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The Technology Adoption and Governance of Artificial Intelligence in the Philippines
Abstract
Artificial intelligence is primed to disrupt our society and the industry. The AI trend of technological singularity is continuously accelerating and is being employed to the different facets of humanity from education, medicine, business, engineering, arts and the like. Government and private companies have been hooked up with this fast pacing technology. AI may displace some non-digital jobs that performs heavy load and repetitive tasks, but it certainly augments labor shortage by realigning the workforce competitiveness to what the technology requires. The diffusion of AI technology is necessary for mental shift of the government and industry leaders to adopt the technology. Research and development is very promising to uplift mankind to faster productivity and positively affect the industries in international perspective. The Philippines is still coping up with the adoption of AI system, but it can steer up globally by strengthening the technology governance of strictly implementing the policies with measures the PDP 2017-2022 and its HNRDA.
... With that, the researchers aim to address absenteeism through the use of a smart attendance acquisition and monitoring system. Quick response, or QR code, is a 2D matrix code that holds more data than a 1D barcode and is being employed in marketing, security, web connections, and academia [17]. The researcher embeds a QR code on the students' identity cards since it is straightforward and inexpensive. ...
... One student was considered as the top absentee with three absences, followed with a student with two absences and one student with one absent. The limitations of this work is with respect to the computational intelligence and sensitivity of processing, hence, the use of big data technologies and advance image processing integrated with machine learning would significantly better the integrity of the system [17][18][19][20][21]. ...
SDG 4 that pertains to quality education can be initialize through schools and universities for direct impact. Thus, one way to have quality education is to ensure that students go to school and receive learning from the academe. However, absenteeism has a significant impact on their academic achievement of every student since those who attend school consistently are more likely to thrive academically. With that, this study aims to address absenteeism using a smart attendance acquisition and monitoring system. The first objective is to design and build a prototype of the Student Attendance Acquisition System (SAAS) utilizing QR code technology and the Raspberry Pi. Then, the establishment of web server for the Attendance Monitoring System (AMS) that can store, retrieve, and display attendance data can be employed. Finally, the constructed system's efficiency must be able to test for reliability and identify the top absentees among the participants. Thus, using the QR code technology, there were no problem in the system after testing to 10 BSECE students over five consecutive days. The prototype is highly efficient and has a strong potential for EVSU attendance system improvement, which might reduce absenteeism and increase student performance in the BSECE department and to the university.
... The most mentioned motivation for AI adoption was to catch up with AI trends and technologies. This motives was given in 10 articles [1], [6], [11], [15], [18], [24], [37], [40], [41], and [13]. Our interpretation is that most articles focused on emerging economies (e.g., China, India, South Africa, and Philippines). ...
... In this case, many countries and companies chose to adopt AI, increase R&D funds, or to develop AI policies, in order to be able to compete with other rivals on the global market. In detail, articles were found, which stated the main reason for adopting AI to be: pressure to competition [5]; competitiveness [10], [24], [37] , [41], [43]; competition [12], [15], [35], [40]; sustain global competitiveness [28]; increase competitiveness [29]; maintain competitiveness advantages [33]; and get competitiveness advantages [13]. ...
In this era of digital revolution, artificial intelligence stands to be one of the emerging technologies to revolutionize the way we live, work, or communicate. While everyone is fighting to lead in this technology, their readiness differs and adoption challenges arise in many sectors. These competitions also result in various economic impacts on countries, firms, and individuals. This paper uses a systematic literature review to analyze the existing economic impact of AI adoption and the technology used. Overall, this paper presents clear evidence that AI adoption has a large effect on an economy. Findings of this research help researchers and practitioners to identify important economic impacts of adopting AI, identify directions for future research, and set policies that need to be put in place.
... Technology-driven solutions, such as AI and big data analytics, are applied in initiatives such as Smart Farming Innovations to boost organic food production, alleviate poverty, and enhance timely decision-making (Matero & Jumawan-Matero, 2020). In the industrial sector, AI would automate redundant tasks, thus making amends for labor shortages and productivity boosts (Concepcion et al., 2019). All these applications are sufficient proof that AI is indeed transforming public safety and would not benefit from integrating AI in bioengineering as a new path toward crime prevention and, at the same time, an ethical and regulatory consideration (Arroyo, 2023). ...
This literature review examines the integration of ABCD (Artificial Intelligence, Blockchain, Cloud Computing, and Data Analytics) technologies in the Philippines, including adoption, worldwide developments, as well as particular applications. Despite the significant potential for ABCD technologies, the integration in healthcare, agriculture, financial, and educational sectors to increase efficiency, security, and ability to make decisions, challenges that are experienced include infrastructure constraints, skill gaps, and regulatory requirements. It manifests the enormous potential of ABCD if applied to engineering models such as Industry 4.0 and digital innovation in health care, e-commerce, and supply chains, predictive maintenance, transparent transactions, and automation. Early adopters can already be discerned in the Philippines, such as AI applications in agriculture and mental health, blockchain for secure records, cloud applications in education, and data analytics in health care-but still, lack of infrastructure and inability to find the people with proper skills slow the movement. Sectors like health and manufacturing, which have high growth potential, these technologies can become competitive engines for the Philippines. Recommendations on digital infrastructure, data privacy, public-private partnerships, and investment in workforce development would facilitate an enabling environment for supporting the culture of ABCD technologies.
... An emerging technology is considered disruptive if it is capable of shaking the current order of things [3] by making existing products and services obsolete, creating new ways of doing things, displacing labor forces and creating new ones, and causing upheaval to the economy and society. Among the prominent but disruptive technologies for Industry 4.0 where policies and regulations are essential are Robotics [4], Augmented Reality and Virtual Reality (AR/VR) [5], and Artificial Intelligence (AI) [6], among others. ...
... The Philippines is still adjusting to the adoption of AI systems, but it can steer up globally by strengthening the technology governance by strictly implementing the policies with measures of the PDP 2017-2022 and its HNRDA regulations (Concepcion, 2019). ...
Artificial Intelligence has been a headline nowadays, it makes human work easier and faster. Organizations adopted this kind of technology to keep up with the demand of globalization and the hotel and leisure industry is amongst to integrate AI to its business model. Highly modernized hotels are more coveted to choose by customers due to their expectation of the excellent services they can be provided. This paper looks on the status of the adoption of artificial intelligence in the hotel and leisure industry in the Philippines as well as its capacity to take full advantage of the perceived benefits of the said technology in the future. It is anchored on Roger’s Innovation Diffusion Theory (IDT), and it rests on the underpinning for comprehending innovation adoption and the elements that influence an individual’s decision to accept a new technology. This research employed qualitative design using secondary data from research journals and articles. It focused on the relative advantage of artificial intelligence in general, efficiency, revenue enhancement, investment cost, reputation management, competitive intelligence, compatibility and complexity as the future in the hospitality, specifically in hotel and leisure industry.
... Smart healthcare represents a disruptive shift propelled by the power of artificial intelligence (AI). This model change makes use of AI's capacity to quickly and effectively analyze enormous volumes of medical data, resulting in improved diagnosis, individualized treatment regimens, and proactive disease prevention [22][23][24]. The field of smart healthcare is ready to provide more effective, affordable, and patient-centered solutions, from predictive analytics that foresee disease outbreaks to wearable technology that continuously records vital signs. ...
Artificial intelligence (AI) has emerged as a transformative force in the field of healthcare, revolutionizing how doctors diagnose, treat, and manage patients. Smart healthcare systems powered by AI technologies have the potential to reshape the landscapes of patient care, medical research, and healthcare management. This abstract discusses the use of artificial intelligence (AI) in smart healthcare, emphasizing its different roles in improving patient care and operational efficiency. Artificial intelligence has shown to be a game changer in smart healthcare, revolutionizing patient care and healthcare operations. As AI technologies advance, the use of AI in healthcare is set to usher in a future in which medical practices are more efficient, personalized, and accessible, resulting in improved health outcomes for people and communities globally. By analyzing genetic information, lifestyle habits, and treatment responses, AI can identify patient-specific treatment options, minimizing adverse effects and optimizing therapeutic benefits. This tailored approach to medicine holds the promise of transforming healthcare into a more precise and effective discipline. AI’s impact on healthcare extends beyond the confines of the hospital. Remote patient monitoring, enabled by AI-driven wearable devices and sensors, facilitates real-time health tracking outside clinical settings. Virtual health assistants and chatbots, provide accessible and round-the-clock healthcare support to patients. These talkative, intelligent assistants can respond to medical questions, give health advice, and even provide mental health care. Healthcare institutions may benefit from the use of AI-powered chatbots since it might reduce the workload for medical staff members and enhance patient engagement and education. As a result of AI’s integration with smart healthcare, a new age of transformational medicine has begun. Patient care might be revolutionized, medical discoveries could be made faster, and healthcare procedures could be optimized with the use of AI’s analytical skill and healthcare knowledge. The future of medicine is set to be more individualized, accessible, and successful, benefiting both patients and healthcare professionals, if ethical and regulatory frameworks are carefully considered.
... Schools need to collaborate with the government to ensure that the graduates' skills are developed through the help of AI tools. Concepcion et al. (2019) pointed out that artificial intelligence was seen as disruptive to industries; however, it is continually accelerating as it has been occupying many facets of society, such as education. In education, for example, AI may not replace non-digital jobs that perform heavy load or repetitive tasks but it helps a lot in ensuring that there would be no manpower shortage. ...
The rise of artificial intelligence (AI) in higher education has sparked significant interest. Artificial intelligence offers opportunities for global learning, personalized instruction, and efficient resource management. In research writing, AI tools streamline processes, from a literature review to data analysis, enhancing efficiency and freeing up
... In this scenario, it is critical for sensor developers to plan and strategize the roll out of new products in a timely manner, so as to address the needs of growing industries [29]. Prior studies have projected the potential of scaling up the adoption of robotics and artificial intelligence in the country [30,31], but the future scenarios presented allowed for the specific analysis of the potential for the sensor technologies development. ...
The advent of the fourth industrial revolution (4IR) offers promising improvements in operational efficiency and profitability for various industries, and can be a key component to leapfrog the many sectors of manufacturing in the Philippines. This will necessitate the reskilling and upskilling of Filipino automation engineers and instrumentation technicians to commission and maintain smart technologies for production facilities. The further expansion of the country's manufacturing capacities presents an opportunity to locally develop products and solutions for process automation. This includes sensor technologies and applications that can boost the Filipino manufacturers' capabilities through locally-sourced automation components. Various sensor technologies and applications can be explored for further improvement through research & development. Through technology foresight, this study looks into the potential of Filipino technology firms to develop sensors that are locally designed and assembled, addressing the needs of growing industries. The use of scenario-building approach allows for the identification and ranking of the key predictable drivers based on the insights of industry professionals. Opportunities and risks are evaluated based on future possible scenarios.
... Canada has been at the forefront of leveraging AI and IT governance in pollution management [56]. Various industries across the country have successfully implemented AI-powered systems and robust IT governance frameworks to enhance their environmental performance [57].For example, in the mining sector, AI-based systems are used to monitor air quality, water quality, and noise levels to ensure compliance with environmental regulations [58]. ...
Artificial Intelligence (AI) and IT governance are playing an increasingly significant role in managing industrial pollution in Canada. With the nation's commitment to reducing environmental impact and promoting sustainable practices, incorporating advanced technologies into pollution management has become a priority. In this article, we will explore how AI and IT governance are transforming the way industries in Canada tackle pollution challenges. Artificial intelligence (AI) is a powerful technology that can help address the global challenge of environmental pollution. This study aims to explore the role of AI on industrial pollution management in Canada, a country that has committed to reducing its greenhouse gas (GHG) emissions by 40-45% by 2030. The study adopts a mixed-methods approach, combining a systematic literature review, a survey of Canadian AI companies and experts, and a case study of AI applications in three industrial sectors: oil and gas, mining, and manufacturing. The results show that AI can help monitor, control, and reduce industrial pollution in various ways, such as optimizing energy efficiency, detecting leaks and spills, enhancing waste management, and enabling circular economy. The study also identifies the barriers and enablers for the adoption and diffusion of AI for industrial pollution management in Canada, such as data availability and quality, regulatory frameworks, ethical and social implications, and stakeholder collaboration. The study concludes with some recommendations for policy makers, industry practitioners, and researchers to foster the development and deployment of AI for industrial pollution management in Canada.
... The average latency for all the classifications done for Case 1 was seen to be 1.5459 s, while the average latency for all the classifications done for Case 2 was seen to be 0.000904 s. A comparison between this work and other works with similar applications can be found in the Table 2. Introducing in-depth computational models in the network might increase the speed of data transmission [29][30][31][32][33]. ...
In the modern day, several different innovations have been created with the sole purpose of improving existing models, products, and systems. One such innovation would be the improvement of brain-computer interfaces (BCI). BCIs tend to require high processing capabilities for the accurate gathering, processing, and classification of different biosignals such as the electroencephalography (EEG) signals. As such, cloud computing and TinyML methods are suggested as a means to solve this issue. In this study, the two methods are compared by simulating two cases, one for cloud computing and another for TinyML implementations. The latencies in which these two implementations can provide a classification for a data point is used as the main metric for comparison. It was observed that TinyML implementations had significantly lower latencies at an average of 0.0009 s, while cloud computing implementation had significantly higher latencies at an average of 1.5459 s. This difference can be attributed to the fact that cloud computing systems are reliant on connectivity between the local machine and the cloud computing services while TinyML implementations do not have the same restrictions and are instead capable of providing a classification as soon as it receives a data point.
... About 50% of the students found the second activity to be easy to perform, but it is necessary to understand the operational sequence. This current study poses a great potential for enhancing the workforce in the mechatronics, robotics, and automation fields, which is one of the trends of IR 5.0 for mass production and interconnectivity [18,19]. Moreover, in meeting the goal of having an effective and efficient mechatronics workforce, they can be the prime mover of the economy of the Philippines, which leans toward faster production systems. ...
Training globally competent students and workers to meet the required standard qualifications for the evolving manufacturing industry is essential. However, At Mindanao State University—Iligan Institute of Technology (MSU-IIT), the equipment and trainers available are minimal, outdated, and need an upgrade to match program requirements to meet industry trends and global competitiveness. The study aims to develop a mechatronics trainer prototype to assess the skill competencies in mechatronics of industry practitioners and students for quality-assured institutional assessment and Technical Education and Skills Development Authority (TESDA) Mechatronics Servicing National Certification (NC) III and IV. The core competency standards addressed were based on the learning outcomes of the 2018 TESDA Migrated Training Regulation (TR) for NC III and NC IV in Mechatronics Servicing. The development of the Mechatronics trainer includes planning the design, identifying the components needed, generating schematic diagrams for electrical and programs in PLC, testing and commissioning the components, and gathering feedback for improvements. The mechatronics trainer has undergone a series of testing in terms of individual devices and system operability based on given tasks to achieve the overall functionality. The NC III and NC IV level of skills competencies of ten Bachelor of Science in Industrial Automation and Mechatronics students at MSU_IIT were assessed and qualified based on the required unit of skills competencies as specified in the mechatronics servicing TR. Based on the results, students with a background in mechatronics were competent in performing the given tasks: (1) Forward-Reverse motor control using Variable Frequency Drive with changing frequency; and (2) Motor speed control with forward-reverse using HMI, PLC, and VFD. Few of the students using the trainer have difficulty in advanced human- machine interface configuration, programmable logic controller programming, and variable frequency drive parameterization due to unfamiliarity with the devices and limited time to finish the tasks.
... PLF aims to provide a farm management system for process automation, real-time animal and environment monitoring, environmental control, and decision support. With the recent advancement in artificial intelligence (AI) [9], researches on precision farming rapidly grow [10][11][12]. ...
Poultry, like quails, is sensitive to stressful environments. Too much stress can adversely affect birds’ health, causing meat quality, egg production, and reproduction to degrade. Posture and behavioral activities can be indicators of poultry wellness and health condition. Animal welfare is one of the aims of precision livestock farming. Computer vision, with its real-time, non-invasive, and accurate monitoring capability, and its ability to obtain a myriad of information, is best for livestock monitoring. This paper introduces a quail detection mechanism based on computer vision and deep learning using YOLOv5 and Detectron2 (Faster R-CNN) models. An RGB camera installed 3 ft above the quail cages was used for video recording. The annotation was done in MATLAB video labeler using the temporal interpolator algorithm. 898 ground truth images were extracted from the annotated videos. Augmentation of images by change of orientation, noise addition, manipulating hue, saturation, and brightness was performed in Roboflow. Training, validation, and testing of the models were done in Google Colab. The YOLOv5 and Detectron2 reached average precision (AP) of 85.07 and 67.15, respectively. Both models performed satisfactorily in detecting quails in different backgrounds and lighting conditions.
... Conversely, the main advantage of the proposed technique is providing an accurate and optimal combination of a material that can be functional for the development of the parasitic antenna filter. This can be applied in land surveying on roads or even in uneven terrain through an underground imaging system apparently being towed with a small vehicle for economic improvement [38]. The filter will be a great help in securing the accuracy of data acquired by blocking unnecessary signals or EM waves as the imaging operation requires high sensitivity to soil resistivity readings. ...
Non-destructive mapping of underground utilities is
one of the fundamental concepts of subsurface imaging technology
that has a great contribution to the improvement of many
infrastructure concerns. It is incorporated with electrical
resistivity measurement of various ground conditions through
electrodes with functional geometric configuration. Due to the
presence of an electrical field, electromagnetic noise and
interference will likely occur and might cause inaccuracy of data.
On that note, it is vital to understand the different factors affecting
the system and its impact as the initial step in the development of
an effective filtering and shielding mechanism. Thus, this paper
discusses the possible impacts of parasitic inductance and
capacitance affecting the performance of low and very lowfrequency
antennas, and the collection of various optimization
methods as well as the tools and software used in the mitigation of
parasitic elements in an electronics system found in different
research publications and journals. Furthermore, an AI-based
framework was also provided as an initial step in the development
of a parasitic antenna filter that performs well for underground
imaging single antenna array. Genetic algorithm is the AI
technique proposed for the optimization of the antenna filter by
providing the best combination of material by considering its
conductivity and thickness.
... With regard to research on the Philippine disaster response, significant work has already been done (Alcantara, 2014;Santiago et al., 2016;Brower et al., 2014). On the other hand, there are also a handful of studies on the impacts of AI technologies in the same country (Concepcion et al., 2019;Manguerra et al., 2020). For example, Kim et al. (2019) looked into this issue specifically in the context of the readiness of the Philippines for a fourth industrial revolution. ...
In general, existential threats are those that may potentially result in the extinction of the entire human species, if not significantly endanger its living population. Among the said threats include, but not limited to, pandemics and the impacts of a technological singularity. As regards pandemics, significant work has already been done on how to mitigate, if not prevent, the aftereffects of this type of disaster. For one, certain problem areas on how to properly manage pandemic responses have already been identified, like the following: (a) not being able to learn from previous experiences, (b) the inability to act on warning signals, and (c) the failure to reach a global consensus on a problem (i.e., in a timely manner). In terms of a singularity, however, it may be said that further research is still needed, specifically on how to aptly respond to its projected negative outcomes. In this paper, by treating the three problem areas noted above as preliminary assessment measures of a country’s capacity to coordinate a national response to large-scale disasters, we examine the readiness of the Philippines in preparing for an intelligence explosion. By citing certain instances of how the said country, specifically its national government, faced the coronavirus disease 2019 pandemic, it puts forward the idea that the likely Philippine disaster response towards a singularity needs to be worked on, appealing for a more comprehensive assessment of such for a more informed response plan.
... The recent bibliometric network of the postharvest storage system is shown in Fig. 1 indicating its closely related applications, topics, and principles. Apparently, there is no clear food storage system policy has been comprehensively implemented in the Philippines [7]. The contributions of the current work are the following: (1) a systematic study on the techniques in phenotyping in agriculture; (2) comparative study of the application of vision processing, and computational intelligence in agri-postharvest systems; and (3) emerging challenges and corresponding future directives for in the field of intelligent postharvest storage system. ...
Agricultural production system does not end with the actual harvesting of crops rather it extends to the postharvest system which primarily consists of crop storing, marketing, and transportation. However, temperature and humidity directly affect the quality of stored agricultural products. In a tropical country like the Philippines, tomato, lettuce, and other thin-skinned and highly moist crops degrade its quality and experience shape deformation over time. This study is a thematic taxonomy of intelligent postharvest storage systems discussing the techniques in the phenotyping of agricultural produce and emerging needs, trends in computer-vision-based postharvest systems, integration of artificial intelligence in postharvest systems, the current issues, challenges, and corresponding future directives in intelligent storage systems. Based on the systematic analysis, technical modeling of the storage system and postharvest crop quality grading are the emerging challenges in effectively storing crops for human consumption. It was found out that non-invasive high throughput methods for evaluation of quality and shelf life are needed. This can be done through vision-based fruit and vegetable quality grading and vision-based adaptive controls in the storage chamber. Overall, computer vision allied with artificial intelligence can make an intelligent postharvest storage system that is sustainable, profitable, and easy to implement.
... According to Oxford Economics forecast, a 30% increase in the global robot stock would lead to 5.3% GDP increased by 2030 which is equivalent to $4.9 trillion annually added to the global economy. A 1% raise of robot stock in every worker is predicted to have a 0.1% increase of output per worker in the manufacturing industry while it is estimated that around 20 million jobs globally, will be displaced by 2030 due to robot adoption, as per Oxford Economics [31][32]. Nowadays, AI is being integrated with robots which made it superior over the early types of robots. ...
Robotics significantly impacts the way we live and work nowadays. It will positively and negatively disrupt every sector of our society, economy, and the industry. Robotics will definitely transform lives, work practices, raise efficiency and safety levels, improve the quality of service, and create new jobs. Its impact will grow over time as well as the interaction between robots and human beings. However, the adoption of technology has also disadvantages to the workforce as human resources are needed to be trained for this new industrial capability. With these, the government need to provide initiatives to combat the negative effect of robotics.
... In aquatic research, the physico-limnological factors serve as fundamental determinants to examine and understand the behavior of aquatic ecological systems [9]. Moreover, advanced computational techniques have been widely adopted in various applications including the prediction and measurement of significant parameters based on acquired features [10,[26][27][28]. Machine learning (ML) algorithms, optimized recurrent neural network (RNN) and deep transfer image networks were configured to measure the spot surface percentage of leaf samples [11,12] and quantify the measures of lettuce canopy water stress which includes the equivalent water thickness (EWT) and full-moisture content (FMC) based on leaf thermo-visible phenotypic signatures [16]. ...
Excess nitrate concentration leads to excessive algal growth that reduces dissolved oxygen for aquatic animals. A significant strategy to preserve the water quality of aquatic systems is through nitrate level assessment. However, use of nitrate sensors and existing laboratory approach is costly and requires a huge effort. This study investigated the application of computational intelligence for measurement of nitrate concentration in a tilapia fishpond at Rizal province, Philippines, based on physico-limnological parameters such as temperature, electrical conductivity, and pH level. Artificial neural network (ANN) algorithms including feed-forward (FNN) and recurrent (RNN) neural networks were developed and optimized using genetic algorithm (GA) to improve their predicting performances. Genetic programming (GP), through GPTIPSv2 tool, was configured to generate a fitness function. This function is the principal component of GA optimization to produce optimal number of hidden neurons for ANN architecture that resulted in 2 neurons for GA-FNN and combination of 92, 31, and 11 neurons for each hidden layer using the GA-RNN model. Based on evaluation results, all models provided acceptable results with error and predictive accuracy values approaching 0 and 1, respectively. However, the GA-FNN model outperformed other models with 3.26 RMSE, 2.23 MAE, and 0.97 R 2 values which proved to be the most effective and suitable model for the indirect measurement of nitrate concentration.
... Conventional approach in classifying bean varieties is through chemometrics and terahertz spectroscopy (0.3-5 THz) [9], and by computing the physical quality index (PQI) [10]. On the other hand, the integration of artificial intelligence in the broad field of plant science and agricultural engineering is one of the national agendas of the Philippines [11]. Combining different machine learning algorithms in tandem with computer vision resulted in a more accurate system and cheaper implementation for grapes, tomatoes, and lettuce [12][13][14][15][16][17][18][19]. ...
Proper identification and categorization of seeds at an earlier stage of the cultivation process is an imperative procedure that contributes to better crop quality and higher production yield. As a strategy to supplement this procedure, integration of computer vision approach and machine learning algorithms including gaussian process regression (GPR), decision trees for regression (RT) and classification (CT), support vector machine regression (SVMR), k-nearest neighbors (KNN), linear discriminant analysis (LDA) classifier, and Naïve Bayes (NB) classifier are explored in this study to predict the extended morphological features (solidity, roundness, compactness) and variety classification of dry bean (Phaseolus vulgaris L.). A total of 13,611 image samples were used. CIELab color channel thresholding was applied in segmenting bean pixels and region properties for extracting the morphological features (bean biomass area, perimeter, major and minor axis lengths, convex area, eccentricity, extent, equivalent diameter, and axis length proportionality, shape factors, roundness, solidity, compactness). Based on RMSE and MAE performances, the optimized GPR is the most reliable model for predicting seed solidity, and regression tree for both seed roundness and compactness. Classification models with seven morphological predictors (LDA7, KNN7, CT7, NB 7 ) exhibited sensitive classification performance, all having accuracies greater than 90%. Further, KNN7 bested out other models with 93.69% accuracy, 93.64% precision, 93.66% specificity, and 93.69% f1-score. The developed machine learning models are innovative approaches in the seed variety classification and phenotyping of dry bean seeds.
... The DICT has also created the National Broadband Plan, "Free Wi-Fi for All," and the Common Tower Initiative to improve the coverage of high-quality internet connections across the country. Companies in the private sectors are also seen to be very likely to invest in the development of AI in the country as no stringent regulations to AI have been set in place at the moment (Concepcion et al., 2019). We also believe that integrating AI systems into Philippine telehealth is certainly possible since both have an identical goal -to create more accessible healthcare services. ...
From a public health perspective, this opinion article discusses why it is necessary to integrate Artificial Intelligence (AI) in the mental health practices in the Philippines. The use of AI systems is an optimum solution to the rising demand for more accessible, cost-efficient, and inclusive healthcare. With the recent developments, the Philippines is deemed to have sufficient capacity to adopt this agendum. This article serves as a call for the introduction of advanced detection tools and predictive
analytics in the medical field, especially in the mental health discipline.
... The decline of one of the variables, especially the policy concerning the diffusion of the innovation in the early stage, will significantly lag by at time 0 < ≤ 0.4 while at time ≥ 0.4 , there will be no significant difference from the ideal scenario (where T is the time when the robots have reached to its maximum advancement). It is also observed in Fig. 3, using all scenarios at = of the diffusion curves, all scenes reach a single point of technology advancement and exploitation (also known as a technological singularity) [61]. However, using two variables declining, it is observed in Fig. ...
... The Science Act of 1958 encourages the integration and intensification of scientific and technical research by funds under the Republic Act No. 2067. This stimulates advances in manufacturing, agricultural, biological, energy [42], food and nutrition research and engineering research to be conducted [43]. The Philippines has ample resources to train them in AI research and development. ...
... Fuzzy structures, NN, and evolutionary computations have historically been the three main pillars of CI [41]. Businesses today such as Google started supporting farmers, through artificial intelligence (AI) and geographic information system (GIS) devices, to increase yields and maximize production [42]. ...
... Algal biomass is the third generation of biofuel that includes both microalgae and macroalgae resources. This promising approach of utilizing microorganisms to yield high-value products with economic impact is one of the attractive sustaining solutions in maintaining ecological balance and speeding up industrial revolution [6][7]. Biofactories, on the average, produces enough microalgae that is a key indicator for domestication [8]. ...
Industrial waste disrupts the natural production of microalgae cultures. Cultivation of microalgae in a controlled environment highly results to biomass with lower contamination necessary as high-valued economic product. In response to the emerging challenges of sustainable energy production, the integration of computational intelligence and biosystems engineering is considered as an open research area. In this study, Chlorella vulgaris microalgae were cultivated in BG-11 growth medium on three customized surface-mount light bioreactors that are equipped with digital camera for growth monitoring in terms of accumulated biomass surface area and color reflectance intensity via IoT. Feature-based machine learning models predicted microalgae growth area in terms of water temperature, pH level and turbidity, and light intensity. Microalgae cultures were exposed to combinations of white artificial light source of 2000 ± 1000 lux and water temperature of 27 ± 5°C using Peltier plate to discriminate biomass growth within a 30-day cultivation period. A total of nine environmental conditions were employed to clearly discriminate the impacts of environmental stressors to microalgae growth. Combined neighborhood component analysis and ReliefF was used to select high impact color features of C, Ye, M, H, and S with biomass area. Electromagnetism-like mechanism optimized-RBNN bested RNN and generalized processing regression with R 2 of 0.985 and RMSE of 6.262. There is also considerable growth in biomass surface area for certain combinations of light intensity and water temperature (2125 ± 625 lux and 28.75 ± 3.25°C), and turbidity and water pH concentrations (3.85 ± 0.15 NTU and 8.025 ± 0.775). However, the photobioreactor with 27°C and 2000 lux exposure is considered having the exact optimum controlled environment condition in cultivating Chlorella vulgaris based on the generated growth in biomass surface area of 38.314%. This developed intelligent system is scalable for seamless microalgae production of any strands for renewable energy resource.
... Evaluation of the prospective impact of automation application in the sector along with the stakeholders' preparation towards the mining system transition is also a key explored component. The analysis of the existing situation and exploring the preparedness of different stakeholders towards the adoption of autonomous mining in developing countries is a dire need that is actively being pursued in Ghana (Kansake et al., 2019) and the Philippines (Concepcion et al., 2019). This is extremely important towards understanding the stakeholders' behavior in adoption to autonomous mining systems and suggests improvements to each stakeholder for a smoother transition to autonomous systems. ...
New technologies including generative artificial intelligence (Gen AI) create opportunities for organizations to become more competitive. However, many employees fail to adopt the technology, which poses a question of how organizations can solve this problem. The main contribution of the study is that it identifies the main drivers of Gen AI adoption intentions amongst Business Process Outsourcing (BPO) workers in the Philippines and reveals the moderating role of age, gender, work experience and frequency of technology use, which will allow organizations. Anchored on the UTAUT framework, this paper investigates how performance expectancy, effort expectancy, social influence, and facilitating conditions affect the Gen AI adoption intention of workers in performing their daily tasks in the business outsourcing organizations. This is done by administering a cross-sectional survey among 385 employees in different BPO companies in the Philippines and applying the structural equation modeling (SEM) technique. The results of the research will help outsourcing firms tackle the factors that slow down the adoption of the new technology among employees through process improvement, training, and communication. The findings also make a contribution by testing the UTAUT model in the context of the new Gen AI technology in a brand new setting that was not researched in the past.
This systematic review synthesizes the existing literature on the impact of artificial intelligence (AI) and automation on Human Resource Development (HRD) practices and outcomes. The study explores how AI and automation affect HRD, highlighting specific HRD processes affected and their influence on outcomes. A comprehensive search was conducted across academic databases, HRD journals, and conference proceedings, resulting in a selection of relevant studies. The findings were analyzed through a narrative synthesis, with subgroup analyses based on specific HRD processes. The review provides insights into AI and automation implications for HRD researchers and practitioners. It also identifies research gaps and future directions.
This article aims to explore, while also recognizing its limitations, the effects of Artificial Intelligence (AI) on worker dedication and performance in the workplace. The study uses a straightforward random sample technique in conjunction with a qualitative research strategy. Google Forms is used to create online surveys that are used to collect data of Bengaluru Urban, Yeshwantpur Industrial Tech Park Employees. Out of the 120 participants, 58% are male and 42% are female, with the bulk of responders (91.8%) being between the ages of 28 and 35. The results show that AI can have a favourable impact on worker engagement and performance. Artificial Intelligence (AI) is the use of computers to mimic intelligent behaviour with little to no human involvement. However, researchers are worried that AI would result in a loss of jobs and a rise in unemployment rates. Moreover, 12.9% of workers believe AI will take their employment from them. About 32.9% of respondents believe that AI will lower employee motivation in businesses. All things considered, the research points to a potential benefit of artificial intelligence on worker commitment and performance. About 74% of respondents expressed interest in utilizing artificially intelligent technologies in their daily lives.. It is said that training has implications for productivity, commitment to the work and personal development. All corporate / companies must train people and develop their staff. Most of the organization are aware of this requirement and make invest and do many things for training and development. The training which we give to the staffs may be technical training or soft skills / behavioral training. It is also said making investment in training and development in companies in generally said to be as good management practice and to maintain appropriate xpertise in the particular field and in future too.. It is said that training has implications for productivity, commitment to the work and personal development. All corporate / companies must train people and develop their staff. Most of the organization are aware of this requirement and make invest and do many things for training and development. The training which we give to the staffs may be technical training or soft skills / behavioral training. It is also said making investment in training and development in companies in generally said to be as good management practice and to maintain appropriate expertise in the particular field and in future too. The objective of the research paper is to collect and analyze data regarding the importance and effectiveness of training.
In this paper, we introduce a groundbreaking approach to criminal network surveillance, utilizing AI-powered drones equipped with advanced cyber penetration tools. Our methodology entails the deployment of drones to intercept wireless networks of targeted criminals, facilitating the collection of crucial evidence without physical intrusion. By harnessing the capabilities of Artificial Intelligence (AI), Machine Learning (ML), and Deep Learning (DL), in conjunction with automated penetration systems, our drones are able to conduct thorough investigations and relay the findings directly to command centers. This innovative technique ensures the acquisition of accurate and reliable evidence, boasting a remarkable success rate of 99.8%, thereby significantly expediting the arrest and prosecution processes. The outcomes of our work underscore the potential of AI drones as vital assets in modern criminal investigations, reshaping the landscape of law enforcement and national security protocols.
Machine Learning, a subset of Artificial Intelligence, is now being integrated into image and speech classification, object and action recognition, medical image analysis, security and surveillance, and autonomous driving. It is usually executed by creating a neural network which is defined as the process of modeling a computer system to mimic the function of the human brain. However, learning to develop a neural network is a challenge for students, especially those who have little background in programming. This study focuses on developing a node-based programming tool called NodalNet, a beginner-friendly and interactive educational tool for creating a neural network designed for image classification applications. Java programming language, Java swing library, and Eclipse integrated development environment (IDE) are used to build the NodalNet. It can be used for offline deployment using laptops, and computers, and for online deployment using a technique called the application programming interface (API) method. NodalNet has built-in components that can be connected through nodes, and it has a data labeling feature that helps the users in classifying the raw data inputs used before the training process. The NodalNet can develop and train simple neural networks, some image recognition samples are FlowerNET, DogCatNET, and NumberNET which have significant results in their accuracy. A survey conducted shows that 77.78% of the respondents answered that it is easy to train using the NodalNet. This tool offers a positive impact as an interactive educational tool for developing a neural network.
In this era of industrial revolution (IR 4.0), coupled with the emergence of the deadly global pandemic of COVID-19, healthcare organizations are compelled to embrace new technologies for their routine decision-making. The Business Intelligence System (BIS) is one of the emphasized innovations, and due to its potential to provide more intellectual information for decision-making processes, has attracted the interest of industry analysts and policymakers. Literature suggests that the BIS is integrated by organizations in different sectors, but most BIS initiatives struggle to produce the anticipated outcomes. Moreover, the adoption of BIS in SMEs generally and healthcare specifically is rather insignificant. This is due to numerous factors. It is therefore necessary to discover and analyze the essential determinants affecting the adoption of BIS in healthcare SMEs. Therefore, this study tries to tackle this gap by exploring the relevant factors for BIS adoption using a systematic literature review (SLR) and an expert-ranking survey of 63 studies that were published in Scopus and WoS databases from 2011 to 2020. A total of 22 determinants are identified and sent to 15 experts. The data that were gathered from these experts were analyzed using SPSS. The results of the analysis indicated 15 determinants were significant, and one determinant was added by an expert. Consequently, a theoretical structure has been developed based on technology, organization, environment, and CEOs determinants and theories. The results of the current study will deepen the current BIS literature and advance the understanding of the significant elements of BIS adoption decision.
This is the official publication of the Prepint "Artificial intelligence as the basis of future control networks", July 2019. - DOI: 10.13140/RG.2.2.30247.50087
‘Green Chemistry Adoption’ prescribes that companies must evaluate toxicity impact throughout the product development lifecycle, minimize the toxic waste generation, and use of toxic raw material. As more companies are adopting Green chemistry, there is a critical need to understand the toxicity of chemicals in terms of ecological and health impact. ViridisChem has built the most comprehensive toxicity database with over 90 million chemicals and 2.4 billion properties and has 10 times more toxicity data than any other chemical database available in the global market. Utilizing this information, it is developing products that can analyze any chemicals, even the new and postulated molecules, mixtures/formulations, and processes.
Using these tools, pharmaceutical, biochemical, agrochemical industries and the chemical suppliers can:
• Predict the toxicity of new molecules and their derivatives in real-time, thereby screen out the toxic drug targets, and avoid spending R&D time on them
• Avoid the use of toxic raw materials, and quickly find better and less toxic chemicals that satisfy the reaction specific requirements
• Fully understand the health, safety and ecological risks of toxic formulations, and find better formulations that offer the same benefits
• Avoid the use of cocktail of formulations that may be non-toxic individually, but become very potent when combined
• Define sustainable product development processes by avoiding toxic reagents during every step, and minimizing the toxic waste
This paper proposes a new methodology for automated design of power electronic systems realized through the use of artificial intelligence. Existing approaches do not consider the system's reliability as a performance metric or are limited to reliability evaluation for a certain fixed set of design parameters. The method proposed in this paper establishes a functional relationships between design parameters and reliability metrics, and uses them as the basis for optimal design. The first step in this new framework is to create a nonparametric surrogate model of the power converter that can quickly map the variables characterizing the operating conditions (e.g. ambient temperature, irradiation) and design parameters (e.g. switching frequency, dc link voltage) into variables characterizing the thermal stress of a converter (e.g. mean temperature and temperature variation of its devices). This step can be carried out by training a dedicated artificial neural network (ANN) either on experimental or simulation data. The resulting network is named as ANN1 and can be deployed as an accurate surrogate converter model. This model can then be used to quickly map the yearly mission profile into a thermal stress profile of any selected device for a large set of design parameter values. The resulting data is then used to train ANN2, which becomes an overall system epresentation that explicitly maps the design parameters into a yearly lifetime consumption. To verify the proposed methodology, ANN2 is deployed in conjunction with the standard converter design tools on an exemplary grid-connected PV converter case study. This study showed how to find the optimal balance between the reliability and output filter size in the system with respect to several design constraints. This paper is also accompanied by a comprehensive data set that was used for training the ANNs.
Big data is increasingly available in all areas of manufacturing and operations, which presents an opportunity for better decision making and discovery of the next generation of innovative technologies. Recently, there have been substantial developments in the field of patent analytics, which describes the science of analysing large amounts of patent information to discover trends. We define Intellectual Property Analytics (IPA) as the data science of analysing large amount of IP information, to discover relationships, trends and patterns for decision making. In this paper, we contribute to the ongoing discussion on the use of intellectual property analytics methods, i.e artificial intelligence methods, machine learning and deep learning approaches, to analyse intellectual property data. This literature review follows a narrative approach with search strategy, where we present the state-of-the-art in intellectual property analytics by reviewing 57 recent articles. The bibliographic information of the articles are analysed, followed by a discussion of the articles divided in four main categories: knowledge management, technology management, economic value, and extraction and effective management of information. We hope research scholars and industrial users, may find this review helpful when searching for the latest research efforts pertaining to intellectual property analytics.
SAE Technical Paper 2018-01-1066 presented at WCX World Congress Experience 2018 in Detroit: One of the major challenges for the automotive industry will be the release and validation of cooperative and automated vehicles. The immense driving distance that needs to be covered for a conventional validation process requires the development of new testing procedures. Further, due to limited market penetration in the beginning, the driving behavior of other human traffic participants, regarding a mixed traffic environment, will have a significant impact on the functionality of these vehicles.In this paper, a generic simulation-based toolchain for the model-in-the-loop identification of critical scenarios will be introduced. The proposed methodology allows the identification of critical scenarios with respect to the vehicle development process. The current development status of cooperative and automated vehicle determines the availability of testable simulation models, software, and components.The identification process is realized by a coupled simulation framework. A combination of a vehicle dynamics simulation that includes a digital prototype of the cooperative and automated vehicle, a traffic simulation that provides the surrounding environment, and a cooperation simulation including cooperative features, is used to establish a suitable comprehensive simulation environment. The behavior of other traffic participants is considered in the traffic simulation environment.The criticality of the scenarios is determined by appropriate metrics. Within the context of this paper, both standard safety metrics and newly developed traffic quality metrics are used for evaluation. Furthermore, we will show how the use of these new metrics allows for investigating the impact of cooperative and automated vehicles on traffic. The identified critical scenarios are used as an input for X-in-the-Loop methods, test benches, and proving ground tests to achieve an even more precise comparison to real-world situations. As soon as the vehicle development process is in a mature state, the digital prototype becomes a “digital twin” of the cooperative and automated vehicle.
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SAE Technical Paper 2018-01-1066 was recommended by WCX 2018 topical chair for publication as a journal article under the original DOI
--> SAE International Journal for Connected and Automated Vehicles. SAE Int. J. of CAV 1(2):93–106, 2018, doi:10.4271/2018-01-1066.
The availability of data feeds, the demand for news on mobile devices, and advances in algorithms are helping to make automated journalism more prevalent. Against the specific backdrop of sports journalism's content, means of production and consumption, the question the paper answers is whether the recent introduction of automatically produced content is merely another evolutionary stage in the field of sport journalism, or has it triggered a revolution that can be defined literally as a sweeping change, both related to production and consumption, in this area?
Introduction:
According to the World Health Organization (WHO), over 130 million people are in constant need of humanitarian assistance due to natural disasters, disease outbreaks, and conflicts, among other factors. These health crises can compromise the resilience of healthcare systems, which are essential for achieving the health objectives of the sustainable development goals (SDGs) of the United Nations (UN). During a humanitarian health crisis, rapid and informed decision making is required. This is often challenging due to information scarcity, limited resources, and strict time constraints. Moreover, the traditional approach to digital health development, which involves a substantial requirement analysis, a feasibility study, and deployment of technology, is ill-suited for many crisis contexts. The emergence of Web 2.0 technologies and social media platforms in the past decade, such as Twitter, has created a new paradigm of massive information and misinformation, in which new technologies need to be developed to aid rapid decision making during humanitarian health crises.
Objective:
Humanitarian health crises increasingly require the analysis of massive amounts of information produced by different sources, such as social media content, and, hence, they are a prime case for the use of artificial intelligence (AI) techniques to help identify relevant information and make it actionable. To identify challenges and opportunities for using AI in humanitarian health crises, we reviewed the literature on the use of AI techniques to process social media.
Methodology:
We performed a narrative literature review aimed at identifying examples of the use of AI in humanitarian health crises. Our search strategy was designed to get a broad overview of the different applications of AI in a humanitarian health crisis and their challenges. A total of 1459 articles were screened, and 24 articles were included in the final analysis.
Results:
Successful case studies of AI applications in a humanitarian health crisis have been reported, such as for outbreak detection. A commonly shared concern in the reviewed literature is the technical challenge of analyzing large amounts of data in real time. Data interoperability, which is essential to data sharing, is also a barrier with regard to the integration of online and traditional data sources. Human and organizational aspects that might be key factors for the adoption of AI and social media remain understudied. There is also a publication bias toward high-income countries, as we identified few examples in low-income countries. Further, we did not identify any examples of certain types of major crisis, such armed conflicts, in which misinformation might be more common.
Conclusions:
The feasibility of using AI to extract valuable information during a humanitarian health crisis is proven in many cases. There is a lack of research on how to integrate the use of AI into the work-flow and large-scale deployments of humanitarian aid during a health crisis.
Artificial intelligence (AI) has penetrated many organizational processes, resulting in a growing fear that smart machines will soon replace many humans in decision-making. To provide a more proactive and pragmatic perspective, this article highlights the complementarity of humans and AI, and examines how each can bring their own strength in organizational decision making processes typically characterized by uncertainty, complexity, and equivocality. With a greater computational information processing capacity and an analytical approach, AI can extend humans' cognition when addressing complexity, whereas humans can still offer a more holistic, intuitive approach in dealing with uncertainty and equivocality in organizational decision-making. This premise mirrors the idea of 'intelligence augmentation': AI systems should be designed with the intention of augmenting, not replacing, human contributions.
Objective:
Given the availability of extensive digitized healthcare data from medical records, claims and prescription information, it is now possible to use hypothesis-free, data-driven approaches to mine medical databases for novel insight. The goal of this analysis was to demonstrate the use of artificial intelligence based methods such as Bayesian networks to open up opportunities for creation of new knowledge in management of chronic conditions.
Materials and methods:
Hospital level Medicare claims data containing discharge numbers for most common diagnoses were analyzed in a hypothesis-free manner using Bayesian networks learning methodology.
Results:
While many interactions identified between discharge rates of diagnoses using this data set are supported by current medical knowledge, a novel interaction linking asthma and renal failure was discovered. This interaction is non-obvious and had not been looked at by the research and clinical communities in epidemiological or clinical data. A plausible pharmacological explanation of this link is proposed together with a verification of the risk significance by conventional statistical analysis.
Conclusion:
Potential clinical and molecular pathways defining the relationship between commonly used asthma medications and renal disease are discussed. The study underscores the need for further epidemiological research to validate this novel hypothesis. Validation will lead to advancement in clinical treatment of asthma & bronchitis, thereby, improving patient outcomes and leading to long term cost savings. In summary, this study demonstrates that application of advanced artificial intelligence methods in healthcare has the potential to enhance the quality of care by discovering non-obvious, clinically relevant relationships and enabling timely care intervention.
We argue that there already exists de facto artificial intelligence policy - a patchwork of policies impacting the field of AI's development in myriad ways. The key question related to AI policy, then, is not whether AI should be governed at all, but how it is currently being governed, and how that governance might become more informed, integrated, effective, and anticipatory. We describe the main components of de facto AI policy and make some recommendations for how AI policy can be improved, drawing on lessons from other scientific and technological domains.
The history and the future of Artificial Intelligence could be summarized into three distinctive phases: embryonic, embedded and embodied. We briefly describe early efforts in AI aiming to mimic intelligent behavior, evolving later into a set of the useful, embedded and practical technologies. We project the possible future of embodied intelligent systems, able to model and understand the environment and learn from interactions, while learning and evolving in constantly changing circumstances. We conclude with the (heretical) thought that in the future, AI should re-emerge as research in complex systems. One particular embodiment of a complex system is the Intelligent Enterprise.
Artificial Intelligence (AI) is influencing the situation where advancement happens. What are the suggestions for our comprehension of the plan? Is AI simply one more advanced innovation that, much the same as numerous others, would not fundamentally question what we think about the plan? Or on the other hand, will it make changes in structure that our present systems cannot catch? To address these inquiries, we have researched two spearheading cases at the boondocks of AI, Netflix and Airbnb (supplemented with investigations in Microsoft and Tesla), which offer a special window on the future advancement of the plan. We found that AI does not subvert the essential standards of Design Thinking (individuals focused, abductive, and iterative). Or maybe, it empowers to defeat past restrictions (in scale, extension, and learning) of human intense configuration measures. With regard to AI production lines, arrangements may even be more client- focused (to an outrageous degree of granularity, for example being intended for everyone), more imaginative, and ceaselessly refreshed through learning emphasizes that length the whole life pattern of an item. However, we found that AI significantly changes the act of plan. Critical thinking errands generally carried on by creators are currently robotized into learning circles that work without restrictions of volume and speed. These circles think in a fundamentally unexpected manner in comparison to a fashioner: they address complex issues through straightforward undertakings, iterated exponentially. The article thusly proposes another system for understanding structure practice in the time of AI. We likewise talk about the suggestions for the plan and development hypothesis. In particular, we see that, as inventive critical thinking is essentially directed by calculations, human structure progressively turns into a movement of sense-making, for example, to comprehend which issues bode well to be tended to. This move in the center calls for new speculations and brings the plan nearer to the initiative, which is, inalienably, an action of sense-making.
Artificial intelligence (AI) has been in existence for over six decades and has experienced AI winters and springs. The rise of super computing power and Big Data technologies appear to have empowered AI in recent years. The new generation of AI is rapidly expanding and has again become an attractive topic for research. This paper aims to identify the challenges associated with the use and impact of revitalised AI based systems for decision making and offer a set of research propositions for information systems (IS) researchers. The paper first provides a view of the history of AI through the relevant papers published in the International Journal of Information Management (IJIM). It then discusses AI for decision making in general and the specific issues regarding the interaction and integration of AI to support or replace human decision makers in particular. To advance research on the use of AI for decision making in the era of Big Data, the paper offers twelve research propositions for IS researchers in terms of conceptual and theoretical development, AI technology-human interaction, and AI implementation.
Germany is falling behind its peers in adopting new digital technologies and financial services offered by non-bank high-tech startups (e.g., FinTech). Using survey data, we analyze which FinTech services households are likely to adopt. Our results indicate that a household's level of trust and comfort with new technologies, financial literacy, and overall transparency impact its propensity to switch to a FinTech. Specifically, households with low levels of trust, good financial education, and preference for transparency are characterized by a higher probability of adopting FinTech. In contrast, household price perceptions do not appear to significantly impact switching probability.
The main aim of the study was to estimate the target values of agrochemical use, and its impact on crop productivity, and human health. Adoptions of protective measures and their impacts on human health during pesticide application were also evaluated. To fulfil the study objectives, using a standardised questionnaire, cross-sectional data of 480 rice growers were collected from September to October, 2017 from the Hafizabad and Sheikhupura districts of Punjab, Pakistan. Various econometric methods were used for data analysis. The artificial neural network (ANN) found an indiscriminate use of agrochemicals in the study area and suggested reducing the applied quantity of Pure N and pesticides by 43.6 and 52.6%, respectively, at a given level of rice yield, while the quantity of Pure P, Pure K, and farmyard manure (FYM) need to increase by 67.6, 15, and 21%, respectively, to obtain an efficient production level. The Cobb-Douglas (CD) production function estimated a positive and significant impact of Pure P, Pure K, FYM, education, and farming experience on rice yield. Results also showed that an indiscriminate use of pesticide not only compromised rice efficiency, it impinged on field worker health. Poisson regression found that occurrence of eye irritation, dizziness, cough, and nausea were significantly related with chemical application. Logistic regression found that education, farming experience, and extension services significantly increased in the adoption of various protective measures. Furthermore, cases of primary health exposures were significantly less among those who were used protective measures during pesticide application. Results of Poison regression function also confirmed that use of protective clothing, goggles, mask, gloves and boots during pesticide spraying significantly reduced human health exposures. It is recommended to apply the quantity of nutrients and chemicals suggested by ANN method. Use of biochemicals is a more sustainable and environmentally friendly strategy. Provision of educational programs and trainings to farmers on safe use of pesticides is required to avert occupational health exposures. Furthermore, governmental actions such as restriction and/or interdiction on use of highly toxic pesticides and enforcement to adopt safety measures during pesticide application, are needed to reduce pesticide exposures.
In the recent era of technological advances and hyper-competition, business intelligence (BI) systems have attracted significant attention from executives and decision makers due to their ability to provide complex and competitive information inputs for the decision process. Following the world of practice, research into the adoption, utilization and success of BI systems has grown substantially over the past two decades. The literature suggests that organizations have largely failed to capture the benefits of BI systems to their full extent and are seeking ways to leverage value from the implemented systems. However, prior studies do not have any comprehensive study that discusses the issues and challenges related to adoption, utilization and success of BI systems. In this study, using a systematic literature review, we present comprehensive knowledge about what has been found in the domain of BI system adoption, utilization and success. A total of 111 peer-reviewed studies, covering three categories – adoption, utilization and success – published between 2000 and 2019, were selected. The findings present the research methods, underpinning theories and key factors employed to study BI system adoption, utilization and success. In addition, the review identified the key issues related to BI adoption, utilization and success and highlighted the areas that have attracted more or less attention. This study also suggests future directions for researchers and practitioners in terms of unexplored themes that may help organizations to obtain value from BI systems.
Stakeholders across the entire healthcare chain are looking to incorporate artificial intelligence (AI) into their decision-making process. From early-stage drug discovery to clinical decision support systems, we have seen examples of how AI can improve efficiency and decrease costs. In this Opinion, we discuss some of the key factors that should be prioritized to enable the successful integration of AI across the healthcare value chain. In particular, we believe a focus on model interpretability is crucial to obtain a deeper understanding of the underlying biological mechanisms and guide further investigations. Additionally, we discuss the importance of integrating diverse types of data within any AI framework to limit bias, increase accuracy, and model the interdisciplinary nature of medicine. We believe that widespread adoption of these practices will help accelerate the continued integration of AI into our current healthcare framework.
Background:
Driven by the recent ubiquity of big data and computing power, we established the Machine Learning Arthroplasty Laboratory (MLAL) to examine and apply artificial intelligence (AI) to musculoskeletal medicine.
Methods:
In this review, we discuss the 2 core objectives of the MLAL as they relate to the practice and progress of orthopedic surgery: (1) patient-specific, value-based care and (2) human movement.
Results:
We developed and validated several machine learning-based models for primary lower extremity arthroplasty that preoperatively predict patient-specific, risk-adjusted value metrics, including cost, length of stay, and discharge disposition, to provide improved expectation management, preoperative planning, and potential financial arbitration. Additionally, we leveraged passive, ubiquitous mobile technologies to build a small data registry of human movement surrounding TKA that permits remote patient monitoring to evaluate therapy compliance, outcomes, opioid intake, mobility, and joint range of motion.
Conclusion:
The rapid rate with which we in arthroplasty are acquiring and storing continuous data, whether passively or actively, demands an advanced processing approach: AI. By carefully studying AI techniques with the MLAL, we have applied this evolving technique as a first step that may directly improve patient outcomes and practice of orthopedics.
Autonomous mining systems (AMS) are being developed and deployed for improving mine productivity, safety and efficiency in countries like Australia and USA. Nonetheless, AMS implementation is known to cause social challenges such as job losses. These systems are expected to eventually be introduced into the Ghanaian mining industry. Thus, it is necessary to understand stakeholder perceptions of AMS to inform policy direction towards their implementation in Ghana. Such knowledge does not exist in the literature. This paper assesses the preparedness of mine stakeholders for the adoption of AMS to surface mining industry in Ghana. Data was gathered using closed, and open-ended questionnaires and analyzed in MS Excel using pivot charts to identify respondents’ knowledge levels, and preparedness for adoption of AMS. Detailed qualitative and quantitative content analyses of the only mining engineering program in Ghana were conducted to assess the adequacy of the program in meeting the future skill demands of the mining industry. The results reveal that even though respondents generally had knowledge of AMS, they expressed unwillingness to accept AMS into Ghanaian mines due to fear of increased unemployment. University courses were perceived by the respondents to focus on mundane and outdated mining technologies. These perceptions were corroborated by detailed content analysis of mining curricula of a Ghanaian university, as 48% of undergraduate and 75% of postgraduate mining courses were focused on these mundane technologies. Thus, avenues for acquiring skill set demands of future mining operations do not exist. We propose setting up a mining education fund (MEF) for equipping mining programs with the needed facilities to train stakeholders (employees and future employees) in AMS to provide adequate local labor that can work with AMS.
Artificial Intelligent (AI) In-home Voice Assistants have seen unprecedented growth. However, we have little understanding on the factors motivating individuals to use such devices. Given the unique characteristics of the technology, in the main hands free, controlled
by voice, and the presentation of a voice user interface, the current technology adoption models are not comprehensive enough to explain the adoption of this new technology. Focusing on voice interactions, this research combines the theoretical foundations of U> with technology theories to gain a clearer understanding on the motivations for adopting and using in-home voice assistants. This research presents a conceptual model on the use of voice controlled technology and an empirical validation of the model through the use of Structural
Equation Modelling with a sample of 724 in-home voice assistant users. The findings illustrate that individuals are motivated by the (1) utilitarian benefits, (2) symbolic benefits and (3) social benefits provided by voice assistants, the results found that hedonic benefits
only motivate the use of in-home voice assistants in smaller households. Additionally, the research establishes a moderating role of perceived privacy risks in dampening and negatively influencing the use of in-home voice assistants.
Environmental impact evaluations in manufacturing fields is a complicated decision-making problem, which involves wastes emission, resource consumption and energy utility, etc. This paper presents an intelligence method for environmental impact evaluation using kernel fuzzy clustering and a back-propagation neural network. The objective of this article is to apply artificial intelligence technology to evaluate environmental influence of cutting process and develop a decision support tool for selecting the optimal environmental solution from various alternative schemes. There are three stages to accomplish this assessment procedure. First, the evaluation index system of the cutting process was analyzed and established. Next, a training sample set which was used for learning the ‘knowledge and experience’ by neural networks, was acquired by kernel fuzzy clustering algorithm. Finally, an evaluation model based on a back-propagation neural network was constructed, and connection weights of the model were determined after training. A case research on the cutting processes of the automobile workpiece was conducted. Results showed that the proposed method is more concise and practicable than existing evaluation methods and provide a feasible and effective decision-making tool for selecting an optimal cutting process while manufacturing.
The paper analyses a model of the EU energy system by means of artificial neural networks. This model is based on the prediction of CO 2 emissions until 2050 taking into account the current Energy Policy of the EU. The results show that artificial neural networks model this system very well and that this model has the ability to predict the behaviour of CO 2 emissions. This will also enable timely response and correction of energy and economic strategy by changing the value of the relevant indicators in order to achieve the ambitious planned reductions of CO 2 emissions by 2050. These plans are specified in the Energy Roadmap 2050 document of the European Commission from 2012 and promote economically cost-effective scenarios that will adapt the European Union's economy to the needs of environmental protection and the reduction of energy consumption. Several structures of Artificial Neural Networks were analysed in order to select the best one for modelling large energy systems. It was determined that the model with the Cascade Forward Back Propagation structure with numerous specific indicators can model such energy systems and predict of CO 2 emissions with acceptable accuracy.
The indiscriminate use of agrochemicals for the maximization of the crop yield has adverse effects on the air, water, soil, non-target organisms, and human health. Reducing the impacts of agrochemicals on the environment and human health is instrumental for agricultural sustainability and cleaner production. To date, limited studies have focused on the issues of rice intensification, realistic agrochemical-saving targets, human health concerns associated with agrochemical use, and protective measures that may help to reduce occupational exposure during pesticide application. Cross-sectional data of 360 rice growers were collected from September to October 2017 from 9 districts of Punjab, Pakistan using multistage sampling technique. A combination of descriptive statistics and econometric methods was used in this study. The results found a 60% rice efficiency, which is evidence that farm resources were not utilized at the optimal level. An artificial neural network method (ANN) was suggested to reduce the quantity of pesticides and pure N by 45.2 and 37.2%, respectively, at a given level of rice yield. However, pure P, pure K, zinc, and farm yard manure (FYM) were recommended to increase by 490.9, 18.4, 64.7, and 32.6%, respectively than existing level. The results of the Cobb-Douglas (CD) production function found positive significant impacts of pure P, pure K, zinc, and FYM on the rice yield. According to a Tobit regression model, the rice efficiency significantly increased with education and farming experience, while it decreased with increasing crop area under rice cultivation and the distance among rice plots. Pesticide application caused skin irritation, eye irritation, cough, dizziness, nausea, and diarrhoea in 33, 41.7, 38, 30.5, 27.5, and 12% of the population, respectively. A few cases of death (3%) and serious illness (10%) due to drinking pesticides intentionally or unintentionally were also discovered. A Poisson regression model confirmed that pesticide poisoning significantly increased the incidence of eye irritation, skin irritation, dizziness, cough, and nausea during pesticide application. Moreover, cases of occupational health exposure were significantly higher among those who did not adopt protective measures. A negative binomial regression suggested that the use of protective measures, such as protective clothes, goggles, mask, gloves and boots, during chemical application significantly reduced the risk to human health. A lack of education and awareness about the appropriate and safe use of agrochemicals are the main reasons for the overutilization of pesticides and for the negative consequences on human health. This study stresses the importance of using agrochemicals at the recommended level and instead using bio-chemicals for agricultural sustainability and to protect human health. Moreover, the use of pesticide protective measures is highly recommended to avoid respiratory and dermal exposure to pesticides.
As a commodity for daily consumption, coffee plays a crucial role in the economy of several African, American and Asian countries; yet, the accurate prediction of coffee yield based on environmental, climatic and soil fertility conditions remains a challenge for agricultural system modellers. The ability of an Extreme Learning Machine (ELM) model to analyse soil fertility properties and to generate an accurate estimation of Robusta coffee yield was assessed in this study. The performance of 18 different ELM-based models with single and multiple combinations of the predictor variables based on the soil organic matter (SOM), available potassium, boron, sulphur, zinc, phosphorus, nitrogen, exchangeable calcium, magnesium, and pH, was evaluated. The ELM model’s performance was compared to that of existing predictive tools: Multiple Linear Regression (MLR) and Random Forest (RF). Individual model performance and inter-model performance comparisons were based on the root mean square error (RMSE), mean absolute error (MAE), Willmott’s Index (WI), Nash-Sutcliffe efficiency
coefficient (ENS), and the Legates and McCabe’s Index (ELM) in the independent testing dataset. In the independent testing phase, an ELM model constructed with SOM, available potassium and available sulphur as predictor variables generated the most accurate coffee yield estimate (i.e., RMSE = 496.35 kg/ha or ± 13.6%, and MAE = 326.40 kg/ha or ± 7.9%). This contrasted with the less accurate MLR (RMSE = 1072.09 kg/ha and MAE = 797.60 kg/ha) and RF (RMSE = 1087.35 kg/ha and MAE = 769.57 kg/ha) model. Normalized metrics showed the ELM model’s ability to yield highly accurate results: WI = 0.9952, ENS = 0.406 and ELM = 0.431. In comparison to the MLR and RF models, the adoption of the ELM model as an improved class of artificial intelligence models for coffee yield prediction in smallholder farms in this study constitutes an original
contribution to the agronomic sector, particularly with respect to the appropriate selection of most optimal soil properties that can be used in the prediction of optimal coffee yield. The potential utility of coupling artificial intelligence algorithms with biophysical-crop models (i.e., as a data-intelligent automation tool) in decision-support systems that implement precision agriculture, in an effort to improve yield in smallholder farms based
on carefully screened soil fertility dataset was confirmed.
Although many studies have demonstrated the good performances of artificial intelligence (AI) approaches for reference evapotranspiration modeling, the applicability of AI approaches for actual crop evapotranspiration (ET) modeling still remains uncertain, especially in plastic mulched croplands. The objective of the present study was to evaluate the applicability of two different artificial intelligence approaches, including support vector machine (SVM) and artificial neural network optimized by genetic algorithm (GANN), in modeling actual ET in a rainfed maize field under non-mulching (CK) and partial plastic film mulching (MFR). A field experiment was conducted for continuous measurements of ET, meteorological variables, leaf area index (LAI) and plant heights (hc) under both CK and MFR during maize seasons of 2011–2013. The meteorological data containing minimum, maximum, mean air temperature, minimum, maximum, mean relative humidity, solar radiation, wind speed and crop data including LAI and hc during maize growing seasons of 2011–2012 were used to trained the SVM and GANN models by using two different input combination, and data of 2013 were used to validate the performances of the models. The results indicated that SVM1 and GANN1 models with meteorological and crop data as input could accurately estimate maize ET, which confirmed the good performances of SVM and GANN models for maize ET estimation. The performances of SVM2 and GANN2 models only with meteorological data as input were relatively poorer than those of SVM1 and GANN1 models, but the estimated results were acceptable when only meteorological data were available. Due to the optimizing of the genetic algorithm, the GANN models performed a slightly better than the SVM models under both CK and MFR, and can be highly recommended to model ET.
Artificial intelligence has been playing an increasingly large role in the economy and this trend seems likely to continue. This paper begins with a high-level overview of artificial intelligence, including some of its important strengths and weaknesses. It then discusses some of the ways that AI affect the evolution of the financial system and financial regulation.
Experts have suggested that the next few decades will herald the fourth industrial revolution. The fourth industrial revolution will be powered by digitization, information and communications technology, machine learning, robotics and artificial intelligence; and will shift more decision-making from humans to machines. The ensuing societal changes will have a profound impact on both personal selling and sales management research and practices. In this article, we focus on machine learning and artificial intelligence (AI) and their impact on personal selling and sales management. We examine that impact on a small area of sales practice and research based on the seven steps of the selling process. Implications for theory and practice are derived.
Aim:
To identify the extent to which transfer learning from deep convolutional neural networks (CNNs), pre-trained on non-medical images, can be used for automated fracture detection on plain radiographs.
Materials and methods:
The top layer of the Inception v3 network was re-trained using lateral wrist radiographs to produce a model for the classification of new studies as either "fracture" or "no fracture". The model was trained on a total of 11,112 images, after an eightfold data augmentation technique, from an initial set of 1,389 radiographs (695 "fracture" and 694 "no fracture"). The training data set was split 80:10:10 into training, validation, and test groups, respectively. An additional 100 wrist radiographs, comprising 50 "fracture" and 50 "no fracture" images, were used for final testing and statistical analysis.
Results:
The area under the receiver operator characteristic curve (AUC) for this test was 0.954. Setting the diagnostic cut-off at a threshold designed to maximise both sensitivity and specificity resulted in values of 0.9 and 0.88, respectively.
Conclusion:
The AUC scores for this test were comparable to state-of-the-art providing proof of concept for transfer learning from CNNs in fracture detection on plain radiographs. This was achieved using only a moderate sample size. This technique is largely transferable, and therefore, has many potential applications in medical imaging, which may lead to significant improvements in workflow productivity and in clinical risk reduction.
Drivers of social commerce usage has been the focus of scholars in recent years, but mobile social media users' resistance behavior towards mobile social commerce has been in the darkness and therefore worth torched lights on. With the data collected from mobile social media users who have no experience in mobile social commerce, Artificial Neural Network analysis was engaged to capture both linear and nonlinear relationships in a research model that consists of innovation barriers and privacy concern. Surprisingly, all resistances positively correlated with usage intention, except for image barrier, which appeared to be the most influencing resistance. Several explanations were offered for such outcomes. The possible coexistence of resistance behavior and usage intention resembles the fitting justification. Mobile social media users intend to embrace mobile social commerce; however, their intentions have been held up by their perceptions on innovation barriers and privacy concern. Based upon these outcomes, this study has reaffirmed the coexistence of resistances and usage intention, as well as the "privacy paradox" phenomenon. These discoveries are believed to have contributed to the existing literature. Practitioners are then advised to act accordingly to these findings, and several methods on catalyzing mobile social media users' adoption decision were suggested.
This work aims to develop a methodology to perform the active demand side management for households in smart grids, which contain distributed solar photovoltaic generation and energy storage. Such methodology outcomes a decision-making system that manages the battery aiming to reduce the consumer electricity cost. It also contributes to postpone the investments in expansion of the electricity grid if the higher loading period coincides with the higher electricity tariff of the day. The decision-making system is a validated neural network, trained with optimized data, which can be used in any household metting certain conditions – specific location and electricity tarrif, and consumption profile like to the standard verified by the local electricity utility. To validate this methodology, it was created three consumption and three solar generation profiles, which were combined to each other. The results show that the ANN-based decision-making system operates the baterry efficiently to achieve the minimum electricity bill.
Artificial Intelligence (AI) systems applications are widespread due to its domain independent characteristics. In this work, an attempt has been made for review on AI applications in Computer Aided Process Planning (CAPP) and manufacturing. Primarily, uniqueness of present review work addressed by analysis of existing review articles. The review work comprise of three main elements; 1. Feature based design, a primary input for a CAPP system, 2. Expert System (ES) usefulness in Process Planning (PP) and manufacturing and 3. Evolutionary approach applications. The review begins with an overview and the use of AI systems in decision making. Research works exemplified for the past three and half decades (1981–2016) are analyzed in terms of feature based modeling, Standards for Exchange of Product Model data approach, ES in PP, scheduling, manufacturing and miscellaneous applications. Role of Evolutionary Techniques (ET) in intelligent system development, execution of PP activities and manufacturing are described. A statistical analysis on existing review articles, number of publications, domain specific articles and percentage contribution of each area are carried out. Finally, research gaps are identified and the possible future research directions are presented.
We propose an extension to the capabiliti es of the Intelligent Autopilot System (IAS) from our previou s work, to be able to learn handling emergencies by observing and imitating human pilots. The IAS is a potential solution to th e current problem of Automatic Flight Control Systems of bein g unable to handle flight uncertainties, and the need to constr uct control models manually. A robust Learning by Imitation app roach is proposed which uses human pilots to demonstrate the task to be learned in a flight simulator while training datase ts are captured from these demonstrations. The datasets are then us ed by Artificial Neural Networks to generate control models automati cally. The control models imitate the skills of the human pilo t when handling flight emergencies including engine(s) failure or f ire, Rejected Take Off (RTO), and emergency landing, while a flig ht manager program decides which ANNs to be fired given the cu rrent condition. Experiments show that, even after being presented with limited examples, the IAS is able to handle such fl ight emergencies with high accuracy.
Gray et al. (2014) examined the productivity of expert systems/artificial intelligence research in accounting and came to the conclusion that both research on and practice use of expert systems/artificial intelligence had waned since the late 1990s. In our study, we reconsider these findings based on a broader view that is ‘artificial intelligence’ centric versus ‘expert systems’ centric. The results show that while there was a bit of a lull in the late 1990s, artificial intelligence research in accounting has continued to steadily increase over the past 30 years. Further consideration of artificial intelligence techniques as embedded modules in integrated audit support systems also suggest that use by practice continues to be robust. Based on these findings, we make a call for much more research on the usability, and use, of artificial intelligence techniques in accounting domains. Contrary to earlier perceptions, the research domain remains vibrant and holds great potential for AIS researchers to take a leadership role in advancing the field.
Robotics for agriculture and forestry (A&F
) represents the ultimate application of one of our society’s latest and most advanced innovations to its most ancient and important industries. Over the course of history, mechanization and automation increased crop output several orders of magnitude, enabling a geometric growth in population and an increase in quality of life across the globe. Rapid population growth and rising incomes in developing countries, however, require ever larger amounts of A&F output. This chapter addresses robotics for A&F in the form of case studies where robotics is being successfully applied to solve well-identified problems. With respect to plant crops, the focus is on the in-field or in-farm tasks necessary to guarantee a quality crop and, generally speaking, end at harvest time. In the livestock domain, the focus is on breeding and nurturing, exploiting, harvesting, and slaughtering and processing. The chapter is organized in four main sections. The first one explains the scope, in particular, what aspects of robotics for A&F are dealt with in the chapter. The second one discusses the challenges and opportunities associated with the application of robotics to A&F. The third section is the core of the chapter, presenting twenty case studies that showcase (mostly) mature applications of robotics in various agricultural and forestry domains. The case studies are not meant to be comprehensive but instead to give the reader a general overview of how robotics has been applied to A&F in the last 10 years. The fourth section concludes the chapter with a discussion on specific improvements to current technology and paths to commercialization.
In this chapter you will learn about the high-level concepts of security intelligence and the definition. Additionally, the chapter will go into great detail on all the various key performance indicators (KPIs) that are associated with security intelligence. Furthermore, the chapter will go into how you build your own security intelligence platform. You will learn about the genesis of security intelligence and the level of effort needed to achieve high-fidelity intelligence. It’s important to note that all of the intelligence feeds you can buy are equal and there is a lot of overlap, but, honestly, this is a good thing, as some will have other pressing intelligence over others. In the case of security intelligence, multiple security intelligence feeds are better than one.
Automation with inherent artificial intelligence (AI) is increasingly emerging in diverse applications, for instance, autonomous vehicles and medical assistance devices. However, despite their growing use, there is still noticeable skepticism in society regarding these applications. Drawing an analogy from human social interaction, the concept of trust provides a valid foundation for describing the relationship between humans and automation. Accordingly, this paper explores how firms systematically foster trust regarding applied AI. Based on empirical analysis using nine case studies in the transportation and medical technology industries, our study illustrates the dichotomous constitution of trust in applied AI. Concretely, we emphasize the symbiosis of trust in the technology as well as in the innovating firm and its communication about the technology. In doing so, we provide tangible approaches to increase trust in the technology and illustrate the necessity of a democratic development process for applied AI.
This study assesses the effect of music listening on the reduction of pain perception in a random pediatric population undergoing skin prick test (SPT) using a therapeutic system based on an innovative technology (Melomics music medicine, or M3). A randomized controlled trial was implemented by randomly assigning 72 children (mean age = 8.87) to either an experimental or a control group. During the prick test procedure, children in the experimental group listened to Melomics music, whereas children in the control group were not provided with any special auditory or other stimulation. The music was administered by a smartphone, in which M3 was running. The intensity of pain experimented by children during the procedure was assessed using a linear 0-10 cm (0: no pain, 10: severe pain) visual analogue scale (VAS). Presence of pain (VAS 3 to 10) was reported by 29% of experimental group compared to 81.1% of control group (p<0.001). VAS median value for M3 group was 2, as compared to a value of 4 for children with no music (p<0.001). Results showed a significant effect of M3 on the reduction of pain perception, diverting the attention of children from pain, and creating a pleasant environment. M3 seems to be a useful tool to easily and effectively manage the pain on a daily basis. Results suggest a potential use of Melomics music in many other clinical setting to reduce the perception of different kinds of pain in the children population.This article is protected by copyright. All rights reserved.
During the past 70+ years of research and development in the domain of Artificial Intelligence (AI) we observe three principal, historical waves: embryonic, embedded and embodied AI. As the first two waves have demonstrated huge potential to seed new technologies and provide tangible business results, we describe likely developments of embodied AI in the next 25-35 years. We postulate that the famous Turing Test was a noble goal for AI scientists, making key, historical inroads - while we believe that Biological Systems Intelligence and the Insect/Swarm Intelligence analogy/mimicry, though largely disregarded, represents the key to further developments. We describe briefly the key lines of past and ongoing research, and outline likely future developments in this remarkable field.
ICT-based decision aids are currently making waves in the modern business world simultaneously with increased pressure on auditors to play a more effective role in the governance and control of corporate entities. This paper aims to review the main research efforts and current debates on auditors’ use of artificial intelligent systems, with a view to predicting future directions of research and software development in the area. The paper maps the development process of artificial intelligent systems in auditing in the light of their identified benefits and drawbacks. It also reviews previous research efforts on the use of expert systems and neural networks in auditing and the implications thereof. The synthesis of these previous studies revealed certain research vacuum which future studies in the area could fill. Such areas include matching the benefits of adopting these intelligent agents with their costs, assessing the impact of artificial intelligence on internal control systems’ design and monitoring as well as audit committees’ effectiveness, and implications of using such systems for small and medium audit firms’ operations and survival, audit education, public sector organisations’ audit, auditor independence and audit expectations-performance gap.
