The paper initially addresses Malaysia’s readiness concerning the adoption of cloud computing services and provides insights into the country’s internet infrastructure and internet users, fixed and mobile broadband deployment, and usage of international bandwidth for improvising cloud services. In addition, the paper investigates the scenario around policy areas, including privacy and security laws, being the chief contributors for the adoption of cloud services across regions and borders. The objective behind conducting the research is to validate the current scenario in Malaysia for the cloud service adoption by healthcare and education segment and identifying the consumer service attribute preferences. By performing the conjoint analysis the research would help in identifying the determinants and prioritizing them based on user preferences with prior use of Multinomial Logit choice model (MNL) and Max Likelihood function. A statistical discussion is also presented concerning the current global market scenario and scenario in the Asia Pacific across the verticals.
One of the key issues of the learning experience is students’ performance during the course, as this is pointed to as one of the main indicators for boosting competences’ development and skills’ improvement. This study explores the roles of spirituality, forgiveness, and gratitude on students’ academic performance, proposing a model of analysis revealing a first-order moderation effect of spirituality in the mediation effect of happiness, on the relation between gratitude and forgiveness with students’ academic performance. Two hundred twenty management students from various Indian universities voluntarily participated in the study. To avoid common method-bias issues, data concerning the study variables were obtained in two distinct moments. To test for the moderated-mediation model of analysis, we have followed the PROCESS analytical procedure. Results showed that forgiveness and gratitude were positively and significantly related to happiness and academic performance. It was also possible to see that spirituality moderates the relationship between forgiveness for self and student happiness. Finally, the moderated-mediating impact of spirituality and happiness on the relationship between gratitude and academic performance was also supported. The present study has taken the lead from positive psychology to assess the students’ character strengths related to their well-being and success. It proposes an innovative model of analysis, supported by theoretical reasoning, pointing to the existence of a moderated-mediation relation predicting students’ academic performance.
Recent period has witnessed benchmarked performance of transfer learning using deep architectures in computer-aided diagnosis (CAD) of breast cancer. In this perspective, the pre-trained neural network needs to be fine-tuned with relevant data to extract useful features from the dataset. However, in addition to the computational overhead, it suffers the curse of overfitting in case of feature extraction from smaller datasets. Handcrafted feature extraction techniques as well as feature extraction using pre-trained deep networks come into rescue in aforementioned situation and have proved to be much more efficient and lightweight compared to deep architecture-based transfer learning techniques. This research has identified the competence of classifying breast cancer images using feature engineering and representation learning over the established and contemporary notion of using transfer learning techniques. Moreover, it has revealed superior feature learning capacity with feature fusion in contrast to the conventional belief of understanding unknown feature patterns better with representation learning alone. Experiments have been conducted on two different and popular breast cancer image datasets, namely, KIMIA Path960 and BreakHis datasets. A comparison of image-level accuracy is performed on these datasets using the above-mentioned feature extraction techniques. Image level accuracy of 97.81% is achieved for KIMIA Path960 dataset using individual features extracted with handcrafted (color histogram) technique. Fusion of uniform Local Binary Pattern (uLBP) and color histogram features has resulted in 99.17% of highest accuracy for the same dataset. Experimentation with BreakHis dataset has resulted in highest classification accuracy of 88.41% with color histogram features for images with 200X magnification factor. Finally, the results are contrasted to that of state-of-the-art and superior performances are observed on many occasions with the proposed fusion-based techniques. In case of BreakHis dataset, the highest accuracies 87.60% (with least standard deviation) and 85.77% are recorded for 200X and 400X magnification factors, respectively, and the results for the aforesaid magnification factors of images have exceeded the state-of-the-art.
The present work deals with the propagation of Rayleigh surface wave in a self-reinforced thermoelastic layer lying over a dry sandy thermoelastic half-space. The expressions for thermal stresses and displacement components have been derived to characterize the dynamics of dry sandy materials. The dispersion equation of Rayleigh wave is obtained using concepts of potential function subjected to suitable boundary conditions. The obtained dispersion equation is complex in nature, so separating the real part of 6th order determinant expression, the dispersion equation of Rayleigh wave in the earth’s crust of sandy media is obtained and analyzed carefully. Some special cases are deduced to obtain the classical equation of Rayleigh wave that is well-consistent with the pre-established developed outcome. A numerical approach has been taken to express the theoretical result graphically.
Promoting mobility by public transport, rather than individual automobiles, is a worldwide accepted and promoted sustainable solution. Traditionally, cycle-rickshaws have been the backbone of localised transportation in cities in India, thus a key component of 'Last Mile Connectivity' (LMC) in the urban transport sector. However, in the last few years, there has been an attempt to promote cycle rickshaws with battery-operated 'e-rickshaws' to make cities smarter. This paper examines the feasibility and consequences of such change in Ranchi, the capital city of Jharkhand state in India, its impact on traditional cycle rickshaw pullers; and how they fit in the overall urban transport sector. This paper presents Passengers and Drivers' overall experience of E-Rickshaws and Cycle Rickshaws in promoting last-mile connectivity. Data collected through in-depth interviews with twenty e-rickshaw drivers, twenty rickshaw pullers, and twenty passengers in Ranchi highlights the user experiences of e-rickshaw and advantages, disadvantages, as well as the effectiveness of e-rickshaw on the streets. The study shows that E-rickshaws and cycle rickshaws may remain a viable Last Mile Connectivity in the urban transport sector.
Health care informatics, also referred as biomedical or medical informatics, is an application of information engineering and management in the medical field. Health care fundamentally covers the management and employment of patient health care information. It is an multidisciplinary field that studies and pursues the effectual use of biomedical data, knowledge for scientific inquiry, information, problem solving, and decision making. Often aided by the use of technology, the main objective is to improve personal health, health care, public health, and biomedical research. Computational Intelligence (CI) is devoted to the solution of non‐algorithm problems, and in this way, artificial intelligence (AI) is a part of CI that is concentrated on the problems associated with higher cognitive functions. In this chapter, some types of smart health care informatics will be addressed.
Sustainable waste management has been a challenge for economies globally, especially in small and isolated regions, like the Arctic communities. Waste management in the Northwest Territories (NWT), Canada is relatively unique and energy and emission-intensive in terms of infrastructure, accessibility, data unavailability, arctic climate, funding limitations, etc. To bridge the evident gap in literature on waste characteristics and management in the Northern and remote communities, this study closely examines the the current practices of waste management, assesses the quantity and characteristics of waste generated through theoretical estimation, analyses the flow of waste through the existing system, and proposes an alternative system that focuses on material recovery. The potential greenhouse gas (GHG) emissions from both the current and proposed alternative system are also estimated to understand the environmental impacts. The average annual waste generation and disposal in NWT is nearly 0.66 tonne/capita/year (T/cap/y) and 0.62 T/cap/y, respectively. It is observed that increased waste diversion through composting and recycling can reduce the GHG emissions in NWT by 22,703 CO2e. With the estimated physico-chemical and thermal characteristics of wastes in NWT, this article serves as a knowledge base for decision-makers and policy analyses for developing a sustainable waste management paradigm, especially in isolated areas like NWT with extreme cold climate.
COVID-19 is a pandemic initially identified in Wuhan, China, which is caused by a novel coronavirus, also recognized as the Severe Acute Respiratory Syndrome (SARS-nCoV-2). Unlike other coronaviruses, this novel pathogen may cause unusual contagious pain, which results in viral pneumonia, serious heart problems, and even death. Researchers worldwide are continuously striving to develop a cure for this highly infectious disease, yet there are no well-defined absolute treatments available at present. Several vaccination drives using emergency use authorisation vaccines have been held across many countries; however, their long-term efficacy and side-effects studies are yet to be studied. Various analytical and statistical models have been developed, however, their outcome rate is prolonged. Thus, modern science stresses the application of state-of-the-art methods to combat COVID-19. This paper aims to provide a deep insight into the comprehensive literature about AI and AI-driven tools in the battle against the COVID-19 pandemic. The high efficacy of these AI systems can be observed in terms of highly accurate results, i.e., > 95%, as reported in various studies. The extensive literature reviewed in this paper is divided into five sections, each describing the application of AI against COVID-19 viz. COVID-19 prevention, diagnostic, infection spread trend prediction, therapeutic and drug repurposing. The application of Artificial Intelligence (AI) and AI-driven tools are proving to be useful in managing and fighting against the COVID-19 pandemic, especially by analysing the X-Ray and CT-Scan imaging data of infected subjects, infection trend predictions, etc.
Background With increased penetration of the internet and social media, there are concerns regarding its negative role in influencing parents’ decisions regarding vaccination for their children. It is perceived that a mix of religious reasons and propaganda by anti-vaccination groups on social media are lowering the vaccination coverage in Malappuram district of Kerala. We undertook a qualitative study to understand the factors responsible for generating and perpetuating vaccine hesitancy, the pathways of trust deficit in immunization programs and the interaction between various social media actors. Methods In-depth interviews and focus group discussions were conducted among parents/caregivers, physicians, public sector health staff, alternative system medical practitioners, field healthcare workers and teachers in areas with highest and lowest vaccination coverage in the district, as well as with communication experts. Results The trust deficit between parents/caregivers and healthcare providers is created by multiple factors, such as providers’ lack of technical knowledge, existing patriarchal societal norms and critical views of vaccine by naturopaths and homeopaths. Anti-vaccine groups use social media to influence caregivers' perceptions and beliefs. Religion does not appear to play a major role in creating vaccine resistance in this setting. Conclusions A long-term, multipronged strategy should be adopted to address the trust deficit. In the short to medium term, the health sector can focus on appropriate and targeted vaccine-related communication strategies, including the use of infographics, soft skills training for healthcare workers, technical competency improvement through a mobile application-based repository of information and creation of a media cell to monitor vaccine-related conversations in social media and to intervene if needed.
This research aims to design and evaluate an organization's performance Use the human resource scorecard method to establish the priority weight of strategic objectives and important network analysis plan performance indicators. This analysis resulted in 16 strategic objectives and 20 key indicators and 17 delays hands. The role of performance assessment in a business is discussed in this report, as it is used to determine its progress. Focusing on the organizational perspectives, the priorities show that the company focuses more on the administrative, strategic, and consumer perspectives than on the financial perspective. A human resources scorecard design strategy was developed based on the design and measuring results obtained from this study and used by the organization.
The efficacy of content-based image classification is dependent on the richness of the feature vectors extracted from the image data. Traditional feature extraction techniques highlight single low level image characteristics like shape, size, texture, color etc. for feature generation. The process often fails to extract meaningful descriptors since considering a single image characteristic will ignore other rich properties of image contents. Mass adoption of Convolutional Neural Network (CNN) has significantly improved classification performances for content-based image data. Recent literature has documented high level of precision for image classification by carrying out transfer learning with open source pre trained Convolutional Neural Network (CNN). However, the concept of transfer learning experiences major setback in case of limited training data due to overfitting of training instances during finetuning. This work has identified this challenge and has attempted to capture probability distribution of input images to a pre trained Convolution Neural Network by utilizing it as a fixed weight feature extractor. The proposed approach has addressed the overfitting problem by removing the finetuning step involved in transfer learning. Further investigation on robust descriptor definition is carried out by concatenating the pre-trained CNN features to handcrafted features. The hybrid architecture has encouraging outcomes that have outclassed the classification accuracies of state-of-the-art handcrafted techniques.
COVID-19 caused by novel coronavirus is a serious pandemic that has affected the various countries all across the globe. The effect of this pandemic is so devastating that many rising nations are brought to their knees and struggling to save the damage posed to their economy. Medical professionals and the healthcare community are paying their best effort to minimize and overcome the spread of this pandemic. To continue to fight against the COVID-19, healthcare delivery systems require the support of novel technologies which can meet their rapid demand for medical equipment and devices. The study explores the damage caused by COVID-19 to the industrial sector and the way AM is contributing to the economy post-COVID-19. State of the art concerning the application of AM in the present scenario especially to support the interrupted global supply chain is collected and analysed to identify its relevance in the battle against COVID-19.
Feature vector extraction is a significant aspect of content-based image classification. Researchers have proposed multiple techniques for representing the image content in the form of feature vectors using important image properties like shape, colour, texture, etc. However, a single feature vector extracted using a particular technique is mostly unable to capture important details of images. This work has attempted a decision fusion-based classification approach using two different features extracted with image binarization and image transform technique, respectively. The results of decision fusion for classification have outperformed the individual approaches.
Recent economy and financial business environment is undergoing a quick and accelerating revolution and paradigm shift, resulting in growing uncertainty and complexity. Therefore, the need for an all-inclusive and far-reaching performance measurement model is universally felt as it can provide management-oriented information and act as a supporting tool in developing, inspecting, and interpreting policy-making strategies of an enterprise to achieve competitive advantages. Hence, this paper proposes an application of the balanced scorecard (BSC) model in an insurance organization for coordinating and regulating its corporate vision, mission, and strategy with organizational performance through the interrelation of different layers of business perspectives. In the next stage, a framework to unify both BSC and best-worst method (BWM) models is implemented for the very first time in the insurance domain to assess its performance over two-time periods. The integrated BSC-BWM model can help managers and decision-makers to figure out and interpret competing strength of the said enterprise and consecutively expedite inefficient and compelling decision making. Nevertheless, this integrated model is embraced and selected for a certain categorical business and there is enough future scope for its application to distinct industries.
Dermoscopic images carry rich information to identify the malignancy in patients at initial stage. Research initiatives in the domain of content-based image classification can be instrumental in identifying fatal diseases like skin cancer by exploring the dermoscopic image database. This paper has carried out feature dimension reduction for representation of significant content-based image descriptors to the classifiers. The approach has resulted in designing an early fusion based classification model with reduced computational overhead to enhance accuracy of malignancy detection at its inception.
Cloud computing is the latest technology. Provides various on-demand services and online for network services, platform services, data storage, etc. Many organizations are not thrilled with using cloud services due to data security concerns, as the data resides on the cloud service provider's servers. To address this problem, various researchers around the world have applied various approaches to strengthen the security of data stored in cloud computing. The latest development in the field of cryptography is DNA encryption. It arose after the disclosure of the computational ability of deoxyribonucleic acid (DNA). DNA encryption uses DNA as a computational tool along with various molecular techniques to manipulate it. Due to the large storage capacity of DNA, this field is becoming very promising. This paper used a layered DNA encryption method for the data encryption and decryption process. Using the four DNA bases (A, C, G, T), we generate dynamic DNA tables to replace the message characters with a dynamic DNA sequence. The implementation of the proposed approach is performed in Python and the experimental results are verified. The resulting encrypted text contains information that will provide greater security against intruder attacks.
Steel has played critical role in the expansionist ambition of successful aristocrats and laying the foundation of enviable empires. Modern economies are built on strong infrastructure, communication, and transport networks. Industries, like automobiles, consumer durables, real estate, cannot be imagined without steel. Its synonymy with growth of an economy can be gauged by the fact that matrices, like per capital steel consumption and its contribution to the GDP, are considered as parameters of economic development. It is one of the most tracked industries by the investors, analysts, and financial institutions. In this chapter, a novel multi-criteria decision-making tool while integrating measurement alternatives and ranking according to compromise solution (MARCOS) and criteria importance through intercriteria correlation (CRITIC) methods is developed for evaluation of steel organisations which are constituents of BSE 200 index. The results derived from the implementation of integrated MARCOS-CRITIC model aid diverse stakeholders in making informed investment decisions.
The social and economic development of a nation in today's modern day world is critically dependent on performance of its banking system. An efficient banking system of a country can only productively channelize the monetary resources in the economy to successfully augment the societal and financial activities in the nation. With incessant transformation in regulation, expertise and competition in Indian banking industry over and above the rising cost-income ratios and non performing assets, it is imperative to analyse the performance of banks operating in India. There are multiple criteria, like long term business profitability, non performing asset (NPA), earning quality etc. which at times are contradictory in nature, impacting the successful operation of the banks. Therefore, in this paper, a novel method on the basis of step-wise weight assessment ratio analysis (SWARA) and weighted sum method (WSM) techniques is presented for the first time to select the best public sector bank in India on selected parameters. The SWARA technique is implemented here to estimate the relative importance of the criteria and the WSM methodology is employed to prioritize the alternatives. The results derived from implementation of proposed integrated SWARA-WSM model can assist policy makers to better understand the preeminent parameters leading towards sustainable success of banks.
Since independence, India has made great progress, but still there exist problem like population explosion, rural backwardness, malnutrition, unemployment, illiteracy, deforestation, etc., which affect the livelihood of rural population immensely. Central and State governments are running various programs with an objective to enhance the standard of living of rural India through linking them to sustainable livelihood opportunities. But, the success of these programs is governed by different factors which are sometimes contradictory in nature. Thus, there is a need for systematic and logical approach that can address these issues. Multi Objective Optimization on the basis of ratio analysis (MOORA) is one of the widely used techniques for the identification of best option in the presence of several conflicting attributes or criteria. This study attempts to find out the factors that are important for the success of social programs run by the government while implementing MOORA method. For this study, nine enablers which impact the success of government social programs are identified and ranked as per their influence on considered cases. Results obtained from the study would aid in allocation of fund in more organized manner so that the objectives of the program may be met successfully.
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