AMA International University
Recent publications
Early and precise knowledge of asthma severity levels may help in effective precautions, proper medication, and follow-up planning for the patients. Keeping this in view, we propose a telemedicine application that is capable of automatically identifying the severity level of asthma patients by using machine learning techniques. Respiratory sounds of 111 asthmatic patients were collected. The 111-patient dataset consisted of 34 mild, 36 moderate, and 41 severe levels. Data was collected from two auscultation locations, i.e., from the trachea and lower lung base. The first dataset was used for the testing and training (cross-validation) of classifiers while a second database was used for the validation of the system. Mel-frequency cepstral coefficient (MFCC) features were extracted to discriminate the severity levels. Then, ensemble and k-nearest neighbor (KNN) classifiers were used for classification. This was performed on both auscultation locations jointly and individually. The developed telemedicine application, based on MFCC features and classifiers, automatically detects wheeze and classifies it into a severity level. The extracted features showed significant differences (p < 0.05) for all severity levels. Based on the testing, training, and validation results, the performance of the ensemble and KNN classifiers were comparable. MFCC-based features classification provides maximum accuracy of 99%, 90%, and 89% for mild, moderate, and severe samples, respectively. The average rate of wheeze detection was observed to be 93%. The maximum accuracy of validation of the telemedicine application was found to be 57%, 72%, and 76% for mild, moderate, and severe levels, respectively.
Tele-training in surgical education has not been effectively implemented. There is a stringent need for a high transmission rate, reliability, throughput, and reduced distortion for high-quality video transmission in the real-time network. This work aims to propose a system that improves video quality during real-time surgical tele-training. The proposed approach aims to minimise the video frame’s total distortion, ensuring better flow rate allocation and enhancing the video frames’ reliability. The proposed system consists of a proposed algorithm for Enhancing Video Quality, Distorting Minimization, Bandwidth efficiency, and Reliability Maximization called (EVQDMBRM) algorithm. The proposed algorithm reduces the video frame’s total distortion. In addition, it enhances the video quality in a real-time network by dynamically allocating the flow rate at the video source and maximizing the transmission reliability of the video frames. The result shows that the proposed EVQDMBRM algorithm improves the video quality with the minimized total distortion. Therefore, it improves the Peak Signal to Noise Ratio (PSNR) average by 51.13 dB against 47.28 dB in the existing systems. Furthermore, it reduces the video frames processing time average by 58.2 milliseconds (ms) against 76.1, and the end-to-end delay average by 114.57 ms against 133.58 ms comparing to the traditional methods. The proposed system concentrates on minimizing video distortion and improving the surgical video transmission quality by using an EVQDMBRM algorithm. It provides the mechanism to allocate the video rate at the source dynamically. Besides that, it minimizes the packet loss ratio and probing status, which estimates the available bandwidth.
Aim/Purpose: The objective of the research was to study the relationship of seven independent factors: administrative support, course content, course design, instructor characteristics, learner characteristics, social support, and technical support on quality of e-learning in higher education during the COVID-19 pandemic. Further, the study analyzes the moderating effect(s) of gender and level of the course on the quality of e-learning in higher education during the COVID-19 pandemic. Background: The COVID-19 pandemic situation has impacted the entire education system, especially universities, and brought a new phase in education “e-learning.” The learning supported with electronic technology like online classes and portals to access the courses outside the classroom is known as e-learning. This study aimed to point out the variables influencing the quality of e-learning, such as administrative support, course content, course design, instructor characteristics, learner characteristics, social support, and technological support. Methodology: An inferential statistics cross-sectional study was conducted of the students of higher education institutions in India and the Kingdom of Saudi Arabia with a self-administered questionnaire to learn the students’ perception of e-learning. All levels of undergraduate and postgraduate students took part in the study with a sample size of 784. Ultimately, this study used a Structural Equation Modelling (SEM) approach to find the positive relationship between the quality of e-learning and the seven independent variables and two moderating variables in the higher education sector. Contribution: The study aims to explore the quality of e-learning in higher education from the students’ perspective. The study was analyzed based on the student’s data collected from the higher educational institutions of India and Saudi Arabia. The study will support the top management and administrators of higher educational institutions in decision making. Findings: The findings revealed that there is a positive relationship between the set of variables and the quality of e-learning in the higher education sector. Also, there is a significant difference in the perception of the students be-tween gender, level of the course, and quality of e-learning in the higher education sector during the COVID-19 pandemic. Recommendations for Practitioners: The results of the study can help top management and administrators of higher educational institutions to improve their actions. Higher educational institutions need to concentrate on the study outcomes related to administrative support, course content, course design, instructor characteristics, learner characteristics, social support, and technological support to enhance the quality of e-learning. The study revealed that there should be a difference in the procedure of providing e-learning based on the level of the course and gender of the students. Recommendations for Researchers: The results were examined and interpreted in detail, based on the perspective of the students, and concluded with a view for future research. The study will be beneficial for academic researchers from different countries with a different set of students and framework. Impact on Society: The study revealed that the positive results of the students’ perspective on the quality of e-learning would help the policy-makers of the country in providing the learning process during the COVID-19 pandemic. Also, the result explored the importance of the quality aspects of e-learning for improvements. Future Research: There is a need for future studies to expose the quality of e-learning in higher education in the post-COVID-19 pandemic. Further researchers will bring the performance level of e-learning during the COVID-19 pandemic.
With the advent of the 20th century, the popularity of digital service usages is increasing every day. The internet has always been a popular communication method, and phishing Webpages have been a challenging issue for more than two decades. Especially, E-commerce and other global companies face enormous challenges due to phishing of websites. Many developed countries have reported substantial economic loss due to unwanted phishing activities. With the exponential increase of digital communications, these phishing activities are going to be increased. There is a need foran effective intrinsic phishing detection technique. Phishing websites have some unique features by which they can be identified. In this research,a Light gradient boosting machine-based phishing email detection model using phisher websites' features of mimic URLs has been proposed. The primary objective is to develop a highly secured and accurate model for successful identification of security breach through websites phishing. With the performance comparison of other ensemble as well as state-of-the-art machine learning models, the proposed model resulted high performance accuracy and proved to a robust approach for phishing activity.
Fuzzy logic controller is one of the most prominent research fields to improve efficiency for process industries, which usually stick to the conventional proportional-integral-derivative (PID) control. The paper proposes an improved version of the three-term PID-like fuzzy logic controller by removing the necessity of having user-defined parameters in place for the algorithm to work. The resulting non-parametric three-term dissimilarity-based clustering fuzzy logic controller algorithm was shown to be very efficient and fast. The performance study was conducted by simulation on armature-controlled and field-controller DC motors, for linguistic type and Takagi-Sugeno-Kang (TSK) models. Comparison of the created algorithm with fuzzy c-means algorithm resulted in improved accuracy, increased speed and enhanced robustness, with an especially high increase for the TSK type model.
Encouraging women as entrepreneurs in the recent scenario are the government initiative over the globe. Some women started these small enterprises to support their living and their families, however, the commitments are minor and significant numbers of projects are probably not increasing in the long run. Thus, the current study aims to explore the factors contributing in motive behind female entrepreneurship to participate in the economic development as an effective entrepreneur by leading a successful business based on their specifics characteristics. Using factor analysis with the data of 80 female entrepreneurs, the study concluded that there are nine indicators that contribute significantly to become a successful entrepreneur. Therefore, the current study suggests that there must be a ceaseless effort to move, stimulate and coordinate with women entrepreneur while developing the policies to promote women in business meanwhile, women itself need to attempt and update themselves inside the changing over occasions by methods for adjusting the pristine time benefits. Women ought to be taught and talented persistently to assemble the capacities and information in the greater part of the viable districts of business administration. This will encourage women to exceed expectations in choice-making process and expand a decent business.
In recent years, electroencephalography-based navigation and communication systems for differentially enabled communities have been progressively receiving more attention. To provide a navigation system with a communication aid, a customized protocol using thought evoked potentials has been proposed in this research work to aid the differentially enabled communities. This study presents the higher order spectra based features to categorize seven basic tasks that include Forward, Left, Right, Yes, NO, Help and Relax; that can be used for navigating a robot chair and also for communications using an oddball paradigm. The proposed system records the eight-channel wireless electroencephalography signal from ten subjects while the subject was perceiving seven different tasks. The recorded brain wave signals are pre-processed to remove the interference waveforms and segmented into six frequency band signals, i. e. Delta, Theta, Alpha, Beta, Gamma 1-1 and Gamma 2. The frequency band signals are segmented into frame samples of equal length and are used to extract the features using bispectrum estimation. Further, statistical features such as the average value of bispectral magnitude and entropy using the bispectrum field are extracted and formed as a feature set. The extracted feature sets are tenfold cross validated using multilayer neural network classifier. From the results, it is observed that the entropy of bispectral magnitude feature based classifier model has the maximum classification accuracy of 84.71 % and the value of the bispectral magnitude feature based classifier model has the minimum classification accuracy of 68.52 %.
In this paper, a speech-to-text translation model has been developed for Malaysian speakers based on 41 classes of Phonemes. A simple data acquisition algorithm has been used to develop a MATLAB graphical user interface (GUI) for recording the isolated word speech signals from 35 non-native Malaysian speakers. The collected database consists of 86 words with 41 classes of phoneme based on Affricatives, Diphthongs, Fricatives, Liquid, Nasals, Semivowels and Glides, Stop and Vowels. The speech samples are preprocessed to eliminate the undesirable artifacts and the fuzzy voice classifier has been employed to classify the samples into voiced sequence and unvoiced sequence. The voiced sequences are divided into frame segments and for each frame, the Linear Predictive co-efficients features are obtained from the voiced sequence. Then the feature sets are formed by deriving the LPC features from all the extracted voiced sequences, and used for classification. The isolated words chosen based on the phonemes are associated with the extracted features to establish classification system input-output mapping. The data are then normalized and randomized to rearrange the values into definite range. The Multilayer Neural Network (MLNN) model has been developed with four combinations of input and hidden activation functions. The neural network models are trained with 60%, 70% and 80% of the total data samples. The neural network architecture was aimed at creating a robust model with 60%, 70%, and 80% of the feature set with 25 trials. The trained network model is validated by simulating the network with the remaining 40%, 30%, and 20% of the set. The reliability of trained network models were compared by measuring true-positive, false-negative, and network classification accuracy. The LPC features show better discrimination and the MLNN neural network models trained using the LPC spectral band features gives better recognition.
In this paper, an intelligent classification system has been developed to command a robot chair by means of direct brain activity, aided by amplification. The intelligent system classifies seven fundamental tasks based on measuring ElectroEncephaloGraphic (EEG) brain activity. The seven tasks were used to control a robot chair and also to interact with others. In this analysis, a simple protocol for the EEG data acquisition procedure has been proposed to perform seven tasks based on thought evoked potentials (TEP’s). The evoked potentials were converted into control signals to navigate the robot chair and also to choose words/letters in an oddball paradigm for communication. In the EEG acquiring process, five volunteers participated and brain activities related to navigational movements (Forward, Left, and Right) and communication (Yes, No, and Help) were recorded from the volunteers to form the database. The acquired EEG signals are visually validated upon recording each trial and pre-processed to eliminate the noise contents. The pre-processed signals were segmented into six frequency bands to extract spectral band energy and spectral band centroid features. The extracted features were then formed to classify the tasks using a feed-forward Multilayer Neural Network algorithm to exhibit customized (subject wise) features. The trained models of the neural networks were compared to validate the classification results. From the results, it is observed that the Spectral centroid features have the highest classification rate of 98.50%.
Background: A simple data collection approach based on electroencephalogram (EEG) measurements has been proposed in this study to implement a brain-computer interface, i.e., thought-controlled wheelchair navigation system with communication assistance. Method: The EEG signals are recorded for seven simple tasks using the designed data acquisition procedure. These seven tasks are conceivably used to control wheelchair movement and interact with others using any odd-ball paradigm. The proposed system records EEG signals from 10 individuals at eight-channel locations, during which the individual executes seven different mental tasks. The acquired brainwave patterns have been processed to eliminate noise, including artifacts and powerline noise, and are then partitioned into six different frequency bands. The proposed cross-correlation procedure then employs the segmented frequency bands from each channel to extract features. The cross-correlation procedure was used to obtain the coefficients in the frequency domain from consecutive frame samples. Then, the statistical measures ("minimum," "mean," "maximum," and "standard deviation") were derived from the cross-correlated signals. Finally, the extracted feature sets were validated through online sequential-extreme learning machine algorithm. Results and conclusion: The results of the classification networks were compared with each set of features, and the results indicated that μ (r) feature set based on cross-correlation signals had the best performance with a recognition rate of 91.93%.
Aim/Purpose The objective of the research was to study the relationship of seven independent factors: administrative support, course content, course design, instructor characteristics, learner characteristics, social support, and technical support on quality of e-learning in higher education during the COVID-19 pandemic. Further, the study analyzes the moderating effect(s) of gender and level of the course on the quality of e-learning in higher education during the COVID-19 pandemic. objective of the research was to study the relationship of seven independent factors: administrative support, course content, course design, instructor characteristics, learner characteristics, social support, and technical support on quality of e-learning in higher education during COVID-19 pandemic. Background The COVID-19 pandemic situation has impacted the entire education system, especially universities, and brought a new phase in education “e-learning.” The learning supported with electronic technology like online classes and portals to access the courses outside the classroom is known as e-learning. This study aimed to point out the variables influencing the quality of e-learning, such as administrative support, course content, course design, instructor characteristics, learner characteristics, social support, and technological support. Methodology An inferential statistics cross-sectional study was conducted of the students of higher education institutions in India and the Kingdom of Saudi Arabia with a self-administered questionnaire to learn the students’ perception of e-learning. All levels of undergraduate and postgraduate students took part in the study with a sample size of 784. Ultimately, this study used a Structural Equation Modelling (SEM) approach to find the positive relationship between the quality of e-learning and the seven independent variables and two moderating variables in the higher education sector. Contribution The study aims to explore the quality of e-learning in higher education from the students’ perspective. The study was analyzed based on the student’s data collected from the higher educational institutions of India and Saudi Arabia. The study will support the top management and administrators of higher educational institutions in decision making. Findings The findings revealed that there is a positive relationship between the set of variables and the quality of e-learning in the higher education sector. Also, there is a significant difference in the perception of the students between gender, level of the course, and quality of e-learning in the higher education sector during the COVID-19 pandemic. Recommendations for Practitioners The results of the study can help top management and administrators of higher educational institutions to improve their actions. Higher educational institutions need to concentrate on the study outcomes related to administrative support, course content, course design, instructor characteristics, learner characteristics, social support, and technological support to enhance the quality of e-learning. The study revealed that there should be a difference in the procedure of providing e-learning based on the level of the course and gender of the students. Recommendation for Researchers The results were examined and interpreted in detail, based on the perspective of the students, and concluded with a view for future research. The study will be beneficial for academic researchers from different countries with a different set of students and framework. Impact on Society The study revealed that the positive results of the students’ perspective on the quality of e-learning would help the policy-makers of the country in providing the learning process during the COVID-19 pandemic. Also, the result explored the importance of the quality aspects of e-learning for improvement. Future Research There is a need for future studies to expose the quality of e-learning in higher education in the post-COVID-19 pandemic. Further researchers will bring the performance level of e-learning during the COVID-19 pandemic.
This study is based on a voluntary organization in the Kingdom of Bahrain (subsequently referred to as Bahrain) and explores the human resource development (HRD) practices adopted by not‐for‐profit organizations and their transferability. Gaps are identified in the existing literature about HRD for voluntary organizations in scrutinizing HRD processes and practices. In‐depth interviews were carried out with volunteers working for a voluntary organization in the Kingdom of Bahrain. Verbatim transcripts of the interviews were then analyzed using interpretative phenomenological analysis and thematic analysis. The results of this study found that HRD practices in for‐profit organizations are transferable and compatible with the HRD practices for not‐for‐profit organizations nongovernmental organizations in Bahrain.
Loneliness is an unpleasant feeling within which an individual finds oneself under strong emptiness and depression due to the poor social connections and consequently leads to deterioration of one's well-being. This phenomenon may become even worst in the case of older adult widows. Therefore, the current research aimed to analyze the moderating effect of education and skill among loneliness of adult widows on their social and mental well-being. The data was collected from 200 widows (60 years old or above) of government pensioners (scale 1 to 16) who receive their pensions from the National Bank of Punjab (NBP) and Bank of Punjab (BOP) through structured interviews using purposive sampling. The partial least squares structural equation modeling (PLS-SEM) was applied for analysis using Smart-PLS (v.3.3). The findings of this study concluded that loneliness has a negative effect on social and mental well-being of older adult widows; however, the moderation effect of skills and education level altered the level of loneliness and brought a positive significant effect on mental and social well-being among the older adult widows. Therefore, it is recommended that projects of older adult education and skill enhancement may be launched based on inclusive models at the government and community level. Furthermore, opportunities of paid or volunteer work may be provided to educated and skilled older adult widows for their active and productive engagement towards society. To heal the negative effects of loneliness among them, drop-in centers may be established for their physical participation in recreational activities with new associates and companions.
In recent years, EEG-based navigation and communication systems for differentially enabled communities have been progressively receiving more attention. To provide a navigation system with a communication aid, a customized protocol using thought evoked sensor potentials has been proposed in this research work to aid the differentially enabled communities. This study presents the higher order robotic sensor spectra-based features to categorize seven basic tasks that include Forward, Left, Right, Yes, NO, Help and Relax; that can be used for navigating a robot chair and also for communications using an oddball paradigm. The proposed system records the eight-channel wireless electroencephalography signal from ten subjects while the subject was perceiving seven different tasks. The recorded brain wave signals are pre-processed to remove the interference waveforms and segmented into six frequency band signals, i.e. Delta, Theta, Alpha, Beta, Gamma1-1 and Gamma-2. The frequency band signals are segmented into frame samples of equal length and are used to extract the features using bispectrum estimation. Further, statistical features such as the mean of bispectral magnitude and entropy using the bispectrum region are extracted and formed as a feature set. The extracted feature sets are tenfold cross validated using multilayer neural network classifier. From the results, it is observed that the entropy of bispectral magnitude feature based classifier model has the maximum classification accuracy of 84.71% and the mean of the bispectral magnitude feature based classifier model has the minimum classification accuracy of 68.52 %.
In current volatile business environment, the owners of the corporations are worried about how diverse board composition influences the strategic performance of the corporations. Therefore, this study considered the agency theory, upper echelon and resource-based view of board heterogeneity as limited literature account for such integrated phenomenon of theories. Accordingly, the key aim of the study is to scrutinize the impacts of occupational heterogeneity (educational) and social heterogeneity (gender and national) on firm performance. At first, Blau’s heterogeneity index was applied to measure the occupational heterogeneity and social heterogeneity, then ordinary least square method was applied for analysis. The data set was obtained from the non-financial sector of Pakistan Stock Exchange for the years 2010–2016 with final sample of 375 firms. The findings of current research concluded that all measures of occupational heterogeneity significantly and positively contribute to firm value expect finance education and other education (defense, arts, political science etc.). However, in social heterogeneity, gender diversity has a negative effect on firm performance while nationally heterogeneous board demonstrate a positive effect on firm performance. Moreover, this study has beneficial implications for the corporate sector as firms can boost their profitability by extracting benefits from their diverse workforce.
This book constitutes the refereed conference proceedings of the Third International Conference on Emerging Technologies in Computing, iCETiC 2020, held in London, UK, in August 2020. Due to COVID-19 pandemic the conference was held virtually.The 25 revised full papers were reviewed and selected from 65 submissions and are organized in topical sections covering blockchain and cloud computing; security, wireless sensor networks and IoT; AI, big data and data analytics; emerging technologies in engineering, education and sustainable development.
This research aimed to find the primary objective of human resource in any organization is to effectively manage its workforce by supporting positive attitude such as motivation, enhancing productivity, and organizational citizenship behavior and job satisfaction. Similarly, human resources should reduce negative employee attitudes such as absenteeism, increased turnover, and deviant workplace culture. This study attempts the relationship of employee empowerment on the performance of employees in the banking sector in the Kingdom of Bahrain, which has particularly experienced various crises in the recent past. The responses were collected through a survey that was based on a structured questionnaire on 150 participants. The collected responses were analyzed and tested on standard statistical tools such as frequency analysis on demographics, mean and Pearson’s correlation test. For instance, it has been found that in the current situation the employee in the Islamic banking sector in the Kingdom of Bahrain is motivated to improve their performance.
Gender differences have been widely observed among developing countries whenever it comes to investigate the position of women on research grounds. Therefore, in current research, the relationship between employment and empowerment is being focused generally and particular attention is paid to the individual’s ability to approach job opportunities and working environment at the communal level. Moreover, women’s access toward resources and contribution level to the accumulated family earnings within the family units is also observed. The study used primary source of data of 500 households which was collected from four districts of Punjab, Pakistan through a multistage random sampling technique. In this study, four indicators have been chosen in order to measure women empowerment and hierarchical multiple regression was applied to have a look at multiple professions and groupings of empowerment. The findings indicated that women in particularly few professions hold the higher chance of getting empowered, in addition, those particular professional attributes seem interconnected by some of the empowerment indicators. Moreover, the study is meant to foster the debate to improve women empowerment through establishing new job market which provides more opportunities to women especially in the rural areas, which is the most neglected part in developing countries.
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Essam Natsheh
  • College of Engineering
Christo Ananth
  • College of Engineering
Husham Mahmood Ahmed
  • College of Engineering
Noaman Noaman
  • Department of Mechatronics Engineering
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