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In this paper, we mainly formulate the problem of predicting smartphone usage based on contextual information, which involves both the user-centric and device-centric contexts. In the area of mobile analytics, traditional machine learning techniques, such as Decision Trees, Random Forests, Support Vector Machines, etc. are popular for building context-aware prediction models. However, real-life smartphone usage data may contain higher dimensions of contexts and can be huge in size considering the daily behavioral data of the users. Thus, the traditional machine learning models may not be effective to build the context-aware model. In this paper, we explore “Mobile Deep Learning”, an artificial neural network learning-based model considering multiple hidden layers for predicting context-aware smartphone usage. Our model first takes into account context correlation analysis to reduce the neurons as well as to simplify the network model through filtering the irrelevant or less significant contexts, and then build the deep learning model with the selected contexts. The experimental results on smartphone usage datasets show the effectiveness of the model.
Iqbal H. Sarker; Yoosef B. Abushark; Asif Irshad Khan; Mottahir Alam; Raza Nowrozy. Mobile Deep Learning: Exploring Deep Neural Network for Predicting Context-Aware Smartphone Usage. SN Computer Science 2021, 2, 1 -12.
AMA StyleIqbal H. Sarker, Yoosef B. Abushark, Asif Irshad Khan, Mottahir Alam, Raza Nowrozy. Mobile Deep Learning: Exploring Deep Neural Network for Predicting Context-Aware Smartphone Usage. SN Computer Science. 2021; 2 (3):1-12.
Chicago/Turabian StyleIqbal H. Sarker; Yoosef B. Abushark; Asif Irshad Khan; Mottahir Alam; Raza Nowrozy. 2021. "Mobile Deep Learning: Exploring Deep Neural Network for Predicting Context-Aware Smartphone Usage." SN Computer Science 2, no. 3: 1-12.
Yoosef B. Abushark; Asif Irshad Khan; Fawaz Jaber Alsolami; Abdulmohsen Almalawi; Mottahir Alam; Alka Agrawal; Rajeev Kumar; Raees Ahmad Khan. Usability Evaluation Through Fuzzy AHP-TOPSIS Approach: Security Requirement Perspective. Computers, Materials & Continua 2021, 68, 1203 -1218.
AMA StyleYoosef B. Abushark, Asif Irshad Khan, Fawaz Jaber Alsolami, Abdulmohsen Almalawi, Mottahir Alam, Alka Agrawal, Rajeev Kumar, Raees Ahmad Khan. Usability Evaluation Through Fuzzy AHP-TOPSIS Approach: Security Requirement Perspective. Computers, Materials & Continua. 2021; 68 (1):1203-1218.
Chicago/Turabian StyleYoosef B. Abushark; Asif Irshad Khan; Fawaz Jaber Alsolami; Abdulmohsen Almalawi; Mottahir Alam; Alka Agrawal; Rajeev Kumar; Raees Ahmad Khan. 2021. "Usability Evaluation Through Fuzzy AHP-TOPSIS Approach: Security Requirement Perspective." Computers, Materials & Continua 68, no. 1: 1203-1218.
In this paper a coplanar waveguide-fed super-wideband antenna is presented for wireless sensor networks. The studied low-profile design is comprised of a modified bow-tie-shaped vertical patch and two asymmetrical ground planes and has been prototyped on a single-sided FR4 microwave substrate. The anticipated antenna has an overall size of $0.25\lambda \times 0.20\lambda $ at 3.035 GHz, the lowest frequency of the operating band. The vertical radiator coupled well with the two coplanar ground planes which enabled the studied antenna to achieve an operating band of 3.035–17.39 GHz (140.56%). The presented antenna demonstrates almost omnidirectional radiation patterns over the entire operating with an average gain of 4.56 dBi and average efficiency of 76.62%. The antenna also features with high fidelity factor, flat group delay, small phase distortion, and good response of transfer function. All these characteristics of the studied antenna make it a robust contender for wireless sensor node applications.
Rezaul Azim; Mohammad Tariqul Islam; Haslina Arshad; Mottahir Alam; Nebras Sobahi; Asif Irshad Khan. CPW-Fed Super-Wideband Antenna With Modified Vertical Bow-Tie-Shaped Patch for Wireless Sensor Networks. IEEE Access 2020, 9, 5343 -5353.
AMA StyleRezaul Azim, Mohammad Tariqul Islam, Haslina Arshad, Mottahir Alam, Nebras Sobahi, Asif Irshad Khan. CPW-Fed Super-Wideband Antenna With Modified Vertical Bow-Tie-Shaped Patch for Wireless Sensor Networks. IEEE Access. 2020; 9 ():5343-5353.
Chicago/Turabian StyleRezaul Azim; Mohammad Tariqul Islam; Haslina Arshad; Mottahir Alam; Nebras Sobahi; Asif Irshad Khan. 2020. "CPW-Fed Super-Wideband Antenna With Modified Vertical Bow-Tie-Shaped Patch for Wireless Sensor Networks." IEEE Access 9, no. : 5343-5353.
Nowadays, machine learning classification techniques have been successfully used while building data-driven intelligent predictive systems in various application areas including smartphone apps. For an effective context-aware system, context pre-modeling is considered as a key issue and task, as the representation of contextual data directly influences the predictive models. This paper mainly explores the role of major context pre-modeling tasks, such as context vectorization by defining a good numerical measure through transformation and normalization, context generation and extraction by creating new brand principal components, context selection by taking into account a subset of original contexts according to their correlations, and eventually context evaluation, to build effective context-aware predictive models utilizing multi-dimensional contextual data. For creating models, various popular machine learning classification techniques such as decision tree, random forest, k-nearest neighbor, support vector machines, naive Bayes classifier, and deep learning by constructing a neural network of multiple hidden layers, are used in our study. Based on the context pre-modeling tasks and classification methods, we experimentally analyze user-centric smartphone usage behavioral activities utilizing their contextual datasets. The effectiveness of these machine learning context-aware models is examined by considering prediction accuracy, in terms of precision, recall, f-score, and ROC values, and has been made an empirical discussion in various dimensions within the scope of our study.
Iqbal H. Sarker; Hamed Alqahtani; Fawaz Alsolami; Asif Irshad Khan; Yoosef B. Abushark; Mohammad Khubeb Siddiqui. Context pre-modeling: an empirical analysis for classification based user-centric context-aware predictive modeling. Journal of Big Data 2020, 7, 1 -23.
AMA StyleIqbal H. Sarker, Hamed Alqahtani, Fawaz Alsolami, Asif Irshad Khan, Yoosef B. Abushark, Mohammad Khubeb Siddiqui. Context pre-modeling: an empirical analysis for classification based user-centric context-aware predictive modeling. Journal of Big Data. 2020; 7 (1):1-23.
Chicago/Turabian StyleIqbal H. Sarker; Hamed Alqahtani; Fawaz Alsolami; Asif Irshad Khan; Yoosef B. Abushark; Mohammad Khubeb Siddiqui. 2020. "Context pre-modeling: an empirical analysis for classification based user-centric context-aware predictive modeling." Journal of Big Data 7, no. 1: 1-23.
Supervisory control and data acquisition (SCADA) systems monitor and supervise our daily infrastructure systems and industrial processes. Hence, the security of the information systems of critical infrastructures cannot be overstated. The effectiveness of unsupervised anomaly detection approaches is sensitive to parameter choices, especially when the boundaries between normal and abnormal behaviours are not clearly distinguishable. Therefore, the current approach in detecting anomaly for SCADA is based on the assumptions by which anomalies are defined; these assumptions are controlled by a parameter choice. This paper proposes an add-on anomaly threshold technique to identify the observations whose anomaly scores are extreme and significantly deviate from others, and then such observations are assumed to be ”abnormal”. The observations whose anomaly scores are significantly distant from ”abnormal” ones will be assumed as ”normal”. Then, the ensemble-based supervised learning is proposed to find a global and efficient anomaly threshold using the information of both ”normal”/”abnormal” behaviours. The proposed technique can be used for any unsupervised anomaly detection approach to mitigate the sensitivity of such parameters and improve the performance of the SCADA unsupervised anomaly detection approaches. Experimental results confirm that the proposed technique achieved a significant improvement compared to the state-of-the-art of two unsupervised anomaly detection algorithms.
Abdulmohsen Almalawi; Adil Fahad; Zahir Tari; Asif Irshad Khan; Nouf Alzahrani; Sheikh Tahir Bakhsh; Madini O. Alassafi; Abdulrahman Alshdadi; Sana Qaiyum. Add-On Anomaly Threshold Technique for Improving Unsupervised Intrusion Detection on SCADA Data. Electronics 2020, 9, 1017 .
AMA StyleAbdulmohsen Almalawi, Adil Fahad, Zahir Tari, Asif Irshad Khan, Nouf Alzahrani, Sheikh Tahir Bakhsh, Madini O. Alassafi, Abdulrahman Alshdadi, Sana Qaiyum. Add-On Anomaly Threshold Technique for Improving Unsupervised Intrusion Detection on SCADA Data. Electronics. 2020; 9 (6):1017.
Chicago/Turabian StyleAbdulmohsen Almalawi; Adil Fahad; Zahir Tari; Asif Irshad Khan; Nouf Alzahrani; Sheikh Tahir Bakhsh; Madini O. Alassafi; Abdulrahman Alshdadi; Sana Qaiyum. 2020. "Add-On Anomaly Threshold Technique for Improving Unsupervised Intrusion Detection on SCADA Data." Electronics 9, no. 6: 1017.
This paper develops an islanding classification mechanism to overcome the problems of non-detection zones in conventional islanding detection mechanisms. This process is achieved by adapting the support vector-based data description technique with Gaussian radial basis function kernels for islanding and non-islanding events in single phase grid-connected photovoltaic (PV) systems. To overcome the non-detection zone, excess and deficit power imbalance conditions are considered for different loading conditions. These imbalances are characterized by the voltage dip scenario and were subjected to feature extraction for training with the machine learning technique. This is experimentally realized by training the machine learning classifier with different events on a 5 kW grid-connected system. Using the concept of detection and false alarm rates, the performance of the trained classifier is tested for multiple faults and power imbalance conditions. The results showed the effective operation of the classifier with a detection rate of 99.2% and a false alarm rate of 0.2%.
Ahteshamul Haque; Abdulaziz Alshareef; Asif Irshad Khan; Mottahir Alam; Varaha Satya Bharath Kurukuru; Kashif Irshad. Data Description Technique-Based Islanding Classification for Single-Phase Grid-Connected Photovoltaic System. Sensors 2020, 20, 3320 .
AMA StyleAhteshamul Haque, Abdulaziz Alshareef, Asif Irshad Khan, Mottahir Alam, Varaha Satya Bharath Kurukuru, Kashif Irshad. Data Description Technique-Based Islanding Classification for Single-Phase Grid-Connected Photovoltaic System. Sensors. 2020; 20 (11):3320.
Chicago/Turabian StyleAhteshamul Haque; Abdulaziz Alshareef; Asif Irshad Khan; Mottahir Alam; Varaha Satya Bharath Kurukuru; Kashif Irshad. 2020. "Data Description Technique-Based Islanding Classification for Single-Phase Grid-Connected Photovoltaic System." Sensors 20, no. 11: 3320.
Data Streams create new challenges for fuzzy clustering algorithms, specifically Interval Type-2 Fuzzy C-Means (IT2FCM). One problem associated with IT2FCM is that it tends to be sensitive to initialization conditions and therefore, fails to return global optima. This problem has been addressed by optimizing IT2FCM using Ant Colony Optimization approach. However, IT2FCM-ACO obtain clusters for the whole dataset which is not suitable for clustering large streaming datasets that may be coming continuously and evolves with time. Thus, the clusters generated will also evolve with time. Additionally, the incoming data may not be available in memory all at once because of its size. Therefore, to encounter the challenges of a large data stream environment we propose improvising IT2FCM-ACO to generate clusters incrementally. The proposed algorithm produces clusters by determining appropriate cluster centers on a certain percentage of available datasets and then the obtained cluster centroids are combined with new incoming data points to generate another set of cluster centers. The process continues until all the data are scanned. The previous data points are released from memory which reduces time and space complexity. Thus, the proposed incremental method produces data partitions comparable to IT2FCM-ACO. The performance of the proposed method is evaluated on large real-life datasets. The results obtained from several fuzzy cluster validity index measures show the enhanced performance of the proposed method over other clustering algorithms. The proposed algorithm also improves upon the run time and produces excellent speed-ups for all datasets.
Sana Qaiyum; Izzatdin Aziz; Mohd Hilmi Hasan; Asif Irshad Khan; Abdulmohsen Almalawi. Incremental Interval Type-2 Fuzzy Clustering of Data Streams using Single Pass Method. Sensors 2020, 20, 3210 .
AMA StyleSana Qaiyum, Izzatdin Aziz, Mohd Hilmi Hasan, Asif Irshad Khan, Abdulmohsen Almalawi. Incremental Interval Type-2 Fuzzy Clustering of Data Streams using Single Pass Method. Sensors. 2020; 20 (11):3210.
Chicago/Turabian StyleSana Qaiyum; Izzatdin Aziz; Mohd Hilmi Hasan; Asif Irshad Khan; Abdulmohsen Almalawi. 2020. "Incremental Interval Type-2 Fuzzy Clustering of Data Streams using Single Pass Method." Sensors 20, no. 11: 3210.
Cyber security has recently received enormous attention in today’s security concerns, due to the popularity of the Internet-of-Things (IoT), the tremendous growth of computer networks, and the huge number of relevant applications. Thus, detecting various cyber-attacks or anomalies in a network and building an effective intrusion detection system that performs an essential role in today’s security is becoming more important. Artificial intelligence, particularly machine learning techniques, can be used for building such a data-driven intelligent intrusion detection system. In order to achieve this goal, in this paper, we present an Intrusion Detection Tree (“IntruDTree”) machine-learning-based security model that first takes into account the ranking of security features according to their importance and then build a tree-based generalized intrusion detection model based on the selected important features. This model is not only effective in terms of prediction accuracy for unseen test cases but also minimizes the computational complexity of the model by reducing the feature dimensions. Finally, the effectiveness of our IntruDTree model was examined by conducting experiments on cybersecurity datasets and computing the precision, recall, fscore, accuracy, and ROC values to evaluate. We also compare the outcome results of IntruDTree model with several traditional popular machine learning methods such as the naive Bayes classifier, logistic regression, support vector machines, and k-nearest neighbor, to analyze the effectiveness of the resulting security model.
Iqbal H. Sarker; Yoosef B. Abushark; Fawaz Alsolami; Asif Irshad Khan. IntruDTree: A Machine Learning Based Cyber Security Intrusion Detection Model. Symmetry 2020, 12, 754 .
AMA StyleIqbal H. Sarker, Yoosef B. Abushark, Fawaz Alsolami, Asif Irshad Khan. IntruDTree: A Machine Learning Based Cyber Security Intrusion Detection Model. Symmetry. 2020; 12 (5):754.
Chicago/Turabian StyleIqbal H. Sarker; Yoosef B. Abushark; Fawaz Alsolami; Asif Irshad Khan. 2020. "IntruDTree: A Machine Learning Based Cyber Security Intrusion Detection Model." Symmetry 12, no. 5: 754.
Cyber security has recently received enormous attention in today’s security concerns, due to the popularity of the Internet-of-Things (IoT), the tremendous growth of computer networks, and the huge number of relevant applications. Thus, detecting various cyber-attacks or anomalies in a network and building an effective intrusion detection system that performs an essential role in today’s security is becoming more important. Artificial intelligence, particularly machine learning techniques, can be used for building such a data-driven intelligent intrusion detection system. In order to achieve this goal, in this paper, we present an Intrusion Detection Tree (“IntruDTree”) machine-learning-based security model that first takes into account the ranking of security features according to their importance and then build a tree-based generalized intrusion detection model based on the selected important features. This model is not only effective in terms of prediction accuracy for unseen test cases but also minimizes the computational complexity of the model by reducing the feature dimensions. Finally, the effectiveness of our IntruDTree model was examined by conducting experiments on cybersecurity datasets and computing the precision, recall, fscore, accuracy, and ROC values to evaluate. We also compare the outcome results of IntruDTree model with several traditional popular machine learning methods such as the naive Bayes classifier, logistic regression, support vector machines, and k-nearest neighbor, to analyze the effectiveness of the resulting security model.
Iqbal H. Sarker; Yoosef B. Abushark; Fawaz Alsolami; Asif Irshad Khan. IntruDTree: A Machine Learning-Based Cyber Security Intrusion Detection Model. 2020, 1 .
AMA StyleIqbal H. Sarker, Yoosef B. Abushark, Fawaz Alsolami, Asif Irshad Khan. IntruDTree: A Machine Learning-Based Cyber Security Intrusion Detection Model. . 2020; ():1.
Chicago/Turabian StyleIqbal H. Sarker; Yoosef B. Abushark; Fawaz Alsolami; Asif Irshad Khan. 2020. "IntruDTree: A Machine Learning-Based Cyber Security Intrusion Detection Model." , no. : 1.
Subjective analysis of thermal comfort of occupants relates to the recording of the level of satisfaction or dissatisfaction of occupants with regard to indoor environmental conditions on a scale which ranges from -5 to +5. This requires recruitment of subjects and matching for gender, age etc. In this study, we have tried to predict the thermal comfort of occupants by observing their real behavior inside the test room fitted with a novel thermoelectric air duct (TE-AD) cooling system rather than a conventional air conditioning system. Firstly, real experimental data were collected for more than two months from the test room equipped with the TE-AD cooling system operated at an input power supply of 6 A and 5 V. After that, the ANN model was developed based on the Levenberg-Marquardt algorithm by taking experimental parameters such as air temperature, relative humidity, globe temperature, wind speed, metabolic rate, and clothing value as model input. The ANN model is optimized by developing different models with different data points as a starting input in the training and validation process. The neuron optimization has been carried out in these models to minimize the mean square error (MSE) for the ANN model. The result shows that among the three models M1, M2, and M3, the optimum predictive mean value (PMV) was obtained from M1 at 10 neurons with MSE of 0.07956, while for predicted percentage dissatisfied (PPD), M3 gives optimum accuracy at 10 neurons with MSE value of 5.1789. The ANN model is then generalized to predict thermal comfort for one week and then for one month. Finally, all the model results were validated with the experimental data.
Kashif Irshad; Asif Irshad Khan; Sayed Ameenuddin Irfan; Mottahir Alam; Abdulmohsen Almalawi; Hasan Zahir. Utilizing Artificial Neural Network for Prediction of Occupants Thermal Comfort: A Case Study of a Test Room Fitted With a Thermoelectric Air-Conditioning System. IEEE Access 2020, 8, 99709 -99728.
AMA StyleKashif Irshad, Asif Irshad Khan, Sayed Ameenuddin Irfan, Mottahir Alam, Abdulmohsen Almalawi, Hasan Zahir. Utilizing Artificial Neural Network for Prediction of Occupants Thermal Comfort: A Case Study of a Test Room Fitted With a Thermoelectric Air-Conditioning System. IEEE Access. 2020; 8 (99):99709-99728.
Chicago/Turabian StyleKashif Irshad; Asif Irshad Khan; Sayed Ameenuddin Irfan; Mottahir Alam; Abdulmohsen Almalawi; Hasan Zahir. 2020. "Utilizing Artificial Neural Network for Prediction of Occupants Thermal Comfort: A Case Study of a Test Room Fitted With a Thermoelectric Air-Conditioning System." IEEE Access 8, no. 99: 99709-99728.
Introduction: The imperative need for ensuring optimal security of healthcare web applications cannot be overstated. Security practitioners are consistently working at improvising on techniques to maximise security along with the longevity of healthcare web applications. In this league, it has been observed that assessment of security risks through soft computing techniques during the development of web application can enhance the security of healthcare web applications to a great extent. Methods: This study proposes the identification of security risks and their assessment during the development of the web application through adaptive neuro-fuzzy inference system (ANFIS). In this article, firstly, the security risk factors involved during healthcare web application development have been identified. Thereafter, these security risks have been evaluated by using the ANFIS technique. This research also proposes a fuzzy regression model. Results: The results have been compared with those of ANFIS, and the ANFIS model is found to be more acceptable for the estimation of security risks during the healthcare web application development. Conclusion: The proposed approach can be applied by the healthcare web application developers and experts to avoid the security risk factors during healthcare web application development for enhancing the healthcare data security.
Jasleen Kaur; Asif Irshad Khan; Yoosef B Abushark; Mottahir Alam; Suhel Ahmad Khan; Alka Agrawal; Rajeev Kumar; Raees Ahmad Khan. Security Risk Assessment of Healthcare Web Application Through Adaptive Neuro-Fuzzy Inference System: A Design Perspective. Risk Management and Healthcare Policy 2020, ume 13, 355 -371.
AMA StyleJasleen Kaur, Asif Irshad Khan, Yoosef B Abushark, Mottahir Alam, Suhel Ahmad Khan, Alka Agrawal, Rajeev Kumar, Raees Ahmad Khan. Security Risk Assessment of Healthcare Web Application Through Adaptive Neuro-Fuzzy Inference System: A Design Perspective. Risk Management and Healthcare Policy. 2020; ume 13 ():355-371.
Chicago/Turabian StyleJasleen Kaur; Asif Irshad Khan; Yoosef B Abushark; Mottahir Alam; Suhel Ahmad Khan; Alka Agrawal; Rajeev Kumar; Raees Ahmad Khan. 2020. "Security Risk Assessment of Healthcare Web Application Through Adaptive Neuro-Fuzzy Inference System: A Design Perspective." Risk Management and Healthcare Policy ume 13, no. : 355-371.
This paper mainly formulates the problem of predicting context-aware smartphone apps usage based on machine learning techniques. In the real world, people use various kinds of smartphone apps differently in different contexts that include both the user-centric context and device-centric context. In the area of artificial intelligence and machine learning, decision tree model is one of the most popular approaches for predicting context-aware smartphone usage. However, real-life smartphone apps usage data may contain higher dimensions of contexts, which may cause several issues such as increases model complexity, may arise over-fitting problem, and consequently decreases the prediction accuracy of the context-aware model. In order to address these issues, in this paper, we present an effective principal component analysis (PCA) based context-aware smartphone apps prediction model, “ContextPCA” using decision tree machine learning classification technique. PCA is an unsupervised machine learning technique that can be used to separate symmetric and asymmetric components, and has been adopted in our “ContextPCA” model, in order to reduce the context dimensions of the original data set. The experimental results on smartphone apps usage datasets show that “ContextPCA” model effectively predicts context-aware smartphone apps in terms of precision, recall, f-score and ROC values in various test cases.
Iqbal H. Sarker; Yoosef B. Abushark; Asif Irshad Khan. ContextPCA: Predicting Context-Aware Smartphone Apps Usage Based On Machine Learning Techniques. Symmetry 2020, 12, 499 .
AMA StyleIqbal H. Sarker, Yoosef B. Abushark, Asif Irshad Khan. ContextPCA: Predicting Context-Aware Smartphone Apps Usage Based On Machine Learning Techniques. Symmetry. 2020; 12 (4):499.
Chicago/Turabian StyleIqbal H. Sarker; Yoosef B. Abushark; Asif Irshad Khan. 2020. "ContextPCA: Predicting Context-Aware Smartphone Apps Usage Based On Machine Learning Techniques." Symmetry 12, no. 4: 499.
There has been a phenomenal increase in the use of web applications in every facet of human endeavor. From education, healthcare, banking, business to governance and so much more now depends on secure web applications. This accelerated growth in the use of web applications has led to increase in the complexity of security and hence the present day developers have to contribute more significantly towards meeting the users’ requirements. However, the high security of web application is not yet efficacious enough because the durability of web application is not as much as it should be. In this context, it is important to consider that ensuring sustainability of security at the early stage of web application development process may reduce costs and rework entailed during the development of secure and durable web applications. Hence, there is a need to focus on increasing the life-span of a secure web application. Quantitative estimation of securitydurability plays a significant role for improving the life-span of a secure web application. Thus, to optimize the security assurance effort for a specific life-span, this paper is aimed at estimating the security-durability of web application. For estimating security-durability, this paper uses a hybrid approach of Hesitant Fuzzy (HF) sets, Analytic Hierarchy Process (AHP) and Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) techniques. The effectiveness of the combined approach of HF-AHP-TOPSIS is tested for its accuracy in a web application for an academic institution, Babasaheb Bhimrao Ambedkar University in India. To check the sensitivity of outcomes, authors of the paper have taken altered forms of the University’s web application. The result established contains the security-durability assessment. This work seeks to be an important contribution in enhancing the security-durability and would be beneficial for experts who are working in this domain.
Rajeev Kumar; Asif Irshad Khan; Yoosef B. Abushark; Mottahir Alam; Alka Agrawal; Raees Ahmad Khan. A Knowledge-Based Integrated System of Hesitant Fuzzy Set, AHP and TOPSIS for Evaluating Security-Durability of Web Applications. IEEE Access 2020, 8, 48870 -48885.
AMA StyleRajeev Kumar, Asif Irshad Khan, Yoosef B. Abushark, Mottahir Alam, Alka Agrawal, Raees Ahmad Khan. A Knowledge-Based Integrated System of Hesitant Fuzzy Set, AHP and TOPSIS for Evaluating Security-Durability of Web Applications. IEEE Access. 2020; 8 (99):48870-48885.
Chicago/Turabian StyleRajeev Kumar; Asif Irshad Khan; Yoosef B. Abushark; Mottahir Alam; Alka Agrawal; Raees Ahmad Khan. 2020. "A Knowledge-Based Integrated System of Hesitant Fuzzy Set, AHP and TOPSIS for Evaluating Security-Durability of Web Applications." IEEE Access 8, no. 99: 48870-48885.
Managing data integrity is a challenging task for any expert or a researcher. This study attempts to collate a Systematic Literature Review of the research efforts done in the domain of healthcare data integrity. The paper highlights the criticalness of data integrity issues in healthcare through attack statistics in the first section. The second section of the paper systematically reviews the previous studies discussing the healthcare related Systematic literature reviews and data integrity techniques in healthcare sector. The third section of this study examines the collated literature through various analysis methodologies and discusses about the most prioritized technique as well as its challenges in healthcare data security. The fourth section illustrates about the various challenges and future directions to take while constructing a roadmap for the future research endeavors in healthcare data integrity management techniques. The concluding segment of the paper presents an objective assessment and sensitivity analysis for finding the implications and difficulties in the studies while outlining feasible solutions. Furthermore, this research endeavour also conducts a Scientometric analysis of all the studies for better understanding of the literature reviewed. Ranking or the Priority analysis part of the paper is totally dedicated to the previously used techniques in healthcare. The paper also discusses about data integrity techniques and postulates that the most prioritized data integrity technique is the blockchain.
Abhishek Kumar Pandey; Asif Irshad Khan; Yoosef B. Abushark; Mottahir Alam; Alka Agrawal; Rajeev Kumar; Raees Ahmad Khan. Key Issues in Healthcare Data Integrity: Analysis and Recommendations. IEEE Access 2020, 8, 40612 -40628.
AMA StyleAbhishek Kumar Pandey, Asif Irshad Khan, Yoosef B. Abushark, Mottahir Alam, Alka Agrawal, Rajeev Kumar, Raees Ahmad Khan. Key Issues in Healthcare Data Integrity: Analysis and Recommendations. IEEE Access. 2020; 8 (99):40612-40628.
Chicago/Turabian StyleAbhishek Kumar Pandey; Asif Irshad Khan; Yoosef B. Abushark; Mottahir Alam; Alka Agrawal; Rajeev Kumar; Raees Ahmad Khan. 2020. "Key Issues in Healthcare Data Integrity: Analysis and Recommendations." IEEE Access 8, no. 99: 40612-40628.
This study investigates the performance of the thermoelectric air conditioning (TE-AC) system smartly controlled by the Internet of Things (IoT)-based configuration for real tropical climatic application. Air cooling management was done through thermoelectric coolers, and an Arduino microcontroller with various sensors such as a temperature sensor, simple RF modules, and actuators was used to control the indoor climatic conditions based on outdoor conditions. The result shows that when the input power supply to the IoT-based TE-AC system is increased, the cooling capacity of the framework is also enhanced. Significant power and carbon emission reduction was observed for the IoT-based TE-AC system as compared to the TE-AC system without IoT. The IoT-incorporated system also ensures better microclimatic temperature control. Additionally, the system cooling capacity improves by 14.0%, and the coefficient of performance is increased by 46.3%. Thus, this study provides a smart solution to the two major energy harvesting issues of traditional air conditioners—an increase in energy efficiency by employing a TE-AC system and a further improvement in efficiency by using an IoT-based thermal management system.
Kashif Irshad; Abdulmohsen Almalawi; Asif Irshad Khan; Mottahir Alam; Hasan Zahir; Amjad Ali. An IoT-Based Thermoelectric Air Management Framework for Smart Building Applications: A Case Study for Tropical Climate. Sustainability 2020, 12, 1564 .
AMA StyleKashif Irshad, Abdulmohsen Almalawi, Asif Irshad Khan, Mottahir Alam, Hasan Zahir, Amjad Ali. An IoT-Based Thermoelectric Air Management Framework for Smart Building Applications: A Case Study for Tropical Climate. Sustainability. 2020; 12 (4):1564.
Chicago/Turabian StyleKashif Irshad; Abdulmohsen Almalawi; Asif Irshad Khan; Mottahir Alam; Hasan Zahir; Amjad Ali. 2020. "An IoT-Based Thermoelectric Air Management Framework for Smart Building Applications: A Case Study for Tropical Climate." Sustainability 12, no. 4: 1564.
Security and usability are often thought of as being contradictive in nature. One affects the other negatively. The relation and trade-offs between usability and security must be detected during developing web application to satisfy the user’s requirements with security perspective. Current approaches of usable-security emphasizes on building systems that are easy to use and secure as well. Hence, this paper is recognizing usability-security as a problem with different attributes contributing towards it. Further, there is a need to assess this problem for the satisfaction of the end user. In this context, this study proposes the track of Fuzzy AHP-TOPSIS (Analytic Hierarchy Process-Technique for Order of Preference by Similarity to Ideal Solution) technique to assess the usable-security of web application and also identifies the most prioritized attribute contributing towards building usable-security of web application. Moreover, to corroborate the efficacy of the proposed technique, the authors have tested the results on the institutional web applications. The results of the assessment undertaken in this study and the findings tabulated thereafter will be a helpful reckoner for the developers while designing web applications that afford optimum usable-security.
Rajeev Kumar; Asif Irshad Khan; Yoosef B. Abushark; Mottahir ALAM; Alka Agrawal; Raees Ahmad Khan. An Integrated Approach of Fuzzy Logic, AHP and TOPSIS for Estimating Usable-Security of Web Applications. IEEE Access 2020, 8, 50944 -50957.
AMA StyleRajeev Kumar, Asif Irshad Khan, Yoosef B. Abushark, Mottahir ALAM, Alka Agrawal, Raees Ahmad Khan. An Integrated Approach of Fuzzy Logic, AHP and TOPSIS for Estimating Usable-Security of Web Applications. IEEE Access. 2020; 8 (99):50944-50957.
Chicago/Turabian StyleRajeev Kumar; Asif Irshad Khan; Yoosef B. Abushark; Mottahir ALAM; Alka Agrawal; Raees Ahmad Khan. 2020. "An Integrated Approach of Fuzzy Logic, AHP and TOPSIS for Estimating Usable-Security of Web Applications." IEEE Access 8, no. 99: 50944-50957.
This paper formulates the problem of building a context-aware predictive model based on user diverse behavioral activities with smartphones. In the area of machine learning and data science, a tree-like model as that of decision tree is considered as one of the most popular classification techniques, which can be used to build a data-driven predictive model. The traditional decision tree model typically creates a number of leaf nodes as decision nodes that represent context-specific rigid decisions, and consequently may cause overfitting problem in behavior modeling. However, in many practical scenarios within the context-aware environment, the generalized outcomes could play an important role to effectively capture user behavior. In this paper, we propose a behavioral decision tree, “BehavDT” context-aware model that takes into account user behavior-oriented generalization according to individual preference level. The BehavDT model outputs not only the generalized decisions but also the context-specific decisions in relevant exceptional cases. The effectiveness of our BehavDT model is studied by conducting experiments on individual user real smartphone datasets. Our experimental results show that the proposed BehavDT context-aware model is more effective when compared with the traditional machine learning approaches, in predicting user diverse behaviors considering multi-dimensional contexts.
Iqbal H. Sarker; Alan Colman; Jun Han; Asif Irshad Khan; Yoosef B. Abushark; Khaled Salah. BehavDT: A Behavioral Decision Tree Learning to Build User-Centric Context-Aware Predictive Model. Mobile Networks and Applications 2019, 25, 1151 -1161.
AMA StyleIqbal H. Sarker, Alan Colman, Jun Han, Asif Irshad Khan, Yoosef B. Abushark, Khaled Salah. BehavDT: A Behavioral Decision Tree Learning to Build User-Centric Context-Aware Predictive Model. Mobile Networks and Applications. 2019; 25 (3):1151-1161.
Chicago/Turabian StyleIqbal H. Sarker; Alan Colman; Jun Han; Asif Irshad Khan; Yoosef B. Abushark; Khaled Salah. 2019. "BehavDT: A Behavioral Decision Tree Learning to Build User-Centric Context-Aware Predictive Model." Mobile Networks and Applications 25, no. 3: 1151-1161.
Currently, comfort analyses of buildings equipped with thermoelectric air cooling or heating systems mainly focus on when occupants are in a wakeful state. In this study, both objective and subjective analyses of the sleeping behavior for fifteen (15) healthy occupants were conducted by exposing the occupants to two sleeping environments (i.e., test room equipped with the thermoelectric air duct cooling system (TE-AD) and naturally ventilated test room (NH)). The result shows that there were significant variations in the sleep satisfaction level in the test room with TE-AD and NH. Occupants felt more comfortable (5) and a slightly cooler thermal environment (3) while sleeping in the test room equipped with the TE-AD system. Their body movements, heart rate and sleeping stages shift from non-rapid eye movement (NREM) to rapid eye movement (REM) and then to the waking stage (WS), was less in test room with the TE-AD system as compared to NH. The occupants gave slightly hot (5) for indoor climatic ratings in NH room and felt a slightly uncomfortable (3) while sleeping. The PMV and PPD analyses showed that occupants were very sensitive to climatic conditions around bed and with slightly change in temperature (1.2 ± 0.4 °C) results in the shifting of sleeping stages. For the TE-AD room, the average occupant sleep onset latency was 19 ± 0.5 min, which is 20 ± 0.4 min lesser than NH room.
Kashif Irshad; Asif Irshad Khan; Salem Algarni; Khairul Habib; Bidyut Baran Saha. Objective and subjective evaluation of a sleeping environment test chamber with a thermoelectric air cooling system. Building and Environment 2018, 141, 155 -165.
AMA StyleKashif Irshad, Asif Irshad Khan, Salem Algarni, Khairul Habib, Bidyut Baran Saha. Objective and subjective evaluation of a sleeping environment test chamber with a thermoelectric air cooling system. Building and Environment. 2018; 141 ():155-165.
Chicago/Turabian StyleKashif Irshad; Asif Irshad Khan; Salem Algarni; Khairul Habib; Bidyut Baran Saha. 2018. "Objective and subjective evaluation of a sleeping environment test chamber with a thermoelectric air cooling system." Building and Environment 141, no. : 155-165.
Ahmed Barnawi; Abdullah Al-Barakati; Asif Khan; Fuad Bajaber; Omar Alhubaiti. A Proposed Architecture for a Heterogeneous Unmanned Aerial Vehicles System. International Journal of Electrical and Electronic Engineering & Telecommunications 2018, 1 .
AMA StyleAhmed Barnawi, Abdullah Al-Barakati, Asif Khan, Fuad Bajaber, Omar Alhubaiti. A Proposed Architecture for a Heterogeneous Unmanned Aerial Vehicles System. International Journal of Electrical and Electronic Engineering & Telecommunications. 2018; ():1.
Chicago/Turabian StyleAhmed Barnawi; Abdullah Al-Barakati; Asif Khan; Fuad Bajaber; Omar Alhubaiti. 2018. "A Proposed Architecture for a Heterogeneous Unmanned Aerial Vehicles System." International Journal of Electrical and Electronic Engineering & Telecommunications , no. : 1.
Mottahir ALAM; Asif Irshad Khan; Aasim Zafar. An Empirical Study of the Improved SPLD Framework using Expert Opinion Technique. IJEACS 2017, 2, 98 -106.
AMA StyleMottahir ALAM, Asif Irshad Khan, Aasim Zafar. An Empirical Study of the Improved SPLD Framework using Expert Opinion Technique. IJEACS. 2017; 2 (3):98-106.
Chicago/Turabian StyleMottahir ALAM; Asif Irshad Khan; Aasim Zafar. 2017. "An Empirical Study of the Improved SPLD Framework using Expert Opinion Technique." IJEACS 2, no. 3: 98-106.