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In machine learning and data science, feature selection is considered as a crucial step of data preprocessing. When we directly apply the raw data for classification or clustering purposes, sometimes we observe that the learning algorithms do not perform well. One possible reason for this is the presence of redundant, noisy, and non-informative features or attributes in the datasets. Hence, feature selection methods are used to identify the subset of relevant features that can maximize the model performance. Moreover, due to reduction in feature dimension, both training time and storage required by the model can be reduced as well. In this paper, we present a tri-stage wrapper-filter-based feature selection framework for the purpose of medical report-based disease detection. In the first stage, an ensemble was formed by four filter methods—Mutual Information, ReliefF, Chi Square, and Xvariance—and then each feature from the union set was assessed by three classification algorithms—support vector machine, naïve Bayes, and k-nearest neighbors—and an average accuracy was calculated. The features with higher accuracy were selected to obtain a preliminary subset of optimal features. In the second stage, Pearson correlation was used to discard highly correlated features. In these two stages, XGBoost classification algorithm was applied to obtain the most contributing features that, in turn, provide the best optimal subset. Then, in the final stage, we fed the obtained feature subset to a meta-heuristic algorithm, called whale optimization algorithm, in order to further reduce the feature set and to achieve higher accuracy. We evaluated the proposed feature selection framework on four publicly available disease datasets taken from the UCI machine learning repository, namely, arrhythmia, leukemia, DLBCL, and prostate cancer. Our obtained results confirm that the proposed method can perform better than many state-of-the-art methods and can detect important features as well. Less features ensure less medical tests for correct diagnosis, thus saving both time and cost.
Moumita Mandal; Pawan Kumar Singh; Muhammad Fazal Ijaz; Jana Shafi; Ram Sarkar. A Tri-Stage Wrapper-Filter Feature Selection Framework for Disease Classification. Sensors 2021, 21, 5571 .
AMA StyleMoumita Mandal, Pawan Kumar Singh, Muhammad Fazal Ijaz, Jana Shafi, Ram Sarkar. A Tri-Stage Wrapper-Filter Feature Selection Framework for Disease Classification. Sensors. 2021; 21 (16):5571.
Chicago/Turabian StyleMoumita Mandal; Pawan Kumar Singh; Muhammad Fazal Ijaz; Jana Shafi; Ram Sarkar. 2021. "A Tri-Stage Wrapper-Filter Feature Selection Framework for Disease Classification." Sensors 21, no. 16: 5571.
The substantial advancements offered by the edge computing has indicated serious evolutionary improvements for the internet of things (IoT) technology. The rigid design philosophy of the traditional network architecture limits its scope to meet future demands. However, information centric networking (ICN) is envisioned as a promising architecture to bridge the huge gaps and maintain IoT networks, mostly referred as ICN-IoT. The edge-enabled ICN-IoT architecture always demands efficient in-network caching techniques for supporting better user’s quality of experience (QoE). In this paper, we propose an enhanced ICN-IoT content caching strategy by enabling artificial intelligence (AI)-based collaborative filtering within the edge cloud to support heterogeneous IoT architecture. This collaborative filtering-based content caching strategy would intelligently cache content on edge nodes for traffic management at cloud databases. The evaluations has been conducted to check the performance of the proposed strategy over various benchmark strategies, such as LCE, LCD, CL4M, and ProbCache. The analytical results demonstrate the better performance of our proposed strategy with average gain of 15% for cache hit ratio, 12% reduction in content retrieval delay, and 28% reduced average hop count in comparison to best considered LCD. We believe that the proposed strategy will contribute an effective solution to the related studies in this domain.
Divya Gupta; Shalli Rani; Syed Hassan Ahmed; Sahil Verma; Muhammad Fazal Ijaz; Jana Shafi. Edge Caching Based on Collaborative Filtering for Heterogeneous ICN-IoT Applications. Sensors 2021, 21, 5491 .
AMA StyleDivya Gupta, Shalli Rani, Syed Hassan Ahmed, Sahil Verma, Muhammad Fazal Ijaz, Jana Shafi. Edge Caching Based on Collaborative Filtering for Heterogeneous ICN-IoT Applications. Sensors. 2021; 21 (16):5491.
Chicago/Turabian StyleDivya Gupta; Shalli Rani; Syed Hassan Ahmed; Sahil Verma; Muhammad Fazal Ijaz; Jana Shafi. 2021. "Edge Caching Based on Collaborative Filtering for Heterogeneous ICN-IoT Applications." Sensors 21, no. 16: 5491.
Decrease in crop yield and degradation in product quality due to plant diseases such as rust and blast in pearl millet is the cause of concern for farmers and the agriculture industry. The stipulation of expert advice for disease identification is also a challenge for the farmers. The traditional techniques adopted for plant disease detection require more human intervention, are unhandy for farmers, and have a high cost of deployment, operation, and maintenance. Therefore, there is a requirement for automating plant disease detection and classification. Deep learning and IoT-based solutions are proposed in the literature for plant disease detection and classification. However, there is a huge scope to develop low-cost systems by integrating these techniques for data collection, feature visualization, and disease detection. This research aims to develop the ‘Automatic and Intelligent Data Collector and Classifier’ framework by integrating IoT and deep learning. The framework automatically collects the imagery and parametric data from the pearl millet farmland at ICAR, Mysore, India. It automatically sends the collected data to the cloud server and the Raspberry Pi. The ‘Custom-Net’ model designed as a part of this research is deployed on the cloud server. It collaborates with the Raspberry Pi to precisely predict the blast and rust diseases in pearl millet. Moreover, the Grad-CAM is employed to visualize the features extracted by the ‘Custom-Net’. Furthermore, the impact of transfer learning on the ‘Custom-Net’ and state-of-the-art models viz. Inception ResNet-V2, Inception-V3, ResNet-50, VGG-16, and VGG-19 is shown in this manuscript. Based on the experimental results, and features visualization by Grad-CAM, it is observed that the ‘Custom-Net’ extracts the relevant features and the transfer learning improves the extraction of relevant features. Additionally, the ‘Custom-Net’ model reports a classification accuracy of 98.78% that is equivalent to state-of-the-art models viz. Inception ResNet-V2, Inception-V3, ResNet-50, VGG-16, and VGG-19. Although the classification of ‘Custom-Net’ is comparable to state-of-the-art models, it is effective in reducing the training time by 86.67%. It makes the model more suitable for automating disease detection. This proves that the proposed model is effective in providing a low-cost and handy tool for farmers to improve crop yield and product quality.
Nidhi Kundu; Geeta Rani; Vijaypal Dhaka; Kalpit Gupta; Siddaiah Nayak; Sahil Verma; Muhammad Ijaz; Marcin Woźniak. IoT and Interpretable Machine Learning Based Framework for Disease Prediction in Pearl Millet. Sensors 2021, 21, 5386 .
AMA StyleNidhi Kundu, Geeta Rani, Vijaypal Dhaka, Kalpit Gupta, Siddaiah Nayak, Sahil Verma, Muhammad Ijaz, Marcin Woźniak. IoT and Interpretable Machine Learning Based Framework for Disease Prediction in Pearl Millet. Sensors. 2021; 21 (16):5386.
Chicago/Turabian StyleNidhi Kundu; Geeta Rani; Vijaypal Dhaka; Kalpit Gupta; Siddaiah Nayak; Sahil Verma; Muhammad Ijaz; Marcin Woźniak. 2021. "IoT and Interpretable Machine Learning Based Framework for Disease Prediction in Pearl Millet." Sensors 21, no. 16: 5386.
Recently, its becomes easy to track down the data due to its availability in a large number. Although for data management, processing, and obtainability, cloud computing is considered a well-known approach for organizational development on the internet. Despite many advantages, cloud computing has still numerous security challenges that can affect the big-data usage on cloud computing. To find the security issues/challenges that are faced by software vendors’ organizations we conducted a systematic literature review (SLR) through which we have find out 103 relevant research publications by developing a search string that is inspired by the research questions. This relevant data was comprised from different databases e.g. Google Scholar, IEEE Explore, ScienceDirect, ACM Digital Library, and SpringerLink. Furthermore, for the detailed literature review, we have accomplished all the steps in SLR, for example, development of SLR protocol, Initials and final assortment of the relevant data, data extraction, data quality assessment, and data synthesis. We identified fifteen (15) critical security challenges which are: data secrecy, geographical data location, unauthorized data access, lack of control, lack of data management, network-level issues, data integrity, data recovery, lack of trust, data sharing, data availability, asset issues, legal amenabilities, lack of quality, and lack of consistency. Furthermore, sixty four (64) standard practices are identified for these critical security challenges using the proposed SLR that could help vendor organizations to overcome the security challenges for big data. The findings of our research study demonstrate the resemblances and divergences in the identified security challenges in different periods, continents, databases, and methods. The proposed SLR will also support software vendor organizations for securing big data on the cloud computing platforms.
Abudul Wahid Khan; Maseeh Ullah Khan; Javed Ali Khan; Arshad Ahmad; Khalil Khan; Muhammad Zamir; Wonjoon Kim; Muhammad Fazal Ijaz. Analyzing and Evaluating Critical Challenges and Practices for Software Vendor Organizations to Secure Big Data on Cloud Computing: An AHP-Based Systematic Approach. IEEE Access 2021, 9, 107309 -107332.
AMA StyleAbudul Wahid Khan, Maseeh Ullah Khan, Javed Ali Khan, Arshad Ahmad, Khalil Khan, Muhammad Zamir, Wonjoon Kim, Muhammad Fazal Ijaz. Analyzing and Evaluating Critical Challenges and Practices for Software Vendor Organizations to Secure Big Data on Cloud Computing: An AHP-Based Systematic Approach. IEEE Access. 2021; 9 (99):107309-107332.
Chicago/Turabian StyleAbudul Wahid Khan; Maseeh Ullah Khan; Javed Ali Khan; Arshad Ahmad; Khalil Khan; Muhammad Zamir; Wonjoon Kim; Muhammad Fazal Ijaz. 2021. "Analyzing and Evaluating Critical Challenges and Practices for Software Vendor Organizations to Secure Big Data on Cloud Computing: An AHP-Based Systematic Approach." IEEE Access 9, no. 99: 107309-107332.
In the modern era, deep learning techniques have emerged as powerful tools in image recognition. Convolutional Neural Networks, one of the deep learning tools, have attained an impressive outcome in this area. Applications such as identifying objects, faces, bones, handwritten digits, and traffic signs signify the importance of Convolutional Neural Networks in the real world. The effectiveness of Convolutional Neural Networks in image recognition motivates the researchers to extend its applications in the field of agriculture for recognition of plant species, yield management, weed detection, soil, and water management, fruit counting, diseases, and pest detection, evaluating the nutrient status of plants, and much more. The availability of voluminous research works in applying deep learning models in agriculture leads to difficulty in selecting a suitable model according to the type of dataset and experimental environment. In this manuscript, the authors present a survey of the existing literature in applying deep Convolutional Neural Networks to predict plant diseases from leaf images. This manuscript presents an exemplary comparison of the pre-processing techniques, Convolutional Neural Network models, frameworks, and optimization techniques applied to detect and classify plant diseases using leaf images as a data set. This manuscript also presents a survey of the datasets and performance metrics used to evaluate the efficacy of models. The manuscript highlights the advantages and disadvantages of different techniques and models proposed in the existing literature. This survey will ease the task of researchers working in the field of applying deep learning techniques for the identification and classification of plant leaf diseases.
Vijaypal Dhaka; Sangeeta Meena; Geeta Rani; Deepak Sinwar; Kavita Kavita; Muhammad Ijaz; Marcin Woźniak. A Survey of Deep Convolutional Neural Networks Applied for Prediction of Plant Leaf Diseases. Sensors 2021, 21, 4749 .
AMA StyleVijaypal Dhaka, Sangeeta Meena, Geeta Rani, Deepak Sinwar, Kavita Kavita, Muhammad Ijaz, Marcin Woźniak. A Survey of Deep Convolutional Neural Networks Applied for Prediction of Plant Leaf Diseases. Sensors. 2021; 21 (14):4749.
Chicago/Turabian StyleVijaypal Dhaka; Sangeeta Meena; Geeta Rani; Deepak Sinwar; Kavita Kavita; Muhammad Ijaz; Marcin Woźniak. 2021. "A Survey of Deep Convolutional Neural Networks Applied for Prediction of Plant Leaf Diseases." Sensors 21, no. 14: 4749.
Deep learning models are efficient in learning the features that assist in understanding complex patterns precisely. This study proposed a computerized process of classifying skin disease through deep learning based MobileNet V2 and Long Short Term Memory (LSTM). The MobileNet V2 model proved to be efficient with a better accuracy that can work on lightweight computational devices. The proposed model is efficient in maintaining stateful information for precise predictions. A grey-level co-occurrence matrix is used for assessing the progress of diseased growth. The performance has been compared against other state-of-the-art models such as Fine-Tuned Neural Networks (FTNN), Convolutional Neural Network (CNN), Very Deep Convolutional Networks for Large-Scale Image Recognition developed by Visual Geometry Group (VGG), and convolutional neural network architecture that expanded with few changes. The HAM10000 dataset is used and the proposed method has outperformed other methods with more than 85% accuracy. Its robustness in recognizing the affected region much faster with almost 2× lesser computations than the conventional MobileNet model results in minimal computational efforts. Furthermore, a mobile application is designed for instant and proper action. It helps the patient and dermatologists identify the type of disease from the affected region’s image at the initial stage of the skin disease. These findings suggest that the proposed system can help general practitioners efficiently and effectively diagnose skin conditions, thereby reducing further complications and morbidity.
Parvathaneni Srinivasu; Jalluri SivaSai; Muhammad Ijaz; Akash Bhoi; Wonjoon Kim; James Kang. Classification of Skin Disease Using Deep Learning Neural Networks with MobileNet V2 and LSTM. Sensors 2021, 21, 2852 .
AMA StyleParvathaneni Srinivasu, Jalluri SivaSai, Muhammad Ijaz, Akash Bhoi, Wonjoon Kim, James Kang. Classification of Skin Disease Using Deep Learning Neural Networks with MobileNet V2 and LSTM. Sensors. 2021; 21 (8):2852.
Chicago/Turabian StyleParvathaneni Srinivasu; Jalluri SivaSai; Muhammad Ijaz; Akash Bhoi; Wonjoon Kim; James Kang. 2021. "Classification of Skin Disease Using Deep Learning Neural Networks with MobileNet V2 and LSTM." Sensors 21, no. 8: 2852.
The widespread acceptance and increase of the Internet and mobile technologies have revolutionized our existence. On the other hand, the world is witnessing and suffering due to technologically aided crime methods. These threats, including but not limited to hacking and intrusions and are the main concern for security experts. Nevertheless, the challenges facing effective intrusion detection methods continue closely associated with the researcher’s interests. This paper’s main contribution is to present a host-based intrusion detection system using a C4.5-based detector on top of the popular Consolidated Tree Construction (CTC) algorithm, which works efficiently in the presence of class-imbalanced data. An improved version of the random sampling mechanism called Supervised Relative Random Sampling (SRRS) has been proposed to generate a balanced sample from a high-class imbalanced dataset at the detector’s pre-processing stage. Moreover, an improved multi-class feature selection mechanism has been designed and developed as a filter component to generate the IDS datasets’ ideal outstanding features for efficient intrusion detection. The proposed IDS has been validated with state-of-the-art intrusion detection systems. The results show an accuracy of 99.96% and 99.95%, considering the NSL-KDD dataset and the CICIDS2017 dataset using 34 features.
Ranjit Panigrahi; Samarjeet Borah; Akash Bhoi; Muhammad Ijaz; Moumita Pramanik; Yogesh Kumar; Rutvij Jhaveri. A Consolidated Decision Tree-Based Intrusion Detection System for Binary and Multiclass Imbalanced Datasets. Mathematics 2021, 9, 751 .
AMA StyleRanjit Panigrahi, Samarjeet Borah, Akash Bhoi, Muhammad Ijaz, Moumita Pramanik, Yogesh Kumar, Rutvij Jhaveri. A Consolidated Decision Tree-Based Intrusion Detection System for Binary and Multiclass Imbalanced Datasets. Mathematics. 2021; 9 (7):751.
Chicago/Turabian StyleRanjit Panigrahi; Samarjeet Borah; Akash Bhoi; Muhammad Ijaz; Moumita Pramanik; Yogesh Kumar; Rutvij Jhaveri. 2021. "A Consolidated Decision Tree-Based Intrusion Detection System for Binary and Multiclass Imbalanced Datasets." Mathematics 9, no. 7: 751.
Family motivation as a mediating mechanism is a novel and under-researched area in the field of positive organizational scholarship. Drawing on Social Exchange Theory (SET), this study empirically validates family motivation as a mediator between family support and work engagement. The process by Hayes (2013) was used to analyze time-lagged data collected from 356 employees of the education sector. Results confirm the mediating role of family motivation in the relationship between family support and work engagement and the moderating role of calling in the relationship between family support and family motivation. This study adds to the literature of family-work enrichment accounts by validating family support as a novel antecedent for family motivation and positive attitudes. The implications of the study are discussed.
Humaira Erum; Ghulam Abid; Aizza Anwar; Muhammad Ijaz; Daisy Kee. My Family Stands Behind Me: Moderated Mediation Model of Family Support and Work Engagement. European Journal of Investigation in Health, Psychology and Education 2021, 11, 321 -333.
AMA StyleHumaira Erum, Ghulam Abid, Aizza Anwar, Muhammad Ijaz, Daisy Kee. My Family Stands Behind Me: Moderated Mediation Model of Family Support and Work Engagement. European Journal of Investigation in Health, Psychology and Education. 2021; 11 (2):321-333.
Chicago/Turabian StyleHumaira Erum; Ghulam Abid; Aizza Anwar; Muhammad Ijaz; Daisy Kee. 2021. "My Family Stands Behind Me: Moderated Mediation Model of Family Support and Work Engagement." European Journal of Investigation in Health, Psychology and Education 11, no. 2: 321-333.
Supervised learning and pattern recognition is a crucial area of research in information retrieval, knowledge engineering, image processing, medical imaging, and intrusion detection. Numerous algorithms have been designed to address such complex application domains. Despite an enormous array of supervised classifiers, researchers are yet to recognize a robust classification mechanism that accurately and quickly classifies the target dataset, especially in the field of intrusion detection systems (IDSs). Most of the existing literature considers the accuracy and false-positive rate for assessing the performance of classification algorithms. The absence of other performance measures, such as model build time, misclassification rate, and precision, should be considered the main limitation for classifier performance evaluation. This paper’s main contribution is to analyze the current literature status in the field of network intrusion detection, highlighting the number of classifiers used, dataset size, performance outputs, inferences, and research gaps. Therefore, fifty-four state-of-the-art classifiers of various different groups, i.e., Bayes, functions, lazy, rule-based, and decision tree, have been analyzed and explored in detail, considering the sixteen most popular performance measures. This research work aims to recognize a robust classifier, which is suitable for consideration as the base learner, while designing a host-based or network-based intrusion detection system. The NSLKDD, ISCXIDS2012, and CICIDS2017 datasets have been used for training and testing purposes. Furthermore, a widespread decision-making algorithm, referred to as Techniques for Order Preference by Similarity to the Ideal Solution (TOPSIS), allocated ranks to the classifiers based on observed performance reading on the concern datasets. The J48Consolidated provided the highest accuracy of 99.868%, a misclassification rate of 0.1319%, and a Kappa value of 0.998. Therefore, this classifier has been proposed as the ideal classifier for designing IDSs.
Ranjit Panigrahi; Samarjeet Borah; Akash Bhoi; Muhammad Ijaz; Moumita Pramanik; Rutvij Jhaveri; Chiranji Chowdhary. Performance Assessment of Supervised Classifiers for Designing Intrusion Detection Systems: A Comprehensive Review and Recommendations for Future Research. Mathematics 2021, 9, 690 .
AMA StyleRanjit Panigrahi, Samarjeet Borah, Akash Bhoi, Muhammad Ijaz, Moumita Pramanik, Rutvij Jhaveri, Chiranji Chowdhary. Performance Assessment of Supervised Classifiers for Designing Intrusion Detection Systems: A Comprehensive Review and Recommendations for Future Research. Mathematics. 2021; 9 (6):690.
Chicago/Turabian StyleRanjit Panigrahi; Samarjeet Borah; Akash Bhoi; Muhammad Ijaz; Moumita Pramanik; Rutvij Jhaveri; Chiranji Chowdhary. 2021. "Performance Assessment of Supervised Classifiers for Designing Intrusion Detection Systems: A Comprehensive Review and Recommendations for Future Research." Mathematics 9, no. 6: 690.
Cloud computing offers the services to access, manipulate and configure data online over the web. The cloud term refers to an internet network which is remotely available and accessible at anytime from anywhere. Cloud computing is undoubtedly an innovation as the investment in the real and physical infrastructure is much greater than the cloud technology investment. The present work addresses the issue of power consumption done by cloud infrastructure. As there is a need for algorithms and techniques that can reduce energy consumption and schedule resource for the effectiveness of servers. Load balancing is also a significant part of cloud technology that enables the balanced distribution of load among multiple servers to fulfill users’ growing demand. The present work used various optimization algorithms such as particle swarm optimization (PSO), cat swarm optimization (CSO), BAT, cuckoo search algorithm (CSA) optimization algorithm and the whale optimization algorithm (WOA) for balancing the load, energy efficiency, and better resource scheduling to make an efficient cloud environment. In the case of seven servers and eight server’s settings, the results revealed that whale optimization algorithm outperformed other algorithms in terms of response time, energy consumption, execution time and throughput.
Shanky Goyal; Shashi Bhushan; Yogesh Kumar; Abu Rana; Muhammad Bhutta; Muhammad Ijaz; YoungDoo Son. An Optimized Framework for Energy-Resource Allocation in a Cloud Environment based on the Whale Optimization Algorithm. Sensors 2021, 21, 1583 .
AMA StyleShanky Goyal, Shashi Bhushan, Yogesh Kumar, Abu Rana, Muhammad Bhutta, Muhammad Ijaz, YoungDoo Son. An Optimized Framework for Energy-Resource Allocation in a Cloud Environment based on the Whale Optimization Algorithm. Sensors. 2021; 21 (5):1583.
Chicago/Turabian StyleShanky Goyal; Shashi Bhushan; Yogesh Kumar; Abu Rana; Muhammad Bhutta; Muhammad Ijaz; YoungDoo Son. 2021. "An Optimized Framework for Energy-Resource Allocation in a Cloud Environment based on the Whale Optimization Algorithm." Sensors 21, no. 5: 1583.
The nonlinear ion-acoustic waves (IAWs) in a space plasma are capable of exhibiting chaotic dynamics which can be applied to cryptography. Dynamical properties of IAWs are examined using the direct method in plasmas composed of positive and negative ions and nonextensive distributed electrons. Applying the wave transformation, the governing equations are deduced into a dynamical system (DS). Supernonlinear and nonlinear periodic IAWs are presented through phase plane analysis. The analytical periodic wave solution for IAW is obtained. Under the influence of an external periodic force, the DS is transformed to a perturbed system. The perturbed DS describes multistability property of IAWs with change of initial conditions. The multistability behavior features coexisting trajectories such as, quasiperiodic, multiperiodic and chaotic trajectories of the perturbed DS. The chaotic feature in the perturbed DS is supported by Lyapunov exponents. This interesting behavior in the windows of chaotic dynamics is exploited to design efficient encryption algorithm. First SHA-512 is used to compute the hash digest of the plain image which is then used to update the initial seed of the chaotic IAWs system. Note that SHA-512 uses one-way function to map input data to the output, consequently it is quite impossible to break the proposed encryption technique. Second DNA coding is used to confuse and diffuse the DNA version of the plain image. The diffused image follows DNA decoding process leading to the cipher image. The security performance is evaluated using some well-known metrics and results indicate that the proposed cryptosystem can resist most of existing cryptanalysis techniques. In addition complexity analysis shows the possibility of practical implementation of the proposed algorithm.
Jharna Tamang; Jean De Dieu Nkapkop; Muhammad Fazal Ijaz; Punam Kumari Prasad; Nestor Tsafack; Asit Saha; Jacques Kengne; YoungDoo Son. Dynamical Properties of Ion-Acoustic Waves in Space Plasma and Its Application to Image Encryption. IEEE Access 2021, 9, 18762 -18782.
AMA StyleJharna Tamang, Jean De Dieu Nkapkop, Muhammad Fazal Ijaz, Punam Kumari Prasad, Nestor Tsafack, Asit Saha, Jacques Kengne, YoungDoo Son. Dynamical Properties of Ion-Acoustic Waves in Space Plasma and Its Application to Image Encryption. IEEE Access. 2021; 9 ():18762-18782.
Chicago/Turabian StyleJharna Tamang; Jean De Dieu Nkapkop; Muhammad Fazal Ijaz; Punam Kumari Prasad; Nestor Tsafack; Asit Saha; Jacques Kengne; YoungDoo Son. 2021. "Dynamical Properties of Ion-Acoustic Waves in Space Plasma and Its Application to Image Encryption." IEEE Access 9, no. : 18762-18782.
Low-cost, vanadium-based mixed metal oxides mostly have a layered crystal structure with excellent kinetics for lithium-ion batteries, providing high energy density. The existence of multiple oxidation states and the coordination chemistry of vanadium require cost-effective, robust techniques to synthesize the scaling up of their morphology and surface properties. Hydrothermal synthesis is one of the most suitable techniques to achieve pure phase and multiple morphologies under various conditions of temperature and pressure. We attained a simple one-step hydrothermal approach to synthesize the reduced graphene oxide coated Nickel Vanadate ([email protected]) composite with interconnected hollow microspheres. The self-assembly route produced microspheres, which were interconnected under hydrothermal treatment. Cyclic performance determined the initial discharge/charge capacities of 1209.76/839.85 mAh g−1 at the current density of 200 mA g−1 with a columbic efficiency of 69.42%, which improved to 99.64% after 100 cycles. High electrochemical performance was observed due to high surface area, the porous nature of the interconnected hollow microspheres, and rGO induction. These properties increased the contact area between electrode and electrolyte, the active surface of the electrodes, and enhanced electrolyte penetration, which improved Li-ion diffusivity and electronic conductivity.
Faizan Ghani; In Wook Nah; Hyung-Seok Kim; JongChoo Lim; Afifa Marium; Muhammad Fazal Ijaz; Abu Ul Hassan S. Rana. Facile One-Step Hydrothermal Synthesis of the [email protected]2O8 Interconnected Hollow Microspheres Composite for Lithium-Ion Batteries. Nanomaterials 2020, 10, 2389 .
AMA StyleFaizan Ghani, In Wook Nah, Hyung-Seok Kim, JongChoo Lim, Afifa Marium, Muhammad Fazal Ijaz, Abu Ul Hassan S. Rana. Facile One-Step Hydrothermal Synthesis of the [email protected]2O8 Interconnected Hollow Microspheres Composite for Lithium-Ion Batteries. Nanomaterials. 2020; 10 (12):2389.
Chicago/Turabian StyleFaizan Ghani; In Wook Nah; Hyung-Seok Kim; JongChoo Lim; Afifa Marium; Muhammad Fazal Ijaz; Abu Ul Hassan S. Rana. 2020. "Facile One-Step Hydrothermal Synthesis of the [email protected]2O8 Interconnected Hollow Microspheres Composite for Lithium-Ion Batteries." Nanomaterials 10, no. 12: 2389.
The majority of imaging techniques use symmetric and asymmetric cryptography algorithms to encrypt digital media. Most of the research works contributed in the literature focus primarily on the Advanced Encryption Standard (AES) algorithm for encryption and decryption. This paper propose an analysis for performing image encryption and decryption by hybridization of Elliptic Curve Cryptography (ECC) with Hill Cipher (HC), ECC with Advanced Encryption Standard (AES) and ElGamal with Double Playfair Cipher (DPC). This analysis is based on the following parameters: (i) Encryption and decryption time, (ii) entropy of encrypted image, (iii) loss in intensity of the decrypted image, (iv) Peak Signal to Noise Ratio (PSNR), (v) Number of Pixels Change Rate (NPCR), and (vi) Unified Average Changing Intensity (UACI). The hybrid process involves the speed and ease of implementation from symmetric algorithms, as well as improved security from asymmetric algorithms. ECC and ElGamal cryptosystems provide asymmetric key cryptography, while HC, AES, and DPC are symmetric key algorithms. ECC with AES are perfect for remote or private communications with smaller image sizes based on the amount of time needed for encryption and decryption. The metric measurement with test cases finds that ECC and HC have a good overall solution for image encryption.
Chiranji Lal Chowdhary; Pushpam Virenbhai Patel; Krupal Jaysukhbhai Kathrotia; Muhammad Attique; Kumaresan P.; Muhammad Fazal Ijaz. Analytical Study of Hybrid Techniques for Image Encryption and Decryption. Sensors 2020, 20, 5162 .
AMA StyleChiranji Lal Chowdhary, Pushpam Virenbhai Patel, Krupal Jaysukhbhai Kathrotia, Muhammad Attique, Kumaresan P., Muhammad Fazal Ijaz. Analytical Study of Hybrid Techniques for Image Encryption and Decryption. Sensors. 2020; 20 (18):5162.
Chicago/Turabian StyleChiranji Lal Chowdhary; Pushpam Virenbhai Patel; Krupal Jaysukhbhai Kathrotia; Muhammad Attique; Kumaresan P.; Muhammad Fazal Ijaz. 2020. "Analytical Study of Hybrid Techniques for Image Encryption and Decryption." Sensors 20, no. 18: 5162.
Globally, cervical cancer remains as the foremost prevailing cancer in females. Hence, it is necessary to distinguish the importance of risk factors of cervical cancer to classify potential patients. The present work proposes a cervical cancer prediction model (CCPM) that offers early prediction of cervical cancer using risk factors as inputs. The CCPM first removes outliers by using outlier detection methods such as density-based spatial clustering of applications with noise (DBSCAN) and isolation forest (iForest) and by increasing the number of cases in the dataset in a balanced way, for example, through synthetic minority over-sampling technique (SMOTE) and SMOTE with Tomek link (SMOTETomek). Finally, it employs random forest (RF) as a classifier. Thus, CCPM lies on four scenarios: (1) DBSCAN + SMOTETomek + RF, (2) DBSCAN + SMOTE+ RF, (3) iForest + SMOTETomek + RF, and (4) iForest + SMOTE + RF. A dataset of 858 potential patients was used to validate the performance of the proposed method. We found that combinations of iForest with SMOTE and iForest with SMOTETomek provided better performances than those of DBSCAN with SMOTE and DBSCAN with SMOTETomek. We also observed that RF performed the best among several popular machine learning classifiers. Furthermore, the proposed CCPM showed better accuracy than previously proposed methods for forecasting cervical cancer. In addition, a mobile application that can collect cervical cancer risk factors data and provides results from CCPM is developed for instant and proper action at the initial stage of cervical cancer.
Muhammad Fazal Ijaz; Muhammad Attique; YoungDoo Son. Data-Driven Cervical Cancer Prediction Model with Outlier Detection and Over-Sampling Methods. Sensors 2020, 20, 2809 .
AMA StyleMuhammad Fazal Ijaz, Muhammad Attique, YoungDoo Son. Data-Driven Cervical Cancer Prediction Model with Outlier Detection and Over-Sampling Methods. Sensors. 2020; 20 (10):2809.
Chicago/Turabian StyleMuhammad Fazal Ijaz; Muhammad Attique; YoungDoo Son. 2020. "Data-Driven Cervical Cancer Prediction Model with Outlier Detection and Over-Sampling Methods." Sensors 20, no. 10: 2809.
This work presents a double asymmetric data hiding technique. The first asymmetry is created by hiding secret data in the complex region of the cover image and keep the smooth region unaffected. Then another asymmetry is developed by hiding a different number of secret bits in the various pixels of the complex region. The proposed technique uses the ant colony optimization (ACO) based technique for the classification of complex and smooth region pixels. Then, the variable least significant bits (VLSB) data hiding framework is used to hide secret bits in the complex region of the cover image. A distance-based substitution technique, namely increasing distance increasing bits substitution algorithm, is used to ensure the asymmetry in the number of hidden bits. The double asymmetric framework enhances the security of the hidden secret data and makes the retrieval of hidden information difficult for unauthorized users. The algorithm results in high-quality stego images, and the hidden information does not attract the human visual system (HVS).
Sahib Khan; Muhammad Abeer Irfan; Khalil Khan; Mushtaq Khan; Tawab Khan; Rehan Ullah Khan; Muhammad Fazal Ijaz. ACO Based Variable Least Significant Bits Data Hiding in Edges Using IDIBS Algorithm. Symmetry 2020, 12, 781 .
AMA StyleSahib Khan, Muhammad Abeer Irfan, Khalil Khan, Mushtaq Khan, Tawab Khan, Rehan Ullah Khan, Muhammad Fazal Ijaz. ACO Based Variable Least Significant Bits Data Hiding in Edges Using IDIBS Algorithm. Symmetry. 2020; 12 (5):781.
Chicago/Turabian StyleSahib Khan; Muhammad Abeer Irfan; Khalil Khan; Mushtaq Khan; Tawab Khan; Rehan Ullah Khan; Muhammad Fazal Ijaz. 2020. "ACO Based Variable Least Significant Bits Data Hiding in Edges Using IDIBS Algorithm." Symmetry 12, no. 5: 781.
The rapid growth of GPS-enabled mobile devices has popularized many location-based applications. Spatial keyword search which finds objects of interest by considering both spatial locations and textual descriptions has become very useful in these applications. The recent integration of social data with spatial keyword search opens a new service horizon for users. Few previous studies have proposed methods to combine spatial keyword queries with social data in Euclidean space. However, most real-world applications constrain the distance between query location and data objects by a road network, where distance between two points is defined by the shortest connecting path. This paper proposes geo-social top-k keyword queries and geo-social skyline keyword queries on road networks. Both queries enrich traditional spatial keyword query semantics by incorporating social relevance component. We formalize the proposed query types and appropriate indexing frameworks and algorithms to efficiently process them. The effectiveness and efficiency of the proposed approaches are evaluated using real datasets.
Muhammad Attique; Muhammad Afzal; Farman Ali; Irfan Mehmood; Muhammad Fazal Ijaz; Hyung-Ju Cho. Geo-Social Top-k and Skyline Keyword Queries on Road Networks. Sensors 2020, 20, 798 .
AMA StyleMuhammad Attique, Muhammad Afzal, Farman Ali, Irfan Mehmood, Muhammad Fazal Ijaz, Hyung-Ju Cho. Geo-Social Top-k and Skyline Keyword Queries on Road Networks. Sensors. 2020; 20 (3):798.
Chicago/Turabian StyleMuhammad Attique; Muhammad Afzal; Farman Ali; Irfan Mehmood; Muhammad Fazal Ijaz; Hyung-Ju Cho. 2020. "Geo-Social Top-k and Skyline Keyword Queries on Road Networks." Sensors 20, no. 3: 798.
With the growth of the internet, electronic (online) business has become an important trend in the economy. This study investigates how retailers could enhance their shopping processes and hence help sustain their e-business development. Therefore, we propose a unified information system-consumer behavior (IS-CB) model for online shopping to analyze factors that impact online shopping. We used an online survey to gather data from 633 online customers to test the theoretical model, matching differences using structural equation modeling. Highly influencing factors for the IS-CB online shopping model included perceived value (PV), perceived risk (PR), social factors (SF), perceived ease of use (PEOU), perceived usefulness (PU), online shopping intention, trust, online shopping experience, actual online shopping purchases, entertainment gratification (EG), website irritation (WI), information design (ID), visual design (VD), and navigation design (ND). This study provides important theoretical and practical implications. PV and trust in online shopping can nurture positive attitudes and shopping intentions among online customers. Well-designed websites produce higher levels of trust and reduced WI. Similarly, online shopping sites with better ID, ND, and VD also reduce WI and increase trust. This study fills gaps in previous studies relating to IS and CB and provides explanations for IS and CB constituent impacts on acceptance and use of online shopping. The proposed unified IS-CB explains consumer online shopping patterns for a sustainable e-business.
Muhammad Fazal Ijaz; Jongtae Rhee. Constituents and Consequences of Online-Shopping in Sustainable E-Business: An Experimental Study of Online-Shopping Malls. Sustainability 2018, 10, 3756 .
AMA StyleMuhammad Fazal Ijaz, Jongtae Rhee. Constituents and Consequences of Online-Shopping in Sustainable E-Business: An Experimental Study of Online-Shopping Malls. Sustainability. 2018; 10 (10):3756.
Chicago/Turabian StyleMuhammad Fazal Ijaz; Jongtae Rhee. 2018. "Constituents and Consequences of Online-Shopping in Sustainable E-Business: An Experimental Study of Online-Shopping Malls." Sustainability 10, no. 10: 3756.
As the risk of diseases diabetes and hypertension increases, machine learning algorithms are being utilized to improve early stage diagnosis. This study proposes a Hybrid Prediction Model (HPM), which can provide early prediction of type 2 diabetes (T2D) and hypertension based on input risk-factors from individuals. The proposed HPM consists of Density-based Spatial Clustering of Applications with Noise (DBSCAN)-based outlier detection to remove the outlier data, Synthetic Minority Over-Sampling Technique (SMOTE) to balance the distribution of class, and Random Forest (RF) to classify the diseases. Three benchmark datasets were utilized to predict the risk of diabetes and hypertension at the initial stage. The result showed that by integrating DBSCAN-based outlier detection, SMOTE, and RF, diabetes and hypertension could be successfully predicted. The proposed HPM provided the best performance result as compared to other models for predicting diabetes as well as hypertension. Furthermore, our study has demonstrated that the proposed HPM can be applied in real cases in the IoT-based Health-care Monitoring System, so that the input risk-factors from end-user android application can be stored and analyzed in a secure remote server. The prediction result from the proposed HPM can be accessed by users through an Android application; thus, it is expected to provide an effective way to find the risk of diabetes and hypertension at the initial stage.
Muhammad Fazal Ijaz; Ganjar Alfian; Muhammad Syafrudin; Jongtae Rhee. Hybrid Prediction Model for Type 2 Diabetes and Hypertension Using DBSCAN-Based Outlier Detection, Synthetic Minority Over Sampling Technique (SMOTE), and Random Forest. Applied Sciences 2018, 8, 1325 .
AMA StyleMuhammad Fazal Ijaz, Ganjar Alfian, Muhammad Syafrudin, Jongtae Rhee. Hybrid Prediction Model for Type 2 Diabetes and Hypertension Using DBSCAN-Based Outlier Detection, Synthetic Minority Over Sampling Technique (SMOTE), and Random Forest. Applied Sciences. 2018; 8 (8):1325.
Chicago/Turabian StyleMuhammad Fazal Ijaz; Ganjar Alfian; Muhammad Syafrudin; Jongtae Rhee. 2018. "Hybrid Prediction Model for Type 2 Diabetes and Hypertension Using DBSCAN-Based Outlier Detection, Synthetic Minority Over Sampling Technique (SMOTE), and Random Forest." Applied Sciences 8, no. 8: 1325.
Current technology provides an efficient way of monitoring the personal health of individuals. Bluetooth Low Energy (BLE)-based sensors can be considered as a solution for monitoring personal vital signs data. In this study, we propose a personalized healthcare monitoring system by utilizing a BLE-based sensor device, real-time data processing, and machine learning-based algorithms to help diabetic patients to better self-manage their chronic condition. BLEs were used to gather users’ vital signs data such as blood pressure, heart rate, weight, and blood glucose (BG) from sensor nodes to smartphones, while real-time data processing was utilized to manage the large amount of continuously generated sensor data. The proposed real-time data processing utilized Apache Kafka as a streaming platform and MongoDB to store the sensor data from the patient. The results show that commercial versions of the BLE-based sensors and the proposed real-time data processing are sufficiently efficient to monitor the vital signs data of diabetic patients. Furthermore, machine learning–based classification methods were tested on a diabetes dataset and showed that a Multilayer Perceptron can provide early prediction of diabetes given the user’s sensor data as input. The results also reveal that Long Short-Term Memory can accurately predict the future BG level based on the current sensor data. In addition, the proposed diabetes classification and BG prediction could be combined with personalized diet and physical activity suggestions in order to improve the health quality of patients and to avoid critical conditions in the future.
Ganjar Alfian; Muhammad Syafrudin; Muhammad Fazal Ijaz; M. Alex Syaekhoni; Norma Latif Fitriyani; Jongtae Rhee. A Personalized Healthcare Monitoring System for Diabetic Patients by Utilizing BLE-Based Sensors and Real-Time Data Processing. Sensors 2018, 18, 2183 .
AMA StyleGanjar Alfian, Muhammad Syafrudin, Muhammad Fazal Ijaz, M. Alex Syaekhoni, Norma Latif Fitriyani, Jongtae Rhee. A Personalized Healthcare Monitoring System for Diabetic Patients by Utilizing BLE-Based Sensors and Real-Time Data Processing. Sensors. 2018; 18 (7):2183.
Chicago/Turabian StyleGanjar Alfian; Muhammad Syafrudin; Muhammad Fazal Ijaz; M. Alex Syaekhoni; Norma Latif Fitriyani; Jongtae Rhee. 2018. "A Personalized Healthcare Monitoring System for Diabetic Patients by Utilizing BLE-Based Sensors and Real-Time Data Processing." Sensors 18, no. 7: 2183.
Ganjar Alfian; Jongtae Rhee; Hyejung Ahn; Jaeho Lee; Umar Farooq; Muhammad Fazal Ijaz; M. Alex Syaekhoni. Integration of RFID, wireless sensor networks, and data mining in an e-pedigree food traceability system. Journal of Food Engineering 2017, 212, 65 -75.
AMA StyleGanjar Alfian, Jongtae Rhee, Hyejung Ahn, Jaeho Lee, Umar Farooq, Muhammad Fazal Ijaz, M. Alex Syaekhoni. Integration of RFID, wireless sensor networks, and data mining in an e-pedigree food traceability system. Journal of Food Engineering. 2017; 212 ():65-75.
Chicago/Turabian StyleGanjar Alfian; Jongtae Rhee; Hyejung Ahn; Jaeho Lee; Umar Farooq; Muhammad Fazal Ijaz; M. Alex Syaekhoni. 2017. "Integration of RFID, wireless sensor networks, and data mining in an e-pedigree food traceability system." Journal of Food Engineering 212, no. : 65-75.