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Muhammad Shafiq
Department of Information and Communication Engineering, Yeungnam University, Gyeongsan 38541, Republic of Korea

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Research article
Published: 27 May 2021 in Journal of Healthcare Engineering
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Object detection plays a vital role in the fields of computer vision, machine learning, and artificial intelligence applications (such as FUSE-AI (E-healthcare MRI scan), face detection, people counting, and vehicle detection) to identify good and defective food products. In the field of artificial intelligence, target detection has been at its peak, but when it comes to detecting multiple targets in a single image or video file, there are indeed challenges. This article focuses on the improved K-nearest neighbor (MK-NN) algorithm for electronic medical care to realize intelligent medical services and applications. We introduced modifications to improve the efficiency of MK-NN, and a comparative analysis was performed to determine the best fuse target detection algorithm based on robustness, accuracy, and computational time. The comparative analysis is performed using four algorithms, namely, MK-NN, traditional K-NN, convolutional neural network, and backpropagation. Experimental results show that the improved K-NN algorithm is the best model in terms of robustness, accuracy, and computational time.

ACS Style

Ijaz Ahmad; Inam Ullah; Wali Ullah Khan; Ateeq Ur Rehman; Mohmmed S. Adrees; Muhammad Qaiser Saleem; Omar Cheikhrouhou; Habib Hamam; Muhammad Shafiq. Efficient Algorithms for E-Healthcare to Solve Multiobject Fuse Detection Problem. Journal of Healthcare Engineering 2021, 2021, 1 -16.

AMA Style

Ijaz Ahmad, Inam Ullah, Wali Ullah Khan, Ateeq Ur Rehman, Mohmmed S. Adrees, Muhammad Qaiser Saleem, Omar Cheikhrouhou, Habib Hamam, Muhammad Shafiq. Efficient Algorithms for E-Healthcare to Solve Multiobject Fuse Detection Problem. Journal of Healthcare Engineering. 2021; 2021 ():1-16.

Chicago/Turabian Style

Ijaz Ahmad; Inam Ullah; Wali Ullah Khan; Ateeq Ur Rehman; Mohmmed S. Adrees; Muhammad Qaiser Saleem; Omar Cheikhrouhou; Habib Hamam; Muhammad Shafiq. 2021. "Efficient Algorithms for E-Healthcare to Solve Multiobject Fuse Detection Problem." Journal of Healthcare Engineering 2021, no. : 1-16.

Journal article
Published: 06 April 2021 in IEEE Access
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This paper proposes a blockchain based trust management method for agents in a multi-agent system. In this work, three objectives are achieved: trust, cooperation and privacy. The trust of agents depends on the credibility of trust evaluators, which is verified using the proposed methods of trust distortion, consistency and reliability. To enhance the cooperation between agents, a tit-3-for-tat (T3FT) repeated game strategy is developed. This strategy is more forgiving than the existing tit-for-tat (TFT) strategy. It encourages cheating agents to re-establish their trust by cooperating for three consecutive rounds of play. Also, a proof-of-cooperation consensus protocol is proposed to improve agents’ cooperation while creating and validating blocks. The privacy of agents is preserved in this work using the publicly verifiable secret sharing mechanism. Simulation results validate the effectiveness of the proposed work. From the simulation results, the proposed trust method outperforms an existing fuzzy logic trust method in terms of detecting cheating behavior of agents in the system. Besides, the proposed T3FT is effective as compared to the existing tit-for- 2-tat and TFT strategies in the literature. Moreover, security analysis of the proposed method is performed. The analysis shows that the proposed work is safe from bad-mouthing and on-off trust related attacks.

ACS Style

Rabiya Khalid; Omaji Samuel; Nadeem Javaid; Abdulaziz Aldegheishem; Muhammad Shafiq; Nabil Alrajeh. A Secure Trust Method for Multi-Agent System in Smart Grids Using Blockchain. IEEE Access 2021, 9, 59848 -59859.

AMA Style

Rabiya Khalid, Omaji Samuel, Nadeem Javaid, Abdulaziz Aldegheishem, Muhammad Shafiq, Nabil Alrajeh. A Secure Trust Method for Multi-Agent System in Smart Grids Using Blockchain. IEEE Access. 2021; 9 (99):59848-59859.

Chicago/Turabian Style

Rabiya Khalid; Omaji Samuel; Nadeem Javaid; Abdulaziz Aldegheishem; Muhammad Shafiq; Nabil Alrajeh. 2021. "A Secure Trust Method for Multi-Agent System in Smart Grids Using Blockchain." IEEE Access 9, no. 99: 59848-59859.

Journal article
Published: 03 April 2021 in International Journal of Environmental Research and Public Health
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Diabetes distress is an alternative disorder that is often associated with depression syndromes. Psychosocial distress is an alternative disorder that acts as a resistance to diabetes self-care management and compromises diabetes control. Yet, in Nigeria, the focus of healthcare centers is largely inclined toward the medical aspect of diabetes that neglects psychosocial care. In this retrospective study, specific distress was measured by the Diabetes Distress Screening (DDS) scale, and depression was analyzed by the Beck Depression Inventory (BDI) and Diagnosis Statistics Manual (DSM) criteria in type 2 diabetes mellitus (T2DM) patients of Northwestern Nigeria. Additionally, we applied the Chi-square test and linear regression to measure the forecast prevalence ratio and evaluate the link between the respective factors that further determine the odd ratios and coefficient correlations in five nonintrusive variables, namely age, gender, physical exercise, diabetes history, and smoking. In total, 712 sample patients were taken, with 51.68% male and 47.31% female patients. The mean age and body mass index (BMI) was 48.6 years ± 12.8 and 45.6 years ± 8.3. Based on the BDI prediction, 90.15% of patients were found depressed according to the DSM parameters, and depression prevalence was recorded around 22.06%. Overall, 88.20% of patients had DDS-dependent diabetes-specific distress with a prevalence ratio of 24.08%, of whom 45.86% were moderate and 54.14% serious. In sharp contrast, emotion-related distress of 28.96% was found compared to interpersonal (23.61%), followed by physician (16.42%) and regimen (13.21%) distress. The BDI-based matching of depression signs was also statistically significant with p < 0.001 in severe distress patients. However, 10.11% of patients were considered not to be depressed by DSM guidelines. The statistical evidence indicates that depression and distress are closely correlated with age, sex, diabetes history, physical exercise, and smoking influences. The facts and findings in this work show that emotional distress was found more prevalent. This study is significant because it considered several sociocultural and religious differences between Nigeria and large, undeveloped, populated countries with low socioeconomic status and excessive epidemiological risk. Finally, it is important for the clinical implications of T2DM patients on their initial screenings.

ACS Style

Sohail Noman; Jehangir Arshad; Muhammad Zeeshan; Ateeq Rehman; Amir Haider; Shahzada Khurram; Omar Cheikhrouhou; Habib Hamam; Muhammad Shafiq. An Empirical Study on Diabetes Depression over Distress Evaluation Using Diagnosis Statistical Manual and Chi-Square Method. International Journal of Environmental Research and Public Health 2021, 18, 3755 .

AMA Style

Sohail Noman, Jehangir Arshad, Muhammad Zeeshan, Ateeq Rehman, Amir Haider, Shahzada Khurram, Omar Cheikhrouhou, Habib Hamam, Muhammad Shafiq. An Empirical Study on Diabetes Depression over Distress Evaluation Using Diagnosis Statistical Manual and Chi-Square Method. International Journal of Environmental Research and Public Health. 2021; 18 (7):3755.

Chicago/Turabian Style

Sohail Noman; Jehangir Arshad; Muhammad Zeeshan; Ateeq Rehman; Amir Haider; Shahzada Khurram; Omar Cheikhrouhou; Habib Hamam; Muhammad Shafiq. 2021. "An Empirical Study on Diabetes Depression over Distress Evaluation Using Diagnosis Statistical Manual and Chi-Square Method." International Journal of Environmental Research and Public Health 18, no. 7: 3755.

Journal article
Published: 02 February 2021 in IEEE Access
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In smart grids, electricity theft is the most significant challenge. It cannot be identified easily since existing methods are dependent on specific devices. Also, the methods lack in extracting meaningful information from high-dimensional electricity consumption data and increase the false positive rate that limit their performance. Moreover, imbalanced data is a hurdle in accurate electricity theft detection (ETD) using data driven methods. To address this problem, sampling techniques are used in the literature. However, the traditional sampling techniques generate insufficient and unrealistic data that degrade the ETD rate. In this work, two novel ETD models are developed. A hybrid sampling approach, i.e., synthetic minority oversampling technique with edited nearest neighbor, is introduced in the first model. Furthermore, AlexNet is used for dimensionality reduction and extracting useful information from electricity consumption data. Finally, a light gradient boosting model is used for classification purpose. In the second model, conditional wasserstein generative adversarial network with gradient penalty is used to capture the real distribution of the electricity consumption data. It is constructed by adding auxiliary provisional information to generate more realistic data for the minority class. Moreover, GoogLeNet architecture is employed to reduce the dataset’s dimensionality. Finally, adaptive boosting is used for classification of honest and suspicious consumers. Both models are trained and tested using real power consumption data provided by state grid corporation of China. The proposed models’ performance is evaluated using different performance metrics like precision, recall, accuracy, F1-score, etc. The simulation results prove that the proposed models outperform the existing techniques, such as support vector machine, extreme gradient boosting, convolution neural network, etc., in terms of efficient ETD.

ACS Style

Abdulaziz Aldegheishem; Mubbashra Anwar; Nadeem Javaid; Nabil Alrajeh; Muhammad Shafiq; Hasan Ahmed. Towards Sustainable Energy Efficiency With Intelligent Electricity Theft Detection in Smart Grids Emphasising Enhanced Neural Networks. IEEE Access 2021, 9, 25036 -25061.

AMA Style

Abdulaziz Aldegheishem, Mubbashra Anwar, Nadeem Javaid, Nabil Alrajeh, Muhammad Shafiq, Hasan Ahmed. Towards Sustainable Energy Efficiency With Intelligent Electricity Theft Detection in Smart Grids Emphasising Enhanced Neural Networks. IEEE Access. 2021; 9 ():25036-25061.

Chicago/Turabian Style

Abdulaziz Aldegheishem; Mubbashra Anwar; Nadeem Javaid; Nabil Alrajeh; Muhammad Shafiq; Hasan Ahmed. 2021. "Towards Sustainable Energy Efficiency With Intelligent Electricity Theft Detection in Smart Grids Emphasising Enhanced Neural Networks." IEEE Access 9, no. : 25036-25061.

Journal article
Published: 01 January 2021 in Intelligent Automation & Soft Computing
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ACS Style

Islam Ali; Wasif Nisar; Waqar Mehmood; Muhammad Qaiser Saleem; Ali S. Ahmed; Haysam E. Elamin; Mahmood Niazi; Muhammad Shafiq. Workflow Models to Establish Software Baselines in SSMEs. Intelligent Automation & Soft Computing 2021, 28, 693 -713.

AMA Style

Islam Ali, Wasif Nisar, Waqar Mehmood, Muhammad Qaiser Saleem, Ali S. Ahmed, Haysam E. Elamin, Mahmood Niazi, Muhammad Shafiq. Workflow Models to Establish Software Baselines in SSMEs. Intelligent Automation & Soft Computing. 2021; 28 (3):693-713.

Chicago/Turabian Style

Islam Ali; Wasif Nisar; Waqar Mehmood; Muhammad Qaiser Saleem; Ali S. Ahmed; Haysam E. Elamin; Mahmood Niazi; Muhammad Shafiq. 2021. "Workflow Models to Establish Software Baselines in SSMEs." Intelligent Automation & Soft Computing 28, no. 3: 693-713.

Journal article
Published: 01 January 2021 in Intelligent Automation & Soft Computing
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ACS Style

Islam Ali; Musawwer Khan; Waqar Mehmood; Wasif Nisar; Waqar Aslam; Muhammad Qaiser Saleem; Majzoob K. Omer; Muhammad Shafiq. CMMI Compliant Workflow Models to Establish Configuration Management Integrity in Software SMEs. Intelligent Automation & Soft Computing 2021, 27, 605 -623.

AMA Style

Islam Ali, Musawwer Khan, Waqar Mehmood, Wasif Nisar, Waqar Aslam, Muhammad Qaiser Saleem, Majzoob K. Omer, Muhammad Shafiq. CMMI Compliant Workflow Models to Establish Configuration Management Integrity in Software SMEs. Intelligent Automation & Soft Computing. 2021; 27 (3):605-623.

Chicago/Turabian Style

Islam Ali; Musawwer Khan; Waqar Mehmood; Wasif Nisar; Waqar Aslam; Muhammad Qaiser Saleem; Majzoob K. Omer; Muhammad Shafiq. 2021. "CMMI Compliant Workflow Models to Establish Configuration Management Integrity in Software SMEs." Intelligent Automation & Soft Computing 27, no. 3: 605-623.

Journal article
Published: 30 December 2020 in IEEE Access
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The drastic increase in real-time vehicle generated data of various types has imparted a great concept of data trading in vehicular networks. Whereas immense usage of Electric Vehicles (EVs) as mobile energy carriers have supported distributed energy trading due to their bidirectional charging and discharging capabilities. The trustless environment of Internet of Electric Vehicles (IoEV), including fuel vehicles and EVs, encounters trading disputes and conflicting interests among trading parties. To address these challenges, we exploit consortium blockchain to maintain transparency and trust in trading activities. Smart contracts are used to tackle trading disputes and illegal actions. Data duplication problem occurs when a dishonest user sell previously traded data multiple times for financial gain. Therefore, data duplication validation is done through previously stored hash-list at roadside units (RSUs) employed with bloom filters for efficient data lookup. Removing data duplication at an earlier stage reduces storage cost. Moreover, an elliptic curve bilinear pairing based digital signature scheme is used to ensure the reliability and integrity of traded data. To ensure persistent availability of traded data, InterPlanetary File System (IPFS) is used, which provides fault-tolerant and a reliable data storage without any single point of failure. On the other hand, the energy trading transactions among EVs face some security and privacy protection challenges. An adversary can infer the energy trading records of EVs, and launch the data linkage attacks. To address this issue, an account generation technique is used that hides the energy trading trends. The new account generation for an EV depends upon its traded volume of energy. The experimental results verify the efficiency of the proposed data and energy trading scheme in IoEV with the reliable and secure data storage.

ACS Style

Ayesha Sadiq; Muhammad Umar Javed; Rabiya Khalid; Ahmad Almogren; Muhammad Shafiq; Nadeem Javaid. Blockchain Based Data and Energy Trading in Internet of Electric Vehicles. IEEE Access 2020, 9, 7000 -7020.

AMA Style

Ayesha Sadiq, Muhammad Umar Javed, Rabiya Khalid, Ahmad Almogren, Muhammad Shafiq, Nadeem Javaid. Blockchain Based Data and Energy Trading in Internet of Electric Vehicles. IEEE Access. 2020; 9 ():7000-7020.

Chicago/Turabian Style

Ayesha Sadiq; Muhammad Umar Javed; Rabiya Khalid; Ahmad Almogren; Muhammad Shafiq; Nadeem Javaid. 2020. "Blockchain Based Data and Energy Trading in Internet of Electric Vehicles." IEEE Access 9, no. : 7000-7020.

Journal article
Published: 04 December 2020 in IEEE Access
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Electricity theft is one of the main causes of non-technical losses and its detection is important for power distribution companies to avoid revenue loss. The advancement of traditional grids to smart grids allows a two-way flow of information and energy that enables real-time energy management, billing and load surveillance. This infrastructure enables power distribution companies to automate electricity theft detection (ETD) by constructing new innovative data-driven solutions. Whereas, the traditional ETD approaches do not provide acceptable theft detection performance due to high-dimensional imbalanced data, loss of data relationships during feature extraction and the requirement of experts’ involvement. Hence, this paper presents a new semi-supervised solution for ETD, which consists of relational denoising autoencoder (RDAE) and attention guided (AG) TripleGAN, named as RDAE-AG-TripleGAN. In this system, RDAE is implemented to derive features and their associations while AG performs feature weighting and dynamically supervises the AG-TripleGAN. As a result, this procedure significantly boosts the ETD. Furthermore, to demonstrate the acceptability of the proposed methodology over conventional approaches, we conducted extensive simulations using the real power consumption data of smart meters. The proposed solution is validated over the most useful and suitable performance indicators: area under the curve, precision, recall, Matthews correlation coefficient, F1-score and precision-recall area under the curve. The simulation results prove that the proposed method efficiently improves the detection of electricity frauds against conventional ETD schemes such as extreme gradient boosting machine and transductive support vector machine. The proposed solution achieves the detection rate of 0.956, which makes it more acceptable for electric utilities than the existing approaches.

ACS Style

Zeeshan Aslam; Fahad Ahmed; Ahmad Almogren; Muhammad Shafiq; Mansour Zuair; Nadeem Javaid. An Attention Guided Semi-Supervised Learning Mechanism to Detect Electricity Frauds in the Distribution Systems. IEEE Access 2020, 8, 221767 -221782.

AMA Style

Zeeshan Aslam, Fahad Ahmed, Ahmad Almogren, Muhammad Shafiq, Mansour Zuair, Nadeem Javaid. An Attention Guided Semi-Supervised Learning Mechanism to Detect Electricity Frauds in the Distribution Systems. IEEE Access. 2020; 8 (99):221767-221782.

Chicago/Turabian Style

Zeeshan Aslam; Fahad Ahmed; Ahmad Almogren; Muhammad Shafiq; Mansour Zuair; Nadeem Javaid. 2020. "An Attention Guided Semi-Supervised Learning Mechanism to Detect Electricity Frauds in the Distribution Systems." IEEE Access 8, no. 99: 221767-221782.

Journal article
Published: 02 December 2020 in IEEE Access
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The rapid deployment of Electric Vehicles (EVs) and the integration of renewable energy sources have ameliorated the existing power systems and contributed to the development of greener smart communities. However, load balancing problems, security threats, privacy leakage issues, etc., remain unresolved. Many blockchain-based approaches have been used in literature to solve the aforementioned challenges. However, they are not sufficient to obtain satisfactory results because of the inefficient energy management methods and time-intensiveness of the primitive cryptographic executions on the network devices. In this paper, an efficient and secure blockchain-based Energy Trading (ET) model is proposed. It leverages the contract theory, incentive mechanism, and a reputation system for information asymmetry scenario. In order to motivate the ET entities to trade energy locally and EVs to participate in smart energy management, the proposed incentive provisioning mechanism plays a vital role. Besides, a reputation system improves the reliability and efficiency of the system and discourages the blockchain nodes from acting maliciously. A novel consensus algorithm, i.e., Proof of Work based on Reputation (PoWR), is proposed to reduce transaction confirmation latency and block creation time. Moreover, a shortest route algorithm, i.e., the Dijkstra algorithm, is implemented in order to reduce the traveling distance and energy consumption of the EVs during ET. The performance of the proposed model is evaluated using peak to average ratio, social welfare, utility of local aggregator, etc., as performance metrics. Moreover, privacy and security analyses of the system are also presented.

ACS Style

Adamu Sani Yahaya; Nadeem Javaid; Muhammad Umar Javed; Muhammad Shafiq; Wazir Zada Khan; Mohammed Y. Aalsalem. Blockchain-Based Energy Trading and Load Balancing Using Contract Theory and Reputation in a Smart Community. IEEE Access 2020, 8, 222168 -222186.

AMA Style

Adamu Sani Yahaya, Nadeem Javaid, Muhammad Umar Javed, Muhammad Shafiq, Wazir Zada Khan, Mohammed Y. Aalsalem. Blockchain-Based Energy Trading and Load Balancing Using Contract Theory and Reputation in a Smart Community. IEEE Access. 2020; 8 (99):222168-222186.

Chicago/Turabian Style

Adamu Sani Yahaya; Nadeem Javaid; Muhammad Umar Javed; Muhammad Shafiq; Wazir Zada Khan; Mohammed Y. Aalsalem. 2020. "Blockchain-Based Energy Trading and Load Balancing Using Contract Theory and Reputation in a Smart Community." IEEE Access 8, no. 99: 222168-222186.

Journal article
Published: 18 November 2020 in Sensors
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The integration of medical signal processing capabilities and advanced sensors into Internet of Things (IoT) devices plays a key role in providing comfort and convenience to human lives. As the number of patients is increasing gradually, providing healthcare facilities to each patient, particularly to the patients located in remote regions, not only has become challenging but also results in several issues, such as: (i) increase in workload on paramedics, (ii) wastage of time, and (iii) accommodation of patients. Therefore, the design of smart healthcare systems has become an important area of research to overcome these above-mentioned issues. Several healthcare applications have been designed using wireless sensor networks (WSNs), cloud computing, and fog computing. Most of the e-healthcare applications are designed using the cloud computing paradigm. Cloud-based architecture introduces high latency while processing huge amounts of data, thus restricting the large-scale implementation of latency-sensitive e-healthcare applications. Fog computing architecture offers processing and storage resources near to the edge of the network, thus, designing e-healthcare applications using the fog computing paradigm is of interest to meet the low latency requirement of such applications. Patients that are minors or are in intensive care units (ICUs) are unable to self-report their pain conditions. The remote healthcare monitoring applications deploy IoT devices with bio-sensors capable of sensing surface electromyogram (sEMG) and electrocardiogram (ECG) signals to monitor the pain condition of such patients. In this article, fog computing architecture is proposed for deploying a remote pain monitoring system. The key motivation for adopting the fog paradigm in our proposed approach is to reduce latency and network consumption. To validate the effectiveness of the proposed approach in minimizing delay and network utilization, simulations were carried out in iFogSim and the results were compared with the cloud-based systems. The results of the simulations carried out in this research indicate that a reduction in both latency and network consumption can be achieved by adopting the proposed approach for implementing a remote pain monitoring system.

ACS Style

Syed Rizwan Hassan; Ishtiaq Ahmad; Shafiq Ahmad; Abdullah AlFaify; Muhammad Shafiq. Remote Pain Monitoring Using Fog Computing for e-Healthcare: An Efficient Architecture. Sensors 2020, 20, 6574 .

AMA Style

Syed Rizwan Hassan, Ishtiaq Ahmad, Shafiq Ahmad, Abdullah AlFaify, Muhammad Shafiq. Remote Pain Monitoring Using Fog Computing for e-Healthcare: An Efficient Architecture. Sensors. 2020; 20 (22):6574.

Chicago/Turabian Style

Syed Rizwan Hassan; Ishtiaq Ahmad; Shafiq Ahmad; Abdullah AlFaify; Muhammad Shafiq. 2020. "Remote Pain Monitoring Using Fog Computing for e-Healthcare: An Efficient Architecture." Sensors 20, no. 22: 6574.

Journal article
Published: 27 October 2020 in IEEE Access
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The significance of electricity cannot be overlooked as all fields of life like material production, health care, educational sector, etc., depend upon it to render consistent and high-quality services, increase productivity and business continuity. To this end, energy operators have experienced a continuous increasing trend in the electricity demand for the past few decades. This may cause many issues like load shedding, increased electricity bills, imbalance between supply and demand, etc. Therefore, forecasting of electricity demand using efficient techniques is crucial for the energy operators to decide about optimal unit commitment and to make electricity dispatch plans. It also helps to avoid wastage as well as the shortage of energy. In this study, a novel forecasting model, known as ELS-net is proposed, which is a combination of an Ensemble Empirical Mode Decomposition (EEMD) method, multi-model Ensemble Bi Long Short-Term Memory (EBiLSTM) forecasting technique and Support Vector Machine (SVM). In the proposed model, EEMD is used to distinguish between linear and non-linear intrinsic mode functions (IMFs), EBiLSTM is used to forecast the non-linear IMFs and SVM is employed to forecast the linear IMFs. Using separate forecasting techniques for linear and non-linear IMFs decreases the computational complexity of the model. Moreover, SVM requires low computational time as compared to EBiLSTM for linear IMFs. Simulations are performed to examine the effectiveness of the proposed model using two different datasets: New South Wales (NSW) and Victoria (VIC). For performance evaluation, Root Mean Square Error (RMSE), Mean Absolute Error (MAE) and Mean Absolute Percentage Error (MAPE) are used as performance metrics. From the simulation results, it is obvious that the proposed ELS-net model outperforms the start-of-the-art techniques, such as EMD-BILSTM-SVM, EMD-PSO-GA-SVR, BiLSTM, MLP and SVM in terms of forecasting accuracy and minimum execution time.

ACS Style

Nadeem Javaid; Aqdas Naz; Rabiya Khalid; Ahmad Almogren; Muhammad Shafiq; Adia Khalid. ELS-Net: A New Approach to Forecast Decomposed Intrinsic Mode Functions of Electricity Load. IEEE Access 2020, 8, 198935 -198949.

AMA Style

Nadeem Javaid, Aqdas Naz, Rabiya Khalid, Ahmad Almogren, Muhammad Shafiq, Adia Khalid. ELS-Net: A New Approach to Forecast Decomposed Intrinsic Mode Functions of Electricity Load. IEEE Access. 2020; 8 ():198935-198949.

Chicago/Turabian Style

Nadeem Javaid; Aqdas Naz; Rabiya Khalid; Ahmad Almogren; Muhammad Shafiq; Adia Khalid. 2020. "ELS-Net: A New Approach to Forecast Decomposed Intrinsic Mode Functions of Electricity Load." IEEE Access 8, no. : 198935-198949.

Journal article
Published: 28 September 2020 in Sustainability
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Due to the increase in the number of electricity thieves, the electric utilities are facing problems in providing electricity to their consumers in an efficient way. An accurate Electricity Theft Detection (ETD) is quite challenging due to the inaccurate classification on the imbalance electricity consumption data, the overfitting issues and the High False Positive Rate (FPR) of the existing techniques. Therefore, intensified research is needed to accurately detect the electricity thieves and to recover a huge revenue loss for utility companies. To address the above limitations, this paper presents a new model, which is based on the supervised machine learning techniques and real electricity consumption data. Initially, the electricity data are pre-processed using interpolation, three sigma rule and normalization methods. Since the distribution of labels in the electricity consumption data is imbalanced, an Adasyn algorithm is utilized to address this class imbalance problem. It is used to achieve two objectives. Firstly, it intelligently increases the minority class samples in the data. Secondly, it prevents the model from being biased towards the majority class samples. Afterwards, the balanced data are fed into a Visual Geometry Group (VGG-16) module to detect abnormal patterns in electricity consumption. Finally, a Firefly Algorithm based Extreme Gradient Boosting (FA-XGBoost) technique is exploited for classification. The simulations are conducted to show the performance of our proposed model. Moreover, the state-of-the-art methods are also implemented for comparative analysis, i.e., Support Vector Machine (SVM), Convolution Neural Network (CNN), and Logistic Regression (LR). For validation, precision, recall, F1-score, Matthews Correlation Coefficient (MCC), Receiving Operating Characteristics Area Under Curve (ROC-AUC), and Precision Recall Area Under Curve (PR-AUC) metrics are used. Firstly, the simulation results show that the proposed Adasyn method has improved the performance of FA-XGboost classifier, which has achieved F1-score, precision, and recall of 93.7%, 92.6%, and 97%, respectively. Secondly, the VGG-16 module achieved a higher generalized performance by securing accuracy of 87.2% and 83.5% on training and testing data, respectively. Thirdly, the proposed FA-XGBoost has correctly identified actual electricity thieves, i.e., recall of 97%. Moreover, our model is superior to the other state-of-the-art models in terms of handling the large time series data and accurate classification. These models can be efficiently applied by the utility companies using the real electricity consumption data to identify the electricity thieves and overcome the major revenue losses in power sector.

ACS Style

Zahoor Khan; Muhammad Adil; Nadeem Javaid; Malik Saqib; Muhammad Shafiq; Jin-Ghoo Choi. Electricity Theft Detection Using Supervised Learning Techniques on Smart Meter Data. Sustainability 2020, 12, 8023 .

AMA Style

Zahoor Khan, Muhammad Adil, Nadeem Javaid, Malik Saqib, Muhammad Shafiq, Jin-Ghoo Choi. Electricity Theft Detection Using Supervised Learning Techniques on Smart Meter Data. Sustainability. 2020; 12 (19):8023.

Chicago/Turabian Style

Zahoor Khan; Muhammad Adil; Nadeem Javaid; Malik Saqib; Muhammad Shafiq; Jin-Ghoo Choi. 2020. "Electricity Theft Detection Using Supervised Learning Techniques on Smart Meter Data." Sustainability 12, no. 19: 8023.

Journal article
Published: 23 September 2020 in IEEE Transactions on Intelligent Transportation Systems
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In this article, we investigate the wireless power transfer and energy-efficiency (EE) optimization problem for nano-empowered vehicular networks operating over the terahertz band. The nano-sensors in air can harvest energy from a power station and then can transmit the trace information to the micro-device under reconnaissance vehicular scenarios. Hence, by considering the properties of the terahertz band, we develop a long-term EE optimization problem. Furthermore, with the help of the equivalent transformation method, we converted the EE optimization problem into a series of energy-efficient resource allocation problems over the time slots. Each reformulated optimization problem becomes a mixed integer nonlinear programming (MINLP) over a time slot. Hence, to obtain the sub-optimal solution of the reformulated optimization problem, we developed a Quantum-behaved Particle swarm-based EE Optimization (QPEEO) algorithm. Furthermore, by exploiting the special structure of the reformulated problem, we propose an Improved Discrete Particle swarm-based EE Optimization (IDPEEO) algorithm. The proposed IDPEEO algorithm handles the problem's constraints effectively, and greatly reduces the search space and the convergence time. Our simulation results validate the theoretical analysis of the proposed scheme.

ACS Style

Li Feng; Amjad Ali; Muddesar Iqbal; Farman Ali; Imran Raza; Muhammad Hameed Siddiqi; Muhammad Shafiq; Syed Asad Hussain. Dynamic Wireless Information and Power Transfer Scheme for Nano-Empowered Vehicular Networks. IEEE Transactions on Intelligent Transportation Systems 2020, PP, 1 -12.

AMA Style

Li Feng, Amjad Ali, Muddesar Iqbal, Farman Ali, Imran Raza, Muhammad Hameed Siddiqi, Muhammad Shafiq, Syed Asad Hussain. Dynamic Wireless Information and Power Transfer Scheme for Nano-Empowered Vehicular Networks. IEEE Transactions on Intelligent Transportation Systems. 2020; PP (99):1-12.

Chicago/Turabian Style

Li Feng; Amjad Ali; Muddesar Iqbal; Farman Ali; Imran Raza; Muhammad Hameed Siddiqi; Muhammad Shafiq; Syed Asad Hussain. 2020. "Dynamic Wireless Information and Power Transfer Scheme for Nano-Empowered Vehicular Networks." IEEE Transactions on Intelligent Transportation Systems PP, no. 99: 1-12.

Journal article
Published: 19 September 2020 in Sustainability
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Wireless Sensor Networks (WSNs) and Internet of Things (IoT) often suffer from error-prone links when deployed in resource-constrained industrial environments. Reliability is a critical performance requirement of loss-sensitive applications, and Signal-to-Noise Ratio (SNR) is a key indicator of successful communications. In addition to the improvement of the physical layer through modulation and channel coding, machine learning offers adaptive solutions by configuring various communication parameters dynamically. In this paper, we apply a Deep Neural Network (DNN) to predict SNR and Packet Delivery Ratio (PDR). Analysis results based on a real dataset show that the DNN can predict SNR and PDR at the accuracy of up to 96% and 98%, respectively, even when trained with very small fraction (≤10%) of data. Moreover, a common subset of features turns out to be useful in predicting both SNR and PDR so as to encourage considering both metrics jointly. We may control the transmission power in the dynamic and adaptive manner when we have predictable SNR and PDR, and thus fulfill the reliability requirements with energy conservation. This can help in achieving sustainable design for the communication system.

ACS Style

Muhammad Ateeq; Muhammad Afzal; Muhammad Naeem; Muhammad Shafiq; Jin-Ghoo Choi. Deep Learning-Based Multiparametric Predictions for IoT. Sustainability 2020, 12, 7752 .

AMA Style

Muhammad Ateeq, Muhammad Afzal, Muhammad Naeem, Muhammad Shafiq, Jin-Ghoo Choi. Deep Learning-Based Multiparametric Predictions for IoT. Sustainability. 2020; 12 (18):7752.

Chicago/Turabian Style

Muhammad Ateeq; Muhammad Afzal; Muhammad Naeem; Muhammad Shafiq; Jin-Ghoo Choi. 2020. "Deep Learning-Based Multiparametric Predictions for IoT." Sustainability 12, no. 18: 7752.

Journal article
Published: 10 September 2020 in Applied Sciences
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Conventional protection schemes in the distribution system are liable to suffer from high penetration of renewable energy source-based distributed generation (RES-DG). The characteristics of RES-DG, such as wind turbine generators (WTGs), are stochastic due to the intermittent behavior of wind dynamics (WD). It can fluctuate the fault current level, which in turn creates the overcurrent relay coordination (ORC) problem. In this paper, the effects of WD such as wind speed and direction on the short-circuit current contribution from a WTG is investigated, and a robust adaptive overcurrent relay coordination scheme is proposed by forecasting the WD. The seasonal autoregression integrated moving average (SARIMA) and artificial neuro-fuzzy inference system (ANFIS) are implemented for forecasting periodic and nonperiodic WD, respectively, and the fault current level is calculated in advance. Furthermore, the ORC problem is optimized using hybrid Harris hawks optimization and linear programming (HHO–LP) to minimize the operating times of relays. The proposed algorithm is tested on the modified IEEE-8 bus system with wind farms, and the overcurrent relay (OCR) miscoordination caused by WD is eliminated. To further prove the effectiveness of the algorithm, it is also tested in a typical wind-farm-integrated substation. Compared to conventional protection schemes, the results of the proposed scheme were found to be promising in fault isolation with a remarkable reduction in the total operation time of relays and zero miscoordination.

ACS Style

Mian Rizwan; Lucheng Hong; Muhammad Waseem; Shafiq Ahmad; Mohamed Sharaf; Muhammad Shafiq. A Robust Adaptive Overcurrent Relay Coordination Scheme for Wind-Farm-Integrated Power Systems Based on Forecasting the Wind Dynamics for Smart Energy Systems. Applied Sciences 2020, 10, 6318 .

AMA Style

Mian Rizwan, Lucheng Hong, Muhammad Waseem, Shafiq Ahmad, Mohamed Sharaf, Muhammad Shafiq. A Robust Adaptive Overcurrent Relay Coordination Scheme for Wind-Farm-Integrated Power Systems Based on Forecasting the Wind Dynamics for Smart Energy Systems. Applied Sciences. 2020; 10 (18):6318.

Chicago/Turabian Style

Mian Rizwan; Lucheng Hong; Muhammad Waseem; Shafiq Ahmad; Mohamed Sharaf; Muhammad Shafiq. 2020. "A Robust Adaptive Overcurrent Relay Coordination Scheme for Wind-Farm-Integrated Power Systems Based on Forecasting the Wind Dynamics for Smart Energy Systems." Applied Sciences 10, no. 18: 6318.

Journal article
Published: 25 June 2020 in Applied Sciences
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The electrical losses in power systems are divided into non-technical losses (NTLs) and technical losses (TLs). NTL is more harmful than TL because it includes electricity theft, faulty meters and billing errors. It is one of the major concerns in the power system worldwide and incurs a huge revenue loss for utility companies. Electricity theft detection (ETD) is the mechanism used by industry and academia to detect electricity theft. However, due to imbalanced data, overfitting issues and the handling of high-dimensional data, the ETD cannot be applied efficiently. Therefore, this paper proposes a solution to address the above limitations. A long short-term memory (LSTM) technique is applied to detect abnormal patterns in electricity consumption data along with the bat-based random under-sampling boosting (RUSBoost) technique for parameter optimization. Our proposed system model uses the normalization and interpolation methods to pre-process the electricity data. Afterwards, the pre-processed data are fed into the LSTM module for feature extraction. Finally, the selected features are passed to the RUSBoost module for classification. The simulation results show that the proposed solution resolves the issues of data imbalancing, overfitting and the handling of massive time series data. Additionally, the proposed method outperforms the state-of-the-art techniques; i.e., support vector machine (SVM), convolutional neural network (CNN) and logistic regression (LR). Moreover, the F1-score, precision, recall and receiver operating characteristics (ROC) curve metrics are used for the comparative analysis.

ACS Style

Muhammad Adil; Nadeem Javaid; Umar Qasim; Ibrar Ullah; Muhammad Shafiq; Jin-Ghoo Choi. LSTM and Bat-Based RUSBoost Approach for Electricity Theft Detection. Applied Sciences 2020, 10, 4378 .

AMA Style

Muhammad Adil, Nadeem Javaid, Umar Qasim, Ibrar Ullah, Muhammad Shafiq, Jin-Ghoo Choi. LSTM and Bat-Based RUSBoost Approach for Electricity Theft Detection. Applied Sciences. 2020; 10 (12):4378.

Chicago/Turabian Style

Muhammad Adil; Nadeem Javaid; Umar Qasim; Ibrar Ullah; Muhammad Shafiq; Jin-Ghoo Choi. 2020. "LSTM and Bat-Based RUSBoost Approach for Electricity Theft Detection." Applied Sciences 10, no. 12: 4378.

Journal article
Published: 18 June 2020 in Energies
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The computing devices in data centers of cloud and fog remain in continues running cycle to provide services. The long execution state of large number of computing devices consumes a significant amount of power, which emits an equivalent amount of heat in the environment. The performance of the devices is compromised in heating environment. The high powered cooling systems are installed to cool the data centers. Accordingly, data centers demand high electricity for computing devices and cooling systems. Moreover, in Smart Grid (SG) managing energy consumption to reduce the electricity cost for consumers and minimum rely on fossil fuel based power supply (utility) is an interesting domain for researchers. The SG applications are time-sensitive. In this paper, fog based model is proposed for a community to ensure real-time energy management service provision. Three scenarios are implemented to analyze cost efficient energy management for power-users. In first scenario, community’s and fog’s power demand is fulfilled from the utility. In second scenario,community’s Renewable Energy Resources (RES) based Microgrid (MG) is integrated with the utility to meet the demand. In third scenario, the demand is fulfilled by integrating fog’s MG, community’s MG and the utility. In the scenarios, the energy demand of fog is evaluated with proposed mechanism. The required amount of energy to run computing devices against number of requests and amount of power require cooling down the devices are calculated to find energy demand by fog’s data center. The simulations of case studies show that the energy cost to meet the demand of the community and fog’s data center in third scenario is 15.09% and 1.2% more efficient as compared to first and second scenarios, respectively. In this paper, an energy contract is also proposed that ensures the participation of all power generating stakeholders. The results advocate the cost efficiency of proposed contract as compared to third scenario. The integration of RES reduce the energy cost and reduce emission of CO 2 . The simulations for energy management and plots of results are performed in Matlab. The simulation for fog’s resource management, measuring processing, and response time are performed in CloudAnalyst.

ACS Style

Rasool Bukhsh; Muhammad Umar Javed; Aisha Fatima; Nadeem Javaid; Muhammad Shafiq; Jin-Ghoo Choi. Cost Efficient Real Time Electricity Management Services for Green Community Using Fog †. Energies 2020, 13, 3164 .

AMA Style

Rasool Bukhsh, Muhammad Umar Javed, Aisha Fatima, Nadeem Javaid, Muhammad Shafiq, Jin-Ghoo Choi. Cost Efficient Real Time Electricity Management Services for Green Community Using Fog †. Energies. 2020; 13 (12):3164.

Chicago/Turabian Style

Rasool Bukhsh; Muhammad Umar Javed; Aisha Fatima; Nadeem Javaid; Muhammad Shafiq; Jin-Ghoo Choi. 2020. "Cost Efficient Real Time Electricity Management Services for Green Community Using Fog †." Energies 13, no. 12: 3164.

Journal article
Published: 21 April 2020 in Sustainability
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With the increase in local energy generation from Renewable Energy Sources (RESs), the concept of decentralized peer-to-peer Local Energy Market (LEM) is becoming popular. In this paper, a blockchain-based LEM is investigated, where consumers and prosumers in a small community trade energy without the need for a third party. In the proposed model, a Home Energy Management (HEM) system and demurrage mechanism are introduced, which allow both the prosumers and consumers to optimize their energy consumption and to minimize electricity costs. This method also allows end-users to shift their load to off-peak hours and to use cheap energy from the LEM. The proposed solution shows how energy consumption and electricity cost are optimized using HEM and demurrage mechanism. It also provides economic benefits at both the community and end-user levels and provides sufficient energy to the LEM. The simulation results show that electricity cost is reduced up to 44.73% and 28.55% when the scheduling algorithm is applied using the Critical Peak Price (CPP) and Real-Time Price (RTP) schemes, respectively. Similarly, 65.15% and 35.09% of costs are reduced when CPP and RTP are applied with demurrage mechanism. Moreover, 51.80% and 44.37% electricity costs reduction is observed when CPP and RTP are used with both demurrage and scheduling algorithm. We also carried out security vulnerability analysis to ensure that our energy trading smart contract is secure and bug-free against the common vulnerabilities and attacks.

ACS Style

Adamu Sani Yahaya; Nadeem Javaid; Fahad A. Alzahrani; Amjad Rehman; Ibrar Ullah; Affaf Shahid; Muhammad Shafiq. Blockchain Based Sustainable Local Energy Trading Considering Home Energy Management and Demurrage Mechanism. Sustainability 2020, 12, 3385 .

AMA Style

Adamu Sani Yahaya, Nadeem Javaid, Fahad A. Alzahrani, Amjad Rehman, Ibrar Ullah, Affaf Shahid, Muhammad Shafiq. Blockchain Based Sustainable Local Energy Trading Considering Home Energy Management and Demurrage Mechanism. Sustainability. 2020; 12 (8):3385.

Chicago/Turabian Style

Adamu Sani Yahaya; Nadeem Javaid; Fahad A. Alzahrani; Amjad Rehman; Ibrar Ullah; Affaf Shahid; Muhammad Shafiq. 2020. "Blockchain Based Sustainable Local Energy Trading Considering Home Energy Management and Demurrage Mechanism." Sustainability 12, no. 8: 3385.

Journal article
Published: 05 February 2020 in Symmetry
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In this paper, we propose innovative schemes for relay selection that jointly explore packet selection and relay selection for buffer-aided amplify and forward (AF) cooperative relaying networks. The first proposed scheme chooses the most suitable channel based on link quality from all active channels, i.e., channels with neither empty nor full corresponding buffers. In the second proposed scheme, the most suitable channel is chosen based on buffer status. When the source-relay channel is determined, the corresponding relay collects data in the buffer. Likewise, when the relay-destination channel is picked, the most suitable packet is dispatched from the buffer. The most suitable packet is one that provides the highest end-to-end equivalent signal-to-noise ratio. We simulated the outage probability, average throughput and packet delay and analyzed the proposed protocol for both symmetric and asymmetric channel conditions. Comparison is made against the existing buffer-aided schemes. The results show that the proposed relay and packet selection systems help to reduce the outage probability, diversity gain and delay.

ACS Style

Hina Nasir; Nadeem Javaid; Waseem Raza; Muhammad Shafiq. Analysis of Packet Diversity in Buffer-Aided Relaying over Symmetric and Asymmetric Rayleigh Fading Channels. Symmetry 2020, 12, 241 .

AMA Style

Hina Nasir, Nadeem Javaid, Waseem Raza, Muhammad Shafiq. Analysis of Packet Diversity in Buffer-Aided Relaying over Symmetric and Asymmetric Rayleigh Fading Channels. Symmetry. 2020; 12 (2):241.

Chicago/Turabian Style

Hina Nasir; Nadeem Javaid; Waseem Raza; Muhammad Shafiq. 2020. "Analysis of Packet Diversity in Buffer-Aided Relaying over Symmetric and Asymmetric Rayleigh Fading Channels." Symmetry 12, no. 2: 241.

Journal article
Published: 04 January 2020 in Entropy
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Over the last decades, load forecasting is used by power companies to balance energy demand and supply. Among the several load forecasting methods, medium-term load forecasting is necessary for grid’s maintenance planning, settings of electricity prices, and harmonizing energy sharing arrangement. The forecasting of the month ahead electrical loads provides the information required for the interchange of energy among power companies. For accurate load forecasting, this paper proposes a model for medium-term load forecasting that uses hourly electrical load and temperature data to predict month ahead hourly electrical loads. For data preprocessing, modified entropy mutual information-based feature selection is used. It eliminates the redundancy and irrelevancy of features from the data. We employ the conditional restricted Boltzmann machine (CRBM) for the load forecasting. A meta-heuristic optimization algorithm Jaya is used to improve the CRBM’s accuracy rate and convergence. In addition, the consumers’ dynamic consumption behaviors are also investigated using a discrete-time Markov chain and an adaptive k-means is used to group their behaviors into clusters. We evaluated the proposed model using GEFCom2012 US utility dataset. Simulation results confirm that the proposed model achieves better accuracy, fast convergence, and low execution time as compared to other existing models in the literature.

ACS Style

Omaji Samuel; Fahad A. Alzahrani; Raja Jalees Ul Hussen Khan; Hassan Farooq; Muhammad Shafiq; Muhammad Khalil Afzal; Nadeem Javaid. Towards Modified Entropy Mutual Information Feature Selection to Forecast Medium-Term Load Using a Deep Learning Model in Smart Homes. Entropy 2020, 22, 68 .

AMA Style

Omaji Samuel, Fahad A. Alzahrani, Raja Jalees Ul Hussen Khan, Hassan Farooq, Muhammad Shafiq, Muhammad Khalil Afzal, Nadeem Javaid. Towards Modified Entropy Mutual Information Feature Selection to Forecast Medium-Term Load Using a Deep Learning Model in Smart Homes. Entropy. 2020; 22 (1):68.

Chicago/Turabian Style

Omaji Samuel; Fahad A. Alzahrani; Raja Jalees Ul Hussen Khan; Hassan Farooq; Muhammad Shafiq; Muhammad Khalil Afzal; Nadeem Javaid. 2020. "Towards Modified Entropy Mutual Information Feature Selection to Forecast Medium-Term Load Using a Deep Learning Model in Smart Homes." Entropy 22, no. 1: 68.