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Project Assessment in Offshore Software Maintenance Outsourcing Using Deep Extreme Learning Machines

computers, Materials & Continua 2023年第1期74卷 1871-1886页

作者： Atif Ikram Masita Abdul Jalil Amir Bin Ngah Saqib Raza Ahmad Salman Khan Yasir Mahmood Nazri Kama Azri Azmi Assad Alzayed Faculty of Ocean Engineering Technology and Informatics University Malaysia TerengganuKuala NerusMalaysia Department of Computer Science&Information Technology Faculty of Information TechnologyThe University of LahoreLahore54000Pakistan Department of Software Engineering Faculty of Information TechnologyThe University of LahoreLahore54000Pakistan Advanced Informatics Department Razak Faculty of Technology and InformaticsUniversiti Teknologi Malaysia54100Kuala LumpurMalaysia Computer Science and Information Systems Department College of Business Studies Public Authority for Applied Education and Training(PAAET)AdailiyahKuwait Department of Computer Science Sharif College of Engineering&TechnologyLahore54000Pakistan

Software maintenance is the process of fixing,modifying,and improving software deliverables after they are delivered to the *** can benefit from offshore software maintenance outsourcing(OSMO)in different ways,including time savings,cost savings,and improving the software quality and *** of the hardest challenges for the OSMO vendor is to choose a suitable project among several clients’*** goal of the current study is to recommend a machine learning-based decision support system that OSMO vendors can utilize to forecast or assess the project of OSMO *** projects belong to OSMO vendors,having offices in developing countries while providing services to developed *** the current study,Extreme Learning Machine’s(ELM’s)variant called Deep Extreme Learning Machines(DELMs)is used.A novel dataset consisting of 195 projects data is proposed to train the model and to evaluate the overall efficiency of the proposed *** proposed DELM’s based model evaluations achieved 90.017%training accuracy having a value with 1.412×10^(-3) Root Mean Square Error(RMSE)and 85.772%testing accuracy with 1.569×10^(-3) RMSE with five DELMs hidden *** results express that the suggested model has gained a notable recognition rate in comparison to any previous *** current study also concludes DELMs as the most applicable and useful technique for OSMO client’s project assessment.

关键词： Software outsourcing deep extreme learning machine(DELM) machine learning(ML) extreme learning machine assessment

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Modeling and Predictive Analytics of Breast Cancer Using Ensemble Learning Techniques:An Explainable Artificial Intelligence Approach

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computers, Materials & Continua 2024年第12期81卷 4033-4048页

作者： Avi Deb Raha Fatema Jannat Dihan Mrityunjoy Gain Saydul Akbar Murad Apurba Adhikary Md.Bipul Hossain Md.Mehedi Hassan Taher Al-Shehari Nasser A.Alsadhan Mohammed Kadrie Anupam Kumar Bairagi Computer Science and Engineering Discipline Khulna UniversityKhulna9208Bangladesh Information and Communication Engineering Noakhali Science and Technology UniversityNoakhali3814Bangladesh School of Computing Sciences and Computer Engineering University of Southern MississippiHattiesburgMS 39401USA Computer Skills Department of Self-Development SkillCommon First Year DeanshipKing Saud UniversityRiyadh11362Saudi Arabia Computer Science Department College of Computer and Information SciencesKing Saud UniversityRiyadh12372Saudi Arabia

Breast cancer stands as one of the world’s most perilous and formidable diseases,having recently surpassed lung cancer as the most prevalent cancer *** disease arises when cells in the breast undergo unregulated proliferation,resulting in the formation of a tumor that has the capacity to invade surrounding *** is not confined to a specific gender;both men and women can be diagnosed with breast cancer,although it is more frequently observed in *** detection is pivotal in mitigating its mortality *** key to curbing its mortality lies in early ***,it is crucial to explain the black-box machine learning algorithms in this field to gain the trust of medical professionals and *** this study,we experimented with various machine learning models to predict breast cancer using the Wisconsin Breast Cancer Dataset(WBCD)*** applied Random Forest,XGBoost,Support Vector Machine(SVM),Multi-Layer Perceptron(MLP),and Gradient Boost classifiers,with the Random Forest model outperforming the others.A comparison analysis between the two methods was done after performing hyperparameter tuning on each *** analysis showed that the random forest performs better and yields the highest result with 99.46%*** performance evaluation,two Explainable Artificial Intelligence(XAI)methods,SHapley Additive exPlanations(SHAP)and Local Interpretable Model-Agnostic Explanations(LIME),have been utilized to explain the random forest machine learning model.

关键词： Breast cancer prediction machine learning models explainable artificial intelligence random forest hyperparameter tuning

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QCVNET - Leveraging Quantum Technology in Convolutional Neural Networks for Improved Cardiovascular Disease Prediction 5

QCVNET - Leveraging Quantum Technology in Convolutional Neur...

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5th International Conference on Circuits, Control, Communication and computing, I4C 2024

作者： Karthigeyan, S. Diviya, M. Bhuvaneswari, R. Sabhanayagam, T. Amrita School of Computing Department of Computer Science and Engineering Amrita Vishwa Vidyapeetham Chennai India Rajalakshmi Engineering College Department of Computer Science and Engineering Chennai India School of Computer Science and Engineering Galgotias University UP Greater Noida India

ISBN: (纸本)9798331528539

The primary issue of global health today is Cardio-vascular diseases (CVDs) and thus requires accurate predictive models for detecting it early. Quantum technology combined with regular deep learning techniques are used by researchers here to improve the accuracy of forecast on heart diseases. While using an expanded dataset with clinical and demographic records, an innovative approach was found to use Quantum Convolutional Neural Network (QCVNET) structure that merged quantum layers into classical CNN framework with Principal Component Analysis (PCA) that allowed for consistency of features with respect to the quantum bits' number. The performance of the QCVNET was evaluated by comparing it to Quantum LSTM, Quantum DNN, Quantum GRU, and Quantum RNN, which are other quantum models enhanced with conventional neural networks, and other traditional non-quantum models like SVM, RF, RNN and DNN, using different indicators of quality like f1-score or recall. In early CVD prediction, the QCVNET model produced an impressive accuracy of 91.73%, beating all other models, showcasing the potential impact of quantum-enhanced deep learning for timely diagnosis and personalized treatment strategies while providing means to reduce the global burden of cardiovascular diseases. © 2024 IEEE.

关键词： Convolutional neural networks

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Battery-Induced Load Hiding and Its Utility Consequences 4

Battery-Induced Load Hiding and Its Utility Consequences

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4th International Conference on Smart Grid and Renewable Energy, SGRE 2024

作者： Aly, Hussein Altamimi, Emran Al-Ali, Abdulaziz Al-Ali, Abdulla Malluhi, Qutaibah Qatar University College of Engineering Department of Computer Science and Engineering Doha Qatar Qatar University Kindi Center for Computing Research Doha Qatar

ISBN: (纸本)9798350306262

The introduction of smart grids allows utility providers to collect detailed data about consumers, which can be utilized to enhance grid efficiency and reliability. However, this data collection also raises privacy concerns. To protect user privacy, some studies suggest using battery-based load hiding. Nevertheless, the impact of widespread adoption of this approach on utility providers remain unclear. Our paper seeks to evaluate the effects of battery-based load hiding on two critical operations: user profiling and anomaly detection. Our findings reveal that the inclusion of battery users in datasets can diminish the quality of conclusions drawn from these data. This can result in a decrease in the Area Under the Curve (AUC) by more than 10% when attempting to profile users within single-occupant and multiple-occupant households. Furthermore, our experiments demonstrate that battery-based load hiding not only conceals information about users employing the batteries but can also lead to an increased rate of false positives for other non-battery users (from 0.15 to 0.37) within the system. To mitigate these adverse effects, our study assessed various mitigation strategies. In the context of user profiling, our experiment demonstrated that identifying and removing battery users from the analytical dataset using unsupervised detection methods can effectively lessen the impact of battery users. For anomaly detection, our experiment revealed that creating separate classification models for battery and non-battery users can significantly reduce the adverse influence of battery users on the detection performance. © 2024 IEEE.

关键词： Smart power grids

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A Machine Learning-Based Attack Detection and Prevention System in Vehicular Named Data Networking

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computers, Materials & Continua 2023年第11期77卷 1445-1465页

作者： Arif Hussain Magsi Ali Ghulam Saifullah Memon Khalid Javeed Musaed Alhussein Imad Rida State Key Laboratory of Networking and Switching Technology Beijing University of Post and TelecommunicationBeijing100876China Information Technology Center Sindh Agriculture UniversityTandojam70050Pakistan Department of Computer Engineering College of Computing and InformaticsUniversity of SharjahSharjah27272United Arab Emirate Department of Computer Engineering College of Computer and Information SciencesKing Saud UniversityP.O.Box 51178Riyadh11543Saudi Arabia Laboratory Biomechanics and Bioengineering University of Technology of CompiegneCompiegne60200France

Named Data Networking(NDN)is gaining a significant attention in Vehicular Ad-hoc Networks(VANET)due to its in-network content caching,name-based routing,and mobility-supporting ***,existing NDN faces three significant challenges,including security,privacy,and *** particular,security attacks,such as Content Poisoning Attacks(CPA),can jeopardize legitimate vehicles with malicious *** instance,attacker host vehicles can serve consumers with invalid information,which has dire consequences,including road *** such a situation,trust in the content-providing vehicles brings a new *** the other hand,ensuring privacy and preventing unauthorized access in vehicular(VNDN)is another ***,NDN’s pull-based content retrieval mechanism is inefficient for delivering emergency messages in *** this connection,our contribution is *** existing rule-based reputation evaluation,we propose a Machine Learning(ML)-based reputation evaluation mechanism that identifies CPA attackers and legitimate *** on ML evaluation results,vehicles accept or discard served ***,we exploit a decentralized blockchain system to ensure vehicles’privacy by maintaining their information in a secure digital ***,we improve the default routing mechanism of VNDN from pull to a push-based content dissemination using Publish-Subscribe(Pub-Sub)*** implemented and evaluated our ML-based classification model on a publicly accessible BurST-Asutralian dataset for Misbehavior Detection(BurST-ADMA).We used five(05)hybrid ML classifiers,including Logistic Regression,Decision Tree,K-Nearest Neighbors,Random Forest,and Gaussian Naive *** qualitative results indicate that Random Forest has achieved the highest average accuracy rate of 100%.Our proposed research offers the most accurate solution to detect CPA in VNDN for safe,secure,and reliable vehicle communication.

关键词： Named data networking vehicular networks reputation caching machine-learning

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Image Retargeting Techniques Identification Using Supervised Deep Learning 3

Image Retargeting Techniques Identification Using Supervised...

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3rd International Conference on Intelligent Data Science Technologies and Applications, IDSTA 2023

作者： Kharsa, Ruba Alzoubi, Rouaa Alsmirat, Mohammad Al-Ayyoub, Mahmoud College of Computing and Informatics University of Sharjah Department of Computer Science Sharjah United Arab Emirates Ajaman United Arab Emirates

ISBN: (纸本)9798350339253

Image retargeting involves the adjustment of an image's dimensions to ensure that its content and visual quality are preserved when the image is resized to fit various screens or devices. This process retains all essential visual elements and details. Different techniques have been developed for this purpose, including cropping (CR), scaling (SCL), seam carving (SC), warping (WARP), scale-and-stretch (SNS), multi-operator (MULTI), and shift-map (SM). However, determining the most suitable method for retargeting a specific image with particular dimensions remains a challenge. Therefore, this research introduces initial work on developing CNN based deep learning model and a transfer learning model based on InceptionV3,to predict the optimal retargeting method for a given image and resolution. The study employed a dataset consisting of 46,716 images with varying resolutions, created using different retargeting techniques, categorized into six groups. Results demonstrates a promising effectiveness of the proposed approach for selecting the appropriate retargeting techniques. © 2023 IEEE.

关键词： Image resolution

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RES-EV: Identifying EV-Households under High AC Load Using a Residual-Based Model 8

RES-EV: Identifying EV-Households under High AC Load Using a...

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8th IEEE International Energy Conference, ENERGYCON 2024

作者： Aly, Hussein Al-Ali, Abdulaziz Al-Ali, Abdulla Malluhi, Qutaibah College of Engineering Department of Computer Science and Engineering Qatar University Doha Qatar Kindi Center for Computing Research Qatar University Doha Qatar

ISBN: (纸本)9798350382150

This paper introduces a novel residual-based model to identify households with Battery Electric Vehicles (EVs) under high Air Conditioning (AC) load. The considerable energy demands of AC units can obscure charging events for EVs. In this work we propose a residual based model which leverages the distinctive characteristics of EV charging patterns, marked by unpredictable spikes in energy consumption, and the more predictable nature of AC load. Our proposed approach involves training a lightweight forecasting model to predict overall household consumption and utilizes the residuals of this model for identifying household with EVs. The residual-based model, ResEV-AR, demonstrated a substantial advantage in F1 score (5.8% and 7.32%) compared to state-of-the-art models such as EVS and KBF, respectively. Additionally, a simpler residual model, ResEV-SRM, exhibited a 3.5% F1 score advantage over EVS, coupled with an impressive 11-fold reduction in computation time. ©2024 IEEE.

关键词： Energy utilization

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An Enhanced Intelligent Intrusion Detection System to Secure E-Commerce Communication Systems

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computer Systems Science & engineering 2023年第11期47卷 2513-2528页

作者： Adil Hussain Kashif Naseer Qureshi Khalid Javeed Musaed Alhussein Department of Transportation Engineering Chang’an UniversityXi’anChina Department of Electronic&Computer Engineering University of LimerickLimerickV94 T9PXIreland Department of Computer Engineering College of Computing and InformaticsUniversity of SharjahSharjahUnited Arab Emirates Department of Computer Engineering College of Computer and Information SciencesKing Saud UniversityP.O.Box 51178Riyadh11543Kingdom of Saudi Arabia

Information and communication technologies are spreading rapidly due to their fast proliferation in many *** number of Internet users has led to a spike in cyber-attack incidents.E-commerce applications,such as online banking,marketing,trading,and other online businesses,play an integral role in our *** Intrusion Detection System(NIDS)is essential to protect the network from unauthorized access and against other *** existing NIDS systems are based on the Backward Oracle Matching(BOM)algorithm,which minimizes the false alarm rate and causes of high packet drop *** paper discussed the existing NIDS systems and different used pattern-matching techniques regarding their weaknesses and *** address the existing system issues,this paper proposes an enhanced version of the BOM algorithm by using multiple pattern-matching methods for the NIDS system to improve the network *** proposed solution is tested in simulation with existing solutions using the Snort and NSL-KDD *** experimental results indicated that the proposed solution performed better than the existing solutions and achieved a 5.17%detection rate and a 0.22%lower false alarm rate than the existing solution.

关键词： E-commerce NIDS security algorithm network applications CIA detection

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CORRECTIVE MAINTENANCE TIME MODEL OF THE 2-PARALLEL CONFIGURATION OF THE EARTH STATION SYSTEM USING CURVE FITTING 51

CORRECTIVE MAINTENANCE TIME MODEL OF THE 2-PARALLEL CONFIGUR...

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51st International Conference on computers and Industrial engineering, CIE 2024

作者： Kamaruddin, Nur Fatin Syamimi Rahim, Nadirah Abdul Mansor, Mahayaudin M. Department of Electrical and Computer Engineering International Islamic University Malaysia Jalan Gombak Kuala Lumpur Malaysia School of Mathematical Sciences College of Computing Informatics and Mathematics Universiti Teknologi MARA Shah Alam Malaysia

An Earth station system serves as a key hub in the ever-changing environment of satellite communication technology, allowing uninterrupted data interchange between the Earth and orbiting satellites. These systems are essential for various applications like telecommunications, remote sensing, and global positioning. In this research, a 2-parallel configuration is chosen for the Earth station system design because the literature suggests that it has the lowest operational cost and is reliably acceptable if compared to other parallel configurations. However, the 2-parallel configuration comes with the issue of care and upkeep, especially in the context of corrective maintenance time, which is the aim of this study. Corrective maintenance, or repairing components and systems after failure, is critical to the Earth station system operation. This study uses curve fitting techniques by using the Monte Carlo method from MATLAB software to foresee system component failures and predict their behavior over time. The useful insights can be extracted from previous maintenance data by using curve fitting, allowing one to make informed decisions regarding when and how to do maintenance. The results demonstrate the curve fitting of the combination of the corrective maintenance for seven subsystems with the mean time between failures (MTBF) of 1, 3, 5, 7, and 10 years, and it is shown that the first-degree polynomial has the lowest Root Mean Square Error (RMSE), which is 1.35. Furthermore, the corrective maintenance time model is developed not only to analyse component failures but also to assist in deriving appropriate maintenance guidelines in the future that can help system engineers repair the failed subsystem. © 2024 computers and Industrial engineering. All rights reserved.

关键词： MATLAB

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Optimizing Energy Usage and Data Storage in Industrial IoT Settings Through Heuristic-Based Data-Rate Processors 2

Optimizing Energy Usage and Data Storage in Industrial IoT S...

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2nd IEEE International Conference for Women in Innovation, Technology and Entrepreneurship, ICWITE 2024

作者： Bharathi Kannan, B. Sriramulu, Srinivasan Gopal, Vidhya Karthick, M. Reddy, Yanna Nithin Natarajan, B. Galgotias University School of Computing Science and Engineering India Sona College of Technology Computer Science and Engineering Salem India K Ramakrishnan College of Engineering Department of Electronics and Communication Engineering Tamil Nadu Tiruchirappalli India Amrita School of Computing Amrita Vishwa Vidyapeetham Department of Computer Science and Engineering Chennai India VIT University School of Computer Science and Engineering Tamilnadu Chennai India

ISBN: (纸本)9798350383287

The Internet of Things (IoT) is a fast-expanding field, with applications in areas as diverse as transportation, aviation, automation, electricity, and healthcare. Sensors, protocols, actuators, cloud services, and layers are just few of the components that make up an IoT architecture. The architecture of the Internet of Things is crucial in delivering the goods. The research community now provides dozens of IoT architectures. interoperability, security, privacy, dependability, energy limits, scalability, and a lack of shared standards are only some of the problems that have been recognized by the research community. The proposed IoT architecture should also consider issues of interoperability, scalability, dependability, and trust. By fixing these issues, this research work builds an IoT ecosystem that can accommodate a wide range of devices and services, guarantee smooth communication and scalability, and lay the groundwork for secure, dependable IoT offerings. In effective offloading and resulting interruptions in task execution might result from high latency or network outages. However, because of the importance of a select few delay-sensitive applications, it is important to guarantee their safety. Therefore, this research work aims to unload data at edge and cloud servers, Heuristic based Energy Efficient Data Rate Controller for Offloading has been developed for sensor networks. The bio-inspired NSGA-2 method is used to assess the performance of the suggested algorithm within the context of the problem's multi-objective function formulation. When compared to the current energy aware scheduling approach, the results show that the offloading method can decrease the amount of time and energy needed to run edge servers, maintain load balancing, and maximize resource utilization while still allowing applications that require delays to meet their deadlines. Finally, simulation results demonstrate the proposed approach outperforms 20% high efficiency compared to the

关键词： Internet of things

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