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IEEE Transactions on Artificial Intelligence 2024年第3期5卷 1077-1086页

作者： Jannu, Srikanth Dara, Suresh Thuppari, Chaitanya Vidyarthi, Ankit Ghosh, Debjani Tiwari, Prayag Muhammad, Ghulam Department of Computer Science and Engineering Vaagdevi Engineering College Singaram506005 India School of Technology Woxsen University HyderabadTS 502 345 India Department of Computer Science and Engineering and Information Technology Jaypee Institute of Information Technology Noida201309 India Department of Computer Science and Engineering Bennett University Greater Noida201309 India School of Information Technology Halmstad University Halmstad301 18 Sweden Department of Computer Engineering College of Computer and Information Sciences King Saud University Riyadh11451 Saudi Arabia

One of themost prominent research areas in information technology is the Internet of Things (IoT) as its applications are widely used, such as structural monitoring, health care management systems, agriculture and battlefield management, and so on. Due to its self-organizing network and simple installation of the network, the researchers have been attracted to pursue research in the various fields of IoTs. However, a huge amount of work has been addressed on various problems confronted by IoT. The nodes densely deploy over critical environments and those are operated on tiny batteries. Moreover, the replacement of dead batteries in the nodes is almost impractical. Therefore, the problem of energy preservation and maximization of IoT networks has become the most prominent research area. However, numerous state-of-The-Art algorithms have addressed this issue. Thus, it has become necessary to gather the information and send it to the base station in an optimized method to maximize the network. Therefore, in this article, we propose a novel quantum-informed ant colony optimization (ACO) routing algorithm with the efficient encoding scheme of cluster head selection and derivation of information heuristic factors. The algorithm has been tested by simulation for various network scenarios. The simulation results of the proposed algorithm show its efficacy over a few existing evolutionary algorithms using various performance metrics, such as residual energy of the network, network lifetime, and the number of live IoT nodes. Impact Statement-Toward IoT-based applications, here we presented the Quantum-inspired ACO clustering algorithm for network lifetime. IoT nodes in the clustering phase choose theirCH through the distance between cluster member IoT nodes and the residual energy. Thus, CH selection reduces the energy consumption of member IoT nodes. Therefore, our significant contributions are summarized as follows. i. Developing Quantum-informed ACO clustered routing algor

关键词： Energy efficiency

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Quadratic and Lagrange interpolation-based butterfly optimization algorithm for numerical optimization and engineering design problem

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Soft computing 2025年第1期29卷 157-194页

作者： Sharma, Sushmita Saha, Apu Kumar Chakraborty, Sanjoy Deb, Suman Sahoo, Saroj Kumar Department of Computer Science and Engineering Indian Institute of Information Technology Tripura Agartala India Department of Computer Science and Engineering National Institute of Technology Tripura Agartala India Department of Mathematics National Institute of Technology Agartala Tripura Agartala India Department of Information Technology Bir Bikram Memorial College Tripura Agartala India

This work proposes a novel and improved Butterfly Optimization Algorithm (BOA), known as LQBOA, to solve BOA’s inherent limitations. The LQBOA uses Lagrange interpolation and simple quadratic interpolation techniques with adapted parameter settings to enhance the search strategy of BOA and to achieve a better balance of diversification and intensification. LQBOA’s performance was examined on 45 traditional benchmark issues as well as the IEEE CEC 2017 benchmark suites with 10, 30 and 50 dimensions, and the results were compared against popular state-of-the-art and modified algorithms. It was discovered that in more than 85% of cases, the proposed LQBOA outperformed the compared algorithms. The Friedman rank test and Wilcoxon rank test were used to validate the suggested LQBOA’s rank and significance. Additionally, convergence and diversity analyses were performed to investigate its convergence speed and searching behavior, respectively. Furthermore, LQBOA has been successfully deployed to solve twelve real-world issues including several engineering design problems and two multiple gravity assist spacecraft trajectory problems. The results of these problems were compared to those of a wide range of algorithms, demonstrating the superior performance of the proposed LQBOA. In conclusion, LQBOA makes a significant addition to the optimization area, and its application could greatly improve the performance of numerous optimization jobs. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2024.

关键词： Lagrange multipliers

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Evaluating the Efficacy of Latent Variables in Mitigating Data Poisoning Attacks in the Context of Bayesian Networks:An Empirical Study

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computer Modeling in Engineering & sciences 2024年第5期139卷 1635-1654页

作者： Shahad Alzahrani Hatim Alsuwat Emad Alsuwat Independent Researcher Specializing in Data Security and Privacy Taif26571Saudi Arabia Department of Computer Science College of Computer and Information SystemsUmm Al-Qura UniversityMakkah24382Saudi Arabia Department of Computer Science College of Computers and Information TechnologyTaif UniversityTaif26571Saudi Arabia

Bayesian networks are a powerful class of graphical decision models used to represent causal relationships among ***,the reliability and integrity of learned Bayesian network models are highly dependent on the quality of incoming data *** of the primary challenges with Bayesian networks is their vulnerability to adversarial data poisoning attacks,wherein malicious data is injected into the training dataset to negatively influence the Bayesian network models and impair their *** this research paper,we propose an efficient framework for detecting data poisoning attacks against Bayesian network structure learning *** framework utilizes latent variables to quantify the amount of belief between every two nodes in each causal model over *** use our innovative methodology to tackle an important issue with data poisoning assaults in the context of Bayesian *** regard to four different forms of data poisoning attacks,we specifically aim to strengthen the security and dependability of Bayesian network structure learning techniques,such as the PC *** doing this,we explore the complexity of this area and offer workablemethods for identifying and reducing these sneaky ***,our research investigates one particular use case,the“Visit to Asia Network.”The practical consequences of using uncertainty as a way to spot cases of data poisoning are explored in this inquiry,which is of utmost *** results demonstrate the promising efficacy of latent variables in detecting and mitigating the threat of data poisoning ***,our proposed latent-based framework proves to be sensitive in detecting malicious data poisoning attacks in the context of stream data.

关键词： Bayesian networks data poisoning attacks latent variables structure learning algorithms adversarial attacks

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Vehicle Detection in Challenging Scenes Using CenterNet Based Approach

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computers, Materials & Continua 2023年第2期74卷 3647-3661页

作者： Ayesha Muhammad Javed Iqbal Iftikhar Ahmad Madini OAlassafi Ahmed SAlfakeeh Ahmed Alhomoud Department of Computer Science University of Engineering and Technology Taxila47050Pakistan Faculty of Computing and Information Technology King Abdulaziz University Jeddah21589Saudi Arabia Faculty of Computing and Information Technology Northern Border UniversityRafhaSaudi Arabia

Contemporarily numerous analysts labored in the field of Vehicle detection which improves Intelligent Transport System(ITS)and reduces road *** major obstacles in automatic detection of tiny vehicles are due to occlusion,environmental conditions,illumination,view angles and variation in size of *** research centers on tiny and partially occluded vehicle detection and identification in challenging scene specifically in crowed *** this paper we present comprehensive methodology of tiny vehicle detection using Deep Neural Networks(DNN)namely *** DNN disregards objects that are small in size 5 pixels and more false positives likely to happen in crowded *** there are two categories of deep learning models single-step and two-step.A single forward pass model is the one in which detection is performed directly to possible location over dense sampling,wherein two-step models incorporated by Region proposals followed by object *** in this research scrutinize one-step State of the art(SOTA)model CenteNet as proposed recently with three different feature extractor ResNet-50,HourGlass-104 and ResNet-101 one by *** train our model on challenging KITTI dataset which outperforms in comparison with SOTA single-step technique MSSD300∗which depicts performance improvement by 20.2%mAPandSMOKEby with 13.2%mAP *** of CenterNet can be justified through the huge improved *** performance of our model is evaluated on KITTI(Karlsruhe Institute of technology and Toyota Technological Institute)benchmark dataset with different backbones such as ResNet-50 gives 62.3%mAP ResNet-10182.5%mAP,last but not the least HourGlass-104 outperforms with 98.2%mAP CenterNet-HourGlass-104 achieved high mAP among above mentioned feature *** also compare our model with other SOTA techniques.

关键词： CenteNet SOTA object detection deep learning MSSD SMOKE DNN KITTI

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Contemporary Study for Detection of COVID-19 Using Machine Learning with Explainable AI

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computers, Materials & Continua 2024年第7期80卷 1075-1104页

作者： Saad Akbar Humera Azam Sulaiman Sulmi Almutairi Omar Alqahtani Habib Shah Aliya Aleryani Department of Computer Science University of KarachiKarachi75270Pakistan Department of Computing Faculty of Engineering Science and TechnologyHamdard UniversityKarachi75540Pakistan Department of Health Informatics College of Applied Medical SciencesQassim UniversityQassim51452Saudi Arabia Department of Computer Science College of Computer ScienceKing Khalid UniversityAbha61421Saudi Arabia

The prompt spread of COVID-19 has emphasized the necessity for effective and precise diagnostic *** this article,a hybrid approach in terms of datasets as well as the methodology by utilizing a previously unexplored dataset obtained from a private hospital for detecting COVID-19,pneumonia,and normal conditions in chest X-ray images(CXIs)is proposed coupled with Explainable Artificial Intelligence(XAI).Our study leverages less preprocessing with pre-trained cutting-edge models like InceptionV3,VGG16,and VGG19 that excel in the task of feature *** methodology is further enhanced by the inclusion of the t-SNE(t-Distributed Stochastic Neighbor Embedding)technique for visualizing the extracted image features and Contrast Limited Adaptive Histogram Equalization(CLAHE)to improve images before extraction of ***,an AttentionMechanism is utilized,which helps clarify how the modelmakes decisions,which builds trust in artificial intelligence(AI)*** evaluate the effectiveness of the proposed approach,both benchmark datasets and a private dataset obtained with permissions from Jinnah PostgraduateMedical Center(JPMC)in Karachi,Pakistan,are *** 12 experiments,VGG19 showcased remarkable performance in the hybrid dataset approach,achieving 100%accuracy in COVID-19 *** classification and 97%in distinguishing normal ***,across all classes,the approach achieved 98%accuracy,demonstrating its efficiency in detecting COVID-19 and differentiating it fromother chest disorders(Pneumonia and healthy)while also providing insights into the decision-making process of the models.

关键词： COVID-19 detection deep neural networks support vector machine CXIs InceptionV3 VGG16 VGG19 t-SNE embedding CLAHE attention mechanism XAI

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SkinMarkNet: an automated approach for prediction of monkeyPox using image data augmentation with deep ensemble learning models

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Multimedia Tools and Applications 2025年第18期84卷 20177-20193页

作者： Akram, Aqsa Jamjoom, Arwa A. Innab, Nisreen Almujally, Nouf Abdullah Umer, Muhammad Alsubai, Shtwai Fimiani, Gianluca Bahawalpur Pakistan Department of Information Systems Faculty of Computing and Information Technology King Abdulaziz University Jeddah21589 Saudi Arabia Department of Computer Science and Information Systems College of Applied Sciences AlMaarefa University Riyadh Diriyah13713 Saudi Arabia Department of Information Systems College of Computer and Information Sciences Princess Nourah bint Abdulrahman University P.O. Box 84428 Riyadh11671 Saudi Arabia Department of Computer Science The Islamia University of Bahawalpur Bahawalpur Pakistan Department of Computer Science College of Computer Engineering and Sciences Prince Sattam bin Abdulaziz University P.O. Box 151 Al-Kharj11942 Saudi Arabia Department of Computer Science University of Salerno Fisciano84084 Italy

Monkeypox, a rare but potentially fatal viral disease, poses a significant public health challenge due to its potential for outbreaks and complications. Detecting monkeypox lesions early and accurately is vital to effectively manage and control the disease. This study introduces a novel method for classifying monkeypox lesions, employing data augmentation methods and a framework based on an ensemble of three transfer learning models called "SkinMarkNet". The dataset used in this research consists of skin lesion images collected from the Kaggle data repository, encompassing diverse demographics and lesion characteristics. This research uses image data augmentation techniques to tackle the scarcity of annotated data. This augmentation enriches the training dataset, thereby improving the model’s ability to perform effectively. Moreover, the novelty of this research work lies in the usage of three popular transfer learning models(Inception, Xception, and ResNet) for feature extraction and ensemble learning. The SkinMarkNet achieves promising results showing an accuracy of 90.615% for monkeypox lesion classification, outperforming traditional machine learning and deep learning methods utilized in recent research works. In addition, thorough comparative analysis is done with machine learning models and contemporary approaches to validate the efficacy of the proposed method. Overall, the findings underscore the potential of leveraging advanced deep learning architectures and data augmentation strategies for improving monkeypox lesion classification, thereby facilitating early diagnosis and intervention in clinical settings and public health surveillance efforts. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Learning systems

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Self-adapting Security Monitoring in Eucalyptus Cloud Environment

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International Journal of Advanced computer science and Applications 2023年第3期14卷 80-93页

作者： Mahmood, Salman Yahaya, Nor Adnan Hasan, Raza Hussain, Saqib Malik, Mazhar Hussain Sarker, Kamal Uddin School of Information Technology Malaysia University of Science and Technology Selangor Malaysia Computing and Information Technology Global College of Engineering and Technology Muscat Oman Department of Computer Science and Creative Technologies University of West of the England Bristol England Bristol United Kingdom Department of Computer Science American International University Bangladesh Dhaka Bangladesh

This paper discusses the importance of virtual machine (VM) scheduling strategies in cloud computing environments for handling the increasing number of tasks due to virtualization and cloud computing technology adoption. The paper evaluates legacy methods and specific VM scheduling algorithms for the Eucalyptus cloud environment and compare existing algorithms using QoS. The paper also presents a self-adapting security monitoring system for cloud infrastructure that takes into account the specific monitoring requirements of each tenant. The system uses Master Adaptation Drivers to convert tenant requirements into configuration settings and the Adaptation Manager to coordinate the adaptation process. The framework ensures security, cost efficiency, and responsiveness to dynamic events in the cloud environment. The paper also presents the need for improvement in the current security monitoring platform to support more types of monitoring devices and cover the consequences of multi-tenant setups. Future work includes incorporating log collectors and aggregators and addressing the needs of a super-tenant in the security monitoring architecture. The equitable sharing of monitoring resources between tenants and the provider should be established with an adjustable threshold mentioned in the SLA. The results of experiments show that Enhanced Round-Robin uses less energy compared to other methods, and the Fusion Method outperforms other techniques by reducing the number of Physical Machines turned on and increasing power efficiency © 2023, International Journal of Advanced computer science and *** Rights Reserved

关键词： Cloud computing

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Optimized Evaluation of Mobile Base Station by Modern Topological Invariants

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computers, Materials & Continua 2023年第1期74卷 363-378页

作者： Khalid Hamid Muhammad Waseem Iqbal Muhammad Usman Ashraf Ahmed Mohammed Alghamdi Adel A.Bahaddad Khalid Ali Almarhabi Department of Computer Science Superior UniversityLahore54000Pakistan Department of Software Engineering Superior UniversityLahore54000Pakistan Department of Computer Science GC Women UniversitySialkot53310Pakistan Department of Software Engineering College of Computer Science and EngineeringUniversity of Jeddah21493Saudi Arabia Department of Information System Faculty of Computing and Information TechnologyKing Abdulaziz UniversityJeddah21589Saudi Arabia Department of Computer Science College of Computing in Al-QunfudahUmm Al-Qura UniversityMakkah24381Saudi Arabia

Due to a tremendous increase in mobile traffic,mobile operators have started to restructure their networks to offload their *** directions will lead to fundamental changes in the design of future Fifthgeneration(5G)cellular *** the formal reason,the study solves the physical network of the mobile base station for the prediction of the best characteristics to develop an enhanced network with the help of graph *** number that can be uniquely calculated by a graph is known as a graph *** the last two decades,innumerable numerical graph invariants have been portrayed and used for correlation *** any case,no efficient assessment has been embraced to choose,how much these invariants are connected with a network *** paper will talk about two unique variations of the hexagonal graph with great capability of forecasting in the field of optimized mobile base station topology in setting with physical *** K-banhatti sombor invariants(KBSO)and Contrharmonic-quadratic invariants(CQIs)are newly introduced and have various expectation characteristics for various variations of hexagonal graphs or *** the hexagonal networks are used in mobile base stations in layered,forms called *** review settled the topology of a hexagon of two distinct sorts with two invariants KBSO and CQIs and their reduced *** deduced outcomes can be utilized for the modeling of mobile cellular networks,multiprocessors interconnections,microchips,chemical compound synthesis and memory interconnection *** results find sharp upper bounds and lower bounds of the honeycomb network to utilize the Mobile base station network(MBSN)for the high load of traffic and minimal traffic also.

关键词： Hexagonal networks k-banhatti sombor invariants maple network graph artificial intelligence contraharmonic-quadratic invariants honeycomb

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Differential Evolution Strategy with Chebyshev Chaos Based Mutation

Journal of Network Intelligence

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Journal of Network Intelligence 2024年第1期9卷 613-628页

作者： Sitenda, Amos Song, Pei-Cheng Chu, Shu-Chuan Chen, Shi-Huang College of Computer Science and Engineering Shandong University of Science and Technology Qingdao266590 China Department of Computer Science and Information Engineering Shu-Te UniversityKaohsiung Kaohsiung824 Taiwan

Differential Evolution (DE) is a potent stochastic evolutionary optimization algorithm garnering increasing research attention. Over the years, it has been found applicable in solving diverse real-world problems. DE employs mutation, crossover, and selection operators to guide populations toward optimal or nearly optimal solutions. However, the standard DE mutation strategies have found limitations in balancing exploration and exploitation effectively, thus prompting research into possible improvements. This study introduces a novel mutation strategy named Chebyshev Infused Chaos Mutation Strategy (CICMS). where a chaotic sequence partially guides the process of donor vector generation. Rigorous evaluations were conducted, comparing our modified DE against the standard DE and seven other metaheuristic algorithms, including Genetic Algorithm, Particle Swarm Optimization and Gravitational Search Algorithm. Experiments were performed using the challenging CEC 2014 benchmark functions, consisting of 30 objective functions. Results indicate substantial improvements in convergence speed and solution quality, highlighting the potential of our novel mutation strategy to enhance DE’s practicality in addressing complex optimization problems. This research contributes valuable insights to the dynamic field of optimization algorithms with implications for a wide range of applications. © 2024, Taiwan Ubiquitous information CO LTD. All rights reserved.

关键词： Chaos theory

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Congruent Feature Selection Method to Improve the Efficacy of Machine Learning-Based Classification in Medical Image Processing

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computer Modeling in Engineering & sciences 2025年第1期142卷 357-384页

作者： Mohd Anjum Naoufel Kraiem Hong Min Ashit Kumar Dutta Yousef Ibrahim Daradkeh Department of Computer Engineering Aligarh Muslim UniversityAligarh202002India College of Computer Science King Khalid UniversityAbha61413Saudi Arabia School of Computing Gachon UniversitySeongnam13120Republic of Korea Department of Computer Science and Information Systems College of Applied SciencesAlMaarefa UniversityRiyadh13713Saudi Arabia Department of Computer Engineering and Information College of Engineering in Wadi AlddawasirPrince Sattam bin Abdulaziz UniversityAl-Kharj16273Saudi Arabia

Machine learning(ML)is increasingly applied for medical image processing with appropriate learning *** applications include analyzing images of various organs,such as the brain,lung,eye,etc.,to identify specific flaws/diseases for *** primary concern of ML applications is the precise selection of flexible image features for pattern detection and region *** of the extracted image features are irrelevant and lead to an increase in computation ***,this article uses an analytical learning paradigm to design a Congruent Feature Selection Method to select the most relevant image *** process trains the learning paradigm using similarity and correlation-based features over different textural intensities and pixel *** similarity between the pixels over the various distribution patterns with high indexes is recommended for disease ***,the correlation based on intensity and distribution is analyzed to improve the feature selection ***,the more congruent pixels are sorted in the descending order of the selection,which identifies better regions than the ***,the learning paradigm is trained using intensity and region-based similarity to maximize the chances of ***,the probability of feature selection,regardless of the textures and medical image patterns,is *** process enhances the performance of ML applications for different medical image *** proposed method improves the accuracy,precision,and training rate by 13.19%,10.69%,and 11.06%,respectively,compared to other models for the selected *** mean error and selection time is also reduced by 12.56%and 13.56%,respectively,compared to the same models and dataset.

关键词： computer vision feature selection machine learning region detection texture analysis image classification medical images

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