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Novel Optimized Feature Selection Using Metaheuristics Applied to Physical Benchmark Datasets

computers, Materials & Continua 2023年第2期74卷 4027-4041页

作者： Doaa Sami Khafaga El-Sayed M.El-kenawy Fadwa Alrowais Sunil Kumar Abdelhameed Ibrahim Abdelaziz A.Abdelhamid Department of Computer Sciences College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Communications and Electronics Delta Higher Institute of Engineering and TechnologyMansoura35111Egypt School of Computer Science University of Petroleum and Energy StudiesDehradun248001India Computer Engineering and Control Systems Department Faculty of EngineeringMansoura UniversityMansoura35516Egypt Department of Computer Science Faculty of Computer and Information SciencesAin Shams UniversityCairo11566Egypt Department of Computer Science College of Computing and Information TechnologyShaqra University11961Saudi Arabia

In data mining and machine learning,feature selection is a critical part of the process of selecting the optimal subset of features based on the target *** are 2n potential feature subsets for every n features in a dataset,making it difficult to pick the best set of features using standard ***,in this research,a new metaheuristics-based feature selection technique based on an adaptive squirrel search optimization algorithm(ASSOA)has been *** using metaheuristics to pick features,it is common for the selection of features to vary across runs,which can lead to *** of this,we used the adaptive squirrel search to balance exploration and exploitation duties more evenly in the optimization *** the selection of the best subset of features,we recommend using the binary ASSOA search strategy we developed *** to the suggested approach,the number of features picked is reduced while maximizing classification accuracy.A ten-feature dataset from the University of California,Irvine(UCI)repository was used to test the proposed method’s performance *** other state-of-the-art approaches,including binary grey wolf optimization(bGWO),binary hybrid grey wolf and particle swarm optimization(bGWO-PSO),bPSO,binary stochastic fractal search(bSFS),binary whale optimization algorithm(bWOA),binary modified grey wolf optimization(bMGWO),binary multiverse optimization(bMVO),binary bowerbird optimization(bSBO),binary hybrid GWO and genetic algorithm 4028 CMC,2023,vol.74,no.2(bGWO-GA),binary firefly algorithm(bFA),and *** results confirm the superiority and effectiveness of the proposed algorithm for solving the problem of feature selection.

关键词： Metaheuristics adaptive squirrel search algorithm optimization methods binary optimizer

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Fake Social Media Profile Identification and Report Using Machine Learning

Fake Social Media Profile Identification and Report Using Ma...

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2024 International Conference on Signal Processing, Computation, Electronics, Power and Telecommunication, IConSCEPT 2024

作者： Chinnasamy, P. Ayyasamy, Ramesh Kumar Subramaniam, Suthashini Sangodiah, Anbuselvan Iftikhar, Aqsa Kiran, Ajmeera Kalsalingam Academy of Research and Education Department of Computer Science and Engineering School of Computing TamilNadu Srivilliputtur India Kampar Malaysia School of Computing Faculty of Computing and Engineering Quest International University Perak Ipoh30250 Malaysia MLR Institute of Technology Department of Computer Science and Engineering Hyderabad India

ISBN: (纸本)9798331540685

In the digital age, Online Social Networking platforms are integral to daily life, but they also attract malicious bots that mimic human behaviour and pose cybersecurity threats. This literature review focuses on recent Machine Learning-based techniques for detecting and classifying bots on major social media platforms. It covers supervised, semi-supervised, and unsupervised methods, dataset details, and feature categories. While the technology is advancing, an ongoing challenge lies in the creation of ever- evolving bots to evade detection. The study offers insights into the evolving landscape of cybersecurity and the need for continual research in this field, but potential drawbacks may include data privacy concerns, The pervasive influence of the internet and social media as platforms for real-time information dissemination and their potential to shape perceptions and lives. It highlights the concern that unverified and malicious content can significantly impact users' perceptions. The paper offers a taxonomy for classifying malicious information at various stages and presents technologies to combat the issue from content generation to containment. The results illustrate the efficacy of machine learning in improving the dependability and safety of social media platforms, offering a scalable approach to address the increasing problem of online deception. © 2024 IEEE.

关键词： Bot (Internet)

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Face image authentication scheme based on MTCNN and SLT

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Multimedia Tools and Applications 2025年 1-43页

作者： Thabit, Rasha Al-Askari, Mohanad A. Mohammed, Dunya Zeki Anaam, Elham Abdulwahab Mahmood, Zainab H. Jabbar, Dina Jamal Salih, Zahraa Aqeel Information System Department College of Computer Sciences and Information Technology University of Al-Anbar Al-Anbar Iraq Computer Engineering Department College of Engineering Al-Iraqia University Baghdad Iraq Electronic and Communications Engineering Department College of Engineering Gilgamesh University Baghdad Iraq Faculty of Computing and Informatics Multimedia University Selangor Cyberjaya Malaysia Department of Cybersecurity Engineering College of Information Engineering Al-Nahrain University Baghdad Iraq Electrical Engineering Technical Collage Middle Technical University Baghdad Iraq Department of Business Administration Alamaal University College Baghdad Iraq

DeepFakes and face image manipulation methods have been widely distributed in the last few years and several techniques have been presented to check the authenticity of the face image and detect manipulation if exists. Most of the available manipulation detection techniques have been successfully applied to reveal one type of manipulation under specific conditions, however, many limitations and challenges can be encountered in this field. To overcome some limitations and challenges, this paper presents a new face image authentication (FIA) scheme based on Multi-Task Cascaded Conventional Neural Networks (MTCNN) and watermarking in Slantlet transform (SLT) domain. The proposed FIA scheme has three main algorithms that are face detection and selection, embedding, and extraction algorithms. Different block sizes have been used to divide the image into non-overlapping blocks followed by classifying them into two groups that are blocks from face area (FA) and blocks from the remaining area (RA) of the image. In the embedding algorithms, the authentication information is generated from FA blocks and embedded in the RA blocks. In the extraction algorithms, the embedded information is extracted from RA blocks and compared with the calculated data from FA blocks to reveal manipulations and localize the manipulated blocks if exist. Extensive experiments have been conducted to evaluate the performance of the proposed FIA scheme for different face images. The experimental work included tests for payload, capacity, visual quality, time complexity, and localization of manipulations. The results proved the efficiency of the proposed scheme in detecting and localizing different face image manipulations such as attributes attacks, retouching attacks, expression swap, face swap, and morphing attacks. The proposed scheme overcomes many limitations and it is 100% accurate in localizing the tampered blocks which makes it a better candidate for practical applications. © The Author(s), un

关键词： Authentication

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Dipper Throated Algorithm for Feature Selection and Classification in Electrocardiogram

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computer Systems Science & engineering 2023年第5期45卷 1469-1482页

作者： Doaa Sami Khafaga Amel Ali Alhussan Abdelaziz A.Abdelhamid Abdelhameed Ibrahim Mohamed Saber El-Sayed M.El-kenawy Department of Computer Sciences College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Computer Science Faculty of Computer and Information SciencesAin Shams University11566CairoEgypt Department of Computer Science College of Computing and Information TechnologyShaqra University11961Saudi Arabia Computer Engineering and Control Systems Department Faculty of EngineeringMansoura UniversityMansoura35516Egypt Electronics and Communications Engineering Department Faculty of EngineeringDelta University for Science and TechnologyMansouraEgypt Department of Communications and Electronics Delta Higher Institute of Engineering and TechnologyMansoura35111Egypt Faculty of Artificial Intelligence Delta University for Science and TechnologyMansoura35712Egypt

Arrhythmia has been classified using a variety of *** of the dynamic nature of electrocardiogram(ECG)data,traditional handcrafted approaches are difficult to execute,making the machine learning(ML)solutions more *** with cardiac arrhythmias can benefit from competent monitoring to save their *** arrhythmia classification and prediction have greatly improved in recent *** are a category of conditions in which the heart's electrical activity is abnormally rapid or *** year,it is one of the main reasons of mortality for both men and women,*** the classification of arrhythmias,this work proposes a novel technique based on optimized feature selection and optimized K-nearest neighbors(KNN)*** proposed method makes advantage of the UCI repository,which has a 279-attribute high-dimensional cardiac arrhythmia *** proposed approach is based on dividing cardiac arrhythmia patients into 16 groups based on the electrocardiography dataset’s *** purpose is to design an efficient intelligent system employing the dipper throated optimization method to categorize cardiac arrhythmia *** method of comprehensive arrhythmia classification outperforms earlier methods presented in the *** achieved classification accuracy using the proposed approach is 99.8%.

关键词： Feature selection electrocardiogram metaheuristics dipper throated algorithm

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A Secure Framework for Blockchain Transactions Protection

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computer Systems Science & engineering 2023年第5期45卷 1095-1111页

作者： Wafaa N.Al-Sharu Majdi K.Qabalin Muawya Naser Omar A.Saraerh Department of Electrical Engineering Faculty of EngineeringThe Hashemite UniversityZarqa13133Jordan Department of Computer Science King Hussein School of Computing SciencesPrincess Sumaya University for TechnologyAmman11941Jordan

One of the most extensively used technologies for improving the security of IoT devices is blockchain *** is a new technology that can be utilized to boost the *** is a decentralized peer-to-peer network with no central *** nodes on the network mine or verify the data recorded on the *** is a distributed ledger that may be used to keep track of transactions between several *** one can tamper with the data on the blockchain since it is *** the blocks are connected by hashes,the transaction data is *** is managed by a system that is based on the consensus of network users rather than a central *** immutability and tamper-proof nature of blockchain security is based on asymmetric cryptography and ***,Blockchain has an immutable and tamper-proof smart contract,which is a logic that enforces the Blockchain’s *** is a conflict between the privacy protection needs of cyber-security threat intelligent(CTI)sharing and the necessity to establish a comprehensive attack chain during blockchain *** paper presents a blockchain-based data sharing paradigm that protects the privacy of CTI sharing parties while also preventing unlawful sharing and ensuring the benefit of legitimate sharing *** builds a full attack chain using encrypted threat intelligence and exploits the blockchain’s backtracking capacity to finish the decryption of the threat source in the attack *** contracts are also used to send automatic early warning replies to possible attack *** tests are used to verify the feasibility and efficacy of the suggested model.

关键词： Manuscript preparation typeset format

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Towards Accurate RPM Detection Using Primary Ignition Pulses 49

Towards Accurate RPM Detection Using Primary Ignition Pulses

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49th Annual Conference of the IEEE Industrial Electronics Society, IECON 2023

作者： Wijayaraja, Jaliya L. Abhayasinghe, Nimsiri Wellalage, Sasini Sliit Faculty of Computing Department of Computer Systems Engineering Malabe Sri Lanka Sliit Faculty of Engineering Department of Electrical and Electronic Engineering Malabe Sri Lanka

ISBN: (纸本)9798350331820

Enhancing the performance and extending the life-cycle of older vehicles through retrofitting with improved systems presents an alluring opportunity. However, achieving accurate sensing of crucial parameters, such as engine revolutions per minute (RPM), poses challenges due to the need for mechanical manipulation and some designs, which may result in diminished accuracy in adverse settings. This study proposes using the primary ignition pulse signal to accurately determine engine speed without mechanical manipulation. We investigate the effectiveness of signal processing methods, including the RC filter, 2nd and 6th order Butterworth filter, to improve the accuracy of the readings. Our proposed method successfully collects data from conventional internal combustion engines, with simulated signal processing methods producing accurate detection of RPM and reduced fluctuations. The proposed method eliminated the noise, leading to accurate RPM measurements, while the maximum standard deviation observed for the RC filter implementation was 38.74. By accurately sensing key parameters, older vehicles can be retrofitted with improved systems, leading to enhanced performance and an extended lifecycle. The introduced methods can be practically implemented using custom electronics or embedded systems and can contribute to future research on automotive retrofitting. © 2023 IEEE.

关键词： Life cycle

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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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Design Principles-Based Interactive Learning Tool for Solving Nonlinear Equations

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computer Systems Science & engineering 2022年第3期40卷 1023-1042页

作者： Ahad Alloqmani Omimah Alsaedi Nadia Bahatheg Reem Alnanih Lamiaa Elrefaei Computer Sciences Department Faculty of Computing and Information TechnologyKing Abdulaziz UniversityJeddahSaudi Arabia Electrical Engineering Department Faculty of Engineering at ShoubraBenha UniversityCairoEgypt

Interactive learning tools can facilitate the learning process and increase student engagement,especially tools such as computer programs that are designed for human-computer ***,this paper aims to help students learn five different methods for solving nonlinear equations using an interactive learning tool designed with common principles such as feedback,visibility,affordance,consistency,and *** also compares these methods by the number of iterations and time required to display the *** study helps students learn these methods using interactive learning tools instead of relying on traditional teaching *** tool is implemented using the MATLAB app and is evaluated through usability testing with two groups of users that are categorized by their level of experience with *** with no knowledge in root-finding confirmed that they understood the root-finding concept when interacting with the designed *** positive results of the user evaluation showed that the tool can be recommended to other users.

关键词： Graphical user interface(GUI) interactive learning tool design principles nonlinear equations experimental design

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Comparative Analysis of News Articles Summarization using LLMs

Comparative Analysis of News Articles Summarization using LL...

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2024 Asia Pacific Conference on Innovation in Technology, APCIT 2024

作者： Archanaa, N. Shivanesh, B. Suwin Kumar, J.D.T. Bharathi Mohan, G. Doss, Srinath Amrita Vishwa Vidyapeetham Amrita School of Computing Department of Computer Science and Engineering Chennai India Botho University Faculty of Engineering and Technology Botswana

ISBN: (纸本)9798350361537

In the rapidly evolving domain of Natural Language Processing (NLP), the efficiency of Large Language Models (LLMs) in generating abstractive text summaries plays a pivotal role in information synthesis. This study advances the understanding of LLM performance by conducting a comprehensive evaluation of seven cutting-edge models on four distinct datasets. The models selected for comparison include Distilbart-cnn-12-6, Led-base-16384, Google's Bigbird, Microsoft's ProphetNet, Facebook's BART, T5 fine-tuned, and Google's PEGASUS. Each model's summarization prowess is rigorously assessed using a battery of metrics: ROUGE, METEOR, BERTScore, Cosine Similarity, and BLEU. The goal is to discern the intricate relationship between dataset characteristics and model efficacy, delivering insights into the inherent advantages and limitations of each model in handling specific data contexts. The results contribute to a refined understanding of LLM applicability, offering empirical evidence to aid in the selection of the most suitable model for varied summarization needs. © 2024 IEEE.

关键词： Natural language processing systems

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Divergent Approaches to Antimicrobial Peptide Classification: A Comparative Evaluation of AI Techniques 7

Divergent Approaches to Antimicrobial Peptide Classification...

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7th International Conference on Circuit Power and computing Technologies, ICCPCT 2024

作者： Rajagopal, Sushma Bharathi Mohan, G. Doss, Srinath Amrita School of Computing Department of Computer Science and Engineering Amrita Vishwa Vidyapeetham Chennai India Botho University Faculty of Engineering and Technology Botswana

ISBN: (纸本)9798350372816

Given the global rise in antibiotic resistance, antimicrobial peptides (AMPs) provide a viable substitute for traditional antibiotics. Through a comparison of transformer-based, deep learning (DL), and machine learning (ML) techniques, this work seeks to classify AMPs. AMP sequences from curated databases were compared with non-AMP sequences obtained from UniProtKB/Swiss-Prot. The study compares performance across many assessment variables to assess how well various algorithms separate AMPs from non-AMPs. Significantly, RoBERTa outperformed DistilBERT (84.43%) in accuracy (86.53%). To provide a thorough knowledge of the algorithms' performance, the research also included a variety of other assessment criteria, including area under the receiver operating characteristic curve (AUC-ROC), recall, F1 score, and accuracy. The performance of the LSTM and GRU models was notable, despite their significantly lower accuracies. Furthermore, conventional machine learning methods such as Naive Bayes and Logistic Regression demonstrated competitive accuracy. This thorough analysis clarifies the benefits and drawbacks of various AMP classification techniques, providing insightful information for next studies on antimicrobial peptides and their potential to fight antibiotic resistance. © 2024 IEEE.

关键词： Contrastive Learning

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