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Adaptive Multi-strategy Rabbit Optimizer for Large-scale Optimization

Journal of Bionic engineering 2025年第1期22卷 398-416页

作者： Baowei Xiang Yixin Xiang Department of Computing School of Electronic and Information Engineering Taizhou UniversityTaizhou318000China Department of Software Engineering College of Software Nankai UniversityTianjin300350China

As optimization problems continue to grow in complexity,the need for effective metaheuristic algorithms becomes increasingly ***,the challenge lies in identifying the right parameters and strategies for these *** this paper,we introduce the adaptive multi-strategy Rabbit Algorithm(RA).RA is inspired by the social interactions of rabbits,incorporating elements such as exploration,exploitation,and adaptation to address optimization *** employs three distinct subgroups,comprising male,female,and child rabbits,to execute a multi-strategy *** parameters,including distance factor,balance factor,and learning factor,strike a balance between precision and computational *** offer practical recommendations for fine-tuning five essential RA parameters,making them versatile and *** is capable of autonomously selecting adaptive parameter settings and mutation strategies,enabling it to successfully tackle a range of 17 CEC05 benchmark functions with dimensions scaling up to *** results underscore RA’s superior performance in large-scale optimization tasks,surpassing other state-of-the-art metaheuristics in convergence speed,computational precision,and ***,RA has demonstrated its proficiency in solving complicated optimization problems in real-world engineering by completing 10 problems in CEC2020.

关键词： Adaptive parameter Large scale optimization Rabbit algorithm Swarm intelligence engineering optimization

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Bias analysis of AI models for undergraduate student admissions

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Neural computing and Applications 2025年第12期37卷 7785-7795页

作者： Van Busum, Kelly Fang, Shiaofen Department of Computer Science and Software Engineering Butler University IndianapolisIN United States Computing and Engineering Indiana University Luddy School of Informatics Indiana University Indianapolis IndianapolisIN United States

Bias detection and mitigation is an active area of research in machine learning. This work extends previous research done by the authors Van Busum and Fang (Proceedings of the 38th ACM/SIGAPP Symposium on Applied computing, 2023) to provide a rigorous and more complete analysis of the bias found in AI predictive models. Admissions data spanning six years was used to create an AI model to determine whether a given student would be directly admitted into the School of Science under various scenarios at a large urban research university. During this time, submission of standardized test scores as part of a student’s application became optional which led to interesting questions about the impact of standardized test scores on admission decisions. We developed and analyzed AI models to understand which variables are important in admissions decisions, and how the decision to exclude test scores affects the demographics of the students who are admitted. We then evaluated the predictive models to detect and analyze biases these models may carry with respect to three variables chosen to represent sensitive populations: gender, race, and whether a student was the first in his/her family to attend college. We also extended our analysis to show that the biases detected were persistent. Finally, we included several fairness metrics in our analysis and discussed the uses and limitations of these metrics. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2024.

关键词： Students

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Evolution Analysis of Network Attack and Defense Situation Based on Game Theory

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Computers, Materials & Continua 2025年第4期83卷 1475-1494页

作者： Haiyan Sun Chenglong Shao Jianwei Zhang Kun Wang Wanwei Huang College of Software Engineering Zhengzhou University of Light IndustryZhengzhou450007China Intelligent Computing Power Research Department Zhengzhou Xinda Advanced Technology Research InstituteZhengzhou450007China

To address the problem that existing studies lack analysis of the relationship between attack-defense game behaviors and situation evolution from the game perspective after constructing an attack-defense model,this paper proposes a network attack-defense game model(ADGM).Firstly,based on the assumption of incomplete information between the two sides of the game,the ADGM model is established,and methods of payoff quantification,equilibrium solution,and determination of strategy confrontation results are ***,drawing on infectious disease dynamics,the network attack-defense situation is defined based on the density of nodes in various security states,and the transition paths of network node security states are ***,the network zero-day virus attack-defense behaviors are analyzed,and comparative experiments on the attack-defense evolution trends under the scenarios of different strategy combinations,interference methods,and initial numbers are conducted using the NetLogo simulation *** experimental results indicate that this model can effectively analyze the evolution of the macro-level network attack-defense situation from the micro-level attack-defense *** instance,in the strategy selection experiment,when the attack success rate decreases from 0.49 to 0.29,the network destruction rate drops by 11.3%,in the active defense experiment,when the interference coefficient is reduced from 1 to 0.7,the network destruction rate decreases by 7%,and in the initial node number experiment,when the number of initially infected nodes increases from 10 to 30,the network destruction rate rises by 3%.

关键词： Network attack-defense situation evolution zero-day virus NetLogo

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Efficient Bit-Plane Based Medical Image Cryptosystem Using Novel and Robust Sine-Cosine Chaotic Map

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Computers, Materials & Continua 2025年第4期83卷 917-933页

作者： Zeric Tabekoueng Njitacke Louai A.Maghrabi Musheer Ahmad Turki Althaqafi Department of Electrical and Electronic Engineering College of Technology(COT)University of BueaBueaP.O.Box 63Cameroon Department of Software Engineering College of EngineeringUniversity of Business and TechnologyJeddah22246Saudi Arabia Department of Computer Engineering Jamia Millia IslamiaNew Delhi110025India Department of Computer Science School of EngineeringComputing and DesignDar Al-Hekma UniversityJeddah22246Saudi Arabia

This paper presents a high-security medical image encryption method that leverages a novel and robust sine-cosine *** map demonstrates remarkable chaotic dynamics over a wide range of *** employ nonlinear analytical tools to thoroughly investigate the dynamics of the chaotic map,which allows us to select optimal parameter configurations for the encryption *** findings indicate that the proposed sine-cosine map is capable of generating a rich variety of chaotic attractors,an essential characteristic for effective *** encryption technique is based on bit-plane decomposition,wherein a plain image is divided into distinct bit *** planes are organized into two matrices:one containing the most significant bit planes and the other housing the least significant *** subsequent phases of chaotic confusion and diffusion utilize these matrices to enhance *** auxiliary matrix is then generated,comprising the combined bit planes that yield the final encrypted *** results demonstrate that our proposed technique achieves a commendable level of security for safeguarding sensitive patient information in medical *** a result,image quality is evaluated using the Structural Similarity Index(SSIM),yielding values close to zero for encrypted images and approaching one for decrypted ***,the entropy values of the encrypted images are near 8,with a Number of Pixel Change Rate(NPCR)and Unified Average Change Intensity(UACI)exceeding 99.50%and 33%,***,quantitative assessments of occlusion attacks,along with comparisons to leading algorithms,validate the integrity and efficacy of our medical image encryption approach.

关键词： Image cryptosystem robust chaos sine-cosine map nonlinear analysis tools medical images

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SQUMUTH squirrel search based algorithm for high order mutant generation in mutation testing

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Discover computing 2025年第1期28卷 1-17页

作者： Mohanty, Subhasish Mishra, Jyotirmaya Mohapatra, Sudhir Kumar Bejo, Seifu Detso Deferisha, Aliazar Deneke Computer Science and Engineering GIET University Odisha Gunupur India Faculty of Engineering and Technology Sri Sri University Cuttack India Department of Software Engineering CCI Wolkite University Wolkite Ethiopia Faculty of Computing and Software Engineering Arba Minch University Arba Minch Ethiopia

In today's software testing community, quality assessment remains critical, with mutation testing standing as a cornerstone technique for evaluating the effectiveness of test cases. This method involves introducing faulty code, or mutants, into the program to assess the quality of the test suite and other testing methods. However, mutation testing faces challenges such as the generation of numerous mutants, the presence of equivalent mutants that are difficult to detect through testing, and the lack of realistic mutant creation. Literature reviews indicate significant efforts to address these issues through formal solutions and heuristic methods. Recent optimization-based methods are now recognized as cost-effective result in an optimized solution. Hence, to overcome these limitations, this study introduces SQUMUTH, a novel approach for high-order mutant generation based on the Squirrel Search Algorithm (SSA). Inspired by the foraging behavior of squirrels, SSA offers a promising solution for enhancing the efficiency and effectiveness of mutation testing. Experimental evaluations on eight well-known Java benchmark programs demonstrate that SQUMUTH outperforms existing methods. Comparative analyses of mutation scores and the rates of realistic mutants consistently show its better performance across all subject programs compared to other state-of-the-art methods such as Social Group Optimization, Binary Genetic Algorithm, and random testing. The experimental results underscore its effectiveness in generating more realistic mutants. The experimental results indicated that the proposed approach has the potential to advance software testing by improving the cost-effectiveness of mutation analysis and the quality of software systems. © The Author(s) 2025.

关键词： First-order mutants Higher-order mutants Mutation score Mutation testing software testing Squirrel search algorithm

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YOLOCSP-PEST for Crops Pest Localization and Classification

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Computers, Materials & Continua 2025年第2期82卷 2373-2388页

作者： Farooq Ali Huma Qayyum Kashif Saleem Iftikhar Ahmad Muhammad Javed Iqbal Department of Software Engineering University of Engineering and TechnologyTaxila47050Pakistan Department of Computer Science&Engineering College of Applied Studies&Community ServiceKing Saud UniversityRiyadh11362Saudi Arabia Department of Information Technology Faculty of Computing and Information TechnologyKing Abdulaziz UniversityJeddah21589Saudi Arabia Department of Computer Science University of Engineering and TechnologyTaxila47050Pakistan

Preservation of the crops depends on early and accurate detection of pests on crops as they cause several diseases decreasing crop production and quality. Several deep-learning techniques have been applied to overcome the issue of pest detection on crops. We have developed the YOLOCSP-PEST model for Pest localization and classification. With the Cross Stage Partial Network (CSPNET) backbone, the proposed model is a modified version of You Only Look Once Version 7 (YOLOv7) that is intended primarily for pest localization and classification. Our proposed model gives exceptionally good results under conditions that are very challenging for any other comparable models especially conditions where we have issues with the luminance and the orientation of the images. It helps farmers working out on their crops in distant areas to determine any infestation quickly and accurately on their crops which helps in the quality and quantity of the production yield. The model has been trained and tested on 2 datasets namely the IP102 data set and a local crop data set on both of which it has shown exceptional results. It gave us a mean average precision (mAP) of 88.40% along with a precision of 85.55% and a recall of 84.25% on the IP102 dataset meanwhile giving a mAP of 97.18% on the local data set along with a recall of 94.88% and a precision of 97.50%. These findings demonstrate that the proposed model is very effective in detecting real-life scenarios and can help in the production of crops improving the yield quality and quantity at the same time.

关键词： Deep learning classification of pests YOLOCSP-PEST pest detection

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Three-Stage Transfer Learning with AlexNet50 for MRI Image Multi-Class Classification with Optimal Learning Rate

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Computer Modeling in engineering & Sciences 2025年第1期142卷 155-183页

作者： Suganya Athisayamani A.Robert Singh Gyanendra Prasad Joshi Woong Cho School of Computing Sastra Deemed to be UniversityThanjavur613401India Department of Computational Intelligence SRM Institute of Science and TechnologyKattankulathur603203India Department of AI and Software Engineering Kangwon National UniversitySamcheok25913Republic of Korea Department of Electronics Information and Communication EngineeringKangwon National UniversitySamcheok25913Republic of Korea

In radiology,magnetic resonance imaging(MRI)is an essential diagnostic tool that provides detailed images of a patient’s anatomical and physiological *** is particularly effective for detecting soft tissue ***,radiologists manually interpret these images,which can be labor-intensive and time-consuming due to the vast amount of *** address this challenge,machine learning,and deep learning approaches can be utilized to improve the accuracy and efficiency of anomaly detection in MRI *** manuscript presents the use of the Deep AlexNet50 model for MRI classification with discriminative learning *** are three stages for learning;in the first stage,the whole dataset is used to learn the *** the second stage,some layers of AlexNet50 are frozen with an augmented dataset,and in the third stage,AlexNet50 with an augmented dataset with the augmented *** method used three publicly available MRI classification datasets:Harvard whole brain atlas(HWBA-dataset),the School of Biomedical engineering of Southern Medical University(SMU-dataset),and The National Institute of Neuroscience and Hospitals brain MRI dataset(NINS-dataset)for *** hyperparameter optimizers like Adam,stochastic gradient descent(SGD),Root mean square propagation(RMS prop),Adamax,and AdamW have been used to compare the performance of the learning ***-dataset registers maximum classification *** evaluated the performance of the proposed classification model using several quantitative metrics,achieving an average accuracy of 98%.

关键词： MRI tumors classification AlexNet50 transfer learning hyperparameter tuning optimizer

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An Empirical Study on Meta Virtual Reality Applications: Security and Privacy Perspectives

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IEEE Transactions on software engineering 2025年第5期51卷 1437-1454页

作者： Guo, Hanyang Dai, Hong-Ning Luo, Xiapu Xu, Gengyang He, Fengliang Zheng, Zibin Hong Kong Baptist University Department of Computer Science 999077 Hong Kong Sun Yat-sen University School of Software Engineering Zhuhai519082 China The Hong Kong Polytechnic University Department of Computing 999077 Hong Kong

Virtual Reality (VR) has accelerated its prevalent adoption in emerging metaverse applications, but it is not a fundamentally new technology. On the one hand, most VR operating systems (OS) are based on off-the-shelf mobile OS (e.g., Android OS). As a result, VR apps also inevitably inherit privacy and security deficiencies from conventional mobile apps. On the other hand, in contrast to traditional mobile apps, VR apps can achieve an immersive experience via diverse VR devices, such as head-mounted displays, body sensors, and controllers. However, achieving this requires the extensive collection of privacy-sensitive human biometrics (e.g., hand-tracking and face-tracking data). Moreover, VR apps have been typically implemented by 3D gaming engines (e.g., Unity), which also contain intrinsic security vulnerabilities. Inappropriate use of these technologies may incur privacy leaks and security vulnerabilities although these issues have not received significant attention compared to the proliferation of diverse VR apps. In this paper, we develop a security and privacy assessment tool, namely the VR-SP detector for VR apps. The VR-SP detector has integrated program static analysis tools and privacy-policy analysis methods. Using the VR-SP detector, we conduct a comprehensive empirical study on 900 popular VR apps. We obtain the original apps from the popular SideQuest app store and extract Android PacKage (APK) files via the Meta Quest 2 device. We evaluate the security vulnerabilities and privacy data leaks of these VR apps through VR app analysis, taint analysis, privacy policy analysis, and user review analysis. We find that a number of security vulnerabilities and privacy leaks widely exist in VR apps. Moreover, our results also reveal conflicting representations in the privacy policies of these apps and inconsistencies of the actual data collection with the privacy-policy statements of the apps. Further, user reviews also indicate their privacy concerns about rele

关键词： Differential privacy

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Adapting CRISP-DM to model enteric fermentation emission: farm level application

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International Journal of Applied Decision Sciences 2025年第3期18卷 330-359页

作者： Guerrón, Philippe Belmont Hallo, María Luján-Mora, Sergio Faculty of Engineering Systems Escuela Politecnica Nacional Ladron de Guevara E11-253 Edif 20 Quito Ecuador Department of Software and Computing Systems University of Alicante Alicante03690 Spain

Enteric fermentation contributes substantially to greenhouse gas emissions (GGEs) in agriculture, but may be reversible in the short-term. To date, numerous attempts have been made to model the environmental impact of agriculture, but have failed to integrate multiple dimensions of production. The objective of this study is to adapt the cross industry standard process for data mining (CRISP-DM) at farm level, using the concept of life cycle assessment (LCA) and implemented a modified version of the global livestock environmental assessment model (GLEAM). Using local data collected over 20 years and secondary data, our results show that for dairy cattle, the methane emissions factor from cattle is lower among marginal farms 86 Kg CH4 head−1 year−1 compared to semi-intensive and intensive farms across time and geographical regions (107.4 and 113.5 respectively) and demonstrate that this type of application is relevant for developing countries and smallholder agriculture, where production data is often unavailable. Copyright © 2025 Inderscience Enterprises Ltd.

关键词： Farms

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Early Diabetic Retinopathy Cyber-Physical Detection System Using Attention-Guided Deep CNN Fusion

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IEEE Transactions on Network Science and engineering 2025年第3期12卷 1898-1910页

作者： Hossain, M. Shamim Shorfuzzaman, Mohammad King Saud University Research Chair of Pervasive and Mobile Computing Department of Software Engineering College of Computer and Information Sciences Riyadh12372 Saudi Arabia Taif University Department of Computer Science College of Computers and Information Technology Taif21944 Saudi Arabia

Diabetic retinopathy is the most common and severe eye complication of diabetes, and it can cause vision loss or even blindness due to retina damage. Automatic and faster detection of various DR stages is crucial and can benefit both patients and ophthalmologists. With the ubiquity of measurement devices and growing processing power, cyber-physical characterization is becoming an enabler technology in many disciplines. This paper proposes a cyber-physical system (CPS) framework that will aid clinicians in establishing an early diagnosis of DR. Particularly;we present a component-based CPS architecture to use a deep learning-based predictive model deployed on the cloud for effective DR diagnosis and incorporate medical devices. To this end, a deep learning-based explainable CNN fusion model has been introduced in the proposed framework for automatic screening and interpretation of DR stages using digital fundus images. We extract salient features using various fine-tuned CNN models in conjunction with an attention network, and we use a locally connected layer to calculate the weighted contribution of these networks. We measure the performance of our approach using a public dataset comprising fundus images of five different categories. Experimental results demonstrate the proposed approach's effectiveness for faster and more accurate detection of various DR stages. © 2013 IEEE.

关键词： Biomedical equipment

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