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Soft computing Based Metaheuristic Algorithms for Resource Management in Edge computing Environment

Computers, Materials & Continua 2022年第9期72卷 5233-5250页

作者： Nawaf Alhebaishi Abdulrhman M.Alshareef Tawfiq Hasanin Raed Alsini Gyanendra Prasad Joshi Seongsoo Cho Doo Ill Chul Department of Information Systems Faculty of Computing and Information TechnologyKing Abdulaziz UniversityJeddahSaudi Arabia Department of Computer Science and Engineering Sejong UniversitySeoulKorea School of Software Soongsil UniversitySeoul06978Korea Artificial Intelligence Education Hankuk University of Foreign StudiesDongdaemun-guSeoul02450Korea

In recent times,internet of things(IoT)applications on the cloud might not be the effective solution for every IoT scenario,particularly for time sensitive applications.A significant alternative to use is edge computing that resolves the problem of requiring high bandwidth by end *** computing is considered a method of forwarding the processing and communication resources in the cloud towards the *** of the considerations of the edge computing environment is resource management that involves resource scheduling,load balancing,task scheduling,and quality of service(QoS)to accomplish improved *** this motivation,this paper presents new soft computing based metaheuristic algorithms for resource scheduling(RS)in the edge computing *** SCBMARS model involves the hybridization of the Group Teaching Optimization Algorithm(GTOA)with rat swarm optimizer(RSO)algorithm for optimal resource *** goal of the SCBMA-RS model is to identify and allocate resources to every incoming user request in such a way,that the client’s necessities are satisfied with the minimum number of possible resources and optimal energy *** problem is formulated based on the availability of VMs,task characteristics,and queue *** integration of GTOA and RSO algorithms assist to improve the allocation of resources among VMs in the data *** experimental validation,a comprehensive set of simulations were performed using the CloudSim *** experimental results showcased the superior performance of the SCBMA-RS model interms of different measures.

关键词： Resource scheduling edge computing soft computing fitness function virtual machines

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Reinforcement Learning Agent for Path Planning with Expert Demonstration

Reinforcement Learning Agent for Path Planning with Expert D...

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IEEE Annual International Computer software and Applications Conference (COMPSAC)

作者： Alan Norkham Mikalus Chalupa Noah Gardner Md Abdullah Al Hafiz Khan Xinyue Zhang Chih-Cheng Hung College of Computing and Software Engineering Kennesaw State University Marietta GA USA

ISBN: (数字)9781665488105

ISBN: (纸本)9781665488112

The problem of path planning is a challenging task for mobile robots. A practical example can be seen in the robots commonly employed in warehouses: they must navigate to pick up goods and move them to certain locations. Therefore, the robot needs a method of moving from an initial location in the warehouse to a final location repeatedly. In this paper, we propose a technique that allows a robot to path plan in generalized environments, from different starting and goal locations. The method is based on a graph representation of the environment, and is capable of finding the shortest path between two points in the environment. In order to generalize appropriately, we show that a neural network is able to effectively choose the correct actions to take at each time step in the path planning problem.

关键词： Navigation Conferences software algorithms Neural networks Decision making Reinforcement learning Path planning

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Explainable AI for Healthcare: An Approach Towards Interpretable Healthcare Models

Explainable AI for Healthcare: An Approach Towards Interpret...

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Information Technology (ACIT)

作者： Kanwal Zahoor Narmeen Zakaria Bawany Usman Ghani Department of Computer Science and Software Engineering Center for Computing Research Jinnah University for Women Institute of Business Administration Karachi Pakistan

Artificial intelligence (AI) along with deep learning techniques has become an integral part of almost all aspects of life. One of the domains significantly impacted by this technological revolution is healthcare. Deep learning-based AI systems assist clinicians and medical professionals in disease diagnosis, personalized treatment, and monitoring through wearables, among other applications. Despite its expedient integration in healthcare, the trustworthiness of deep learning models remains a concern, primarily due to a lack of understanding of their underlying processes. However, Explainable AI (XAI) offers explanations through various methods, including Local Interpretable Model-agnostic Explanations (LIME), Shapley Additive explanation (SHAP), and GRAD-CAM. XAI is utilized to enhance transparency, allowing users to understand and trust AI decisions. In this study, we present deep learning models for the classification of pneumonia disease in Chest X-ray Images followed by their explanations. Convolutional Neural Networks (CNNs) and other pre-trained models, including VGG16, MobileNetV3, and ResNet50, were used for classification of images as ‘normal’ or ‘pneumonia’. The VGG16 model, known for its exceptional image understanding capabilities, achieved the highest accuracy, with an impressive 93% score. Further, we used XAI techniques including SHAP, LIME, and Grad-CAM for explanation of models. LIME and Grad-CAM provided more accurate results than SHAP in our experiments. This approach was taken to evaluate the fairness and transparency of the model. The insights gained from XAI can be used to refine and improve machine learning models by identifying areas of weakness or misinterpretation which increases overall model robustness.

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An Efficient High-Dimensional Gene Selection Approach based on Binary Horse Herd Optimization Algorithm for Biological Data Classification

arXiv

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arXiv 2023年

作者： Mehrabi, Niloufar Boroujeni, Sayed Pedram Haeri Pashaei, Elnaz School of Computing Clemson University ClemsonSC United States Department of Software Engineering Istanbul Aydin University Istanbul Turkey

The Horse Herd Optimization Algorithm (HOA) is a new meta-heuristic algorithm based on the behaviors of horses at different ages. The HOA was introduced recently to solve complex and high-dimensional problems. This paper proposes a binary version of the Horse Herd Optimization Algorithm (BHOA) in order to solve discrete problems and select prominent feature subsets. Moreover, this study provides a novel hybrid feature selection framework based on the BHOA and a minimum Redundancy Maximum Relevance (MRMR) filter method. This hybrid feature selection, which is more computationally efficient, produces a beneficial subset of relevant and informative features. Since feature selection is a binary problem, we have applied a new Transfer Function (TF), called X-shape TF, which transforms continuous problems into binary search spaces. Furthermore, the Support Vector Machine (SVM) is utilized to examine the efficiency of the proposed method on ten microarray datasets, namely Lymphoma, Prostate, Brain-1, DLBCL, SRBCT, Leukemia, Ovarian, Colon, Lung, and MLL. In comparison to other state-of-the-art, such as the Gray Wolf (GW), Particle Swarm Optimization (PSO), and Genetic Algorithm (GA), the proposed hybrid method (MRMR-BHOA) demonstrates superior performance in terms of accuracy and minimum selected features. Also, experimental results prove that the X-Shaped BHOA approach outperforms others methods. Copyright © 2023, The Authors. All rights reserved.

关键词： Support vector machines

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Privacy-Aware Web APIs Recommendation for Consumer Mashup Creation Based on Iterative Quantification

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IEEE Transactions on Consumer Electronics 2024年 1-1页

作者： Zhang, Ruowei Qi, Lianyong Yan, Chao Chen, Zige Gong, Wenwen Xu, Yanwei Rafique, Wajid School of Computer Science Qufu Normal University China China Tsinghua University China College of Intelligence and Computing Tianjin University China Department of Electrical and Software Engineering University of Calgary Canada

With the emergence of the "unmanned" field, unmanned supermarket software has entered consumers’ lives in line with the pace of development of the times. Nowadays, developers of unmanned supermarket software often tend to create feature-rich consumer mashup applications by invoking a variety of Web APIs (Application Programming Interfaces) in order to save time and costs. The number of APIs available on edge servers has significantly increased with the rise of mobile edge computing. To cope with the continuous growth in API volume and the increasing diversity of API functions in API sharing communities (e.g., ***), lightweight recommendation techniques have been employed to assist consumer mashup developers in finding their desired Web APIs from a vast pool of candidates. However, during the data processing in edge computing, traditional Web API recommendation approaches often prioritize the accuracy of recommended APIs, neglecting the potential privacy risks associated with disclosure. This significantly reduces the willingness and incentive of consumer mashup developers to share API information, such as historical edge mashup-API invocation records. To address this issue, we propose a privacy-preserving Web API recommendation approach, WARecITQ, based on Iterative Quantification (ITQ). Specifically, we convert the sensitive mashup-API invocation records into less-sensitive mashup indices using Iterative Quantization hash coding. We then utilize these less-sensitive mashup indices as the primary decision-making criteria for Web API recommendations, thereby achieving the goal of completing top-k API recommendations. Lastly, we crawled a real-world dataset of mashup-API invocation records from *** and conducted a series of experiments on the dataset to evaluate performance. The results of the experiments demonstrate the superiority of our WARecITQ approach in multiple performance metrics compared to other related approaches, while ensu

关键词： Iterative methods

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Think Before You Act: A Two-Stage Framework for Mitigating Gender Bias Towards Vision-Language Tasks

arXiv

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arXiv 2024年

作者： Zhang, Yunqi Li, Songda Deng, Chunyuan Wang, Luyi Zhao, Hui Software Engineering Institute East China Normal University China Georgia Institute of Technology United States Shanghai Key Laboratory of Trustworthy Computing Shanghai China

Gender bias in vision-language models (VLMs) can reinforce harmful stereotypes and discrimination. In this paper, we focus on mitigating gender bias towards vision-language tasks. We identify object hallucination as the essence of gender bias in VLMs. Existing VLMs tend to focus on salient or familiar attributes in images but ignore contextualized nuances. Moreover, most VLMs rely on the co-occurrence between specific objects and gender attributes to infer the ignored features, ultimately resulting in gender bias. We propose GAMA, a task-agnostic generation framework to mitigate gender bias. GAMA consists of two stages: narrative generation and answer inference. During narrative generation, GAMA yields all-sided but gender-obfuscated narratives, which prevents premature concentration on localized image features, especially gender attributes. During answer inference, GAMA integrates the image, generated narrative, and a task-specific question prompt to infer answers for different vision-language tasks. This approach allows the model to rethink gender attributes and answers. We conduct extensive experiments on GAMA, demonstrating its debiasing and generalization ability. © 2024, CC BY-NC-ND.

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MFA-DAF: Unsupervised Multimodal Medical Image Fusion via Multiscale Fourier Attention and Detail-Aware Fusion Strategy

MFA-DAF: Unsupervised Multimodal Medical Image Fusion via Mu...

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Image Processing, Computer Vision and Machine Learning (ICICML), International Conference on

作者： Xinyu Xie Xiaozhi Zhang Dongping Xiong Lijun Ouyang School of Electrical Engineering University of South China Hengyang China School of Computing/Software University of South China Hengyang China

Multimodal medical image fusion is vital for extracting complementary information and generating comprehensive images in clinical applications. However, existing deep learning-based fusion approaches face challenges in effectively utilizing frequency-domain information, designing appropriate integration strategies and modelling long-range context correlation. To address these issues, we propose a novel unsupervised multimodal medical image fusion method called Multiscale Fourier Attention and Detail-Aware Fusion (MFA-DAF). Our approach employs a multiscale Fourier attention encoder to extract rich features, followed by a detail-aware fusion strategy for comprehensive integration. The fusion image is obtained using a nested connected Fourier attention decoder. We adopt a two-stage training strategy and design new loss functions for each stage. Experiment results demonstrate that our model outperforms other state of the art methods, producing fused images with enhanced texture information and superior visual quality.

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software Defect Prediction Harnessing on Multi 1-Dimensional Convolutional Neural Network Structure

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Computers, Materials & Continua 2022年第4期71卷 1521-1546页

作者： Zuhaira Muhammad Zain Sapiah Sakri Nurul Halimatul Asmak Ismail Reza M.Parizi Department of Information Systems College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityRiyadhSaudi Arabia Department of Computer Science and Information Technology College of CommunityPrincess Nourah bint Abdulrahman UniversityRiyadhSaudi Arabia College of Computing and Software Engineering Kennesaw State UniversityMariettaGA30060USA

Developing successful software with no defects is one of the main goals of software *** order to provide a software project with the anticipated software quality,the prediction of software defects plays a vital *** learning,and particularly deep learning,have been advocated for predicting software defects,however both suffer from inadequate accuracy,overfitting,and complicated *** this paper,we aim to address such issues in predicting software *** propose a novel structure of 1-Dimensional Convolutional Neural Network(1D-CNN),a deep learning architecture to extract useful knowledge,identifying and modelling the knowledge in the data sequence,reduce overfitting,and finally,predict whether the units of code are defects *** design large-scale empirical studies to reveal the proposed model’s effectiveness by comparing four established traditional machine learning baseline models and four state-of-the-art baselines in software defect prediction based on the NASA *** experimental results demonstrate that in terms of f-measure,an optimal and modest 1DCNN with a dropout layer outperforms baseline and state-of-the-art models by 66.79%and 23.88%,respectively,in ways that minimize overfitting and improving prediction performance for software *** to the results,1D-CNN seems to be successful in predicting software defects and may be applied and adopted for a practical problem in software ***,in turn,could lead to saving software development resources and producing more reliable software.

关键词： Defects software defect prediction deep learning convolutional neural network machine learning

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On Text Granularity and Metric Frameworks for Large Language Model Content Detection

On Text Granularity and Metric Frameworks for Large Language...

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IEEE International Conference on Big Data

作者： Linh Le Dung Tran College of Computing and Software Engineering Kennesaw State University Kennesaw Georgia USA Brigham and Women’s Hospital Harvard Medical School Boston Massachusetts USA

ISBN: (数字)9798350362480

ISBN: (纸本)9798350362497

Breakthroughs in Large Language Models (LLMs) have allowed Artificial Intelligence (AI) assistant systems to provide quality information with conveniences. An issue is paralleling the advantages, however. One among the problems of LLM generated content is that they seem indistinguishable from that of human which leads to numerous issues in areas like science, education, information security, etc. Furthermore, approaches in LLM content detection are either computationally expensive or need the LLMs’ internal computations which make them more difficult to be used by the public. Addressing the research gap, we present a metric learning framework for LLM text detection that is balanced for resources, accessibility, and performance. Specifically, the detection framework relies on metric learning to evaluate the similarity between a given text to an equivalent example from LLMs and verify whether the former is from human or AI. The framework can be trained in triplets or pairs of text instances from the same contexts at either the full-text or the sentence granularity levels. For benchmarking, five corpora totalling over 95,000 contexts and responses from human and GPT-3.5 TURBO or GPT-4 TURBO are developed. In term of performance, our architectures maintain 0.87 to 0.95 F1 scores throughout multiple experiment settings. Our framework also requires much less time in training and inference compared to RoBERTa, LLaMA 3, and Ghostbuster, while having 90% to 150% performances of the best benchmark.

关键词： Measurement Training Computational modeling Large language models Training data Text detection Computer architecture Benchmark testing Transformers Vectors

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Transparent AI for Adaptive Fraud Detection 19

Transparent AI for Adaptive Fraud Detection

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19th International Conference on Ubiquitous Information Management and Communication, IMCOM 2025

作者： Agomuo, Okechukwu Clement Uzoma, Agomuo Kingsley Khan, Zohaib Otuomasirichi, Agomuo Ijeoma Muzamal, Junaid Hussain Department of Operations and Information Systems University of Utah Salt Lake City United States Center for Environmental Management and Green Energy University of Nigeria Enugu Nigeria Dept. of Software Engineering FAST NUCES Lahore Pakistan Department of Agricultural Economics University of Nigeria Enugu Nigeria Fast School of Computing Fast NUCES Lahore Pakistan

ISBN: (纸本)9798331507817

The rapid evolution of digital financial services has significantly increased the vulnerability of financial systems to fraudulent activities. Traditional machine learning models have offered substantial progress in detecting fraudulent transactions;however, they often lack adaptability and transparency, critical for dynamic financial environments and regulatory compliance. This paper introduces a novel approach that integrates Deep Reinforcement Learning (DRL) with Explainable Artificial Intelligence (XAI) to enhance fraud detection capabilities in cloud-based financial platforms. Our model, tested on the synthetic PaySim mobile money transaction dataset, leverages the adaptive nature of DRL to continuously learn and update its fraud detection strategies based on evolving transaction patterns. Concurrently, the integration of XAI provides clear, interpretable insights into the decision-making process, thus ensuring transparency and building trust. The proposed model demonstrates superior performance with an achieved accuracy of 99.5% and an F1-score of 99.0%, significantly outperforming traditional fraud detection systems. These results not only underscore the efficacy of our model in improving the accuracy of fraud detection but also highlight its capability in offering significant improvements in understanding the underlying reasons for each decision, thereby aiding compliance with financial regulations. The practical implications of this research highlight its potential to provide a robust, scalable, and transparent solution for real-time fraud detection, which is essential for safeguarding the integrity of modern financial systems. © 2025 IEEE.

关键词： Decentralized finance

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