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A Developer-Focused Genetic Algorithm for IoT Application Placement in the Computing Continuum

IEEE Transactions on Services Computing 2025年第3期18卷 1185-1198页

作者： Herrera, Juan Luis Moya, Alejandro Berrocal, Javier Murillo, Juan Manuel Navarro, Elena University of Bologna Department of Computer Science Engineering Italy University of Castilla-La Mancha LoUISE Research Group Spain University of Extremadura Department of Computer Science and Communications Engineering Spain

The rise of the Internet of Things (IoT) paradigm has led to an interest in applying it not only in tasks for the general public but also to stringent domains such as healthcare. However, the developers of these next-generation IoT applications must consider additional non-functional requirements related to the criticality of the processes they automate, such as low response times or low deployment costs, as well as technical constraints, which include organizational, legal and policy-related constraints on where data can be processed or stored. While the Computing Continuum paradigm emerges as a valuable alternative for placing such applications, identifying the deployments that satisfy all these requirements becomes a tough challenge. The NP-hard nature of the problem makes it impractical to manually find such a deployment, and traditional approaches fail to consider the technical constraints. In this paper, we present the Genetic Algorithm for Application Placement (GAAP), an evolutionary computing-based meta-heuristic designed to help IoT application developers find deployments that satisfy their Quality of Service, business and technical constraints. Our evaluation of an Internet of Medical Things use case shows that GAAP supports larger scenarios than traditional approaches and gives IoT application developers more options while providing better scalability. © 2008-2012 IEEE.

关键词： Internet Of Things Microservice Architectures Quality Of Service Costs Time Factors Electrocardiography Biomedical Monitoring Blood Pressure Genetic Algorithms Business Internet Of Things Computing Continuum Quality Of Service Service Placement Deployment Genetic Algorithm Internet Of Things Internet Of Things Applications Response Time Service Quality Technical Constraints Deployment Cost Low Response Time Internet Of Medical Things Heuristic Objective Function Replica Decision Variables Solution Space Quality Of Experience Internet Of Things Devices Pareto Front Multi Objective Algorithm Capital Expenditure Crossover Operator Quality Of Service Requirements Operational Expenditure Set Of Hosts Workflow Dijkstras Algorithm Non Dominated Solutions Average Response Time Pareto Optimal Solutions Response Cost Multi Objective Genetic Algorithm

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Optimizing classifier performance using PCA-FLANN: a fast and reliable approach

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Iran Journal of computer science 2025年第2期8卷 457-470页

作者： Jena, Monalisa Behera, Ranjan Kumar Dehuri, Satchidananda Cho, Sung-Bae Department of Computer Science Yonsei University Seoul 03722 South Korea Department of Computer Science & Engineering Indian Institute of Information Technology Jharkhand Ranchi India Department of Computer Science Fakir Mohan University Odisha Balasore India

This work introduces PCA-FLANN, an innovative hybrid model combining principal component analysis (PCA) with functional link artificial neural network (FLANN) to achieve efficient non-linear dimensionality reduction and robust classification. The approach is motivated by the need to optimize feature selection, recognizing that not all features equally contribute to achieving optimal accuracy. PCA is employed to extract the most relevant features from datasets, retaining essential information while significantly reducing dimensionality, and FLANN processes these reduced features to handle non-linear correlations effectively. To assess its performance, PCA-FLANN is evaluated on six widely-used real-world datasets from the KEEL repository and compared against hybrid models such as PCA-RBFN, PCA-SVM, PCA-RF, PCA-MLP, and standalone FLANN. The results reveal that PCA-FLANN outperforms these models in accuracy, with improvements of 8.5%, 2.94%, 9.09%, 11.1%, and 9.69%, respectively. Additionally, the model demonstrates enhanced computational efficiency and reduced complexity, establishing PCA-FLANN as a powerful and precise solution for classification tasks involving dimensionality reduction. © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2025.

关键词： Classification Feature extraction FLANN Neural network PCA

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The Security of Using Large Language Models:A Survey With Emphasis on ChatGPT

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IEEE/CAA Journal of Automatica Sinica 2025年第1期12卷 1-26页

作者： Wei Zhou Xiaogang Zhu Qing-Long Han Lin Li Xiao Chen Sheng Wen Yang Xiang the School of Science Computing and Engineering Technologies Swinburne University of Technology IEEE the School of Computer and Mathematical Sciences the University of Adelaide Adelaide the Faculty of Science and Engineering Southern Cross University the School of Information and Physical Sciences the University of Newcastle

ChatGPT is a powerful artificial intelligence(AI)language model that has demonstrated significant improvements in various natural language processing(NLP) tasks. However, like any technology, it presents potential security risks that need to be carefully evaluated and addressed. In this survey, we provide an overview of the current state of research on security of using ChatGPT, with aspects of bias, disinformation, ethics, misuse,attacks and privacy. We review and discuss the literature on these topics and highlight open research questions and future *** this survey, we aim to contribute to the academic discourse on AI security, enriching the understanding of potential risks and mitigations. We anticipate that this survey will be valuable for various stakeholders involved in AI development and usage, including AI researchers, developers, policy makers, and end-users.

关键词： Artificial intelligence (AI) ChatGPT large language models (LLMs) security

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Local Fine-grained Visual Tracking

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IEEE Transactions on Multimedia 2025年 27卷 3426-3436页

作者： Wu, Jingjing Sun, Yifan Hong, Richang Hefei University of Technology School of Computer Science and Information Engineering Hefei230601 China Baidu China

This paper introduces a novel local fine-grained visual tracking task, aiming to precisely locate arbitrary local parts of objects. This task is motivated by our observation that in many realistic scenarios, the user demands to track a local part instead of a holistic object. However, the absence of an evaluation dataset and the distinctive characteristics of local fine-grained targets present extra challenges in conducting this research. To tackle these issues, firstly, this paper constructs a local fine-grained tracking (LFT) dataset to evaluate the tracking performance for local fine-grained targets. Secondly, this paper designs a cutting-edge solution to handle the challenges posed by properties of local objects, including ambiguity and high-proportion backgrounds. It consists of a hierarchical adaptive mask mechanism and foreground-background differentiated learning. The former adaptively searches for and masks ambiguity, which drives the network to concentrate on the local target instead of the holistic objects. The latter is constructed to distinguish foreground and background in an unsupervised manner, which is beneficial to mitigate the impacts of high-proportion backgrounds. Extensive analytic experiments are performed to verify the effectiveness of each submodule in the proposed fine-grained tracker. Our datasets and code will be publicly available soon: https://***/hfutwujingjing/local-fine-grained-tracking. © 1999-2012 IEEE.

关键词： Coarse-grained modeling

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Face Recognition Using LBPH and CNN

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Recent Advances in computer science and Communications 2024年第5期17卷 48-58页

作者： Shukla, Ratnesh Kumar Tiwari, Arvind Kumar Mishra, Ashish Ranjan Department of Computer Science & Engineering Shambhunath Institute of Engineering & Technology Uttar Pradesh Prayagraj211012 India Department of Computer Science & Engineering Kamla Nehru Institute of Engineering & Technology Uttar Pradesh Sultanpur228118 India Department of Computer Science & Engineering Rajkiya Engineering College Uttar Pradesh Sonbhadra231206 India

Objective: The purpose of this paper was to use Machine Learning (ML) techniques to extract facial features from images. Accurate face detection and recognition has long been a problem in computer vision. According to a recent study, Local Binary Pattern (LBP) is a superior facial descriptor for face recognition. A person's face may make their identity, feelings, and ideas more obvious. In the modern world, everyone wants to feel secure from unauthorized authentication. Face detection and recognition help increase security;however, the most difficult challenge is to accurately recognise faces without creating any false identities. Methods: The proposed method uses a Local Binary Pattern Histogram (LBPH) and Convolution Neural Network (CNN) to preprocess face images with equalized histograms. Results: LBPH in the proposed technique is used to extract and join the histogram values into a single vector. The technique has been found to result in a reduction in training loss and an increase in validation accuracy of over 96.5%. Prior algorithms have been reported with lower accuracy when compared to LBPH using CNN. Conclusion: This study demonstrates how studying characteristics produces more precise results, as the number of epochs increases. By comparing facial similarities, the vector has generated the best result. © 2024 Bentham science Publishe.

关键词： Face recognition

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Intelligent Session Management for URLLC in 5G Open Radio Access Network: A Deep Reinforcement Learning Approach

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IEEE Transactions on Industrial Informatics 2023年第2期19卷 1844-1853页

作者： Lien, Shao-Yu Deng, Der-Jiunn National Chung Cheng University Department of Computer Science and Information Engineering Chiayi62102 Taiwan National Changhua University of Education Department of Computer Science and Information Engineering Changhua50007 Taiwan

To sustain ultra-reliable and low latency communication for the fifth generation (5G) networks, the latency of data forwarding over the core network is conventionally ignored. To significantly reduce the latency, a base station shall not permit to service a new session before the case of unacceptable latency taking place. To this end, the fundamental challenge turns out to proactively cognize that the requirements of reliability/latency are about to be violated. To address this challenge, in this article, a deep reinforcement learning based intelligent session management for the open radio access network is proposed to efficiently allocate the resources for the serving sessions and new sessions. The experimental testing results sufficiently show the practicability of our scheme for the 5G networks. © 2005-2012 IEEE.

关键词： Quality of service

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Pose-Invariant Face Recognition via Facial Landmark Based Ensemble Learning

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IEEE Access 2023年 11卷 44221-44233页

作者： Lin, Shinfeng D. Linares Otoya, Paulo E. National Dong Hwa University Department of Computer Science and Information Engineering Hualien97401 Taiwan

In recent years, pose-invariant face recognition has been mainly approached from a holistic insight. DCNNs (ArcFace, Elastic Face, FaceNet) are used to compute a face image embedding, which is used later to perform face recognition. This paper presents a novel approach to pose-invariant face recognition through the use of ensemble learning and local feature descriptors. The proposed method trains a base learner for each person's face recognition ensemble system, based on feature vectors (SIFT, GMM, LBP) extracted from image regions surrounding specific facial landmarks. Three different classification models (SVM, Naive Bayes, GMM) are exclusively used as base learners, and the training procedure for each of these models is detailed. The proposed methodology includes a novel face pose descriptor referred to as the Face Angle Vector (FAV) which is utilized by a head pose classification model to determine the pose class of a face image. This model works together with a Base Learner Selection (BLS) block, to determine a set of facial landmarks to extract local feature descriptors, and uses them as the input to their corresponding base learners. Experimental results show a better performance over state-of-the-art methods using the CMU-PIE as the testing dataset, and face poses within ±90°. © 2013 IEEE.

关键词： Face recognition

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Frame aggregation aware cluster based association management for heterogeneous users

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Journal of Ambient Intelligence and Humanized Computing 2023年第3期14卷 2811-2826页

作者： Tewari, Babul P. Department of Computer Science and Engineering Indian Institute of Information Technology Bhagalpur813 210 India

In this paper, a cluster based association management for heterogeneous users has been addressed. The aim is to explore frame aggregation for the high throughput (HT) users such as 802.11n type, avoiding the drawback of mixed contention from the presence of the coexisting legacy users such as 802.11 g. It has been demonstrated that restricting an access point (AP) to serve only similar type of users may lead suboptimal performance both in terms of throughput and service coverage as well. Additionally, frame aggregation can not perform well under this common contending scenario. Therefore, a cluster based association management has been proposed which uses frame aggregation and suitably designs a user-AP association where both HT users and legacy users coexist and access services from a common AP. A mathematical formulation of the proposed approach has been developed followed by a greedy solution. Extensive simulations have been performed that establish the fact that the proposed approach judiciously associates the heterogeneous users to the selected target AP and outperforms the other existing approaches including some benchmark algorithms. The results also confirm the effective use of frame aggregation and novelty of the proposed approach in terms of throughput, fairness and delay overhead. © 2023, The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.

关键词： Wireless local area networks (WLAN)

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Advanced UAV - based leaf disease detection: Deep Radial Basis Function Networks with multidimensional mixed attention

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Multimedia Tools and Applications 2024年 1-29页

作者： Das, Bappaditya Raghuvanshi, C.S. Research Scholar Department of Computer Science and Engineering FET Rama University Kanpur India Professor Department of Computer Science and Engineering FET Rama University Kanpur India

Plant disease causes a highly significant impact on production due to its destructive characteristics. A variety of chemical techniques should be applied to agriculture at different stages of disease infestation to enable precise plant protection. The integration of unmanned aerial vehicles (UAVs) for obtaining high-resolution images has become necessary due to the growing demand for precision agriculture, making crop health monitoring more efficient. In this study, we developed an innovative approach that combines the Deep Radial Basis Function Network (DRBFN) with the Multidimensional Mixed Attention (MMA) method. Since DRBFNs use radial basis functions as effective feature extractors, the model can detect even the smallest changes in the composition, color, and texture of leaves. This capability is particularly helpful when dealing with a diversity of plant species and pathogens exhibiting different symptoms. By deliberately considering pertinent spatial and spectral attributes, MMA is applied to enhance the discriminatory abilities of the network. By integrating spatial and spectral attention, the network can efficiently acquire information from both local and global sources, thereby enhancing the overall resilience and precision of the detection system. Our proposed model leverages UAV-captured images to achieve a classification accuracy of 96.95% in identifying leaf diseases across two different plant species. The integration of mixed attention mechanisms improved feature representation, leading to a 47.63% reduction in Root Mean Square Error (RMSE) compared to traditional methods. Furthermore, our model demonstrates significant efficiency by processing 2000 images, making it suitable for real-time applications. These results underscore the potential of UAV-based systems combined with advanced deep learning techniques for precision agriculture, offering scalable solutions for early disease detection and management. © The Author(s), under exclusive licence to S

关键词： Deep learning

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A Genetic Algorithm-Based Optimized Transfer Learning Approach for Breast Cancer Diagnosis

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computer Modeling in engineering & sciences 2024年第12期141卷 2575-2608页

作者： Hussain AlSalman Taha Alfakih Mabrook Al-Rakhami Mohammad Mehedi Hassan Amerah Alabrah Department of Computer Science College of Computer and Information SciencesKing Saud UniversityRiyadh11543Saudi Arabia Department of Information Systems College of Computer and Information SciencesKing Saud UniversityRiyadh11543Saudi Arabia

Breast cancer diagnosis through mammography is a pivotal application within medical image-based diagnostics,integral for early detection and effective *** deep learning has significantly advanced the analysis of mammographic images,challenges such as low contrast,image noise,and the high dimensionality of features often degrade model *** these challenges,our study introduces a novel method integrating Genetic Algorithms(GA)with pre-trained Convolutional Neural Network(CNN)models to enhance feature selection and classification *** approach involves a systematic process:first,we employ widely-used CNN architectures(VGG16,VGG19,MobileNet,and DenseNet)to extract a broad range of features from the Medical Image Analysis Society(MIAS)mammography ***,a GA optimizes these features by selecting the most relevant and least redundant,aiming to overcome the typical pitfalls of high *** selected features are then utilized to train several classifiers,including Linear and Polynomial Support Vector Machines(SVMs),K-Nearest Neighbors,Decision Trees,and Random Forests,enabling a robust evaluation of the method’s effectiveness across varied learning *** extensive experimental evaluation demonstrates that the integration of MobileNet and GA significantly improves classification accuracy,from 83.33%to 89.58%,underscoring the method’s *** detailing these steps,we highlight the innovation of our approach which not only addresses key issues in breast cancer imaging analysis but also offers a scalable solution potentially applicable to other domains within medical imaging.

关键词： Deep learning convolution neural network(CNN) support vector machine(SVM) genetic algorithmic(GA) breast cancer an optimized smart diagnosis

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