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5th International Conference on Sustainable Communication Networks and Application, ICSCNA 2024

作者： Thilakavathy, P. Manikandan, G. Deepa, R. Jayalakshmi, V. Surendran, R. Computer Science and Engineering Vels Institute of Science and Engineering Vels Institute of Science Technology and Advanced Studies Chennai India St. Joseph's College of Engineering Anna University Department of Artificial Intelligence and Machine Learning Chennai India School of Computing SRM institute of science and technology Department of Computing Technologies Chennai India SRM Institute of Science and Technology Department of Computer Science and Applications Ramapuram Chennai India Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Department of Computer Science and Engineering Chennai India

ISBN: (数字)9798331530013

ISBN: (纸本)9798331530013

With the growth of large-scale text datasets from reviews, social media, and other online sources, sentiment analysis is essential for understanding public opinion. Traditional models struggle to understand complicated linguistic sentiments and interdependencies. This research could improve sentiment classification in customer feedback analysis, market sentiment predictions, and social media monitoring. Addressed issues include ambiguous language, sentence context flipping, and the need for robust models that can manage massive datasets. Contextual Detection Text Analysis Using Deep Learning (CDTA-DL) improves contextual knowledge and emotion identification in this research. RNNs and CNNs are used in the CDTA-DL approach to extract spatial and sequential information from text. CDTA-DL helps large-scale sentiment analysis detect sarcasm, implicit emotions, and many expressions. In a comprehensive simulation investigation employing open-access sentiment analysis datasets, CDTA-DL surpassed standard deep learning models in accuracy, recall, and F1 scores. The recommended strategy may increase emotion recognition and contextual understanding, making it ideal for large-scale sentiment analysis in various domains. © 2024 IEEE.

关键词： Contrastive Learning

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SELECTING FEATURES BY UTILIZING INTUITIONISTIC FUZZY ENTROPY METHOD

Decision Making: Applications in Management and Engineering

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Decision Making: Applications in Management and engineering 2023年第1期6卷 111-133页

作者： Pandey, Kiran Mishra, Arunodaya Raj Rani, Pratibha Ali, Jabil Chakrabortty, Ripon Department of Computer Science and Engineering Technocrats Institute of Technology Madhya Pradesh Bhopal India Department of Mathematics Government College Raigaon Madhya Pradesh Satna India Department of Engineering Mathematics Koneru Lakshmaiah Education Foundation Andhra Pradesh Vaddeswaram India School of Engineering and Technology Department of Computer Science and Engineering Sharda University Uttar Pradesh Greater Noida India Capability Systems Centre School of Engineering and Information Technology UNSW Canberra Australia

Feature selection is the most significant pre-processing activity, which intends to reduce the data dimensionality for enhancing the machine learning process. The evaluation of feature selection must consider classification, performance, efficiency, stability, and many factors. Nowadays, uncertainty is commonly occurred in the feature selection process due to time limitations, imprecise information, and the subjectivity of human minds. Moreover, the theory of intuitionistic fuzzy set has been proven as an extremely valuable tool to tackle the uncertainty and ambiguity that arises in many practical situations. Thus, this study introduces a novel feature selection framework using intuitionistic fuzzy entropy. In this regard, new entropy for IFS is proposed first and then compared with some of the previously developed entropy measures. As entropy is a measure of uncertainty present in data (features), features with higher entropy values are filtered out, and the remaining features having lower entropy values have been used to classify the data. To verify the effectiveness of the proposed entropy-based feature selection, some experiments are done with ten standard benchmark datasets by employing a support vector machine, K-nearest neighbor, and Naïve Bias classifiers. The outcomes of the study validate that the proposed entropy-based filter feature selection is more feasible and impressive than existing filter-based feature selection methods. © 2023 by the authors.

关键词： Feature Selection

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Joint Task Offloading and Resource Allocation for Streaming Applications in Cooperative Mobile Edge Computing

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IEEE Transactions on Vehicular technology 2025年第6期74卷 9549-9564页

作者： Li, Xiang Fan, Rongfei Hu, Han Li, Xiangming Gong, Shimin Yang, Jian China Mobile Research Institute Beijing100053 China Beijing Institute of Technology School of Information and Electronics Beijing100081 China Beijing Institute of Technology School of Cyberspace Science and Technology Beijing100081 China Beijing Institute of Technology Zhuhai519088 China Shenzhen Campus of Sun Yat-sen University School of Intelligent Systems Engineering Shenzhen518000 China Guangdong Provincial Key Laboratory of Fire Science and Intelligent Emergency Technology China University of Science and Technology of China School of Information Science and Technology Hefei230027 China

Streaming applications like smart monitoring and real-time data processing are characterized by long data-collecting duration and delay stringent computation. Mobile edge computing can enable mobile devices to execute such applications more smoothly. However, achieving timely completion of streaming applications necessitates processing a flow of computation tasks in an assembly-line fashion, which requires an unprecedented system model and thus needs further study. This work addresses the above concern by investigating a scenario where multiple mobile devices run streaming tasks and offload them to a nearby BS for edge computing through a cooperative node. In this system, the duration of data collection, task offloading and edge computation together with multiuser offloading ratio and bandwidth allocation are jointly optimized to achieve low power consumption of the mobile devices and the cooperative node. The introduction of streaming tasks and cooperation mechanisms turns the task execution into multi-stage process and thus greatly exacerbate the complexity of overall solution. To this end, Dinkelbach method is first utilized for problem transformation. Subsequently, a hybrid approach of block coordinate descent (BCD) and Lagrangian multiplier method is employed to find local optimal solution when the BS has abundant computation capacity and difference of convex algorithm (DCA) is leveraged to attain convergent solution when the BS has finite computation capacity. Finally, numerical results are demonstrated to verify the effectiveness of the proposed methods and offer some insightful results about our proposed strategy. © 1967-2012 IEEE.

关键词： Mobile edge computing

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Underpinning of Resource-Constrained IoT Networks: Analysis and Countermeasures for Attacks and Vulnerabilities 14

Underpinning of Resource-Constrained IoT Networks: Analysis ...

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14th IEEE International Conference on Communication systems and Network Technologies, CSNT 2025

作者： Singh, Umeshwer Sood, Manu Bhadoria, Robin Singh Department of Computer Science Himachal Pradesh University Shimla India Department of Computer Science and Engineering National Institute of Technology Hamirpur Himachal Pradesh India

ISBN: (纸本)9798331531935

The Internet of Things integrate various electronics devices on common platform as objects over the Internet. IoT devices have limited capabilities, such as low power, less memory, and low computational processing. Besides these constraints, IoT deals with a large amount of sensitive and personal data, making the IoT networks highly prone to attacks and various vulnerabilities. This paper summarizes the different possible attacks and vulnerabilities on resource-constrained IoT networks and the security requirements compromised. It also presents available countermeasures for these possible attacks. Furthermore, the paper discusses the simulation-based parameters for results on detection of such vulnerabilities and attacks in IoT networks. The paper presents protocols for resource-constrained IoT networks and maps different attacks with existing protocols. Finally, the paper summarizes the vulnerabilities in the IoT specially designed for resource-constrained environments and, discusses the potential impact of these vulnerabilities, and outlines countermeasures to address the same. © 2025 IEEE.

关键词： HTTP

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Design and beam dynamics study of a magnet system for an 11 MeV superconducting isochronous cyclotron

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Nuclear science and Techniques 2025年第6期36卷 56-65页

作者： Pan-Pan Zheng Xiang-Hui Wang Zi-Feng He Yue Wu Zhi-Min Dai Wei-Shi Wan School of Physical Science and Technology ShanghaiTech UniversityShanghai 201210China Shanghai Institute of Applied Physics Chinese Academy of SciencesShanghai 201800China School of Computer Engineering Wefang UniversityWeifang 261061China

In recent years,due to the scarcity of domestic radioisotopes,the Chinese government has strongly supported the development of dedicated radioisotope production *** paper presents conceptual design simulations of an 11 MeV,50μA,H^(-) compact superconducting cyclotron for radioisotope *** paper focuses primarily on four aspects:magnet system design,central region configuration,beam dynamics analysis,and extraction system *** paper outlines the cyclotron's primary parameters and key steps in the development process.

关键词： Superconducting cyclotron magnet Beam dynamics Central region Stripping extraction

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An Experimental Study of Machine Learning Algorithms for Predicting Start-Up Success 6th

An Experimental Study of Machine Learning Algorithms for Pre...

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6th World Conference on Smart Trends in systems, Security and Sustainability, WS4 2022

作者： Mishra, Ajai Jat, Dharm Singh Mishra, Durgesh Kumar Computer Science Namibia University of Science and Technology Windhoek Namibia Computer Science and Engineering Sri Aurobindo Institute of Technology Indore India

ISBN: (纸本)9789811976599

In recent years, our data storage and processing abilities have been enhanced significantly. Machine learning (ML) has become able to capture and analyze huge data without human interference. In this context, the ML techniques will also be used for measuring, predicting, and recommending the consequences of start-up success. Therefore, first, we studied some essential contributions which employ the ML models for business success. Next, a system architecture has been demonstrated that will be able to predict the success of a start-up and also recommend the key insights. Further, we proposed and implemented a start-up success prediction model. In this modeling, we initially used four supervised learning algorithms, namely support vector machine (SVM), k-nearest neighbor (KNN), Naïve Bayes, and logistic regression. In addition, two unsupervised algorithms such as k-means and fuzzy c-means (FCM) are implemented. During experiments, we have found these models are able to provide only 58% of classification accuracy. Therefore, we have proposed to use convolutional neural network (CNN) for training and prediction. In this context, we have crafted three variants of CNN models for experimental study. According to obtained results, we found the first model provides only 48% classification accuracy, the second model provides (69%) accuracy, and the third model provides higher accuracy (74%). Thus, we found promising to work with the CNN and Crunchbase datasets. Therefore, in near future, we proposed to design a CNN model with the feature engineering processes. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Algorithm design Business data analytics Handholding of start-ups Machine learning Recommendation system Support system

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An Opposition-Based Learning-Based Search Mechanism for Flying Foxes Optimization Algorithm

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computers, Materials & Continua 2024年第6期79卷 5201-5223页

作者： Chen Zhang Liming Liu Yufei Yang Yu Sun Jiaxu Ning Yu Zhang Changsheng Zhang Ying Guo Software College Northeastern UniversityShenyang110169China School of Information Science and Engineering Shenyang Ligong UniversityShenyang110159China China Telecom Digital Intelligence Technology Co. Ltd.Beijing100035China College of Computer Science and Engineering Ningxia Institute of Science and TechnologyShizuishan753000China

The flying foxes optimization(FFO)algorithm,as a newly introduced metaheuristic algorithm,is inspired by the survival tactics of flying foxes in heat wave *** preferentially selects the best-performing *** tendency will cause the newly generated solution to remain closely tied to the candidate optimal in the search *** address this issue,the paper introduces an opposition-based learning-based search mechanism for FFO algorithm(IFFO).Firstly,this paper introduces niching techniques to improve the survival list method,which not only focuses on the adaptability of individuals but also considers the population’s crowding degree to enhance the global search ***,an initialization strategy of opposition-based learning is used to perturb the initial population and elevate its ***,to verify the superiority of the improved search mechanism,IFFO,FFO and the cutting-edge metaheuristic algorithms are compared and analyzed using a set of test *** results prove that compared with other algorithms,IFFO is characterized by its rapid convergence,precise results and robust stability.

关键词： Flying foxes optimization(FFO)algorithm opposition-based learning niching techniques swarm intelligence metaheuristics evolutionary algorithms

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Pre-trained SAM as data augmentation for image segmentation

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CAAI Transactions on Intelligence technology 2025年第1期10卷 268-282页

作者： Junjun Wu Yunbo Rao Shaoning Zeng Bob Zhang Yangtze Delta Region Institute(Huzhou) University of Electronic Science and Technology of ChinaHuzhouChina School of Information and Software Engineering University of Electronic Science and Technology of ChinaChengduChina Pattern Analysis and Machine Intelligence Research Group Department of Computer and Information ScienceUniversity of MacaoMacaoChina

Data augmentation plays an important role in training deep neural model by expanding the size and diversity of the ***,data augmentation mainly involved some simple transformations of ***,in order to increase the diversity and complexity of data,more advanced methods appeared and evolved to sophisticated generative ***,these methods required a mass of computation of training or *** this paper,a novel training-free method that utilises the Pre-Trained Segment Anything Model(SAM)model as a data augmentation tool(PTSAM-DA)is proposed to generate the augmented annotations for *** the need for training,it obtains prompt boxes from the original annotations and then feeds the boxes to the pre-trained SAM to generate diverse and improved *** this way,annotations are augmented more ingenious than simple manipulations without incurring huge computation for training a data augmentation *** comparative experiments on three datasets are conducted,including an in-house dataset,ADE20K and *** this in-house dataset,namely Agricultural Plot Segmentation Dataset,maximum improvements of 3.77%and 8.92%are gained in two mainstream metrics,mIoU and mAcc,***,large vision models like SAM are proven to be promising not only in image segmentation but also in data augmentation.

关键词： data augmentation image segmentation large model segment anything model

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Invasive weed optimization for multi-AGVs dispatching problem in a matrix manufacturing workshop

Invasive weed optimization for multi-AGVs dispatching proble...

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作者： Li, Zhong-Kai Sang, Hong-Yan Li, Jun-Qing Han, Yu-Yan Gao, Kai-Zhou Zheng, Zhi-Xin Liu, Li-li School of Computer Science Liaocheng University Liaocheng 252059 China Institute of Systems Engineering Macau University of Science and Technology Macau999078 China

This paper studies automatic guided vehicle dispatching problem (AGVDP) with time and capacity constraints. The purpose of this problem is to find a solution with low transportation cost. To reduce the transportation cost, punishment for violation of time and AGV cost have to be considered. A mathematical model is formulated according to practical problems. A new type of AGV is proposed to reduce the AGV cost. An effective discrete invasive weed optimization algorithm (DIWO) is proposed with some new strategies and methods to solve the problem. In the proposed algorithm, a nearest-neighbor-based heuristic is used to generate initial population with high quality. Two effective neighborhood operators are proposed to generate new solutions with high efficiency. Two theorems are proposed to reduce unnecessary calculations. A local search based on insertion is proposed to enhance the local search capability. A new strategy of delaying departure is proposed to reduce the punishment for violation of time. A heuristic method is proposed to balance the delayed departure time and vehicle carrying capacity. A heuristic method is proposed to reduce the computational time complexity of local search. A comprehensive simulation experiment based on the 110 instances from an actual production factory validates the effectiveness of the proposed algorithm. © 2023

关键词： Automatic guided vehicles

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Automated Skin Cancer Classification and Detection Using Convolutional Neural Networks and Dermoscopy Images 4

Automated Skin Cancer Classification and Detection Using Con...

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4th International Conference on Evolutionary Computing and Mobile Sustainable Networks, ICECMSN 2024

作者： Sara, Umme Das, Utpol Kanti Sikder, Juel Chowdhury, Sudipta Roy Dept. of Computer Science and Engineering National Institute of Textile Engineering and Research Savar Dhaka Bangladesh Dept. of Computer Science and Engineering Rangamati Science and Technology University Rangamati Sadar Rangamati4500 Bangladesh

The uncontrolled growth of skin cells in the epidermis producing the creation of a mass termed a tumor is a dangerous condition known as skin cancer. Current developments in deep learning artificial intelligence have greatly improved image-based diagnosis. In this study, we included a Skin Lesion Cancer feature extractor Convolutional Neural Network (SLC-CNN) model, which is used for both classification with the SVM classifier and segmentation with XGBoost for skin cancer. In our proposed system, a test image of skin cancer is taken and pre-processed for both classification and segmentation purposes. After applying pre-processing, the test image features are extracted using the SLC-CNN feature extractor, which features are used in SVM to classify the types of skin cancer (Benign and Malignant), and based on the classification result, a trained XGBoost model is called to segment the cancer region. We have tested our system using the dermoscopy image collection from the International Skin Imaging Collaboration (ISIC) and built it in Google Colab to best use the GPU. Our suggested approach has gained a segmentation accuracy of 95.25% and a classification accuracy of 99.6%. © 2025 The Authors. Published by Elsevier B.V.

关键词： Lung cancer

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