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3rd International Conference on Frontiers of Electronics, information and Computation Technologies, ICFEICT 2023

作者： Wang, Hui Ding, Di Li, Zhiwen Li, Shengchang Zeng, Shuai Huaxin Consulting Design Research Institute Group Customer Department Hangzhou China China Ordnance Industry Computer Application Technology Institute Internet and Information Technology Department Beijing China

ISBN: (纸本)9798350302356

The recommendation algorithms based on graph convolutional network can extract the association relationship between users and items by interaction data, but most of these algorithms ignore the general preferences in the review data of users and items. The recommendation algorithms based on convolutional neural network with review can learn user behaviors and item properties. In order to combine the advantages of two kinds of algorithm and make use of both review text data and user-item interaction data fully, we proposed a new model called Joint Graph Convolutional Network and Convolutional Neural Network for Recommendation (JGCR) in this paper. This model used the graph convolutional network to extract association relations, and used convolutional neural network to extract the general preferences from review text, and carried out feature fusion to improve the recommendation effect. This paper conducted comparative experiments on four datasets of amazon dataset to verify the effectiveness of the proposed model. The evaluation metric is mean square error. Experiments results demonstrate that JGCR significantly outperforms all baseline recommendation algorithms. The feature from jointing graph convolutional network and convolutional neural network can improve the recommendation effect. © 2023 IEEE.

关键词： Convolution

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Social Recommendation Algorithm Based on Fusion Graph Convolutional Neural Network and Gated Recurrent Unit Network 4

Social Recommendation Algorithm Based on Fusion Graph Convol...

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4th International Conference on computer, Big Data and Artificial Intelligence, ICCBD+AI 2023

作者： Ding, Di Wang, Hui Li, Zhiwen Huaxin Consulting Design & Research Institute Group Customer Department Hangzhou China China Ordnance Industry Computer Application Technology Institute Internet and Information Technology Department Beijing China

ISBN: (纸本)9798350373233

This paper proposes a fusion model named FGCN-GRU (Fusion Graph Convolutional Neural network and Gated Recurrent Unit network) to predict users' ratings on items. Firstly, FGCN-GRU constructs a new graph convolutional neural network by regarding users' ratings on items as relative ratings and introduces the GRU network by considering the fact that the user's interest in items will change over time. Then the improved graph convolutional neural network, GRU network and users' social network are used to model users, items, and social relations respectively. The experimental results on the datasets of Ciao and Epinions show that the recommendation performance of the proposed model is better. © 2023 IEEE.

关键词： Convolutional neural networks

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Light gradient boosting machine with optimized hyperparameters for identification of malicious access in IoT network

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Digital Communications and Networks 2023年第1期9卷 125-137页

作者： Debasmita Mishra Bighnaraj Naik Janmenjoy Nayak Alireza Souri Pandit Byomakesha Dash S.Vimal Department of Computer Application Veer Surendra Sai University of TechnologyBurlaSambalpur768018OdishaIndia Department of Computer Science Maharaja Sriram Chandra Bhanja Deo(MSCB)UniversityBaripada757003OdishaIndia Department of Software Engineering HaliçUniversity34394IstanbulTurkey Department of Information Technology Aditya Institute of Technology and Management(AITAM)Tekkali532201Andhra PradeshIndia Data Analytics Lab Department of Artificial Intelligence and Data ScienceRamco Institute of TechnologyNorth Venganallur VillageRajapalayam 626117 Virudhunagar DistrictTamilnaduIndia

In this paper,an advanced and optimized Light Gradient Boosting Machine(LGBM)technique is proposed to identify the intrusive activities in the Internet of Things(IoT)*** followings are the major contributions:i)An optimized LGBM model has been developed for the identification of malicious IoT activities in the IoT network;ii)An efficient evolutionary optimization approach has been adopted for finding the optimal set of hyper-parameters of LGBM for the projected ***,a Genetic Algorithm(GA)with k-way tournament selection and uniform crossover operation is used for efficient exploration of hyper-parameter search space;iii)Finally,the performance of the proposed model is evaluated using state-of-the-art ensemble learning and machine learning-based model to achieve overall generalized performance and *** outcomes reveal that the proposed approach is superior to other considered methods and proves to be a robust approach to intrusion detection in an IoT environment.

关键词： IoT security Ensemble method Light gradient boosting machine Machine learning Intrusion detection

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Analyzing the Performance of Machine Learning Algorithms for Real-Time Data Analysis Under Optimization 3

Analyzing the Performance of Machine Learning Algorithms for...

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3rd International Conference on Smart Generation Computing, Communication and Networking, SMART GENCON 2023

作者： Vijayalakshmi, V.J. Senthilkumar, K. Mirajkar, Riddhi R. Adlin Jebakumari, S. Goyal, Manish Kumar Sakhare, Nitin N. Karpagam Academy of Higher Education Department of Electrical and Electronics Engineering Coimbatore India Prince Shri Venkateshwara Padmavathy Engineering College Department of Electronics and Communication Engineering Chennai India Vishwakarma Institute of Information Technology Department of Information Technology Pune India Jain Department of Computer Science and Information Technology Karnataka Bangalore India Vivekananda Global University Department of Computer Science & Application Jaipur India Vishwakarma Institute of Information Technology Department of Computer Engineering Pune India

ISBN: (纸本)9798350319125

In current years, studies has shifted away from conventional statistical methods to include more sophisticated gadget studying algorithms including support vector machines, random forests, and deep getting to know for real-time statistics analysis. But, such algorithms have their very own precise challenges, particularly associated with optimization and performance. This studies paper seeks to discover the challenges posed by applying gadget studying algorithms to actual-time datasets and talk the tactics and strategies to optimize the algorithms for stepped forward performance. Using numerous optimization strategies inclusive of evolutionary algorithms, simulated annealing, and evolutionary techniques are introduced to beautify the performance of the machine mastering algorithms. Additionally, the usage of go-validation, hyper parameter tuning, and ensemble methods to enhance the accuracy and pace of the algorithms is investigated. This paper additionally presents an in depth evaluation of the results received from exceptional optimization strategies with regards to overall performance. In the end, potential areas for in addition studies are discussed in detail. © 2023 IEEE.

关键词： Evolutionary algorithms

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Comparative study of IoT-and AI-based computing disease detection approaches

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Data Science and Management 2025年第1期8卷 94-106页

作者： Wasiur Rhmann Jalaluddin Khan Ghufran Ahmad Khan Zubair Ashraf Babita Pandey Mohammad Ahmar Khan Ashraf Ali Amaan Ishrat Abdulrahman Abdullah Alghamdi Bilal Ahamad Mohammad Khaja Shaik Department of Computer Application Lovely Professional UniversityPhagwaraPunjab144411India Department of Computer Science and Engineering Koneru Lakshmaiah Education FoundationGunturAndhra Pradesh522502India School of Computing Science and Engineering VIT Bhopal UniversityKothrikalanSehore466114India Babasaheb Bhimrao Ambedkar University Lucknow226025India College of Commerce and Business Administration Dhofar UniversitySalalah211Oman Faculty of Computer Studies Arab Open University18211Bahrain Department of Computer Application Shri Ramswaroop Memorial UniversityLucknow225003India College of Computing and Information Technology Shaqra UniversityShaqra11961Saudi Arabia

The emergence of different computing methods such as cloud-,fog-,and edge-based Internet of Things(IoT)systems has provided the opportunity to develop intelligent systems for disease *** to other machine learning models,deep learning models have gained more attention from the research community,as they have shown better results with a large volume of data compared to shallow ***,no comprehensive survey has been conducted on integrated IoT-and computing-based systems that deploy deep learning for disease *** study evaluated different machine learning and deep learning algorithms and their hybrid and optimized algorithms for IoT-based disease detection,using the most recent papers on IoT-based disease detection systems that include computing approaches,such as cloud,edge,and *** analysis focused on an IoT deep learning architecture suitable for disease *** also recognizes the different factors that require the attention of researchers to develop better IoT disease detection *** study can be helpful to researchers interested in developing better IoT-based disease detection and prediction systems based on deep learning using hybrid algorithms.

关键词： Deep learning Internet of Things(IoT) Cloud computing Fog computing Edge computing

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An Integrated Image Classification Approach to Detect the Flood Prone Areas using Sentinel-1 Images 10

An Integrated Image Classification Approach to Detect the Fl...

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17th INDIACom;2023 10th International Conference on Computing for Sustainable Global Development, INDIACom 2023

作者： Siddique, Mohammed Ahmed, Tasneem Husain, Mohd Shahid Integral University Advanced Computing Research Lab Department of Computer Application Lucknow India Majan University College Faculty of Information Technology Muscat Oman CAS-Ibri University of Technology and Applied Sciences Department of Information Technology Muscat Oman

ISBN: (纸本)9789380544472

The untimely monsoon floods and the rise in the level of rivers in the north Indian state of Uttar Pradesh (UP) is a major challenge for coordinating relief and rescue operations. To address this challenge, the experts that are deploying a variety of techniques to evaluate the damages might get useful information from synthetic aperture radar (SAR) images which offer images in close to real-time. The Sentinel-1A satellite images from the European Space Agency provide C-band SAR images that have helped researchers achieve enhanced processes of flood detection and flood mapping in flood-prone areas. In regular cases, the Random Forest (RF) and K-Nearest Neighbor (KNN) supervised classification techniques provide higher accuracies than the unsupervised K-Means classification techniques. The integrated approach with the resultant accuracy provides long-reliable elucidation towards flood damages and assessment control. Thenceforth, the RF and KNN supervised classifications based outcomes were integrated with the outcomes of K-Means iterative clustering unsupervised classification producing an enhanced overall accuracy. The overall accuracy from the integrated approach for the city of Basti and Ayodhya is 97% and 93.8% which is higher than the accuracies derived from the supervised and unsupervised classifications. Further, these processed results when refined by time-series analysis over a number of years along with implementing change detection techniques would help in monitoring the flood situation and undertaking precautionary measures. © 2023 Bharati Vidyapeeth, New Delhi.

关键词： Synthetic aperture radar

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Efficient energy-preserving methods for the Schrödinger-Boussinesq equation

Efficient energy-preserving methods for the Schrödinger-Bou...

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作者： Yan, Jinliang Zheng, Lianghong Lu, Fuqiang Zhang, Qiongyue Fujian Key Laboratory of Big Data Application and Intellectualization for Tea Industry College of Mathematics and Computer Fujian354300 China Department of Information and Computer Technology No.1 Middle School of Nanping Fujian353000 China Changzhou Institute of Technology Changzhou213032 China

In this paper, we present two novel energy-preserving schemes for the Schrödinger-Boussinesq system based on the second-order Adams prediction-correction method. We first introduce supplementary variables to the initial model simultaneous its invariants to obtain a consistent and structural stable system. Then, we discretize the extended system by directly using the Fourier pseudo-spectral method in space and second-order Adams prediction-correction method in time to construct conservative schemes. Numerical results show that the proposed schemes can reach second-order accuracy and can conserve the discrete invariants very well. © 2022 John Wiley & Sons, Ltd.

关键词： Forecasting

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Machine Learning-Based Cybersecurity Optimization in Internet of Things-Enabled Autonomous Vehicles 1

Machine Learning-Based Cybersecurity Optimization in Interne...

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1st International Conference on Innovative Engineering Sciences and Technological Research, ICIESTR 2024

作者： Musa, Usman Ibrahim Musa, Aminu Ibrahim Galadima, Yakubu Ibrahim Kantunsung, Ramadan Osman Gupta, Sakshi Dua, Sakshi Lovely Professional University School of Computer Application Punjab India Esgt Benin University Department of Information Technology Cotonou Benin Al-Qalam University Department of Computer Science Katsina Nigeria University College of Engineering Osmania University Telangana India Amity University Amity Institute of Information Technology Uttar Pradesh Noida India

ISBN: (纸本)9798350348637

The swift proliferation of Internet of Things-Enabled Autonomous Vehicles, or IoT-AVs, caused serious apprehensions about potential security weaknesses. In response, we provide a unique solution that improves cybersecurity in IoT-AVs by applying machine learning (ML) approaches. Three critical areas are the focus of our research: intrusion detection optimization (IDOpt), network reliability and latency optimization (NetOpt), and user authentication optimization (AuthOpt). Through biometric approaches which include fingerprint recognition, we accomplish powerful user authentication with 99% accuracy by using convolutional neural networks (CNNs). For Network Reliability and Latency Optimization, the Random Forest Regressor algorithm minimizes downtime and latency issues, improving vehicle performance with 97% accuracy. Additionally, we use the K-Nearest Neighbors (KNN) technique in intrusion detection optimization to protect IoT-AVs against harmful assaults. We use this ML model and obtain a 99% accuracy rate, allowing us to quickly identify and stop intrusions. The entire security posture of IoT-AVs is strengthened greatly by this optimization. To validate the effectiveness of our proposed approach, we compare our results with existing models and algorithms. The outcomes demonstrate the superiority of our ML-based optimizations over the previous approaches. Our integrated methodology establishes new benchmarks for security enhancement in IoT-AVs. © 2024 IEEE.

关键词： Adversarial machine learning

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MiniNet: Dense squeeze with depthwise separable convolutions for image classification in resource-constrained autonomous systems

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ISA Transactions 2023年 132卷 120-130页

作者： Tseng, Fan-Hsun Yeh, Kuo-Hui Kao, Fan-Yi Chen, Chi-Yuan Department of Computer Science and Information Engineering National Cheng Kung University Tainan Taiwan Department of Information Management National Dong Hwa University Hualien Taiwan Computer Science and Engineering Department of National Sun Yat-sen University Kaohsiung804 Taiwan Department of Technology Application and Human Resource Development National Taiwan Normal University Taipei Taiwan Department of Computer Science and Information Engineering National Ilan University Yilan Taiwan

In recent years, artificial intelligence (AI) has been developed vigorously, and a great number of AI autonomous applications have been proposed. However, how to decrease computations and shorten training time with high accuracy under the limited hardware resource is a vital issue. In this paper, on the basis of MobileNet architecture, the dense squeeze with depthwise separable convolutions model is proposed, viz. MiniNet. MiniNet utilizes depthwise and pointwise convolutions, and is composed of the dense connection technique and the Squeeze-and-Excitation operations. The proposed MiniNet model is implemented and experimented with Keras. In experimental results, MiniNet is compared with three existing models, i.e., DenseNet, MobileNet, and SE-Inception-Resnet-v1. To validate that the proposed MiniNet model is provided with less computation and shorter training time, two types as well as large and small datasets are used. The experimental results showed that the proposed MiniNet model significantly reduces the number of parameters and shortens training time efficiently. MiniNet is superior to other models in terms of the lowest parameters, shortest training time, and highest accuracy when the dataset is small, especially. © 2022 ISA

关键词： Convolution

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Investigating Domain Adaptation for Medical Image Classification 5th

Investigating Domain Adaptation for Medical Image Classifica...

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5th International Conference on Data Science, Machine Learning and applications, ICDSMLA 2023

作者： Agarwal, Ankita Seth, Jyoti Sharma, Ambrish Kumar Karthikeyan, M.P. Maharishi School of Engineering and Technology Maharishi University of Information Technology Uttar Pradesh Lucknow India Department of Computer Science and Engineering Vivekananda Global University Jaipur India Master of Computer Application Noida Institute of Engineering and Technology Uttar Pradesh Greater Noida India Karnataka Bangalore India

ISBN: (纸本)9789819780426

Area version goals bridge the space between supply and target domain names by remodeling statistics from the source domain to the target area without providing categorized goal facts. Medical image domain version is a critical trouble in scientific imaging obligations, consisting of most cancers prognosis, because it lets in for the switch of understanding throughout particular medical imaging modalities, obligations, and affected person cohorts. It can help address record distribution shifts and make undertaking medical photo classes more dependable and accurate. In this examination, we look into several area model techniques and benchmark their performance in the venture of the medical photo category. We examine supervised and unsupervised methods, as well as deep studying-based methods. The results of our experiments offer insights into the performance of numerous area model techniques for clinical picture class that could assist in deciding the pleasant method to use in any given medical imaging task. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Medical imaging

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