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2nd International Conference on Sustainable Computing and data Communication Systems, ICSCDS 2023

作者： Garg, Shalini Pooja Chaoudhary, Shailee Lohmor Dixit, Rinku Govil, Nikhil Design and Technology University Mit Art MH Pune India Galgotias University School of Cse Delhi - NCR Gr. Noida India New Delhi Institute of Management Department of Aiml - Data Science New Delhi India Gla University Department of Computer Engineering & Applications U.P. Mathura India

ISBN: (纸本)9781665491990

Music gives listeners a chance to relax, relieve fatigue and connect one culture with another. In such a situation, if the favorite song-music is heard one after another and according to the emotion, it gives a unique and wonderful experience to the listener. It is determined based on the emotion and interest, ratings provided by listeners and search patterns. In this research article, the proposed method presents Tamil songs compilation and recommendation system with the help of these factors. The behavior of this recommendation system is also analyzed in this system. The proposed system is developed in Java programming language by adopting the techniques of Machine Learning. Through this system, listeners have the choice to play category wise songs. As per the listening history and patterns, upcoming songs are predicted to the listeners. This system also provides ratings to each song. This developed application can also be used commercially as it has been validated that the ratings provided by the listeners are in line with IMDb ratings. © 2023 IEEE.

关键词： Recommender systems

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Revolutionizing IoT Network Security with Deep Learning-Anomaly Detection Model 4

Revolutionizing IoT Network Security with Deep Learning-Anom...

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4th International Conference on Electronics and Sustainable Communication Systems, ICESC 2023

作者： Vyas, Manish Vijayaganth, R. Chandhok, Jigyasha Srivastava, Aviral Arumugam, S. Tiwari, Mohit College Name & Address: Vikrant Institute of Technology and Management Computer Science Department Indore India Artificial Intelligence and Data Science M.Kumarasamy College of Engineering Thalavapalayam Karur639113 India Tula's Institute of Engineering and Management Naac Grade A+ Accredited Institute Computer Science & Engineering UttarakhandDhoolkot Dehradun India Amity University Rajasthan NH 11 C kant kalwar Jaipur300202 India Periyar Maniammai Institute of Science & Technology Periyar Nagar Vallam Computer Science and Applications Thanjavur613403 India Bharati Vidyapeeth's College of Engineering Department of Computer Science and Engineering A-4 Rohtak RoadPaschim Vihar Delhi India

ISBN: (纸本)9798350300093

The term 'Internet of Things' (IoT) is used to describe the collection of data and the connectivity of items to the web that requires little to no human interaction. The IoT is a network of interconnected devices that can collect and disseminate information. Increased security and privacy worries accompany the launch of new devices due to the proliferation of Internet connections and the development of cutting-edge technology like the IoT. These days, the IoT is used everywhere, but especially in logistics, manufacturing, and healthcare. While these emerging IoT applications greatly enhance the usefulness of smart objects, they also present new security risks. Because of this, adapting existing intrusion detection systems (IDS) for use with IoT networks is a topic of intense study. Many IDS experts have found success with machine learning (ML) and deep learning (DL) techniques. By combining deep extraction through the Convolutional autoencoder with deep learning to identify the best features, this work delivers an improved IDS that can be used for anomaly detection. Improves to the deep learning approach include an evaluation of hyperparameter effectiveness, a stage of feature pruning using an autoencoder neural network, and an examination of the sturdiness of the most effective deep neural networks for circumstances exaggerated by Gaussian noise over some of the features in question. Despite the noise, the results show that the formed IoT dataset is useful for anomaly detection with deep learning methods. © 2023 IEEE.

关键词： Internet of things

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A hybrid method to select morphometric features using tensor completion and F-score rank for gifted children identification

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science China(Technological sciences) 2021年第9期64卷 1863-1871页

作者： ZHANG Jin FENG Fan HAN TianYi DUAN Feng SUN Zhe CAIAFA Cesar F. SOLÉ-CASALS Jordi College of Computer Science Nankai UniversityTianjin 300350China Department of Artificial Intelligence Nankai UniversityTianjin 300350China Computational Engineering Applications Unit Head Office for Information Systems and CybersecurityRIKEN351-0198 SaitamaJapan Instituto Argentino de Radioastronomía-CCT La Plata CONICET/CIC-PBA/UNLP1894 V.ElisaArgentina Department of Psychiatry University of CambridgeCambridge CB20SZUnited Kingdom Data and Signal Processing Group University of Vic-Central University of Catalonia08500 VicCataloniaSpain

Gifted children are able to learn in a more advanced way than others, probably due to neurophysiological differences in the communication efficiency in neural pathways. Topological features contribute to understanding the correlation between the brain structure and intelligence. Despite decades of neuroscience research using MRI, methods based on brain region connectivity patterns are limited by MRI artifacts, which therefore leads to revisiting MRI morphometric features, with the aim of using them to directly identify gifted children instead of using brain connectivity. However, the small, high-dimensional morphometric feature dataset with outliers makes the task of finding good classification models challenging. To this end, a hybrid method is proposed that combines tensor completion and feature selection methods to handle outliers and then select the discriminative features. The proposed method can achieve a classification accuracy of 93.1%, higher than other existing algorithms, which is thus suitable for the small MRI datasets with outliers in supervised classification scenarios.

关键词： gifted children identification morphometric features tensor completion feature selection

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A hybridized parallel bats algorithm for combinatorial problem of traveling salesman

A hybridized parallel bats algorithm for combinatorial probl...

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作者： Nguyen, Trong-The Qiao, Yu Pan, Jeng-Shyang Chu, Shu-Chuan Chang, Kuo-Chi Xue, Xingsi Dao, Thi-Kien Intelligent Information Processing Research Center Fujian University of Technology Fujian Fuzhou China Fujian Provincial Key Laboratory of Big Data Mining and Applications Fujian University of Technology Fujian China College of Computer Science and Engineering Shandong University of Science and Technology Qingdao China Department of Information Technology University of Manage and Technology Haiphong Viet Nam

Metaheuristic algorithms have been applied widely for real-world problems in many fields, e.g., engineering, financial, healthcare. Bats algorithm (BA) is a recent metaheuristic algorithm with considering as a robust optimization method that can outperform existing algorithms. However, when dealing with complicated combinatorial problems such as traveling salesman problems (TSP), the BA can be fallen in a local optimum. This paper proposes a new hybridizing Parallel BA (HPBA) with a mutation in local-search to escape such its drawback scenario for TSP. A graph theory mutation method is used to embed for hybridizing BA with exploiting similarities among individuals. The proposed method is extensively evaluated in TSP with series instances of the benchmark from TSPLIB to test its performance. The compared experimental result with the previous method and the best-known solutions (B.K.S) in the literature shows that the proposed approach offers competitive results. © 2020 - IOS Press and the authors. All rights reserved.

关键词： Traveling salesman problem

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An Enhanced Traffic Incident Detection using Factor Analysis and Weighted Random Forest Algorithm 5

An Enhanced Traffic Incident Detection using Factor Analysis...

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5th International Conference on IoT Based Control Networks and Intelligent Systems, ICICNIS 2024

作者： Kanna, P. Rajesh Vanithamani, S. Karunakaran, P. Pandiaraja, P. Tamilarasi, N. Nithin, P. Bannari Amman Institute of Technology Department of Computer Science and Engineering Tamil Nadu Erode India M. Kumarasamy College of Engineering Department of Master of Computer Applications Tamil Nadu Karur India Nandha Engineering College Department of Artificial Intelligence and Data Science Tamil Nadu Erode India Vel Tech Rangarajan Dr. Sagunthala Department of Computer Science and Engineering R&D Institute of Science and Technology Tamil Nadu Chennai India Nehru Institute of Technology Department of Electronics and Communication Engineering Tamil Nadu Coimbatore India Bannari Amman Institute of Technology Department of Artificial Intelligence and Machine Learning Tamil Nadu Erode India

ISBN: (纸本)9798331518097

Efficient and precise traffic incident detection is essential to reduce casualties and property damage. To address the issue of unbalanced event data, this work offers a novel methodology known as FA-WRF (Factor Analysis and Weighted Random Forest). This approach combines dimensionality reduction through factor analysis with classification using weighted random forests, data preparation through the Synthetic Minority Over-Sampling Technique (SMOTE), and data preparation with SMOTE. The included feature of severity detection is highlighted, as are the performance metrics of the FA-WRF model using well-established metrics such as detection level rate, number of false count alarm rate, classification type rate, and area specification under the receiver operating characteristic curve (AUC). The superiority of the FA-WRF model is illustrated using real-world expressway traffic data represented by imbalanced incidents through thorough comparisons with various machine learning-based algorithms such as Support Vector Machine, k-nearest neighbours algorithms, Logistic Regression algorithms, and decision-making trees. In addition to advancing incident detection, this technique has encouraging prospects for enhancing traffic management procedures and well-informed decision-making procedures in transportation networks. © 2024 IEEE.

关键词： Dimensionality reduction

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applications of AI to Age-Related Macular Degeneration: A case study and a brief review

Applications of AI to Age-Related Macular Degeneration: A ca...

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2022 International Conference on computer Engineering and Artificial Intelligence, ICCEAI 2022

作者： Wang, Han Lung Chong, Kelvin Kam Li, Zefeng City University of Macau Faculty of Data Science Macau999078 China Zhuhai Institute of Advanced Technology Chinese Academy of Sciences Department of Artificial Intelligence and Big Data Applications Zhuhai519000 China School of Computer College of Beijing University of Technology Zhuhai Zhuhai519000 China Chinese University of Hong Kong Faculty of Medicine Department of Ophthalmology and Visual Sciences Hong Kong999077 Hong Kong

ISBN: (数字)9781665468039

ISBN: (纸本)9781665468039

Age-Related Macular Degeneration (AMD) is one of the major causes of blindness. The artificial intelligence (AI) based method is a novel solution computer-Aided diagnosis approach. This study implemented a brief review by utilizing a case study on AI-based AMD diagnosis studies. Challenges and solutions are discussed., which makes a reference value for future research. © 2022 IEEE.

关键词： computer aided diagnosis

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Research on recognition algorithm for gesture page turning based on wireless sensing

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Intelligent and Converged Networks 2023年第1期4卷 15-27页

作者： Lin Tang Sumin Wang Meng Zhou Yinfan Ding Chao Wang Shengbo Wang Zhen Sun Jie Wu Key Laboratory of Specialty Fiber Optics and Optical Access Networks Joint International Research Laboratory of Specialty Fiber Optics and Advanced CommunicationShanghai Institute for Advanced Communication and Data ScienceSchool of Communication and Information EngineeringShanghai UniversityShanghai 200444China School of Electronic Information Engineering Gannan University of Science and TechnologyGanzhou 341000China Affiliated High School of Peking University Beijing 100086China Peking University&National Engineering Laboratory for Big Data Analysis and Applications Beijing 100871China Department of Computer and Information Sciences Temple UniversityPhiladelphiaPA 19122-6096USA

When a human body moves within the coverage range of Wi-Fi signals,the reflected Wi-Fi signals by the various parts of the human body change the propagation path,so analysis of the channel state data can achieve the perception of the human *** extracting the Channel State Information(CSI)related to human motion from the Wi-Fi signals and analyzing it with the introduced machine learning classification algorithm,the human motion in the spatial environment can be *** the basis of this theory,this paper proposed an algorithm of human behavior recognition based on CSI wireless sensing to realize deviceless and over-the-air slide *** algorithm collects the environmental information containing upward or downward wave in a conference room scene,uses the local outlier factor detection algorithm to segment the actions,and then the time domain features are extracted to train Support Vector Machine(SVM)and eXtreme Gradient Boosting(XGBoost)classification *** experimental results show that the average accuracy of the XGBoost module sensing slide flipping can reach 94%,and the SVM module can reach 89%,so the module could be extended to the field of smart classroom and significantly improve speech efficiency.

关键词： Wi-Fi signal Channel State Information(CSI) wireless sensing human behavior recognition

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Comments on Yu et al’s Shared data Integrity Verification Protocol 4th

Comments on Yu et al’s Shared Data Integrity Verification P...

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4th Euro-China Conference on Intelligent data Analysis and applications, ECC 2017

作者： Wu, Tsu-Yang Lin, Yueshan Wang, King-Hang Chen, Chien-Ming Pan, Jeng-Shyang Fujian Provincial Key Lab of Big Data Mining and Applications Fujian University of Technology Fuzhou350118 China National Demonstration Center for Experimental Electronic Information and Electrical Technology Education Fujian University of Technology Fuzhou350118 China School of Computer Science and Technology Harbin Institute of Technology - Shenzhen Shenzhen518055 China Department of Computer Science and Engineering Hong Kong University of Science and Technology Clear Water Bay Kowloon Hong Kong

ISBN: (纸本)9783319685267

Recently, Yu et al. proposed a secure shared data integrity verification protocol called SDVIP 2 to ensure the integrity of outsourced file in the cloud. Unfortunately, we exploit the vulnerability of their protocol in this paper. We demonstrate an active adversary can modify the outsourced file such that the auditor is unable to detect in the auditing process. At the end of this paper, we also provide two suggestions to fix the proposed attack. © 2018, Springer International Publishing AG.

关键词： Cloud storage

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AgriLLM:Harnessing Transformers for Framer Queries

arXiv

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arXiv 2024年

作者： Didwania, Krish Seth, Pratinav Kasliwal, Aditya Agarwal, Amit Manipal Institute of Technology Manipal Academy of Higher Education Manipal India Department of Computer Science and Engineering India Department of Data Science & Computer Applications India Enterprise Analytics & Data Science Artificial Intelligence - Center of Excellence Wells Fargo International Solutions Private Limited Bangalore India

Agriculture, vital for global sustenance, necessitates innovative solutions due to a lack of organized domain experts, particularly in developing countries where many farmers are impoverished and cannot afford expert consulting. Initiatives like Farmers Helpline play a crucial role in such countries, yet challenges such as high operational costs persist. Automating query resolution can alleviate the burden on traditional call centers, providing farmers with immediate and contextually relevant information. The integration of Agriculture and Artificial Intelligence (AI) offers a transformative opportunity to empower farmers and bridge information gaps. Language models like transformers, the rising stars of AI, possess remarkable language understanding capabilities, making them ideal for addressing information gaps in agriculture. This work 1 explores and demonstrates the transformative potential of Large Language Models (LLMs) in automating query resolution for agricultural farmers, leveraging their expertise in deciphering natural language and understanding context. Using a subset of a vast dataset of real-world farmer queries collected in India, our study focuses on approximately 4 million queries from the state of Tamil Nadu, spanning various sectors, seasonal crops, and query types. Copyright © 2024, The Authors. All rights reserved.

关键词： Agriculture

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A Cutting Edge Deep Learning Models for Paddy Leaf Disease Detection and Classification

A Cutting Edge Deep Learning Models for Paddy Leaf Disease D...

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Artificial Intelligence For Internet of Things (AIIoT), International Conference on

作者： A. Dhanalakshmi K. Ponmozhi A. Ajmal Department of Computer Applications Kalasalingam Academy of Research and Education KrishnanKovil Tamil Nadu India Department of Data Science Vellore Institute of Technology(ICER) Bangalore Karanataka India

ISBN: (数字)9798350372120

ISBN: (纸本)9798350372137

The world’s main grain crop is rice (Oryza sativa). It provides the majority of the world’s population with nourishment and energy. Numerous rice leaf diseases are a major threat to the world’s agricultural output because they deteriorate crop quality in addition to lowering crop yield. Traditional methods of classifying and managing diseases mostly rely on laborious and subjective manual examination procedures. However, recent advancements in computer vision methods have made it possible to construct automated systems that can recognize illnesses from picture and video data. The paper emphasizes the potential application of cutting-edge models like Detectron2 to address issues with agricultural leaf disease detection. Together with other cutting-edge models, Detectron2 provides a promising means of enhancing the accuracy and effectiveness of disease diagnosis and control techniques. The use of automated disease detection systems holds the potential to reduce subjectivity in the evaluation of illnesses, optimize the allocation of resources in agriculture, and reduce dependence on human labour. These systems are able to continually learn from and adapt to changing disease patterns by incorporating complex algorithms and machine learning techniques, ensuring robust and stable agricultural operations.

关键词： Deep learning computer vision Adaptation models Computational modeling Scalability Sociology Agriculture

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