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

作者： Manjunatha, A.S. Venkatramana Bhat, P. Ramakrishna, M. Prabhu, Matti Nidhi Dept. of Computer and Communication Engineering NMAM Institute of Technology Nitte Karnataka Mangaluru India Dept. of Computer Science and Engineering NMAM Institute of Technology Nitte Karnataka Mangaluru India Manipal Academy of Higher Education Dept. of Data Science and Computer Applications Manipal Institute of Technology Manipal576104 India

ISBN: (纸本)9798331518097

Wireless Sensor Networks (WSNs) are highly implemented in harsh and dynamic environments. In such configurations, faults and anomalies easily occur, causing unwanted disruptions in data accuracy and network performance. In the context of reliability and stability in WSN operations, effective fault detection techniques are vital. This research work discusses how several machine learning models may be applied, including Logistic Regression, Decision Tree, Random Forest, and K-Nearest Neighbors in order to detect fault in WSNs. The performance evaluation was performed based on major criteria, which are accuracy, precision, recall, and Fl score. This, therefore, makes these approaches to machine learning effective in identifying faults efficiently, hence offering a well-balanced solution that is scalable and robust for integrity and operational effectiveness of WSN s. © 2024 IEEE.

关键词： Random forests

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A probabilistic public key encryption switching scheme for secure cloud storage

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International Journal of Information technology (Singapore) 2023年第2期15卷 675-690页

作者： Bhat, Radhakrishna Sunitha, N.R. Iyengar, S.S. Department of Computer Science and Engineering Manipal Institute of Technology Manipal Academy of Higher Education Karnataka Manipal 576104 India Department of Computer Science and Engineering Siddaganga Institute of Technology Karnataka Tumkur 572103 India School of Computing and Information Science Florida International University Miami FL United States

The high demand for user-centric applications such as secure cloud storage laid the foundation for the development of user-centric security schemes with multiple security features in recent years. But, the current state-of-art techniques primarily emphasized only one type of security feature i.e., either homomorphism or non-malleability. To fill this gap and provide a common platform for both homomorphic and non-malleable cloud applications, we have introduced a new public key-based probabilistic encryption switching method. The proposed scheme generates O(m+2 log N) bits of ciphertext where m∈ N and m< n, n∈ N is the plaintext size, N is the RSA composite. The superior performance of the proposed scheme has been tested in comparison to existing methods and is reported in this paper. © 2022, The Author(s).

关键词： Encryption Switching Homomorphic encryption Probabilistic encryption Public key cryptosystem Secure Cloud storage and retrieval

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MLP-Based Speech Emotion Recognition for Audio and Visual Features 1

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7th International Conference on Microelectronics and Telecommunication engineering, ICMETE 2023

作者： Kothai, G. Boora, Prabhas Bhanu Muzammil, S. Venkata Subhash, L. Naga Raju, B. KPR Institute of Engineering and Technology Tamil Nadu Uthupalayam Coimbatore India Department of Computer Science and Engineering Kalasalingam Academy of Research and Education Virudhunagar Krishnankoil India

ISBN: (数字)9789819995622

ISBN: (纸本)9789819995615

Due to its potential applications in domains involving psychology, social smart machines, and human–computer interaction, speech emotion recognition has become an important and growing topic field in the study. That article offers the service comparative analysis of strategies in artificial intelligence classifiers for speech emotion recognition, including MLP, decision tree, SVM, and random forest. Using visual analysis methods like wave plot and spectrogram analysis as well as audio feature extraction, we evaluate these classifiers. According to our observations, MLP achieves better performance than the other classifiers, obtaining an accuracy of 85% when identifying different emotions. Moreover, we illustrate how audio feature extraction and visual analysis help to improve emotion recognition efficiency. Our research has applications for creating speech emotion recognition algorithms that may be applied in real-life scenarios. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Decision trees

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Advanced Knee Osteoarthritis Detection and Severity Prediction 15

Advanced Knee Osteoarthritis Detection and Severity Predicti...

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15th International Conference on Advances in Computing, Control, and Telecommunication Technologies, ACT 2024

作者： Santosh, B. Rama Sai Reddy, N. Vinay Kumar Krishna, B. Jathin Vadlamudi, Venkata Naveen Jeyaraj, Jane Rubel Angelina Subhashini, S.J. Department of Computer Science and Engineering Kalasalingam Academy of Research and Education Tamilnadu Virudhnagar India Department of IT SRM Madurai College for Engineering and Technology Tamil Nadu India

ISBN: (纸本)9798331300579

Millions of individuals worldwide suffer from knee osteoarthritis (OA), a crippling joint ailment that is marked by joint stiffness, discomfort, and functional impairment. To make a decision severity, physical symptoms, medical history, and further joint screening exams including radiography, MRIs, and CT scans are frequently considered. Unfortunately, the old methods are quite subjective, which makes it difficult to identify early illness development. Worldwide, Osteoarthritis affects close to 240 million individuals. Arthritis's most widespread kind, particularly in older people, is knee OA. Doctors utilize the Kellgren and Lawrence (KL) measure in order to assess the level of severity of knee OA. visually using X-ray or MR imaging. For the intent of detecting and predicting the severity of Knee Osteoarthritis, we are putting forth a model that uses novel Deep Learning models, such as Inception and Exception. By utilizing the KL Grading Scale, we propose ahead a model that can determine the degree of knee osteoarthritis in people. © Grenze Scientific Society, 2024.

关键词： Diseases

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NLP using Wordnet Database for Monitor the Social Platform in Digital Forensic Investigation 14

NLP using Wordnet Database for Monitor the Social Platform i...

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14th International Conference on Advances in Computing, Control, and Telecommunication Technologies, ACT 2023

作者： Senthil, P. Alagumuthukrishnan, S. Kannan, K.S. CMR College of Engineering & Technology Department of Computer Science and Engineering Kandlakoya Medchal Road Hyderabad501401 India CMR Institute of Technology Department of Computer Science and Engineering Kandlakoya Medchal Road Hyderabad501401 India Kalasalingam Academy of Research and Education Department of Computer Science and Engineering Tamilnadu Krishnankoil626126 India

Cyberbullying has made people too vulnerable to hatred and abuse which makes cyberbullying an important subject of matter all over the world. We propose a framework that prevents the use from using abusive words and predict user crime probability in social application with the help of one of the machine learning technique Natural Language Processing (NLP). NLP plays an important role in analyzing the given words and find out the alternative meaning and provide which category these words belongs to with the help of the WordNet database. In the previous system, social application user information is processed and kept aside and required is action not taken. We proposed an application that solves that existing system issue and provides a solution by making a complaint on the user in the social application. The proposed system consists of three application social book, social admin, crime reporting. First social book is social media application that provides activities like adding friends posting status and etc. Second social admin has the control of the whole social media application admin has all details of the user and so on Third crime application contains two-part public and police this application acts as a compliant system to our application where public user can raise a complaint against a person in social application to take some action. © Grenze Scientific Society, 2023.

关键词： Crime

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Effect of temperature on heavy ion-induced single event transient on 16-nm FinFET inverter chains

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Chinese Physics B 2023年第4期32卷 504-510页

作者：蔡莉池雅庆叶兵刘郁竹贺泽王海滨孙乾孙瑞琪高帅胡培培闫晓宇李宗臻刘杰 Institute of Modern Physics Chinese Academy of SciencesLanzhou 730000China University of Chinese Academy of Sciences Beijing 100049China College of Computer Science National University of Defense TechnologyChangsha 410073China College of IoT Engineering Hohai UniversityChangzhou 213022China

The variations of single event transient(SET)pulse width of high-LET heavy ion irradiation in 16-nm-thick bulk silicon fin field-effect transistor(Fin FET)inverter chains with different driven strengths are measured at different ***-dimensional(3D)technology computer-aided design simulations are carried out to study the SET pulse width and saturation current varying with *** and simulation results indicate that the increase in temperature will enhance the parasitic bipolar effect of bulk Fin FET technology,resulting in the increase of SET pulse *** the other hand,the increase of inverter driven strength will change the layout topology,which has a complex influence on the SET temperature effects of Fin FET inverter *** experimental and simulation results show that the device with the strongest driven strength has the least dependence on temperature.

关键词： heavy ion single event effect single event transient Fin FET inverter chain

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A Dynamic and Secure Join Query Protocol for Multi-User Environment in Cloud Computing

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IEEE Transactions on Cloud Computing 2025年第2期13卷 512-525页

作者： Li, Hongjun He, Debiao Feng, Qi Yang, Xiaolin Luo, Qingcai Wuhan University Key Laboratory of Aerospace Information Security and Trusted Computing Ministry of Education School of Cyber Science and Engineering Wuhan430072 China Wuhan University School of Cyber Science and Engineering Wuhan430072 China Qilu University of Technology Shandong Academy of Sciences Key Laboratory of Computing Power Network & Information Security Ministry of Education Shandong Computer Science Center Jinan250014 China National Key Laboratory of Security Communication Chengdu610041 China Inspur Cloud Information Technology Company Ltd. Shandong Jinan250000 China Inspur Academy of Science and Technology Shandong Jinan250101 China

The development of cloud computing needs to continuously improve and perfect the privacy-preserving techniques for the user’s confidential data. Multi-user join query, as an important method of data sharing, allows multiple legitimate data users to perform join query over the data owner’s encrypted database. However, some existing join query protocols may face some challenges in the practical application, such as practicality, security, and efficiency. In this article, we put forward a dynamic and secure join query protocol in the multi-user environment. Compared with some existing protocols, the proposed protocol has the following advantages. On the one hand, we utilize the dynamic oblivious cross tags structure to realize an efficient join query with forward and backward security. On the other hand, we combine the randomizable distributed key-homomorphic pseudo-random functions with join query to support multiple data users, which can provide resilience against the single user’s key leakage and resist collusion attacks between the cloud server and a subset of data users. We formally define and prove the security of proposed protocol. In addition, we give a detailed analysis of computation and communication overheads to demonstrate the efficiency of proposed protocol. Finally, we carry out some experimental evaluations to further demonstrate the superiority of functionality and efficiency. © 2013 IEEE.

关键词： Structured Query Language

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Real-time Animal Identification and Alert System using IoT and Deep Learning 2

Real-time Animal Identification and Alert System using IoT a...

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2nd IEEE International Conference on Advances in Information technology, ICAIT 2024

作者： Manujakshi, B.C. Shashidhar, T.M. Ravikiran, H.N. Asha, R. Bilagi, Rajeev Karnataka Bengaluru India Department of Computer Science and Engineering Acharya Institute of Technology Karnataka Bengaluru India Department of Electronics and Communication Engineering Adichunchanagiri University Karnataka Mandya Nagamangala India Department of Electronics and Communication Engineering Vidya Vikas Institute of Engineering and Technology Karnataka Mysore India

ISBN: (纸本)9798350383867

The amount of traffic accidents that result in fatalities and injuries is one of the most urgent problems facing the world today. Many tragic incidents are caused by collisions between humans and animals. Worldwide, there are startlingly many automobile accidents' "one every few seconds. Our primary objective is to decrease the probability of traffic accidents caused by animals by identifying them and informing cars of their presence. We suggest using IoT technology to create a detection and warning system that can significantly lower animal-related incidents. The purpose of this warning system is to alert cars on the roadway as soon as a detection event takes place by means of a signal. Our solution is based on Internet of Things technology, with the Raspberry Pi3 Model B acting as the main component for car warning and animal identification. Real-time photographs and videos of animal movements are captured by the Pi camera, which is equipped with a Raspberry Pi, through the use of sensors to identify impediments. After that, it alerts any vehicles and pedestrians using the woodland road. Additionally, it marks particular distances at night with lights. In the end, the document' s primary goal is to safeguard numerous people from possible harm and to preserve animals from mishaps. In order to do this, the YOLO (You Only Look Once) algorithm was created. It processes approximately 45 frames per second and examines the complete picture in order to determine if objects are present or not. © 2024 IEEE.

关键词： Deep learning

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Fast Mesh Generation in Multi-layer Lattice Boltzmann Simulation with Moving Boundaries 1

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41st computer Graphics International Conference, CGI 2024

作者： He, Peng Wang, Liangjun Xu, Lei Zhang, Wu Zhu, Wenhao School of Computer Engineering and Science Shanghai University Shanghai200444 China Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Shenzhen518055 China School of Mechanics and Engineering Science Shanghai University Shanghai200072 China

ISBN: (数字)9783031820243

ISBN: (纸本)9783031820236

Large-scale computational fluid dynamics methods rely on efficient and accurate mesh generation algorithms, especially when handling complex geometries, as mesh generation in such cases can be time-consuming. This paper presents a new approach to mesh generation, integrating the multi-layer lattice Boltzmann method with moving boundaries. This technique minimizes the impact of machine errors on computational geometry, thereby improving the precision of geometric object recognition within the mesh. Furthermore, it significantly cuts down the time needed for mesh generation. Theoretical analyses and numerical experiments reveal that the time required for mesh generation in this study has been reduced by an order of magnitude, facilitating the rapid creation of multi-layer grid nodes numbering in the tens of millions within seconds. By combining moving grids with moving boundaries, this method verifies airflow around airfoils in various grid setups. Moreover, we have extended its application to three-dimensional space, effectively simulating submarine movements. In scenarios with moving boundaries, where mesh generation is necessary at each time step, this method particularly shines. It requires minimal additional computation time, which is a significant advantage. This makes it a practical solution for mesh generation in large-scale simulations. Such simulations often use the multi-layer lattice Boltzmann method and involve moving boundary flow. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Mesh generation

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Cryptocurrency Transaction Network Embedding From Static and Dynamic Perspectives: An Overview

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IEEE/CAA Journal of Automatica Sinica 2023年第5期10卷 1105-1121页

作者： Yue Zhou Xin Luo MengChu Zhou the School of Computer Science and Technology Chongqing University of Posts and TelecommunicationsChongqing 400065 the Chongqing Key Laboratory of Big Data and Intelligent Computing Chongqing Institute of Green and Intelligent TechnologyChinese Academy of SciencesChongqing 400714 the Chongqing School University of Chinese Academy of SciencesChongqing 400714China the College of Computer and Information Science Southwest UniversityChongqing 400715China the School of Information and Electronic Engineering Zhejiang Gongshang UniversityHangzhou 310018China the Helen and John C.Hartmann Department of Electrical and Computer Engineering New Jersey Institute of TechnologyNewark NJ 07102 USA

Cryptocurrency, as a typical application scene of blockchain, has attracted broad interests from both industrial and academic communities. With its rapid development, the cryptocurrency transaction network embedding(CTNE) has become a hot topic. It embeds transaction nodes into low-dimensional feature space while effectively maintaining a network structure,thereby discovering desired patterns demonstrating involved users' normal and abnormal behaviors. Based on a wide investigation into the state-of-the-art CTNE, this survey has made the following efforts: 1) categorizing recent progress of CTNE methods, 2) summarizing the publicly available cryptocurrency transaction network datasets, 3) evaluating several widely-adopted methods to show their performance in several typical evaluation protocols, and 4) discussing the future trends of CTNE. By doing so, it strives to provide a systematic and comprehensive overview of existing CTNE methods from static to dynamic perspectives,thereby promoting further research into this emerging and important field.

关键词： Big data analysis cryptocurrency transaction network embedding(CTNE) dynamic network network embedding network representation static network

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