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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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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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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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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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A Review on Alzheimer's Disease Detection using Machine Learning 2

A Review on Alzheimer's Disease Detection using Machine Lear...

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2nd International Conference on Augmented Intelligence and Sustainable Systems, ICAISS 2023

作者： Moorthy, Deepthi K Nagaraj, P. Subhashini, S.J. Mar Baselios College of Engineering and Technology Department of Computer Science and Engineering Nalanchira Trivandrum India Kalasalingam Academy of Research and Education Department of Computer Science and Engineering Krishnankoil Virudhunagar India Srm Madurai College of Engineering and Technology Department of Information Technology Sivagangai India

ISBN: (纸本)9798350325799

Alzheimer's disease is a progressive and irreversible brain disorder affecting millions worldwide. The timely detection of Alzheimer's Disease (AD) is essential for the effective management and treatment of the disease. Machine learning (ML) approaches have shown promising results in detecting AD at an early stage. This review article provides an overview of the recent developments in the application of ML techniques for AD detection. Also discusses the application of certain image processing, feature extraction, and ML techniques for detecting AD. The article covers various ML algorithms and their performance in detecting AD with different data sources, such as MRI, PET, etc. with their advantages and limitations. Overall, this research study demonstrates the potential of ML approaches in improving the accuracy and efficiency of Alzheimer's disease detection, which could lead to better patient outcomes and disease management. © 2023 IEEE.

关键词： Neurodegenerative diseases

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ASHA: Machine Learning-Based Polycystic Ovary Syndrome (PCOS) Detection and Prediction System 1

ASHA: Machine Learning-Based Polycystic Ovary Syndrome (PCOS...

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1st International Conference on AIML-Applications for engineering and technology, ICAET 2025

作者： Dixit, Jayati Rai, Anshuman Tandon, Pulak Pandey, Dhiraj JSS Academy of Technical Education Noida Information Technology Department Noida India JSS University Noida Computer Science and Engineering Department Noida India

ISBN: (纸本)9798350355611

About 17.5% of adults worldwide are infertile, which emphasizes the urgent need for cutting-edge reproductive healthcare solutions. Based on statistics from June 2023. According to the World Health Organization (WHO), 8-13% of females on earth who are in their reproductive age have PCOS, and about 70% of cases go undiagnosed. With the integration of PCOS detection, IVF success rate prediction, and a period tracking system, this work offers a comprehensive women's health application. In addition, a large percentage of women suffer from menstruation abnormalities, such as dysmenorrhea (38.1%) and irregular frequency (80.7%). This approach improves early illness detection and fertility planning by using data analysis and machine learning. The techniques used are Random Forest, K-Nearest Neighbor, Logistic Regression, Naive Bayes, Decision Tree and SVM;which provides us better accuracy than previous works. © 2025 IEEE.

关键词： Nearest neighbor search

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Refining Pancreatic Cancer Detection and Classification through a Hybrid Approach: Stacked Deep Learning Integrated with the Sparrow Search Algorithm for Medical Image Analysis 2

Refining Pancreatic Cancer Detection and Classification thro...

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

作者： Arunachalam, P. Devi, M. Kalpana Jose, Naduvathezhath Nessariose Vivekanandan, G. Sharma, Rinku Mahesh Yadav, Ganesh Kumar Department of Biomedical Engineering Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology MCA Department Sreenivasa Institute of Technology and Management Studies Chittoor Architect Denken Solutions Department of CSE Sri Sairam Institute of Technology Chennai Department of Computer Science and Engineering SVKM Institute of technology Dhule Department of Computer Science and Engineering JSS Academy of Technical Education

ISBN: (纸本)9798350383867

In the endeavour to improve the precision and efficiency of diagnosing and classifying pancreatic cancer, there is a new idea that fuses deep learning and the Sparrow Search Algorithm with a stacked structure. That is why we consider this approach as revolutionary in terms of utilizing medical image analysis for the diagnosis in the field of pancreatic oncology with more accurate results. Transforming using stacked deep learning allows the systemto recursively learn its own complex feature in medical images, which is a basis for the identification of cancer. SBA or Sparrow Search Algorithm has been developed based on this social sparrow model and is a separate optimization technique which helps in optimizing the method of classification in this case. The foremost aim of this particular paper is to incorporate these two strategies and use the endproduct to improve the diagnostics of pancreatic cancer and also theclassification of the same. The cyclic process is applied together with the stacked deep learning process as well as the Sparrow Search Algorithm to make the systems' continuous optimization for sensitivity and specificity in detecting pancreatic malignancies. It is well to remember that this is not just a pure technological innovationthat holds promise of changing the landscape of pancreatic diagnosisand therapy. This approach of diagnosis and treatment also suggests early diagnosis, develop individual management plan and likely to yield higher survival rates for such patients. In addition, it is also worth considering at the potential that has been provided in enhancement of all sorts of branches of medicine linked to image analysis, along with the potential that has been developed when the best of technologies is paired with the best of ideas in computation. In conclusion, this paper captures the notion of intentionality and exigency in medical procedures. This union of stacked deep learningwith the Sparrow Search Algorithm unveils a new epoch in the medic

关键词： Deep learning

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Fuzzy clustering for electric field characterization and its application to thunderstorm interpretability

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Digital Communications and Networks 2025年第2期11卷 299-307页

作者： Xu Yang Hongyan Xing Xinyuan Ji Wei Xu Witold Pedrycz School of Electronics and Information Engineering Nanjing University of Information Science and TechnologyNanjing 210044China Department of Electrical and Computer Engineering University of AlbertaEdmonton AB T6G 2R3Canada Systems Research Institute Polish Academy of Sciences00-901 WarsawPoland Department of Computer Engineering Faculty of Engineering and Natural SciencesIstinye University34010 IstanbulTurkey

Changes in the Atmospheric Electric Field Signal(AEFS)are highly correlated with weather changes,especially with thunderstorm ***,little attention has been paid to the ambiguous weather information implicit in AEFS *** this paper,a Fuzzy C-Means(FCM)clustering method is used for the first time to develop an innovative approach to characterize the weather attributes carried by ***,a time series dataset is created in the time domain using AEFS *** AEFS-based weather is evaluated according to the time-series Membership Degree(MD)changes obtained by inputting this dataset into the ***,thunderstorm intensities are reflected by the change in distance from a thunderstorm cloud point charge to an AEF ***,a matching relationship is established between the normalized distance and the thunderstorm dominant MD in the space ***,the rationality and reliability of the proposed method are verified by combining radar charts and expert *** results confirm that this method accurately characterizes the weather attributes and changes in the AEFS,and a negative distance-MD correlation is obtained for the first *** detection of thunderstorm activity by AEF from the perspective of fuzzy set technology provides a meaningful guidance for interpretable thunderstorms.

关键词： Atmospheric electric field(AEF) Thunderstorm Fuzzy C-means(FCM) Attribute

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