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International Conference on Control Computing Communication & Materials (ICCCCM)

作者： Anil Singh Umesh Kumar Pandey Ankita Singh Harsh Bhatt Shahshank Mishra Vivek Kumar Mishra Dept. of Computer Science United Institute of Technology Dept. of Computer Science United College of Engineering & Research Computer Science and Engineering United Institute of Technology

ISBN: (数字)9798350371680

ISBN: (纸本)9798350371697

Coronary Artery Disease (CAD) is a significant global health issue that requires reliable and prompt prediction approaches. This research proposes an innovative approach to improvise CAD prediction by implementing different machine learning algorithms like Support Vector Machine (SVM), Logistic Regression, Random Forest and KNN [5]. To deal with the mixed types of data, FAMD is incorporated. It is used for dimensionality reduction purpose addressing the complexities of a mixed dataset comprising both categorical and numerical variables. We applied all these machine learning algorithms without reducing the dataset and evaluated the accuracy, precision, recall and f1-score of all the algorithms. After that, we reduced the dataset with the help of FAMD and than again applying all those included algorithms on the reduced dataset and calculated the accuracy, precision, recall and f1-score. All these included algorithms without the application of FAMD and with the application of FAMD undergo a comparative analysis to distinguish their performances in CAD prediction. With the help of the compaprative analysis, it is discovered that the results of the models are improved and better with the application of FAMD than without FAMD.

关键词： Support vector machines Dimensionality reduction Solid modeling Machine learning algorithms Accuracy Prediction algorithms Reliability Arteries Random forests Diseases

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Leveraging Machine Learning Algorithms to Optimize Diet Maintenance Based on User Food Intake and Fitness Data 5

Leveraging Machine Learning Algorithms to Optimize Diet Main...

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

作者： Clarinda, K. Deepa, N. Dhamotharan, R. Krishnan, R. Santhana Raj, J. Relin Fransis Anchitaalagammai, J.V. SCAD College of Engineering and Technology Department of Management Studies Cheranmahadevi India PSNA College of Engineering and Technology Department of Artificial Intelligence and Data Science Dindigul India Kalasalingam Academy of Research and Education Department of Computer Science and Information Technology Krishnankoil India SCAD College of Engineering and Technology Department of Electronics and Communication Engineering Cheranmahadevi India Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Department of Electronics and Communication Engineering Chennai India Velammal College of Engineering and Technology Department of Computer Science and Engineering Madurai India

ISBN: (纸本)9798331530013

The increasing prevalence of lifestyle-related health issues has necessitated the development of innovative approaches to diet management. This research introduces the Adaptive Eating Pattern Predictor (AEPP), a novel machine learning algorithm designed to optimize diet maintenance by analyzing user food intake and fitness data. The AEPP combines the predictive capabilities of Artificial Neural Networks (ANN) with the probabilistic modeling of Markov Chains, enabling accurate forecasting of future eating patterns based on historical dietary and activity data. A comprehensive simulation analysis was conducted to evaluate the performance of the AEPP against existing algorithms, including traditional recommendation systems and basic machine learning models. Various simulation metrics, such as accuracy, precision, recall, and F1 score, were employed to compare the effectiveness of the AEPP in providing personalized dietary recommendations. The results demonstrate that the AEPP significantly outperforms existing algorithms, highlighting its potential as a valuable tool for individuals seeking to maintain a balanced diet in alignment with their fitness goals. This study contributes to the field of nutritional science by showcasing the advantages of integrating advanced machine learning techniques in personal diet management. © 2024 IEEE.

关键词： Adversarial machine learning

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Deployment of neural networks for WLAN thumbprint indoor positioning system

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International Journal of Information technology (Singapore) 2025年 1-7页

作者： Kushwaha, Ajay Agarwal, Trapty Singh, Manpreet MP, Sunil Sharma, Amit Jain, Romil Bhuvaneswari, P. Computer Science and Engineering Rungta College of Engineering and Technology Chhatisgarh Bhilai India Maharishi School of Engineering & Technology Maharishi University of Information Technology Uttar Pradesh Lucknow India Centre of Research Impact and Outcome Chitkara University Punjab India Department of Electronics and Communication Engineering Faculty of Engineering and Technology Jain (Deemed-to-be University) Karnataka Bangalore India School of Computer Applications Lovely Professional University Punjab India Chitkara Centre for Research and Development Chitkara University Himachal Pradesh Chandigarh India Department of Electronics and Communication Engineering Vel Tech Rangarajan Dr Sagunthala R & D Institute of Science and Technology Chennai India

With the application and development of related technologies based on user location information, location-based services have become essential for daily lives. Despite this fact, Wireless Local area networks (WLAN’s) often face time complexities in streamlining and collection of data. Thus, the given paper introduces a deep-learning model based on convolutional neural network (CNN) to predict user’s indoor location from the received wireless signal strength fingerprint maps. The aim is to enhance WLAN indoor localization by streamlining the data collection process. The proposed indoor positioning model leverages a region of interest (ROI) extraction technique to simulate the wireless received signal strength intensity in WLAN’s. By incorporating this advanced technique, the model aims to improve the accuracy and efficiency of WLAN localization. To optimize and evaluate the model’s performance in real-world scenarios, experiments are conducted using simulated datasets obtained from the publicly available database such as UCI repository. The results show that the proposed model attains the highest validation accuracy (97.11%) and training accuracy (97.34%) as compared to the existing deep learning algorithms used for indoor localization. © Bharati Vidyapeeth's institute of computer Applications and Management 2024.

关键词： CNN Deep learning Indoor localization Region of interest (ROI) Signal strength WLAN

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SC-JBR22 based Method to Secure the Analyzed Data 14

SC-JBR22 based Method to Secure the Analyzed Data

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

作者： Jaichandran, R. Muthuselvan, S. Shree, Alok Gowda, Shreya N. Krishna, Thirpula Murali Department of Computer Science and Engineering Aarupadai Veedu Institute of Technology Vinayaka Mission’s Research Foundation Tamil Nadu Chennai India

The current global is very important is internet for each and every people for every work mandatory. With the help of internet every people used many social media, because social media is mandatory for people life. The people decided to do the any new work before analyzed that work in "social media". This analyzed "data not have good security" in the people systems. In this paper, proposed the new method SC-JBR22-01. This method has 3 stages;1st stage is to convert the matrix format from analyzed data;2nd stage is apply the Salsa operations in the matrix;and 3rd stage is apply the ChaCha operations in the *** new SC-JBR22-01 method has provide the good security and performance while compared to ChaCha method. © Grenze Scientific Society, 2023.

关键词： Cryptography

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Fake Social Media Profile Identification and Report Using Machine Learning

Fake Social Media Profile Identification and Report Using Ma...

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2024 International Conference on Signal Processing, Computation, Electronics, Power and Telecommunication, IConSCEPT 2024

作者： Chinnasamy, P. Ayyasamy, Ramesh Kumar Subramaniam, Suthashini Sangodiah, Anbuselvan Iftikhar, Aqsa Kiran, Ajmeera Kalsalingam Academy of Research and Education Department of Computer Science and Engineering School of Computing TamilNadu Srivilliputtur India Kampar Malaysia School of Computing Faculty of Computing and Engineering Quest International University Perak Ipoh30250 Malaysia MLR Institute of Technology Department of Computer Science and Engineering Hyderabad India

ISBN: (纸本)9798331540685

In the digital age, Online Social Networking platforms are integral to daily life, but they also attract malicious bots that mimic human behaviour and pose cybersecurity threats. This literature review focuses on recent Machine Learning-based techniques for detecting and classifying bots on major social media platforms. It covers supervised, semi-supervised, and unsupervised methods, dataset details, and feature categories. While the technology is advancing, an ongoing challenge lies in the creation of ever- evolving bots to evade detection. The study offers insights into the evolving landscape of cybersecurity and the need for continual research in this field, but potential drawbacks may include data privacy concerns, The pervasive influence of the internet and social media as platforms for real-time information dissemination and their potential to shape perceptions and lives. It highlights the concern that unverified and malicious content can significantly impact users' perceptions. The paper offers a taxonomy for classifying malicious information at various stages and presents technologies to combat the issue from content generation to containment. The results illustrate the efficacy of machine learning in improving the dependability and safety of social media platforms, offering a scalable approach to address the increasing problem of online deception. © 2024 IEEE.

关键词： Bot (Internet)

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Adaptive high dynamic range 3D measurement method based on speckle-assisted fringe projection

Adaptive high dynamic range 3D measurement method based on s...

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2024 International Conference on Automation Control, Algorithm, and Intelligent Bionics, ACAIB 2024

作者： Zhang, Xiaocai Zheng, Zhao Lei, Mingyang Shao, Long Fan, Jingfan Yang, Jian Beijing Engineering Research Center of Mixed Reality and Advanced Display Beijing100081 China School of Optics and Photonics Beijing Institute of Technology Beijing100081 China School of Computer Science and Technology Beijing Institute of Technology Beijing100081 China

ISBN: (纸本)9781510682597

Conventional phase-shift method can obtain the topography information of the object surface by projecting phase-shift fringe sequence and capturing deformed pictures. However, overexposure on shiny surfaces causes the loss of fringe information received by the camera, thus reducing the measurement accuracy and integrity. To solve this problem, an adaptive fringe projection method based on speckle-assisted fringes and mask fusion is proposed. Firstly, the optimal projection intensities are obtained by fusing multiple mask images. Then, the pixel-by-pixel mapping relationship between the camera and projector is established by using speckle-assisted phase shift method under unsaturated condition. Finally, the adaptive intensity fringe is generated and projected by the constraint of the mapping relationship. The 3D reconstruction is performed by the speckle-assisted phase shift method. Experimental results show that the RMS phase error of the proposed method is reduced by 94%, 33% and 12% compared with the contrast methods. The proposed method can accurately match the phase information of the overexposed surface and reconstruct objects with large surface fluctuations, thereby effectively solving the problem of 3D reconstruction of high reflective objects. © 2024 SPIE.

关键词： Cameras

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ToC-Net: Combining Channel Attention for Targeted Optimization of Stroke Segmentation Networks 1

ToC-Net: Combining Channel Attention for Targeted Optimizati...

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1st IEEE International Conference on Medical Artificial Intelligence, MedAI 2023

作者： Li, Yazhou Li, Yang Zhou, Yan Computer Science and Engineering Changchun University of Technology Changchun China Frontier Cross Research Institute Northeast Normal University Changchun China

ISBN: (纸本)9798350358780

Ischemic stroke is a common cerebrovascular disease, and rapid and accurate identification of stroke lesions in patients to assess risk and obtain the most timely and effective treatment can reduce long-term disability and mortality. Therefore, it is clinically important to study stroke segmentation methods. This paper propose a combining channel attention for targeted optimization of stroke segmentation networks (ToC-Net) to achieve accurate segmentation of 2D stroke lesions by addressing the problems of vague lesion contours and insignificant intra-class differences. U-Net++ is used as the baseline network, and the channel attention module with weights (WCA) and atrous spatial pyramid pooling module are added in the feature extraction part to unify the feature map size of each channel, highlight the lesion information in different scales, and then extract the global features. Finally, the network output is corrected by the target calibration (TC) module to obtain the final segmentation results. This paper compares with five state-of-the-art methods on the public dataset ISLES2022 and private dataset (AIS), and the results validate that ToC-Net can segment stroke lesion regions effectively and the segmentation results are better than the comparison network. The project website can be found at https://***/lucky-asia/ToC-Net. © 2023 IEEE.

关键词： Calibration

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An automatic isotropic/anisotropic hybrid grid generation technique for viscous flow simulations based on an artificial neural network

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Chinese Journal of Aeronautics 2022年第4期35卷 102-117页

作者： Peng LU Nianhua WANG Xinghua CHANG Laiping ZHANG Yadong WU School of Information Engineering Southwest University of Science and TechnologyMianyang 621010China School of Intelligent Manufacturing Engineering Chongqing University of Arts and ScienceChongqing 402160China Stake Key Laboratory of Aerodynamics China Aerodynamics Research and Development CenterMianyang 621000China Unmanned Systems Research Center National Innovation Institute of Defense TechnologyBeijing 100071China School of Computer Science and Technology Sichuan University of Science&EngineeringYibin 644005China

Based on the author’s previous research, a novel hybrid grid generation technique is developed by introducing an Artificial Neural Network(ANN) approach for realistic viscous flow simulations. An initial hybrid grid over a typical geometry with anisotropic quadrilaterals in the boundary layer and isotropic triangles in the off-body region is generated by the classical mesh generation method to train two ANNs on how to predict the advancing direction of the new point and to control the grid size. After inputting the initial discretized fronts, the ANN-based Advancing Layer Method(ALM) is adopted to generate the anisotropic quadrilaterals in boundary layers. When the high aspect ratio of the anisotropic grid reaches a specified value, the ANN-based Advancing Front Method(AFM) is adopted to generate isotropic triangles in the off-body computational *** initial isotropic triangles are smoothed to further improve the grid quality. Three typical cases are tested and compared with experimental data to validate the effectiveness of grids generated by the ANN-based hybrid grid generation method. The experimental results show that the two ANNs can predict the advancing direction and the grid size very well, and improve the adaptability of the isotropic/anisotropic hybrid grid generation for viscous flow simulations.

关键词： Advancing front method Advancing layer method Anisotropic quadrilateral grid generation Artificial neural network Isotropic triangular grid generation Machine learning

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Information-Bearing RIS Enabled NOMA System

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IEEE Transactions on Vehicular technology 2025年第6期74卷 9904-9909页

作者： Han, Shuai Sun, Zeyang Xu, Sai Xie, Dan Li, Cheng Harbin Institute of Technology School of Electronics and Information Engineering Heilongjiang Harbin150001 China Southeast University National Mobile Communications Research Laboratory Nanjing211111 China Memorial University Electrical and Computer Engineering Faculty of Engineering and Applied Science St. John'sA1B 3X5 Canada

In this correspondence, we investigate an information-bearing reconfigurable intelligent surface (RIS) assisted non-orthogonal multiple access (NOMA) system. In this system, the RIS is segmented into multiple sub-RIS units, each serving as a passive transmitter to deliver information to users via extended backscatter modulation under the NOMA scheme. We aim to maximize the system's weighted sum rate (WSR) by jointly optimizing the active beamformer at the power beacon (PB) and the RIS coefficient vectors, while adhering to constraints related to PB transmission power, RIS coefficients, successive interference cancellation (SIC) decoding rate, and rate fairness. Extensive simulation results verify the feasibility and superiority of the proposed scheme. © 2025 IEEE.

关键词： Beamforming

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ElephantGuardNet: A Comprehensive Solution for Elephant Conservation and Human-Elephant Conflict Reduction 4

ElephantGuardNet: A Comprehensive Solution for Elephant Cons...

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4th International Conference on Sentiment Analysis and Deep Learning, ICSADL 2025

作者： Naveen, S. Dhamotharan, R. Sahebzathi, S. Krishnan, R. Santhana Kumar, V. Vinoth Sundaravadivel, P. Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Department of Mechanical Engineering Chennai India Kalasalingam Academy of Research and Education Department of Computer Science and Information Technology Krishnankoil India Velammal College of Engineering and Technology Department of Computer Science and Engineering Madurai India Scad College of Engineering and Technology Department of Electronics and Communication Engineering Cheranmahadevi India Vel Tech Rangarajan Dr. Sagunthala R&d Institute of Science and Technology Department of Electronics and Communication Engineering Chennai India Saveetha Engineering College Department of Artificial Intelligence and Machine Learning Chennai India

ISBN: (纸本)9798331523923

The ElephantGuardNet system is an advanced technological framework designed to monitor and protect elephants while minimizing human-elephant conflict. The system leverages a combination of real-time data collection, AI-driven analysis, and IoT sensors to track elephant movements across vast territories. By integrating wildlife camera traps, drones, satellite imagery, and IoT sensors such as infrared motion detectors and seismic sensors, the system provides continuous monitoring of elephant activity, enabling proactive management of potential risks. A custom dataset, ElephantMovementTracker, compiled from diverse data sources, forms the foundation for the model's training. Preprocessing techniques such as data normalization, augmentation, and noise filtering enhance the dataset's quality and ensure its suitability for deep learning applications. EfficientNet B7 and YOLO are employed for image and real-time detection tasks, respectively, while 3D CNNs are used for video analytics, enabling accurate tracking of dynamic movement patterns. GRU-based models predict future elephant movements, helping assess the likelihood of conflict. XGBoost is utilized for classification tasks, evaluating the risk of human-elephant conflict. The system is deployed on AWS and incorporates NVIDIA Jetson Nano for real-time detection and automated alerting via SMS and email. IoT sensors and automated deterrent systems, including sound emitters and drones, further mitigate conflict risk. The system's effectiveness in reducing human-elephant conflicts and providing a scalable solution for elephant conservation demonstrates the potential of AI and IoT integration in wildlife protection efforts, offering a comprehensive approach to both conservation and human-elephant coexistence. © 2025 IEEE.

关键词： Drones

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