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Networking and Communications (ICNWC), International Conference on

作者： Jayabharathi Ramasamy E. Srividhya V. Vaidehi S. Vimaladevi N. Mohankumar S. Murugan Department of Computer Science and Engineering RMK College of Engineering and Technology Chennai Tamil Nadu India Department of Computer Science and Engineering Sathyabama Institute of Science and Technology Chennai Tamil Nadu India Department of Computer Science Vinayaka Mission's Kirupananda Variyar Arts and Science College Vinayaka Mission's Research Foundation (DU) Salem Tamil Nadu India Department of Commerce and Business Administration School of Management Veltech Rangarajan Dr.Sagunthala R&D Institute of Science and Technology Chennai Tamil Nadu India Department of Electronics and Communication Engineering B.V. Raju Institute of Technology Telangana India Department of Biomedical Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Chennai Tamil Nadu India

ISBN: (数字)9798350365269

ISBN: (纸本)9798350365276

Unmanned Aerial Vehicles (UAVs) gather data efficiently for power line inspection. Anomaly detection is essential for power infrastructure dependability and security. It proposes a Cloud-Enabled Isolation Forest (CEIF) method for UAV-based power line inspection. It improves the isolation forest algorithm's efficiency and scalability in cloud computing. It can process huge UAV inspection datasets by dispersing cloud computing. The technique, which effectively isolates anomalies, is applied to the cloud for fast power line inspection and anomaly identification. It describes the CEIF system's cloud service integration and distributed computing algorithm optimization. Real-world UAV-based power line inspection datasets show it can accurately detect abnormalities with low false-positive rates. It is scalable and robust for improving power infrastructure dependability and security. It allows cloud services to deploy real-world settings to implement different inspection scales.

关键词： Cloud computing Scalability Forestry Inspection Autonomous aerial vehicles Sensors Security

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Dynamic 3D UAV Placement Optimization: Improved Bonobo Optimizer for Enhanced Coverage and Communication 35

Dynamic 3D UAV Placement Optimization: Improved Bonobo Optim...

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35th IEEE International Symposium on Personal, Indoor and Mobile Radio Communications, PIMRC 2024

作者： Yahia, Selma Taleb, Sylia Mekhmoukh Loscri, Valeria Saadi, Amylia Ait Ho, Tu Dac Vo, Van Nhan Eldeeb, Hossien Muhaidat, Sami Inria Lille-Nord Europe Lille 59000 France University of m'Hamed Bougara List Laboratory Boumerdes35000 Algeria Esme Engineering School Ivry-sur-Seine94200 France Norwegian University of Science and Technology Department of Information Security and Communication Technology Norway The Arctic University of Norway Department of Electrical Engineering Norway Duy Tan University Faculty of Information Technology Viet Nam Duy Tan University The Institute of Research and Development Viet Nam Khalifa University 6G Research Center Computer and Communication Engineering Abu Dhabi United Arab Emirates

ISBN: (纸本)9798350362244

Unmanned aerial vehicles (UAVs) offer a promising solution for enhancing network coverage, reliability, and data speed in future wireless network generations. However, deploying UAVs as aerial base stations requires careful consideration of crucial design factors, including three-dimensional (3D) placement and performance optimization tailored to specific applications. In this paper, the 3D placement of multiple UAVs, acting as aerial base stations, is investigated in a dynamic user scenario. First, a closed-form expression for the coverage probability is derived. Then, to maximize the network coverage and sum rate while ensuring reliable and energy efficient system, a joint multi-objective optimization problem is formulated considering the real-time user movements. To solve the problem, an improved Chaos-based Bonobo Optimizer (CBO) scheme is proposed which combines chaotic maps with the Bonobo Optimizer (BO) algorithm. The obtained results demonstrate the superior performance of the proposed approach compared with different benchmark algorithms. The results reveal that the proposed CBO algorithm offers a minimum of 1 5 % and 9 0 ~ M b i t / s ~ i m p r o v e m e n t s ~ in coverage and sum rate, respectively. © 2024 IEEE.

关键词： Unmanned aerial vehicles (UAV)

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Machine learning assisted identification of homobilayer sliding ferroelectrics with large out-of-plane polarization and low sliding energy barriers

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Physical Review B 2025年第9期111卷 094106-094106页

作者： Xian Wang Yifan Li Ying Zhang Peng Wang Yi-Ming Zhao Jun Zhou Jie Yang Xuesen Wang Lei Shen Department of Physics National University of Singapore Singapore 117551 Singapore Department of Mechanical Engineering National University of Singapore Singapore 117575 Singapore Department of Electrical and Computer Engineering National University of Singapore Singapore 117576 Singapore National University of Singapore (Chongqing) Research Institute Chongqing 401123 China College of Electronic and Information Engineering Shandong University of Science and Technology Qingdao 266590 China Institute of Materials Research and Engineering (IMRE) Agency for Science Technology and Research (ASTAR6) Singapore 138634 Singapore Key Laboratory of Material Physics Ministry of Education School of Physics Zhengzhou University Zhengzhou 450001 China

Two-dimensional (2D) ferroelectric materials hold great promise for ultrahigh-density data storage and ultrafast memory applications in flexible or wearable electronic devices. Because of depolarizing field effects and high-energy barriers for switching, only a handful of 2D ferroelectrics have been reported experimentally, such as In2Se3 and CuInP2S6. Through high-throughput calculations we identified a catalog of 25 synthesizable sliding ferroelectrics with large out-of-plane polarization and low sliding energy barriers. The high-throughput process started with 6351 monolayer materials in 2dmatpedia, narrowed down to 79 monolayer semiconductors with honeycomb structures, leading to the construction of 474 homobilayers sliding ferroelectrics, and ultimately identifying 25 high-performance 2D sliding ferroelectrics. Using big-data analysis and machine learning, we also revealed strong correlations between polarization and key physical properties, especially a hidden factor of effective van der Waals radius, which has been overlooked in previous fewer-sample studies. Based on these mechanistic insights, we proposed machine learning descriptors for predicting sliding ferroelectric properties.

关键词： Van der Waals forces

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Subspace Gaussian Mixture Modeling for low-resource non-native Punjabi Language Speech Recognition

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Procedia computer science 2025年 259卷 408-415页

作者： Puneet Bawa Virender Kadyan Gunjan Chhabra Ashish Chopra Centre of Excellence for Speech and Multimodal Laboratory Chitkara University Institute of Engineering and Technology Chitkara University Punjab India Machine Intelligence Research Centre School of Computer Science UPES Energy Acres Bidholi Dehradun-248007 Uttarakhand India Department of CSE Graphic Era Hill University； Graphic Era Deemed to be University Dehradun Uttarakhand-248007 Department of Computer Science and Applications Seth Jai Parkash Mukand Lal Institute of Engineering and Technology Radaur Haryana

The advancement of non-native recognition of speech is becoming more significant as individual research interest in communicating with various languages has developed. However, because of the limited ability to include semantic level contextual signals for non-native language, the resilience and performance of Automatic Speech Recognition (ASR) systems have been degraded. The objectives of this research include the collection and pre-processing of speech data, the training of an acoustic model using Gaussian Mixture Models (GMMs), the development of a Punjabi-specific language model, and the creation of a comprehensive pronunciation dictionary. Similarly, the performance of proposed ASR system has been evaluated using relevant measures that consider the unique characteristics of excellent non-native Punjabi speakers’ speech. Furthermore, three three distinct kinds of language models using a combined subspace Gaussian Mixture Modeling and Hidden Markov Modeling (sGMM-HMM) technique has been employed with an objective of tackling the issue of shared-state parameters due to a limited dataset. Overall, the findings obtained on sGMM-HMM yielded a Word Error Rate (WER) of 16.41%, which appears respectable in contrast to the native language ASR system.

关键词： Non-Native Speech Recognition Acoustic Modeling Gaussian Mixture Modeling Punjabi Speech Recognition

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IoT Based Mobile App for Continuous Health Monitoring of the Person 4

IoT Based Mobile App for Continuous Health Monitoring of the...

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4th International Conference on Emerging research in Electronics, computer science and technology, ICERECT 2022

作者： Priyadarsini, Indira Tejaswini, B. Kumar, Ashok Manochitra, I.S. Chakrapani, I.S. Alaskar, Kamal SKR Government College Department of Botany Nagari India East Point College of Engineering and Technology Department of Information Science and Engineering Bengaluru India Banasthali Vidyapith Department of Computer Science Rajasthan India Kalasalingam Academy of Research & Education Department of Computer Science & Information Technology Srivilliputhur India PRR&VS Government College Department of Zoology Nellore India Bharati Vidyapeeth Institute of Management Department of Computer Application Kolhapur India

ISBN: (纸本)9781665456357

In the sphere of medicine, IOT is meant to keep people safe and healthy plays a crucial part in communicating with doctors and patients through the use of health monitoring equipment and lowering healthcare costs in the future years. The internet of things (IoT) is making the world a smarter and more efficient village by allowing a variety of sensors and smart gadgets to gather and analyse data for a variety of reasons. As a result of these smart things, the healthcare system is growing wiser. When basic health facilities lack comprehensive medical care infrastructure, emerging countries gain. However, there is currently no specialized architecture for smart health units that can allow for this gathering and transferring patient health information to headquarters hospitals where live patient assistance is offered. Here, a smart IoT -based healthcare system is proposed, which includes a smart medical kit linked to sensors and a server for frequent health tracking. This smart medical kit is associated with sensors to measure the health parameters like body temperature, blood pressure, and heart rate for the effective function of the body. The proposed idea can alert the patient and their relatives in case of any abnormalities in their health parameters and also get suggestions from the doctor without physical contact with the doctor. © 2022 IEEE.

关键词： Blood pressure

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Customized Billboard Advertisement through Age and Gender Identification

Customized Billboard Advertisement through Age and Gender Id...

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2024 International Conference on Distributed Systems, computer Networks and Cybersecurity, ICDSCNC 2024

作者： Hariharan, Shanmugasundaram Gogula, Sreenivasulu Kukreja, Vinay Sheeba, R. D. S Terese Perumal, V. Jose, P. Vardhaman College of Engineering Department of Artificial Intelligence and Data Science Hyderabad India Vardhaman College of Engineering Department of Computer Science and Engineering Hyderabad India Chitkara University Centre for Research Impact & Outcome Rajpura India Dmi Engineering College Department of Electronics and Communication Engineering Chennai India Vardhaman College of Engineering Dept. of Electrical and Electronics Engineering Hyderabad India Vel Tech Rangarajan Dr. Sagunthala R&d Institute of Science and Technology Department of Cse Chennai India

ISBN: (纸本)9798350375442

This work provides a novel and sustainable advertisement display system that uses trained models, such as CNN and SSD, to adapt to the age and gender preferences of bystanders. By integrating a webcam with the advertising screen, the system is able to identify the age and gender of passersby based on the faces that are detected and processed by trained models. Customers can personalize adverts to certain groups by accessing the classification results via a website, which is accessed and stored in real-time on a database server. The system has a motion sensor for energy economy and runs continuously, updating every two seconds. With 91.7% and 95.5% accuracy rates for age and gender classification, respectively, the technology guarantees accurate content delivery. It also has the capacity to show news and guidance in crowded places, thereby fostering societal awareness and knowledge. The work well analyzes the gender detection using conventional methods and reveled it significance. © 2024 IEEE.

关键词： advertisement display system age classification decision making deep learning gender recognition

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Driver Inattentiveness Detection Techniques for Intelligent Transportation Systems: A Review

Driver Inattentiveness Detection Techniques for Intelligent ...

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Delhi Section Conference (DELCON), IEEE

作者： Anwesha Patel Rishu Chhabra C. Rama Krishna Department of Computer Science and Engineering National Institute of Technical Teachers Training and Research (NITTTR) Chandigarh India Chitkara University Institute of Engineering and Technology Chitkara University Punjab India

Due to technical advancement, Intelligent Transportation System (ITS) aims to maximize driver safety and security. ITS helps us increase the safety and convenience of the overall transportation system. It aims to incorporate new technology into an already existing traditional transportation system to create a more efficient traffic system that both drivers and others in-charge of managing the traffic can use conveniently. ITS plays a crucial role in development of future smart cities. The core of any transportation system is its drivers. Multiple factors, including distracted driving while using a smartphone, driving while intoxicated, driving while talking on the phone, and many more, have dramatically increased the number of traffic accidents. Driver fatigue is another factor that negatively affects driving attention. Hence, detecting the driver's inattentiveness is an integral part of the ITS as it heavily ensures the safety of both drivers and passengers on the road. In this paper, we present a survey of various driver inattentiveness detection techniques using IoT, Machine Learning, or Deep Learning detection techniques. We initially require input before we can implement any of the detection strategies. Specific wearables, bio-signal sensors, cameras, and smartphone sensors, which include the magnetometer, gyroscope, GPS, and accelerometer, which are embedded into a smartphone, can be used to collect the required input. A comparative analysis has been carried out based on the benefits, drawbacks, and methods used in various techniques. Furthermore, new research directions for driver inattentiveness detection on the road have been discussed.

关键词： Surveys Smart cities Magnetometers Roads Wearable computers Safety Biosensors

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E-Learning Privacy in the Context of Machine Learning

E-Learning Privacy in the Context of Machine Learning

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Data science, Agents & Artificial Intelligence (ICDSAAI), International Conference on

作者： N. Kirubakaran Varadha Rajan S R. Parijatham Ramya Bharathi K P. Jegadeeshwari V. Vijayaraja Chennai Institue Of Technology Chennai India Department Of Computer Science And Business System Chennai Institue Of Technology Chennai India Madha Institute of Engineering and Technology Chennai S. A. Engineering College Chennai India Dr. MGR Educational and Research Institute (Deemed to be University) Chennai India RMK College of Engineering and Technology Chennai

ISBN: (数字)9798331537555

ISBN: (纸本)9798331537562

The present paper discusses the privacy issues associated with the usage of machine learning in e-learning systems. Although ML is a promising technology that increases personalization and engagement, it also poses significant security concerns in terms of gathering and using sensitive data. E-learning platforms are vulnerable to problems such as breaches, unauthorized access, and misutilization of personal information. The paper addresses two major concerns: recommendation engines and predictive performance systems, which emphasize the weaknesses and shortcomings in current data privacy and protection measures. In an attempt to address these challenges, the paper develops a privacy-preserving framework based on federated learning and data anonymization technologies. Together, these technologies work in the decentralization of data storage and reduction of exposure yet provide personalized experiences. There is a comparative study that will analyze whether such solutions could help improve data privacy and at the same time maintain functionality in ML. Finally, a balance between security and user experience is brought into discussion, but this time it puts emphasis on the need to have personalization without sacrificing privacy. The final result is a framework for privacy that supports the fulfillment of laws like GDPR, while in the meantime maintaining the best of the personalization that this brings with the ML approach so that an effective solution towards modern e-learning systems can be availed.

关键词： Training Data privacy Privacy Electronic learning Federated learning Data breach User experience Information filtering Protection Information integrity

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Micromagnetics Simulation of Highly Efficient Oscillation of Magnetization in a Ferromagnetic Local Areas via Spin Waves Induced by Spin-Orbit Torque

Micromagnetics Simulation of Highly Efficient Oscillation of...

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2023 IEEE International Magnetic Conference - Short Papers, INTERMAG Short Papers 2023

作者： Koda, Tetsunori Muroga, Sho Endo, Yasushi Oshima College National Institute of Technology Department of Electronic Mechanical Engineering Suo-Oshima742-2193 Japan Akita University Graduate School of Engineering Science Department of Mathematical Science and Electrical-Electronic-Computer Engineering Akita010-8502 Japan Department of Electrical Engineering Graduate School of Engineering Tohoku University Sendai980-8579 Japan Tohoku University Center for Spintronics Research Network Sendai980-8577 Japan Sendai980-8577 Japan

ISBN: (纸本)9798350338362

We study numerical simulation of highly efficient oscillation of magnetization in a ferromagnetic local region using the spin-orbit torque. It is found that in the presence of multiple SOT regions, the precession motion can be excited at a lower current density through the interaction. This interaction is caused by spin waves, and as a result, phase control between the precession motions is possible through the gap between the areas. Our new finding indicates that the interaction between the wave source and the spin wave is very complex in devices that utilize the interference effects of spin waves. © 2023 IEEE.

关键词： Spin waves

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Evaluating Segmentation Accuracy with Diverse Prompt Strategies in Medsam

Evaluating Segmentation Accuracy with Diverse Prompt Strateg...

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IEEE International Symposium on Biomedical Imaging

作者： Muhammad Nouman Ghada Khoriba Essam A. Rashed Graduate School of Information Science University of Hyogo Kobe Japan School of Information Technology and Computer Science Nile University Giza Egypt Advanced Medical Engineering Research Institute University of Hyogo Himeji Japan

ISBN: (数字)9798331520526

ISBN: (纸本)9798331520533

Segment Anything Model (SAM) shows robustness in prompt-based segmentation of natural images. However, adapting SAM to the medical domain as a foundation model, MedSAM, presents unique challenges arising from clinical settings. This study evaluates MedSAM's performance across five medical imaging modalities (MRI, CT, ultrasound, endoscopy, and fundus IR) using four different prompting strategies: standard bounding boxes (SBB), rotated bounding boxes (RBB), contour points (CP), and simulated human annotations (SHA). MedSAM's performance is assessed under precise and perturbed conditions for each prompt type to evaluate its segmentation capabilities comprehensively. We empirically evaluate MedSAM's abilities and adaptability by experimenting with zero-shot learning and fine-tuning approaches. The results indicate that performance varies markedly with prompt type, accuracy, and imaging modality. The study concludes that MedSAM is sensitive to prompt precision and anatomical complexity. Fine-tuning improves performance, but further refinements are necessary to make it robust in various clinical settings. This work highlights the need for prompt engineering and provides practical insights to enhance segmentation accuracy in SAM-like foundation models.

关键词： Image segmentation Ethics Accuracy Ultrasonic imaging Foundation models Magnetic resonance imaging Robustness Prompt engineering Standards Biomedical imaging

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