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5th International Conference on Mobile Computing and Sustainable Informatics, ICMCSI 2024

作者： Shankar, K. Akash, S. Akash Sarvesh, M. Gokulakrishnan, K.J. Easwari Engineering College Computer Science and Engineering Chennai India

ISBN: (纸本)9798350395235

Terrain regions pose unique challenges, from accidents at hazardous hairpin bends to wildlife encounters, floods in hill areas, and road obstacles like rockfalls and treefalls. Addressing these challenges requires innovative solutions. This research paper explores the integration of advanced detection technologies to enhance terrain safety and resilience. We present a unified system architecture that combines CCTV cameras, deep learning, and object detection techniques, including YOLOv8 for real-time hazard monitoring. Our study provides a comprehensive overview of each detection module, including accident detection, wildlife monitoring, flood detection, and road obstacle detection. We detail the deep learning algorithms and custom datasets employed in training these modules. The system's multithreading controller ensures concurrent execution for real-time analysis of video feeds, allowing for swift response to potential hazards. Moreover, an alert system is seamlessly integrated, promptly notifying authorities upon detection of hazards. Furthermore, we discuss the benefits and advantages of this integrated approach, emphasizing proactive hazard mitigation, precise resource allocation, ecosystem conservation, and enhanced infrastructure resilience. This study addresses the challenges and limitations while highlighting future research opportunities in terrain safety and resilience. In conclusion, this research study underscores the transformative potential of integrating advanced detection technologies for terrain safety. Moreover, this approach not only mitigate the hazards but also empowers communities, fosters environmental stewardship, and enhances overall safety and resilience in challenging terrain regions. © 2024 IEEE.

关键词： Object detection

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An IoT Framework for Effective Railway Looping and Obstacles Detection 7

An IoT Framework for Effective Railway Looping and Obstacles...

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7th International Conference on Inventive Computation Technologies, ICICT 2024

作者： Sanjana, D.N. Soumya, V. Sneha, L. Shama, G. Pooja, M.R. Vidyavardhaka College of Engineering Computer Science And Engineering Mysuru India

ISBN: (数字)9798350359299

ISBN: (纸本)9798350359299

Current railway infrastructure faces the critical challenge of preventing looping incidents, where trains unintentionally re-enter previously traversed tracks. To address this, we propose a robust and comprehensive IoT framework for real-time looping detection and intervention. The proposed framework integrates a network of strategically positioned sensors, a centralized data processing unit, intelligent actuators, and a comprehensive alert system. Sensors such as weight sensors or acoustic detectors capture precise train data, which is then securely transmitted and analyzed for abnormal patterns indicative of looping. Upon detection, actuators like signal control systems and track switches are triggered to prevent further incidents. Simultaneously, alert systems notify relevant personnel to ensure swift response. By prioritizing redundancy, compliance, and user-friendly interfaces, this framework guarantees long-term reliability and operational efficiency. Through thorough testing and training, we pave the way for the effective implementation of this IoT solution, ultimately enhancing railway safety and preventing catastrophic looping incidents. © 2024 IEEE.

关键词： Redundancy

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A Novel Approach to Energy Optimization:Efficient Path Selection in Wireless Sensor Networks with Hybrid ANN

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computers, Materials & Continua 2024年第5期79卷 2945-2970页

作者： Muhammad Salman Qamar Ihsan ulHaq Amil Daraz Atif MAlamri Salman A.AlQahtani Muhammad Fahad Munir Department of Electrical and Computer Engineering International Islamic UniversityIslamabad44000Pakistan School of Information Science and Engineering NingboTech UniversityNingbo315100China Software Engineering Department College of Computer and Information SciencesKing Saud UniversityRiyadh11495Saudi Arabia Computer Engineering Department College of Computer and Information SciencesKing Saud UniversityRiyadh11495Saudi Arabia

In pursuit of enhancing the Wireless Sensor Networks(WSNs)energy efficiency and operational lifespan,this paper delves into the domain of energy-efficient routing ***,the limited energy resources of Sensor Nodes(SNs)are a big challenge for ensuring their efficient and reliable *** data gathering involves the utilization of a mobile sink(MS)to mitigate the energy consumption problem through periodic network *** mobile sink(MS)strategy minimizes energy consumption and latency by visiting the fewest nodes or predetermined locations called rendezvous points(RPs)instead of all cluster heads(CHs).CHs subsequently transmit packets to neighboring *** unique determination of this study is the shortest path to reach *** the mobile sink(MS)concept has emerged as a promising solution to the energy consumption problem in WSNs,caused by multi-hop data collection with static *** this study,we proposed two novel hybrid algorithms,namely“ Reduced k-means based on Artificial Neural Network”(RkM-ANN)and“Delay Bound Reduced kmeans with ANN”(DBRkM-ANN)for designing a fast,efficient,and most proficient MS path depending upon rendezvous points(RPs).The first algorithm optimizes the MS’s latency,while the second considers the designing of delay-bound paths,also defined as the number of paths with delay over bound for the *** methods use a weight function and k-means clustering to choose RPs in a way that maximizes efficiency and guarantees network-wide *** addition,a method of using MS scheduling for efficient data collection is *** simulations and comparisons to several existing algorithms have shown the effectiveness of the suggested methodologies over a wide range of performance indicators.

关键词： Wireless Sensor Networks(WSNs) mobile sink(MS) rendezvous point(RP) machine learning Artificial Neural Networks(ANNs)

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Denoising graph neural network based on zero-shot learning for Gibbs phenomenon in high-order DG applications

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Chinese Journal of Aeronautics 2025年第3期38卷 234-248页

作者： Wei AN Jiawen LIU Wenxuan OUYANG Haoyu RU Xuejun LIU Hongqiang LYU College of Aerospace Engineering Nanjing University of Aeronautics and AstronauticsNanjing 211106China College of Computer Science and Technology Nanjing University of Aeronautics and AstronauticsNanjing 211106China

With the availability of high-performance computing technology and the development of advanced numerical simulation methods, Computational Fluid Dynamics (CFD) is becoming more and more practical and efficient in engineering. As one of the high-precision representative algorithms, the high-order Discontinuous Galerkin Method (DGM) has not only attracted widespread attention from scholars in the CFD research community, but also received strong development. However, when DGM is extended to high-speed aerodynamic flow field calculations, non-physical numerical Gibbs oscillations near shock waves often significantly affect the numerical accuracy and even cause calculation failure. Data driven approaches based on machine learning techniques can be used to learn the characteristics of Gibbs noise, which motivates us to use it in high-speed DG applications. To achieve this goal, labeled data need to be generated in order to train the machine learning models. This paper proposes a new method for denoising modeling of Gibbs phenomenon using a machine learning technique, the zero-shot learning strategy, to eliminate acquiring large amounts of CFD data. The model adopts a graph convolutional network combined with graph attention mechanism to learn the denoising paradigm from synthetic Gibbs noise data and generalize to DGM numerical simulation data. Numerical simulation results show that the Gibbs denoising model proposed in this paper can suppress the numerical oscillation near shock waves in the high-order DGM. Our work automates the extension of DGM to high-speed aerodynamic flow field calculations with higher generalization and lower cost.

关键词： Computational fluid dynamics High-order discon tinuous Galerkin method Gibbs phenomenon Graph neural networks Zero-shot learning

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Advancing Safety and Efficiency in Human-Robot Interaction 2

Advancing Safety and Efficiency in Human-Robot Interaction

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2nd International Conference on Distributed Computing and Optimization Techniques, ICDCOT 2024

作者： Kurian, Smitha Faiz, Mohammed Mohammed Mustafa, C. Zubair, Mohammed Ahamed, Sagheer Hkbk College of Engineering Computer Science and Engineering Bengaluru India

ISBN: (纸本)9798350382952

The modern-day studies in video surveillance, fireplace detection, face reputation, crowd detection, augmented reality-based totally navigation, and artificial intelligence programs. In video surveillance, deep mastering-based totally anomaly detection algorithms, consisting of convolution and recurrent neural networks, revolutionize performance in item segmentation and tracking. Fire detection studies introduces algorithms combining static and dynamic functions, making use of deep studying for precise scene identification. Face reputation advancements include algorithms primarily based on invariant features, rework domain strategies, and the implementation of YOLO.V3 for actual-time popularity. Crowd detection studies unveils algorithms using pre-educated 2D convolution neural networks and hybridized YOLOv4 fashions for real-time monitoring. Augmented fact-based totally navigation leverages ORB-SLAM algorithms for indoor placement. Lastly, the paper introduces the Network-in-the-Loop (N-HiL) framework, incorporating algorithms to evaluate wireless conversation effects on robotic systems. This summary succinctly offers key algorithms and studies findings, presenting insights into the trendy technology in protection and surveillance. © 2024 IEEE.

关键词： Internet of things

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Smart Helmet for Riders to Avoid Accidents Using IoT 2

Smart Helmet for Riders to Avoid Accidents Using IoT

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2nd International Conference on computer, Communication and Control, IC4 2024

作者： Bhagya Lakshmi, A. Ragunath, C. Yeswanth, Raja Rajkumar, M. Easwari Engineering College Computer Science and Engineering Chennai India

ISBN: (纸本)9798350387933

MotorBike accidents are rampant due to the negligence of helmet use, resulting in injuries and fatalities. To address this, a solution emerges in the form of smart helmets with integrated control systems. This project aims to enhance road safety for motorcyclists by preventing the bike from starting unless the rider wears the helmet. The innovation comprises an RF receiving system and RF transmitter. With the persistent surge in motorcycle accidents, the imperative to prioritize safety has led to this technological intervention, potentially saving countless lives. By enforcing helmet usage through a fail-safe mechanism, the smart motorcycle helmet seeks to curb the violation of traffic laws and mitigate the devastating consequences of accidents. The smart motorcycle helmet includes an alcohol sensor to detect intoxication in the rider. If alcohol is detected, a red light on the helmet illuminates, providing a visible indication of impairment. © 2024 IEEE.

关键词： Internet of things

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Breast Cancer Detection Using Neural Networks

Breast Cancer Detection Using Neural Networks

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2024 International Conference on Advances in Data engineering and Intelligent Computing Systems, ADICS 2024

作者： Preethi, J. Rahul Chiranjeevi, V. Dhanya Lakshmi, A. Hosala Devi, N.K. Rajalakshmi Engineering College Computer Science And Engineering Chennai India

ISBN: (纸本)9798350364828

Cancer is the deadliest disease in the world, and it primarily strikes women. As early cancer detection can aid in the disease's treatment, it is imperative that the primary goal be the scientific discovery of a cancer cure. After analyzing 41 publications, we discovered that there are numerous techniques for detecting cancer. We demonstrated a deep learning neural network method for breast cancer diagnosis in this work using the Breast Cancer database. This study illustrates how deep technology and datasets can be used to learn about breast cancer diagnosis. There are three sections to this work. We started by gathering the dataset, dividing it into training and testing sets, applying a pre-processing technique to scale and filter the data, and making some graphs for data visualization. A training dataset was used to test the model, and it achieved an accuracy of 99.67%. We have shown that deep learning technologies can accurately diagnose breast cancer using the Wisconsin Breast Dataset. The dataset comprises 30 features in total, and the database contains 569 rows. In this paper, we use eleven pre-processed features that we obtained to identify breast cancer. But first, we used several pre-processing techniques including Label Encoder, Normalizer, and Standard Scaler, to apply in the model and achieve good accuracy to enhance the model for breast cancer detection. © 2024 IEEE.

关键词： Data visualization

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Detection of Polycystic Ovary Syndrome Using Clinical Data and Ultrasound Images 7

Detection of Polycystic Ovary Syndrome Using Clinical Data a...

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7th International Conference on Circuit Power and Computing Technologies, ICCPCT 2024

作者： Thomson, Esther Sara Ram, Ajai College of Engineering Trivandrum Department of Computer Science and Engineering India

ISBN: (纸本)9798350372816

Polycystic Ovary Syndrome (PCOS) is a hormonal issue that occurs in women of adulthood. The World Health Organization (WHO) has identified PCOS as a prevalent endocrine illness that affects around 10% of women globally. It can result in various health complications, which includes infertility, metabolic complications such as insulin resistance, obesity, as well as cardiovascular problems, sleep apnea, endometrial cancer, and psychological disorders like anxiety and depression. Hence early diagnosis of PCOS is crucial. One of the diagnosis methods used for its detection is the Rotterdam criteria or Consensus. This diagnostic approach includes three criteria: Oligovulation or anovulation, presence of hyperandrogenism, and the identification of polycystic ovaries through ultrasound examination. Patients who meet two or more of these criteria can be diagnosed with PCOS. Cysts may indicate Polycystic Ovarian Disease (PCOD), a condition similar to PCOS. In PCOD, the ovaries release numerous immature or partially-mature eggs, which can develop into cysts over time. Among the numerous available techniques in the machine learning domain, only one criterion is typically assessed at a time, either clinical data or ultrasound, but not both simultaneously. The proposed system considers both and has two sections to aid in this process - one for detection through images and the other through clinical data. The dataset for the system includes 781 PCOS and 1143 Non-PCOS images, as well as clinical data from 541 patients, including 177 with PCOS and 43 features collected from open sources. Several models and techniques are used for the detection individually. A novel feature selection approach for CS-PCOS is employed, utilizing an optimized chi-squared mechanism. Additionally, overfitting is assessed using ten-fold cross-validation. Different pre-trained models are tried out for ultrasound images and the best is taken. Random forest is considered the best model for clinical data with

关键词： Ultrasonic imaging

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Cardiovascular Disease Prediction using Patient History and Real Time Monitoring 2

Cardiovascular Disease Prediction using Patient History and ...

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2nd International Conference on Intelligent Data Communication Technologies and Internet of Things, IDCIoT 2024

作者： Ananthajothi, K. David, Joshua Kavin, A. Computer Science and Engineering Rajalakshmi Engineering College Chennai India

ISBN: (纸本)9798350327533

A comprehensive healthcare solution through a unified web application is offered Centered on cardiovascular disease prediction and broader health prognosis based on patient treatment history and recent health data, it integrates three distinct datasets and deploys top-Tier machine learning algorithms tailored to each dataset's unique objectives. For cardiovascular disease prediction, Random Forest, Support Vector Machine, and Gradient Boosting algorithms are employed, meticulously analyzing patient health records and lifestyle factors to accurately identify disease risks. In the domain of disease prediction using health data, Logistic Regression, Long Short-Term Memory networks, and Multilayer Perceptrons provide insights into various health conditions by assessing recent health metrics. The third dataset, which tracks individuals' activity and health metrics over a month, relies on K-Means Clustering, Decision Trees, and Recurrent Neural Networks to monitor activity patterns and deviations, issuing timely health warnings to users. This all-inclusive web application unites predictive power with real-Time health monitoring, empowering individuals to proactively manage their well-being, fos tering a culture of preventive care, and facilitating personalized health management in a user-friendly and accessible online platform. © 2024 IEEE.

关键词： Decision Tree Long Short-Term Memory Machine Learning Patient Historical Treatment Random Forest Recurrent Neural Networks

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ResQ - Intelligent Real-Time Disaster Management Using Machine Learning and IoT

ResQ - Intelligent Real-Time Disaster Management Using Machi...

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2024 International Conference on Innovative Computing, Intelligent Communication and Smart Electrical Systems, ICSES 2024

作者： Duraimurugan, N. Reshma, A. Reshma, S. Rajalakshmi Engineering College Computer Science and Engineering Chennai India

ISBN: (纸本)9798331543617

The project, therefore, is to be a ResQ using deep learning capabilities for an enhanced response and coordination in cases of disaster. The best sources of real-time data on the weather services, government alerts, and user reports then integrate into critical updates and predictions during a disaster. The ResQ has such as features: real-time alerts, access to emergency contact information, route optimization evacuation, resource mapping, user-generated incident reports, first aid tips, offline functionality, volunteer coordination, and multi-language support. Deep learning algorithms in fact enhance predicting capabilities and thereby improve better management of disasters and effective allocation of resources. It is a means through which the effects of disasters will be reduced, rationalizing rescue procedures and saving more lives in the long term. © 2024 IEEE.

关键词： Resource allocation

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