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检索条件"任意字段=5th International Conference on Informational Processing in Sensor Networks"

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Utilization of Li-Fi Technology for Black Box in Ground Vehicles

Utilization of Li-Fi Technology for Black Box in Ground Vehi...

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Trends in Material Science and Inventive Materials (ICTMIM), international conference on

作者： Sabari L. Uma Maheswari Magesh T Sukhi Y Jayasree V Jeeva G Oviya B Department of EEE R. M. K. Engineering College Tiruvallur India

ISBN: (数字)9798331501488

ISBN: (纸本)9798331501495

Inspired by the aircraft flight data recorders, the study aims to invent a universal Black Box prototype for automobiles-for safe vehicle usage and accident analysis, plug on minimum circuitry and inexpensive model to provide complete security over the vehicle, better medical assistance for the casualty, and fast insurance claim process. It uses Li-Fi technology for enabling real-time, fast data delivery in vehicle and infrastructure communication without lag. Additionally, the Black Box also enables Car-to-Car Communication for the detection of probable collision threats through sudden movements of proximity vehicles. It also analyzes in real-time through simulation, which works towards collision risk reduction, and real-time safety improvement involves quality vehicle diagnosis and providing timely hazard information. With predictive analytics and rapid data sharing, this model gears up for intelligent transport networks that benefit emergency action and insurance processing while accident prevention is possible by proactive detection of hazards and creation of situational awareness. It is a very important breakthrough on road safety technology.

关键词： Electronic equipment Flight recording Vehicular ad hoc networks Prototypes Insurance Light fidelity Real-time systems Hazards Monitoring Accidents

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Optimized Clustering Approach for WSN Using WCLUST Algorithm

Optimized Clustering Approach for WSN Using WCLUST Algorithm

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Innovative Research in Applied Science, Engineering and Technology (IRASET),conference on

作者： Soukaina Bouarourou Chaimae Kanzouai Abderrahim Zannou El Habib Nfaoui Abdelhak Boulaalam Faculty of Sciences Mohamed V University Rabat Morocco LSATE Laboratory Faculty of Sciences Dhar El Mahraz Sidi Mohamed Ben Abdellah University Fez Morocco ERCI2A Faculty of Sciences and Techniques Al Hoceima Morocco Abdelmalek Essaâdi Morocco Computer Science Department L3IA Laboratory Faculty of Sciences Dhar El Mahraz Sidi Mohamed Ben Abdellah University Fez Morocco LSATE Laboratory National School of Applied Sciences Sidi Mohamed Ben Abdellah University Fez Morocco

ISBN: (数字)9798331532970

ISBN: (纸本)9798331532987

the proliferation of Internet of things applications has resulted in easy device integration in Wireless sensor networks, enabling real-time data collection and coordination. However, seamless integration is still plagued by issues such as energy constraints and high energy consumption due to inefficient data communication. Many studies explored clustering methods to address such issues. Here, we introduce the Whale-Based Algorithm for cluster establishment and a Selection Factor for Cluster Head selection—key steps that minimize paths and save energy. With the use of the proposed method, our approach improves clustering quality with the establishment of dense and energy-saving clusters. the Selection Factor provides dynamic selection of the most suitable Cluster Heads according to residual energy, neighboring nodes, and distance to the sink. the proposed method attains the clustering quality accuracy of 97% and drastically decreases the run time with respect to the Ant-Based Clustering algorithm.

关键词： Wireless sensor networks Energy consumption Accuracy Heuristic algorithms Clustering methods Clustering algorithms Data collection Real-time systems Internet of things Data communication

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Innovative Rumor Detection on Social Media Text: A Comprehensive Study of Dual Co-Attention Ensemble Based Voting Approach 5th

Innovative Rumor Detection on Social Media Text: A Comprehen...

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5th international conference on Recent Trends in Machine Learning, IoT, Smart Cities, and Applications, ICMISC 2024

作者： Gangone, Anjali Bharat Kumar, G.J. Swapna, Mandu Department of Computer Science and Engineering CMR Institute of Technology Hyderabad India Department of Computer Science and Engineering Sreyas Institute of Engineering and Technology Telangana Hyderabad India

ISBN: (纸本)9789819788606

this study delves into the crucial task of rumor detection amidst the rapid spread of information online, focusing on the efficacy of advanced dual co-attention ensemble models for ensuring digital communication’s reliability. By integrating Gated Recurrent Unit (GRU) Representation, Self-Attention (SA) Models, and Bidirectional Encoder Representation from Transformers (BERT) Sentiment Models, alongside various Natural Language processing (NLP) techniques, the research examines the potential of these models to improve rumor detection accuracy significantly. the investigation encompasses eight distinct models: the GRU Representation Model, the SA Model, the BERT Sentiment Model, and various dual co-attention ensemble combinations thereof (GRU + SA, SA + BERT, BERT + GRU, and GRU + SA + BERT), employing a sophisticated voting mechanism for rumor classification. this comprehensive evaluation across key performance metrics Accuracy, Precision, Recall, and F1 Score revealed the substantial impact of leveraging dual co-attention ensemble model strengths, particularly through dual co-attention and a strategic voting mechanism. Among the dual co-attention ensemble models, the SA + GRU + BERT configuration emerged as a superior performer, achieving remarkable results with an accuracy of 93.5%, a precision of 92%, a perfect recall of 99%, and an F1 score of 95%. these findings underscore the dual co-attention ensemble models’ exceptional capability to address rumor detection challenges effectively. the study not only showcases the promising prospects of utilizing cutting-edge AI for rumor detection but also provides insights for refining these models further. It suggests avenues for future research, including model finetuning, ethical considerations, and the necessity for real-time monitoring. this research underscores the transformative potential of AI in enhancing the accuracy of online information, paving the way for future advancements in rumor detection. © the Author(s),

关键词： Natural language processing systems

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Application of Deep Learning in Natural Language processing Algorithm for User Service

Application of Deep Learning in Natural Language Processing ...

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Power Electronics, Computer Applications (ICPECA), IEEE international conference on

作者： Zongbao Sun Harbin University of Science and Technology Harbin China

ISBN: (数字)9798331533694

ISBN: (纸本)9798331533700

Natural language processing (NLP) is becoming more and more widely used in the field of user services. this paper discusses the innovative application of NLP algorithm based on deep learning in improving user service quality, and focuses on several key algorithms and their optimization methods. Firstly, by introducing pre-trained language models such as BERT and GPT-3, the efficient understanding of user services and the generation of responses are realized, which greatly improves the accuracy and fluency of the dialogue system. Secondly, for the intent recognition task in the user service, a multi-head attention mechanism based on Transformer architecture is proposed, which enhances the ability of the model to capture long-distance dependencies and enables the system to analyze the user's intent more accurately. In addition, in order to solve the challenge of context understanding in multi-turn dialogue, we designed a memory-enhanced recurrent neural network (RNN) with a fusion memory network, which can maintain and update the dialogue state in multiple rounds of interaction, so as to provide a coherent service experience. Finally, this paper discusses the challenges and solutions in the process of model deployment, real-time inference efficiency and data privacy protection, and looks forward to the application prospects of supervised learning and adaptive transfer learning.

关键词： Deep learning Adaptation models Accuracy Recurrent neural networks Computational modeling Transfer learning Transformers Natural language processing Data models Unsupervised learning

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Development of a Multi-Dimensional Image processing Model for Personalized Diagnosis and Severity Assessment of Skin Diseases

Development of a Multi-Dimensional Image Processing Model fo...

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Advanced Research in Computing (ICARC), international conference on

作者： Nethmi Shihari Wijerama PPG Dinesh Asanka thilini V. Mahanama Department of Industrial Management Faculty of Science University of Kelaniya Sri Lanka

ISBN: (数字)9798331530983

ISBN: (纸本)9798331530990

Skin diseases affect millions worldwide, and more than 3,000 different conditions present significant challenges to diagnose and suggest treatments. this study introduces a novel multi-dimensional image processing model that gives three critical outputs: skin type classification, Skin disease prediction, and severity level assessment. Unlike the traditional image-only diagnostic models, this approach puts forward a customized convolutional neural network (CNN) architecture that utilizes high-resolution dermatological images with clinical data, including patient demographics, and symptomatology, to provide a personalized and accurate diagnostic solution. Considering the variation in skin type and disease manifestation, the model has been adapted to adjust its prediction based on the unique characteristics of every patient. the final model, trained on such a diverse and clinically representative dataset, includes the diagnosis based on the severity—(mild, moderate, or severe)—and skin types, such as dry, oily, or normal, which represents critical gaps in current AI applications. the developed model could be implemented in real-time clinical use, as it is scalable and data-driven, effectively addressing diagnostic challenges. It is particularly beneficial in resource-constrained settings, offering accurate and tailored skin disease diagnostics to meet individual patient needs.

关键词： Adaptation models Accuracy Image processing Predictive models Skin Data models Robustness Real-time systems Convolutional neural networks Diseases

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Integrated IoT-based Hazard Detection and Automated Waste Management System for Environmental Safety

Integrated IoT-based Hazard Detection and Automated Waste Ma...

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Trends in Material Science and Inventive Materials (ICTMIM), international conference on

作者： Samyudha K Swethaa Sree R Swetha A Fatima Vincy R Department of Computer Science and Engineering Easwari Engineering College Chennai India

ISBN: (数字)9798331501488

ISBN: (纸本)9798331501495

Waste management is a critical challenge in urban environments, where inefficient disposal methods contribute to pollution, public health risks, and operational inefficiencies. Traditional waste collection systems lack real-time monitoring, leading to overfilled bins, delayed disposal, and increased environmental hazards. In this research, an IoT-based smart dustbin system is designed to optimize waste management through automated real-time monitoring. the system integrates an ultrasonic sensor to detect waste levels, an IR sensor for user interaction, and a temperature sensor for fire hazard detection. A key novelty of this research is the incorporation of three MQ gas sensors for toxic gas detection, ensuring early identification of hazardous emissions from waste decomposition. the data is transmitted via a Wi-Fi module to a web-based dashboard, displaying waste levels, gas concentrations, timestamps, and geolocation. Additionally, an automated GSM-based SMS notification system alerts municipal authorities when the dustbin is full or detects hazardous gases. While IoT integration enhances urban sanitation and operational efficiency, challenges such as power consumption and network dependency persist. To address these concerns, our system optimizes data transmission intervals and leverages energy-efficient components. this approach enables seamless waste management, reducing manual monitoring efforts while fostering a cleaner and safer urban environment.

关键词： Waste management Urban areas Data processing Real-time systems Hazards Energy efficiency Data communication Internet of things Monitoring Gas detectors

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Smart Energy Management Leveraging Twin Adaptive Pulse Coupled networks for Dynamic Energy Optimization in IoT-based Electrical WSN

Smart Energy Management Leveraging Twin Adaptive Pulse Coupl...

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Trends in Material Science and Inventive Materials (ICTMIM), international conference on

作者： Ravi Kumar M B. Md. Irfan Sugunadevi C Umang Soni Madhu B K Ramya Maranan Department of Electronics and Communication Engineering Cambridge Institute of Technology Bengaluru Karnataka Department of Information Technology Nalsar University of Law Hyderabad Telengana Department of Information Science and Engineering CMR Institute of Technology Bengaluru Karnataka Electronics & Communication Engineering Faculty of Engineering and Technology Parul Institute of Engineering and Technology Parul University Gujarat Department of Computer Science and Engineering Vidya Vikas Institute of Engineering and Technology Mysore Karnataka Department of Research and Innovation Saveetha School of Engineering SIMATS Chennai Tamil Nadu

ISBN: (数字)9798331501488

ISBN: (纸本)9798331501495

Internet of things (IoT)-driven Wireless sensor networks (WSNs) undergo fast growth hence requiring sophisticated energy optimization methods to keep the networks operational longer with reliable data handling. Traditional energy management practices lead to early node failure combined with inefficient network routes and non-even energy distribution which blocks network development and operational performance expansion. the three main factors that cause WSNs to be energy inefficient are improper node positioning along with excessive routing overhead and uneven distribution of power consumption across sensor nodes. the current network management approaches do not provide adequate dynamic energy distribution which results in premature network failure. this research establishes "Smart Energy Management leveraging Twin Adaptive Pulse Coupled networks for Dynamic Energy Optimization in IoT-Based WSN (MG-TwinAPC-ReP)" to address these challenges. the proposed framework MG-TwinAPC-ReP features four layers which (1) strategic node deployment coverage, (2) Cluster-Based Routing Protocol Using Modified Greylag-Goose Optimization, and (3) Energy management through adaptive load balancing using Twin Adaptive Pulse Coupled Network's dual synchronization model and (4) uses Reformed Poplar Optimization to optimize networking parameters. Experimental results indicate remarkable performance capabilities which lead to a 99.82% increase in network lifetime and 99.74% energy conservation together with 99.91% reliable data delivery and 99.65% reduced latency compared to traditional IoT-WSN systems. the proposed scalable self-adapting energy-efficient WSN model provides an optimal solution for smart cities together with healthcare and agriculture and industrial IoT applications which drives sustainable IoT-driven WSN deployment into the future.

关键词： Wireless sensor networks Adaptive systems Smart cities Medical services Routing Load management Mathematical models Reliability Optimization Industrial Internet of things

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Integration of Piezoelectric and Light-Dependent Resistor sensors for Efficient Generation and Utilization of Electricity for Pathways

Integration of Piezoelectric and Light-Dependent Resistor Se...

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Trends in Material Science and Inventive Materials (ICTMIM), international conference on

作者： Saranya G Kumaran K Daneshwaran M Shyam Venkatraman Jey Pradeep A Anish Anandhan A L School of Computer Science and Engineering Vellore Institute of Technology Chennai India

ISBN: (数字)9798331501488

ISBN: (纸本)9798331501495

the ever-increasing demand for energy in our modern world necessitates exploration of innovative and sustainable solutions. However, the research in the integration of piezoelectric materials has highlighted limitations with respect to scalability. this research investigates the potential of piezoelectric materials for harvesting energy from everyday human movement. We look into cost-effective system utilizing piezoelectric sensors embedded under flooring to capture the energy generated by footsteps. this harvested energy can be stored in and boosted for powering devices. this research explores the utilization of piezoelectric energy harvesting technology to create electricity-generating pathways, offering a sustainable solution for powering pathway lights in urban environments. Piezoelectric sensors strategically placed along the pathway surface capture kinetic energy from pedestrian movement, converting it into electrical energy. this energy is stored in rechargeable batteries and undergoes voltage conversion to power pathway lights effectively. A light-dependent resistor (LDR) sensor ensures lights only activate when necessary, optimizing energy usage. An Arduino Uno microcontroller orchestrates light operation based on ambient light conditions. the methodology focuses on efficient energy utilization and provides a practical solution for sustainable pathway lighting. Several prototypes and studies are discussed, highlighting the potential and limitations of piezoelectric energy harvesting technology. the results demonstrate consistent power generation, high energy conversion efficiency, and cost-effectiveness, underscoring the feasibility and effectiveness of the proposed approach.

关键词： Resistors Mechanical sensors Wireless sensor networks Pedestrians Costs Electricity Piezoelectric materials Lighting sensor systems Energy harvesting

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Analyzing the Link Lifetime and Doppler Shift Between Connected Vehicles in VANET 5th

Analyzing the Link Lifetime and Doppler Shift Between Connec...

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5th international conference on Recent Developments in Control, Automation and Power Engineering, RDCAPE 2023

作者： Nagar, Vishvendra Pal Singh Sagar, Anil Kumar Department of Computer Science and Engineering Sharda University Greater Noida India Sharda University Greater Noida India

ISBN: (纸本)9789819600465

the highly dynamic new technology known as Vehicular Ad Hoc networks (VANETs) supports wireless communication between hybrid vehicles. Wireless sensor networks’ (WSNs) technology is used by hybrid cars for a range of tasks, including real-time data gathering, monitoring, control, and sharing that data for remote monitoring and system diagnostics. Hybrid vehicles may interface with infrastructure via Intelligent Transport System (ITS) to receive real-time traffic, road, and weather information. Depending on the road's specifications, a vehicle’s mobility speed may vary. Certain VANET characteristics, such as dynamic vehicle speed, vehicle mobility in terms of movement, network density, etc., make wireless communication challenging to maintain the connectivity of dynamic wireless network. As speed is one of the factors that leads to a rapid change in topology, this paper examines and analyzes the effects of speed and mobility on the performance of wireless communication protocols. the findings show that raising a vehicle's speed has an adverse impact on the connection of the network, with a negative correlation between speed and wireless network link lifetime. © the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Mobile ad hoc networks

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Performance Analysis of Text Classification Algorithms for Dhivehi Language Documents

Performance Analysis of Text Classification Algorithms for D...

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Advanced Research in Computing (ICARC), international conference on

作者： Fathimath Rashma Mohamed Prasanna S. Haddela Mohamed Jamaaludhin School Maldives Faculty of Computing Sri Lanka Institute of Information Technology Sri Lanka

ISBN: (数字)9798331530983

ISBN: (纸本)9798331530990

this study examines the effectiveness of various machine learning algorithms in classifying text written in “Dhivehi,” the official language of the Maldives. As a low-resource language with limited research in text analytics, “Dhivehi” poses unique challenges due to its distinctive linguistic properties. To address these challenges, this research evaluates the performance of algorithms, including Support Vector Machines, Naive Bayes, Decision Trees, Neural networks, XGBoost, and Random Forest, leveraging a newly curated “Dhivehi” language dataset. the evaluation highlights that K-Neighbors achieved the highest performance, with an accuracy of 64.7% and F1 scores (macro: 0.640, weighted: 0.642), demonstrating a strong balance between precision and recall. Support Vector Machines (accuracy: 63.9%) and XGBoost (accuracy: 62.8%) also showed competitive results, with SVM slightly outperforming XGBoost in F1 metrics. Decision Tree exhibited the lowest performance across all metrics. the findings provide critical insights into improving text classification for low-resource languages and contribute to developing natural language processing tools adapted explicitly for “Dhivehi.” Furthermore, the dataset is publicly available on Mendeley data under the name “Dhivehi Categories data set” to foster future research and innovation in this domain.

关键词： Support vector machines Measurement Accuracy Computational modeling Text categorization Neural networks Nearest neighbor methods Bayes methods Decision trees Random forests

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