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5th IEEE International Conference on Recent Trends in computer Science and Technology, ICRTCST 2024

作者： Hariprasath, V.Y. Mukesh, S. Aruna, T.N. Gayana, R. Akshya, J. Choudhry, Mani Deepak Sundarrajan, M. SRM Institute of Science and Technology Department of Computational Intelligence Chennai India KGiSL Institute of Technology Department of Computer Science and Engineering Coimbatore India K. Ramakrishnan College of Engineering Department of Information Technology Tiruchirappalli India KGiSL Institute of Technology Department of Information Technology Coimbatore India SRM Institute of Science and Technology Department of Networking and Communications Chennai India

ISBN: (纸本)9798350351378

Unmanned Ground Robots (UGVs) are increasingly becoming vital tools in disaster relief operations, offering a means to navigate through hazardous environments where human intervention may be risky or impossible. These intelligent robots are designed to perform a variety of tasks, such as searching for survivors, delivering supplies, and assessing damage. However, the effectiveness of UGVs in such critical missions largely depends on their ability to navigate complex and unpredictable terrains, a challenge that necessitates advanced path-planning strategies. The paper introduces a novel approach to path planning for UGVs in disaster relief scenarios. This approach combines the strengths of advanced rapidly Rapidly-exploring Random Trees (RRT) algorithms with the capabilities of YOLOv4, a state-of-the-art technique for object detection, specifically tailored here for human detection. RRT algorithms are known for their efficiency in exploring complex spaces, making them ideal for navigating through the unpredictable terrains typical of disaster zones. Meanwhile, YOLOv4 enhances the UGVs' ability to detect and respond to human presence, a critical requirement in rescue operations. The UGVs equipped with this novel path-planning strategy were able to navigate safely and efficiently, adapting to the dynamic nature of the environment. More importantly, the accuracy in detecting human presence was significantly improved, a crucial factor in ensuring that aid and rescue efforts are directed where they are most needed. By enhancing the path planning capabilities of UGVs with advanced algorithms and human detection technology, this approach holds the potential to revolutionize disaster response operations, making them more efficient and impactful, ultimately leading to faster and more effective aid delivery to those in dire need. © 2024 IEEE.

关键词： Motion planning

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Effect on Public Health Due to Environment Condition- Role of IoT 14

Effect on Public Health Due to Environment Condition- Role o...

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14th International Conference on Computing Communication and networking Technologies, ICCCNT 2023

作者： Joshi, Kapil Rastogi, Ritesh Singh, Anupam Kaushik, Vishal Anandaram, Harishchander Kumar, Anil Uttaranchal University Uttaranchal Institute of Technology Department of Cse Dehradun India Niet Department of Computer Science & Engineering Greater Noida India Graphic Era Hill University Department of Computer Science and Engineering Uttarakhand Dehradun India Upes School of Computer Science Dehradun India Amrita Vishwa Vidyapeetham Centre for Computational Engineering and Networking Tamil Nadu Coimbatore India Dit University School of Computing Dehradun India

ISBN: (纸本)9798350335095

The biggest environmental issues are poor air quality, polluted water, contaminated food, polluted transportation, and radiation contamination. In order to ensure sustainable growth and preserve a stable social structure, adequate monitoring is required. With the development of contemporary sensors and advancements in the internet of things (IoT), environmental surveillance has evolved into a smart environment monitoring (SEM) platform in recent decades. The emergence and widespread use of Internet of Things (IoT) devices and its supporting technologies are the driving forces behind the growth of ubiquitous systems, as shown in this study. This study analysisIoT has great promise in the environment, health, & safety (EHS) industries that involve high risk. The social health can be improved with the use of technology that manages and organises information about individuals. IoT may actually serve as the foundation for a society that is health. In this study, we discussed how the Internet of Things (IoT) may benefit individuals and how it can be used for public health. and also highlight IoT-related problems and a suggested remedy. © 2023 IEEE.

关键词： Internet of things

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AoI-Delay Tradeoff in Mobile Edge Caching: A Lyapunov Optimization-Based Method

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IEEE Internet of Things Journal 2025年第11期12卷 16266-16281页

作者： Li, Ran Huang, Chuan Qin, Xiaoqi Fu, Zhanhong Yang, Lei Yang, Dong The Future Network of Intelligence Institute The Chinese University of HongKong Shenzhen518172 China The Department of Information Engineering The Chinese University of Hong Kong Hong Kong The School of Science and Engineering The Future Network of Intelligence Institute The Chinese University of HongKong Shenzhen518172 China The State Key Laboratory of Networking and Switching Technology Beijing University of Posts and Telecommunications Beijing100876 China SF Technology Shenzhen518052 China The Department of Computer Science and Engineering University of Nevada RenoNV89557 United States The School of Electronic and Information Engineering Beijing Jiaotong University Beijing100044 China

Mobile edge caching (MEC) is a promising technique to improve the quality of service (QoS) for mobile users (MU) by bringing data to the network edge. However, optimizing the crucial QoS aspects of message freshness and service promptness, measured by age of information (AoI) and service delay, respectively, entails a tradeoff due to their competition for shared edge resources. This paper investigates this tradeoff by formulating their weighted sum minimization as a sequential decision-making problem, incorporating high-dimensional, discrete-valued, and linearly constrained design variables. First, to assess the feasibility of the considered problem, we characterize the corresponding achievable region by deriving its superset with the rate stability theorem and its subset with a novel stochastic policy, and develop a sufficient condition for the existence of solutions. Next, to efficiently solve this problem, we propose a mixed-order drift-plus-penalty algorithm by jointly considering the linear and quadratic Lyapunov drifts and then optimizing them with dynamic programming (DP). Finally, by leveraging the Lyapunov optimization technique, we demonstrate that the proposed algorithm achieves an O(1/V) versus O(V) tradeoff for the average AoI and average service delay. © 2014 IEEE.

关键词： Stochastic systems

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Data-Driven Recycling Transformation for Enhancing Paper and Cardboard Bin Efficiency through IoT and Random Forest

Data-Driven Recycling Transformation for Enhancing Paper and...

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2024 International Conference on Advances in Modern Age Technologies for Health and engineering Science, AMATHE 2024

作者： Srinivasan, S. Swathy, R. Srividhya, V. Murugan, S. Srinivasan, C. Muthulekshmi, M. Saveetha School Of Engineering Saveetha Institute Of Medical And Technical Sciences Saveetha University Department Of Biomedical Engineering Tamil Nadu Chennai India Srm Institute Of Science And Technology Department Of Networking And Communications Chenagalpattu Chennai Tamil Nadu Kattankulathur India Meenakshi College Of Engineering Department Of Electrical And Electronics Engineering Tamil Nadu Chennai India Saveetha School Of Engineering Saveetha Institute Of Medical And Technical Sciences Saveetha University Department Of Computer Science And Engineering Tamil Nadu Chennai India

ISBN: (纸本)9798350371567

This study uses data to enhance paper and cardboard recycling bin efficiency for sustainable waste management. It uses Internet of Things (IoT) and Random Forest algorithms to dynamically optimize bin use to improve recycling. It starts by installing IoT sensors on paper and cardboard recycling bins to track fill levels and use. A Random Forest system trained on past data predicts bin fill levels from this continuous data stream. The predictive algorithm adapts collection schedules and resource distribution based on temporal trends, weather, and community events. IoT and Random Forest increase fill-level forecasts and enable data-driven recycling bin management. This reduces wasted collections, fuel use, and carbon emissions, making recycling more sustainable and cost-effective. The study also analyzes the system's real-world urban application, demonstrating its scalability and flexibility to varied waste management circumstances. Our empirical study and case studies show that the technique improves paper and cardboard recycling efficiency, contributing to data-driven sustainability programs. © 2024 IEEE.

关键词： Predictive analytics

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An ideal deeply contextual prediction model for an effective prediction and mitigation of cyberbullying in social networks

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International Journal of Information Technology (Singapore) 2025年第3期17卷 1561-1579页

作者： Prathaban, Banu Priya Vaishnavi, T. Ramesh, Addanki Purna Sujaritha, M. Rama, R. Senthil Gracewell, J. Jeffin Department of Networking and Communications School of Computing SRM Institute of Science and Technology SRM Nagar Kattankulathur Chennai 603203 India Department of Electronics and Communication Engineering Vel Tech Rangarajan Dr. Sagunthala R & D Institute of Science and Technology Avadi Tamil Nadu Chennai 600062 India Department of Electronics and Communication Engineering DNR College of Engineering and Technology West Godavari Andhra Pradesh Bhimavaram India Department of Computer Science and Engineering Sri Krishna College of Engineering and Technology Tamil Nadu Kuniyamuthur 641008 India Department of Electrical and Electronics Engineering DMI College of Engineering Tamil Nadu 600123 Chennai India Department of Electronics and Communication Engineering Saveetha Engineering College Chennai 602105 India

Social media have evoked a host of problems, especially Twitter and Instagram, considering misconceived notions of free speech. Among the concerns that have been seen as crucial is the impact of cyberbullying;it is a global issue affecting an individual and society in general. Though various methods have been suggested for deterring cyberbullying, most of these depend on victim interaction and are hence ineffective in the task of independently identifying and preventing cyberbullying. Within this context, the research introduces a new, innovative framework that is ideal and deeply contextual in terms of prediction, thus creating an approach to detecting cyberbullying on social networks without directly engaging with any victim. Consequently, the key contribution of this work is developing the Embedded Residual Recurrent Convolution Network, a hybrid deep learning architecture for the detection of cyberbullying data with more precision and accuracy. Further, this is optimized by a Tenacious Projectile Optimizer for minimizing computational time and improving classifier performance. In this regard, the novelty of the proposed I-DCP framework is established in its superior performances on different social media datasets, with an average accuracy of over 99% and an execution time as low as 13 s, hence highly efficient for real-time detection of cyberbullying. These represent important advances compared to previous methodologies and constitute a strong and scalable response in the fight against cyberbullying. © Bharati Vidyapeeth's Institute of computer Applications and Management 2024.

关键词： And classification Artificial intelligence (AI) Cyberbullying detection Deep learning Instagram Optimization Social network Twitter

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Development and optimization of deep convolutional neural network using Taguchi method for real-time electricity conservation system

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International Journal of Information Technology (Singapore) 2023年第3期15卷 1521-1534页

作者： Ushasukhanya, S. Jothilakshmi, S. Sridhar, S.S. Department of Networking and Communications SRM Institute of Science and Technology kattankulathur Tamil Nadu 603203 India Department of Computer Science and Engineering Annamalai University Tamil Nadu Chidambaram 608002 India Department of Computing Technologies SRM Institute of Science and Technology Kattankulathur 603203 India

Conservation of energy is imperative for the development of the country. The critical electrical energy situation is one of the immense challenges in the developing nations of the globe. Though there are many ways of preservations, one of the simplest and efficient ways is to turn off the electricity when it is not needed. Thus, in this study, we have developed an automatic smart system to enable the electricity turned on only when a human being is detected in Closed Circuit Television footage (CCTV). Human detection in the footage is done by classifying the frames with and without a human by Convolutional Neural Network (CNN) in an indoor environment. As the system is processing live CCTV footage, the reduction of training and overall processing time of the model is done by Extended Orthogonal array tuning method (E-OATM) which tunes the parameters and hyper-parameters of the model using Taguchi method. The constructed model is further pruned and the less important parameters are eliminated based on H-Rank algorithm to keep the model simple for fast processing. Finally, optimization of the network is done using Nadam technique, and the results (images) are fed into the Arduino UNO microcontroller. This controls the power supply that turns off/on the lights and fans only when human is unavailable/available respectively without the assist of a sensor. © 2022, The Author(s), under exclusive licence to Bharati Vidyapeeth's Institute of computer Applications and Management.

关键词： Arduino CCTV CNN E-OATM Nadam Smart electrical energy consumption

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Multimodal Sentiment Analysis: A GRU Approach for Improved Accuracy

Multimodal Sentiment Analysis: A GRU Approach for Improved A...

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2024 International Conference on Electrical, Electronics and Computing Technologies, ICEECT 2024

作者： Manogaran, Nalini Annamalai, Suresh Anbarasu, V. Nair, Resmi R Balusamy, Balamurugan Veluval, Parimala Sushamass, Sushamass Arunprasath, S. Department of CSE Tamil Nadu Chennai600077 India SRM Institute of Science and Technology School of Computing College of Engineering and Technology Department of Networking and Communications Kattankulathur603203 India Department of Electronics and Communication Engineering Tamil Nadu Chennai602105 India Uttar Pradesh 201314 India Pune India Manipal University Jaipur Department of Computer Science and Engineering Rajasthan Jaipur303007 India

ISBN: (纸本)9798350378092

In the real world, data often comes in different forms, such as text, audio, and video, making it multi-modal. This poses a challenge for machine learning models, which need to be able to process and understand this type of data. One way to address this challenge is to use models that can handle multiple data types in a single model. This enables the model to learn from the diverse information offered by the various data types. This research explores various methods for sentiment analysis in a multi-modal context, incorporating text, images, and potentially audio data. The methods evaluated include lexicon-based methods, machine learning with text features, convolutional neural networks (CNNs) for text, and a proposed multi-modal GRU model. The objective of the multi-modal GRU model is to surpass existing methods by efficiently capturing and integrating sentiment information from various modalities. The evaluation of the model's capabilities and constraints encompasses multiple performance indicators, including accuracy, precision, recall, and F1-score. Our Multi-modal GRUs model achieved an accuracy rate of 85%, precision of 82%, recall of 80%, and an F1-score of 81%. These metrics indicate that the model adeptly captures the underlying patterns within the multimodal data. © 2024 IEEE.

关键词： Convolutional neural networks

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A Cooperative Transmission Scheme for Wearable Sensor Networks

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IEEE Sensors Journal 2025年

作者： Peng, Yuyang AL-Hazemi, Fawaz Mirza, Mohammad Meraj Macau University of Science and Technology School of Computer Science and Engineering Macau China University of Jeddah Department of Computer and Networking Engineering Jeddah Saudi Arabia Taif University College of Computers and Information Technology department of Computer Science Taif21944 Saudi Arabia

As we become more aware of the connection between physical health and physiological signal measurement, healthcare monitoring becomes an interesting field in body area networks (BANs). Healthcare monitoring systems use sensor network and communication technologies to detect the health state of a person and transmit the related information to the monitoring center. In BANs, the sensors attached on or embedded in the human body for collecting and transmitting the physiological information are energy limited. Therefore, saving energy is one of the key issues in designing BANs. Because the application of cooperative transmission provides energy saving, it is necessary to form the cooperative transmission using body sensors together. In addition, the modulation constellation size also affects the energy consumption in BANs. Therefore, according to the transmission distance, it is necessary to choose the right transmission scheme by selecting the appropriate modulation to save energy consumption in BANs. In this paper, an adaptive cooperative transmission scheme is proposed for BANs in healthcare systems. In the proposed scheme, the cooperative transmission is constructed over intra-body phase and on-off body phase whereas the adaptive transmission is constructed over the forwarding phase. Results from the simulations show that the proposed adaptive cooperative transmission scheme achieves significant energy savings when compared with the existing schemes. © 2001-2012 IEEE.

关键词： Energy saving

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IoT-Based Car Safety System With Airbag Notification for Emergency Assistance 2

IoT-Based Car Safety System With Airbag Notification for Eme...

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2nd International Conference on Smart Technologies for Smart Nation, SmartTechCon 2023

作者： Ramalingam, L. Muthumarilakshmi, S. Venkatesh, M. Jambulingam, Umamagewaran Malathi, N. School of Computer Science and Engineering Sriram Engineering College Perumalpattu Tamil Nadu Chennai India Department of Computer Science and Engineering S.A. Engineering College Tamil Nadu Chennai India Department of Electrical and Electronics Engineering Madanapalle Institute of Technology & Science Andhra Pradesh Madanapalle India Department of Networking and Communications School of Computing College of Engineering and Technology SRM Institute of Science and Technology Tamil Nadu Kattankulathur India Department of Computer Science and Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Tamil Nadu Chennai India

ISBN: (纸本)9798350305418

The development of Internet of Things (IoT) technology has provided the potential for creative uses in many fields, including car safety systems. This research presents an IoT-based automotive safety system that intends to improve the safety of passengers during accidents by providing quick airbag deployment notification and assistance in an emergency. The system uses a network of in-car communication modules, actuators, and sensors. These sensors continually monitor several parameters, including impact intensity, deceleration, and acceleration. The technology senses the force of a collision and activates the airbags, significantly lowering the potential for serious injury. Additionally, the IoT-based system instantly notifies emergency response agencies of the collision, including the vehicle's position, the collision's severity, and if it is occupants. This makes it possible for first responders to respond swiftly and provide the occupants with quick assistance. The technology uses Wi-Fi communication protocols to send data between the car and emergency response services, ensuring a reliable and real-time connection. The collected data is securely transmitted and stored on a cloud-based platform, enabling effective analysis and historical accident tracking. Reduced response times, increased occupant safety, and better accident data collecting for further research and preventive measures are just several advantages of the proposed IoT-based automobile safety system with airbag notification for emergency help. IoT technology in automobile safety systems brings new benefits for enhancing general road safety and emergency response capabilities. © 2023 IEEE.

关键词： Airbag deployment Airbag notification Car safety system Internet of Things Sensors

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Mutual information oriented deep skill chaining for multi‐agent reinforcement learning

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CAAI Transactions on Intelligence Technology 2024年第4期9卷 1014-1030页

作者： Zaipeng Xie Cheng Ji Chentai Qiao WenZhan Song Zewen Li Yufeng Zhang Yujing Zhang Key Laboratory of Water Big Data Technology of Ministry of Water Resources Hohai UniversityNanjingChina College of Computer and Information Hohai UniversityNanjingChina Center for Cyber‐Physical Systems University of GeorgiaAthensGeorgiaUSA Information Networking Institute Carnegie Mellon UniversityPittsburghPennsylvaniaUSA Department of Electrical and Systems Engineering University of PennsylvaniaPhiladelphiaPennsylvaniaUSA

Multi‐agent reinforcement learning relies on reward signals to guide the policy networks of individual ***,in high‐dimensional continuous spaces,the non‐stationary environment can provide outdated experiences that hinder convergence,resulting in ineffective training performance for multi‐agent *** tackle this issue,a novel reinforcement learning scheme,Mutual Information Oriented Deep Skill Chaining(MioDSC),is proposed that generates an optimised cooperative policy by incorporating intrinsic rewards based on mutual information to improve exploration *** rewards encourage agents to diversify their learning process by engaging in actions that increase the mutual information between their actions and the environment *** addition,MioDSC can generate cooperative policies using the options framework,allowing agents to learn and reuse complex action sequences and accelerating the convergence speed of multi‐agent *** was evaluated in the multi‐agent particle environment and the StarCraft multi‐agent challenge at varying difficulty *** experimental results demonstrate that MioDSC outperforms state‐of‐the‐art methods and is robust across various multi‐agent system tasks with high stability.

关键词： artificial intelligence techniques decision making intelligent multi‐agent systems

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