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2025 IEEE International Conference on Computational, Communication and Information Technology, ICCCIT 2025

作者： Mittal, Mudit Sehgal, Archana Varshney, Neeraj Kumar, Sunil Prashanth Boob, Nandini Shirish Reddy, R. Akhilesh Graphic Era Deemed to Be University Department of Computer Science & Engineering Dehradun India Lovely Professional University Phagwara India GLA University Department of Computer Engineering and Applications Mathura India New Horizon College of Engineering Department of Mechanical Engineering Bangalore India Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Tamilnadu Chennai India MLR Institute of Technology Department of CSE-AI&ML Telangana Hyderabad India

ISBN: (纸本)9798331512965

Urban traffic networks are efficient route design problems due to the intricacy and dynamic character of contemporary cities. Conventional methods hamper real time adjustment to continually changing traffic conditions, thereby providing suboptimal routes, a greater burden on the roadways, and greater air pollution. This thesis presents a novel deep reinforcement learning (DRL) method to optimize the route planning for urban traffic networks. With this rendering of real time traffic data, we use DRL to make autonomous decisions to reduce travel time and congestion of routes. One advantage of using sophisticated neural networks for feature extraction and policy learning is that, since the model is so adaptable to changing traffic patterns, the model guarantees that. And, in comparison to common routing algorithms, the results of simulations using real world traffic data show that this method is able to greatly enhance route efficiency and overall traffic flow management. The suggested DRL based system is feasible as a smart city program based on intelligent transportation system which can scale to grand metropolitan regions. The research presents that DRL can enhance the commuter run experience, reduce the environmental effect, and alter urban transportation. Future study will also investigate how this system can be better optimized when integrated with traffic infrastructure enabled by the Internet of Things. © 2025 IEEE.

关键词： Deep reinforcement learning

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An end-to-end smart IoT-Driven navigation for social distance monitoring framework for Covid-19 using deep convolutional neural networks in Deep Learning

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Measurement: Sensors 2024年 32卷

作者： Banothu, Balaji Ramdas, Vankdothu Department of Computer Applications National Institute of Technology Trichy India Department of Computer Science and Engineering Balaji Institute of Technology & Science Laknepally Warangal India

The extraordinary worldwide spread of the COVID-19 coronavirus pandemic has considerably boosted the development of new Internet-of-things (IoT)-based strategies to stop, stop, monitor, or foresee virus propagation among people. These technologies are being used more often for a variety of practical purposes that include enhanced safety, discipline, and control. The novel coronavirus creates a global pandemic and affects a human, besides a huge death rate. As there is no proper medication, at least the spread should be stopped or it should be minimized. One way is to avoid physical contact. People should maintain a minimum distance to avoid direct contact. This paper presents detecting the social distance between pedestrians in public places. The idea is to take live videos from the camera we set up in a public place and create bounding boxes for each person in the video. If two or more people are close to each other violating social distance, then we put a red box around them otherwise we put a green box. The proposed framework's experimental results exhibit good results in monitoring the distance among humans in public areas compared to the existing methods. © 2024 The Authors

关键词： Coronavirus

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Smartening Energy Consumption: A Comparative Study of Energy Optimization Strategies in IoT Environments 8th

Smartening Energy Consumption: A Comparative Study of Energy...

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8th International Conference on Information System Design and Intelligent applications, ISDIA 2024

作者： Biswal, Sudhansu Mohan Mohapatra, Ambarish G. Panigrahi, Sunil K. Mishra, Sunil K. Mohanty, Jnyana Ranjan Bajaj, Mohit Barik, Rabindra K. Department of Electronics Silicon Institute of Technology Bhubaneswar India Department of Computer Science and Engineering Einstein Academy of Technology and Management Bhubaneswar India School of Electronics Engineering Kalinga Institute of Industrial Technology Bhubaneswar India School of Computer Applications KIIT Deemed to be University Bhubaneswar India Dehradun India Graphic Era Hill University Dehradun248002 India

ISBN: (纸本)9789819748945

Nowadays, the Internet of Things (IoT) platform is being utilized extensively to integrate the real world with cyber-physical systems. The IoT is being used to share data among small and resource-constrained devices. Thus, optimization has the key to providing energy efficiency as well as the suitability of network architectures of IoTs. This article portrays a brief introduction to optimization strategies applied in different energy-efficient IoT and M2M technologies. In this article, a basic energy optimization strategy for IoT architecture is demonstrated. The simulation focuses on minimizing energy consumption by implementing a simple optimization algorithm. The optimization algorithm iterates through each device, selectively turning off devices that fall below their energy thresholds. The simulation results reveal the total energy consumption before and after optimization, highlighting the percentage reduction achieved. While this simulation provides a basic framework, real-world energy optimization in IoT systems involves more complex algorithms that consider various factors such as data processing requirements, network conditions, and communication protocols. Nevertheless, the presented result serves as a starting point for researchers and practitioners to explore energy optimization strategies in IoT networks. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Energy efficiency Energy optimization IoT IoT architecture M2M

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Harnessing Tech Innovations for Diversified Multigenerational Work Force and Work Life Balance

Harnessing Tech Innovations for Diversified Multigenerationa...

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2025 IEEE International Conference on Computational, Communication and Information Technology, ICCCIT 2025

作者： Mittal, Mudit Sharma, Vishal Varshney, Neeraj Kumar, Sunil Prashanth Boob, Nandini Shirish Reddy, R. Akhilesh Graphic Era Deemed to Be University Department of Computer Science & Engineering Dehradun India Lovely Professional University Phagwara India GLA University Department of Computer Engineering and Applications Mathura India New Horizon College of Engineering Department of Mechanical Engineering Bangalore India Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Tamil Nadu Chennai India MLR Institute of Technology Department of CSE-AI&ML Telangana Hyderabad India

ISBN: (纸本)9798331512965

This research aims to examine how technologically innovative breakthroughs may help a multigenerational heterogeneous workforce to maintain a good work and life balance. We investigate how technology can be used to strategically raise up the rate of participation and align with the particular needs of different age groups. The research findings show how AI powered personal learning, collaborative digital communication tools and flexible remote working solutions promote a healthy and mature culture in the workplace. The findings explain how key it is to utilize technology in a refined manner such that all workers can thrive and maintain a sustainable balance between their work and home life. © 2025 IEEE.

关键词： Collaborative learning

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On Efficient Processing of Queries for Live Multi-Streaming Soiree Organization

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IEEE Transactions on Services Computing 2023年第4期16卷 2812-2826页

作者： Kankeu Fotsing, C.P. Shen, Chih-Ya Huang, Liang-Hao Chen, Yi-Shin Lee, Wang-Chien Yang, De-Nian International Graduate Program Academia Sinica Social Networks and Human-Centered Computing Program Taipei115 Taiwan National Tsing Hua University Institute of Information Systems and Applications Hsinchu300 Taiwan National Tsing Hua University Department of Computer Science Hsinchu300 Taiwan Academia Sinica Institute of Information Science Taipei115 Taiwan Pennsylvania State University Department of Computer Science and Engineering State CollegePA16801 United States

Real-time social interactions and multi-streaming are two critical features of live streaming services. In this paper, we formulate a new fundamental service query, Social-aware Diverse and Preferred Organization Query (SDSQ), that jointly selects a set of diverse and preferred live streaming channels and a group of socially tight viewers for organization of a live multi-streaming soiree. We prove that SDSQ is NP-hard and inapproximable within any factor, and design SDSSel, a 2-approximation algorithm with a guaranteed error bound. Moreover, we study SDSQ-T, a special case of SDSQ, where the social graph is a threshold graph, and propose TDSSel, a 2-approximation algorithm without any error to SDSQ-T. We propose two pruning strategies, PCP and CDP to boost SDSSel and TDSSel. We further propose a more challenging but practical service query, Generalized Social-aware Maximum Preferred and Diverse Query (GSPQ), a generalization of SDSQ. We design GPDSel, a 4-approximation algorithm for GSPQ with a guaranteed error bound. We propose a strategy to improve the approximation ratios of the proposed algorithms. A user study on Twitch validates SDSQ, and the large-scale experiments on real datasets demonstrate the superiority of the proposed algorithms over several baselines for live-streaming services. © 2008-2012 IEEE.

关键词： Approximation algorithms

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Secrecy Performance Analysis of Space-to-Ground Optical Satellite Communications 16

Secrecy Performance Analysis of Space-to-Ground Optical Sate...

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16th International Conference on Advanced Technologies for Communications, ATC 2023

作者： Nguyen, Thang V. Phamz, Thanh V. Phamx, Anh T. Ngoc, Dang T. Posts and Telecommunications Institute of Technology Wireless Systems and Applications Lab. Hanoi Viet Nam Shizuoka University Department of Mathematical and Systems Engineering Japan The University of Aizu Department of Computer Science and Engineering Japan

ISBN: (纸本)9798350301328

To cope with the scarcity of radio frequency (RF) counterparts, free-space optics (FSO) based on satellite communications technologies have recently gained a lot of interest. Firstly, this investigates the security of space-to-ground intensity modulation/direct detection FSO satellite communications in various effects from the physical world, such as propagation loss, beam misalignment, cloud attenuation, and fading caused by atmospheric turbulence. Next, A wiretap channel composed of a genuine broadcaster Alice (i.e., the satellite), a legitimate user Bob, and an eavesdropper Eve is considered over turbulent channels characterized by the Fisher-Snedecor mathcal{F} distribution. In addition, the closed-form expressions of the secrecy performance metrics, including average secrecy capacity, secrecy outage probability, and strictly positive secrecy capacity, are derived in our study. Finally, the numerical findings indicate that the satellite altitude of 600 km should be used to achieve a secrecy outage of roughly 50% for the whole turbulence regime. In addition, high levels of cloud liquid water content (CLWC) could improve the secrecy performance as it is shown that, the average secrecy capacity increases by about 12% when the CLWC increases from 1mathrm{~mg}/mathrm{m}{3} to 2mathrm{~mg}/mathrm{m}{3}. © 2023 IEEE.

关键词： Atmospheric turbulence

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A SPB based LB Algorithm in Order to Schedule the Sequence Task in CCS 4

A SPB based LB Algorithm in Order to Schedule the Sequence T...

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4th IEEE International Conference on Advance Computing and Innovative Technologies in engineering, ICACITE 2024

作者： Bindu, G. B. Hima Manjula, L. Tellur, Anil Vincy Devi, V.K. Nasrulaah, Najlaa Muhsin, Omar The Apollo University Department of Cse School of Technology Chittoor India The Oxford College of Engineering Department of Computer Science and Engineering Bangalore India Karnataka Bengaluru India Adi Shankara Institute of Engineering&Technology Department of Computer Applications kalady India National University of Science and Technology Faris Collage of Nursing Dhi Qar64001 Iraq Al-Farahidi University Baghdad Iraq

ISBN: (纸本)9798350360165

The increasing pervasiveness of computers in almost every aspect of our everyday lives has led to a commensurate rise in the need for computing resources, including bandwidth, processing power, and storage space. Originally, computer networks were created to address this difficulty, which helped ideas and paradigms like distributed computing, grid computing, and cluster computing to develop. But providing such services calls for advanced methods to effectively handle workloads and quickly attend to customer demands. One of the major problems with cloud computing is load balancing, which is a vital procedure that distributes work among virtual computers to avoid excess or underuse. In order to support the increasing number of users, the algorithms controlling this work assignment need to balance robustness with efficient use of resources. The related costs and hazards increase, especially for larger organisations, which emphasises how crucial it is to evaluate the risks involved in deploying load balancing algorithms in order to achieve desired service levels. Assessing the algorithms, applications, and rules in a virtualized setting is a useful method for risk assessments. With the use of simulation tools, one may practically mimic the intricacies of actual cloud settings and gain insights into possible problems and performance. As a powerful simulation toolset, CloudSim is intended to fully represent and simulate various cloud environments. It makes experimentation and optimisation easier by providing the ability to design and test different load balancing algorithms and cloud broker policies. © 2024 IEEE.

关键词： Cluster computing

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Cloud-Connected Wearable Devices for Lone Worker Protection With Wireless Sensor Networks 4

Cloud-Connected Wearable Devices for Lone Worker Protection ...

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4th International Conference on Sustainable Expert Systems, ICSES 2024

作者： Mohankumar, N. Manivannan, K.K. Babu, A. Ramesh Sampoornam, M. Maria Kovarasan, Rajesh Kambattan Srinivasan, C. Nagpur Campus Maharashtra Pune India Kcg College of Technology Department of Mechatronics Engineering Tamil Nadu Chennai India School of Computer Applications Vidya Vikas Institute of Engineering and Technology Karnataka Mysore India J.J College of Engineering and Technology Department of Information Technology Tamil Nadu Tiruchirappalli India Vel Tech Rangarajan Dr. Sagunthala R&d Institute of Science and Technology Department of Computer Science and 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)9798331540364

Various sectors continue to prioritize lone worker safety, which requires creative ways to provide quick support and monitoring. To safeguard lone workers, this paper introduces a new method that makes use of cloud-connected wearable devices that are coupled with Wireless Sensor Networks (WSNs). The system design relies on sensors embedded in wearable devices that can monitor a range of environmental elements as well as the vital signs of workers. A central cloud platform receives data in real-time via wireless communication from these devices and processes and analyzes it. The system may detect possible dangers or crises using smart techniques and immediately notify those who are supposed to react or the proper authorities. Cloud computing also makes it easy to store, analyze, and monitor large amounts of data over time, which is great for doing proactive risk assessments and improving safety standards. It demonstrates solution's capacity to improve lone worker safety, reduce reaction times, and mitigate hazards via simulations and actual deployment scenarios, efficacy and dependability. This growing body of knowledge on how to protect lone workers in various workplaces by using the power of IoT and cloud computing. © 2024 IEEE.

关键词： Cloud platforms

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Estimating Fetal Gestational Age from Ultrasound Images Using Deep Learning Techniques

Estimating Fetal Gestational Age from Ultrasound Images Usin...

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2025 International Conference on Multi-Agent Systems for Collaborative Intelligence, ICMSCI 2025

作者： Suganthi, N. Mohana Subhashni, R. Chitra, A. Rajakumari, S. Priya, T. Anitha, P. Caroline Vel Tech Rangarajan Dr.Sagunthala R&d Institute of Science and Technology Department of Computer Science and Engineering Avadi Chennai India St.Peter's Institute of Higher Education and Research Department of Computer Applications Avadi Chennai54 India EastPoint College of Higher Education Department of Computer Application Jnana Prabha East Point Campus Karnataka Bengaluru560049 India

ISBN: (纸本)9798331509828

Monitoring fetal development and guaranteeing mother health depend on correct estimate of fetal gestational age. Conventional approaches of gestational age estimate mostly depend on manual estimations from ultrasonic scans, which may be laborintensive and prone to human mistake. Variability in operators, picture quality, and fetal position might potentially affect current methods and cause possible errors and inconsistent outcomes. We offer the Ultrasound Images Using Deep Learning Techniques (UI-DLT) framework, a strong, automated method for fetal gestational age estimate, to handle these problems. UI-DL T analyzes ultrasonic pictures and enhances the accuracy and consistency of gestational age estimations by using deep learning-more especially, convolutional neural networks (CNNs). UI-DLT reduces human error and standards outcomes across several clinical environments by automating the analytic process. CNNs are used in the proposed system to categorize and examine ultrasonic pictures, therefore spotting important information on fetal development and growth. By extracting deep picture elements that match developmental phases, our method guarantees accurate and consistent gestational age estimations, thereby beyond the capacity of conventional manual techniques. Experimental data on ultrasonic image datasets shows significant increases in estimating accuracy of the UI-DLT framework. This approach not only improves the accuracy of gestational age prediction but also helps to progress clinical decision-making and mother-fetal health care. © 2025 IEEE.

关键词： Ultrasonic imaging

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Smart Auto Image Captioning Using LSTM and Densenet Network 6

Smart Auto Image Captioning Using LSTM and Densenet Network

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6th International Conference on Contemporary Computing and Informatics, IC3I 2023

作者： Arumugam, M. Vijayakumar, P. Siddharrth, G.M. Rohini, J. Karthikeyan, A. Rohith Bhat, C. Kongu Engineering College Department of Computer Technology - PG Perundurai India Kongu Engineering College Department of Master of Computer Applications Tamil Nadu Perundurai638060 India Erode Sengunthar Engineering College Department of Computer Science and Engineering Thuduppathi638057 India PSNA College of Engineering and Technology Department of Electrical and Electronics Engineering Dindigul624622 India Saveetha School of Engineering Department of Computer Science and Engineering Saveetha Institute of Medical and Technical Sciences Chennai India

ISBN: (纸本)9798350304480

The integration of semantic attention increase photo captioning ability. The premise underlying semantic attention-based techniques is that the model should prioritize words or qualities with high semantic value. Prior studies focused on the attribute detector and the captioning network as distinct entities. This hampers the best use of semantic data. Image captioning is the computational process of producing accurate written descriptions of an image's or video's visual content. Captioning images makes it easier to construct contextualized interpretations, allowing people to situate photographs within a larger context. Using photo captions can be useful in a variety of contexts, such as conducting research with an unlabeled image collection or attempting to identify. previously unknown patterns. Subtitles added to the photos using a Deep Learning Model. Recent advances in the fields of deep learning and natural language processing have made it much easier to generate captions for input photographs. This study will use neural networks to generate image captions on their own. The Long Short-Term Memory (LSTM) model, a type of recurrent neural network, used as an encoder, together with a pre-trained lexicon and visual attributes. This enables the extraction of image data and the generation of captions. © 2023 IEEE.

关键词： Deep Learning Model Densenet Network Long Short-Term Memory Remote Sensing

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