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16th IEEE International Conference on Computational Intelligence and Communication Networks, CICN 2024

作者： Segu, Girish Sai Pavan Kumar Sivannarayana, Annam Devi Satya Naga Ramesh, S. School of Computing College of Engineering and Technology Srm Institute of Science and Technology Department of Computing Technologies India

ISBN: (纸本)9798331505264

Modern strides in autonomous vehicles and embedded advanced driver assistant part systems(ADAS) have forced the need of an efficient in addition to accurate system intended for clear road lane as in addition to vehicle detection. For road lane detection, this study introduces a new proposal through the combination of YOLOv8 (You Only Look Once), and YOLO for vehicle detection, creating a complete package for road understanding and navigation. To achieve accurate road lane detection, YOLOv8, which is the fastest and most accurate model to date, is used to ensure that it can keep track of these lanes in real-time with consistent accuracy whenever the camera is placed under various scenarios with insufficient lighting and occlusions as well. At the same time it uses YOLO for vehicle detection, which improves the recognition and interpretation of the presence and motion of vehicles in real time. The system uses Streamlit, which is an open-source app framework for Machine Learning and Data Science projects to provide an intuitive user experience. Finally, we build an interface to provide results of lane and vehicle detection in realtime manner to allow ease of monitoring and evaluation on the overall system. The combination of YOLOv8 and YOLO with Streamlit provides a powerful, scalable, and deployable solution for practical computer vision applications. Combining well established object detection algorithms with a simplified deployment platform, the proposed system tackles some of the hardest and time consuming areas of autonomous driving. The goal is to make significant contributions to both the safety and efficiency of autonomous vehicles and pave the way for further inevitable advances in ADAS. © 2024 IEEE.

关键词： Advanced driver assistance systems

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Efficient allocation of independent gridlet on small, medium, and large grid

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Personal and Ubiquitous computing 2023年第3期27卷 1029-1037页

作者： Rajeswari, D. Ramamoorthy, S. Srinivasan, R. Department of Data Science and Business Systems School of Computing College of Engineering and Technology SRM Institute of Science and Technology Tamil Nadu Kattankulathur603203 India Department of Computing Technologies School of Computing College of Engineering and Technology SRM Institute of Science and Technology Tamil Nadu Kattankulathur603203 India

Gridlet allocation in a computational grid environment is a major research issue to obtain not only the efficient gridlet allocation technique but also the time needed to obtain the efficient allocation technique. Grid computing networks have various nodes to process user jobs. To achieve the high performance of computational grid, task scheduling is an important issue. The users who are using services of grid systems are more cautious about time to complete their job. Hence, this work concentrates on gridlet allocation method used for time reduction by a genetic algorithm with the MapReduce programming model for independent tasks in computational grid. In computational grid environment, multi-objective problem formulation minimization of makespan and flowtime is considered. In this proposed technique, fitness function formulation for makespan and flowtime has been formulated mathematically. The genetic algorithm with the MapReduce programming model is implemented using MapReduce written in Java and then combined with GridSim. The experimental outcome with regard to time needed, flowtime, and makespan clearly reveals that the genetic algorithm with the MapReduce model effectively optimizes time, makespan, and flowtime in computational grid environment. A comparative study of performance efficiency among genetic algorithm with the MapReduce and sequential genetic algorithm (SGA) and parallel genetic algorithm (PGA) depicts the usefulness of the model. The execution time achieved by GA with the MapReduce model in small grid is 10.48 s, medium grid is 18.76 s, and large grid is 33.73 s. © 2023, The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature.

关键词： MapReduce

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A Multi-path Droplet Routing Protocol for Digital Microfluidic Biochip

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Informatica (Slovenia) 2024年第7期48卷 95-102页

作者： Swain, Jyotiranjan Pyne, Sumanta School of Computing and Information Technology REVA University Bangalore India Department of Computer Science and Engineering National Institute of Technology Rourkela India

Digital microfluidic biochip provides an alternative platform to synthesize the biochemical protocols. Droplet routing in biochemical synthesis involves moving multiple droplets across the biochip simultaneously. It involves challenges like moving droplets without violating the fluid constraints. This article proposed a multi-path droplet routing protocol. Multiple routes are explored and validated using special packets. These routes are then classified based on user-defined heuristics. A single route is sequentially selected for each droplet. The routes are then compacted to generate a parallel moving sequence. The simulation result shows 2.087% and 4.952% improvement in the latest arrival time for free and virtual topology, respectively. © 2024 Slovene Society Informatika. All rights reserved.

关键词： Biochips

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Dynamic Offloading and Scheduling Strategy for Telematics Tasks Based on Latency Minimization

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Computers, Materials & Continua 2024年第8期80卷 1809-1829页

作者： Yu Zhou Yun Zhang Guowei Li Hang Yang Wei Zhang Ting Lyu Yueqiang Xu North China Institute of Computing Technology Beijing Beijing100080China School of Computer and Communication Engineering University of Science and Technology BeijingBeijing100083China

In current research on task offloading and resource scheduling in vehicular networks,vehicles are commonly assumed to maintain constant speed or relatively stationary states,and the impact of speed variations on task offloading is often *** is frequently assumed that vehicles can be accurately modeled during actual motion ***,in vehicular dynamic environments,both the tasks generated by the vehicles and the vehicles’surroundings are constantly changing,making it difficult to achieve real-time modeling for actual dynamic vehicular network *** into account the actual dynamic vehicular scenarios,this paper considers the real-time non-uniform movement of vehicles and proposes a vehicular task dynamic offloading and scheduling algorithm for single-task multi-vehicle vehicular network scenarios,attempting to solve the dynamic decision-making problem in task offloading *** optimization objective is to minimize the average task completion time,which is formulated as a multi-constrained non-linear programming *** to the mobility of vehicles,a constraint model is applied in the decision-making process to dynamically determine whether the communication range is sufficient for task offloading and ***,the proposed vehicular task dynamic offloading and scheduling algorithm based on muti-agent deep deterministic policy gradient(MADDPG)is applied to solve the optimal solution of the optimization *** results show that the algorithm proposed in this paper is able to achieve lower latency task computation ***,the average task completion time of the proposed algorithm in this paper can be improved by 7.6%compared to the performance of the MADDPG scheme and 51.1%compared to the performance of deep deterministic policy gradient(DDPG).

关键词： Component vehicular dynamic task offloading resource scheduling

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Classification of Aerial Images and Applications: A Deep Learning Perspective 16

Classification of Aerial Images and Applications: A Deep Lea...

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16th IEEE International Conference on Computational Intelligence and Communication Networks, CICN 2024

作者： Monika, Pralayakaveri Reddy, Chinta Midhilesh Venkata Sai Ramesh, S. College of Engineering and Technology Srm Institute of Science and Technology Department of Computing Technologies School of Computing India

ISBN: (纸本)9798331505264

Aerial image detection is a crucial technology for a variety of applications, including but not limited to urban planning, environmental monitoring, and disaster management. In this project, we implement deep learning algorithms, in particular the AlexNet model to classify aerial images taken from different landscapes. The dataset includes cityscape, road, and nature images, which are resized and normalized to make sure these image inputs conform to the input size for the AlexNet model. Fine-tuning means changing the top layer of AlexNet to have the corresponding number of classes as that of the dataset. Using pre-trained weights helps speed up the time it will take to learn the model set up in pytorch. We use momentum Stochastic Gradient Descent (SGD) to optimize the model, and a learning rate scheduler to boost the performance. The data is then divided into training and testing sets, and the accuracy of the model is assessed using the test set. The classification results indicate that fine-tuned alexnet model work well in different aerial image classification. We present the full potential of deep learning for fast aerial image classifications, which will serve as an accurate tool for aerial image classification with applications in disaster response, urban development and environmental monitoring. This model can be further improved for different datasets and for real time image processing problems. © 2024 IEEE.

关键词： Environmental monitoring

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A Transformer-Based Approach for Fake News and Spam Detection in Social Media Using RoBERTa

A Transformer-Based Approach for Fake News and Spam Detectio...

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

作者： Pal, Ankit Abhijit Mondal, Sudin Kumar, C. Ashok Kumar, C. Jothi School of Computing College of Engineering and Technology Srm Institute of Science and Technology Department of Computing Technologies Kattankulathur Chennai India

ISBN: (纸本)9798331509828

The spread of misinformation and spam on social media has become a critical challenge, undermining information integrity and online security. Addressing this pressing issue, this study introduces an advanced solution utilizing RoBERTa, a transformer-based deep learning model, for the effective detection of fake news and spam. By employing sophisticated Natural Language Processing (NLP) techniques, the proposed method demonstrates exceptional accuracy in classifying unreliable content, making it a valuable tool in today's digital communication landscape. The approach is designed to provide real-time detection capabilities, ensuring swift identification and mitigation of misinformation and spam. Its adaptability allows seamless application across different domains, enhancing its versatility. Furthermore, the solution emphasizes sustainability by incorporating continuous monitoring and retraining mechanisms, which enable the model to maintain its performance even as the nature of misinformation evolves. This work underscores the transformative potential of deep learning models like RoBERTa in combating the widespread dissemination of online threats. By ensuring precise, scalable, and timely detection, the study contributes to creating a safer, more trustworthy digital ecosystem. In doing so, it addresses a critical need in the current era, where the reliability of information is paramount for societal and individual well-being. © 2025 IEEE.

关键词： Contrastive Learning

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Maximizing Energy Efficiency of Mobile Biomechanical Decoding with Spatial Variability Mining

Maximizing Energy Efficiency of Mobile Biomechanical Decodin...

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2024 IEEE International Conference on Consumer Electronics, ICCE 2024

作者： Gangadharan, Kiirthanaa Daniel, Cody Zhang, Qingxue Purdue University School of Engineering and Technology Electrical and Computing Engineering Indiana United States Purdue University School of Engineering and Technology Electrical and Computing Engineering Biomedical Engineering Indiana United States

ISBN: (纸本)9798350324136

Mobile sensing and data analytics usually take a substantial amount of energy, which limits the durability of the wearable devices. Especially, when deep learning is applied for data mining, the energy need is even more hungry. In this study, we take a special interest in wearable biomechanical data analytics, and propose to leverage data mining to tackle this challenge. More specifically, we leverage deep learning to mine the spatial variability of motion sensors embedded in mobile devices placed on the forearm and upper arm. In addition to compare the sensor locations, we further compare six different channels of each sensor location, thereby making twelve different configurations. Ultimately, we determine the optimal sensor location and the optimal sensor channel, which indicates the minimized data processing need in the real-world applications. The results indicate that the upper arm location and Y-axis of the accelerometer is the optimal configuration. This study will advance the field of maximizing energy efficiency of mobile biomechanical monitors, towards continuous data-driven precision medicine. © 2024 IEEE.

关键词： Big Data Deep Learning Mobile Devices Spatial Variability

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From Buzz to Blockbuster: Predicting Movie Revenue Using a Hybrid Approach Combining Machine Learning and Sentiment Analysis

From Buzz to Blockbuster: Predicting Movie Revenue Using a H...

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

作者： Vardhan, Senapathi Vishnu Balaji, Killemsetty Viswa Sai Kumar, C. Ashok Kumar, C. Jothi School of Computing College of Engineering and Technology Srm Institute of Science and Technology Department of Computing Technologies Kattankulathur Chennai India

ISBN: (纸本)9798331509828

This research investigates a hybrid approach for predicting movie revenue by integrating machine learning models with sentiment analysis. The growing influence of social media and online discussions offers a valuable opportunity to understand audience engagement and sentiment, which are critical in forecasting box office performance. By analyzing data from various social media platforms alongside movie metadata from sources like IMDb and TMDb, this study seeks to create a comprehensive model that can predict a movie's opening weekend revenue and classify it as a 'Hit,' 'Average,' or 'Flop.' The work highlights the value of audience sentiment, pre-release engagement, and historical box office trends in predicting financial outcomes. By using a combination of quantitative metrics (likes, shares, views) and qualitative data (sentiment scores, discussion themes), the study aims to refine revenue predictions and improve classification accuracy. The hybrid model's ability to incorporate both traditional and real-time data offers several advantages, including better responsiveness to audience perceptions, the ability to identify emerging trends, and more accurate revenue forecasts. This research provides actionable insights that can aid movie studios, marketers, and investors in decision-making processes, ultimately reducing financial risks associated with movie releases. The findings contribute to the growing field of predictive analytics in the entertainment industry, offering a novel and data-driven approach to understanding and forecasting movie success. © 2025 IEEE.

关键词： Predictive analytics

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Editorial:Driving into Future with Reliable,Secure,Efficient and Intelligent MetaVehicles

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IEEE/CAA Journal of Automatica Sinica 2023年第6期10卷 1355-1356页

作者： Qing-Long Han School of Science Computing and Engineering TechnologiesSwinburne University of TechnologyMelbourneVIC 3122Australia

Welcome to the sixth issue of the IEEE/CAA Journal of Automatica Sinica(JAS)in 2023!In the first issue in 2023,I briefly discussed about MetaVehicles,a technological expression of intelligent vehicles,robotics,AI,blockchain,knowledge automation,networking and communication[1].As the concept of the Metaverse continues to evolve and expand,the range of vehicle entities is diversely enlarged,including passenger vehicles,heavy duty vehicles,rail vehicles,marine vehicles,aerial vehicles,and other types of *** development and use of MetaVehicles are likely to become more ***,from a systems and control perspective,new methods of control,estimation and optimization should be developed to achieve the improved vehicular control system stability and reliability in the physical space.

关键词： estimation likely evolve

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Study of the Inhibition of Schumann Resonance-inspired Electromagnetic Field on Cancer Cell Proliferation

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Journal of Bionic engineering 2025年第1期22卷 341-353页

作者： Xianqiang Yan Xuelei Liu Shujun Zhang Zhenning Liu Luquan Ren Key Laboratory of Bionic Engineering(Ministry of Education) College of Biological and Agricultural EngineeringJilin UniversityChangchun130022China School of Computing and Engineering University of GloucestershireThe ParkCheltenhamGL502RHUK School of Mechanical Engineering Shandong University of TechnologyZibo255000China

Organisms on Earth evolve and coexist with natural Electromagnetic Fields(EMFs).Although many reports have suggested the potential anti-neoplastic effects of EMFs with specific parameters,the studies on the influence of natural EMFs on cancers are still ***,an EMF emitter has been developed to investigate the effects of the extremely-low frequency SR-mimicking EMF(SREMF)on cancer and normal cell *** numerical simulation has revealed that the emitter with specific parameters is able to enhance EMF intensity and uniformity on the designated plane above the *** importantly,honeycomb-like emitter array can generate a stronger EMF intensity on the 20 mm plane above the *** colony formation assays have demonstrated that SREMF generated by the honeycomb-like emitter array can significantly inhibit Hela cell proliferation in a cell-density-dependent *** morphological changes of SREMF-exposed Hela cells suggest that the anti-proliferative effect of SREMF may be caused by apoptosis *** contrast,no detrimental effect is observed for SREMF-treated normal cells,which probably can be explained by the evolutionary ***,this work can not only contribute to understanding the impact of natural EMF on creatures,but also afford a novel strategy to personalized cancer prevention and treatment.

关键词： Electromagnetic fields(EMFs) Schumann resonance EMF emitter Cancer Cell proliferation

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