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11th International Conference on Vehicle technology and Intelligent Transport Systems, VEHITS 2025

作者： Singh, Ashutosh Gupta, Arobinda Dept. of Computer Science & Engineering Indian Institute of Technology KharagpurWB-721302 India

ISBN: (纸本)9789897587450

Ridesharing systems have become an important part of urban transportation. At the same time, electric vehicle (EV) adoption is also growing at a fast pace as an eco-friendly and sustainable transportation option. To operate a ridesharing system with EV fleets, scheduling an EV fleet to serve passenger requests requires consideration of both the requests, and the available charge and potential future charge requirements of the EVs. In this paper, we address the problem of scheduling EVs by a ridesharing operator, and propose four algorithms that schedule passenger requests while taking into consideration charging requirements of the EVs. Detailed simulation results are presented on a real world data set to show that the algorithms perform well. Copyright © 2025 by SCITEPRESS - Science and technology Publications, Lda.

关键词： Urban transportation

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Smart ANPR: A Checkpoint-Based Toll and Parking Management System Using Hybrid OCR and Blockchain 3

Smart ANPR: A Checkpoint-Based Toll and Parking Management S...

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3rd International Conference on Intelligent and Innovative Technologies in Computing, Electrical and Electronics, IITCEE 2025

作者： Samarth, M.N. Gopisetti, Sampriti Chaitra, M. Bnm Institute of Technology Affiliated to Vtu Dept. of Computer Science and Engineering Bangalore India

ISBN: (纸本)9798331515911

Automated Number Plate Recognition is one of the most important applications of computer vision, employed in the enforcement of traffic law, vehicular management, security, and even at toll collection. This paper proposed an advanced system for ANPR that can move away from the traditional booth and establish a checkpoint. The strategically located cameras with checkpointing will automatically capture the plates of passing vehicles, permitting road tax and parking fees to be deducted without making contact. A hybrid approach that combines the capabilities of OpenCV, Tesseract OCR, and deep-learningbased OCR models helps tackle such challenges as stable images from live video feeds and highly accurate text recognition. To this end, since the system integrates high-resolution frame stabilization using OpenCV to ensure crystal clear captures, it further employs the use of YOLOv8 to boost the precision of real-time detection. The blockchain technology is further implemented to ensure that transactions are tamper proof, therefore ensuring secure and transparent billing systems. This makes the ANPR solution deployable on edge devices, which opens real-time processing capabilities for high-speed and high-volume environments, thereby offering a scalable and efficient approach for intelligent transportation and smart city applications. © 2025 IEEE.

关键词： Blockchain

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Dynamic Charging on the Go: Optimizing Mobile Charging Stations for Electric Vehicle Infrastructure 11

Dynamic Charging on the Go: Optimizing Mobile Charging Stati...

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11th International Conference on Vehicle technology and Intelligent Transport Systems, VEHITS 2025

作者： Jain, Suhas Gupta, Arobinda Dept. of Computer Science & Engineering Indian Institute of Technology WB Kharagpur721302 India

ISBN: (纸本)9789897587450

With the growing adoption of electric vehicles (EVs) as an eco-friendly and sustainable means of transportation, availability of adequate EV charging infrastructure has become very important. While fixed charging stations are the primary means for public charging, they can be effectively augmented by mobile charging stations. In this paper, we address the problem of planning and operating a mobile charging station fleet by an operator. We propose an algorithm for the operator to use for route planning of the MCSs and for scheduling charging requests of EVs to them to try to maximize the number of charging requests served. Detailed simulation results are presented in different realistic scenarios to show that the proposed algorithm works well. Copyright © 2025 by SCITEPRESS - Science and technology Publications, Lda.

关键词： Electric vehicles

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Exploring Agile Methodologies in Educational Software Development- A Comparative Analysis and Project Management Insights 3

Exploring Agile Methodologies in Educational Software Develo...

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3rd IEEE International Conference on ICT in Business Industry and Government, ICTBIG 2023

作者： Kate, Vandana Bhalerao, Shilpa Sharma, Vanshika Acropolis Institute of Technology and Research Dept. of Computer Science and Information Technology Indore India Acropolis Institute of Technology and Research Dept. of Computer Science and Technology Indore India

ISBN: (纸本)9798350343274

In the realm of academic research, the task of determining the most suitable Software Development Life Cycle (SDLC) becomes complex, owing to the diverse project requirements, rapidly changing technology, team skills and experience, organizational culture and readiness, flexibility and adaptability needs, and decision-making complexity. It requires a thorough analysis of various factors to make a wise decision that aligns with the project's goals and constraints. A variety of models, including Waterfall, Prototyping, and Spiral, offer distinct sets of advantages and disadvantages. Agile modeling is a flexible and iterative software development strategy that emphasizes collaborative modeling and rapid feedback. The agile philosophy promotes transparency, effective communication, and embracing change to meet evolving project needs efficiently and effectively. An In-dept. Examination of Agile Methodologies versus Conventional Approaches gives a deeper understanding of their respective strengths, weaknesses, and applicability. Also as the educational institutes increasingly recognize the importance of providing industry-relevant skills and preparing students for dynamic work environments, agile methodologies are likely to play a vital role in shaping the future of education and fostering the success of students in their careers. In this paper, we have explored the practical application of well-established Agile methodologies including Scrum, Kanban, Lean, Extreme Programming (XP), Feature Driven Development, and Dynamic Systems Development Method (DSDM) within the context of educational institutions and project management. © 2023 IEEE.

关键词： Project management

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Deep Learning-Driven Cloud Intrusion Detection: Internet of Things and Cloud Networks 1

Deep Learning-Driven Cloud Intrusion Detection: Internet of ...

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1st International Conference on Advances in computer Science, Electrical, Electronics, and Communication Technologies, CE2CT 2025

作者： Moon, Iffath Tanjim Islam Mondal, Md. Nazrul Rajshahi University of Engineering & Technology Dept. of Computer Science & Engineering Rajshahi6204 Bangladesh

ISBN: (纸本)9798331518578

In this paper, we have utilized deep learning approaches to detect cloud intrusion for the Internet of Things (IoT). The emerging growth of IoT and cloud environments has revolutionized many industries by allowing real-time data sharing and storage. Cloud platforms offer the essential infrastructure for managing the massive volumes of data produced by IoT devices. However, due to several flaws, including insufficient encryption, insecure APIs, and weak authentication, intruders can exploit the vulnerabilities to interrupt services and obtain unauthorized access. Therefore, deep learning can be an effective solution to detect cloud intrusion in IoT. In this research, we have developed individual convolutional neural network (CNN) and long short-term memory (LSTM) models, along with a hybrid CNN-LSTM model for multiclass classification and binary classification to detect cloud intrusion. In addition, we have examined which models perform the best for cloud intrusion detection for IoT among the individual and hybrid models. The proposed models are effective in detecting cloud intrusion, as CNN can extract spatial features, and LSTM can capture temporal dependencies from network traffic data. Furthermore, we have utilized one of the most recent and comprehensive datasets for IoT, the ToN-IoT dataset, containing real-world data. In multiclass classification, the hybrid CNN-LSTM has given the best performance, achieving an accuracy of 97.65%, precision of 97.72%, recall of 97.65%, f1 score of 97.67%, and AUC of 99.93%. In binary classification, all CNN, LSTM, and hybrid CNN-LSTM have demonstrated high performance, where LSTM has achieved the highest accuracy of 99.97%. Moreover, we have evaluated the performance of multiclass classification for detecting nine distinct kinds of attacks. In this instance, CNN has shown better performance in detecting backdoor attacks. Thus, this research can make the IoT platform more secure by efficiently detecting cloud intrusion. © 2025

关键词： Network intrusion

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Assessing Interaction Mechanics in Extended Reality for Control Engineering Applications 32

Assessing Interaction Mechanics in Extended Reality for Cont...

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32nd International Conference on Software, Telecommunications and computer Networks, SoftCOM 2024

作者： Remenyi, Armin Alsaleh, Saleh Tepljakov, Aleksei Tallinn University of Technology Dept. of Software Science Tallinn Estonia Tallinn University of Technology Dept. of Computer Systems Tallinn Estonia

ISBN: (纸本)9789532901382

This research paper delves into the evaluation of human-machine interactions within the realm of Extended Reality (XR) applications, focusing on the integration of Digital Twins (DT) for control systems. The research explores multiple XR interaction methods and assesses their efficacy in enhancing user experience and operational efficiency in industrial settings, specifically in control systems. The study employs advanced tools such as the Microsoft HoloLens 2 headset, Unreal Engine 5, and the Mixed Reality Toolkit (MRTK) to develop and test XR interaction methods. Data collection and analysis involves usability testing using the System Usability Scale (SUS) and the NASA Task Load Index (NASA-TLX) to measure cognitive, physical and temporal demands on users. The findings highlight the most effective XR interaction strategies, providing valuable insight for future research and practical implementations in intelligent manufacturing environments. © 2024 University of Split, FESB.

关键词： NASA

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Securing of Cloud Storage Data Using Hybrid AES-ECC Cryptographic Approach

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Journal of Mobile Multimedia 2023年第2期19卷 363-388页

作者： Kumar, Sunil Kumar, Dilip Dept. of Computer Science and Engineering National Institute of Technology Jamshedpur India

Internet has revolutionized the world in a way no one could have ever imagined. It paved the way for various different technologies, that have transformed the world exceptionally. Internet enabled cloud technology which provides cost-effective, scalable, on-demand computing resources with little to no downtime. Cloud storage allows its users to store and access private data from anywhere in the world without needing any high-end computing system. Cloud storage isn’t always secure, but that doesn’t imply it isn’t. The security of a data depends on the security policies followed by the provider along with the security of the communication channel via which the data is being sent. Encryption is used to obfuscate the data so that it can only be viewed when correct credentials, known as encryption keys, are provided. Following study proposes an encryption technique using ECC (Elliptic Curve Cryptography) along with AES (Advance Encryption Standard) to provide data confidentiality in an efficient way for securing data on cloud and hence, protect the personal information of user from any adversary. This new method is more effective, and the results are superior as a consequence. © 2022 River Publishers.

关键词： AES asymmetric encryption authentication blowfish cloud storage security ECC Symmetric encryption

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Effective Pest Detection by YOLOv5 with BottleneckCSP 2

Effective Pest Detection by YOLOv5 with BottleneckCSP

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2nd International Conference on Power, Electrical, Electronics and Industrial Applications, PEEIACON 2024

作者： Tani, Ishrat Zahan Jannat, Tasmia Rajshahi University of Engineering & Technology Dept. of Computer Science & Engineering Rajshahi Bangladesh

ISBN: (纸本)9798331517984

Pest damage poses a significant risk to crop quality and productivity, underscoring the necessity for accurate and efficient detection methods for farmers. Artificial intelligence (AI) facilitates targeted pest management with reduced reliance on broad-spectrum insecticides and enables early detection of insect infestations. Object detection, a computer vision approach, is revolutionizing pest identification in agriculture by locating pests in digital crop photographs. However, the detection of tiny objects within these datasets presents challenges due to sparse spatial information. In response to this challenge, we have replaced the C3 block of YOLOv5 with the BottleneckCSP module, which is more effective for feature mapping of tiny objects. Our enhanced model demonstrates superior performance on the IP102 dataset, which is characterized by the presence of tiny objects. Comparative analysis reveals that our BottleneckCSP-integrated model significantly improves feature mapping, resulting in a notable increase in mean average precision (mAP). Specifically, our modified model achieves an mAP of 63.9%, outperforming many state-of-the-art methods on this dataset. This advancement in the YOLOv5 architecture sets a new benchmark for AI-powered precision agriculture, providing farmers with a highly efficient tool for crop protection and pest management. The significant performance gains highlight the potential of our approach to transform current agricultural practices, paving the way for more sustainable and effective pest control strategies. © 2024 IEEE.

关键词： Damage detection

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Churn Buster: Uncovering Patterns and Predicting Churn in OTT Platforms 6

Churn Buster: Uncovering Patterns and Predicting Churn in OT...

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6th Parul International Conference on Engineering and technology, PICET 2024

作者： Anand, Akshat Sarika, D. Malviya, Harshit Vatsa, Dipti Yadwad, Sunita Adarsh Dholariya, Shreya M. Dept. Computer Science Engineering Parul Institute of Engineering and Technology Vadodara India

ISBN: (纸本)9798350369748

Churn, a common concern among business leaders, poses a significant challenge in today's fast-paced world where consumer attention spans are short. This research focuses on churn analysis within the OTT streaming platform sector, essential for retaining subscribers and ensuring business longevity. Utilising data preprocessing methods and machine learning algorithms, this study aims to identify factors influencing churn and provide insights for targeted retention strategies. The dataset includes 16 variables, with churn serving as the binary-dependent variable. Data preprocessing involved addressing missing values and converting categorical data into numerical format. Four machine learning models-Random Forest, XGBoost, K-Nearest Neighbors, and Support Vector Machine-were applied, with Random Forest achieving the highest accuracy of 89%. Evaluation metrics like confusion matrices, ROC curves, and Precision-Recall curves were used to evaluate model performance, revealing Random Forest's effectiveness in churn prediction. Principal Component Analysis (PCA) was employed to handle the complexity of user data, aiding in the identification of influential churn factors. This study emphasises the importance of understanding customer behaviour and utilising machine learning to forecast subscription choices. The insights gained provide valuable guidance to OTT companies for enhancing customer satisfaction and retention strategies, ensuring competitiveness in a dynamic industry landscape. © 2024 IEEE.

关键词： Sales

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Enhancing VANET Efficiency with ACRR Technique for Improved Traffic Management and Network Scalability

Enhancing VANET Efficiency with ACRR Technique for Improved ...

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2024 International Conference on Science, technology, Engineering and Management, ICSTEM 2024

作者： Karthick, K. Talari, Praveen Nishadevi, V. Krishna Prakash, R. Asokan, R. Velusamy, A. Dept. of Computer Science and Engineering Kongunadu College of Engineering and Technology Tamilnadu Trichy India Dept. of Computer Science and Engineering Vignan Bharathi institute of technology Telangana Hyderabad India Dept. of Computer Science and Engineering Mahendra College of Engineering Tamilnadu Salem India Dept. of Information Technology Sona College of Technology Salem India Dept. of Electronics and Communication Engineering Kongunadu College of Engineering and Technology Tamilnadu Trichy India Dept. of Information Technology Sona College of Technology Tamil Nadu Salem India

ISBN: (纸本)9798350376913

The role of Vehicular Ad Hoc Networks (VANETs) is crucial in enabling Intelligent Transportation System (ITS) technologies such as safe financial transactions, media applications, and effective traffic control. As traffic increases, the topology of vehicular networks is in constant flux, and the sparse distribution of vehicles, particularly on highways, presents challenges for network scalability. This situation makes it difficult for cars to keep consistent routes inside the network, which affects the stability of the network. To address these challenges, the developed Adaptive-ant Colony based Randomized Recommendation (ACRR) technique emerges as a unique solution for enhancing VANETs by reducing travel time. In instances of high traffic density on busy roads, the ACRR algorithm is effectively utilized to group vehicles. Leveraging data collected from these densely populated road segments, the system identifies congestion-prone areas and formulates optimal vehicle routes based on customized vehicle groupings. The framework's performance evaluation encompasses various parameters, including packet loss, message transmission rate, energy consumption, and average cluster growth. The proposed VANET framework, empowered by the ACRR algorithm, achieves an impressive message transmission rate of approximately 80%. In comparison, alternative methods like Re-RouTE exhibit a limited transmission rate of 70%, while others such as Net Run Rate (NRR), DIVERT 30, and DIVERT-60 demonstrate rates below 20%. Furthermore, the framework's parcel loss is significantly reduced to only 33% of that observed in the standard VANET framework. As a result, the ACRR algorithm integrated into the VANET framework demonstrates notable efficiency when compared to other approaches. It is crucial to recognize that, even with a refined technique, managing traffic congestion remains challenging if drivers disregard the recommended routing suggestions. Overall, this research offers insights into the p

关键词： Traffic congestion

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