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3rd International Conference on Intelligent Data Communication Technologies and Internet of Things, IDCIoT 2025

作者： Kumar, Boddu Manoj Rajeswari, G. Duraipandian, Kavitha David, Shiela Dept. of Computer Science and Engineering with specialization in Blockchain Technology Sathyabama Institute of Science and Technology Tamil Nadu Chennai India Dept. of Computer Science and Engineering Sathyabama Institute of Science and Technology Tamil Nadu Chennai600119 India

ISBN: (纸本)9798331527549

Blockchain technology has become more widespread in our lives revolutionizing various industries through improved security, transparency, and operational efficiency. Its decentralized ledger system is particularly effective in areas like finance and supply chain management ensuring data integrity and reducing the need for middlemen. This study explores how blockchain technology is used in the process of buying vehicles highlighting its benefits compared to methods. Traditional vehicle purchases involve a network of dealers, banks and regulators leading to inefficiencies, higher costs and potential fraud. The usual process involves steps like financing checking vehicle history and changing ownership. Each requiring extensive paperwork and third-party intervention. In contrast using blockchain for vehicle purchases can simplify things through contracts that automate tasks like verifying history securely transferring ownership instantly settling payments. The unchangeable nature of records ensures that all transaction data remains secure against tampering fostering trust between buyers and sellers. Furthermore, blockchain can cut costs significantly by removing the need for middlemen such, as escrow services and title companies while reducing burdens. The advanced security elements built into technology help reduce the chances of fraudulent actions like altering odometer readings or selling stolen cars. This study showcases a comparison illustrating how blockchain can simplify the process of buying vehicles offering an economical and secure experience. © 2025 IEEE.

关键词： Decentralized finance

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J-NECORA: A Framework for Optimal Resource Allocation in Cloud-Edge-Things Continuum for Industrial Applications With Mobile Nodes

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IEEE Internet of Things Journal 2025年第11期12卷 16525-16542页

作者： Pettorali, Marco Righetti, Francesca Vallati, Carlo Das, Sajal K. Anastasi, Giuseppe Univ. of Pisa Dept. of Information Engineering Italy Missouri Univ. of Science and Technology Dept. of Computer Science RollaMO United States

In the Industrial Internet of Things (IIoT) landscape, where the Cloud-to-Things Continuum (C2TC) paradigm is now a reality, industrial applications need to cope with highly heterogeneous network and computing resources. Moreover, many industrial applications also involve Mobile Nodes (MNs). Efficient allocation of network and computing resources to meet the stringent requirements of such applications is often a very challenging task. In this paper, we propose J-NECORA (Joint NEtwork and COmputing Resource Allocation), a comprehensive analytical framework to derive the optimal joint allocation of network and computing resources in the C2TC, that guarantees the application requirements, even in the presence of MNs. If the optimal allocation does not exist, J-NECORA provides a best-effort allocation that minimizes the delay. The proposed framework is highly modular and flexible, and can be easily customized to different scenarios. We validated its effectiveness through simulation experiments, focusing on a specific use case. For the considered scenarios, the performance predicted by our framework matches quite closely the simulation results. © 2014 IEEE.

关键词： Cloud platforms

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Real-Time Object Detection from Surveillance using Deep Learning 3

Real-Time Object Detection from Surveillance using Deep Lear...

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

作者： Tanisha, A. Tanisha, N. Chaitra, M. Tejasri, P.R. Bnm Institute of Technology Vtu Dept. of Computer Science and Engineering Bangalore India

ISBN: (数字)9798331515911

ISBN: (纸本)9798331515911

Object detection in surveillance systems leverages advanced deep learning techniques to enhance security measures through real-time analysis of dynamic video feeds. This project integrates the YOLOv5 model for detecting weapons in both pre-recorded videos and live camera feeds. The model, trained on a dataset with 4000 images labeled for handguns and knives, utilizes image preprocessing steps such as resizing, normalization, and augmentation to improve detection accuracy. Implementing the system with OpenCV and Tkinter, the application processes video streams, identifying potential threats with high precision rate of 0.85 and recall rate of 0.90. The integration of a graphical user interface ensures user-friendly operation for security personnel. Key outcomes demonstrate the efficacy of deep learning models in real-time object detection, significantly contributing to proactive surveillance and enhanced situational awareness. © 2025 IEEE.

关键词： Graphical user interfaces

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Fake Job Post Detection Using Machine Learning

Fake Job Post Detection Using Machine Learning

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2025 International Conference on Computing for Sustainability and Intelligent Future, COMP-SIF 2025

作者： Pradeep Kumar, B.P. Bhagya, D.J. Gagana, G. Mani, N. Dept. of Computer Science and Engineering Atria Institute of Technology Karnataka Bangalore India

ISBN: (数字)9798331538538

ISBN: (纸本)9798331538538

By examining patterns in the text data and metadata linked to job adverts, The use of learning algorithms has grown in popularity in the detection of fraudulent job postings. For this, Research Machine emphasises the application of a number of algorithms, such as Naive Bayes and Random Forest, Together with models for deep learning, like CNN and Bidirectional LSTM, and AdaBoost. To extract useful features These techniques depend on text preparation techniques such as lemmatisation, tokenisation, and TF-IDF vectorisation. To differentiate between authentic and fraudulent posts, models are instructed in datasets and evaluated using measures such as accuracy, accuracy, memory, and F1-score. Advanced classifiers and group techniques have proven to be quite successful in enhancing detection performance while lowering false positives and negatives. © 2025 IEEE.

关键词： Random forests

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A Hybrid Approach Using Cryptography and K-Least Significant Bit Steganography Algorithm in Cloud Computing 1

A Hybrid Approach Using Cryptography and K-Least Significant...

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

作者： Goyal, Anshika Gupta, Bhumika VMSB Uttarakhand Technical University Dept. of Computer Science & Engineering Dehradun India G.B. Pant Institute of Engineering & Technology Dept. of Computer Science & Engineering Pauri India

ISBN: (纸本)9798331518578

The exponential growth of communication technologies has broadened the cloud computing ecosystem horizon to meet main communication needs. However, in-parallel upsurge in online attacks has alarmed industries to ensure optimal data security. The combination of steganography and cryptosystems has been recognized as an enhanced security model for data transfer among the main security systems now in use. In this paper, a hybrid approach using cryptography and K- Least Significant Bit Steganography algorithm in Cloud Computing (HYBC-KLSBS-CC) are proposed. Initially, input text data is retrieved from cloud storage and compressed using Huffman Compression Coding (HCC) to reduce data size. The compressed data is then encrypted with a Hybrid Cryptography Algorithm (Hyb-MRFC-ALC), combining Modified Random Fibonacci Cryptography and Asymmetric Lightweight Cryptography to ensure robust encryption. Finally, encrypted data is embedded within an image using the K-Least Significant Bit (KLSB) steganography technique, providing additional layer of security. The proposed method enhances data confidentiality, integrity, and storage efficiency in cloud environments. The proposed HYBC-KLSBS-CC approach attains 26.89%, 29.49% and 18.64% lower PSNR and 26.89%, 29.49% and 18.64% lower MSE when analyzed through existing techniques like data security dependent on cryptography steganography related with OTP procedure and Huffman coding in the cloud environment (CS-OTP-CE), a novel hybrid cryptographic framework for secure data storage in cloud computing: Integrating AES-OTP and RSA with adaptive key management and Time-Limited access control (AES-OTP-RSA-CC) and Crypto-Stegno dependent model for securing medical information on IOMT platform (CS-SMI-IOMT). © 2025 IEEE.

关键词： Cloud platforms

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AI-Powered DeepFake Defense 3

AI-Powered DeepFake Defense

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

作者： Chaitra, M. Kushal, B. Likhitha, H. Priyanka, H.R. Bnm Institute of Technology Affiliated to Vtu Dept. of Computer Science and Engineering Bengaluru India

ISBN: (数字)9798331515911

ISBN: (纸本)9798331515911

Deepfake technology, known for its ability to produce convincingly face-swapped videos, presents significant risks to privacy, security, and public trust. Misuse of this technology for spreading misinformation, defamation, and influencing politics is on the rise, creating a pressing need for more effective detection mechanisms. As deepfake generation methods continue to advance, conventional detection techniques are increasingly unable to keep up, often falling short in accuracy and adaptability. This project aims to create an AI-driven detection system specialized in identifying face-swap deepfakes. The system focuses on subtle irregularities in facial details, movements, and the synchronization between audio and visuals. Employing neural network models such as Convolutional Neural Networks (CNNs), Long Short-Term Memory (LSTM) networks, and Capsule Networks, it pinpoints anomalies like unnatural expressions, atypical skin textures, and lighting inconsistencies. The system achieves high reliability with metrics including accuracy (96%), precision (95%), recall (94%), and F1 score (95%), showing marked improvement compared to traditional techniques. It outperforms existing methods such as XceptionNet (92%) on the Celeb-DF dataset, underscoring its superior generalizability and robustness. This scalable solution has broad applications in media, law enforcement, and cybersecurity, reinforcing efforts to combat deepfake threats and fostering improved trust and security across various sectors. © 2025 IEEE.

关键词： Long short-term memory

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Routing and Charge Planning Strategies for Ridesharing EV Fleets 11

Routing and Charge Planning Strategies for Ridesharing EV Fl...

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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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Advances in Deep Learning for Tuberculosis Detection in Chest X-Rays: A Review of Diagnostic Accuracy 3

Advances in Deep Learning for Tuberculosis Detection in Ches...

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3rd International Conference on Intelligent Data Communication Technologies and Internet of Things, IDCIoT 2025

作者： Jayakumar, K. Muthupandi, G. Dhayanidhi, R. Pavithra, S. Dept. of Computer Science and Engineering Chennai Institute of Technology Tamilnadu Chennai600069 India

ISBN: (纸本)9798331527549

Tuberculosis (TB) is one of the leading causes of deaths globally, mainly in low- and middle-income countries. Early and accurate detection is crucial for effective treatment and disease control. In this paper, models like CNNs-VGG16, ResNet, and DenseNet, to name a few, are taken into an exhaustive study. Optimization techniques, including transfer learning and ensemble methods, which can increase accuracy in the challenging diverse clinical settings, will be presented. Equitable healthcare outcome is achieved in terms of future research directions which include representative datasets and interpretability of the model. © 2025 IEEE.

关键词： Disease control

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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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Smart Oral Health Monitor 3

Smart Oral Health Monitor

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

作者： Kamkar, Spandana Anilkumar Sanjay, Srushti Vaishnavi, P.S. Karthik, S. Bnm Institute of Technology Affiliated to Vtu Dept. of Computer Science and Engineering Bangalore India

ISBN: (纸本)9798331515911

Oral health is vital to overall well-being but is often overlooked due to inefficient monitoring tools and delayed diagnosis. This project presents a smart handheld device featuring a miniaturized camera for detecting oral issues like plaque, cavities, teeth discoloration and gum diseases in real-time. Using image processing and deep learning algorithms, the device provides precise feedback and actionable insights. Paired with a mobile application offering detailed analysis and recommendations, it empowers users to adopt proactive oral hygiene practices to prevent teeth disorder. © 2025 IEEE.

关键词： Electronic health record

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