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

作者： Sadhasivam, Shankaripriya Gayathri, G. Poongothai, P. Pavithra, S. Dept. of Computer Science and Engineering Chennai Institute of technology Chennai India

ISBN: (数字)9798331527549

ISBN: (纸本)9798331527549

This review explores recent advancements that are made in remote sensing, enabling detailed analysis of our ecosystem through high resolution and multispectral imaging. The recent advancements in remote sensing includes developments useful for generation of synthetic images using GAN such as StyleGAN3 and for estimation of carbon stocks in forest using OBIA. Ecotones like mangroves and saltmarsh are crucial for carbon sequestration but mapping them accurately was challenging due to spectral similarity and seasonal variations. Focusing on coastal protection by this review examines the mapping methodologies such as random forest classification, OBIA and FID for habitat distinction, canopy area assessment and analysis. Through these technologies data accuracy can be enhanced for better monitoring. This review concludes with a discussion of AI-powered image synthesis and classification. © 2025 IEEE.

关键词： Carbon sequestration

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Multi-Layered Approach to IoT Cybersecurity in Smart Cities 3

Multi-Layered Approach to IoT Cybersecurity in Smart Cities

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

作者： Benjamine, Daniel Anto Madhavan, K. Pavithra, S. Dept. of Computer Science and Engineering Chennai Institute of Technology Chennai India

ISBN: (纸本)9798331527549

Internet of Things (IoT) has been rapidly growing over the past years. There have been many cybersecurity challenges faced by IoT-based smart cities in existing work for the energy, health and transportation sectors. These cybersecurity challenges are crucial to remove in the IoT sector to implement a safe and secure IoT based smart city. Vulnerabilities across layers in IoT are analyzed and a multi-layered model of security is proposed to enhance data integrity and communication security by using AI, blockchain and ML. This paper would also present several policy recommendations to stakeholders and IoT developers for building a safe urban IoT ecosystem. © 2025 IEEE.

关键词： Cybersecurity frameworks IoT smart city technologies urban

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DDoS Attack Detection in Blockchain Networks 5

DDoS Attack Detection in Blockchain Networks

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5th International Conference on Data Intelligence and Cognitive Informatics, ICDICI 2024

作者： Jadhav, Swati Bhosale, Vaibhavi Choudhari, Gauri Bura, Rishita Bhavik, Manasi Vishwakarma Institute of Technology Dept. Of Computer Engineering Pune India

ISBN: (纸本)9798350389609

DDoS attacks pose a significant threat to the security and stability of blockchain networks. Low generalization, high misreporting rates, and ineffective detection capabilities are just a few of the flaws in the detection methods currently in use. DDoS attacks threaten blockchain network security and stability. Current detection methods suffer from low generalization, high misreporting rates, and inefficacy. This project proposes a solution to detect and mitigate the DDOS attack happening on Blockchain. Scapy and Wireshark's real-time data analysis helps identify attack patterns and adapt defenses to evolving threats. This proactive approach ensures the functionality and integrity of blockchain networks. Through thorough experimentation, the solution provides a robust defense against DDoS attacks, enhancing the security of the blockchain ecosystem. © 2024 IEEE.

关键词： Blockchain

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Enhancing Doodling Experience Using Generative Adversarial Networks 2

Enhancing Doodling Experience Using Generative Adversarial N...

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2nd IEEE Global Conference on Communications and Information Technologies, GCCIT 2024

作者： Ashtagi, Rashmi Rathi, Devanshu Handoo, Ripul Purohit, Rishi Madhavaswala, Abbas Nogamawala, Mohammad Vishwakarma Institute of Technology Dept. Of Computer Engineering Pune India

ISBN: (纸本)9798350388916

Interacting with AI systems solely through textual prompts often encounters limitations due to the challenges of expressing complex visual ideas in text. This paper introduces an advanced AI system that interprets and visualizes users' sketches by employing state-of-the-art Generative Adversarial Networks (GANs), such as pix2pix, SketchGAN, DCGAN, and ESRGAN. This system not only significantly enhances real-time interactions but also shows exceptional proficiency in image editing tasks, thus facilitating more intuitive communication between humans and AI. By enabling rapid and accurate visualizations, this technology substantially streamlines and improves design workflows in the architecture and fashion industries. The advancements fostered by this system contribute significantly towards achieving greater capabilities akin to Artificial General Intelligence, enhancing creative processes and production efficiencies. © 2024 IEEE.

关键词： Generative adversarial networks

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Towards Vision Based Bi-Cycles Detection and An Autonomous Decision Making System for Driving Situation Awareness 13

Towards Vision Based Bi-Cycles Detection and An Autonomous D...

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13th International Symposium on computer Science and Educational technology, ISCSET 2024

作者： Nine, Julkar Mehta, Ayushkumar Rajkumar, Sai Rohit Hardt, Wolfram Chemnitz University of Technology Dept. of Computer Engineering Chemnitz Germany

ISBN: (纸本)9798350390803

In response to the growing global emphasis on healthy living and environmental consciousness, the popularity of eco-friendly transportation options like bicycles has surged. However, cyclist safety remains a critical concern, particularly in interactions with automobiles, where accidents often result in severe consequences. This project aims to enhance cyclist safety through machine learning-based bicycle detection and situational awareness techniques, aiming to mitigate vehicle-bicycle collisions. By integrating OpenCV lane detection and YOLOv8-based vehicle/bicycle detection with distance estimation, the system can analyze real-Time road conditions and make informed decisions, including speed reduction, overtaking safety assessment, and maintaining safe distances from cyclists. Testing validates the system's accurate detection, precise analysis, and effective decision-making capabilities, ensuring vehicles respond appropriately to encountered road scenarios. Ultimately, this project addresses the pressing need for cyclist safety in vehicle-rich environments, leveraging machine learning, real-Time analysis, and advanced detection technologies to foster safer roads. © 2024 IEEE.

关键词： Bicycles

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A Study on Performance Improvement of Degraded Image Processing Using Learning Method 13

A Study on Performance Improvement of Degraded Image Process...

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13th IEEE Global Conference on Consumer Electronic, GCCE 2024

作者： Kojima, Iori Goto, Tomio Nagoya Institute of Technology Dept. of Computer Science Nagoya Japan

ISBN: (纸本)9798350355079

While the performance of denoising and deblurring has continued to improve, there is a lack of prior research on methods that address both simultaneously. Therefore, this paper employs a model known as NAFNet, which has been shown to be capable of removing either noise or blur. We verify that it is possible to simultaneously remove both noise and blur using this model. © 2024 IEEE.

关键词： Adversarial machine learning

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A Decentralized Document Storage Platform using IPFS with Enhanced Security 8

A Decentralized Document Storage Platform using IPFS with En...

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8th IEEE International Conference on Computing, Communication, Control and Automation, ICCUBEA 2024

作者： Jadhav, Swati Choudhari, Gauri Bhavik, Manasi Bura, Rishita Bhosale, Vaibhavi Vishwakarma Institute of Technology Dept. Of Computer Engineering Pune India

ISBN: (纸本)9798350391770

In response to the evolving challenges of centralized file sharing and transport-layer issues in decentralized file systems, this research paper, "A Decentralized File Sharing using IPFS with Enhanced Security,"delves into the potential of the InterPlanetary File System (IPFS) made secure with Hybrid Encryption. This decentralized approach not only addresses issues of reliability and scalability but also prioritizes enhanced security measures. By integrating cutting-edge security protocols, our study aims to fortify the IPFS framework, creating a resilient platform for secure file sharing in the digital age using algorithms such as AES and ECC-based methods. Through this exploration, we contribute to the ongoing discourse on decentralized systems, offering insights into the future of efficient and secure digital information exchange. © 2024 IEEE.

关键词： AES Blockchain Data Security ECC Encryption IPFS Privacy

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Image Quality Improvement of Denoising Method based on DRUNet for Real Noise 13

Image Quality Improvement of Denoising Method based on DRUNe...

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13th IEEE Global Conference on Consumer Electronic, GCCE 2024

作者： Yasue, Sho Goto, Tomio Nagoya Institute of Technology Dept. of Computer Science Nagoya Japan

Collecting a large amount of real noise images is difficult, and effective learning must occur from a small amount of datasets. In this study, we use DRUNet, a noise eliminator. By following an effective learning proc... 详细信息

ISBN: (纸本)9798350355079

关键词： Adversarial machine learning

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A Hybrid approach for Deepfake Detection using CNN-RNN

A Hybrid approach for Deepfake Detection using CNN-RNN

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2024 OPJU International technology Conference on Smart Computing for Innovation and Advancement in Industry 4.0, OTCON 2024

作者： Antad, Sonali Arthamwar, Vaishnavi Vijay Deshmukh, Rohan Kishor Chame, Akshay Uttam Chhangani, Harshita Pawan Vishwakarma Institute of Technology Dept. of Computer Engineering Pune India

ISBN: (纸本)9798350373783

Reliable deepfake detection of falsified videos generated by deep learning is still a pressing challenge with its growing popularity. Deep adversarial neural networks, which educate on film and target faces to ahead facial resources and facial expressions to targets, underneath DF can without difficulty idiot facial recognition structures the usage of revealed, 3-D photos mask, or video recordings from the valid consumer's face to sensors. We make significant contributions, including building adaptive datasets, comparing primitive and deep models for training, and tunable CNN-RNN tools. The overall evaluation model shows that the results are better than the findings of the most advanced methods. It was acknowledged that Deep faking today greatly impacts our environment;It is done by placing faces on original images/videos using deep neural networks. Together with the sharing of other misinformation via digital social networks, deepfake has formed digital fakery, which has become a real problem of negative social impact and as such there is a great need for successful measures to be taken to examine Deep Fakes [1]. The approach in this research generalizes by capturing physical cues in body shape and size. Our method combines CNN for spatial analysis, capturing facial features and skin texture, and LSTM and RNN for temporal analysis, monitoring expression progress. This method provides stable performance on complex deepfake videos. The modular design allows for future expansion, such as the integration of audio analysis. © 2024 IEEE.

关键词： Convolutional neural networks

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An Advance Boosting Approach for Multiclass Dry Bean Classification

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Journal of Engineering Science and technology Review 2023年第2期16卷 107-115页

作者： Nayak, Janmenjoy Dash, Pandit Byomokesha Naik, Bighnaraj Dept. of Computer Science Maharaja Sriram Chandra Bhanja Deo University Odisha Baripada757003 India Dept. of Information Technology Aditya Institute of Technology and Management AP Tekkali532201 India Dept. of Computer Application Veer Surendra Sai University of Technology Burla768018 India

Dry beans, which are produced in large quantities, have the highest level of genetic diversity. The quality of seeds has a significant impact on crop yield. The importance of seed classification to both marketing and production can be shown by realizing that sustainable agricultural systems depend on these principles. This research is primarily aimed at providing a means to generate uniform seed varieties, as seed is not certified as a single variety. To achieve consistent seed classification, we have proposed Extreme Gradient Boosting ensembles using the Synthetic Minority Over-Sampling Methodology (SMOTE) to differentiate seven distinct registered types of dry beans with similar characteristics. There were a total of 13,611 grains from seven different varieties of dry beans sampled for the classification model. Classification algorithms based on machine learning like Decision Tree (DT), Support Vector Machine (SVM), Multilayer Perceptron (MLP), Adaptive Boosting classifier, Bagging Classifier, and Extreme Gradient Boosting ensembles using the Synthetic Minority Over-Sampling Methodology (SMOTE) were developed and compared. Overall correct classification rates for SVM, MLP, DT, Adaboost, Bagging, and Extreme Gradient Boost classifiers were 94.44%, 94.48%, 96.53%, 96.35%, 96.89%, and 97.32%, respectively. Extreme Gradient Boosting ensembles using the SMOTE classification model have the best accuracy. The results of this study satisfy the producers' and customers' demand for uniform bean varieties. © 2023 School of Science, IHU. All rights reserved.

关键词： Adaptive boosting

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