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Traffic Threshold and Potholes Detection Road Using Supervised Learning 4

Traffic Threshold and Potholes Detection Road Using Supervis...

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4th International Conference on Sentiment Analysis and Deep Learning, ICSADL 2025

作者： Vani Lavanya, A. Monish, D. Praveenraj, N. Ezhil Dyana, M.V. St. Joseph's Institute of Technology Department of Computer Science and Engineering Chennai India

ISBN: (数字)9798331523923

ISBN: (纸本)9798331523923

This research study proposes a Traffic Threshold and Pothole Detection Using Supervised Learning. Detection of traffic thresholds and potholes on roads is done by employing a specific type of Artificial Neural Network called convolutional neural network (CNN). This proposed method works with training CNN on a huge dataset of labeled road images for learning visual features of traffic thresholds & potholes. Then, for detection of road hazard's locations, the learned CNN is employed by sending its information to a real-time map in order to further present on it. Several experiments are performed on images of roads under varying lighting conditions, types of road textures, and weather conditions to evaluate and analyze the overall performance of the proposed method. The result showed that the proposed approach can detect traffic thresholds and potholes with a high accuracy that can be used to renovate road safety through giving early warning to drivers. In general, the proposed method gives a practical solution for road hazards detection which can reduce accidents and improve the overall driving experience. Lastly, this model is deployed to Python. This method uses Image Processing and Segmentation classifiers. The required image can be of any resolution, and it can extract features from those images. This, therefore, increases the accuracy value to 97.7%. © 2025 IEEE.

关键词： Convolutional neural networks

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Exploratory Analysis of Methods, Techniques, and Metrics to Handle Class Imbalance Problem 2

Exploratory Analysis of Methods, Techniques, and Metrics to ...

引用

2nd International Conference on Machine Learning and Data engineering, ICMLDE 2023

作者： Sneha, H.R. Annappa, B. Department of Information Science & Engineering Nitte Meenakshi Institute of Technology Bengaluru560064 India Department of Computer Science & Engineering National Institute of Technology Karnataka Surathkal575025 India

Class imbalance a common challenge in machine learning, often results in skewed predictions and misrepresentative model assessments, highlighting the need for effective countermeasures. Our detailed survey dives into three primary techniques for addressing this issue: data-level interventions, algorithmic modifications, and integrated hybrid solutions. We thoroughly dissect each approach, delineating its merits, drawbacks, and ideal use cases. Data-level methods aim to restructure the dataset for class balance, while algorithmic techniques recalibrate the learning process to better detect the minority class. The hybrid strategies merge the benefits of both for a holistic remedy. The study further emphasizes the importance of precise evaluation metrics, elaborating on both conventional metrics and those tailored for imbalanced scenarios. Our objective is to arm professionals with a deep insight into tackling class imbalance, especially within the big data framework. The insights shared aspire to inspire the creation of resilient, equitable machine learning models adapt at navigating imbalanced data, ensuring enhanced prediction fidelity and consistency. © 2024 Elsevier B.V.. All rights reserved.

关键词： Machine learning

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Enhancing Accuracy and Efficiency in Diabetic Retinopathy Detection: A Deep Learning Framework for Fundus Image Analysis 1

引用

3rd International Conference on Advanced Computing and Intelligent Technologies, ICACIT 2023

作者： Tayal, Mahima Singh, Jagendra Kumar, Vinish School of Computer Science Engineering and Technology Bennett University Greater Noida India Department of Computer Science Engineering Raj Kumar Goel Institute of Technology AKTU Lucknow Ghaziabad India

ISBN: (数字)9789819719617

ISBN: (纸本)9789819719600

The present research investigates the challenge of accurately and efficiently diagnosing diabetic retinopathy using a deep learning architecture. Our research is based on a sizable dataset of 54,325 fundus images that have been meticulously classified as diabetic retinopathy (DR) positive or negative by medical professionals. The dataset is carefully split into training and validation subsets, with 80% designated for training and 20% for validation, to ensure the robustness of model performance. Convolutional neural networks (CNN), artificial neural networks (ANN), support vector machines (SVM), recurrent neural networks (RNN), and k-nearest neighbors (KNN) are just a few of the machine learning models whose diagnostic performance we examine. CNN came out on top with a precision rate of 98.66% and excellent standards for recall, precision, and F1 score. The results of this study have the potential to significantly improve patient care and treatment by allowing for a quick and precise identification of diabetic retinopathy. The outcomes also demonstrate how medical image processing and diagnosis can be revolutionized by deep learning frameworks. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Image enhancement

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Disease Detection in Tomato Plant Leaf Using Deep Learning Techniques 8th

Disease Detection in Tomato Plant Leaf Using Deep Learning T...

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8th International Symposium on Intelligent Informatics, ISI 2023

作者： Choudhary, Piyush Vinothini, A. School of Computer Science and Engineering Vellore Institute of Technology Tamil Nadu Chennai600127 India

ISBN: (纸本)9789819721467

The identification of diseases in plants is crucial for sustaining crop yield and securing global food resources. This study delves into the utilization of deep learning methodologies to precisely detect diseases in tomato plant leaves. Focused on the PlantVillage dataset, a comprehensive collection of tomato plant leaf images categorized as healthy or diseased is analyzed. Augmentation techniques, encompassing random transformations like rotations, flips, and zooms, are employed to diversify the training dataset, aiming to improve the models’ accuracy and generalization capacities. The deep learning models, including AlexNet, DenseNet, LeNet, and two bespoke models, undergo training with well-suited loss functions and are optimized using appropriate techniques. Assessment of the models’ performance involves metrics such as accuracy, loss, recall, precision, and F1 score, calculated on distinct validation sets. The outcomes indicate that the specifically tailored models for disease detection outperform established architectures. Notably, Custom Model 2 achieves an accuracy of 86.10% and an F1 score of 85%, while Custom Model 3 attains an accuracy of 86.84% and an F1 score of 86%. Contrastingly, AlexNet records an accuracy of 79% and an F1 score of 79.29%, DenseNet exhibits an accuracy of 89% and an F1 score of 88%, and LeNet scores an accuracy of 81% and an F1 score of 82%. This study underscores the potential of deep learning in precisely identifying plant diseases in tomato leaves, contributing to the advancement of sophisticated diagnostic techniques. Implementing these models in agricultural systems holds promise for proactive disease identification, safeguarding crops, and ensuring global food stability. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Deep reinforcement learning

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Bridging Gaps: Federated Multi-View Clustering in Heterogeneous Hybrid Views 38

Bridging Gaps: Federated Multi-View Clustering in Heterogene...

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38th Conference on Neural Information Processing Systems, NeurIPS 2024

作者： Chen, Xinyue Ren, Yazhou Xu, Jie Lin, Fangfei Pu, Xiaorong Yang, Yang School of Computer Science and Engineering University of Electronic Science and Technology of China China Shenzhen Institute for Advanced Study University of Electronic Science and Technology of China China

Recently, federated multi-view clustering (FedMVC) has emerged to explore cluster structures in multi-view data distributed on multiple clients. Many existing approaches tend to assume that clients are isomorphic and all of them belong to either single-view clients or multi-view clients. While these methods have succeeded, they may encounter challenges in practical FedMVC scenarios involving heterogeneous hybrid views, where a mixture of single-view and multi-view clients exhibit varying degrees of heterogeneity. In this paper, we propose a novel FedMVC framework, which concurrently addresses two challenges associated with heterogeneous hybrid views, i.e., client gap and view gap. To address the client gap, we design a local-synergistic contrastive learning approach that helps single-view clients and multi-view clients achieve consistency for mitigating heterogeneity among all clients. To address the view gap, we develop a global-specific weighting aggregation method, which encourages global models to learn complementary features from hybrid views. The interplay between local-synergistic contrastive learning and global-specific weighting aggregation mutually enhances the exploration of the data cluster structures distributed on multiple clients. Theoretical analysis and extensive experiments demonstrate that our method can handle the heterogeneous hybrid views in FedMVC and outperforms state-of-the-art methods. The code is available at https://***/5Martina5/FMCSC. © 2024 Neural information processing systems foundation. All rights reserved.

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Mitigating Security Challenges in Edge Computing: Attacks, Defence Strategies, and Algorithms 3

Mitigating Security Challenges in Edge Computing: Attacks, D...

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

作者： Matta, Venkata Durga Rao Maheswara Rao, V.V.R. Battula, Venkata Sai Jayasurya Addagatla, Mahesh Babu Computer Science and Engineering Department Vishnu Institute of Technology Andhra Pradesh Bhimavaram India Computer Science and Engineering Department Shri Vishnu Engineering College for Women Andhra Pradesh Bhimavaram India

ISBN: (纸本)9798331527549

Edge computing has emerged as a crucial paradigm to fulfill the increasing demand for rapid data processing, low latency, and efficient resource utilization, particularly in applications such as the Internet of Things (IoT), smart cities, autonomous vehicles, and automated industrial systems. By processing data closer to its source, edge computing minimizes reliance on centralized cloud infrastructures, thereby improving response times and optimizing bandwidth. However, this transition also presents significant security challenges, including heightened risks of Distributed Denial of Service (DDoS) attacks, data breaches, and unauthorized physical access to edge nodes. This paper conducts a comprehensive analysis of these security vulnerabilities, presenting a framework that integrates advanced defence mechanisms and algorithms tailored to enhance data protection and resource management in edge environments. Our findings indicate that the proposed strategies effectively mitigate identified threats while maintaining system performance. Furthermore, we outline ongoing challenges related to scalability, interoperability, and data governance, providing insights into future research avenues aimed at fortifying edge computing systems against evolving security threats. This study contributes to a deeper understanding of the critical role edge computing plays in the domain of next-generation computing technologies. © 2025 IEEE.

关键词： Cloud security

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Electronic Patient Records Management Using Federated Blockchain 7th

Electronic Patient Records Management Using Federated Blockc...

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7th International Conference on Communication, Device and Networking, ICCDN 2024

作者： Dhingra, Yatharth Gangwar, Siddhant Shukla, Yagyesh Ranjan Sharma, Ravi Sathish Kumar, J. Department of Computer Science and Engineering Motilal Nehru National Institute of Technology Allahabad Prayagraj India

ISBN: (纸本)9789819764648

The healthcare industry is increasingly recognizing the need for efficient management of electronic patient records (EPRs). This paper explores the potential of using Hyperledger Fabric (HLF), an open-source blockchain framework, for the adoption of EPRs in the healthcare industry. We investigate the concepts of blockchain, distributed networks, and smart contracts, and analyze how HLF can provide a secure and decentralized platform for storing and managing sensitive medical data. By leveraging HLF’s features such as permissioned networks, chaincode, and private channels, healthcare providers can ensure data integrity, confidentiality, and availability while granting and revoking access to authorized personnel. This paper contributes to the understanding of how blockchain technology, specifically HLF, can revolutionize the healthcare industry and improve the quality of care provided to patients. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Electronic health record

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AI-Powered DeepFake Defense 3

AI-Powered DeepFake Defense

引用

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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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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Machine Learning Based Pattern Recognition with HOG Features for Detection of Islanding 3

Machine Learning Based Pattern Recognition with HOG Features...

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3rd IEEE International Conference on Electrical Power and Energy Systems, ICEPES 2024

作者： Pal, Kumaresh Goswami, Subhadip Bhuyan, Anshuman Koley, Santanu Bera, Bidisha Basu, Sarmistha National Institute of Technology Department of Electrical Engineering Jamshedpur India National Institute of Technology Department of Electrical Engineering Arunachal Pradesh India Gandhi Institute for Education & Technology Department of Electrical Engineering Bhubaneswar India Haldia Institute of Technology Department of Computer Science Engineering Haldia India Global Institute of Science and Technology Department of Computer Science and Technology Haldia India Calcutta Institute of Engineering and Management Department of Physics Kolkata India

ISBN: (纸本)9798350390728

Islanding is a significant difficulty that arises from the high integration of distributed generation sources. Islanding could potentially damage the clients and their equipment. According to the IEEE 1547 DG interconnection requirements, islanding must be detected within two seconds, and the DG must be shut down. This research introduces a new method for identifying islanding using image pattern recognition utilising a Machine Learning classification algorithm. The suggested technique utilises the Random Forest (RF) classification algorithm. Images are utilised to extract the histogram of oriented gradient (HOG) features in order to detect both non-islanding and islanding occurrences. The spectrogram images are formed from the time-series signal collected from the point of common coupling. The images are utilised to extract features from histogram of oriented gradient, which are subsequently used as feature vector inputs for training and testing the RF classifiers. The spectrogram image is derived using the rate of change of the voltage parameter. The efficacy of the RF classifier is evaluated using 5, 10, 20, and 25-fold cross-validations. The classification findings indicates that islanding detection utilising a Random Forest classifier and Histogram of Oriented Gradients (HOG) feature for image pattern recognition obtained outstanding performance with 98.75% accuracy and a detection time of 192 milliseconds. © 2024 IEEE.

关键词： Spectrographs

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