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

作者： Kothai, G. Povammal, E. Amutha, S. Deepa, V. KPR Institute of Engineering and Technology Coimbatore641407 India Department of Computing Technologies SRM Institute of Science and Technology Kattankulathur60203 India Department of Computer Science and Engineering Kalasalingam Academy of Research and Education Krishnankoil626126 India

In the domain of traffic management, road toll collection, and parking lot systems, vehicle number plate detection and identification play a pivotal role. Unlike conventional methods that treat license plate detection and character recognition as separate tasks, the system simultaneously addresses both challenges within a single neural network. Our Proposed methodology capitalizes on the efficiency and accuracy of the one-stage object detection algorithm known as YOLO (You Only Look Once) to locate license plates under diverse and challenging conditions. To augment the quality of input images with low resolution or poor clarity, we employ super-resolution generative adversarial networks (SRGANs). The image enhancement process substantially improves the visual quality of captured license plate images, facilitating more precise character recognition. Quantitative assessment of propounded system reveals compelling results. The license plate detection component achieves an outstanding average accuracy rate of 98.5%, surpassing previous methods by 15.2%. This comprehensive approach not only reduces the dependency on manual labour but also elevates processing precision. It seamlessly integrates into existing transportation infrastructure, resulting in heightened operational efficiency, reduced traffic congestion, and enhanced security measures. The rapid evolution of neural networks and deep learning techniques has streamlined the deployment of such applications, revolutionizing the field of traffic monitoring and management with unprecedented ease and precision. One-stage object detector, widely referred to as YOLO, is used to find licence plates in difficult circumstances. © 2024 Elsevier B.V.. All rights reserved.

关键词： Image enhancement

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A novel recursive privacy-preserving information retrieval approach for private retrieval

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International Journal of Intelligent Information and Database Systems 2022年第3期15卷 269-274页

作者： Bhat, Radhakrishna Kumar, K.M. Mahesh Sunitha, N.R. Department of Computer Science and Engineering Manipal Institute of Technology Manipal Academy of Higher Education Karnataka Manipal576104 India Department of Information Science and Engineering Nitte Meenakshi Institute of Technology Karnataka Bangalore560064 India Department of Computer Science and Engineering Siddaganga Institute of Technology Karnataka Tumkur572102 India

The major drawback of existing information retrieval schemes in preserving user privacy is that they either exhibit computationally bounded privacy with intractability assumptions or perfect privacy with high bandwidth utilisation. Today, the essential requirement is to have a bandwidth efficient perfect privacy preserving information retrieval scheme in order to provide effective and guaranteed service to the information retrieving user. Therefore, in this paper, we have constructed a new single database perfect privacy preserving private block retrieval scheme called ‘pepperPBR’ using quadratic residuosity as the underlying primitive where private block retrieval (PBR) is a natural extension to private information retrieval (PIR). In this paper, user generates O(6k) bit query and server generates O(2ku + o(n)) bit response where n is the database size, o(n) is the non-trivial server communication cost, u is the number of database blocks, k is the security parameter. Copyright © 2022 Inderscience Enterprises Ltd.

关键词： Information retrieval

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An Intelligent Flood Automation System Using IoT and Machine Learning 1

An Intelligent Flood Automation System Using IoT and Machine...

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1st International Conference on Recent Developments in Electronics and Communication Systems, RDECS 2022

作者： Verma, Bhumika Yudheksha, G.K. Sanjana Reddy, P. Maheswari, R. Parvathi, R. Vignesh, U. School of Computer Science and Engineering Vellore Institute of Technology Chennai India Manipal Academy of Higher Education India

ISBN: (纸本)9781643683607

Floods are one of the most common and well-known natural calamities that create havoc, causing damage to one's life and economy while adversely affecting the environment. This Research focuses on receiving emergency notifications whenever the situation is critical and also being able to predict the flood in time by automatically measuring the riverbed water level periodically. In this work, the objective is to measure the level using ultrasonic sensors and process the acquired data using ESP8266, where the information will be transferred to ThingSpeak IoT cloud which uses HTTP Protocol over Local Area Network thus enabling it to graphically monitor the world. It checks whether it is not exceeding the threshold limit and when it reaches beyond the limit the system should be able to alert individuals through Telegram and web cloud application, and when the system predicts a flood in the following timeline. Here the Servo Motor acts as a dam gate i.e., it opens when the flood is alerted and closes back once it reaches below the threshold value. For prediction, various machine-learning algorithms such as Random Forest, Support Vector Machine, KNN, Decision Tree, and Logistic Regression were implemented. Due to its high accuracy of about 95%, the logistic regression model was considered suitable for predicting the changes and detecting various possibilities. © 2023 The authors and IOS Press.

关键词： HTTP

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Hybrid Routing Techniques for Location Privacy in IoT-Enabled Wireless Sensor Healthcare Networks

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SN computer science 2024年第8期5卷 1164页

作者： Arpitha, T. Chouhan, Dharamendra Shreyas, J. Department of Computer Science and Engineering University Visvesvaraya College of Engineering Ambedka Beedi Karnataka Bengaluru 560001 India Department of Information Technology Manipal Institute of Technology Manipal Academy of Higher Education Karnataka Bengaluru 560064 India

Ensuring patient privacy is critical for healthcare systems reliant on the Internet of Things. While prior work has addressed patient privacy extensively, a comprehensive end-to-end strategy protecting patients’ location and data privacy under the assumption that system entities are untrusted has yet to be developed. In response, this paper proposes a novel hybrid Deep Q-learning Neural Network (DQ-NN) technique tailored to Source Location Privacy (SLP) integrated with Blockchain technology for routing in Internet of Things (IoT)-enabled Wireless Sensor Networks (WSNs). To overcome the shortcomings of the existing systems, the study offers extremely random routing paths between the source and sink nodes. The plan uses strategically selected phantom nodes to transmit packets to the sink node randomly, ensuring the paths are incredibly confusing to the enemy. To attain maximum privacy and optimize energy consumption, the suggested method employs a hybrid DQNN model to select optimal paths among multiple paths generated. Blockchain integration adds an extra layer of security to the routing network. The results of the simulation show that the proposed scheme outperforms other schemes by offering a more extended safety period, optimized energy, and enhanced privacy for the healthcare environment. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2024.

关键词： Blockchain for privacy Hybrid phantom routing Location privacy Neural network solutions Wireless sensor network privacy

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CNN-Based Pneumonia Detection and Severity Analysis in Chest X-Ray Images for Enhanced Diagnostic Support

CNN-Based Pneumonia Detection and Severity Analysis in Chest...

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

作者： Muthukumar, D. Surendiran Chowdary, Bommineni Sreekar Kumar, D. Senthil Jagadeesan, P. Mythili, B. Swapna, B. Kalasalingam Academy of Research and Education Department of computer science and Engineering Tamilnadu Srivilliputhur India Kalasalingam Academy of Research and Education Department of Computer Science and Engineering Tamil Nadu Virudhunagar India Rmk College of Engineering and Technology Department of Computer Science and Engineering Tamil Nadu Chennai India R.M.D. Engineering College Department of Computer Science and Engineering Tamil Nadu Chennai India Vel tech Multi tech Dr.Rangarajan Dr. Sakunthala Engineering college Department of Computer Science and Engineering Tamil Nadu Chennai India Dr.M.G.R Educational and Research Institute Department of Electronics and communication engineering Chennai India

ISBN: (纸本)9798331509828

Pneumonia continues to pose a substantial global health challenge, necessitating prompt and precise diagnosis to enhance patient outcomes. In order to detect pneumonia and classify its severity into mild, moderate, and severe phases, the web-based program uses deep learning algorithms to analyse chest X-ray pictures. A Convolutional Neural Network (CNN) model that has been trained on publicly accessible datasets and achieved high accuracy is integrated into the application. Furthermore, it provides personalized health recommendation, downloadable PDF reports, and a local hospital database to facilitate users in locating nearby treatment facilities. The system utilizes Fast API for backend processing and user-friendly interfaces to enhance accessibility and efficiency. This solution underscores the potential of artificial intelligence in supporting early diagnosis and effective pneumonia management. © 2025 IEEE.

关键词： Convolutional neural networks

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Straightforward Guess and Determine Analysis Based on Genetic Algorithm

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Journal of Systems science & Complexity 2022年第5期35卷 1988-2003页

作者： CAO Chunping CEN Zhe FENG Xiutao WANG Zhangyi ZHU Yamin Department of Computer Science and Technology University of Shanghai for Science and TechnologyShang-hai 200093China Key Laboratory of Mathematics Mechanization Academy of Mathematics and Systems ScienceChinese Academy of SciencesBeijing 100190China School of Cyber Science and Engineering Wuhan UniversityWuhan 430072China School of Science Xi'an Technological UniversityXi'an 710021China

With the development of artificial intelligence,the genetic algorithm has been widely used in many *** cryptography,the authors find it is natural to code an individual and design its fitness in a genetic algorithm for a straightforward guess and determine analysis(SGDA,in short).Based on this observation,the authors propose an SGDA based on genetic *** it with the other three SGDAs based on exhaustive search,MILP method and CPP method respectively,the authors illustrate its effectiveness by three stream ciphers:Small scale SNOW 2.0,medium scale Enocoro-128v2 and large scale *** results show our method is significantly superior to them,especially for Trivium,the method can find a solution of 165 variables in less than one hour,while the other three methods are not applicable due to its enormous search space of size 2^(619.37).As far as we know,it is a best solution in an SGDA for Trivium so far.

关键词： Enocoro-128v2 genetic algorithm guess and determine analysis SNOW 2.0 trivium

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Unveiling the epilepsy enigma: an agile and optimal machine learning approach for detecting inter-ictal state from electroencephalogram signals

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International Journal of Information technology (Singapore) 2024年第8期16卷 5149-5172页

作者： Kaushik, Shoibolina Balachandra, Mamatha Olivia, Diana Khan, Zaid Manipal Institute of Technology Karnataka Manipal 576104 India Department of Computer Science and Engineering Manipal Institute of Technology Manipal Academy of Higher Education Karnataka Manipal India Department of Information and Communication Technology Manipal Institute of Technology Manipal Academy of Higher Education Karnataka Manipal India

Epilepsy is a chronic neurological disorder characterized by the occurrence of paroxysmal recurrent seizures, which are caused by abnormal electrical activity in the brain. Seizures vary widely in their presentation, depending on the specific region of the brain involved and the extent of the abnormal electrical discharges. The disease can affect cognitive function posing a serious threat to the patients’ lives. Epilepsy causes emotional and behavioral changes, along with sleep disorders and migraines, leading to social isolation and discrimination. Timely administration of medication can cure most cases of epilepsy. However, identifying epileptic patients requires reviewing multiple EEG signal sheets, which can delay disease prediction. Therefore, the aim of our study is to apply simplistic machine learning algorithms that can study the EEG signal data swiftly to identify individuals in seizure, inter-ictal, and normal states, thereby aiding in medical diagnosis. The novelty of this study lies in the utilization of pre-built methods and develop a fast and efficient model that is lightweight and easy to integrate in healthcare to provide relief to epileptic patients. While previous studies have achieved high accuracy, the discussion involving time complexity of their models has been scarce. Given the importance of timely medication in managing epilepsy, it is crucial to consider the runtime of the model rather than solely focusing on accuracy. Therefore, a model that balances both a short runtime (2.9 min) and a satisfactory accuracy (97.46%) has been developed in this project. Integration of this project's findings will catalyze transformative changes within the healthcare industry, enabling healthcare professionals to detect epilepsy at earlier stages and provide timely interventions, ultimately fostering a system that prioritizes precision, innovation, and improved patient outcomes. © The Author(s) 2024.

关键词： EEG signals Epilepsy Improved quality of life Machine learning Medical diagnosis

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Diversity and Freshness-Aware Regret Minimizing Set Queries 29th

Diversity and Freshness-Aware Regret Minimizing Set Queries

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29th International Computing and Combinatorics Conference, COCOON 2023

作者： Guo, Hongjie Li, Jianzhong Shen, Fangyao Gao, Hong Faculty of Computing Harbin Institute of Technology Heilongjiang Harbin China School of Computer Science and Technology Zhejiang Normal University Zhejiang Jinhua China Faculty of Computer Science and Control Engineering Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Shenzhen China

ISBN: (纸本)9783031491924

Multi-criteria decision-making often involves selecting a small representative set from a database. A recently proposed method is the regret minimization set (RMS) queries. It aims to rectify the shortcomings of needing a utility function in top-k queries and the overly large result size of skyline queries. However, the existing definition of RMS only ensures one result under any utility function, and do not consider the diversity and freshness of the returned results. In this paper, we define a strong regret set, which guarantees the utility value error of k data points under any utility function. Given this new definition, we propose two problems, namely the Minimum Size problem and the Max-sum Diversity and Freshness problem. Both proposed problems have been proven to be NP-hard. Correspondingly, we devise approximation algorithms for them, and analyze algorithms’ time complexities and the approximation ratios of the solutions obtained. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Approximation algorithms

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Hybrid Models for Plant Disease Detection using Transfer Learning Technique 10

Hybrid Models for Plant Disease Detection using Transfer Lea...

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17th INDIACom;2023 10th International Conference on Computing for Sustainable Global Development, INDIACom 2023

作者： Sharma, Pavan Yadav, Rakesh Kumar Rawat, Sur Singh Iftm University Department of Computer Science and Engineering Moradabad244102 India Maharishi University of Information Technology Department of Computer Science and Engineering Lucknow226013 India Jss Academy of Technical Education Department of Computer Science and Engineering Noida201301 India

ISBN: (纸本)9789380544472

Plant diseases can sometimes be so bad that there is no grain harvest at all. Consequently, there is a great need for automatic plant disease identification and diagnosis in the realm of agricultural informatics. Many approaches have been put forth to solve this problem, but deep learning is quickly winning popularity due to its outstanding results. In this study, we assess the transfer learning capabilities of deep convolutional neural networks for the diagnosis of plant leaf diseases. We also consider how well the trained model will perform when applied to the specific task identified by our own data. One of the deep learning methods, the convolutional neural network algorithm (CNN), has been successfully applied to solve computer vision issues such as image classification, object segmentation, picture analysis, etc. In our study, we apply the Densenet201, Xception, InceptionResnetv2, NasNetMobile and VGG-16, hybrid with Support vector machine (SVM) along with a transfer learning approach to recognize diseases in images of different leaves. Instead of starting the training from scratch and arbitrarily initializing the weights, we use the pre-trained networks on the large labelled dataset, ImageNet, to build the weights. The proposed method's validity is supported by experimental findings, and it effectively detects plant diseases. The suggested method significantly outperforms other cutting-edge approaches in terms of performance like (F-scores of 0.93) for VGGG16+SVM, (F-score: 0.97) for DenseNet201+SVM, (F-score: 0.87) for Xception+SVM, (F-score: 0.97) for InceptionResnetv2+SVM, and (F-score: 0.93) NasNetMobile+SVM hybrid model on the dataset of plant images. © 2023 Bharati Vidyapeeth, New Delhi.

关键词： Deep neural networks

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Particle Swarm Optimization based Detection of Diabetic Retinopathy using a Novel Deep CNN 3

Particle Swarm Optimization based Detection of Diabetic Reti...

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3rd International Conference on Artificial Intelligence and Smart Energy, ICAIS 2023

作者： Revathi, B. Elizabeth, S.K.Kezial Nagaraj, P. Birunda, S.Selva Nithya, D. Ramco Institute of Technology Department of Artificial Intelligence and Data Science Virudhunagar India Mangayarkarasi College of Engineering Department of Computer Science and Engineering Madurai India Department of Computer Science and Engineering Kalasalingam Academy of Research and Education Virudhunagar India Theni Kammavar Sangam College of Technology Department of Computer Science and Engineering Theni India

ISBN: (纸本)9781665462167

Diabetic Retinopathy (DR) is a disease. Diabetic patients are mostly affected by this disease. This causes damage to the eyes. Most probably the disease affects both the back of the eye and the blood vessels of the light-sensitive. Initially, this DR disease causes only minor problems with vision. But eventually, the eyesight will go away. This condition may occur in any diabetic patient. When the disease is more affected, the blood sugar level is high. DR detection is the application of image processing to medical examinations. To diagnose DR the retinal images should be evaluated. However, it takes a lot of time and resources to physically assess the images so that the severity of the DR can be removed. The blood vessels in the retina are damaged due to this problem can be noticed. When it starts to bleed the small vessels, then it forms a way on the retina. To overcome the challenges of DR, CNN with PSO is introduced. There are different stages implicated in the diabetic retinopathy detection technique. They are pre-processing, optimization and classification. The main objective of the proposed work is to use the CNN algorithm to analyze the disease that seems to be most affected and classify and report only that area from the given input. PSO is used to optimize the given classified images, and then finally PSO with CNN technique will produce accurate results. © 2023 IEEE.

关键词： Blood vessels

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