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15th International Conference on Computing Communication and Networking Technologies, ICCCNT 2024

作者： Sasipriyaa, N. Ravikumar, R. Sharmila, C. Gothai, E. Krishnakumar, B. Nithya Sri, R.K. Kongu Engineering College Department of Computer Science and Engineering Tamilnadu India The Erode College of Pharmacy Pharmaceutical Analysis Tamilnadu India SRM IST Trichy Department of Computer Science and Engineering India Kongu Engineering College Department of Computer Science and Engineering Tamilnadu India

ISBN: (纸本)9798350370249

Corona Virus caused a pandemic outbreak all over the world during 2020-2021. Identification of such diseases in the X-ray images needs help of deep learning methodologies involving classifiers. Achieving proficiency in training convolutional neural networks requires a considerable volume of data. Higher accuracy is needed while dealing with medical fieldrelated problems especially. When dealing with small datasets, networks tend to exhibit overfitting problems. In order to get larger dataset, techniques for data augmentation serve to enhance the neural networks' capacity for generalization by expanding existing training data more efficiently. While conventional data augmentation methods do enhance performance, their scope remains restricted. Generative Adversarial Networks (GANs) have proven instrumental in producing fresh data, thereby elevating the performance of CNNs. Methodologies for augmenting data with the help of GAN's is not as much notable in Convolutional neural networks. In this research study, a GAN architecture is used for augmenting X -ray images of chest in order to expand the dataset. Auxiliary Classifier Generative Adversarial Network model has been used to produce images. It consists of a generator that produces new images with random noise and a discriminator whose job is to identify fake & real images. The results showcase that the new image generated is fine-tuned over each epoch and the significance can be seen in the output images. We compare the quality of our ACGAN images with the conventionally augmented images. © 2024 IEEE.

关键词： COVID-19

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Electronic health records protection strategy by using blockchain approach

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Multimedia Tools and Applications 2024年第39期83卷 86883-86894页

作者： Kathole, Atul B. Patil, Sonali D. Kumbhare, Savita Vhatkar, Kapil Kshirsagar, Aniruddha P. Kimbahune, Vinod V. Computer Engineering Dr. D. Y. Patil Institute of Technology Pune India IT Dept. Pimpri Chinchwad College of Engineering Pune India Computer Science and Engineering Department K.B.P College of Engineering Satara Satara India

Electronic health records (EHRs) provide significant challenges for medical organizations due to the digitalization of traditional medical information. When using EHRs, both doctors and patients are accustomed to spending considerable time conducting the necessary queries;nevertheless, the acquired data is not always related to the sought one, and access may be revoked if this is the case. The exchange and maintenance of medical health records are among the most crucial tasks in the healthcare system. The integrity of health records is crucial, as is maintaining patient anonymity, and either would have devastating effects in the event of a breach. Therefore, it is one of the most important reasons to keep electronic health records safe. Modern healthcare infrastructure is notoriously difficult to use, wasteful, and fraught with problems related to data integrity, security, and privacy. Better EHR management and monitoring, however, has the potential to lessen the severity of the problems with security and complexity. The blockchain's potential, with its decentralized and trustworthy nature in the healthcare industry, makes this a reality. Problems with data management and the way information is validated and disseminated are at the heart of the healthcare delivery system's inherent difficulties. Improved access to medication tracking, hospital assets, drug systems, patient information, and so on is the primary benefit of employing blockchain technology in healthcare data management. Because of how important it is to have access to a patient's medical history when prescribing medication, blockchain technology has the potential to significantly enhance the current healthcare delivery system. Therefore, it is critical to develop a blockchain-based system for verifying and protecting medical records. The primary objective of this article is to create a permissioned blockchain-based Ciphertext Policy-Attribute-based encryption system to protect privacy and regulate acces

关键词： Blockchain

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Medicinal Plant Identification and Information Provision Using AI

Medicinal Plant Identification and Information Provision Usi...

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

作者： Anslam Sibi, S. Sundaresan, C. Surya, S. Sujith, J.P. Srm Valliammai Engineering College Department of Computer Science and Engineering Chennai India

ISBN: (数字)9798331509828

ISBN: (纸本)9798331509828

Medicinal plants have been integral to traditional medicine, offering a wide range of health benefits and natural remedies. However, accurate identification is essential for safe use and the preservation of this valuable knowledge. This project proposes an AI-based system that combines EfficientNet and XGBoost models to enhance the accuracy of medicinal plant identification from images. While traditional systems rely on manual identification or basic models like CNN and SVM, they often fail to account for variations in plant appearance due to growth stages or environmental conditions. To address these limitations, the proposed system leverages EfficientNet for robust feature extraction from plant images, utilizing its superior ability to handle image variations across diverse conditions. These features are then passed to an XGBoost classifier, which excels in handling structured data and fine-tuning predictions. Together, this combination ensures high identification accuracy across a wide variety of plant species. Additionally, the system integrates a Hugging Face Transformer for natural language understanding and generation, allowing it to provide comprehensive information about each plant's medicinal properties and uses. This approach bridges the gap between accurate identification and practical application, promoting safe usage and preserving traditional medicinal knowledge. © 2025 IEEE.

关键词： Medicinal chemistry

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Tri-UnityNet: A Multifaceted Ensemble Model for Pneumonia Detection

Tri-UnityNet: A Multifaceted Ensemble Model for Pneumonia De...

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2024 International Conference on Advances in Data engineering and Intelligent Computing Systems, ADICS 2024

作者： Dhivyaa, C.R. Nithya, K. Shanmugapriya, P. Deebika, R. Kumar, K. Sathis Sudhakar, R. School Of Computer Science And Engineering Vellore Institute Of Technology Vellore India Srm Institute Of Science And Technology Computer Science And Engineering Trichy India Computer Science And Engineering Erode India K.S.R College Of Engineering Computer Science And Engineering Tiruchengode India Bannari Amman Institute Of Technology Computer Science And Engineering Sathyamangalam India Nandha College Of Technology Computer Science And Engineering Erode India

ISBN: (纸本)9798350364828

Pneumonia is an infection often caused by several viral infections and prediction of pneumonia requires expertise from radiotherapists, posing challenges, especially in remote areas. Developing an automatic pneumonia detection system is crucial to detect and treat pneumonia disease. Hence, an innovative approach is proposed to detect pneumonia by utilizing the ensemble of deep learning models, specifically VGG16, ResNet50, and InceptionV3. The ensemble method integrates predictions from these various architectures to enhance overall classification performance. Rather than starting the training of each model from the beginning, transfer learning techniques are utilized to fine-tune Convolutional Neural Network (CNN) models using a pre-existing knowledge base. This involves leveraging pre-trained models and adapting them specifically for the disease diagnose on Chest X-ray image dataset. The features are extracted from VGG16, ResNet50, and InceptionV3 models and the classes of disease are predicted by using softmax layers. Finally, ensemble learning is employed to fuse the predictions of multiple neural network architectures within Tri-UnityNet, enhancing its accuracy and robustness in pneumonia detection. By leveraging the complementary strengths of diverse models, the ensemble approach achieves superior performance, contributing to more reliable diagnostic outcomes. © 2024 IEEE.

关键词： Forecasting

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Deep Learning Enabled Social Media Recommendation Based on User Comments

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computer Systems science & engineering 2023年第2期44卷 1691-1702页

作者： K.Saraswathi V.Mohanraj Y.Suresh J.Senthilkumar Department of Computer Science and Engineering Government College of EngineeringSalem636011India Department of Information Technology Sona College of TechnologySalem636005India

Nowadays,review systems have been developed with social media Recommendation systems(RS).Although research on RS social media is increas-ing year by year,the comprehensive literature review and classiﬁcation of this RS research is limited and needs to be *** previous method did notﬁnd any user reviews within a time,so it gets poor accuracy and doesn’tﬁlter the irre-levant comments efﬁ*** Recursive Neural Network-based Trust Recom-mender System(RNN-TRS)is proposed to overcome this method’s *** it is efﬁcient to analyse the trust comment and remove the irrelevant sentence ***ﬁrst step is to collect the data based on the transactional reviews of social *** second step is pre-processing using Imbalanced Col-laborative Filtering(ICF)to remove the null values from the *** the features from the pre-processing step using the Maximum Support Grade Scale(MSGS)to extract the maximum number of scaling features in the dataset and grade the weights(length,count,etc.).In the Extracting features for Training and testing method before that in the feature weights evaluating the softmax acti-vation function for calculating the average weights of the ***,In the classiﬁcation method,the Recursive Neural Network-based Trust Recommender System(RNN-TRS)for User reviews based on the Positive and negative scores is analysed by the *** simulation results improve the predicting accuracy and reduce time complexity better than previous methods.

关键词： Recommendation systems(RS) social media recursive neural network-based trust recommender system(RNN-TRS) user reviews

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A Base Station Deployment Algorithm for Wireless Positioning Considering Dynamic Obstacles

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computers, Materials & Continua 2025年第3期82卷 4573-4591页

作者： Aiguo Li Yunfei Jia Department of Network Engineering Xi’an University of Science and TechnologyXi’an710600China College of Computer Science and Technology Xi’an University of Science and TechnologyXi’an710600China

In the context of security systems,adequate signal coverage is paramount for the communication between security personnel and the accurate positioning of *** studies focus on optimizing base station deployment under the assumption of static obstacles,aiming to maximize the perception coverage of wireless RF(Radio Frequency)signals and reduce positioning blind ***,in practical security systems,obstacles are subject to change,necessitating the consideration of base station deployment in dynamic ***,research in this area still needs to be *** paper proposes a Dynamic Indoor Environment Beacon Deployment Algorithm(DIE-BDA)to address this *** algorithm considers the dynamic alterations in obstacle locations within the designated *** determines the requisite number of base stations,the requisite time,and the area’s practical and overall signal coverage *** experimental results demonstrate that the algorithm can calculate the deployment strategy in 0.12 s following a change in obstacle *** results show that the algorithm in this paper requires 0.12 s to compute the deployment strategy after the positions of obstacles *** 13 base stations,it achieves an effective coverage rate of 93.5%and an overall coverage rate of 97.75%.The algorithm can rapidly compute a revised deployment strategy in response to changes in obstacle positions within security systems,thereby ensuring the efficacy of signal coverage.

关键词： Wireless positioning base station deployment dynamic obstacles dynamic obstacle wireless positioning

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HDR Reconstruction from LDR Scenes Using Basic Image Formation Procedure 14

HDR Reconstruction from LDR Scenes Using Basic Image Formati...

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14th International Conference on Computing Communication and Networking Technologies, ICCCNT 2023

作者： Sisira, C.S. Ann Thomas, Princy Government Engineering College Computer Science & Engineering Department of Computer Science & Engineering Thrissur India Government Engineering College Department of Computer Science & Engineering Thrissur India

ISBN: (纸本)9798350335095

High dynamic range (HDR) images are the one's which includes all the objects from the original scene with correct exposure. The HDR content can be very helpful in analysis purposes as it possess all features of the scene as it is. Although it is useful the creation of HDR is very much expensive. Low dynamic range (LDR) images are less expensive but may include saturation regions leads to the absence of some content. For this reason there is a necessity to think of an alternative. The reconstruction of HDR from LDR content can be the alternative solution for the problem. This process can be done using different *** studies mainly represents different kinds of machine learning techniques. The methods include machine learning , deep learning , different kinds of CNN architectures specially designed for particular purposes , GAN models , reverse tone mapping operators etc. This survey also analyses evaluation techniques for assessing quality of images and techniques like image declipping, tone mapping operator etc. Some of the analyzed methods also deals with reconstruction of HDR videos from LDR videos rather than from raw *** in the survey reveals accuracy of different methods. © 2023 IEEE.

关键词： Inverse problems

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Adaptive Deep Learning Model for Software Bug Detection and Classification

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computer Systems science & engineering 2023年第5期45卷 1233-1248页

作者： S.Sivapurnima D.Manjula Department of Computer Science and Engineering College of EngineeringGuindyAnna UniversityChennai600025TamilnaduIndia

Software is unavoidable in software development and *** literature,many methods are discussed which fails to achieve efficient software bug detection and *** this paper,efficient Adaptive Deep Learning Model(ADLM)is developed for automatic duplicate bug report detection and classification *** proposed ADLM is a combination of Conditional Random Fields decoding with Long Short-Term Memory(CRF-LSTM)and Dingo Optimizer(DO).In the CRF,the DO can be consumed to choose the efficient weight value in *** proposed automatic bug report detection is proceeding with three stages like pre-processing,feature extraction in addition bug detection with ***,the bug report input dataset is gathered from the online source *** the pre-processing phase,the unwanted information from the input data are removed by using cleaning text,convert data types and null value *** pre-processed data is sent into the feature extraction *** the feature extraction phase,the four types of feature extraction method are utilized such as contextual,categorical,temporal and ***,the features are sent to the proposed ADLM for automatic duplication bug report detection and *** proposed methodology is proceeding with two phases such as training and testing *** on the working process,the bugs are detected and classified from the input *** projected technique is assessed by analyzing performance metrics such as accuracy,precision,Recall,F_Measure and kappa.

关键词： Software bug detection classification pre-processing feature extraction deep belief neural network long short-term memory

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computer Vision and Deep Learning-enabled Weed Detection Model for Precision Agriculture

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computer Systems science & engineering 2023年第3期44卷 2759-2774页

作者： R.Punithavathi A.Delphin Carolina Rani K.R.Sughashinir Chinnarao Kurangit M.Nirmala Hasmath Farhana Thariq Ahmed S.P.Balamurugan Department of Information Technology M.Kumarasamy College of EngineeringKarur6391 I3India Department of Computer Science and Engineering K.Ramakrishnan College of TechnologyTiruchirapalli621112India Department of Electronics and Instrumentation Easwani Engineering CollegeTamil Nadu600089India Pondichery University Puducherry605014India Department of computer science and engineering The Oxford college of EngineeringBangalore560068India Department of Computer Science Engineering Saveetha School of EngineeringSaveetha Institute of Medical and Technical SciencesSaveetha UniversityChennai602105India Department of Computer and Information Science Faculty of ScienceAnnamalai University Chidambaram608002India

Presently,precision agriculture processes like plant disease,crop yield prediction,species recognition,weed detection,and irrigation can be accom-plished by the use of computer vision(CV)*** plays a vital role in influencing crop *** wastage and pollution of farmland's natural atmosphere instigated by full coverage chemical herbicide spraying are *** the proper identification of weeds from crops helps to reduce the usage of herbicide and improve productivity,this study presents a novel computer vision and deep learning based weed detection and classification(CVDL-WDC)model for precision *** proposed CVDL-WDC technique intends to prop-erly discriminate the plants as well as *** proposed CVDL-WDC technique involves two processes namely multiscale Faster RCNN based object detection and optimal extreme learning machine(ELM)based weed *** parameters of the ELM model are optimally adjusted by the use of farmland fertility optimization(FFO)algorithm.A comprehensive simulation analysis of the CVDL-WDC technique against benchmark dataset reported the enhanced out-comes over its recent approaches interms of several measures.

关键词： Precision agriculture smart farming weed detection computer vision deep learning

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Automated Virtual Toll Booth System Based on Number Plate Recognition Using YOLOv8 and EasyOCR 15

Automated Virtual Toll Booth System Based on Number Plate Re...

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15th International Conference on Computing Communication and Networking Technologies, ICCCNT 2024

作者： Kumar, Vinod S. Ponmani, S. Anand, K. Ananthajothi, K. Department of Computer Science and Engineering Rajalakshmi Engineering College Chennai India

ISBN: (纸本)9798350370249

In this work, a virtual toll booth system utilizing cutting-edge technologies like EasyOCR with optical character recognition, or OCR, and YOLOv8 for object detection is introduced. By connecting the accounts stored in a database with vehicle number plate recognition, the system is meant to streamline toll collection procedures. By using this technique, the system may efficiently handle toll transactions without the need for actual toll booths or human involvement. The registration of car number plates as well as owner data, which is kept in a database based on CSV files, is the first step in the system's process. The system's camera takes a picture of the license plate when a car gets close to the toll station. After that, the image is processed using the EasyOCR engine to extract the information from the number plate and the YOLOv8 algorithm to determine whether a car is present. An extra toll is charged if the license plate is within a set distance of the toll booth and matches a registered number in the database. The toll amount, the date and time of the transaction, the vehicle's license plate, and other transaction details are all stored in the database by the system. There is no toll if the registration number is not found or is found outside of the predetermined range. Comprehensive testing was used to assess the system's performance, and the results showed that it could recognize license plates and manage toll transactions with high accuracy. The results show that the system can increase the overall efficiency of toll management systems, reduce operational expenses, and speed up toll collection procedures. ©2024 IEEE.

关键词： Optical character recognition

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