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2nd IEEE International Conference on Distributed Computing and Electrical Circuits and Electronics, ICDCECE 2023

作者： Pentela, Ujwala Meesala, Anjali Nirupama Karingula, Deekshitha Seelamsetti, Nandini Krishna Veerlapalli, Swapna Gokaraju Rangaraju Institute of Engineering and Technology Dept. of Computer Science and Engineering Hyderabad India

ISBN: (纸本)9798350347456

Every day, many individuals encounter different illnesses. The prognosis of a disease is the most pivotal part of treatment. Enormous increase in healthcare and medical data enabled accurate medical data analysis, which aids in early sickness discovery and beforehand patient care. This study focuses on performing research on the enormous medical data by exercising numerous supervised classification algorithms like Decision Tree, Support Vector Machine, K- Nearest Neighbor, Logistic Regression, Naive Bayes and Random Forest to anticipate the most probable disease grounded on the symptoms and also to directly prognosticate the possibility of whether or not the person might be suffering from that particular illness. Based on the symptoms, the model uses the results of the supervised classification algorithms and gives a final validation indicating the disease that the existent might be suffering. On combining the prognostications from all the below classification algorithms, the model returns a more accurate verification when compared to the prognostications made by individual models. This study enhances the swiftness of decision-making and can reduce the rate of false cons. It helps healthcare associations make better decisions about how to proceed with early patient care. It also aids healthcare professionals in developing further effective ways of treating patients. © 2023 IEEE.

关键词： Support vector machines

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Detection of Tuberculosis Using Deep Learning Models

Detection of Tuberculosis Using Deep Learning Models

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2024 International Conference on Artificial Intelligence and Emerging technology, Global AI Summit 2024

作者： Srivastava, Samridhi Sharma, Anukrati Nair, Aarushi Srivastava, Prakash Jaypee Institute of Information Technology Noida India Department of Computer Science & Engineering Dehradun India

ISBN: (纸本)9798350379716

Tuberculosis is a major international concern, with approximately 10 million new emerging cases and 1 million deaths reported annually. Developing a semiautomated system for identifying TB through scan images is crucial for improving diagnostic accuracy. Current deep learning approaches face challenges such as lowquality datasets, limited model generalization, and high computational costs. Our paper addresses these issues by evaluating three deep learning models: SqueezeNet, ResNet, and DenseNet-121. SqueezeNet and ResNet were trained on 4,200 trillion bytes images of chest X-ray from the TB Chest X-ray Database, achieving test accuracies of 90% and 83%, respectively. DenseNet-121 was trained on the Shenzhen Chest X-ray Dataset, achieving an accuracy of 85%. After conducting a comparative study and implementing these models, we concluded that SqueezeNet provides the highest accuracy at 90%. Therefore, SqueezeNet is the most suitable model for accurately detecting tuberculosis and can be utilized for this purpose moving *** and can be utilized for this purpose moving forward. © 2024 IEEE.

关键词： Contrastive Learning

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Tomato Plant Disease Detection using Deep Learning Based Techniques: A Comparative study 2

Tomato Plant Disease Detection using Deep Learning Based Tec...

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2nd International Conference on Electrical, Electronics, Information and Communication Technologies, ICEEICT 2023

作者： Deepika, P. Arthi, B. SRM Institute of Science and Technology Chennai India Easwari Engineering College Department of Information Technology TN Chennai India College of Engineering and Technology Faculty of Engineering and Technology SRM Institute of Science and Technology Department of Computer Science and Engineering Chennai India

ISBN: (纸本)9798350397635

Tomato is most widely used and important crop in India. It plays a significant role in agriculture. Tomato plant grows in short period of time yields more tomatoes. Tomato is good in both nutrition and income. It can be grown in any soil condition and temperature. There are various diseases that affect tomato. To increase the production of tomato early detection of disease is important. There are various diseases in tomato plant like bacterial wilt, early blight, Late blight, Leaf, septorial leaf spot, leaf mold, bacterial spot etc. Deep Learning technologies provides better result in disease identification and detection. Deep learning architectures AlexNet, ResNet50, DenseNet121, EfficientNetB5, Inception-Resnet-V2, InceptionV3, MobileNet, VGG16 and VGG19 are tested against tomato plant diseases. The implementation is done by taking the tomato leaf images from real environment. The nine architectures are compared and in which DenseNet121 and MobileNet provides better accuracy than other deep learning architectures. © 2023 IEEE.

关键词： Deep learning

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Online Sales Prediction in E-Commerce Market Using Machine Learning 4

Online Sales Prediction in E-Commerce Market Using Machine L...

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4th International Conference on Signal Processing and Communication, ICSPC 2023

作者： Ajaykrishna, S. Suganya, T.S. Rao, Beema Pughazendi, N. Computer Science and Engineering Panimalar Engineering College India Srm Institute of Science and Technology Ramapuram Campus India

ISBN: (纸本)9798350300772

Product sales anticipation assumes a key part in upgrading idealness of item conveyance in E-Commerce. Among numerous heterogeneous provisions pertinent to sales estimating, advancement crusades held in E-Commerce and contending connection amid substitutable items would significantly muddle the substance. Unlikely, these variables are typically neglected in the current research;since the regular time-series investigation-based procedures principally believe the business records unaided. In this article, we make use of the previous data in online trade market to build up the framework to anticipate sales. As stated by the qualities of diverse data, 3-sorts of expectation techniques are: Incentive-Auto-Regressive-Integrated-Moving-Average (I-ARIMA), Long-Short-Term-Memory (LSTM) and Artificial-Neural-Network (ANN). These 3-techniques can handle the concern with diverse exactness necessities and various data types. Broad investigations are led more than two genuine datasets in diverse areas from E-Commerce. Outcomes reveal that the LSTM gets munificent implementation attain over others and modern intense learning choices as far as anticipating precision. This research considers the benefits and inconveniences of the three kinds of strategies on diverse informational repositories and gives considerable rules to traders on their advertising methodologies. © 2023 IEEE.

关键词： Long short-term memory

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Optimizing Diagnostic Accuracy in Ultrasound Imaging Through Cloud-Enabled Deep Learning Techniques 4

Optimizing Diagnostic Accuracy in Ultrasound Imaging Through...

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4th International Conference on Soft Computing for Security Applications, ICSCSA 2024

作者： Kalpana, V. Girija, M. Tharageswari, K. Yobu, D. Revathi, J. Aruna, R. Department of Computer Science and Engineering Vel Tech Rangarajan Dr.Sagunthala R&D Institute of Science and Technology Chennai India Jeppiaar University School of Engineering and Technology Chennai India Department of Computer Science and Engineering Karpagam Academy of Higher Education Coimbatore India Computer Science and Engineering Cyber Security J.J College of Engineering and Technology Tricy India Department of Mathematics Kongu Engineering College Erode India Department of Computer Science and Engineering School of Computing Vel Tech Rangarajan Dr.Sagunthala R&D Institute of Science and Technology Chennai India

ISBN: (纸本)9798331515720

This research aims to study predicting and optimizing the diagnostic accuracy by cloud-enabled deep learning techniques in ultrasound imaging for kidney disease diagnosis. The main findings are that CNN gives 92% accurate outcomes in terms of sensitivity, precision, specificity, AUC-ROC, and other terms used for predictive outcomes in the current research. The other outcomes are 88% accuracy and 91% specificity in predicting kidney diseases by using the RNN model. The other two models used for learning are transfer learning and federated learning which also provide good outcomes which are 94% accurate and 91% accurately in diagnosing with the above-said models. These learning approaches optimize or increase the accuracy rates such as 93% by the transfer learning approach and 90% accuracy outcomes by the federated learning mechanism. These above-given models have been taken based on advanced technologies that are used to predict or forecast the diseases through learning processes. The success of these learning mechanisms is explained by the reliable processes used in the learning of the models that are possible by the neural network used in the learning mechanisms. The high accuracy rates of the models show the successfulness of such clinics, who are in rural areas and are unable to meet their necessary outcome-oriented goals, then measures can be taken for running tele-clinics using the above models of learning techniques to diagnose the diseases at the correct time by 'n' number of people in rural areas. Mine is one of them working as a health worker for which measures need to be taken for finding an accurate solution that operates on cloud-enabled deep learning. This model help run family life and be able to diagnose certain conditions at desired rural clinics out of the above learning models. These tools of learning are studied on cloud-based deep learning techniques and performed well in diagnosing kidney diseases on rural tele-medicine and these learning techni

关键词： Ultrasonic imaging

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Smart Agriculture: Advanced Rice Disease Detection Using YOLOv10 for Enhanced Crop Health Management 3

Smart Agriculture: Advanced Rice Disease Detection Using YOL...

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3rd International Conference on Automation, Computing and Renewable Systems, ICACRS 2024

作者： Surya, V. Santhi, S. Department of Computer Science and Engineering KIT – Kalaignarkarunanidhi Institute of technology Coimbatore India KIT – Kalaignarkarunanidhi Institute of technology Coimbatore India

ISBN: (纸本)9798331532420

Rice, an essential crop for global food security, faces significant threats from diseases that impact both yields and economic stability. While traditional methods like SVM, KNN, and CNNs have contributed to disease detection, their limitations in real-time application and scalability hinder their effectiveness in practical field scenarios. This research highlights YOLOv10 as a groundbreaking deep learning model capable of detecting rice leaf diseases with remarkable speed and accuracy. Its advanced design overcomes the shortcomings of earlier techniques, facilitating real-time diagnosis and classification of plant diseases. By ensuring timely interventions and minimizing crop damage, YOLOv10 supports sustainable farming practices and reduces agricultural losses. This innovative solution represents a significant step forward in modern, efficient crop management. © 2024 IEEE.

关键词： Smart agriculture

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Efficient load balancing for Micro Services based applications

Efficient load balancing for Micro Services based applicatio...

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2023 International Conference on computer Communication and Informatics, ICCCI 2023

作者： Javahar, A. Ananth, R. Arun Ritthik, K.K. Dharun, R. KPR Institute of Engineering and Technology Department of Computer Science Engineering Tamil Nadu Coimbatore India

ISBN: (纸本)9798350348217

Any micro service-based programme, including alumni management software, must include load balancing. The effective use of resources is made possible by load balancing, which also ensures that application performance is unaffected by unexpected increases in requests or user usage. In an alumni management application, load balancing can be achieved with a combination of techniques such as round-robin scheduling, weighted load balancing, and dynamic scheduling. These techniques help to ensure that the application remains up and running, even during peak usage times. Additionally, load balancing can be used to ensure users get the best possible experience when using the application. By distributing traffic among multiple back-end services, users can expect faster response times and more reliable *** balancing in a micro services-based alumni management application is a way of distributing incoming web traffic across multiple back end servers in order to efficiently manage traffic, reduce latency, and improve overall performance. Load balancing works by distributing incoming requests across a cluster of servers, and can be done either statically or dynamically. Static load balancing assigns each request to a specific server, while dynamic load balancing uses algorithms to decide which server should handle each request. In the alumni management application, load balancing is important for ensuring that requests are handled efficiently and that the system is not overwhelmed by too many requests. This is especially important when multiple users are accessing the application simultaneously. By distributing requests across multiple servers, the application can remain responsive and reliable even under heavy load. © 2023 IEEE.

关键词： Response time (computer systems)

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Comparative Analysis of Dynamic Data Placement Strategy in Distributed Cloud Environment 1st

Comparative Analysis of Dynamic Data Placement Strategy in D...

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1st International Conference on Data engineering and Machine Intelligence, ICDEMI 2023

作者： Shankar, B. Prabhu Singh, Uday Kumar Vijayalakshmi Viji, C. Rajkumar, N. Stalin, M. Nachiappan, Balusamy Department of Computer Science and Engineering Alliance College of Engineering and Design Alliance University Karnataka Bangalore India Department of Computer Science and Engineering CMR Institute of Technology Karnataka Bangalore India Prologis DenverCO80202 United States Department of Computer Science and Engineering Vel Tech Rangarajan Dr.Sagunthala R & D Institute of Science and Technology Chennai India

ISBN: (纸本)9789819776153

In general, the dynamic data optimization technique is used to place the user’s data in a cloud system to reduce the latency, intercommunication traffic, data transfer cost, execution cost, load balancing, and bandwidth utilization. The user data is dispersed and stored in a distributed cloud environment, so it must ensure data availability and reliability in cloud data centers. These data placement techniques involve two steps: first user data identification and second identifying the node to place the data in a geographically distributed cloud. The primary goal of the data placement approach is to find the best or optimal node to store user data in terms of latency and storage costs. This comparative analysis provides various survey data placement algorithms and techniques used to analyze latency delay, execution time, access cost, load balance, server over, bandwidth, and server capacity with different parameters. This study also analyzes the merits and demerits of data placement strategies. This survey helps to concentrate on the dynamic data optimization technique in the Distributed or multiple cloud environment. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Distributed cloud

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OCSO-CA:opposition based competitive swarm optimizer in energy efficient IoT clustering

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Frontiers of computer science 2022年第1期16卷 77-87页

作者： Arpita BISWAS Abhishek MAJUMDAR Soumyabrata DAS Krishna Lal BAISHNAB Department of Electronics and Communication Engineering National Institute of Technology SilcharSilchar788010India Department of Computer Science&Engineering Karunya Institute of Technology&SciencesCoimbatore641114India Department of Electrical Engineering National Institute of Technology SilcharSilchar788010India

With the advent of modern technologies,IoT has become an alluring field of *** IoT connects everything to the network and transmits big data frequently,it can face issues regarding a large amount of energy *** this respect,this paper mainly focuses on reducing the energy loss problem and designing an energy-efficient data transfer scenario between IoT devices and ***,a layered architectural framework for IoT-cloud transmission has been proposed that endorses the improvement in energy efficiency,network lifetime and ***,an Opposition based Competitive Swarm Optimizer oriented clustering approach named OCSO-CA has been proposed to get the optimal set of clusters in the IoT device *** proposed strategy will help in managing intra-cluster and inter-cluster data communications in an energy-efficient ***,a comparative analysis of the proposed approach with the state-of-the-art optimization algorithms for clustering has been performed.

关键词： competitive swarm optimization cloud computing clustering IoT

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Face Recognition System Using Multicolor Image Analysis and Template Protection with BioCryptosystem 37th

Face Recognition System Using Multicolor Image Analysis and...

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37th International Conference on Image and Vision Computing New Zealand, IVCNZ 2022

作者： Sardar, Alamgir Umer, Saiyed Rout, Ranjeet Kumar Department of Computer Science and Engineering Aliah University Kolkata India Department of Computer Science and Engineering National Institute of Technology Srinagar India

ISBN: (纸本)9783031258244

This paper presents a multicolor image analysis-based face recognition system in an encrypted domain using the Tree Structure Part Model (TSPM) for face detection and a statistical feature extraction technique. The features are extracted from the RGB and YCbCr components independently of the input images. Then, an enhanced FaceHashing technique has been employed on the extracted feature vectors to generate cancelable biometrics (CB), i.e., cancelable biometrics for red (CBR ), green (CBG ), and blue (CBB ) of an RGB image, and similarly green (CBY ), blue (CBCb ), and red (CBCr ) for an YCbCr image. Further, we encrypted these cancelable biometrics using a novel BioCryptosystem technique and then stored them in a template database. This BioCryptosystem generates a security code called "CryptoCode" during the encryption process, which is stored on a smart card and in the template database as well. This "CryptoCode" is used as a decryption key and smart card verification. Then, a multi-class linear SVM classifier has been employed on the decrypted and query-cancelable feature vectors for both RGB and YCbCr biometrics individually. Finally, we employed the score fusion technique between the best score obtained from the classification of RGB biometrics and the best score obtained from YCbCr biometrics. For the performance evaluation, two benchmark facial databases, CVL and FEI, have been used. The system provides 100 % identification accuracy even after reducing the dimension from 500 to 100. The optimal security, reliability, efficiency, and speed of the proposed BioCryptosystem provide the novelty of this system. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Face recognition

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