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Comparing Convolutional Neural Networks for Covid-19 Detection in Chest X-Ray Images 10

Comparing Convolutional Neural Networks for Covid-19 Detecti...

10th IEEE Uttar Pradesh Section International Conference on Electrical, Electronics and computer engineering, UPCON 2023

作者： Varshney, Neeraj Madan, Parul Shrivastava, Anurag Kumar, C Praveen Mehta, Jyoti Kumar, Anil Gla University Department of Computer Engineering and Applications Mathura India Graphic Era Deemed to Be University Department of Computer Science & Engineering Uttarakhand Dehradun India Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Tamilnadu Chennai India Institute of Aeronautical Engineering Department of Computer Science and Engineering Telangana Hyderabad India Lloyd Institute of Engineering & Technology Greater Noida India Lloyd Law College Greater Noida India

ISBN: (纸本)9798350382471

The study has been conducted to understand the effectiveness of CNN in the case of 'COVID-19' detection by using all the X-ray images of the chest. This helps the health sector to facilitate medical performance and conduct the analytical procedure by using ML. The aim and objective of the study have been properly mentioned in the first segment of the study. The issues have also been identified and the second chapter has identified all the previous work related to the study. The methodology segment has illustrated all the methods that have been applied to implement the project. The resulting segment has illustrated all the themes with proper evaluation in a comprehensive way. The last chapter represented a conclusion segment with proper recommendations and future work that can be implemented later. © 2023 IEEE.

关键词： CNN COVID-19 pandemic situation Social Repercussions X-ray images

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Edge Computing And Convolutional Neural Networks For Real-Time Object Detection In Healthcare Iot 1

Edge Computing And Convolutional Neural Networks For Real-Ti...

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1st International Conference on Artificial Intelligence for Innovations in Healthcare Industries, ICAIIHI 2023

作者： Kumar, Rakesh Pundir, Sumit Shrivastava, Anurag Kumar, C Praveen Rao, A.L.N. Sankhyan, Akhil Gla University Department of Computer Engineering & Applications Mathura India Graphic Era Deemed to Be University Department of Computer Science & Engineering Dehradun India Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Tamilnadu Chennai India Institute of Aeronautical Engineering Department of Computer Science and Engineering Telangana Hyderabad India Lloyd Institute of Engineering and Technology Greater Noida India Lloyd Law College Greater Noida India

ISBN: (纸本)9798350330915

In order to provide real-time identification of objects in Internet of Things (IoT) software for the healthcare industry, this research examines the incorporation of cutting-edge computing with convolutional neural networks (CNNs). A deductive method is used to methodically optimize CNN representations for constrained by resources edge devices, and it is based on an interpretivist mindset. To give a thorough explanation of the technical complexities concerned, a design based on descriptive statistics is used. The analysis is built on secondary data gathered from reliable sources. The topics covered range from enhancing CNN machine learning models on edge devices to putting secure processing of information practices in place, analyzing the effects on medical diagnostics particularly monitoring, and examining scalability across various healthcare contexts. Results indicate significant gains in object detection precision and decreased latency, demonstrating the viability of implementing Edge-CNN methods. Critical study underlines the possibility for live installations and the necessity for specific approaches to integration in broader healthcare organizations. Conducting clinical trials, modifying the framework for particular healthcare areas, and investigating cost-benefit assessments are all recommendations. Enhanced optimization methods, adaptive safeguards, and additional scalability investigations should all be the focus of future research. Future research around real-time analytics gives an intriguing opportunity to go beyond recognizing objects as well. Overall, this research revolutionizes real-time object identification in medical IoT for better patient care by advancing the practical use of edge technologies and CNNs. © 2023 IEEE.

关键词： applications bandwidth Convolutional neural networks hierarchical framework IoT device

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Large Scale Follower Recommendation in Instagram 3

Large Scale Follower Recommendation in Instagram

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3rd International Conference for Emerging Technology, INCET 2022

作者： Dash, Bibhuti Bhusan Banerjee, Shobhan Samant, Tapaswini Swain, Tanmaya Rath, Manas Kumar Kalinga Institute of Industrial Technology School of Computer Applications Bhubaneswar India Birla Institute of Technology and Science Data Science and Engineering Pilani India

ISBN: (纸本)9781665494991

With advancements in predictive and prescriptive analytics over the past few years, various businesses have been extensively using recommender systems for marketing purposes. Say whether it's some sort of association analysis for customer market or any social networking for connecting people together, this very concept of recommenders has been utilized up to a very large extent over the past decade. When it comes to implementing the underlying concepts behind these systems, most of the time we get to have a paradox of choice of various techniques, which need to be carefully analyzed and acted upon to get the desired accuracy of the model. In cases where the recommendations are made based on a connected graph, we need proper feature generation techniques which play an important role in training the model as per our objective. In this paper, we propose a combination of feature extraction techniques employed over a Gradient Boost Decision Tree model for recommending people whom a person is likely to follow. We also shall investigate a few ways where the recommendation is made based on the common followers of two or more profiles and how likely two or more people may or may not know each other. In any problem, hyperparameter tuning plays the most important part which is a key aspect to optimize the model (so that it doesn't overfit or underfit the data available) and is totally problem specific. © 2022 IEEE.

关键词： Decision trees

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Play Store App Analysis & Rating Prediction Using Classical ML Models & Artificial Neural Network 7

Play Store App Analysis & Rating Prediction Using Classical ...

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2023 7th International Conference On Computing, Communication, Control And Automation, ICCUBEA 2023

作者： Moharana, Bhimasen Biswal, Bhramara Bar Dey, Snehasis Rath, Manas Kumar Banerjee, Shobhan Lovely Professional University Department of Computer Science and Engineering Punjab India College of Engineering P.G. Department of Computer Science & Applications Bhubaneswar India College of Engineering Electronics & Telecommunication Engineering Bhubaneswar India Maharaja Sriram Chandra Bhanja Deo University Department of Computer Application Baripada India Birla Institute of Technology and Science Department of Engineering Technology Pilani India

ISBN: (纸本)9798350304268

Each app available in the App Store or Play Store has various aspects linked to it such as its version, category, number of installations, genre, etc. which makes it unique, robust & popular. The reviews and ratings given by end users play an important role for both the developers and other users for the performance and survival of the app in the marketplace. Reviews are just a description for justification of the rating given by a user & the rating plays an important role in the first look. In this paper, we have used the Play Store Analysis Dataset to descriptively analyze the various attributes present in it and create models that predict the rating of an app, given its specifications. The models have been compared based on a common accuracy metric based on which their reliability can be judged and used in real-time rating predictions for a new application in the store for which a certain amount of data is available over some period. © 2023 IEEE.

关键词： Descriptive Statistics ML Classification Models Model Comparison Play Store App Analysis Rating Prediction

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Behavioral and Clinical Data Analysis for Autism Spectrum Disorder Screening with Machine Learning 10

Behavioral and Clinical Data Analysis for Autism Spectrum Di...

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10th IEEE Uttar Pradesh Section International Conference on Electrical, Electronics and computer engineering, UPCON 2023

作者： Kumar, Rakesh Bordoloi, Dibyhash Shrivastava, Anurag Kumar, C Praveen Kumari, Vandna Kumar, Anil Gla University Department of Computer Engineering & Applications Mathura India Graphic Era Deemed to Be University Department of Computer Science & Engineering Uttarakhand Dehradun India Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Tamilnadu Chennai India Institute of Aeronautical Engineering Department of Computer Science and Engineering Telangana Hyderabad India Lloyd Institute of Engineering & Technology Greater Noida India Lloyd Law College Greater Noida India

ISBN: (纸本)9798350382471

The study of appropriate and accurate classification for 'autism spectrum disorder (ASD)' is crucial, and this study, 'Behavioral and Clinical Data Analysis for Autism Spectrum Disorder Screening with Machine Learning,' aims to fulfil this requirement. The study integrates both 'quantitative and qualitative methodologies' through an integrated approach and accessible study philosophy. Approaches for gathering data include compiling datasets, reviewing relevant research, and obtaining EEG, emotions, and eye motion data. In order to boost the accuracy of ASD screening, statistical models including 'logistic regression, neural networks, and support vector machines have been created.' This quantitative analysis is enhanced by a thematic approach, which pinpoints recurrent themes and characteristics. Data protection and permission from subjects are given top priority in this study's ethical concerns. The theoretical and practical divide, the studies hope to improve effective ASD diagnosis and treatments. © 2023 IEEE.

关键词： Autism Spectrum Disorder (ASD) Electroencephalogram (EEG) Logistic Regression Neural network restricted and repetitive behaviours SVM

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RBCET: A Reputation based Blockchain Consensus Mechanism for Fast and Secured Energy Trading in Internet of Electric Vehicles (IoEV)

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IEEE Transactions on Consumer Electronics 2025年

作者： Kumar, Amritesh Mondal, Koustav Kumar Vishwakarma, Lokendra Das, Debasis Indian Institute of Technology Department of IDRP: IoT and Applications Jodhpur India Indian Institute of Technology Inter-Disciplinary Research Division: Centre for AIoT and Applications Jodhpur India Department of Computer Science and Engineering Thapar Institute of Engineering and Technology Patiala India Indian Institute of Technology Department of Computer Science and Engineering Jodhpur India

The state-of-the-art consensus mechanisms in blockchain-based Internet of Electric Vehicles (IoEV) prove suboptimal for energy trading due to their heavy CPU workload, high bandwidth consumption, energy inefficiencies, and the backlog of pending transactions. Also, the network is susceptible to attacks such as double-spending, Distributed Denial of Service (DDoS), eclipse, Sybil, and collusion attacks. Addressing these challenges, this paper introduces RBCET, a reputation-based consensus mechanism explicitly designed for energy trading within the IoEV blockchain network. The RBCET computes the reputation score (RS) of nodes (e.g. Electric Vehicles (EVs) and Charging Stations (CSs)) in the IoEV network using a decision tree regressor (DTR) model. This model utilizes the reputation parameter dataset generated by a relational mathematical model. RBCET allows only those EVs and CSs to participate in energy trading whose RS value exceeds the average RS value. Additionally, we also propose a hardware-based incentive mechanism to calculate and store incentives to enhance the efficiency of energy trading. Security analysis shows that RBCET resists the attacks mentioned above. Furthermore, we have developed a testbed using 10 Raspberry Pi 4 devices to demonstrate the performance of RBCET compared with state-of-the-art schemes. This comparison shows that RBCET reduces CPU workload by 47%, energy consumption by 10%, bandwidth usage by 5%, the volume of pending transactions by 75%, and overall improves efficiency by 62%. © 1975-2011 IEEE.

关键词： Denial-of-service attack

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Sustainable Health Care System for Cognitive Breast Cancer Detection using Machine Learning 2

Sustainable Health Care System for Cognitive Breast Cancer D...

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2nd IEEE Mysore Sub Section International Conference, MysuruCon 2022

作者： Ranjitha, P. Bhanu, K.N. Jain, Sachin Malapriya, S. Presidency University Computer Science and Engineering Bangalore India Global Institute of Management Science Department of Computer Applications Bangalore India Jss Science and Technology University Information Science and Engineering Mysuru India

ISBN: (数字)9781665497909

ISBN: (纸本)9781665497909

Cancer of the breast is a common cause of death in women. It is the second most origin of cancer deaths in women. For classifying breast tumors using image processing techniques, the researchers had developed image classification systems. Breast cancer disease becomes complicated as the causes remain unspecified. The regular indication of this disease masses and micro calcifications which is appeared on mammograms. They can be used to improve initial diagnosis approaches over time, which is important for women's lives. Mammography is an x-ray of the breasts to execute in the absence of symptoms. X-ray mammography is the major test used for screening and processing is the key to developing breast cancer. The first method comprises determining the cells present with various processes. An abnormal cell growth that serves no purpose is a tumor. In this work machine learning technique-SVM is used to detect the breast cancer and this work also predict the various stages of breast cancer. Proposed model results in 97% accuracy which is better than previous work. © 2022 IEEE.

关键词： Support vector machines

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Dynamic Trust Management in IoT Networks using Reputation-Based Systems

Dynamic Trust Management in IoT Networks using Reputation-Ba...

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2024 International Conference on Distributed Systems, computer Networks and Cybersecurity, ICDSCNC 2024

作者： Naidu, D J Samatha Jain, Upma Shruthi, S. Rao, K. B. V. Brahma Iqbal, Syed Mohammad Uzair Natrayan, L. Annamacharya Pg College of Computer Studies Department of Master of Computer Applications India Graphic Era Deemed to Be University Department of Computer Science & Engineering Dehradun India Bms Institute of Technology and Management Department of Artificial Intelligence and Machine Learning Bengaluru India Koneru Lakshmaiah Education Foundation Department of Computer Science and Engineering India Symbiosis Law School Department of Law Pune India Simats Saveetha School of Engineering Department of Mechanical Engineering India

ISBN: (纸本)9798350375442

The Internet of Things (IoT) enables intelligent data exchange across many kinds of interconnected devices in a diverse and unpredictable physical setting. The Internet of Things (IoT) network used in this study is a cluster-based sensor network that includes a base station and an application server that offers various services in a cloud setting. In this setting, assaults may occur in both device-to-device and user-to-device communication. For pervasive devices with limited resources, the use of conventional encryption techniques to address this problem is not practical. In addition, the majority of current trust-based security solutions either address the user level or the devices level. Therefore, this study aims to tackle environment-specific security concerns at the device and user levels in light of the environment's inherent dynamic and dispersed nature. This research explored the usage of a Trust Based Security Framework (TBSF) and Reputation Based Systems in the Internet of Things (IoT) to accurately describe the actions of devices and users. Fuzzy logic was used to calculate both the device trust and the user trust. A trust-based access control system, secure cluster head selection, and safe routing were all built using these trust values. At first, the security of device-to-device communication was addressed by the design and effective implementation of the Trust Aware Device Access Control System (TADACS). © 2024 IEEE.

关键词： Access control

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An Effective Forest Fire Prediction based on Contrastive Vision Transformer with Pool Former 2

An Effective Forest Fire Prediction based on Contrastive Vis...

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2nd IEEE International Conference on Integrated Intelligence and Communication Systems, ICIICS 2024

作者： Deepa, N. Husain, Saef Obad Ezhil, G. Indhumathi, V. Revathi, R. Department of Computer Science and Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Chennai India Department of Computers Techniques Engineering College of Technical Engineering The Islamic University Najaf Iraq Department of Humanities Bannari Amman Institute of Technology Sathyamangalam India Department of Computer Applications Sona College of Arts and Science Salem India Department of Electrical and Electronics Engineering New Prince Shri Bhavani College of Engineering and Technology Chennai India

ISBN: (纸本)9798331504960

Since years, forest fires are a significant concern globally, causing damage to ecosystems and human settlements. For effective prevention and mitigation predicting forest fires is important, different factors contribute to forest fires which includes human activities, topography, climate change and weather conditions. Traditional approaches for predicting forest fires had faced significant challenges which include limited validation of prediction models and lack of standardization. Therefore, this paper proposes Contrastive Vision Transformer (CViT)-Pool Former for predicting forest fire which is employed on Forest Fire Big Data dataset which enabled better comparability. This dataset is preprocessed by using data augmentation, noise reduction and data enhancement techniques. Then, the features are extracted by using CViT which extracted optimal features based on weather conditions and captured contextual relationships by maximizing mutual information. After that, Pool Former light weight attention module is used for predicting fires which gave more accurate predictions by enabling targeted prevention and mitigation, minimized unwanted deployments and resource waste, boosted prediction accuracy by token fusion and enhanced representation learning. The proposed CViT-Pool Former method established better results including accuracy (92.8%), precision (89.4%), recall (91.4%) and F1-score (90.2%) when compared with existing Region Proposed Convolutional Neural Network (R-CNN). © 2024 IEEE.

关键词： Premixed flames

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Scalable Image Processing with Apache Spark and Convolutional Neural Networks

Scalable Image Processing with Apache Spark and Convolutiona...

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2024 International Conference on Communication, computer sciences and engineering, IC3SE 2024

作者： Sharma, Deepika Rawat, Ruchira Alsalami, Zaid Chinthamu, Narender Varshney, Neeraj Saraswat, Manish Noida International University Department Of Computer Science And It U. P India Graphic Era Deemed To Be University Department Of Computer Science & Engineering Dehradun India College Of Technical Engineering The Islamic University Department Of Computers Techniques Engineering Najaf Iraq Mit Cto DallasTX United States Gla University Department Of Computer Engineering And Applications Mathura India Lloyd Institute Of Engineering & Technology Greater Noida India

ISBN: (纸本)9798350366846

This research presents a new framework called Spark-CNN that incorporates Apache Sparks distributed computing capabilities with Convolutional Neural Networks (CNNs) for images work processing. The results of experimental evaluation show that the proposed framework is indeed effective in handling large-scale image datasets, wherein substantial improvements have been noted regarding reduced processing time, scalability and accuracy. In Trial 1, Spark-CNN outperformed standalone -Spark (180 minutes), and all CNNs (240minutes) with an estimated processing season of 120 mins. Scale2 demonstrated the scalability of analyzing 2, sustaining constant processing times despite an increase in image dataset size from 1 million to 10 million. Further, in the third examination, Spark-CNN got an accuracy of 89%, surpassing standalone CNNs with a percentage score of 85%. Comparing the Trial 4 with Horovod-CNN, Spark-CNN showed a cutthroat processing time (120 minutes) and enhanced accuracy (89% vs., Trial 5 presented the influence of cluster setup on Spark-CNN revealing reduced execution times and better precision with growing numbers of executors beneficiaries per agent. These results establish Spark-CNN as a robust and effective solution to address scalable image processing challenges across different areas. © 2024 IEEE.

关键词： Convolution

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