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共 1031 条记录，以下是131-140 订阅

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Early Detection: Deep Learning-Driven ECG Image Analysis Forcardiovascular Screening

Early Detection: Deep Learning-Driven ECG Image Analysis For...

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soft computing for Security Applications (ICSCSA), international conference on

作者： J. R. V. Jeny K. Nikhil Goud Divya Sai Leela Amrutha Kishore Azmira Department of Computer Science and Engineering (AI&ML) Vignan Institute of Technology and Science

ISBN: (数字)9798331515720

ISBN: (纸本)9798331515737

the heart is a very important organ in the circulatory system. Heart disease is extremely severe and can even lead to death, therefore it is essential to make an accurate and timely diagnosis. However, health data analysis may be complicated because there may not be any evident patterns. Cardiovascular diseases (CVDs) are the number one cause of death throughout the world. Early detection of them is vital for successful management. For this purpose, electrocardiograms (ECGs) are used which indicate electrical activities in our hearts; however, computers and machine learning systems are ways by which we could analyze patients’ data more swiftly. It concentrates on using deep as well as machine learning techniques in assisting diagnosing diseases. the purpose was to evaluate how well deep learning could identify four significant heart conditions: arrhythmia, myocardial infarction or heart attacks, history of myocardial infractions (past heart attacks), and normal patterns. We utilized a real-world ECG dataset reflecting actual scenarios and complexities. the Proposed model performed even better than the existing ones with an accuracy of 98.23% implying that it could correctly identify heart issues in ECG images 98.23% times out of 100%. Also recall, precision and F1 score were very high which are other measures about how good a model is performing. By using Machine Learning & Deep learning Algorithms like Support Vector Machine, Efficient Net, Residual Neural Network, and *** Algorithms created their own deep learning model specifically for predicting heart problems.

关键词： Heart Deep learning Support vector machines Machine learning algorithms Accuracy Computational modeling Cardiac arrest Electrocardiography Myocardium Prediction algorithms

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Enhancing the Accuracy of Forecasting of the partern of load usage of the Electrical appliences via soft computing

Enhancing the Accuracy of Forecasting of the partern of load...

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Advance computing and Innovative Technologies in Engineering (ICACITE), international conference on

作者： Subuddi Nagaraju C. Rohith Bhat M. Baskar Gnanajeyaraman Rajaram L. Pattathu Rani K. Vijaya Bhaskar Department of EEE Aditya Institute of Technology and Management Tekkali Andhra Pradesh India Saveetha School of Engineering Saveetha institute of Medical and Technical Sciences Saveetha University Chennai India Department of Mechatronics Chennai Institute of Technology Chennai India Department of Computer Science and Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences SIMATS Chennai Saveetha School of Engineering SIMATS University Chennai Department of Computer Science and Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Chennai India

ISBN: (数字)9798350360165

ISBN: (纸本)9798350360172

precise estimations of consuming load profiles are crucial for the successful oversight of electrical energy systems in both financial & environmental terms. Based on this foundation, it is feasible to strategize & execute the utilization of manageable power generation and storage systems, along with power acquisition, considering the necessary preparation as well as technological and contract limitations. the documented electrical load profiles will significantly rise throughout the digitization of the energy sector. To enhance prediction accuracy, it is essential to create and analyze models that consider the increasing data volume and aim to enhance predicting accuracy through an increased number of data. Artificial neural networks (ANN) are being more commonly employed to address not linear issues involving large datasets influenced by humans and various other unexpected factors. the artificial neural network (ANN) technique is selected to forecast load patterns. the study aims to simulate and evaluate the efficacy & optimal performance of prediction models using an Artificial Neural Network for power consumption profiles.

关键词： Solid modeling Accuracy Power demand Artificial neural networks Predictive models State-space methods Forecasting

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Temporal Analysis and Prediction of Lung Tumor Growth Using LSTM Networks: A Deep Learning Approach

Temporal Analysis and Prediction of Lung Tumor Growth Using ...

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soft computing for Security Applications (ICSCSA), international conference on

作者： Ajanthaa Lakkshmanan D. Reddy Sai Geethesh N. Saketh Reddy Department of Computing Technologies SRM Institute of Science and Technology

ISBN: (数字)9798331515720

ISBN: (纸本)9798331515737

Lung cancer is one of major causes of cancer related disease globally, and early detection, combined with precise tumor growth prediction, is essential for efficient treatment planning and better patient outcomes. this study offers a deep learning-based technique that employs Long-Short-Term Memory (LSTM) networks to predict the growth rate of lung tumors over time by using sequential imaging data from longitudinal CT scans. the proposed method, which analyses temporal sequences of lung scans, ensures not only detect tumors but also predict their progression, so aiding in the proactive management of lung cancer. this technique includes several steps. Initially, sequential CT scan data from the NSCLC radio genomics dataset is preprocessed to identify the region of interest (ROI) that contains the tumor in each scan. these ROIs are used to extract features such as tumor volume, shape and texture which then fed into the LSTM network. the LSTM method is then trained on these temporal features sequences to identify patterns that represent tumor growth. the model’s architecture is intended to capture temporal dependencies in the data, allowing it to generate accurate predictions about the upcoming tumor size and growth rate. the effectiveness of the proposed method is tested using common performance metrics such as Root Mean Squared Error (RMSE), Mean squared error (MSE), R-Squared(R2) and mean absolute error. Experimental results show that the LSTM based method can reliably forecast tumor developed parts offering useful advancements for therapeutic decision such as modifying treatments based on expected tumor growth. this study demonstrated the potential of temporal analysis with LSTM networks to improve lung cancer growth. By predicting tumor growth, this method can help doctors make prompt and personalized treatment decisions, ultimately improving the quality of the care for lung cancer patients. Future work will concentrate on improving the method and investigating it

关键词： Deep learning Accuracy Computed tomography Lung cancer Medical services Feature extraction Real-time systems Long short term memory Tumors Biomedical imaging

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the Effective Detection of Alzheimer in the System via ML Algorithm

The Effective Detection of Alzheimer in the System via ML Al...

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Advance computing and Innovative Technologies in Engineering (ICACITE), international conference on

作者： R. Kishore Kanna Kapil Joshi Gunjan Chhabra Ambali Pacholi X.S. Blessing NimaSajai Flosy R. Menezes Department of Biomedical Engineering Jerusalem College of Engineering (Autonomous) Chennai India Department of CSE Uttaranchal Institute of Technology Uttaranchal University Dehradun India Department of CSE Graphic Era Hill University Adjunct Professor Graphic Era Deemed to be University Dehradun Uttarakhand School of Nursing Science ITM University Gwalior (MP) India College of Nursing Khamis Mushayt King Khalid University Kingdom of Saudi Arabia Institute of Nursing Education Bambolim(Goa) India

ISBN: (数字)9798350360165

ISBN: (纸本)9798350360172

Effective therapy for Alzheimer’s disease depends on early diagnosis. Key results from four research that used machine learning to detect Alzheimer’s disease during its initial level. Early research indicates that combining cognitive and neuropsychological data may enhance diagnosis. the second research interprets neuroimaging data using machine learning methods. Finding the first signs of illness is the objective here. In the third research, risk variables for Alzheimer’s defect conditions are detected using machine learning algorithms. they stress the need of comprehensive data for precise prediction. A novel algorithm is presented in the fourth research. the soft voting classifier method increases recognition accuracy by operating on an averaging mechanism. these papers demonstrate how machine learning may enhance approaches for diagnosing Alzheimer’s disease. To enhance therapeutic results, combine everything from cognitive evaluations to multimodal data.

关键词： Neuroimaging Machine learning algorithms Accuracy Sensitivity Machine learning Classification algorithms Alzheimer's disease

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DeepDrive: Innovations in Distance Detection and Intervehicle Communication through Deep Learning

DeepDrive: Innovations in Distance Detection and Intervehicl...

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Communication, computing and Industry 4.0 (C2I4), international conference on

作者： Chettiyar Vani Vivekanand Inbamalar T M Amala Justus Selvam M Monika Devi A Mohanambikai A Pooia Shree K Dept. of ECE R.M.K. College of Engineering and Technology Puduvoyal Tamilnadu India Department of Automobile Engineering Veltech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Avadi Tamil Nadu

Over 1.2 million people lost their lives in traffic accidents in 2018, according to the World Health Organization (WHO). this conference paper proposes ground-breaking research that integrates deep learning approaches for object identification and distance estimates utilising onboard sensors with intervehicle communication using Li-Fi (Light Fidelity) technology. Li-Fi integration allows for fast wireless communication between vehicles, while deep learning improves the precision and dependability of object identification and distance calculation, leading to improvements in traffic flow and road safety. Traditional radio frequency-based systems are supplemented by this communication protocol, which offers faster and more secure data transfer between cars. A sensor suite that includes cameras and distance sensors simultaneously documents the area around the vehicle. Our method integrates sensor data with Li-Fi-based intervehicle communication for distance calculation. this study concludes by highlighting an innovative experiment that combines deep learning and Li-Fi inter-vehicle communication to detect a wide range of objects and estimate their distance with great precision. these technologies have been used to show a viable route towards safer and more effective road transport systems, advancing intelligent vehicles and upcoming smart cities.

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Detecting and Diagnosing Alzheimer's Disease: A Transformer-Based Survey (DAD-T)

Detecting and Diagnosing Alzheimer's Disease: A Transformer-...

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Mobile Networks and Wireless Communications (ICMNWC), IEEE international conference on

作者： Prabu Selvam C. Gunasundari M. Tamilselvi Pinjari Haneef T. Suresh R. Jini School of Computing SRM Institute of Science & Technology Tiruchirapalli Campus Tiruchirapalli India Department of Computer Science & Engineering Roever Engineering College Tiruchirapalli India Department of Computer Science and Engineering (AIE) Amrita School of Computing Amrita Vishwa Vidyapeetham India Department of AIML K. Ramakrishnan College of Engineering Tiruchirapalli India Department of Information Technology Sri Muthukumaran Institute of Technology Chennai India

ISBN: (数字)9798331528348

ISBN: (纸本)9798331528355

Alzheimer's Disease (AD) represents a significant health challenge in today's world. Applying deep learning across various disciplines has demonstrated remarkable potential and has garnered substantial interest. the Transformer model has been shown to have exceptional performance across various computer vision tasks, significantly enhancing its application in medical imaging. Compared to conventional CNN, RNNs, and traditional deep learning algorithms, the Transformer network has demonstrated encouraging results in numerous studies and tasks. this paper reviews multiple works for Mild Cognitive Impairment, early diagnosis, AD and related conditions in the Transformer network over the last half-decade. this paper also presents a comprehensive overview of the data sources, pre-processing, feature extraction, training protocols, and evaluation methods to serve as a resource for future research on AD. Additionally, this survey offers a performance comparison of Transformer models across different datasets, a concise examination of these challenges and suggests potential directions for future research.

关键词： Surveys Deep learning Wireless communication Training Protocols Reviews soft sensors Transformers Feature extraction Alzheimer's disease

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Intelligent Accident Recognition and Notification System using Hybrid Deep Learning and IoT

Intelligent Accident Recognition and Notification System usi...

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soft computing for Security Applications (ICSCSA), international conference on

作者： P. Sathish Kumar Ramesh Singh Rawat Manish Shrimali Swapnil Parikh Harshal Patil Ramya Maranan Department of Robotics & Automation United Institute of Technology Coimbatore Tamil Nadu India Department of Computer Science & Engineering Graphic Era Deemed to be University Dehradun Uttarakhand India Department of Computer Science and IT Janardan Rai Nagar Rajasthan Vidyapeeth (Deemed to be University) Udaipur Rajasthan India Department of Computer Science and Engineering Parul Institute of Engineering and Technology Parul University Gujarat India Computer Science and Engineering Symbiosis Institute of Technology Symbiosis International (Deemed University) Pune Maharashtra India Department of Research and Innovation Saveetha School of Engineering SIMATS Chennai Tamil Nadu India

ISBN: (数字)9798331515720

ISBN: (纸本)9798331515737

Ensuring the safety of individuals should be a top priority. Everyone wants to return home safely. Many riders continue to disregard safety constraints, increasing the chance of accidents that result in death. It is critical to incorporate the latest technology into road monitoring systems to make cities smarter and to ensure rider safety. For this research, Deep Learning (DL) and the Internet of things (IoT) were chosen for effective road surveillance and alert systems. the primary goal of the study is to detect and inform riders about accidents that occur along their path and to provide details about the location of the detected accident. Additionally, the system provides the vehicle's speed. To identify automobile accidents in the footage, a hybrid DL model that includes Convolutional Neural Network - Bidirectional Long Short-Term Memory (CNN-Bi-LSTM) is suggested. For the model's training, both accident and non-accident data were collected. the model is evaluated using three metrics: accuracy, loss, and execution time. the next step is to upload the recommended model to the thingSpeak cloud. A Graphical User Interface (GUI) is being created in the thingSpeak cloud. the GUI displays the vehicle's speed, accident images, and the precise location of the accident scene using the Raspberry Pi, speed sensor, Pi-camera, and Global Positioning System (GPS) module. An LED is also integrated into the GUI to alert the user. A real-world test is conducted on the designed system. the proposed solution has many advantages and will ensure driver safety.

关键词： Deep learning Training Cloud computing Accuracy Roads Surveillance Feature extraction Safety Accidents Graphical user interfaces

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Comparative Analysis of Apriori, FP-Growth, and ECLAT Algorithms for Effective Grocery Store Management and Product Recommendations

Comparative Analysis of Apriori, FP-Growth, and ECLAT Algori...

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soft computing for Security Applications (ICSCSA), international conference on

作者： Sivaramakrishnan S Anusha V Madhumitha A Karthika M Harshitha N Kumari Vaishnavi Chourasia Dept of Information Science and Engineering New Horizon College of Engineering Bengaluru India Dept of Electrical and Electronics Engineering New Horizon College of Engineering Bengaluru India

ISBN: (数字)9798331515720

ISBN: (纸本)9798331515737

In the dynamic landscape of grocery retail, administrators face the challenge of efficiently managing inventory and making informed decisions to enhance sales and customer satisfaction. Traditional methods of inventory management and product recommendation may not fully leverage the wealth of transactional data available. this study aims to tackle these obstacles through the introduction of a Grocery Recommendation System that utilizes Market Basket Analysis (MBA). the goal of this system is to successfully recommend products and anticipate client preferences by utilizing transactional data taken from the custom e-commerce website. the website is made to record a variety of transactional data when a customer makes a purchase. By using sophisticated algorithms like Apriori, FP-Growth (Frequent Pattern Growth), and ECLAT (Equivalence Class Clustering and Bottom-Up Lattice Traversal), the system detects recurring itemsets and association rules that emphasize the significant connections across products. this study evaluates the performance of all three algorithms, focusing on metrics like time complexity, support, confidence, and lift. FP-Growth demonstrates superior time efficiency, particularly with large datasets, due to its memory-efficient frequent itemset generation. Apriori's straightforward solution, meanwhile, might be appropriate for smaller datasets that need to be interpretable. ECLAT’s vertical transaction method offers advantages in dense data scenarios. Selection among Apriori, FP-Growth, or ECLAT should align with specific dataset characteristics and business goals. these algorithms provide actionable insights crucial for inventory management and customer engagement strategies. Administrators are then able to increase inventory turnover, adjust stocking techniques , and eventually increase sales.

关键词： Itemsets Clustering algorithms Organizations Inventory management Inventory control Association rule learning Security Reliability Time complexity Recommender systems

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First-Order Meta-Learning in Node Classification with Graph Convolutional Network 4

First-Order Meta-Learning in Node Classification with Graph ...

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4th international conference on Artificial Intelligence and Big data, ICAIBD 2021

作者： Cao, Jing Xu, Yi Song, Xuening School of Information and Software Engineering University of Electronic Science and Technology of China China Trusted Cloud Computing and Big Data Key Laboratory of Sichuan Province Chengdu China

ISBN: (纸本)9780738131702

In recent years, research on graph representation learning within graph neural networks has made great progress. Among them, graph convolutional neural networks (GCN), which is based on spectral domain, have drawn great attention and become one of the mainstream graph embedding approaches. However, few-shot learning on graphs has little related work, as we know. Toward this problem, we propose a meta-learning method on graphs, FOML-GCN (First order meta-learning on GCN), aimed to be applied in various kinds of models of graph convolutional networks. As many common approaches in meta-learning, FOML-GCN involves two phases, meta-training and meta-testing. By learning the potential Knowledge from a small number of samples in existing classes during meta-training, our method enhances the ability in node classification of the base model by training within new few data that only appear in the meta-testing step. the experiment on two benchmark datasets shows that our FOML-GCN performs better than base GCN and other models of graph network in few-shot node classification tasks. © 2021 IEEE.

关键词： Graph neural networks

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Enhancing Predictive Accuracy in Cardiovascular Disease Diagnosis: A Hybrid Approach Using RFAP Feature Selection and Random Forest Modeling

Enhancing Predictive Accuracy in Cardiovascular Disease Diag...

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soft computing for Security Applications (ICSCSA), international conference on

作者： Vikram Pasupuleti Bharadwaj thuraka Chandra Shikhi Kodete V Priyadarshini Koti Mani Kumar Tirumanadham Vahiduddin Shariff School of Technology Eastern Illinois University Charleston IL USA School of Computer Science and Information Systems Northwest Missouri State University Maryville MO USA Department of CSE SRKR Engineering College Bhimavaram India Department of CSE Sir C R Reddy College of Engineering Eluru India

ISBN: (数字)9798331515720

ISBN: (纸本)9798331515737

Cardiovascular disease remains one of the leading causes of death all over the world and is characterized by ailments affecting cardiovascular structures, such as the heart failure, arrhythmia, and coronary artery diseases. It has multiple roots based on genetic influences on the disease, meals, drinks, physical inactivity, or presence of other diseases including hypertension, and diabetes. Heart diseases are still rampant in the world despite the development in the research on diseases and in healthcare. this is mainly because of the increasing average life span of individuals as well as increased cases of urbanization and adoption of poor-quality diets and leading inactive or sedentary lives. Such problems further escalate due to shortcomings in healthcare provision and preventive options by socioeconomic differences, especially affecting the needy populace. Hence, there is a rising focus on using complex technologies such as artifices and machine intelligence, along with big data analytics and predictive models, in handling these issues. these methodologies utilize computational methods and large-scale data to enhance risk assessment, optimize treatment pathways, and alleviate the overall burden of cardiac disease on healthcare systems and individuals. the concentration of this research is on the systematic application of rigorous data preprocessing, class imbalance correction using techniques such as SMOTE, and outlier management using methods such as IQR, in order to improve the effectiveness of predictive modelling. this study intends to improve predictive accuracy and model generalizability, thereby contributing to improvements in healthcare decision-making and patient care outcomes, by utilizing the RFAP hybrid technique for feature selection and Random Forest for modelling.

关键词： Accuracy Systematics Computational modeling Urban areas Medical services Predictive models Feature extraction Socioeconomics Random forests Standards

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