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Classifying Alzheimer's Disease Stages via Bootstrap Aggregation of CNN Architectures on MRI Scans 1

Classifying Alzheimer's Disease Stages via Bootstrap Aggrega...

引用

1st International Conference on Pioneering Developments in computer science and Digital Technologies, IC2SDT 2024

作者： Neeharika, S. Rao, D. Rajeswara Nagaveni, V. Lalitha V.R. Siddhartha Engineering College Computer science Dept. Vijayawada India

ISBN: (纸本)9798350365016

Alzheimer's disease (AD) presents a pressing challenge in healthcare globally, necessitating timely and accurate diagnosis for effective management. Magnetic Resonance Imaging (MRI) holds promise for AD detection due to its ability to reveal structural brain changes. However, manual MRI analysis is labor-intensive and prone to human error. To tackle these issues, this study proposes a novel method for AD classification using ensemble learning with Convolutional Neural Network (CNN) architectures-specifically, CNN, Inception V3,DenseNet121, and MobileNet-leveraging bootstrap aggregation. This ensemble approach diversifies training data subsets, leading to enhanced accuracy. The proposed model achieved an impressive 84% accuracy with bootstrapping and 94 % without. Additionally, specificity values underscore the model's robustness, with 0.93 and 0.97 for bootstrapped and non-bootstrapped ensembles, respectively. This innovative approach not only improves accuracy but also addresses scalability and generalization challenges, offering promise for AD diagnosis and management based on neuroimaging data. © 2024 IEEE.

关键词： Convolutional neural networks

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Epileptic Seizure Detection using Variational Quantum Classifier

Epileptic Seizure Detection using Variational Quantum Classi...

引用

2024 International Conference on Computational Intelligence for Green and Sustainable Technologies, ICCIGST 2024

作者： Meesala, Gopichand Kumar, Lalit Pandey, Manish Khare, Nilay Maulana Azad National Institute of Technology Dept. of Computer Science and Engineering Bhopal India

ISBN: (纸本)9798350381887

Epilepsy, a neurological disorder characterized by recurrent and unpredictable seizures, demands timely and accurate detection for effective management and patient safety. While traditional machine learning approaches have shown promise in seizure detection, this study explores the potential of quantum computing, specifically utilizing a Variational Quantum Classifier. The Variational Quantum Classifier, grounded in quantum mechanics, aims to enhance computational capabilities for seizure detection. Leveraging quantum computing's intrinsic parallelism and superposition properties, the model focuses on processing complex and high-dimensional Electroencephalogram signals commonly used in epilepsy diagnosis. The model undergoes training on a dataset containing both seizure and non-seizure instances, optimizing parameters for increased accuracy. Performance evaluations utilize metrics such as sensitivity, specificity, and overall classification accuracy. © 2024 IEEE.

关键词： Quantum computers

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NutriSpy: Investigate Your Food's Health Quotient 3

NutriSpy: Investigate Your Food's Health Quotient

引用

3rd IEEE North Karnataka Subsection Flagship International Conference, NKCon 2024

作者： Thanay Kumar, Y.R. Joshi, Sudhanva S Sujan, A.T. Ujwal Karanth, K.V. Asha Rani, K.P. Gowrishankar, S. Dr. Ambedkar Institute of Technology Dept. of Computer Science and Engineering Bengaluru India

ISBN: (纸本)9798350364569

NutriSpy is a comprehensive web and mobile application designed to enhance users' health and wellbeing through personalized food and exercise recommendations. At its core, NutriSpy integrates three powerful features: a tailored workout recommendation system, an advanced food identification tool, and a robust diet advisory service. By leveraging state-of-the-art machine learning algorithms, NutriSpy accurately identifies food items, assesses their nutritional and caloric content, and generates customized dietary guidance. The food identification system employs image recognition and natural language processing to provide precise nutritional information from photographs or descriptions of meals. This is combined with a diet advice system that considers individual health profiles, dietary restrictions, and fitness goals to offer personalized nutrition plans. Additionally, NutriSpy's workout recommendation system aligns exercise routines with users' dietary needs and fitness aspirations, creating a harmonious balance between food intake and physical activity. NutriSpy's sophisticated technology facilitates real-time adjustments and recommendations, making it a dynamic tool for promoting healthier lifestyles. By analyzing user data and integrating current health trends, NutriSpy not only aids in immediate dietary and fitness decisions but also contributes to long-term health improvements. This study explores the development and functionality of NutriSpy, detailing its components, underlying technologies, and potential impact on fostering healthier living habits. © 2024 IEEE.

关键词： Diet Recommendation system Exercise Recommendation System Food Recognition System

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Automated tiered Detection of Cyber-Attacks in Operational Distribution Networks 8

Automated tiered Detection of Cyber-Attacks in Operational D...

引用

8th International Conference on Inventive Systems and Control, ICISC 202

作者： Brintha, N.C. Balaji, M. Arun Teja, B. Chandra Anwesh, G. Shashank, P. Eswar, C. Kapil Kalasalingam Academy of Research and Education Dept of Computer Science and Engineering Tamil Nadu Krishnankovil India

ISBN: (纸本)9798350386578

The increasing dependence on digital technologies within power grids brings forth a pressing concern: the vulnerability to cyber-attacks poses a significant threat to its security and reliability. In response, this research proposes an innovative approach leveraging ensemble learning techniques to predict and mitigate cyber-attacks in Smart Grids. By combining the strengths of diverse models such as Extra Trees, XGBoost, Random Forest, and Bagging, the ensemble model aims to bolster attack detection accuracy and robustness. Through real-time monitoring, anomaly detection, and rapid response mechanisms, the framework enhances the resilience of Smart Grids against evolving cyber threats. The research focuses on refining a predictive model capable of distinguishing legitimate data from malicious injections, thus ensuring the ongoing security and reliability of critical energy infrastructure. This innovative ensemble learning approach represents a promising strategy to fortify modern power grids, safeguarding against sophisticated cyber threats and ensuring continued operational integrity. © 2024 IEEE.

关键词： Cyber attacks

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Modular Design of Solid-State Transformer with High Flexibility

Modular Design of Solid-State Transformer with High Flexibil...

引用

2024 IEEE Energy Conversion Congress and Exposition, ECCE 2024

作者： Ma, Zhuxuan Cao, Hui Zhao, Yue University of Arkansas Dept. of Electrical Engineering and Computer Science FayettevilleAR United States

ISBN: (纸本)9798350376067

This paper presents a modular solid state transformer (SST) design incorporating dual active bridge and cascaded H-Bridge topologies to enhance electric vehicle (EV) charging infrastructure. By using modular design concept for the power cell hardware, the proposed SST can be suitable for industrial usage with low manufacturing cost, and low maintenance complexity. With various combinations of the power cells, the whole SST converter has high flexibility for the power scale and circuit topology. This guarantees the fast installation and high transportability of the SST, suitable for the high-demanded EV charging applications. The design detail of the busbar used for the SST has been presented, which shows how to reduce the stray inductance of the busbar while keeping its high current conduction ability and high insulation. To validate the high current conduction ability of the busbar, finite element analysis is conducted to assess busbar current density and temperature. Finally, an advanced control strategy is introduced to effectively reduce DC link ripple, eliminating the need for bulky components. © 2024 IEEE.

关键词： Busbars

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Cancer Classification using Gene Expression Dataset to Detect Cancer 15

Cancer Classification using Gene Expression Dataset to Detec...

引用

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

作者： Nishanth, Y. Meghraj, G.K. Kuttan, Akshat Sahay, Apurvanand Bangalore Amrita Vishwa Vidyapeetham Amrita School of Computing Dept of Computer Science and Engineering India

ISBN: (纸本)9798350370249

Cancer classification plays a very important role in personalized medicine and targeted therapy. To classify the kinds of cancer based on the gene expression data, three different kinds of algorithms are applied in this paper: the greedy method, the Recursion Tree Method, and Hashing. The selection of these algorithms forms a large combination to address the complexity of the gene expression datasets in an attempt to increase the accuracy of cancer classification and minimize the computational time. The study evaluates the performance of each of the algorithms and also show how well they do in cancer classification through detailed experimentation and review. The Greedy Algorithm and Feature Hashing enable accuracy of 88% and 85%, respectively. Feature Hashing reduces the computational complexity significantly. Such findings bring out the trade-offs between accuracy and computational efficiency, giving insights that might be of importance for cancer detection and treatment methods. © 2024 IEEE.

关键词： Trees (mathematics)

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Personal Finance Management and Prediction using ML Algorithms 8

Personal Finance Management and Prediction using ML Algorith...

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8th IEEE International Conference on Computational System and Information Technology for Sustainable Solutions, CSITSS 2024

作者： Kaushik, Sujay G. Kashyap, Sumukha S. Surabhi, B.V. Chaitra, M. Reddy, Santhosh Anitha, N. Bnm Institute of Technology Affiliated to Vtu Dept. of Computer Science and Engineering Bangalore India

ISBN: (纸本)9798331505462

Effective personal finance management is essential for achieving financial stability and long-term financial health. This paper presents a comprehensive approach to summarizing, analyzing, and show monthly expenses using machine learning models. By collecting user inputs such as age, place of stay, number of dependents, monthly expenses on transport, education, subscriptions, savings, medical expenses, and more, the system uses linear regression, decision trees and random forest algorithms to predict financial values. The system provides detailed information of expenses, savings, and overall financial health, along with personal health analysis based on dietary habits. Users have access to a wide range of financial and lifestyle information, allowing the system to provide personalized recommendations to increase financial security and achieve financial goals. The model also evaluates the impact of recurring expenses and notable investments, helping users understand their spending patterns and make informed financial decisions. The results demonstrate the system's capability to deliver accurate predictions and actionable advice, enable users to better manage their finances. This approach has led to significant advances in personal financial management by integrating machine learning techniques to provide insight into people's financial health. © 2024 IEEE.

关键词： Adversarial machine learning

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Evaluation of Movie Recommender Systems Using Hesitant Fuzzy Logic and Deep Convolutional Spiking Neural Networks 4

Evaluation of Movie Recommender Systems Using Hesitant Fuzzy...

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4th International Conference on Advancement in Electronics and Communication engineering, AECE 2024

作者： Shrivastava, Vineet Kumar, Suresh Manav Rachna International Institute of Research & Studies Dept of Computer Science and Engineering Faridabad India

ISBN: (纸本)9798350364729

Within the entertainment industry, movie recommendation systems have become important tools that enable users to explore films in large catalogues in line with their tastes and experiences by suggesting content relating to their preferences. This paper examines the efficiency of an innovative approach that combines Hesitant Fuzzy Clustering (HFC) with Deep Convolutional Neural Networks (DCNNs) to improve accuracy of movie recommendations. The proposed system was validated on Rotten Tomatoes and MovieLens 10m dataset, which showed considerable enhancements compared to traditional approaches like collaborative filtering and matrix factorization for accuracy, precision and Mean Absolute error as well. An integrated model consisting of HFC and DCNN demonstrated an accuracy level of 98% for MovieLens 10m dataset while it was equal to 99% for Rotten Tomatoes. It further shows a decrement of Mean Absolute Error (MAE) when compared with previous Matrix Factorization along with neural network. It is therefore evident from these findings that the developed model is robust enough, thereby creating higher levels of user satisfaction through more accurate recommendations. Future research will extend the application of this technique to other areas and further refine it to be able to accommodate a wider range dynamic user preference. © 2024 IEEE.

关键词： Convolutional neural networks

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Enhancing Decentralized Finance for Scalability, Interoperability, and User Experience 8

Enhancing Decentralized Finance for Scalability, Interoperab...

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8th IEEE International Conference on Computational System and Information Technology for Sustainable Solutions, CSITSS 2024

作者： Nalini, N. Rao, Vishak Rao, Karthik Shobha, P. Nitte Meenakshi Institute of Technology Dept. of Computer Science and Engineering Karnataka Bengaluru India

ISBN: (纸本)9798331505462

This research explores Decentralized Finance (DeFi) through the lens of enhanced applications, introducing the concept of Decentralized Finance Enhanced (DeFine). It examines the transformative potential of DeFi, analyzing its evolution and the innovative solutions driving its progress. The objective is to investigate how DeFine addresses scalability, interoperability, security, and user experience challenges in the DeFi ecosystem. Through case studies and analysis, this proposed work provides insights into the implications, opportunities, and risks associated with the adoption and maturation of DeFi enhancements. © 2024 IEEE.

关键词： Decentralized finance

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On - Device Translation of Dynamically Typed and Interpreted Languages 15

On - Device Translation of Dynamically Typed and Interpreted...

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

作者： Pranav, H. Sriharshini, Muppavarapu Tarun, Rachuri Belwal, Meena Amrita Viswa Vidyapeetham Amrita school of computing Dept of Computer Science Engineering Bengaluru India

ISBN: (纸本)9798350370249

On Device - translation of expression form languages with dynamic nature and using interpreted languages like MATLAB and Python requires a precise and automated form of processing the error-free conversion. With the help of the automation, the code in MATLAB is well-defined to Python so that translation is precise and the originality as well as functionality is ensured. This paper describes a robust and redundant methodology for converting maintaining the functionality and originality of the MATLAB code helping in smooth transition of code from MATLAB to python. The procedure being entirely local also ensures to protect privacy of the code. © 2024 IEEE.

关键词： Code Generator Dynamical Type Interpreting language MATLAB On - device Code Translation Parser Python Token Tokenizer

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