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A Transfer Learning Approach for Neurodegenerative Disease Classification from Brain MRI Images: Distinguishing Alzheimer's, Parkinson's, and Control Cases 6

A Transfer Learning Approach for Neurodegenerative Disease C...

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6th International Conference on Electrical engineering and information and Communication technology, ICEEICT 2024

作者： Siddiqua, Ayesha Oni, Atib Mohammad Miah, Md. Jarez Northern University Bangladesh Department of Computer Science and Engineering Dhaka Bangladesh Brac University Department of Electrical & Electronic Engineering Dhaka Bangladesh Institute of Information and Communication Technology Bangladesh University of Engineering and Technology Dhaka Bangladesh

ISBN: (纸本)9798350385779

Like many other critical medical conditions, different neurogenerative diseases, including Alzheimer's and Parkinson's diseases, need to get diagnosed in the primary stage. Deep learning algorithms show excellent performance in detecting neurodegenerative diseases from images obtained by magnetic resonance imaging (MRI) of the human brain. Recently, transfer learning has also shown promising outcomes in the classification of neurological conditions using brain MRI data. Here, we examine the efficacy of the transfer learning strategy utilizing four distinct CNN architectures, namely EfficientNetB0, ResNet50, InceptionV3,and Xception. Used dataset for the study has three classes;Parkinson's disease (PD), Alzheimer's disease (AD), and control (healthy). The study compares the accuracy, precision, recall, and F1- score metrics for the investigated CNN models. The result demonstrates that the EfficientNetB0 model shows the best training and testing accuracy, reaching an accuracy as high as 99.4%. © 2024 IEEE.

关键词： Magnetic resonance imaging

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Ontology-Based Data Modeling for NoSQL Databases: A Case Study in e-Healthcare Application

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SN computer science 2023年第1期4卷 3页

作者： Sen, Poly Sil Mukherjee, Nandini Department of Information Technology Techno Main Salt Lake Kolkata India Department of Computer Science and Engineering Jadavpur University Kolkata India

With the advent of e-healthcare applications, managing health data manually is no longer possible. Health data being voluminous, complex in nature and generally described with specialized terminologies, requires to be specified with standards like ISO EN13606RM, openEHR, HL7, etc. for reference implementation and data storage. These standards are not completely interoperable and cannot fit to sensor data, cloud and database implementation for various requirements. Thus, a generic ontology is necessary to accommodate basic clinical care. This paper first proposes one such ontology and describes the process of development of the ontology as suggested in the literature. Next, the paper also proposes an algorithm which uses a domain ontology and a set of queries to output the design of a NoSQL data store. The ontology developed in the paper is used by the proposed algorithm to output a NoSQL design for storing semi-structured and unstructured healthcare data. The performance of the algorithm is also analyzed theoretically using the NoSQL database schema generated for healthcare domain. © 2022, The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd.

关键词： Big data e-Health data e-Health sensor Healthcare ontology NoSQL data model

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Enhancing Medical Image Diagnosis with Convolutional Neural Networks 2

Enhancing Medical Image Diagnosis with Convolutional Neural ...

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2nd IEEE International Conference on Advances in information technology, ICAIT 2024

作者： Mahadevan, K. Pushparani, S. SashiRekha, K. Sivagami, V.M. Usharani, R. Department of Computer Science and Engineering CARE College of Engineering Tamil Nadu Trichy India Department of Computer Science and Engineering Meenakshi College of Engineering Tamil Nadu Chennai India Department of Computer Science and Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Tamil Nadu Chennai India Department of Information Technology Sri Venkateswara College of Engineering Tamil Nadu Chennai India Department of Computational Intelligence School of Computing Faculty of Engineering and Technology SRM Institute of Science and Technology Kattankulathur Chennai India

ISBN: (纸本)9798350383867

This paper is a comparative analysis of medical image diagnosis algorithms with Convolutional Neural Networks (CNNs) and other methods;such as Support Vector Machine (SVM), Random Forest, and k-nearest Neighbors (k-NN) The study performs evaluations on datasets with multimodalities like medical images and diagnostic and various imaging modalities. Results shows that CNNs have the upper hand over traditional, static algorithms across several metrics that assesses the performance of the system such as accuracy, precision, recall, and F1. In particular, CNN precision was 90.5% while the recall was 93.7%, and the accuracy was 92.1% as well as F1-score 92.0%. In addition, the high area under the Receiver Operating Characteristic curve (AUC-ROC) of over 0.95 for the CNN also signed for its excellent discriminatory power. These outcomes only exemplify CNN usefulness in the diagnostic area because it is an exact and accurate method for analyzing medical images. Although the nets CNNs are deployed in clinical cases may help address concerns regarding interpretability, compliance with regulations, and ethical opinions, such issues still exist and require further exploration. Overall, this research takes the first steps in directing algorithmic approach towards medical diagnosis and show the feasibility in deployment of CNNs as inseparable pieces of equipment in modern healthcare. © 2024 IEEE.

关键词： Random forests

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Diabetes Prediction using Enhanced and Optimized DRL-CNN Approaches 21

Diabetes Prediction using Enhanced and Optimized DRL-CNN App...

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21st International Learning and technology Conference, L and T 2024

作者： Usha Nandhini, A. Dharmarajan, K. Sharma, Bhisham Chowdhury, Subrata Ben Dhaou, Imed Vistas School of Computing Chennai India Vistas Department of Information Technology Chennai India Chitkara University Institute of Engineering and Technology Chitkara University Department of Computer Science and Engineering Punjab India Sreenivasa Institute of Technology and Management Studies Department of Computer Science and Engineering Andra Pradesh Chittoor India Dar Al-Hekma University Hekma School of Engineering Computing and Design Department of Computer Science Jeddah Saudi Arabia

ISBN: (纸本)9798350393569

This study develops an advanced automated prediction system using Machine Learning (ML) techniques to identify diabetes early. The research employs the WBSMOTE method for data preprocessing, addresses class imbalances with the WB-SMOTE approach, and introduces two novel classification methods, DRL-CNN and DRL-OCNN. Reliable datasets from sources like The Data World repository, PIMA Indian Diabetes (PIDD) dataset, and Real Time (RT) Diabetes Dataset are used, with an emphasis on data consistency and feature relevance. If we examine these datasets, it contains general attributes and no internal specific attributes related to diabetes is used. So, prediction results will be what type of diabetes we won't get any specific characteristics and risk factors. Evaluation is based on Accuracy, Precision, and Recall criteria. The DRL-OCNN model, integrating SGD optimization and Delta Rule Learning, outperforms other ML models with a accuracy of 96%, precision of 0.93 and recall of 0.91, demonstrating its potential for accurate early diabetes identification and classification. © 2024 IEEE.

关键词： Machine learning

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Design and Implementation of an Artificial Intelligence of Things-Based Autonomous Mobile Robot System for Cleaning Garbage

Design and Implementation of an Artificial Intelligence of T...

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作者： Chen, Liang-Bi Huang, Xiang-Rui Chen, Wei-Han Pai, Wen-Yu Huang, Guan-Zhi Wang, Wei-Chien National Penghu University of Science and Technology Department of Computer Science and Information Engineering Magong Penghu880011 Taiwan The University of Sydney Faculty of Engineering and Information Technologies SydneyNSW2006 Australia

This article proposes an autonomous mobile robot (AMR) system based on the artificial intelligence of things (AIoT) for collecting garbage. The proposed system consists of an AMR subsystem, a robot operating system (ROS) robot subsystem, an artificial intelligence (AI) garbage recognition subsystem, a solar power supply subsystem, and a smart trash can subsystem. The AMR subsystem uses a motor drive, which is built on an aluminum alloy metal base and is connected to an AI edge computing module via an app, to control the robot. The ROS subsystem is built on an edge computing module, which is coupled with the motor drive module to control the robot arm and the robot gripper and is connected to the AI edge computing module to control the robot arm through the app. The AI garbage recognition subsystem recognizes the types of waste in real time through an image sensor (camera). This subsystem adopts multiple sensors that can monitor the level of waste in the trash can. Moreover, a map on a web platform locates all trash cans. Servomotors enable automatic opening and closing of the lid to prevent the unpleasant smell of garbage from wafting. A user connects to the AI edge computing module through an app to monitor the robot and control the movement of the robot to any location on the map. © 2001-2012 IEEE.

关键词： Edge computing

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Real-Time Multi-Feature Approximation Model-Based Efficient Brain Tumor Classification Using Deep Learning Convolution Neural Network Model

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computer Systems science & engineering 2023年第9期46卷 3883-3899页

作者： Amarendra Reddy Panyala M.Baskar Department of Computer Science and Engineering School of ComputingSRM Institute of Science and TechnologyKattankulathurChengalpattuChennai603203TamilnaduIndia Department of Information Technology MLR Institute of TechnologyHyderabad500043TelanganaIndia Department of Computing Technologies School of ComputingSRM Institute of Science and TechnologyKattankulathurChengalpattuChennai603203TamilnaduIndia

The deep learning models are identified as having a significant impact on various *** same can be adapted to the problem of brain tumor ***,several deep learning models are presented earlier,but they need better classification *** efficient Multi-Feature Approximation Based Convolution Neural Network(CNN)model(MFACNN)is proposed to handle this *** method reads the input 3D Magnetic Resonance Imaging(MRI)images and applies Gabor filters at multiple *** noise-removed image has been equalized for its quality by using histogram ***,the features like white mass,grey mass,texture,and shape are extracted from the *** features are trained with deep learning Convolution Neural Network(CNN).The network has been designed with a single convolution layer towards dimensionality *** texture features obtained from the brain image have been transformed into a multi-dimensional feature matrix,which has been transformed into a single-dimensional feature vector at the convolution *** neurons of the intermediate layer are designed to measure White Mass Texture Support(WMTS),GrayMass Texture Support(GMTS),WhiteMass Covariance Support(WMCS),GrayMass Covariance Support(GMCS),and Class Texture Adhesive Support(CTAS).In the test phase,the neurons at the intermediate layer compute the support as mentioned above values towards various classes of *** on that,the method adds a Multi-Variate Feature Similarity Measure(MVFSM).Based on the importance ofMVFSM,the process finds the class of brain image given and produces an efficient result.

关键词： CNN deep learning brain tumor classification MFA-CNN MVFSM 3D MRI texture Gabor

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Real Time Monitoring of Water Quality for Rural Areas: A Machine Learning and Internet of Things Approach 4

Real Time Monitoring of Water Quality for Rural Areas: A Mac...

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4th International Conference on Intelligent engineering and Management, ICIEM 2023

作者： Paul, Tushar Raj, Rohit Garg, Priyansh Jain, Shruti Jaypee University of Information Technology Department of Electronics and Communication Engineering Himachal Pradesh Solan India Jaypee University of Information Technology Department of Computer Science and Engineering Himachal Pradesh Solan India

ISBN: (纸本)9798350341126

A water quality monitoring system can aid in preserving the environment, ensuring the security of nearby water sources, and fostering economic growth in rural areas. This results in the development of a system, employing the Internet of Things (IoT) and Machine Learning to monitor the quality of water. This paper discusses the characteristics of water that explains whether it is fit for human consumption or not. The pH and Turbidity sensors are dipped in water samples acquired from wells, lakes, rivers, ponds, and other places are used to inform the development of an effective model. The data will be delivered from the sensors to the IDE, where it will then be sent to the cloud server. The model effectively accounts for test tables, where 1 indicates the water is fit for drinking and 0 indicates the water is unfit. The model is validated by employing Support Vector Machine, Random Forest and XG Boost methods. The maximum accuracy of 95.12% was observed using XGBoost. We have successfully implemented the real-time data for the water quality monitoring system for rural areas using Machine Learning and IoT which lets us know whether the water is fit for drinking or not. © 2023 IEEE.

关键词： Internet of things

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Leveraging Deep Learning Methodologies in Data science Practices: A Systematic analysis 15

Leveraging Deep Learning Methodologies in Data Science Pract...

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

作者： Gupta, Eesha Senthilkumar, K.K. Gaikwad, Vidya S. Nagpal, Manish Singh, Akhilendra Pratap Sharma, Sonal Vivekananda Global University Department of Computer Science & Engineering Jaipur India Prince Shri Venkateshwara Padmavathy Engineering College Department of Electronics and Communication Engineering Chennai127 India Vishwakarma Institute of Technology Department of Computer Engineering Pune India Chitkara University Institute of Engineering and technology Chitkara University Centre of Interdisciplinary Research in Business and Technology Punjab India Maharishi University of Information Technology Maharishi School of Engineering and Technology Uttar Pradesh India Department of Computer Science and Engineering Karnataka Bangalore India

ISBN: (纸本)9798350370249

This paper proposes a systematic analysis of using deep mastering methodologies in statistics technology practices for the cause of leveraging the strength of deep mastering algorithms. It presents an overview of the exceptional types of deep gaining knowledge of methodologies and architectures in addition to their application within the various areas of records technology. Similarly, it critiques the prevailing literature and famous several useful findings regarding the usage of deep mastering in facts science and also the potential demanding situations and possibilities that could come in conjunction with it. It details the coding and implementation of numerous deep mastering fashions such as convolutional neural networks, deep belief networks, recurrent neural networks, and lengthy quick term memory (LSTM). Additionally, it presents an in-intensity analysis of the professionals and cons of the usage of deep gaining knowledge of for information technology. Eventually, it gives guidelines on a way to effectively use deep studying in facts technological know-how. © 2024 IEEE.

关键词： Fashions Guidelines Mastering Networks Technological

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Generate a Single Heuristic for Multiple Dynamic Flexible Job Shop Scheduling Tasks by Genetic Programming 13

Generate a Single Heuristic for Multiple Dynamic Flexible Jo...

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13th IEEE Congress on Evolutionary Computation, CEC 2024

作者： Chen, Jiayin Jia, Ya-Hui Bi, Ying Chen, Wei-Neng School of Future Technology South China University of Technology Guangzhou China School of Electrical and Information Engineering Zhengzhou University Zhengzhou China School of Computer Science and Engineering South China University of Technology Guangzhou China

ISBN: (纸本)9798350308365

Genetic programming (GP) hyper-heuristic method has been extensively studied to solve multiple dynamic job shop scheduling tasks by generating an effective heuristic for each task simultaneously. However, a fundamental question has not been answered. Do we need to customize a specific heuristic for each task? To fill this research gap, we propose to generate a single heuristic for handling multiple tasks. Without designing complex evolution mechanisms, only during the evaluation process of GP, the fitness of a heuristic is evaluated by multiple tasks. Since there are multiple tasks, a heuristic has multiple objective values. A rank aggregation (RA) fitness evaluation strategy is designed to convert multiple objective values of multiple tasks into a fitness value for a single heuristic. To validate the effectiveness of the generated solution and the proposed RA strategy, we design multitask scenarios that encompass tasks with diverse objectives, utilization levels, and maximum operation times. The results demonstrate that the performance of the single heuristic generated in multitask scenarios is comparable to solutions generated by GP using the single-task learning paradigm, meaning that with an appropriate training method, GP can generate a heuristic with good generality. © 2024 IEEE.

关键词： Genetic programming

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Optimal Load Forecasting Model for Peer-to-Peer Energy Trading in Smart Grids

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computers, Materials & Continua 2022年第1期70卷 1053-1067页

作者： Lijo Jacob Varghese K.Dhayalini Suma Sira Jacob Ihsan Ali Abdelzahir Abdelmaboud Taiseer Abdalla Elfadil Eisa Department of Electrical and Electronics Engineering Christian College of Engineering and TechnologyOddanchatram624619India Department of Electrical and Electronics Engineering K.Ramakrishnan College of EngineeringTiruchirappalli621112India Department of Computer Science and Engineering Christian College of Engineering and TechnologyOddanchatram624619India Department of Computer System and Technology Faculty of Computer Science and Information TechnologyUniversity of Malaya50603Kuala LumpurMalaysia Department of Information Systems King Khalid UniversityMuhayel Aseer62529Saudi Arabia Department of Information Systems-Girls Section King Khalid UniversityMahayil62529Saudi Arabia

Peer-to-Peer(P2P)electricity trading is a significant research area that offers maximum fulfilment for both prosumer and *** also decreases the quantity of line loss incurred in Smart Grid(SG).But,uncertainities in demand and supply of the electricity might lead to instability in P2P market for both prosumer and *** recent times,numerous Machine Learning(ML)-enabled load predictive techniques have been developed,while most of the existing studies did not consider its implicit features,optimal parameter selection,and prediction *** order to overcome fulfill this research gap,the current research paper presents a new Multi-Objective Grasshopper Optimisation Algorithm(MOGOA)with Deep Extreme Learning Machine(DELM)-based short-term load predictive technique i.e.,MOGOA-DELM model for P2P Energy Trading(ET)in *** proposed MOGOA-DELM model involves four distinct stages of operations namely,data cleaning,Feature Selection(FS),prediction,and parameter *** addition,MOGOA-based FS technique is utilized in the selection of optimum subset of ***,DELM-based predictive model is also applied in forecasting the load *** proposed MOGOA model is also applied in FS and the selection of optimalDELM parameters to improve the predictive *** inspect the effectual outcome of the proposed MOGOA-DELM model,a series of simulations was performed using UK Smart Meter *** the experimentation procedure,the proposed model achieved the highest accuracy of 85.80%and the results established the superiority of the proposed model in predicting the testing data.

关键词： Peer to Peer energy trade smart grid load forecasting machine learning feature selection

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