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检索条件"任意字段=3rd International Conference on Machine Learning and Data Engineering, ICMLDE 2024"

共 6842 条记录，以下是271-280 订阅

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Breast Cancer Prediction by Ensembling machine learning Algorithms and Explainable AI 3

Breast Cancer Prediction by Ensembling Machine Learning Algo...

引用

3rd international conference for Innovation in Technology, INOCON 2024

作者： Sobhana, M. Palaketi, Anil Kumar Nalabothu, Ramya Vr Siddhartha Engineering College Department of CSE Andhra Pradesh Vijayawada India

ISBN: (纸本)9798350381931

A primary cause of death is cancer, which is a result of abnormal cell growth. Globally, breast cancer is a significant contributor to female fatalities, and its prevention is challenging due to unidentified causes. However, early detection is pivotal for reducing risk and improving survival rates. Advanced imaging techniques like mammography and ultrasound are instrumental in diagnosing breast cancer. This model integrates machine learning and Explainable AI to predict breast cancer. Trained on a dataset with diverse features from fine needle aspiration of breast masses, the model not only determines whether a patient is positive or negative but also sheds light on the importance of specific features of the cancerous cell. In cases of a positive diagnosis, early detection empowers patients to promptly seek essential treatment, significantly enhancing their chances of survival. © 2024 IEEE.

关键词： machine learning

来源：评论

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Sentiment Analysis and Forecasting for Improved Business Performance in E-Commerce using machine learning Algorithms 3

Sentiment Analysis and Forecasting for Improved Business Per...

引用

3rd international conference on Electronics and Renewable Systems, ICEARS 2025

作者： Tiwari, Naveen Kumar Arora, Himanshu Arya College of Engineering Department of Cse Rajasthan Jaipur India

ISBN: (纸本)9798331509675

The study explores how sentiment analysis and forecasting can improve the business performance, especially for e-commerce platforms like Amazon to analyze customer feedback for enhancing future work. This study focuses on integrating machine learning models, including SVM, Random Forest and Naive Bayes to categorize product reviews into positive, negative or neutral sentiment analysis, achieving accuracy rates of 0.85, 0.87 and 0.85 based on more than 11,000 datasets. The process of analysis includes data Acquisition, data Pre-processing, Feature Extraction, Model Training and using a GUI that allows users to write their reviews for sentiment prediction. Additionally, the study also involves ARIMA model to train the model for likes forecasting analysis on historical data, that helps many businesses sectores with valuable and meaningful insights to optimize the engagement of the customer and their decision-making. © 2025 IEEE.

关键词： Adversarial machine learning

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Convolutional Neural Network for Detection of Fluid-Filled Inclusions in Solids from Measured Elastodynamic Responses 3

Convolutional Neural Network for Detection of Fluid-Filled I...

引用

3rd international conference on Computing and machine Intelligence (ICMI)

作者： Hahn, Jinho Abdelgawad, Salma Kim, Boyoung Jeong, Chanseok Cent Michigan Univ Sch Engn & Technol Mt Pleasant MI 48859 USA

ISBN: (纸本)9798350372977;9798350372984

We present a data-informed convolutional neural network (CNN) to detect fluid-filled inclusions within a two-dimensional (2D) solid domain using wave responses measured at surficial sensors. ANSYS Mechanical is utilized to simulate the propagation of elastic waves in the solid containing internal fluid inclusions. This work produces data comprising input-layer features (measured wave responses) and output-layer features (a contour map representing solid or fluid cells in the domain). The CNN is trained on synthetic data to classify each cell as solid or fluid based on the measured wave responses at sensors. Our numerical results show that the proposed CNN can effectively detect fluid inclusions of various locations, sizes, and shapes. We also present numerical experiments with respect to various parameters, including the number of sensors, the amount of training data, and frequency, and provide a comprehensive analysis of their effect on outcomes.

关键词： Detection of fluid-filled inclusions convolutional neural network (CNN) machine learning hyperparameter optimization KerasTuner

来源：评论

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Suggesting Playlist and Playing Preferred Music Based on Emotion from Facial Expression 3

Suggesting Playlist and Playing Preferred Music Based on Emo...

引用

3rd international conference for Innovation in Technology, INOCON 2024

作者： Akter Asha, Mst. Lima Ahmed Rafi, Muntasir Ahamed, Md. Sazzadur Imran, Md. Hafizul Daffodil International University Department of Computer Science and Engineering Dhaka Bangladesh

ISBN: (纸本)9798350381931

Facial expression is a powerful indicator of human emotion and plays a crucial role in interpersonal communication. The mood of a person or his intention can be analyzed by detecting his expression. For detecting the expression, artificial intelligence and machine learning are very useful for automated detection. By analyzing facial features and expressions, a music recommendation system can predict the user's mood and recommend songs that align with their emotional state. Many researchers have worked on *** proposed system works on 8 moods of humans which are angry, contempt, disgust, fear, happy, neutral, sad, and surprise. This study utilizes a machine learning concept to achieve this goal. The methodology involves data collection, model training using a combination of Convolutional Neural Network (CNN) and VGG16 CNN, and recommending songs from the Spotify music dataset. The results show that both CNN and VGG16 CNN performed well in detecting facial expressions, with CNN achieving 66% accuracy and VGG16 achieving 92% accuracy. This system effectively recommends songs from the Spotify dataset based on the user's mood. © 2024 IEEE.

关键词： machine learning

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Empowering Diabetic Care: Building an Edge-Enabled machine learning Model for Classification 3

Empowering Diabetic Care: Building an Edge-Enabled Machine L...

引用

3rd Odisha international conference on Electrical Power engineering, Communication and Computing Technology, ODICON 2024

作者： Sahoo, Suchi Prangya Dash, Subhashree Subudhi, Anisha Agarwal, Shiva Dabata, Prajna Paramita Agarwal, Shashank Computer Science and Engineering Odisha Siksha 'O' Anusandhan Bhubaneswar India Utkarsh Business School Bachelor of Computer Applications Uttar Pradesh Bareilly India

ISBN: (纸本)9798350354379

Diabetes mellitus is a chronic metabolic dis-order characterised by elevated amounts of glucose in the bloodstream. The categorization and forecasting of diabetes are crucial for the effective administration of this prevalent long-term condition. machine learning (ML) enables real-time monitoring and personalised therapy on edge devices. machine learning can be employed to predict the likelihood of developing diabetes mellitus using advanced technology. Many symptoms are too subtle to be readily observed. It is a primary factor contributing to global blindness. It has a gradual lethal effect and there is currently no recognised remedy for it. This study explores the potential of edge-based predictive modelling for early identification of diabetes. Neural networks have the ability to accurately detect and analyse patterns and trends within patient data, leading to more accurate predictions of diabetes. Principal component analysis (PCA), quantization, dynamic pruning, and model compression are employed to optimise model performance while working within the limitations of the available re-sources. The proposed methodology is used and evaluated on the Pima Indian diabetic datasets, demonstrating its efficacy in achieving precise classification within the constraints of edge devices. Additionally, the possibility of shifting trained models from cloud servers to edge devices is being examined in order to reduce the computational burden. © 2024 IEEE.

关键词： Adversarial machine learning

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Rainfall Prediction using machine learning Algorithms 3

Rainfall Prediction using Machine Learning Algorithms

引用

3rd international conference on Applied Artificial Intelligence and Computing, ICAAIC 2024

作者： Sai, Pallem Sumanth Thiyagu, T.M. Soumya, Pallem Geetha Institute of Technology and Sciences Division of Cse Karunya Coimbatore India Karunya Institute of Technology and Sciences Division of Ai and Ml Coimbatore India Sumathi Reddy Institute of Technology for Women Division of Cse Hasanparthy India

ISBN: (纸本)9798350375190

In several industries, including agriculture, water resource management, and disaster planning, rainfall prediction is essential. Because machine learning algorithms can examine historical rainfall data and provide precise projections, they have attracted a lot of interest. In this work, several meteorological characteristics including temperature, humidity, wind speed, and pressure are used to forecast rainfall patterns using machine learning algorithms. To determine which method best fits the prediction goal, a series of steps including feature selection, model training, assessment, and data pretreatment are involved. The models may be used to provide precise and timely forecasts by using historical rainfall data to study the intricate correlations between meteorological factors and rainfall patterns. Decision-making in a variety of weather-dependent industries is supported by the use of machine learning approaches into rainfall prediction, which provide a data-driven approach that improves forecast accuracy and dependability. © 2024 IEEE.

关键词： Decision making

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Hybrid machine learning Model for EV Battery SoC and SoH Prediction 3

Hybrid Machine Learning Model for EV Battery SoC and SoH Pre...

引用

3rd international conference on Electrical, Electronics, Information and Communication Technologies, ICEEICT 2024

作者： Varshini, C.R. Amrutha Jha, Arya Tiwari, Ayush Deepa, K. Amrita School of Engineering Amrita Vishwa Vidyapeetham Department of Electrical and Electronics Engineering Bengaluru India

ISBN: (纸本)9798350369083

As electrification advances in the automotive industry, attention towards the safety and reliability of the battery becomes important. The Battery Management System (BMS) which is the heart of the whole Battery system needs to be periodically inspected to ensure the safety, reliability, and longevity of EVs Li-ion batteries. Therefore, the study covers different hybrid machine learning (ML) approaches for monitoring remaining charge and EV battery health through hybrid machine learning (ML) such as LSTM-SVR, LSTM-ARIMA and LSTM-ATTENTION in two different platforms PYTHON IDLE and RASPBERRYPI 4B and ultimately suggest the best ML Hybrid Model to determine the state of charge and health of the battery. © 2024 IEEE.

关键词： Automotive industry

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Detecting Malicious Threats On Mobile Applications Using machine learning Approaches 3

Detecting Malicious Threats On Mobile Applications Using Mac...

引用

3rd international conference on Communication, Control, and Intelligent Systems, CCIS 2024

作者： Padmavathi, A. Yasaswy, K. Rahul, K.Jayesh Amrita Vishwa Vidyapeetham Amrita School of Computing Department of Computer Science Engineering Chennai India

ISBN: (纸本)9798331528201

In Today scenario , Android is one of the most frequently used mobile operatings systems, thus it is a priority for advanced threat actors and hackers. Malicious code is often found in Android applications to which security solutions have a problem of identifying the threats. Today's Android malware is sophisticated enough that it can avoid most ***, machine learning is more efficient in stopping a large number of diverse contemporary threats to Android devices. These machine learning methods essentially first rely on the detection of sequential patterns of malicious behaviors, and secondly, distinguish between existing and new types of threats that have previously exhibited typical and atypical behavior. Our proposed approach builds on Ensemble learning where the methods include Support Vector machines, Decision Trees, Logistic regression and XGBoost to bolster the detection abilities of the model. This methodology serves to enhance the specific model performance on identifying known malware. © 2024 IEEE.

关键词： Adversarial machine learning

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Comparative Analysis of machine learning Algorithms for Fault Detection and Localization in Photovoltaic Systems 3

Comparative Analysis of Machine Learning Algorithms for Faul...

引用

3rd international conference on Smart Grids and Energy Systems, SGES 2024

作者： Hamad, Muhannad Muhammad Sheikholeslami, Abdolreza Faculty of Electrial and Computer Engineering Babol Noshirvani University of Technology Babol Iran

ISBN: (纸本)9798331539634

This paper presents a comprehensive comparative analysis of four machine learning algorithms (SVM, DT, FNN, RF) for fault detection and localization in photovoltaic (PV) systems. The study aims to address the limitations of traditional methods, such as manual inspections and rule-based approaches, which can be time-consuming, error-prone, and inflexible. By leveraging advanced machine learning techniques, we aim to improve the accuracy, efficiency, and scalability of fault detection and localization in PV systems. We conducted a rigorous evaluation of these algorithms using a unified dataset and performance metrics, focusing on common faults like shading, open circuit, and short circuit. Our findings demonstrate the superior performance of Random Forest (RF) for both fault classification and localization tasks, highlighting its potential for enhancing the reliability and efficiency of PV systems. © 2024 IEEE.

关键词： Power distribution faults

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A Comparative Survey of machine learning Approaches for Alzheimer's Disease Prediction and Detection 3

A Comparative Survey of Machine Learning Approaches for Alzh...

引用

3rd international conference for Innovation in Technology, INOCON 2024

作者： Shilka, M.V. Subhashini, R. Sathyabama Institute of Science and Technology School of Computing Chennai600119 India Providence Women's College Department of Computer Science Kerala Kozhikode673009 India

ISBN: (纸本)9798350381931

Alzheimer's disease (AD) is a neurological illness that affects a large proportion of the senior population. Early detection and prognosis are crucial for improving patient care and developing effective treatment strategies. machine learning methods, such as voice analysis, neuroimaging, and clinical data, have showed promise in detecting and predicting Alzheimer's disease. This study presents at thorough assessment and comparative analysis of 15 research papers, highlighting their contributions to the area. Deep learning models like convolutional neural networks and recurrent neural networks have demonstrated great accuracy in differentiating between cognitively normal, dementia, and Alzheimer's dementia. Incorporating hybrid data sources, developing interpretive approaches, and researching disease-specific traits and mechanisms are all possible future paths. Overall, machine learning approaches have the potentialto increase the accuracy and efficiency of Alzheimer's disease detection, resulting in earlier intervention and better patient outcomes. © 2024 IEEE.

关键词： Forecasting

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