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共 11874 条记录，以下是71-80 订阅

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Ensemble-based Machine learning Classification for Early Detection of Autism Spectrum Disorder 3

Ensemble-based Machine Learning Classification for Early Det...

引用

3rd International conference on intelligent Data Communication Technologies and Internet of Things, IDCIoT 2025

作者： Subhash, Ambika Rani Ashwin Kumar, U.M. School of Computer Science and Engineering REVA University Bengaluru India Department of Information Science and Engineering BMS Institute of Technology and Management Bengaluru India

ISBN: (纸本)9798331527549

A complicated neurological illness, autism spectrum disorder (ASD) impacts social skills, communication, and other behavioral elements. Because the symptoms of ASD might be confused with those of other mental health conditions, diagnosing it can take a long time. The goal of this study is to develop a categorization model that can forecast the risk of ASD in toddlers, kids, teens, and adults. To improve speed, we used a variety of machine learning methods and ensembled them. Four publicly accessible non-clinical ASD screening datasets from Kaggle and the UCI library were used to assess the models. These datasets included 1,054 cases with 19 features for toddlers and different characteristics for older age groups. The classifiers' accuracy was almost 99%, according to the results. © 2025 IEEE.

关键词： Adversarial machine learning

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Fake News Detection System: An Ensemble Approach 3

Fake News Detection System: An Ensemble Approach

引用

3rd International conference on intelligent Data Communication Technologies and Internet of Things, IDCIoT 2025

作者： Kavitha, A.R. Lita Pansy, D. Vishnu Balaji, R. Harsh, J. Suki Sivam, S. Chennai Institute of Technology India

ISBN: (纸本)9798331527549

Fake news has become a critical challenge in digital communication, threatening information integrity and public discourse, especially in this internet era where misunderstanding can lead to dispute of opinions, conflicts and wars. This research presents a comprehensive machine learning framework for fake news detection using ensemble architecture involving Machine learning and Deep learning. By integrating natural language processing, content-based feature extraction, and lexical analysis, a robust detection model was developed. Our methodology leveraged a diverse dataset of 72,134 news articles, applying ensemble learning algorithms to classify potential misinformation. The proposed approach achieved a remarkable 0.97 accuracy. Our findings demonstrate the potential of ensemble techniques in combating digital misinformation, offering a scalable solution for identifying and mitigating fake news across digital platforms. © 2025 IEEE.

关键词： Adversarial machine learning

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A Multiclass Deep learning Approach using CRelu - To Enhance Brain Tumor Identification 3

A Multiclass Deep Learning Approach using CRelu - To Enhance...

引用

3rd International conference on intelligent and Innovative Technologies in Computing, Electrical and Electronics, IITCEE 2025

作者： Kusuma, P. Selvam, Sheba Ronihal, Shreedevi S. Bnm Institute of Technology Bangalore India Bnm Institute of Technology Bangalore India Bnm Institute of Technology Bangalore India

ISBN: (纸本)9798331515911

Research analyses the deployment of Convolutional-Neural-Networks (CNN) equipped with the CReLU (Concatenated Rectified Linear Units) activation function for identifying brain tumors MRI scan images. The CNN model is designed to be trained to classify various types of tumors, including glioma, meningiomas, pituitary tumors, and cases with no tumor present. By leveraging MRI images, the study evaluates how the CReLU activation function can enhance the model's capability to reliably distinguish between these tumor types, aiming to improve diagnostic accuracy and facilitate early detection through advanced image analysis techniques using deep learning. © 2025 IEEE.

关键词： Convolutional neural networks

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Machine learning Models for Android Malware Detection–A Comparative Study 3

Machine Learning Models for Android Malware Detection–A Com...

引用

3rd International conference on intelligent Data Communication Technologies and Internet of Things, IDCIoT 2025

作者： Arun, N. Nisha Dayana, T.R. Department of Computer Science Vel’s Institute of Science Technology and Advanced Studies Tamil Nadu Chennai India

ISBN: (纸本)9798331527549

In the mobile ecosystem, Android apps are expanding quickly, but malicious software for Android is also proliferating in ever-increasing quantities. The issue of identifying malware on Android has been examined by several researchers, who have proposed ideas and approaches from various angles. According to current research, machine learning (ML) is a promising and successful method of detecting Android malware. This study is performed to compare the various ML methods for Android malware detection, including Random Forest (RF), Decision Tree,(DT) Random Tree(RT), Support Vector Machine (SVM), and XGBoost. The study assessed these models using a Androzoo dataset of Android apps, taking into account the important performance parameters as detection latency, accuracy, precision, and false alarm rate (FAR). © 2025 IEEE.

关键词： Adversarial machine learning

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Autism Spectrum Disorder Analysis and Detection System 3

Autism Spectrum Disorder Analysis and Detection System

引用

3rd International conference on intelligent Data Communication Technologies and Internet of Things, IDCIoT 2025

作者： Ananthajothi, K. Mohanapriya, E. Mukhilan, S.S. Department of CSE Engineering College Chennai India

ISBN: (纸本)9798331527549

Autism Spectrum Disorder is a neurodevelopmental disorder characterized by social interaction, communication impairments, and repetitive behaviors. In recent years, there has been an increase in prevalence, and it is important to identify individuals early for appropriate intervention to improve their quality of life. The following study was conducted to deepen the understanding and management of the disorder by employing a comprehensive two-module approach. Module one emphasizes the detection of ASD through sophisticated machine learning techniques for early and accurate diagnosis. Module two is on improving social and communication skills of children with ASD via game-based assessments, offering them an interactive yet enjoyable learning environment. This project, not only in contributing to the scientific community, but also through practical applications contributes and offers easy solutions for caregivers and educators. Integration of technology with both detection and intervention holds huge advancement in this area, promising better outcomes for ASD individuals. © 2025 IEEE.

关键词： Adversarial machine learning

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Research on Flight intelligent Sorting Algorithm in AI-based Air Traffic Control Systems 24

Research on Flight Intelligent Sorting Algorithm in AI-based...

引用

3rd International conference on Algorithms, Data Mining, and Information Technology, ADMIT 2024

作者： Wang, Minghua Qiao, Jiantong Air Force Engineering University ATC&GCL College Shaanxi Xi'an China

ISBN: (纸本)9798400718120

Flight delay is a significant challenge faced by air traffic control systems, severely affecting passenger travel experience and airline operational efficiency. This paper addresses this issue by proposing an intelligent flight sorting method based on machine learning and deep learning. By preprocessing and feature engineering of flight historical data, airport data, and route data, a set of high-quality feature sets is constructed. On this basis, models such as random forests and LSTM are employed to predict flight delays and intelligently sort flights according to the prediction results. Experimental results demonstrate that this method significantly enhances the accuracy and efficiency of flight sorting, reducing the impact of flight delays on passenger travel. This study provides a novel and effective approach to addressing flight delay problems, offering broad application prospects and practical value. © 2024 Copyright held by the owner/author(s).

关键词： Delay control systems

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Epilepsy Seizure Detection based on EEG Signals using Machine learning 3

Epilepsy Seizure Detection based on EEG Signals using Machin...

引用

3rd International conference on intelligent and Innovative Technologies in Computing, Electrical and Electronics, IITCEE 2025

作者： Goel, Aadya School of Computer Science and Engineering Vellore Institute of Technology Vellore632014 India

ISBN: (纸本)9798331515911

Seizure detection is a critical aspect of epilepsy management, and accurate identification can significantly improve patient outcomes. This study explores the application of machine learning techniques, specifically the Random Forest classifier, for the detection of seizures using electroencephalogram (EEG) data. By employing a systematic approach that includes feature extraction and channel reduction, we enhanced the model's performance. Relevant features were extracted from EEG signals, including time-domain statistics and frequency-domain characteristics, which are vital for effective classification. The model was evaluated using standard classification metrics such as precision, recall, F1-score, and accuracy, yielding high performance, particularly in identifying non-seizure events. These findings highlight the potential of ensemble learning methods for real-time seizure monitoring and underscore the importance of this research in developing reliable diagnostic tools. Overall, our results demonstrate that machine learning approaches can significantly contribute to improved patient outcomes in epilepsy management, paving the way for future advancements in clinical applicability. © 2025 IEEE.

关键词： Electroencephalography

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Optimizing Symptom Prediction in Healthcare: An Integrative Machine learning Approach to Regression and Classification Models 3

Optimizing Symptom Prediction in Healthcare: An Integrative ...

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3rd International conference on intelligent and Innovative Technologies in Computing, Electrical and Electronics, IITCEE 2025

作者： Goddilla, Nagarjuna Reddy Sivasangari, A. Sathyabama Institute of Science and Technology Dept of Cse Chennai India

ISBN: (纸本)9798331515911

This research explores the application of combined machine learning techniques for predicting disease symptoms, aiming to support early diagnosis and improve healthcare outcomes. By analyzing both regression and classification models, the study evaluates their respective performance, strengths, and challenges in predicting symptoms. Using a comprehensive dataset with medical records, demographic data, and clinical variables, we developed predictive models to capture the complexities of healthcare data. To enhance accuracy and manage issues like data imbalance and feature diversity, ensemble methods and feature engineering were applied, with the Random Forest classifier achieving a leading accuracy of 91.84%. Additionally, the study emphasizes model interpretability, which is crucial for healthcare professionals to trust and adopt these predictive tools. The findings provide valuable guidance for healthcare professionals and data scientists in choosing machine learning approaches suited to specific medical applications. By bridging the roles of regression and classification models, this research offers a well-rounded perspective on the potential of predictive analytics in healthcare. © 2025 IEEE.

关键词： Adversarial machine learning

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Dynamic Traffic Signal optimization Congestion Management and Emergency Vehicle Prioritization 3

Dynamic Traffic Signal Optimization Congestion Management an...

引用

3rd International conference on intelligent Data Communication Technologies and Internet of Things, IDCIoT 2025

作者： Deoghare, Seema U. Dhond, Yash Gaikwad, Saakshi Gorkhe, Parth Department of Electronics and Telecommunication Pimpri Chinchwad College of Engineering Akurdi India

ISBN: (纸本)9798331527549

These delays have been linked to the traditional traffic signal systems that are used as they are time bound with pre-set schedules that do not take into account changing operational surroundings. The real life integration of technology into traffic management has the potential of alleviating these issues. In this paper, a Smart Traffic Management System that uses YOLOv8 object detection, IoT inter-signal communication and Real Time adaptive signal control is examined. The proposed system does automatic adjustments to signal times according to the density of the vehicles and gives higher priority to emergency vehicles to promote seamless traffic. Through the examination of existing systems, an analysis is conducted pointing out the issues revolving around those systems which this research aims to solve. The STMS incorporates including but not limited to USB cameras, Raspberry Pi, and sophisticated machine learning models to do real life identification and decision making. In consideration of modern day urban traffic troubles, it can be said with confidence that this system is able to terminate congestion with the help of emergency vehicles and also provide limitlessly scalable solutions. Future considerations will include identifying field implementation limitations and addressing them during the research. © 2025 IEEE.

关键词： Decision making

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3rd International conference on intelligent Computing Techniques for Smart Energy Systems, ICTSES 2023

3rd International Conference on Intelligent Computing Techni...

引用

3rd International conference on intelligent Computing Techniques for Smart Energy Systems, ICTSES 2023

ISBN: (纸本)9789819784639

The proceedings contain 50 papers. The special focus in this conference is on intelligent Computing Techniques For Smart Energy Systems. The topics include: Analysis of Fault and Current-Limiting Methods in the Power System;UDS for Data Communication in Temperature Monitoring;approximation in the Weighted Lipschitz Class by (C,α,θ)(E,q) Mean of Its Fourier Series;demand Response and Load Profile Analysis in Modern Electricity Systems;First-Principles Study of the Structural, Optical and Electronic Properties of Zinc Blende GaN Through PBEsol and TB-mBJ Functionals;optimization-Based Cost Analysis for Energy Management System in Microgrids;classification and Prediction of Knee Osteoarthritis by Deep learning Approach;Predicting Stock Price and Market Direction Using Statistical and LSTM Models;Comprehensive Evaluation of Time Management with Balancing Resistors in Passive Cell Balancing Using the 1RC Model;PAWnnect: Pioneering IoT-ML Driven Pet Monitoring;transactive Energy Management for Microgrids: Peer-To-Peer Trading Using Stackelberg Game Approach;Unravelling the Mind's Emotions: A Review of EEG-Based Emotion Detection Using Machine learning and Deep learning;unlocking IoT Security: Enabling the Future with Lightweight Cryptographic Ciphers;Optimal Design of Nano Scale Voltage Amplifier Using Evolutionary Techniques for DG-MOSFET;flower Patterned with Elliptical Petals Multiband Microstrip Fractal Patch Antenna for Wireless Applications;Optimal Scheduling of Solar PV Generators and Network Assets in Distribution Network;revolutionizing Biomedical Engineering with Microcontroller Applications: A Comprehensive Review;investigation of Battery Temperature Impact on High Voltage Cable of Electric Vehicles;deep learning Technique for Computer Vision-Based Pose Estimation for Augmented Reality;Rectangular Shape Patch Antenna with Defected Ground Plane for 5G Sub 6 GHz Wireless Communication Systems.

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