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Multi-Agent Systems for Collaborative Intelligence (ICMSCI), International Conference on

作者： Jerin Akther Munia K.B.M. Tahmiduzzaman Izazul Haque Saad Md Mynul Islam Abdul Hady Akash Md. Thoufiq Zumma Department of Computer Science and Engineering Daffodil International University Dhaka Bangladesh Faculty of Engineering and Information Technology University of Technology Sydney Sydney Austrialia Department of Computer Science and Engineering University of Asia Pacific Dhaka Bangladesh

ISBN: (数字)9798331509828

ISBN: (纸本)9798331509835

This study uses survey data and machine learning algorithms to forecast social media disorder in people. A total of 600 individuals answered questions on their social media usage patterns, internet habits, demographics, and signs of social media disorder. This dataset was used to train a number of machine learning models, such as Gaussian Naive Bayes (GaussianNB), Random Forest, K-Nearest Neighbors (KNN), Decision Tree, and Support Vector Machine (SVM). According to the findings, the GaussianNB model had the greatest accuracy of 97%, followed by SVM (94%), Random Forest (91%), Decision Tree (92%), and KNN (90%). These results demonstrate how well machine learning can identify those who may be at risk for social media disorder. In order to improve prediction performance, future research will concentrate on increasing feature size, diversifying datasets, and refining models. Furthermore, incorporating these models into user-friendly apps and intervention programs may offer useful resources for the early diagnosis and treatment of social media illness.

关键词： Support vector machines Surveys Accuracy Machine learning algorithms Social networking (online) Nearest neighbor methods Predictive models Media Decision trees Random forests

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Scalable Subspace Clustering via Purity Kernel Tensor Learning 3

Scalable Subspace Clustering via Purity Kernel Tensor Learni...

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3rd International Conference on Integrated Circuits and Communication Systems, ICICACS 2025

作者： Adhvaryu, Rachit Kannan, S. Rajes Sanjay, C.P. Dange, Pravin Parul University Department of Computer Science and Engineering Faculty of Engineering and Technology Gujarat India Chennai Institute of Technology Department of Computer Science and Engineering Kundrathur India Sri Balaji University Pune India Pune India

ISBN: (纸本)9798331508456

Subspace clustering is a fundamental task in machine learning, particularly for high-dimensional datasets where the goal is to group data points that lie in distinct subspaces. Traditional methods, such as Sparse Subspace Clustering (SSC) and Low-Rank Representation (LRR), have demonstrated good performance but often suffer from high computational costs, particularly when applied to large-scale data. To address these limitations, this paper introduces a novel technique called Purity Kernel Tensor Learning (PKTL). PKTL combines the power of tensor decomposition with a purity kernel function to improve both the efficiency and effectiveness of subspace clustering. By utilizing tensor-based representations, the method captures higher-order relationships in the data, allowing for a more robust clustering process. The purity kernel function measures the coherence within the clusters, enhancing the identification of subspaces in complex datasets. The proposed approach not only reduces computational complexity but also improves the clustering accuracy compared to existing methods. Experiments on synthetic and real-world datasets, including ORL, Yale, and MNIST, show that PKTL outperforms conventional subspace clustering algorithms, both in terms of clustering accuracy and scalability. The results demonstrate the potential of PKTL in handling large datasets while maintaining high performance, making it a promising approach for scalable subspace clustering in real-world applications. © 2025 IEEE.

关键词： Large datasets

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Bioacoustics Signal Classification Using Hybrid Feature Space with Machine Learning

Bioacoustics Signal Classification Using Hybrid Feature Spac...

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International Conference on computer and Automation engineering, ICCAE

作者： Usman Haider Muhammad Hanif Hiroki Kobayashi Laxmi Kumar Parajuli Daisuke Shimotoku Ahmar Rashid Sonia Safeer Faculty of Computer Science and Engineering GIK Institute of Engineering Sciences and Technology Topi Pakistan Information Technology Center The University of Tokyo Tokyo Japan Faculty of Computer Science and Engineering GIK Institute of Engineering Sciences and Technology Swabi

The vocal sounds emitted by animals and birds possess distinctive signatures. They are vital for acoustic monitoring to extract useful ecological data and track biodiversity. Recently, automated bioacoustics classification drew attention from the research community due to its diverse application. To that aim, we present a novel classification method for acoustic data by fusing optimally selected signal features. The proposed method extracts the distinctive statistical features, calculated using coefficients of Discrete Wavelet Transform (DWT), and fuses them with the descriptive features estimated using the Mel-Frequency Cepstral Coefficients (MFCC). The combined feature set is then passed to different machine learning classifiers, including Random Forest (RF), Support Vector Machine (SVM), and k-Nearest Neighbors (kNN) for sound classification of different animals and birds. The evaluation results show that the proposed method improves the classification accuracy and achieved high precision on all classifiers.

关键词： Support vector machines Radio frequency Fuses Transforms Feature extraction Birds Discrete wavelet transforms

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Novel hybrid data-driven models for enhanced renewable energy prediction

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Frontiers in Energy Research 2024年 12卷

作者： Alharbi, Talal Iqbal, Saeed Department of Electrical Engineering College of Engineering Qassim University Buraydah Saudi Arabia Department of Computer Science Faculty of Information Technology and Computer Science University of Central Punjab Lahore Pakistan

Global power grid management depends on accurate solar energy estimation, yet present prediction techniques frequently suffer from unreliability as a result of abnormalities in solar energy data. Solar radiation projections are affected by variables such as anticipated horizon length, meteorological classification, and power measuring techniques. Therefore, a Solar Wind Energy Prediction System (SWEPS) is proposed as a solution to these problems. It improves renewable energy projections by taking sun trajectories and atmospheric characteristics into account. In addition to using a variety of optimization methods and pre-processing techniques, such as Principal Component Analysis (PCA), Recursive Feature Elimination (RFE), Least Absolute Shrinkage Selection Operator (LASSO), and recursive feature addition processes (RFA), complemented by a genetic algorithm for feature selection (GAFS). The SWEPS also makes use of sophisticated machine learning algorithms and Statistical Correlation Analysis (SCA) to find important connections. Neural Network Algorithms (NNA) and other metaheuristic techniques like Cuckoo Search Optimization (CSO), Social Spider Optimization (SSO), and Particle Swarm Optimization (PSO) are adopted in this work to increase the predictability and accuracy of models. Utilizing the strengths of machine learning and deep learning techniques (Artificial Neural Networks (ANN), Decision Trees, Support Vector Machine (SVM), Recurrent Neural Networks (RNN), and Long Short Term Memory (LSTM)) for robust forecasting, as well as meta-heuristic optimization techniques to fine-tune hyper-parameters and achieve near-optimal values and significantly improve model performance, are some of this work contributions to the development of a comprehensive prediction system. Copyright © 2024 Alharbi and Iqbal.

关键词： Decision trees

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Enhancing Network Intrusion Detection with Deep Learning: A Comprehensive Analysis

Enhancing Network Intrusion Detection with Deep Learning: A ...

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International Conference on computer and Information technology (ICCIT)

作者： Md. Mahbubur Rahman Md. Shahriar Rahman Chinmay Bepery Md. Atikqur Rahaman Md. Samsuzzaman Department of Computer Science and Information Technology Patuakhali Science and Technology University Patuakhali Bangladesh Faculty of Computer Science and Engineering Patuakhali Science and Technology University Patuakhali Bangladesh Department of Computer and Communication Engineering Patuakhali Science and Technology University Patuakhali Bangladesh

ISBN: (数字)9798331519094

ISBN: (纸本)9798331519100

With the growing sophistication of cyberattacks, traditional intrusion detection systems (IDS) are often inadequate. This research addresses these shortcomings by leveraging deep learning techniques to enhance the detection of network intrusions. Previous studies have either retained unnecessary features, leading to biased results, or overly simplified feature sets, resulting in low accuracy. This study systematically refines feature selection and employs advanced deep learning models to improve detection performance. The result is a robust IDS that mitigates bias and enhances accuracy, offering a significant improvement over existing methods. Our findings provide valuable insights into the effective application of machine learning for network security.

关键词： Deep learning Support vector machines Accuracy Computational modeling Training data Network intrusion detection Network security Nearest neighbor methods Feature extraction Principal component analysis

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Artificial Intelligence Based Solar Radiation Predictive Model Using Weather Forecasts

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computers, Materials & Continua 2022年第4期71卷 109-124页

作者： Sathish Babu Pandu A.Sagai Francis Britto Pudi Sekhar P.Vijayarajan Amani Abdulrahman Albraikan Fahd N.Al-Wesabi Mesfer Al Duhayyim Department of Electrical and Electronics Engineering University College of EngineeringPanruti607106India Department of Mechanical Engineering Rohini College of Engineering&TechnologyPalkulam629401India Department of Electrical and Electronics Engineering Vignan’s Institute of Information TechnologyAndra Pradesh530046India Department of Electrical and Electronics Engineering University College of EngineeringBIT CampusTiruchirappalli620024India Department of Computer Science College of Computer and Information SciencesPrincess Nourah Bint Abdulrahman UniversitySaudi Arabia Department of Computer Science King Khalid UniversityMuhayel AseerSaudi Arabia&Faculty of Computer and ITSana’a UniversitySana’aYemen Department of Natural and Applied Sciences College of Community-AflajPrince Sattam bin Abdulaziz UniversitySaudi Arabia

Solar energy has gained attention in the past two decades,since it is an effective renewable energy source that causes no harm to the *** Irradiation Prediction(SIP)is essential to plan,schedule,and manage photovoltaic power plants and grid-based power generation *** models have been proposed for SIP in the literature while such studies demand huge volumes of weather data about the target location for a lengthy period of *** this scenario,commonly available Artificial Intelligence(AI)technique can be trained over past values of irradiance as well as weatherrelated parameters such as temperature,humidity,wind speed,pressure,and ***,in current study,the authors aimed at developing a solar irradiance prediction model by integrating big data analytics with AI models(BDAAI-SIP)using weather forecasting *** order to perform long-term collection of weather data,Hadoop MapReduce tool is *** proposed solar irradiance prediction model operates on different ***,data preprocessing take place using various sub processes such as data conversion,missing value replacement,and data ***,Elman Neural Network(ENN),a type of feedforward neural network is also applied for predictive *** is divided into input layer,hidden layer,loadbearing layer,and output *** overcome the insufficiency of ENN in choosing the value of weights and hidden layer neuron count,Mayfly Optimization(MFO)algorithm is *** order to validate the performance of the proposed model,a series of experiments was *** experimental values infer that the proposed model outperformed other methods used for comparison.

关键词： Solar irradiation prediction weather forecast artificial intelligence Elman neural network mayfly optimization

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Optimal Machine Learning Enabled Intrusion Detection in Cyber-Physical System Environment

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computers, Materials & Continua 2022年第9期72卷 4691-4707页

作者： Bassam A.Y.Alqaralleh Fahad Aldhaban Esam A.AlQarallehs Ahmad H.Al-Omari MIS Department College of Business AdministrationUniversity of Business and TechnologyJeddah21448Saudi Arabia School of Engineering Princess Sumaya University for TechnologyAmman11941Jordan Faculty of Science Computer Science DepartmentNorthern Border UniversityArar91431Saudi Arabia

Cyber-attacks on cyber-physical systems(CPSs)resulted to sensing and actuation misbehavior,severe damage to physical object,and safety *** learning(ML)models have been presented to hinder cyberattacks on the CPS environment;however,the non-existence of labelled data from new attacks makes their detection quite *** Detection System(IDS)is a commonly utilized to detect and classify the existence of intrusions in the CPS environment,which acts as an important part in secure CPS *** developments in deep learning(DL)and explainable artificial intelligence(XAI)stimulate new IDSs to manage cyberattacks with minimum complexity and high *** this aspect,this paper presents an XAI based IDS using feature selection with Dirichlet Variational Autoencoder(XAIIDS-FSDVAE)model for *** proposed model encompasses the design of coyote optimization algorithm(COA)based feature selection(FS)model is derived to select an optimal subset of ***,an intelligent Dirichlet Variational Autoencoder(DVAE)technique is employed for the anomaly detection process in the CPS ***,the parameter optimization of the DVAE takes place using a manta ray foraging optimization(MRFO)model to tune the parameter of the *** order to determine the enhanced intrusion detection efficiency of the XAIIDS-FSDVAE technique,a wide range of simulations take place using the benchmark *** experimental results reported the better performance of the XAIIDSFSDVAE technique over the recent methods in terms of several evaluation parameters.

关键词： Cyber-physical systems explainable artificial intelligence deep learning security intrusion detection metaheuristics

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Enhanced Pediatric Pneumonia Diagnosis with Chest X-Ray using Deep Attention Mechanism 11

Enhanced Pediatric Pneumonia Diagnosis with Chest X-Ray usin...

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11th IEEE/ACM International Conference on Big Data Computing, Applications and Technologies, BDCAT 2024

作者： Zaman, Muhammad Fatima, Tahseen Hameed, Sana Haider, Shahzeb Akhunzada, Adnan Azeem, Muhammad University of Lahore Faculty of Computer Science Sargodha Pakistan University of Saint Thomas School of Engineering Saint PaulMN United States University of Doha for Science and Technology Department of Data and Cybersecurity Doha24449 Qatar

ISBN: (纸本)9798350367300

Development of deep learning technologies has resulted in a major revolution in the field of medical diagnostics in recent years. The purpose of this research is to examine the usage of Convolutional Neural Networks (CNNs) in combination with an integrated attention mechanism known as CoATNet to improve the accuracy and responsiveness of paediatric pneumonia diagnosis using chest X-ray analysis. The major goal of this research project is to build and verify a powerful deep learning model capable of detecting mild pneumonia symptoms in children's chest X-rays. CoATNet's innovative attention approach allows it to provide more weight to areas deemed to be extremely important. This is in addition to its ability to recognise major patterns in images. Furthermore, the paper considers the issues posed by class imbalances in medical imaging datasets and undertakes an in-depth assessment of how effectively CoATNet works across a large dataset of paediatric chest X-rays. CoATNet's remarkable performance was shown by its ability to achieve a Micro Average of 98.2%, a Weighted Average of 98.7%, and an Accuracy of 99.7%. These measures show that the model can distinguish between instances of pneumonia and those do not have the illness, and that it is resilient when faced with class imbalances. F1 Score of 98.9% reflects a well-balanced trade-off between Precision 98.7% and Recall 98.2%. The integration of deep learning technologies with attention processes in CoATNet has resulted in a big step forward in the development of diagnostic models that are more visible and useful. © 2024 IEEE.

关键词： Pediatrics

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LayOut Loud: An AI-Powered Augmented Reality and Mobile Application for Room Interior Design and Layout Optimization 4

LayOut Loud: An AI-Powered Augmented Reality and Mobile Appl...

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4th International Conference on Intelligent Cybernetics technology and Applications, ICICyTA 2024

作者： Cuevas, John Raphael G. Rili, John Kenneth R. Santillan, Anny Neolicia G. Agustin, Vivien A. Gonzales, Mary Grace N. Centeno, Criselle J. Regala, Richard C. College of Information Systems and Technology Management Pamantasan ng Lungsod ng Maynila Department of Information Technology Manila Philippines College of Information Systems and Technology Management Pamantasan ng Lungsod ng Maynila Department of Computer Science Manila Philippines Pamantasan ng Lungsod ng Maynila Faculty of College of Engineering and Technology Department of Information Technology Manila Philippines College of Information Systems and Technology Management Pamantasan ng Lungsod ng Maynila Department of Information Technology Manila Philippines Pamantasan ng Lungsod ng Maynila College of Engineering and Technology Department of Computer Science Manila Philippines

ISBN: (纸本)9798331506490

LayOut Loud is an AI-powered augmented reality (AR) and mobile application designed to revolutionize room interior design by offering tailored, real-time solutions for layout optimization. The primary objective of this tool is to design AI-powered features that analyze user-provided room dimensions, furniture types, and style preferences. By leveraging advanced algorithms, LayOut Loud generates personalized design suggestions that cater to the individual needs and aesthetic tastes of users, ensuring that each interior design experience is customized and unique. A key goal of the application is to enhance user experience and decision-making through a clustering algorithm. This algorithm categorizes furniture pieces based on their suitability and appearance in living spaces, streamlining the selection process for users. By enabling the visualization of these items in a 3D model, users can make informed decisions about their interior design choices, ensuring that each piece contributes to the overall aesthetic and functional harmony of the space. Additionally, LayOut Loud focuses on improving its AR capabilities by allowing users to capture their room layouts and view virtual furniture arrangements in real time. This feature provides practical, interactive design solutions, empowering users to experiment with different configurations and instantly see how potential changes would appear in their actual living environments. Ultimately, LayOut Loud offers a user-friendly, immersive platform that transforms the way individuals approach interior design. © 2024 IEEE.

关键词： Architectural design

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Using Supervised Machine Learning Model For Predicting Fake Deliveries

Using Supervised Machine Learning Model For Predicting Fake ...

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Computing and Informatics (ICCI), International Conference on

作者： Mohamed A. Sadek Khaled M. Fouad Faculty of Information Technology and Computer Science Nile University Cairo Egypt Faculty of Computers and Artificial Intelligence Benha University Faculty of Computer Science and Engineering New Mansoura University

The home delivery service is a crucial part of the delivery process. Still, it has the highest financial and trustworthiness costs because it's the only part that directly touches the customer. If it works, the customer will be happy, and the company will make money, but if it doesn't, the customer will be skeptical, and the product will not be trusted. One big issue with the delivery process that this study will try to address is the prevalence of fraudulent or inaccurate deliveries. Making the most money possible from each journey is the primary goal of most riders. The delivery management system allows riders to falsely record orders as completed, allowing them to receive many orders simultaneously. Because the consumer usually gets a message or notification saying their item has been delivered when it hasn't, this weakens their faith in the organization and messes up the delivery process. Return to Origin (RTO) rates rose, and consumer confidence in the brand fell due to the fraudulent shipments. The suggested approach is based on ML models that are supervised.

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