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Dipper Throated Optimization Algorithm for Unconstrained Function and Feature Selection

computers, Materials & Continua 2022年第7期72卷 1465-1481页

作者： Ali E.Takieldeen El-Sayed M.El-kenawy Mohammed Hadwan Rokaia M.Zaki Faculty of Artificial Intelligence Delta University for Science and TechnologyMansoura35712Egypt Department of Communications and Electronics Delta Higher Institute of Engineering and TechnologyMansoura35111Egypt Department of Information Technology College of ComputerQassim UniversityBuraydah51452Saudi Arabia Department of Computer Science College of Applied SciencesTaiz UniversityTaizYemen Intelligent Analytics Group(IAG) College of ComputerQassim UniversityBuraydahSaudi Arabia Higher Institute of Engineering and Technology KafrelsheikhEgypt Department of Electrical Engineering Shoubra Faculty of EngineeringBenha UniversityEgypt

Dipper throated optimization(DTO)algorithm is a novel with a very efficient metaheuristic inspired by the dipper throated *** has its unique hunting technique by performing rapid bowing *** show the efficiency of the proposed algorithm,DTO is tested and compared to the algorithms of Particle Swarm Optimization(PSO),Whale Optimization Algorithm(WOA),Grey Wolf Optimizer(GWO),and Genetic Algorithm(GA)based on the seven unimodal benchmark ***,ANOVA and Wilcoxon rank-sum tests are performed to confirm the effectiveness of the DTO compared to other optimization ***,to demonstrate the proposed algorithm’s suitability for solving complex realworld issues,DTO is used to solve the feature selection *** strategy of using DTOs as feature selection is evaluated using commonly used data sets from the University of California at Irvine(UCI)*** findings indicate that the DTO outperforms all other algorithms in addressing feature selection issues,demonstrating the proposed algorithm’s capabilities to solve complex real-world situations.

关键词： Metaheuristic optimization swarmoptimization feature selection function optimization

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Exploring the Impact of Carbon Emissions on Supply Chains of Imperfect Quality Items: A Game Theory Perspective 2nd

Exploring the Impact of Carbon Emissions on Supply Chains of...

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2nd Biennial International Symposium on Fluids and Thermal Engineering, FLUTE 2023

作者： Mittal, Mandeep Dhanda, Anil Raghav, Tisha Phanden, Rakesh Kumar Department of Mathematics School of Computer Science Engineering and Technology Bennett University Greater Noida India Department of Mathematics Amity Institute of Applied Sciences Amity University Uttar Pradesh Noida India Amity School of Engineering and Technology Amity University Uttar Pradesh Noida India

ISBN: (纸本)9789819731725

This research study proposes a cooperative and non-cooperative game theory strategy. The study’s objectives are to examine how carbon emissions impact the supply chain, especially in means of quality control and cost restriction, and to evaluate the effectiveness of game theory in addressing the issue. To gather and evaluate both quantitative and qualitative data for the study, a mixed-methods technique is used. The importance of reducing carbon emissions in supply chains is emphasized in the study, as are the benefits of using cooperative and non-cooperative game theory strategies to achieve this goal. The paper improves knowledge on supply chain management by offering an approach for simulating carbon emissions in a network of supply chains. The results of this study benefit supply chain management and assist academics, policymakers, and sustainability practitioners. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Game theory

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Combining the AIML Model with Acoustic Signal to Evaluate the Bowel Sound 2

Combining the AIML Model with Acoustic Signal to Evaluate th...

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2nd International Conference Computational and Characterization Techniques in Engineering and sciences, IC3TES 2024

作者： Kumar Reddy, S.Pavan Sreevani, N. Revathi, V. Babu, Samuel Singh, Navdeep Kareem, Ali Narsapur Campus Hyderabad India Hyderabad India New Horizon College of Engineering Department of Applied Sciences Bangalore India Mlr Institute of Technology Department of Computer Science and Engineering Hyderabad India Lovely Professional University Phagwara India Hilla University College Babylon Iraq

ISBN: (纸本)9798350364699

Automated methods for assessing bowel sounds (BS) were well developed by the early 2000s. Several teams, using diverse analytical techniques, achieved high levels of accuracy. BS has shown potential in doing non-invasive research on irritable bowel syndrome, studying gastrointestinal motility, and performing surgical procedures. This article introduces hybrid convolution and recursive neural networks that are used for BS analysis. It was among the first deep-learning approaches to be extensively studied. An experimental workflow and a novel dataset from a contact microphone device were used to assess the results. Data were obtained at night, the most neuro gastroenterological intriguing time. Previous studies solely preserved daytime information, ignoring this period. The programme accurately detects bowel noises. In addition, they acquired good diagnostic specificity. Medical professionals confirmed the data, supporting the clinical diagnosis. Medical professionals may submit patient recordings and have them analyzed online using the client-server solution. Technologies are established, but BS research needs a consistent methodology, a worldwide venue for debate, & a free platform for data interchange, hence it is not widely employed. A common BS research framework may start with the server. © 2024 IEEE.

关键词： Diagnosis

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Exogenous Data in Forecasting: FARM - A New Measure for Relevance Evaluation

arXiv

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arXiv 2023年

作者： Christen, Ramón Mazzola, Luca Denzler, Alexander Portmann, Edy School of Computer Sciences Lucerne University of Applied Sciences and Arts Switzerland Human-IST Institute University of Fribourg Switzerland

Evaluating the relevance of an exogenous data series is the first step in improving the prediction capabilities of a forecast algorithm. Inspired by existing metrics for time series similarity, we introduce a new approach named FARM - Forward Aligned Relevance Metric. Our forward method relies on an angular measure that compares changes in subsequent data points to align time-warped series in an efficient way. The proposed algorithm combines local and global measures to provide a balanced relevance metric. This results in considering also partial, intermediate matches as relevant indicators for exogenous data series significance. As a first validation step, we present the application of our FARM approach to synthetic but representative signals. While demonstrating the improved capabilities with respect to existing approaches, we also discuss existing constraints and limitations of our idea. © 2023, CC BY.

关键词： Forecasting

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Customer Churn Prediction in the Telecom Sector with Machine Learning and Adaptive k-Means Cluster using Imbalance Data 2

Customer Churn Prediction in the Telecom Sector with Machine...

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2nd International Conference on Distributed Computing and Optimization Techniques, ICDCOT 2024

作者： Aravinda, N.L. Reddy, R. Archana Bala Dhandayuthapani, V. Nayak, Jithendra P. R. Husseen, Ahmad Electronics and Communication Engineering Department Telangana Hyderabad India School of Computer Science and Engineering Sr University Telangana Warangal India University of Technology and Applied Sciences Shinas Branch Department of It College of Computing and Information Sciences Oman Srinivas Institute of Technology Department of Cse Valachil Mangalore India The Islamic University Najaf Iraq

ISBN: (纸本)9798350382952

A crucial aspect of maintaining a customer-oriented business in the telecommunications sector with machine learning (ML) is understanding the reasons and factors that lead to customer churn. However, the dataset is difficult by noise, misclassifications, duplicated data, and imbalanced customer information complicating the process of identifying ways to split the dataset based on events. The under-sampling method address the imbalance data by decreasing the data quantity of majority class, thereby achieving a balanced dataset. This method, utilizing Adaptive k-means clustering, directly determines the reduction based on that data quality in each class. The datasets are combined with class labels to generate a new balanced dataset used for classification in the SVM algorithm, which distinguishes between customer churn and non-churn in the prediction for the telecom sector. The SVM algorithm can perform linear and non-linear classification with a kernel function to control overfitting improve generalization and handle high-dimensional data. The obtained results demonstrate that the proposed method achieves better accuracy of 95.70%, precision of 56.01%, Recall of 55.05%, and F1-score of 96.05% on the telecom churn dataset. These results ensure superior detection and classification performance compared to other existing methods, such as Random Forest (RF), Decision Tree (DT) and K-Nearest Neighbour (KNN). © 2024 IEEE.

关键词： Decision trees

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Efficient local search based on dynamic connectivity maintenance for minimum connected dominating set

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Journal of Artificial Intelligence Research 2021年 71卷 89-119页

作者： Zhang, Xindi Li, Bohan Cai, Shaowei Wang, Yiyuan State Key Laboratory of Computer Science Institute of Software Chinese Academy of Sciences Beijing China School of Computer Science and Technology University of Chinese Academy of Sciences Beijing China School of Computer Science and Information Technology Northeast Normal University China Key Laboratory of Applied Statistics of MOE Northeast Normal University China

The minimum connected dominating set (MCDS) problem is an important extension of the minimum dominating set problem, with wide applications, especially in wireless networks. Most previous works focused on solving MCDS problem in graphs with relatively small size, mainly due to the complexity of maintaining connectivity. This paper explores techniques for solving MCDS problem in massive real-world graphs with wide practical importance. Firstly, we propose a local greedy construction method with reasoning rule called 1hopReason. Secondly and most importantly, a hybrid dynamic connectivity maintenance method (HDC+) is designed to switch alternately between a novel fast connectivity maintenance method based on spanning tree and its previous counterpart. Thirdly, we adopt a two-level vertex selection heuristic with a newly proposed scoring function called chrono-safety to make the algorithm more considerate when selecting vertices. We design a new local search algorithm called FastCDS based on the three ideas. Experiments show that FastCDS significantly outperforms five state-of-the-art MCDS algorithms on both massive graphs and classic benchmarks. © 2021 AI Access Foundation. All rights reserved.

关键词： Local search (optimization)

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PARTICLE MULTI-AXIS TRANSFORMER FOR JET TAGGING

arXiv

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arXiv 2024年

作者： Usman, Muhammad Shahid, M. Husnain Ejaz, Maheen Hani, Ummay Fatima, Nayab Khan, Abdul Rehman Khan, Asifullah Mirza, Nasir Majid Pattern Recognition Lab Department of Computer & Information Science Pakistan Institute of Engineering & Applied Sciences Nilore Islamabad45650 Pakistan Pakistan Institute of Engineering & Applied Sciences Nilore Islamabad45650 Pakistan Center for Mathematical Sciences Pakistan Institute of Engineering & Applied Sciences Nilore Islamabad45650 Pakistan Department of Physics & Applied Mathematics Pakistan Institute of Engineering & Applied Sciences Nilore Islamabad45650 Pakistan

Jet tagging is an essential categorization problem in high energy physics. In recent times, Deep Learning has not only risen to the challenge of jet tagging but also significantly improved its performance. In this article, we proposed an idea of a new architecture, Particle Multi-Axis transformer (ParMAT) which is a modified version of Particle transformer (ParT). ParMAT contains local and global spatial interactions within a single unit which improves its ability to handle various input lengths. We trained our model on JETCLASS, a publicly available large dataset that contains 100M jets of 10 different classes of particles. By integrating a parallel attention mechanism and pairwise interactions of particles in the attention mechanism, ParMAT achieves robustness and higher accuracy over the ParT and ParticleNet. The scalability of the model to huge datasets and its ability to automatically extract essential features demonstrate its potential for enhancing jet tagging. © 2024, CC BY.

关键词： Deep learning

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Comparative Analysis of Multinomial Naïve Bayes and XG Boost for Sentiment Analysis

Comparative Analysis of Multinomial Naïve Bayes and XG Boos...

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Signal Processing and Advance Research in Computing (SPARC), International Conference on

作者： Richa Mehortra Shilpi Bisht Neeraj Bisht Anshul Srivastava Amity School of Applied Sciences Amity University Lucknow Campus Lucknow India Department of Applied Sciences and Humanities Birla Institute of Applied Sciences Bhimtal India Department of Computer Science Birla Institute of Applied Sciences Bhimtal India Amity International Business School Amity University Noida Uttar Pradesh India

ISBN: (数字)9798350385205

ISBN: (纸本)9798350385212

Social-media provides a vast amount of user-generated content which serves as an invaluable source of real-time data for analyzing public sentiments about various aspects. Public sentiment shapes various services and strategies as per the targeted users. Analyzing text for sentiments or opinions comes under a branch of Natural language processing (NLP), known as Sentiment analysis. Various approaches have been used for sentiment analysis from time to time, viz., machine learning methods, lexicon-based approaches and hybrid of these approaches. This paper uses machine leaning methodologies for sentiment analysis. It explores the effectiveness of Multinomial Naive Bayes (MNB) and Gradient boosting using XG Boost in sentiment analysis. The strength and limitations of both approaches are explored using cross-validation technique. This approach contributes to existing research in the area of sentiment investigation by critically assessing the performance of proposed model and by providing valuable implications in the areas like brand monitoring and public opinion.

关键词： Sentiment analysis Analytical models Shape Computational modeling User-generated content Signal processing Boosting Real-time systems Bayes methods Monitoring

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Human Fall Detection Using Transfer Learning-Based 3D CNN 1st

Human Fall Detection Using Transfer Learning-Based 3D CNN

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1st International Conference on Computational Technologies and Electronics, ICCTE 2023

作者： Alam, Ekram Sufian, Abu Dutta, Paramartha Leo, Marco Department of Computer Science Gour Mahavidyalaya Old Malda India Department of Computer Science University of Gour Banga English Bazar India Department of Computer and System Sciences Visva-Bharati University Santiniketan India National Research Council of Italy Institute of Applied Sciences and Intelligent Systems Lecce Italy

ISBN: (纸本)9783031819346

Unintentional or accidental falls are one of the significant health issues in senior persons. The population of senior persons is increasing steadily. So, there is a need for an automated fall detection monitoring system. This paper introduces a vision-based fall detection system using a pre-trained 3D CNN. Unlike 2D CNN, 3D CNN extracts not only spatial but also temporal features. Proposed model leverages the original learned weights of a 3D CNN model pre-trained on the Sports1M dataset to extract the spatio-temporal features. Only the SVM classifier was trained, which saves the time required to train the 3D CNN. Stratified shuffle five split cross-validation has been used to split the dataset into training and testing data. Extracted features from the proposed 3D CNN model were fed to an SVM classifier to classify the activity as fall or ADL. Two datasets, GMDCSA and CAUCAFall, were utilized to conduct the experiment. The source code for this work can be accessed via the following link: https://***/ekramalam/HFD_3DCNN. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Fall detection

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Secure Channel Estimation Using Norm Estimation Model for 5G Next Generation Wireless Networks

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computers, Materials & Continua 2025年第1期82卷 1151-1169页

作者： Khalil Ullah Song Jian Muhammad Naeem Ul Hassan Suliman Khan Mohammad Babar Arshad Ahmad Shafiq Ahmad Faculty of Information Engineering and Automation Kunming University of Science and TechnologyKunming650500China Department of Computer Science Abbottabad University of Science and TechnologyHavelianAbbottabad22500Pakistan Department of Computing and Electronics Engineering Middle East CollegeMuscat124Oman Department of CS&IT Pak-Austria Fachhochschule Institute of Applied Sciences and Technology MangHaripur22621Pakistan Industrial Engineering Department College of EngineeringKing Saud UniversityRiyadh11421Saudi Arabia

The emergence of next generation networks(NextG),including 5G and beyond,is reshaping the technological landscape of cellular and mobile *** networks are sufficiently scaled to interconnect billions of users and *** in academia and industry are focusing on technological advancements to achieve highspeed transmission,cell planning,and latency reduction to facilitate emerging applications such as virtual reality,the metaverse,smart cities,smart health,and autonomous *** continuously improves its network functionality to support these *** input multiple output(MIMO)technology offers spectral efficiency,dependability,and overall performance in *** article proposes a secure channel estimation technique in MIMO topology using a norm-estimation model to provide comprehensive insights into protecting NextG network components against adversarial *** technique aims to create long-lasting and secure NextG networks using this extended *** viability of MIMO applications and modern AI-driven methodologies to combat cybersecurity threats are explored in this ***,the proposed model demonstrates high performance in terms of reliability and accuracy,with a 20%reduction in the MalOut-RealOut-Diff metric compared to existing state-of-the-art techniques.

关键词： Next generation networks massive mimo communication network artificial intelligence 5G adversarial attacks channel estimation information security

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