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Sports Events Recognition Using Multi Features and Deep Belief Network

computers, Materials & Continua 2024年第10期81卷 309-326页

作者： Bayan Alabdullah Muhammad Tayyab Yahay AlQahtani Naif Al Mudawi Asaad Algarni Ahmad Jalal Jeongmin Park Department of Information Systems College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Computer Science Air UniversityIslamabad44000Pakistan Department of Computer Science Applied CollegeKing Khalid UniversityAbha61421Saudi Arabia Department of Computer Science College of Computer Science and Information SystemNajran UniversityNajran55461Saudi Arabia Department of Computer Sciences Faculty of Computing and Information TechnologyNorthern Border UniversityRafha91911Saudi Arabia Department of Computer Engineering Korea Polytechnic UniversitySiheung-siGyeonggi-do237Republic of Korea

In the modern era of a growing population,it is arduous for humans to monitor every aspect of sports,events occurring around us,and scenarios or *** recognition of different types of sports and events has increasingly incorporated the use of machine learning and artificial *** research focuses on detecting and recognizing events in sequential photos characterized by several factors,including the size,location,and position of people’s body parts in those pictures,and the influence around those *** approaches utilized,here are feature descriptors such as MSER(Maximally Stable Extremal Regions),SIFT(Scale-Invariant Feature Transform),and DOF(degree of freedom)between the joint points are applied to the skeleton ***,for the same purposes,other features such as BRISK(Binary Robust Invariant Scalable Keypoints),ORB(Oriented FAST and Rotated BRIEF),and HOG(Histogram of Oriented Gradients)are applied on full body or *** integration of these techniques increases the discriminative nature of characteristics retrieved in the identification process of the event,hence improving the efficiency and reliability of the entire *** extracted features are passed to the early fusion and DBscan for feature fusion and *** deep belief,network is employed for *** results demonstrate a separate experiment’s detection average recognition rate of 87%in the HMDB51 video database and 89%in the YouTube database,showing a better perspective than the current methods in sports and event identification.

关键词： Machine learning silhouettes extremal regions joint points scalable keypoints

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Collaborative Search With Knowledge Sharing And Summarization 4

Collaborative Search With Knowledge Sharing And Summarizatio...

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4th International Conference on Sustainable Expert Systems, ICSES 2024

作者： Mohan, Venkateshwaran Sivaganesan, D. Balasubramaniam, S. Vikhas, S.G. Dhinesh, S.P. Psg Institute of Technology and Applied Research Department of Computer Science and Engineering Coimbatore India Dr Mahalingam College of Engineering and Technology Department of Computer Science and Engineering Pollachi India Psg College of Technology Department of Computer Science and Engineering Coimbatore India Sri Krishna Arts and Science College Department of Computer Science Coimbatore India

ISBN: (纸本)9798331540364

A collaborative search engine is used for a group of users who want to search information together for various group activities. This will be time saving as the users can split the work between them. Summarization is the process of condensing a document into a shorter version which will contain the important details about the document. This will be useful to quickly get the main ideas without having to go through the entire document. Our main aim is to combine collaborative search with document summarization so that it will be helpful when users in a group want to quickly read the summary of the documents whenever required. In the collaborative search, the results will be re-ranked based on the user's interaction with the system, so that feedback driven ranking of results is attained and the users in a session will be able to view pages that were even ranked by their co-mates. This re-raking process is done by obtaining the user's interest on a particular page using decision logic and updating it in the priority queue. User's meta-data like page size, click-through rate, time spent and scrolling speed of the document will be calculated and these will be used as parameters for the decision logic to find the interest level of the user. Extractive summarization is implemented with the concept of Maximum Independent Set. The raw text document is first converted into a graph having its sentences as nodes and the edge weight is the similar instances between sentences. Then graph MIS is identified and removed after which the top-n sentences are fetched upon its eigenvector centrality value and the summary is generated. © 2024 IEEE.

关键词： Computation theory

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Utilizing Clustering Analysis and Machine Learning Techniques for Providing Student Learning Recommendations 24

Utilizing Clustering Analysis and Machine Learning Technique...

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2024 10th International Conference on Education and Training Technologies, ICETT 2024

作者： Woo, Bary Cheong, Ngai Peng, Li Liu, Gaojian Faculty of Applied Sciences Macao Polytechnic University China Computer Science and Technology Hunan University of Science and Technology China

ISBN: (纸本)9798400717895

This research endeavors to scrutinize the influence of courses on students' final year project (FYP) scores and prognosticate FYP scores by applying methodologies such as clustering analysis, decision trees, logistic regression, and support vector machines. By scrutinizing the correlation between students' course grades and FYP scores, the objective is to gain deeper insights into the impact of diverse courses on FYP performance and offer tailored learning recommendations. Initially, clustering analysis is employed to categorize students into distinct groups, discerning the potential impact of specific course combinations on FYP outcomes. Subsequently, machine learning algorithms, including decision trees, logistic regression, and support vector machines, are leveraged to forecast FYP scores based on students' course grades. Through these analyses, customized learning recommendations and guidance can be provided to augment students' FYP performance, fostering their academic advancement and maturation. The findings of this research are pivotal for educational institutions and individual students, contributing to the enhancement of curriculum design and pedagogical methodologies, thereby elevating the standard of education and enriching students' learning encounters. Furthermore, drawing upon assessment data spanning over 16 years and encompassing more than 2,000 non-CS majors at MPU, we delve into the factors influencing student learning quality and proffer timely learning recommendations based on amassed student subject grades and FYP scores. © 2024 ACM.

关键词： Contrastive Learning

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Synergizing vision transformer with ensemble of deep learning model for accurate kidney stone detection using CT imaging

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Alexandria Engineering Journal 2025年 127卷 357-373页

作者： Alzughaibi, Arwa Alanazi, Adwan A. Alshahrani, Mohammed Hilali Jaghdam, Ines Hassaballa, Abaker A. Applied college Taibah University Madinah Saudi Arabia Department of Computer Science and Information College of Computer Science and Engineering University of Hail Hail Saudi Arabia Department of Computer Sciences Applied College Najran University Najran66462 Saudi Arabia Department of Computer Science and Information Technology Applied College Princess Nourah bint Abdulrahman University P.O. Box 84428 Riyadh11671 Saudi Arabia Center for Scientific Research and Entrepreneurship Northern Border University Arar73213 Saudi Arabia Department of Mathematics Faculty of Science Northern Border University Arar73213 Saudi Arabia

The disease of Kidney stones is a risky disease for individuals all over the world. Many people with kidney stones in the early phase do not detect it as an illness, and it harms the organ gradually. Precise analysis of kidney illness is vital, as it is a major health concern that needs accurate detection for appropriate and effective treatment. CT scans are one of the most extensively accessible imaging models, and they are employed for effective diagnosis. Deep learning (DL) techniques are gradually identified as beneficial tools for analyzing illness in the medical field. However, present techniques employing deep networks often meet low accuracy and overfitting challenges, demanding further alteration for optimum performance. This study presents a Leveraging Flying Foxes Optimization with an Ensemble of Deep Learning for Accurate Kidney Stone Detection (LFFOEDL-AKSD) technique in CT scans. The presented LFFOEDL-AKSD technique mainly focuses on detecting kidney stones using CI imaging. At first, the presented LFFOEDL-AKSD technique applies the pre-processing phase, which involves image resizing for uniform CT scan dimensions and data augmentation through transformations like rotation and flipping to reduce overfitting, sobel filtering (SF) sharpens edges, and the data is separated into training, validation, and testing sets for model development. The presented LFFOEDL-AKSD technique employs the swin transformer (ST) model for the feature extraction method. Furthermore, the majority voting ensemble of three DL approaches, such as the graph convolutional network (GCN), temporal convolutional network (TCN), and three-dimensional convolutional autoencoder (3D-CAE) approaches, are employed to increase the precision and reliability of the kidney stone recognition. Finally, the presented LFFOEDL-AKSD technique implements the flying foxes optimization (FFO) approach for the hyperparameter tuning involved in the ensemble learning models. An extensive experiment is conduct

关键词： Flying foxes optimization Ensemble deep learning Kidney stone detection CI imaging Swin transformer

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An Empirical Study of Image based Steganography and Watermarking

An Empirical Study of Image based Steganography and Watermar...

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2024 International Conference on Knowledge Engineering and Communication Systems, ICKECS 2024

作者： Padma, R. Yendapalli, Vamsidhar Jyothi, A.P. Gitam University Computer Science and Engineering Gitam School of Technology Bengaluru India Ramaiah University of Applied Sciences Computer Science and Engineering Bengaluru India

ISBN: (纸本)9798350359688

In the current technological landscape, the transmis-sion of data through communication channels has experienced significant advancements. The rapid growth in communication technology has brought about improved outcomes. However, challenges arise in ensuring the security and integrity of the transmitted data. It becomes crucial to safeguard from unauthorized access or users without proper authorization, it is important to implement various security parameters and best practices. This need has led to the development of various data hiding techniques. The objective of this paper is to conduct a comprehensive study on the taxonomy of Steganography and Watermarking types, as well as the taxonomy of techniques employed in Steganography and Watermarking. The paper aims to provide an overview and classification of different types of Steganography and Watermarking techniques. It also focuseson analyzing the advantages and disadvantages associated with these *** examining the taxonomy and assessing the pros and cons of Steganography and Watermarking techniques, this paper aims to contribute to a better understanding of their applications and implications in data security. The findings of this study can serve as a valuable resource for researchers and practitioners working in the field of data hiding and information security. © 2024 IEEE.

关键词： Discrete Fourier transforms

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A Machine Learning-Based Approach for Network Optimization in WSNs

A Machine Learning-Based Approach for Network Optimization i...

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International Conference on Data science, Computation and Security, IDSCS 2023

作者： Bhattad, Bharti Sethi, Kamal Kumar Pal Singh Rathore, Narendra Bhanodia, Praveen Mishra, Durgesh Kumar Computer Science Engineering Acropolis Institute of Technology and Research Indore India School of Computer Science and Information Technology Symbiosis University of Applied Sciences Madhya Pradesh Indore India

ISBN: (纸本)9789819709748

With the advancements in interconnected devices and automation, wireless sensor networks have gained substantial importance. Energy constraint is one of the main issues that wireless sensor networks (WSNs) must deal with. Moreover, to enhance the effectiveness, it is necessary to reduce the latency as much as possible. Wireless sensor networks can be optimized for latency and energy consumption using a variety of protocols, with their own pros and cons. This paper investigates different aspects related to wireless sensor networks along with the relevant approaches. The intent of this paper is to present a reinforcement learning-based approach for optimized routing in WSNs. The protocol is designed to cater to the constraints of limited energy and latency, with the evaluation of the proposed algorithm in terms of network lifetime and latency. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Reinforcement learning

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Quantum-secure plaintext checkable signcryption

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Iran Journal of computer science 2023年第1期6卷 63-68页

作者： Alornyo, Seth School of Applied Science and Technology Computer Science Department Koforidua Technical University Koforidua Ghana

A public plaintext query on a ciphertext using plaintext checkable encryption is a cryptographic primitive studied extensively to promote the search on ciphertext using a plaintext keywords or phrase. Most existing schemes are vulnerable to an adversary with quantum computers. In this paper, we propose quantum-secure plaintext checkable signcryption in a lattice environment. Our scheme is post-quantum secure and achieves the dual benefits of digital signature and public key encryption. Learning with errors cryptographic primitive ensures the attainment of this goal. We achieve a desirable security feature of existential unforgeable chosen message attack using the random oracle model. © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2022. Springer Nature or its licensor holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s);author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

关键词： Existential unforgeability Lattice Plaintext checkable signcryption Post-quantum secure

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Explainable AI-Based DDoS Attacks Classification Using Deep Transfer Learning

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computers, Materials & Continua 2024年第9期80卷 3785-3802页

作者： Ahmad Alzu’bi Amjad Albashayreh Abdelrahman Abuarqoub Mai A.M.Alfawair Department of Computer Science Jordan University of Science and TechnologyIrbid22110Jordan Department of Computer Science The University of JordanAmman11942Jordan Cardiff School of Technologies Cardiff Metropolitan UniversityCardiffCF52YBUK Prince Abdullah bin Ghazi Faculty of Information and Communication Technology Al-Balqa Applied UniversitySalt19117Jordan

In the era of the Internet of Things(IoT),the proliferation of connected devices has raised security concerns,increasing the risk of intrusions into diverse *** the convenience and efficiency offered by IoT technology,the growing number of IoT devices escalates the likelihood of attacks,emphasizing the need for robust security tools to automatically detect and explain *** paper introduces a deep learning methodology for detecting and classifying distributed denial of service(DDoS)attacks,addressing a significant security concern within IoT *** effective procedure of deep transfer learning is applied to utilize deep learning backbones,which is then evaluated on two benchmarking datasets of DDoS attacks in terms of accuracy and time *** leveraging several deep architectures,the study conducts thorough binary and multiclass experiments,each varying in the complexity of classifying attack types and demonstrating real-world ***,this study employs an explainable artificial intelligence(XAI)AI technique to elucidate the contribution of extracted features in the process of attack *** experimental results demonstrate the effectiveness of the proposed method,achieving a recall of 99.39%by the XAI bidirectional long short-term memory(XAI-BiLSTM)model.

关键词： DDoS attack classification deep learning explainable AI cybersecurity

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Computational Web Drug Discovery Application forSARS- Cov-2 Disease Using Predictive and Analytical Approach 10

Computational Web Drug Discovery Application forSARS- Cov-2 ...

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10th International Conference on Advanced Computing and Communication Systems, ICACCS 2024

作者： Hemkiran, S. Kartik, R. Aswath, S. Rathan Kabilan, K. Sudha Sadasivam, G. Psg Institute of Technology and Applied Research Dept. of Computer Science and Engineering Coimbatore India Psg College of Technology Dept. of Computer Science and Engineering Coimbatore India

ISBN: (纸本)9798350384369

In response to the urgent need for coronavirus treatments, this research focuses on leveraging bioactivity data collection and processing for efficient drug discovery, employing computational methods to predict potential antiviral compounds. Exploratory data analysis was performed to identify patterns and trends, and various molecular descriptors were computed for the Mpro inhibitors in the descriptor dataset preparation step. A regression model was trained using the developed random forest algorithm to predict the bioactivity of new compounds against the standard value and compared with other regression models. Additionally, a web-based application was created using the random forest model to allow users to obtain predicted bioactivity values against Mpro by providing information about molecular structures. Machine learning-based bioactivity prediction offers an intriguing plan for drug discovery, and the proposed work provides a comprehensive workflow for COVID- 19 drug discovery. The web application provides a user-friendly interface for drug discovery researchers to evaluate the potential of compounds against the Mpro target quickly. © 2024 IEEE.

关键词： COVID-19

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Sentiment Analysis of Low-Resource Language Literature Using Data Processing and Deep Learning

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computers, Materials & Continua 2024年第4期79卷 713-733页

作者： Aizaz Ali Maqbool Khan Khalil Khan Rehan Ullah Khan Abdulrahman Aloraini Department of IT and Computer Science Pak-Austria Fachhochschule:Institute of Applied Sciences and TechnologyHaripur22620Pakistan Software Competence Center Hagenberg Softwarepark 32aHagenberg4232Austria Department of Computer Science School of Engineering and Digital SciencesNazarbayev UniversityAstana010000Kazakhstan Department of Information Technology College of ComputerQassim UniversityP.O.Box 1162BuraydahSaudi Arabia

Sentiment analysis, a crucial task in discerning emotional tones within the text, plays a pivotal role in understandingpublic opinion and user sentiment across diverse *** numerous scholars conduct sentiment analysisin widely spoken languages such as English, Chinese, Arabic, Roman Arabic, and more, we come to grapplingwith resource-poor languages like Urdu literature which becomes a challenge. Urdu is a uniquely crafted language,characterized by a script that amalgamates elements from diverse languages, including Arabic, Parsi, Pashtu,Turkish, Punjabi, Saraiki, and more. As Urdu literature, characterized by distinct character sets and linguisticfeatures, presents an additional hurdle due to the lack of accessible datasets, rendering sentiment analysis aformidable undertaking. The limited availability of resources has fueled increased interest among researchers,prompting a deeper exploration into Urdu sentiment analysis. This research is dedicated to Urdu languagesentiment analysis, employing sophisticated deep learning models on an extensive dataset categorized into fivelabels: Positive, Negative, Neutral, Mixed, and Ambiguous. The primary objective is to discern sentiments andemotions within the Urdu language, despite the absence of well-curated datasets. To tackle this challenge, theinitial step involves the creation of a comprehensive Urdu dataset by aggregating data from various sources such asnewspapers, articles, and socialmedia comments. Subsequent to this data collection, a thorough process of cleaningand preprocessing is implemented to ensure the quality of the data. The study leverages two well-known deeplearningmodels, namely Convolutional Neural Networks (CNN) and Recurrent Neural Networks (RNN), for bothtraining and evaluating sentiment analysis performance. Additionally, the study explores hyperparameter tuning tooptimize the models’ efficacy. Evaluation metrics such as precision, recall, and the F1-score are employed to assessthe effectiv

关键词： Urdu sentiment analysis convolutional neural networks recurrent neural network deep learning natural language processing neural networks

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