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Association Rule Analysis-Based Identification of Influential Users in the Social Media

computers, Materials & Continua 2022年第12期73卷 6479-6493页

作者： Saqib Iqbal Rehan Khan Hikmat Ullah Khan Fawaz Khaled alarfaj Abdullah Mohammed alomair Muzamil Ahmed College of Engineering Al Ain UniversityAl AinUnited Arab Emirates Department of Computer Science COMSATS University IslamabadWah CampusWah Cantt47040Pakistan Department of Computer Science and Information Sciences Imam Mohammad Ibn Saud Islamic University(IMSIU)Al-HoufufSaudi Arabia

The exchange of information is an innate and natural process that assist in content *** networking sites emerge to enrich their users by providing the facility for sharing information and social *** extensive adoption of social networking sites also resulted in user content *** are diverse research areas explored by the researchers to investigate the influence of social media on users and confirmed that social media sites have a significant impact on markets,politics and social *** is extensively used platform to share information,thoughts and opinions through posts and *** identification of influential users on the social web has grown as hot research field because of vast applications in diverse areas for instance political campaigns marketing,e-commerce,commercial and,*** research studies either uses linguistic content or graph-based representation of social network for the detection of influential *** this article,we incorporate association rule mining algorithms to identify the top influential users through frequent *** association rules have been computed using the standard evaluation measures such as support,confidence,lift,and *** verify the results,we also involve conventional metrics for example accuracy,precision,recall and F1-measure according to the association rules *** detailed experiments are carried out using the benchmark college-Msg dataset extracted by *** obtained results validate the quality and visibility of the proposed *** outcome of propose model verify that the association rule mining is able to generate rules to identify the temporal influential users on Facebook who are consistent on regular *** preparation of rule set help to create knowledge-based systems which are efficient and widely used in recent era for decision making to solve real-world problems.

关键词： Association rule mining ranking social web influential users social media

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IoT Devices Authentication Using Artificial Neural Network

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computers, Materials & Continua 2022年第2期70卷 3701-3716页

作者： Syed Shabih Ul Hasan Anwar Ghani Ikram Ud Din Ahmad almogren Ayman altameem Department of Computer Science and Software Engineering International Islamic University Islamabad45000Pakistan Department of Information Technology The University of Haripur22620HaripurPakistan Chair of Cyber Security Department of Computer ScienceCollege of Computer and Information SciencesKing Saud UniversityRiyadh11633Saudi Arabia Chair of Cyber Security Department of Natural and Engineering SciencesCollege of Applied Studies and Community ServicesKing Saud UniversityRiyadh11543Saudi Arabia

User authentication is one of the critical concerns of information *** tend to use strong textual passwords,but remembering complex passwords is hard as they often write it on a piece of paper or save it in their mobile *** passwords are slightly unprotected and are easily *** attacks include dictionary,shoulder surfing,and brute *** passwords overcome the shortcomings of textual passwords and are designed to aid memorability and ease of *** paper proposes a Process-based Pattern Authentication(PPA)system for Internet of Things(IoT)devices that does not require a server to maintain a static password of the login *** server stores user’s information,which they provide at the time of registration,i.e.,the R-code and the symbol,but the P-code,i.e.,the actual password,will change with every login attempt of *** this scheme,users may draw a pattern on the basis of calculation from the P-code and Rcode in the PPA pattern,and can authenticate themselves using their touch dynamic behaviors through Artificial Neural Network(ANN).The ANN is trained on touch behaviors of legitimate users reporting superior performance over the existing *** experimental purposes,PPA is implemented as a prototype on a computer system to carry out experiments for the evaluation in terms of memorability and *** experiments show that the system has an effect of 5.03%of the False Rejection Rate(FRR)and 4.36%of the False Acceptance Rate(FAR),respectively.

关键词： Implicit authentication behavioral authentication artificial neural network processed pattern authentication

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Concatenated Residual Attention U-Net with DenseNet: A Hybrid Deep Learning Framework for Pavement Condition Evaluation and Maintenance Planning 2

Concatenated Residual Attention U-Net with DenseNet: A Hybri...

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2nd IEEE International Conference on Integrated Intelligence and Communication Systems, ICIICS 2024

作者： Devi, S. Rukmani alabdeli, Haideer M. Abbas, Haider Mohammed Hindumathy, N. Sivasubramanian, S. Department of Computer Science Saveetha College of Liberal Arts and Sciences SIMATS Deemed to be University Chennai India Department of Computers Techniques Engineering College of Technical Engineering The Islamic University Najaf Iraq Department of Computers Techniques Engineering College of Technical Engineering The Islamic University of Al Diwaniyah Al Diwaniyah Iraq Department of Artificial intelligence School of Computing Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Avadi Chennai India Department of Information Technology New Prince Shri Bhavani College of Engineering and Technology Chennai India

ISBN: (纸本)9798331504960

Recent days, pavement management systems are important for country’s economic well-being as road authorities strive to protect their valuable road assets and minimize the maintenance costs which optimize resource allocation. Traditional approaches for accurate pavement condition evaluation faced significant challenges which include infrequent evaluations lead to delayed maintenance by causing further deterioration, insufficient funding lead to inadequate maintenance. Therefore, this paper proposes a Deep Learning (DL) based Concatenated Residual Attention U-Net with DenseNet (CRAUNet-DenseNet) for Accurate Pavement Condition Evaluation and Maintenance Planning. Initially, data is collected from pavement surface images by using two images. Then, preprocessing is done by using data augmentation to improve training process. After that, image segmentation is done by CRAUNet where, it has encoder, decoder, and skip connection parts to make image segments as well as restore feature maps to its original size. The image features were extracted by utilizing Spatial Pyramid Pooling Layer (SPPL) which allows arbitrary scales of any sizes of image. At last, DenseNet is used for determining and classify condition of road from pavement surface images. The proposed CRAUNet-DenseNet model generated outperforming results in precision 97.91%, recall 98.36%, and F1 score 99.10% when compared with existing Convolutional Neural Network (CNN). © 2024 IEEE.

关键词： Resource allocation

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Body Part Segmentation of Anime Characters

Body Part Segmentation of Anime Characters

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作者： Ou, Zhenhua Liu, Xueting Li, Chengze Wen, Zhenkun Li, Ping Gao, Zhijian Wu, Huisi College of Computer Science and Software Engineering Shenzhen University Shenzhen China School of Computing and Information Sciences Saint Francis University Hong Kong Department of Computing The Hong Kong Polytechnic University Hong Kong College of Electronic and Information Engineering Shenzhen University Shenzhen China

Semantic segmentation is an important approach to present the perceptual semantic understanding of an image, which is of significant usage in various applications. Especially, body part segmentation is designed for segmenting body parts of human characters to assist different editing tasks, such as style editing, pose transfer, and animation production. Since segmentation requires pixel-level precision in semantic labeling, classic heuristics-based methods generally have unstable performance. With the deployment of deep learning, a great step has been taken in segmenting body parts of human characters in natural photographs. However, the existing models are purely trained on natural photographs and generally obtain incorrect segmentation results when applied on anime character images, due to the large visual gap between training data and testing data. In this article, we present a novel approach to achieving body part segmentation of cartoon characters via a pose-based graph-cut formulation. We demonstrate the use of the acquired body part segmentation map in various image editing tasks, including conditional generation, style manipulation, pose transfer, and video-to-anime. © 2024 John Wiley & Sons Ltd.

关键词： Semantic Segmentation

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Optimal Deep Hybrid Boltzmann Machine Based Arabic Corpus Classification Model

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computer Systems Science & engineering 2023年第9期46卷 2755-2772页

作者： Mesfer al Duhayyim Badriyya B.al-onazi Mohamed K.Nour Ayman Yafoz Amal S.Mehanna Ishfaq Yaseen Amgad Atta Abdelmageed Gouse Pasha Mohammed Department of Computer Science College of Computer Engineering and SciencesPrince Sattam bin Abdulaziz UniversityAl-Kharj16273Saudi Arabia Department of Language Preparation Arabic Language Teaching InstitutePrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Computer Sciences College of Computing and Information SystemUmm Al-Qura UniversityMakkah 24211Saudi Arabia Department of Information Systems Faculty of Computing and Information TechnologyKing Abdulaziz UniversityJeddahSaudi Arabia Department of Digital Media Faculty of Computers and Information TechnologyFuture University in EgyptNew Cairo11845Egypt Department of Computer and Self Development Preparatory Year DeanshipPrince Sattam bin Abdulaziz UniversityAlKharjSaudi Arabia

Natural Language Processing(NLP)for the Arabic language has gained much significance in recent *** most commonly-utilized NLP task is the‘Text Classification’*** main intention is to apply the Machine Learning(ML)approaches for automatically classifying the textual files into one or more pre-defined *** ML approaches,the first and foremost crucial step is identifying an appropriate large dataset to test and train the *** of the trending ML techniques,i.e.,Deep Learning(DL)technique needs huge volumes of different types of datasets for training to yield the best *** current study designs a new Dice Optimization with a Deep Hybrid Boltzmann Machinebased Arabic Corpus Classification(DODHBM-ACC)model in this *** presented DODHBM-ACC model primarily relies upon different stages of pre-processing and the word2vec word embedding *** Arabic text classification,the DHBM technique is *** technique is a hybrid version of the Deep Boltzmann Machine(DBM)and Deep Belief Network(DBN).It has the advantage of learning the decisive intention of the classification *** adjust the hyperparameters of the DHBM technique,the Dice Optimization algorithm(DOA)is exploited in this *** experimental analysis was conducted to establish the superior performance of the proposed DODHBM-ACC *** outcomes inferred the better performance of the proposed DODHBM-ACC model over other recent approaches.

关键词： Arabic corpus text classification machine learning deep learning dice optimization

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A Use of Fuzzy Logic Based Decision Making tree for Developing Smart Healthcare System 4

A Use of Fuzzy Logic Based Decision Making tree for Developi...

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4th IEEE International Conference on Advance Computing and Innovative Technologies in engineering, ICACITE 2024

作者： Gandhimathi, G. Rajaram, Gnanajeyaraman Gandhi, Vaibhav C. Putta, Prakash Abed, Riyad E. Meassar, al-Hussain ECESrm Trp Engineering College Trichy India Saveetha Institute of Medical and Technical Sciences Simats Saveetha School of Engineering Department of Computer Science and Engineering India Madhuben & Bhanubhai Patel Institute of Technology Cvm University Computer Engineering Department India Mohan Babu University Department of Mechanical Engineering andhra pradesh Tirupati India National University of Science and Technology College of Health and Medical Technology Dhi qar64001 Iraq Al-farahid University Baghdad Iraq

ISBN: (纸本)9798350360165

in order to improve the flexibility and adaptation of the omnidirectional mobile robot in dynamic situations, this work provides a hierarchical control strategy with an emphasis on point stability. It is believed that in order for robotic systems to function well during the sense-plan-act cycle, they must be flexible and adaptable. The recommended control structure makes use of a behavior fuzzy decision tree (bt) that integrates high-level decision-making with continuous execution monitoring. Moreover, it incorporates non-linear model predictive control (nmpc) for point stabilization of the mobile robotic healthcare system. The robot's architecture enables it to operate in many configurations inside the workspace, all the while adhering to input and state constraints, including obstacle avoidance and motor restrictions. The efficacy was confirmed in realistic simulation scenarios and with an industrial omni-directional mobile robot operating in an actual environment, executing point stabilization and obstacle avoidance. © 2024 IEEE.

关键词： Predictive control systems

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Machine Learning Techniques for Protein Structure Prediction in Bioinformatics

Machine Learning Techniques for Protein Structure Prediction...

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2024 IEEE International Conference on Communication, Computing and Signal Processing, IICCCS 2024

作者： Abdiqayumovich, aliyev Nurillo Jaisudha, J. al-Farouni, Mohammed Khare, Neha Kaur, Gurmandeep Koduru, Siva Sai Fergana Medical Institute Of Public Health Assistants Of The Department Of Biophysics And Information Technologies Fergana Uzbekistan Prince Shri Venkateshwara Padmavathy Engineering College Chennai India The Islamic University College of Technical Engineering Department of Computers Techniques Engineering Najaf Iraq The Islamic University of Al Diwaniyah College of Technical Engineering Department of Computers Techniques Engineering Al Diwaniyah Iraq Ies University Ies Institute of Technology and Management Department of Computer Science & Engineering M.P. Bhopal India Chandigarh Group of Colleges Chandigarh Engineering College Department of Computer Application Punjab Jhanjeri Mohali140307 India Saveetha Institute of Medical and Technical Sciences Dapartment of Ai & Ml Tamilnadu Chennai India

ISBN: (纸本)9798350390759

This work analyses and discusses the use of machine learning methods in the field of bioinformatics concerning protein structure prediction. The steps of the proposed methodology include: acquiring and cleaning the relevant proteins sequences datasets;and creation and training of deep learning models, including Convolutional Neural Networks (CNNs), and Recurrent Neural Networks, (RNNs). Overall, adding data augmentation and transfer learning boosts the performance of the models to a mean accuracy of 87%. Predictors Evaluation: In evaluating the predictors, the model used an accuracy of 6% and a Matthews correlation coefficient (MCC) of 0. 74. The generality and efficiency of the proposed model are tested and compared with results from other similar standard datasets. Study shows that the results of the model have higher reliability than the traditional method of the machine-learning algorithm especially in the classification of the second and third levels of varied types of proteins. The research will demonstrate the applicability and possibility of the large-scale and cost-efficient approaches to the protein structure prediction using machine learning, while emphasizing the drawbacks of experimental tools. Recommendations for future work are to enhance the interpretability of the resulting models, better combine methods based on the use of information from hybrid schemes with physical models, and use more extensive databases for enhancing accuracy. This research highlights the profound impact that machine learning can have in the field of bioinformatics as a tool to enhance knowledge of such protein structures and to further practice improvements in the understanding of life processes and a and drug design. © 2024 IEEE.

关键词： Federated learning

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Vibrating Particles System algorithm for Solving Classification Problems

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computer Systems Science & engineering 2022年第12期43卷 1189-1206页

作者： Mohammad Wedyan Omar Elshaweesh Enas Ramadan Ryan alturki Department of Autonomous Systems Faculty of Artificial IntelligenceAl-Balqa Applied UniversityAl-Salt19117Jordan Department of Software Engineering Information Technology CollegeAL-Hussein bin Talal UniversityMa’an71111Jordan Department of Computer Science Prince Abdullah Bin Ghazi Faculty of Communication and Information TechnologyAl-Balqa Applied UniversityAl-Salt19117Jordan Department of Information Science College of Computer and Information SystemsUmm Al-Qura UniversityMakkah21961Saudi Arabia

Big data is a term that refers to a set of data that,due to its largeness or complexity,cannot be stored or processed with one of the usual tools or applications for data management,and it has become a prominent word in recent years for the massive development of *** immediately thereafter,the term“big data mining”emerged,i.e.,mining from big data even as an emerging and interconnected field of *** is an important stage in data mining since it helps people make better decisions in a variety of situations,including scientific endeavors,biomedical research,and industrial *** probabilistic neural network(PNN)is a commonly used and successful method for handling classification and pattern recognition *** this study,the authors proposed to combine the probabilistic neural network(PPN),which is one of the data mining techniques,with the vibrating particles system(VPS),which is one of the metaheuristic algorithms named“VPS-PNN”,to solve classi-fication problems more *** data set is eleven common benchmark medical datasets from the machine-learning library,the suggested method was *** suggested VPS-PNN mechanism outperforms the PNN,biogeography-based optimization,enhanced-water cycle algorithm(E-WCA)and the firefly algorithm(FA)in terms of convergence speed and classification accuracy.

关键词： Vibrating particles system(VPS) probabilistic neural network(PNN) classification problem data mining

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Intelligent Caching Strategies for 5G Edge Networks using Machine Learning 4

Intelligent Caching Strategies for 5G Edge Networks using Ma...

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4th International Conference on Smart Electronics and Communication, ICOSEC 2023

作者： Guduri, Naga Venkata Ramakrishna Sethu, S. Shalinirajan, R. Reena, R. Karthikeyan, M. Uthayakumar, G.S. University of Technology and Applied Sciences-Al Musannah Electrical and Electronics Section Engineering Department Oman Department of Computer Science and Engineering Tamilnadu Chennai India Panimalar Engineering College Department of Computer Science and Business System Tamilnadu Chennai India Prince Shri Venkateshwara Padmavathy Engineering College Department of Computer Science and Engineering Tamilnadu Chennai India Tamilnadu Chennai India St. Joseph's Institute of Technology Department of Electronics and Communication Engineering Tamilnadu Chennai India

ISBN: (纸本)9798350300888

To reduce latency and improve the user experience, content caching solutions at the network periphery will become increasingly important as 5G networks and data-intensive applications. In this proposed work, the deployment of a caching architecture for 5G periphery networks using machine learning is done. Utilizing information on user behavior, network conditions, and content prominence enables more accurate forecasts and cache management decisions. This trains the prediction models using a wide range of machine-learning techniques to accommodate the ever-changing needs of both networks and content. To evaluate the efficiency of the proposed architecture by measuring its ability to increase cache hit rates and decrease latency. These techniques significantly outperform the standard caching methods. With the intelligent caching architecture of 5G periphery networks, throughput is increased, latency is reduced, and congestion is alleviated. The findings of this study contribute to the growing corpus of literature on AI-driven optimization in 5G networks and shed light on how such solutions could be enhanced for future networks. Neural Networks achieved 96% accuracy than SVM which is 93%. © 2023 IEEE.

关键词： 5G mobile communication systems

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A Novel Conv-Mamba-Hybrid Network for Medical Image Segmentation

A Novel Conv-Mamba-Hybrid Network for Medical Image Segmenta...

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2024 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2024

作者： Qiao, Sibo Zhao, Zhiyuan Xie, Pengfei Yin, Wenjing Wang, Min Zhang, Yuanyuan Pang, Shanchen Tiangong University The College of Software Tianjin China China University of Petroleum The College of Computer Science and Technology Qingdao China Khalifa University of Science and Technology The Department of Electrical Engineering and Computer Science Abu Dhabi United Arab Emirates Tiangong University The College of Life Sciences Tianjin China Qingdao University of Technology The College of Information and Control Engineering Qingdao China

ISBN: (纸本)9798350386226

In the rapidly advancing landscape of intelligent medical technology, high-precision and widely applicable medical image segmentation techniques are pivotal in advancing personalized treatment plans and enhancing patient experiences. To address inherent obstacles, such as complex structures and features that are difficult to capture in segmentation tasks, we first propose a CMH module that merges convolutional neural networks (CNNs) with state space models (e.g., Mamba). This module hierarchically integrates CNN's local priors into the Mamba layer, adeptly capturing specific details of target objects and broader contextual information from different perspectives. Subsequently, we introduce an information fusion module based on Mamba (MIH), aimed at dynamically fusing feature information from different paths and levels to enhance feature expression and discrimination. Building upon the abovementioned modules, we develop an unexplored segmentation model, CMHNet, capable of flexibly capturing feature maps of varying scales and angles from medical images. In the experiments, we present CMHNet's distinguished performance in segmentation tasks through massive experiments on four medical datasets. © 2024 IEEE.

关键词： Convolutional neural networks

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