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Gender Identification Using Marginalised Stacked Denoising Autoencoders on Twitter Data

Intelligent Automation & Soft Computing 2023年第6期36卷 2529-2544页

作者： Badriyya B.Al-onazi Mohamed K.Nour Hassan Alshamrani Mesfer Al Duhayyim Heba Mohsen Amgad Atta Abdelmageed Gouse Pasha Mohammed Abu Sarwar Zamani 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 UniversityMakkah24211Saudi Arabia Department of Teachers Training Arabic Linguistics InstituteKing Saud UniversityP.O.BOX 145111RiyadhZIP 4545Saudi Arabia Department of Computer Science College of Sciences and Humanities-AflajPrince Sattam bin Abdulaziz UniversityAl-Aflaj16733Saudi Arabia Department of Computer Science Faculty of Computers and Information TechnologyFuture University in EgyptNew Cairo11835Egypt Department of Computer and Self Development Preparatory Year DeanshipPrince Sattam bin Abdulaziz UniversityAlKharjSaudi Arabia

Gender analysis of Twitter could reveal significant socio-cultural differ-ences between female and male *** had been made to analyze and auto-matically infer gender formerly for more commonly spoken languages’content,but,as we now know that limited work is being undertaken for *** of the research works are done mainly for English and least amount of effort for non-English *** study for Arabic demographic inference like gen-der is relatively uncommon for social networking users,especially for ***,this study aims to design an optimal marginalized stacked denoising autoencoder for gender identification on Arabic Twitter(OMSDAE-GIAT)*** presented OMSDAE-GIAR technique mainly concentrates on the identifica-tion and classification of gender exist in the Twitter *** attain this,the OMS-DAE-GIAT model derives initial stages of data pre-processing and word ***,the MSDAE model is exploited for the identification of gender into two classes namely male and *** the final stage,the OMSDAE-GIAT technique uses enhanced bat optimization algorithm(EBOA)for parameter tuning process,showing the novelty of our *** performance validation of the OMSDAE-GIAT model is inspected against an Arabic corpus dataset and the results are measured under distinct *** comparison study reported the enhanced performance of the OMSDAE-GIAT model over other recent approaches.

关键词： Arabic twitter gender identification bat algorithm hybrid deep learning social media arabic corpus

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Towards Task-Free Privacy-Preserving Data Collection

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China Communications 2022年第7期19卷 310-323页

作者： Zhibo Wang Wei Yuan Xiaoyi Pang Jingxin Li Huajie Shao School of Cyber Science and Engineering Wuhan University430072 China School of Cyber Science and Technology Zhejiang University310027 China State Key Laboratory of Integrated Services Networks Xidian University710071 China Department of Computer Science College of William and MaryWilliamsburgVA23186 USA

With the rapid developments of Internet of Things(IoT)and proliferation of embedded devices,large volume of personal data are collected,which however,might carry massive private information about attributes that users do not want to *** privacy-preserving methods have been proposed to prevent privacy leakage by perturbing raw data or extracting task-oriented features at local ***,they would suffer from significant privacy leakage and accuracy drop when applied to other tasks as they are designed and optimized for predefined *** this paper,we propose a novel task-free privacy-preserving data collection method via adversarial representation learning,called TF-ARL,to protect private attributes specified by users while maintaining data utility for unknown downstream *** this end,we first propose a privacy adversarial learning mechanism(PAL)to protect private attributes by optimizing the feature extractor to maximize the adversary’s prediction uncertainty on private attributes,and then design a conditional decoding mechanism(ConDec)to maintain data utility for downstream tasks by minimizing the conditional reconstruction error from the sanitized *** the joint learning of PAL and ConDec,we can learn a privacy-aware feature extractor where the sanitized features maintain the discriminative information except *** experimental results on real-world datasets demonstrate the effectiveness of TF-ARL.

关键词： IoT privacy protection data collection adversarial learning deep learning

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Enhancing Network Design through Statistical Evaluation of MANET Routing Protocols

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computers, Materials & Continua 2024年第7期80卷 319-339页

作者： Ibrahim Alameri Tawfik Al-Hadhrami Anjum Nazir Abdulsamad Ebrahim Yahya Atef Gharbi Faculty of Medical Sciences Jabir ibn Hayyan Medical UniversityAlkufa QuNajaf54001Iraq Computer Science Department School of Science and TechnologyNottingham Trent UniversityNottinghamNG118NSUK Department of Computing Faculty of Engineering Sciences&Technology(FEST)Hamdard UniversityKarachi74600Pakistan Department of Information Technology College of Computing and Information TechnologyNorthern Border UniversityArar91431Saudi Arabia Department of Information System College of Computing and Information TechnologyNorthern Border UniversityArar91431Saudi Arabia

This paper contributes a sophisticated statistical method for the assessment of performance in routing protocols salient Mobile Ad Hoc Network(MANET)routing protocols:Destination Sequenced Distance Vector(DSDV),Ad hoc On-Demand Distance Vector(AODV),Dynamic Source Routing(DSR),and Zone Routing Protocol(ZRP).In this paper,the evaluation will be carried out using complete sets of statistical tests such as Kruskal-Wallis,Mann-Whitney,and *** articulates a systematic evaluation of how the performance of the previous protocols varies with the number of nodes and the mobility *** study is premised upon the Quality of Service(QoS)metrics of throughput,packet delivery ratio,and end-to-end delay to gain an adequate understanding of the operational efficiency of each protocol under different network *** findings explained significant differences in the performance of different routing protocols;as a result,decisions for the selection and optimization of routing protocols can be taken effectively according to different network *** paper is a step forward in the general understanding of the routing dynamics of MANETs and contributes significantly to the strategic deployment of robust and efficient network infrastructures.

关键词： Routing protocols statistical approach friedman MANET

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A Way of Making Smart Health Through Collaborating Machine Learning with the Bibliometrics 4

A Way of Making Smart Health Through Collaborating Machine L...

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

作者： Vignesh, T. Sruthi, P.S. Mohanraj, T. Kumar, Gaurav Varkale, Akshay Sheela, M.Sahaya Prince Shri Venkateshwara Padmavathy Engineering College Department of Computer Science and Engineering TN India Nehru Institute of Technology Department of Computer Science and Business System Tamil Nadu T.M. Palayam India Karpagam Academy of Higher Education Department of Computer Science Engineering India Chitkara University Chitkara Centre for Research and Development Himachal Pradesh India M.P Bhopal India Vel Tech Rangarajan Dr. Sagunthala R&d Institute of Science and Technology Department of Electronics and Communication Engineering Tamil Nadu Chennai India

ISBN: (纸本)9798350360165

It datasets are widely utilized, shared, and repurposed without providing enough context for understanding the decision-making processes that led to their creation. It is necessary to modify system creation and use procedures to handle the concrete effects of how knowledge for model building is envisioned and used in real-world applications as AI systems assume more and more important roles. Documents are created at every phase of the data generation lifecycle to improve communication, aid in decision-making, and highlight the need of careful data work. By putting out this paradigm, we want to shed light on the choices and duties that are often disregarded while creating datasets, which is a crucial first step in bridging the duty of care gap in AI. This framework provides an essential and important resource for guaranteeing ethical AI activities, and it is in line with current developments in auditing procedures. © 2024 IEEE.

关键词： Adversarial machine learning

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START: Automatic Sleep Staging with Attention-based Cross-modal Learning Transformer

START: Automatic Sleep Staging with Attention-based Cross-mo...

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2023 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2023

作者： Sun, Jingpeng Wang, Rongxiao Zhao, Gangming Chen, Chen Qu, Yixiao Li, Jinpeng Hu, Xiyuan Yu, Yizhou Nanjing University of Science and Technology School of Computer Science and Engineering China Chinese Academy of Sciences Institute of Automation China Xinjiang University College of Mathematics and System Science China The University of Hong Kong Department of Computer Science Hong Kong University of Chinese Academy of Sciences Ningbo Institute of Life and Health Industry China

ISBN: (纸本)9798350337488

Automatic sleep staging is vital to scale up sleep assessment and diagnosis to serve millions experiencing sleep deprivation and disorders and enable longitudinal sleep monitoring in home environments. However, how to learn from multi-channel raw physiological signal inputs (e.g., EEG and EOG) to capture the sleep stage and physiological signal relations remains a big challenge. In this paper, we propose a sleep staging model, named Sleep Staging Cross-modal Transformer (START), which is a transformer-only method for sleep stage classification. Our model is capable of learning a joint representation from both EEG and EOG signals by using a cross-modal fusion strategy. Experimental results show that our model outperforms the state-of-the-art methods on two public datasets. Furthermore, our model provides considerable reductions in parameters and training time compared to previous methods. © 2023 IEEE.

关键词： Attention Mechanism Cross-modal Network Sleep Staging Transformer

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Indian Supreme Court Judgement Dataset for Prediction Models

Indian Supreme Court Judgement Dataset for Prediction Models

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Intelligent Computing and Control for Engineering and Business systems (ICCEBS)

作者： N. Sivaranjani Karthik R Jaya Sancith Siva P Department of Computer Science and Business System Sri Sairam Engineering College

The development of legal records requires extra time and their absurd length raises the need for programmed legal record handling frameworks. One of the handling steps is to recognize the essence of the reports expressions for legitimate case records. A computerized framework that could help a legal professional in anticipating the result of a case would help facilitate the legal interaction. For such a framework to be essentially valuable, expectations by the framework ought to be reasonable. To advance exploration in growing such a framework, this paper presents the Indian Legal Judgment Dataset (ILJD). ILJD is a huge corpus of 35k Indian Supreme Legal disputes commented on with unique court choices. A part of the corpus (a different test set) is commented on with best quality level clarifications by lawful specialists.

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Crafting Compelling Argumentative Writing for Undergraduates: Exploring the Nexus of Digital Annotations, Conversational Agents, and Collaborative Concept Maps

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Journal of Educational Computing Research 2024年第5期62卷 1327-1357页

作者： Sandra, Randi Proska Hwang, Wu-Yuin Zafirah, Afifah Hariyanti, Uun Engkizar, Engkizar Hadi, Ahmaddul Fauzan, Ahmad Universitas Negeri Padang West Sumatra Indonesia Graduate Institute of Network Learning Technology National Central University Taoyuan Taiwan College of Science and Engineering National Dong Hwa University Hualien Taiwan Department of Information System Faculty of Computer Science Universitas Brawijaya East Java Indonesia

Argumentative writing is a fundamental aspect of undergraduate students’ academic and scientific writing related to critical thinking and problem-solving skills. However, previous studies have shown that students face various difficulties with argumentative writing, such as unclear and illogical ideas, less-structured arguments, and unbalanced interpretation of issues, data, and evidence. This study aims to improve the argumentative writing skills of undergraduate students by integrating computer-supported argumentative writing tools, such as annotation, conversational agents (CAs), and collaborative concept maps, into an online learning management system. Since the study was conducted during the COVID-19 pandemic, these tools can support meaningful learning activities and investigation in argumentative writing. The researchers divided sixty participants into the experimental group (N = 30) and the control group (N = 30). The results showed that the experimental group’s writing achievements outperformed the control group, and the three tools effectively improved the five elements of argumentative writing, including claims, grounds, warrants, backings, and rebuttal. Furthermore, a deep analysis found that the number of annotations, valid CAs’ responses, and argument nodes on collaborative concept maps can significantly predict students’ argumentative writing development. Moreover, students perceived that the incorporated tools could effectively improve their argumentative writing skills. © The Author(s) 2024.

关键词： argumentative writing collaborative concept map conversational agents digital annotation undergraduate students

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Robust Spectral Efficiency Maximization in 5G networks Using Non-Linear Stochastic Precoding 2

Robust Spectral Efficiency Maximization in 5G Networks Using...

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2nd International Conference on Disruptive Technologies, ICDT 2024

作者： Ponnaian, Geetha Jayanthi, N. Sarasu, R. Sajitha, L.P. Ramalakshmi, R. Manimegalai, L. Department of Computer Science and Engineering Thandalam Chennai India Pallavaram Tamil Nadu Chennai600117 India Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences India R. M. K Engineering College Department of Computer Science and Business System Tamilnadu Kavaraipettai India Ramco Institute of Technology Department of Electronics and Communication Engineering Rajapalayam India R.M.K. College of Engineering and Technology Department of Electronics and Communication Engineering Chennai India

ISBN: (纸本)9798350371055

This research focuses on enhancing the spectral efficiency of 5G networks through the utilization of nonlinear stochastic precoding techniques. The proposed approach aims to mitigate the detrimental effects of channel impairments and noise by employing advanced precoding schemes that exploit the statistical properties of the wireless channel. By incorporating nonlinear precoding algorithms and stochastic optimization methods, the system can dynamically adapt to varying channel conditions, resulting in improved data transmission rates and overall spectral efficiency. Through extensive simulations and analysis, the effectiveness of the proposed method is demonstrated, showcasing its potential to significantly enhance the performance of 5G networks in real-world scenarios. © 2024 IEEE.

关键词： 5G networks channel impairments nonlinear stochastic precoding spectral efficiency stochastic optimization

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An Efficient Intrusion Detection Framework in Software-Defined Networking for Cybersecurity Applications

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computers, Materials & Continua 2022年第8期72卷 3529-3548页

作者： Ghalib H.Alshammri Amani K.Samha Ezz El-Din Hemdan Mohammed Amoon Walid El-Shafai Department of Computer Science Community CollegeKing Saud UniversityRiyadh28095Saudi Arabia Deanship of Scientific Research Saudi Electronic UniversityRiyadhSaudi Arabia Management Information System Department College of Business AdministrationKing Saud UniversityRiyadh28095Saudi Arabia Department of Computer Science and Engineering Faculty of Electronic EngineeringMenoufia UniversityMenouf32952Egypt Security Engineering Lab Computer Science DepartmentPrince Sultan UniversityRiyadh11586Saudi Arabia Electronics and Electrical Communications Eng.Depart. Faculty of Electronic EngineeringMenoufia UniversityMenouf32952Egypt

Network management and multimedia data mining techniques have a great interest in analyzing and improving the network traffic *** recent times,the most complex task in Software Defined Network(SDN)is security,which is based on a centralized,programmable ***,monitoring network traffic is significant for identifying and revealing intrusion abnormalities in the SDN ***,this paper provides an extensive analysis and investigation of the NSL-KDD dataset using five different clustering algorithms:K-means,Farthest First,Canopy,Density-based algorithm,and Exception-maximization(EM),using the Waikato Environment for Knowledge Analysis(WEKA)software to compare extensively between these five ***,this paper presents an SDN-based intrusion detection system using a deep learning(DL)model with the KDD(Knowledge Discovery in Databases)***,the utilized dataset is clustered into normal and four major attack categories via the clustering ***,a deep learning method is projected for building an efficient SDN-based intrusion detection *** results provide a comprehensive analysis and a flawless reasonable study of different kinds of attacks incorporated in the KDD ***,the outcomes reveal that the proposed deep learning method provides efficient intrusion detection performance compared to existing *** example,the proposed method achieves a detection accuracy of 94.21%for the examined dataset.

关键词： Deep neural network DL WEKA network traffic intrusion and anomaly detection SDN clustering and classification KDD dataset

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Machine learning algorithms in Bigdata Analysis and its applications: A review 4

Machine learning algorithms in Bigdata Analysis and its appl...

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4th International Congress on Human-computer Interaction, Optimization and Robotic Applications, HORA 2022

作者： Khoshaba, Farah Kareem, Shahab Awla, Hoshang Mohammed, Chnar Erbil Polytechnic University Technical Engineering College Information System Engineering Department Erbil Iraq Lebanese French University College of Engineering and Computer Science Dep. of Information Technology Kurdistan Erbil Iraq Soran University College of Science Department of Computer Science Erbil Iraq

ISBN: (纸本)9781665468350

A wide range of disparate variety of heterogeneous and even disparate data sources has been integrated into the computer science research principles through the assistance of Artificial Intelligence (AI) and Machine Learning (ML) delivering excellent results in accuracy and data quality. Even so, taking machine learning approaches to very broad and dynamic datasets is both logistically and computationally costly. Some people are generating so much data regularly, which means that advanced analytics platforms have greater importance than ever before. Spark Machine Learning has extensive capabilities for a variety of ML activities such as regression, grouping, dimension reduction, and extraction of rules. While it has numerous datasets of high quality and consistency of structure. The specification of the MapReduce programming model was intended for the massive density of large-volume computation. It accomplishes this by splitting the job into several well-defined sub-sized units. There are various sets of data mining frameworks that link to a distributed device. Hadoop serves as the infrastructure for the distributed system. Several resources claim that Mahout will scale to handle massive datasets. This article discusses and demonstrates the use of algorithms and their significant impact on Bigdata, as well as a comparison between their implementations among Apache Spark and Apache Mahout. © 2022 IEEE.

关键词： Big data

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