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Biometric Verification System UsingHyperparameter Tuned Deep Learning Model

computer systems Science & engineering 2023年第7期46卷 321-336页

作者： Mohammad Yamin Saleh Bajaba Sarah B.Basahel E.Laxmi Lydia Department of Management Information Systems Faculty of Economics and AdministrationKing Abdulaziz UniversityJeddah21589Saudi Arabia Department of Business Administration Faculty of Economics and AdministrationKing Abdulaziz UniversityJeddah21589Saudi Arabia Department of Management Information Systems Faculty of Economics and AdministrationKing Abdulaziz UniversityJeddahSaudi Arabia Department of Computer Science and Engineering Vignan’s Institute of Information TechnologyVisakhapatnam530049India

Deep learning(DL)models have been useful in many computer vision,speech recognition,and natural language processing tasks in recent *** models seem a natural fit to handle the rising number of biometric recognition problems,from cellphone authentication to airport security *** approaches have recently been utilized to improve the efficiency of various biometric recognition *** recognition was considered the more reliable and accurate biometric detection method *** recognition has been an active research region in the last few decades due to its extensive applications,from security in airports to homeland security border *** article presents a new Political Optimizer with Deep Transfer Learning Enabled Biometric Iris Recognition(PODTL-BIR)*** presented PODTL-BIR technique recognizes the iris for biometric *** the presented PODTL-BIR model,an initial stage of pre-processing is carried *** addition,the MobileNetv2 feature extractor is utilized to produce a collection of feature *** PODTL-BIR technique utilizes a bidirectional gated recurrent unit(BiGRU)model to recognise iris for biometric ***,the political optimizer(PO)algorithm is used as a hyperparameter tuning strategy to improve the PODTL-BIR technique’s recognition ***-ranging experimental investigation was executed to validate the enhanced performance of the PODTL-BIR *** experimental outcome stated the promising performance of the PODTL-BIR system over other existing algorithms.

关键词： Biometric verification iris recognition political optimizer deep learning feature extraction

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Toward Drought Modeling in South Asia: Machine Learning Approaches, Challenges, and Opportunities

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IEEE Access 2025年 13卷 87654-87671页

作者： Reshan, Mana Saleh Al Raza, Muhammad Owais Mahoto, Naeem Ahmed Rajab, Adel Shaikh, Asadullah Elmagzoub, Mohamed A. Rajab, Khairan D. Najran University College of Computer Science and Information Systems Department of Information Systems Najran61441 Saudi Arabia College of Computer Science and Information Systems Najran61441 Saudi Arabia Istanbul Sabahattin Zaim University Department of Computer Engineering İstanbul34303 Turkey Mehran University of Engineering and Technology Department of Software Engineering Sindh Jamshoro76062 Pakistan Najran University College of Computer Science and Information Systems Department of Computer Science Najran61441 Saudi Arabia Najran University College of Computer Science and Information Systems Department of Network and Communication Engineering Najran61441 Saudi Arabia

Drought is an environmental and economic problem. Sustainable ecosystems, water resources, food security, and ecosystem sustainability. Machine all are severely affected by drought. Due to the increasing frequency and severity of droughts caused by climate change. Effective drought modeling is crucial for early warning systems and risk mitigation. Recent advances in machine learning (ML) and deep learning (DL) techniques have been developed as potential drought modeling tools, which offer accurate and reliable drought detection. This review paper summarizes the drought modeling(Drought Prediction, Drought Detection and Drought Forecasting) approaches. This paper focuses on three main aspect. 1) The selection of the region for this study, for this study South Asia(SA) is selected as region of interest (ROI) that offer accurate drought modeling, providing policymakers and decision-makers with insightful information. The geographical scope of this study is the region of South Asia. This region is selected because of its heavy reliance on agriculture. 2) This paper focuses on the current and future trends, challenges, and advances of and vulnerability to droughts. The review offers a thorough grasp of how drought conditions are evaluated by gathering and analyzing the most important drought indicators and metrics specific to South Asia. The paper explores the current state-of-the-art in ML and DL for drought modeling. 3) This review encapsulates the indicator and metrics (Complex Machine learning and deep learning models) for drought modeling which are most relevant to the SA region. This study sum up as most common challenges in drought modeling are, highlighting current challenges such as incomplete and inconsistent datasets, lack of explainable and interpretable models, and unavailability of data for model uncertainty analysis. This study proposes that these problems can be solved with modern machine learning techniques such as explainable machine learning and federa

关键词： Federated learning

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Optimizing Secure Multi-User ISAC systems With STAR-RIS: A Deep Reinforcement Learning Approach for 6G Networks

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IEEE Access 2025年 13卷 31472-31484页

作者： Kamal, Mian Muhammad Zain Ul Abideen, Syed Al-Khasawneh, M.A. Alabrah, Amerah Sohail Ahmed Larik, Raja Irfan Marwat, Muhammad Southeast University School of Electronic Science and Engineering Jiangning Jiangsu Nanjing211189 China Qingdao University College of Computer Science and Technology Qingdao266071 China Al-Ahliyya Amman University Hourani Center for Applied Scientific Research Amman19111 Jordan Skyline University College School of Computing University City Sharjah Sharjah United Arab Emirates King Saud University College of Computer and Information Science Department of Information Systems Riyadh11543 Saudi Arabia Ilma University Department of Computer Science Sindh Karachi75190 Pakistan University of Science and Technology Bannu Department of Software Engineering Bannu28100 Pakistan

The rapid evolution of wireless communication technologies and the increasing demand for multi-functional systems have led to the emergence of integrated sensing and communication (ISAC) as a key enabler for future 6G networks. Simultaneously transmitting and reflecting reconfigurable intelligent surfaces (STAR-RISs) have recently garnered significant attention for their ability to enhance signal coverage and improve system efficiency. This paper investigates a STAR-RIS-assisted ISAC system designed to secure communication for multiple legitimate users (LUs) while safeguarding against multiple eavesdroppers (Eves). By jointly optimizing the base station (BS) transmit beamforming, STAR-RIS transmission and reflection coefficients, and receive filters, the proposed framework aims to maximize the long-term average secrecy rate for all LUs. Constraints are imposed to ensure minimum echo signal-to-noise ratios (SNRs) for sensing and meet the achievable rate requirements of LUs. To address the inherent complexity of this non-convex problem, two deep reinforcement learning (DRL) algorithms are proposed. Numerical results demonstrate that the proposed system achieves significant improvements in secrecy rate compared to conventional RIS setups. This work provides a scalable and efficient approach for secure multi-user ISAC systems, making it highly relevant for future 6G networks, smart cities, and IoT applications. © 2025 The Authors.

关键词： Deep reinforcement learning

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Comprehensive Functional ETL Testing Methodologies for Real-World Data 24

Comprehensive Functional ETL Testing Methodologies for Real-...

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24th IEEE International Conference on Software Quality, Reliability and Security Companion, QRS-C 2024

作者： Homayouni, Hajar Pourebadi, Maryam Mary Nguyen, Steven T. Hashemi, Mohammad Shirazi, Hossein Department of Computer Sciencc San Diego State University United States University of California Department of Computer Science San Diego United States Hamedan University of Technology Department of Computer Engineering Iran Management Information Systems San Diego State University United States

ISBN: (纸本)9798350365658

In today's data-driven enterprises, data warehouses are crucial for aggregating diverse datasets for analysis and research. The ETL (Extract, Transform, Load) process is central to this, involving data extraction, integration, cleansing, and loading into the warehouse. Challenges in ETL can cause inaccuracies, diminishing the value of high-quality source data. Robust ETL testing methodologies are essential to ensure correct implementation and efficient execution. This paper surveys existing functional ETL testing approaches, categorizing them into data quality and balancing tests. Data quality tests identify issues like missing values and outliers in the target data, while balancing tests verify data consistency between source and target. The research highlights unresolved challenges in completeness and scalability, proposes future research directions, and includes an empirical study of ETL testing on the Our World in Data warehouse, identifying data disparities and abnormalities. © 2024 IEEE.

关键词： Data quality

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A Study of Cyber Bullying Classification Using Social Media and Texual Analysis Based on Machine Learning Approches 14

A Study of Cyber Bullying Classification Using Social Media ...

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14th International Conference on Computing Communication and Networking Technologies, ICCCNT 2023

作者： Rahman Aronno, Md. Shafiur Zumma, Md.Thoufiq Prodhan, Rashed Zohora, Fatema Tuz Sakib, Nazmus Tahmiduzzaman, K.B.M. Daffodil International University Department of Computer Science and Engineering Dhaka Bangladesh Wilmington University Department of Information Systems Technologies DE United States Bangladesh University of Business &technology Department of Computer Science and Engineering Dhaka Bangladesh

ISBN: (纸本)9798350335095

In today's world, cyberbullying is a problem that is becoming more and more common, especially among teenagers and young people. The prevalence of social networking sites and other digital communication tools has made it simpler for offenders to harass their victims in secret and without repercussions. Natural language processing (NLP) methods have been used in recent years to categorize instances of cyberbullying and assist identify them. The language used in online conversations is examined using these approaches to look for trends and signs of cyberbullying behavior. The purpose of this study is to investigate how well NLP approaches can be used to recognize and categorize cyberbullying behavior. To provide a thorough knowledge of the many types of cyberbullying, the study will use a variety of data sources, including social media posts, chat logs, and other online conversations. Overall, this research will further our knowledge of the intricate nature of cyberbullying and shed light on the potential applications of NLP approaches to lessen its negative impacts. © 2023 IEEE.

关键词： Digital communication systems

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Optimizing Satellite-Based Entanglement Distribution in Quantum Networks via Quantum-Assisted Approaches 1

Optimizing Satellite-Based Entanglement Distribution in Quan...

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1st International Conference on Quantum Communications, Networking, and Computing, QCNC 2024

作者： Wei, Xinliang Fan, Lei Guo, Yuanxiong Han, Zhu Wang, Yu Temple University Department of Computer and Information Sciences PhiladelphiaPA United States University of Houston Department of Engineering Technology HoustonTX United States University of Houston Department of Electrical and Computer Engineering HoustonTX United States University of Texas at San Antonio Department of Information Systems and Cyber Security San AntonioTX United States

ISBN: (纸本)9798350366778

Satellite-based quantum networks leverage the advantageous properties of optical signals from satellites to ground stations, to enable the distribution of high-fidelity quantum entanglements over vast distances, thus circumventing the limitations of traditional terrestrial-based systems. However, the satellite-based entanglement distribution coupled with terrestrial quantum swapping in the space-terrestrial integrated network becomes very complex when considering the joint optimization with satellite assignment, resource allocation, and path selections. To tackle this issue, we introduce a hybrid quantum-classical Dantzig-Wolfe decomposition technique by leveraging the strengths of both quantum and classical computing, to solve the joint optimization problem. Through a series of experiments, the paper demonstrates the efficiency and robustness of the proposed methods in addressing large-scale network optimization and balancing qubit usage. The insights generated by this work offer valuable guidance for the design and implementation of satellite-based entanglement distribution for quantum networks, thereby laying the groundwork for the realization of a secure quantum communication infrastructure on a global scale. © 2024 IEEE.

关键词： Quantum entanglement

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The Impact of Blockchain and AI in the Healthcare 3

The Impact of Blockchain and AI in the Healthcare

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3rd IEEE International Conference on Electrical Power and Energy systems, ICEPES 2024

作者： Thatikonda, Ramya Kempanna, M. Tatikonda, Reshmi Bhuvanesh, A. University of the Cumberlands PhD in Information Technology Williamsburg United States Bangalore Institute of Technology Department of Artificial Intelligence and Machine Learning Karnataka Bengaluru India University of the Central Missouri Masters in Computer Information Systems Warrensburg United States PSN College of Engineering and Technology Department of Electrical and Electronics Engineering Tamil Nadu Tirunelveli India

ISBN: (纸本)9798350390728

Integrating blockchain and artificial intelligence (AI) in healthcare presents a transformative opportunity. Therefore, this can revolutionize patient care by improving data management and medical research. Decentralization and invulnerability granted by blockchain technology foster improved data consistency and interoperability. AI enhances blockchain by streamlining data examination, automatic tasks, and decision-making processes, resulting in more precise medical diagnoses and tailored therapy programs. The intersection of blockchain and AI is explored, along with their potential benefits and practical applications in healthcare. The document examines the hurdles encountered when integrating innovations, including scalability, interoperability, and regulatory adherence issues. Additionally, the study investigates moral questions surrounding data ownership and patient consent, thereby protecting confidentiality and trust. The synergy between blockchain, AI, and healthcare will lead to transformative advancements. It pledges to fortify data safety and expedite information sharing. Consequently, this will result in an improved, price-effective, and patient-focused healthcare environment. Recommendations were made for additional research and practical implementation in healthcare technology. The study should concentrate on creating scalable and compatible blockchain systems. A vital factor for extensive use is tackling regulatory and moral issues. Hybrid blockchain designs and federated learning strategies will bolster information privacy and utility. © 2024 IEEE.

关键词： Data consistency

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Pretrained Model with CNN for Lung Cancer Identification and Classification 7

Pretrained Model with CNN for Lung Cancer Identification and...

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7th International Conference on Pattern Recognition and Artificial Intelligence, PRAI 2024

作者： Kanwal, Shaista Ali, Sajid Alahmadi, Tahani Jaser Assam, Muhammad Ansari, Ghulam Jillani Bhatti, Uzair Aslam University of Education Department of Information Sciences Lahore Pakistan College of Computer and Information Sciences Princess Nourah Bint Abdulrahman University Department of Information Systems P.O. Box 84428 Riyadh Saudi Arabia University of Science and Technology Department of Software Engineering KPK Bannu Pakistan School of Information and Communication Engineering Hainan University China

ISBN: (纸本)9798350350890

Lung cancer impacts both genders, and its early detection is key to lowering death rates. Current deep learning techniques for automated identification and classification of lung carcinoma face issues with interpretability, data variability handling, and broader applicability. In our research, we propose a novel method that merges a convolutional neural network (CNN) with a streamlined RESNET18 framework. This approach is designed for lung cancer detection and categorization from CT scans, involving image preprocessing like resizing and rescaling. It effectively differentiates between healthy and cancerous tissues, further classifying them as either Normal, Benign, or Malignant, including variants such as adenocarcinoma and squamous cell carcinoma. Our CNN+RESNET18 model boasts an impressive 99% accuracy, supporting radiologists in the intricate task of image analysis, reducing risks of incorrect or delayed diagnosis, and fostering prompt treatment for improved patient outcomes. © 2024 IEEE.

关键词： Lung cancer

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Requirements for Building a Business-Driven Reference Architecture for Implementation of Big Data Analytics by Public Sector Organizations: A Case Study of Uganda 1

Requirements for Building a Business-Driven Reference Archit...

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1st International Conference on the Advancements of Artificial Intelligence in African Context, AAIAC 2023

作者： Didas, Matendo Chali, Frederick Elisa, Noe Matogoro, Jabhera The University of Dodoma Department of Information Systems and Technology Dodoma Tanzania United Republic of The University of Dodoma Department of Computer Science and Engineering Dodoma Tanzania United Republic of

ISBN: (纸本)9798350330137

Big Data Analytics (BDA) technology is an emerging desire within public sector organizations. It can be applied to meet the rising global demand for informed decision-making and cost-effective and reliable public services. As a result, BDA has been put forward on the planning agendas of different public sector organizations and governments at large. However, regardless of BDA technologies being widely spread, there is a high failure rate associated with BDA implementation projects in public sector organizations. The paper determines the requirements for designing business-driven reference architecture for the implementation of BDA by public sector organizations, taking Uganda as a case study for developing countries, considering The Uganda Bureau of Statistics, the Ministry of Health, and the Ministry of Education purposively chosen. To accomplish the study's goals, the mixed research methodology (Questionnaire and Mini-literature review) was used. The study used a questionnaire and conducted a mini literature review executed under design science research methodology. The questionnaire was given to 36 information technology (IT) and Managerial (M) staff. Some of the derived requirements by the study that need public sector organizations' attention include;the component for identifying the business process/Use case first for which data is needed so that these business processes generate added business value to the public sector organization in question, need to support diversified processing and analysis (like predictive, batch-oriented, real-time, and stream, among others), need to support high-level/advanced data security measures such as time multiplexing and Key management, component to support low, medium, and high volumes of data among others. © 2023 IEEE.

关键词： Data Analytics

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Correction to: Automated explainable and interpretable framework for anomaly detection and human activity recognition in smart homes (Neural Computing and Applications, (2025), 10.1007/s00521-025-10991-3)

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Neural Computing and Applications 2025年 1-2页

作者： Kim, Tae Hoon Ojo, Stephen Krichen, Moez Alamro, Meznah A. Mihoub, Alaeddine Sampedro, Gabriel Avelino School of Information and Electronic Engineering Zhejiang University of Science and Technology No. 318 Zhejiang Hangzhou China Department of Electrical and Computer Engineering College of Engineering Anderson University AndersonSC29621 United States Al-Baha University Al-Baha65528 Saudi Arabia ReDCAD Laboratory University of Sfax Sfax3038 Tunisia Department of Information Technology College of Computer and Information Science Princess Nourah Bint Abdul Rahman University Riyadh11564 Saudi Arabia Department of Management Information Systems College of Business and Economics Qassim University P.O. Box: 6640 Buraidah51452 Saudi Arabia Department of Computer Science University of the Philippines Diliman Quezon City1101 Philippines

In this article, the author’s name Stephen Ojo was incorrectly written as Stepehn Ojo and the affiliation details for author Stephen Ojo was incorrectly given as ‘department of Electrical and computer engineering, College of engineering Anderson, Anderson University, Anderson, SC 29621, USA’ but should have been ‘department of Electrical and computer engineering, College of engineering, Anderson University, Anderson, SC 29621, USA’. In addition, the affiliation details for author Gabriel Avelino Sampedro were incorrectly given as ‘College of computer Studies, De La Salle University, 2401 Taft Ave., Malate, 1004 Manila, Philippines and Faculty of information and Communication Studies, University of the Philippines Open University, 4031 Los Baños, Philippines’ but should have been ‘department of computer Science, University of the Philippines Diliman, 1101 Quezon City, Philippines’. The original article has been corrected. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2025.

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