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25th International Arab Conference on Information technology, ACIT 2024

作者： Asassfeh, Mahmoud Al-Mousa, Mohammad Rasmi Al-Dweikat, Hasan Al-Mashagbeh, Malak Hamad Afaneh, Suha Samara, Ghassan Zaid, Ali Abu Khouj, Mohammed Al Daoud, Essam Zarqa University Department of Cybersecurity Zarqa Jordan Applied Science Private University Cybersecurity and Cloud Computing Department Amman Jordan Zarqa University Department of Computer Science Zarqa Jordan University of Business and Technology Department of Electrical Engineering Jeddah21432 Saudi Arabia Zarqa University Faculty of Information Technology Zarqa Jordan

ISBN: (纸本)9798331540012

Network forensics pertains to a subfield of digital forensics that deals with network security. It is utilized in conjunction with computer network traffic monitoring and analysis, which acts as an intrusion detection system and firewall and facilitates the collection of data and digital proof for network security. This platform is designed to analyze and monitor data in a real-time network environment. It is a crucial instrument for identifying and resolving instances of real-time network misuse. This research shows the differences between network forensics and digital forensics as well as the tools and techniques used in network forensics. It also discusses building forensics platforms, developing network analysis methodologies, and talking about the future of network forensics. © 2024 IEEE.

关键词： Network intrusion

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SELECTED ASPECTS OF EXPERIMENTAL-NUMERICAL RESEARCH OF A HYBRID REMOTELY CONTROLLED MULTI-TASKING ROBOT FOR CONSTRUCTION WORK 38

SELECTED ASPECTS OF EXPERIMENTAL-NUMERICAL RESEARCH OF A HYB...

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38th Danubia-Adria Symposium on Advances in Experimental Mechanics, DAS 2022

作者： Derlukiewicz, Damian Andruszko, Jakub Department of Machine and Vehicle Design and Research Faculty of Mechanical Engineering Wroclaw University of Science and Technology Wybrzeże Wyspiańskiego 27 Wroclaw Poland

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Detecting Parkinson's Disease using High-Dimensional Feature-Based Support Vector Regression

Detecting Parkinson's Disease using High-Dimensional Feature...

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2024 International Conference on Intelligent Algorithms for Computational Intelligence Systems, IACIS 2024

作者： Aarthi, E. Jagadeesh, S. Lingineni, Sree Lakshmi Dharani, N.P. Pushparaj, T. Satyanarayana, A.N. Srm Institute of Science and Technology Faculty of Science and Humanities Department of Computer Science Kattankulathur India Saveetha University Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Department of Cse Chennai India R&d Institute of Science and Technology Vel Tech Rangarajan Dr Sagunthala Department of Mathematics Chennai India Department of Ece Tirupati India The American College Department of Information Technology Madurai India Sreenidhi Institute of Science and Technology Department of Ece Hyderabad India

ISBN: (纸本)9798350360660

Parkinson's disease (PD) is a progressive neurological disorder that significantly impacts patients' quality of life. Accurate and early detection of PD is crucial for effective management and treatment. A study proposes a High-Dimensional Feature-Based Support Vector Regression (HDFSVR) method for enhancing the accuracy of PD detection. This method integrates advanced normalization and feature selection techniques to improve diagnostic accuracy. Sensitivity analysis shows a prediction accuracy of 83%, while precision analysis indicates an accuracy of 87%. Recall analysis further highlights the method's efficacy with a prediction accuracy of 92.78%. The HDFSVR method achieves an accuracy of 94.78%, outperforming contemporary approaches such as Convolutional Neural Networks (CNN), Long Short-Term Memory (LSTM), and Adaptive Genetic Weighted Optimizer (AGWO). It represents a substantial advancement in PD diagnostics, addressing the limitations of traditional support vector machines in handling high-dimensional data. The method can be seamlessly adopted in clinical settings, enhancing early and accurate detection of Parkinson's disease. © 2024 IEEE.

关键词： Support vector regression

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Customer Segment Classification Prediction in the Australian Retail Based on Machine Learning Algorithms 4

Customer Segment Classification Prediction in the Australian...

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4th International Conference on Machine Learning and computer Application, ICMLCA 2023

作者： Luo, Yiming Zhang, Rui Wang, Fei Wei, Tao Business Schoo The University of Sydney Sydney Australia Computer and Information Sciences Program Harrisburg University of Science and Technology Harrisburg United States Faculty of Science and Technology Beijing Normal University-Hong Kong Baptist University United International College Zhuhai China School of Chemical and Environmental Engineering Sichuan University of Science and Engineering Zigong China

ISBN: (纸本)9798400709449

Customer segmentation is an essential area of business analytics today. Accurate customer segmentation is access to improves the efficiency of marketing campaigns and customer satisfaction. This study employs multiple machine learning methods to classify Australian Retail company BIGW's customer segments and first to apply multiple model explanation methods to find insights related to customer segmentation identification. After rigorous comparison and hyperparameter fine-tuning, XGBoost is the most adept for this dataset. We derive three key insights through the model results and model interpretive methods. First, BIGW's primary clientele comprises young families in urban areas who prefer cost-effective products, establishing the foundation of their consumer base. Second, the model result indicates a notable gap in BIGW's understanding of its high-end customers, suggesting an area for immediate attention. Third, a specific customer segment emerges from the data: individuals favoring online shopping, demonstrating high total expenditure but low interest in promotions, representing a high-value segment. © 2023 ACM.

关键词： Customer satisfaction

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Eco-Detect: A Sustainable Solution to Advanced Waste Sorting using AI and IoT

Eco-Detect: A Sustainable Solution to Advanced Waste Sorting...

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Ubiquitous Computing and Intelligent Information Systems (ICUIS), International Conference on

作者： Nandini Prasad K S Manjula G Aishwarya K J Chetankumar K N Bhanu Prakash V Sai Chandan Department of Information Science and Engineering Dayananda Sagar Academy of Technology and Management Department of Computer Science and Design Dayananda Sagar Academy of Technology and Management Department of Information Science and Engineering Dayananda Sagar Academy of Technology and Management Bangalore INDIA

ISBN: (数字)9798331529635

ISBN: (纸本)9798331529642

The traditional waste management system, prevalent in many regions, relies heavily on manual sorting processes carried out by human workers. These processes are often labor- intensive and time-consuming, leading to inefficiencies in waste management operations. Manual sorting methods struggle to cope with the increasing volume and complexity of waste generated, resulting in challenges in accurately categorizing and classifying different types of waste materials. This limitation not only hampers the effectiveness of waste sorting but also impedes efforts to maximize recycling rates and minimize environmental impact. In response to the limitations of traditional waste management approaches, the proposed Eco Detect Advanced Waste Sorting system presents a transformative solution. Leveraging the advancements in artificial intelligence and computer vision, this system introduces the utilization of the YOLOv7 algorithm for real-time interference. YOLOv7, renowned for its exceptional accuracy and speed in object detection, is integrated into the waste sorting process to enable rapid and precise identification and classification of diverse waste materials. By employing deep learning methodologies, the system is capable of recognizing a wide array of waste categories, including plastics, paper, glass, metals, and organic materials, with remarkable efficiency. The integration of the YOLOv7 algorithm into the proposed waste sorting system represents a significant advancement in waste management technology. By enabling swift and accurate identification of various waste types, the system facilitates the optimization of recycling practices, promotes the establishment of a circular economy, and contributes to the overall sustainability agenda. Its adaptability and scalability make it well-suited for widespread implementation, addressing the pressing need for sustainable waste management for clean energy solutions on a global scale. Ultimately, the paper envisions a future where te

关键词： Waste management Waste materials Accuracy Manuals Ubiquitous computing Real-time systems Recycling Classification algorithms Artificial intelligence Sorting

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Implementing Augmented Reality (AR) Textbooks in Primary Schools: A Systematic Analysis

Implementing Augmented Reality (AR) Textbooks in Primary Sch...

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2023 International Conference for Technological engineering and its Applications in Sustainable Development, ICTEASD 2023

作者： Ramli, Noraini Binti Bin Hashim, Mohd Ekram Alhafis Wan Mustafa, Wan Azani Md Ghani, Miharaini Almusawi, Muntather Hanafi, Hafizul Fahri Sultan Idris Educational University Faculty of Art Sustainability & Creative Industry Multimedia Creative Departmet Perak Malaysia University Malaysia Perlis Faculty of Electrical Engineering & Technology Perlis Malaysia University Sains Malaysia School of Communication Pulau Pinang Malaysia The Islamic University Faculty of Engineering Najaf Iraq Sultan Idris Educational University Faculty of Computing and Meta-Technology Department of Computer Science and Digital Technology Perak Malaysia

ISBN: (纸本)9798350336474

This evaluation examined 28 major papers to see if textbooks and augmented reality may be used as instructional technologies. This study, "Implementing Augmented Reality (AR) Textbooks in Elementary Schools: A Systematic Analysis,"examined current status of AR integration, perceived benefits, and impact on the learning environment. The "What is the current status of augmented reality (AR) integration in elementary schools?"review seeks to answer. Some schools have adopted AR textbooks, while others are just starting. Theme AR Integration Studies Today Some schools have adopted AR textbooks, while others are just starting. AR textbooks adapt to varied learning methods and match each student's needs to promote dynamic and engaging learning. Second, perceived benefits emphasise how they boost student motivation, understanding of complex concepts, and passion for learning. The study reveals how augmented reality (AR) textbooks can simplify and deepen learning on hard topics. Impact on the Learning Environment examines how AR textbooks improve classroom engagement, interactivity, and dynamicness. Augmented reality creates immersive and dynamic learning environments, making textbooks more valuable. Synthesising findings illuminates the contributions of various components of augmented reality (AR) textbooks and future research and implementation approaches. This The synthesis of findings shows how augmented reality (AR) textbooks can improve educational technology through the current status of AR integration, perceived benefits, and impact on the learning environment. This study teaches educators, researchers, and policymakers about the potential of textbook material and augmented reality to improve education by answering research questions. © 2023 IEEE.

关键词： Textbooks

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Positive and unlabeled learning with controlled probability boundary fence 24

Positive and unlabeled learning with controlled probability ...

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Proceedings of the 41st International Conference on Machine Learning

作者： Changchun Li Yuanchao Dai Lei Feng Ximing Li Bing Wang Jihong Ouyang College of Computer Science and Technology and Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education Jilin University China Information Systems Technology and Design Pillar Singapore University of Technology and Design Singapore

Positive and Unlabeled (PU) learning refers to a special case of binary classification, and technically, it aims to induce a binary classifier from a few labeled positive training instances and loads of unlabeled instances. In this paper, we derive a theorem indicating that the probability boundary of the asymmetric disambiguation-free expected risk of PU learning is controlled by its asymmetric penalty, and we further empirically evaluated this theorem. Inspired by the theorem and its empirical evaluations, we propose an easy-to-implement two-stage PU learning method, namely Positive and Unlabeled Learning with Controlled Probability Boundary Fence (PUL-CPBF). In the first stage, we train a set of weak binary classifiers concerning different probability boundaries by minimizing the asymmetric disambiguation-free empirical risks with specific asymmetric penalty values. We can interpret these induced weak binary classifiers as a probability boundary fence. For each unlabeled instance, we can use the predictions to locate its class posterior probability and generate a stochastic label. In the second stage, we train a strong binary classifier over labeled positive training instances and all unlabeled instances with stochastic labels in a self-training manner. Extensive empirical results demonstrate that PUL-CPBF can achieve competitive performance compared with the existing PU learning baselines.

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A Drones Optimal Path Planning Based on Swarm Intelligence Algorithms

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computers, Materials & Continua 2022年第7期72卷 365-380页

作者： Mahmoud Ragab Ali Altalbe Abdullah Saad Al-Malaise ALGhamdi SAbdel-khalek Rashid A.Saeed Information Technology Department Faculty of Computing and Information TechnologyKing Abdulaziz UniversityJeddah 21589Saudi Arabia Center of Artificial Intelligence for Precision Medicines King Abdulaziz UniversityJeddah 21589Saudi Arabia Information Systems Department Faculty of Computing and Information TechnologyKing Abdulaziz UniversityJeddah 21589Saudi Arabia Department of Mathematics Faculty of ScienceAl-Azhar UniversityNaser City 11884CairoEgypt Department of Mathematics Faculty of ScienceTaif UniversityTaifSaudi Arabia Department of Mathematics Faculty of ScienceSohag UniversitySohagEgypt Department of Computer Engineering College of Computers and Information TechnologyTaif UniversityTaifSaudi Arabia

The smart city comprises various interlinked elements which communicate data and offers urban life to *** Aerial Vehicles(UAV)or drones were commonly employed in different application areas like agriculture,logistics,and *** improving the drone flying safety and quality of services,a significant solution is for designing the Internet of Drones(IoD)where the drones are utilized to gather data and people communicate to the drones of a specific flying region using the mobile devices is for constructing the Internet-of-Drones,where the drones were utilized for collecting the data,and communicate with *** addition,the SIRSS-CIoD technique derives a tuna swarm algorithm-based clustering(TSA-C)technique to choose cluster heads(CHs)and organize clusters in IoV ***,the SIRSS-CIoD technique involves the design of a biogeography-based optimization(BBO)technique to an optimum route selection(RS)*** design of clustering and routing techniques for IoD networks in smart cities shows the novelty of the study.A wide range of experimental analyses is carried out and the comparative study highlighted the improved performance of the SIRSS-CIoD technique over the other approaches.

关键词： Drones smart city swarm intelligence route selection internet of drones networking

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Seam Strength Prediction for Different Stitch Types Considering Stitch Density of Cotton Woven Fabrics

Textile and Leather Review

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Textile and Leather Review 2022年 5卷 53-69页

作者： Islam, Md. Mazharul Hossain, Md. Tanjim Repon, Md. Reazuddin Islam, Tarikul Kibria, Golam Jalil, Mohammad Abdul Department of Textile Engineering Northern University of Bangladesh Dhaka Bangladesh ZR Research Institute for Advanced Materials Sherpur Bangladesh Department of Textile Engineering Khwaja Yunus Ali University Sirajganj Bangladesh Department of Production Engineering Faculty of Mechanical Engineering and Design Kaunas University of Technology Kaunas Lithuania Department of Textile Engineering Jashore University of Science and Technology Jashore Bangladesh Department of Apparel Manufacturing and Technology BGMEA University of Fashion and Technology Dhaka Bangladesh Department of Textile Engineering Khulna University of Engineering & Technology Khulna Bangladesh

Seam strength is critical in achieving the proper and adequate quality seam, which eventually determines the overall quality of any garment. The target of this research was to investigate exactly how different stitch density affected seam strength for different stitch types. Commercially available, 100% cotton plain weave structured woven fabric with mass per unit area of 270 g/m2was used in this analysis. Two different stitch types, such as lock stitch (ISO#301) and chain stitch (ISO#401), along with four stitch densities like 8, 10, 12 and 14 per inch were utilized. The seam strength of the prepared samples was determined using a tensile strength tester followed by the ASTM D1683 standard procedure. The impact of independent variables on superimposed seam strength was statistically analyzed using a regression correlation with the help of SPSS software to construct a regression equation to predict seam strength before the manufacturing process. The study found a non-linear relationship between seam strength and stitch density. When the stitch density is too low, the seam strength is shown low as well, because the cloth cannot be held under tensile load. In a very compact woven fabric, structural jamming or intrinsic puckering occurs if it is too high. As a result, the best stitch density should be chosen to achieve the best seam strength. This study will help the manufacturers choose a suitable SPI for lock and chain stitches from polynomial regression equations before the sewing production of cotton woven fabric, which was not predicted by any researcher before. Therefore, the optimum stitch density can be chosen using regression equations to get the best result for seam strength. © 2022 The authors.

关键词： Weaving

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OMNI- CHANNEL RETAILING FOR ENHANCING CUSTOMER ENGAGEMENT AMIDST SUPPLY CHAIN DISRUPTION: AN EMERGING MARKET PERSPECTIVE

Decision Making: Applications in Management and Engineering

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Decision Making: Applications in Management and engineering 2023年第1期6卷 449-473页

作者： Joshi, Sudhanshu Sharma, Manu Chatterjee, Prasanjit Operations and Supply Chain Management Research Laboratory School of Management Doon University Dehradun India School of Computer Science Faculty of Engineering & Information Technology University of Technology Sydney Sydney Australia Department of Management Studies Graphic Era Deemed to be University Dehradun India Guidhall School of Business and Law London Metropolitan University London United Kingdom MCKV Institute of Engineering West Bangal India

The research aims to explore the strength of enablers and adoption barriers in omnichannel retailing (OCR) and discuss how organizations may focus on redesigning their business models in emerging markets to manage the disruptive environment. The major enablers may enhance the omnichannel' performance to deliver a unified experience across all channels during the pandemic. The paper has used hybrid Multi-Criteria Decision-Making (MCDM) Methods. Organizations widely use these methods to explore the interrelationship among barriers and enablers affecting their performance. In the current study, 18 experts from different domains have examined and evaluated the 10 barriers and 7 enablers. The study reveals that integration, Visibility, internet accessibility, and advanced distribution centers are the primary enablers and driving the customer analytics enabler to strengthen their customer engagement and providing a unified experience to the. During the pandemic time the usage of the online channels have increased and thus retail channels may consider these enablers to enhance the unified experience level of the customers. The study also shows that inconsistency in price is the main adoption barrier followed by inconsistency in product discounts that should be minimized to engage customers effectively. The retail organizations need to understand the roadblocks in adopting OCR and should take relevant actions to minimize them. The retail organization or marketers may redesign their existing strategies based on price consistency, integration, Visibility, information systems, and coordination to develop a unified experience across channels during the pandemic situation. © 2023 by the authors.

关键词： Visibility

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