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1st International Conference on Computer Science and Emerging Technologies, CSET 2023

作者： Alzoubi, Sharaf Amayreh, Khalid Thaher Farea, Mazen Mohammed Baker El-Ebiary, Yousef A. Ahmad Saany, Syarilla Iryani Bisht, Neeraj Information Technology Department Jordan Irbid National University Faculty of Finance Science and Business Business Administration Department Jordan Al-Madinah International University Faculty of Finance & Administrative Sciences Malaysia UniSZA Faculty of Informatics and Computing Malaysia Birla Institute of Applied Sciences Bhimtal India

ISBN: (纸本)9798350341737

In today's highly competitive global business environment, effective supplier selection plays a crucial role in the success and sustainability of organizations. Decision Support Systems (DSS) have emerged as powerful tools to aid in this complex process by providing valuable insights and data-driven recommendations for selecting suppliers. This research paper presents a comprehensive review of the methodologies used to assess the effectiveness and efficiency of Decision Support Systems employed in supplier selection processes. The primary objective of this study is to evaluate and compare the various methodologies applied in Decision Support Systems, focusing on their effectiveness and efficiency in assisting decision-makers during supplier selection. The research critically analyzes a range of published literature, academic papers, and case studies to highlight the strengths and limitations of different DSS methodologies in this context. The paper commences by elucidating the fundamental concepts of supplier selection and the significance of Decision Support Systems in streamlining this multifaceted process. Subsequently, a systematic review of existing literature is conducted to identify the key methodologies employed in DSS frameworks. Furthermore, the research explores the novel integration of emerging technologies, such as Artificial Intelligence, Machine Learning, and Big Data analytics, within Decision Support Systems for supplier selection. This investigation aims to discern how these advanced techniques contribute to the enhancement of decision-making precision and the overall efficiency of the selection process. By critically analyzing and comparing the effectiveness and efficiency of various Decision Support System methodologies, this research seeks to provide valuable insights for both academics and practitioners in supply chain management. The findings of this study will enable decision-makers to make informed choices regarding the adoption and customiz

关键词： Decision support systems

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Churn Rate Prediction System using Logistic Regression, SVM, and Random Forest Models 2

Churn Rate Prediction System using Logistic Regression, SVM,...

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2nd IEEE Global Conference on Communications and Information Technologies, GCCIT 2024

作者： Jain, Paras Gupta, Vishan Kumar Pandey, Priyank Anupriya Ghai, Kapil Singh, Ajay Vit Bhopal University School of Computing Engineering and Artificial Intelligence Kothri Kalan MP Bhopal India Amity University Punjab Amity School of Engineering and Technology Punjab Mohali India Graphics Era Deemed to be University Department of Cse Dehradun India Graphic Era Hill University School of Computing Dehradun India Graphic Era Hill University Department of Chemistry Dehradun India Uttaranchal University School of Applied and Life Sciences Uttarakhand Dehradun248007 India

ISBN: (纸本)9798350388916

Customers are the life blood of any business, and their importance cannot be overstated. Customers are a business's primary source of revenue. Retaining and gaining new clients is crucial to profitability. Over time, repeat customers typically spend more, and gaining new customers is frequently more expensive than keeping current ones. Consequently, establishing a foundation of devoted clients might boost revenue. Without customers purchasing products or services, there would be no income to sustain the business operations or drive growth. It might have several detrimental implications, such as a decline in market share, loss of revenue, bad impact on staff morale, etc. To tackle this problem supervised learning algorithms are trained on historical data, where the input features and corresponding churn labels are known. The goal is to learn patterns from the data that can help and predict churn accurately for newly unseen data. These algorithms guarantee a high degree of accuracy when forecasting customer attrition, allowing businesses to precisely identify consumers who pose a danger. Businesses frequently use focused tactics, such as re-engagement campaigns with discounts or offers that are specifically catered to the requirements and interests of each churned consumer, to entice and re-engage these customers through individualized communication. © 2024 IEEE.

关键词： Random forests

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Improving Existing VANETs by Incorporating UAVS Using Ant Colony and PSO Algorithms

Improving Existing VANETs by Incorporating UAVS Using Ant Co...

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Data Engineering and Communication Systems (ICDECS), International Conference on

作者： S Thangam Aniket Dixit Alok Kumar Karn Arnav Thakur Cg Arunbalaji J Jesy Janet Kumari A. Saleem Raja Dept. of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Bengaluru India IT Department College of Computing and Information Sciences University of Technology and Applied Sciences-Shinas Sultanate of Oman

Vehicular Ad Hoc Networks (VANETs) are a type of wireless communication network that enable vehicles to communicate with each other and with roadside infrastructure in a peer-to-peer fashion; despite their vast applications, they come with several shortcomings. The key challenges in their real-world implementation include scalability, connectivity, and coverage issues. Unmanned Aerial Vehicles (UAVs), also known as drones, can complement existing VANETs in several ways to enhance their functionality and address some of their limitations. This paper studies the incorporation of UAVs in VANETs to overcome the challenges faced by the present networks. The approach is based on the dynamic deployment of UAVs in the most optimal positions, found by utilizing Particle Swarm Optimization (PSO) and Ant Colony algorithms which analyze the vehicle density, and previous coverage information in the network. The deployment of UAVs is intended to provide a seamless network coverage for ground vehicles. The impact of dynamic UAV mobility in communicating VANETs is comparatively studied. The simulation is done using Network Simulator-3 (NS3) simulator to evaluate the performance of 4 VANET protocols, AODV, DSR, OLSR, and DSDV, in terms of packet delivery ratio (PDR), Average End-to-End Delay, Throughput, Average Throughput per Packet, Packet Drop Rate, and Normalized Routing Load after incorporating the proposed modification designs. The paper concludes that the incorporation of Ant Colony is better suited to enhance VANETs than PSO.

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Adaptive Strategies for Scalable and Heterogeneous VANET Network

Adaptive Strategies for Scalable and Heterogeneous VANET Net...

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Intelligent Communication Technologies and Virtual Mobile Networks (ICICV), International Conference on

作者： S Thangam Priyanshu Jha Jawed Hawari Nabin Kumar Sah Nikunj Jain J Jesy Janet Kumari A.Saleem Raja Dept. of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Bengaluru India IT Department College of Computing and Information Sciences University of Technology and Applied Sciences Shinas Sultanate of Oman

Despite technological advancements, Vehicular Ad-hoc Networks (VANETs) continue to face challenges related to reliability and high-speed mobility. This study focuses on enhancing scalability, improving mobility, and addressing network heterogeneity in the context of data dissemination. Data dissemination involves the distribution or transmission of statistical data to users. Tools like NS3, SUMO utilizing OSM for realistic traffic simulations, MATLAB for Data Analysis and evaluation of various protocols like ADHOC, Dynamic Source Routing (DSR), Optimized Link State Routing Protocol (OLSR), Destination Sequenced Distance Vector Routing (DSDV). The research aimed to refine VANET simulations for effective decision making, as its primary objective data dissemination i.e. distribution or transmission of statistical data to users, forming the central focus of this investigation.

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Smart Traffic Management in Green VANETs: Ant-Inspired Routing Strategies with Local Search Optimization

Smart Traffic Management in Green VANETs: Ant-Inspired Routi...

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Recent Innovation in Smart and Sustainable technology (ICRISST), International Conference on

作者： Thangam S Jahnavi M Chandana K Dheeraj K J Jesy Janet Kumari A Saleem Raja Dept. Of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Bengaluru India IT Department College of Computing and Information Sciences University of Technology and Applied Sciences-Shinas Sultanate of Oman

ISBN: (数字)9798350309140

ISBN: (纸本)9798350309157

Green Vehicular Ad-hoc Networks (VANETs) are gaining significance in smart mobility because of growing concerns about the environment. The research introduces a novel method for managing traffic in such networks by combining ant-inspired routing methods with local search optimization approaches. The solution, which draws inspiration from the efficient path-finding abilities of ants, facilitates decentralized communication and sharing of traffic data among cars. It allows rapid adaptation to changing traffic circumstances and environmental elements. Local search optimization enhances routing decisions by considering the near vicinity of each vehicle. Extensive simulations demonstrate notable enhancements in traffic efficiency, decreased congestion, and improved energy conservation as compared to conventional routing methods. The suggested solutions also enhance the development of environmentally friendly and enduring transportation networks.

关键词： Technological innovation Costs Green products Energy conservation Vehicular ad hoc networks Transportation Routing Sustainable development Optimization Pattern matching

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Stacked Gated Recurrent Unit Classifier with CT Images for Liver Cancer Classification

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Computer Systems Science & Engineering 2023年第3期44卷 2309-2322页

作者： Mahmoud Ragab Jaber Alyami Information Technology Department Faculty of Computing and Information TechnologyKing Abdulaziz UniversityJeddah21589Saudi Arabia Centre of Artificial Intelligence for Precision Medicines King Abdulaziz UniversityJeddah21589Saudi Arabia Department of Mathematics Faculty of ScienceAl-Azhar UniversityNaser City11884CairoEgypt Department of Diagnostic Radiology Faculty of Applied Medical SciencesKing AbdulazizJeddah21589Saudi Arabia Imaging Unit King Fahd Medical Research CentreKing AbdulazizJeddah21589Saudi Arabia

Liver cancer is one of the major diseases with increased mortality in recent years,across the *** detection of liver cancer is a tedious and laborious task due to which Computer Aided Diagnosis(CAD)models have been developed to detect the presence of liver cancer accurately and classify its ***,liver cancer segmentation outcome,using medical images,is employed in the assessment of tumor volume,further treatment plans,and response ***,there is a need exists to develop automated tools for liver cancer detection in a precise *** this motivation,the current study introduces an Intelligent Artificial Intelligence with Equilibrium Optimizer based Liver cancer Classification(IAIEO-LCC)*** proposed IAIEO-LCC technique initially performs Median Filtering(MF)-based pre-processing and data augmentation ***,Kapur’s entropy-based segmentation technique is used to identify the affected regions in ***,VGG-19 based feature extractor and Equilibrium Optimizer(EO)-based hyperparameter tuning processes are also involved to derive the feature *** last,Stacked Gated Recurrent Unit(SGRU)classifier is exploited to detect and classify the liver cancer *** order to demonstrate the superiority of the proposed IAIEO-LCC technique in terms of performance,a wide range of simulations was conducted and the results were inspected under different *** comparison study results infer that the proposed IAIEO-LCC technique achieved an improved accuracy of 98.52%.

关键词： Liver cancer image segmentation artificial intelligence deep learning CT images parameter tuning

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Fabric4show: real-time vision system for fabric defect detection and post-processing

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Visual Intelligence 2024年第1期2卷 1-13页

作者： Lin, Huaizhou Cai, Dan Xu, Zengmin Wu, Jinsong Sun, Lixian Jia, Haibin School of Materials Science and Engineering Guangxi Key Laboratory of Information Guilin University of Electronic Technology Guilin China School of Mathematics and Computing Science Guangxi Colleges and Universities Key Laboratory of Data Analysis and Computation Guilin University of Electronic Technology Center for Applied Mathematics of Guangxi (GUET) Guilin China Anview.ai Guilin China School of Artificial Intelligence Guilin University of Electronic Technology Guilin China Department of Electrical Engineering University of Chile Santiago Chile

The exploration of computer vision applications for fabric defect detection has immense potential value. However, current relevant research in this area has primarily focused on detection models that aim for high detection accuracy and algorithmic efficiency, while neglecting the practical industrial production requirements. Therefore, we propose a fabric defect detection and post-processing system that integrates an optimized region with convolutional neural network (CNN) features (i.e., Faster R-CNN) for defect detection, defect localization and detection model evaluation. In addition, the proposed intelligent system incorporates novel approaches, such as a rearranged fabric dataset, anomaly detection, recommended clipping region division, and a replenishment device. This study illustrates an example of artificial intelligence (AI)-driven automated technology in fabric manufacturing. The accuracy and detection speed of different detection models under identical hardware conditions are evaluated and compared with related work. Experimental results demonstrate that the proposed approach achieves comparable performance to other models, while significantly reducing computational resource requirements. The potential efficiency of using two-stage networks on hardware systems for fabric defect detection tasks is highlighted, which is likely to have relevant implications for the textile industry. © The Author(s) 2024.

关键词： Automation Computer vision Defect detection Fabric manufacturing Faster R-CNN

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Automatic detection of ischemic-stroke-lesion with CNN segmentation: A study 8

Automatic detection of ischemic-stroke-lesion with CNN segme...

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8th International Virtual Conference on Biosignals, Images, and Instrumentation, ICBSII 2022

作者： Al Attar, Ferasnadhimhasoon Kadry, Seifedine Manic, K. Suresh Meqdad, Maytham N. Department of Electrical and Communication Engineering National University of Science and Technology Muscat Oman Faculty of Applied Computing and Technology Noroff University College Kristiansand94612 Norway Al-Mustaqbal University College Babil Hillah Iraq

The vital organ in human physiology is the brain, and abnormality in the brain will reason for various behavioural problems. Ischemic-Stroke is a medical emergency, and early detection and action will help the patient recover quickly. This scheme aims to implement Convolutional-Neural-Network (CNN) segmentation method to extract and evaluate the infected portion from the MRI slice of the brain. In our study the pre-trained UNet scheme is adopted to extract the stroke region from the Flair modality MRI slice with axial-, coronal- and sagittal plane. In this work, the ISLES2015 database is used for the experimental investigation. The segmented portion is further evaluated to the ground-truth and the metrics such as Jaccard, Dice and Accuracy are computed. The experimental investigation is implemented using Python software. The experimental outcome of this research proves that the proposed CNN scheme aids to improve segmentation accuracy on axial-plane images compared with other images. The performance of the CNN segmentation scheme is then validated with other related results existing in the literature. The outcome of this study confirms that UNet supported technique helps extract the stroke lesion from the MRI slice with more accurate accuracy. © Published under licence by IOP Publishing Ltd.

关键词： Image enhancement

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Divide-and-train: A new approach to improve the predictive tasks of bike-sharing systems

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Mathematical Biosciences and Engineering 2024年第7期21卷 6471-6492页

作者： Ali, Ahmed Salah, Ahmad Bekhit, Mahmoud Fathalla, Ahmed Department of Computer Science College of Computer Engineering and Sciences Prince Sattam Bin Abdulaziz University Al-Kharj Saudi Arabia Higher Future Institute for Specialized Technological Studies Cairo3044 Egypt College of Computing and Information Sciences University of Technology and Applied Sciences Ad-Dhahirah Ibri Oman Zagazig University Department of Computer Science Faculty of Computers and Informatics Sharkeya Zagazig Egypt Sydney Australia Kaplan Business School Sydney Australia Department of Mathematics Faculty of Science Suez Canal University Ismailia Egypt

Bike-sharing systems (BSSs) have become commonplace in most cities worldwide as an important part of many smart cities. These systems generate a continuous amount of large data volumes. The effectiveness of these BSS systems depends on making decisions at the proper time. Thus, there is a vital need to build predictive models on the BSS data for the sake of improving the process of decision-making. The overwhelming majority of BSS users register before utilizing the service. Thus, several BSSs have prior knowledge of the user’s data, such as age, gender, and other relevant details. Several machine learning and deep learning models, for instance, are used to predict urban flows, trip duration, and other factors. The standard practice for these models is to train on the entire dataset to build a predictive model, whereas the biking patterns of various users are intuitively distinct. For instance, the user’s age influences the duration of a trip. This endeavor was motivated by the existence of distinct user patterns. In this work, we proposed divide-and-train, a new method for training predictive models on station-based BSS datasets by dividing the original datasets on the values of a given dataset attribute. Then, the proposed method was validated on different machine learning and deep learning models. All employed models were trained on both the complete and split datasets. The enhancements made to the evaluation metric were then reported. Results demonstrated that the proposed method outperformed the conventional training approach. Specifically, the root mean squared error (RMSE) and mean absolute error (MAE) metrics have shown improvements in both trip duration and distance prediction, with an average accuracy of 85% across the divided sub-datasets for the best performing model, i.e., random forest. © 2024 the Author(s).

关键词： Forecasting

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A Comparative Study of Secure and Efficient Data Duplication Mechanisms for Cloud-Based IoT Applications 1st

A Comparative Study of Secure and Efficient Data Duplication...

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1st International Conference on Advances in computing Research, ACR’23

作者： Baligodugula, Vishnu Vardhan Amsaad, Fathi Tadepalli, Varshitha Vunnam Radhika, Vojjala Sanjana, Yetukuri Shiva, Sai Meduri, Supriya Maabreh, Majdi Alsaadi, Naser Darwish, Omar Razaque, Abdul Tashtoush, Yahya Computer Science Department Wright State University DaytonOH United States Department of Information Technology The Hashemite University Zarqa Jordan Computer Science Department Taibah University Medina Saudi Arabia Information Security and Applied Computing Eastern Michigan University YpsilantiMI United States Department of Cybersecurity IITU Almaty Kazakhstan Computer Science Department Jordan University of Science and Technology Irbid22110 Jordan

ISBN: (纸本)9783031337420

Cloud-based processes store enormous amounts of data to support several daily applications, especially in intelligent systems and the Industrial Internet of Things (IIoT) applications. The advancement of hosted cloud storage services endorses the productivity of IIoT applications. It is critical to enabling businesses to increase data security, privacy, efficiency, accessibility, and flexibility. There are many concerns for cloud providers, including storage overhead, upload/download bandwidth, and data security and privacy. Utilizing secure data deduplication technology, cloud service providers may maximize their available storage capacity, resulting in effective and secure disk space use. This review focuses on a comparative study of secure data deduplication mechanisms for cloud-based applications, including MD5 and various versions of SHA. These techniques remove duplicate data in different storage levels and enhance users’ data integrity, confidentiality, and privacy protection. It is computationally more efficient than traditional compression methods, which may support IoT applications to manage the exponential growth of their data. We also present an overview and classification of the most recent research on data deduplication based on essential mechanisms. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Cloud storage

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