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2023 International Symposium on Networks, computers and Communications, ISNCC 2023

作者： Mustafa, Dheya Khabour, Safaa M. Shatnawi, Ahmed S. Taqieddin, Eyad The Hashemite University Faculty of Engineering Dept. of Computer Engineering Zarqa13133 Jordan Yarmouk University Faculty of Information Technology and Computer Sciences Dept. of Information Systems Irbid21163 Jordan Jordan University of Science & Technology Dept. of Software Engineering Irbid21110 Jordan School of Computing and Informatics Dept. of Cyber Security Al-Hussein Technical University Amman Jordan

ISBN: (纸本)9798350335590

Customer reviews on online platforms have grown to become an important source of insight into a company's performance. Food delivery services (FDS) companies aim to effectively use customers' feedback to identify areas for improvement of customer satisfaction. Although Arabic is becoming one of the most widely used languages on the Internet, only a few studies have focused on Arabic sentiment analysis to date. The present study conducts an extensive emotion mining and sentiment analysis on FDS-related reviews in Arabic, exploiting natural language processing, and machine learning techniques to extract information that reflects customers' subjective viewpoints, recognize their feelings, and determine polarity in the FDS domain. This work begins with collecting the FDS dataset (Talabat), and then extracting and creating a dialects lexicon for Arabic dialects, followed by walking the reader through detailed steps of cleaning and pre-processing a manually annotated dataset. Finally, we examined classification algorithms including Decision Tree (DT), and Support Vector Machine (SVM). We achieved a maximum accuracy of about 82% using the SVM classifier. © 2023 IEEE.

关键词： Sentiment analysis

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Automated, Unsupervised, and Auto-parameterized Inference of Data Patterns and Anomaly Detection

arXiv

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arXiv 2024年

作者： Qin, Qiaolin Li, Heng Merlo, Ettore Lamothe, Maxime Dept. of Computer and Software Engineering Polytechnique Montreal Montreal Canada

With the advent of data-centric and machine learning (ML) systems, data quality is playing an increasingly critical role for ensuring the overall quality of software systems. Data preparation, an essential step towards high data quality, is known to be a highly effort-intensive process. Although prior studies have dealt with one of the most impacting issues, data pattern violations, these studies usually require data-specific configurations (i.e., parameterized) or use carefully curated data as learning examples (i.e., supervised), relying on domain knowledge and deep understanding of the data, or demanding significant manual effort. In this paper, we introduce RIOLU: Regex Inferencer autO-parameterized Learning with Uncleaned data. RIOLU is fully automated, automatically parameterized, and does not need labeled samples. RIOLU can generate precise patterns from datasets in various domains, with a high F1 score of 97.2%, exceeding the state-of-the-art baseline. In addition, according to our experiment on five datasets with anomalies, RIOLU can automatically estimate a data column’s error rate, draw normal patterns, and predict anomalies from unlabeled data with higher performance (up to 800.4% improvement in terms of F1) than the state-of-the-art baseline, even outperforming ChatGPT in terms of both accuracy (12.3% higher F1) and efficiency (10% less inference time). A variant of RIOLU, with user guidance, can further boost its precision, with up to 37.4% improvement in terms of F1. Our evaluation in an industrial setting further demonstrates the practical benefits of *** Codes 68P01 © 2024, CC BY.

关键词： Self-supervised learning

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Effective Disease Recognition in Cucumbers: A Web-Based Application Using Transfer Learning Models 3

Effective Disease Recognition in Cucumbers: A Web-Based Appl...

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3rd IEEE International Conference on Robotics, Automation, Artificial-Intelligence and Internet-of-Things, RAAICON 2024

作者： Rahman, Md Shahidur Ahamed, Asif Pranto, Md Nuruzzaman Islam, Md Ariful Al Masum, Abdullah Al-Sakib, Abdullah Rahman, S.M. Arafat Haque, Rezaul Rahman, Shafiur Westcliff University Dept. of Engineering Management CA92614 United States Westcliff University College of Technology and Engineering CA92614 United States International American University Dept. of Business Administration CA90010 United States Westcliff University Dept. of Information Technology CA92614 United States East West University Dept. of Computer Science and Engineering Dhaka1212 Bangladesh Daffodil International University Dept. of Software Engineering Dhaka1216 Bangladesh

ISBN: (纸本)9798331534400

Cucumber farming plays a crucial role in Bangladesh's agricultural economy, significantly contributing to vegetable production. However, diseases like Downy Mildew, Bacterial Wilt, Anthracnose, and Belly Rot threaten crop quality and lead to financial losses. Effective management of these diseases is crucial for maintaining high crop yields and ensuring economic stability. Current disease identification methods are labor-intensive, costly, and dependent on expert knowledge. To address these challenges, this study presents an automated disease categorization system using advanced transfer learning models, including MobileNet, ShuffleNet, InceptionResNet-v2, AlexNet, and Ensemble Transfer Learning (ETL). Utilizing a dataset of 8,000 well-annotated images, enhanced through preprocessing techniques like GLRM feature extraction and InfoMAX-GAN feature selection, the ETL model achieved a remarkable accuracy of 99.79% and an F1-score of 99.78%. We also developed a user-friendly web tool for real-time diagnostics, providing invaluable support to farmers and agricultural specialists. This tool enables rapid disease detection, leading to increased productivity and a transformative approach to cucumber disease management. By reducing the reliance on chemical interventions, it promotes sustainable farming practices and safeguards human and animal health. © 2024 IEEE.

关键词： Feature Selection

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Investigating Online Searching Behavior Based on Google Trends in MENA Region Before and After COVID-19 53

Investigating Online Searching Behavior Based on Google Tren...

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53rd IEEE ASEE Frontiers in Education International Conference, FIE 2023

作者： Belkacem, Abdelkader Nasreddine Jamil, Nuraini Alrabaee, Saed College of It Uae University Dept. of Computer and Network Engineering Al Ain United Arab Emirates College of It Uae University Dept. of Computer Science and Software Engineering Al Ain United Arab Emirates College of It Uae University Dept. of Information Systems and Security Al Ain United Arab Emirates

ISBN: (纸本)9798350336429

The outbreak of the coronavirus disease (COVID-19) has had a profound impact on education worldwide. The rise of remote learning is one of the most significant changes in this regard, as many schools and universities were forced to close down by regional health authorities. This has also caused people to become more conservative in trade-offs between healthcare and education. Google Trends is the most common tool for analyzing online search behaviors. It is a free resource that provides information on the trends and changes in users' online interests over time based on certain terms and subjects. The online search queries on Google can be used to assess users' behaviors concerning online learning to forecast their choices regarding online education. This paper examines the frequency of users' web searches for online communication tools, courses, and learning terms. We statistically compared users in the Middle East and North Africa regions by using the volumes of searches recorded on Google Trends from January 2016 to August 2022. Moreover, we used machine learning techniques to identify differences among the keywords used. The findings statistically show that COVID-19 has led to an increase in the extent of students' attention to and interest in online learning. © 2023 IEEE.

关键词： COVID-19 Google Trends MENA Online learning Remote learning Web Behavior

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Cloud-Based Framework for Data Exchange to Enhance Global Healthcare 19

Cloud-Based Framework for Data Exchange to Enhance Global He...

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19th International Conference on Future Networks and Communications, FNC 2024 / 21st International Conference on Mobile Systems and Pervasive Computing, MobiSPC 2024 / 14th International Conference on Sustainable Energy Information Technology, SEIT 2024

作者： Naz, Ammerha Ali, Muhammad Cheema, Sehrish Munawar Pires, Ivan Miguel Dept. of Computer Science University of Sialkot Punjab Sialkot Pakistan Dept. of Software Engineering Superior University Lahore Pakistan Dept. of Computer Science University of Management and Technology Lahore Pakistan Instituto de Telecomunicações Escola Superior de Tecnologia e Gestão de Águeda Universidade de Aveiro Águeda Portugal

Healthcare is a global pillar, with a surge in the adoption of information technology, particularly in hospital information systems (HIS). However, global protocols are needed to meet the growing demand for data interchange, practical implementations for sharing healthcare data among facilities, and a pressing need for processing and storage infrastructure to handle the escalating volume of healthcare data. This study proposes a solution for efficient data transmission using electronic health records (EHR) and Platform-as-a-Service (PaaS) to leverage cloud computing resources. This framework's architecture boasts robustness and adaptability, providing all registered software programs access to data interchange services. Through a comprehensive examination of the framework's structure, the essay also explores the most effective data-sharing methods. It identifies the healthcare system's optimal EHR data model. According to multiple healthcare experts, the operational building of this framework is expected to catalyze the growth of healthcare institutions both nationally and within specific industries. © 2024 The Authors. Published by Elsevier B.V.

关键词： Electronic health record

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Utilizing Machine Learning to Enhance Infrastructure Resilience in Cold Regions 20

Utilizing Machine Learning to Enhance Infrastructure Resilie...

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20th International Conference on Cold Regions engineering: Sustainable and Resilient engineering Solutions for Changing Cold Regions, ICCRE 2024

作者： Shohel Rana, Md Gudla, Charan Ahmed, Feroz Nobi, Mohammad Nur Dept. of Computing and Software Engineering Florida Gulf Coast Univ. Fort MyersFL United States Dept. of Computer Science and Engineering Mississippi State Univ. StarkvilleMS United States Prediction 3D Technologies BiloxiMS United States Dept. of Computer Science Univ. of Texas at San Antonio San AntonioTX United States

ISBN: (纸本)9780784485460

In the challenging domain of engineering, where cold regions present formidable challenges, we confront the relentless forces of nature. From sub-zero temperatures to the unpredictable dance of snowfall and the silent buildup of ice, these regions demand innovative solutions to fortify the resilience of critical infrastructure. This initiative harnesses the potential of cutting-edge technology and leverages the extensive historical weather data tapestry. It introduces a pioneering strategy by integrating machine learning algorithms with extensive weather data, steering cold region engineering into an era defined by foresight and adaptability. This paper studies a transformative approach designed to forecast, prevent, and ultimately enhance infrastructure resilience in the face of rigid cold. Addressing the distinct challenges of cold region engineering, arising from harsh winter conditions such as extreme cold temperature, snowfall, and ice accumulation, we offer a comprehensive study using machine learning algorithms applied to historical weather data to construct a deeper analysis model capable of highlighting adverse weather effects. This, in turn, covers the way for optimized resource allocation, streamlined maintenance planning, and design enhancements. Our proposed study follows a systematic process, encompassing meticulous data collection, appropriate feature selection, and aiming seamless integration of the model into existing infrastructure management systems. Additionally, it facilitates the implementation of efficient and proactive measures to mitigate the impact of severe weather conditions on infrastructure. The paper also conducts three different hypotheses testing: temperature impact hypothesis, precipitation influence hypothesis, and ice accumulation and infrastructure resilience hypothesis, propelling engineering practices to new heights, particularly in the face of challenging cold environments. © ASCE.

关键词： Snow

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Uplink Max-Min Spectral Efficiency for IRS-Aided Cell-Free Massive MIMO Networks: Analysis and Deep Learning Design 13

Uplink Max-Min Spectral Efficiency for IRS-Aided Cell-Free M...

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13th International Conference on Information and Communication Technology Convergence, ICTC 2022

作者： Tran, Thong-Nhat Ant, Beongku Graduate School Hongik University Dept. of Electronics and Computer Engineering Korea Republic of Hongik University Dept. of Software and Communications Engineering Korea Republic of

ISBN: (数字)9781665499392

ISBN: (纸本)9781665499392

In this paper, we study on a max-min uplink Spectral Efficiency (SE) problem for IRS-aided cell-free massive MIMO networks. Firstly, we derive exact closed-forms of channel estimates with spatially correlated Rayleigh fading model. Secondly, we derive an analytical expression of network uplink SE to formulate the max-min uplink SE problem. Thirdly, we propose a mathematical method and a deep learning design to solve the optimization problem. Finally, the simulation results show that the proposed scheme overcomes conventional cell-free massive MIMO networks. Moreover, the deep learning design can be used to save computational costs compared to the mathematics analysis method. © 2022 IEEE.

关键词： Spectrum efficiency

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Towards the Utilization of Parallel Programming to Speed Up RAM Forensics 10

Towards the Utilization of Parallel Programming to Speed Up ...

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10th International Conference on software Defined Systems, SDS 2023

作者： Al-Sharif, Ziad A. Alhamouri, Rahaf Jararweh, Yaser Lewis University Dept. of Engineering Computing and Mathematical Sciences Romeoville United States Jordan University of Sci. and Tech. Dept. of Software Engineering Irbid Jordan Jordan University of Sci. and Tech. Dept. of Computer Science Irbid Jordan

ISBN: (纸本)9798350382235

Memory forensics uses volatile digital artifacts as evidence about criminal activities. Analyzing captured memory dumps for volatile data requires time and effort. This paper studies the utilization of parallel programming to speed up RAM forensics. It presents a performance-based evaluation of parallel programming in the domain of memory forensics and compares sequential and parallel approaches to speed up the memory analysis process. First, it evaluates the sequential approach and uses it as a base case for further parallel approaches. Second, it evaluates two of the parallel approaches that can be performed on a typical user machine. Our experiments evaluate the use of two parallel programming paradigms: the in-process parallelization approach using OpenMP, and the inter-process parallelization approach using MPI. Our results compare the performance of the sequential approach, OpenMP thread-based approach, and MPI process-based approach. Experimentations compare the performance of three scenarios using six files of different sizes and various numbers of threads and/or processes. The results show that the use of MPI is slightly better than OpenMP approaches on the use of 2 and 4 processes/threads. However, when the number of processors/threads is increased to 8 and 16, OpenMP slightly outperforms the MPI approach. Additionally, the parallelization approach using OpenMP and MPI provides 3X to 5X speed up over the traditional sequential approach. Moreover, it is worth mentioning that this speed-up is achieved on traditional user machines without the use of HPC computers. © 2023 IEEE.

关键词： Parallel programming

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Web Accessibility of the Cyprus Island Food Retailers' Websites 5

Web Accessibility of the Cyprus Island Food Retailers' Websi...

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5th International Congress on Human-computer Interaction, Optimization and Robotic Applications, HORA 2023

作者： Iseri, Erkut Inan Uyar, Kaan Ilhan, Umit Near East University Dept. of Physics TRNC Mersin 10 Nicosia Turkey Near East University Dept. of Software Engineering TRNC Mersin 10 Nicosia Turkey Near East University Dept. of Computer Engineering TRNC Mersin 10 Nicosia Turkey

ISBN: (纸本)9798350337525

Accessibility is the process of making information and electronic communication environments meaningful and usable for most people including those with disabilities. It is not always an easy task to provide all user communities with different areas of interests and needs with exact styles and contents. It is a good practice to employ responsible designs and a certain degree of interaction that provide equitable conditions to suit the needs of the most user communities. It became evident that in the passed two years the world went thru a terrifying Coronavirus disease which made the accessibility of web sites of retailers even more challenging. Most of the food supplying companies introduced shop-to door deliveries of food products with web based applications. Although, technically speaking, these web sites accomplished the task for most of the people but with some accessibility issues. These issues can be identified and solved by following the international standards to gain world wide acceptance. This study covers the investigation of the accessibility of the Food Retailers' websites in the whole of Cyprus Island. Web Content Accessibility Guidelines 2.1 (WCAG 2.1) published by the World Wide Web Consortium (W3C) is followed in the process. Three testing software are used to determine the degree of compliance of the food retailers with the WCAG 2.1 in Cyprus Island. The aim of this study is to raise awareness about web accessibility among general public. The findings are not so promising and the sites examined are not in compliance with the standard of the guidelines defined in WCAG. © 2023 IEEE.

关键词： Websites

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Synthetic Data: Revisiting the Privacy-Utility Trade-off

arXiv

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arXiv 2024年

作者： Sarmin, Fatima Jahan Sarkar, Atiquer Rahman Wang, Yang Mohammed, Noman Dept. of Computer Science University of Manitoba Canada Dept. of Computer Science and Software Engineering Concordia University Canada

Synthetic data has been considered a better privacy-preserving alternative to traditionally sanitized data across various applications. However, a recent article challenges this notion, stating that synthetic data does not provide a better trade-off between privacy and utility than traditional anonymization techniques, and that it leads to unpredictable utility loss and highly unpredictable privacy gain. The article also claims to have identified a breach in the differential privacy guarantees provided by PATE-GAN and PrivBayes. When a study claims to refute or invalidate prior findings, it is crucial to verify and validate the study. In our work, we analyzed the implementation of the privacy game described in the article and found that it operated in a highly specialized and constrained environment, which limits the applicability of its findings to general cases. Our exploration also revealed that the game did not satisfy a crucial precondition concerning data distributions, which contributed to the perceived violation of the differential privacy guarantees offered by PATE-GAN and PrivBayes. We also conducted a privacy-utility tradeoff analysis in a more general and unconstrained environment. Our experimentation demonstrated that synthetic data indeed achieves a more favorable privacy-utility trade-off compared to the provided implementation of k-anonymization, thereby reaffirming earlier conclusions. © 2024, CC BY.

关键词： Anonymity

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