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10th International Conference on Web Research, ICWR 2024

作者： Nazarizadeh, Ali Samavati, Taha Mansouri, Sina Sayyadpour, Minoo Islamic Azad University Central Tehran Branch Department of Computer Engineering Tehran Iran Iran University of Science and Technology Faculty of Computer Engineering Tehran Iran K. N. Toosi University of Technology Faculty of Computer Engineering Tehran Iran Kharazmi University Dept. Math and Computer Science Tehran Iran

ISBN: (纸本)9798350394986

Today, data is more valuable to us than gold. When observing the environment, a substantial amount of data, particularly textual information, can be identified, tagged, prepared, and published in the form of a corpus or datasets. The primary objective of our paper is to gather, prepare, tag, and develop a vast dataset of Fidibo users' opinions regarding educational content and e-books. This dataset enables in-depth analysis of emotions and opinion mining, particularly within the educational content realm. A common flaw in nearly all similar datasets in the Farsi language is their restriction to user opinions on services and products available on online platforms. The dataset we refer to as LDPSA (A Large Dataset of Persian Sentiment Analysis) offers several advantages over comparable datasets in the Persian language. Notably, this dataset consists of 253,368 comments, each categorized into 5 classes. LDPSA represents the sole extensive Iranian dataset suitable for scrutinizing educational content and e-books. Moreover, significant insights were gleaned from data analysis. For example, during the COVID-19 pandemic, Iranian individuals dedicated more time to studying and engaging with educational platforms significantly. Nearly 80% of users expressed favorable opinions concerning the informational materials available on the Fidibo website. Users' inclination towards utilizing audio books has escalated, along with other analysis referenced in the paper. © 2024 IEEE.

关键词： Sentiment analysis

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ResNet50-Based Deep Learning Model for Lung Disease Detection

ResNet50-Based Deep Learning Model for Lung Disease Detectio...

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2025 International Conference on Multi-Agent Systems for Collaborative Intelligence, ICMSCI 2025

作者： Hajbe, Janhavi R. Patle, Sneha K. Talvekar, Shreya V. Khonde, Tanvi W. Yesankar, Prajyot R. Faculty of Engineering and Technology Department of Computer Science & Design Sawangi Maharashtra Wardha442001 India

ISBN: (纸本)9798331509828

By enabling a highly accurate examination of the chest x-ray, deep learning, for example, is changing the methods of recognizing lung disorders. In order to classify lung diseases, such as bacterial pneumonia, viral pneumonia, TB, and COVID-19, this work describes the use of ResNet50, a kind of CNN and a deep learning technique. We prove the potential of deep learning to discern these diseases from the two conventional approaches and most importantly, the patterns one could implicitly observe. The article also discusses the challenges that deep learning may pose on to the healthcare industry including data privacy, availability of data and the issue of overfitting. The paper links with this part by looking at the future of this area of specialization, which includes;multimodal fusion, operational AI, real-time identification, personalized care, data issues, and concerns, and ethics. But if deep learning is successful in addressing all the above-discussed challenges then it can bring the situation to the real world where the identification of lung diseases happens in a previous phase which in turn results in better treatment procedures and higher patient outcomes. © 2025 IEEE.

关键词： Lung cancer

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Analysis and Assessment of Wind Energy Potential of Socotra Archipelago in Yemen

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computers, Materials & Continua 2022年第1期70卷 1177-1193页

作者： Murad A.Almekhlafi Fahd N.Al-Wesabi Imran Khan Nadhem Nemri Khalid Mahmood Hany Mahgoub Noha Negm Amin M.El-Kustaban Ammar Zahary Department of Electrical Engineering–Communication Engineering Sana’a UniversityYemen Department of Computer Science King Khalid UniversityMuhayel AseerKingdom of Saudi Arabia Faculty of Computer and IT Sana’a UniversitySana’aYemen Department of Electrical Engineering University of Engineering and TechnologyPeshawarP.O.B 814Pakistan Department of Information Systems King Khalid UniversityMayahel AseerKSA Computer Science Department King Khaled UniversityKSA&Faculty of Computers and InformationComputer Science DepartmentMenoufia UniversityEgypt Computer Science Department King Khaled UniversityKSA&Faculty of ScienceDepartment of Mathematics and Computer ScienceMenoufia UniversityEgypt Electronics Department Faculty of EngineeringUSTSana’aYemen Faculty of Computer and IT Sana’a UniversityYemen

The increasing use of fossil fuels has a significant impact on the environment and ecosystem,which increases the rate of *** the high potential of renewable energy sources inYemen and the absence of similar studies in the region,this study aims to examine the potential of wind energy in Socotra *** was done by analyzing and evaluating wind properties,determining available energy density,calculating wind energy extracted at different altitudes,and then computing the capacity factor for a number of wind turbines and determining the *** average wind speed in Socotra Island was obtained from the Civil Aviation and Meteorology Authority data,only for the five-year data currently *** results showed high wind speeds from June to September(9.85-14.88 m/s)while the wind speed decreased for the rest of the *** average wind speed in the five years was 7.95 m/*** average annual wind speed,wind energy density,and annual energy density were calculated at different altitudes(10,30,and 50 m).According to the International Wind Energy Rating criteria,the region of Socotra Island falls under Category 7 and is classified as‘Superb’for most of the *** study provides useful information for developing wind energy and an efficient wind approach.

关键词： Energy potential wind characteristics Weibull distribution wind power density Socotra island

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Enhancing Cyber-Physical Systems Security: A Review of Deep Learning and Blockchain Integration 5

Enhancing Cyber-Physical Systems Security: A Review of Deep ...

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5th International Conference on Image Processing and Capsule Networks, ICIPCN 2024

作者： Selvi, K. Dilip, Golda SRM Institute of Science and Technology Faculty of Engineering and Technology Department of Computer Science and Engineering Vadapalani Campus Chennai India

ISBN: (纸本)9798350367171

Cyber-Physical Systems (CPS) combine physical and computational elements to produce intelligent systems communicating with their surroundings. By integrating digital and physical processes, CPS provides increased functionality in various industries, including industry, transportation, and healthcare. However, there are security, data integrity, and privacy issues brought on by the extensive integration of CPS across several domains. Therefore, addressing these issues is significant for an effective and reliable CPS model. This study presents a comprehensive review exploring the synergistic relationship between two cutting-edge technologies, Deep Learning (DL) and Blockchain, deployed to resolve these issues in CPS. This study explores the efficiency of leveraging DL and blockchain to enhance the reliability and effectiveness of the CPS models. Also, this study examines the challenges and limitations faced by those DL and blockchain approaches during their implementation across different CPS domains. This comprehensive review provides promising directions for future research in this dynamic and interdisciplinary field for mitigating the challenges in CPS models. It also aids researchers, practitioners, and policymakers by providing a deeper understanding of the possible effects, difficulties, and opportunities related to utilizing DL and Blockchain technologies to improve the security and functionality of CPS. © 2024 IEEE.

关键词： Blockchain technologies Cyber-Physical Systems Deep Learning Internet of Things

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Validation Without Rules: A Data Integration Case Study 28th

Validation Without Rules: A Data Integration Case Study

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28th International Symposium on Database Engineered Applications, IDEAS 2024

作者： Dzalev, Stefan Velinov, Goran Faculty of Computer Science and Engineering Ss. Cyril and Methodius University Skopje1000 Macedonia

ISBN: (纸本)9783031834714

Data quality assessment is one of the most fundamental operations executed during data integration. Data validity is a collection of validation rules applied to the dataset’s attributes. The validation rules provided by domain experts must be known during the data validation checks. In practice domain experts may not be available, or their number is insufficient, and the project timeline may be strict, resulting in unknown data validity rules. In this paper, we present a low code framework for outlier and anomaly detection as an alternative to traditional data validation rules. The framework comprises statistical and machine learning methods that recognize outliers and label them as invalid data. We evaluated the accuracy and scalability of four methods using the TPC-DI benchmark tests, and the results indicate a high level of correctness when the appropriate method is employed for a specific distribution. The framework is exceptionally effective for data with skewed and normal distributions for local and global outliers. Additionally, for different scaling factors, we observed that the ratio of validation time to data integration execution time is low and stable as datasets increase. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Data integrity

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YOLOCSP-PEST for Crops Pest Localization and Classification

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computers, Materials & Continua 2025年第2期82卷 2373-2388页

作者： Farooq Ali Huma Qayyum Kashif Saleem Iftikhar Ahmad Muhammad Javed Iqbal Department of Software Engineering University of Engineering and TechnologyTaxila47050Pakistan Department of Computer Science&Engineering College of Applied Studies&Community ServiceKing Saud UniversityRiyadh11362Saudi Arabia Department of Information Technology Faculty of Computing and Information TechnologyKing Abdulaziz UniversityJeddah21589Saudi Arabia Department of Computer Science University of Engineering and TechnologyTaxila47050Pakistan

Preservation of the crops depends on early and accurate detection of pests on crops as they cause several diseases decreasing crop production and quality. Several deep-learning techniques have been applied to overcome the issue of pest detection on crops. We have developed the YOLOCSP-PEST model for Pest localization and classification. With the Cross Stage Partial Network (CSPNET) backbone, the proposed model is a modified version of You Only Look Once Version 7 (YOLOv7) that is intended primarily for pest localization and classification. Our proposed model gives exceptionally good results under conditions that are very challenging for any other comparable models especially conditions where we have issues with the luminance and the orientation of the images. It helps farmers working out on their crops in distant areas to determine any infestation quickly and accurately on their crops which helps in the quality and quantity of the production yield. The model has been trained and tested on 2 datasets namely the IP102 data set and a local crop data set on both of which it has shown exceptional results. It gave us a mean average precision (mAP) of 88.40% along with a precision of 85.55% and a recall of 84.25% on the IP102 dataset meanwhile giving a mAP of 97.18% on the local data set along with a recall of 94.88% and a precision of 97.50%. These findings demonstrate that the proposed model is very effective in detecting real-life scenarios and can help in the production of crops improving the yield quality and quantity at the same time.

关键词： Deep learning classification of pests YOLOCSP-PEST pest detection

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Study on optimization of multi-UAV nucleic acid sample delivery paths in large cities under the influence of epidemic environment

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Journal of Ambient Intelligence and Humanized Computing 2023年第6期14卷 7593-7620页

作者： Han, Yuhang Xiang, Hongyu Cao, Jianing Yang, Xiaohua Pan, Nan Huang, Linhai Mechanical Engineering Faculty of Civil Aviation and Aeronautics Kunming University of Science and Technology Yunnan China Computer Science and Technology Metrology Center Yunnan Power Grid Co. Ltd. Yunnan China Computer Science and Technology Faculty of Information Engineering and Automation Kunming University of Science and Technology Yunnan China

In the context of global novel coronavirus infection, we studied the distribution problem of nucleic acid samples, which are medical supplies with high urgency. A multi-UAV delivery model of nucleic acid samples with time windows and a UAV (Unmanned Aerial Vehicle) dynamics model for multiple distribution centers is established by considering UAVs’ impact cost and trajectory cost. The Golden Eagle optimization algorithm (SGDCV-GEO) based on gradient optimization and Corsi variation is proposed to solve the model by introducing gradient optimization and Corsi variation strategy in the Golden Eagle optimization algorithm. Performance evaluation by optimizing test functions, Friedman and Nemenyi test compared with Golden Jackal Optimization (GJO), Hunter-Prey Optimization (HPO), Pelican Optimization Algorithm (POA), Reptile Search Algorithm (RSA) and Golden Eagle Optimization (GEO), the convergence performance of SGDCV-GEO algorithm was demonstrated. Further, the improved RRT (Rapidly-exploring Random Trees) algorithm is used in the UAV path planning, and the pruning process and logistic chaotic mapping strategy are introduced in the path generation method. Finally, simulation experiments are conducted based on 8 hospitals and 50 randomly selected communities in the Pudong district of Shanghai, southern China. The experimental results show that the developed algorithm can effectively reduce the delivery cost and total delivery time compared with simulated annealing algorithm (SA), crow search algorithm (CSA), particle swarm algorithm (PSO), and taboo search algorithm (TS), and the developed algorithm has good uniformity, robustness, and high convergence accuracy, which can be effectively applied to the multi-UAV nucleic acid sample delivery path optimization in large cities under the influence of an epidemic environment. © 2023, The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.

关键词： COVID-19

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Intelligent Real-Time Hypoglycemia Prediction for Type 1 Diabetes 2

Intelligent Real-Time Hypoglycemia Prediction for Type 1 Dia...

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2nd International Conference of Intelligent Methods, Systems and Applications, IMSA 2024

作者： Mahdy, Nevien A. Elnokrashy, Ahmed F. Hammad, D.A. Mohamed, Wael A. Benha University Benha Faculty of Engineering Electrical Engineering Department Benha Egypt Nile University Faculty of Information Technology and Computer Science Computer Science Department Giza Egypt Benha University Benha Faculty of Engineering Basic Engineering Sciences Department Benha Egypt

ISBN: (纸本)9798350362633

Hypoglycemia in Type 1 Diabetes (T1D) refers to a condition where blood glucose (BG) levels drop to abnormally low levels, typically below 70 mg/dL. This can occur when there is an excessive amount of insulin relative to the blood glucose level, leading to an imbalance that can be dangerous and potentially life-threatening if not promptly treated. The availability of large amounts of data from continuous glucose monitoring (CGM), insulin doses, carbohydrate intake, and additional vital signs, together with deep learning (DL) techniques, has revolutionized algorithmic approaches for BG prediction in T1D, achieving superior performance. In our study, we employed a Long Short-Term Memory (LSTM) neural network architecture to predict hypoglycemia events in patients with T1D. For the training and testing, we utilized the OhioT1DM (2018) dataset. In addition, real-time data collected from an individual patient for the evaluation. This patient utilized the CGM FreeStyle Libre (FSL) system, along with a smartwatch to monitor step count. The LSTM model exhibited performance demonstrating exceptional levels of sensitivity, specificity, and accuracy scores of 97.09%, 94.17%, and 95.63%, respectively, when assessed using the Ohio test dataset. Our research provides strong evidence supporting the system's efficacy in managing hypoglycemia events in individuals diagnosed with T1D. © 2024 IEEE.

关键词： Classification Deep learning Glucose prediction Hypoglycemia Type 1 diabetes

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Comparison of K-Means, K-Means++, X-Means and Single Value Decomposition for Image Compression 27

Comparison of K-Means, K-Means++, X-Means and Single Value D...

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27th International Conference on Circuits, Systems, Communications and computers, CSCC 2023

作者： Zalik, Krista Rizman Zalik, Mitja University of Maribor Faculty of Electrical Engineering and Computer Science Faculty of Natural Sciences and Mathematics Maribor Slovenia University of Maribor Faculty of Electrical Engineering and Computer Science Maribor Slovenia

ISBN: (纸本)9798350337594

Uncompressed digital images need huge storage space and large transmission bandwidth for the transmission over the network. Image compression reduces the image size and enables the efficient storage and transmission of image files. The main goal of image compression is the best image quality at the given compression rate. In this paper, the performance of Singular Value Decomposition (SVD), K-means, K-means++, and X-means for image compression is discussed. K-means, K-means++, and X-means clustering reduce the number of colors of images. Singular Value Decomposition performs a low-rank approximation of the image matrix. Image compression parameters mean-square error (MSE), peak signal-to-noise ratio (PSNR). and compression ratio (CR) are evaluated for the considered compression methods. © 2023 IEEE.

关键词： Singular value decomposition

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Are Fact Checkers Effective in the Post Truth World? Assessing Impact of Fact Checkers Cross Medium and Platforms 25th

Are Fact Checkers Effective in the Post Truth World? Asses...

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25th International Conference on Web Information Systems engineering, WISE 2024

作者： Masurkar, Hrishikesh Suleiman, Basem Yaqub, Waheeb Alibasa, Muhammad Johan School of Computer Science The University of Sydney Sydney Australia School of Computer Science and Engineering The University of New South Wales Sydney Australia Faculty of Engineering and Technology Sampoerna University Jakarta Indonesia

ISBN: (纸本)9789819605668

Social media platforms help users share opinions and find new information but also spread rumors, which misinforms the public. These rumour threads often prompt users (called guardians) to respond with fact-checking articles to debunk or verify the rumour. We examine how general users’ and rumour starter respond to these fact-checking (FC) comments. This study explores these aspects by analysing users’ textual and non-textual engagements. These engagements are examined across three main factors: platforms (Twitter and Reddit), rumour topics, and the primary medium (text, image, video, and GIF) in the rumour post. We observed minimal differences across primary mediums by analyzing 147K news posts and FC comments with 20.7 million replies. Furthermore, we found that Reddit users engage with FC comments more than Twitter users in terms of likes and the rumour starter’s reply-back rates. We found that no general users engaged with the rumour post before and after the guardian intervention. The most significant differences in the emotive characteristics of replies were across topics, with rumour starters expressing more anger, disgust, and offensive language when discussing rumours about terrorism and racism. These emotions indicate the backfire effect. Our findings raise concerns about the effectiveness of fact-checking interventions in updating misinformed users. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Tweets

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