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1st Renewable Energy Technologies and Modern Communications systems: Future and Challenges Conference, RETMCS 2024

作者： AL-Shehri, Bushra A. Sabbeh, Sahar F. Dessouky, Mohamed M. Department of Data Science College of Computer Science and Engineering University of Jeddah Jeddah23218 Saudi Arabia Faculty of Computers and Artificial Intelligence Information Systems Department Benha University Benha13518 Egypt Department of Computer Science and Artificial Intelligence College of Computer Science and Engineering University of Jeddah Jeddah23218 Saudi Arabia Department of Computer Science and Engineering Faculty of Electronic Engineering Menoufia University Menouf32952 Egypt

ISBN: (纸本)9798331519445

COVID-19 is a respiratory disease for which reverse transcription-polymerase chain reaction (RT-PCR) is the standard detection method. This study introduces a hybrid deep learning approach to support the diagnosis of respiratory abnormalities, using COVID-19 as a case study. Multiple datasets were utilized, including the COVID-19 Radiography Dataset (Kaggle), the SARS-CoV-2 CT-Scan Dataset, and proprietary data from the King Faisal Medical Complex. Seven pre-trained deep learning models—VGG16, VGG19, MobileNet, DenseNet201, ResNet50, InceptionResNetV2, and InceptionV3—were evaluated for their performance. InceptionResNetV2 and InceptionV3 achieved perfect accuracy (100%) on the COVID-19 Radiography Dataset and 99% accuracy on the SARS-CoV-2 CT-Scan Dataset. Additionally, augmentation techniques were explored to improve model robustness. This research contributes significantly to medical imaging, offering practical insights for advancing diagnostic and monitoring practices for COVID-19 and other respiratory conditions. ©2024 IEEE.

关键词： COVID-19

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Fake News Classification: Past, Current, and Future

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computers, Materials & Continua 2023年第11期77卷 2225-2249页

作者： Muhammad Usman Ghani Khan Abid Mehmood Mourad Elhadef Shehzad Ashraf Chaudhry Department of Computer Science University of Engineering and TechnologyLahore54890Pakistan Department of Computer Science&Information Technology Abu Dhabi UniversityAbu Dhabi59911United Arab Emirates Department of Software Engineering Faculty of Engineering and ArchitectureNisantasi UniversityIstanbulTurkey

The proliferation of deluding data such as fake news and phony audits on news web journals,online publications,and internet business apps has been aided by the availability of the web,cell phones,and social *** can quickly fabricate comments and news on social *** most difficult challenge is determining which news is real or ***,tracking down programmed techniques to recognize fake news online is *** an emphasis on false news,this study presents the evolution of artificial intelligence techniques for detecting spurious social media *** study shows past,current,and possible methods that can be used in the future for fake news *** different publicly available datasets containing political news are utilized for performing *** supervised learning algorithms are used,and their results show that conventional Machine Learning(ML)algorithms that were used in the past perform better on shorter text *** contrast,the currently used Recurrent Neural Network(RNN)and transformer-based algorithms perform better on longer ***,a brief comparison of all these techniques is provided,and it concluded that transformers have the potential to revolutionize Natural Language Processing(NLP)methods in the near future.

关键词： Supervised learning algorithms fake news classification online disinformation transformers recurrent neural network(RNN)disinformation transformers recurrent neural network(RNN)

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Enhancement of UAV Data Security and Privacy via Ethereum Blockchain Technology

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computers, Materials & Continua 2023年第8期76卷 1797-1815页

作者： Sur Singh Rawat Youseef Alotaibi Nitima Malsa Vimal Gupta Department of Computer Science and Engineering JSS Academy of Technical EducationNoida201301India Department of Computer Science College of Computer and Information SystemsUmm Al-Qura UniversityMakkah21955Saudi Arabia

Unmanned aerial vehicles(UAVs),or drones,have revolutionized a wide range of industries,including monitoring,agriculture,surveillance,and supply ***,their widespread use also poses significant challenges,such as public safety,privacy,and ***,targetingUAVs have become more frequent,which highlights the need for robust security *** technology,the foundation of cryptocurrencies has the potential to address these *** study suggests a platform that utilizes blockchain technology tomanage drone operations securely and *** incorporating blockchain technology,the proposed method aims to increase the security and privacy of drone *** suggested platform stores information on a public blockchain located on Ethereum and leverages the Ganache platform to ensure secure and private blockchain *** wallet for Ethbalance is necessary for BCT *** present research finding shows that the proposed approach’s efficiency and security features are superior to existing *** study contributes to the development of a secure and efficient system for managing drone operations that could have significant applications in various *** proposed platform’s security measures could mitigate privacy concerns,minimize cyber security risk,and enhance public safety,ultimately promoting the widespread adoption of *** results of the study demonstrate that the blockchain can ensure the fulfillment of core security needs such as authentication,privacy preservation,confidentiality,integrity,and access control.

关键词： Unmanned aerial vehicles(UAVs) blockchain data privacy network security smart contract Ethereum

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A new family of q-Bernstein polynomials: probabilistic viewpoint

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Arab Journal of Basic and Applied sciences 2025年第1期32卷 42-50页

作者： Karagenc, Ayse Acikgoz, Mehmet Araci, Serkan Department of Mathematics Faculty of Science and Arts Gaziantep University Gaziantep Turkey Department of Computer Engineering Faculty of Engineering Hasan Kalyoncu University Gaziantep Turkey

In this paper, we introduce a new class of polynomials, called probabilistic q-Bernstein polynomials, alongside their generating function. Assuming (Formula presented.) is a random variable satisfying moment conditions, we use the generating function of these polynomials to establish new relations. These include connections to probabilistic Stirling numbers of the second kind and higher-order probabilistic Bernoulli polynomials associated with (Formula presented.) Additionally, we derive recurrence and differentiation properties for probabilistic q-Bernstein polynomials. Utilizing Leibniz’s formula, we give an identity for the generating function of these polynomials. In the latter part of the paper, we explore applications by choosing appropriate random variables, such as Poisson, Bernoulli, Binomial, Geometric, Negative Binomial, and Uniform distributions. This allows us to derive relationships among probabilistic q-Bernstein polynomials, Bell polynomials, Stirling numbers of the second kind, higher-order Frobenius-Euler numbers, and higher-order Bernoulli polynomials. We also present p-adic q-integral and fermionic p-adic q-integral representations for probabilistic q-Bernstein polynomials. © 2025 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group on behalf of the University of Bahrain.

关键词： 05D40 11B73 11B83 distributions generating functions moment generating functions probabilistic q-Bernstein polynomials

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Revitalizing Paddy Yields with computer Vision 2

Revitalizing Paddy Yields with Computer Vision

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2nd International Conference on Intelligent Data Communication Technologies and Internet of Things, IDCIoT 2024

作者： Siva M.e, K.P. Vibin, K.C. Kaarnika, A. Ramkumar, T.R. Priyanka, D. SriEshwarCollege of Engineering Department of Computer Science and Business Systems Coimbatore India

ISBN: (纸本)9798350327533

A groundbreaking innovation in the field of plant science revolutionizes the precise identification of nutrient deficiencies in paddy plants through meticulous leaf analysis. The methodology commences by breaking down input leaf images into manageable blocks, subsequently subjecting them to rigorous evaluation using specialized convolutional neural networks (CNNs), each meticulously trained to discern specific nutrient deficiencies. The advanced network architecture systematically scrutinizes these blocks for characteristic deficiency symptoms. To achieve an all-encompassing evaluation of the leaf's condition, the responses generated by all CNNs are aggregated, and a multi-layer perceptron synthesizes them into a singular block response. The proposed approach results in a comprehensive assessment of the overall leaf health. In a comprehensive study encompassing seven different deficiency types - including Nitrogen (N), Phosphorus (P), Potassium (K), and Zinc (Zn) deficiencies - a vast dataset comprising 2,750 carefully collected leaf images was employed. The results demonstrate the proposed method's superiority, surpassing the accuracy of expert human assessments in identifying various nutrient deficiencies. The proposed method harnesses the power of CNNs, effectively merging the image analysis, machine learning, and Convolutional Neural Networks (CNNs) to propel nutrient deficiency analysis in paddy plants to unprecedented heights. © 2024 IEEE.

关键词： Convolutional Neural Networks Image Segmentation Leaf Analysis Machine Learning Nutrient Deficiency

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ROUTESENSE - An AI powered solution to Car pooling systems 10

ROUTESENSE - An AI powered solution to Car pooling systems

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10th International Conference on Communication and Signal Processing, ICCSP 2024

作者： Poornimathi, K. Deepak Kumar, A. Srinithi, A. Rajalakshmi Engineering College Department of Computer Science and Business Systems India

ISBN: (纸本)9798350353068

Revolutionizing the urban commuting by introducing a ground-breaking AI-powered carpooling portal. This comprehensive platform provides an ultimate solution combining all the latest technologies for transportation challenges. The system ensures effortless scalability and real-time synchronization of user profiles, tip histories and route information across devices. Using the integration of emerging technologies such as Artificial intelligence and Machine learning algorithms, commuters are given a unique advantage of having a seamless travel and an optimized cost for the travel using AI and ML models and they are matched based on their preferences, schedules and historical data that makes the travel more convenient. The system also provides enhanced route security by analyzing the geospatial information to suggest the safest and most efficient routes for carpooling groups. It also streamlines the billing process by introducing Robotic process automation (RPA), ensuring accurate and tamper-free billing process for the carpooling groups. This combination of different technologies makes carpooling into a sophisticated, seamless and efficient mode of sustainable transportation that also is secure while delivering unparalleled convenience and peace of mind to its users. © 2024 IEEE.

关键词： Robotics

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Abnormal event detection in surveillance videos through LSTM auto-encoding and local minima assistance

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Discover Internet of Things 2025年第1期5卷 1-25页

作者： Sengonul, Erkan Samet, Refik Abu Al-Haija, Qasem Alqahtani, Ali Alsemmeari, Rayan A. Alghamdi, Bandar Alturki, Badraddin Alsulami, Abdulaziz A. Department of Computer Engineering Ankara University Ankara06100 Turkey Department of Cybersecurity Faculty of Computer & Information Technology Jordan University of Science and Technology PO Box 3030 Irbid22110 Jordan Department of Networks & Communications Engineering College of Computer Science & Information Systems Najran University Najran61441 Saudi Arabia Department of Information Technology Faculty of Computing and Information Technology King Abdulaziz University Jeddah21589 Saudi Arabia Department of Information Systems Faculty of Computing and Information Technology King Abdulaziz University Jeddah21589 Saudi Arabia

Abnormal event detection in video surveillance is critical for security, traffic management, and industrial monitoring applications. This paper introduces an innovative methodology for anomaly detection in video data, encompassing three primary stages: preprocessing, feature learning, and anomaly detection. We employ background subtraction and noise reduction during preprocessing to refine the data. The feature-learning stage involves training an LSTM autoencoder to capture the essential features of normal video sequences. For anomaly detection, we map video data to a lower-dimensional space (latent code) and compare it against the distribution of codes from normal sequences. We determine regularity scores and identify local minima points exceeding a specified threshold while scrutinizing shadows between adjacent maxima to confirm and pinpoint anomalies. When tested on the CUHK Avenue and UMN datasets, our methodology demonstrated performance with AUCs of 93.8% and 94.1%, respectively, outperforming several baseline models. Our results show the high precision of our method that can detect anomalies, highlighting its potential advantages that it can achieve for enhancing systems of surveillance. © The Author(s) 2025.

关键词： Noise abatement

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Designing a Fuzzy Mathematical Model for a Two-Echelon Allocation-Routing Problem by Applying Route Conditions: A New Interactive Fuzzy Approach

Decision Making: Applications in Management and Engineering

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Decision Making: Applications in Management and engineering 2024年第2期7卷 172-196页

作者： Yadegari, Zahra Molana, Seyyed Mohammad Hadji Kashan, Ali Husseinzadeh Najafi, Seyed Esmaeil Department of Industrial Engineering Science and Research Branch Islamic Azad University Tehran Iran Faculty of Industrial and Systems Engineering Tarbiat Modares University Tehran Iran

In vehicle routing problems (VRP), the optimal allocation of transportation by considering factors such as route hardness, driver experience and vehicle worn-out has a significant effect on costs reduction and approaching real-world conditions. In this paper, a novel fuzzy mixed integer non-linear mathematical model to address the two-echelon allocation-routing problem under uncertainty is proposed by applying route and fleet conditions. The cost of allocating drivers to diverse vehicles is computed at the first echelon of the problem, considering factors such as vehicle type, vehicle wear-out, and driver experience. Additionally, different routes are defused with varying levels of hardness. The goal of the second echelon of the model is to improve reliability by defining the reliability of routes within each section. To solve the model, the Torabi and Hessini (TH), the Selimi and Ozkarahan (SO) methods, and a newly proposed approach (PIA) were utilized to transform the multi-objective model into a single-objective one. Numerical tests and performance indicators were used to validate the effectiveness of both the multi-objective mathematical model and the proposed solution method. The validation computation results indicate that the proposed solution approach outperforms both the TH and SO approaches. © 2024 Regional Association for Security and crisis management. All rights reserved.

关键词： Multiobjective optimization

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Proposal of a Document Layout Analysis Method Adopting Pre-processing Based on Document Image Classification 16th

Proposal of a Document Layout Analysis Method Adopting Pre...

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16th International Conference on Genetic and Evolutionary Computing, ICGEC 2024

作者： Kakinoki, Kanta Katayama, Tetsuro Department of Computer Science and Systems Engineering Faculty of Engineering University of Miyazaki 1-1 Gakuen-Kibanadai Nishi Miyazaki889-2192 Japan

ISBN: (纸本)9789819615346

To extract important information from the document images, document layout analysis research has been carried out. Previous research analyzes document layouts only for specific document formats. This paper proposes a document layout analysis method that adopts pre-processing based on document image classification to analyze document layouts for various document formats. From the application examples, we have confirmed that the proposed method can correctly analyze document layout by using training models trained for each document format. Furthermore, to evaluate the usefulness of the proposed method, we have compared the F score of the proposed method for document layout analysis using a single training model covering various document formats and using a training model generated for each document format. As a result, we have confirmed that our proposed method is useful in document layout analysis. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： document image classification document layout analysis rule-based correction

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Harmony in transcripts: a systematic literature review of transcriptome-wide association studies

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Journal of engineering and Applied science 2024年第1期71卷 167页

作者： Mashhour, Mahinaz A. Kandil, Ahmed Hisham AbdElwahed, Manal Mabrouk, Mai S. Biomedical Engineering Department Misr University for Science and Technology Giza Egypt Systems and Biomedical Engineering Department Cairo University Giza Egypt School of Information Technology and Computer Science Nile University Sheikh Zayed City Egypt

Transcriptome-wide association studies (TWAS) goal is to better understand the etiology of diseases and develop preventative and therapeutic approaches by examining the connections between genetic variants and phenotypes while overcoming the limitations of the genome-wide association study (GWAS). It is a valuable complement to GWAS, reducing the negative effects of multiple tests and enabling a more thorough investigation of gene expression patterns in various tissues. A systematic review is presented in this paper to identify articles that utilize TWAS to understand the genetic factors behind complex diseases. A detailed selection process was carried out using standard PRISMA criteria to select relevant articles for the review. Twenty-five articles passed the inclusion criteria and were selected for additional review. The studies cover a diverse range of disorders, including Tourette’s syndrome, Alzheimer’s disease, rheumatoid arthritis, and major depression. Leveraging gene expression data from different tissues and populations, these investigations successfully identified novel genes and pathways associated with the studied conditions. The collective findings highlight the transformative impact of integrative genomics in advancing our understanding of complex diseases, providing insights into potential therapeutic targets, and laying the foundation for precision medicine approaches. © The Author(s) 2024.

关键词： Genome

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