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A Dynamic Resource-Aware Routing Protocol in Resource-Constrained Opportunistic Networks

computers, Materials & Continua 2022年第2期70卷 4147-4167页

作者： Aref Hassan Kurd Ali Halikul Lenando Slim Chaoui Mohamad Alrfaay Medhat A.Tawfeek Department of Computer Systems and Communication Technologies Faculty of Computer Science and Information TechnologyUniversiti Malaysia Sarawak(UNIMAS)Kota Samarahan94300Malaysia Department of Computer Engineering and Networks College of Computer and Information SciencesJouf UniversitySakaka2014KSA Unit-Lab of Sciences of Electronics Technologies of Information and TelecommunicationsSfax UniversitySfaxTunisia Department of Basic Sciences First Common Year DeanshipJouf UniversitySakaka2014KSA Department of Computer Sciences College of Computer and Information SciencesJouf UniversitySakaka2014KSA Department of Computer Sciences Faculty of Computers and InformationMenoufia UniversityEgypt

Recently,Opportunistic Networks(OppNets)are considered to be one of the most attractive developments of Mobile Ad Hoc Networks that have arisen thanks to the development of intelligent *** are characterized by a rough and dynamic topology as well as unpredictable contacts and contact *** is forwarded and stored in intermediate nodes until the next opportunity ***,achieving a high delivery ratio in OppNets is a challenging *** is imperative that any routing protocol use network resources,as far as they are available,in order to achieve higher network *** this article,we introduce the Resource-Aware Routing(ReAR)protocol which dynamically controls the buffer usage with the aim of balancing the load in resource-constrained,stateless and non-social *** ReAR protocol invokes our recently introduced mutual informationbased weighting approach to estimate the impact of the buffer size on the network performance and ultimately to regulate the buffer consumption in real *** proposed routing protocol is proofed conceptually and simulated using the Opportunistic Network Environment *** show that the ReAR protocol outperforms a set of well-known routing protocols such as EBR,Epidemic MaxProp,energy-aware Spray and Wait and energy-aware PRoPHETin terms of message delivery ratio and overhead ratio.

关键词： Opportunistic networks mobile ad hoc networks routing protocols resource-constrained networks load balancing buffer management

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Traffic Management in Internet of Vehicles Using Improved Ant Colony Optimization

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computers, Materials & Continua 2023年第6期75卷 5379-5393页

作者： Abida Sharif Imran Sharif Muhammad Asim Saleem Muhammad Attique Khan Majed Alhaisoni Marriam Nawaz Abdullah Alqahtani Ye Jin Kim Byoungchol Chang Department of Computer Science COMSATS University IslamabadPakistan Faculty of Computing Riphah International UniversityFaisalabadPakistan Department of Computer Science HITEC UniversityTaxilaPakistan Computer Sciences Department College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityRiyadh11671Saudi Arabia Department of Computer Science UET TaxilaTaxilaPakistan Department of Software Engineering UET TaxilaTaxilaPakistan Software Engineering Department College of Computer Engineering and SciencesPrince Sattam bin Abdulaziz UniversityP.O.Box 151Al-Kharj11942KSA Department of Computer Science Hanyang UniversitySeoul04763Korea Center for Computational Social Science Hanyang UniversitySeoul04763Korea

The Internet of Vehicles(IoV)is a networking paradigm related to the intercommunication of vehicles using a *** a dynamic network,one of the key challenges in IoV is traffic management under increasing vehicles to avoid ***,optimal path selection to route traffic between the origin and destination is *** research proposed a realistic strategy to reduce traffic management service response time by enabling real-time content distribution in IoV systems using heterogeneous network ***,this work proposed a novel use of the Ant Colony Optimization(ACO)algorithm and formulated the path planning optimization problem as an Integer Linear Program(ILP).This integrates the future estimation metric to predict the future arrivals of the vehicles,searching the optimal *** the mobile nature of IOV,fuzzy logic is used for congestion level estimation along with the ACO to determine the optimal *** model results indicate that the suggested scheme outperforms the existing state-of-the-art methods by identifying the shortest and most cost-effective ***,this work strongly supports its use in applications having stringent Quality of Service(QoS)requirements for the vehicles.

关键词： Internet of vehicles internet of things fuzzy logic optimization path planning

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Evaluating Pretrained Deep Learning Models for COVID-19 Detection in Medical Imaging 1

Evaluating Pretrained Deep Learning Models for COVID-19 Dete...

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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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Bipartite Graph-Based Recommendation systems: Introducing the Enhanced Graph Convolutional Neural Network Method for Indonesian Tourism 7

Bipartite Graph-Based Recommendation Systems: Introducing th...

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7th International Conference on New Media Studies, CONMEDIA 2023

作者： Kurniadi, Felix Indra Sutomo, Rudi School of Computer Science Bina Nusantara University Computer Science Department Jakarta11530 Indonesia Universitas Multimedia Nusantara Information Systems Faculty of Engineering & Informatics Banten15810 Indonesia

ISBN: (纸本)9798350307504

Indonesia's tourism sector, a cornerstone of its economy, has seen significant growth with both domestic and international tourists, drawn by its diverse landscapes and cultural sites. In 2022, the country welcomed around 10 million international visitors, indicating the industry's potential. In this context, employing artificial intelligence for personalized travel recommendations is crucial. This study proposes an innovative approach, converting tourism data into a bipartite format focused on the user-place relationship. We introduce the Enhanced Graph Convolutional Neural Network (Enhanced GCN), tailored for the tourism sector's complexities. Compared to the traditional Graph Convolutional Network (GCN), the Enhanced GCN shows superior predictive accuracy, reducing the Mean Squared Error (MSE) to 2.38. This advance represents a significant step in providing more accurate, customized travel suggestions. However, the current MSE suggests room for further improvement. Future research will aim to refine the Enhanced GCN, enhancing its ability to meet and predict tourists' needs in Indonesia's dynamic tourism environment. Ultimately, the goal is to use artificial intelligence to transform how visitors experience Indonesia's cultural and natural offerings, thereby bolstering the tourism industry's growth.. © 2023 IEEE.

关键词： Recommender systems

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A Combined Approach of Principal Component Analysis and Support Vector Machine for Early Development Phase Modeling of Ohrid Trout(Salmo Letnica)

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computer Modeling in engineering & Sciences 2021年第3期126卷 991-1009页

作者： Sunil Kr.Jha Ivan Uzunov Xiaorui Zhang School of Computer and Software Nanjing University of Information Science and TechnologyNanjing210044China Faculty of Computer Science and Engineering University of Information Science and TechnologyOhrid6000Republic of Macedonia

Ohrid trout(Salamo letnica)is an endemic species of fish found in Lake Ohrid in the Former Yugoslav Republic of Macedonia(FYROM).The growth of Ohrid trout was examined in a controlled environment for a certain period,thereafter released into the lake to grow their natural *** external features of the fish were measured regularly during the cultivation period in the laboratory to monitor their *** data mining methods-based computational model can be used for fast,accurate,reliable,automatic,and improved growth monitoring procedures and classification of Ohrid *** this motivation,a combined approach of principal component analysis(PCA)and support vectormachine(SVM)has been implemented for the visual discrimination and quantitative classification of Ohrid trout of the experimental and natural breeding and their growth *** PCA results in better discrimination of breeding categories of Ohrid trout at different development phases while the maximum classification accuracy of 98.33% was achieved using the combination of PCA and *** classification performance of the combination of PCA and SVM has been compared to combinations of PCA and other classification methods(multilayer perceptron,naive Bayes,randomcommittee,decision stump,random forest,and random tree).Besides,the classification accuracy of multilayer perceptron using the original features has been studied.

关键词： Salamo letnica growth phase modeling PCA SVM

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SUPPLYDECK - A Blockchain-Based Pharmaceutical Supply Chain Management 4

SUPPLYDECK - A Blockchain-Based Pharmaceutical Supply Chain ...

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4th International Conference on Advancements in Computing, ICAC 2022

作者： Amarasinghe, A.A.B.G. Gamagedara, G.M.H.S. Pitigala, K.K. Subawickrama, H.D.A.E. Abeywardena, Kavinga Yapa Yapa, Kanishka Department of Computer Science and Software Engineering Faculty of Computing Sri Lanka Institute of Information Technology Malabe Sri Lanka Department of Information Technology Faculty of Computing Sri Lanka Institute of Information Technology Malabe Sri Lanka Department of Information Systems Engineering Faculty of Computing Sri Lanka Institute of Information Technology Malabe Sri Lanka

ISBN: (纸本)9798350398090

With the rising competition among pharmaceutical companies, the current drug supply chain market has become more competitive with high-quality product segments. The rapid growth of internet pharmacies has made it more difficult to standardize drug safety throughout in complex distributed supply chain networks. There is a high chance of introducing counterfeit drugs which are almost the same as the original pharmaceuticals. Due to the lack of transparency the possibility of tampering with drugs in the current manual and web-based pharmaceutical systems is extremely high. Throughout the past recent years, these counterfeit drugs were recognized as the one of major worldwide problems. This situation is worse in developing countries. The unavailability of verifying the legitimacy of drug suppliers and poor mechanisms to trace the drugs were identified as critical points that need to be resolved. SUPPLYDECK comes as a solution for the critical scenario mentioned. This solution addresses the above problems using blockchain technology, as a distributed digital ledger that ensures transparency, traceability, and security. This can show promise for solving some global supply chain management problems using smart contracts and user authentication along with IoT technology and machine learning concepts that are critically examined with this potential application. © 2022 IEEE.

关键词： Smart contract

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Sentiment Analysis On Arabic Companies Reviews 6

Sentiment Analysis On Arabic Companies Reviews

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6th International Conference on Computing and Informatics, ICCI 2024

作者： Fouda, Aya E. Ahmed, Karim Salah Mohamed, Karim Ashraf Noshy, Mayer Mamdouh ElKattan, Youssef Mhran, Amany Ahmed Abdelbaky, Ibrahim Fouad, Khaled M. School of Computer Science Nile University Giza Egypt Artificial Intelligence Department Faculty of Computers and Artificial Intelligence Benha University Banha13518 Egypt Information Systems Faculty of Computers and Artificial Intelligence Benha University Egypt Faculty of Computer Science and Engineering New Mansoura University Dakhlia Egypt

ISBN: (纸本)9798350373875

This study introduces an innovative approach to sentiment analysis, specifically tailored for the Arabic language, a domain that poses unique challenges due to its complex morphology and diverse dialects. Utilizing a substantial dataset of over 108,000 reviews related to Arabic companies, our primary objective was to develop a robust and reliable sentiment scoring system that caters to the intricacies of the Arabic language, aimed at assisting businesses in understanding customer sentiments more *** methodology encompassed an extensive preprocessing phase, crucial for preparing the dataset for accurate analysis. This phase included converting emojis and emoticons into textual descriptions a contemporary and expressive facet of digital communication, removing Arabic diacritics to standardize text input, and normalizing the text for consistency. The preprocessing was carefully designed to address the specific challenges posed by the Arabic script and dialectal *** the core sentiment analysis, we employed two robust machine learning models: Logistic Regression and Naive Bayes. Each model was meticulously chosen and adapted for its proven effectiveness in text classification, especially in handling the nuances of sentiment analysis within the Arabic text. Our approach not only leverages the strengths of these models but also adapts them to accommodate the linguistic characteristics of *** results of our study provide significant contributions to the field of sentiment analysis in Arabic. By addressing the unique challenges of the language and adapting conventional machine learning techniques accordingly, our research offers valuable insights and tools for businesses and researchers focused on the Arab market. These insights are instrumental for companies seeking to understand and respond to customer sentiments in a linguistically diverse and culturally rich region. © 2024 IEEE.

关键词： Sentiment analysis

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Entropy Based Feature Fusion Using Deep Learning for Waste Object Detection and Classification Model

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computer systems Science & engineering 2023年第12期47卷 2953-2969页

作者： Ehab Bahaudien Ashary Sahar Jambi Rehab B.Ashari Mahmoud Ragab Electrical and Computer Engineering Department Faculty of EngineeringKing Abdulaziz UniversityJeddah21589Saudi Arabia Information Systems Department Faculty of Computing and Information TechnologyKing Abdulaziz UniversityJeddah21589Saudi Arabia Information Technology Department Faculty of Computing and Information TechnologyKing Abdulaziz UniversityJeddah21589Saudi Arabia Department of Mathematics Faculty of ScienceAl-Azhar UniversityNaser CityCairo11884Egypt

Object Detection is the task of localization and classification of objects in a video or *** recent times,because of its widespread applications,it has obtained more *** the modern world,waste pollution is one significant environmental *** prominence of recycling is known very well for both ecological and economic reasons,and the industry needs higher *** object detection utilizing deep learning(DL)involves training a machine-learning method to classify and detect various types of waste in videos or *** technology is utilized for several purposes recycling and sorting waste,enhancing waste management and reducing environmental *** studies of automatic waste detection are difficult to compare because of the need for benchmarks and broadly accepted standards concerning the employed data ***,this study designs an Entropy-based Feature Fusion using Deep Learning forWasteObject Detection and Classification(EFFDL-WODC)*** presented EFFDL-WODC system inherits the concepts of feature fusion and DL techniques for the effectual recognition and classification of various kinds of waste *** the presented EFFDL-WODC system,two major procedures can be contained,such as waste object detection and waste object *** object detection,the EFFDL-WODC technique uses a YOLOv7 object detector with a fusionbased backbone *** addition,entropy feature fusion-based models such as VGG-16,SqueezeNet,and NASNetmodels are ***,the EFFDL-WODC technique uses a graph convolutional network(GCN)model performed for the classification of detected waste *** performance validation of the EFFDL-WODC approach was validated on the benchmark *** comprehensive comparative results demonstrated the improved performance of the EFFDL-WODC technique over recent approaches.

关键词： Object detection object classification waste management deep learning feature fusion

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Covid-19 CT Lung Image Segmentation Using Adaptive Donkey and Smuggler Optimization Algorithm

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computers, Materials & Continua 2022年第4期71卷 1133-1152页

作者： P.Prabu K.Venkatachalam Ala Saleh Alluhaidan Radwa Marzouk Myriam Hadjouni Sahar A.El_Rahman Department of Computer Science CHRIST(Deemed to be University)BengaluruIndia Department of Computer Science and Engineering CHRIST(Deemed to be University)BengaluruIndia Information Systems Department College of Computer and Information SciencePrincess Nourah Bint Abdulrahman UniversityRiyadhKSA 84428Saudi Arabia Department of Mathematics Faculty of ScienceCairo UniversityGiza12613Egypt Computer Sciences Department College of Computer and Information SciencePrincess Nourah Bint Abdulrahman UniversityRiyadhSaudi Arabia Department of Electrical Engineering Faculty of Engineering-ShoubraBenha UniversityCairoEgypt

COVID’19 has caused the entire universe to be in existential healthcrisis by spreading globally in the year 2020. The lungs infection is detected inComputed Tomography (CT) images which provide the best way to increasethe existing healthcare schemes in preventing the deadly virus. Nevertheless,separating the infected areas in CT images faces various issues such as lowintensity difference among normal and infectious tissue and high changes inthe characteristics of the infection. To resolve these issues, a new inf-Net (LungInfection Segmentation Deep Network) is designed for detecting the affectedareas from the CT images automatically. For the worst segmentation results,the Edge-Attention Representation (EAR) is optimized using AdaptiveDonkey and Smuggler Optimization (ADSO). The edges which are identifiedby the ADSO approach is utilized for calculating dissimilarities. An IFCM(Intuitionistic Fuzzy C-Means) clustering approach is applied for computingthe similarity of the EA component among the generated edge maps andGround-Truth (GT) edge maps. Also, a Semi-Supervised Segmentation(SSS) structure is designed using the Randomly Selected Propagation (RP)technique and Inf-Net, which needs only less number of images and unlabelleddata. Semi-Supervised Multi-Class Segmentation (SSMCS) is designed usinga Bi-LSTM (Bi-Directional Long-Short-Term-memory), acquires all theadvantages of the disease segmentation done using Semi Inf-Net and enhancesthe execution of multi-class disease labelling. The newly designed SSMCSapproach is compared with existing U-Net++, MCS, and *** such as MAE (Mean Absolute Error), Structure measure, Specificity(Spec), Dice Similarity coefficient, Sensitivity (Sen), and Enhance-AlignmentMeasure are considered for evaluation purpose.

关键词： Adaptive donkey and snuggler optimization.bi-directional long short term memory coronavirus disease 2019 randomly selected propagation semi-supervised learning

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Digitalization of Railway Transportation as a Factor for Improving the Quality of the Service 3

Digitalization of Railway Transportation as a Factor for Imp...

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3rd International Conference on Applied Mathematics and computer Science, ICAMCS 2023

作者： Petriashvili, Lily Modebadze, Zurab Lominadze, Tamar Kiknadze, Mzia Otkhozoria, Nona Zhvania, Taliko Georgian Technical University Software Engineering Tbilisi Georgia Georgian Technical University Information Systems Tbilisi Georgia Georgian Technical University Information Technology Tbilisi Georgia Georgian Technical University Computer Engineering Tbilisi Georgia Georgian Technical University Microprocessors and Measurement Systems Tbilisi Georgia

ISBN: (纸本)9798350324266

Current global events, such as the COVID-19 pandemic and the Russian-Ukrainian war, have had a notable impact on the rise in the volume of freight transportation on the Georgian railway. Consequently, the efficient utilization of railway systems, along with monitoring the state of operational processes and technical equipment, plays a crucial role in ensuring smooth and uninterrupted transportation. Key factors to consider include speed, reliability, and the quality of shipments. Railway transportation is progressing worldwide, and effective management is undeniably a priority for our *** essential element for identifying and managing freight wagons is the Arabic numerals printed on them, which indicate their main characteristics and ownership. According to internationally recognized agreements, foreign wagons, as well as those owned or temporarily used by railway administrations, must be promptly returned to their country of dispatch. Within the first 24 hours of crossing the border, "inventory"freight wagons are subject to an average daily charge of 20 Swiss francs, while owned wagons incur an average daily rental fee of US$30. This circumstance, along with rolling stock management, presents one of the critical ***, hundreds of railway employees are involved in identifying the rolling stock in our country. These workers walk along the stations where the parked rolling stock is located. Once they visually recognize the freight car numbers, they relay the information to the relevant station operators via transmitters. Initially, these operators manually record the numbers in special journals and then enter the data into a unified electronic registry system. As a result, railway freight shipments require significant human and time resources, with a high probability of error. This situation considerably slows down the speed of railway service and increases its costs. © 2023 IEEE.

关键词： Radio frequency identification (RFID)

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