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Metric-Based Resolvability of Quartz Structure

computers, Materials & Continua 2022年第4期71卷 2053-2071页

作者： Muhammad Imran Ali Ahmad Muhammad Azeem Kashif Elahi Department of Mathematical Sciences United Arab Emirates UniversityAl AinUnited Arab Emirates College of Computer Science&Information Technology Jazan University JazanSaudi Arabia Department of Aerospace Engineering Faculty of EngineeringUniversiti Putra MalaysiaMalaysia Deanship of E-learning and Information Technology Jazan UniversityJazanSaudi Arabia

Silica has three major varieties of crystalline. Quartz is the main andabundant ingredient in the crust of our earth. While other varieties are formedby the heating of quartz. Silica quartz is a rich chemical structure containingenormous properties. Any chemical network or structure can be transformedinto a graph, where atoms become vertices and the bonds are converted toedges, between vertices. This makes a complex network easy to visualize towork on it. There are many concepts to work on chemical structures in termsof graph theory but the resolvability parameters of a graph are quite advanceand applicable topic. Resolvability parameters of a graph is a way to getting agraph into unique form, like each vertex or edge has a unique identification bymeans of some selected vertices, which depends on the distance of vertices andits pattern in a particular graph. We have dealt some resolvability parametersof SiO2 quartz. We computed the resolving set for quartz structure and itsvariants, wherein we proved that all the variants of resolvability parameters ofquartz structures are constant and do not depend on the order of the graph.

关键词： Quartz polycyclic aromatic hydrocarbon related structure metric dimension metric or distance-based resolvability parameters

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A model of service-oriented architecture of e-certification system to support boat registration and site visit inspection to support maritime safety and crew health inspection

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Marine systems and Ocean Technology 2023年第1-2期18卷 45-63页

作者： Saputra, Diaz Gaol, Ford Lumban Abdurachman, Edi Sensuse, Dana Indra Matsuo, Tokuro Department of Computer Science BINUS Graduate Program - Doctor of Computer Science Bina Nusantara University Jakarta11480 Indonesia Department of Computer Science Binus Graduate Program - Master in Information System Bina Nusantara University Jakarta11480 Indonesia Department of Research in Management Bina Nusantara University Jakarta Indonesia Faculty of Computer Science University of Indonesia Jakarta Indonesia Advanced Institute of Industrial Technology Tokyo Japan City University of Macau China Asia University Taichung Taiwan

This article describes the architecture of software services that provide registration of small boat data to build maritime safety. The proposed system architecture which so-called SIMKAPEL aims to support small boat registration for maritime safety. It is developed from a conventional service-oriented architecture (SOA) with an open standard for data interoperability between elements. The goal of this research is to implement and evaluate the proposed architecture to support small boat registration and e-certification. Several parts of the architecture have been implemented as a platform that provides core services and data sources to be used by Ministry of Transportation, Directorate General of Sea Transportation, and other government agencies. Finally, the proposed SIMKAPEL platform with the SOA architecture is evaluated for its benefits to users through the expert opinion survey to measure its efficiency. The analysis results showed that SOA-based SIMKAPEL is feasible to use as a boat e-certification system. Furthermore, experiment results provide three parameters (e.g., maritime safety, namely, cargo/passengers, boat safety facilities, and maritime safety systems) with efficiency values of 42%, 46%, and 42%, respectively. The parameter with the highest efficiency shows a big effect on the level of safety of the boat and passengers. Thus, the parameters that need to be prioritized and need boat owners’ attention are cargo/passenger, boat safety equipment, and maritime safety system. © 2023, The Author(s).

关键词： Boat e-certification Boat online certification Cloud-based service Integrated system SOA architecture

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Natural Language Processing with Optimal Deep Learning-Enabled Intelligent Image Captioning system

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computers, Materials & Continua 2023年第2期74卷 4435-4451页

作者： Radwa Marzouk Eatedal Alabdulkreem Mohamed KNour Mesfer Al Duhayyim Mahmoud Othman Abu Sarwar Zamani Ishfaq Yaseen Abdelwahed Motwakel Department of Information Systems College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Computer Sciences College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Computer Sciences College of Computing and Information SystemUmm Al-Qura UniversitySaudi Arabia Department of Computer Science College of Sciences and Humanities-AflajPrince Sattam bin Abdulaziz UniversitySaudi Arabia Department of Computer Science Faculty of Computers and Information TechnologyFuture University in EgyptNew Cairo11835Egypt Department of Computer and Self Development Preparatory Year DeanshipPrince Sattam bin Abdulaziz UniversityAlKharjSaudi Arabia

The recent developments in Multimedia Internet of Things(MIoT)devices,empowered with Natural Language Processing(NLP)model,seem to be a promising future of smart *** plays an important role in industrial models such as speech understanding,emotion detection,home automation,and so *** an image needs to be captioned,then the objects in that image,its actions and connections,and any silent feature that remains under-projected or missing from the images should be *** aim of the image captioning process is to generate a caption for *** next step,the image should be provided with one of the most significant and detailed descriptions that is syntactically as well as semantically *** this scenario,computer vision model is used to identify the objects and NLP approaches are followed to describe the *** current study develops aNatural Language Processing with Optimal Deep Learning Enabled Intelligent Image Captioning system(NLPODL-IICS).The aim of the presented NLPODL-IICS model is to produce a proper description for input *** attain this,the proposed NLPODL-IICS follows two stages such as encoding and decoding ***,at the encoding side,the proposed NLPODL-IICS model makes use of Hunger Games Search(HGS)with Neural Search Architecture Network(NASNet)*** model represents the input data appropriately by inserting it into a predefined length ***,during decoding phase,Chimp Optimization Algorithm(COA)with deeper Long Short Term Memory(LSTM)approach is followed to concatenate the description sentences 4436 CMC,2023,vol.74,no.2 produced by the *** application of HGS and COA algorithms helps in accomplishing proper parameter tuning for NASNet and LSTM models *** proposed NLPODL-IICS model was experimentally validated with the help of two benchmark *** comparative analysis confirmed the superior performance of NLPODL-IICS model over other models.

关键词： Natural language processing information retrieval image captioning deep learning metaheuristics

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3D Object Detection for Autonomous Driving: A Comprehensive Review

3D Object Detection for Autonomous Driving: A Comprehensive ...

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Computing and Informatics (ICCI), International Conference on

作者： Ahmed Soliman Nagiub Mahmoud Fayez Heba Khaled Said Ghoniemy Computer System Department Faculty of Computer and information Science Ain Shams University Cairo Egypt

accurate perception is a critical component in enabling autonomous vehicles to understand their driving environment. The acquisition of 3D information about objects, including their location and pose, is essential for achieving this understanding. This survey paper presents a comprehensive review of 3D object detection techniques specifically tailored for autonomous vehicles. The survey begins with an introduction to 3D object detection, elucidating the significance of the third dimension in perceiving the driving environment. It explores the types of sensors utilized in this context and the corresponding data extracted from these sensors. Additionally, the survey investigates the different types of datasets employed, including their formats, sizes, and provides a comparative analysis. Furthermore, the paper categorizes and thoroughly examines the perception methods employed for 3D object detection, based on the diverse range of sensors utilized. Each method is evaluated based on its effectiveness in accurately detecting objects in a three-dimensional space. Additionally, the evaluation metrics used to assess the performance of these methods are discussed. By offering a comprehensive overview of 3D object detection techniques for autonomous vehicles, this survey aims to advance the field of perception systems. It serves as a valuable resource for researchers and practitioners, providing insights into the techniques, sensors, and evaluation metrics employed in 3D object detection for autonomous vehicles.

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A Framework for Food Availability Forecasting Using Temporal Knowledge Graph

A Framework for Food Availability Forecasting Using Temporal...

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International Conference on computer and Applications (ICCA)

作者： Nada Eltaki Khaled Nagaty Doaa ElZanfaly Noura Maghawry Department of Information System Faculty of Informatics and Computer Science The British University in Egypt Cairo Egypt Department of Computer Science Faculty of Informatics and Computer Science The British University in Egypt Cairo Egypt Department of Information System Faculty of Informatics and Computer Science Helwan University Cairo Egypt

ISBN: (数字)9798350367560

ISBN: (纸本)9798350367577

The entire world encounters a significant problem which is food availability. “Zero-hunger” is the second Sustainable Development Goal (SDG) of the goals established by the United Nations in 2015. With the increasing number of people and various factors affecting food availability such as climate and country-based food production rate, food forecasting models have become increasingly essential. However, traditional forecasting techniques are limited in dealing with the data and identifying the relationships between them within a specific timestamp. This paper proposes a framework for an intelligent food availability forecasting model built using deep learning techniques and based on a temporal knowledge graph where different timestamps are taken into consideration. The proposed framework helps in enhancing and overcoming the challenges and limitations of the recent forecasting techniques leading to a dynamic, scalable, intelligent forecasting model.

关键词： Surveys Food manufacturing Computational modeling Knowledge graphs Predictive models Solids Dynamic scheduling Forecasting Sustainable development Meteorology

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Optimal Bottleneck-Driven Deep Belief Network Enabled Malware Classification on IoT-Cloud Environment

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computers, Materials & Continua 2023年第2期74卷 3101-3115页

作者： Mohammed Maray Hamed Alqahtani Saud S.Alotaibi Fatma S.Alrayes Nuha Alshuqayran Mrim M.Alnfiai Amal S.Mehanna Mesfer Al Duhayyim Department of Information Systems College of Computer ScienceKing Khalid UniversityAbhaSaudi Arabia Department of Information Systems College of Computer ScienceCenter of Artificial IntelligenceUnit of CybersecurityKing Khalid UniversityAbhaSaudi Arabia Department of Information Systems College of Computing and Information SystemUmm Al-Qura UniversitySaudi Arabia Department of information systems College of Computer and Information SciencesPrincess Nourah Bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Information Systems College of Computer and Information SciencesImam Mohammad Ibn Saud Islamic UniversitySaudi Arabia Department of Information Technology College of Computers and Information TechnologyTaif UniversityP.O.Box 11099Taif21944Saudi Arabia Department of Digital Media Faculty of Computers and Information TechnologyFuture University in EgyptNew Cairo11845Egypt Department of Computer Science College of Sciences and Humanities-AflajPrince Sattam Bin Abdulaziz UniversitySaudi Arabia

Cloud Computing(CC)is the most promising and advanced technology to store data and offer online services in an effective *** such fast evolving technologies are used in the protection of computerbased systems from cyberattacks,it brings several advantages compared to conventional data protection *** of the computer-based systems that effectively protect the data include Cyber-Physical systems(CPS),Internet of Things(IoT),mobile devices,desktop and laptop computer,and critical *** software(malware)is nothing but a type of software that targets the computer-based systems so as to launch cyberattacks and threaten the integrity,secrecy,and accessibility of the *** current study focuses on design of Optimal Bottleneck driven Deep Belief Network-enabled Cybersecurity Malware Classification(OBDDBNCMC)*** presentedOBDDBN-CMCmodel intends to recognize and classify the malware that exists in IoT-based cloud *** attain this,Zscore data normalization is utilized to scale the data into a uniform *** addition,BDDBN model is also exploited for recognition and categorization of *** effectually fine-tune the hyperparameters related to BDDBN model,GrasshopperOptimizationAlgorithm(GOA)is *** scenario enhances the classification results and also shows the novelty of current *** experimental analysis was conducted upon OBDDBN-CMC model for validation and the results confirmed the enhanced performance ofOBDDBNCMC model over recent approaches.

关键词： Malware detection security Internet of Things cloud computing machine learning parameter adjustment

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A Hierarchal Clustered Based Proactive Caching in NDN-Based Vehicular Network

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computer systems science & Engineering 2023年第10期47卷 1185-1208页

作者： Muhammad Yasir Khan Muhammad Adnan Jawaid Iqbal Noor ul Amin Byeong-Hee Roh Jehad Ali Department of Computer Science&Information Technology Hazara University Mansehra21120Pakistan Department of Electronics Engineering Kookmin University Seoul Korea Faculty of Computing Riphah International University Islamabad Pakistan Department of AI Convergence Network Ajou UniversitySuwon16499Korea

An information-Centric Network(ICN)provides a promising paradigm for the upcoming internet architecture,which will struggle with steady growth in data and changes in *** ICN architectures have been designed,including Named Data Networking(NDN),which is designed around content delivery instead of *** data is the central part of the ***,NDN was developed to get rid of the dependency on IP addresses and provide content *** is one of the major research dimensions for this upcoming internet *** research has been carried out to solve the mobility issues,but it still has problems like handover delay and packet loss ratio during real-time video streaming in the case of consumer and producer *** solve this issue,an efficient hierarchical Cluster Base Proactive Caching for Device Mobility Management(CB-PC-DMM)in NDN Vehicular Networks(NDN-VN)is proposed,through which the consumer receives the contents proactively after handover during the mobility of the *** a consumer moves to the next destination,a handover interest is sent to the connected router,then the router multicasts the consumer’s desired data packet to the next hop of neighboring ***,once the handover process is completed,consumers can easily get the content to the newly connected router.A CB-PCDMM in NDN-VN is proposed that improves the packet delivery ratio and reduces the handover delay aswell as cluster ***,the intra and inter-domain handover handling procedures in CB-PC-DMM for NDN-VN have been *** the validation of our proposed scheme,MATLAB simulations are *** simulation results show that our proposed scheme reduces the handover delay and increases the consumer’s interest satisfaction *** proposed scheme is compared with the existing stateof-the-art schemes,and the total percentage of handover delays is decreased by up to 0.1632%,0.3267%,2.3437%,2.3255%,and 3.7313%at the mobil

关键词： Vehicular network named data networking caching hierarchical architecture

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Rice Leaves Disease Diagnose Empowered with Transfer Learning

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computer systems science & Engineering 2022年第9期42卷 1001-1014页

作者： Nouh Sabri Elmitwally Maria Tariq Muhammad Adnan Khan Munir Ahmad Sagheer Abbas Fahad Mazaed Alotaibi College of Computer and Information Sciences Jouf UniversitySakaka72341Saudi Arabia Department of Computer Science Faculty of Computers and Artificial IntelligenceCairo University12613Egypt School of Computer Science National College of Business Administration and EconomicsLahore54000Pakistan Department of Computer Science Lahore Garrison UniversityLahore54000Pakistan Pattern Recognition and Machine Learning Lab Department of SoftwareGachon UniversitySeongnam13557Korea Department of Information System Faculty of Computing and Information TechnologyKing Abdulaziz UniversityJeddah21589Saudi Arabia

In the agricultural industry,rice infections have resulted in significant productivity and economic *** infections must be recognized early on to regulate and mitigate the effects of the *** diagnosis of disease severity effects or incidence can preserve production from quantitative and qualitative losses,reduce pesticide use,and boost ta country’s *** the health of a rice plant through its leaves is usually done as a manual ocular *** this manuscript,three rice plant diseases:Bacterial leaf blight,Brown spot,and Leaf smut,were identified using the Alexnet *** research shows that any reduction in rice plants will have a significant beneficial impact on alleviating global food hunger by increasing supply,lowering prices,and reducing production's environmental impact that affects the economy of any *** would be able to get more exact and faster results with this technology,allowing them to administer the most acceptable treatment *** Using Alex Net,the proposed approach achieved a 99.0%accuracy rate for diagnosing rice leaves disease.

关键词： Rice bacterial leaf blight brown spot leaf smut machine learning alexnet

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BHJO: A Novel Hybrid Metaheuristic Algorithm Combining the Beluga Whale, Honey Badger, and Jellyfish Search Optimizers for Solving Engineering Design Problems

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computer Modeling in Engineering & sciences 2024年第10期141卷 219-265页

作者： Farouq Zitouni Saad Harous Abdulaziz S.Almazyad Ali Wagdy Mohamed Guojiang Xiong Fatima Zohra Khechiba Khadidja Â Kherchouche Department of Computer Science and Information Technology Kasdi Merbah UniversityP.O.Box 30000OuarglaAlgeria Department of Computer Science College of Computing and InformaticsUniversity of SharjahP.O.Box 27272SharjahUnited Arab Emirates Department of Computer Engineering College of Computer and Information SciencesKing Saud UniversityP.O.Box 51178Riyadh11543Saudi Arabia Operations Research Department Faculty of Graduate Studies for Statistical ResearchCairo UniversityGiza12613Egypt Applied Science Research Center Applied Science Private UniversityAmman11937Jordan Guizhou Key Laboratory of Intelligent Technology in Power System College of Electrical EngineeringGuizhou UniversityGuiyang550025China

Hybridizing metaheuristic algorithms involves synergistically combining different optimization techniques to effectively address complex and challenging optimization *** approach aims to leverage the strengths of multiple algorithms,enhancing solution quality,convergence speed,and robustness,thereby offering a more versatile and efficient means of solving intricate real-world optimization *** this paper,we introduce a hybrid algorithm that amalgamates three distinct metaheuristics:the Beluga Whale Optimization(BWO),the Honey Badger Algorithm(HBA),and the Jellyfish Search(JS)*** proposed hybrid algorithm will be referred to as *** this fusion,the BHJO algorithm aims to leverage the strengths of each *** this hybridization,we thoroughly examined the exploration and exploitation capabilities of the BWO,HBA,and JS metaheuristics,as well as their ability to strike a balance between exploration and *** meticulous analysis allowed us to identify the pros and cons of each algorithm,enabling us to combine them in a novel hybrid approach that capitalizes on their respective strengths for enhanced optimization *** addition,the BHJO algorithm incorporates Opposition-Based Learning(OBL)to harness the advantages offered by this technique,leveraging its diverse exploration,accelerated convergence,and improved solution quality to enhance the overall performance and effectiveness of the hybrid ***,the performance of the BHJO algorithm was evaluated across a range of both unconstrained and constrained optimization problems,providing a comprehensive assessment of its efficacy and applicability in diverse problem ***,the BHJO algorithm was subjected to a comparative analysis with several renowned algorithms,where mean and standard deviation values were utilized as evaluation *** rigorous comparison aimed to assess the performance of the BHJOalgorithmabout its counterparts,shedd

关键词： Global optimization hybridization of metaheuristics beluga whale optimization honey badger algorithm jellyfish search optimizer chaotic maps opposition-based learning

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An Efficient Schema Transformation Technique for Data Migration from Relational to Column-Oriented Databases

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computer systems science & Engineering 2022年第12期43卷 1175-1188页

作者： Norwini Zaidi Iskandar Ishak Fatimah Sidi Lilly Suriani Affendey System Development and Engineering Center Universiti Sains Islam Malaysia71800Negeri SembilanMalaysia Faculty of Computer Science and Information Technology Universiti Putra MalaysiaUPM Serdang43400SelangorMalaysia

Data transformation is the core process in migrating database from relational database to NoSQL database such as column-oriented database. However,there is no standard guideline for data transformation from relational database toNoSQL database. A number of schema transformation techniques have been proposed to improve data transformation process and resulted better query processingtime when compared to the relational database query processing time. However,these approaches produced redundant tables in the resulted schema that in turnconsume large unnecessary storage size and produce high query processing timedue to the generated schema with redundant column families in the transformedcolumn-oriented database. In this paper, an efficient data transformation techniquefrom relational database to column-oriented database is proposed. The proposedschema transformation technique is based on the combination of denormalizationapproach, data access pattern and multiple-nested schema. In order to validate theproposed work, the proposed technique is implemented by transforming data fromMySQL database to MongoDB database. A benchmark transformation techniqueis also performed in which the query processing time and the storage size arecompared. Based on the experimental results, the proposed transformation technique showed significant improvement in terms query processing time and storagespace usage due to the reduced number of column families in the column-orienteddatabase.

关键词： Data migration data transformation column-oriented database relational database big data

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