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A Multi-Carrier Deep Learning CSK Communication System

IEEE Communications Letters 2025年第6期29卷 1191-1195页

作者： Mobini, Majid Kaddoum, Georges Herceg, Marijan Babol Noshirvani University of Technology Department of ECE Babol Iran University of Québec École de technologie supérieure Département de génie électrique MontréalQC Canada Lebanese American University Cyber Security Systems and Applied AI Research Center Beirut Lebanon University of Osijek Faculty of Electrical EngineeringComputer Science and Information Technology Department of Communications Croatia

A new Multi-Carrier Deep Learning Chaos Shift Keying (MC-DLCSK) modulation is designed in this paper. The proposed receiver includes a Neural Network (NN)-based classifier that recovers MC-DLCSK signals. The results demonstrate that the MC-DLCSK system achieves a considerable Bit Error Rate (BER) gain compared to all existing similar systems under both Additive White Gaussian Noise (AWGN) and Rayleigh fading channels. © 1997-2012 IEEE.

关键词： White noise

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Analysis and Assessment of Wind Energy Potential of Almukalla in Yemen

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computers, Materials & Continua 2022年第8期72卷 3113-3129页

作者： Murad A.A.Almekhlafi Fahd N.Al-Wesabi Majdy M.Eltahir Anwer Mustafa Hilal Amin M.El-Kustaban Abdelwahed Motwakel Ishfaq Yaseen Manar Ahmed Hamza Department of Electrical Engineering–Communication Engineering Sana’a UniversityYemen Department of Computer Science King Khalid UniversityMuhayelAseerSaudi Arabia Faculty of Computer and IT Sana’a UniversitySana’aYemen Department of Information Systems King Khalid UniversityMuhayelAseerSaudi Arabia Department of Computer and Self Development Preparatory Year DeanshipPrince Sattam bin Abdulaziz UniversityAlKharjSaudi Arabia Electronic Department Faculty of EngineeringUniversity of Science and TechnologySana’aYemen

Energy is an essential element for any civilized country’s social and economic development,but the use of fossil fuels and nonrenewable energy forms has many negative impacts on the environment and the *** Republic of Yemen has very good potential to use renewable ***,we find few studies on renewable wind energy in *** the lack of a similar analysis for the coastal city,this research newly investigates wind energy’s potential near the Almukalla area by analyzing wind ***,evaluation,model identification,determination of available energy density,computing the capacity factors for several wind turbines and calculation of wind energy were extracted at three heights of 15,30,and *** wind speeds were obtained only for the currently available data of five recent years,2005–*** study involves a preliminary assessment of Almukalla’s wind energy potential to provide a primary base and useful insights for wind engineers and *** research aims to provide useful assessment of the potential of wind energy in Almukalla for developing wind energy and an efficient wind *** Weibull distribution shows a perfect approximation for estimating the intensity of Yemen’s wind *** on both theWeibullmodel and the results of the annual wind speed data analysis for the study site in Mukalla,the capacity factor for many turbines was also calculated,and the best suitable turbine was *** to the International Wind Energy Rating criteria,Almukalla falls under Category 7,which is,rated“Superb”most of the year.

关键词： Almukalla energy potential Rayleigh distribution Weibull distribution wind power density wind speed

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Load Balancing Framework for Cross-Region Tasks in Cloud Computing

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computers, Materials & Continua 2022年第1期70卷 1479-1490页

作者： Jaleel Nazir Muhammad Waseem Iqbal Tahir Alyas Muhammad Hamid Muhammad Saleem Saadia Malik Nadia Tabassum Department of Software Engineering The Superior CollegeLahorePakistan Department of Computer Science Lahore Garrison UniversityLahore54000Pakistan Department of Statistics and Computer Science University of Veterinary and Animal SciencesLahore54000Pakistan Department of Industrial Engineering Faculty of EngineeringRabighKing Abdulaziz UniversityJeddah21589Saudi Arabia Department of Information Systems Faculty of Computing and Information Technology-RabighKing Abdulaziz UniversityJeddah21589Saudi Arabia Department of Computer Science Virtual University of PakistanLahore54000Pakistan

Load balancing is a technique for identifying overloaded and underloaded nodes and balancing the load between *** maximize various performance parameters in cloud computing,researchers suggested various load balancing *** store and access data and services provided by the different service providers through the network over different regions,cloud computing is one of the latest technology systems for both end-users and service *** volume of data is increasing due to the pandemic and a significant increase in usage of the internet has also been *** of the cloud are looking for services that are intelligent,and,can balance the traffic load by service providers,resulting in seamless and uninterrupted *** types of algorithms and techniques are available that can manage the load balancing in the cloud *** this paper,a newly proposed method for load balancing in cloud computing at the database level is *** database cloud services are frequently employed by companies of all sizes,for application development and business *** balancing for distributed applications can be used to maintain an efficient task scheduling process that also meets the user requirements and improves resource *** balancing is the process of distributing the load on various nodes to ensure that no single node is *** avoid the nodes from being overloaded,the load balancer divides an equal amount of computing time to all *** results of two different scenarios showed the cross-region traffic management and significant growth in revenue of restaurants by using load balancer decisions on application traffic gateways.

关键词： Load balancing performance database region virtualization

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Automated BPMN Diagram Generation

Automated BPMN Diagram Generation

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Advancements in Computing (ICAC), International Conference on

作者： Farhath Ajmal Poorna Wijekoon Haritha Dhanamina Yasiru Ravishan Dasuni Nawinna Buddhima Attanayaka Department of Information Technology Faculty of Computing Sri Lanka Institute of Information Technology Malabe Sri Lanka Department of Computer Systems Engineering Faculty of Computing Sri Lanka Institute of Information Technology Malabe Sri Lanka

ISBN: (数字)9798331517878

ISBN: (纸本)9798331517885

The automation of business process modelling has become crucial for organizations seeking to improve their operational efficiency. This research presents a novel methodology that leverages fine-tuned GPT models to automate the conversion of textual business process descriptions into Business Process Model and Notation (BPMN) diagrams. The study builds on previous work, addressing gaps related to the handling of unstructured text and the absence of anaphora resolution in our solution we use fine-tuned GPT models to accurately extract and structure business process activities from text input from end users, which are then converted into BPMN diagrams using a custom generation framework. The evaluation results demonstrate the system's accuracy with an average precision of 80%, recall of 76%, and F1 score of 78%. These findings outline the potential of using advanced language models to streamline business process modelling, offering significant improvements in accuracy and efficiency. The research concludes by suggesting enhancements for future iterations, including fine-tuning with advanced models and expanding support for complex BPMN elements, aiming to further optimize workflow for organizations.

关键词： Training Accuracy Process modeling Large language models Computational modeling Organizations Writing Logic gates User experience Robustness

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Suggestion Mining from Opinionated Text of Big Social Media Data

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computers, Materials & Continua 2021年第9期68卷 3323-3338页

作者： Youseef Alotaibi Muhammad Noman Malik Huma Hayat Khan Anab Batool Saif ul Islam Abdulmajeed Alsufyani Saleh Alghamdi Department of Computer Science College of Computer and Information SystemsUmm Al-Qura UniversitySaudi Arabia Department of Computer Science Faculty of Engineering and Computer SciencesNational University of Modern LanguagesIslamabadPakistan Department of Software Engineering Faculty of Engineering and Computer SciencesNational University of Modern LanguagesIslamabadPakistan Department of Computer Sciences Institute of Space TechnologyIslamabadPakistan Department of Computer Science College of Computers and Information TechnologyTaif UniversityTaif21944Saudi Arabia Department of Information Technology College of Computers and Information TechnologyTaif UniversityTaifSaudi Arabia

:Social media data are rapidly increasing and constitute a source of user opinions and tips on a wide range of products and *** increasing availability of such big data on biased reviews and blogs creates challenges for customers and businesses in reviewing all content in their decision-making *** overcome this challenge,extracting suggestions from opinionated text is a possible *** this study,the characteristics of suggestions are analyzed and a suggestion mining extraction process is presented for classifying suggestive sentences from online customers’reviews.A classification using a word-embedding approach is used via the XGBoost *** two datasets used in this experiment relate to online hotel reviews and Microsoft Windows App Studio discussion reviews.F1,precision,recall,and accuracy scores are *** results demonstrated that the XGBoost classifier outperforms—with an accuracy of more than 80%.Moreover,the results revealed that suggestion keywords and phrases are the predominant features for suggestion ***,this study contributes to knowledge and practice by comparing feature extraction classifiers and identifying XGBoost as a better suggestion mining process for identifying online reviews.

关键词： Suggestion mining word embedding Naïve Bayes random forest XGBoost dataset

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Deep Transfer Learning-Enabled Activity Identification and Fall Detection for Disabled People

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computers, Materials & Continua 2023年第5期75卷 3239-3255页

作者： Majdy M.Eltahir Adil Yousif Fadwa Alrowais Mohamed K.Nour Radwa Marzouk Hatim Dafaalla Asma Abbas Hassan Elnour Amira Sayed A.Aziz Manar Ahmed Hamza Department of Computer Science College of Science&Art at MahayilKing Khalid UniversitySaudi Arabia Faculty of Arts and Science Najran UniversitySharourahSaudi Arabia Department of Computer Sciences College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Industrial Engineering College of Engineering at AlqunfudahUmm Al-Qura UniversitySaudi Arabia Department of Information Systems College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Computer Science College of Applied SciencesKing Khalid UniversityMuhayil63772Saudi Arabia Department of Digital Media 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 human motion data collected using wearables like smartwatches can be used for activity recognition and emergency event *** is especially applicable in the case of elderly or disabled people who live self-reliantly in their *** sensors produce a huge volume of physical activity data that necessitates real-time recognition,especially during *** is one of the most important problems confronted by older people and people with movement *** previous techniques were introduced and a few used webcam to monitor the activity of elderly or disabled ***,the costs incurred upon installation and operation are high,whereas the technology is relevant only for indoor ***,commercial wearables use a wireless emergency transmitter that produces a number of false alarms and restricts a user’s *** this background,the current study develops an Improved WhaleOptimizationwithDeep Learning-Enabled Fall Detection for Disabled People(IWODL-FDDP)*** presented IWODL-FDDP model aims to identify the fall events to assist disabled *** presented IWODLFDDP model applies an image filtering approach to pre-process the ***,the EfficientNet-B0 model is utilized to generate valuable feature vector ***,the Bidirectional Long Short Term Memory(BiLSTM)model is used for the recognition and classification of fall ***,the IWO method is leveraged to fine-tune the hyperparameters related to the BiLSTM method,which shows the novelty of the *** experimental analysis outcomes established the superior performance of the proposed IWODL-FDDP method with a maximum accuracy of 97.02%.

关键词： Fall detection disabled people deep learning improved whale optimization assisted living

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Implementation of AI-Based Support Vector Machine Algorithm for Fraud Detection in Digital Payment systems

Implementation of AI-Based Support Vector Machine Algorithm ...

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International Seminar on Research of information technology and Intelligent systems (ISRITI)

作者： Edy Prayitno Rikie Kartadie Uning Lestari Munengsih Sari Bunga Accounting Information Systems Faculty of Information Technology Universitas Teknologi Digital Indonesia Yogyakarta Indonesia Computer Engineering Study Program Faculty of Information Technology Universitas Teknologi Digital Indonesia Yogyakarta Indonesia Informatics Study Program Faculty of Science and Information Technology Universitas Akprind Indonesia Yogyakarta Indonesia Department of Computer Engineering Smart City Information Systems Politeknik Negeri Indramayu Indramayu Indonesia

ISBN: (数字)9798331519643

ISBN: (纸本)9798331519650

The increasing use of digital payment systems has led to a rise in fraudulent activities, presenting a significant challenge in ensuring secure transactions. This research focuses on implementing the Support Vector Machine (SVM) algorithm with a Radial Basis Function (RBF) kernel to detect fraud in digital payment systems. One of the main challenges addressed in this study is the severe class imbalance in the dataset, where fraudulent transactions account for only 0.17% of total transactions. To overcome this, the SMOTE (Synthetic Minority Over-sampling Technique) method was applied to balance the dataset, allowing the model to better recognize fraudulent patterns. The results indicate that the SVM model achieved an accuracy of 99.93%, with a precision of 86.23% and a recall of 75.51%. These results demonstrate that SVM, combined with SMOTE and RBF kernel, is highly effective in detecting fraudulent transactions while minimizing false positives. This research provides a strong foundation for improving fraud detection models in the context of digital payment systems, offering enhanced security and trust for users. Further research could explore hybrid models and real-time data analysis to improve performance.

关键词： Support vector machines Seminars Adaptation models Accuracy Data models Real-time systems Fraud Security Kernel Intelligent systems

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Web Application to predict Skin Disease: An Approach Using Deep Learning Algorithm

Web Application to predict Skin Disease: An Approach Using D...

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Human-computer Interaction, Optimization and Robotic Applications (HORA), International Congress on

作者： Eelanila Thayalan Ayingaran Arumugavel Sharujan Ganeshan Makenthiran Dalaxshan Harinda Fernando Kaushalya Rajapakse Department of Information Technology Faculty of Computing Sri Lanka Institute of Information Technology Malabe Sri Lanka Department of Computer Systems Engineering Faculty of Computing Sri Lanka Institute of Information Technology Malabe Sri Lanka Department of Computer Science and Software Engineering Faculty of Computing Sri Lanka Institute of Information Technology Malabe Sri Lanka

ISBN: (数字)9798350394634

ISBN: (纸本)9798350394641

The Disease Prediction System revolutionizes healthcare with advanced machine learning techniques for early detection of skin diseases, notably focusing on skin cancer. Through image processing and Transfer Learning, it excels in identifying potential malignancies, enabling timely interventions. Its capabilities extend to benign tumors and growth disorders, utilizing the ResNet50 Model for precise identification. Additionally, it predicts skin reactions associated with various dermatological conditions, such as Urticaria Hives and Warts, leveraging the efficientnetB0 Model. Integration of the VGG16 Model enhances diagnostic accuracy for inflammatory skin conditions. This holistic approach prioritizes patient-centric care, leveraging diverse datasets and intricate pattern recognition in medical images. The system’s proactive nature embodies personalized solutions for early detection, timely intervention, and improved patient outcomes. Its versatility and accuracy underscore itstransformative potential in healthcare delivery. By harnessing diverse datasets and recognizing intricate patterns within medical images, it heralds a new era of personalized healthcare solutions. In essence, the Disease Prediction System exemplifies the transformative potential of machine learning in healthcare, ensuring the highest standards of diagnostic accuracy and efficacy, while prioritizing patient well-being and quality oflife.

关键词： Transfer learning Medical services Skin Pattern recognition Medical diagnostic imaging Standards Robots

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Intelligent DoS Attack Detection with Congestion Control Technique for VANETs

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computers, Materials & Continua 2022年第7期72卷 141-156页

作者： R.Gopi Mahantesh Mathapati B.Prasad Sultan Ahmad Fahd N.Al-Wesabi Manal Abdullah Alohali Anwer Mustafa Hilal Department of Computer Science and Engineering Dhanalakshmi Srinivasan Engineering CollegePerambalur621212India Department of Computer Science and Engineering RajaRajeswari College of EngineeringBengaluru560074India Department of Information Technology Vignan’s Institute of Information TechnologyVisakhapatnam530049India Department of Computer Science College of Computer Engineering and SciencesPrince Sattam Bin Abdulaziz UniversityAlkharj11942Saudi Arabia Department of Computer Science College of Science&Arts at MahayilKing Khalid UniversityMuhayel Aseer62529Saudi Arabia&Faculty of Computer and ITSana’a UniversitySana’a15347Yemen Department of Information Systems College of Computer and Information SciencesPrincess Nourah Bint Abdulrahman UniversityRiyadh11564Saudi Arabia Department of Computer and Self Development Preparatory Year DeanshipPrince Sattam bin Abdulaziz UniversityAl-Kharj16278Saudi Arabia

VehicularAd hoc Network(VANET)has become an integral part of Intelligent Transportation systems(ITS)in today’s *** is a network that can be heavily scaled up with a number of vehicles and road side units that keep fluctuating in real *** is susceptible to security issues,particularly DoS attacks,owing to maximum unpredictability in ***,effective identification and the classification of attacks have become the major requirements for secure data transmission in *** the same time,congestion control is also one of the key research problems in VANET which aims at minimizing the time expended on roads and calculating travel time as well as waiting time at intersections,for a *** this motivation,the current research paper presents an intelligent DoS attack detection with Congestion Control(IDoS-CC)technique for *** presented IDoSCC technique involves two-stage processes namely,Teaching and Learning Based Optimization(TLBO)-based Congestion Control(TLBO-CC)and Gated Recurrent Unit(GRU)-based DoS detection(GRU-DoSD).The goal of IDoS-CC technique is to reduce the level of congestion and detect the attacks that exist in the *** algorithm is also involved in IDoS-CC technique for optimization of the routes taken by vehicles via traffic signals and to minimize the congestion on a particular route instantaneously so as to assure minimal fuel *** is applied to avoid congestion on ***,GRU-DoSD model is employed as a classification model to effectively discriminate the compromised and genuine vehicles in the *** outcomes from a series of simulation analyses highlight the supremacy of the proposed IDoS-CC technique as it reduced the congestion and successfully identified the DoS attacks in network.

关键词： VANET intelligent transportation systems congestion control attack detection dos attack deep learning

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A Metamodel for a DSL to Design Decision Support systems in Smart Tourism

A Metamodel for a DSL to Design Decision Support Systems in ...

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Decision Aid Sciences and Application (DASA), International Conference on

作者： Charaf Ouaddi Lamya Benaddi Adnane Souha Lahbib Naimi El mahi Bouziane Abdeslam Jakimi Department of Computer Sciences Software Engineering and Information Systems Engineering Team Faculty of Science and Technology Errachidia Moulay Ismail University Morocco

ISBN: (数字)9798350369106

ISBN: (纸本)9798350369113

The development of Decision Support systems (DSS) for several companies operating in sectors such as tourism, healthcare, or others, presents significant challenges due to the nature of their multi-component architecture. A typical DSS consists of a data component, a model component incorporating machine learning and deep learning techniques, and a user interface component, each one requires diverse technologies. This complexity increases development time and effort, hindering the efficient design of DSS tailored to specific domains like tourism, where real-time decision-making is essential. One promising approach to overcome these challenges is the use of Domain-Specific Languages (DSLs). DSLs allow developers to model and generate code more efficiently by providing high-level abstractions suited to the domain. By leveraging DSLs, it is possible to simplify the design process and automate the generation of code or parts of the DSS, thereby reducing the overall complexity and time required for development. This paper proposes a metamodel for designing DSS, specifically for the Smart Tourism sector. The metamodel serves as a foundation for the creation of DSL. The goal is to provide a solution to facilitate the development of DSS for the demands of tourism. The paper outlines the key concepts, approach, and results, presenting the first element of the proposed DSL which is the metamodel capturing the essential components and relationships for DSS in Smart Tourism.

关键词： Decision support systems Codes Source coding Medical services User interfaces Syntactics Real-time systems Complexity theory DSL Software engineering

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