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arXiv 2023年

作者： Mukala, Patrick Department of Software engineering Faculty of Information and Communication Technology Tshwane University of Technology Kinshasa South Africa

The Democratic Republic of Congo is a sleeping giant at the heart of Africa. Though endowed with incredible natural resources spanning from significant reserves of gold, diamond, coltan, copper to vast African forests and impressive water resources, it is still considered as one of the poorest countries on the planet. For a long time, its economy has had nothing to show for all these resources as the Congolese people continue to live in extreme poverty. To survive, the Congolese people engage in a range of activities to generate income and most families rely on "solidarity grants" from family members and relatives that either work or conduct a personal business. Most of these families remain unbanked. Due to the absence of reliable and private financial institutions, almost 90% of all financial transactions are cash-based. However, as the number of banks increases throughout the past few years, there is a possibility to encourage and perform cashless transactions. In this paper, we demonstrate an implementation of a Service-Oriented Architecture approach in the design of a eWallet system. A typical scenario has been presented to highlight the major components of the system to be implemented as contextualized in the ecosystem. A series of models, from the business processes to the technical architecture and service model for the system, has been developed to represent every step of the solution. The paper is structured as follows: In section 1, we give a brief introduction of the paper, section 2 gives a generic description and contextualization of eWallet, section 3 gives a description of the scenario as chosen for this case study, section 4 describes the ecosystem for the scenario, section 5. © 2023, CC BY.

关键词： Ecosystems

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Optimal Resource Allocation Method for Device-to-Device Communication in 5G Networks

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Computers, Materials & Continua 2022年第4期71卷 1-15页

作者： Fahd N.Al-Wesabi Imran Khan Saleem Latteef Mohammed Huda Farooq Jameel Mohammad Alamgeer Ali M.Al-Sharafi Byung Seo Kim Department of Computer Science King Khalid UniversityMuhayel AseerKSA Faculty of Computer and IT Sana’a UniversityYemen Department of Electrical Engineering University of Engineering and TechnologyPeshawarPakistan Department of Medical Instrumentation Techniques Engineering Electrical Engineering Technical CollegeMiddle Technical UniversityBaghdad10013Iraq Department of Information Systems King Khalid UniversityMayahel AseerKSA Department of Computer Science College of Computers and Information TechnologyUniversity of BishaKSA Department of Software and Communications Engineering Hongik UniversityKorea

With the rapid development of the next-generation mobile network,the number of terminal devices and applications is growing ***,how to obtain a higher data rate,wider network coverage and higher resource utilization in the limited spectrum resources has become the common research goal of ***-to-Device(D2D)communication technology and other frontier communication technologies have ***-to-Device communication technology is the technology that devices in proximity can communicate directly in cellular *** has become one of the key technologies of the fifth-generation mobile communications system(5G).D2D communication technology which is introduced into cellular networks can effectively improve spectrum utilization,enhance network coverage,reduce transmission delay and improve system throughput,but it would also bring complicated and various interferences due to reusing cellular resources at the same *** resource management is one of the most challenging and importing issues to give full play to the advantages of D2D *** resource allocation is an important factor that needs to be addressed in D2D ***,this paper proposes an optimization method based on the game-matching *** main idea is to model the optimization problem of the quality-of-experience based on user fairness and solve it through game-matching *** results show that the proposed algorithm effectively improved the resource allocation and utilization as compared with existing algorithms.

关键词： D2D communication resource allocation optimization networks

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Comparative Analysis of Classifier Performance on Reduced Feature Sets

Comparative Analysis of Classifier Performance on Reduced Fe...

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Computational Intelligence Approaches and Applications (ICCIAA), International Conference on

作者： Raed Alazaidah Wael Hadi Suhaila Abu Owaida Alaa Bani-bakr Mo'ath Alluwaici Muhyeeddin Alqaraleh Department of Data Science Faculty of Information Technology Zarqa University Zarqa Jordan Department of Information Security Faculty of Information Technology University of Petra Amman Jordan Department of Computer Science Faculty of Information Technology Al-al-Bayt University Mafraq Jordan Department of Cyber Security Faculty of Information Technology Zarqa University Zarqa Jordan Department of Computer Science Faculty of Information Technology Zarqa University Zarqa Jordan Software Engineering Department Faculty of Information Technology Zarqa University Zarqa Jordan

ISBN: (数字)9798331523657

ISBN: (纸本)9798331523664

In this study, we examine the impact of feature reduction on the performance of various classifiers within the Weka environment. Utilizing the Pima Indians Diabetes Database, we compare the Accuracy, Precision, Recall, F1-measure, and other relevant metrics across classifiers like BayesNet, NaiveBayes, and NaiveBayesMul when applied to full and reduced feature sets. The reduction process involves halving the attribute count, aiming to assess the efficacy of machine learning models in scenarios with constrained feature availability. Our results indicate significant differences in the performance of the models, offering insights into the trade-offs between model complexity and predictive power in medical dataset applications.

关键词： Measurement Productivity Data analysis Accuracy Systematics Databases Machine learning Predictive models Diabetes Reliability

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SPUFChain: Permissioned Blockchain Lightweight Authentication Scheme for Supply Chain Management Using PUF of IoT

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IEEE Access 2025年 13卷 88662-88682页

作者： Assaqty, Mohammad Iqbal Saryuddin Gao, Ying Alqarni, Abeer D. Khan, Siraj Chakraborty, Mohuya Nur Siddiqi, Hafied Ahmad, Sadique South China University of Technology School of Computer Science and Engineering Guangzhou510006 China Universitas Nahdlatul Ulama Indonesia Faculty of Engineering and Computer Science Jakarta10320 Indonesia Princess Nourah bint Abdulrahman University College of Computer and Information Sciences Department of Information Technology P.O. Box 84428 Riyadh11671 Saudi Arabia Research Institute for Future Media Computing College of Computer Science and Software Engineering Shenzhen University Shenzhen518060 China University of Engineering and Management Cybersecurity Centre of Excellence Kolkata700160 India Riyadh12435 Saudi Arabia

The complexity of the entire process of supply chain management (SCM) is quite cumbersome and traditional way of handling it is devoid of proper authentication and security and very often suffers from human errors in dealing with flaws in quality control process of SCM. While it may have started with shipment tracking, the outcome of using IoT on supply chains has spread to every link in the chain. For instance, manufacturers are employing Internet-enabled sensors in production to find product faults, resulting in higher-quality production runs. Physical Unclonable Function (PUF) is a defense countermeasure that exploits the exclusive, unrepeatable features of hardware components to generate distinct cryptographic keys or identifiers, typically for a semiconductor device like an Internet of Things (IoT) device. The distinctive proof of identity of IoT devices in the supply chain is employed by means of PUFs as inviolable IDs. Blockchain, the distributed, immutable ledger, on the other hand is the disruptive technology that provides higher security as compared to traditional centralized systems. The integration of PUF and blockchain proves to be quite interesting while handling the above issues of authentication. A smart contract on the blockchain is a software code that executes spontaneously as and when the conditions of the contract or agreement are satisfied. Hence after authentication process the results are fed to blockchain smart contract for the final validation. This paper presents a novel permissioned blockchain smart contract-based lightweight authentication scheme for SCM using PUF of IoT known as SPUFChain. Informal and formal security analysis (using AVISPA and BAN logic) of the proposed framework show its efficiency to resist several threat scenarios like man-in-the middle, non-repudiation, impersonation, replay attacks and many other security features as compared to other related schemes. The processing time (~13.8ms) is better than existing lightweig

关键词： Supply chain management

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ENCL: Empirical Evaluation of Consumer Behaviour Analysis Model Using Internet of Things (IoT) Based Enhanced Neural Classification Logic

ENCL: Empirical Evaluation of Consumer Behaviour Analysis Mo...

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2024 International Conference on Innovative Computing, Intelligent Communication and Smart Electrical Systems, ICSES 2024

作者： Merlin, S. Sudha Malathy, K. Ravi, S. Arun, M. Raut, S.P. Fawareh, H. Dmi College of Engineering Department of Electrical and Electronics Engineering Palanchur Chennai India S.A. Engineering College Department of Mca Chennai India Department of Vlsi Microelectronics Chennai India Kings College of Engineering Dept of Computer Science and Engineering Punalkulam Thanjavur India Yeshwantrao Chavan College of Engineering Department of Civil Engineering Nagpur India Zarqa University Faculty of Information Technology Software Engineering Dept Zarqa Jordan University of Business and Technology Jeddah21448 Saudi Arabia

ISBN: (纸本)9798331543617

The proliferation of consumer data can be attributed to 'the Internet of Things (IoT),' which makes it easier to access and more detailed than before. In this research our purpose is to evaluate the effectiveness of a consumer behavior analysis model using data from IoT. The quality of the model is evaluated on multiple parameters - accuracy, predictability and business sense for marketing. It uses the analytics of huge amounts for data from IoT devices to predict patterns, customer preferences and trends. The proposed method called as Enhanced Neural Classification Logic (ENCL) is cross-validated with the already existing method, which is termed as Consumer behavior Forecasting Strategy (CBFS), and helps users to share gather or process information in a fast and cost-effective way through the proposed method. The results are useful to grasp the promise of IoT in more effective consumer behavior analysis. The research portrays the strengths, weaknesses and possible areas of improvement in this model which will be beneficial for both academic and industry practitioners. As such, the objective of this study is to show how an IoT-based consumer behavior analysis can increase business growth and customer satisfaction. The experimental evaluations demonstrate significant improvements in predictive performance over traditional methods, providing actionable insights for targeted marketing and strategic decision-making. The results highlight ENCL's potential to transform consumer behavior analysis by delivering more precise and nuanced understandings of market dynamics. © 2024 IEEE.

关键词： CBFS Consumer Behavior Data Analysis ENCL Internet of Things IoT Marketing Strategy Model Evaluation

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Enhancing Collaborative and Geometric Multi-Kernel Learning Using Deep Neural Network

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Computers, Materials & Continua 2022年第9期72卷 5099-5116页

作者： Bareera Zafar Syed Abbas Zilqurnain Naqvi Muhammad Ahsan Allah Ditta Ummul Baneen Muhammad Adnan Khan Department of Mechatronics and Control Engineering University of Engineering and TechnologyLahore54890Pakistan Department of Information Sciences Division of Science and TechnologyUniversity of EducationLahore54000Pakistan Riphah School of Computing&Innovation Faculty of ComputingRiphah International UniversityLahore CampusLahore54000Pakistan Department Software Gachon UniversitySeongnam13120Korea

This research proposes a method called enhanced collaborative andgeometric multi-kernel learning (E-CGMKL) that can enhance the CGMKLalgorithm which deals with multi-class classification problems with non-lineardata distributions. CGMKL combines multiple kernel learning with softmaxfunction using the framework of multi empirical kernel learning (MEKL) inwhich empirical kernel mapping (EKM) provides explicit feature constructionin the high dimensional kernel space. CGMKL ensures the consistent outputof samples across kernel spaces and minimizes the within-class distance tohighlight geometric features of multiple classes. However, the kernels constructed by CGMKL do not have any explicit relationship among them andtry to construct high dimensional feature representations independently fromeach other. This could be disadvantageous for learning on datasets with complex hidden structures. To overcome this limitation, E-CGMKL constructskernel spaces from hidden layers of trained deep neural networks (DNN).Due to the nature of the DNN architecture, these kernel spaces not onlyprovide multiple feature representations but also inherit the compositionalhierarchy of the hidden layers, which might be beneficial for enhancing thepredictive performance of the CGMKL algorithm on complex data withnatural hierarchical structures, for example, image data. Furthermore, ourproposed scheme handles image data by constructing kernel spaces from aconvolutional neural network (CNN). Considering the effectiveness of CNNarchitecture on image data, these kernel spaces provide a major advantageover the CGMKL algorithm which does not exploit the CNN architecture forconstructing kernel spaces from image data. Additionally, outputs of hiddenlayers directly provide features for kernel spaces and unlike CGMKL, do notrequire an approximate MEKL framework. E-CGMKL combines the consistency and geometry preserving aspects of CGMKL with the compositionalhierarchy of kernel spaces extracted from DNN hidde

关键词： CGMKL multi-class classification deep neural network multiplekernel learning hierarchical kernel spaces

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Chirped Large Mode Area Photonic Crystal Modal Fibers and its Resonance Modes Based on Finite Element Technique

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Journal of Optical Communications 2023年第3期44卷 333-338页

作者： Amiri, I.S. Rashed, Ahmed Nabih Zaki Sarker, Kaushik Paul, Bikash Kumar Ahmed, Kawsar Computational Optics Research Group Advanced Institute of Materials Science Ton Duc Thang University Ho Chi Minh City Viet Nam Faculty of Applied Sciences Ton Duc Thang University Ho Chi Minh City Viet Nam Electronics and Electrical Communications Engineering Department Faculty of Electronic Engineering Menoufia University Menouf32951 Egypt Department of Software Engineering Daffodil International University 102 Mirpur Road Shukrabad Dhanmondi Dhaka1207 Bangladesh Group of Bio-photomatiχ Information and Communication Technology Mawlana Bhashani Science and Technology University Tangail Santosh1902 Bangladesh Department of Information and Communication Technology Mawlana Bhashani Science and Technology University Tangail Santosh1902 Bangladesh

The study has outlined the finite element technique used for the fiber modal analysis of photonic crystal fiber (PCF) structure with a hexagonal/circular air holes arrangement on the cladding of pure fiber-optic silica. The used fibers that are namely highly nonlinear fiber (HNLF), single model silica fiber (SMSF) are combined with PCFs with different dopants concentration. Leakage loss, number of guided resonant modes, fiber birefringence, effective refractive index and cross-section areas and nonlinear coefficient parameters are measured inaccurate estimation. The optimum resonant guided modes are also estimated for different lattice infrastructure in circular PCFs. © 2019 Walter de Gruyter GmbH, Berlin/Boston.

关键词： Modal analysis

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Modeling of Sensor Enabled IrrigationManagement for Intelligent Agriculture Using Hybrid Deep Belief Network

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Computer Systems Science & engineering 2023年第8期46卷 2319-2335页

作者： Saud Yonbawi Sultan Alahmari B.R.S.S.Raju Chukka Hari Govinda Rao Mohamad Khairi Ishak Hend Khalid Alkahtani JoséVarela-Aldás Samih M.Mostafa Department of Software Engineering College of Computer Science and EngineeringUniversity of JeddahJeddahSaudi Arabia King Abdul Aziz City for Science and Technology RiyadhKingdom of Saudi Arabia Aditya Engineering College Affiliated to JNTUK(Kakinada) SurampalemIndia Department of MBA Vignan’s Institute of Information and Technology(A)DuvvadaVisakhapatnamAP530049India School of Electrical and Electronic Engineering Engineering CampusUniversiti Sains Malaysia(USM)Nibong TebalPenang14300Malaysia Department of Information Systems College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityRiyadh11564Saudi Arabia SISAu Research Group Universidad Tecnológica IndoaméricaAmbato180103Ecuador Faculty of Computers and Information South Valley UniversityQena83523Egypt

Artificial intelligence(AI)technologies and sensors have recently received significant interest in intellectual *** the application of AI technologies and agriculture sensors in intellectual agriculture is urgently required for the growth of modern agriculture and will help promote smart *** irrigation scheduling systems were highly required in the agricultural field due to their capability to manage and save water deficit irrigation *** learning systems devise an alternative to conventional irrigation management through the automatic elaboration of predictions related to the learning of an *** this motivation,this study develops a modified black widow optimization with a deep belief network-based smart irrigation system(MBWODBN-SIS)for intelligent *** MBWODBN-SIS algorithm primarily enables the Internet of Things(IoT)based sensors to collect data forwarded to the cloud server for examination ***,the MBWODBN-SIS technique applies the deep belief network(DBN)model for different types of irrigation classification:average,high needed,highly not needed,and not *** MBWO algorithm is used for the hyperparameter tuning process.A wideranging experiment was conducted,and the comparison study stated the enhanced outcomes of the MBWODBN-SIS approach to other DL models with maximum accuracy of 95.73%.

关键词： Agriculture smart farming hyperparameter tuning artificial intelligence irrigation management sensors deep learning

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An Approach for Early Detection of Schizophrenia

An Approach for Early Detection of Schizophrenia

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International Conference on Computing and Networking technology (ICCNT)

作者： Sachin Paranagama Isiwara Uditha Pamodya Daundasekara Shehani Hettiarachchi Lokesha Weerasinghe Dharshana Kasthurirathna Department of Computer Science and Software Engineering Faculty Of Computing Sri Lanka Institue Of Information Technology Malabe Sri Lanka

ISBN: (数字)9798350370249

ISBN: (纸本)9798350370270

Schizophrenia is a chronic mental disorder with distorted thinking, hallucinations, and social difficulties. Early diagnosis is vital, but current methods are limited. This research proposes a web-based application integrating machine learning, emotion recognition, and game-based elements to track symptoms and aid in early diagnosis. Interactive exercises focus on specific symptoms like cognitive evaluation, emotional analysis, and memory assessment. By combining computer science, psychology, and machine learning, the project aims to improve early detection and ultimately enhance the quality of life for affected individuals. Key components include using Deep Convolutional Neural Network (DCNN) architecture for recognizing facial and physical expressions associated with schizophrenia with an 87% accuracy rate, and utilizing Vector Regressor (SVR) with a 93% accuracy rate, along with Vision Transformer (ViT) and Custom CNN models for cognitive assessment, achieving accuracy rates of 99% and 98%, respectively.

关键词： Computer science Emotion recognition Computer vision Accuracy Face recognition Psychology Machine learning Transformers Vectors Convolutional neural networks

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Discussion Manipulation, Language and Domain Dependent Models: An Overview

Discussion Manipulation, Language and Domain Dependent Model...

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Zooming Innovation in Consumer Electronics International Conference (ZINC)

作者： Filip Chalás Igor Stupavský Valentino Vranić Institute of Informatics Information Systems and Software Engineering Faculty of Informatics and Information Technologies Slovak University of Technology in Bratislava

The amount of antisocial online behavior (AOB) in the political field has been on the rise in recent years. In the research, we are dealing with the reason for confusing AOB with other forms of antisocial behavior. We specifically dealt with NLP in the political field in the Slovak language, with a specific number of prefixes and suffixes. In this paper, we focus on the area of detection of manipulation of political discussions. The data source consisted of previously collected data in the period from April 2018 with an update until November 19, 2022 realized within this research from the site ***, which we supplemented with new claims. We trained several classification models on preprocessed data and evaluated them. For multi-class classification, the best results were achieved using logistic regression and a support vector machine trained on the resampled dataset—both achieving an accuracy of 0.56 and a macro F1 score of 0.39. In the case of binary classification, the best results were achieved by logistic regression—accuracy 0.7 and macro F1 score 0.56. These models could help detect manipulation in online political discussions.

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