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A Holistic Anti-Counterfeiting Platform Using NFC and Blockchain Technologies

computers, Materials and Continua 2025年第3期83卷 4257-4280页

作者： Rajendren Subramaniam Saaidal Razalli Azzuhri Teh Ying Wah Atif Mahmood Vimala Balakrishnan Department of Information System Faculty of Computer Science & Information Technology Universiti Malaya Kuala Lumpur 50603 Malaysia Department of Computer System & Technology Faculty of Computer Science & Information Technology Universiti Malaya Kuala Lumpur 50603 Malaysia Faculty of Data Science and Information Technology INTI International University Nilai 71800 Malaysia

Counterfeiting is still a pervasive global issue, affecting multiple industries and hindering industrial innovation, while causing substantial financial losses, reputational damage, and risks to consumer safety. From luxury goods and pharmaceuticals to electronics and automotive parts, counterfeit products infiltrate supply chains, leading to a loss of revenue for legitimate businesses and undermining consumer trust. Traditional anti-counterfeiting measures, such as holograms, serial numbers, and barcodes, have proven to be insufficient as counterfeiters continuously develop more sophisticated replication techniques. As a result, there is a growing need for more advanced, secure, and reliable methods to prevent counterfeiting. This paper presents a novel, holistic anti-counterfeiting platform that integrates Near Field Communication (NFC)-enabled mobile applications with blockchain technology to provide an innovative, secure, and consumer-friendly authentication mechanism. Our approach addresses key gaps in existing solutions by incorporating dynamic product identifiers, which make replication significantly more difficult. The system enables consumers to verify the authenticity of products instantly using their smartphones, enhancing transparency and trust in the supply chain. Blockchain technology plays a crucial role in our proposed solution by providing an immutable, decentralized ledger that records product authentication data. This ensures that product verification records cannot be tampered with or altered, adding a layer of security that is absent in conventional systems. Additionally, NFC technology enhances security by offering unique identification capabilities, enabling real-time product verification. To validate the effectiveness of the proposed system, real-world testing was conducted across different industries. The results demonstrated the platform’s ability to significantly reduce counterfeit products in the supply chain, offering businesses and cons

关键词： Blockchain near field communication (NFC) anti-counterfeiting

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Optimal Deep Convolutional Neural Network for Vehicle Detection in Remote Sensing Images

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computers, Materials & Continua 2023年第2期74卷 3117-3131页

作者： Saeed Masoud Alshahrani Saud S.Alotaibi Shaha Al-Otaibi Mohamed Mousa Anwer Mustafa Hilal Amgad Atta Abdelmageed Abdelwahed Motwakel Mohamed I.Eldesouki Department of Computer Science College of Computing and Information TechnologyShaqra UniversityShaqraSaudi 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 Electrical Engineering Faculty of Engineering&TechnologyFuture University in EgyptNew Cairo11845Egypt Department of Computer and Self Development Preparatory Year DeanshipPrince Sattam Bin Abdulaziz UniversityAlKharjSaudi Arabia Department of Information System College of Computer Engineering and SciencesPrince Sattam Bin Abdulaziz UniversityAlKharjSaudi Arabia

Object detection(OD)in remote sensing images(RSI)acts as a vital part in numerous civilian and military application areas,like urban planning,geographic information system(GIS),and search and rescue *** recognition from RSIs remained a challenging process because of the difficulty of background data and the redundancy of recognition *** latest advancements in deep learning(DL)approaches permit the design of effectual OD *** study develops an Artificial Ecosystem Optimizer with Deep Convolutional Neural Network for Vehicle Detection(AEODCNN-VD)model on Remote Sensing *** proposed AEODCNN-VD model focuses on the identification of vehicles accurately and *** detect vehicles,the presented AEODCNN-VD model employs single shot detector(SSD)with Inception network as a baseline *** addition,Multiway Feature Pyramid Network(MFPN)is used for handling objects of varying sizes in *** features from the Inception model are passed into theMFPNformultiway andmultiscale feature ***,the fused features are passed into bounding box and class prediction *** enhancing the detection efficiency of the AEODCNN-VD approach,AEO based hyperparameter optimizer is used,which is stimulated by the energy transfer strategies such as production,consumption,and decomposition in an *** performance validation of the presentedmethod on benchmark datasets showed promising performance over recent DL models.

关键词： Object detection remote sensing vehicle detection artificial ecosystem optimizer convolutional neural network

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Possibility of Profit Gain in Futures Cryptocurrency Trading Plans Using a Patterned Dataset Model under Moon Conditions 10

Possibility of Profit Gain in Futures Cryptocurrency Trading...

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10th IEEE information Technology International Seminar, ITIS 2024

作者： Parlika, Rizky Isnanto, R. Rizal Rahmat, Basuki Universitas Diponegoro Doctoral Programe of Information Systems Semarang Indonesia University of Pembangunan Nasional "Veteran"Jawa Timur Faculty of Computer Science Department of Information System Surabaya Indonesia

ISBN: (纸本)9798331521295

Among the various components of cryptocurrency trading are futures (2-way trading based on short or long contracts) and spot schemes (which operate in the same direction as buying and selling crypto assets). The futures scheme transaction was selected for this study's test since it frequently results in trades with comparatively larger swings than spot scheme deals. If these variations are not correctly anticipated, they may result in losses. Real-time position reading for cryptocurrency trading is made possible by the Patterned Dataset Model. Based on observations of the pattern of influence that Bitcoin price changes have on a variety of other cryptocurrency asset values in a trading market, this model was developed. Three types of data are generated by this model, and they are mined nonstop every few seconds. The entire conditions (a mix of moon and crash conditions), price increase conditions (moon), and price fall conditions (crash) make up the patterned dataset. In this study, the topic of discussion is patterned datasets in rising price (moon) environments. It is possible to demonstrate the possibility of reasonably consistent gains from short contract purchase positions using the Patterned Dataset in Moon Conditions. © 2024 IEEE.

关键词： Marketplaces

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Malicious URL Classification Using Artificial Fish Swarm Optimization and Deep Learning

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computers, Materials & Continua 2023年第1期74卷 607-621页

作者： Anwer Mustafa Hilal Aisha Hassan Abdalla Hashim Heba G.Mohamed Mohamed K.Nour Mashael M.Asiri Ali M.Al-Sharafi Mahmoud Othman Abdelwahed Motwakel Department of Electrical and Computer Engineering International Islamic University Malaysia 53100 Kuala LumpurMalaysia Department of Computer and Self Development Preparatory Year DeanshipPrince Sattam bin Abdulaziz UniversityAlKharjSaudi Arabia Department of Electrical Engineering College of EngineeringPrincess 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 Science&Art at MahayilKing Khalid UniversitySaudi Arabia Department of Computer Science College of Computers and Information TechnologyUniversity of BishaSaudi Arabia Department of Computer Science Faculty of Computers and Information TechnologyFuture University in EgyptNew Cairo11835Egypt

Cybersecurity-related solutions have become familiar since it ensures security and privacy against cyberattacks in this digital *** Uniform Resource Locators(URLs)can be embedded in email or Twitter and used to lure vulnerable internet users to implement malicious data in their *** may result in compromised security of the systems,scams,and other such *** attacks hijack huge quantities of the available data,incurring heavy financial *** the same time,Machine Learning(ML)and Deep Learning(DL)models paved the way for designing models that can detect malicious URLs accurately and classify *** this motivation,the current article develops an Artificial Fish Swarm Algorithm(AFSA)with Deep Learning Enabled Malicious URL Detection and Classification(AFSADL-MURLC)*** presented AFSADL-MURLC model intends to differentiate the malicious URLs from genuine *** attain this,AFSADL-MURLC model initially carries out data preprocessing and makes use of glove-based word embedding *** addition,the created vector model is then passed onto Gated Recurrent Unit(GRU)classification to recognize the malicious ***,AFSA is applied to the proposed model to enhance the efficiency of GRU *** proposed AFSADL-MURLC technique was experimentally validated using benchmark dataset sourced from Kaggle *** simulation results confirmed the supremacy of the proposed AFSADL-MURLC model over recent approaches under distinct measures.

关键词： Malicious URL cybersecurity deep learning machine learning metaheuristics gated recurrent unit

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Feature Selection Technique Model for Forest and Land Fire Data Sentiment Analysis: Comparison of SSA, PSO, and ALO 7

Feature Selection Technique Model for Forest and Land Fire D...

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7th International Conference on New Media Studies, CONMEDIA 2023

作者： Kristiyanti, Dinar Ajeng Sitanggang, Imas Sukaesih Annisa Nurdiati, Sri Ipb University Department of Computer Science Bogor Indonesia Universitas Multimedia Nusantara Department of Information System Tangerang Indonesia Ipb University Department of Mathematics Bogor Indonesia

ISBN: (纸本)9798350307504

The forest and land fires in Indonesia caused a trending topic on social media, namely Twitter, especially in 2019 with 316,782 tweets. Mining it with a sentiment analysis approach will yield valuable knowledge. Mining data from Twitter that contain huge quantities cannot be handled with a sentiment analysis approach using conventional machine learning, where the data are very rich in features. This research aims to select the most relevant and informative features using the feature selection technique (FST), which adopts a Swarm Intelligence-based algorithm to produce accurate and fast model performance in processing time. This study involves several phases, including data collection, feature selection, labeling, data splitting, text pre-processing, modeling, and performance assessment. Salp Swarm Algorithm (SSA), Particle Swarm Optimization (PSO), and Ant Lion Optimization (ALO) will be compared in this research as feature selection technique optimization using classification algorithms such as K-Nearest Neighbor (KNN), Support Vector Machine (SVM), and Naive Bayes (NB). Research results based on algorithm performance evaluation show that the KNN Algorithm using the PSO algorithm is the algorithm that performs best, with an accuracy of 90.17% and the fastest processing time, namely 0.038 seconds. KNN Algorithm using the SSA algorithm is also no less superior, achieving accuracy reaching 90.02% with a processing time of 0.039 seconds. © 2023 IEEE.

关键词： Sentiment analysis

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MobileViTv2-MLP: Arabic Traffic Sign Recognition with Enhanced Lightweight Vision Transformers 17

MobileViTv2-MLP: Arabic Traffic Sign Recognition with Enhanc...

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17th International Conference on Machine Vision, ICMV 2024

作者： Alqahtani, A. Alqaysi, E. Alsarhani, W. Lee, C.P. Alsharafi, A. Alzahrani, M. Almosa, A. Computer Science Department King Khalid University Saudi Arabia King Khalid University Saudi Arabia Faculty of Information Science and Technology Multimedia University Malaysia English Department King Khalid University Saudi Arabia

ISBN: (纸本)9781510688278

Traffic Sign Recognition (TSR) plays a pivotal role in advanced driver-assistance systems (ADAS) and autonomous vehicles, serving as a fundamental element for safe and efficient road navigation. Due to a diverse array of factors such as variations in sign appearance, fluctuating lighting conditions, occlusions, and environmental influences, the task of TSR poses significant challenges. These challenges have been addressed with advanced deep learning techniques, particularly with Convolutional Neural Networks (CNNs) and other architectures proficient in handling visual data. While such approaches have shown promise, they are often limited in their ability to effectively manage the aforementioned challenges. To overcome these obstacles, we propose a novel approach called the MobileViTv2-MLP method. This approach aims to develop a resilient model specifically tailored to the task of identifying and categorizing traffic signs encountered during driving. Our methodology involves training the model on an extensive dataset comprising diverse representations of traffic signs under varying conditions, ensuring its adaptability and reliability in real-world scenarios. Through experimentation and evaluation, our refined approach has demonstrated exceptional accuracy, achieving a remarkable 99.75%. This outcome underscores the robustness and effectiveness of our proposed method in addressing the challenges inherent in TSR, thereby contributing to the advancement of intelligent transportation systems. © 2025 SPIE.

关键词： Advanced driver assistance systems

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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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Hybridization of deep learning models with crested porcupine optimizer algorithm-based cybersecurity detection on industrial IoT for smart city environments

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Alexandria Engineering Journal 2025年 127卷 239-250页

作者： Alzakari, Sarah A. Aljebreen, Mohammed Asiri, Mashael M. MANSOURI, Wahida Alahmari, Sultan Alqahtani, Mohammed Sorour, Shaymaa Bedewi, Wafi Department of Computer Sciences College of Computer and Information Sciences Princess Nourah bint Abdulrahman University P.O. Box 84428 Riyadh11671 Saudi Arabia Department of Computer Science Community College King Saud University P.O. Box 28095 Riyadh11437 Saudi Arabia Department of Computer Science Applied College at Mahayil King Khalid University Saudi Arabia Department of Computer Science and Information Technology Faculty of Sciences and arts Turaif Northern Border University Arar91431 Saudi Arabia Cybersecurity Institute Riyadh Saudi Arabia Department of Information System and Cyber Security College of Computing and Information Technology University of Bisha Bisha61922 Saudi Arabia Department of Management Information Systems School of Business King Faisal University Al-Ahsa31982 Saudi Arabia Department of Information Systems Faculty of Computing and Information Technology King Abdulaziz University Jeddah Saudi Arabia

Smart cities have attracted extensive coverage from multidisciplinary studies, and many artificial intelligence (AI) solutions have been designed. Conversely, cybersecurity has constantly been a crucial issue and is becoming gradually dangerous in smart cities. The attack defence models are inappropriate for perceiving multistep assaults as the recognition rules are restricted, and efficacy is partial due to many false security alarms. Therefore, an innovative solution is immediately required to progress cybersecurity defence ability. Machine learning (ML) methods are commonly employed to recognize numerous attacks because they could help network administrators grab analogous initials to avert intrusion. This study presents Cybersecurity using a Crested Porcupine Optimizer Algorithm with Hybrid Deep Learning Models (CCPOA-HDLM). The foremost intention of this study is to improve cybersecurity detection and classification in smart city environments. To accomplish that, the CCPOA-HDLM method comprises distinct processes such as min-max normalization, improved Salp swarm algorithm (ISSA)-based feature selection, Multi-Channel Convolutional Neural Network - Recurrent Neural Network (MCNN-RNN)-based cybersecurity detection, and crested porcupine optimizer (CPO)-based parameter selection process. Primarily, data normalization utilizing min-max normalization is implemented. Next, the CCPOA-HDLM method utilizes ISSA based feature selection method to select optimum features. The CCPOA-HDLM technique employs a hybrid of the MCNN-RNN model for the cybersecurity detection and classification process. Moreover, the hyperparameter range of the hybrid of DL techniques occurs using the CPO technique. The experimental validation of the CCPOA-HDLM approach is performed on the UNSW-NB15 dataset, and the outcome portrayed a superior accuracy value of 99.04 % over other recent approaches under various measures. © 2025 The Authors

关键词： Convolutional neural networks

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Long Short-Term Memory and Word Embedding For Sentiment Analysis of User Review 1

Long Short-Term Memory and Word Embedding For Sentiment Anal...

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1st IEEE International Conference on Smart Technology, ICE-SMARTec 2023

作者： Silviana, Lia Nababan, Erna Budhiarti Zarlis, Muhammad Universitas Sumatera Utara Faculty of Computer Science and Information Technology Medan Indonesia Bina Nusantara University Department of Information System Jakarta Indonesia

ISBN: (纸本)9798350306156

Accuracy in training sentiment analysis models for large number of review datasets is affected by the correct classification of sentiment labels. Improving the accuracy of sentiment labels, and text representation also affects the performance of sentiment analysis models. Deep learning methods have been widely used to solve various sentiment analysis problems. To improve the performance of deep learning in sentiment analysis, it is necessary to use the right labeling method and good text representation to be used as an embedding layer. This study proposes sentiment labeling using Lexicon and Long Short-Term Memory (LSTM) as well as used FastText as embedding words in sentiment classification. As a corpus, the InSet Lexicon Dictionary is employed for feature extraction. The sentiment data used is the reviews given by users on several applications provided on Google Play. The results showed that the LSTM network using Word-embedded FastText with a dimension of 300 words received a small error value of 0.111 with an accuracy of 95.55% for data labeled based on Lexicon. © 2023 IEEE.

关键词： Long short-term memory

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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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