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9th International Conference on informatics and Computing, ICIC 2024

作者： Raharja, Agung Rachmat Putra, Vito Hafizh Cahaya Kanugrahan, Ghanim Tho, Cuk Bandung University Faculty of Health and Engineering Bandung Campus Bandung 40286 Indonesia Satu University Faculty of Creative Technology Informatics Department Bandung Campus Bandung 40253 Indonesia BinaNusantara University School of Computer Science Computer Science Department Jakarta Campus Jakarta 11480 Indonesia

ISBN: (纸本)9798331517601

This paper discusses computer vision-based sign language by utilizing landmark detection and Scikit-Learn. The objective of this research is to develop a model that proficient in the accurate recognition of hand gestures specific to sign language. Pictures of hand gestures are taken using a camera and hand landmark are detected by utilizing MediaPipe. Following to landmark detection, a machine learning model is developed using Scikit-Learn employing features extracted from these landmarks. Evaluation of the model using the Indonesian sing language (SIBI) yielded competitive result, proving the efficacy of the proposed model without specific hardware. This work significantly improves the communication technology for the deaf, leveraging computer vision to broaden the research opportunity in sign language recognition utilizing available equipment. The accuracy obtained from this model is 100% which means the classification is correct, and reinforces the effectiveness of the approach to the recognition and classification of hand movements in sign language. © 2024 IEEE.

关键词： computer Vision Landmark Detection MediaPipe Scikit-learn Sign Language

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Concentric Blocking Techniques for Improved Feature Extraction in Local Binary Pattern (LBP) Systems

Concentric Blocking Techniques for Improved Feature Extracti...

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International Conference on Adaptive Science & technology (ICAST)

作者： Obed Appiah Ezekiel Mensah Martey Department of Computer Science and Informatics University of Energy and Natural Resources (UENR) Sunyani Ghana Department of Computer Science and Engineering University of Mines and Technology (UMaT) Tarkwa Ghana

ISBN: (数字)9798350385403

ISBN: (纸本)9798350385410

The domain of image analysis has seen rapid advancements, particularly feature extraction techniques, driven by the increasing demand for accurate and efficient results. Among these advancements, Local Binary Patterns (LBP) have gained prominence due to their simplicity and computational efficiency. However, traditional LBP methods often need help with image orientation and scale variations, common in real-world scenarios. To address these challenges, this study explores the potential of concentric blocking methods as an alternative to conventional grid-based blocking techniques. Concentric blocking, which organizes image data into circular or spiral patterns, is hypothesized to enhance the robustness and accuracy of LBP-based systems by better accommodating geometric transformations. The study uses multiple LBP variants to compare traditional grid-based blocking with concentric blocking techniques across various image analysis tasks, including face recognition and texture classification. Experimental results demonstrate that the proposed ring-centric technique significantly outperforms traditional methods in precision and computational efficiency. Precision values for the proposed Ring Centric technique reach up to 90.73% for the basic LBP and 88.47% for Neighboring LBP with Distance (NLBP D ), indicating its superior performance in handling variations in orientation and scale. Additionally, the technique demonstrates lower running times, making it a highly efficient and accurate choice for image retrieval tasks. This study highlights the importance of image-blocking strategies in enhancing the performance of LBP-based systems and underscores the potential of concentric blocking methods in overcoming the limitations of traditional approaches. The findings contribute to developing more resilient and efficient image analysis systems, with significant implications for applications in computer vision, medical diagnostics, and security systems.

关键词： Image analysis Accuracy Spirals Face recognition Image retrieval Feature extraction Robustness Computational efficiency Security Medical diagnosis

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Enhanced Redundant Weighted Clustered Scheme for Damaged S-UAV

Enhanced Redundant Weighted Clustered Scheme for Damaged S-U...

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IEEE International Conference on Communications (ICC)

作者： Grace Khayat Constandinos X Mavromoustakis Andreas Pitsillides Jordi Mongay Batalla Evangelos K. Markakis Department of Computer Science Mobile Systems Laboratory (MoSys Lab) University of Nicosia Nicosia Cyprus Department of Computer Science Cyprus and Department of Electrical & Electronic Engineering Science University of Cyprus University of Johannesburg (Visiting Professor) Warsaw University of Technology Warsaw Poland Department of Electrical and Computer Engineering Hellenic Mediteranean University Estavromenos Heraklion Greece

A spontaneous group of unmanned aerial vehicles (UAVs) is denoted as a swarm of UAVs (S-UAVs). The UAVs communicate wirelessly and cooperate to accomplish tasks. In crisis scenarios such as flooding or earthquakes, all UAVs are at risk of getting damaged and thus non-functional. A non- functional UAV will result in a disconnected network, especially if that UAV is highly responsible for packet forwarding. S- UAV s are dynamic networks; clustering is one of the most adopted routing schemes in S-UAVs. The clustering scheme groups the UAVs into clusters where each cluster is formed of a cluster head (CH) and cluster members (CMs). Only the CH can handle inter-cluster communication. Due to the crucial role played by the CH, its selection is a continuous field of research. This paper proposes an enhanced clustered weighted scheme with redundancy to ensure end-to-end communication. The proposed scheme is based on a weighted formula for the primary CH, redundant CH, and CMs selection. The weighted formula calculates a cluster index based on the distance, the speed, and the reward index. A new component is added to the reward index which is performance. The redundant CH is selected to automatically replace the primary CH whenever it is damaged. If the redundant CH becomes inoperable, the second redundant CH will take over. Each cluster is formed of n CMs and will have n-2 redundant CHs. The results obtained from the conducted simulation experiments concluded that this promising scheme decreases data loss in a crisis case scenario.

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Artificial neural networks in supply chain management, a review

Journal of Economy and Technology

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Journal of Economy and technology 2023年 1卷 179-196页

作者： Mohsen Soori Behrooz Arezoo Roza Dastres Department of Aeronautical Engineering University of Kyrenia Via Mersin 10 Kyrenia North Cyprus Turkey CAD/CAPP/CAM Research Center Department of Mechanical Engineering Amirkabir University of Technology (Tehran Polytechnic) 424 Hafez Avenue Tehran 15875-4413 Iran Department of Computer Engineering Cyprus International University Via Mersin 10 North Cyprus Turkey

Artificial Neural Networks (ANNs) are a type of machine learning algorithm inspired by the structure and function of the human brain. In the context of supply chain management, ANNs can be used for demand forecasting, inventory optimization, logistics planning, and anomaly detection. ANNs help companies to optimize their inventory levels, production schedules and procurement activities in terms of productivity enhancement of part production. By considering multiple variables and constraints, ANNs can identify the most efficient routes, allocate resources effectively, and reduce costs. Furthermore, ANNs can identify anomalies as well as abnormalities in supply chain data, such as unexpected demand patterns, quality issues and disruptions in logistics operations in order to minimize their impact on the supply chain. ANNs can also analyze supplier performance data, including quality, delivery times and pricing in order to assess the reliability and effectiveness of suppliers. This information can support decision-making processes in supplier evaluation and selection processes. Moreover, ANNs can continuously monitor supplier performance, raising alerts for deviations from predefined criteria to provide safe and secure supply chain in part production processes. By analyzing various data sources, including weather conditions, and political instability, ANNs can identify and mitigate risks in terms of safety enhancement of supply chain processes. Artificial neural networks in supply chain management is studied in the research work to analyze and enhance performances of supply chain management in process of part manufacturing. New ideas and concepts of future research works are presented by reviewing and analyzing of recent achievements in applications of artificial neural networks in supply chain management. Thus, productivity of part manufacturing can be enhanced by promoting the supply chain management using the artificial neural networks.

关键词： Artificial neural networks Supply chain management

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Microservice API Implementation for E-Government Service Interoperability

Microservice API Implementation for E-Government Service Int...

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2019 International Conference of Science and Information technology in Smart Administration, ICSINTeSA 2019

作者： Puspitasari, N. Budiman, E. Sulaiman, Y.N. Firdaus, M.B. Department of Informatics Engineering Faculty of Computer Science and Information Technology Mulawarman University Indonesia

To improve e-government services released by Communication and Information technology Office of Samarinda City, each system needs to be able to interoperate even when developed by different developers. Interoperation can be achieved by using one data source which is API (Application Programming Interface) for general data objects such as announcements. Given this condition, API built by using microservice can support further enhancement even if API is developed by developers who use different programming languages. The result shows that microservice API can be used to interoperate in relaying data between e-government services and can be developed using more than one programming language and base codes. Further development of this API can be done by adding more data objects, using AWS Cognito as authorization management, adding AWS Elasticsearch to load and filter data, and by showing data objects in real-time on the front end. © Published under licence by IOP Publishing Ltd.

关键词： Interoperability

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Improved RC6 Block Cipher Based on Data Dependent Rotations

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computers, Materials & Continua 2022年第1期70卷 1921-1934页

作者： Osama S.Faragallah Ibrahim F.Elashry Ahmed AlGhamdi Walid El-Shafai S.El-Rabaie Fathi E.Abd El-Samie Hala S.El-sayed Mohamed A.Elaskily Department of Information Technology College of Computers and Information TechnologyTaif UniversityP.O.Box 11099Taif21944Saudi Arabia Department of Electrical Communications Faculty of EngineeringKafrelsheikh UniversityKafrelsheikhEgypt Department of Computer Engineering College of Computers and Information TechnologyTaif UniversityP.O.Box 11099Taif21944Saudi Arabia Department of Electronics and Electrical Communications Faculty of Electronic EngineeringMenoufia UniversityMenouf32952Egypt Department of Electrical Engineering Faculty of EngineeringMenoufia UniversityShebin El-Kom32511Egypt Department of Informatics Electronic Research Institute(ERI)CairoEgypt

This paper introduces an Improved RC6(IRC6)cipher for data encryption based on data-dependent *** proposed scheme is designed with the potential of meeting the needs of the Advanced Encryption Standard(AES).Four parameters are used to characterize the proposed *** parameters are the size of the word(w)in bits,the number of rounds(r),the length of the secret key(b)in bytes,and the size of the block(L)in *** main feature of IRC6 is the variable number of working registers instead of just four registers as in RC6,resulting in a variable block size for plaintext and *** IRC6 cipher is designed to improve the robustness against attacks by increasing the diffusion for each round and providing greater security with fewer *** effectiveness of the proposed IRC6 scheme is verified against theoretical *** proposed IRC6 scheme depends on full diffusion and confusion mechanisms regardless of the utilized block *** proposed IRC6 scheme saves 70%of the encryption time and 64%of the decryption time of *** simulation results prove that the IRC6 achieves a better encryption/decryption time compared to the ***,the proposed IRC6 is anticipated to fulfill the market needs and system security requirements.

关键词： Cryptography block cipher RC6,IRC6,AES

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AI-powered blockchain technology in industry 4.0, a review

Journal of Economy and Technology

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Journal of Economy and technology 2023年 1卷 222-241页

作者： Mohsen Soori Roza Dastres Behrooz Arezoo Department of Aeronautical Engineering University of Kyrenia Via Mersin 10 Kyrenia North Cyprus Turkey Department of Computer Engineering Cyprus International University Via Mersin 10 North Cyprus Turkey CAD/CAPP/CAM Research Center Department of Mechanical Engineering Amirkabir University of Technology (Tehran Polytechnic) 424 Hafez Avenue Tehran 15875–4413 Iran

Artificial Intelligence (AI) algorithms can be employed to enhance the security of the blockchain networks in the era of industry 4.0. Smart contracts, powered by blockchain, can be developed by using the AI capabilities. These contracts can execute themselves based on predefined conditions, automating various processes and reducing the need for intermediaries. Blockchain provides a transparent and immutable ledger for supply chain transactions. AI can be integrated to analyze this data, providing insights into the entire supply chain. This helps in tracking and tracing products, ensuring authenticity, and optimizing the supply chain based on data-driven decisions. AI algorithms can be employed to analyze data from the manufacturing process recorded on the blockchain. This allows for predictive maintenance and quality control, helping to identify and address issues before they result in defects or downtime. AI and blockchain can work together to secure the massive amount of data generated by IoT devices in an Industry 4.0 setting. Blockchain ensures the integrity and immutability of IoT data, while AI can analyze this data for insights and optimization. AI and blockchain can facilitate the creation of decentralized energy grids where AI algorithms optimize energy distribution and blockchain ensures transparent and secure energy trading through smart contracts. AI and blockchain can support decentralized and collaborative manufacturing networks where different entities can securely and efficiently collaborate on production processes. Blockchain enables individuals or organizations to securely monetize their data. AI can help analyze and determine the value of data, ensuring fair compensation in tokenized ecosystems. In this paper, applications of AI-Powered Blockchain technology in Industry 4.0 is reviewed and discussed and future research works are also suggested. As a result, efficiency, transparency, and security across various industrial processes can be enhanced

关键词： Artificial Intelligence Blockchain AI-Powered Blockchain Industry 4.0

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A Blockchain-Based Transactive Energy Management Scheme for Nano-Grids using Power Flow Coloring

A Blockchain-Based Transactive Energy Management Scheme for ...

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IEEE International Conference on Consumer Electronics-Taiwan (ICCE-TW)

作者： Abdullah Bin Masood Saher Javaid Yasuo Tan Vasos Vassiliou Marios Lestas Department of Computer Science University of Cyprus Nicosia Cyprus CYENS - Centre of Excellence Nicosia Cyprus School of Information Science Japan Advanced Institute of Science and Technology Ishikawa Japan Department of Electrical Engineering Frederick University Nicosia Cyprus

The proliferation of distributed energy resources (DER) and the widespread adoption of the Internet of Things (IoT) in Prosumer Nano-Grids (P-NGs) pose severe challenges in existing energy management schemes, thus necessitating the need for the development of novel solutions which can guarantee system efficiency and security. To address such challenges, we propose a novel scheme leveraging the benefits of distributed power flow coloring, blockchain technology, and Transactive Energy Management (TEM). The proposed system model offers P-NGs a secure, transparent, trustworthy, and efficient energy management and transactions platform that can: maintain the overall voltage stability and specified power ratios against power fluctuations within the P-NG, offer an increase in energy security, provide real-time monitoring, and reduce energy waste.

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Deep integration of conditional gan, attention mechanism, and image clustering for automated color separation and correction in textile screen printing

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Applied Intelligence 2025年第11期55卷 1-16页

作者： Yang, Chao-Lung Harjoseputro, Yulius Chien, Chi-Hao Chen, Yung-Yao Department Industrial Management National Taiwan University of Science and Technology Taipei Taiwan Department Electronics and Computer Engineering National Taiwan University of Science and Technology Taipei Taiwan Department Informatics Universitas Atma Jaya Yogyakarta Yogyakarta Indonesia

Manual color separation and image processing are needed for pretreatment in traditional textile screen printing but the shortage of engineers has created a significant bottleneck in improving printing quality. Therefore, this research aimed to introduce an innovative method by proposing the squeeze-excitation attention and Pix2PixHD (SEAPix) incorporating KMeans clustering algorithm, forming KMeans+SEAPix model. The proposed SEAPix integrates a squeeze and excitation attention (SEA) mechanism into the conditional generative adversarial network (cGAN), Pix2PixHD, architecture. KMeans algorithm facilitates the separation of similar colors into distinct branches. Furthermore, SEAPix method improves the accuracy of high-resolution image generation by focusing on important regions and reducing irrelevant noise. To verify the sequence of color separation and image processing, this research developed two different frameworks, namely the Image Generation-Separation Framework (PF1) and the Image Generation-Separation Framework (PF2). PF1 starts with image generation, followed by Kmeans color separation, while PF2 adopted a different method by using color separation first, followed by generating images with cGAN. The experimental results of this research showed that PF2 was superior to PF1. Specifically, PF2 had better structural similarity index measure (SSIM), peak signal-to-noise ratio (PSNR), intersection over union (IoU), and pixel accuracy increased by 15.5643%, 0.5249%, 11.3757%, and 1.6508%, respectively. PF2 also has lower learned perceptual image patch similarity (LPIPS) and mean squared error (MSE) decreased by 24.3518% and 2.5963% compared with PF1. In conclusion, the proposed KMeans+SEAPix could revolutionize traditional textile screen printing methods by providing automated color separation and image correction.

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Fixed-Length Arbitrary-Phase Tunable NRI-TL Metamaterial Phase Shifter for Antenna Beam-Steering Applications

Fixed-Length Arbitrary-Phase Tunable NRI-TL Metamaterial Pha...

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European Conference on Antennas and Propagation, EuCAP

作者： Asif Bilal Abdul Quddious Atsushi Kanno Tetsuya Kawanishi Marco A. Antoniades Stavros Iezekiel EMPHASIS Research Centre University of Cyprus Nicosia Cyprus Department of Electrical and Computer Engineering University of Cyprus Nicosia Cyprus Chair for RF and Photonics Engineering Technische Universitat Dresden Dresden Germany National Institute of Information and Communications Technology Tokyo Japan Japan Graduate School of Engineering Nagoya Institute of Technology Aichi Japan School of Fundamental Science and Engineering Waseda University Tokyo Japan Department of Electrical Computer & Biomedical Engineering Ryerson University Toronto ON Canada

A fixed-length arbitrary-phase tunable phase shifter, based on the concept of negative-refractive-index transmission-line (NRI-TL) metamaterials for sub-6 GHz antenna beam steering applications is proposed. The design consists of a fixed microstrip transmission line which is reconfigured by loading varactor diodes in both the series and shunt branches. A continuously tunable phase of 210° at 3.2 GHz for the transmitted signal is achieved at the output by varying the biasing voltage from 0 – 7 V for the varactor diodes. The insertion loss varies from 0.9 – 2.5 dB for the design in the frequency range of 3.2 – 3.6 GHz. The total length of the tunable NRI-TL metamaterial phase shifter is λg/4.

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