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Insights into research on blockchain for smart contracts: a bibliometric analysis

Multimedia Tools and Applications 2024年 1-25页

作者： Singh, Renu Gupta, Ashlesha Mittal, Poonam Faculty of Informatics and Computing J.C. Bose University of Science and Technology YMCA Faridabad India Department of Computer Science & amp Engineering Indraprastha Institute of Information Technology IIIT New Delhi India

Over the past few years, blockchain technology has gained significant attention. This surge in popularity can be attributed to the emergence of cryptocurrencies and the development of smart contracts. Cryptocurrency is a digital currency that eliminates the problem of double spending. Cryptocurrencies like Bitcoin, Ethereum, Litecoin, Stellar, Zcash, Maker, Aave, etc. become popular and are preferred for money transfers. Smart contracts are the next popular technology on the blockchain after cryptocurrency. It can be considered a piece of code that can execute automatically when the predefined conditions are fulfilled. Researchers believe that the potential of blockchain with smart contracts is only in its initial stages and that its true potential has yet to be fully discovered. Hence, an extensive bibliometric analysis is conducted to understand blockchain trends for smart contracts and to give future directions in this field. For this analysis, various steps are followed, starting with formulating the research question, defining the scope of our research, extracting and analyzing data, answering the research question, and finally, drawing a conclusion. This research paper will be fruitful for scholars and researchers, providing an extensive statistical and network analysis of extracted smart contracts publications. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024.

关键词： Bibliometric analysis Blockchain Crypto contract Scientometrics Scopus Smart contracts VOSViewer

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A transformer-based approach to Nigerian Pidgin text generation

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International Journal of Speech Technology 2024年第4期27卷 1027-1037页

作者： Garba, Kabir Kolajo, Taiwo Agbogun, Joshua B. Department of Computer Science Federal University Lokoja P.M.B 1154 Kogi State Lokoja Nigeria Department of Informatics Faculty of Engineering Built Environment & IT University of Pretoria Pretoria South Africa

This paper describes the development of a transformer-based text generation model for Nigerian Pidgin also known as Naijá, a popular language in West Africa. Despite its wide use, Nigerian Pidgin remains under-resourced, particularly in areas related to text generation and natural language processing. These difficulties are primarily due to technological constraints rather than the language’s fundamental attributes. There is currently a demand for Nigerian Pidgin-specific solutions because it is used in everyday communication and has a unique linguistic blend. This paper aims to close this gap by exploring the application of state-of-the-art transformer technology to develop a text generation model for Nigerian Pidgin. This work uses the public Afriberta-corpus dataset to optimize the Generative Pre-trained Transformer (GPT-2) model across a sizeable dataset. The performance evaluators, BLEU and Perplexity metrics provide a detailed breakdown of the model’s text quality and predictive accuracy. Despite the difficulties caused by a limited amount of training data, preliminary evaluations show that the model can generate coherent Nigerian Pidgin text. The performance evaluation yielded perplexity scores of 43.56 for variable target reference length and 43.26 for fixed text length. BLEU scores of 0.15 for fixed max length and 0.56 for variable reference target length. This highlights the quality of generated text and the significant improvement when the generated text length is aligned with the reference target. Our work was benchmarked against African American Vernacular (AAVE) revealing that BLEU scores for AAVE are significantly lower than those for Standard American English, with BLEU given as 0.26. Our Nigerian Pidgin model, with a BLEU score of 0.56, shows a better performance. However, both results suggest that both dialects are challenging for language models. Leveraging the pre-trained transformer-based language model and evaluation metrics, we showcase the mo

关键词： Controllable text Generation Natural Language Generation Natural Language Processing Nigerian pidgin Pre-trained Language models Transformers

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YOLOv8 vs. YOLOv9: Evaluating Object Detection Algorithms for Marine Waste Recognition 1

YOLOv8 vs. YOLOv9: Evaluating Object Detection Algorithms fo...

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1st IEEE International Symposium on Consumer Technology, ISCT 2024

作者： Wajdi, Achmad Zahir Wibowo, Prasetyo Sasaki, Shiori Politeknik Elektronika Negeri Surabaya Department of Informatics and Computer Engineering Surabaya Indonesia Faculty of Data Science Musashino University Tokyo Japan

ISBN: (纸本)9798350365191

Marine debris, the majority of which is composed of plastic (61 % to 87 %), is a significant environmental issue facing the world. Between 4.8 million and 12.7 million metric tons of plastic are thought to have entered the ocean in 2010 alone, increasing the estimated total amount of particles in the water to at least 5 trillion. Global plastic production, which reached 348 million tons in 2017, is expected to double in the next 20 years. To overcome this challenge, effective waste detection methods are needed, one of which is by utilizing computer vision, specifically the You Only Look Once (YOLO) algorithm. YOLOv9, as the latest version, offers improved object detection performance compared to its predecessor, YOLOv8. This research aims to evaluate the performance and efficiency of YOLOv9 and YOLOv8 in detecting marine waste. Using the Trash Annotations in Context dataset (TACO), We train both models and conduct tests to evaluate their accuracy and efficiency. Experimental results show that YOLOv9 has higher precision at 45% but lower recall at 6%, compared to YOLOv8, which has a precision of 33 % and a recall of 15 %. Overall, YOLOv8 shows better performance with higher mAP50 at 13% and mAP50-95 values at 10%, compared to YOLOv9, which has mAP50 at 4% and mAP50-95 values at 3%. © 2024 IEEE.

关键词： Marine Waste Neural Net-work Object Detection TACO Dataset YOLO

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Atomic Lifecycle for Cyber Threat Intelligence 17

Atomic Lifecycle for Cyber Threat Intelligence

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17th International Conference on Information Security and Cryptology, ISCTurkiye 2024

作者： Arikan, Suleyman Muhammed Kocak, Aynur Alkan, Mustafa Computer Forensics Institute of Informatics Gazi University Ankara Turkey Electrical and Electronics Engineering Faculty of Technology Gazi University Ankara Turkey

ISBN: (纸本)9798331533649

The utilisation of cyber threat intelligence provides a multitude of advantages in the assurance of security. A growing body of research is being published on the subject of cyber threat intelligence. However, the precise sequence of steps involved in the cyber threat intelligence life cycle remains unclear. The life cycle presented in each study is not consistent. Moreover, the transition from data to intelligence throughout the life cycle is not sufficiently delineated. The intelligence produced in many studies can be of different types. In our study, all existing lifecycles were examined and their naming approaches were analysed. The objective was to present a cyber threat intelligence life cycle that is as atomic as possible, with each step clearly expressed. This should facilitate a more rigorous comparison of studies in the existing literature on cyber threat intelligence. Furthermore, the life cycle can be utilised in new cyber threat intelligence studies. Researchers who aspire to implement best practices in their study are able to run more effective processes by following the life cycle. © 2024 IEEE.

关键词： Phishing

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Modified U-Net with attention gate for enhanced automated brain tumor segmentation

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Neural Computing and Applications 2025年第7期37卷 5521-5558页

作者： Saifullah, Shoffan Dreżewski, Rafal Yudhana, Anton Wielgosz, Maciej Caesarendra, Wahyu Faculty of Computer Science AGH University of Krakow Krakow30-059 Poland Department of Informatics Universitas Pembangunan Nasional Veteran Yogyakarta Yogyakarta55281 Indonesia Department of Electrical Engineering Universitas Ahmad Dahlan Yogyakarta55191 Indonesia Faculty of Computer Science Electronics and Telecommunications AGH University of Krakow Krakow30-059 Poland Faculty of Mechanical Engineering Opole University of Technology 76 Proszkowska St. Opole45-758 Poland

This study addresses the formidable challenges encountered in automated brain tumor segmentation, including the complexities of irregular shapes, ambiguous boundaries, and intensity variations across MRI modalities. Manual segmentation, plagued by subjectivity and time constraints, further exacerbates the problem. To address these issues, we propose a modified U-Net architecture with an integrated attention gate. The proposed model demonstrates high performance, with notable Dice Similarity Coefficient (DSC) and Jaccard Index (JI) values across various tumor classes, consistently exceeding 0.93 and 0.87, respectively. Incorporating Contrast-Limited Adaptive Histogram Equalization and Histogram Equalization improves segmentation accuracy, particularly in cases of Meningioma. Comparative analyses against established models reveal a DSC of 0.9521 and a JI of 0.9093, underscoring the superiority of our method. Validation in the BraTS 2021 dataset underscores the robustness of the method, achieving high DSC and JI scores in four MRI modalities, with the T2 modality demonstrating the highest performance (DSC: 0.9216, JI: 0.8556). While acknowledging these achievements, we recognize challenges related to dataset specificity and computational intensity associated with the attention gate. Future research efforts should address these issues to improve the generalizability and applicability of the method in real-world scenarios. In addition to presenting a novel automated brain tumor segmentation method, this study contributes comprehensive result values and comparative analyses with previous research, providing valuable insights into the evolving landscape of medical image analysis. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2025.

关键词： Image segmentation

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Performance Comparison of the Invasive Weed Optimization K-Means Algorithm With Particle Swarm Optimization K-Means for Banking Data 6

Performance Comparison of the Invasive Weed Optimization K-M...

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6th International Conference on Cybernetics and Intelligent System, ICORIS 2024

作者： Suwirmayanti, Ni Luh Gede Pivin Putra, I. Ketut Gede Darma Sudarma, Made Sukarsa, I. Made Setyaningsih, Emy Department of Computer Systems Faculty of Informatics and Computer Institut Teknologi dan Bisnis STIKOM Bali Bali Indonesia Department of Information Technology Faculty of Engineering Udayana University Jimbaran Hill Bali Indonesia Department of Electrical Engineering Faculty of Engineering Udayana University Jimbaran Hill Bali Indonesia Department of Computer Systems Engineering Faculty of Applied Science Universitas AKPRIND Indonesia Yogyakarta Indonesia

ISBN: (纸本)9798331539603

Banking produces extensive and diverse data, so a clustering process is needed to understand customer behavior patterns and transactions more effectively. This clustering has been widely utilized with the K-Means algorithm due to its simplicity and efficiency. However, this algorithm is limited in determining the optimal cluster center, affecting the accuracy of the clustering results. This study proposes applying two metaheuristic algorithms, Invasive Weed Optimization (IWO) and Particle Swarm Optimization (PSO), as additional solutions combined with K-Means. These two algorithms were chosen because of their ability to find global solutions and convergence speed to handle large and complex banking data. The results show the superiority of IWOKM compared to PSOKM in terms of SSE and DBI. From the SSE value, IWOKM produces a lower value (3029.77), which indicates that this method is better at minimizing clustering errors and producing more accurate clustering than PSOKM (80007.09). From the DBI value, IWOKM also produces a better value (1.6684) than PSOKM (1.6782), which shows that the clusters produced by IWOKM are more compact and more separated. However, in terms of computation time, PSOKM is more efficient, with a faster average computation time. Although IWOKM produces better clustering quality, PSOKM offers advantages in terms of processing speed. This finding confirms that the selection of the IWO algorithm is more appropriate for use on datasets with characteristics such as the German Credit Dataset. © 2024 IEEE.

关键词： Invasive weed optimization

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Revolutionizing Recruitment Management: Optimizing Advanced Digital Solutions for Manpower Planning in Higher Education 12

Revolutionizing Recruitment Management: Optimizing Advanced ...

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12th IEEE Conference on Systems, Process and Control, ICSPC 2024

作者： Andrian, Ahmad Mukafi Khamis, Nor Kamaliah Subbiah, Ahgalya Zuhriyah, Halimatuz Faizah, Safira Management and Science University Faculty of Information Sciences and Engineering Section 13 Selangor Shah Alam40100 Malaysia Jakarta Global University Faculty Engineering and Computer Science Department Informatics Engineering Depok16412 Indonesia

ISBN: (纸本)9798350391398

Effective manpower planning is crucial for any organization, including universities, as it directly influences performance, productivity, and overall success. Inefficient recruitment processes can lead to increased costs, higher turnover rates, and suboptimal utilization of human resources, negatively impacting the institution's ability to achieve its goals and maintain a competitive advantage. The objective of this study is to develop a comprehensive digital solution to streamline and enhance recruitment and manpower planning processes, replacing traditional manual methods. This paper presents the Recruitment and Manpower Planning System, an innovative web-based platform designed using Extreme Programming (XP) methodologies. The system integrates modern programming tools such as Visual Studio Code, Laragon, Laravel 9, and MySQL, and utilizes Unified Modelling Language (UML) for precise system design. A mixed-methods approach, incorporating quantitative data from structured questionnaires administered to 500 users, informed the system's design. Data analysis indicates a 40% reduction in recruitment time, a 35% increase in successful job placements, and a 90% user satisfaction rate. Rigorous testing phases ensured system accuracy and quality. This digital solution not only optimizes recruitment processes but also provides a robust prototype for future advancements in manpower planning systems. © 2024 IEEE.

关键词： Unified Modeling Language

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A Review of Fintech Management and Security Development 10

A Review of Fintech Management and Security Development

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10th IEEE Information Technology International Seminar, ITIS 2024

作者： Ritonga, Ali Akbar Zuhri Harahap, Syaiful Sooai, Adri Gabriel Nugroho, Aryo Samsir Prasetyo Adi, Puput Dani Universitas Labuhanbatu Faculty of Science and Technology Department of Informatics Management North Sumatera Labuhanbatu Indonesia Universitas Labuhanbatu Faculty of Science and Technology Department of Information System North Sumatera Labuhanbatu Indonesia Universitas Katolik Widya Mandira Faculty of Computer Science Department of Computer Engineering NTT Kupang Indonesia Universitas Narotama Faculty of Computer Science Department of Information System Surabaya Indonesia Universitas Labuhanbatu Faculty of Computer Science Department of Informatics Engineering North Sumatera Labuhanbatu Indonesia Department of Telecommunication Bandung Indonesia

ISBN: (纸本)9798331521295

This research focuses on the review of Fintech and its development on the IoT Platform and also the risks that can be posed to the IoT network used. Finance is the most essential side of several other sectors which in this case often experience many losses, one of the biggest losses is the existence of hackers and theft of money through the Internet platform or Internet Server. This research reviews how the ins and outs of the flow of funds located on servers or online banks. And ways to overcome hackers and bypass data. Comprehensively reviewed in this research review. This research discusses Cryptocurrency Analysis, Bitcoin, and Ethereum Comparison which are some of the methods used in online-based Finance, which is without currency but has the same value even more than currency if the conversion process is carried out at the bank. In this research, it is comprehensively discussed how methods are carried out in the process of securing data, Cryptocurrency, Bitcoin, and Ethereum are comprehensively discussed to get a lot of data and comparisons and the right steps to deal with crime via the internet. The predicted use of IoT in the Fintech sector is 140 billion connected devices, IoT Loans 110 billion connected devices, and IoT insurance with an estimated 90 billion connected devices, in the approaching 2026. © 2024 IEEE.

关键词： Ethereum

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The Basics of Neutrosophic Simulation for Converting Random Numbers Associated with a Uniform Probability Distribution into Random Variables Follow an Exponential Distribution

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Neutrosophic Sets and Systems 2023年 53卷 358-366页

作者： Jdid, Maissam Alhabib, Rafif Salama, A.A. Faculty of Informatics Engineering Al-Sham Private University Damascus Syria Department of Mathematical Statistics Faculty of Science Albaath University Homs Syria Department of Mathematics and Computer Science Faculty of Science Port Said University Port Said Egypt

When performing the simulation process, we encounter many systems that do not follow by their nature the uniform distribution adopted in the process of generating the random numbers necessary for the simulation process. Therefore, it was necessary to find a mechanism to convert the random numbers that follow the regular distribution over the period [0, 1] to random variables that follow the probability distribution that works on the system to be simulated. In classical logic, we use many techniques in the transformation process that results in random variables that follow irregular probability distributions. In this research, we used the inverse transformation technique, which is one of the most widely used techniques, especially for the probability distributions for which the inverse function of the cumulative distribution function can found. We applied this technique to generate neutrosophic random variables that follow an exponential distribution or a neutrosophic exponential distribution. This based on classical or neutrosophic random numbers that follow a regular distribution. We distinguished three cases according to the logic that each of the random numbers or the exponential distribution follows. We arrived at neutrosophic random variables that, when we use them in systems that operate according to an exponential distribution, such as queues and others, will provide us with a high degree of accuracy of results, and the reason for this is due to the indeterminacy provided by neutrosophic logic © 2023, Neutrosophic Sets and *** Rights Reserved.

关键词： Random variables

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Towards Utilizing Unlabeled Data for Flood Forecasting with Weakly Supervised Adaptive Learning

Towards Utilizing Unlabeled Data for Flood Forecasting with ...

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2023 Innovations in Intelligent Systems and Applications Conference, ASYU 2023

作者： Abdulla, Nawaf Demirci, Mehmet Ozdemir, Suat Gazi University Graduate School of Informatics Department of Information Systems Ankara Turkey Gazi University Faculty of Engineering Department of Computer Engineering Ankara Turkey Hacettepe University Faculty of Engineering Department of Computer Engineering Ankara Turkey

ISBN: (纸本)9798350306590

The importance of accurate flood forecasting is rising as climate change makes it more difficult to predict when and where floods will occur. Flood predictions use supervised machine learning models, and flood levels are being tracked via the Internet of Things (IoT). Despite advancements in data-driven hydrological forecasting, producing reliable flood forecasts remains challenging. This research paper explores the possibility of utilizing adaptive deep learning with weak supervision to predict flooding in coastal cities that are equipped with Internet of Things technology. This approach uses weakly supervised learning for data labeling and enhancement, a bidirectional long-short-term memory (LSTM) RNN for time-series prediction and classification, and adaptive learning to handle seasonality. The results of the proposed system show that flood forecasting accuracy could be improved by 40% compared to the baseline models. © 2023 IEEE.

关键词： Floods

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