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2024 International Conference on Information Technology Systems and Innovation, ICITSI 2024

作者： Aditama, Raditya Maulana, Maulvi Ziadinda Harisman, M. Rifki Virziadeili Hariyanto, Agus Susanto, Teguh T. D Universitas Indonesia Faculty of Computer Science Depok Indonesia Institut Teknologi Bandung School of Electrical Engineering and Informatics Bandung Indonesia Universitas Kristen Teknologi Solo Department of Informatics Engineering Surakarta City Indonesia Universitas Negeri Jakarta Department of Educational Management Jakarta Indonesia

ISBN: (纸本)9798331511470

Even if an explosive number of proposals and prototype systems of blockchain based certificates and verification have been suggested and implemented since 2015, still in the public service areas paper certificates are prevalent. Because of advancement of color duplication and editing tools obtainable at almost no price in the internet, paper certificates are easily forged and modified. For the reason, the verification technology of the paper certificates needs need regressive research and can provide abundant research opportunities. In this research, various research suggestions and technical implementation are reviewed and a new system, called 'one-shot' verification is briefly described. The one-shot verification captures the image and QR code of the printed certificates with mobile devices before sending them to block-chained server. The block-chained server decides whether the printed certificates are tampered or not, with the OCR content by comparing hash value with hash value of the original content. The image OCR of the paper was trained with more than 1,700 images using YOLOv8. In the testing with preprocessing, the technology showed 96% accuracy. © 2024 IEEE.

关键词： Blockchain

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Detection of Katokkon Chili Maturity using Convolutional Neural Network with Transfer Learning Model DenseNet169

Detection of Katokkon Chili Maturity using Convolutional Neu...

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2023 International Workshop on Artificial Intelligence and Image Processing, IWAIIP 2023

作者： Kaswar, Andi Baso Ilham, Muhammad Ryan Tahir, Renisa Amalia Mappalotteng, Abdul Muis Djawad, Yasser Abdul Andayani, Dyah Darma State University of Makassar Department of Computer Engineering Makassar Indonesia State University of Makassar Department of Informatics and Computer Engineering Makassar Indonesia State University of Makassar Department of Electronics Engineering Makassar Indonesia

ISBN: (纸本)9798350382914

Chili is a plant that originates from America and is included in the Solonaceae and Capsicum genera. One type of chili that is included in the large chili category is Katokkon chili, commonly referred to as Toraja chili, which comes from the Tana Toraja district, South Sulawesi, Indonesia. Research related to the detection of fruit ripeness and quality has also been conducted. However, the research that has been conducted still uses relatively few images, and the accuracy needs to be improved. In addition, no research has discussed the detection of Katokkon chili maturity level using images or images of Katokkon chili fruit taken directly from the tree. Therefore, research was conducted with the title Detection of the Maturity Level of Katokkon Chili using the CNN pretrained DenseNet169 model method. In this study, the level of maturity of chili peppers was classified into three categories: raw chilies, half-ripe chilies, and ripe chilies. Based on the results of several tests that have been carried out, the best accuracy results are obtained in testing using the DenseNet169 method, with an accuracy of 95.03%, a loss value of 16.72%, a recall and specificity value of 95.03% and 97.51% respectively, a precision value of 95.03%, and an accuracy CF of 96.69%. This shows that the DenseNet169 method with a combination (Swish, Swish, and Adamax) can be considered the best choice for classifying or detecting the level of maturity of Toraja chili compared other methods that have been tested. © 2023 IEEE.

关键词： Convolutional neural networks

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Distributed Page Table: Harnessing Physical Memory as an Unbounded Hashed Page Table 57

Distributed Page Table: Harnessing Physical Memory as an Unb...

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57th Annual IEEE/ACM International Symposium on Microarchitecture, MICRO 2024

作者： Kwon, Osang Lee, Yongho Park, Junhyeok Jang, Sungbin Tak, Byungchul Hong, Seokin Sungkyunkwan University Department of Electrical and Computer Engineering Korea Republic of School of Computer Science and Engineering Kyungpook National University Korea Republic of

ISBN: (纸本)9798350350579

Virtual memory systems rely on the page table, a crucial component that maps virtual addresses to physical addresses (i.e., address translation). While the Radix Page Table (RPT) has traditionally been used for this task, its limitations have become more apparent with the rise of memory-intensive applications. Recently, Hashed Page Tables (HPTs) have been explored as an alternative page table structure to offer faster address translation. However, the HPT introduces its own set of challenges particularly in resizing the page table and allocating contiguous physical memory space for storing the table. To tackle the fundamental problem of the existing HPT designs, this paper introduces Distributed Page Table (DPT), a novel approach that utilizes the physical memory as a huge hashed page table. DPT distributes Page Table Entries (PTEs) across the entire physical memory space, significantly reducing the hash collisions while avoiding the table resizing overheads. When distributing the PTEs across the physical memory, they can be mapped to memory locations already allocated to data pages. This new type of collision, referred to as address collision, may reduce the effectiveness of the DPT. This paper showcases that the DPT can effectively resolve the address collision with three simple yet efficient techniques: Strided Open Addressing (SOA), Collision-Aware Virtual Address Allocation (CVA) and Collided Page Displacement (CPD). Our experimental results demonstrate that DPT achieves average performance improvements of 12.6%, 11.6%, and 8.7% compared to traditional RPT, the latest large-coverage TLB design, and state-of-the-art HPTs, respectively. © 2024 IEEE.

关键词： Virtual addresses

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Global household infectious waste management strategies:a systematic review

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Waste Disposal and Sustainable Energy 2024年第3期6卷 371-384页

作者： Leila Mohammadinia Khadijeh Raei Fathollah Gholami-Borujeni Health in Disasters and Emergencies Department of Health Policy and ManagementSchool of Management and Medical InformaticsTabriz University of Medical SciencesTabrizIran Department of Health Policy and Management School of Management and Medical InformaticsTabriz University of Medical SciencesTabrizIran Department of Environmental Health Engineering Faculty of HealthMazandaran University of Medical SciencesSariIran

Infectious waste management is a major environmental and public health challenge *** infectious wastes are often mixed with other waste,collected,and disposed of without taking measures to reduce *** the emergence of the COVID-19 virus in early December 2019,there have been concerns about the dangers of infectious waste generated in healthcare facilities and ***,this review was conducted with the aim of investigating the methods of household infectious waste management in different countries/regions and evaluating the factors affecting the management of household infectious *** this study,we conducted a systematic literature search in four electronic databases(PubMed,Web of Science,Scopus,and ProQuest)to answer the research *** process was based on the new version of PRISMA *** total,52 documents out of 6742 were selected and *** review of studies based on content analysis led to the emergence of 12 main themes,36 sub-themes,and 112 *** main topics included education and culture,management measures,legal social support,economic support,technology,control and evaluation,waste generation,classification and collection,temporary storage,transportation,processing and recycling,and safe *** factors affecting household infectious waste management identified in this research can serve as a basis for future *** is also suitable for providing a comprehensive guide to the management of household infectious waste.

关键词： Infectious waste Waste management Household waste Domestic waste COVID-19

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Profit Demand Delegated Proof of Stake: Hypothetical Design Concept for Blockchain

Profit Demand Delegated Proof of Stake: Hypothetical Design ...

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2024 IEEE International Conference on Computing, Applications and Systems, COMPAS 2024

作者： Gupta, Udayan Das Haque, Md. Mokammel Reza, Ahmed Wasif Arefin, Mohammad Shamsul Chittagong University of Engineering and Technology Department of Computer Science and Engineering Chittagong Bangladesh Sciences University of Adelaide School of Computer and Mathematical Adelaide Australia East West University Department of Computer Science and Engineering Dhaka Bangladesh

ISBN: (纸本)9798331529765

Delegated Proof of Stake (DPoS) is indeed a fascinating consensus algorithm used on various blockchain platforms. It was designed to address some of the scalability and energy consumption issues associated with Proof of Work (PoW) while maintaining decentralization to a certain extent. But, DPoS has some disadvantages in the distribution of profit share for stake forwarding nodes to delegate nodes. The profit share is controlled by the delegate nodes rather than the staking nodes or token holder voting nodes. Here, in Profit Demand Delegated Proof of Stake (PD-DPoS), we introduced a new mechanism to facilitate the token holder voting nodes to demand a rate of profit/profit share. PD-DPoS will encourage the delegate nodes to invest in infrastructure for high connectivity and fast computing crypto systems. As the system builds targeting fast and reliable computing by extending the opportunity of demanding profit share for token holder voter nodes will experimentally perform and prove better than the DPoS. © 2024 IEEE.

关键词： Blockchain

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Realizing Predictive Maintenance in Production Machinery Through Low-Cost IIoT Framework and Anomaly Detection: A Case Study in a Real-World Manufacturing Environment 19

Realizing Predictive Maintenance in Production Machinery Thr...

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19th Annual International Conference on Distributed Computing in Smart Systems and the Internet of Things, DCOSS-IoT 2023

作者： Filios, Gabriel Karatzas, Stylianos Krousarlis, Marios Nikoletseas, Sotiris Panagiotou, Stefanos H. Spirakis, Paul University of Patras Computer Engineering and Informatics Department Greece Greece University of Patras Civil Engineering Department Greece University of Liverpool Department of Computer Science United Kingdom

ISBN: (纸本)9798350346497

Digital transformation and predictive maintenance are still some of the key challenges faced by the industrial sector as it moves towards Industry 4.0. However, the lack of resources, experienced personnel, and the absence of digital-savvy culture in manufacturing companies, often hinder the adoption of the required technologies. In this context, maintenance is a required process to ensure the smooth and continuous operation of the production, but to this day, engineers are still required to physically inspect equipment on site. By applying digital technologies, a predictive maintenance framework will enable the industry to maximize the life of the equipment and to act on time in order to prevent failures and thus reduce costs and optimize maintenance efficiency. In this effort, integrating such technologies and the required modern computing devices for monitoring legacy industrial equipment remains a challenge. Therefore, in this paper, we present a low-cost IIoT system for an AI-based predictive maintenance of machinery in a real-world industrial environment. In specific, a data collection system, consisting of a Raspberry Pi device and several sensors (namely, an accelerometer, temperature sensors and a microphone), was designed and developed. The collected data was then utilized to feed a deep-learning LSTM-autoencoder model for anomaly detection through a semi-supervised learning approach. Despite the small amount of data collected, the model is able to effectively detect anomalies. Finally, ways in which the existing system can be improved are proposed. © 2023 IEEE.

关键词： Internet of things

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Hand Sign Detection and Voice Conversion for the Hearing and Speech Impaired Using Convolutional Neural Networks 4th

Hand Sign Detection and Voice Conversion for the Hearing and...

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4th International Conference on Recent Trends in Machine Learning, IoT, Smart Cities, and Applications, ICMISC 2023

作者： Belgaum, Mohammad Riyaz Sowmya, Kurni Sireesha, Kuruva Priyanka, Tatagari Tony Vyshnavi, Uppara Uravakonda Department of Computer Science and Engineering G. Pullaiah College of Engineering and Technology Kurnool India Faculty of Computing and Informatics Multimedia University Cyberjaya Malaysia

ISBN: (纸本)9789819994410

Sign Language Recognition (SLR) tries to convert sign language into written or spoken form to enable communication between a deaf-mute person and a normal person. The task of communicating with disabled and impaired persons is extremely challenging, owing to its complexity and variety of hand motions that have a major societal impact. In order to depict sign language motions, SLR approaches are increasingly being used. These classification models are based on hand-crafted characteristics. Building reliable features that can adapt to the diverse range of hand signs is nonetheless challenging. To address this issue, the authors proposed a special 3D convolutional neural network (CNN) to ease the communication between normal and mute communities. Our proposed model eliminates the requirement to create features by automatically extracting discriminative spatial–temporal features from the actual livestream without any prior information. To improve performance, the 3D CNN is fed with multiple channels of video feeds that include colour, depth, trajectory information, joint body locations, and depth clues. We illustrate the proposed model's superiority to the conventional methods based on hand-crafted characteristics by testing them against a real dataset collected using a depth camera. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Audition

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Classification of Adversarial Attacks Using Ensemble Clustering Approach

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computers, Materials & Continua 2023年第2期74卷 2479-2498页

作者： Pongsakorn Tatongjai Tossapon Boongoen Natthakan Iam-On Nitin Naik Longzhi Yang Center of Excellence in AI&Emerging Technologies School of ITMae Fah Luang UniversityChiang RaiThailand Department of Computer Science Aberystwyth UniversityAberystwythUnited Kingdom School of Informatics and Digital Engineering Aston UniversityBirminghamUnited Kingdom Department of Computer and Information Sciences Northumbria UniversityNewcastleUnited Kingdom

As more business transactions and information services have been implemented via communication networks,both personal and organization assets encounter a higher risk of *** safeguard these,a perimeter defence likeNIDS(network-based intrusion detection system)can be effective for known *** has been a great deal of attention within the joint community of security and data science to improve machine-learning based NIDS such that it becomes more accurate for adversarial attacks,where obfuscation techniques are applied to disguise patterns of intrusive *** current research focuses on non-payload connections at the TCP(transmission control protocol)stack level that is applicable to different network *** contrary to the wrapper method introduced with the benchmark dataset,three new filter models are proposed to transform the feature space without knowledge of class *** ECT(ensemble clustering based transformation)techniques,i.e.,ECT-Subspace,ECT-Noise and ECT-Combined,are developed using the concept of ensemble clustering and three different ensemble generation strategies,i.e.,random feature subspace,feature noise injection and their *** on the empirical study with published dataset and four classification algorithms,new models usually outperform that original wrapper and other filter alternatives found in the *** is similarly summarized from the first experiment with basic classification of legitimate and direct attacks,and the second that focuses on recognizing obfuscated *** addition,analysis of algorithmic parameters,i.e.,ensemble size and level of noise,is provided as a guideline for a practical use.

关键词： Intrusion detection adversarial attack machine learning feature transformation ensemble clustering

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Cross-Layer Optimization Strategies for Energy-Efficient and Reliable Machine-Type Communication in LPWANs 2

Cross-Layer Optimization Strategies for Energy-Efficient and...

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2nd IEEE International Conference on Emerging Research in Computational Science, ICERCS 2024

作者： Amanullah, M. Manoranjini, J. Sathiya, V. Helen Prabbu Sankar, P. Saveetha Institute of Medical and Technical Sciences Saveetha School of Engineering Department of Computer Science & Engineering Chennai India Rajalakshmi Engineering Department of Artificial Intelligence and Data Science Chennai India Panimalar Engineering College Department of Computer Science & Engineering Chennai India

ISBN: (纸本)9798331534950

In the massive Machine-Type Communication (mMTC), the exponential growth of Internet of Things (IoT) devices over Low Power Wide Area Networks (LPWANs) presents substantial issues regarding energy efficiency and stability. The existing frameworks frequently fails in the the intricate relationships among the MAC-layer and physical parameters. To enhanced cross-layer evaluation methodology that integrates adaptive data rate (ADR) optimization, real-time energy consumption monitoring, and dynamic downlink message handling to address the problems. To maintain stability and performance with the network situations, the framework makes use of a Control-Lyapunov function. To enable effective resource allocation uses the Successive Convex Approximation that improve the effects of various transmission systems under quasi-static and dynamic channel conditions. To verify the cross-layer interactions and it impacts the energy efficiency, the proposed method begins with the data collection and it moves to payload size and packet length, are regularly changed to examines the affect throughput and energy consumption. The findings show the high-density node situations, energy consumption that reduced while retaining reliable communication. Furthermore, the results show the interdependencies among network parameters, highlighting the necessity of an integrated strategy. This framework helps to ensure the mMTC scenarios that optimized by improving the long-term viability and dependability of IoT networks. © 2024 IEEE.

关键词： Resource allocation

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A Comprehensive Model Leveraging Convolutional Neural Networks for Online Transaction Fraud Detection

A Comprehensive Model Leveraging Convolutional Neural Networ...

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2025 International Conference on Pervasive Computational Technologies, ICPCT 2025

作者： Mishra, Sanskar Goyal, Anjali Sahu, Deepti Sharda University Sharda School of Engineering and Technology Department of Computer Science & Engineering Uttar Pradesh Greater Noida India

ISBN: (纸本)9798331508685

Financial fraud detection is a major challenge faced by financial institutions. For transaction fraud detection, Deep learning and machine learning algorithms are frequently used;however, their efficacy is severely limited by the issue of class imbalance, particularly with regard to the minority fraud class. A thorough study on literature has been done through this research for the examination of the techniques used to handle problems like class imbalance. This work analysed various works on usage of classical machine learning algorithms for transaction fraud detection on unbalanced and balanced datasets produced by under and over sampling. Works on Graph Neural Network that can capture relationships among entities in a graph constructed from the credit card transaction data has also been examined. Furthermore, this work proposes a Convolutional Neural Network (CNN) based approach combined with Adaptive Synthetic Sampling to address the class imbalance problem. The effectiveness has been demonstrated through our preliminary results on AUC, accuracy and other metrics of the approach proposed in learning from the data that is imbalanced as evaluated and compared against other classifiers like Random Forest and K-Nearest Neighbour. The proposed approach achieves an accuracy of 97.5% for fraud detection, outperforming other learners. This study provides insights on leveraging Deep Learning (DL) and Machine Learning (ML) techniques to address data imbalance for improved fraud detection performance. © 2025 IEEE.

关键词： Adaptive Synthetic Sampling Class Imbalance Convolutional Neural Network Credit Card Fraud Detection Financial fraud

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