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2023 1st DMIHER International Conference on Artificial Intelligence in Education and Industry 4.0, IDICAIEI 2023

作者： Kadu, Aishwarya Reddy, K.T.V. Artificial Intelligence and Data Science Faculty of Engineering and Technology Wardha India

ISBN: (纸本)9798350338423

Historically, farmers' practical knowledge was a significant factor in agricultural decisions. The start of climate change has, nevertheless, harmed crop output. Consequently, farmers face challenges when choosing the optimal crops considering the soil and environmental elements. Traditional manual methods of predicting the best crops to grow on particular types of terrain have typically yielded unsatisfactory results. Acquiring accuracy in crop forecasts is essential for increasing agricultural productivity. It is the precise point in the crop forecast process that machine learning plays a significant role. Crop forecast accuracy depends on several factors, including soil type, location, and regional weather variations. A fundamental aspect of prognostication is carefully identifying important qualities matched to particular crops, supported by feature selection methods. The current study orchestrates a comparison of several selection strategies for wrapper features. The experimental results show that the Recursive Feature Elimination method combined with the Adaptive Bagging classifier performs better than the alternative approaches. These procedures, which fall under the category of crop prediction and use categorization techniques, not only pinpoint the appropriate crops for specific plots of land but also plot the route for future development. © 2023 IEEE.

关键词： Crops

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SourcePlag - Source Code Plagiarism Detection Based on Abstract Syntax Trees 3

SourcePlag - Source Code Plagiarism Detection Based on Abstr...

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3rd IEEE International Conference on Blockchain and Distributed Systems Security, ICBDS 2024

作者： Sharma, Nakul Shinde, Siddharth Bhosale, Swarup Patil, Suyog Vishwakarma Institute of Information Technology Faculty of Engineering Department of Artificial Intelligence and Data Science Pune India Vishwakarma Institute of Information Technology Department of Artificial Intelligence and Data Science Pune India

ISBN: (纸本)9798350354348

Code plagiarism poses a significant challenge in programming communities, necessitating effective detection mechanisms. This paper introduces a novel system that employs Abstract Syntax Trees (ASTs) for code representation and comparison. The system utilizes ASTs to capture the structural essence of code, facilitating a comprehensive analysis of code similarity, it offers flexibility for application across multiple programming languages. In addition to leveraging the Levenshtein Distance Algorithm for Python code comparison, the system incorporates node counting for other languages such as Java and C/C++. By integrating AST-based representation and a combination of comparison techniques, the system offers a robust solution for identifying plagiarized code instances accurately across various programming environments. Through a detailed exploration of the system's methodology, this paper underscores its potential to address the pervasive issue of code plagiarism in programming communities. © 2024 IEEE.

关键词： C++ (programming language)

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Latency- and Privacy-Aware Convolutional Neural Network Distributed Inference for Reliable Artificial Intelligence Systems

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2025年第2期6卷 365-377页

作者： Hu, Yuhao Xu, Xiaolong Qi, Lianyong Zhou, Xiaokang Xia, Xiaoyu Nanjing University of Information Science and Technology School of Software Nanjing210044 China College of computer science and technology Qingdao266850 China Shiga University Faculty of Data Science Hikone522-8522 Japan RMIT University School of Computing Technologies MelbourneVIC3000 Australia

Reliable artificial intelligence (AI) systems not only propose a challenge on providing intelligent services with high quality for customers but also require customers' privacy to be protected as much as possible during process of the services. Given the ultrahigh computing load brought by deep-learning-based intelligent services to edge devices and the ultralong distance between edge and cloud, low-latency requirement of intelligent services is hard to meet in single edge computing or cloud computing. Edge-cloud collaborative inference of deep neural networks (DNNs) is considered a feasible solution to the problem. However, former work has not reduced the inference latency to the greatest extent and has not considered privacy protection in distributed systems. To solve the problem, we first establish a novel queue mechanism. Then, the convolution layer split decisions are made based on deep reinforcement learning (DRL) to realize the parallel inference of convolutional neural networks (CNNs) for inference latency reduction. Next, for each CNN, the partition decision is made based on brute force algorithm to further reduce inference latency and protect customers' privacy. Finally, simulation results show that our method performs better than the existing other methods. © 2020 IEEE.

关键词： Cloud computing

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Computer vision and Generative AI for yield prediction in Digital Agriculture 2

Computer vision and Generative AI for yield prediction in Di...

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2nd IEEE International Conference on Networking and Communications, ICNWC 2024

作者： Majumder, Sayan Khandelwal, Yash Sornalakshmi, K. SRM Institute of Science and Technology School of Computing Faculty of Engineering and Technology Department of Data Science and Business Systems Kattankulathur Chennai603203 India

ISBN: (纸本)9798350365269

For the cause of evolution of agriculture to its next generation, the introduction of A.I. and data-driven approach is going to be an important part of the agricultural industry that as per our vision would offer numerous economic, environmental and social benefits. Controlled Environment Agriculture (CEA) is providing more benefits since the weather and other environmental conditions is controlled and predictable. In CEA, the benefits include enhanced productivity in yield, reduced environmental footprints and better resource management. Our solution uses the adoption of Generative AI to visually forecast the potential yield of a crop using stable diffusion and conditional prompts additionally implementing several Generative Adversarial Networks (GAN) architectures for data generation, augmentation and super-resolution. Our proposed system also adopts generative AI for real time monitoring of plants. studying their respective conditions and their autonomous cultivation and harvesting patterns. The dataset used contained over 8479 examples for different fruits alongside annotations. This data was used to train the detection models as well categorically feed the GANs for data generation. data generated by DCGAN has a structural similarity of 0.5-0.7 whereas data generated by pix2pix network returns us a structural similarity of 0.7-0.9. © 2024 IEEE.

关键词： Cultivation

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Peer-to-Peer International Trade Model Based on Blockchain and Smart Contracts: Theory and Practice of BTLC 2

Peer-to-Peer International Trade Model Based on Blockchain a...

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2nd International Conference on Big data and Privacy Computing, BDPC 2024

作者： Yan, Junjie Wang, Wennan Feng, Duanyang Song, Shiyang Li, Chengyifan Application College Zhuhai College of Science and Technology Alibaba Cloud Big Data Zhuhai China Academy of Management Guangdong University of Science and Technology Dongguan China City University of Macau Faculty of Data Science China

ISBN: (纸本)9798350307597

This study focuses on BTLC, a peer-to-peer international trade model based on blockchain and smart contracts, which aims to reform the traditional let- ter of credit process, improve international trade efficiency, reduce costs and improve security. By elaborating the BTLC model, this study demonstrates how blockchain technology as well as smart contract technology can be applied in the field of international trade to effectively solve the problems existing in the tra- ditional letter of credit process, such as high transaction costs due to too many intermediaries, high transaction risks due to an imperfect trust mechanism, and inefficiency in transactions. In addition, this paper further proves the superiority of the BTLC model in theory and practice through empirical research. It provides an in-depth discussion of the potential application and development direction of the model in future research. However, there are still some challenges and lim- itations in the practical application of the BTLC model. These challenges and limitations include the need for further empirical studies to validate the model's effectiveness and the problems faced. These include technical standards, laws and regulations. Therefore, this study suggests that future research directions should focus on the implementation challenges of blockchain technology, as well as the application of blockchain technology in other fields, in order to promote the wide application of blockchain technology in international trade. © 2024 IEEE.

关键词： Smart contract

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Community Clustering on Fraud Transactions Applied the Louvain-Coloring Algorithm

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International Journal of Electronics and Telecommunications 2023年第3期69卷 593-598页

作者： Mardiansyah, Heru Suwilo, Saib Nababan, Erna Budiarti Efendi, Syahril Department Computer Science Faculty of Computer Science and Information Technology Universitas Sumatera Utara Medan Indonesia Department Mathematics Faculty of Mathematics and Natural Sciences Universitas Sumatera Utara Medan Indonesia Department Data Science and Artificial Intelligence Faculty of Computer Science and Information Technology Universitas Sumatera Utara Medan Indonesia

The contribution main from this research is modularity and better processing time in detecting community by using K-1 coloring. Testing performed on transaction datasets remittance on P2P platforms where the Louvain Coloring algorithm is better in comparison to Louvain Algorithm data used is data transfer transactions made by customers on the P2P Online platform. The data is the User data that has information transfer transactions, Card data that has information card, IP data that has IP information, and Device data that has information device. Every user owns unique 128-bit identification, and other nodes representing card, device, and IP are assigned a random UUID. The Device node has the guide, and device properties. IP nodes only have property guide and node User has property fraud Money Transfer, guide, money Transfer Error Cancel Amount, first Charge back Date. Each node has a unique 128-bit guide, with the amount whole of as many as 789,856 nodes. Application technique K-1 staining on Louvain algorithm shows enhancement value modularity and better processing time for detecting community on the network large scale. Through a series of exercises and tests carried out in various scenarios, it shows that the experiments carried out in this paper, namely the Louvain Coloring algorithm, are more effective and efficient than the Louvain algorithm in scenario 1,3, and 5 meanwhile For Scenarios 2 and 4 Louvain Algorithm is better. © 2023 Polish Academy of sciences. All rights reserved.

关键词： Clustering algorithms

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CONSTRUCTING ERGODIC NETWORKS USING DISCRETE MARKOV CHAINS 51

CONSTRUCTING ERGODIC NETWORKS USING DISCRETE MARKOV CHAINS

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51st International Conference on Computers and Industrial Engineering, CIE 2024

作者： Ohba, H. Sadakuni, S. Matsuda, T. Mizuno, S. Faculty of Health Data Science Juntendo University Chiba Japan Faculty of Information Technology Kanagawa Institute of Technology Kanagawa Japan Department of Information Design Shizuoka Institute of Science and Technology Shizuoka Japan

In a region's tourism strategy, connecting famous locations to surrounding areas can stimulate activity throughout the region. Connections between locations can be established by identifying the central locations in the area and creating routes from them to other locations. This study proposes an algorithm to obtain ergodicity in discrete Markov chains by utilizing network centrality measures. The number of calculations to obtain ergodicity was compared using several definitions of network centrality, enabling the selection of an appropriate network centrality measure for the target network. The proposed method identifies the central locations within the network and determines the necessary adjustments to transition probabilities between locations to achieve ergodicity. If the location network holds ergodicity, a stationary distribution of the Markov chain exists and optimization applications are expected. Numerical calculations were used to demonstrate the method's applicability to various networks. Furthermore, using a Wi-Fi log, we observed that the proposed method can reconstruct a network with ergodicity for networks that use access points as locations, indicating the social applicability of the proposed method. The proposed method can be used to analyze various networks in modern society, such as facility operations and tourism stimulation. In facility operations, this method can be applied to manage human traffic and analyze stay duration. To promote tourism, by considering tourist sites as network locations, the method clarifies which routes should be prioritized to construct an uninterrupted tourist network, making the construction and operation of an extensive tourist network possible. © 2024 Computers and Industrial Engineering. All rights reserved.

关键词： Equivalence classes

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Surface Defect Detection Algorithm for PCB Based on Improved YOLOv8 8

Surface Defect Detection Algorithm for PCB Based on Improved...

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8th International Conference on Electrical, Mechanical and Computer Engineering, ICEMCE 2024

作者： Ai, Yan Ye, TingDong City University of Macau Faculty of Data Science China Guangdong Industry Polytechnic University Faculty of Information Technology GuangZhou China

ISBN: (纸本)9798331506230

With the rapid development of the electronics industry, printed circuit boards (PCBs) play a crucial role in electronic products. However, traditional methods for PCB defect detection suffer from low efficiency and insufficient accuracy. To address this issue, this paper proposes an improved YOLO algorithm for PCB defect detection. In the field of PCB defect detection, due to the dense components and complex wiring on PCBs, traditional detection methods struggle to accurately and quickly identify various types of defects. This study aims to improve upon the traditional YOLO algorithm by introducing a new dual-layer routing attention mechanism and a feature extraction module, which enhances the ability to extract features of PCB defects. Additionally, the algorithm's loss function, Shape-IoU, has been optimized to better accommodate the shape characteristics of PCB defects, thereby improving both detection accuracy and recall rate. Experimental results demonstrate that the improved YOLO algorithm exhibits higher performance in PCB defect detection. Compared to traditional methods, the detection accuracy has increased by 2%, and the detection speed has improved by 3%, providing a more effective technical means for quality control in the PCB production process. © 2024 IEEE.

关键词： Electronics industry

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Implementing Hadoop and Moodle Integrated Student Support System 5

Implementing Hadoop and Moodle Integrated Student Support Sy...

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5th International Conference on Advanced Information Technologies, ICAIT 2024

作者： Aung, Myint Myat Min Htet, Shin Set, Hein Myo Hpone Thant, Aung Htut, Sithu Htet, Kyaw Zin Nwe Myint Thein, Nwe University of Information Technology Data Distribution Management Lab Yangon Myanmar University of Information Technology Data Distribution Management Lab Faculty of Computer Science Yangon Myanmar

ISBN: (纸本)9798331510046

Learning management systems (LMS) generate large amounts of real-time data which has the potential to provide valuable insights. Hadoop MapReduce is a scalable framework that is designed to handle such data. This paper presents a system architecture that integrates Hadoop MapReduce into Moodle, a popular LMS, for analyzing academic data. The implementation of the architecture allows users to generate a variety of reports from student attendance logs through a Moodle plugin which utilizes the Hadoop cluster for processing data. Users such as lecturers can then utilize these reports to identify which students do and do not engage in which courses as well as monitor their eligibility for exams. The use of Hadoop MapReduce provides high performance computing (HPC) capabilities that offer distributed parallel processing and economical scalability by means of adding commodity nodes to the cluster. © 2024 IEEE.

关键词： MapReduce

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AW-YOLOv9: Adverse Weather Conditions Adaptation for UAV Detection 24th

AW-YOLOv9: Adverse Weather Conditions Adaptation for UAV De...

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24th International Conference on Algorithms and Architectures for Parallel Processing, ICA3PP 2024

作者： Yang, Runjie Guo, Yongde Yan, Jun Yang, Haonan Dai, Zifeng Faculty of Data Science City University of Macau SAR Macau China School of Data Science Qingdao University of Science and Technology Qingdao266000 China Zhuhai Aerospace Microchips Science and Technology Co. Ltd. Zhuhai519000 China School of Cyberspace Science and Technology Beijing Institute of Technology Beijing100039 China

ISBN: (纸本)9789819615445

Effective and accurate detection of unmanned aerial vehicles (UAVs) is crucial for combating malicious UAV systems. However, adverse weather conditions, such as haze or low light, often degrade the quality of captured UAV images. Traditional target detection models applied to these low-quality UAV images usually suffer from decreased accuracy. To address this challenge, this paper proposes AW-YOLOv9, a UAV target detection method designed for adverse weather conditions. Specifically, an image enhancement processing (IEP) module with adaptive learning parameters is introduced to suppress weather-related interference in the images and restore hidden target information. The DP module embedded within YOLOv9 was designed to reduce the network’s parameter count while improving the detection speed. We established a dataset of UAV images under adverse weather conditions and utilized this dataset to validate the superiority of AW-YOLOv9. The experiments demonstrate that the proposed method achieves a 3.30% improvement in mean average precision compared to SOTA methods, effectively enhancing the detection accuracy of UAV targets under adverse weather conditions. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Aircraft detection

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