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11th International Conference on Future data and Security Engineering, FDSE 2024

作者： D. T. Do, Huy D. Nguyen, Hien T. Pham, Vuong Nguyen, Tri-Hai Faculty of Computer Science University of Information Technology Ho Chi Minh City Viet Nam Faculty of Mathematics and Computer Science University of Science Ho Chi Minh City Viet Nam Vietnam National University Ho Chi Minh City Viet Nam Computer Science Department New Mexico State University Las Cruces United States Institute of Data Science and Artificial Intelligence Sai Gon University Ho Chi Minh City Viet Nam Faculty of Information Technology School of Technology Van Lang University Ho Chi Minh City Viet Nam

ISBN: (纸本)9789819604333

In the rapidly evolving economic and social landscape of Vietnam, land remains a pivotal resource, crucial for economic growth and social stability. The recent enactment of the Land Law 2024, which supersedes the 2013 legislation, necessitates continuous updates for land users, investors, and regulatory authorities to ensure compliance and optimize land utilization. Traditional legal information retrieval systems are constrained by keyword-based searches and pre-set responses, which are inadequate for addressing specific user queries or rapidly integrating new legislative updates. This paper presents an advanced technological solution employing the Legal-Onto model to represent and update legal knowledge. The Legal-Onto model, inspired by relational ontology and keyphrase graphs, captures the semantic content of legal documents. By employing natural language processing techniques, the system automatically compares old and new legal texts, identifying differences at the article, point, and clause levels. This enables efficient updates and provides a valuable tool for legal professionals. It demonstrates the potential of artificial intelligence in legal contexts, offering a more effective and accessible approach to legal information. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： information Retrieval Intelligent System Knowledge-Based Systems Legal Document Ontology Vietnamese Land Law

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A Novel 3D Medical Image Segmentation Model Using Improved SAM

A Novel 3D Medical Image Segmentation Model Using Improved S...

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2024 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2024

作者： Kuang, Yuansen Ma, Xitong Zhao, Jing Wang, Guangchen Zeng, Yijie Liu, Song Key Laboratory of Computing Power Network and Information Security Jinan China Shandong Engineering Research Center of Big Data Applied Technology Faculty of Computer Science and Technology Jinan China Shandong Fundamental Research Center for Computer Science Shandong Provincial Key Laboratory of Computer Networks Jinan China

ISBN: (纸本)9781665410205

3D medical image segmentation is an essential task in the medical image field, which aims to segment organs or tumours into different labels. A number of issues exist with the current 3D medical image segmentation task: existing models cannot simultaneously obtain the space correlation and depth correlation of 3D slices;previous models suffer from local detail loss of positional embedding in 3D images;previous approaches often have blurring of boundaries in segmenting 3D images. To solve these shortcomings, we propose a 3D medical image segmentation model named TPM-SAM. In our model, we design a twinchannel image encoder to simultaneously capture the space correlation and depth correlation of 3D slices through a multi-head attention mechanism and improved adapters. Furthermore, we design a prompt encoding generator, which divides the volumetric image into small blocks and better captures the local detail information. In addition, we introduce a multi-layer aggregation decoder by employing U-Net with multi-level skip connection to solve the blurring of boundaries in processing 3D images. Finally, we experimented and evaluated our model on KiTS21 and LiTS17 datasets to compare with other baseline models. © 2024 IEEE.

关键词： Image segmentation

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A Survey of Artificial Intellegence Techniques to Detect Intrusion Attack in Communication Network 1

A Survey of Artificial Intellegence Techniques to Detect Int...

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1st International Conference on Cognitive Computing and Engineering Education, ICCCEE 2023

作者： Shankar Siva, S. Thanh Hung, Bui Chakrabarti, Prasun Industrial University of Ho Chi Data Science Laboratory Faculty of IT Minh City Viet Nam KG Reddy College of Engineering and Technology Department of CSE Telangana Moinabad India Industrial University of Ho Chi Data Science Laboratory Faculty of Information Technology Minh City Viet Nam LTM SLS Baroda University Gujarat Vadodara391510 India

ISBN: (纸本)9798350332803

Networks are a huge part of modern life, and cyber security has become a crucial area of research. An intrusion detection system (IDS) is a device that examines network traffic to confirm security, availability, and integrity. Despite years of research, conventional IDSs continue to struggle with enhancing detection performance, lowering False Alarm Rates (FAR), and identifying novel assaults. Many academics have concentrated on constructing IDSs that leverage Artificial Intelligence (AI) technologies to overcome the difficulties mentioned above. With extreme accuracy, AI algorithms can reliably identify the main distinctions among normal and anomalous data. Furthermore, AI approaches are very generalizable, allowing them to identify previously undiscovered assaults. IDS methods based on ML, DL, and meta-heuristic optimization have recently been proposed as workable alternatives for spotting intrusions throughout the network. This survey presents an IDS taxonomy that classifies and summarizes AI-based IDS research using data items as the primary dimension. This article offers a thorough analysis of recent studies on the application of AI in IDSs, including surveys and suggestions for cutting-edge techniques. Following that, recent developments and advances in AI-based network IDS are explored, as well as the proposed strategy, assessment metrics, and dataset selection. This study highlighted many research obstacles and presented future direction for research in developing AI-based Network IDS using the inadequacies of the existing approaches. © 2023 IEEE.

关键词： Wireless networks

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A Security Enhanced Chaotic-Map-Based Authentication Protocol for Internet of Drones

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IEEE Internet of Things Journal 2024年第12期11卷 22301-22309页

作者： Mahmood, Khalid Ghaffar, Zahid Farooq, Muhammad Yahya, Khalid Das, Ashok Kumar Chaudhry, Shehzad Ashraf National Yunlin University Of Science And Technology Graduate School Of Intelligent Data Science Douliu64002 Taiwan National Yunlin University Of Science And Technology Graduate School Of Engineering Science And Technology Douliu64002 Taiwan Abu Dhabi University Department Of Mathematics And Statistics College Of Arts And Sciences Abu Dhabi United Arab Emirates Istanbul Gelisim University Department Of Electrical And Electronics Engineering Istanbul34310 Turkey Sabratah University Department Of Electrical Engineering Faculty Of Engineering Regdalen Libya Center For Security Theory And Algorithmic Research Hyderabad500 032 India Abu Dhabi University Department Of Computer Science And Information Technology College Of Engineering Abu Dhabi United Arab Emirates Nisantasi University Faculty Of Engineering And Architecture Department Of Software Engineering Istanbul34398 Turkey

The Internet of Drones (IoD) extends the capabilities of unmanned aerial vehicles, enabling them to participate in a connected network. In IoD infrastructure, drones communicate not only among themselves but also with users and a control center. This interconnected communication framework holds promise for various applications, from collaborative decision-making to real-time data exchange. However, the expansion of communication in IoD also introduces new challenges, particularly in terms of security, privacy and authentication. Unfortunately, the current authentication protocols are inadequate in offering robust security features against various attacks in the IoD environment. To address these security issues and limitations, we proposed a secure protocol for the IoD environment using chaotic maps and hash functions. In addition, we also employed a physically unclonable function in the development of the proposed protocol. We assess the security of the protocol through both informal and formal security analysis. The formal security analysis is conducted through a widely used random or real (RoR) model. The informal analysis shows the rigorous security features against various attacks, such as masquerading, anonymity violation, and physical cloning attacks. Moreover, we compare the performance of the devised protocol with similar existing protocols across important performance parameters, such as communication overhead, computation overhead, and security features. The devised protocol provides a 67.86% and 17.80% reduction in computation and communication overheads, respectively, as compared to related protocols. The analysis demonstrates the proposed protocol's capacity to support secure communication in the IoD environment and satisfy desirable security attributes. © 2014 IEEE.

关键词： Drones

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BookChain: A Secure Library Book Storing and Sharing in Academic Institutions using Blockchain technology

BookChain: A Secure Library Book Storing and Sharing in Acad...

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2023 International Conference on Computer Communication and Informatics, ICCCI 2023

作者： Chinnasamy, P. Elumalai, Anupriya Ayyasamy, Ramesh Kumar Kavya, S.P. Dhanasekaran, S. Kiran, Ajmeera MLR Institute of Technology Department of Computer Science and Engineering Hyderabad India VIT - AP University School of Computer Science and Engineering India Faculty of Information and Communication Technology Kampar Malaysia KPR Institute of Engineering and Technology Department of Artificial Intelligence and Data Science Coimbatore India Deemed to Be University Kalasalingam Academy of Research and Education Department of Information Technology Tamilnadu Srivilliputtur India

ISBN: (纸本)9798350348217

By exploiting wireless operators, contemporary bookcrossing allows people to share books conveniently and hastens the spread of knowledge. Nevertheless, the widespread use of smartphone bookcrossing has been seriously hampered by the lack of tracking, resulting in missing and disappearance of textbooks, additionally, it leads to organizational inefficiency. Hence, we suggest using the emerging blockchain technology to exploit the advantages of its immutability and encrypted smart contract in order to overcome these difficulties. In this research, we propose a BookChain, a blockchain-based intra-campus book-sharing system that is verifiable and effective. BookChain securely records all of the book's interacting entities on blockchain because every consumer could retrace the book's renting activity, hence lowering the risk of a book being misplaced. Additionally, BookChain promotes the use of smart contracts to streamline the distribution of books with very little human interference, maximizing productivity. The experimental findings demonstrate the efficiency and affordability of the suggested system even with a large number of concurrent participants. © 2023 IEEE.

关键词： Smart contract

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A Deep Graph Convolutional Network based Web API Classification Approach for Mashup Creation 25

A Deep Graph Convolutional Network based Web API Classificat...

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25th IEEE International Conferences on High Performance Computing and Communications, 9th International Conference on data science and Systems, 21st IEEE International Conference on Smart City and 9th IEEE International Conference on Dependability in Sensor, Cloud and Big data Systems and Applications, HPCC/DSS/SmartCity/DependSys 2023

作者： Qi, Lianyong Yan, Boyuan Zheng, Jiaqi Xu, Xiaolong Dou, Wanchun Zhou, Xiaokang Zhang, Jie School of Computer Science Qufu Normal University China China Nanjing University Department of Computer Science and Technology China School of Computer and Software Nanjing University of Information Science and Technology China Shiga University Faculty of Data Science Japan Riken Center for Advanced Intelligence Project Japan

ISBN: (纸本)9798350330014

Software developers create mashups of their interest by searching, selecting, and combining web Application Programming Interfaces (APIs) with diverse functionalities. However, achieving the selection of APIs relies on a crucial prerequisite: accurate classification of APIs. However, the traditional web API classification methods are often based on a single piece of API function's information such as manual annotations and description documents which decreases the accuracy of API classification results significantly, especially for the web API sharing communities with sparse data (e.g., ***). To tackle this issue, we propose BDGCN which improves some existing web API classification approaches by adding isomorphic graph of strong correlation among different web APIs on the basis of API description documents. Concretely, at the first, we integrate the information of multiple dimensions of web APIs as embedded inputs in which we then build an API-API strongly correlated isomorphic network to strengthen the association among API individuals, and to initialize the embedding vector from the description of each API by tuning the pre-trained Bidi-rectional Encoder Representation from Transformers (BERT) model. Besides, in order to smooth the impact of high-order features between neighboring nodes, a deep graph convolutional network is introduced in this paper that is consistent with the problem. In fact, we set up a categorization layer and utilize cross-entropy function to achieve accurate classification of APIs. Experimental results demonstrate the superiority of our method compared to the existing methods. © 2023 IEEE.

关键词： Classification (of information)

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Enhancing Durian Cultivation Efficiency Through data-Driven Smart Farming Using Cluster Analysis and Machine Learning

Enhancing Durian Cultivation Efficiency Through Data-Driven ...

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Big data Analytics and Practices (IBDAP), International Conference on

作者： Pattharaporn Thongnim Jakkrapan Sreekajon Thanaphon Pukseng Department of Mathematics Faculty of Science Burapha University Chonburi Thailand Information Technology and Data Science Faculty of Science and Arts Burapha University Chanthaburi Thailand Data Center Faculty of Science and Arts Burapha University Chanthaburi Thailand

ISBN: (数字)9798350391749

ISBN: (纸本)9798350391756

This study explores the application of k-means clustering, combined with the elbow and silhouette methods, to analyze durian farm yields and production areas in Eastern Thailand from 2012 to 2023. The aim is to identify optimal farming practices and land use patterns to enhance productivity and sustainability. Using data on yield and area of production, the analysis reveals distinct clusters representing different farming characteristics. The evaluation metrics, including the Davies-Bouldin Index and Dunn Index, indicate that the Elbow method generally provides better defined clusters, although the Silhouette method occasionally shows superior clustering quality. The results show significant shifts in cluster centroids over the years, reflecting changes in smart farming. These insights suggest targeted interventions to optimize resource allocation and improve farm management.

关键词： Smart agriculture Measurement Productivity Machine learning Big data Indexes Resource management

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Dynamic Offloading Control for Waste Sorting Based on Deep Q-Network 24th

Dynamic Offloading Control for Waste Sorting Based on Deep...

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

作者： Wang, Jing Wang, Xiaoyang Guo, Jianxiong Tang, Zhiqing Ding, Xingjian Wang, Tian Advanced Institute of Natural Sciences Beijing Normal University Zhuhai China Guangdong Key Lab of AI and Multi-modal Data Processing Department of Computer Science BNU-HKBU United International College Zhuhai China Faculty of Information Technology Beijing University of Technology Beijing China

ISBN: (纸本)9789819615278

With the increasing concern for environmental protection and resource optimization, efficient waste sorting has become a serious challenge today. In this paper, we propose a new offloading control problem that aims to solve waste sorting in wireless bin communication networks. Due to limited computational power, bins belonging to embedded devices rely on simple classification models with varying accuracy. In this scenario, consider a network of intelligent bins, each acting as an independent agent capable of deciding to offload an image to the edge server with a more accurate but resource-intensive model when the local classification is deemed inaccurate. Thus, Our goal is to find a lightweight online offloading policy that can achieve the best possible sorting accuracy while balancing transmission traffic. The method utilizes a Deep Q-network algorithm that enables each intelligent bin to make image-processing decisions autonomously. In the experiment, we validate the effectiveness of improving the performance of waste classification compared with existing flow control methods. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Distributed Resource Allocation Intelligent Waste Management Waste Sorting Wireless Communication Network

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Formation of Research Area for Malaysian Urban data Centre and Waste Heat Recovery

Formation of Research Area for Malaysian Urban Data Centre a...

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2022 International Conference on Digital Transformation and Intelligence, ICDI 2022

作者： Othman, Marini Maidin, Siti Sarah Suliman, Azizah Zakaria, Nur Zakri Nassar, Dua' Al-Ghaili, Abbas M. Inti International University Faculty of Data Science and Information Technology Nilai Malaysia Independent Researcher Sepang Malaysia Princess Nourah Bint Abdulrahman University Faculty of Computer & Information Science Riyadh Saudi Arabia Malaysia

ISBN: (纸本)9798350397000

The realization that the energy consumption due to digital application will continue to rise significantly while climate change has persistently demand efficiency in the energy use, has triggered data centers to become more accountable in shouldering the task to operate sustainably. Green strategy for data centers, among others, should look at improving treatment of waste heat generated by their operation and cooling process. While intensive efforts are taking place in Scandinavian countries, many countries are yet to follow in their steps. A case study involving three urban data centres in Malaysia is conducted to gain insight on their potential in implementing waste heat recovery for utilization by relevant systems within their vicinity. It is found that the data centers have attained favourable in term of safety, security and reliability. However, there are a lot to be desired from the aspect of energy efficiency. A way forward suggests for further study to explore adoption and innovation of waste heat recycling models to improve the performance of Malaysian urban data centers in alignment to improve use and reuse of energy whilst making them more climate-neutral. © 2022 IEEE.

关键词： Waste heat

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SourcePlag - Source Code Plagiarism Detection Based on Abstract Syntax Trees

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

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Blockchain and Distributed Systems Security (ICBDS), IEEE International Conference on

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

ISBN: (数字)9798350354348

ISBN: (纸本)9798350354355

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.

关键词： Java Codes Plagiarism Source coding Syntactics Programming Security Reliability Python Testing

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