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Interdisciplinary Humanitarian Conference for Sustainability (IIHC), International

作者： Prashanth M V Shambhubhardwaj Ramana Raju Kundan Kumar Pramanik Swaroop Mohanty Kuntal Barua Information Science and Engineering Vidyavardhaka College of Engineering India Department of Computing Sciences & IT Faculty of Engineering Teer thanker Mahaveer University Moradabad Uttar Pradesh India Department of Data Science & Mathematics Department of Data Science & Mathematics Jain (Deemed to be University) Bangalore India Department of School of Engg. &IT ARKA Jain University Jamshedpur Jharkhand India management cv Raman global university Bhubaneswar SAGE University Indore Madhya Pradesh

Pattern analysis is one of the most important tasks for strong data and useful data from unstructured data. The item-sets for this work must include the aforementioned homogeneity, but rather frequency in the data. Despite the fact that numerous efficient algorithms have been developed in this field, the rising demand for information has led to deterioration in the effectiveness of present pattern mining techniques. The goal of this study is to provide fresh, efficient methods for mining huge data patterns. To accomplish this, a variety of methods based on the Map reduction algorithm as well as the Apache expansive implementation have been proposed. The recommended algorithms may be divided into three main groups. The Attribute Mapper (AprioriMR) and recurrent AprioriMR techniques, which eliminate any previous objects in data without employing a pruning methodology, are the first two described. Second, two recommended approaches (space cutting AprioriMR or top AprioriMR) to improve the search space leverage certain well-generalized pro property. The last approach for mining compressed versions of frequent patterns is the maximum AprioriMR algorithm. To evaluate the efficacy of the offered strategies, up to 3 1018 interactions and more than 5 million distinct ones have been examined in a range of big data datasets. Evaluations of well-known and efficient pattern mining algorithms are part of the testing process. The results show both the benefit of employing Dremel versions when taking into consideration intricate issues and the insufficiency of this strategy when dealing with little amounts of data.

关键词： Big data Market research Information retrieval data mining Task analysis Sustainable development Pattern analysis

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Skeleton Recall Loss for Connectivity Conserving and Resource Efficient Segmentation of Thin Tubular Structures

arXiv

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arXiv 2024年

作者： Kirchhoff, Yannick Rokuss, Maximilian R. Roy, Saikat Kovacs, Balint Ulrich, Constantin Wald, Tassilo Zenk, Maximilian Vollmuth, Philipp Kleesiek, Jens Isensee, Fabian Maier-Hein, Klaus Heidelberg Division of Medical Image Computing Germany Faculty of Mathematics and Computer Science Heidelberg University Germany HIDSS4Health - Helmholtz Information and Data Science School for Health Heidelberg Karlsruhe Germany Medical Faculty Heidelberg Heidelberg University Heidelberg Germany Helmholtz Imaging German Cancer Research Center Heidelberg Germany Clinic for Neuroradiology University Hospital Bonn Bonn Germany Medical Faculty Bonn University of Bonn Bonn Germany University Hospital Essen Essen Germany West German Cancer Center Essen University Hospital Essen Essen Germany Pattern Analysis and Learning Group Department of Radiation Oncology Heidelberg University Hospital Germany

Accurately segmenting thin tubular structures, such as vessels, nerves, roads or concrete cracks, is a crucial task in computer vision. Standard deep learning-based segmentation loss functions, such as Dice or Cross-Entropy, focus on volumetric overlap, often at the expense of preserving structural connectivity or topology. This can lead to segmentation errors that adversely affect downstream tasks, including flow calculation, navigation, and structural inspection. Although current topology-focused losses mark an improvement, they introduce significant computational and memory overheads. This is particularly relevant for 3D data, rendering these losses infeasible for larger volumes as well as increasingly important multi-class segmentation problems. To mitigate this, we propose a novel Skeleton Recall Loss, which effectively addresses these challenges by circumventing intensive GPU-based calculations with inexpensive CPU operations. It demonstrates overall superior performance to current state-of-the-art approaches on five public datasets for topology-preserving segmentation, while substantially reducing computational overheads by more than 90%. In doing so, we introduce the first multi-class capable loss function for thin structure segmentation, excelling in both efficiency and efficacy for topology-preservation. Our code is available to the community, providing a foundation for further advancements, at: https://***/MIC-DKFZ/Skeleton-Recall. © 2024, CC BY.

关键词： Topology

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6G communications through sub-Terahertz CMOS power amplifiers: Design challenges and trends

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Heliyon 2025年第10期11卷

作者： Lee, Jun Yan Wu, Duo Guo, Xuanrui Tan, Jian Ding Yew, Teh Jia Ng, Zi Neng Bhuiyan, Mohammad Arif Sobhan Miraz, Mahdi H. School of Artificial Intelligence and Robotics Xiamen University Malaysia Selangor Malaysia School of Electronic Information and Electrical Engineering Shanghai Jiao Tong University Shanghai China School of Computing and Data Science Xiamen University Malaysia Selangor Malaysia School of Computing Faculty of Arts Science and Technology Wrexham University Wrexham United Kingdom Faculty of Computing Engineering and Science University of South Wales Pontypridd United Kingdom

The fifth-generation (5G) network faces limitations in supporting emerging applications, such as artificial intelligence (AI), virtual reality (VR) and digital twins. To overcome these confines, sub-Terahertz (sub-THz) and Terahertz (THz) technologies are considered to be key enablers of effective 6G wireless communications, offering higher transmission speeds, longer range and wider bandwidth. Achieving these capabilities requires careful engineering of 6G transceivers, with a focus on efficient power amplifiers (PAs) in the front-end, which play a critical role in effectively amplifying and transmitting signals over long distances. Complimentary metal-oxide-semiconductor (CMOS) technology-based PA in sub-THz suffers severe parasitic and limited maximum frequency, however, this has eventually been solved by different design architectures and scaling down of CMOS technology to break through the frequency limitations. In this article, we reviewed the potentials and capabilities of CMOS technology for designing 6G hardware, identified the state-of-art PA designs in the sub-THz band and then examined as well as compared the designs to identify the suitable design strategies for better performance. The circuit optimisation techniques, such as coupled-line, passive gain boosting method, zero-degree power splitting, load-pull matching, diode and capacitor linearisation for better gain, saturated output power and power added efficiency, are considered for the PA design architectures at different sub-THz bands. Furthermore, these methods are summarised and discussed with their advantages and disadvantages in lieu with their performances. The PA design trends, challenges and future perspectives are also presented and discussed. Therefore, this comprehensive review article will serve as a comparative study and reference for future PA designs for radio frequency integrated circuits (RFIC). © 2025 The Authors

关键词： 6G CMOS Front-end Power amplifier Sub-THz Transceiver Transmitter

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Deep Graph Reinforcement Learning for UAV-Enabled Multi-User Secure Communications

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IEEE Transactions on Mobile computing 2025年

作者： Tang, Xiao Zhao, Kexin Shen, Chao Du, Qinghe Wang, Yichen Niyato, Dusit Han, Zhu Research & Development Institute Northwestern Polytechnical University Shenzhen518063 China National Key Laboratory of Wireless Communications Chengdu611731 China State Key Laboratory of Intelligent Game Taicang215400 China Northwestern Polytechinical University School of Electronics and Information Xi'an710072 China Xi'an Jiaotong University Faculty of Electronics and Information Engineering Xi'an710049 China Nanyang Technological University College of Computing and Data Science Singapore University of Houston Department of Electrical and Computer Engineering Houston77004 United States Kyung Hee University Department of Computer Science and Engineering Seoul446-701 Korea Republic of

While unmanned aerial vehicles (UAVs) with flexible mobility are envisioned to enhance physical layer security in wireless communications, the efficient security design that adapts to such high network dynamics is rather challenging. The conventional approaches extended from optimization perspectives are usually quite involved, especially when jointly considering factors in different scales such as deployment and transmission in UAV-related scenarios. In this paper, we address the UAV-enabled multi-user secure communications by proposing a deep graph reinforcement learning framework. Specifically, we reinterpret the security beamforming as a graph neural network (GNN) learning task, where mutual interference among users is managed through the message-passing mechanism. Then, the UAV deployment is obtained through soft actor-critic reinforcement learning, where the GNN-based security beamforming is exploited to guide the deployment strategy update. Simulation results demonstrate that the proposed approach achieves near-optimal security performance and significantly enhances the efficiency of strategy determination. Moreover, the deep graph reinforcement learning framework offers a scalable solution, adaptable to various network scenarios and configurations, establishing a robust basis for information security in UAV-enabled communications. © 2002-2012 IEEE.

关键词： Deep reinforcement learning

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Discrepancy and Thematic Bibliometric Analyses of the Remaining Limitations in Artificial Intelligence

Discrepancy and Thematic Bibliometric Analyses of the Remain...

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computing (ICOCO), IEEE International Conference on

作者： Mahayaudin M. Mansor Nurain Ibrahim Noor Fadhilah Ahmad Radi Nadirah Abdul Rahim Syarul Heiry Yahaya Mohd Aftar Abu Bakar Roslinazairimah Zakaria School of Mathematical Sciences College of Computing Informatics and Mathematics Universiti Teknologi MARA Malaysia Institute of Big Data Analytics and Artificial Intelligence Universiti Teknologi MARA Malaysia Department of Electrical and Computer Engineering Kuliyyah of Engineering International Islamic University Malaysia Department of Science Computer and Mathematics Universiti Teknologi MARA (Penang Campus) Malaysia Department of Mathematical Sciences Faculty of Science & Technology Universiti Kebangsaan Malaysia Centre for Mathematical Sciences Universiti Malaysia Pahang Al-Sultan Abdullah Malaysia

ISBN: (数字)9798331530303

ISBN: (纸本)9798331530310

As artificial intelligence (AI) advances, it is essential to continuously comprehend its limitations to optimise the integration of AI into autonomous systems that empower humans. The first objective of this study is to highlight the key capabilities of both humans and AI, focusing on the differences and identifying limitations of AI from the literature. Through discrepancy analysis, this study uses a Venn diagram to visualise the remaining limitations of AI into five key domains: (i) emotional intelligence, (ii) consciousness and awareness, (iii) creative imagination, (iv) communication, and (v) ethical decision-making. Furthermore, this study employs thematic bibliometric analysis to provide a more detailed examination of each AI limitation as the second objective. This study has identified underdeveloped and emerging research themes with potential for future development, such as emotion recognition, human-computer interaction, digital health, and situational awareness, which may require further research. Additionally, this study commends the ongoing efforts to harness AI’s computational power and algorithmic innovations to enhance AI’s overall performance and applicability.

关键词： Visualization Technological innovation Ethics Emotion recognition Bibliometrics Decision making Focusing Electronic healthcare Object recognition Artificial intelligence

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Fast Learning-Based Split Type Prediction Algorithm for VVC

Fast Learning-Based Split Type Prediction Algorithm for VVC

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IEEE International Conference on Image Processing

作者： Dayong Wang Liulin Chen Xin Lu Frederic Dufaux Weisheng Li Ce Zhu Chongqing Key Laboratory on Big Data for Bio Intelligence Chongqing University of Posts and Telecommunications School of Computer Science and Technology Chongqing University of Posts and Telecommunications Faculty of Computing Engineering and Media (CEM) De Montfort University Université Paris-Saclay CNRS CentraleSupélec Laboratoire des Signaux et systèmes Chongqing Key Laboratory of Image Cognition Chongqing University of Posts and Telecommunications University of Electronic Science and Technology of China

As the latest video coding standard, Versatile Video Coding (VVC) is highly efficient at the cost of very high coding complexity, which seriously hinders its widespread application. Therefore, it is very crucial to improve its coding speed. In this paper, we propose a learning-based fast split type (ST) prediction algorithm for VVC using a deep learning approach. We first construct a large-scale database containing sufficient STs with diverse video resolution and content. Next, since the ST distributions of coding units (CUs) of different sizes are significantly distinct, so we separately design neural networks for all different CU sizes. Then, we merge ambiguous STs into four merged classes (MCs) to train models to obtain probabilities of MCs and skip unlikely ones. Experimental results demonstrate that the proposed algorithm can reduce the encoding time of VVC by 67.53% with 1.89% increase in Bjøntegaard delta bit-rate (BDBR) on average.

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Cybersecurity Threats in FinTech: A Systematic Review

arXiv

引用

arXiv 2023年

作者： Javaheri, Danial Fahmideh, Mahdi Chizari, Hassan Lalbakhsh, Pooia Hur, Junbeom Department of Computer Science and Engineering Korea University Seoul02841 Korea Republic of School of Business University of Southern Queensland SpringfieldQLD4300 Australia School of Business Computing and Social Sciences University of Gloucestershire The Park CampusCheltenham GloucesterGL50 2RH United Kingdom Department of Data Science and Artificial Intelligence Faculty of Information Technology Monash University ClaytonVIC3168 Australia

The rapid evolution of the Smart-everything movement and Artificial Intelligence (AI) advancements have given rise to sophisticated cyber threats that traditional methods cannot counteract. Cyber threats are extremely critical in financial technology (FinTech) as a data-centric sector expected to provide 24/7 services. This paper introduces a novel and refined taxonomy of security threats in FinTech and conducts a comprehensive systematic review of defensive strategies. Through PRISMA methodology applied to 74 selected studies and topic modeling, we identified 11 central cyber threats, with 43 papers detailing them, and pinpointed 9 corresponding defense strategies, as covered in 31 papers. This in-depth analysis offers invaluable insights for stakeholders ranging from banks and enterprises to global governmental bodies, highlighting both the current challenges in FinTech and effective countermeasures, as well as directions for future research. © 2023, CC BY.

关键词： Cybersecurity

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An Intelligent Resource Scheduling Method with Edge Channel Deployment for BPM

An Intelligent Resource Scheduling Method with Edge Channel ...

引用

Symposia and Workshops on Ubiquitous, Autonomic and Trusted computing, UIC-ATC

作者： Bowen Liu Wanchun Dou Xiaokang Zhou Xuyun Zhang Lianyong Qi Fei Dai Chaochao Chen State Key Laboratory for Novel Software Technology Nanjing University Nanjing China Faculty of Data Science Shiga University Hikone Japan Department of Computing Macquarie University Sydney Australia College of Computer and Software China University of Petroleum (East China) Qingdao China College of Big Data and Intelligent Engineering Southwest Forestry University Kunming China College of Computer Science and Technology Zhejiang University Hangzhou China

Edge computing is a novel computing paradigm that offers kinds of resources at the network edge. In edge computing, terminal users are connected to edge servers via the wireless network and there are various channels in each wireless link. These wireless channels are limited resource while different channel has different cost and service ability. The dynamic changes of users’ status make it harder to find an appropriate method to satisfy the BPM requirements of channel deployment. With this observation, it is a tricky challenge to make a trade-off between the system cost(rental price) and the service ability(number of users). In view of this challenge, an intelligent resource scheduling method, named EdgeIRS, is proposed in this paper. In the technical sense, the EdgeIRS method can accommodate most users at the edge with a minimum cost of deploying channel resources in an online way. Its performance is analyzed theoretically and the experiments verify the superiority of the method.

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Correction: Comparative study of ML models for IIoT intrusion detection: impact of data preprocessing and balancing

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Neural computing and Applications 2024年第19期36卷 11661-11661页

作者： Eid, Abdulrahman Mahmoud Soudan, Bassel Nassif, Ali Bou Injadat, MohammadNoor Department of Computer Engineering College of Computing and Informatics University of Sharjah Sharjah UAE Department of Data Science and AI Faculty of Information Technology Zarqa University Zarqa Jordan

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"Challenges and future in deep learning for sentiment analysis: a comprehensive review and a proposed novel hybrid approach"

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Artificial Intelligence Review 2024年第3期57卷 62-62页

作者： Islam, Md. Shofiqul Kabir, Muhammad Nomani Ghani, Ngahzaifa Ab Zamli, Kamal Zuhairi Zulkifli, Nor Saradatul Akmar Rahman, Md. Mustafizur Moni, Mohammad Ali Faculty of Computing Universiti Malaysia Pahang Al-Sultan Abdullah Pahang Pekan26600 Malaysia Mirpur Cantonment Dhaka Mirpur-101216 Bangladesh United City Dhaka Madani Ave1212 Bangladesh Faculty of Mechanical and Automotive Engineering Technology Universiti Malaysia Pahang Al-Sultan Abdullah Pahang Pekan26600 Malaysia Artificial Intelligence and Cyber Futures Institute Charles Stuart University Panorama Ave BathurstNSW2795 Australia Centre for Artificial Intelligence & amp Data Science Universiti Malaysia Pahang Al-Sultan Abdullah Pahang Pekan26600 Malaysia

Social media is used to categorise products or services, but analysing vast comments is time-consuming. Researchers use sentiment analysis via natural language processing, evaluating methods and results conventionally through literature reviews and assessments. However, our approach diverges by offering a thorough analytical perspective with critical analysis, research findings, identified gaps, limitations, challenges and future prospects specific to deep learning-based sentiment analysis in recent times. Furthermore, we provide in-depth investigation into sentiment analysis, categorizing prevalent data, pre-processing methods, text representations, learning models, and applications. We conduct a thorough evaluation of recent advances in deep learning architectures, assessing their pros and cons. Additionally, we offer a meticulous analysis of deep learning methodologies, integrating insights on applied tools, strengths, weaknesses, performance results, research gaps, and a detailed feature-based examination. Furthermore, we present in a thorough discussion of the challenges, drawbacks, and factors contributing to the successful enhancement of accuracy within the realm of sentiment analysis. A critical comparative analysis of our article clearly shows that capsule-based RNN approaches give the best results with an accuracy of 98.02% which is the CNN or RNN-based models. We implemented various advanced deep-learning models across four benchmarks to identify the top performers. Additionally, we introduced the innovative CRDC (Capsule with Deep CNN and Bi structured RNN) model, which demonstrated superior performance compared to other methods. Our proposed approach achieved remarkable accuracy across different databases: IMDB (88.15%), Toxic (98.28%), CrowdFlower (92.34%), and ER (95.48%). Hence, this method holds promise for automated sentiment analysis and potential deployment. © The Author(s) 2024.

关键词： Sentiment analysis

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