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International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud) (I-SMAC)

作者： V. Suganthi J. Jebathangam Department of Computer Science Vels Institute of Science Technology & Advanced Studies(VISTAS) Chennai India

ISBN: (数字)9798350376425

ISBN: (纸本)9798350376432

Mobile payment systems have become increasingly popular with the rise of smartphones, but this has also attracted fraudsters. As fraud rates continue to rise, researchers have turned to machine learning methods to detect and analyze fraudulent transactions effectively. The purpose of this study is to propose the use of the Gated Recurrent Unit (GRU) is an effective method for analyzing and detecting patterns in sequential data, particularly for applications such as fraud detection in mobile payment systems. The GRU is chosen for its ability to capture temporal dependencies in data while being computationally efficient compared to other recurrent neural network variants like LSTM. This study will explore the implementation of GRU, evaluate its performance, and compare it with existing algorithms to highlight its advantages in real-time fraud detection scenarios. The proposed GRU method provides an efficient and effective approach for detecting fraud in credit card transactions by leveraging its ability to process and learn from sequential data. The use of GRUs can improve detection accuracy and efficiency, contributing to more robust fraud detection systems, and ultimately helping protect consumers and financial institutions from financial losses.

关键词： Accuracy Recurrent neural networks Online banking Machine learning Logic gates Credit cards Real-time systems Fraud Smart phones Long short term memory

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Pushing AI to wireless network edge: an overview on integrated sensing, communication, and computation towards 6G

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science China(Information sciences) 2023年第3期66卷 7-25页

作者： Guangxu ZHU Zhonghao LYU Xiang JIAO Peixi LIU Mingzhe CHEN Jie XU Shuguang CUI Ping ZHANG Shenzhen Research Institute of Big Data Future Network of Intelligence Institute (FNii) The Chinese University of Hong Kong (Shenzhen) School of Science and Engineering (SSE) The Chinese University of Hong Kong (Shenzhen) State Key Laboratory of Advanced Optical Communication Systems and Networks School of Electronics Peking University Department of Electrical and Computer Engineering and Institute for Data Science and Computing University of Miami Peng Cheng Laboratory State Key Laboratory of Networking and Switching Technology Beijing University of Posts and Telecommunications

Pushing artificial intelligence(AI) from central cloud to network edge has reached board consensus in both industry and academia for materializing the vision of artificial intelligence of things(AIoT) in the sixth-generation(6G) era. This gives rise to an emerging research area known as edge intelligence, which concerns the distillation of human-like intelligence from the vast amount of data scattered at the wireless network edge. Typically, realizing edge intelligence corresponds to the processes of sensing, communication,and computation, which are coupled ingredients for data generation, exchanging, and processing, ***, conventional wireless networks design the three mentioned ingredients separately in a task-agnostic manner, which leads to difficulties in accommodating the stringent demands of ultra-low latency, ultra-high reliability, and high capacity in emerging AI applications like auto-driving and metaverse. This thus prompts a new design paradigm of seamlessly integrated sensing, communication, and computation(ISCC) in a taskoriented manner, which comprehensively accounts for the use of the data in downstream AI tasks. In view of its growing interest, this study provides a timely overview of ISCC for edge intelligence by introducing its basic concept, design challenges, and enabling techniques, surveying the state-of-the-art advancements, and shedding light on the road ahead.

关键词： sixth-generation (6G) edge intelligence artificial intelligence of things (AIoT) integrated sensing communication and computation (ISCC)

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LSTM-MI: Revolutionizing Intrusion Detection Through Adaptive Learning and Mutual Information Analysis

LSTM-MI: Revolutionizing Intrusion Detection Through Adaptiv...

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Distributed computing and Electrical Circuits and Electronics (ICDCECE), IEEE International Conference on

作者： J. Archana A.S. Aneetha Department of Computer science Vels Institute of Science Technologies and Advanced Studies (VISTAS) Chennai India

ISBN: (数字)9798350318609

ISBN: (纸本)9798350318616

This study introduces two innovative approaches to augment intrusion detection capabilities. The first method employs Principal Component Analysis (PCA) for dimensionality reduction, streamlining the dataset's complexity while retaining crucial features. The second method delves into feature selection using Mutual Information (MI), identifying and preserving the most informative attributes crucial for intrusion detection. These selected features are then incorporated into LSTM-based models and it evaluates the efficacy of both PCA and MI alongside LSTM, providing a comparative analysis of their impact on intrusion detection performance. Notably, the LSTM-PCA model exhibits an accuracy of 97.73%, while the LSTM-MI model achieves a higher accuracy of 98.21 %. These results underscore the significance of integrating PCA and MI in conjunction with LSTM, offering nuanced insights into their individual contributions to accurate intrusion detection. Through extensive experimentation on benchmark datasets, the proposed methodologies aim to advance the development of robust intrusion detection systems (IDS).

关键词： Dimensionality reduction Circuits Intrusion detection Benchmark testing Feature extraction Complexity theory Distributed computing

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Air Quality Index Prediction Using Support Vector Regression Based on African Buffalo Optimization 9th

Air Quality Index Prediction Using Support Vector Regression...

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9th International Conference on computing and Informatics, ICOCI 2023

作者： Yusof, Yuhanis Maijama’a, Inusa Sani School of Computing Institute for Advanced and Smart Digital Opportunities Universiti Utara Malaysia UUM Sintok Kedah Bukit Kayu Hitam06010 Malaysia Department of Computer Science Hussaini Adamu Federal Polytechnic Kazaure Kazaure Nigeria

ISBN: (纸本)9789819995912

Support Vector Regression (SVR) is one of the machine learning models widely used in regression analysis. As an alternative for fitting a line to the data points like typical linear regression algorithms, it finds a hyperplane that is effective in fitting data points in a continuous space. The kernel type and hyperparameters significantly influence the performance and effectiveness of SVR. Determination of the optimal values is crucial in ensuring the success of prediction, regardless of the application domain. This study adapts the African Buffalo Optimization (ABO) algorithm to determine SVR’s regularization and kernel parameters. The ABO algorithm mirrors African buffaloes’ hunting and defensive behavior, offering ability to track the best position and extensive memory capacity to discover the best solution for problems under analysis. Evaluation is then performed on the air quality index benchmark dataset, and the prediction results of SVR-ABO are compared against other optimized SVR prediction models. The results show that SVR-ABO is a better algorithm because it produces smaller errors and best fits the data. Such an outcome indicates that the proposed SVR optimized by ABO is a competitive prediction model in data analytics. © 2024, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Air quality

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Physics-based fuid simulation in computer graphics: Survey, research trends, and challenges

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Computational Visual Media 2024年第5期10卷 803-858页

作者： Xiaokun Wang Yanrui Xu Sinuo Liu Bo Ren Jiri Kosinka Alexandru C.Telea Jiamin Wang Chongming Song Jian Chang Chenfeng Li Jian Jun Zhang Xiaojuan Ban Beijing Advanced Innovation Center for Materials Genome Engineering and School of Intelligence Science and Technologyand Shunde Innovation School University of Science and Technology BeijingBeijing 100083China National Center for Computer Animation Faculty of Media and CommunicationBournemouth UniversityPoole BH125BBUK. Bernoulli Institute University of GroningenGroningen 9700 AKthe Netherlands School of Computer Science Peking UniversityBeijing 100871China. TMCC College of Computer ScienceNankai UniversityTianjin 300350China Department of Information and Computing Sciences Utrecht UniversityUtrecht 3584 CCthe Netherlands. Zienkiewicz Institute for Modelling Data and AISwansea UniversitySwansea SA18ENUK. Institute of Materials Intelligent Technology Liaoning Academy of MaterialsShenyang 110004China.

Physics-based fluid simulation has played an increasingly important role in the computer graphics *** methods in this area have greatly improved the generation of complex visual effects and its computational *** techniques have emerged to deal with complex boundaries,multiphase fluids,gas-liquid interfaces,and fine *** parallel use of machine learning,image processing,and fluid control technologies has brought many interesting and novel research *** this survey,we provide an introduction to theoretical concepts underpinning physics-based fuid simulation and their practical implementation,with the aim for it to serve as a guide for both newcomers and seasoned researchers to explore the field of physics-based fuid simulation,with a focus on developments in the last *** by the distribution of recent publications in the field,we structure our survey to cover physical background;discretization approaches;computational methods that address scalability;fuid interactions with other materials and interfaces;and methods for expressive aspects of surface detail and *** a practical perspective,we give an overview of existing implementations available for the above methods.

关键词： computer graphics physical simulation fluid simulation fluid coupling

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Exploring 2D representation and transfer learning techniques for indoor localization

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Multimedia Tools and Applications 2025年 1-13页

作者： Kerdjidj, O. Himeur, Y. Atalla, S. Copiaco, A. Sohail, S.S. Amira, A. Fadli, F. Mansoor, W. Gawanmeh, A. Centre of Development of Advanced Techniques Algiers Algeria College of Engineering and Information Technology University of Dubai Dubai United Arab Emirates Department of Computer Science University of Sharjah Sharjah United Arab Emirates Institute of Artificial Intelligence De Montfort University Leicester United Kingdom School of Computing Science and Engineering VIT Bhopal University Kothri Kalan India Department of Architecture & Urban Planning Qatar University Doha2713 Qatar

Indoor localization (IL) plays a critical role in various applications, including asset tracking, emergency response, and indoor navigation. Traditional methods relying on one-dimensional (1D) representations of received signal strength indicator (RSSI) data often suffer from limitations such as low accuracy, environmental sensitivity, and susceptibility to interference. To overcome these challenges, this paper proposes Scalogram-Based Transfer Learning for Indoor Localization (STL-IL), a novel framework that significantly enhances localization performance. The proposed approach transforms 1D RSSI signals into two-dimensional (2D) representations using scalogram-based time-frequency analysis, coupled with advanced transfer learning (TL) techniques. By leveraging the scalogram method to generate image representations of the data and employing diverse wavelet types. Furthermore, pre-trained deep learning models, including GoogleNet and SqueezeNet, are utilized to achieve state-of-the-art performance. Experimental results demonstrate that the STL-IL system achieves remarkable accuracy levels of 95% and 96% for SqueezeNet and GoogleNet, respectively. The proposed framework not only improves localization accuracy but also enhances robustness in dynamic and challenging environments, making it a promising solution for real-world indoor localization applications. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2025.

关键词： Deep learning

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ThickV-Stain: Unprocessed Thick Tissues Virtual Staining for Rapid Intraoperative Histology 7

ThickV-Stain: Unprocessed Thick Tissues Virtual Staining for...

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7th International Conference on Medical Imaging with Deep Learning, MIDL 2024

作者： Shi, Lulin Hou, Xingzhong Wong, Ivy H.M. Chan, Simon C.K. Chen, Zhenghui Lo, Claudia T.K. Wong, Terence T.W. Translational and Advanced Bioimaging Laboratory Department of Chemical and Biological Engineering The Hong Kong University of Science and Technology Hong Kong State Key Laboratory of Computer Architecture Institute of Computing Technology Chinese Academy of Sciences Beijing China

Virtual staining has shown great promise in realizing a rapid and low-cost clinical alternative for pathological examinations, eliminating the need for chemical reagents and laborious staining procedures. However, most of the previous studies mainly focus on thin slice samples, which still require tissue sectioning and are unsuitable for intraoperative use. In this paper, we propose a multi-scale model to virtually stain label-free and slide-free biological tissues, allowing hematoxylin- and eosin- (H&E) staining generation in less than a minute for an image with 100 million pixels. We name this ThickV-Stain model, specifically developed to virtually stain intricated and unprocessed thick tissues. We harness the ability of a multi-scale network to encourage the model to capture multiple-level micromorphological characteristics from low-resolution images. Experimental results highlight the advantages of our method for virtual staining on unprocessed thick samples. We also show the effectiveness of ThickV-Stain on thin sections, showing generalizability to other clinical workflows. The proposed method enables us to obtain virtually stained images from unstained samples within minutes and can be seamlessly integrated with downstream pathological analysis tasks, providing an efficient alternative scheme for intraoperative assessment as well as general pathological examination. © 2024 CC-BY 4.0, L. Shi, X. Hou, I.H.M. Wong, S.C.K. Chan, Z. Chen, C.T.K. Lo & T.T.W. Wong.

关键词： Tissue

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Information Gerrymandering in Elections 1

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19th Principle and Practice of Data and Knowledge Acquisition Workshop, PKAW 2023, held in conjunction with the 20th Pacific Rim International Conference on Artificial Intelligence, PRICAI 2023

作者： Liu, Xiaoxue Kato, Shohei Ren, Fenghui Su, Guoxin Zhang, Minjie Gu, Wen School of Computing and Information Technology University of Wollongong Wollongong Australia Department of Computer Science and Engineering Nagoya Institute of Technology Nagoya Japan Center for Innovative Distance Education and Research Japan Advanced Institute of Science and Technology Ishikawa Japan

ISBN: (数字)9789819978557

ISBN: (纸本)9789819978540

When making decisions, individuals often seek advice from their family, friends, and social network connections. In this way, individ-uals’ opinions are susceptible to influence from their connections’ viewpoints. For example, in elections, individuals may initially support one party, while social influence may sway their choice to another. A recent study by Stewart et al. introduced a metric, influence assortment, to quantify the phenomenon of information gerrymandering that one party can influence more voters from other parties by strategically distributing their members among the social networks. While this metric correlates strongly with voting outcomes, the finding is only suitable for two-party elections. In this paper, we define the influence assortment that incorporates the level of similarity (homophily) among neighbours in the social network and extend it to multi-party elections. We examine its ability to predict voting outcomes when all parties initially have equal votes and average degrees. Through simulations using a stochastic model of voting behaviour, we demonstrate that the correlation between the influence gap and vote difference is a strong predictor in both small and large scale-free networks. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023.

关键词： Stochastic systems

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Performance Juxtapose of Plant Leaf Disease Detection using Adaptive Deep Convolutional Recurrent Neural Network (ADCRNN) in MATLAB Versus Python

Performance Juxtapose of Plant Leaf Disease Detection using ...

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Intelligent Algorithms for Computational Intelligence Systems (IACIS), International Conference on

作者： S. Jayashree V. Sumalatha Department of Information Technology Vels Institute of Science Technology & Advanced Studies Chennai India Department of Computer Application Vels Institute of Science Technology & Advanced Studies Chennai India

ISBN: (数字)9798350360660

ISBN: (纸本)9798350360677

Leaf diseases can cause several detriments in crops' overall yield and fertility. While analyzing the various diseases that affect plants is imperative, identifying the diseases using algorithmic techniques that render optimal performance is crucial. In agriculture, detecting multiple diseases in plants is difficult. However, the provision to automate disease identification through machine learning approaches through the various phases of preprocessing and segmentation is implemented in this study. This indagation delineates the leaf disease identification using the proposed hybrid algorithmic Adaptive Deep Convolutional Recurrent Neural Network (ADCRNN) in bifurcated platforms such as MATLAB and Python. The Radial basis function is incorporated to optimize the throughput in MATLAB before observing the juxtaposed results in both platforms. The purpose of utilizing ADCRNN is to effectively combine the advantages and parallelly overcome the challenges of Deep Convolutional Neural Network (DCNN), RNN, and adaptive techniques that unsheathe the pivotal features from the provided input. The ADCRNN simulation is carried out in MATLAB and Python, and the results are successfully obtained. In addition, after optimization, the proposed ADCRNN technique’s accuracy increased to 94.5% in performance evaluation in MATLAB.

关键词： Plant diseases Accuracy Recurrent neural networks Adaptive systems Machine learning algorithms Plants (biology) Prediction algorithms MATLAB Optimization Python

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Design of AED Based Drone to Deliver the Medical Kit on an Emergency Basis 3

Design of AED Based Drone to Deliver the Medical Kit on an E...

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3rd International Conference on Advancement in Electronics and Communication Engineering, AECE 2023

作者： Nagarani, P. Ahmad, Mohd Wazih Shalini, R. Singh, Pavitar Parkash Cvr College of Engineering Department of Emerging Technologies Hyderabad India Shri Ram Murti Smarak College of Engineering and Technology Department of Computer Science and Engineering Bareilly India Vels Institute of Science Technology and Advanced Studies Department of Bca & It School of Computing Sciences Chennai India Lovely Professional University Department of Management Phagwara India

ISBN: (纸本)9798350330724

Access to reliable modes of transportation is essential. Postal delivery to multi-million-dollar freight transports, practically everything is linked to its connections. Wheeled motor vehicles and manned aeroplanes are now the only options for transporting medical supplies at times of urgent need, which may be expensive, time-consuming, and often impossible if the emergency site is far. Drones are a game-changer in the medical industry because they can transfer vital supplies like blood and medical equipment. Unfortunately, given the state of the art, the drone's battery life is a constraint that may slow down the delivery process. This article details the research into the mechanisms designed for drones to increase their battery life and efficiently deliver first aid supplies. The drone's system safely delivers and drops medical supplies, reducing the drone's takeoffs and landings. The primary goal of this research is to develop a device that can safely transport medical equipment to its destination and then carefully unload it there. Results showed the system could safely transport and deploy first aid supplies during flight and landing. © 2023 IEEE.

关键词： Drones

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