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Intelligent Systems and Computational Networks (ICISCN), International Conference on

作者： Pavan V Omprakash Omprakash Bhagya Prasad Bugge S Thangamani Anvesh Perada Sampathirao Suneetha Manipal Law School Manipal Academy of Higher Education India School of Law Jain University Bengaluru India Department of Electronics and Communication Engineering S.R.K.R Engineering College Bhimavaram India Department of Information Technology Nandha Engineering College Erode India Department of Electrical and Computer Engineering Drexel University Philadelphia Pennsylvania USA Department of Computer Science and Engineering Koneru Lakshmaiah Education Foundation Vijayawada India

ISBN: (数字)9798331529246

ISBN: (纸本)9798331529253

The swift advancement of fintech has transformed financial services, offering users seamless digital experiences. Nonetheless, this expansion has also presented intricate cybersecurity difficulties, especially in fraud detection. This research investigates novel methods for addressing fintech fraud through the utilization of GCNN and the ResNet101-C model. GCNN adeptly addresses dynamic fraud trends via graph-based feature aggregation, whereas ResNet101-C, augmented with dropout and fully connected layers, attains enhanced classification performance. Preprocessing procedures, such as imputation, noise reduction, and feature standardization, enhance data quality, whereas feature extraction emphasizes essential transaction variables like frequency and value. Assessment of synthetic datasets reveals that ResNet101-C achieves an accuracy of 93.46%, surpassing leading models. These results underscore the transformational capability of GCNN and ResNet101-C in tackling the dynamic nature of fraud, providing scalable, precise, and privacy-preserving solutions for the financial sector.

关键词： Training Industries Accuracy Federated learning Fintech Data preprocessing Feature extraction Fraud Convolutional neural networks Synthetic data

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Adaptive Networking and Routing technology for Time-Varying Topology Group Nodes in Lunar Exploration

Adaptive Networking and Routing Technology for Time-Varying ...

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International Conference on Advanced Infocomm technology (ICAIT)

作者： Jiyang Yu Dan Huang Jinyang Li Wenjie Li Xianjie Wang Xiaolong Shi China Academy of Space Technology Beijing Institute of Spacecraft System Engineering Beijing China China Research & Development Academy of Machinery Equipment Beijing China Chongqing University of Technology Chongqing China Beijing Blackstone Matrix Technology Co. LTD Chongqing Feima Innovation Research Institute Chongqing China College of Computer Science and Engineering Chongqing University of Technology Chongqing China

A routing method based on hierarchical adaptive switching of heterogeneous subnetworks is proposed, which is used to address the local speed reduction and interruption effects caused by complex terrain ground environment interference on group mobile nodes. Firstly, by designing a universal networking and routing model, taking into account the impact of changes in energy, storage, and communication links on the network. Then, a two-level network architecture is formed based on sub network clustering and partitioning. At last, local and global predictions are introduced to design the optimal model for dealing with time-varying topology, improving the accuracy and reliability of routing during lunar data transmission. Compared to conventional single constraint models, independent resource calculation and path planning are performed on each subnet to ensure that even in the event of individual carrier interruption loss during the task process, the optimal replacement topology of the network can be quickly formed. After simulation experiment analysis, statistical results of routing request ratio, number of faults, data packets, and routing rate delay were obtained. The routing delay is at the millisecond level, indicating that the algorithm model has good fidelity and real-time performance in applications above 10 nodes on the lunar surface.

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A Single-Ion Information Engine for Charging Quantum Battery

arXiv

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arXiv 2024年

作者： Zhang, Jialiang Wang, Pengfei Chen, Wentao Cai, Zhengyang Qiao, Mu Li, Riling Huang, Yingye Tian, Haonan Tu, Henchao Cui, Kaifeng Yan, Leilei Zhang, Junhua Zhang, Jingning Yung, Manhong Kim, Kihwan State Key Laboratory of Low Dimensional Quantum Physics Department of Physics Tsinghua University Beijing100084 China Beijing Academy of Quantum Information Sciences Beijing100193 China State Key Laboratory of Computer Science Institute of Software Chinese Academy of Sciences Beijing100086 China School of Physics Zhengzhou University Zhengzhou450001 China Shenzhen Institute for Quantum Science and Engineering Southern University of Science and Technology Shenzhen518055 China International Quantum Academy Shenzhen518048 China Hefei National Laboratory Hefei230088 China Frontier Science Center for Quantum Information Beijing100084 China

Information engines produce mechanical work through measurement and adaptive control. For information engines, the principal challenge lies in how to store the generated work for subsequent utilization. Here, we report an experimental demonstration where quantized mechanical motion serves as a quantum battery and gets charged in repeated cycles by a single trapped-ion in-formation engine. This is enabled by a key technological advancement in rapid state discrimination, allowing us to suppress measurement-induced disturbances. Consequently, we were able to obtain a charging efficiency over 50% of the theoretical limit at the optimal temperature. The experimental results substantiate that this approach can render trapped ions a promising platform for microscopic information engines with potential applications in the future upon scaling up. Copyright © 2024, The Authors. All rights reserved.

关键词： Trapped ions

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Combining Deep Features with the Invariant Local Binary Pattern features for Skin Cancer Classification

Combining Deep Features with the Invariant Local Binary Patt...

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IEEE Punecon

作者： Ritesh Maurya Drishti Arora T Gopalakrishnan Ankita R Deshpande Malay Kishore Dutta Mohan Karnati Amity Centre for Artificial Intelligence Amity University Noida India Dept. of Computer Science and Engineering Amity University Noida India Department of Information Technology Manipal Academy of Higher Education Manipal Institute of Technology Bengaluru Manipal India

Melanoma, a potentially fatal form of skin cancer, represents a global health concern, necessitating efficient and precise detection for improved patient outcomes. However, the subjective and daunting nature of manual examination and diagnosing skin lesions by dermatologists often leads to delayed identification or misdiagnosis. This research paper introduces a novel technique for automated melanoma classification to address this pressing problem. The proposed research methodology combines deep features with rotation-invariant handcrafted local-binary pattern (RI-LBP) features for skin lesion classification. RI-LBP extracts microstructures present at the local level whereas deep features capture the high-level intuitive features present in a skin lesion image. After combining RI-LBP with the deep features, the combined feature vector is used to train and test the XGBoost machine learning classifier for classification purposes. Moreover, the investigation undertakes a comparative analysis of numerous machine learning algorithms to evaluate their efficacy in the classification of melanoma using the suggested combined feature set. The XGBoost classifier has obtained an Area Under the Curve value of 0.91 in differentiating pernicious melanomas and benign skin lesion dermoscopic images. Thus, the proposed method is suitable for aiding dermatologists, enhancing the efficacy and precision of melanoma identification and treatment, and ultimately yielding superior patient care and outcomes.

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Panicle-Cloud:An Open and Al-Powered Cloud Computing Platform for Quantifying Rice Panicles from Drone-Collected Imagery to Enable the Classification of Yield Production in Rice

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Plant Phenomics 2023年第4期5卷 905-916页

作者： Zixuan Teng Jiawei Chen Jian Wang Shuixiu Wu Riqing Chen Yaohai Lin Liyan Shen Robert Jackson Ji Zhou Changcai Yang Digital Fujian Research Institute of Big Data for Agriculture and Forestry College of Computer and Information SciencesFujian Agriculture and Forestry UniversityFuzhou 350002China State Key Laboratory of Crop Genetics&Germplasm Enhancement academy for Advanced Interdisciplinary StudiesNanjing Agricultural UniversityNanjing 210095China Ningxia Academy of Agriculture and Forestry Sciences Yinchuan 750002China College of Mechanical and Electrical Engineering Fujian Agriculture and Forestry UniversityFuzhou 350002China Cambridge Crop Research National Institute of Agricultural Botany(NIAB)Cambridge CB3 OLEUK Key Laboratory of Smart Agriculture and Forestry(Fujian Agriculture and Forestry University) Fujian Province UniversityFuzhou 350002China Center for Agroforestry Mega Data Science School of Future TechnologyFujian Agriculture and Forestry UniversityFuzhou 350002China

Rice(Oryza sativa)is an essential stable food for many rice consumption nations in the world and,thus,the importance to improve its yield production under global climate *** evaluate different rice varieties'yield performance,key yield-related traits such as panicle number per unit area(PNpM^(2))are key indicators,which have attracted much attention by many plant research ***,it is still challenging to conduct large-scale screening of rice panicles to quantify the PNpM^(2)trait due to complex field conditions,a large variation of rice cultivars,and their panicle morphological ***,we present Panicle-Cloud,an open and artificial intelligence(AI)-powered cloud computing platform that is capable of quantifying rice panicles from drone-collected *** facilitate the development of Al-powered detection models,we first established an open diverse rice panicle detection dataset that was annotated by a group of rice specialists;then,we integrated several state-of-the-art deep learning models(including a preferred model called Panicle-AI)into the Panicle-Cloud platform,so that nonexpert users could select a pretrained model to detect rice panicles from their own aerial *** trialed the Al models with images collected at different attitudes and growth stages,through which the right timing and preferred image resolutions for phenotyping rice panicles in the field were ***,we applied the platform in a 2-season rice breeding trial to valid its biological relevance and classified yield production using the platform-derived PNpM^(2)trait from hundreds of rice *** correlation analysis between computational analysis and manual scoring,we found that the platform could quantify the PNpM^(2)trait reliably,based on which yield production was classified with high ***,we trust that our work demonstrates a valuable advance in phenotyping the PNpM^(2)trait in rice,which provides a useful toolkit to enable rice br

关键词： breeding cultivar hundreds

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Leveraging an Optimized Deep Convolutional Neural Network Architecture for the Automated Diagnosis of Pathological Conditions Through Acoustic Analysis of Human Cough Signatures

Leveraging an Optimized Deep Convolutional Neural Network Ar...

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Smart Systems for applications in Electrical sciences (ICSSES), International Conference on

作者： Tamilarasan M Keshavamurthy T G vidya K V Nicy Siby Sriraksha Devaraj Revanna B R Department of Computer Science and Engineering Dr.N.G.P Institute of Technology Coimbatore Anna University Tamil Nadu India Department of Electronics and Communication Engineering Sri Siddhartha Institute of Technology Sri Siddhartha Academy of Higher Education Tumkur Karnataka India Department of Electronics and Communication and Engineering B.M.S College of Engineering Affiliated to Visvesvaraya Technological University Belgavi Bangalore Karnataka India

ISBN: (数字)9798331507671

ISBN: (纸本)9798331507688

The usage of artificial intelligence (AI) techniques particularly deep learning algorithms, has recently increased dramatically and the healthcare sector is only one of many industries affected Using deep convolutional neural networks (CNNs) enhanced with human auditory inputs we present a novel approach to disease detection in this paper The proposed system aims to provide an effective economical and non invasive method of early disease detection we employ a deep convolutional neural network (CNN) architecture that has been optimized for audio input processing. Further more we use data augmentation methodologies to strengthen the model's capacity to generalize and handle varied patient groups and diseases more effectively. Our all encompassing approach incorporates denoising techniques using Wavelet transform denoising segmentation via K-mean clustering, feature extraction using Recursive Feature Elimination (RFE) and classification using Enhanced Deep Convolutional Neural Networks (EDCNN) which improves the accuracy and dependability of the system in identifying diseases based on human sound signatures.

关键词： Deep learning Accuracy Noise reduction Medical services Feature extraction Data augmentation Data models Convolutional neural networks Artificial intelligence Diseases

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Expression of Concern for: A Comprehensive Analysis of the Use of Machine Learning Techniques to Optimize the Information Retrieval Process

Expression of Concern for: A Comprehensive Analysis of the U...

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Smart Generation Computing, Communication and Networking (SMART GENCON), International Conference on

作者： S. Hemelatha Kalanandhini. G Prasad B. Chaudhari Rajeev Sharma Jambi Ratna Raja Kumar Dipannita Mondal Department of Computer Science Engineering Karpagam Academy of Higher Education Coimbatore Department of Electronics and Communication Engineering Prince Shri Venkateshwara Padmavathy Engineering College Chennai Department of Artificial Intelligence & Data Science Vishwakarma Institute of Information Technology Pune India Department of Management Mangalayatan University Aligarh India Department of Computer Engineering Genba Sopanrao Moze College of Engineering Balewadi Pune Maharashtra India Artificial Intelligence And Data Science Department Dr. D.Y.Patil College Of Engineering And Innovation Talegaon

ISBN: (数字)9798350319125

ISBN: (纸本)9798350319132

This explores using a system to gain knowledge of strategies that allow you to optimize the facts retrieval manner. Facts retrieval is a complicated technique; it is becoming even more complicated as advancements in generation have furnished tools to make the gathering, employing, and evaluating information less complicated and faster. Research techniques, including artificial intelligence, neural networks, and machine learning, can speed up records retrieval and improve outcomes' accuracy. This look examines the different machine learning techniques to optimize the facts retrieval process and the advantages and downsides of each technique. Furthermore, this study looks at how gadgetstudying algorithms improve a person's enjoyment of retrieval systems and how their use can be integrated into cutting-edge practices. Subsequently, methods for improving the performance of machine learning algorithms are discussed in addition to capacity destiny directions inside the use of gadget studying to optimize statistics retrieval.

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Medical Multimodal Foundation Models in Clinical Diagnosis and Treatment: Applications, Challenges, and Future Directions

arXiv

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arXiv 2024年

作者： Sun, Kai Xue, Siyan Sun, Fuchun Sun, Haoran Luo, Yu Wang, Ling Wang, Siyuan Guo, Na Liu, Lei Zhao, Tian Wang, Xinzhou Yang, Lei Jin, Shuo Yan, Jun Dong, Jiahong School of Biomedical Engineering Tsinghua University Beijing China Department of Computer Science and Technology Tsinghua University Beijing China Xi’an Research Institute of Hi-Tech Shaanxi China Hepato-Pancreato-Biliary Center Beijing Tsinghua Changgung Hospital School of Clinical Medicine Tsinghua University Beijing China School of Computer and Communication Engineering University of Science and Technology Beijing China College of Electronic Information Engineering Tongji University Shanghai China Research Unit of Precision hepatobiliary Surgery Paradigm Chinese Academy of Medical Sciences Beijing China

Recent advancements in deep learning have significantly revolutionized the field of clinical diagnosis and treatment, offering novel approaches to improve diagnostic precision and treatment efficacy across diverse clinical domains, thus driving the pursuit of precision medicine. The growing availability of multi-organ and multimodal datasets has accelerated the development of large-scale Medical Multimodal Foundation Models (MMFMs). These models, known for their strong generalization capabilities and rich representational power, are increasingly being adapted to address a wide range of clinical tasks, from early diagnosis to personalized treatment strategies. This review offers a comprehensive analysis of recent developments in MMFMs, focusing on three key aspects: datasets, model architectures, and clinical applications. We also explore the challenges and opportunities in optimizing multimodal representations and discuss how these advancements are shaping the future of healthcare by enabling improved patient outcomes and more efficient clinical workflows. Copyright © 2024, The Authors. All rights reserved.

关键词： Personalized medicine

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Real-Time Parallel LU Decomposition of Space Debris Features On-Board

Real-Time Parallel LU Decomposition of Space Debris Features...

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International Conference on Advanced Infocomm technology (ICAIT)

作者： Jiyang Yu Dan Huang Jinyang Li Wenjie Li Xianjie Wang Qunzhi Li China Academy of Space Technology Beijing Institute of Spacecraft System Engineering Beijing China China Research & Development Academy of Machinery Equipment Chongqing University of Technology Beijing China Chongqing China Beijing Blackstone Matrix Technology Co. LTD Chongqing Feima Innovation Research Institute Chongqing China College of Computer Science and Engineering Chongqing University of Technology Chongqing China

This paper provides a parallel LU decomposition calculation method for on orbit Real-time computing calculation of space debris target characteristics. The purpose is to overcome the shortcomings of the traditional LU decomposition design, carry out high-precision approximate calculation of division in the LU calculation process, design a full pipeline parallel computing architecture based on digital logic devices according to the interrelationship between Land $U$ matrix elements, and design the calculation process and timing of each sub step by combing the correlation of each element in the calculation process, Real time LU decomposition of space target is realized on the basis of ensuring the calculation accuracy and executable frequency, and the target principal component features are obtained. The LU solution based on this design can effectively distinguish the characteristics of stars and debris targets in batches. Through experimental verification, the overall calculation performance has been improved by an order of magnitude compared to previous designs, and the designed fixed point calculation error is not more than 1%.

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Design and Implementation of Real-Time Localization System (Rtls) Based on Uwb and Tdoa Algorithm

SSRN

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SSRN 2022年

作者： Zhang, Fengyun Yang, Li Liu, Yuhuan Ding, Yulong Yang, Shuang-Hua Li, Hao Department of Computer Science and Engineering Southern University of Science and Technology Shenzhen China Army Engineering University PLA Nanjing China Academy for Advanced Interdisciplinary Studies Southern University of Science and Technology Shenzhen China PCL Research Center of Networks and Communications Peng Cheng Laboratory Shenzhen China Science and Technology on Near-surface Detection Laboratory Wuxi China

Nowadays, accurate localization plays an essential role in many fields, like target tracking and path planning. The challenges of indoor localization include inadequate localization accuracy, unreasonable anchor deployment in complex scenarios, lack of stability, and the high cost. So the universal positioning technologies cannot meet the real application requirements scarcely. To overcome these shortcomings, a comprehensive Ultra Wide-Band (UWB) based RTLS is presented in this paper. We introduce the architecture of real-time localization system, then propose a new wireless clock synchronization (WCS) scheme, finally discuss the time difference of arrival (TDoA) algorithm. We define the time-base selection strategy for the TDoA algorithm, and analyze the relationship between anchor deployment and positioning accuracy. The Extended Kalman Filter (EKF) method is presented for non-linear dynamic localization estimation, and it performs well in terms of stability and accuracy in moving target. © 2022, The Authors. All rights reserved.

关键词： Extended Kalman filters

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