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Automatic type Identification of Sub - Axial Vertebral Column Fractures using Deep learning Technique 2

Automatic type Identification of Sub - Axial Vertebral Colum...

2nd International Conference on Artificial Intelligence, Computational Electronics and communication System, AICECS 2023

作者： Sindhura, D.N. Pai, Radhika M Bhat, Shyamasunder N Manohara Pai, M.M. Department of Information and Communication Technology Manipal Institute of Technology Manipal Academy of Higher Education Manipal India Department of Data Science and Computer Applications Manipal Institute of Technology Manipal Academy of Higher Education Manipal India Department of Data Science and Department of Orthopaeadics Kasturba Medical College Manipal Manipal Academy of Higher Education Manipal India Department of Data Science Information and Communication Technology Manipal Institute of Technology Manipal Academy of Higher Education Manipal India

Traumas like falls, sports injuries, or vehicle accidents are the main causes of Vertebral Column Fracture(VCFs) or spine fracture. The symptoms of VCF include back pain, swelling, numbness, and height decrease. VCF causes extreme pain, paralysis, difficulty in movement, etc. CT scans are inexpensive and effective in providing precise and quick VCF type detection. The determination of type VCF, however subject to inter-observer variability. To address this limitation this work introduces an automatic system for the detection of VCF type based on an ensemble of deep fine tuned models. This can assist the orthopaedicians in type identification of VCF which decreases the image interpretation time and increases the patient care time. For type identification, a total of two fine-tuned CNN architectures are used. Then, to further enhance the type identification performance, we developed an ensemble model of fine tuned VGG16 and ResNet50 deep learning models, which average the outputs of the models during final prediction. We used a dataset of 2820 CT images from eight different VCF types that were collected from Kasturba Medical college, Manipal Academy of Higher education, Manipal. The results shows that the ensemble model performs well compare to individual fine-tuned models with accuracy of 82%, precision of 0.84, recall of 0.82 and F1-score of 0.81 for VCF type identification from CT scans. This study shows that type identification of VCF in CT scans may be successfully accomplished using an ensemble deep transfer learning system with various fine tuned CNN architectures. © Published under licence by IOP Publishing Ltd.

关键词： Deep learning

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STUDY ON PLANETARY TRANSMISSION COUPLING FAULT DIAGNOSIS BASED ON MACHINE LEARNING 9

STUDY ON PLANETARY TRANSMISSION COUPLING FAULT DIAGNOSIS BAS...

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9th International Symposium on Test Automation and Instrumentation, ISTAI 2022

作者： Wang, Botao Dong, Shaojun Ma, Chao Zhou, Fuqiang Beijing Information Science and Technology University Beijing China Institute of Data Communication Science and Technology Beijing China

ISBN: (纸本)9781839538391

In this paper, the fault diagnosis of a planetary gearbox is studied theoretically and experimentally. A fault diagnosis method combining variational mode decomposition (VMD) and relevance vector machine (RVM) was proposed. Through VMD decomposition of gearbox vibration signals, Through VMD decomposition of gearbox vibration signals, multi-parameter feature vectors were constructed and input into RVM fault identification model to realize effective identification of single fault The simulation model of planetary transmission system and the data acquisition and analysis of scale experiment were used to verify the results. The results show that the diagnostic accuracy of the proposed method is 98.3%, which is true and effective. © 2022 IET Conference Proceedings. All rights reserved.

关键词： Variational mode decomposition

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ResPho(SC)Net: A Zero-Shot Learning Framework for Norwegian Handwritten Word Image Recognition 11th

ResPho(SC)Net: A Zero-Shot Learning Framework for Norwegian...

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11th Iberian Conference on Pattern Recognition and Image Analysis, IbPRIA 2023

作者： Gurav, Aniket Jensen, Joakim Krishnan, Narayanan C. Chanda, Sukalpa Department of Computer Science and Communication Østfold University College Halden1757 Norway Department of Data Science Indian Institute of Technology Palakkad Kerala India

ISBN: (纸本)9783031366154

Recent advances in deep Convolutional Neural Networks (CNNs) have established them as a premier technique for a wide range of classification tasks, including object recognition, object detection, image segmentation, face recognition, and medical image analysis. However, a significant drawback of utilizing CNNs is the requirement for a large amount of annotated data, which may not be feasible in the context of historical document analysis. In light of this, we present a novel CNN-based architecture ResPho(SC)Net, to recognize handwritten word images in a zero-shot learning framework. Our method proposes a modified version of the Phosc(Net) architecture with a much lesser number of trainable parameters. Experiments were conducted on word images from two languages (Norwegian and English) and encouraging results were obtained. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Optical character recognition

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Design of 2X1 Monopole Array Antenna for WiMAX and WiLAN Applications 1

Design of 2X1 Monopole Array Antenna for WiMAX and WiLAN App...

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1st International Conference on Computational Intelligence for Security, communication and Sustainable Development, CISCSD 2024

作者： Vennela, R. Neeraj, N. Lakshmi Feroze, Abdul Ushasree, A. Kodipalli, Ashwini Satyanarayana, M. Kumar, N. Udaya Griet Department of Electronics and Communication Engineering Hyderabad India Global Academy of Technology Artificial Intelligence and Data Science Bangalore India Mvgr College of Engineering Department of Electronics and Communication Engineering Vizag India Vivekananda Institute of Technology Department of Electronics and Communication Engineering Bangalore India

ISBN: (纸本)9798350365405

This paper presents a thorough examination of the performance attributes of a 2x1 monopole array antenna employing variations in strip configurations. The study focuses on evaluating the influence of single, double, and triple strips on key parameters including return loss, VSWR, and gain. The design methodology involves rigorous simulations and optimization techniques to achieve optimal antenna performance. Results reveal significant differences in the performance metrics among the different configurations. The single strip configuration demonstrates a certain level of performance, while the addition of multiple strips, particularly the double strip configuration, exhibits enhanced characteristics in terms of lower return loss, VSWR, and higher gain. Comparative analysis is conducted to highlight the advantages and limitations of each configuration, providing valuable insights for designing monopole array antennas tailored to specific application requirements. This study contributes to advancing the understanding of monopole array antenna design principles and optimization strategies for wireless communication systems. © 2024 IEEE.

关键词： Monopole antennas

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Deep Learning Based Autonomous Transport System for Secure Vehicle and Cargo Matching

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Intelligent Automation & Soft Computing 2023年第1期35卷 957-969页

作者： T.Shanthi M.Ramprasath A.Kavitha T.Muruganantham Department of Electronics and Communication Engineering Sona College of TechnologySalem636005Tamil NaduIndia Department of Data Science and Business Systems School of ComputingSRM Institute of Science and TechnologyKattankulathur603203TamilnaduIndia Department of Electronics and Communication Engineering K.Ramakrishnan College of TechnologyTiruchirapalli621112TamilnaduIndia Department of Electronics and Communication Engineering K.Ramakrishnan College of EngineeringTiruchirapalli621112TamilnaduIndia

The latest 6G improvements secured autonomous driving's realism in Intelligent Autonomous Transport Systems(IATS).Despite the IATS's benefits,security remains a significant *** technology has grown in popularity as a means of implementing safe,dependable,and decentralised independent IATS systems,allowing for more utilisation of legacy IATS infrastructures and resources,which is especially advantageous for crowdsourcing *** technology can be used to address security concerns in the IATS and to aid in logistics *** light of the inadequacy of reliance and inattention to rights created by centralised and conventional logistics systems,this paper discusses the creation of a blockchain-based IATS powered by deep learning for secure cargo and vehicle matching(BDL-IATS).The BDL-IATS approach utilises Ethereum as the primary blockchain for storing private data such as order and shipment ***,the deep belief network(DBN)model is used to select suitable vehicles and goods for ***,the chaotic krill herd technique is used to tune the DBN model’s *** performance of the BDL-IATS technique is validated,and the findings are inspected under a variety of *** simulationfindings indicated that the BDL-IATS strategy outperformed recent state-of-the-art approaches.

关键词： Blockchain ethereum intelligent autonomous transport system security deep belief network

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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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Partitioned Edge Learning over Fast Fading Channels

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IEEE Transactions on Vehicular technology 2025年第6期74卷 8561-8576页

作者： Jiang, Zhihui Wen, Dingzhu Liu, Shengli Zhu, Guangxu Yu, Guanding Zhejiang University College of Information Science and Electronic Engineering Hangzhou China ShanghaiTech University School of Information Science and Technology Shanghai China Shanghai University School of Communication and Information Engineering Shanghai China Shenzhen Research Institute of Big Data Shenzhen International Center for Industrial and Applied Mathematics Shenzhen China

The implementation of the partitioned edge learning (PARTEL) framework in practical fast-fading wireless systems with time-varying channels is investigated in this paper. By exploiting the benefit of only training and transmitting a light-size sub-model on devices, PARTEL enjoys both enhanced computation and communication efficiency compared to federated edge learning framework where each device needs to train and transmit all model parameters. In this work, we aim at further enhancing the learning efficiency via minimizing the training latency of each training round in practical fast fading channels, where each round includes multiple channel time-coherence time durations. The challenges arise from the unknown channel state information (CSI) of future durations and the coupling between load balancing and bandwidth allocation among devices. To this end, an equivalent Markov decision process (MDP) problem is derived, where each decision step corresponds to one channel coherence-time duration. The learning load balancing is first determined based on an existing design for static channels by using the expected channel gains. Then, the bandwidth allocation for uploading the local sub-model updates in each duration is sequentially determined by the proposed optimization algorithm. The spectrum efficiency is enhanced since the bandwidth is adaptively allocated to all devices according to their communication and computation statuses in each coherence-time duration. Finally, extensive simulations are conducted to show the superiority of our proposed algorithms over the existing benchmarks. Specifically, the proposed algorithm can reduce the one-round latency by up to 25.51% for the bandwidth of 70 MHz. © 1967-2012 IEEE.

关键词： Bandwidth

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Multi-layer network embedding on scc-based network with motif

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Digital communications and Networks 2024年第3期10卷 546-556页

作者： Lu Sun Xiaona Li Mingyue Zhang Liangtian Wan Yun Lin Xianpeng Wang Gang Xu Department of Communication Engineering Institute of Information Science TechnologyDalian Maritime UniversityDalian116026China Key Laboratory of Big Data Intelligent Computing Chongqing University of Posts and TelecommunicationsChongqing400065China Key Laboratory for Ubiquitous Network and Service Software of Liaoning Province DUT School of Software Technology&DUT-RU International School of Information Science and EngineeringDalian University of TechnologyDalian 116620China College of Information and Communication Engineering Harbin Engineering UniversityHarbin 150001China School of Information and Communication Engineering Hainan UniversityHaikou 570228China State Key Laboratory of Millimeter Waves School of Information Science and EngineeringSoutheast UniversityNanjing 210096China

Interconnection of all things challenges the traditional communication methods,and Semantic communication and Computing(SCC)will become new *** is a challenging task to accurately detect,extract,and represent semantic information in the research of SCC-based *** previous research,researchers usually use convolution to extract the feature information of a graph and perform the corresponding task of node ***,the content of semantic information is quite *** graph convolutional neural networks provide an effective solution for node classification tasks,due to their limitations in representing multiple relational patterns and not recognizing and analyzing higher-order local structures,the extracted feature information is subject to varying degrees of ***,this paper extends from a single-layer topology network to a multi-layer heterogeneous topology *** Bidirectional Encoder Representations from Transformers(BERT)training word vector is introduced to extract the semantic features in the network,and the existing graph neural network is improved by combining the higher-order local feature module of the network model representation network.A multi-layer network embedding algorithm on SCC-based networks with motifs is proposed to complete the task of end-to-end node *** verify the effectiveness of the algorithm on a real multi-layer heterogeneous network.

关键词： Semantic communication and computing Multi-layer network Graph neural network Motif

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Deep Neural Networks for Image-Based Indian Sign Language Recognition: A Comprehensive Review and Practical Analysis 1

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2nd International Conference on Advances in data-driven Computing and Intelligent Systems, ADCIS 2023

作者： Kaur, Harleen Bedi, Sanjam Kaur Rao K, Mallikharjuna Lekhana, M.A. Data Science and Artificial Intelligence International Institute of Information Technology Naya Raipur Chhattisgarh Naya Raipur India Electronics and Communication Engineering International Institute of Information Technology Naya Raipur Chhattisgarh Naya Raipur India

ISBN: (数字)9789819995318

ISBN: (纸本)9789819995301

Addressing the communication barriers faced by individuals with vocal and hearing disabilities, sign language serves as a visual means of expression. However, the limited understanding of sign language among the general population creates significant challenges in various settings, such as banks, airports, and supermarkets. To overcome this, a sign language recognition (SLR) system is essential. This study focuses on developing a real-time word-level sign language recognition system that translates sign language into text, with a specific emphasis on Indian sign language (ISL) to cater to the needs of the deaf and hard-of-hearing community in India. Our research presents an ISL-based sign language recognition system, enabling users to capture hand movement images through a web camera, with the system accurately predicting and displaying the corresponding sign name. The captured images undergo several processing stages, incorporating computer vision techniques like grayscale conversion, dilatation, and masking. To achieve robust recognition, a convolutional neural network (CNN) is trained on the collected dataset, demonstrating exceptional performance with a 99% accuracy rate. This research contributes to the advancement of assistive technologies by providing an effective solution for real-time ISL recognition, promoting inclusive communication and accessibility for individuals with hearing and vocal impairments. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Audition

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data-Driven Exploration and Visualization of Diverse fields of Startup in India

Data-Driven Exploration and Visualization of Diverse fields ...

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2023 International Conference on Innovative Computing, Intelligent communication and Smart Electrical Systems, ICSES 2023

作者： Blessy, Sardar Rechel Supraja, Balerao Rathi, Kushal Prathima, T. Vasanth, K. Artificial Intelligence and Data Science Chaitanya Bharathi Institute of Technology Hyderabad India Information Technology Chaitanya Bharathi Institute of Technology Hyderabad India Electronics and Communication Engineering Chaitanya Bharathi Institute of Technology Hyderabad India

ISBN: (纸本)9798350319200

The objective of this research is to analyze and comprehend the trends in the startup culture of India. Startup insights refer to the knowledge and understanding gleaned from the analysis and visualization of various startup venture information. Startup insights can assist entrepreneurs, investors, and other stakeholders in the startup ecosystem to better understand the factors that come up while building a new business, and to make informed decisions regarding strategy, resource allocation, and risk management. The research involved collection and analysis of data on numerous startup companies. Descriptive statistics were used to explain trends and patterns in the data such as, which sectors are attracting the most startups, which locations are seeing the most startup activity, how many number of startups that have been established yearly and so on. © 2023 IEEE.

关键词： Descriptive statistics Entrepreneurs Stakeholders Startup

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