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International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Jiaxiong Fang Juan Liu Hua Chen Wei Liu Ye Tian Gang Wang Faculty of Electrical Engineering and Computer Science Ningbo University Ningbo China Zhejiang Key Laboratory of Mobile Network Application Technology Ningbo China School of Electronic Engineering and Computer Science Queen Mary University of London London UK

Based on the exact source-sensor spatial geometry, a three-dimensional (3-D) spatial-temporal localization algorithm for multiple near-field (NF) sources is proposed without adopting the Fresnel approximation, which simplifies the spatial phase difference by Taylors polynomial. In addition, considering the propagation attenuation which varies from different sensors, the spatial and temporal information can be exploited to construct a third-order parallel factor (PARAFAC) data model and the array manifold matrices can be extracted by trilinear decomposition; then, estimation of the unambiguous range and angle parameters of the NF sources is achieved from the spatial amplitude-phase factors by the least squares method. The obtained 3-D parameters associated with each source require no additional pairing process, as also demonstrated by simulation results.

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Dynamic Access Control in Multi-Layer Satellite Remote Sensing System Using Multi-Agent Deep Reinforcement Learning

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IEEE Transactions on Wireless Communications 2025年

作者： Hu, Han Lyu, Yifeng Fan, Rongfei Sui, Xiufeng Zhan, Cheng Niyato, Dusit Beijing Institute of Technology School of Information and Electronics Beijing100081 China Norinco Group Institute of Computer Application Technology Beijing100089 China Beijing Institute of Technology School of Cyberspace Science and Technology Beijing100081 China Beijing Institute of Technology School of Management Beijing100081 China Southwest University School of Computer and Information Science Chongqing400715 China Nanyang Technological University School of Computer Science and Engineering 639798 Singapore

The Multi-Layer Satellite Remote Sensing (SRS) integrates data collection by Low Earth Orbit (LEO) satellites and data processing assistance from Medium Earth Orbit (MEO) satellites, thereby playing a crucial role in scientific exploration. However, effectively controlling access to LEO satellites for processing data, especially considering the frequent handovers caused by speed differences, presents a significant challenge to achieving high energy efficiency services. To address this challenge, we explore cooperative dynamic access control based on efficient communication mechanisms, with the aim of prioritizing processed data volume and meeting energy consumption requirements for satellites. Specifically, we formulate the access control issue as an optimization problem and integrate it into the framework of partially observable Markov decision process (POMDP), considering MEO satellites' limited observation ability. By employing Multi-agent Deep Reinforcement Learning (MADRL), we propose a novel dynamic access control algorithm named DAC to solve our featured problem. Specifically, for improving performance, communication-efficient cooperation among MEOs is enhanced through modeling decision-relevant information of fellow MEO satellites and maximizing mutual information with their actual data to extract precise awareness and enable the generation of concise message. Finally, we conduct comprehensive experiments and an ablation study spanning the Starlink, OneWeb, and Telesat mega-constellations. The results demonstrate that DAC increases the average system data processing volume by at least 13.5%, while meeting energy consumption constraints and outperforming baseline algorithms. © 2002-2012 IEEE.

关键词： Deep reinforcement learning

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Integration of AI and Cloud-Native Technologies for Personalized Mobile Banking Experiences

Integration of AI and Cloud-Native Technologies for Personal...

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2024 International Conference on Recent Innovation in Smart and Sustainable Technology, ICRISST 2024

作者： Legapriyadharshini, N. Thirumalaikumari, T. Senbagam, K. Sarasu, R. Basha, H. Anwer Malathi, P. Saveetha Institute of Medical and Technical Sciences Saveetha College of Liberal Arts and Sciences Department of Computer Application Tamil Nadu Chennai India Saveetha College of Liberal Arts and Sciences Simats Deemed to Be University Department of Computer Science Tamil Nadu Chennai India Dhanalakshmi Srinivasan College of Engineering and Technology Department of Computer Science and Engineering Tamil Nadu Mamallapuram India Saveetha Institute of Medical and Technical Sciences Saveetha School of Engineering Tamil Nadu Chennai India Saveetha Institute of Medical and Technical Sciences Department of Computer Science Saveetha College of Liberal Arts and Sciences Tamil Nadu Chennai India Saveetha Institute of Medical and Technical Sciences Saveetha College of Liberal Arts and Sciences Department of Data Analytics Tamil Nadu Chennai India

ISBN: (纸本)9798350309140

In today's rapidly evolving digital landscape, using Artificial Intelligence (AI) and Cloud-Native Technologies is essential for improving personalized mobile banking experiences. The limitations and errors of existing financial systems necessitate such integration. Existing systems often struggle to adapt to modern consumers dynamic tastes and expectations, resulting in unsatisfactory customer experiences. Identifying the gap, the proposed system revolutionizes mobile banking applications by leveraging AI algorithms such as machine learning and natural language processing within a microservices-based cloud infrastructure. As a result, the proposed system solves the limitations of outdated architectures by providing personalized and efficient services while addressing security issues. The study indicates considerable gains in terms of user pleasure, system scalability, and security robustness through an in-depth evaluation of the results. The results highlight AI's and Cloud-Native Technologies transformational potential in revolutionizing the landscape of tailored mobile banking experiences. The study provides helpful information to financial institutions looking to adopt innovation and fulfill the evolving needs of modern consumers. © 2024 IEEE.

关键词： Legacy systems

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Leveraging Machine Learning for Early Detection of Cardiovascular Diseases

Leveraging Machine Learning for Early Detection of Cardiovas...

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Computational, Communication and Information Technology (ICCCIT), International Conference on

作者： Anurag Shrivastava Meenakshi Maindola Rakesh Kumar H Pal Thethi S P Sreeja N Sirisha Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Chennai Tamilnadu Department of Computer Science & Engineering Graphic Era Deemed to be University Dehradun Department of Computer Engineering & Applications GLA University Mathura Lovely Professional University Phagwara India Master of Computer Application New Horizon College of Engineering Bangalore Department of Computer Science & Engineering MLR Institute of Technology Hyderabad Telangana

ISBN: (数字)9798331512965

ISBN: (纸本)9798331512972

In order to enhance patient outcomes, rapid and precise detection approaches are needed for cardiovascular diseases (CVDs), which are among the top causes of death globally. By examining a wide range of demographic and clinical data, this study investigates how machine learning approaches could improve the early diagnosis of CVDs. We explore how many different machine learning methods could identify different heart diseases. Decision trees, support vector machines and more, are included in these algorithms. By using feature selection and optimization methods we want to strengthen these models' prediction abilities and make them more resilient. The dataset used by this research includes all of the medical information of patients as they include all the pertinent biomarkers such as age, blood pressure, cholesterol levels and etc. The results suggest that machine learning models, especially ensemble methods like Random Forest and gradient boosting, are indeed able to predict the risk of CVD better than conventional diagnostic strategies. Results suggest that early risk assessment using machine learning embedded within healthcare processes is a reliable and non-invasive approach. This study has found machine learning to have promise for change and further research into the practical use of these models to revolutionize treatment of cardiovascular diseases.

关键词： Support vector machines Accuracy Computational modeling Machine learning Medical services Predictive models Blood pressure Cardiovascular diseases Risk management Random forests

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Analysis of Energy Quality Issues in the Solar Energy System

Analysis of Energy Quality Issues in the Solar Energy System

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International Conference on Electrical Power and Energy Systems (ICEPES)

作者： Ishita Uniyal Shweta Tiwari Vishal Shiv Ashish Dhondiyal Department of Electrical Engineering Graphic Era Hill University Dehradun Department of Computer Science & Engineering University Institute of Engineering Chandigarh University Mohali Punjab School of Computer Application Lovely Professional University Jalandhar Punjab Dept. of Computer Science and Engineering Graphic Era Deemed to be University Dehradun India

ISBN: (数字)9798350390728

ISBN: (纸本)9798350390735

Photovoltaic systems are being used more and more in the production of electricity because of the high cost of fossil fuel-based energy. The main issue that happens between grid and end-user transmission lines is power quality. DSTATCOM is a power quality compensating device that corrects voltage sag and swell are examples of power quality concerns. In high voltage line for power transmission this can be happened. The study was carried out with MATLAB or Simulink software to resolve the voltage sag and swell, and the model was created. The simulation results show the problem of voltage sag and swell is minimize to very large extent.

关键词： Power transmission lines Voltage fluctuations Software packages Simulation Power quality Energy resolution Power transmission

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Empirical Analysis of Prediction of Crime and Forecasting With IGWO-SVM Approach 2

Empirical Analysis of Prediction of Crime and Forecasting Wi...

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2nd International Conference on Edge Computing and applications, ICECAA 2023

作者： Vasuki, R. Ganvir, Neha Sharma, Rohita Kumar, K Senthil Krishna Kumari, Svhn Singh, Kamlesh Mannar Thirumalai Naicker College Department of Computer Application Tamilnadu Madurai India Kj College of Engineering and Management Maharashtra Pune India Lovely Professional University Department of Fine Arts Punjab India School of Computing College of Engineering and Technology Srm Institute of Science and Technology Department of and Communications Tamil Nadu Chennai India Telangana India Graphic Era Hill University Department of Computer Science and Engineering Dehradun India

ISBN: (纸本)9798350347579

In order to understand the nuances of the crime data from recent years, in-depth analysis is necessary. Involved parties in law enforcement will benefit from this procedure in terms of making arrests and guiding efforts to reduce crime. Strategically or tactically, it benefits from having a better understanding of where, when, and how crimes will occur in the future. However, given the rising incidence of crime in the modern world, developing an effective method for predicting future criminal behavior is a difficult issue. As a result, it is crucial to develop a strategy for predicting criminal activity in order to prevent further crimes and lower crime rates. Researchers are currently attempting to predict criminal behavior based on a variety of factors. SVM, CNN and ANN are just few of the prediction approaches that can be used to assess a model's accuracy. Although their studies have yielded encouraging findings, there are still major blind spots that preclude an accurate prediction of crime hubs. There have been many earlier publications of academic papers on this subject. So, the results are compiled and conducted a comprehensive assessment of all of them in this report. Proposed goal is to catalog the various forms the crime prediction method has taken and the ways in which it might be improved in the future. Preprocessing, feature extraction, and model training are the three main components of the proposed method. Preprocessing includes activities including data cleansing, integration, transformation, reduction, and discretization. It uses principal component analysis to glean features. The model is trained with IGWO-SVM. When compared to two existing models, the proposed model performs exceptionally well. © 2023 IEEE.

关键词： Support vector machines

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A Comparative Analysis of Cloud based Services Platform 6

A Comparative Analysis of Cloud based Services Platform

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6th Smart Cities Symposium, SCS 2022

作者： Bajaj, Karan Sharma, Bhisham Singh, Raman Kumar, Mukesh Chowdhury, Subrata Department of Computer Science & Engineering Chitkara University School of Engineering and Technology Chitkara University Himachal Pradesh India School of Computing Engineering and Physical Sciences University of the West of Scotland Lanarkshire United Kingdom School of Computer Application Lovely Professional University Punjab Phagwara India MCA Department Sri Venkateswara College of Engineering and Technology India

ISBN: (纸本)9781839538544

Data processing at high speeds and storage solutions on a massive scale are two things that are becoming increasingly important as a result of the proliferation of new start-up companies and the vast volume of data that users consume daily. The cloud platforms offer a solution to these issues in the shape of virtualization, which entails the creation of several distinct virtual machines within a single actual system. Because of this, the efficiency of the processor increases, and it now has far less downtime. The four major cloud platforms are discussed, and each has its own set of benefits, which helps make them successful in their unique conducts. Although Amazon EC2 is one of the most used and earliest that offers assistance for the utmost numeral of already configured system software termed as operating systems, which falls short in terms of both its scope and the support that it makes available. In a similar vein, the Google Cloud Platform can support the greatest number of databases and has an excellent repository of in-built libraries. The Microsoft Azure has the greatest number of users out of four, however, it is badly lacking in the database supports it offers. IBM Watson offers user friendly services but has scalability issues. As a result, a conclusion has been reached, that the selecting a particular cloud platform, there is extreme dependence on the needs of the users. These requirements may vary from client to client which are the end-users of the applications. Before deciding on a particular platform, it is best to conduct a thorough analysis of the needs. © 2022 IET Conference Proceedings. All rights reserved.

关键词： Digital storage

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Multi-Modal Emotion and Gesture Recognition System for Humans and Animals Using computer Vision and Deep Learning

Multi-Modal Emotion and Gesture Recognition System for Human...

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IoT Based Control Networks and Intelligent Systems (ICICNIS), International Conference on

作者： Rekha R Nair Tina Babu Kishore S Smitha Shivaswamy Aruna S Manchikalapudi Bhargavi Department of Computer Science and Engineering Alliance School of Advanced Computing Alliance University Bengaluru India Department of Computer Application Nitte Institute of Professional Education Nitte(Deemed to be University) Mangaluru Karnataka India Department of Computer Applications Acharya Institute of Technology Bengaluru India School of Science and Computer studies CMR University Bangalore India Department of Computer Science and Engineering(AIML) Malla Reddy College of Engineering and Technology Hyderabad India

ISBN: (数字)9798331518097

ISBN: (纸本)9798331518103

Emotion recognition through facial expressions and gesture analysis has emerged as a key area of research in computer vision and human-computer interaction. The ability to automatically detect and classify human emotions has wide-ranging applications, from enhancing user experiences to improving mental health monitoring and surveillance systems. This project builds on this background to develop a comprehensive system for recognizing emotions and gestures in both humans and animals. The system integrates facial emotion recognition, hand gesture interpretation, and animal emotion detection into a single platform. Using advanced deep learning models and computer vision libraries, the system classifies facial expressions into categories such as happiness, sadness, anger, fear, surprise, and neutral. The gesture recognition component interprets hand signals to convey messages like “need help” or “need water.” An innovative aspect of this work is the extension of emotion recognition to animals, particularly focusing on species like dogs and monkeys. Implemented primarily in Python, the system leverages libraries such as OpenCV and DeepFace. By combining these different modalities of nonverbal communication analysis, the project presents a versatile solution with potential applications in human-computer interaction, mental health monitoring, security, and animal welfare.

关键词： Hands Human computer interaction Emotion recognition computer vision Accuracy Animals Face recognition Computational modeling Gesture recognition Mental health

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A Multimodal Deep Learning Approach for Advancing Liver Disease Diagnosis and Prognosis Prediction

A Multimodal Deep Learning Approach for Advancing Liver Dise...

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IoT, Communication and Automation Technology (ICICAT), International Conference on

作者： Mukesh Kumar Nitin Arvind Shelke Jagendra Singh Kamal Nain Sharma Rajat Sharma Rakesh Kumar Electronics and Communication Engineering The Technological Institute of Textile & Sciences Bhiwani India School of Computer Science Engineering & Technology Bennett University Greater Noida India School of Computer Application Lovely Professional University Jalandhar India Department of Computer Science and Engineering - SOES G D Goenka University Gurugram India Department of Computer Science and I.T. Magadh University Bodh Gaya India

ISBN: (数字)9798350368109

ISBN: (纸本)9798350368116

Using a multimodal deep learning framework, we have demonstrated the possibility to predict liver disease subtyping and prognosis with high accuracy. The framework includes over 1600 patients, for which medical imaging and clinical data have been collected. Convolutional Neural Networks (CNNs), Recurrent Neural Network (RNNs), and Long short-term memory (LSTMs) have been combined in the work to extract features from the input data and demonstrate correlation between input and output and model complexity. Such an approach allowed analyzing images and predicting liver diseases based on structural abnormality analysis with the use of CNNs. RNNs and LSTMs, in their turn, allowed combining features for multimodal data source analysis and sequential clinical data analysis providing information about patient history. The obtained results were accurate for each feature extraction and modeling, with the best performance demonstrated by CNN with RNN where the accuracy was 97.8% and another CNN with LSTM where the accuracy was around 94.5%. The model proved to be feasible and accurate in the context of its application to hepatology since multiple data sources were combined in the work. This approach allows accounting for all modes of data source and offers a comprehensive assessment including disease diagnosis and prognosis. The obtained results can be also applied to clinical practice where doctors can add data about their patients to receive more information on diagnosis or prognosis for better decision-making. Finally, the idea to use a multilayered feature from multimodal data sources can be transferable to other types of sequential and structural data and applied to other diseases for their diagnosis and prognosis. However, it is essential to test the framework in other settings to refine it or find limitations to its application.

关键词： Deep learning Accuracy Recurrent neural networks Liver diseases Soft sensors Feature extraction Data models Convolutional neural networks Prognostics and health management Long short term memory

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A Deep Learning Aided Intelligent Framework for Condition Monitoring of Electrical Machinery 5

A Deep Learning Aided Intelligent Framework for Condition Mo...

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5th International Conference on Devices for Integrated Circuit, DevIC 2023

作者： Ganguly, Biswarup Ray, Radha Kumari Chatterjee, Arunava Paul, Subho Meghnad Saha Institute of Technology Electrical Engineering Department Kolkata India Application Development Associate Accenture Kolkata India Raghunathpur Govt. Polytechnic Electrical Engineering Department Purulia India Washington State University School of Electrical Engineering and Computer Science PullmanWA United States

ISBN: (纸本)9798350347265

Single-phase induction motors (SPIM) are fairly efficiently employed in household devices and possess wide applications in the industrial system. Typically, current signature-Analysis (CSA) based methods have been used for fault diagnosis, but not always been an efficient solution, especially for fractional kW SPIMs that may be replaced entirely iffaulty. This paper presents a deep learning (DL) based fault detection scheme of a SPIM which is in-service in household devices. The fault detection is done employing a wavelet based deep dense convolutional neural network (W-D2CNN), where several wavelets are used as convolutional filters for convolution operation. The convoluted feature maps are further passed through a proposed nonlinearity, i.e., SLReLU, for richer feature extraction. The uniqueness of the proposed method lies in the feature learning ability, where upper-And lower-layer features of the dense block fuse via concatenation operation. Also, the SL-ReLU has been designed to combine logarithmic function, softsign, and ReLU operation. The designed SL-ReLU minimizes the probability of convoluted output and gradient explosion, due to which training has been improved along with feature learning ability. Comprehensive results reveal the efficacy of the proposed work in terms of performance metrics with state-of-The-Art DL frameworks using the same dataset. The proposed work can also be extendable to multiphase-induction machines or any other electrical device working in any environment for fault identification. © 2023 IEEE.

关键词： Failure analysis

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