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Innovations in Communications, Electrical and Computer Engineering (ICICEC), International Conference on

作者： K. Bhagya Laxmi John Benito Jesudasan Peter P. rajalakshmi K. dhivya Nilamadhab Mishra S. Srinivasan Department of Computer Science and Engineering Matrusri Engineering College Hyderabad Telangana India Cyber | Data Science & Engineering Capital One Services LLC Richmond Virginia USA Department of Computer Science and Engineering Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Chennai Tamil Nadu India Department of Electronics and Communication Engineering SRM Institute of Science and Technology Chennai Tamil Nadu India School of Computing Science and Engineering VIT Bhopal University Madhya Pradesh India Department of Biomedical Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Chennai Tamil Nadu India

ISBN: (数字)9798350376517

ISBN: (纸本)9798350376524

A novel method for monitoring and controlling humidity levels in smart hospital settings by means of the Internet of Things (IoT). To maximize patient comfort and infection control measures, IoT device connection allows for real-time humidity level monitoring and modification. Improving the system's responsiveness to humidity data is possible with the use of the k-Nearest Neighbors (KNN) algorithm. Machine learning (ML) method, the system can forecast the ideal relative humidity from past data and present weather. A major factor in preventing the spread of infectious diseases in healthcare facilities is keeping the relative humidity at an optimal level. The general health and success of the healing process depend on the patient's level of comfort. The proposed IoT system for smart hospital humidity management considers medical and patient needs. The system's capacity to dynamically adjust humidity levels based on data improves infection control and patient satisfaction.

关键词： Technological innovation Hospitals Humidity measurement Weather forecasting Humidity Nearest neighbor methods Control systems real-time systems Internet of Things Monitoring

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reconstructing human activities via coupling mobile phone data with location-based social networks

arXiv

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arXiv 2023年

作者： Huang, Le Xia, Fan Chen, Hui Hu, Bowen Zhou, Xiao Li, Chunxiao Jin, Yaohui Xu, Yanyan MoE Key Laboratory of Artificial Intelligence Ai Institute Shanghai Jiao Tong University Shanghai200240 China Data-Driven Management Decision Making Lab Shanghai Jiao Tong University Shanghai200030 China School of Information Science and Technology Shandong University Qingdao266237 China Technology R&d Department Data Intelligence Division China Unicom Digital Technology Co. Ltd. Beijing100166 China Antai College of Economics and Management Shanghai Jiao Tong University Shanghai200030 China Gaoling School of Artificial Intelligence Renmin University of China Beijing100872 China

In the era of big data, the ubiquity of location-aware portable devices provides an unprecedented opportunity to understand inhabitants' behavior and their interactions with the built environments. Among the widely used data resources, mobile phone data is the one passively collected and has the largest coverage in the population. However, mobile operators cannot pinpoint one user within meters, leading to the difficulties in activity inference. To that end, we propose a data analysis framework to identify user's activity via coupling the mobile phone data with location-based social networks (LBSN) data. The two datasets are integrated into a Bayesian inference module, considering people's circadian rhythms in both time and space. Specifically, the framework considers the pattern of arrival time to each type of facility and the spatial distribution of facilities. The former can be observed from the LBSN data and the latter is provided by the points of interest (POIs) dataset. Taking Shanghai as an example, we reconstruct the activity chains of 1,000,000 active mobile phone users and analyze the temporal and spatial characteristics of each activity type. We assess the results with some official surveys and a real-world check-in dataset collected in Shanghai, indicating that the proposed method can capture and analyze human activities effectively. Next, we cluster users' inferred activity chains with a topic model to understand the behavior of different groups of users. This data analysis framework provides an example of reconstructing and understanding the activity of the population at an urban scale with big data fusion. Copyright © 2023, The Authors. All rights reserved.

关键词： big data

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A dynamics Theory of Implicit regularization in deep Low-rank Matrix Factorization

arXiv

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arXiv 2022年

作者： Cao, Jian Qian, Chen Huang, Yihui Chen, dicheng Gao, Yuncheng dong, Jiyang Guo, di Qu, Xiaobo Department of Electronic Science Biomedical Intelligent Cloud R&D Center Fujian Provincial Key Laboratory of Plasma and Magnetic Resonance Xiamen University Xiamen361005 China School of Computer and Information Engineering Fujian Engineering Research Center for Medical Data Mining and Application Xiamen University of Technology Xiamen361024 China

Implicit regularization is an important way to interpret neural networks. recent theory starts to explain implicit regularization with the model of deep matrix factorization (dMF) and analyze the trajectory of discrete gradient dynamics in the optimization process. These discrete gradient dynamics are relatively small but not infinitesimal, thus fitting well with the practical implementation of neural networks. Currently, discrete gradient dynamics analysis has been successfully applied to shallow networks but encounters the difficulty of complex computation for deep networks. In this work, we introduce another discrete gradient dynamics approach to explain implicit regularization, i.e. landscape analysis. It mainly focuses on gradient regions, such as saddle points and local minima. We theoretically establish the connection between saddle point escaping (SPE) stages and the matrix rank in dMF. We prove that, for a rank-r matrix reconstruction, dMF will converge to a second-order critical point after r stages of SPE. This conclusion is further experimentally verified on a low-rank matrix reconstruction problem. This work provides a new theory to analyze implicit regularization in deep learning. Copyright © 2022, The Authors. All rights reserved.

关键词： deep learning

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Individual Intelligent recognition Method Based on Fingerprint Features of radar Emitter

Individual Intelligent Recognition Method Based on Fingerpri...

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IEEE International Conference on radar

作者： Zhongwei Liu Hongwei Gao Jie Chen dongming Zhou Yingchun Li Shuyan Sun rongrong Xiang School of Electronic and Information Engineering Beihang University Beijing China Radar R&D Center Beijing Institute of Radio Measurement Beijing China Department of Data Technology and Application Beijing Institute of Computer Technology and Application Beijing China

ISBN: (纸本)9781665468893

Aiming at the problem of radar emitter individual recognition with unintentional modulation feature, a method of emitter fingerprint identification based on deep learning is proposed. In this method, the deep fingerprint features of radar emitter are extracted by deep Convolutional neural network, and the fingerprint feature library is constructed. Through similarity calculation, the individual recognition results of radar emitter signal are obtained. The results show that the new method can effectively identify both known and unknown emitter individuals.

关键词： deep learning Modulation radar Fingerprint recognition Feature extraction Libraries Convolutional neural networks

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Beyond reality: Crafting Logical Systems for Autonomous Vehicles through Virtual reality

Beyond Reality: Crafting Logical Systems for Autonomous Vehi...

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Communication & Computing (WCONF), World Conference on

作者： N.K. Senthil Kumar Sumalatha A dhivyabharathi r K. Suresh Kumar A. Hency Juliet S. rajarajeswari Department of CSE School of Computing Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Chennai Tamil Nadu India Department of Computer Science Kristu Jayanti College Bengaluru Karnataka India Department of Computer Science and Engineering S.A. Engineering College Chennai Tamil Nadu India Department of MBA Panimalar Engineering College Chennai Tamil Nadu India Department of Data Analytics Saveetha College of Liberal Arts and Sciences Saveetha Institute of Medical and Technical Sciences Saveetha University Chennai Tamil Nadu India Department of Science and Humanities Prince Shri Venkateshwara Padmavathy Engineering College Chennai Tamil Nadu India

ISBN: (数字)9798350395327

ISBN: (纸本)9798350395334

The current paradigm for advancing autonomous driving functionalities on public roads predominantly operates within the tangible realm. Autonomous vehicles (AVs) are equipped with an array of sensors, including Lidar, cameras, radar, GPS, and on-board-unit (OBU) modems, each actively collecting data about the surrounding environment. This data is then processed through sophisticated algorithms, enhancing the vehicle's situational awareness. In our pursuit of refining autonomous driving capabilities, we leverage the AVTSC Simulator as our chosen Autonomous Vehicle (AV) simulation environment. Notably, this simulator stands out for its ability to replicate realistic urban settings and seamlessly integrate with widely used platforms such as Apollo and rOS. By harnessing the capabilities of this simulation environment, we aim to bridge the gap between real-world testing and simulated scenarios. This abstract encapsulates the essence of our strategy, emphasizing the integration of advanced sensor technologies and simulation environments for the development of autonomous driving features. Preliminary results indicate a promising 94% success rate for the proposed methodology, showcasing the efficacy of our approach in enhancing the robustness and reliability of autonomous systems.

关键词： Laser radar roads Virtual reality Modems Cameras robustness Autonomous vehicles Testing Global Positioning System Sensor arrays

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Enhancing Biomedical Image Interpretation Through a Hybrid Machine Learning Algorithm

Enhancing Biomedical Image Interpretation Through a Hybrid M...

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International conference of Electronics, Communication and Aerospace technology (ICECA)

作者： r rajkumar Lakshmi Namratha Vempaty r Thiyagarajan Celine Kavida A Harshal Hemane Vijayakumar S Department of Electronics and Communication Engineering Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Chennai Tamilnadu India Lead Data Scientist Avis Budget Group - Decision Technology and Business Intelligence New York University NY USA Department of Biomedical Engineering Shreenivasa Engineering college Dharmapuri Department of Physics Vel Tech Multi Tech Dr Rangarajan Dr. Sakunthala Engineering College Chennai India Bharati Vidyapeeth (Deemed to be University) College of Engineering Pune India Department of Electronics and Communication Engineering Paavai Engineering College Namakkal Tamil Nadu India

In contemporary medicine, biomedical image interpretation is essential for disease diagnosis and the selection of appropriate treatments. However, manually scrutinizing these images is time-consuming and may lead to erroneous conclusions. The proposed work offers a novel approach to resolving these issues by utilizing a mixed machine-learning technique to improve the interpretation of biological images. The proposed system accurately evaluates physical images using machine learning techniques, including convolutional neural networks and decision trees. The algorithm aims to integrate the most beneficial aspects of multiple image analysis methods to enhance their overall performance. Using a carefully selected dataset, demonstrate the algorithm's precision and robustness compared to other approaches. The findings imply that the algorithm could substantially alter the interpretation of biological images in clinical and academic settings. This discovery has far-reaching implications, paving the way for improved diagnostic precision and further study of the human body. This hybrid method is a promising step toward automating image processing and paves the way for new research and implementation opportunities in healthcare technology.

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Fuzzy Logic Controlled Photovoltaic system with IoT technology

Fuzzy Logic Controlled Photovoltaic system with IoT Technolo...

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International Conference on Smart Technologies For Smart Nation (SmartTechCon)

作者： L. M. Merlin Livingston Elangovan Guruva reddy K S rajesh M. Muthulekshmi E. N. Ganesh Professor Department of Electronics and Communication Engineering Jeppiaar Institute of Technology Chennai Tamil Nadu India Department of Artificial Intelligence and Data Science Koneru Lakshmaiah Education Foundation Vaddeswaram Andhra Pradesh India Assistant Professor Department of Electrical and Electronics Engineering Madanapalle Institute of Technology & Science Madanapalle Andhra Pradesh India Adjunct Professor Department of Biomedical Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Chennai Tamil Nadu India Director R&D Department of Electronics and Communication Engineering St. Peters Institute of Higher Education and Research (SPIHER) Avadi Chennai Tamil Nadu India

due to its affordability and environmental sustainability, solar photovoltaic (PV) systems are gaining popularity as a source of renewable energy. These types of systems, however, deal with a number of issues, such as insufficient energy efficiency, unstable systems, and poor weather adaptation. Conventional control techniques, which are employed to monitor and manage PV systems, have drawbacks, including inadequate data processing capacity and not enough scalability. The Internet of Things (IoT) and fuzzy logic control (FLC) are used in this paper's unique technique to improve PV system monitoring and control in order to meet these difficulties. The FLC technique enhances the PV system output voltage and attains the maximum power point (MPP). Using FLC method, PV systems can be controlled more precisely and effectively, which boosts system stability and energy efficiency. Benefits of the suggested approach include increased flexibility and scalability to various PV system configurations, decreased energy costs, and decreased carbon emissions. This method could assist with the design of more environmentally friendly and energy-efficient systems and has potential uses in commercial as well as residential PV systems.

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Early detection of Lung Cancer Using deep Learning-Based Analysis of CT Scans

Early Detection of Lung Cancer Using Deep Learning-Based Ana...

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International Conference on Smart Structures and Systems (ICSSS)

作者： d. Muthukumaran rabnawaz Ahmad Sajja Suneel K. Laxman Kumar Sheetal Mujoo Syed Noeman Taqui Vel Tech Rangarajan Dr.Sagunthala R&D Institute of Science and Technology Chennai Tamil Nadu Department of Mechanical Engineering Government Engineering College Jehanabad Department of CSE(Data Science) Institute of Aeronautical Engineering Hyderabad Telangana India Department of CSD St Martin's Engineering College Hyderabad Telangana Department of Maxillofacial Surgery and Diagnostic Sciences College of Dentistry Jazan University Jazan Saudi Arabia Department of VLSI Microelectronics Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Chennai Tamil Nadu India

The advanced stage at which most occurrences of lung cancer (LC) are detected results in a dismal prognosis and few available therapeutic options for the disease. The ability to diagnose lung cancer at an early stage is an important clinical and scientific emphasis since it can greatly improve patient outcomes. recent advances in deep learning have made it possible to use these methods effectively on medical images, which might improve the speed and precision with which lung cancer is diagnosed. Using deep learning to analyze computed tomography (CT) scans, this study introduces a unique method for the early diagnosis of lung cancer. The proposed method leverages a combination of two deep neural network architectures, UNET and LeNet, to achieve robust and accurate lung cancer detection. The UNET architecture is employed for semantic segmentation of lung regions from CT scans, allowing for precise localization and isolation of potential cancerous lesions. Subsequently, the LeNet architecture is applied to classify the segmented regions as either benign or malignant. This two-stage approach enables the model to identify lung cancer with high accuracy while minimizing false positives. Based on the results of this study, combining UNET and LeNet for CT scan analysis may result in an accuracy of 97.89%, providing doctors with a useful tool to enhance patient outcomes through prompt intervention.

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Application of deep Neural Networks for Weed detection and Classification

Application of Deep Neural Networks for Weed Detection and C...

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Applied Artificial Intelligence and Computing (ICAAIC), International Conference on

作者： r. Ganesan G. Manjula Madhavarapu Chandan I. Thamarai S. rajavelu Upendra Mohan Bhatt Swagata Sarkar Department of Computer Science and Engineering Vel Tech RangarajanDr.Sagunthala R&D institute of science and Technology Chennai Tamil Nadu India Department of Computer Science and Engineering RMK College of Engineering and Technology Thiruvallur Tamil Nadu India Department of Computer Science Engineering Koneru Lakshmaiah Education Foundation Vaddeswaram Andhra Pradesh India Department of Computer Science and Engineering Panimalar Engineering College Chennai city campus Tamil Nadu India Department of Physics PSNA College of Engineering and Technology (Autonomous) Dindigul Tamil Nadu India Department of Computer Science and Engineering Graphic Era Deemed to be University Dehradun Uttarakhand India Department ofArtificial Intelligence and Data Science Sri Sairam Engineering College Chennai Tamil Nadu India

Weeds compete for natural resources both in forest areas, harming the development of native vegetation, and in agricultural areas, affecting crop quality. The need then arises to classify these species, so that mechanical or chemical methods can be applied appropriately to contain the pests. This research presents the application and comparison of machine learning techniques, with the aim of automating the classification of images for agricultural challenges, such as the detection of defective seeds, and weeds and the category between these and native vegetation, while finally, the architecture of a convolutional neural network is presented. As a differential, the network's self-learning ability stands out, as images are captured in less than ideal conditions at varying heights and lighting levels in most cases. This research is expected to provide important information on artificial intelligence techniques that can be used in the classification of weed images, a factor that will contribute to the forestry and agricultural sector.

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Performance analysis of a novel hybrid deep learning approach in classification of quality-related English text

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Measurement: Sensors 2023年 28卷

作者： Orosoo, Myagmarsuren Govindasamy, Santhakumar Bayarsaikhan, Narmandakh rajkumari, Yaisna Fatma, Gulnaz Manikandan, r. Kiran Bala, B. School of Humanities and Social Sciences Mongolian National University of Education Mongolia Department of Electronics and Communication Engineering Sri Krishna College of Technology Tamil Nadu Coimbatore India Theory and Methodology of Physical Education Mongolian National University of Education India Dept of Humanities and Social Sciences NIT Hamirpur India Dept of English Language Center Jazan University Jazan Saudi Arabia Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Avadi Tamil Nadu Chennai600062 India Department of Artificial Intelligence and Data Science K.Ramakrishnan College of Engineering Tamil Nadu Trichy India

Text classification technique is advancing rapidly alongside AI technology, showing signs of maturity. Moreover, there are always many unrestricted constraints that text classification must deal with in practical settings. English text is indeed a significant component of textual data and a significant source of data for persons seeking data from other countries. This study enhances the text classification method currently in use using text classification depending upon English quality. By using an illustration of English quality-related text classification systems, the concept as well as execution of text classification systems is demonstrated, concluding the study on text classification algorithms. The main task of this article is to categorize, and analyze huge volumes of information in English text using technique of integrating qualitative. Therefore, the fundamental components of superior English compositions are attained using Lion Optimization Algorithm (LOA). Convolutional Neural Network-Long Short Term Memory (CNN-LSTM) is utilized to classify the obtained texts. If there is a lot of training input, the typical English text classification method can easily display flaws like ambiguous characteristic elements. In light of such issues, the research suggests a quality-related English text classification approach based on convolutional neural network in order to enhance the precision and adaptability of English text classification. © 2023

关键词： Long short-term memory

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