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检索条件"任意字段=International Conference on Trends in Automation, Communications and Computing Technology"

共 3241 条记录，以下是21-30 订阅

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CampusSight—College Management automation Using Full Stack and RPA 12th

CampusSight—College Management Automation Using Full Stack ...

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12th international conference on Recent trends in computing, ICRTC 2024

作者： Manohar, Eashaan Duraimurugan, N. Vijay, K. Narayana, K.E. Department of Computer Science and Engineering Rajalakshmi Engineering College Chennai India Department of Artificial Intelligence and Machine Learning Rajalakshmi Engineering College Chennai India Department of Information Technology Rajalakshmi Engineering College Chennai India

ISBN: (纸本)9789819789450

The college management diaspora encapsulates various stakeholders such as students, faculty members, administrative personnel, and other staff. Unfortunately, most of the management workflows are undertaken manually, leading to silos, long paper work and admin overhauls. The pivotal aim of this research project is to digitalize the college management by bringing the entire setup into an intelligent application, leveraging key technologies like automation, data analytics, full stack development. This application ultimately streamlines cumbersome tasks and enhance working across the college environment, promoting transparency and facilitating seamless interfaces for the entire user base. Swapping the monotonous manual workings with state-of-art digital platform will elevate the overall student experience by bringing down the high latency issue with the manual undertakings. Prime features of the application features management in terms of attendance, absences, on-duty requests, result disbursement, course registration, and facilitation of placement processes. Also, the system will promote enhanced organizing of events related to campus, industrial visits, increasing the net student engagement within the college and opening new opportunities. The system prioritizes core functionalities such as maintaining clean design and nimble experience, aligning with the increasing trend of digital transformation, granting greater college management autonomy. The application not only brings down the complexities prevailing in the existing college administration but also helps the higher education and placement processes alike, by leveraging key concepts like Data Analysis, Machine Learning and Artificial Intelligence from the data and insights stored, thereby adopting agile college management practices and emphasizing rapid digital growth, contributing to the entire education community. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Students

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Analysis of Energy Consumption Using Support Vector Regression 3rd

Analysis of Energy Consumption Using Support Vector Regress...

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3rd international conference on Emerging Electronics and automation, E2A 2023

作者： Pavan Kumar, V. Likhith Reddy, G. Mahadev, H. Harish Kumar, V. Krishna Prasad, K. Computer Science and Engineering B V Raju Institute of Technology Telangana Narsapur India

ISBN: (纸本)9789819730896

Energy consumption analysis is essential for maximizing energy use and improving energy efficiency across a range of applications. In this study, we suggest a unique method for analyzing energy use based on real-time data from an energy meter. The information gathered comprises the voltage, current, power, and overall energy consumption’s root mean square (RMS) values. We use the Support Vector Regression (SVR) model, a potent machine learning method known for its ability to handle non-linear connections and high-dimensional data, to reliably forecast future energy usage. A popular interactive computing tool called Jupyter Notebook has the SVR model incorporated, making experimentation and analysis simple. Our proposed methodology proves its effectiveness in anticipating future energy use through testing and evaluation. The SVR-based prediction model’s findings offer useful insights into trends in energy consumption, empowering users to make deft choices that would optimize energy use, cut down on waste, and enhance overall energy sustainability. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Support vector regression

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An AlexNet Model for Diagnosing Left Ventricular Hypertrophy Using ECG Signals 12th

An AlexNet Model for Diagnosing Left Ventricular Hypertrophy...

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12th international conference on Recent trends in computing, ICRTC 2024

作者： Ryakala, Deepika Balaji Srikaanth, P. Department of Networking and Communications SRM Institute of Science and Technolgy College of Engineering and Technology Kattankulathur India

ISBN: (纸本)9789819789450

Left ventricular hypertrophy represents the thickened wall condition related to the main pumping chamber of the heart which wither results in blood pressure elevation within the heart or poor action of blood pumping at certain times. The existing methods which contributed to the literature for diagnosing left ventricular hypertrophy using electrocardiogram (ECG) are determined to confirm a comparatively low sensitivity and classification accuracy. Deep learning (DL) approaches which are capable in attaining feature extraction automatically from ECG signals are determined to be potent in detecting cardiac diseases. In this paper, an AlexNet-based deep learning model is proposed for effective and rapid diagnosis of left ventricular hypertrophy using 12 lead ECG. This methodology initially removed the baseline drift and fine noise by denoising signals using discrete wavelet transformation. Then AlexNet model is applied dedicatedly by training and testing 90 and 10% of the data and determining the patterns from the samples and identify unknown patterns. The experimental investigation of the proposed AlexNet-based deep learning model confirmed better sensitivity of 97.21%, superior specificity of 96.79%, and maximized classification accuracy of 99.12%, compared to the CNN-LSTM, CNN-LSTM-AM and ResNet-based left ventricular hypertrophy diagnosis models used for comparison. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Heart

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Constructing a Hierarchical Model Based on the Blockchain in Health Care 5th

Constructing a Hierarchical Model Based on the Blockchain in...

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5th international conference on computing, communications, and Cyber Security, IC4S 2023

作者： Singh, Ravendra Singh, Anupam Singh, Raj Pal Department of Computer Science and Engineering Pranveer Singh Institute of Technology Kanpur India Department of Computer Science and Engineering Graphic Era Hill University Uttarakhand Dehradun India Department of Computer Science and Engineering Graphic Era Deemed to be University Uttarakhand Dehradun India

ISBN: (纸本)9789819773701

Building a hierarchical conceptual structure for smart health care, identifying the effects of distributed ledger technology on clever medical treatment, and then developing a system of applications for smart health care via blockchain that utilizes the theory of involvement of stakeholders are the goals of the research. However, besides the essential characteristics and the connections between different features and characteristics, such a hierarchy conceptual framework should also consider the involvement of several stakeholders. The study offers decision-making including analytical lab (DEMATEL) to control the intricate relationships between various features and attributes. Finally, a hierarchical conceptual framework is created by splitting the hierarchy employing Interpretive Structural Modeling (ISM). The results show that the top-level organization, medical records leadership, and general doctor management are among the system's core reasons. A specific application of blockchain in the field of healthcare automation requires a decentralized agreement that depends on how health information is handled within system constraints. The secret to updating a system is application optimization. Environmental control, health insurance, and internal and external regulation all contribute to the system's development and the successful protection of stakeholder interests. The financing layer, data layer, as well as stakeholder layer must form the basis of a blockchain-based application structure for smart health care. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Decentralized finance

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Evolution and Future trends in Wireless Communication: From 2G to 6G Technologies

Evolution and Future Trends in Wireless Communication: From ...

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Computational Intelligence, Communication technology and Networking (CICTN), international conference on

作者： Deepanshu Goel Deepanshu Jaiwal Dhruv Taruna Chopra School of Computer Applications Manav Rachna International Institute of Research & Studies Faridabad India

ISBN: (数字)9798331530389

ISBN: (纸本)9798331530396

The journey from 2G to emerging 6G is impressive, mirroring the long evolutionary path of wireless communication technology. We address critical transitions in cellular networks in light of architectural transformation, performance metrics, and the enablers of new technologies. Key trends identified: from Kbps data rate growth exponentially in 2G to Tbps in 6G; a fall of latency from the 500-ms level to sub-millisecond latency; and more importantly, from human-centric communication to machine-centric communication. It will bring some breakthrough technologies, like terahertz communications, intelligent reflecting surfaces, and quantum computing integration, revolutionizing the network configuration and capability. 6G networks will highlight many unprecedented applications such as holographic communications and extended reality; however, that is not possible without addressing various challenges related to spectrum efficiency, energy optimization, and security challenges. This report synthesized technical literature, standardization documents, and industry insights to propose a roadmap of future wireless networks with innovative and AI-integrated spectrum utilization concepts and sustainable designs. The results are critical to researchers, policymakers, and industry stakeholders in the shaping of the next generation of wireless communication systems.

关键词： 6G mobile communication Industries Measurement Quantum computing Wireless networks Standardization Market research Security Stakeholders Terahertz communications

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Innovative Technological Transformation in Mining Industry: Sustainable Outlook and Futuristic Approach

Innovative Technological Transformation in Mining Industry: ...

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Intelligent Control, computing and communications (IC3), international conference on

作者： Kalpana Bhandari Rakesh Kumar Shashank Awasthi Manas Kumar Mishra Pooja Devi Department of Higher Education Government Degree College Haldwani City Nainital India Department of management Studies Graphic Era Deemed to be Universiity Dehradun India G.L. Bajaj Institute of Technology and Management Greater Noida India Uttaranchal Institute of Management Uttaranchal University Dehradun India

ISBN: (数字)9798331527495

ISBN: (纸本)9798331527501

Innovation is crucial in the mining industry for improving efficiency, reducing costs, and addressing social and environmental concerns. Technological advancements enable exploitation of new deposits in complex scenarios. This paper identifies current digital transformation and key technologies in the mining industry. And identifies Mining's future is being shaped by technological trends such as continuous mining, invisible zero-waste mining, electro mobility, and digital transformation (DT). To improve processes across the value chain, DT involves implementing technologies 4.0, such as automation, robotics, and remotization of activities for providing orientation points for future digital transformation processes. In this study is qualitative data is used from several Scopus based journal and conferences papers. This paper also includes technological challenges faced by mining industry. On behalf of several studies, we also explore few analyses and recommendations also for smooth running of mining industry.

关键词： Productivity Technological innovation Costs Service robots Digital transformation Companies Market research Safety Data mining Mining industry

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Incentivizing Crowdsensing for DT-Enabled Metaverse 20th

Incentivizing Crowdsensing for DT-Enabled Metaverse

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20th IFIP WG 10.3 international conference on Network and Parallel computing, NPC 2024

作者： Ye, Dongdong Huang, Xumin Wu, Yuan Kang, Jiawen Zhong, Weifeng Niyato, Dusit School of Automation Guangdong University of Technology Guangzhou China State Key Laboratory of Internet of Things for Smart City University of Macau Taipa China Department of Computer and Information Science University of Macau Taipa China Collge of Computing and Data Science Nanyang Technological University Singapore Singapore

ISBN: (纸本)9789819628292

Digital Twin (DT) has been proposed as a key enabling technology to produce a digital clone of the physical world and facilitate the convergence of the physical and virtual worlds for building a real-world Metaverse. For physical-virtual synchronization, crowdsensing is applied to employ Internet of Things (IoT) devices to sense physical objects and collect status information for timely updating the associated DTs. However, an incentive mechanism is necessitated to stimulate the devices to perform sensing tasks while meeting task requirements such as Age of Information (AoI) requirements. Thus, we adopt a contract theoretic approach to design an incentive mechanism for the devices that are classified into several types and the type parameters are unknown to others. The contract consists of a set of contract items with each contract item representing a device type, which specifies the required data updating frequency and monetary reward. We formulate an optimal contract design problem subject to feasible constraints including the average AoI requirement for each device type. Furthermore, we solve the problem by transforming it into a convex optimization problem that can be directly tackled by the existing methods. Finally, we provide numerical results to demonstrate the effectiveness and efficiency of our scheme. © IFIP international Federation for Information Processing 2025.

关键词： Convex optimization

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Leveraging Machine Learning for Dynamic Resource Allocation in Cloud automation

Leveraging Machine Learning for Dynamic Resource Allocation ...

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automation and Computation (AUTOCOM), international conference on

作者： Gunveen Ahluwalia Vaibhav Goutham Suresh M D Anto Praveena Kalyan Acharjya Trushna Paridaname Centre of Research Impact and Outcome Chitkara University Rajpura Punjab India School of Mangement - PG Management JAIN (Deemed to be University) Bangalore Karnataka Department of Computer Science and Engineering Sathyabama Institute of Science and Technology Chennai Tamil Nadu India Maharishi School of Engineering & Technology Maharishi University of Information Technology Uttar Pradesh India Department of Computer Science and Engineering Siksha ‘O’ Anusandhan (Deemed to be University) Bhubaneswar Odisha India

ISBN: (数字)9798331542375

ISBN: (纸本)9798331542382

Cloud automation enables the dynamic management and allocation of resources in a computing environment and is one of the most common uses available. Yet despite their power when cloud systems were simple and small, traditional approaches to allocating resources have proven far less feasible lately. This scenario has also created the need for new evolution methods that will adapt to dynamic cloud environments and improve the system's overall performance. A solution to this challenge comes from machine learning, which has been increasingly used recently. ML algorithms can process vast amounts of data and identify patterns and trends to predict resource use and user needs. Deep learning helps cloud-enabled automation systems adapt in real-time to changing conditions, distribute resources efficiently and improve user experience. For example, reinforcement learning and deep learning are ML techniques that can be applied to resource allocation algorithms or demand prediction models to increase accuracy. Integrating cloud automation and machine learning brings effectiveness in dynamic resource allocation, boosting the overall performance of a system, which leads to savings, better experience, and greater flexibility in using cloud resources. Reinforcement and deep learning are machine learning techniques to optimize order allocation algorithms and enhance demand estimation models.

关键词： Deep learning Cloud computing automation Machine learning algorithms Heuristic algorithms Estimation Dynamic scheduling Prediction algorithms Real-time systems Resource management

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Using Convolutional Neural Networks for Image Recognition in IoT Applications

Using Convolutional Neural Networks for Image Recognition in...

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Pervasive Computational Technologies (ICPCT), international conference on

作者： Durgam Rajababu Sampenga Veerraju K. Suresh Kumar Sonali Gupta V B Thurai Raaj Rahul Joshi Department of EEE School of Engineering SR University Warangal Telangana India Department of Electronics and Communications Engineering Dadi Institute of Engineering and Technology Anakapalli Visakhapatnam Andhra Pradesh MBA Department Panimalar Engineering College Varadarajapuram Poonamallee Chennai J C Bose University of Science and Technology YMCA Faridabad Electrical and Electronics Engineering Madanapalle Institute of Technology & Science Madanapalle Andhra Pradesh Department of Journalism & Mass Communication SMeH Manav Rachna International Institute of Research and Studies Haryana India

ISBN: (数字)9798331508685

ISBN: (纸本)9798331519476

Convolutional Neural Networks (CNNs) have grown into a powerful picture identification tool. They are an important part of making Internet of Things (IoT) apps. In this regard, CNNs make it possible to analyse visual data gathered from networked devices, like cameras and sensors, effectively and accurately. With an emphasis on its potential for real-time applications for example smart surveillance, healthcare monitoring, autonomous cars, and industrial automation, this study investigates the incorporation of CNN-based image identification into IoT contexts. We tackle important issues such as the requirement for low-latency processing, energy economy, and computing limitations on edge devices. In order to maximise CNN performance in the resource-constrained IoT ecosystem, strategies like model compression, edge computing, and distributed architectures are covered. The ability to interpret large volumes of visual data is improved by the integration of CNNs with IoT, providing creative solutions for automation and intelligent decision-making across numerous industries.

关键词： Training Visualization automation Computational modeling Surveillance Training data Feature extraction Convolutional neural networks Internet of Things Image classification

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RFID based Smart Trolley Billing System using IoT

RFID based Smart Trolley Billing System using IoT

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international conference on ent computing and Control Systems (ICICCS)

作者： Manikandan S Harish Murugan A Kamal S Udhayakumar S Yokesh D Department of ETE Karpagam College of Engineering Coimbatore India

ISBN: (数字)9798331512088

ISBN: (纸本)9798331512095

Customers expect to experience better retail interactions while operations become more efficient in the modernizing retail industry. Shopping through traditional methods requires lengthy manual scanning for products and billing operations leading retailers to require automation solutions for streamlining these processes. It is not bad thing to mention about but why going a lengthy process when technology is all we have implement to make it better with a help of designed system. This system consists of RFID detection alongside 8051 microcontroller processing and load cells monitoring and uses IoT communications and relay controls to power an intelligent automated shopping cart. Real-time data tracking functions alongside cloud service readiness to deliver advanced monitoring and management capabilities as the system's standout feature. A Smart Trolley consumes RFID technology to recognize products that go inside the cart through unique product tags linked with RFID. Product details that the 8051 microcontroller retrieves are displayed through an LCD screen interface. The system delivers automated operations together with precise product tracking along with weight measurement functionality and billing support. Data transmission to the cloud enables distant storage functions along with distant system monitoring that produces meaningful information about trolley utilization while bolstering inventory handling capabilities. The system allows product tracking and billing automation and IoT integration for data management which enables its deployment into multiple retail settings to deliver enhanced customer service alongside operational efficiency. It is an attempt to make the shopping experience lighter and making the automation process a part of the shopping Eco system and to interpreted much more easily than with the trouble of going through all lengthy billing process by also ensuring the safety and security of the purchased product and also to neglect the idea of the

关键词： Weight measurement Cloud computing automation Microcontrollers Smart cities Real-time systems Security Monitoring Radiofrequency identification Ultra wideband technology

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