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检索条件"任意字段=3rd IEEE Intelligent Data Acquisition and Advanced Computing Systems"

共 1251 条记录，以下是21-30 订阅

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Smart Servo-Operated Ball Valve with IoT Integration and PID Control for Precise Fluid Management 3

Smart Servo-Operated Ball Valve with IoT Integration and PID...

引用

3rd International Conference on intelligent and Innovative Technologies in computing, Electrical and Electronics, IITCEE 2025

作者： Bhanuse, Vijaykumar Bhosale, Shreyash Nitin Bhilare, Abhishek Mahadev Bauskar, Aneesh Rajesh Vishwakarma Institute of Technology Pune411037 India

ISBN: (纸本)9798331515911

This paper presents the design and implementation of an innovative control valve system that integrates a ball valve run by a servo motor, IoT capabilities, and PID control for precise operation. The system uses a 1/4-inch ball valve mounted in a custom 3D printed housing, driven by an MG995 servo motor. An ESP32 WROOM module microcontroller enables Wi-Fi connectivity and data transmission to the cloud via the Thingspeak platform. Ultrasonic sensors are used for tank level measurement, and the valve operation is controlled using a PID algorithm implemented in C++ on the Arduino IDE. The PID parameters are fine-tuned to achieve best performance. This solution offers a cost-effective and precise method for fluid control in various industrial applications. © 2025 ieee.

关键词： Three term control systems

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Integrating Big data Analytics in Soil Quality Assessment: Techniques, Challenges, and Applications 3

Integrating Big Data Analytics in Soil Quality Assessment: T...

引用

3rd International Conference on intelligent and Innovative Technologies in computing, Electrical and Electronics, IITCEE 2025

作者： Mohammed, Shehu Singh, Arun Hanif, Md Abu Lovely Professional University Department of Computer Applications Phagwara India Lovely Professional University Department of Computer Science and Engineering Phagwara India Department of Computer Science 365 5th Ave New YorkNY110016 United States

ISBN: (纸本)9798331515911

This review paper investigates the application of Big data analytics, focusing on soil quality assessment, with an emphasis on the innovative benefits offered by modern data-driven techniques for improved soil management. With world food demands rising, the importance for real-time and exact soil quality monitoring has increased. Although still useful, traditional approaches to modeling are limited in their ability to capture the spatial and temporal variability of soil properties. Big data analytics sits as the answer to the above by facilitating extensive and large volume data processing resulting in better, on-demand and scalable soil quality assessment. In the paper, we describe machine learning algorithms, remote sensing and geospatial analysis as key techniques to mine soil data at scales and resolutions unprecedented so far. We also critically evaluate the challenge of data integration (heterogeneity, scalability and multi-disciplinarity). These analytics are scalable for assessment and decision-making in precision agriculture, sustainable land management and environmental conservation. Thus, this paper synthesizes current research and practical implications in support of the need for Big data analytics within soils and soil sciences to cover also future challenges. Future research directions are also highlighted, including the importance of improved data acquisition and algorithmic advances as well greater synergy between Big data and conventional soil science practices.. © 2025 ieee.

关键词： data quality

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Load balancing promotes effective IoT-based data transfer in Snowflake Storage systems 3

Load balancing promotes effective IoT-based data transfer in...

引用

3rd International Conference on intelligent and Innovative Technologies in computing, Electrical and Electronics, IITCEE 2025

作者： Jesupriya, J. Kavitha, M. Deeksha Presidency University India Presidency College Bengaluru India

ISBN: (数字)9798331515911

ISBN: (纸本)9798331515911

In networked devices, efficient data transportation is important for guaranteeing dependable and scalable Internet of Things (IoT) infrastructures. For this purpose, in the current work, a dynamic load-balancing mechanism is proposed to optimize the data transfer between Internet of Things nodes and Snowflake data storage. It may be fairly distributing the distributed data between IoT nodes, but traditional round-robin load balancing often overlooks the difference in node performance, which may lead to inefficient data transfer. A solution to this problem is a dynamic load-balancing mechanism that depends on nodes in real-time performing, hence its variation with transfer speed and latency. This system would dynamically select the most effective nodes that are to transmit the data and would ensure quicker and more dependable delivery of information. In the following simulation, round-robin and dynamic load-balancing strategies are compared using ten IoT nodes that are sending segmented data to a Snowflake server. Outcomes: These show that although the round-robin accomplishes reduced latency and faster transfer rates, the dynamic approach presents a fairer distribution of the load as well as reduces the possibility of node overloading while enhancing overall system robustness in the long term. This work adds to the future prospects of IoT load-balancing solutions, thus vividly showing how round-robin and dynamic approaches could present trade-offs between one another. © 2025 ieee.

关键词： Snow melting systems

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RFID Door Lock Access Control systems: Trends, Technologies and Applications 3

RFID Door Lock Access Control Systems: Trends, Technologies ...

引用

3rd International Conference on intelligent data Communication Technologies and Internet of Things, IDCIoT 2025

作者： Nair, Adwaith A. Adithyan, R. Unni, Akshara Nalinakshan, Shankar Department of Electrical and Electronics Engineering Amrita Vishwa Vidyapeetham Amritapuri India

ISBN: (纸本)9798331527549

Radio Frequency Identification (RFID) door lock systems are at the forefront of modern access control technology, combining security, convenience, and scalability. This paper presents a comprehensive review of RFID-based access control systems, highlighting their fundamental components, operational mechanisms, and advantages over traditional methods. Key innovations in RFID technology, such as integration with IoT platforms and mobile devices, are discussed in the context of enhancing system functionality and user experience. Moreover, this paper examines the challenges associated with RFID implementations, including data security vulnerabilities, signal interference, and high installation costs, while suggesting potential solutions. Through this analysis, the paper highlights the growing relevance of RFID access systems in residential, commercial, and industrial environments, paving the way for further advancements in secure and intelligent access control *** research identifies key challenges in traditional access control systems, such as susceptibility to breaches, lack of scalability, and operational inefficiency. RFID door lock systems emerge as an advanced solution, integrating wireless communication, IoT platforms, and mobile devices to address these limitations. This study presents an in-depth analysis of RFID systems, highlighting their components, operational mechanisms, and comparative advantages. Considering issues such as vulnerability to data security, interference signals, and a high price tag for the installation, this paper suggests possibilities with solutions of viable, secure, scalable, and intelligent access control systems. Hence, the transformative RFID technology into a residential, commercial, or industrial domain holds an immense study area. © 2025 ieee.

关键词： Radio frequency identification (RFID)

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Hybrid Approach for Electricity Price Forecasting using AlexNet and LSTM 3

Hybrid Approach for Electricity Price Forecasting using Alex...

引用

3rd International Conference on intelligent data Communication Technologies and Internet of Things, IDCIoT 2025

作者： Sambana, Bosubabu Devi, Kotamsetty Geethika Reddy, Bandi Rajeswara Hussain, Galeti Mohammad Siddartha, Gownivalla School of Computing Mohan Babu University Tirupati India Department of Computer Science and Systems Engineering Sree Vidyanikethan Engineering College Tirupati India

ISBN: (纸本)9798331527549

The recent development of advanced machine learning methods for hybrid models has greatly addressed the need for the correct prediction of electrical prices. This method combines AlexNet and LSTM algorithms, which are used to introduce a new model with higher accuracy in price forecasting. Despite RNN and ANN being effective, they often fail to deal with forex time sequence data. The traditional methods do not accurately forecast the prices. These traditional methods only focus on demand and price which leads to insufficient analysis of data. To address this issue, using the hybrid approach, which focuses on external variables that also effect the predicted prices. Nevertheless, due to AlexNet's excellent feature extraction and LSTM's learning sequential patterns, the prediction accuracy is vastly increased. The model is built on the past data, which has been supplied with the most significant elements like demand, temperature, sunlight, and rain. For example, the model applies methods, such as minimum-maximum scaling and a time window, to predict the electricity prices of the future. The results show that this hybrid model is good than the standalone ones in terms of accuracy. Although we got our accuracy rating of 97.08, it shows higher accompaniments than remaining models RNN and ANN with accuracies of 96.64 and 96.63 respectively. © 2025 ieee.

关键词： Prediction models

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Multilevel Discrete Cosine Transform and CNN-Based Image Steganography 3

Multilevel Discrete Cosine Transform and CNN-Based Image Ste...

引用

3rd International Conference on intelligent systems, advanced computing, and Communication, ISACC 2025

作者： Subburaj, Brindha Sai, Aditya Shantanu, Shantanu Won, Daehan Vellore Institute of Technology School of Computer Science and Engineering Chennai India Binghamton University Department of Systems Science and Industrial Engineering United States

ISBN: (纸本)9798331523893

This paper investigates the growing role of steganography in cybersecurity and presents a hybrid implementation called Multi-level Discrete Cosine Convolution (MDCC) that applies a Multi-level Discrete Cosine Transform (MDCT) and a Convolutional Neural Network (CNN). Steganography provides some advantages compared to cryptography, mainly its invisibility, but there are weaknesses in information capacity and resistance to attacks in traditional techniques. The paper suggests solving these difficulties with a new steganography method using deep learning. The proposed MDCC method provides an opportunity to encrypt large amounts of data in realistic images that are beyond human visual perception. The system is trained on the ImageNet dataset by using a loss function to balance the quality of the cover image and the information it conceals within it. The MDCC model proposed yields accurate results for all metrics since PSNR, SSIM, and bits per pixel come out to be 80.13dB, 0.9918, and 12.5 bpp, respectively. © 2025 ieee.

关键词： Convolutional Neural Networks Deep Learning Discrete Cosine Transform Embedding Steganography

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Optimizing data Movement in Heterogeneous computing: A LASSA-based Approach for Efficient Nucleation List Precomputation 3

Optimizing Data Movement in Heterogeneous Computing: A LASSA...

引用

3rd International Conference on intelligent systems, advanced computing, and Communication, ISACC 2025

作者： Bhowmik, Biswajit Girish, K.K. Pandey, Himanshu Prabhanjans, Piyus National Institute of Technology Karnataka BRICS Laboratory Dept. of Computer Science and Engineering Mangalore India National Institute of Technology Karnataka Ishwarchandra Vidyasagar AIT Lab BRICS Laboratory Dept. of Computer Science and Engineering Mangalore India National Institute of Technology Karnataka Maharshi Sushrut CAS Lab BRICS Laboratory Dept. of Computer Science and Engineering Mangalore India

ISBN: (纸本)9798331523893

In the rapidly evolving landscape of heterogeneous computing, the efficiency of data movement between CPUs and GPUs can make or break system performance. Despite advancements in parallel processing, existing methods for managing data transfers - particularly in GPU offloading scenarios - suffer from significant inefficiencies. These inefficiencies are particularly evident in nucleation list precomputation for non-equilibrium solidification models, where redundant data movements and complex dynamic work-sharing in OpenMP lead to significant performance overhead. To tackle this issue, this paper proposes a novel solution that integrates the Location-Aware Heap Static Single Assignment (LASSA) algorithm into the compilation process. This approach identifies and eliminates redundant memory copy operations, optimizing data transfers and reducing overhead. The findings reveal a dramatic performance boost, with up to a 9.6-fold increase in efficiency. By addressing the specific challenges of nucleation list precomputation, this work provides valuable insights into optimizing data movement in heterogeneous computing environments, paving the way for enhanced performance in parallel programming models. © 2025 ieee.

关键词： Cellular Automata Models Heterogeneous computing Nucleation List Precomputation OpenMP Performance Issues System Efficiency

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A No-Code Approach to Classifying Neurological Disorders Using Orange data Mining 3

A No-Code Approach to Classifying Neurological Disorders Usi...

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3rd International Conference on intelligent systems, advanced computing, and Communication, ISACC 2025

作者： Davidraj, Yabez Elavarasan, S. Vivek, T. Munirathinam, Ramesh Karpagam Academy of Higher Education Department of Biomedical Engineering Coimbatore India

ISBN: (纸本)9798331523893

Neurodegenerative diseases are progressive disorders causing neuron damage, leading to cognitive and motor impairments. This study presents a machine learning approach for classifying multiple neurological disorders using MRI data with the Orange data analytics software. A total of 165 MRI images representing conditions such as Alzheimer's disease, Huntington's disease, motor neuron disease, and Pick's disease were sourced from the AANLIB database. Image features were extracted using the VGG-16 deep learning model, and classification was conducted via Support Vector Machine (SVM) with polynomial, linear, radial basis function (RBF), and sigmoid kernels. The polynomial and sigmoid kernels achieved the highest classification accuracy of 93.3%, outperforming the linear and RBF kernels. These findings highlight that MRI-based classification using deep learning features and SVM in Orange can effectively differentiate neurological disorders, showing promise for early and accurate diagnosis. © 2025 ieee.

关键词： AANLIB Alzheimer disease Alzheimer visual agnosia Cerebral disease Huntington disease Motor neuron disease Pick's disease SVM

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ResuMatcher: An intelligent Resume Ranking System 3

ResuMatcher: An Intelligent Resume Ranking System

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3rd International Conference on intelligent data Communication Technologies and Internet of Things, IDCIoT 2025

作者： Dhobale, Prasad Bhoir, Vinayak Vyavhare, Abhijeet Yelkar, Pratik Dharmadhikari, Pramod A. School of Computer Engineering MIT Academy of Engineering Alandi Pune India

ISBN: (纸本)9798331527549

The growing complexity of recruitment processes necessitates advanced solutions for resume screening and ranking. In this paper, we present ResuMatcher, an AI-powered resume ranking system that leverages the power of Large Language Models (LLMs) for semantic understanding. Unlike traditional keyword-based systems, ResuMatcher evaluates the context and meaning behind terms in resumes and job descriptions, providing a more accurate and efficient matching process. Using models such as BERT and transformer-based architectures, ResuMatcher can assess candidate qualifications by considering the underlying semantics of job requirements and candidate skills. The system was evaluated on a diverse dataset across industries such as IT, healthcare, and finance, and showed a significant improvement in both accuracy and efficiency compared to conventional Applicant Tracking systems (ATS). Results from user testing demonstrated high satisfaction rates among both job seekers and recruiters, highlighting ease of use, relevance of matches, and efficiency gains in the hiring process. © 2025 ieee.

关键词： Semantics

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Dual-Plate Verification System for Vehicle Identifcation using IoT and Embedded systems 3

Dual-Plate Verification System for Vehicle Identifcation usi...

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3rd International Conference on intelligent data Communication Technologies and Internet of Things, IDCIoT 2025

作者： Sampath, A.V.S.S. Malagati, Nischal S. Kanapathi, V. Belwal, Meena Department of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Bengaluru India

ISBN: (纸本)9798331527549

This research uses a dual-plate verification system for improving vehicle security by using IoT and embedded systems. The system compares front and rear license plates to detect any anomaly, which signals unauthorized modifications. Using real-time image processing and IoT-based data communication, this solution gives us an accurate plate matching system, making sure of reliable vehicle identity verification. Its possible applications include traffic monitoring, parking management, and detecting any unlawful activity. This project is unique because it integrates the dual plate verification system with an Arduino IoT setup. For hassle-free text recognition, this system uses deep learning frameworks combined with a wireless mobile setup that serves as a camera. The system’s accuracy is shown through experiments, displaying its value in improving transportation security with automation. The number plates were detected, and the respective penalties for the vehicles were imposed accordingly. © 2025 ieee.

关键词： Anomaly detection

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