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

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Uniting the Power of Text and Emojis to Revolutionizing Sentiment Analysis of Twitter data 3

Uniting the Power of Text and Emojis to Revolutionizing Sent...

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

作者： Musengamana, Adalbert Patadiya, Kunjesh Moteriya, Jay Faldu, Shreyal Palaniappan, Damodharan Jain, Rituraj Parmar, Kumar Marwadi University Department of Information Technology Gujarat Rajkot India

ISBN: (纸本)9798331523893

This research innovates sentiment analysis by incorporating emoji data alongside text collected from Twitter, which points to the problem of text-based analysis in the identification of discrete emotional contexts. A new multimodal sentiment analysis model was created using LSTM networks based on a dataset of 199708 tweets that can be positive, neutral or negative. The proposed approach also improved the classification of sentiments to an overall accuracy of 90.20%, with positive sentiments and neutral sentiments being well classified, although there was difficulty in classifying negative sentiments. Due to the presence of textual as well as emoji features it can generate more profound emotional interpretation and can potentially be used in social media analysis and survey analysis. This research is useful to the field because it shows that combining multiple types of sentiment analysis is can enhance the comprehension of emotion in computer-mediated interaction. © 2025 ieee.

关键词： Deep Learning Emoji Long-Short Term Memory Natural Language Processing Sentiment analysis Text

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A hybrid CNN-GNN approach for enhanced early detection of schizophrenia using EEG data 3

A hybrid CNN-GNN approach for enhanced early detection of sc...

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

作者： Reetha, L. Gnanajeyaraman, R. Saveetha Institute of Medical and Technical Sciences Saveetha School of Engineering Department of CSE TN Chennai 602105 India

ISBN: (纸本)9798331523893

Schizophrenia, a complex neuropsychiatric disorder, poses significant challenges in early detection due to its impact on cognitive, emotional, and social functioning. Hybrid approach integrating CNNs and Graph Neural Networks has shown promise in addressing this challenge. CNNs excel at capturing local spatial-temporal features from EEG signals, while GNNs are designed to model the brain's connectivity patterns, providing a holistic representation of brain function. This study presents a hybrid CNN-GNN model that extracts both spatial-temporal features and models global brain connectivity for schizophrenia detection. Using EEG data from the Lomonosov Moscow State University (LMSU) dataset, this model achieves high accuracy (99.12%), sensitivity (98.83%), and specificity (99.35%). An improvement in schizophrenia detection, the CNN-GNN hybrid model gives doctors a more precise and trustworthy instrument for early diagnosis. © 2025 ieee.

关键词： Continuous Wavelet Transform Convolutional neural networks EEG Signals Graph Neural Networks Schizophrenia

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Time-Series Forecasting Using ARIMA and SARIMA Models for Solar NASA POWER data 3

Time-Series Forecasting Using ARIMA and SARIMA Models for So...

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

作者： Patil, Yashwant Shruti, Tanya Rajeswari, K. Pimpri-Chinchwad College of Engineering Pune Department of Computer Science Pune India

ISBN: (纸本)9798331523893

Solar energy forecasting is vital, particularly in energy planning for power utilities, as it provides insights for integrating renewable energy into existing power distribution networks. This study investigates the effectiveness of ARIMA and SARIMA models in forecasting solar radiation (ALLSKY_SFC_SW_DWN) using NASA POWER data from 2019 to 2024. The dataset includes solar irradiance and meteorological parameters critical for renewable energy research. Methodologically, both ARIMA and SARIMA models were implemented and evaluated through R², MAE, and RMSE metrics, highlighting their capacity to predict temporal trends and seasonal variations. Results demonstrated ARIMA's superior accuracy with an R² of 0.9343, while SARIMA, incorporating more seasonal factors, achieved an R² of 0.8664. This work not only compares these models' practical applications but also contributes to solar energy forecasting research by offering insights into their strengths and limitations for decision-making in renewable energy systems. © 2025 ieee.

关键词： ARIMA SARIMA Solar energy forecasting Solar radiation prediction Time series analysis

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Comprehensive Security Analysis and Threat Mitigation Strategies for Software-Defined Networking (SDN) Environments 3

Comprehensive Security Analysis and Threat Mitigation Strate...

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

作者： Karki, Ajit Deb, Raktim The ICFAI University Sikkim School of Information Technology Gangtok India The ICFAI University Tripura School of Science and Technology Agartala India

ISBN: (纸本)9798331523893

Software Defined Networking (SDN) is a new networking standard that significantly reduces the complexity of network administration tasks. SDN is considered a networking breakthrough. Additionally, it provides a flexible interface that allows for network innovation by controlling the network's general behavior. However, for many years, traditional IP networks were difficult to maintain, prone to errors, and difficult to grow with new features. The idea is derived from the separation and abstraction of the data plane and the control of conventional network devices. Better segmentation, scalability, programmability, faster delivery, and increased service and experience quality are all advantages of this separation for network operations. To make system administrators' tasks more at ease, SDN has been suggested as a prospective standard for a symbolized and rationally integrated network administration plane. By separating the control and data planes, SDN enables network configuration through programmability. This paper provides a comprehensive explanation of SDN, covering its numerous levels and their purposes, to keep up with the new technologies accelerated development. © 2025 ieee.

关键词： Control plane data plane IP Software defined networking

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Leveraging IoT and Machine Learning for intelligent Water Quality Monitoring systems

Leveraging IoT and Machine Learning for Intelligent Water Qu...

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2025 International Conference on intelligent Control, computing and Communications, IC3 2025

作者： Baranwal, Gaurav Singh, Kaushtubh Gupta, Ayush Tiwari, Harsh N. Gupta, Tukur Ajay Kumar Garg Enginnering College Department of Electronics and communication Engineering Ghaziabad India

ISBN: (纸本)9798331527495

Water quality monitoring is a crucial aspect of public health, particularly in mitigating the risks associated with waterborne diseases and ensuring safe drinking water. Traditional monitoring methods are often labor-intensive, costly, and lack real-time capabilities, making them unsuitable for widespread and rural deployment. This paper examines the integration of advanced technologies, specifically the Internet of Things (IoT) and Machine Learning (ML), to design a sophisticated, low-cost water quality monitoring system. Leveraging an array of sensors, including pH, turbidity, temperature, total dissolved solids (TDS), and dissolved oxygen (DO), combined with the advanced embedded processor, the system ensures seamless data acquisition and processing. IoT enables continuous remote monitoring, while ML algorithms provide predictive analytics and intelligent anomaly detection to identify potential contaminants and trends. These value-driven solutions underscore the transformative potential of advanced technologies in revolutionizing water quality assessment. This review article carefully reviews the available literature on water quality assessment in order to lay out an assortment of conclusions on the concerns, problems, challenges, and research gaps that have emerged during the last few years. Research gaps are identified by thoroughly examining the previously suggested methodologies. The study presented in this article primarily identified the problems with the accuracy of the models, the generation of data collection mechanisms, and the types of data that were used in the suggested frameworks. Subsequently, research avenues towards smart water quality monitoring are offered by examining key concepts crucial to the expansion of this field of research. © 2025 ieee.

关键词： Damage detection

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FPGA implementation of Hardware Accelerated 64-bit Floating Point Matrix-Multiplier for GNSS data Processing 3

FPGA implementation of Hardware Accelerated 64-bit Floating ...

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

作者： Digdarsini, Durga Shau, Hem Raj Jadav, Nilay Guru Vignesh, G. SAC-ISRO Gujarat Ahmedabad India EC CHARUSAT Gujarat Anand India EE PSG College of Technology Tamilnadu Coimbatore India

ISBN: (纸本)9798331523893

This paper presents FPGA-based implementation of hardware accelerated matrix multiplier using High Level Synthesis (HLS) technique with a specific linkage to GNSS data processing. Matrix multiplication operation is a vital block for GNSS data processing. The multiplication of matrices of order greater than 10 consumes significant time in FPGA-based design. The total time for data processing need to be reduced for real-time scenario specially in case of space-based GNSS applications. For this purpose, HLS IP for matrix multiplication is developed to reduce the processing time substantially. In this work, 64-bit floating point data processing is achieved on 32-bit softcore processor, by design of control logic for seamless dataflow and synchronization between the HLS IP and other key modules in the FPGA. The design is tested in Xilinx Kintex Ultra FPGA-based KCU105 evaluation board. The hardware result for 40x40 matrices show that HLS-based approach achieves speedup factor up to 286 over standalone MicroBlaze softcore processor, this speedup is achieved at the expense of less than 3% percent of available major resources in the FPGA. © 2025 ieee.

关键词： FPGA HLS Matrix Multiplication Softcore processor etc

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Generative Adversarial Networks for Synthetic data Generation in Privacy-Preserving Health Informatics 3

Generative Adversarial Networks for Synthetic Data Generatio...

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

作者： Kumar, Vineet Ahmed, Hassan Safi Shailaja, M. Karthikeyan, B. Alazzam, Malik Bader Saravanakumar, R. Symbiosis International (Deemed University) Symbiosis Centre for Management Studies Pune Maharashtra Pune India Al-Farahidi University Department of Biomedical Engineering Baghdad Iraq MallaReddy University Department of DataScience Telangana Hyderabad India KPR Institute of Engineering and Technology Department of CSE Coimbatore India Jadara University Faculty of Information Technology Irbid Jordan Saveetha Institute of Medical and Technical Sciences Saveetha University Saveetha School of Engineering Department of ECE Chennai India

ISBN: (纸本)9798331523893

Privacy preserving health informatics deals with the problem of how to obtain informative analysis from the health-related information while ensuring that the privacy of individuals is protected. This paper presents the first framework that employs GANs for synthetic data generation in the context of health informatics to overcome the presented shortcomings and expand on existing literature. In this framework, the solution is presented through the utilization of GANs which presents a nucleation point in developing synthetic datasets that reproduce the density and distribution of real patient data. Employing PLMs in conjunction with synthetic as well as original datasets, led to accurate classification rates of above 98%. These results raise the possibility of the synthetic data generated by model substituting the original data for machine learning purposed while maintaining high accuracy and, arguably most importantly, privacy of the individual in question. Therefore, our work presents a major advance toward bridging the paradigm of health data usability and protection. © 2025 ieee.

关键词： Differential Privacy Generative Adversarial Networks (GANs) Privacy-Preserving Health Informatics Synthetic data Generation

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Edu-Metaverse: A Structured Architecture and Workflow Model for Digital and Blended Learning

Edu-Metaverse: A Structured Architecture and Workflow Model ...

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International Symposium on advanced computing and Communication (ISACC)

作者： Sanskar Garg Rishi Dev Ashwini Kumar Singh Sahil Sahani Kiran Kumari Anjan Bandyopadhyay School of Computer Engineering Kalinga Institute of Industrial Technology Bhubaneswar India School of Law Kalinga Institute of Industrial Technology Bhubaneswar India School of Law Mody University Lakshmangarh India

ISBN: (数字)9798331523893

ISBN: (纸本)9798331523909

The Metaverse is transforming digital engagement across sectors like education, healthcare, and technology. This survey examines how edge computing, combined with 5G, IoT, and AI, supports real-time, immersive experiences. In education, the Edu-Metaverse uses virtual platforms for simulations, STEM learning, language acquisition, and digital education, while addressing challenges such as privacy and scalability. In healthcare, it employs federated learning for secure applications like remote monitoring and diagnostics. Decentralized edge computing is key to solving issues related to interoperability and data security. The framework proposed identifies four essential hubs—design, knowledge, technology, and training connected through infrastructure, accessibility, and policy. The review emphasizes the importance of collaboration across disciplines and the need for infrastructure and training to fully realize the Metaverse’s potential.

关键词： Training Surveys Metaverse Reviews Computational modeling Scalability Medical services Real-time systems Remote monitoring Edge computing

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A Unified IoT-based Smart Home System: Integration of Security and Convenience Features for Consumer 3

A Unified IoT-based Smart Home System: Integration of Securi...

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

作者： Oeishee, Moslema Hoque Ahammad Muyeed, Munyem Alvi, Mishkat Mehedi, Rejoan Shariar Emon Shaikat, M. Molla, Md Nayan Brac University Electrical and Electronic Engineering Department Dhaka Bangladesh United International University Computer Science and Engineering Department Dhaka Bangladesh Brac University Computer Science and Engineering Department Dhaka Bangladesh

ISBN: (纸本)9798331523893

Home automation is growing rapidly in the Fourth Industrial Revolution (4IR), providing users with unwavering convenience and enhanced security. This paper presents a comprehensive Internet of Things (IoT) smart home automation solution with a focus on energy management, user-friendliness, and security. Our system includes real-time integrated dashboards accessible via web and mobile interfaces, smart irrigation for indoor gardening, passkeys for door security, voice commands, fingerprint access, night security lighting, and morning alarms. The hardware consists of an Arduino MEGA microcontroller, various sensors (smoke, temperature, humidity, and soil), an ESP Wi-Fi module for connectivity, and additional components. The system demonstrated a satisfactory effectiveness level with an accuracy of 78% based on various tests. The voice assistant bot and accessible dashboard significantly improve user experience. Despite the complexity of home automation processes, increased affordability and accessibility are the aims of this paper. Future work will focus on enhancing real-time capabilities, integrated circuits, data security, and web-based functionalities to improve system performance, scalability, and user accessibility. © 2025 ieee.

关键词： data security home appliances control IoT sensor integration smart home user-friendly dashboard

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Pyramidal Gravitational Search clustering for Hybrid Gene Expression data 3

Pyramidal Gravitational Search clustering for Hybrid Gene Ex...

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

作者： Kukreti, Swapnil Singh, Urshi College of Engineering Roorkee Uttarakhand India Indira Gandhi Delhi Technical University for Women Delhi India

ISBN: (纸本)9798331523893

Since many real-world performance concerns are becoming bigger, more complex, and more dynamic, natural-inspired algorithms are becoming more and more popular. Due to the scale and complexity of the problems, it is necessary to develop an optimization strategy whose effectiveness is measured by finding the nearly optimal solution in a reasonable amount of time. The bio-inspired metaheuristic techniques are based on swarm intelligence, biological, physical, and chemical features, depending on the source of inspiration. Cancerous tumors form and propagate largely as a result of genetic factors. A high-dimensional data processing difficulty exists with the identification of the pertinent genes. The Pyramid-GSA hybrid framework was developed to tackle the issue of dimensionality reduction through a cyclical gene-reduction method. This framework integrates two advanced modules: a high-throughput filtering component and an iterative wrapper technique inspired by the gravitational search algorithm (GSA). Using a recursive selection approach, top-performing genes are retained in each reduction cycle, facilitating efficient dimensionality compression while preserving essential genetic features. This methodology aims to optimize the gene subset to enhance classification accuracy and minimize interference from non-informative genes. Testing was conducted on a multi-class breast cancer microarray gene expression dataset, with comparisons to established feature selection methods to ensure a comprehensive analysis. The various approaches are assessed and contrasted using datasets from the UCI repository using a set of assessment metrics. Findings demonstrate that in spite of initialization and the use of stochastic operators, for the most favorable feature combination, the suggested version of GSA aims to search the feature space for the optimal feature combination. © 2025 ieee.

关键词： Convergence Rate Feature Extraction(FE) Feature Selection(FS) Gravitational Search Algorithm(GSA) Pyramid Gravitational Search Algorithm(PGSA)

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