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10th International Conference on Electrical Energy Systems, ICEES 2024

作者： Avinash, S. Sujatha, S. Poornima, D. Vedhashree, S.K. Saravanan, V. Malathi, P. St. Josephs College of Engineering Department of MBA Tamil Nadu Chennai India Sri Sairam College of Engineering Department of EEE Anekal Karnataka Bangalore India Sathyabama Institute of Science and Technology Department of Computer Science and Engineering Chennai India M.Kumarasamy College of Engineering Department of Electronics and Communication Engineering Tamil Nadu Chennai India Saveetha Institute of Medical and Technical Sciences Saveetha University Saveetha School of Engineering Department of Nano Electronics Materials and Sensors Tamilnadu Chennai India SIMATS Saveetha College of Liberal Arts and Sciences Department of Data Analytics Tamil Nadu Chennai India

ISBN: (纸本)9798350353778

The incorporation of cutting-edge technology into intelligent power grid management becomes a top priority as the globe keeps searching for effective and environmentally friendly energy solutions. The arrival of 6G wireless networks is expected to completely transform the methods used to track, manage, and enhance energy grids. An effort to provide high data speeds has already been made with the deployment of commercial fifth-generation (5G) wireless communications networks. The advent of IoT applications and the proliferation of smart devices will put a heavy strain on 5G wireless networks since they need extremely dependable and lowlatency connection. So, the massive continuous data traffic boom is probably going to be too much for 5G networks to handle. Research into enhancing current wireless networks in preparation for the 6G cellular systems, the next generation of mobile communication, has been spurred by this. This paper introduces the Neural 6G Wireless Routing Protocol (N6GWRP), a new routing protocol that supports intelligent communication. To evaluate the effectiveness of the suggested approach, it is cross-validated with the Multiple-Access Routing Protocol (MARP), a traditional routing model. Users should not experience any disruptions when mobile power and data transmission systems interact. Another major obstacle is connecting the vast array of devices and people with varying degrees of processing power in a way that is both sustainable and environmentally conscious. This study delves into the smart solution to the aforesaid difficulties and examines how 6G wireless networks might improve smart energy grid resilience and dependability through seamless interaction with other sophisticated technologies like blockchain. A more environmentally friendly and smart vision regarding electrical grid administration is possible thanks to the extensive study that strives to illuminate the revolutionary significance of 6G wireless technologies. © 2024 IEEE.

关键词： 5G mobile communication systems

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Development of Business Intelligence Framework for Open Government data Portal Usage Analysis: A Case Study of Thailand 13

Development of Business Intelligence Framework for Open Gove...

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13th International Conference Knowledge and Systems Engineering, KSE 2021

作者： Sripramong, Sahaporn Anutariya, Chutiporn Buranarach, Marut Tumsangthong, Patipat Wutthitasarn, Theerawat School of Engineering and Technology Asian Institute of Technology Ict Department Thailand National Electronics and Computer Technology Center Data Science and Analytics Research Group Thailand

ISBN: (纸本)9781665499750

The usage of Open Government data (OGD) can enable the government transparency for better governance, increase citizen collaboration, and promote innovation through public service improvement. OGD initiatives involve different stakeholders and complex publishing guidelines. In general, governments initiate OGD in their countries and provide the government-run portals to support the provision and consumption process of OGD. Insufficient usage of OGD and limited provision by the government is a great challenge to OGD development. Monitoring the operation and constantly analyzing the usage can be beneficial for OGD policymakers to gain insight into usage, to plan, and to enact the law to support the OGD initiative. This study proposes a Business Intelligence (BI) framework to analyze OGD portal datasets and its usage. The data from Thailand's OGD (ThOGD) portal was derived to validate our framework. The work focuses on designing and implementing the BI framework. We demonstrated the features of the prototyped BI system in supporting the analysis through various data visualization. © 2021 IEEE.

关键词： Open data

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Swarm-LSTM: Condition Monitoring of Gearbox Fault Diagnosis Based on Hybrid LSTM Deep Neural Network Optimized by Swarm Intelligence Algorithms

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computers, Materials & Continua 2021年第2期66卷 2041-2059页

作者： Gopi Krishna Durbhaka Barani Selvaraj Mamta Mittal Tanzila Saba Amjad Rehman Lalit Mohan Goyal School of Electrical and Electronics Sathyabama Institute of Science and TechnologyChennai600119India Department of Computer Science and Engineering G.B.Pant Government Engineering CollegeNew Delhi110020India Artificial Intelligence and Data Analytics(AIDA)Laboratory CCISPrince Sultan UniversityRiyadh11586Saudi Arabia Department of Computer Engineering J.C.Bose University of Science and TechnologyFaridabad121006India

Nowadays,renewable energy has been emerging as the major source of energy and is driven by its aggressive expansion and falling *** of the renewable energy sources involve turbines and their operation and maintenance are vital and a difficult *** monitoring and fault diagnosis have seen remarkable and revolutionary up-gradation in approaches,practices and technology during the last *** mostly do use a rotating type of machinery and analysis of those signals has been challenging to localize the *** paper proposes a new hybrid model wherein multiple swarm intelligence models have been evaluated to optimize the conventional Long Short-Term Memory(LSTM)model in classifying the faults from the vibration signals data acquired from the *** helps to analyze the performance and behavioral patterns of the system more effectively and efficiently which helps to suggest for replacement of the unit with higher *** results have demonstrated that the proposed hybrid modeling approach is effective in classifying the faults of the gearbox from the time series data and achieve higher diagnostic accuracy in comparison to the conventional LSTM methods.

关键词： Gearbox long short term memory fault classification swarm intelligence optimization condition monitoring

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Metaverse Empowered Healthcare 5.0 in B5G: Use Cases, Opportunities and Challenges

Metaverse Empowered Healthcare 5.0 in B5G: Use Cases, Opport...

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Computing and Information Technology (ICCIT-1441), 2020 International Conference on

作者： Saeed Hamood Alsamhi Ammar Hawbani Santosh Kumar Navin Singh Rajput Edward Curry Insight Centre for Data Analytics University of Galway Galway Ireland School of Computer Science Shenyang Aerospace University Shenyang China CSE IIIT Naya Raipur Chhattisgarh India Department of EE IIT (BHU) Varanasi India

ISBN: (数字)9798350353839

ISBN: (纸本)9798350353846

Patient tracking on the go, emotional telemedicine, telesurgery, wellness tracking, virtual clinics, and customized treatment are all made possible by Healthcare 5.0. Healthcare services offered by metaverse have the potential to transform the sector entirely, and the development of Beyond Fifth Generation (B5G) networks offers the infrastructure required to make these services a reality. We demonstrate a range of use cases, including emotional telemedicine, telesurgery, virtual physical therapy sessions, remote patient monitoring, and well-being monitoring, as we examine the possibilities of Metaverse-enabled Healthcare 5.0 in B5G networks. It is stated how these services can lead to better patient outcomes, more individualized treatment, and lower costs. In addition, we address the difficulties that these services present, including issues with data interoperability, privacy and security, and regulatory compliance, and we offer answers. Finally, we highlight future direction insights for metaverse-empowered healthcare5.0 in B5G networks.

关键词： Patient monitoring Privacy Costs Metaverse Telemedicine Medical treatment Medical services Transforms Security Interoperability

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Comparing the Performance of Individual and Hybrid Classifiers for Medical datasets

Comparing the Performance of Individual and Hybrid Classifie...

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data analytics for Business and Industry: Way Towards a Sustainable Economy (ICDABI), International Conference on

作者： Aysha Alkhayyat Elham Musaaed Isa AlHeddi Nabil Hewahi MSc. in Big Data Science and Analytics College of Science University of Bahrain Department of Computer Science College of Information Technology University of Bahrain

ISBN: (纸本)9781665490597

An accurate prediction of a certain medical condition is a crucial matter that is of great benefit to patients and healthcare systems. Machine Learning can be of a great help to the medical professionals in assisting them to precisely predict the occurrence of medical conditions. This research paper aims to enhance the prediction capabilities of machine learning models, so it become more adopted in the medical field. To fill in this gap, the capabilities of the ensemble hybrid model were explored in comparison to individual models. The machine learning algorithms were tested using five medical datasets pertaining to five different diseases. The Random Forest model was the best performing individual model. In terms of accuracy, the ensemble hybrid model performance was close to the individual models in all datasets, except for the Liver Disease dataset where it exceled. This research could be extended to experiment the integrated hybrid models along with the optimization techniques.

关键词： Recurrent neural networks Machine learning algorithms Support vector machine classification Stochastic processes Predictive models Diabetes Particle swarm optimization

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Enhancing Transfer Learning for Medical Image Classification with SMOTE: A Comparative Study

arXiv

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arXiv 2024年

作者： Alam, Md. Zehan Roy, Tonmoy Kawsar, H.M. Nahid Rimi, Iffat Department of Mathematics University of Dhaka Dhaka Bangladesh Department of Data Analytics and Information Systems Utah State University LoganUT United States Department of Applied Mathematics Noakhali Science and Technology University Noakhali Bangladesh Department of Computer Science and Engineering Daffodil International University Dhaka Bangladesh

This paper explores and enhances the application of Transfer Learning (TL) for multilabel image classification in medical imaging, focusing on brain tumor class and diabetic retinopathy stage detection. The effectiveness of TL—using pre-trained models on the ImageNet dataset—varies due to domain-specific challenges. We evaluate five pre-trained models—MobileNet, Xception, InceptionV3, ResNet50, and DenseNet201—on two datasets: Brain Tumor MRI and APTOS 2019. Our results show that TL models excel in brain tumor classification, achieving near-optimal metrics. However, performance in diabetic retinopathy detection is hindered by class imbalance. To mitigate this, we integrate the Synthetic Minority Over-sampling Technique (SMOTE) with TL and traditional machine learning(ML) methods, which improves accuracy by 1.97%, recall (sensitivity) by 5.43%, and specificity by 0.72%. These findings underscore the need for combining TL with resampling techniques and ML methods to address data imbalance and enhance classification performance, offering a pathway to more accurate and reliable medical image analysis and improved patient outcomes with minimal extra computation powers. Copyright © 2024, The Authors. All rights reserved.

关键词： Convolutional neural networks

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Providing and evaluating a model for big data anonymization streams by using in-memory processing

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Knowledge and Information Systems 2025年

作者： Shamsinejad, Elham Banirostam, Hamid BaniRostam, Touraj Pedram, Mir Mohsen Rahmani, Amir Masoud Computer Engineering (Software) Specializing in Data Science Tehran Iran Master of Data Analytics University of Niagara Falls Niagara Falls ON Canada Electrical and Computer Engineering Department Faculty of Engineering Kharazmi University Tehran Iran Future Technology Research Center National Yunlin University of Science and Technology Yunlin Taiwan

Extracting valuable information from vast sources of social networks while protecting confidentiality and preventing data disclosure is a significant challenge in big data environments. Traditional anonymization methods often fall short in handling the volume, variety, and velocity of big data, leading to high data loss and inefficiency. This article addresses these challenges by proposing a novel anonymization method based on K-means clustering within the Spark framework, leveraging its in-memory processing capabilities. Our model uses K-means clustering to determine optimal cluster heads, significantly reducing data loss and identity disclosure risks. By utilizing Spark's RDD abilities and the MLlib component, our method achieves faster processing times compared to traditional methods that rely on non-in-memory big data tools. Performance evaluation demonstrates that at k = 9, the cost factor is minimized to 0.20, indicating the efficiency and effectiveness of our approach. The proposed method not only enhances processing speed but also ensures minimal data loss, making it suitable for real-time anonymization of big data streams. This work provides a balanced solution that addresses the critical need for high-speed data anonymization while maintaining data privacy and utility. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2025.

关键词： Anonymity confidentiality Big data data disclosure Privacy

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Classification of Water Potability Using Machine Learning Algorithms

Classification of Water Potability Using Machine Learning Al...

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data analytics for Business and Industry: Way Towards a Sustainable Economy (ICDABI), International Conference on

作者： Husain Yusuf Salman Alhaddad Salman Yusuf Nabil Hewahi MSc. in Big Data Science and Analytics College of Science University of Bahrain Department of Computer Science College of Information Technology University of Bahrain

ISBN: (纸本)9781665490597

Water is the most important resource for sustaining life. Everyone has the right to have access to pollution-free water. The achievement of safe drinking water leads to tangible benefits to health. According to the World Health organization (WHO), the number of global deaths due to water diseases is about two and a half million deaths per year. Therefore, it is important to study new applications for analyzing and classifying safe water quality. In this research, the classification algorithms of K Nearest Neighbors (KNN), Decision Tree (DT), Random Forest (RF), Artificial Neural Network (ANN), Logistic Regression (LR), and Support Vector Machine (SVM) are used to classify the potability of the drinking water. RF and DT achieved the highest performance with an accuracy of 83.78% and 74.98% respectively. The lowest classifier accuracy is LR with 48.74%. In addition, the most important features of the RF model are pH, followed by Hardness, followed by Sulfate. On other hand, the least important feature is Turbidity.

关键词： Support vector machines Industries Machine learning algorithms Artificial neural networks Water quality Organizations Water pollution

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A Survey on Intrusion Detection System for IoT Networks Based on Artificial Intelligence

A Survey on Intrusion Detection System for IoT Networks Base...

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Electrical, Electronics, Communication and computers (ELEXCOM), International Conference on

作者： Saksham Checker Madhuri Yadav Rahul Katarya Department of Applied Physics Delhi Technological University New Delhi India Department of Computer Science Big Data Analytics and Web Intelligence Laboratory Delhi Technological University New Delhi India

Internet of Things(IoT) devices is the prime factor in automating the real world in the past few years. These networks provide efficient utilization of resources, quality data, and benefits with reduced human input as well as have secured processing which results in a high potential for automating the healthcare and defence systems. Noticing the large number of attacks on these networks in previous years, there is a vast study to detect the intrusion by attackers to ease the use of these devices without the fear of losing data. This survey is a deep study of works on detecting the intrusion in IoT networks and devices revolving around the use of Machine Learning and Deep Learning based algorithms on real-world datasets like “Bot-IoT” and “KDD”. The survey provides a building block in understanding the basics of Intrusion and also the benefits of Intrusion detection systems and thus promotes the researchers to focus more on prevention of such attacks. Furthermore, it compiles the studies from the year 2016 and compares the models of Machine Learning and Deep Learning segregated based on data sets used in training; also, summarizes the performance of such mechanisms.

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Embedded Graph Reconstruction under Hausdorff Noise

arXiv

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arXiv 2024年

作者： Fritze, Halley Majhi, Sushovan Masden, Marissa Mitra, Atish Stickney, Michael Department of Mathematics University of Oregon United States Data Science George Washington University United States Data Analytics Computer Science and Mathematics University of Puget Sound United States Department of Mathematical Sciences Montana Technological University United States Montana Bureau of Mines and Geology Montana Technological University United States

Filamentary structures (topologically embedded graphs with a metric structure) are ubiquitous in science and engineering. A challenging problem in topological data analysis (TDA) is to reconstruct the topology and geometry of such an underlying (usually unknown) metric graph from possibly noisy data sampled around it. Reeb graphs have recently been successfully employed in abstract metric graph reconstruction under Gromov–Hausdorff noise: the sample is assumed to be metrically close to the ground truth. However, such a strong global density assumption is hardly achieved in applications, making the existing Reeb graph-based methods untractible. We relax the density assumption to give provable geometric reconstruction schemes, even when the sample is metrically close only locally. A very different yet more relevant paradigm focuses on the reconstruction of metric graphs—embedded in the Euclidean space—from Euclidean samples that are only Hausdorff-close. We further extend our methodologies to provide novel, provable guarantees for the successful geometric reconstruction of Euclidean graphs under the Hausdorff noise model. Our technique produces promising results in reconstructing earthquake plate tectonic boundaries from the global earthquake catalog. Copyright © 2024, The Authors. All rights reserved.

关键词： Earthquakes

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