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Microgrid Recent Innovations & Uses 15

Microgrid Recent Innovations & Uses

引用

15th International Conference on Computing Communication and Networking Technologies, ICCCNT 2024

作者： Kaushik, Neeraj Goswami, Shubhashis Sachan, Keerti Vijayalakshmi, V.J. Gajendran, P. Mishra, V.A. Tmu Department of Ece India Dev Bhoomi Uttarakhand University School of Engineering and Computer Uttarakhand India Iimt University School of computer science and application Meerut India Karpagam Academy of Higher Education Department of Electrical and Electronics Engineering Coimbatore India Karpagam College of Engineering Department of Electrical and Electronics Engineering Coimbatore India Vishwakarma Institute of Information Technology Computer Engg Pune India

ISBN: (纸本)9798350370249

Concerns about global warming brought on by increasing carbon dioxide levels in the environment have sped up technological development in the sustainable energy sector over the past few years. The advancement in science while the shift to less polluting materials sources possesses increased the appeal for DG, subsequent generations distributed generation, using renewable supplies. Nevertheless, it has become widely recognized that increasing DG entry can negatively affect the matrix's layout and operations. A suggestion made to reduce the effect about DG while improve the suitability of traditional grids over massive DG installation involves the Located concept. encompassing a variety of electrical devices', the elements and Micro-grids and related power gadgets fields are now very well-liked research areas. This essay examines the history and theory behind microgrids, as well as the present situation regarding the writing, active research initiatives, and pertinent requirements. It additionally provides a more thorough analysis of the numerous Located pilot initiatives which have been implemented surrounding the globe and addresses possible directions for future study. © 2024 IEEE.

关键词： Distributed generation IEEE Standard 1547 Microgrid control Microgrid management system Protection Renewable energy

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News-Driven Stock Price Forecasting in Indian Markets: A Comparative Study of Advanced Deep Learning Models 3

News-Driven Stock Price Forecasting in Indian Markets: A Com...

引用

3rd International Conference for Advancement in Technology, ICONAT 2024

作者： Attaluri, Kaushal Tripathi, Mukesh Kumar Reddy, Srinithi Shivendra Software Engineer-Machine Learning and AI Pegasystems India Worldwide Pvt Ltd Hyderabad India Vardhaman College of Engineering Department of Computer Science and Engineering Hyderabad India Vasavi College of Engineering Department of Information Technology Hyderabad India D. K. College Department of Computer Application Bihar Dumraon India

ISBN: (纸本)9798350354171

Forecasting stock market prices is a challenging task for traders, analysts, and engineers due to the myriad of variables influencing stock prices. However, the advent of artificial intelligence (AI) and natural language processing (NLP) has significantly advanced stock market forecasting. AI's ability to analyze complex data sets allows for more informed predictions. Despite these advancements, stock price forecasting remains an area where AI has not yet achieved optimal results. In this paper, we forecast stock prices using 30 years of historical data from various national banks in India sourced from the National Stock Exchange. We employ advanced deep learning models, including multivariate multi-step Long Short-Term Memory (LSTM), Facebook Prophet with LightGBM and Optuna, and Seasonal Auto-Regressive Integrated Moving Average (SARIMA). Additionally, we analyze news data from tweets and reliable sources like Business Standard and Reuters, recognizing their significant impact on stock price movements. © 2024 IEEE.

关键词： Contrastive Learning

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Emotion Recognition in Speech: Exploiting ResNet50 and Attention Mechanism 2

Emotion Recognition in Speech: Exploiting ResNet50 and Atten...

引用

2nd IEEE International Conference on Emerging Research in Computational science, ICERCS 2024

作者： Priya, V. Savithri Padma Meyyappan, Senthilkumar Vallathan, G. Ammai, N. Meenatchi Geethanjali College of Engineering & Technology Department of ECE Hyderabad India Nalla Malla Reddy Engineering College Department of ECE Hyderabad India Vinayaka Mission's Kirupananda Variyar Arts and Science College Department of Computer Application Salem India

ISBN: (纸本)9798331534950

This research explores the application of ResNet50 combined with an attention mechanism for emotion classification in speech signals. With the increasing need for effective emotion recognition systems in applications such as virtual assistants and mental health monitoring, leveraging advanced deep learning techniques becomes crucial. ResNet50, a deep convolutional neural network, excels at extracting complex features from audio representations like spectrograms and Mel-frequency cepstral coefficients (MFCCs). By integrating an attention mechanism, the model can focus on significant temporal and spectral features, enhancing its ability to capture emotional nuances. This study evaluates the proposed model's performance on benchmark emotion datasets, comparing it with traditional approaches and other deep learning architectures. Results indicate that the ResNet50- attention model achieves superior classification accuracy and improved interpretability, effectively distinguishing various emotional states in speech. The findings suggest that this hybrid approach offers a promising direction for future research in emotion recognition, facilitating more responsive and empathetic AI systems. © 2024 IEEE.

关键词： Emotion Recognition

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A Hybrid Intelligent Model SFAHP-ANFIS-PSO for Technical Capability Evaluation of Manufacturing Enterprises 9th

A Hybrid Intelligent Model SFAHP-ANFIS-PSO for Technical Cap...

引用

19th International Conference on Advanced Data Mining and applications, ADMA 2023

作者： Liu, Tingting Ding, Xuefeng Jiang, Yuming Hu, Dasha College of Computer Science Sichuan University Chengdu610065 China Big Data Analysis and Fusion Application Technology Engineering Laboratory of Sichuan Province Chengdu610065 China

ISBN: (纸本)9783031466731

In the collaborative production environment of manufacturing tasks, the evaluation of enterprise technical capability in advance has a direct impact on the high-performance collaboration between the supplier and the demander of production tasks. In order to solve the problem of efficiency and accuracy in the past evaluation methods, this paper applies the adaptive neuro fuzzy inference system (ANFIS) to the field of enterprise technical capability evaluation for the first time, and proposes an improved hybrid intelligent model SFAHP-ANFIS-PSO based on fuzzy theory. The model takes the four key indexes which have the deepest impact on enterprise technical capability evaluation as the input variables of the model, and the input data are evaluated by ANFIS with high accuracy. Spherical fuzzy analytic hierarchy process (SFAHP) calculated the weight of each evaluation index to preprocess the ANFIS model. Particle swarm optimization algorithm (PSO) continuously optimized the model parameters in the training process, shortened the model training time, improved the evaluation efficiency, and further improved the accuracy of the evaluation results. The experimental results show that the SFAHP-ANFIS-PSO model has high convergence speed and accuracy of results, and can be used to evaluate the technical capability of enterprises with high efficiency and high accuracy. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023.

关键词： Analytic hierarchy process

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A Survey on Green IoT and Its Opportunities for Future Directions

A Survey on Green IoT and Its Opportunities for Future Direc...

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2023 International Conference on Advanced Computing and Communication Technologies, ICACCTech 2023

作者： Upadhye, Sachin Shelke, Nilesh Thakur, Uma Patel Shri Ramdeobaba College of Engineering and Management Department of Computer Application Nagpur India Symbiosis Institute of Technology Nagpur Campus Symbiosis International Deemed University Pune India Jhulelal Institute of Technology Department of Computer Science and Engineering Nagpur India

ISBN: (纸本)9798350380880

The Internet of Things (IoT) has changed the way technology has evolved, integrating it with other aspects such as home appliances for home automation, smart city projects, e-waste management, and many more. But with the increase in the usage of IoT, there are several challenges that come along, like an increase in energy consumption. Due to this, the Green IoT comes into play. It looks for alternative solutions to the present style of implementing IoT technology, which leads to eco-friendly, sustainable, and cost-effective solutions. This survey points towards effective ways of implementing the IoT for eco-friendly, sustainable, and cost-effective management. The Internet of Things (IoT) has transformed the way technology is integrated into various aspects of life, including home automation, smart city projects, and e-waste management. However, the increasing usage of IoT technology has led to challenges, such as higher energy consumption. This has given rise to Green IoT, which seeks alternative solutions for implementing eco-friendly, sustainable, and cost-effective IoT technology. This survey focuses on effective ways to implement IoT technology for sustainable and eco-friendly cost management. It also explores the potential of IoT technology in new areas, its future sustainability, and its challenges. The adoption of green IoT practices can reduce pollution hazards, traffic waste, and energy consumption while enhancing public safety and quality of life. To create sustainable and eco-friendly cities, researchers, policymakers, and industry leaders must collaborate to address IoT implementation challenges. The survey concludes by highlighting opportunities for future research and innovation in this field. © 2023 IEEE.

关键词： Internet of things

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Utilization of ECC in Smart Grids: A Review

Utilization of ECC in Smart Grids: A Review

引用

2023 International Conference on Power Energy, Environment and Intelligent Control, PEEIC 2023

作者： Gopala Krishna, K. Shekhawat, Jyoti Agarwal, Trapty Faculty of Engineering and Technology Department of Electronics and Communication Engineering Bangalore India Vivekananda Global University Department of Computer Science & Application Jaipur India Maharishi University of Information Technology Engineering Department Uttar Pradesh India

ISBN: (纸本)9798350357769

Within the realm of smart grids, a power service provider plays a crucial function in coordinating predicted power production with actual power demand in order to support power system stability. Insufficient protection means that, even with these advancements, data sent inside smart grids is still vulnerable to a variety of security risks and assaults. To enhance the security of electronic grid networks, a robust and effective authentication procedure must be put in place. The trouble of ensuring the security of communications among substations and electronic machines stays, even in shrewd lattice settings where SCADA (supervisory controls and data acquisition) frameworks assume a part in shielding associations between the administration place and substations. In this present circumstance, predominant security strategies frequently neglect to accomplish an unpretentious harmony among security and execution. With the expansion of an safe part on the smart machine side, this work incorporates security insurance into a moderation verification framework in view of elliptic bend cryptography. This clever convention handles the requirement for a finely tuned balance between the security and execution parts of shrewd lattices. Key arrangement, common verification, and character security are significant parts of the proposed convention. The convention's viability is upheld by a complete assessment utilizing the Gong-Needham-Yahalom rationale. This procedure is a critical improvement in reinforcing the security position of smart grids, particularly taking into account the continually changing climate of cyber threats and weaknesses. © 2023 IEEE.

关键词： Authentication Elliptic Curve Cryptography Privacy Protection SCADA Systems Security Smart Grids

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Algorithms for Detection of Anomalies in Data From Clinical Studies and Medical Registries

Algorithms for Detection of Anomalies in Data From Clinical ...

引用

2024 International Conference on Intelligent Systems for Cybersecurity, ISCS 2024

作者： Patil, Harshal Swati Sudheendramouli, H.C. Pratyush, Kumar Parikh, Swapnil Vishwakarma, Rajesh Kumar Sri Balaji University Maharashtra India Graphic Era Deemed to Be University Department of Computer Science and Engineering Uttarakhand 248002 India Gsss Simha SubbaMahalakshmi First Grade College Department of Computer Science and Application Mysuru570016 India Shri Vile Parle Kelavani Mandal's Institute of Pharmacy Department of Pharmaceutical Chemistry Maharashtra 424001 India Parul Institute of Engineering and Technology Parul University Department of Computer Science and Engineering Gujarat India Jaypee University of Engineering and Technology Department of Electronics and Communication Engineering Madhya Pradesh 473226 India

ISBN: (纸本)9798350375237

Anomaly detection is a burgeoning trend in data quality, underpinned by the fundamental principles of honesty and truthfulness in scientific research. Adherence to these principles is critical for advancing science and ensuring public trust in scientific findings. Deviations from these principles can lead to scientific misconduct or fraud, with potentially severe consequences, particularly in clinical research where human lives may be at stake. This work introduces an implemented algorithm for anomaly detection, leveraging basic machine learning concepts such as training on original datasets and testing on new datasets. Two training methods, resubstitution, and external validation (hold-out), were employed. To assess classification accuracy, the input dataset was deliberately augmented with anomalous records, and the detection results were compared against a vector of artificially generated anomalies. Prior studies may have overlooked factors such as varying anomaly occurrence rates and the need for manual threshold adjustments, which our algorithm addresses. By addressing these limitations, the algorithm contributes to the advancement of anomaly detection methodologies in clinical research and beyond. © 2024 IEEE.

关键词： Clinical research

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A Method for Identifying the Timeliness of Manufacturing Data Based on Weighted Timeliness Graph 9th

A Method for Identifying the Timeliness of Manufacturing Dat...

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19th International Conference on Advanced Data Mining and applications, ADMA 2023

作者： Liu, Zehua Ding, Xuefeng Jiang, Yuming Hu, Dasha College of Computer Science Sichuan University Chengdu610065 China Big Data Analysis and Fusion Application Technology Engineering Laboratory of Sichuan Province Chengdu610065 China

ISBN: (纸本)9783031466601

Timeliness is one of the important indicators of data quality. In industrial production processes, a large amount of dependent data is generated, often resulting in unclear timestamps. Therefore, this article combines the conclusion dependency graph into a process dependency graph to determine the identification order of the timeliness of each process data;By constructing a weighted timeliness graph (WTG) and path single flux, a data timeliness identification method that does not completely rely on timestamps is proposed. Finally, a time-effectiveness identification method based on weighted time-effectiveness graph was discussed through an example and 9 dependency rules, and the effectiveness of the method was verified through a set of experiments. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023.

关键词： Big data

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Research of trajectory similarity metric based on improved graph edit distance and ITS application 4

Research of trajectory similarity metric based on improved g...

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4th International Conference on Machine Learning and computer application, ICMLCA 2023

作者： Li, Zefeng Feng, Ji Yang, Degang College of Computer and Information Science Chongqing Normal University Chongqing401331 China Chongqing Engineering Research Center of Educational Big Data Intelligent Perception and Application Chongqing Normal University Chongqing401331 China

ISBN: (数字)9781510680265

ISBN: (纸本)9781510680258

In order to better analyze and process National Day highway travel trajectory data, a method and its calculation method for measuring the similarity between trajectories were proposed. Firstly, a new method for measuring trajectory similarity, namely TGI_GED, was introduced in this paper. Secondly, a graph neural network model was employed to enhance the computational efficiency of TGI_GED, enabling the calculation of TGI_GED between complex trajectories. Finally, this method combined clustering algorithms to further explore the characteristics and patterns of trajectories. Experimental results indicated that the graph neural network model not only significantly improved the computational efficiency of TGI_GED but also allowed for the calculation of TGI_GED between complex trajectories. Additionally, clustering results suggested that TGI_GED was more suitable for measuring the similarity between vehicle trajectories than graph edit distance. TGI_GED facilitated the discovery of latent patterns in trajectory data, offering a fresh perspective and solution for research in the field of vehicle trajectories. © 2024 SPIE.

关键词： Trajectories

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Deep U-Net Architecture with Curriculum Learning for Left Atrial Segmentation 1

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1st Left Atrial and Scar Quantification and Segmentation Challenge, LAScarQS 2022, held in Conjunction with the 25th International Conference on Medical Image Computing and computer-Assisted Intervention, MICCAI 2022

作者： Jiang, Lei Li, Yan Wang, Yifan Cui, Hengfei Xia, Yong Zhang, Yanning National Engineering Laboratory for Integrated Aero -Space -Ground -Ocean Big Data Application Technology School of Computer Science Northwestern Polytechnical University Xi’an710072 China School of Computer Science Northwestern Polytechnical University Xi’an710072 China

ISBN: (数字)9783031317781

ISBN: (纸本)9783031317774

Segmentation of the late-stage gadolinium-enhanced magnetic resonance imaging (LGE-MRI) is a critical step in the ablation therapy for atrial fibrillation (AF). In this work, we propose an end-to-end deep learning-based segmentation method for delineating 3D left atrial (LA) structures in multiple domains. The proposed method uses the 6 layers deep U-Net architecture as the segmentation backbone. Curriculum learning is integrated into the deep U-Net architecture, helping the network learn step by step from easy to difficult scene. We have tested normal and strong version of data augmentation methods, to verify the effect of reducing domain shifts. Other techniques like Fourier-based data augmentation and Swin Transformer Block have also been explored to further improve the segmentation performance. The experimental results demonstrate that the strong version of data augmentation method can reduce the domain shifts and achieve more accurate result, with mean Dice score of 0.881 on the validation set of LAScarQS 2022 challenge. The evaluation results demonstrate our method’s effectiveness on left atrial segmentation in multi-sequence cardiac magnetic resonance (CMR) data. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Curricula

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