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Trends in Material science and Inventive Materials (ICTMIM), International Conference on

作者： K. Venkatesh Gulshan Dhasmana Swapnil Uttamrao Deokar Chintan Thacker K G Saravanan R. K. Padmashini Department of Computer Science and Engineering Kalasalingam Academy of Research and Education Srivilliputhur Tamil Nadu Department of Computer Science & Engineering Graphic Era Deemed to be University Dehradun Departement of Mechanical Engineering Smt. Kashibai Navale College of Engineering Pune Computer Science and Engineering Faculty of Engineering and Technology Parul Institute of Engineering and Technology Parul University Gujarat Department of Mechatronics Engineering Sona College of Technology Salem Tamil Nadu Department of Electrical and Electronics Engineering AMET Deemed to be University Chennai Tamil Nadu

ISBN: (数字)9798331501488

ISBN: (纸本)9798331501495

Optimized Vehicle-to-Grid (V2G) Control System enhances energy management by leveraging Electric Vehicle (EV) as flexible storage units, enabling controlled energy exchange with the grid to improve power distribution and overall system efficiency. The system encounters difficulties in reducing total EV charging and discharging plan costs while maintaining high operational efficiency and power grid integrity throughout varying electrical circumstances. To address these challenges, this paper proposes a hybrid approach for optimized V2G control system. The proposed method integrates the Osprey Optimization Algorithm (OOA) and Position-Aware Graph Neural Network (PAGNN); therefore it is called the OOA-PAGNN technique. The main goal is to reduce total costs, enhance grid stability, and generate revenue for EV owners through an optimized V2G framework. OOA is utilized to optimize the charging and discharging schedules of EVs, ensuring effective energy flow management and enhancing grid stability. While, PAGNN is utilized to predict energy demand and generation by analyzing the spatial distribution of EVs, enabling optimized energy allocation. By then, the proposed method is implemented on the MATLAB platform and evaluated with various existing approaches such as Artificial Neural Network (ANN), Mixed-Integer Quadratic Programming (MIQP), Particle Swarm Optimization-Trained Artificial Neural Network (PSO-TANN), Recurrent Proximal Policy Optimization-Long Short-Term Memory (RPPO-LSTM) and Competitive Swarm Optimization-Imperialist Competitive Algorithm (CSO-ICA). The OOA-PAGNN model successfully optimizes V2G control while lowering expenses by -1.458$ while maintaining 98.6% system efficiency.

关键词： Vehicle-to-grid Costs Artificial neural networks Predictive models Power system stability Control systems Stability analysis Power grids Mathematical models Optimization

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Causal Fusion for Recommender System 4

Causal Fusion for Recommender System

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4th International Conference on Computing and Data science, CONF-CDS 2022

作者： Sun, Liwen Zhang, Chi Su, Zilin Zhang, Feiran Zhou, Yuen Department of Computer Science University of Illinois at Urbana-Champaign Urbana-Champaign United States School of Electronic Engineering Xidian University Xi'an China Faculty of Science University of Melbourne Melbourne Australia Department of Art & Science University of Toronto Toronto Canada Faculty of Art and Science Queen's University Kingston Canada

Recommender system suffers from huge selection bias from users, which makes utilizing causal inference to solve this problem becomes a necessary problem. However, traditional rating model only utilizing biased dataset and unbiased estimator cannot remarkably be improved. Thus, we fused the unbiased dataset into training model and combine it with jointly learning method to better improve the original baselines. Extensive experiments verify that the proposed doubly robust joint leaning method after fusing unbiased, missing rating data can significantly outperform the state-of-the-art methods and can dramatically reduce the error from rating model with the increasing number of unbiased data. © Published under licence by IOP Publishing Ltd.

关键词： Recommender systems

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Latent Communication in Artificial Neural Networks

arXiv

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arXiv 2024年

作者： Moschella, Luca GLADIA research lab Department of Computer Science Faculty of Information Engineering Informatics and Statistics Italy

As NNs (Neural Networks) permeate various scientific and industrial domains, understanding the universality and reusability of their representations becomes crucial. At their core, these networks create intermediate neural representations, indicated as latent spaces, of the input data and subsequently leverage them to perform specific downstream tasks. This dissertation focuses on the universality and reusability of neural representations. Do the latent representations crafted by a NN remain exclusive to a particular trained instance, or can they generalize across models, adapting to factors such as randomness during training, model architecture, or even data domain? This adaptive quality introduces the notion of Latent Communication – a phenomenon that describes when representations can be unified or reused across neural spaces. A salient observation from our research is the emergence of similarities in latent representations, even when these originate from distinct or seemingly unrelated NNs. By exploiting a partial correspondence between the two data distributions that establishes a semantic link, we found that these representations can either be projected into a universal representation (Moschella*, Maiorca*, et al., 2023), coined as Relative Representation, or be directly translated from one space to another (Maiorca* et al., 2023). Intriguingly, this holds even when the transformation relating the spaces is unknown (Cannistraci, Moschella, Fumero, et al., 2024) and when the semantic bridge between them is minimal (Cannistraci, Moschella, Maiorca, et al., 2023). Latent Communication allows for a bridge between independently trained NN, irrespective of their training regimen, architecture, or the data modality they were trained on – as long as the data semantic content stays the same (e.g., images and their captions). This holds true for both generation, classification and retrieval downstream tasks;in supervised, weakly supervised, and unsupervised settings;and

关键词： Neural networks

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A Novel Deep Neural Network-Based Prediction Model for Identifying Diseases in Tomato Leaves

A Novel Deep Neural Network-Based Prediction Model for Ident...

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2023 IEEE International Conference on Electrical, computer and Energy Technologies, ICECET 2023

作者： Kumar, T. Ananth Oviya, S. Ajagbe, Sunday Adeola Ananth, Christo Aiyeniko, Olukayode Adigun, Matthew O. Ifet College of Engineering Computer Science and Engineering Villupuram India University of Zululand South Africa First Technical University Ibadan200255 Nigeria Samarkand State University Faculty of International Education Programs Uzbekistan Lagos State University Computer Science Department Lagos Nigeria University of Zululand Kwadlangezw 3886 South Africa

ISBN: (纸本)9798350327816

Ensuring global food security is essential for the various stakeholders involved. Accurate identification and categorization of plant diseases are imperative. The emergence of novel solutions in image categorization can be attributed to the advancements in deep learning-based techniques. However, the integration of these technologies in low-end devices requires processing systems that are efficient, precise, and cost-effective. This study presents a concise and practical approach utilizing transfer learning to detect anomalies in tomato leaves. The utilization of illumination correction to enhance leaf images represents an effective preprocessing technique for improving categorization. The methodology employed in our study involves utilizing a hybrid model consisting of a pre-trained MobileNetV2 architecture and a classifier network to gather data and generate accurate predictions. Runtime augmentation assumes the responsibility of conventional augmentation methods to prevent data breaches and facilitate management. © 2023 IEEE.

关键词： CLAHE Data Augmentation MobileNetV2 transfer learning

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An Unsupervised Machine Learning Approach for Heart Disease Prediction

An Unsupervised Machine Learning Approach for Heart Disease ...

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International Conference on Artificial Intelligence and Data sciences (AiDAS)

作者： Wing Son Loh Yu Jiun Lim Lloyd Ling Ren Jie Chin Chen Kang Lee Ming Jie Lee Department of Mathematical and Engineering Sciences Lee Kong Chian Faculty of Engineering and Science Universiti Tunku Abdul Rahman Kajang Malaysia Department of Civil Engineering Lee Kong Chian Faculty of Engineering and Science Universiti Tunku Abdul Rahman Kajang Malaysia Department of Internet Engineering and Computer Science Lee Kong Chian Faculty of Engineering and Science Universiti Tunku Abdul Rahman Kajang Malaysia

ISBN: (数字)9798331528553

ISBN: (纸本)9798331528560

Cardiovascular diseases (CVDs) persist as a primary cause of mortality on a global scale, necessitating effective prediction methods. This study presents a novel modelling approach utilizing the Self-Organizing Map (SOM), an unsupervised machine learning approach, for CVD prediction. The SOM model employed to analyze and cluster patient data based on intrinsic patterns without predefined labels, enabling the identification of high-risk individuals. Results demonstrated that the SOM model effectively differentiates between healthy and at-risk patients, offering a robust approach for early detection. The SOM presented to be the best predictive model for the CVD data prediction with the highest accuracy (84.44%); precision (89.13%); recall (82.00%); F1-score (85.42%). The U-Matrix of SOM as a visualization tool provided insightful representations that highlighted two discernible clusters, effectively illustrating the health status of individuals with similar health conditions concerning CVD. This facilitates a better understanding and management of CVD. The research findings suggested the potential of integrating SOM into clinical workflows, offering a potent tool for healthcare professionals in the fight against CVD.

关键词： Self-organizing feature maps Accuracy Machine learning Predictive models Prediction algorithms Data models Robustness Particle swarm optimization Optimization Genetic algorithms

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Distance Metric-Based Classification Comparisons for a Brain-computer Interface Authentication

Distance Metric-Based Classification Comparisons for a Brain...

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IEEE International Conference on Systems, Man and Cybernetics

作者： Tyree Lewis Rupal Agarwal Marvin Andujar Department of Computer Science and Engineering University of South Florida University of South Florida United States Department of Computer Science and Engineering Tampa FL USA Faculty of Computer Science and Engineering Tampa FL USA

The rise of security concerns has spurred on-going research into Brain-computer Interfaces (BCI) based authentication. These applications utilize electroencephalogram (EEG) signals, due to their properties that can enhance security systems. In previous studies, EEG data has been incorporated into various authentication systems to compare the performance of new and existing classification methods. However, using EEG data to compare the performance of distance metrics in a P300-based BCI authentication system has not been explored yet. In this study, EEG data is used to determine the most effective distance metric for authenticating users in a closed-loop system. To accomplish this task, we conducted a longitudinal study to evaluate three distance metrics (Cosine, Correlation and Chebyshev) while participants interacted with our BCI authentication system. Our results indicated that the Cosine similarity outperformed all other distance metrics for each user.

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Genetic Algorithm-Based Feature Selection for Accurate Breast Cancer Classification

Genetic Algorithm-Based Feature Selection for Accurate Breas...

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2023 International Conference on IT and Industrial Technologies, ICIT 2023

作者： Shabbir, Attia Ali, Raja Hashim Shabbir, Muhammad Zeeshan Abideen, Zain Ul Khan, Talha Ali Ijaz, Ali Zeeshan Ali, Nisar Imad, Muhammad Bakar, Muhammad Abu Gik Institute of Engg. Sciences & Technology Faculty of Computer Science & Engineering Topi Pakistan University of Europe for Applied Sciences Dept. of Technology & Software Engineering Berlin Germany Department of Electrical Engineering Lahore Pakistan BCMaterials Basque Center for Materials Applications & Nanostructure Basque Country Leioa Spain University of Regina Faculty of Electronic Systems Engineering Regina Canada

ISBN: (纸本)9798350315974

Breast cancer is a significant global healthcare challenge, particularly in developing and underdeveloped countries, with profound physical, emotional, and psychological consequences, including mortality. Timely diagnosis and accurate treatment are crucial in addressing this issue. We propose the utilization of a feature selection technique to identify the most relevant features from among all features for breast cancer diagnosis, and show that Genetic Algorithms are impressive for this task. The study compares the results of GA with no selection and an alternative method, Principle Component Analysis (PCA). Three machine learning models, all based on supervised learning with data split into training and test data, are employed for binary classification using the selected feature subset. The evaluation metrics employed encompass accuracy, precision, recall, and F1-score. Among the selected models, Random Forest demonstrates the most favorable outcomes, achieving an accuracy score of 0.96, precision score of 0.96, recall value of 0.98, and an F1-score of 0.97. These results underscore the effectiveness of GA in feature selection for breast cancer diagnosis. Consequently, the integration of Genetic Algorithms (GA) with Random Forest showcases the superior performance among the evaluated models. © 2023 IEEE.

关键词： Genetic algorithms

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Harnessing Long Short Term Memory Networks for Stock Market Forecasts

Harnessing Long Short Term Memory Networks for Stock Market ...

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engineering & Computing Technologies (ICECT), International Conference on

作者： Alishba Faheem Usama Arshad Department of Artificial Intelligence Faculty of Computer Science & Engineering Ghulam Ishaq Khan Institute of Engineering Sciences and Technology Topi Pakistan

ISBN: (数字)9798350349719

ISBN: (纸本)9798350349726

Stock market trend prediction is a classical re-gression problem with effects on stock exchange. This research is presenting a detailed exploration of how Long Short Term Memory (LSTM) networks may be used for predicting stock market trends. The LSTM model was employed instead of the conventional Recurrent Neural Networks (RNNs), to learn from historical financial data. The study examines the operations of the LSTM model and the optimization of hyper parameters that lead to accurate prediction outcomes from the given dataset. Our model gave encouraging results that underlined the LSTM model's prowess in capturing intricate temporal dependencies in data. The model's result were in a low Root Mean Square Error (RMSE) score and an impressively high accuracy level that were good indicators of the model's adaptability at trend identification. Our study amplifies the need for advanced machine learning algorithms like LSTM that increase prediction accuracy while offering insights into decision-making processes for both investors and financial analysts at the same time. Further, it also hints at the potential of deep learning within the stock market. The paper concludes by discussions on all possible improvements to the existing model, future directions, and how incorporating additional data types can increase our predictive accuracy. This research primarily underscores the significant role LSTM plays in deciphering complex financial data trends accurately.

关键词： Deep learning Adaptation models Accuracy Recurrent neural networks Machine learning algorithms Predictive models Market research

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Optimized Control Strategy for LC L-Filtered Three-Phase Voltage-Source Converters in Adverse Grid Conditions

Optimized Control Strategy for LC L-Filtered Three-Phase Vol...

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IEEE Industry Applications Society Annual Meeting (IAS)

作者： Ahmad Ali Nazeri Jens Friebe Peter Zacharias Department of Power Electronics Faculty of Electrical Engineering / Computer Science University of Kassel Kassel Germany

ISBN: (数字)9798350372717

ISBN: (纸本)9798350372724

This paper presents the precise AC current control within three-phase grid-connected voltage source converters featuring an $L C L$ filter, particularly in distributed generation systems. Traditional control methodologies such as proportionalintegral (PI) and proportional resonant (PR) are conventionally employed to ensure zero steady-state error at the fundamental frequency. However, to effectively address high-order harmonic currents, this study introduces the utilization of proportional resonant plus multi-resonant (PR-MR) techniques within the current control loop to mitigate current harmonics induced by dead time and distorted grid voltages. The paper offers a comprehensive current control design methodology, incorporating a rigorous system stability analysis. Experimental findings demonstrate a significant reduction in total harmonic distortion of grid currents, achieving approximately 1.1 % under distorted grid conditions, thereby meeting the stringent IEEE 1547 standard. The proposed controllers are implemented on the TMDSDOCK28379D 32bit floating-point digital signal processor (DSP). The system's performance is evaluated through simulations conducted in the MATLAB/Simulink environment and validated experimentally on a 7.5 kW three-phase $L C L$ -filtered grid-connected VSC employing the proposed control strategies.

关键词： Current control Total harmonic distortion System performance Digital signal processors Harmonic analysis Steady-state Voltage control Power harmonic filters Standards Voltage-source converters

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Adaptive Memory Differential Evolutionary Algorithm for Network Management Using Variants of Dominating Set Models

Adaptive Memory Differential Evolutionary Algorithm for Netw...

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Intelligent Methods, Systems, and Applications (IMSA)

作者： Abdel-Rahman Hedar Fatma El-Zahraa Tarek Mostafa Mohamed Gamal A. El-Sayed Department of Computer Science Faculty of Computers & Information Assiut University Assiut Egypt Department of Electrical Engineering Faculty of Engineering Assiut University Assiut Egypt Department of Computer Science in Jamoum Umm Al-Qura University Makkah Saudi Arabia

ISBN: (数字)9798350362633

ISBN: (纸本)9798350362640

In wireless sensor networks, where multiple sensors are typically concentrated in a confined area, determining the optimal size of wireless sensors to use for communication and coordination over the network is essential due to concerns about the preservation of energy and battery. In this paper, we introduce new competitive memory-based differential evolution methods to solve wireless sensor network management problems using variants of dominating set models. The problems considered are formulated as constrained optimization problems that are transformed into unconstrained optimization problems using the penalty principle. Then, differential evolution with several memory-based enhanced procedures is designed to find solutions to the reformed optimization problems. Two versions of the proposed Adaptive Memory Differential Evolutionary Algorithm (AMDEA) are designed to deal with the minimum dominating set problem and its variants. The results obtained from the proposed methods show that they are promising compared to some benchmark methods.

关键词： Wireless communication Wireless sensor networks Adaptation models Memory management Evolutionary computation Benchmark testing Sensor systems and applications

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