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International Conference on Electrical Energy Systems (ICEES)

作者： Virum Ranka Dwisha Shah Devam Shah Pradnya Saval Dept. of Computer Science and Engineering (Data Science) Dwarkadas J. Sanghvi College of Engineering Mumbai India

ISBN: (数字)9798350353778

ISBN: (纸本)9798350353785

In the pursuit of sustainable living and enhanced user comfort, the integration of smart technologies into everyday devices has become paramount. This paper highlights the importance of dynamic lighting control in improving energy efficiency and the overall quality of life. Traditional lighting systems often fail to account for the dynamic nature of human activities, resulting in unnecessary energy wastage and suboptimal lighting conditions. The proposed system seeks to bridge the gap between existing lighting systems and the evolving needs of modern indoor spaces, with its focus on adaptability, efficiency, and user-centricity, promoting a more comfortable and nurturing environment. Although earlier studies have explored various aspects of smart lighting technologies, there remains a paucity of comprehensive solutions that integrate machine learning with sensor technologies to create truly adaptive environments. Novel algorithms have been utilized to optimize both visual comfort and energy efficiency. This system achieves up to 45-70% energy savings compared to existing lighting systems, making it a promising solution for both environmental and economic concerns.

关键词： Visualization Machine learning algorithms Energy conservation Smart homes Activity recognition Energy efficiency Real-time systems Trajectory Sensors Smart lighting

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Empirical Analysis of Classical and Quantum Algorithms for Portfolio Optimization: Enhancing Financial Decision-Making Through Quantum Computing

Empirical Analysis of Classical and Quantum Algorithms for P...

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Sustainable Communication Networks and Application (ICSCNA), International Conference on

作者： Krish Valecha Anuradha Yeole Atharv Salian Shruti Mathur Dept. of Computer Science and Engineering (Data Science) Dwarkadas J. Sanghvi College of Engineering Mumbai India

ISBN: (数字)9798331530013

ISBN: (纸本)9798331530020

This research investigates the efficacy of quantum and classical algorithms in the context of portfolio optimization, focusing on a dataset comprising 20 equities from India's National Stock Exchange over a duration of three years. The study employs quantum methodologies such as the Quantum Approxi-mate Optimization Algorithm (QAOA), the Variational Quantum Eigensolver (VQE), and the Exact Minimum Eigen Solver, alongside conventional strategies including Mean-Variance Op-timization, Genetic Algorithms, and Simulated Annealing. The methodology for this study includes data preprocessing, issue designing, and performance evaluation based on metrics such as MSE, RMSE, and Mean Absolute Error. The results obtained show quantum algorithms exhibit better performance over the classical methods regarding prediction accuracy and risk-return profiles, especially the Exact Minimum Eigen hSolver and QAOA. The Exact Minimum Eigen Solver and QAOA had the lowest MSE values (0.006 and 0.005, respectively), whereas the best-performing classical technique, the Genetic Algorithm, had an MSE of 0.0105. These findings imply that quantum algorithms provide significant advantages in portfolio optimization problems, with the potential to revolutionize financial optimization tactics. The research can be further unfolded into avenues for future inquiry, including expansion of the choice of assets, alternative data sources integration, as well as hybrid quantum-classical models to efficiently enhance portfolio management strategies.

关键词： Quantum algorithm Accuracy Soft sensors Simulated annealing Transforms Prediction algorithms Stock markets Optimization Portfolios Genetic algorithms

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Comparative Analysis of Machine Learning Techniques for Arabic Text Classification Using the SANAD dataset 11

Comparative Analysis of Machine Learning Techniques for Arab...

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11th International Conference on Mechatronics and Robotics engineering, ICMRE 2025

作者： Attar, Hani Sharrab, Yousef Smadi, Saja Jbaily, Safa Zarqa University Faculty of Engineering Zarqa Jordan University of Business and Technology College of Engineering Jeddah21448 Saudi Arabia Isra University Department of Data Science and Artificial Intelligence Amman Jordan Yarmouk University Dept. Computer Science Irbid Jordan

ISBN: (纸本)9798331509293

Applying Machine Learning (ML) models for Arabic text classification is a growing research field that has recently raised a large number of research. Generally, classifying text involves selecting a category based on its content from a list of pre-established categories. data classification is among the most popular Natural Language Processing (NLP) activities. Moreover, automatic article classifications, such as the Single-label Arabic News Articles dataset (SANAD), are desperately needed to improve the retrieval of the requested data, which this paper investigates. The ML leverages large datasets to improve efficiency;indeed, by locating focal spots within the text, the learning mechanisms facilitate attention tools better suited for handling lengthy texts. The proposed work employs a dataset of 7,000 articles of separately classified Arab news items and then breaks them into seven categories of 1,000 news stories each: politics, education, sports, religion, culture, finance, and medicine. The investigated SANAD consists of three primary datasets from three news portals: Akhbarana, AlArabiva, and AlCtrl. Finally, to obtain trustable information, this study implements a variety of Conventional ML techniques, K-Nearest Neighbor (KNN), Artificial Neural Network (ANN), Support Vector Machine (SVM), and Logistic Regression model to classify the investigated dataset. Our results show that the suggested solution outperformed the analytical search in the Arabic language in terms of accuracy, whereas the Logistic Regression model shows the best results among the seven utilized publicly available dataset classifications. © 2025 IEEE.

关键词： Logistic regression

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Opportunistic Balancing and Strategic Insights through Deep Reinforcement Learning in Asset Allocation

Opportunistic Balancing and Strategic Insights through Deep ...

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International Conference on Computing and Networking Technology (ICCNT)

作者： Harsh Shetye Vaidehi Shah Jay Chotalia Pradnya Saval Dept. of Computer Science and Engineering (Data Science) Dwarkadas J. Sanghvi College of Engineering Mumbai India

ISBN: (数字)9798350370249

ISBN: (纸本)9798350370270

Asset allocation, diversification of investments across various asset classes, presents significant challenges in accurately forecasting expected returns and adapting to dynamic market conditions. This research explores a novel approach, “Opportunistic Balancing and Strategic Insights through Deep Reinforcement Learning in Asset Allocation,” by leveraging Deep Reinforcement Learning (DRL) to optimize dynamic asset allocation strategies. Specifically, we investigate the Advantage Actor-Critic (A2C) Model, renowned for its stability and superior performance with large batch sizes. Our approach aims to capture short to medium-term market trends and anomalies, enabling strategic asset allocation to enhance risk-adjusted returns. We evaluate the DRL models’ performance against traditional benchmarks, including the portfolio index, the naive equal-weight allocation, and the maximum Sharpe strategy (mean-variance optimization). The DRL models exhibit relatively superior performance compared to the mean-variance portfolio during periods preceding 2020, with the A2C model outperforming the benchmark DJIA index across all considered market conditions. Notably, the A2C model demonstrated a Sharpe ratio of 0.733, surpassing the DJIA index’s Sharpe ratio of 0.406. Additionally, during the 2020 market downturn, the A2C model exhibited a faster recovery rate than the benchmark index, regaining positive returns swiftly.

关键词： Analytical models Uncertainty Stability criteria Benchmark testing Deep reinforcement learning Dynamic scheduling Resource management Indexes Portfolios Optimization

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Performance Analysis of Hybrid Quantum-Classical Convolutional Neural Networks for Audio Classification

Performance Analysis of Hybrid Quantum-Classical Convolution...

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International Conference on Computing and Networking Technology (ICCNT)

作者： Yash Thakar Bhuvi Ghosh Vishma Adeshra Kriti Srivastava Dept. of Computer Science and Engineering (Data Science) Dwarkadas J. Sanghvi College of Engineering Mumbai India

ISBN: (数字)9798350370249

ISBN: (纸本)9798350370270

Audio signals being high-dimensional and complex pose challenges for classical machine learning techniques in terms of computation and generalization on real-world data. This paper evaluates the use of hybrid quantum-classical convolutional neural networks (QC-CNN) that leverage quantum effects like superposition and entanglement for audio classification using mel-spectrograms obtained from audio data. Evaluated on both small-sized and large-sized datasets, the proposed QC-CNN model gave comparable training accuracy with classical CNN (Convolutional Neural Network) on the smaller dataset but outperformed classical CNN on test accuracy ($\mathbf{95.04 \%}$ vs $\mathbf{92.88 \%}$) for a larger birdsong dataset and reduced overfitting, thus highlighting the potential advantages of QC-CNNs for audio data. The QC-CNN exhibited higher cross-entropy loss in case of the small-sized dataset which was further significantly reduced when evaluated on the large-sized birdsong dataset. The work demonstrates the application of QC-CNN for audio classification.

关键词： Training Accuracy Quantum computing Quantum entanglement Neural networks Computer architecture data models Performance analysis Convolutional neural networks Testing

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Long Short-Term Memory-Based Next Keyword Prediction 6

Long Short-Term Memory-Based Next Keyword Prediction

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6th IEEE International Conference on Emerging Smart Computing and Informatics, ESCI 2024

作者： Rajkumar, K. Karthikeyan, S. Hariharan, U. College of Engineering and Technology SRM Institute of Science and Technology Dept. of Data Science and Business Systems Tamilnadu Kattankulathur India Department of CSE Bangalore562112 India Apex Institute of Technology Chandigarh University Department of CSE Punjab India

ISBN: (纸本)9798350306613

This paper focuses on a method that uses an LSTM model to predict the next word of a sentence. Based on Katz's Backoff model, it has been designed to improve and further succeed in our previous work. The main intention here is to show how effective the LSTM model is when compared to Katz's backoff model in terms of the number of n-grams required to predict the next word, and it also helps you predict more accurately. Here LSTM is used for processing, prediction and classification. Since the LSTM model is efficient and has been modified to the task at hand, the model offers very highly accurate predictions even though have used only a tiny portion of the entire corpus. When comparing the existing model, the proposed model predicts the word with accuracy of 90%. © 2024 IEEE.

关键词： Katz's backoff Model LSTM Prediction

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Real-Time Injury Risk Assessment in Athletes Based on Relative Joint Angles

Real-Time Injury Risk Assessment in Athletes Based on Relati...

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Emerging Smart Computing and Informatics (ESCI), Conference on

作者： Atharv Salian Bhuvi Ghosh Harsh Shetye Parth Kapadia Kriti Srivastava Dept. of Computer Science and Engineering (Data Science) Dwarkadas J. Sanghvi College of Engineering Mumbai India

Preventing running injuries is critical for track and field athletes. This work presents a novel machine learning methodology to automatically detect injury risk from videos by analyzing biomechanical technique. Joint angles from anatomical pose estimates serve as descriptive features input to ML classifiers. Statistical tests verified significant distribution differences in angles between injured and non-injured states, evidencing utility as injury markers. Multi-frame analysis integrating temporal context attained 0.735 accuracy on held-out test data using SVM, surpassing single-frame approaches. The scoring function showed no significant statistical drift from training to test, providing evidence of strong generalization. By automating nuanced pose pattern quantification imperceptible to the naked eye, the proposed video screening enables early at-risk athlete identification for targeted preventative intervention before injuries develop.

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Optimizing Fashion Recommendations for Diverse Body Types: An Epsilon Greedy Approach

Optimizing Fashion Recommendations for Diverse Body Types: A...

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International Conference on Computing and Networking Technology (ICCNT)

作者： Anuradha Yeole Ami Desai Kriti Srivastava Dept. of Computer Science and Engineering (Data Science) Dwarkadas J. Sanghvi College of Engineering Mumbai India

ISBN: (数字)9798350370249

ISBN: (纸本)9798350370270

As the fashion industry evolves, the need for sophisticated recommendation systems on e-commerce platforms becomes increasingly critical. This paper introduces a novel reinforcement learning-based recommendation system tailored for diverse body types using the Epsilon Greedy algorithm. Our dual-layered model architecture integrates content-based filtering to precisely identify user body types through innovative physical measurements and photographic analysis. This groundwork allows for a dynamic reinforcement learning strategy to refine and personalize fashion recommendations. Statistical analysis confirmed that our system significantly enhances recommendation precision, achieving a novelty score of $\mathbf{84\%}$ and a click-through rate of 46%, surpassing traditional static models. The adaptive reinforcement mechanism demonstrated robust generalization across various user interactions without significant deviation in performance. By personalizing fashion recommendations to individual user preferences and body types, our system facilitates more accurate and engaging shopping experiences, effectively addressing the challenges of personalization in the fast-evolving e-commerce sector.

关键词： Industries data privacy Analytical models Filtering Statistical analysis Reinforcement learning Market research Electronic commerce Object recognition Recommender systems

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Revolutionizing Residential EV Charging Through IoT-Enabled Smart Energy Metering for Sustainability

Revolutionizing Residential EV Charging Through IoT-Enabled ...

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Mobile Computing and Sustainable Informatics (ICMCSI), International Conference on

作者： N. Sivakumar S. Charles Raja M.Meenakshi Devi Saraswathi Meena R Dept. of EEE Thiagarajar College of Engineering Madurai Tamilnadu India Dept. of Applied Mathematics and Computational Science Thiagarajar College of Engineering Madurai Tamilnadu India

ISBN: (数字)9798331522667

ISBN: (纸本)9798331522674

The rapid growth in Electric Vehicle (EV) adoption poses substantial challenges to residential energy infrastructures, especially in terms of the strain it places on local grids during charging. This paper proposes an IoT-based Smart Energy Meter System (SEM) designed to efficiently manage EV charging in residential environments. The system integrates IoT technology with advanced energy metering features, enabling real-time monitoring and control of charging operations. The system is composed of intelligent energy meters for real-time monitoring, an IoT-enabled hub for data collection and processing, and an intuitive consumers interface for customizing and managing EV charging schedules. By continuously monitoring energy consumption and charging demands, the system optimizes charging schedules to reduce peak loads, lower energy costs, and enhance grid stability. It provides residents with insights into their energy usage and charging patterns, empowering them to make informed decisions to reduce their carbon footprint. Furthermore, the system seamlessly integrates with renewable energy sources and energy storage solutions, promoting clean energy usage for EV charging. Through cloud-based analytics and predictive maintenance, it ensures reliable and uninterrupted charging services. The proposed IoT-based SEM system offers a scalable and sustainable solution for managing EV charging in residential settings, supporting electric mobility while improving energy efficiency and promoting environmental sustainability.

关键词： Meters Schedules Power system stability Message services Electric vehicle charging Real-time systems Stability analysis Reliability Monitoring Predictive maintenance

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Distributionally Robust Optimization as a Scalable Framework to Characterize Extreme Value Distributions

arXiv

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arXiv 2024年

作者： Kuiper, Patrick Hasan, Ali Yang, Wenhao Ng, Yuting Bidkhori, Hoda Blanchet, Jose Tarokh, Vahid Dept. of Electrical and Computer Engineering Duke University DurhamNC United States Dept. of Management Science and Engineering Stanford University StanfordCA United States Computational and Data Sciences Dept. George Mason University FairfaxVA United States

The goal of this paper is to develop distributionally robust optimization (DRO) estimators, specifically for multidimensional Extreme Value Theory (EVT) statistics. EVT supports using semi-parametric models called max-stable distributions built from spatial Poisson point processes. While powerful, these models are only asymptotically valid for large samples. However, since extreme data is by definition scarce, the potential for model misspecification error is inherent to these applications, thus DRO estimators are natural. In order to mitigate over-conservative estimates while enhancing out-of-sample performance, we study DRO estimators informed by semi-parametric max-stable constraints in the space of point processes. We study both tractable convex formulations for some problems of interest (e.g. CVaR) and more general neural network based estimators. Both approaches are validated using synthetically generated data, recovering prescribed characteristics, and verifying the efficacy of the proposed techniques. Additionally, the proposed method is applied to a real data set of financial returns for comparison to a previous analysis. We established the proposed model as a novel formulation in the multivariate EVT domain, and innovative with respect to performance when compared to relevant alternate proposals. © 2024, CC BY.

关键词： Poisson distribution

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