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2025 IEEE PES Grid Edge Technologies Conference and Exposition, Grid Edge 2025

作者： Bhattacharjee, Kaustav Kundu, Soumya Chakraborty, Indrasis Dasgupta, Aritra New Jersey Institute of Technology Department of Data Science United States Pacific Northwest National Laboratory Optimization and Control Group United States Lawrence Livermore National Laboratory Center for Applied Scientific Computing United States

ISBN: (纸本)9798350352528

Net load forecasting is crucial for energy planning and facilitating informed decision-making regarding trade and load distributions. However, evaluating forecasting models' performance against benchmark models remains challenging, thereby impeding experts' trust in the model's performance. In this context, there is a demand for technological interventions that allow scientists to compare models across various timeframes and solar penetration levels. This paper introduces a visual analytics-based application designed to compare the performance of deep-learning-based net load forecasting models with other models for probabilistic net load forecasting. This application employs carefully selected visual analytic interventions, enabling users to discern differences in model performance across different solar penetration levels, dataset resolutions, and hours of the day over multiple months. We also present observations made using our application through a case study, demonstrating the effectiveness of visualizations in aiding scientists in making informed decisions and enhancing trust in net load forecasting models. © 2025 IEEE.

关键词： AI/ML net load forecasting visual analytics

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A generalized software reliability prediction model for module based software incorporating testing effort with cost model

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Life Cycle Reliability and Safety Engineering 2024年第4期13卷 419-436页

作者： Yadav, Akshay Kumar Srivastava, Shilpa Pant, Millie Department of Applied Mathematics and Scientific Computing Indian Institute of Technology Roorkee 247001 India Christ University Karnataka Bengaluru 560029 India Mehta Family School of Data Science and Artificial Intelligence Indian Institute of Technology Roorkee 247667 India

As software innovation has advanced, it has been noted that the testing effort function (TEF) is one of the key factors influencing the improvement of software reliability. This paper presents a simplified model which incorporates the testing effort for the reliability growth of a software. A closed form solution has been derived for the reliability of the software. This study examines the impact of testing efforts on a software reliability model based on NHPP. The sensitivity analysis has been made available to investigate how the created model's system parameters affect the cost function, mean value function, and software’s reliability. The parameters of the model have been estimated using the non-linear least square estimation (MLE) method in MATLAB software. Additionally, a warranty cost model is constructed to assess the optimal release policy for the software. The general form of the reliability expression involves elliptic integrals, which can be computed easily through a software like Mathematica. We have derived analytical solutions for reliability pertaining to several particular cases. Optimal release time for the software product has been calculated for some particular cost-sets. Goodness of fit curves have been plotted to compare the proposed model with some well-known existing SRGMs. Numerical illustrations are provided to bolster the analytical outcomes. © The Author(s), under exclusive licence to Society for Reliability and Safety (SRESA) 2024.

关键词： Maximum likelihood estimation (MLE) Non-homogeneous Poisson process (NHPP) Optimal release time (ORT) Software reliability growth model (SRGM) Testing effort function (TEF) Warranty cost model

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Basketball Win Percentage Prediction using Ensemble-based Machine Learning 6

Basketball Win Percentage Prediction using Ensemble-based Ma...

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6th International Conference on Electronics, Communication and Aerospace Technology, ICECA 2022

作者： Sikka, Dhruv Rajeswari, D. School of Computing College of Engineering and Technology Srm Institute of Science and Technology Department of Networking and Communications Kattankulathur603203 India School of Computing Srm Institute of Science and Technology Department of Data Science and Business Systems Kattankulathur603203 India

ISBN: (纸本)9781665482714

Basketball is a prominent team sport played on a rectangular court between two teams of five players each. The goal is to shoot the ball through the defender's hoop, which is high on a backboard at each end of the court, while stopping the other team from shooting through its own hoop. Predicting how sports will turn out is an interesting sports analytics problem because it gives valuable data for running the sports market. In this work, data from NBA (National Basketball Association) basketball teams is analyzed, and a machine learning model is made that uses advanced statistical measures and performance data to evaluate and predict the winning percentage of different teams. Voting Regression is used to put together algorithms like Random Forest, Decision Trees, Linear Regression, Gaussian Regression, and Gradient Boosting into a single unit that can predict a team's win percentage over the course of a season with 93.3% accuracy. © 2022 IEEE.

关键词： Forecasting

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Semantic Segmentation of Satellite Imagery Using Optimized U-Net Model 1st

Semantic Segmentation of Satellite Imagery Using Optimized U...

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1st International Research Conference on computing Technologies for Sustainable Development, IRCCTSD 2024

作者： Kavipriya, J. Vadivu, G. Department of Data Science and Business Systems School of Computing SRM Institute of Science and Technology Tamilnadu Kattankulathur603203 India

ISBN: (纸本)9783031823855

The classification of land cover is crucial due to increasing demands and population expansion. Segmentation of the terrain is utilized in environmental monitoring to accurately identify and delineate areas of agriculture, buildings, and water bodies. Satellite imagery is quite valuable for the purpose of segmentation. Deep learning models offer automated and precise segmentation of satellite imagery. The U-Net architecture is widely employed as an encoder-decoder structure for the purpose of segmenting medical and remote sensing images. This study investigates the process of dividing satellite images into segments using an optimized U-Net model. Our model utilizes improved equal weights to calculate loss functions. In addition, we have implemented a dropout layer for each convolutional block in order to decrease the computational time required for feature extraction in the encoder layer of the optimized U-Net model. Our study examines the Dubai dataset, which has six distinct classes. The optimized U-Net model we propose achieves an accuracy of 81.5% and Intersection over Union (IoU) values of 61.7% for segmenting the dataset. These metrics are acquired with a reduced number of epochs, hence decreasing the computational expense. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Semantic Segmentation

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Exploring Sensor-Based Smart Farming Technologies in the Internet of Things (IoT) 1

Exploring Sensor-Based Smart Farming Technologies in the Int...

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1st IEEE International Conference on computing and data science, ICCDS 2024

作者： Palarimath, Suresh Maran, Pyingkodi Thenmozhi, K. Balakumar, C. Sujatha, T. Wilfred Blessing, N.R. College of Computing and Information Sciences University of Technology and Applied Sciences Information Technology Department Salalah Oman Kongu Engineering College Department of Computer Applications Erode India Department of Compute Science Bengaluru India Karpagam Academy of Higher Education Department of Computer Science Coimbatore India Sri Krishna College of Engineering and Technology Department of Artificial Intelligence and Data Science Coimbatore India

ISBN: (纸本)9798350365337

The Internet of Things (loT) is a new technology trend that is being used in almost every area of human life. IoT is used almost every aspect of people's lives. Significantly, with a projected increase in the world's population to 9.7 billion by 2050, agricultural output would need to increase at an even more rapid rate to fulfill the requirement. Modern tools, notably the Internet of Things, make this a reality. The IoT makes it possible for farms to function without human labor. It has several potential applications in agriculture, including large- scale farming, greenhouse farming and management. The sensors serve as the most crucial component of the loT. Sensing devices are mostly used for the purpose of learning about the soil and its surroundings. The sensor has several applications in agriculture, including but not limited to NPK (nitrogen, phosphorus, and potassium) measurement, disease detection, and soil moisture analysis. This study discusses how IoT applications contribute to efficient farming practices. It shows how the IoT can be applied to agriculture and exhibits the many sensors, applications, problems, strengths, and shortcomings that underpin this field. © 2024 IEEE.

关键词： Internet of things

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Metaheuristic Optimization for Path Planning in UAV Networks for Long-Distance Inspection Tasks 13

Metaheuristic Optimization for Path Planning in UAV Networks...

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13th International Conference on System Modeling and Advancement in Research Trends, SMART 2024

作者： Akshya, J. Manochitra, M. Indu Poornima, R. Jeevitha, T. Saravanakumar, M. Sundarrajan, M. Choudhry, Mani Deepak Srm Institute of Science and Technology Department of Computational Intelligence Chennai India Coimbatore India Institute of Technology Department of Information Technology KGiSL Coimbatore India Nehru Institute of Engineering and Technology Department of Artificial Intelligence and Data Science Coimbatore India Srm Institute of Science and Technology Department of Networking and Communications Chennai India Srm Institute of Science and Technology Deportment of Computing Technologies Chennai India

ISBN: (纸本)9798350380583

With broad applicability in surveillance, delivery, and environmental monitoring, multi-drone path planning has received a great deal of attention recently. So far, wide applicability has been explored by various applications of Genetic Algorithms, Particle Swarm Optimization, and Ant Colony Optimization, which have already established reliable solutions to navigation and coverage through drone path optimization. However, in dynamic, obstacle-dense environments, these algorithms fail to adapt in real-time with energy efficiency. Traditional GA and PSO approaches can be effective for generating an initial path but fail to respond to collision avoidance and efficient resource management. In complex and unpredictable conditions, ACO fails to converge rapidly, which leads to suboptimal paths and increased energy consumption. To overcome such barriers, we present here an HGARL for enhanced multi-drone path optimization. The proposed algorithm thus combines the population-based search capability of GA with RL's adaptive learning ability, therefore providing an algorithm that in addition to finding optimal paths adjusts the paths according to changing environmental situations. HGARL presents better performance compared to existing methods. In path convergence, it reaches up to 15%, and in energy consumption, it reduces up to 20% with safe collision-free navigation. The strong potential of HGARL for robust, efficient, and adaptive solutions of real-time multi-drone operation in complex and dynamic environments has set a new benchmark for UAV path planning. © 2024 IEEE.

关键词： Genetic algorithms

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MLPFormer: MLP-integrated transformer for colorectal histopathology whole slide image segmentation

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Neural computing and Applications 2025年第6期37卷 4651-4661页

作者： Wang, Yuxuan Li, Dan Li, Xuechen Guo, Yan Zuo, Yanfei Shen, Linlin Department of Computer Science Wenzhou-Kean University Wenzhou China School of Electronics and Information Engineering Wuyi University Jiangmen China Department of Histo Pathology Shanghai Histo Pathology Diagnostic Center Shanghai China College of Computer Sicence and Software Engineering Shenzhen University Shenzhen China National Engineering Laboratory for Big Data System Computing Technology Shenzhen University Shenzhen China

Colorectal intraepithelial neoplasia is a precancerous lesion of colorectal cancer, which is mainly diagnosed using pathological images. According to the characteristics of lesions, precancerous lesions can be classified into four different grades, i.e., background, normal, low and high level. However, due to the continuity of lesion development, tissue in different stages has high similarity. Recently, visual transformers have achieved impressive results in different visual tasks, due to their capacity of capturing long-range dependencies. However, due to computational cost, transformer cannot well extract detailed feature, which is important for lesion grading. To accurately segment pathological tissues with high similarity, in this work, we embed the multi-head MLP mixer module in the transformer network to extract cell and edge information. Furthermore, we propose a hierarchical MLP decoder to facilitate multi-scale feature fusion. The proposed transformer, namely MLPFormer, achieves remarkable results against competitive baselines on the Histo-CRC Biopsy dataset. The experimental results demonstrate that MLPFormer significantly outperforms the competitive baselines, i.e., a 3% dice improvement is achieved over the SegFormer series. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2024.

关键词： Image segmentation

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Warts Disease Treatment Suggestion Using Images and Numerical data

Warts Disease Treatment Suggestion Using Images and Numerica...

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Advances in computing, Communication and Applied Informatics (ACCAI), International Conference on

作者： Daketi Yatin Challa Avinash D Hemavathi Department of Data Science and Business Systems School of Computing SRM Institute of Science and Technology Kattankulathur Chennai India

ISBN: (数字)9798350389449

ISBN: (纸本)9798350389456

To classify wart treatment methods, this research paper examines the effectiveness of using machine learning (ML) and deep learning algorithms in conjunction with numerical and image data. Human papillomavirus (HPV)–induced warts are a common dermatological concern. Several factors can affect the severity and spread of these lesions. Making use of both picture and numerical data, the study suggests a thorough method for classifying treatments. The paper shows that the suggested methodology is effective through thorough experimentation. The study achieves remarkable classification accuracy, specifically $91.67 \%$ for cryotherapy and $85 \%$ for immunotherapy datasets, by utilising machine learning and deep learning algorithms. Notably, accuracy rates of $76 \%$ for cryotherapy and $74 \%$ for immunotherapy are obtained by combining synthetic and raw data, demonstrating the potential benefits of integrating various data sources. The study adds a comprehensive framework that makes accurate classification of wart treatments possible. The model provides a comprehensive understanding of wart types and treatment outcomes by combining image analysis with numerical data. This creative method uses both quantitative and visual data to enable users to make well-informed decisions. All things considered, the study highlights the potential of AI and ML methods to improve the classification of wart treatments, offering dermatologists and other medical professionals a useful tool. This study is a major step towards more individualised and data- driven dermatological care strategies, which could lead to better patient outcomes and more effective treatments.

关键词： Deep learning Visualization Accuracy Machine learning algorithms Soft sensors Immunotherapy Classification algorithms

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Exponential Capacity of Dense Associative Memories

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Physical Review Letters 2024年第7期132卷 077301-077301页

作者： Carlo Lucibello Marc Mézard Department of Computing Sciences Bocconi University Milano 20136 Italy and Bocconi Institute for Data Science and Analytics (BIDSA) Milano 20136 Italy.

Recent generalizations of the Hopfield model of associative memories are able to store a number P of random patterns that grows exponentially with the number N of neurons, P=exp(αN). Besides the huge storage capacity, another interesting feature of these networks is their connection to the attention mechanism which is part of the Transformer architecture widely applied in deep learning. In this work, we study a generic family of pattern ensembles using a statistical mechanics analysis which gives exact asymptotic thresholds for the retrieval of a typical pattern, α1, and lower bounds for the maximum of the load α for which all patterns can be retrieved, αc, as well as sizes of attraction basins. We discuss in detail the cases of Gaussian and spherical patterns, and show that they display rich and qualitatively different phase diagrams.

关键词： Disordered systems Spin glasses Deep learning Machine learning Replica theory

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Block Broyden's Methods for Solving Nonlinear Equations 37

Block Broyden's Methods for Solving Nonlinear Equations

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37th Conference on Neural Information Processing Systems, NeurIPS 2023

作者： Liu, Chengchang Chen, Cheng Luo, Luo Lui, John C.S. Department of Computer Science & Engineering The Chinese University of Hong Kong Hong Kong Shanghai Key Laboratory of Trustworthy Computing East China Normal University China School of Data Science Fudan University China

ISBN: (纸本)9781713899921

This paper studies quasi-Newton methods for solving nonlinear equations. We propose block variants of both good and bad Broyden's methods, which enjoy explicit local superlinear convergence rates. Our block good Broyden's method has a faster condition-number-free convergence rate than existing Broyden's methods because it takes the advantage of multiple rank modification on Jacobian estimator. On the other hand, our block bad Broyden's method directly estimates the inverse of the Jacobian provably, which reduces the computational cost of the iteration. Our theoretical results provide some new insights on why good Broyden's method outperforms bad Broyden's method in most of the cases. The empirical results also demonstrate the superiority of our methods and validate our theoretical analysis. © 2023 Neural information processing systems foundation. All rights reserved.

关键词： Nonlinear equations

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