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检索条件"机构=Dept. of Computer and Electrical Engineering and Computer Science"

共 55371 条记录，以下是341-350 订阅

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Optimizing Wind Speed Estimation with Least Squares Generative Adversarial Networks: A Case Study of Pakistan 6

Optimizing Wind Speed Estimation with Least Squares Generati...

引用

6th International Conference on Robotics and Automation in Industry, ICRAI 2024

作者： Ashfaq, Iqra Zahid, Hasan Naveed, Fajr Abid, Aaisha Uddin, Zaki NUST CEME Dept. of Electrical Engineering Islamabad Pakistan GIKI Dept. of Electrical Engineering Swabi Pakistan Fast NUCES Dept. of Computer Science Islamabad Pakistan NUST CEME Dept. of Mechatronics Engineering Islamabad Pakistan

ISBN: (纸本)9798350355703

Wind energy has emerged as a key player in sustainable power generation. Accurate prediction of wind speed is critical for optimizing wind farm operations, energy output forecasting, and reducing dependency on traditional power sources. In this study, we propose a Least-Squares Generative Adversarial Network (LSGAN) for wind speed prediction in a specific region of Pakistan, utilizing data from Tandoghulamali, which includes features like wind speeds at various altitudes and wind direction. The model is designed to predict wind speed at 80 meters, a crucial target variable for wind turbine operations. The LSGAN model effectively leverages adversarial training, achieving high accuracy with a Mean Absolute Error (MAE) of 1.54 and a Root Mean Squared Error (RMSE) of 1.77. Through a detailed comparison of actual versus predicted values, the model demonstrates its ability to generalize well across unseen data, with stable performance during the adversarial learning process. This approach opens up new possibilities for applying advanced machine learning techniques in renewable energy forecasting, particularly in regions with sparse historical data. © 2024 IEEE.

关键词： Wind forecasting

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Efficacy of Customer Churn Prediction System 6

Efficacy of Customer Churn Prediction System

引用

6th International Conference on Computational Intelligence and Communication Technologies, CCICT 2024

作者： Raj, Ritik Gupta, Siddhant Mishra, Ritik Malik, Medha Dept. of Computer Science and Engineering ABES Engineering College Ghaziabad India Department of Computer Science & Engineering ABES Engineering College Ghaziabad India

ISBN: (纸本)9798350374629

Customer churn, defined as customer defection is a big problem for businesses in today's competition. This paper takes an in-dept. look at the application of machine learning (ML) in customer churn prediction to identify the best models for various industries. quantitative performance metrics of seven commonly used machine learning algorithms: Logistic Regression, Decision Trees, Random Forests, Naive Bayes, K-Nearest Neighbors (KNN), AdaBoost, and XGBoost. The theoretical background, application context, and performance evaluation of each model are examined, focusing on precision, accuracy, recall, and F1 score. Evaluation of the performance of the model on different business data is also done to ensure that the application is real in the world. This comparison shows the advantages and disadvantages of each model and guides providers in their selection based on the specific needs of the business and profile features. Additionally, this paper explores future changes in predictions and highlights the changing role of machine learning technologies, especially deep learning, in improving the accuracy of predictions and personalization. This review is very useful for professionals and experts;It provides a better understanding of the current machine learning model for customer churn prediction and provides insight into future research and practical applications. ©2024 IEEE.

关键词： Forecasting

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BERN-NN: Tight Bound Propagation For Neural Networks Using Bernstein Polynomial Interval Arithmetic 26

BERN-NN: Tight Bound Propagation For Neural Networks Using B...

引用

26th ACM International Conference on Hybrid Systems: Computation and Control, HSCC 2023, Part of CPS-IoT Week 2023

作者： Fatnassi, Wael Khedr, Haitham Yamamoto, Valen Shoukry, Yasser University of California Irvine United States Dept. of Electrical Engineering and Computer Science

ISBN: (纸本)9798400700330

In this paper, we present BERN-NN as an efficient tool to perform bound propagation of Neural Networks (NNs). Bound propagation is a critical step in wide range of NN model checkers and reachability analysis tools. Given a bounded input set, bound propagation algorithms aim to compute tight bounds on the output of the NN. So far, linear and convex optimizations have been used to perform bound propagation. Since neural networks are highly non-convex, state-of-the-art bound propagation techniques suffer from introducing large errors. To circumvent such drawback, BERN-NN approximates the bounds of each neuron using a class of polynomials called Bernstein polynomials. Bernstein polynomials enjoy several interesting properties that allow BERN-NN to obtain tighter bounds compared to those relying on linear and convex approximations. BERN-NN is efficiently parallelized on graphic processing units (GPUs). Extensive numerical results show that bounds obtained by BERN-NN are orders of magnitude tighter than those obtained by state-of-the-art verifiers such as linear programming and linear interval arithmetic. Moreoveer, BERN-NN is both faster and produces tighter outputs compared to convex programming approaches like alpha-CROWN. © 2023 Copyright held by the owner/author(s).

关键词： Convex optimization

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Analytical Approach Towards Radiological Image-based Fracture Assessment Using Artificial Intelligence 1

Analytical Approach Towards Radiological Image-based Fractur...

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1st International Conference on Computing, sciences and Communications, ICCSC 2024

作者： Sylvia, Takhellambam Dubey, Sanjay Kumar Thongam, Khelchandra Amity University Computer Science & Engineering Dept. Uttar Pradesh Noida India National Institute of Technology Manipur Computer Science & Engineering Dept. Imphal India

ISBN: (纸本)9798350353648

The utilization of Artificial Intelligence (AI) techniques has been prevalent in various applications and the gradual increase in implementation towards healthcare sectors has scaled up. computer aided diagnosis has emerged as a major research area directed toward efficient use of resources. Fracture of bone has been a popular health problem associated with risk for mortality. Fractures are frequent in the long bones or the weight-bearing bones. AI techniques to accurately detect and classify fractures are still a challenging task beyond the conventional methods. Our objective is to assess the effectiveness of multiple algorithms in identifying and categorizing ankle fractures using radiological images. This review paper focuses on several available approaches of Deep-Learning (DL) and Machine-Learning (ML) regarding Ankle Fracture Classification and Identification based on Radiological Images for Medical Applications. The goal is to assist physicians and orthopaedics with simple and realistic decision support systems. In addition to a comprehensive analysis of the research, this study looked at several real-world examples of AI applications in the healthcare industry. Consequently, it is critical to have a fundamental grasp of these ML and DL algorithms to be ready for upcoming advancements in AI. We have reviewed and classified scientific conference and journal articles used for various AI techniques and offer future utility as a comparative analysis for further research in healthcare sectors. © 2024 IEEE.

关键词： Deep learning

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EV Allocation and Charging within Parking Lots Using a Locational Marginal Pricing Mechanism

EV Allocation and Charging within Parking Lots Using a Locat...

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2023 IEEE PES Innovative Smart Grid Technologies Europe, ISGT EUROPE 2023

作者： Arias, Nataly Bañol Giraldo, Juan S. López, Juan Camilo Hoogsteen, Gerwin Hurink, Johann Dept. of Electrical Engineering Mathematics and Computer Science University of Twente Netherlands Netherlands

ISBN: (纸本)9798350396782

Electrifying parking lots (PLs) is becoming increasingly challenging as the number of electric vehicle (EV) chargers operating within PLs increases, putting a strain on the internal electricity grid. Reinforcing the local grid can be an expensive solution. Therefore, an alternative approach is to design a mechanism that optimizes the distribution of incoming EVs along the PL infrastructure, minimizing operating costs for the PL owner and the charging costs for EV users. This paper proposes a user-aware pricing mechanism for EV charging in PLs based on locational marginal prices (LMPs), which consists of a base tariff and a location-dependent tariff. A mixed-integer second-order programming model is developed to determine the optimal charging spot for each EV upon arrival at the PL. Results show that the proposed LMP-based allocation mechanism distributes the strain more evenly throughout the local grid, increasing the EV hosting capacity of the PL, while minimizing charging costs for users. Compared to a random allocation approach, the proposed mechanism resulted in 30.9% less energy not served and 17.5% lower charging costs. © 2023 IEEE.

关键词： Electric vehicles locational marginal prices optimization parking lots pricing mechanism

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Utilising Convolutional Neural Networks for the Automated Detection of Diabetic Retinopathy

Utilising Convolutional Neural Networks for the Automated De...

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2024 International Conference on System, Computation, Automation and Networking, ICSCAN 2024

作者： Lakshmi, R Poornima Shopika, S. Yaazhini, A. Hemalatha, M. Saraswathi, V. Ponnusamy, R. Chennai Institute of Technology Dept. of Computer Science and Engineering Tamil Nadu India Chennai Institute of Technology Dept. of Computer Science and Engineering Tamilnadu Chennai India

ISBN: (纸本)9798331510022

Diabetic retinopathy has emerged as one of the leading causes of eye diseases among people suffering from long-term diabetes. Indeed, it raises the risk of being blinded without proper detection and treatment. Conventional detection of retinal fundus images by an ophthalmologist is time-consuming and prone to mistakes owing to human intervention. This is particularly a cause for alarm in the wake of the growing incidence of diabetes globally. The need for automated, accurate detection systems for early diagnosis of diabetic retinopathy has never been more relevant. In this work, we develop a deep learning model based on CNN for classifying four stages of diabetic retinopathy, from no DR to proliferative DR. We tested the model in a public dataset to extract features from retinal images and to screen for abnormalities automatically. Our CNN model achieved 98.5% classification and detection accuracy, thus indicating the ability to make a real difference in the early detection and treatment plan, thereby preserving the vision of many diabetic patients. Practical value The present research is very relevant to clinical practice and, therefore, practically useful because of its relevance to healthcare technology. Using accuracy and loss function metrics, the proposed model performs well compared to the latest techniques in DR detection. An approach based on CNNs is expected to ease much of the workload that healthcare professionals bear in diagnosis and improve the precision, potentially resulting in a very high cutback in vision loss in diabetic patients with better patient outcomes. Our study has highlighted the practical utility of the CNN model, which has improved patient outcomes. DR is a serious condition affecting the eyes, which, in case of untimely detection and untreated in diabetic patients, can cause loss of vision. For centuries, the conventional diagnosis for this disease has been through the manual inspection of retinal fundus images taken through a camera

关键词： Clinical research

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A Review of Disease Detection in Sugarcane Using Machine Learning and Deep Learning Techniques 15

A Review of Disease Detection in Sugarcane Using Machine Lea...

引用

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

作者： Pavan, Undavalli Chandra Pateriya, R.K. Tomar, Deepak Singh Singh, Surendra Maulana Azad National Institute of Technology Dept. of computer science & engineering Bhopal India National Institute of Technology Dept. of computer science & engineering Uttarakhand India

ISBN: (纸本)9798350370249

Agriculture has been an active source of food and economic growth, but it faces significant challenges from diseases and climate change. In Indonesia, sugarcane production is severely impacted by viral infections such as Sugarcane mosaic virus (SCMV) and Sugarcane streak mosaic virus (SCSMV). These infectious diseases pose a threat to the world's sugarcane industry, resulting in significant crop losses in nations like Brazil and India, along with other viruses like the Fiji Disease Virus (FDV), and Sugarcane Yellow Leaf Virus (SCYLV). For such diseases to be managed and performance to be maintained, early detection is essential. Disease detection is accomplished by both conventional and cutting-edge approaches, such as molecular, serological, and imaging methods. However, the advancements in Machine learning, Deep learning and computational image processing algorithms have demonstrated significant potential in the early and precise diagnosis of disease. In this study, different methods used for machine learning (ML) and deep learning (DL) for plant disease diagnosis were reviewed and it highlights the need of high-quality datasets, various pre-processing techniques, robust extraction of features, and efficient classification techniques. It also discussed the issues with data collection, symptom variability, over-fitting, and imbalances in classes, along with potential avenues for further advancement in diagnosing diseases systems. © 2024 IEEE.

关键词： challenges deep learning detection hybrid models machine learning Plant diseases

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Robust and Predictive Charging of Large Electric Vehicle Fleets in Grid Constrained Parking Lots 14

Robust and Predictive Charging of Large Electric Vehicle Fle...

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14th IEEE International Conference on Communications, Control, and Computing Technologies for Smart Grids, SmartGridComm 2023

作者： Hoogsteen, Gerwin Winschermann, Leoni Nijenhuis, Bart Arias, Nataly Banol Hurink, Johann L. University of Twente Dept. of Electrical Engineering Mathematics and Computer Science Enschede Netherlands

ISBN: (纸本)9781665455541

Grid congestion forms an obstacle for the adoption of electric vehicles (EVs) by companies for their sustainability goals. This paper presents a robust approach to smart charging of large fleets of EVs that only utilizes currently available technologies and data, such that it can be applied in practice. The method relies only on available data at charge point operators. The presented approach uses offline aggregated planning to determine a robust fill-level. Subsequently, this fill-level is used in online control to maximise the quality of service to users while being able to provide flexibility to the energy system. Simulation results using real data show that the self-consumption of PV energy can be improved from 72.0% to 85.4%. Furthermore, the energy not served is reduced from 5.9% to 2.8%. © 2023 IEEE.

关键词： Charging (batteries)

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Computational Web Drug Discovery Application forSARS- Cov-2 Disease Using Predictive and Analytical Approach 10

Computational Web Drug Discovery Application forSARS- Cov-2 ...

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10th International Conference on Advanced Computing and Communication Systems, ICACCS 2024

作者： Hemkiran, S. Kartik, R. Aswath, S. Rathan Kabilan, K. Sudha Sadasivam, G. Psg Institute of Technology and Applied Research Dept. of Computer Science and Engineering Coimbatore India Psg College of Technology Dept. of Computer Science and Engineering Coimbatore India

ISBN: (纸本)9798350384369

In response to the urgent need for coronavirus treatments, this research focuses on leveraging bioactivity data collection and processing for efficient drug discovery, employing computational methods to predict potential antiviral compounds. Exploratory data analysis was performed to identify patterns and trends, and various molecular descriptors were computed for the Mpro inhibitors in the descriptor dataset preparation step. A regression model was trained using the developed random forest algorithm to predict the bioactivity of new compounds against the standard value and compared with other regression models. Additionally, a web-based application was created using the random forest model to allow users to obtain predicted bioactivity values against Mpro by providing information about molecular structures. Machine learning-based bioactivity prediction offers an intriguing plan for drug discovery, and the proposed work provides a comprehensive workflow for COVID- 19 drug discovery. The web application provides a user-friendly interface for drug discovery researchers to evaluate the potential of compounds against the Mpro target quickly. © 2024 IEEE.

关键词： COVID-19

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Joint Estimation of DOA and Distance in Noisy Reverberant Conditions 48

Joint Estimation of DOA and Distance in Noisy Reverberant Co...

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48th IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2023

作者： Bu, Suliang Zhao, Tuo Zhao, Yunxin University of Missouri-Columbia Dept. of Electrical Engineering and Computer Science United States

ISBN: (纸本)9781728163277

Sound Source localization (SSL) using microphone arrays is an active research topic with many applications, but noise and reverberation make the direction-of-arrival (DOA) and distance estimation a challenging problem. In this work, we propose a novel method to jointly estimate the DOA and distance in noisy and reverberant environments. Our method exploits the linear phase structure across frequencies in a steering vector (SV). We convert the joint estimation issue into an optimization problem, which can be solved by Newton's method augmented by a gradient ascent method. Our method does not depend on certain microphone array geometry, and it can also be extended to estimate the elevation angle. We conducted experimental evaluations in simulated noisy and reverberant acoustic conditions, which verified the superiority of our proposed method to several established methods in estimation accuracy and computation efficiency. © 2023 IEEE.

关键词： distance estimation DOA estimation microphone array Sound source localization

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