检索结果-内蒙古大学图书馆

2nd International Conference on Artificial Intelligence and Machine Learning Applications, AIMLA 2024

作者： Devanath, B. Dharunish, A.P. Mohana Bharathi, S. Suganth, R. Banupriya, V. Ramesh, M. M Kumarasamy College of Engineering Department of Electrical and Electronics Engineering Karur India M Kumarasamy College of Engineering Department of Computer Science and Business Systems Karur India

ISBN: (数字)9798350349221

ISBN: (纸本)9798350349221

The fridge is the most constantly employed menage/ kitchen electrical device encyclopedically for conserving food. The leading sectors of smart widgets include refrigerators in the kitchen. The Web of Effects (IoT) denotes the tools and systems employed to link the factors deduced from the detectors and selectors. Primarily this contrivance is employed for different purposes such as storing flora, fruits, etc. This design transforms any pre-existing fridge into an intelligent, budget-friendly outfit with exercise sensors. The proposed design is educated and worked grounded on the medium of learning machines, and it contrasts the condition of the aliment, for case, Mass, quantum, etc. The significance of this undertaking will involve barring food decay, dwindling affections, and promoting a more restorative way of life for contemporary mortal beings. The arrangement of dish recommendations grounded on the veggies being in the caddy is carried out through an abecedarian algorithm of machine literacy, which categorizes the vegetables grounded on the tinges, latterly suggesting a specific form. It'll be canny enough to warn the stoner IoT about the present condition of food papers through an Android operation on our cellular device. © 2024 IEEE.

关键词： Refrigerators

来源：评论

学校读者我要写书评

暂无评论

Hybrid attention residual deep convolution learning network for bio medical image analysis

引用

Journal of Intelligent and Fuzzy Systems 2025年第4期48卷 537-554页

作者： Sundar Raj, A. Naidu, Paruchuri Chandra Babu Sivakumar, S. Senthilkumar, S. Department of Biomedical Engineering E.G.S. Pillay Engineering College Tamilnadu Nagapattinam India Department of Electrical and Electronics Engineering BSA Crescent Institute of Science and Technology Vandalur Tamilnadu Chennai India Department of Computer Science and Engineering Nehru Institute of Engineering and Technology Tamilnadu Coimbatore India Department of Electronics and Communication Engineering E.G.S. Pillay Engineering College Tamilnadu Nagapattinam India

Biomedical image analysis has progressed significantly with the integration of artificial intelligence, presenting new opportunities for early diagnosis and treatment of diseases with high mortality rates, such as skin cancer. This research work introduces a novel Hybrid Attention Residual Deep Convolution Learning (HARDCL) Model designed to enhance the accuracy of skin lesion classification, which is often challenging due to the subtle characteristics of lesions. The HARDCL Network employs an innovative hybrid attention mechanism within a residual learning framework, effectively distinguishing between benign and melanoma skin lesions with a high degree of precision. The encoder-decoder network segments the lesion from dermoscopic images, followed by feature extraction and classification, achieving a maximum accuracy of 97.11%, precision of 98.10%, recall of 98.81%, and an F1-score of 98.45%. This performance surpasses conventional deep learning algorithms such as AlexNet, InceptionV3, and ResNet18, demonstrating the effectiveness of the attention-based features in identifying critical lesion characteristics. However, despite its strengths, the HARDCL Network’s computational intensity and the need for a substantial dataset for optimal training highlight areas for further refinement. The proposed network advances the field of skin cancer diagnostics, offering a robust tool for medical professionals and setting a foundation for future enhancements in automated biomedical image analysis techniques. © 2025 – IOS Press. All rights reserved.

关键词： Deep learning

来源：评论

学校读者我要写书评

暂无评论

An AI-Enabled Framework for Transparency and Interpretability in Cardiovascular Disease Risk Prediction

引用

computers, Materials & Continua 2025年第3期82卷 5057-5078页

作者： Isha Kiran Shahzad Ali Sajawal ur Rehman Khan Musaed Alhussein Sheraz Aslam Khursheed Aurangzeb Department of Computer Science University of Engineering and TechnologyLahore39161Pakistan Dipartimento di Farmacia e Biotecnologie Alma Mater Studiorum—Universita di BolognaBologna40126Italy Department of Information Sciences University of EducationLahore54770Pakistan Department of Computer Science National Textile UniversityKarachi CampusKarachi74900Pakistan Department of Computer Science National Textile UniversityFaisalabad37610Pakistan Department of Computer Engineering College of Computer and Information SciencesKing Saud UniversityRiyadh11543Saudi Arabia Department of Electrical Engineering Computer Engineeringand InformaticsCyprus University of TechnologyLimassol3036Cyprus Department of Computer Science CTL EurocollegeLimassol3077Cyprus

Cardiovascular disease(CVD)remains a leading global health challenge due to its high mortality rate and the complexity of early diagnosis,driven by risk factors such as hypertension,high cholesterol,and irregular pulse *** diagnostic methods often struggle with the nuanced interplay of these risk factors,making early detection *** this research,we propose a novel artificial intelligence-enabled(AI-enabled)framework for CVD risk prediction that integrates machine learning(ML)with eXplainable AI(XAI)to provide both high-accuracy predictions and transparent,interpretable *** to existing studies that typically focus on either optimizing ML performance or using XAI separately for local or global explanations,our approach uniquely combines both local and global interpretability using Local Interpretable Model-Agnostic Explanations(LIME)and SHapley Additive exPlanations(SHAP).This dual integration enhances the interpretability of the model and facilitates clinicians to comprehensively understand not just what the model predicts but also why those predictions are made by identifying the contribution of different risk factors,which is crucial for transparent and informed decision-making in *** framework uses ML techniques such as K-nearest neighbors(KNN),gradient boosting,random forest,and decision tree,trained on a cardiovascular ***,the integration of LIME and SHAP provides patient-specific insights alongside global trends,ensuring that clinicians receive comprehensive and actionable *** experimental results achieve 98%accuracy with the Random Forest model,with precision,recall,and F1-scores of 97%,98%,and 98%,*** innovative combination of SHAP and LIME sets a new benchmark in CVD prediction by integrating advanced ML accuracy with robust interpretability,fills a critical gap in existing *** framework paves the way for more explainable and transparent decision-making in he

关键词： Artificial Intelligence cardiovascular disease(CVD) explainability eXplainable AI(XAI) interpretability LIME machine learning(ML) SHAP

来源：评论

学校读者我要写书评

暂无评论

High-Speed Visible Light Communication Using Phenothiazine/Dimesitylborane Derivatives as Color Conversion Materials in Semipolar Micro-LED-Based White-Light Systems

引用

ACS Photonics 2024年第2期11卷 489-497页

作者： Sadhu, Annada Sankar Chen, Li-Yin Pai, Yi-Hua Hsieh, Chung-An Lin, Hao-Wu Chow, Chi-Wai Kuo, Hao-Chung Department of Photonics Institute of Electro-Optical Engineering College of Electrical and Computer Engineering National Yang Ming Chiao Tung University Hsinchu30010 Taiwan Semiconductor Research Center Hon Hai Research Institute Taipei11492 Taiwan College of Electrical and Computer Engineering National Yang Ming Chiao Tung University Hsinchu30010 Taiwan Department of Materials Science and Engineering National Tsing Hua University Hsinchu30013 Taiwan

Visible light communication (VLC) has emerged as a cutting-edge high-speed communication technology, poised to meet the surging capacity demands of 6G networks. Micro-light-emitting diodes (μLEDs) are considered as the light sources for achieving high-speed VLC, distinguished by their remarkable modulation bandwidths. However, achieving broadband white light emission hinges on the utilization of color-conversion materials with wide emission spectra. The transmission speed of the white-light system is inherently constrained by the characteristics of these color-conversion materials. In this work, we demonstrate CC-MP7 and CC-MP8, two derivatives of phenothiazine/dimesitylborane, as color conversion materials in a semipolar (20-21) micro-LED-based white-light system for high-speed VLC. The color conversion layers possess wide emission spectra, enabling them to achieve excellent color rendering performance when combined with blue micro-LEDs. CCMP7 and CC-MP8 demonstrate rapid photoluminescence decay characteristics, thereby enhancing the modulation bandwidth of the color-conversion layer in the white-light system. The resulting bandwidths achieved by CC-MP7 and CC-MP8 are 210 and 240 MHz, respectively, which represents an approximately 45-fold increase compared to ordinary phosphors. By combining semipolar (20-21) micro-LEDs with CC-MP7 and CC-MP8, the resulting white-light systems exhibit correlated color temperatures of 6860 and 7500 K, CIE coordinates of (0.3009, 0.3577) and (0.2958, 0.3129), and color-rendering indexes of 80 and 85, respectively. Furthermore, both systems offer high bandwidths of 1063 and 1084 MHz with the data rates of 1.72 Gbps and 1.74 Gbps using non-return-to-zero on-off keying (NRZ-OOK) format, respectively, indicating the significant potential of CC-MP7 and CC-MP8 for practical applications in VLC. © 2024 The Authors.

关键词： Bandwidth

来源：评论

学校读者我要写书评

暂无评论

Impact of Grid Strength on Sub-Synchronous Oscillations in AC Systems with Type-4 Wind Farms Integrated 50

Impact of Grid Strength on Sub-Synchronous Oscillations in A...

引用

50th Annual Conference of the IEEE Industrial Electronics Society, IECON 2024

作者： Zamzam, Tassneem Umar, Muhammad F. Qiblawey, Yazan Alassi, Abdulrahman Ghrayeb, Ali Abu-Rub, Haitham Department of Electrical and Computer Engineering Texas A&M University College Station United States Department of Electrical and Computer Engineering Texas A&M University at Qatar Doha Qatar Iberdrola Innovation Middle East Doha Qatar Department of Electrical and Computer Engineering College of Science and Engineering Hamad Bin Khalifa University Qatar Foundation Doha Qatar

ISBN: (纸本)9781665464543

The modern power system dominated by renewable energy resources such as Type-4 wind farms (WF) has recently seen significant increase in cases of sustained oscillations at the sub-synchronous frequency range that is typically referred to sub-synchronous oscillations (SSOs) in the existing literature. Although thorough studies have been conducted on prior types of WFs to understand the triggering factors for SSO, however, in the case of Type-4 WFs the triggering factors and causes still remain unclear. Therefore, this paper studies the SSO characteristics exhibited by Type-4 WFs operating within a weak grid. To emulate real-world grid conditions, an extensive AC network modeled after the IEEE 39-bus system is employed. Through simulations incorporating the integration of Type-4 WFs at various locations within the grid, the resulting effects on SSO triggering factors are analyzed. The investigation is structured around three distinct case studies, conducted at buses 31, 37, and 38. These studies involve varying network reactance to explore and establish the relationship between SSO events and the short-circuit ratio (SCR) of the network. The consistent findings across diverse case studies underscore the general relationship between SCR, grid strength, and SSOs. The analysis reaffirms the significant impact of increased reactance on SSO characteristics and confirms that weaker grids are more susceptible to SSOs. © 2024 IEEE.

关键词： Wind power

来源：评论

学校读者我要写书评

暂无评论

A novel U-net model for brain tumor segmentation from MRI images

引用

Alexandria engineering Journal 2025年 126卷 220-230页

作者： Obayya, Marwa Alshuhail, Asma Mahmood, Khalid Alanazi, Meshari H. Alqahtani, Mohammed Aljehane, Nojood O. Almansour, Hamad Al-Hagery, Mohammed Abdullah Department of Biomedical Engineering College of Engineering Princess Nourah bint Abdulrahman University PO Box 84428 Riyadh11671 Saudi Arabia Department of Information Systems College of Computer Sciences & Information Technology King Faisal university Saudi Arabia Department of Information Systems Applied College at Mahayil King Khalid University Saudi Arabia Department of Computer Science College of Sciences Northern Border University Arar Saudi Arabia Department of Information System and Cyber Security College of Computing and Information Technology University of Bisha Bisha61922 Saudi Arabia Department of Computer Science Faculty of Computers and Information Technology University of Tabuk Tabuk Saudi Arabia Applied Collage Najran University Najran Saudi Arabia Department of Computer Science College of Computer Qassim University Saudi Arabia

Segmentation of brain tumors aids in diagnosing the disease early, planning treatment, and monitoring its progression in medical image analysis. Automation is necessary to eliminate the time and variability associated with traditional segmentation methods. Convolutional neural networks (CNNs) and U-Net architectures have demonstrated their efficiency and effectiveness in segmenting brain tumors from MRI images using deep learning techniques. The paper presents an improved U-Net-based segmentation algorithm that integrates nested skip paths to improve encoder-decoder feature fusion. The performance of segmentation was optimized by utilizing a variety of activation functions and loss functions, including Dice Loss and Intersection over Union (IoU). A high level of accuracy was demonstrated in the proposed model when it was evaluated using the LGG Segmentation Dataset. The proposed approach for segmenting medical images has been shown to be both robust and efficient in a comparative analysis. © 2025 The Authors

关键词： Convolutional neural networks

来源：评论

学校读者我要写书评

暂无评论

Real-Time Non-Gaussianity Evaluation for Anomaly Detection in Noisy Power Grid Data Stream 7

Real-Time Non-Gaussianity Evaluation for Anomaly Detection i...

引用

7th International Conference on Energy Conservation and Efficiency, ICECE 2024

作者： Liu, Maohua Ahmad, Faraz Ahmad, Irshad Beyette, Fred R. School of Electrical and Computer Engineering College of Engineering University of Georgia Athens30602 United States University of Engineering and Technology Department of Electrical Engineering Lahore38600 Pakistan

ISBN: (纸本)9798350370898

In response to concerns regarding energy sustainability and increasing energy demand, a substantial number of renewable energy systems (RESs) are being integrated into the power grid. Unlike conventional power plants that can be adjusted to meet demand, RESs rely on weather conditions, introducing numerous unstable factors and abnormal events. Anomaly detection is a critical data analysis task, beneficial for pinpointing grid irregularities, failures, or intrusions. By examining anomaly patterns in integrated energy systems, a deeper comprehension of their operational status can be attained, leading to enhanced reliability and efficiency of the power grid. This paper suggests a statistical algorithm based on assessing nonGaussianity. In contrast to computationally expensive learning-based anomaly detection, the proposed algorithm can efficiently detect anomalies by evaluating kurtosis and skewness of the time series. It proves to be both time and resource-efficient, making it suitable for real-time and resource-constrained scenarios. © 2024 IEEE.

关键词： Anomaly detection

来源：评论

学校读者我要写书评

暂无评论

Analysis of CMOS Full Adder Circuits for Multiplier Logic Architectures 2

Analysis of CMOS Full Adder Circuits for Multiplier Logic Ar...

引用

2nd International Conference on Distributed computing and Optimization Techniques, ICDCOT 2024

作者： Ganesh Reddy, Jyothi Lokesh Krishna, K. Priyanka Keerthi, Jinka Revanth, Karamala Sai Deekshitha, Kalluri Doraswamy, B. Sri Venkateswara College of Engineering Department of Ece Andhra Pradesh Tirupati India College of Engineering & Technology Bule Hora University Department of Electrical & Computer Engineering Ethiopia

ISBN: (纸本)9798350382952

High efficient digital adder logic circuits are very essential and critical modules in the processor and controller architectures. For example, an adder circuit in an arithmetic logic unit circuit must offer high speed operation and low power per calculation to make it an optimized processor. Apart from this, adder logic is also used to carry out subtract operations, multiply and division operations. Logic circuits such as Full adders and multipliers have become very elementary components of signal processing operations. Full adder logic circuits are implemented by using different technologies such as conventional complementary metal oxide semiconductor transistors, transmission gate logic, using different transistor counts, gate diffusion input adder and pass transistor logic based circuits. Improvements and scaling in integrated circuit design technologies have led to the circuits to operate at less critical path delay, less power consumption and final less values of power delay product. In this work, two full adder logics are simulated at 0.09 micrometer CMOS process technology in analog design environment of cadence tools. The simulation results shows that the pass transistor logic offers a power dissipation reduction of 18.46% when compared to conventional circuit topologies and is designed using fourteen transistor count, and contributes better performance with respect to power delay product values. © 2024 IEEE.

关键词： Adders

来源：评论

学校读者我要写书评

暂无评论

Graph Theory Based N-1 Transmission Contingency Selection and Its Application in Security-constrained Unit Commitment

引用

Journal of Modern Power Systems and Clean Energy 2021年第6期9卷 1458-1467页

作者： Lu Nan Yikui Liu Lei Wu Tianqi Liu Chuan He College of Electrical Engineering Sichuan University the Department of Electrical and Computer Engineering Stevens Institute of Technology Department of Electrical and Computer Engineering Stevens Institute of Technology

Contingency analysis is an important building block in the stability and reliability analysis of power grid operations. However, due to the large number of transmission lines,in practice only a limited number of contingencies could be evaluated. This paper proposes a graph theory based N-1 contingency selection method to effectively identify the most critical contingencies, which can be used in security-constrained unit commitment(SCUC) problems to derive secure and economic operation decisions of the power grid. Specifically, the power flow transferring path identification algorithm and the vulnerability index are put forward to rank individual contingencies according to potential transmission line overloads. Effectiveness of the proposed N-1 contingency selection method is quantified by applying the corresponding SCUC solution to the full N-1 security evaluation, i. e., quantifying total post-contingency generation-load imbalance in all N-1 contingencies. Numerical results on several benchmark IEEE systems, including5-bus, 24-bus, and 118-bus systems, show effectiveness of the proposed method. Compared with existing contingency selection methods which usually resort to full power flow calculations,the proposed method relies on power gird topology to effectively identify critical contingencies for facilitating the optimal scheduling of SCUC problems.

关键词： Concentric relaxation contingency analysis graph theory security-constrained unit commitment(SCUC) shortest path vulnerability index

来源：评论

学校读者我要写书评

暂无评论

Utilising Machine Learning for Precise State-of-Charge Prediction in Li-Ion Batteries 12

Utilising Machine Learning for Precise State-of-Charge Predi...

引用

12th IEEE International Conference on Intelligent Systems, IS 2024

作者： Khatri, Avinash Lota, Jaswinder Nepal, Prasanna Amirhosseini, Mohammad Hossein School of Architecture Computing and Engineering University of East London London United Kingdom Nepal Engineering College Department of Electrical and Electronics Engineering Bhaktapur Nepal

ISBN: (纸本)9798350350982

As the popularity of electric propulsion using batteries rises alongside the demand for renewable energy, effective battery management and monitoring are crucial for sustain ability and efficiency in electric vehicles (EVs). The battery monitoring system (BMS) employs IoT/sensor networks to estimate crucial battery metrics like remaining useful life (RUL), state-of-health (SoH), and state-of-charge (SoC), using data on current status, temperature, and voltage. Machine learning (ML) and artificial intelligence (AI) are increasingly utilized to enhance BMS accuracy, addressing challenges like real-time data processing and the accuracy of estimations. This paper investigates the effectiveness of Linear Regression and Random Forest models in estimating SoC. During the hyperparameter tuning phase, the models were optimized using the Grid Search method, and their performance was evaluated at various temperatures: 25°C, 10°C, 0°C, and -10°C. The findings indicate that the models' effectiveness enhances as the temperature increases. The Random Forest model exhibited superior performance at 25°C, achieving an R2 score of 0.99646 and an RMSE score of 0.000264. This paper not only contributes to advancing Li-ion battery monitoring system, but also empowers professionals in this field to harness machine learning capabilities effectively. © 2024 IEEE.

关键词： State of charge

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：