检索结果-内蒙古大学图书馆

2024 IEEE International Conference on Modeling, Simulation and Intelligent Computing, MoSICom 2024

作者： Gopalakrishnan, Leechita Barrera, Julian L Cardenas Fatima, N. Sabiyath B.S. Abdur Rahman Crescent Institute of Science and Technology Dept. of Computer Science and Engineering Chennai India University of New Brunswick Dept. of Electrical and Computer Engineering Fredericton Canada

ISBN: (纸本)9798331533311

As wind energy becomes a key player in the global transition to renewable energy, its variability presents significant challenges. Accurate wind ramp forecasting is crucial for managing such rapid fluctuations in wind power output, ensuring grid stability, and enhancing the reliability of renewable energy integration. This paper presents a short-term wind ramp forecasting model using a sequential approach: initially forecasting wind power through the integration of Variational Mode Decomposition (VMD) and the XGBoost algorithm, and subsequently detecting wind ramps using the Definition Based Sign Indicator (DSI) method. The historical wind speed and power data for Turkey was obtained from Kaggle. The dataset comprises readings from a wind turbine's SCADA system in Turkey, with measurements recorded at 10minute intervals. Variational Mode Decomposition (VMD) was applied to deconstruct and restore the historical wind power of the wind field, resulting in the formation of three training datasets. Wind power projections for the subsequent 8 hours, at 10 -minute intervals, are generated using historical wind power, wind speed, and wind direction as inputs. The DSI algorithm is employed to identify ramps. To assess the predictive capability of the proposed model, two algorithms, SVR and XGBoost, were employed for forecasting and the resulting error is minimal. The proposed model, VMD + XGBOOST is also compared without VMD to evaluate the performance. Taking mean square error (MSE), root mean square error (RMSE) mean absolute error (MAE), and symmetric mean absolute percentage error (sMAPE) as evaluation indicators, the results show that the forecasted accuracy of wind power is significantly improved after VMD processing. The performance of XGBOOST is better than SVR in the two evaluation indicators. Precision, Recall, and F1-score are calculated to evaluate the accuracy of the predicted wind ramps. © 2024 IEEE.

关键词： Wind forecasting

来源：评论

学校读者我要写书评

暂无评论

Nova: Revolutionizing Agile Project Management with Scrum and Kanban Integration for Enhanced Team Collaboration and Productivity 5

Nova: Revolutionizing Agile Project Management with Scrum an...

引用

5th IEEE Global Conference for Advancement in Technology, GCAT 2024

作者： Manaswini, Poluri Shaik, Sajjad Ashwani, Siddhant Balakrishnan, Roshni M. Rajagopal, Shinu M. Amrita Vishwa Vidyapeetham Amrita School of Computing Dept. of Computer Science Engineering Bengaluru India Amrita Vishwa Vidyapeetham Amrita School of Computing Dept. of Computer Science and Engineering Bengaluru India

ISBN: (纸本)9798350376685

In the realm of software project management, Agile methodologies such as Scrum and Kanban have become quintessential for fostering adaptability and efficiency. "Nova,"a novel project management tool, embodies these methodologies to offer a user-friendly interface that supports the dynamic needs of modern software development teams. This paper explores Nova's unique approach that integrates the foundational principles of Agile which are Scrum, and Kanban, thereby facilitating a seamless and effective management platform. Through a comparative analysis with traditional project management tools, this study evaluates how Nova enhances team collaboration, project tracking, and outcome predictability. Emphasizing its ease of use and adaptability, the research highlights Nova's potential to improve project delivery times and overall team productivity. © 2024 IEEE.

关键词： Lean production

来源：评论

学校读者我要写书评

暂无评论

Deep Learning Techniques for Object Detection in Underwater Environments

Deep Learning Techniques for Object Detection in Underwater ...

引用

2025 IEEE International Conference on Computational, Communication and Information Technology, ICCCIT 2025

作者： Praveen, R.V.S. Maindola, Meenakshi Anandhi, R.J. Thandra, Nagarjuna Bansal, Saloni Sharma, Vishal LTIMindtree Limited Telangana Hyderabad India Graphic Era Deemed to Be University Department of Computer Science & Engineering Dehradun India New Horizon College of Engineering Department of Information Science Engineering Bangalore India MLR Institute of Technology Dept. Computer Science and Engineering Telangana Hyderabad India GLA University Department of Computer Engineering and Applications Mathura India Lovely Professional University Phagwara India

ISBN: (纸本)9798331512965

The degradation of image quality caused by things like light absorption, scattering, and distortion makes object detection in underwater environments a unique challenge. applications like marine research, environmental monitoring, and autonomous underwater vehicles (AUVs) are restricted in their effectiveness by traditional image processing techniques, which often struggle with these complexities. Recent years have seen the rise of deep learning techniques as a potent weapon in the fight against these obstacles. In order to accurately detect objects underwater, this paper investigates the use of deep learning models, specifically convolutional neural networks (CNNs). We explore cutting-edge models and methods like attention mechanisms, transfer learning, and hybrid deep learning architectures to improve detection performance in challenging underwater environments. Furthermore, we utilize data augmentation techniques and synthetic dataset generation to tackle the critical issue of limited labeled underwater datasets. From a detection accuracy and robustness perspective, the experimental results show that our suggested deep learning method is light years ahead of the competition. Marine exploration, autonomous underwater vehicle navigation, and underwater surveillance systems can all benefit from this study's new insights into how to better detect objects in real time while submerged. © 2025 IEEE.

关键词： Convolutional neural networks

来源：评论

学校读者我要写书评

暂无评论

Advances and Adaptation in Artificial General Intelligence Based on Novel Q∗ Framework 27

Advances and Adaptation in Artificial General Intelligence B...

引用

27th International Symposium on Wireless Personal Multimedia Communications, WPMC 2024

作者： Upreti, Meenakshi Bhatnagar, Abhinav Birla Institute of Applied Sciences Dept. of Master of Computer Applications Bhimtal India Birla Institute of Applied Sciences Dept. of Electronics and Communication Engineering Bhimtal India

ISBN: (纸本)9798350392319

A priority in the researches on artificial intelligence is the creation of Artificial General Intelligence. Even though the regular artificial intelligence was making some progress in certain areas, there were challenges and untried domains. This study introduces a novel Q∗ framework which is intended for improving AGI. Understanding, obtaining and using machine-generated knowledge across multiple domains are some of the aspects of AGI. It integrates concepts form cognitive psychology and reinforcement learning to produces a cohesive approach that includes learning as well as introspection and flexibility. By leveraging the advantages of Q-learning and refining existing methods, achieving AGI through Q∗ is feasible. Q* prioritizes metalearning and hierarchical representations over traditional AI systems that perform well within narrow contexts yet fail when asked to generalize more broadly. This makes it possible to be adaptable and learn well in unfamiliar settings. The paper gives a detailed explanation of the theoretical underpinnings and possible applications of AGI, emphasizing Q∗′ 's commitmentto advancing its pursuit. We discuss the conceptual framework proposed by Q∗ and speculate on its implications for future AI research. © 2024 IEEE.

关键词： Adversarial machine learning

来源：评论

学校读者我要写书评

暂无评论

Deep Learning Techniques for Bangladeshi Coin Detection and Automated Counting System: A Comparative Study of Multiple Algorithms 3

Deep Learning Techniques for Bangladeshi Coin Detection and ...

引用

3rd International Conference on Advancement in Electrical and Electronic engineering, ICAEEE 2024

作者： Babor, Ashraf Hussan Choity, Umme Habiba Kaspia, Most. Paul, Subrata Kumer Paul, Rakhi Rani Haque, Md. Momenul Hamid, Md. Ekramul Qadirabad Dayarampur Dept. of Computer Science and Engineering Natore6431 Bangladesh University of Rajshahi Department of Computer Science and Engineering Rajshahi6205 Bangladesh

ISBN: (纸本)9798350388282

Efficient currency detection and counting systems are crucial for the smooth operation of any financial ecosystem. This paper explores the development, implementation, and potential impact of a specialized currency detection and counting system for Bangladeshi coin currencies. Through this comprehensive study, this paper addresses the unique challenges and opportunities in Bangladesh's coin currency management landscape, highlighting the motivations and results of such a system's introduction. computer vision technology includes currency detection. In this paper, we present a coin currency detection system that can detect coin currency from images. Designing a CNN-based coin recognition system for the recognition of Bangladeshi coins of denominations '1tk', '2tk', and '5tk'. We have created a dataset that contains more than 10,000 images. We photographed coin currencies from both sides, and the system can recognize coin currencies from both sides. For high efficiency, we test on various backgrounds. Experimental results are presented successfully with a training accuracy of 99.99% with VGG16, 99.38% with VGG19, and 99.99% with EfficientNetB0. We also got validation accuracy with VGG16 of 90.90%, 99.96% with VGG19, and 99.51% with EfficientNetB0. © 2024 IEEE.

关键词： Deep learning

来源：评论

学校读者我要写书评

暂无评论

Pattern Recognition in Lung Disease Classification: Leveraging Convolutional Neural Networks that Employs Local Phase Quantization Features 15

Pattern Recognition in Lung Disease Classification: Leveragi...

引用

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

作者： Sree, M. Ramya Siddhartha, Mettu Reddy, Poli Vamsi Vardhan Singh, Tripty Nair, Rekha R. Amrita Vishwa Vidyapeetham Amrita School of Computing Dept. of Computer Science and Engineering Bengaluru India Alliance University Alliance School of Advanced Computing Dept. of Computer Science and Engineering Bangalore India

ISBN: (纸本)9798350370249

Lung diseases are wide group of diseases related to the respiratory system, and is ranging from acute infections to chronic disorders with multifactorial causes. Recognition of the lung disease is very crucial in the proper diagnosis, forecasting through prognosis, and remedies. Hence proposed lung disease classification with feature extraction technique. The proposed work carried out feature extraction with Gray-Level Co-occurrence Matrix (GLCM) and Local Phase Quantization (LPQ) combined with Convolutional Neural Network(CNN). The goal is to create a model that reduces diagnostic errors, ensuring more precise early detection and classification of pulmonary disorders. This initiative facilitates the advancement of pulmonary medicine by promoting accurate radiologic imaging, while future directions emphasize integrating medical workflows to enable personalized and timely diagnosis and treatment. Additionally, the ResNet model demonstrates superior performance in hernia detection, achieving a test accuracy of 89.43% for GLCM based feature extraction and Pneumonia detection with 79.20% test accuracy for LPQ based feature extraction. © 2024 IEEE.

关键词： CNNs Deep learning Early detection Feature extraction GLCM Local Phase Quantization Lung disease diagnosis Medical imaging Texture analysis

来源：评论

学校读者我要写书评

暂无评论

COMPARING WSN PERFORMANCE: TRADITIONAL VS. SDN APPROACH

UPB Scientific Bulletin, Series C: Electrical Engineering an...

引用

UPB Scientific Bulletin, Series C: Electrical engineering and computer science 2024年第4期86卷 81-94页

作者： Hamad, Diyar Jamal Yalda, Khirota Gorgees Okumus, Ibrahim Taner Tapus, Nicolae Faculty of Automatic Control and Computer Science University of Science and Technology POLITEHNICA Bucharest Soran Technical college Erbil Polytechnic University Erbil Iraq Computer Engineering Kahramanmaras Sutcu Imam University Kahramanmaras Turkey Dept. of Computer Science University of Science and Technology POLITEHNICA Bucharest Romania

The proliferation of Wireless Sensor Networks (WSN) in various applications has necessitated the exploration of network architectures that can ensure efficient, scalable, and reliable communication. This study presents a comparative performance evaluation of traditional networking paradigms and Software-Defined Networking (SDN)-based architectures within WSN. Utilizing the Floodlight SDN controller and Mininet-WiFi emulator, we conducted a series of experiments across three different network topologies, varying the number of hosts and access points. The performance metrics focused on are average latency, packet loss percentage, and throughput. The results demonstrate a considerable improvement in all measured parameters under the SDN-based environment compared to the traditional setup. Specifically, SDN environments consistently showcased lower latency, minimal packet loss, and higher throughput, with the most notable difference observed in smaller network configurations. The findings suggest that SDN, with its centralized control plane, can significantly enhance the performance of WSNs, particularly in scenarios demanding rapid scalability and dynamic traffic management. This study underscores the potential of SDN architectures in optimizing WSN operations and lays the groundwork for future research in this domain. © 2024, Politechnica University of Bucharest. All rights reserved.

关键词： Floodlighting

来源：评论

学校读者我要写书评

暂无评论

DMDA: A Computational Resource Allocation Approach for IoT Devices in Fog Computing 4

DMDA: A Computational Resource Allocation Approach for IoT D...

引用

4th International Conference on Intelligent Cybernetics Technology and applications, ICICyTA 2024

作者： Pramono, Luthfan Hadi Shen, Shan-Hsiang National Taiwan University of Science and Technology Dept. of Computer Science and Information Engineering Taipei Taiwan

ISBN: (纸本)9798331506490

Fog Computing enables the efficient offloading of computational tasks from IoT Devices to Fog nodes, enhancing processing efficiency and reducing latency. This paper introduces the Dynamic Matching with Deferred Acceptance (DMDA) algorithm, designed to optimize the allocation of GPU and CPU resources within Fog Computing environments, specifically for smart city applications. The DMDA algorithm allocates resources to IoT Devices based on parameters such as data type, computational load, priority, and responsiveness. Devices processing image or video data are given priority for GPU allocation, while other devices are assigned to CPUs based on their cumulative performance scores. Devices with lower priority are reassigned to CPU resources or excluded when resource availability is exhausted, significantly enhancing performance under high-demand scenarios. The algorithm employs a Mixed-Integer Linear Programming (MILP) model to maximize the aggregate score of allocated devices while adhering to resource constraints. Experiments conducted with 50 to 1,000 devices demonstrate that both the total Device Score rate and Allocated Device rate reach 100%, indicating optimal allocation. Empirical findings show that the DMDA algorithm improves resource utilization, reduces processing times, and ensures effective workload distribution across large-scale IoT deployments. © 2024 IEEE.

关键词： Fog computing

来源：评论

学校读者我要写书评

暂无评论

Routing and Charge Planning Strategies for Ridesharing EV Fleets 11

Routing and Charge Planning Strategies for Ridesharing EV Fl...

引用

11th International Conference on Vehicle Technology and Intelligent Transport Systems, VEHITS 2025

作者： Singh, Ashutosh Gupta, Arobinda Dept. of Computer Science & Engineering Indian Institute of Technology KharagpurWB-721302 India

ISBN: (纸本)9789897587450

Ridesharing systems have become an important part of urban transportation. At the same time, electric vehicle (EV) adoption is also growing at a fast pace as an eco-friendly and sustainable transportation option. To operate a ridesharing system with EV fleets, scheduling an EV fleet to serve passenger requests requires consideration of both the requests, and the available charge and potential future charge requirements of the EVs. In this paper, we address the problem of scheduling EVs by a ridesharing operator, and propose four algorithms that schedule passenger requests while taking into consideration charging requirements of the EVs. Detailed simulation results are presented on a real world data set to show that the algorithms perform well. Copyright © 2025 by SCITEPRESS - science and Technology Publications, Lda.

关键词： Urban transportation

来源：评论

学校读者我要写书评

暂无评论

Analysing Breast Cancer Classification Using Explainable Artificial Intelligence 12

Analysing Breast Cancer Classification Using Explainable Art...

引用

12th International Conference on Internet of Everything, Microwave, Embedded, Communication and Networks, IEMECON 2024

作者： Fardeen, Kazi Bhaduri, Harshadeep Hazra, Soham Mondal, Praloy Manna, Sarbajit Si, Tapas Dept. of Computer Science & Electronics Ramakrishna Mission Vidyamandira Belur Math West Bengal Howrah711202 India Dept. of Computer Science & Engineering University of Engineering & Management Rajasthan Jaipur India

ISBN: (纸本)9798350387315

Worldwide, breast cancer is becoming the most serious illness that affects women. It is believed that early diagnosis and treatment of breast cancer can increase survival rates and decrease the need for surgery. Machine Learning model is very reliant on features for their proper training. However, understanding how a prediction is being affected by specific features is very important for a model’s interpretation. Understanding what features support a prediction is important as it provides some transparency to the inner workings of the model. Gaussian Naive Bayes (GNB), Decision Tree (DT), Random Forest (RF), K-Nearest Neighbors (KNN), and Support Vector Machine (SVM) are used to classify the breast cancer data, and accuracy, sensitivity, specificity, false-positive rate, precision, F1-score, and Geometric-Mean (GM) are used for the performance assessment. Furthermore, Multi-Criteria Decision Making (MCDM) is used to evaluate overall performance assessment based on the aforementioned performance measures and DT is found to be best among all the classifiers. Finally, an Explainable AI model namely LIME is used to interpret the predicted outcomes and impact of the different features of the data on the model’s prediction. © 2024 IEEE.

关键词： Lung cancer

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：