检索结果-内蒙古大学图书馆

4th International Conference on Advancement in Electronics and Communication engineering, AECE 2024

作者： Madhavi, G Bindu Sumithra, M. Devi, P Yamini Mahalaxmi, U.S.B.K. Saravanan, T. Shaker Reddy, Pundru Chandra Geethanjali College of Engineering and Technology Department of Cse -AI &ml Telangana Hyderabad India Vel Tech Rangarajan Dr. Sagunthala R&d Institute of Science and Technology Computer Science and Engineering School of Computing Tamil Nadu Chennai India Computer Science and Engineering Vignana Bharathi Institute of Technology Telangana Hyderabad India Electronics Communication Engineering Aditya University Andhra Pradesh Surampalem India P. S. R. Engineering College Department of Information Technology Tamil Nadu Sivakasi India School of Computer Science and Artificial Intelligence Sr University Telangana Warangal India

ISBN: (纸本)9798350364729

Pigmented skin lesions must be identified in order to detect hazardous skin-related disorders, especially cancer. To improve the correctness of skin cancer recognition, image detection strategies and classification abilities can be utilized. Based on the HAM10000 dataset, which contains 10015 photos, this research paper makes use of the dataset. An augmentation has been carried out on a subset of the dataset in the proposed work. Compared to an approach with &without data augmentation, is more likely to learn unique traits and attributes. To make the model more accurate, data augmentation is often used. However, until the model is strong, it will not be able to produce meaningful results when tested with the testing data. This work's model is robust since it makes use of the k-fold cross-validation technique. Machine learning methods and models from Convolutional Neural Networks have been evaluated for their classification accuracy. After comparing the suggested work's use of several machine learning methods, we found that Convolutional Neural Network produced the best results in terms of accuracy. We achieved a maximum accuracy of 95.53% using the CNN in the suggested setup. Early detection of seven types of skin illness is made easier by the suggested work, which may be authenticated and treated properly by medicinal professionals. © 2024 IEEE.

关键词： Convolutional neural networks

来源：评论

学校读者我要写书评

暂无评论

Futuristic IoT based Smart Precision Agriculture: Brief Analysis

引用

Journal of Mobile Multimedia 2022年第3期18卷 935-956页

作者： Swamidason, Iwin Thanakumar Joseph Pandiyarajan, Shanthini Velswamy, Karunakaran Leela Jancy, P. Department of Computer Science and Engineering Koneru Lakshmaiah Education Foundation Andhrapradesh Vijayawada India Department of Electronics and Communication Engineering Karunya Institute of Technology and Sciences Tamilnadu Coimbatore India Department of Computer Science and Engineering (AI & ML) and (Cybersecurity) Jain University Karnataka Bengaluru India Department of Information Technology Sri Sai Ram Institute of Technology Tamilnadu Chennai India

Agriculture is considered as the backbone of any nation across the globe. With the advent of modern technologies, smart tools and techniques are used in the agriculture/farming to build on the quantity as well as quality of the agriculture production to feed the basic necessity of the humans. Smart technology such as Internet of Things play a vital role in monitoring and analyzing various environmental parameters such as water level, humidity, soil moisture, air quality, UV level, rain etc. which are highly essential to ensure the fruitful yield of any nutritious crops. In this research article, precision agriculture concepts are investigated widely with the focus of improving the productivity level and also the effective utilization of resources with the minimal cost while compared with the conventional methodologies. © 2022 River Publishers.

关键词： Drones Internet of things (IoT) Irrigation Precision agriculture Sensors

来源：评论

学校读者我要写书评

暂无评论

Advanced Deep Learning Approaches for Early Detection of Diabetic Retinopathy in Retinal Images

Advanced Deep Learning Approaches for Early Detection of Dia...

引用

Women in Computing (InCoWoCo), International Conference for

作者： D. Vetrithangam G. Pinki Azra Fathima Naresh Kumar Pegada V. Devi Aparajita Mukherjee Department of Computer Science and Engineering Chandigarh University Mohali India Department of CSE Methodist College of Engineering and Technology Hyderabad India Department of CSE(AI &ML) Vignana Bharathi Institute of Technology Hyderabad India Department of Computer Science and Engineering(AI &ML) Keshav Memorial Engineering College Hyderabad India Department of CSE Hindusthan College of Engineering and Technology Coimbatore India Department of CSE University of Engineering and Management Kolkata India

ISBN: (数字)9798331518943

ISBN: (纸本)9798331518950

Diabetic Retinopathy (DR) is one of the major causes of blindness worldwide and a complication of diabetes. Therefore, early identification and intervention are important to reduce the extent of vision loss and improve prognosis for patients. This paper presents an improved model for screening DR at its initial stage in retinal images using deep learning methods. The developed system employs convolutional neural networks (CNNs) to detect abnormalities in retinal images that indicate early signs of DR, thereby increasing the chances of screening in clinics. The proposed system utilizes large-scale Kaggle DR and Messidor datasets, with an optimized CNN for the analysis of retinal images. To address issues related to variations in image quality and acquisition methods, various data enhancement strategies are incorporated into the model. Additionally, an interpretability module based on Grad-CAM is included to explain the model's classifications and build trust with clinicians. Based on experimental results, it can be concluded that the proposed model demonstrates higher accuracy, sensitivity, and specificity compared to benchmark methods. This system shows potential in reducing the workload of ophthalmologists and improving early DR screening, particularly in resource-limited healthcare settings. Furthermore, it can be extended to other ophthalmic diseases, offering significant benefits to the broader healthcare system.

关键词： Deep learning Diabetic retinopathy Accuracy Computational modeling Blindness Sensitivity and specificity Retina Data models Convolutional neural networks Usability

来源：评论

学校读者我要写书评

暂无评论

Selective Kernel Spatial Feature Extraction based Deep Learning Approach for Identification and Classification of Archaeological Sites

Selective Kernel Spatial Feature Extraction based Deep Learn...

引用

2024 International Conference on Intelligent Algorithms for Computational Intelligence Systems, IACIS 2024

作者： Sajja, Sri Lavanya Alsalami, Zaid Sunil, G. Sunil Kumar, V. Mohamad Ramadan, Ghazi Malla Reddy Engineering College for Women Secunderabad India The Islamic University Engineering College of Technical Engineering Department of Computers Techniques Najaf Iraq Sr University School of Computer Science & Artificial Intelligence Warangal India Nitte Meenakshi Institute of Technology Dept. of Ai and Ml Bengaluru India Ahl Al Bayt University College of Mlt Karbala Iraq

ISBN: (纸本)9798350360660

Deep Learning (DL) based identification and classification of archaeological sites are being used progressively from satellite images obtained from Geographic Information System (GIS). Even though, there were promising results from the existing researches, there is still need to enhance the classification with distinct features of the satellite images. The methods found challenging to accurately or partially identify an archaeological feature especially in a complex landscape due to lack of distinction between archaeological and non-archaeological features. For this, the geo-referenced satellite images are considered in this research, which are pre-processed using min-max normalization for efficient data analysis and to remove unwanted data. The Selective kernel spatial Feature Extraction (SFE) module is used to improve the feature extraction process where the spatial features are extracted. The extracted features are then classified using Fully Convoluted Deep Neural Network (FCDNN) for effective identification and classification of archaeological sites. The results showed that the proposed SFE-FCDNN achieved better classification accuracy with 98.83%, precision of 97.41%, and an error rate of 1.71 when compared to the existing methods such as Neural Network (NN), and Backpropagation Neural Network (BNN). © 2024 IEEE.

关键词： Satellite imagery

来源：评论

学校读者我要写书评

暂无评论

Bayesian Belief Network Analysis for SPAD System in Railways

Bayesian Belief Network Analysis for SPAD System in Railways

引用

Multimedia Processing, Communication & Information Technology (MPCIT), International Conference on

作者： Manjunath Sargur Krishnamurthy Bommisetty Rahul Chandrahas Reddy Bondala M Vishnu Vardhan Reddy Magha Raghul G Ambika B J JP Morgan & Chase Co. Houston Texas USA Dept. of AI&ML BMS Institute of Technology and Management Bengaluru Dept. of Computer Science and Engineering Manipal Institute of Technology Bengaluru Manipal Karnataka

ISBN: (数字)9798350375466

ISBN: (纸本)9798350375473

Even with a powerful network of signaling and warning systems in the country, there have been many examples of trains crossing the red signal due to various factors, even today. These occurrences, known as Signal Passed At Danger (SPAD) events, could potentially result in severe consequences such as train derailments, train collisions, infrastructure collisions, and other dangerous events. Traditionally, these events have been analyzed using the Fault Tree Analysis (FTA) approach Bayesian Network (BN) is considered to be a better model to represent this situation when it comes to handling complexity. Bayesian Network allows for integration and includes systematic knowledge about the system, which helps to get a very flexible concise, and simple graphed representation. This study demonstrates applications of Bayesian Belief Networks (BBN) analysing and enlightening the Signal Passed at Danger (SPAD) system in railway processes. Execution of BBN offers more dynamic and flexible models compared to outmoded Fault Tree Analysis (FTA), struggling with complexity. BBN efficiently catches the inter dependencies between factors that are contributing to SPAD events, such as signal conditions, human errors, and mechanical issues. Over simulations and probability calculations using OpenMarkov software, Study revealed key intuitions, such as identifying highly risk apparatuses within SPAD system. The outcomes include enhanced risk calculation accuracy and recommendations for improving signal systems and safety protocols. These conclusions contribute to a safer and reliable railway structure, highlighting the success of BBN for complex, safety critical systems in real world applications.

关键词： Analytical models Systematics Rail transportation Software Bayes methods Safety Complexity theory Software reliability Single-photon avalanche diodes Fault trees

来源：评论

学校读者我要写书评

暂无评论

Research on Optimization Algorithm of Real Estate Enterprise Value Evaluation Based on BP Neural Network 2

Research on Optimization Algorithm of Real Estate Enterprise...

引用

2nd International Conference on Smart Technologies for Smart Nation, SmartTechCon 2023

作者： Niranjanamurthy, M. Saravanan, S. Mahaveerakannan, R. Surendran, S. Nakrani, Tulsidas Arun, M.R. Department of AI and ML BMS Institute of Technology and Management Avalahalli Karnataka Bangalore India Department of Computer Science and Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Chennai India Tamilnadu Chennai India Department of Computer Science and Engineering Tagore Engineering College Tamil Nadu Chennai India Department of Computer Application Sankalchand Patel College of Engineering Sankalchand Patel University Gujarat Visnagar India Department of ECE Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Avadi Tamilnadu Chennai India

ISBN: (纸本)9798350305418

Because of its professional skills and expertise, it is able to persuade both the buyer and the selling and make the transaction easier. An optimal design scheme for real estate businesses' worth appraisals is proposed here, and it is based on BPNN (BP neural network). A worth appraisal system is developed by doing an analysis of several worth indicators pertaining to real estate companies, and then the system is optimized with the use of BPNN. In the final step, an analysis of the simulation test is performed. According to the findings of the simulation, the arithmetic has a certain accuracy, which is 8.34% higher than the accuracy of traditional arithmetic. On the premise of ensuring the efficient processing of data in batches and finding the rules therein, reduce the impact of human subjectivity, improve the accuracy and scientificity of real estate appraisal, and provide certain reference for the national and local governments to reasonably regulate house prices, so as to promote the stable operation and healthy development of the real estate market. In doing so, you will be able to promote the growth of the real estate market in a healthy manner. © 2023 IEEE.

关键词： BP neural network Enterprise worth appraisal Real estate

来源：评论

学校读者我要写书评

暂无评论

Predictive Maintenance of aircraft Engine using Empirical Mode Decomposition based Long Short Term Memory

Predictive Maintenance of Aircraft Engine using Empirical Mo...

引用

Data science and Information System (ICDSIS), IEEE International Conference on

作者： Shaik Abdul Nabi B. Raveendra Naick Muntather Almusawi B S Shruthi R Rana Veer Samara Sihman Bharattej Department of Computer Science and Engineering AVN Institute of Engineering & Technology Hyderabad Telangana India Dept. of CSE(AI&ML) School of Computing Mohan Babu University Tirupati India Department of Computers Techniques Engineering College of Technical Engineering The Islamic University Najaf Iraq Department of Civil Engineering Nitte Meenakshi Institute of Technology Bengaluru India Department of Computer Science Saint Leo University Florida United States

ISBN: (数字)9798350381665

ISBN: (纸本)9798350381672

In this modern era of Industry 4.0, efficiency and safety of industrial machines are a primary concern in trade sectors and prediction of Remaining Useful Life (RUL) of an engine helps to plan effectively maintenance operation. However, the Predictive Maintenance is to acquire reliable prognostics and estimation RUL in turbofan engines is a difficult task. To solve this problem, an Empirical Mode Decomposition based Long Short-Term Memory (EMD-LSTM) for prediction of RUL in aircraft engine utilizing C-MAPSS dataset. Initially, the most relevant sensor signals are selected by Correlation analysis and filtered using Moving Median Filter (MMF). After, the signals are normalized by Z-score technique to train the prediction model and signals are fed as input to proposed EMD-LSTM approach for prediction of RUL span of the engine. The evaluation results of the proposed method using performance metrics are Root Mean Square Error (RMSE), Score, Accuracy and Precision. The presented EMD-LSTM model achieved great accuracy of 99.3%, Precision of 98.9%, RMSE of 13.02 and Score of 282.0 which is greater than the previous models like Deep Convolutional Neural Network (DCNN)-LightGBM, LSTM, Graph CN (GCN), DCNN and Denoising Online Sequential Extreme Learning Machine (DOSELM).

关键词： Analytical models Empirical mode decomposition Accuracy Atmospheric modeling Predictive models aircraft propulsion aircraft

来源：评论

学校读者我要写书评

暂无评论

Advancements in Brain Disease Diagnosis for Comparing Traditional Methods and Deep Learning Approaches in Medical Imaging

Advancements in Brain Disease Diagnosis for Comparing Tradit...

引用

Emerging Technologies and Innovation for Sustainability (EmergIN), International Conference on

作者： Sunita Kumari Laxman Singh Moghal Irfan Pasha M. Kumaraswamy Amar Choudhary Monika Agarwal Department Computer Science and Engineering ABES Institute of Technology Ghaziabad India School of Computer Science Engineering and Technology Bennett University Greater Noida India Department of CSE-AI&ML CVR College of Engineering Hyderabad India Department of CST Sasi Institute of Technology and Engineering Tadepalligudem India Department Electronics and Communication Engineering Alliance College of Engineering and Design (ACED) Alliance University Bengaluru India Department of Computer Science and Engineering Raj Kumar Goel Institute of Technology Ghaziabad India

ISBN: (数字)9798350391268

ISBN: (纸本)9798350391275

This work explores improvements in the diagnosis of brain disease through traditional vs deep learning base techniques with respect to Medical Imaging. The dataset containing 3,000 images of brain tumors was preprocessed to resize (224×224), normalize and augmentation techniques like rotation, flipping were further implemented. The dataset are splitted into70% training and 30%. testing across four deep learning models, Convolutional Neural Networks (CNN), VGG16. The models were trained to classify brain tumor images into more or less complex categories. After training VGG19 gave top accuracy of 98.6 per cent, followed byVVG16 at second place with an accuracy rate of 96.75%, while the ResNet50 and CNN were rated third and fourth in terms of their accuracies; as neither performed well with just 92.3% and 89.3%. VGG19 showed the best precision (98.8%), recall(98.5%) and F1-score(98.65) among metrics of all models utilised in this research(which ever they might be). VGG19 had low false positives and negatives which are important for accurate diagnosis as can be seen in the confusion matrices. The type of research exemplifies how deep learning can improve the accuracy and certainty within medical imaging diagnostics, particularly in difficult tasks such as brain tumor classification. This study highlights the strong possibilities deep learning could lead to transformative improvements in clinical neuroscience by employing advanced neural network architectures and meticulous preprocessing strategies.

关键词： Deep learning Training Accuracy Brain tumors Brain modeling Feature extraction Convolutional neural networks Medical diagnosis Biomedical imaging Residual neural networks

来源：评论

学校读者我要写书评

暂无评论

Real-Time Load Forecasting and Demand Response Optimization Using Deep Learning

Real-Time Load Forecasting and Demand Response Optimization ...

引用

Cognitive Computing in engineering, Communications, sciences and Biomedical Health Informatics (IC3ECSBHI), International Conference on

作者： B Rajalakshmi S Sreekanth Ginni Nijhawan K. Anusha Baydaa Fathil Mohammed Saurabh Sharma Priyanka Agrawal Department of Computer Science New Horizon College of Engineering Bangalore India Computer Science and Engineering (Data Science) Institute of Aeronautical Engineering Dundigal Hyderabad India LPU Phagwara India Department of CSE-AI&ML MLR Institute of Technology Hyderabad Telangana India Radiology Techniques Department College of Medical Technology The Islamic University Najaf Iraq Lloyd Law College Greater Noida Uttar Pradesh Department of Electronics & Electrical Engineering IILM University Greater Noida

ISBN: (数字)9798331518523

ISBN: (纸本)9798331518530

The application of deep learning approaches for use in power system to enhance load forecasting accuracy is accurate real time load forecasting an essential element in managing the stability and efficiency of the power network. This research proposes a new deep learning-based architecture that improves STLP accuracy and directly provides input for demand response applications. Based on deep neural network, the proposed approach is able to capture temporal characteristics and non-linear relationship of the load data, and hence provides better performance of load forecasting in various operation conditions. Incorporation of these forecasts in real-time to manage demand response is shown to facilitate modulations of load in order to minimize costs of operation and at the same time ensure stability of power grid networks. The study shows that the implementation of this deep learning framework will go a long way in enhancing the stability of the contemporary power systems as the load forecasts are positively enhanced by demand response actions to achieve an enhanced supply of energy.

关键词： Deep learning Accuracy Load forecasting Power system stability Demand response Real-time systems Stability analysis Smart grids Thermal stability Optimization

来源：评论

学校读者我要写书评

暂无评论

Collaborative and Integrated Platform to Support Distributed Manufacturing System Using a Service-Oriented Approach Based On Cloud Computing Paradigm

Collaborative and Integrated Platform to Support Distributed...

引用

Computational, Communication and Information Technology (ICCCIT), International Conference on

作者： Ankita Nainwal Vishal Sharma Neeraj Varshney Nandini Shirish Boob NV Uma Reddy K. Jyothsna Reddy Department of Computer Science & Engineering Graphic Era Deemed to be University Dehradun Lovely Professional University Phagwara India Department of Computer Engineering and Applications GLA University Mathura Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Chennai Tamilnadu Department of Artificial Intelligence & Machine Learning New Horizon College of Engineering Bangalore Department of CSE-AI&ML MLR Institute of Technology Hyderabad Telangana

ISBN: (数字)9798331512965

ISBN: (纸本)9798331512972

Increasing interest in distributed manufacturing systems is a result of the dynamic nature of today's business environment and with that comes the popularity of their use. This has given rise in the interest in distributed manufacturing systems. The indirect consequence of this has led to much higher use of distributed manufacturing systems. A large number of production processes, dispersed across a multitude of locations geographically separate, must be controlled and integrated in order to achieve production agility, flexibility and efficiency of costs. In our most recent project, we developed an integrated, collaborative manufacturing platform, the centerpiece of which was LAYMOD. To address the issues posed by such systems, this research presents a collateral and integrated platform based on a service oriented approach that can operate within the cloud computing paradigm. These systems present problems which the study is conducted in order to solve. Utilising the features of cloud computing, it aims to serve as a platform in solving process coordination and hence improving efficiency and scalability of distant manufacturing processes and potential real time communications among various stake holders. Furthermore, statistical research on the rates of platform module adoption in different settings provides specifics of (a) use patterns of some platform modules and (b) what capabilities are provided by some platform modules. When extrapolated to a wider size, our work is applied towards the creation of collaborative and integrated platforms for a number of the various manufacturing processes which are currently in use.

关键词： Cloud computing Manufacturing processes Scalability Collaboration Production Companies Real-time systems Service-oriented architecture Standards Business

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：