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检索条件"任意字段=2023 International Conference on Computer Engineering and Distance Learning, CEDL 2023"

共 40626 条记录，以下是4881-4890 订阅

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Translational Deep learning-Based Diagnostics: Enhancing Pathology With AI

Translational Deep Learning-Based Diagnostics: Enhancing Pat...

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2023 international conference on Advances in Computation, Communication and Information Technology, ICAICCIT 2023

作者： Ashwini, B. Sinha, Deepak Kumar Chaudhary, Chetan Presidency University Department of Electronics and Communication Engineering Karnataka Bangalore India Faculty of Engineering and Technology Department of Computer Science Engineering Karnataka Bangalore India Maharishi University of Information Technology Maharishi School of Engineering & Technology Uttar Pradesh India

ISBN: (纸本)9798350344387

Transformative innovation in healthcare is led by the constantly changing field of deep learning techniques, especially designed for cellular imaging in diagnostic pathology. This paper provides a thorough examination of several deep learning approaches in the field of pathology, highlighting new uses and tackling important issues that are essential to the field's progress. The successful deployment of AI in clinical settings depends on the seamless integration of these apps into processes in digital pathology. This all-inclusive diagnostic method not only simplifies processes but also provides pathologists with cutting-edge instruments that accelerate work, improve diagnosis accuracy, and lower mistakes. The US has recently approved a limited number of manufacturers' digital pathology products for diagnostic usage, which offers a strong basis for the creation of useful AI solutions. Researchers, physicians, business executives, government agencies, and patient advocacy organizations working together will make it much easier for these cutting-edge technologies to spread and become widely used by medical professionals. These technologies usher in a new age in healthcare, distinguished by their exceptional performance, faster processing rates, cost effectiveness, increased accuracy, and strict safety regulations. AI and computational pathology will continue to develop in harmony, opening the door to a new age of improved healthcare delivery. © 2023 IEEE.

关键词： Artificial Intelligence Cellular Imaging Deep learning Diagnostic Imaging and Machine Intelligence Diagnostics Digital Pathology Healthcare Pathology

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Empowering Farmers with AI: Federated learning of CNNs for Wheat Diseases Multi-Classification 4

Empowering Farmers with AI: Federated Learning of CNNs for W...

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4th international conference for Emerging Technology, INCET 2023

作者： Mehta, Shiva Kukreja, Vinay Vats, Satvik Chitkara University Institute of Engineering and Technology Chitkara University Punjab India Graphic Era Hill University Computer Science and Engineering Uttarakhand Dehradun India

ISBN: (纸本)9798350335750

Higher agricultural outputs are required due to the rising worldwide population, shifting nutritional preferences, and growing demand for food and basic materials for the industry. However, the farming sector confronts several difficulties, such as climate change and a rise in the severity of production risks, which have had a detrimental effect on food output. Crop production forecast algorithms have been created using machine learning and deep learning techniques to handle this issue. Traditional machine learning techniques, however, are less effective because many characteristics related to meteorological data, earth data, and agricultural management data are dispersed and isolated to specific organization computers or smart farming devices. Using about 9,876 pictures, a collaborative learning CNN method for wheat disease identification is suggested. The suggested method used the federated averaging technique to train the model on laterally dispersed datasets across various client devices. The suggested method beat current state-of-the-art models for detecting wheat illness, according to the findings of our experiments, obtaining high accuracy, precision, recall, and F1 score. The suggested method shows how federated learning can enhance machine learning models in a distributed environment. It can also be applied to other agricultural uses, such as crop forecast, soil analysis, and insect detection. © 2023 IEEE.

关键词： Crops

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Classification And Prediction of Brain Tumors and its Types using Deep learning 1

Classification And Prediction of Brain Tumors and its Types ...

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1st international conference on Computational Intelligence, Communication Technology and Networking, CICTN 2023

作者： Bajaj, Madhvan Rawat, Priyanshu Bhatt, Aastha Sharma, Vikrant Jain, Ayushi Kumar, Navneet Graphic Era Hill University Computer Science and Engineering Dehradun India BSc Nursing Uttar Pradesh Lucknow India Jawaharlal Nehru University School of Computer & System Sciences New Delhi India

ISBN: (纸本)9798350338027

Brain tumors are a major source of illness and mortality globally, with about 11,700 persons diagnosed with one each year. Brain tumor diagnosis and categorization are critical for proper treatment planning, which can increase patients' life expectancy. Due to the intricacy of brain tumors and their attributes, the traditional approach of manual assessment of brain MRI images might be error-prone. Automated classification approaches based on Machine learning (ML) and Artificial Intelligence (AI) have been shown to be more accurate than manual categorization. Deep learning techniques including Convolutional Neural Networks (CNNs), Artificial Neural Networks (ANNs), and Transfer learning (TL) have shown promising results for the automatic categorization of brain MRI images in this context. These automated procedures can save time and lessen the possibility of misdiagnosis, but they should always be used in tandem with clinical experience. The suggested method has the potential to significantly improve the accuracy and efficiency of brain tumor identification and categorization for doctors and radiologists globally. © 2023 IEEE.

关键词： Tumors

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Enhancing the Accuracy and Explainability of Heart Disease Prediction Models through Interpretable Machine learning Techniques 2

Enhancing the Accuracy and Explainability of Heart Disease P...

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2nd international conference on Information and Communication Technology for Sustainable Development, ICICT4SD 2023

作者： Moushi, Owana Marzia Ara, Nishat Helaluddin, Md Mondal, Himadri Shekhar Electrical and Computer Engineering University of Nebraska - Lincoln Omaha Nebraska United States Pharmaceutical Sciences Texas A&M University College Station Texas United States Computer Science & Engineering New Mexico Institute of Mining and Technology Socorro New Mexico United States

ISBN: (纸本)9798350358667

Cardiovascular disease is a significant worldwide health concern that has a devastating impact on mortality rates around the globe. Leveraging machine learning techniques for cardiovascular diseases prediction has the potential to prevent further consequences significantly. This study proposed a Grid Search Cross Validation-based model to predict heart diseases. The dataset has been utilized in this study consists of 51.3% heart disease samples and the rest of them have healthy conditions. A combination of the Pearson Correlation Coefficient and Permutation Important algorithm is used for selecting the best features for the dataset. Several Machine learning models such as Multi-layer Perceptron, Logistic Regression, Extra Trees, Decision trees, Random forests, Native Bayes, k-nearest neighbors, and C5.0 have been applied in this research. This experimental analysis is focused on different machine-learning models with default parameters which give the best outcome of 94.63% accuracy by Random Forest model. The proposed Grid Search Cross Validation model with the selected best features has been used on top of the classic machine learning approaches, giving a significant accuracy gain of 98.57% by the Extra Trees classifier. The excellent proposed result suggests that applying the Grid Search Cross Validation on the classic machine learning approach might be a promising method for cardiovascular disease detection. © 2023 IEEE.

关键词： Decision trees

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Cyber-Attacks Detection Using Intelligent Intrusion System (IDS) Along With Deep learning: Novel Approach 14

Cyber-Attacks Detection Using Intelligent Intrusion System (...

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14th international conference on Computing Communication and Networking Technologies, ICCCNT 2023

作者： Shrivastava, Prateek Yadav, Rajesh Kumar Delhi Technological University Dept. of Computer Science and Engineering Delhi India

ISBN: (纸本)9798350335095

In present scenario network congestion problem occurs during data transferring and receiving on any networks. The various reason of slowing down of this data flow is due to various kind of cyber-attacks occurring at a rapid speed. The cyber-attacks work internally and damage the server system, it is very dangerous to run system while it is prone to cyber-attacks which may interrupt the smooth flow. This research paper presents various kind of cyber related attacks and its detection through machine learning approach by the help of various simulation tools. the various kind of attacks like DDOS, UDP, ICMP, TCP, many more attacks finding out by suing simulate machine learning algorithms. The machine learning techniques perform generalise data set on the other hand data mining techniques perform specific data set and detection of cyber-attacks the objectives of cyber-attacks detection and categories along with simulation process also elaborate. © 2023 IEEE.

关键词： Network security

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Dynamic CNN: An Automatic Dynamic Model for Lung Cancer Lesion Detection 5

Dynamic CNN: An Automatic Dynamic Model for Lung Cancer Lesi...

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5th IEEE international conference on Electrical, computer and Communication Technologies, ICECCT 2023

作者： Chandrika G, Naga Shaik, Amreen Reddy G, Vamsi Krishna Sandeep V, Sai Teddu, Nikhil Information Technology VNR Vignana Jyothi Institute of Engineering and Technology Hyderabad India

ISBN: (纸本)9781665493604

Lung cancer is one of the most deadly diseases in emergent world, and early discovery of lung cancer is challenging. The diagnosis and treatment of lung cancer has been one of the most difficult challenges people have faced in recent decades. Early detection of the tumour will consistently save a large number of lives worldwide. The current computer-aided detection techniques have a convoluted workflow and poor detection precision. This research addresses a novel measure for classifying lung tumours as malignant or harmful using Dynamic Convolutional Neural Networks (Dynamic CNN). The proposed Dynamic CNN model attained an accuracy of 96%, which outperforms to other cutting-edge deep learning measures. © 2023 IEEE.

关键词： Convolutional neural networks

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Detection Plant Diseases Using Deep learning Algorithms 22

Detection Plant Diseases Using Deep Learning Algorithms

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22nd international conference on Cyberworlds, CW 2023

作者： Al-Shalout, Mooad Elleuch, Mohamed Douik, Ali University of Sousse Computer Science Department ISITCom Sousse Tunisia Carthage University Higher Institute of Management of Bizerte Bizerte Tunisia University of Sousse National Engineering School of Sousse NOCCS-ENISO Lab Sousse Tunisia

ISBN: (纸本)9798350315653

This study aimed to propose a detection approach for plant disease based on deep learning (DL) algorithms. The study sought to discover diseases affecting three plants, which are: Common Rust, Vercospora Leaf Spot, Northern Leaf Blight for Corn Plant, And Early Blight, Late Blight for Potato, And Bacterial Spot, Septoria Leaf Spot, Target Spot for Tomato. To implement this approach, three deep learning algorithms were used including VGG16, VGG19, and CNN. This model was trained on a dataset contain 25272 image from the Kaggle database. The results demonstrated that the proposed approach could accurately detect plant diseases. In comparison, the VGG19 algorithm has a high detection accuracy of up to 95%, while the VGG16 algorithm has a detection accuracy of up to 86%. Then followed the CNN algorithm, which had detection accuracy up to as 86%. © 2023 IEEE.

关键词： learning algorithms

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Digitization Workflow for Data Mining in Production Technology applied to a Feed Axis of a CNC Milling Machine 5

Digitization Workflow for Data Mining in Production Technolo...

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5th international conference on Industry 4.0 and Smart Manufacturing (ISM)

作者： Drowatzky, Lucas Maelzer, Mauritz Wejlupek, Kim A. Wiemer, Hajo Ihlenfeldt, Steffen TUD Dresden Univ Technol IMD D-01062 Dresden Germany

Condition monitoring and predictive maintenance applications receive ongoing scientific attention in production technology. Larger companies, especially machine and component manufacturers, already offer related products. Small and medium-sized enterprises (SMEs) in particular show interest in developing and offering solutions in this market themselves to gain economic advantages, to improve resource utilization of their machines or to be able to offer these advantages to their own customers. In the development process, however, they often encounter problems already in the digitization of the machines. The first hurdle is to obtain an analysiscapable data set. This is due to the fact that common and established general data mining development process models, such as CRISP-DM, do not focus on production technology, causing difficulties for engineers during deployment. A problem with existing process models is the limited practicality in the engineering domain due to restricted adaptability. In a previous paper, a guideline for engineers for data mining suitable digitization of production machines was developed in order to solve these problems. The related results were provided in the context of a project for condition monitoring of mixing machines. In this paper, the proposed method is applied to components of a 5-axis CNC milling machine in three different monitoring use cases. A complete workflow is presented, including effect analysis, sensor selection, formulation of predictive scenarios, data preparation, training of machine learning algorithms and vizualization. Data and documentation are provided alongside this publication. (c) 2023 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (http://***/licenses/by-nc-nd/4.0/) Peer-review under responsibility of the scientific committee of the 5th international conference on Industry 4.0 and Smart Manufacturing.

关键词： Condition Monitoring Machine learning Digitization Production engineering Production Technology Anomaly Detection Classification Usable Artificial Intelligence UAI Data Mining

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Graph Neural Network and Reinforcement learning Based Routing for Mega LEO Satellite Constellations 9

Graph Neural Network and Reinforcement Learning Based Routin...

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9th international conference on computer and Communications, ICCC 2023

作者： Zhang, Senbai Liu, Aijun Han, Chen Wang, Rui Liang, Xiaohu Xu, Xin Lin, Xin National University of Defense Technology Sixty-Third Research Institute Nanjing China Air Force Communication Nco Academy Dalian China Army Engineering University of Pla Nanjing China The School of Information Science and Engineering in Southeast University Nanjing China

ISBN: (纸本)9798350317251

We investigated the routing problem in the mega low earth orbit (LEO) satellite constellations. In Walker-Delta constellation, each satellite establishes 4 stable inter satellite links (ISLs) with neighboring satellites to forward data packets. We considered the factors such as traffic volume of the satellites, the length and the interference of the ISLs in routing. To minimize the delay from the source satellite to the destination satellite, we formulated it as an optimization problem and proposed a GRLR routing algorithm. The GRLR integrates the benefits of reinforcement learning (RL) and graph neural network (GNN), which builds the decision network based on Actor-Critic RL framework and builds feature extraction network based on GNN to realize distributed intelligent routing decision. The simulation results demonstrate that the proposed strategy exhibits accelerated convergence and reduced latency compared to the baseline strategies. © 2023 IEEE.

关键词： Reinforcement learning

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A Comprehensive Review on Early Diagnosis of Alzheimer's Disease Detection

A Comprehensive Review on Early Diagnosis of Alzheimer's Dis...

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2023 IEEE international conference on Research Methodologies in Knowledge Management, Artificial Intelligence and Telecommunication engineering, RMKMATE 2023

作者： Gayathri, G.S. Nachiyappan, S. School of Computer Science and Engineering Vellore Institute of Technology Tamil Nadu Chennai India

ISBN: (纸本)9798350305708

A progressive neurodegenerative Alzheimer's Disease (AD) shrinks (atrophy) the brain and kills brain cells. On a global scale, Finland and the United Kingdom have the highest prevalence of AD, with 54.7 and 42.7 cases per 100, 000 inhabitants respectively. In India, there are 14.60 AD cases for every 100, 000 people and it is ranked 137th in the world and 13th in India among the 50 causes of death. At the beginning of the disease, a person may forget things, or have trouble remembering recent conversations. As the disease progresses, a person may experience severe memory loss and difficulty performing everyday tasks. Consequently, the only way to predict AD at an early stage is to use treatments that prevent the disease from progressing. This paper provides an overview of the current approaches to AD diagnosis and detection, with a focus on the use of Biomarkers and discussed the advantages and disadvantages of Machine learning (ML)/Deep learning (DL) techniques in early detection of AD. It also reviews the collection of AD datasets and the pre-processing techniques used in the studies. Although DL methods gives the best outcome measures such as accuracy, precision, F-measures, recall and Area Under the Curve (AUC) still DL techniques have some limitations that can be overcome via Quantum Computing (QC). QC is currently emerging new technology based on quantum theory where qubits are used to store the data instead of bits. It accomplishes 1000 times faster and also solves the complex problem within a fraction of time than the classical computers. © 2023 IEEE.

关键词： Alzheimer's Disease Biomarkers Datasets Deep learning Machine learning Quantum Computing

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