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

共 40755 条记录，以下是4381-4390 订阅

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Transforming Healthcare with MedInsight: A Guide to Intelligent Decision Support

Transforming Healthcare with MedInsight: A Guide to Intellig...

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2023 international conference on Data Science, Agents and Artificial Intelligence, ICDSAAI 2023

作者： Uma Haimavathi, K. Kandavel, N. Vignesh, D. Hubert Chinnadurai, B. Chennai Institute of Technology Department of Biomedical Engineering Chennai600069 India Chennai Institute of Technology Department of Computer Science Engineering Chennai600069 India

ISBN: (纸本)9798350348910

A novel Prediction System has been developed to address the lack of a unified approach for multi-disease prediction in healthcare machine learning models. The To detect various ailments, the system uses machine learning neural network as well as artificial intelligence. simultaneously, specifically targeting heart, liver, and diabetes conditions. The system uses Support Vector Machine and logistic regression models to analyze heart disorders, diabetes, and breast cancer. The logistic regression model, trained on 8,000 patients, achieved an impressive overall accuracy of 85%. It was particularly effective in predicting hypertension and hyperlipidaemia, with accuracies of 90% and 87%, respectively. The system also showed proficiency in predicting diabetes and heart disease, with accuracies of 82% and 78%. This research represents a promising step towards a more comprehensive and accurate multi-disease prediction system. © 2023 IEEE.

关键词： Diseases

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Detection of Early Fault in Power Electronic Converters through Machine learning and Data Mining Techniques 3

Detection of Early Fault in Power Electronic Converters thro...

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3rd international conference on Artificial Intelligence and Smart Energy, ICAIS 2023

作者： Pradeep, K.V. Gowrishankar, K. Sivanantham, E. Rao, Katta Subba Kiran, Nagulapati Vimal, A. Vellore Institute of Technology School of Computer Science and Engineering Tamil Nadu Chennai India Amet University Department of Electrical and Electronics Engineering Tamil Nadu Chennai India Rajalakshmi Institute of Technology Department of Electronics and Communication Engineering Tamil Nadu Chennai India B v Raju Institute of Technology Department of Computer Science and Engineering Telangana Medak India Anil Neerukonda Institute of Technology and Sciences Department of Electrical and Electronics Engineering Andhra Pradesh Visakhapatnam India Sri Eshwar College of Engineering Department of Mechanical Engineering Tamil Nadu Coimbatore India

ISBN: (纸本)9781665462167

The Power electronic system plays a significant role in versatile applications. The power electronic converters are largely used in energy conversion mechanisms. A fault is defined as the abnormal condition of the system that results in various consequences. The important constraints in the modelling of power electronic systems involve losses, Electromagnetic Interference (EMI) and harmonics. This includes the fault detection in the power electronic converters that includes three phase rectifier, d-dc converter and single-phase inverter. These parameter affects the overall efficiency and quality of the system. To overcome the fault in the power electronic converters, the machine learning with data mining techniques is adopted. This helps to predict the early fault and helps to increase the efficiency of the system. © 2023 IEEE.

关键词： Signal interference

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A Review of Convolutional Neural Network-based Approaches for Disease Detection in Plants

A Review of Convolutional Neural Network-based Approaches fo...

引用

2023 international conference on Intelligent Data Communication Technologies and Internet of Things, IDCIoT 2023

作者： Biswas, Barsha Yadav, Rajesh Kumar Delhi Technological University Department of Computer Science and Engineering Delhi India

ISBN: (纸本)9781665474511

Around 60.3% of land in India is used for agricultural purposes and the whole population depends on agriculture. That's why crop yield is very crucial to get high agricultural output. The economical loss will be very high if the agricultural output is low. So, that's why the diagnosis of disease in plants is very important. And the detection should be in the early stage not in a later stage. Using Deep learning (DL) i.e. a branch of Artificial Intelligence (AI), a farmer can detect plant diseases very easily. In Deep learning(DL), Convolutional Neural Networks (CNNs) are a cutting-edge method for image classification tasks. And Plant Disease Detection is an image classification task in which image is given as input and a class of plant disease is obtained as an output. This research study reviews the CNN-based approaches that are used to detect various diseases in plants. © 2023 IEEE.

关键词： Convolutional neural networks

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An Effective FL-CNN Based Data Securing Model for Heart Disease Prediction 6

An Effective FL-CNN Based Data Securing Model for Heart Dise...

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6th international conference on Contemporary Computing and Informatics, IC3I 2023

作者： Sharma, Pakhi Sharma, Sanjiv KIET Group of Institutions Computer Science and Engineering Ghaziabad India

ISBN: (纸本)9798350304480

Cardiovascular disease is the leading cause of death worldwide, according to the WHO. Coronary heart disease is most dangerous. 2015 saw 360,000 US heart attack deaths. Effective heart disease treatment prevents global deaths. An updated FL-CNN model improved cardiac disease diagnosis and prognosis for doctors and patients. Hospitals cannot disclose patient data for security and privacy reasons. Thus, centralizing data is hard. Federated learning can train machine learning and deep learning models using massive volumes of distributed data. On the UCI Cleveland dataset, CNN with Federated learning achieves 94.99% accuracy, while CNN with centralized learning achieves 97% accuracy. © 2023 IEEE.

关键词： Cardiovascular disease Centralized learning Convolutional Neural Network Federated learning UCI Dataset

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Bengali Music Genre Classification Using WaveNet 26

Bengali Music Genre Classification Using WaveNet

引用

26th international conference on computer and Information Technology, ICCIT 2023

作者： Khan, Md. Nabil Rahman Tuli, Khandaker Iffat Jahan Salsabil, Most. Sadia Reza, S.M. Raiyan Shah, Faisal Muhammad Joy, Sajib Kumar Saha Ahsanullah University of Science and Technology Department of Computer Science and Engineering Dhaka Bangladesh

ISBN: (纸本)9798350359015

Music genre classification plays a crucial role in music recommendation and information retrieval. While there have been numerous studies on classifying genres of English music using various machine learning approaches, there is a noticeable absence of similar work for Bangla music classification. Bangla music encompasses a wide range of types and styles that can be classified into different genres. In this research, we focus on six distinct Bangla music genres, including 'Bangla Adhunik,' 'Bangla Hip-Hop,' 'Bangla Band Music,' 'Nazrulgeeti,' 'Palligeeti,' and 'Rabindra Sangeet.' Our dataset consists of 250-300 songs (***3 format) for each genre. By comparing the performances of different models, we demonstrate that our proposed model, using a combination of Principal Component Analysis and K-Fold on the model WaveNet, achieves high accuracy in the multiclass classification of Bangla music genres by comparing with other machine learning and deep learning. This work contributes to the advancement of music genre classification specifically for Bangla music and paves the way for enhanced music recommendation systems and information retrieval in this rich musical tradition. © 2023 IEEE.

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Enhancing neural network with LIDAR data for navigation 24

Enhancing neural network with LIDAR data for navigation

引用

24th international conference on Control Systems and computer Science, CSCS 2023

作者： Baicu, Diana Craciunescu, Mihai Duvinage, Thomas Mocanu, Stefan University Politehnica of Buchaerst Faculty of Automatic Control and Computer Science Bucharest Romania University of Belfort Montbéliard Belfort France

ISBN: (纸本)9798350313390

In this paper, a method of fusing distance information and camera data is presented, the objective being that of combining LIDAR measurements with the output of a segmentation neural network, applied on the merged image from three frontal cameras, for navigating in an indoor environment. Usually, these types of environments are not structured, being diverse in their composition: for example, some buildings have more glass walls and doors, others might have large corridors and concrete structures. The proposed fusing method is used for reducing the errors generated by both techniques individually, which is then applied to control the trajectory of an electric wheelchair, as well as to gather a dataset of corrected and annotated images for reducing the dependency on the LIDAR data in the future. © 2023 IEEE.

关键词： Cameras

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A Machine learning based Facial Expression and Emotion Recognition for Human computer Interaction through Fuzzy Logic System 6

A Machine Learning based Facial Expression and Emotion Recog...

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6th international conference on Inventive Computation Technologies, ICICT 2023

作者： Vinutha, K. Niranjan, Manoj Kumar Makhijani, Jagdish Natarajan, B. Nirmala, V. Vijaya Lakshmi, T.R. Department of Information Science and Engineering BMS Institute of Technology and Management Karnataka Bengaluru India Department of Computer Applications Rustamji Institute of Technology Madhya Pradesh Gwalior India Department of Computer Science and Engineering Rustamji Institute of Technology Madhya Pradesh Gwalior India Department of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Tamil Nadu Chennai India Department of Science and Humanities Faculty of Engineering Karpagam Academy of Higher Education Tamil Nadu Coimbatore India Department of Electronics and Communication Engineering Mahatma Gandhi Institute of Technology Telangana Hyderabad India

ISBN: (纸本)9798350398496

Facial recognition is in use for the past decade there are many applications that needs facial expression to learn the human behaviour and emotions for certain activities. Facial recognition is in a development phase where many service providers use this feature to find the expression of the people on using their BlogSpot or website or reading any news article. This recognition of facial expression is highly possible with the help of machine learning technology. This research study has developed a facial expression recognizing algorithm using Python programming language with the help of Keras software package. This algorithm is purely based on machine learning approach that enables the programmer to process the facial image and convert it into data that is helpful in prediction of facial expression using the fuzzy logic technique. The fuzzy logic technique is a prediction method that helps programmer to predict the intermediate data by providing the initial and ending conditions. For enabling the facial recognition to process any system or a mobile device the algorithm needs permission to access the camera, once the onto the access is permitted the algorithm retrieves the image from the Vision sensor and with the help of image processing technology of the machine learning algorithm the program the program converts the data from the vision sensor into required facial expression and emotional content. © 2023 IEEE.

关键词： Emotion Recognition

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Enhancing Text-to-Image Model Evaluation: SVCS and UCICM 6

Enhancing Text-to-Image Model Evaluation: SVCS and UCICM

引用

6th international conference on Recent Trends in Advance Computing, ICRTAC 2023

作者： Gabriel Joshua, R. Khadar Nawas, K. Varsini, S.R. Vellore Institute of Technology School of Computer Science and Engineering Chennai India

ISBN: (纸本)9798350394702

As the world gradually embraces the integration of AI into various applications, there is a growing need for methods to enhance and evaluate AI-based technologies. One such a remarkable innovation is text to image models, which are currently revolutionizing the content generation, computer vision, design industries etc. Among the existing deep learning techniques utilized for text to image synthesis, stable diffusion model set apart due to its ability to generate high quality images. It transforms images into their latent space representation, mitigating computational memory and time requirements while minimizing instability and complexity. In this paper, we propose two evaluation metrics Semantic Visual Consistency Score (SVCS) and User-Centric Image Coherence Metric (UCICM) to analyze the coherence of the image with respect to the user prompt. SVCS assesses the model's capability to interpret human language, while UCICM is tailored to enhance application-specific image generation through personalization. This paper primarily focuses on the processes entailed in a transfer learning-based stable diffusion model and subsequently evaluates its performance using the newly proposed metrics. © 2023 IEEE.

关键词： Semantics

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Channel Attention-Guided Lightweight CNN Meets Extreme learning Machine: Multi-Class Crop Leaf Disease Classification with Explainable AI 26

Channel Attention-Guided Lightweight CNN Meets Extreme Learn...

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26th international conference on computer and Information Technology, ICCIT 2023

作者： Islam, Md. Minhazul Hasan, S. M. Mahedy Hossain, Md. Rakib Srizon, Azmain Yakin Akter, Farzana Faruk, Md. Farukuzzaman Shraboni Akter, Sheikh Rajshahi University of Engineering & Technology Department of Electronics & Telecommunication Engineering Rajshahi6204 Bangladesh Rajshahi University of Engineering & Technology Department of Computer Science & Engineering Rajshahi6204 Bangladesh

ISBN: (纸本)9798350359015

Crop leaf diseases pose a significant challenge to global agriculture, leading to substantial economic losses and reduced yields. These ailments, caused by various pathogens, manifest as symptoms such as leaf discoloration and impaired photosynthesis, severely impacting crop health and productivity. Existing classification methods face challenges related to accuracy, computational efficiency, and interpretability. This study introduces an innovative solution that combines a meticulously designed lightweight convolutional neural network (CNN) architecture with a channel attention (CA) mechanism for extracting crucial features and leverages the discriminative power of an extreme learning machine (ELM) for classification. The lightweight CNN developed demonstrates significantly reduced training times. Additionally, the CA mechanism within the model effectively prioritizes channel-wise critical features while eliminating irrelevant and redundant ones. An empirical evaluation is conducted using a publicly available dataset and the proposed model achieves a flawless 100% accuracy in distinguishing between 39 distinct crop leaf diseases. Furthermore, the study employs gradient class activation maps (Grad-CAM) visualization to delve into the model's decision-making process, shedding light on influential factors and regions. It holds the promise of transforming crop health and agricultural yields, instilling hope for farmers and researchers dedicated to achieving agricultural prosperity and sustainability. © 2023 IEEE.

关键词： Channel Attention Convolutional Neural Network Crop Leaf Disease Extreme learning Machine Grad-CAM

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Maximising Efficiency of Network Resources in SDN through Optimal Packet Routing using Reinforcement learning

Maximising Efficiency of Network Resources in SDN through Op...

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2023 international conference on computer Communication and Informatics, ICCCI 2023

作者： Byakodi, Anush K Hungund, Mohammad Zakee Athani, Aman Khan N Afghan, Khadar Wali B Patil, Somashekar Narayan, D.G. KLE Technological University School of Computer Science and Engineering Hubballi India

ISBN: (纸本)9798350348217

Software-defined networking (SDN) has increased in popularity in recent years owing to its ability to centralize network device control. This is the cornerstone of data center networks, and as more IoT-enabled devices are added, it is expected to skyrocket. The employment of Machine Intelligence to handle and route the majority of traffic can enhance centralized SDN-based control, allowing for high and occasionally subpar network management. Owing to the development of Reinforcement learning (RL) combined with Deep learning, which allows autonomous flow management in SDN, intelligent routing has gained popularity. However, the existing SDN routing algorithm has poor link usage and is unable to update and change according to real-time network conditions, making it challenging to forecast and predict how communication networks will evolve in the future. Consequently, we present a routing optimization strategy based on Quality of Service (QoS) variables for SDN-based Data Center Networks using Deep Reinforcement learning. We provide a reinforcement learning-based routing method for SDN that outperforms Dijkstra's shortest path in terms of the throughput and average latency. In the proposed method, we integrated SDN with RL, which takes input from the state in which the current network is and routes traffic to achieve the maximum QoS parameters. The parameters considered were the throughput and delay. The presented solution is a network-oriented, self-learning routing system named DeepQoSR, based on Advantage Actor-Critic Networks. We compared our proposed technique with traditional algorithms, such as the shortest path, and present our analysis. © 2023 IEEE.

关键词： Reinforcement learning

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