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

共 40714 条记录，以下是4741-4750 订阅

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Deep learning-Based Waste Classification: Enhancing Recycling Efficiency and Sustainability 1

Deep Learning-Based Waste Classification: Enhancing Recyclin...

引用

1st IEEE international conference on Emerging Research in Computational Science, ICERCS 2023

作者： Trivedi, Naresh Kumar Maheshwari, Himani Agarwal, Ambuj Kumar Tiwari, Raj Gaurang Gautam, Vinay Chitkara University Institute of Engineering and Technology Chitkara University Punjab India Graphic Era Hill University School of Computing Uttrakhand Dehradun India Sharda University Sharda School of Engineering and Technology Department of Computer Science and Engineering Greater Noida India

ISBN: (纸本)9798350359756

All countries should make recycling a priority. To recycle efficiently and save money, there is a need to classify trash into different categories. In this study, the focus is on the problem of locating a single garbage item in an image and assigning it to one of several possible recycling bins. Several ways have been studied to devise a complete solution. The models studied are a convolutional neural network (CNN) with Random Forest, kNN, naive Bayes, and a support vector machine (SVM). This research presents a new waste classification approach that uses deep learning technology to determine the type of trash automatically. It helps in reducing waste and improves the efficiency. The experimental results show that simple CNN and ANN networks produce promising results. Using deep learning techniques, the garbage categorization problem has been resolved with an accuracy of 97%. © 2023 IEEE.

关键词： Support vector machines

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Deep learning-Based Classification of Indian Classical Music Based on Raga 6

Deep Learning-Based Classification of Indian Classical Music...

引用

6th international conference on Information Systems and computer Networks, ISCON 2023

作者： Singha, Anupam Rajalakshmi, N.R. Arun Pandian, J. Saravanan, S. R and D Institute of Science and Technology Avadi Department of Computer Science and Engineering Vel Tech Ranagrajan Dr Sagunthala Chennai600062 India

ISBN: (纸本)9798350346961

Classical music is an integral part of Indian culture's lineage. The raga serves as the foundation for all Indian classical music, including both Hindustani and Carnatic music. The term 'raga' is typically used to refer to the melodic structure in Indian classical music. Traditional approaches to the classification of ragas are time consuming and inefficient. In this work, a convolutional neural network was proposed for raga classification. The proposed convolutional neural network was trained on 70 classes of ragas. The proposed convolutional neural network takes the spectrograms of the audio note and identifies the raga of the note. The proposed convolutional neural network model achieved a precision of 97.9% on raga classification. The performance of the proposed convolutional neural network was compared with the traditional classification techniques using standard performance metrics such as precision, recall, F1 score, and AUC-ROC. The comparison results show that the classification performance of the proposed convolutional neural network was superior to the state-of-the-art machine learning techniques.. © 2023 IEEE.

关键词： Convolutional neural networks

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Research on the Application of New Intelligent Sensor Electronic Equipment in Automobile 3

Research on the Application of New Intelligent Sensor Electr...

引用

2023 IEEE 3rd international conference on Data Science and computer Application, ICDSCA 2023

作者： Zhou, Shaoxuan Sichuan Technology & Business College Intelligent Manufacturing and Information Engineering College Chengdu China

ISBN: (纸本)9798350341546

There is great randomness in multi-sensor acquisition of intelligent unmanned vehicles. There are great differences in the characteristics of the collected information. This paper presents an adaptive multi-sensor data fusion algorithm. The learning machine based on support vector machine regression is applied to the intelligent sensor system of single chip microcomputer. With STC12LE5410AD single chip as the core controller and nRF905 wireless transceiver control chip, the data collected by the sensor is transmitted wirelessly. Experiments show that this algorithm can greatly improve the fusion rate of multi-sensor differential information, increase the amount of effective information, and achieve good results. © 2023 IEEE.

关键词： automotive electronics control system electronic technology Information fusion Intelligent vehicle sensor

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Applications of Machine learning in Viral Disease Diagnosis 10

Applications of Machine Learning in Viral Disease Diagnosis

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17th INDIACom;2023 10th international conference on Computing for Sustainable Global Development, INDIACom 2023

作者： Chaudhary, Jitendra Kumar Sharma, Himanshu Tadiboina, Sai Nitisha Singh, Rajesh Khan, Mohammed Shais Garg, Ashish School of Computing Graphic Era Hill University Bhimtal Campus Uttrakhand India Chandigarh University Department of Cse Punjab Mohali India Geico Department of Information Technology Chevy ChaseMD United States Uttaranchal Institute of Technology Uttaranchal University India Mechanical Engineering Osmania University Telangana Hyderabad India Graphic Era Deemed to Be University Department of Computer Science and Engineering Uttarakhand Dehradun248002 India

ISBN: (纸本)9789380544472

Viral diseases are common and natural in human it spreads from animals and other humans. It seeks to identify the proper, reliable, and effective disease detection as quickly as possible so that patients can receive the right care. It becomes vital for medical field searches to have assistance from other disciplines like statistics and computer science because this detection is frequently a challenging process. These fields must overcome the difficulty of learning novel, non-traditional methodologies. Because so many new techniques are being developed, a thorough overview must be given while avoiding some specifics. In order to do this, we suggest a thorough analysis of machine learning which is used for the diagnosis of viral diseases caused in humans as well as plans. Predictions are made which is not obvious at the first glance does machine learning will be more helpful in making decisions. The study focuses on the machine learning algorithms for diagnosis of viral diseases for early diagnosis and treatment of viral diseases with greater accuracy. The work helps the researchers and medical professionals for learning and to give treatment for determining the applications of different machine learning techniques run to evaluate the parameters. Through examination of various parameters new machine learning model is proposed understanding the applications of machine learning in viral disease diagnosis like imaging techniques, plant virus diagnosis and the solution for the problem, Covid 19 diagnosis. © 2023 Bharati Vidyapeeth, New Delhi.

关键词： Machine learning

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A LSTM-Based Power Generation Forecasting Method For The Gird With Multiple Power Generation Modes

A LSTM-Based Power Generation Forecasting Method For The Gir...

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2023 international conference on Intelligent Communication and computer engineering, ICICCE 2023

作者： Honghao, Huo Wang, Qiong Zhang, Yu School of Computer and Information Technology Beijing Jiaotong University China The State Grid Beijing Electric Power Company China State Grid Energy Research Institute Co. Ltd China

ISBN: (纸本)9798350395617

Forecasting generation holds substantial importance for managing load control within power grids and similar applications. The evolution of diverse power generation systems has introduced significant challenges to the precise forecasting of electricity production. To address this issue, this study introduces a forecasting technique for multiple power generation methods using the long short-term memory (LSTM) neural network, leveraging its proficiency in handling extensive sequential data, adapting to dynamic alterations, managing nonlinear relationships, and employing bidirectional learning. This LSTM-based approach aims to model and predict the output of various combined power production methodologies. Experimental evaluations reveal that, in contrast to conventional methods such as Temporal Convolutional Networks (TCN) and the autoregressive integrated moving average model (ARIMA), our proposed LSTM-based forecasting approach generates predictions with fewer errors. The model's precision increased by 55% when compared to ARIMA and by 16.83% when compared to TCN, demonstrating its superior efficacy. © 2023 IEEE.

关键词： Long short-term memory

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Research on Applying Dense Convolutional Neural Network in Chinese Character Font Recognition 3

Research on Applying Dense Convolutional Neural Network in C...

引用

3rd IEEE international conference on Electronic Technology, Communication and Information, ICETCI 2023

作者： Zhang, Xiangyang Xihua University School of Computer and Software Engineering Chengdu610039 China

ISBN: (纸本)9798350398410

The emergence of computer and artificial intelligence technology has subverted the traditional work model of pattern recognition and classification. Chinese character font recognition is one of the essential steps in developing and disseminating digitalized traditional calligraphy. In the traditional machine learning environment, dealing with the automatic recognition of a large number of fonts is often challenging. At the same time, font recognition under manual intervention also has substantial limitations. With the development of deep learning, the construction of font recognition algorithms using convolutional neural networks has become a mainstream research direction. This paper proposes a dense convolutional neural network, especially using fonts such as regular script, cursive script, and seal character as training targets to construct a mixed training set. By optimizing the model in three aspects: pooling rules, training strategies, and model pruning, the recognition performance of the model is further improved. It is hoped that the research in this paper will provide some help for the recognition of Chinese characters and the dissemination of digitalized traditional calligraphy. © 2023 IEEE.

关键词： Convolutional neural networks

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Research on Teaching Innovation of computer Rendering Course of Environmental Design Specialty Based on PTP Mode 2nd

Research on Teaching Innovation of Computer Rendering Course...

引用

2nd EAI international conference on Application of Big Data, Blockchain, and Internet of Things for Education Informatization, BigIoT-EDU 2022

作者： Rao, Changming Ankang University Shaanxi Ankang725000 China

ISBN: (纸本)9783031239434

Based on the characteristics of computer rendering course of environmental design specialty, using "PTP" The teaching mode tries to innovate the teaching methods to replace the traditional computer renderings teaching mode, and mobilize the students’ initiative to participate in the learning content in the way of practice project practice, so as to enhance the students’ learning ability and improve the professional skill level through teaching, so as to meet the talent needs of the employer of environmental design specialty. © 2023, ICST Institute for computer Sciences, Social Informatics and Telecommunications engineering.

关键词： Teaching

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Reentrancy Vulnerability Detection Based on Semantic-Aware Graph Convolutional Neural Network 3

Reentrancy Vulnerability Detection Based on Semantic-Aware G...

引用

3rd international conference on computer Science and Blockchain, CCSB 2023

作者： Dong, Likai Li, Wenlong School of Information Science and Engineering University of Jinan Jinan China

ISBN: (纸本)9798350359077

Aiming at the problems of low accuracy of conventional smart contract vulnerability detection methods, as well as the single form of feature extraction of deep learning-based methods, which are unable to adequately represent the rich semantic and structural information present in the source code, we propose a smart contract reentrancy vulnerability detection method based on semantic-aware graph convolutional neural network. The method first constructs the smart contract graph, extracts the graph structure features in the smart contract source code by the degree-free graph convolutional neural network, then slices the contract source code, extracts the sequence features by using BiGRU and attention mechanism, and finally splices the extracted graph structure features and the sequence features to find reentrancy vulnerability. The experimental findings demonstrate that, when compared to deep learning and conventional detection approaches, the suggested method in this work exhibits improved accuracy, recall, precision, and Fl score, with a detection accuracy of 89.77%. © 2023 IEEE.

关键词： Smart contract

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Kidney Disease X-Ray Image Classification Using a ResNet50V2 Model based Machine learning Approach 2

Kidney Disease X-Ray Image Classification Using a ResNet50V2...

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2nd IEEE international conference on Futuristic Technologies, INCOFT 2023

作者： Gill, Kanwarpartap Singh Anand, Vatsala Chauhan, Rahul Rawat, Ramesh Singh Gupta, Rupesh Chitkara University Institute of Engineering and Technology Chitkara University Punjab India Graphic Era Hill University Computer Science and Engineering Uttarakhand Dehradun India Graphic Era Hill University Computer Science and Engineering Punjab India Graphic Era Deemed to Be University Computer Science & Engineering Uttarakhand Dehradun248002 India

ISBN: (纸本)9798350308846

The process of Kidney Disease Classification entails the categorization of kidney pictures or patient data into several disease classifications, including but not limited to chronic kidney disease (CKD) phases, polycystic kidney disease (PKD), kidney stones, renal tumours, and many other kidneyrelated illnesses. Machine learning algorithms have the capability to undergo training using datasets that are annotated with labels, enabling them to automatically detect and categorise certain circumstances. The primary objective of Kidney Disease Classification is to categorise kidney tumours or lesions into distinct categories, including renal cell carcinoma (RCC), angiomyolipoma (AML), oncocytoma, and several benign or malignant tumour subtypes. The use of this categorization system may provide valuable support in the process of diagnosing and formulating treatment strategies for individuals afflicted with kidney tumours. The process of Kidney Disease Classification entails the categorization of biopsy samples from patients who have had kidney transplantation, with the aim of ascertaining the presence or absence of rejection in the transplanted kidney. The categorization may include many classifications such as acute cellular rejection, acute antibody-mediated rejection, chronic rejection, or absence of rejection. The advancement of novel traits or combinations thereof, which have the potential to enhance the accuracy of categorization, is facilitated by social research in this domain, therefore contributing to the enhancement of quality of life. The objective of this research is to enhance patient health by using deep learning techniques to develop an X-ray classification system capable of detecting renal illness. The ResNet50V2 model demonstrated robust classification abilities in identifying renal disease, with a notable accuracy rate of 97%. © 2023 IEEE.

关键词： Classification CNN Confusion matrix Deep learning Kidney Disease Model Training ResNet50V2 Model

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A Comprehensive Review of Machine learning and Deep learning Techniques for Addressing Class Imbalance Issues in Network Intrusion Detection Systems 6

A Comprehensive Review of Machine Learning and Deep Learning...

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

作者： Saikam, Jalaiah Koteswararao, Ch. VIT-AP University School of Computer Science and Engineering Andhra Pradesh India

ISBN: (纸本)9798350394702

Nowadays, a lot of information is shared between various connecting devices due to the quick development of internet technologies and the growing reliance on online services. Security systems are one of the most important scientific topics in today's computer network, so data should be transmitted safely between connected devices. The intrusion detection system is crucial for network security because it can identify and stop malicious activities. IDS still has trouble identifying novel intrusions due to data imbalance problems and a high false alarm rate. Many different methods have been applied to either prevent or detect intrusions. We discussed several research problems as well as potential future directions for NIDS. This study investigates ML-based and DL-based intrusion detection strategies and dataset selection. © 2023 IEEE.

关键词： Intrusion detection

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