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

共 40711 条记录，以下是4531-4540 订阅

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Crop Prediction using Machine learning Techniques and IoT 3

Crop Prediction using Machine Learning Techniques and IoT

引用

3rd international conference on Pervasive Computing and Social Networking, ICPCSN 2023

作者： Haritha, M. Lakshmi Sutha, J. Jasmine, J. Jeno Deepa, R. Thilagavathy, A. Raju, D. Naveen R.M.K Engineering College Department of Computer Science and Engineering Tamil Nadu Kavaraipettai India Aaa College of Engineering and Technology Department of Computer Science and Engineering Sivakasi626005 India

ISBN: (纸本)9798350322842

Agriculture being the backbone of the Indian economy faces multiple challenges in the production of quality food and good yield due to the unnatural climatic changes, which reduces crop yield adversely. Farmers lack the knowledge to forecast the future crop yield and take the necessary precautions. Therefore, this research aims to assist farmers in crop selection by predicting suitable crops for a given region using supervised machine learning algorithms. The work focusses on achieving the improved accuracy of crop prediction when compared to the existing models. Seed data with the appropriate parameters such as humidity, soil pH, temperature, rainfall, nitrogen, potassium, and phosphorus content are collected to support its maximum growth. Based on the obtained data, the crop prediction is performed. Performance of the Naïve Bayes Classifier, Ada Boost Classifier, Decision Tree Classification, and Voting Classifier algorithms are compared based on the accuracy, confusion matrix, and other performance metrics like precision, recall, and F1 score. The Decision Tree classifier algorithm is chosen as the best algorithm for crop prediction and the model is hence deployed with it. The finest accuracy of around 99.4 % is achieved by the proposed model. © 2023 IEEE.

关键词： Forecasting

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Research on Apriori Algorithm Based on Compression Processing and Hash Table 3

Research on Apriori Algorithm Based on Compression Processin...

引用

3rd international conference on Machine learning and computer Application, ICMLCA 2022

作者： Yu, Chengqi Liang, Ying Zhang, Xinxin School of Information Science and Engineering Shenyang Ligong University Shenyang China

ISBN: (纸本)9781510664814

Apriori improved algorithm based on compression processing and hash table is given to solve the problem that the traditional Apriori (association rules) algorithm must repeatedly scan the database when performing data mining frequent itemsets. It makes full use of the method of transaction compression, firstly compressing the data, and then compressing the obtained data into the hash table for the second time through hashing technology. In this process, you only need to scan the database once, and then even if the support degree changes, there is no need to perform a second scan. The experimental results show that the improved algorithm can not only mine the association rules of the dataset correctly, but also greatly improve the operation efficiency of the algorithm. © 2023 SPIE.

关键词： Association rules

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Analysis of Micro-grid Structure and Operation Mode 3

Analysis of Micro-grid Structure and Operation Mode

引用

3rd international conference on Machine learning and computer Application, ICMLCA 2022

作者： Xiao, Yanhui Yan, Hongkui Yin, Wenxue Sun, Bo Shan, Yiyu Ma, Shiyi Ren, Junge Ma, Zheng Jia, Zijian Shenyang Institute of Engineering Shenyang110136 China

ISBN: (纸本)9781510664814

Microgrid is a small power supply and distribution network, which connects multiple loads and multiple power units with energy storage systems together by power electronic converter interface. Because of the inherent intermittent and random characteristics of distributed generation units in microgrid, it is very important to study the structure and control mode of microgrid system. Firstly, this paper analyzes the structure and control modes of microgrid system under different operation modes, then proposes to optimize microgrid system, and analyzes the advantages and disadvantages of traditional centralized control and distributed control modes, which provides new ideas for the future development of distributed energy and microgrid system. © 2023 SPIE.

关键词： Electric power distribution

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Location-Aware Reinforcement learning-Based Live Virtual Machine Migration in Fog Computing 16

Location-Aware Reinforcement Learning-Based Live Virtual Mac...

引用

16th international conference on Advanced computer Theory and engineering, ICACTE 2023

作者： Alqam, Shahd Suleiman Al Zeidi, Nasser Touzene, Abderrezak Day, Khaled Sultan Qaboos University Department of Computer Science Muscat Oman

ISBN: (纸本)9798350317534

One of the most existing problems in live virtual machine migration in a fog environment is the type of devices. The studies assume that all fog nodes in the fog layer are homogeneous and dedicated to fog functionality. However, fog nodes can be implemented over different types of devices (routers, switches, servers, etc.). They are naturally heterogeneous and originally erected to do specific functions. This would create an issue while migrating the virtual machines. Appropriate destinations should be chosen before the migration to avoid any delay in accomplishing the time-critical tasks and to increase the success rate of migrations. Further, fog nodes are location-aware devices. This feature needs to be incorporated while modeling any VM migration solution. To address these challenges, we propose a novel approach to live VM migration in a fog environment that employs Reinforcement learning (RL). Extensive experiments show that our approach significantly reduces the latency of time- critical applications. Our proposed system, called EVM_MIG, outperforms other models in terms of reduced latency by approximately 50% and this proved that considering device type, function, and location in deciding VM migration in a fog environment can effectively reduce the response time in time- critical applications. © 2023 IEEE.

关键词： Reinforcement learning

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Automatic Spelling Error Classification in Malayalam 8th

Automatic Spelling Error Classification in Malayalam

引用

8th international conference on Information and Communication Technology for Competitive Strategies, ICTCS 2023

作者： Dhanya, S. Kaimal, M.R. Nedungadi, Prema Department of Computer Science and Engineering Amrita School of Computing Coimbatore India Amrita CREATE Amrita Vishwa Vidyapeetham Amritapuri India

ISBN: (纸本)9789819707430

Spelling errors, a commonplace phenomenon among students dominated by those with learning disabilities, call for effective systems that automatically and accurately identify and classify spelling mistakes. This research focused on developing an automatic spelling error classification tool to identify the Malayalam writing system’s phonological and orthographic spelling error categories. For the analysis of spelling errors in Malayalam, the study considered the input pair of real words and spelled words for extraction of six linguistic features: the difference in word length, edit distance, character overlapping percentage, the number of common phonemes, the number of common bigrams, and the number of common trigrams. After analysing spelling errors, machine learning techniques classify them. The research appraised the effectiveness of feature engineering apropos automatic spelling error classification in Malayalam. Results indicate that the fine-tuned Random Forest classifier achieved the highest classification accuracy of 71%, effectively distinguishing between the two error categories. The findings pinpoint the impact of linguistic features on accuracy and afford insights into the imperative of language technology tools for educational purposes. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Machine learning

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Radiology Report Generation via Structured Knowledge-Enhanced Multi-modal Attention and Contrastive learning

Radiology Report Generation via Structured Knowledge-Enhance...

引用

2023 IEEE international conference on Bioinformatics and Biomedicine, BIBM 2023

作者： Xu, Dexuan Chen, Yanyuan Zhang, Jiayu Lou, Yiwei Wang, Hanpin He, Jing Huang, Yu Peking University National Engineering Research Center for S.E. School of Software & Micro. China Peking University School of Computer Science China School of Computer Science China Peking University People's Hospital Department of Rheumatology and Immunology China Peking University National Engineering Research Center for Software Engineering China

ISBN: (纸本)9798350337488

The automated generation of radiology reports has attracted significant attention in the field of bioinformatics. Currently, the main limitations of this task include insufficient utilization of prior medical knowledge, lack of efficient knowledge fusion algorithms, and less distinctiveness between different generated reports. To address these issues, we propose a novel algorithm for radiology report generation, which includes Structured Knowledge-Enhanced Multi-modal Attention (SKEMA) and Dual-Branch Contrastive learning (DBCL) for the first time. SKEMA aims to effectively bridge the gap between visual and prior knowledge by leveraging the high-order adjacency matrix of the knowledge graph to weightedly fuse image features and knowledge features. We enhance both features through masking, and use the original features and augmented features as positive and negative samples in the dual-branch contrastive learning (DBCL). DBCL increases the differences between positive and negative samples to avoid generating templated results, and enhances the robustness of the model. Finally, we conducted experiments to demonstrate the effectiveness of our model on two public radiology datasets, IU-Xray and MIMIC-CXR. Our model outperformed previous baseline methods on both datasets and achieved excellent evaluation scores. © 2023 IEEE.

关键词： Contrastive learning Knowledge Graph Multi-modal Fusion Radiology Report Generation

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Boosting Text Classification Performance for Unlabeled Data with Semi-Supervised learning 9

Boosting Text Classification Performance for Unlabeled Data ...

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9th IEEE international Women in engineering (WIE) conference on Electrical and computer engineering, WIECON-ECE 2023

作者： Khondokar, Tanvina Islam, Saiful Alam Chadni, Tasmia Ishrat Khandokar, Iftakhar Ali United International University Department of Computer Science & Engineering Dhaka Bangladesh

ISBN: (纸本)9798350319651

Text classification has always been an engrossing problem in the field of Natural Language Processing. To build a model on a specific domain, it needs to gain the ability to distinguish text that is related to that domain and others that are not. To make the model achieve this type of domain knowledge, we need to provide a large amount of labeled data within the domain. Having said that, labeling such an amount of data is challenging, as it demands hard labor, a tremendous amount of time, and cost. These could slow the progress of any language or domain model, achieving the performance that is expected. In this paper, we have attempted to address the above-discussed problem, proposing a semi-supervised performance-boosting technique for large-volume unlabeled datasets. The target of this work is to demonstrate the semi-supervised un-labeled data labeling mechanism that can boost the accuracy of text classification models. We have developed an ensemble semi-supervised labeling technique that utilizes the weighted output of different feature-based predictive classifiers to classify unlabelled datasets which makes the labeling process more reliable than unsupervised approaches. The proposed approach overcomes the above-mentioned challenges. After evaluating our approach, we observed that our Semi-Supervised boosting technique enhanced the performance of the predictive models to 20% approx. © 2023 IEEE.

关键词： Large datasets

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ZxCAPTCHA: New Security-Enhanced CAPTCHA 15

ZxCAPTCHA: New Security-Enhanced CAPTCHA

引用

15th international conference on Knowledge and Smart Technology, KST 2023

作者： Dinh, Nghia Nguyen, Trung Truong, Vinh VSB-Technical University of Ostrava Faculty of Electrical Engineering and Computer Science Department of Computer Science 17 Listopadu 15/2172 Ostrava708 33 Czech Republic FPT University Department of Information Technology Ho Chi Minh City Viet Nam Ho Chi Minh City Open University Faculty of Information Technology 97 Vo Van Tan Street Ho Chi Minh722000 Viet Nam

ISBN: (纸本)9781665477123

Automated attacks using CNN (Convolutional Neural Network), ML (Machine learning), and DNN (Deep Neural Network have been successful in bypassing traditional CAPTCHAs. However, Deep learning techniques, adversarial examples and style neural transfer, have been shown to be particularly effective in protecting CAPTCHAs. In this study, the authors proposed zxCAPTCHA, a new CAPTCHA that combines cognitive-based, image-based, and text-based CAPTCHA characteristics with Deep learning techniques to improve security. Extensive evaluations were conducted to assess the improvement of the CAPTCHA security. The experiment shows that zxCAPTCHA considerably enhances the security while maintaining comparable usability. We also demonstrate the effectiveness of combining cognitive techniques and Deep learning to improve CAPTCHA security. © 2023 IEEE.

关键词： Deep neural networks

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Random Forest Regressor Model for Rainfall Prediction

Random Forest Regressor Model for Rainfall Prediction

引用

2023 international conference on New Frontiers in Communication, Automation, Management and Security, ICCAMS 2023

作者： Raut, Archana Theng, Dipti Khandelwal, Sarika G H Raisoni College of Engineering Department of Computer Science and Engineering Nagpur India

ISBN: (纸本)9798350317060

Rainfall is cause of natural calamities like flood. Rainfall prediction is a major problem everywhere. Rainfall is a factor that affects several activities like agriculture, production, construction, generation of power, and tourism. Machine learning is very useful nowadays to predict rainfall which is basically needed for today. In the current scenario, accurate rainfall prediction is a difficult task. Rainfall Prediction is an attempt to predict the rainfall for a region. Different algorithms for predicting rainfall is applied in this paper which includes classifiers well as a Regressor like Random Forest Regressor, Random Forest Classifier, Linear Regression, Support vector regressor (SVR), and Decision Tree Algorithm. Effective rainfall plays a huge role in the growth rate of crops in agriculture during crop growth. Applying Machine learning will calculate the amount of predictable rainfall which will be used for water resources, crop productivity and pre-planning of water usage. The proposed machine learning (ML) based approach will help to predict the amount of rainfall every year, with the help of a machine learning algorithm, the comparison of different algorithm results is also given in the paper. It is evident from the application of a different algorithm that the Random forest regressor outperforms over other algorithms with an accuracy score of 89%. © 2023 IEEE.

关键词： Rain

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Hate speech detection by classic machine learning 3

Hate speech detection by classic machine learning

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3rd IEEE international conference on Electronic engineering, ICEEM 2023

作者： El-Sayed, Tharwat Mustafa, Abdallah El-Sayed, Ayman Elrashidy, Mohamed Menoufia University Faculty of Electronic Eng. Computer Science & Eng. Dept. Menouf32952 Egypt

ISBN: (纸本)9798350323511

It is becoming increasingly important for society to identify hate speech on social media. Differentiating hate speech from other instances involving offensive language is a significant difficulty for automatic hate speech tracking on social media. To distinguish between these categories, we train various classical machine learning models such as logistic regression, decision trees, random forest, naive Bayes, k-nearest neighbors, and support vector machines (SVM) - support vector classifier (SVC) on a dataset divided into three groups: those containing hate speech, those containing only offensive language, and those containing neither. From our practical trials, we found that the Logistic Regression algorithm and the SVM-SVC algorithm perform well in detecting hate speech and offensive language. © 2023 IEEE.

关键词： Decision trees

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