2023 International Conference on Disruptive Technologies (ICDT)最新文献_第9页

Systematic Review on Maize Plant Disease Identification Based on Machine Learning 基于机器学习的玉米病害识别系统综述

2023 International Conference on Disruptive Technologies (ICDT) Pub Date : 2023-05-11 DOI: 10.1109/ICDT57929.2023.10151064

Vijaya Nagendra Gandham, Lovish Jain, S. Paidipati, Sathvik Pothuneedi, Surinder Kumar, Arpit Jain

{"title":"Systematic Review on Maize Plant Disease Identification Based on Machine Learning","authors":"Vijaya Nagendra Gandham, Lovish Jain, S. Paidipati, Sathvik Pothuneedi, Surinder Kumar, Arpit Jain","doi":"10.1109/ICDT57929.2023.10151064","DOIUrl":"https://doi.org/10.1109/ICDT57929.2023.10151064","url":null,"abstract":"Agriculture plays a crucial role in everyone's life. In this technological world, 75 out of 100 are taking steps towards automated workflow solutions rather than staying in the same position of manual solution replica of analyzing the product to detect the disease affecting the product's production. This study focuses primarily on wheat, which is a significant crop farmed globally owing to its substantial contribution to human nutrition and provides for around 14% of global food consumption. However, various diseases affect wheat yield, which can reduce 30% (31 million metric tons approx.) of wheat production, out of which 106.41 million measured tones of wheat for 2021-22 in India, a severe hazard to food security. Therefore, it is required to early detection of the disease during the growing stage of the plant by applying plant disease detection approaches. While analyzing the product, we would use various techniques to classify the classes. To perform various operations to detect diseases, we collected different information and images related to wheat which we considered a dataset. The dataset would help us concentrate on the loopholes to work on so that the algorithm would have a more accurate percentage to isolate the disease in plants, especially wheat.","PeriodicalId":266681,"journal":{"name":"2023 International Conference on Disruptive Technologies (ICDT)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-05-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116609819","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1

Suitable Crop Prediction based on affecting parameters using Naïve Bayes Classification Machine Learning Technique 利用Naïve贝叶斯分类机器学习技术进行基于影响参数的合适作物预测

2023 International Conference on Disruptive Technologies (ICDT) Pub Date : 2023-05-11 DOI: 10.1109/ICDT57929.2023.10150814

Latha Banda, Aarushi Rai, Ankit Kansal, Animesh Kumar Vashisth

{"title":"Suitable Crop Prediction based on affecting parameters using Naïve Bayes Classification Machine Learning Technique","authors":"Latha Banda, Aarushi Rai, Ankit Kansal, Animesh Kumar Vashisth","doi":"10.1109/ICDT57929.2023.10150814","DOIUrl":"https://doi.org/10.1109/ICDT57929.2023.10150814","url":null,"abstract":"Agriculture is one of the most important occupations for the majority of people in the world’s second largest populated country, India. However, due to a lack of education, accurate information, and India's rapid climate change, farmers frequently produce the same crops or the incorrect crops, regardless of whether they are appropriate given the soil, climate, and other elements in that particular place or not. This has caused an impact negatively on agricultural crop efficiency and output over the past few decades. Predicting the absolutely correct crops to grow based on the most important parameters for crop production would be of good help to farmers in choosing the right crops, improving crop quality, production and yield. In order to tackle the above problem, we have worked on a project using Naive Bayes Classification Machine Learning algorithm and Web Scraping. Our project consists of a friendly interactive chatbot with which the farmers can easily interact. The chatbot would make the farmer to provide some of the important parameters for crop production and would also fetch real time data through Web Scraping. The results of the crop prediction would be available to the farmer through that chatbot itself. By analyzing the parameters such as current weather conditions, location, soil, season and many more, our crop prediction system will be able to predict the right crops for the farmers to grow. This project will help to bridge the digital gap between farmers and right information and will help them to make smart choices about their crops to reduce the chances of crop failures.","PeriodicalId":266681,"journal":{"name":"2023 International Conference on Disruptive Technologies (ICDT)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-05-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116931153","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Risk Modelling and Prediction of Financial Management in Macro Industries using CNN Based Learning 基于CNN学习的宏观行业财务管理风险建模与预测

2023 International Conference on Disruptive Technologies (ICDT) Pub Date : 2023-05-11 DOI: 10.1109/ICDT57929.2023.10151085

S. C. Sekhar, Sai Bhaskar Reddy Kovvuri, Kanuparthi S R M S Sai Vyshnavi, Sahithi Uppalapati, Kondepu Yaswanth, Rama Krishna Teja

引用次数: 0

Analysis of Achievable Rate for SISO, MISO and MIMO-Orthogonal Frequency-Division Multiplexed (OFDM) Systems with Reconfigurable Intelligent Surface 具有可重构智能表面的SISO、MISO和mimo正交频分复用(OFDM)系统的可达速率分析

2023 International Conference on Disruptive Technologies (ICDT) Pub Date : 2023-05-11 DOI: 10.1109/ICDT57929.2023.10150999

Sharzeel Saleem, U. Chauhan, S. Pratap Singh

{"title":"Analysis of Achievable Rate for SISO, MISO and MIMO-Orthogonal Frequency-Division Multiplexed (OFDM) Systems with Reconfigurable Intelligent Surface","authors":"Sharzeel Saleem, U. Chauhan, S. Pratap Singh","doi":"10.1109/ICDT57929.2023.10150999","DOIUrl":"https://doi.org/10.1109/ICDT57929.2023.10150999","url":null,"abstract":"The world has started using fifth generation mobile network, with over 1billion people now feeling the speed and power of 5G technology, testing and experiments for beyond 5G networks have started already. One of the most popular technical hardware that shows a potential to enhance this technology is Intelligent reflecting surfaces (IRSs). These have been in the picture for considerable amount of time, these are a low-cost passive element made up on PIN diodes. Intelligent reflecting surfaces (IRSs) also known as reconfigurable intelligent surfaces (RISs) have the capacity to direct the electromagnetic (EM) waves to a particular path. The 3D- Passive meta surface is digitally controlled and has low energy consumption and operates in full-duplex mode. The various Orthogonal Frequency-Division Multiplexed (OFDM) Systems when configured with IRS shows different trends, in the research article the behavior of Achievable rate for Single-Input Single Output (SISO), Multiple-Input Single-Output (MISO) and Multiple-Input Multiple-Output (MIMO) systems have been analyzed. The analyses have been done taking into consideration the mid-band 5G spectrum.","PeriodicalId":266681,"journal":{"name":"2023 International Conference on Disruptive Technologies (ICDT)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-05-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130271765","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Deploying of Artificial Intelligence and Blockchain in Domain of Non-Fungible Token 人工智能和区块链在不可替代代币领域的应用

2023 International Conference on Disruptive Technologies (ICDT) Pub Date : 2023-05-11 DOI: 10.1109/ICDT57929.2023.10151017

Aditi Bansal, Rajesh Bahuguna, Shweta Pandey, Rajesh Singh, Abhinav Kathuria, Manish Gupta

引用次数: 2

Performance Evaluation of Text Document Using Machine Learning Models for Information Retrieval 基于机器学习模型的文本文档信息检索性能评价

2023 International Conference on Disruptive Technologies (ICDT) Pub Date : 2023-05-11 DOI: 10.1109/ICDT57929.2023.10150858

Subhasish Chowdhury, Suresh Kumar

引用次数: 0

Vertical Handover in WLAN Systems Using Cooperative Scheduling 基于协同调度的WLAN系统垂直切换

2023 International Conference on Disruptive Technologies (ICDT) Pub Date : 2023-05-11 DOI: 10.1109/ICDT57929.2023.10151045

V. Saravanan, A. Jayanthiladevi

引用次数: 0

Efficient Detection and Classification of Orange Diseases using Hybrid CNN-SVM Model 基于CNN-SVM混合模型的柑橘病害高效检测与分类

2023 International Conference on Disruptive Technologies (ICDT) Pub Date : 2023-05-11 DOI: 10.1109/ICDT57929.2023.10150721

N. Garg, Radhika Gupta, M. Kaur, Suhaib Ahmed, H. Shankar

{"title":"Efficient Detection and Classification of Orange Diseases using Hybrid CNN-SVM Model","authors":"N. Garg, Radhika Gupta, M. Kaur, Suhaib Ahmed, H. Shankar","doi":"10.1109/ICDT57929.2023.10150721","DOIUrl":"https://doi.org/10.1109/ICDT57929.2023.10150721","url":null,"abstract":"Orange is an important citrus fruit grown globally, and its consumption is encouraged by health-conscious individuals due to its nutritional value. Classifying oranges is important for quality control, sorting, and grading in the food industry. For the production of high-quality oranges, farm-based disease prediction is not utilizing technology to its full potential. A hybrid version is proposed in this research paper for the categorization of six common disorders of oranges, namely Penicillium, Scab, Anthracnose, Melanose, Phytophthora, and Citrus Canker, using a blend of the classifier - Support Vector Machine and ANN prototype - Convolutional Neural Network. With CNN being accustomed for feature derivation and SVM being utilized for classification, the suggested model leverages the best aspects of both algorithms. Using a dataset of 4,864 orange photos, the suggested hybrid model’s performance is assessed, and as a result, an accuracy of 88.13734% is achieved. Our sensitivity analysis indicates that the form, size, and texture of the lesions were the most crucial characteristics for categorizing orange-colored illnesses, followed by their texture and color. The effectiveness of utilizing a hybrid model for illness diagnosis in citrus fruits is shown by the postulated hybrid model’s superior performance over existing classification models like SVM, Random Forest, and K-Nearest Neighbor (KNN). The impeccable competence of the proposed hybrid model makes it suitable to be employed in automated disease detection systems to make prompt and well-informed decisions about disease management and prevention, thereby enhancing citrus crop productivity and quality.","PeriodicalId":266681,"journal":{"name":"2023 International Conference on Disruptive Technologies (ICDT)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-05-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129373649","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1

IoT Based Smart Extension Board 基于IoT的智能扩展板

2023 International Conference on Disruptive Technologies (ICDT) Pub Date : 2023-05-11 DOI: 10.1109/ICDT57929.2023.10150457

Shiv Narain Gupta, Rahul Dev, Abdul Samad, A. Asadullah, R. Bhardwaj, Dhiraj Gupta

引用次数: 0

Precision Agriculture Using Internet of Things and Wireless Sensor Networks 利用物联网和无线传感器网络的精准农业

2023 International Conference on Disruptive Technologies (ICDT) Pub Date : 2023-05-11 DOI: 10.1109/ICDT57929.2023.10150678

Pallabi Saha, Prof Vikas Kumar, Samta Kathuria, A. Gehlot, Vikrant Pachouri, Angel Swastik Duggal

引用次数: 3