2023 International Conference on Disruptive Technologies (ICDT)最新文献_第3页

Plant Disease Classification Using Machine Learning 利用机器学习进行植物病害分类

2023 International Conference on Disruptive Technologies (ICDT) Pub Date : 2023-05-11 DOI: 10.1109/ICDT57929.2023.10151118

Ravi Shankar Singla, A. Gupta, Richa Gupta, Vikas Tripathi, Mahaveer Singh Naruka, Shashank Awasthi

{"title":"Plant Disease Classification Using Machine Learning","authors":"Ravi Shankar Singla, A. Gupta, Richa Gupta, Vikas Tripathi, Mahaveer Singh Naruka, Shashank Awasthi","doi":"10.1109/ICDT57929.2023.10151118","DOIUrl":"https://doi.org/10.1109/ICDT57929.2023.10151118","url":null,"abstract":"In the event of a sharp rise in global population, agriculture strives to provide food to it. In agriculture, the detection and diagnosis of diseases occurring in the plants continues to be the arduous task. That is why it is endorsed to predict the diseases when the crops are in early stage. This work is done to develop and implement a disease prediction system by using different machine learning algorithms and a convolutional neural network. The objective of the paper is to grab the attention among the organisations to employ innovative technologies to decrease the diseases that are persistent in plants. The different approaches of ML and image processing with the algorithm that will provide explicit results to recognize the leaves that are healthy and classification algorithms and techniques so that we come to a result of categorically conclusive factor that the leaf is infected by any disease or not. Firstly, the dataset of leaves is divided into directories based on some features extracted from the leaves. Now, the logistic regression estimates the probability of the leave being healthy, based on the dataset of independent variables. The same dataset is also provided to Neural Networks (NN), Support Vector Machines (SVM) and Naïve Bayes algorithms. All the models are analysed with the confusion matrix and K-Fold Cross validation techniques. The proposed model gave the accuracy of 94% using Neural Networks.","PeriodicalId":266681,"journal":{"name":"2023 International Conference on Disruptive Technologies (ICDT)","volume":"71 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114837423","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

Study on Zero-Trust Architecture, Application Areas & Challenges of 6G Technology in Future 未来6G技术零信任架构、应用领域与挑战研究

2023 International Conference on Disruptive Technologies (ICDT) Pub Date : 2023-05-11 DOI: 10.1109/ICDT57929.2023.10150745

Richa Singh, Gaurav Srivastav, Rekha Kashyap, Satvik Vats

引用次数: 0

Convergence of Geo-IoT with Advanced Technologies 地理物联网与先进技术的融合

2023 International Conference on Disruptive Technologies (ICDT) Pub Date : 2023-05-11 DOI: 10.1109/ICDT57929.2023.10151106

Monalisha Sinha, Shalini, M. Thejaswini

引用次数: 0

An Enhanced Machine Learning Security Algorithm for the Anonymous user Detection in Ultra Dense 5G Cloud Networks 超密集5G云网络中匿名用户检测的增强机器学习安全算法

2023 International Conference on Disruptive Technologies (ICDT) Pub Date : 2023-05-11 DOI: 10.1109/ICDT57929.2023.10150909

Ramesh Babu P, Tariku Birhanu, K. R. N. K. Kumar, Manjunath Gadiparthi

引用次数: 0

Modelling and Simulation of Needle-Tissue Interaction in Robotic Surgery 机器人手术中针-组织相互作用的建模与仿真

2023 International Conference on Disruptive Technologies (ICDT) Pub Date : 2023-05-11 DOI: 10.1109/ICDT57929.2023.10150819

J. Shah, Prashant Johri, Pawan Kumar Singh Nain

{"title":"Modelling and Simulation of Needle-Tissue Interaction in Robotic Surgery","authors":"J. Shah, Prashant Johri, Pawan Kumar Singh Nain","doi":"10.1109/ICDT57929.2023.10150819","DOIUrl":"https://doi.org/10.1109/ICDT57929.2023.10150819","url":null,"abstract":"In robotic surgery practical applications one of the main bottlenecks is to accurately model tissue and needle interactions, in such modelling generally needle is taken as biocompatible material and tissue a elastic, plastic and viscous material. In this study, we present an adaptive finite element algorithm for simulating the indentation of the needle into tissue which is gelatin like viscoelastic material, the path of the needle takes a unique and non-predetermined route. Apart from the modelling the tissue and needle other aspect of the work requires proper boundary conditions and application of the load which mimic the real-world scenario. A cohesive zone model is employed to describe the fracture process, The distribution of strain energy density in the surrounding tissue is utilized to determine the direction of crack propagation. The simulation results presented in this study are centered on the deep penetration of a bevel-tip needle with a programmable design, which offers steering control by modifying the offset between interlocked needle segments. We primarily discuss the relationship between how size and number of mesh affect the stress in modelling tissue-needle interaction. We have done modelling and simulation in ANSYS software.","PeriodicalId":266681,"journal":{"name":"2023 International Conference on Disruptive Technologies (ICDT)","volume":"27 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132209583","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

Deep Learning Model on Blockchain for Secured Mobile Communication 安全移动通信区块链深度学习模型

2023 International Conference on Disruptive Technologies (ICDT) Pub Date : 2023-05-11 DOI: 10.1109/ICDT57929.2023.10150581

P. Iyappan, Shikha Maheshwari, A. Saranya, M. Jayaprakash

引用次数: 0

Job and Internship Assistance Application 工作及实习援助申请

2023 International Conference on Disruptive Technologies (ICDT) Pub Date : 2023-05-11 DOI: 10.1109/ICDT57929.2023.10150490

Disha Tyagi, Daniyal Kazim, Soumen Bhadra, Avantika Gupta, Praveen Kumar, Abhishek Sharma, Himanshu Chaudhary

引用次数: 0

Deep Neural Networks for Comprehensive Multimodal Emotion Recognition 综合多模态情绪识别的深度神经网络

2023 International Conference on Disruptive Technologies (ICDT) Pub Date : 2023-05-11 DOI: 10.1109/ICDT57929.2023.10150945

Ashutosh Tiwari, Satyam Kumar, Tushar Mehrotra, Rajneesh Kumar Singh

{"title":"Deep Neural Networks for Comprehensive Multimodal Emotion Recognition","authors":"Ashutosh Tiwari, Satyam Kumar, Tushar Mehrotra, Rajneesh Kumar Singh","doi":"10.1109/ICDT57929.2023.10150945","DOIUrl":"https://doi.org/10.1109/ICDT57929.2023.10150945","url":null,"abstract":"Emotions may be expressed in many different ways, making automatic affect recognition challenging. Several industries may benefit from this technology, including audiovisual search and human- machine interface. Recently, neural networks have been developed to assess emotional states with unprecedented accuracy. We provide an approach to emotion identification that makes use of both visual and aural signals. It’s crucial to isolate relevant features in order to accurately represent the nuanced emotions conveyed in a wide range of speech patterns. We do this by using a Convolutional Neural Network (CNN) to parse the audio track for feature extraction and a 50-layer deep ResNet to process the visual track. Machine learning algorithms, in addition to needing to extract the characteristics, should also be robust against outliers and reflective of their surroundings. To solve this problem, LSTM networks are used. We train the system from the ground up, using the RECOLA datasets from the AVEC 2016 emotion recognition research challenge, and we demonstrate that our method is superior to prior approaches that relied on manually constructed aural and visual cues for identifying genuine emotional states. It has been demonstrated that the visual modality predicts valence more accurately than arousal. The best results for the valence dimension from the RECOLA dataset are shown in Table III below.","PeriodicalId":266681,"journal":{"name":"2023 International Conference on Disruptive Technologies (ICDT)","volume":"51 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116768004","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

Rideshare Transportation Fare Prediction using Deep Neural Networks 基于深度神经网络的拼车交通票价预测

2023 International Conference on Disruptive Technologies (ICDT) Pub Date : 2023-05-11 DOI: 10.1109/ICDT57929.2023.10150947

Namrata Mohan Bagal, Madhuri Dinesh Gabhane, C. Mahamuni

引用次数: 0

URL Based Malicious Activity Detection Using Machine Learning 基于URL的机器学习恶意活动检测

2023 International Conference on Disruptive Technologies (ICDT) Pub Date : 2023-05-11 DOI: 10.1109/ICDT57929.2023.10150899

Tagba Zoukarneini Difaizi, Ouedraogo Pengd-Wende Leonel Camille, Tadiwanashe Caleb Benhura, Ganesh Gupta

引用次数: 1