2022 International Conference on Intelligent Systems and Computer Vision (ISCV)最新文献_第2页

Toward a Stage-wise Classification of Date Palm White Scale Disease using Features Extraction and Machine Learning Techniques 基于特征提取和机器学习技术的枣树白斑病分期分类研究

2022 International Conference on Intelligent Systems and Computer Vision (ISCV) Pub Date : 2022-05-18 DOI: 10.1109/ISCV54655.2022.9806134

Abdelaaziz HESSANE, Ahmed El Youssefi, Yousef Farhaoui, Badraddine Aghoutane

{"title":"Toward a Stage-wise Classification of Date Palm White Scale Disease using Features Extraction and Machine Learning Techniques","authors":"Abdelaaziz HESSANE, Ahmed El Youssefi, Yousef Farhaoui, Badraddine Aghoutane","doi":"10.1109/ISCV54655.2022.9806134","DOIUrl":"https://doi.org/10.1109/ISCV54655.2022.9806134","url":null,"abstract":"Date palms cultivation is a crucial factor in oasis agriculture. The latter plays an essential role in the socioeconomics of many countries, including Morocco. However, many diseases threaten this valuable tree causing economic and environmental damages. White Scale is one of the most harmful pests that negatively affect the quality of date fruits and reduce their yield. Therefore, early detection of infected plants is essential in any Pest Management System. In this study, we propose a framework based on feature extraction and Machine Learning techniques to automatically classify the degree of infestation by Date Palm White Scale Disease. Gray-Level Co-occurrence Matrix features and HSV Color Moments were extracted and combined, then fitted to a K-Nearest Neighbors classifier that outperformed an average accuracy of 96.90%. In addition, other metrics such as Precision, Recall, F1-score, and confusion matrix are calculated to obtain more details about the stage-wise classification performance of the proposed framework. Finally, we conducted a comparative analysis between the proposed framework and some state-of-the-art-based methods. As a result, the proposed approach surpasses the state-of-the-art models in terms of various performance metrics. It is anticipated that this application will help farmers and stakeholders across the country and worldwide on both a macro and micro scale.","PeriodicalId":426665,"journal":{"name":"2022 International Conference on Intelligent Systems and Computer Vision (ISCV)","volume":"52 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-05-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131408073","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

IT Agility and Moroccan HEI’s Innovation Performance: The Moderating Role of IT Ambidexterity IT敏捷性与摩洛哥高等企业创新绩效:IT双灵巧性的调节作用

2022 International Conference on Intelligent Systems and Computer Vision (ISCV) Pub Date : 2022-05-18 DOI: 10.1109/ISCV54655.2022.9806112

Mah'd Taleb, Y. Pheniqi

引用次数: 1

Graph based method for Arabic text summarization 基于图的阿拉伯语文本摘要方法

2022 International Conference on Intelligent Systems and Computer Vision (ISCV) Pub Date : 2022-05-18 DOI: 10.1109/ISCV54655.2022.9806127

Nabil Burmani, H. Alami, Said Lafkiar, Mohamed Zouitni, Mohammed Taleb, Noureddine En Nahnahi

引用次数: 0

Automatic counting of chickens in confined area using the LCFCN algorithm 基于LCFCN算法的密闭区域鸡群自动计数

2022 International Conference on Intelligent Systems and Computer Vision (ISCV) Pub Date : 2022-05-18 DOI: 10.1109/ISCV54655.2022.9806092

Diab Abuaiadah, Alexander Switzer, M. Bosu, Yun Liu

{"title":"Automatic counting of chickens in confined area using the LCFCN algorithm","authors":"Diab Abuaiadah, Alexander Switzer, M. Bosu, Yun Liu","doi":"10.1109/ISCV54655.2022.9806092","DOIUrl":"https://doi.org/10.1109/ISCV54655.2022.9806092","url":null,"abstract":"Grouping chickens based on their weights is an important process that takes place in many chicken farms in New Zealand where chickens are grouped into three categories: small, medium and large. Each category has pins (cages) to temporarily hold the chickens during the process and a permeant bigger section to hold the chickens after grouping. Chickens are weighed and placed in respective pins. Thereafter they are released to the permanent section. Currently, the chickens are counted manually when they are released from a pin to a bigger section. The task of weighing chickens, placing them in a pin and releasing them to a bigger section is repeated until all chickens are moved to their respective bigger section and the total number of chickens in each section is calculated. This manual effort is done by several employees and takes several hours. This study investigated the feasibility of using deep learning algorithms to replace the manual counting. We applied the localized fully convolutional network (LCFCN) algorithm to count and locate chickens from images of the pins. LCFCN was applied to a dataset of 4092 images containing 114132 chickens. The algorithm was evaluated using the Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE) and Root Mean Square Error (RMSE) metrics and achieved the values of 0.5592, 1.36% and 1.67 respectively which are promising results in this setting. Furthermore, we modified the implementation of LCFCN to enable a user to manually alter the predicted labels to guarantee error free counting and localization.","PeriodicalId":426665,"journal":{"name":"2022 International Conference on Intelligent Systems and Computer Vision (ISCV)","volume":"68 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-05-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114272048","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

VMM: Viewpoint-based Memory Mechanism for Object Detection of Moving Sensors 基于视点的运动传感器目标检测记忆机制

2022 International Conference on Intelligent Systems and Computer Vision (ISCV) Pub Date : 2022-05-18 DOI: 10.1109/ISCV54655.2022.9806119

Jiyuan Hu, Tao Wang, Shiqiang Zhu

引用次数: 2

Admissibility Analysis of T-S Fuzzy singular system with time varying-delay 时变时滞T-S模糊奇异系统的可容许性分析

2022 International Conference on Intelligent Systems and Computer Vision (ISCV) Pub Date : 2022-05-18 DOI: 10.1109/ISCV54655.2022.9806090

Driss El-Jimi, N. Chaibi, I. Boumhidi, Charqi Mohammed, Ghizlane El Khaloufi

引用次数: 1

Quaternion Discrete Racah Moments Convolutional Neural Network for Color Face Recognition 彩色人脸识别的四元数离散Racah矩卷积神经网络

2022 International Conference on Intelligent Systems and Computer Vision (ISCV) Pub Date : 2022-05-18 DOI: 10.1109/ISCV54655.2022.9806123

Abdelmajid El Alami, Abderrahim Mesbah, Nadia Berrahou, Aissam Berrahou, H. Qjidaa

引用次数: 0

Iconic registration based on the local dissimilarity card 基于本地不相似卡的图标注册

2022 International Conference on Intelligent Systems and Computer Vision (ISCV) Pub Date : 2022-05-18 DOI: 10.1109/ISCV54655.2022.9806120

Hanae Mahmoudi, Hiba Ramadan, J. Riffi, H. Tairi

引用次数: 0

Robust Traffic Signs Classification using Deep Convolutional Neural Network 基于深度卷积神经网络的鲁棒交通标志分类

2022 International Conference on Intelligent Systems and Computer Vision (ISCV) Pub Date : 2022-05-18 DOI: 10.1109/ISCV54655.2022.9806122

Amine Kherraki, Muaz Maqbool, Rajae El Ouazzani

引用次数: 0

In silico molecular investigations of derived cyclohexane-1,3-dione compounds as potential inhibitors of protein tyrosine kinase C-met: 2D QSAR, molecular docking and ADMET 作为蛋白酪氨酸激酶C-met潜在抑制剂的衍生环己烷-1,3-二酮化合物的硅分子研究:2D QSAR，分子对接和ADMET

2022 International Conference on Intelligent Systems and Computer Vision (ISCV) Pub Date : 2022-05-18 DOI: 10.1109/ISCV54655.2022.9806058

Khaoula Mkhayar, O. Daoui, S. Elkhattabi, Samir CHTITA, Rachida Elkhalabi

{"title":"In silico molecular investigations of derived cyclohexane-1,3-dione compounds as potential inhibitors of protein tyrosine kinase C-met: 2D QSAR, molecular docking and ADMET","authors":"Khaoula Mkhayar, O. Daoui, S. Elkhattabi, Samir CHTITA, Rachida Elkhalabi","doi":"10.1109/ISCV54655.2022.9806058","DOIUrl":"https://doi.org/10.1109/ISCV54655.2022.9806058","url":null,"abstract":"The C-met receptor tyrosine kinase represents an interesting anti-cancer target. In this work, we present a theoretical study of the quantitative structure-activity relationship, QSAR, inhibitor of the enzymatic activity of said C-met protein. Using statistical techniques, RLM, RNLM and Y-randomization assay of the field of applicability, we studied a series of 36 molecules derived from cyclohexane-1,3-dione, dimedon, as anticancer agents capable of inhibiting C-met receptor tyrosine kinase. In this study we developed models showing excellent statistical results for multiple linear regression $text{R}^{2}$=0,913; $text{R}^{2}$ cv=0,85, $text{R}_{text{t}text{e}text{s}text{t}}^{2}$=0,934) and for multiple nonlinear regression ($text{R}^{2}$=0,991$;text{R}^{2}$ cv=0,82; $text{R}_{text{t}text{e}text{s}text{t}}^{2}$ = 0,997). These results demonstrate the great ability of multiple linear regression to effectively model the inhibitory activity of the enzymatic activity of the C-met protein and its predictive capacity. Motivated by these results, we designed 16 molecules adopted for the treatment of non-small cell lung cancer (NSCLC) to evaluate the properties of ADMET in silico which will be supplemented by a molecular Docking.","PeriodicalId":426665,"journal":{"name":"2022 International Conference on Intelligent Systems and Computer Vision (ISCV)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-05-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131191608","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: 2