2022 International Conference on Intelligent Systems and Computer Vision (ISCV)最新文献_第4页

Meta-heuristics Applied to Multiple DG Allocation in Radial Distribution Network: A comparative study 元启发式方法在径向配电网多DG分配中的应用比较研究

2022 International Conference on Intelligent Systems and Computer Vision (ISCV) Pub Date : 2022-05-18 DOI: 10.1109/ISCV54655.2022.9806131

Ali Tarraq, Faissal Elmariami, Abdelaziz Belfqih, Touria Haidi, Naima Agouzoul, Rabiaa Gadal

{"title":"Meta-heuristics Applied to Multiple DG Allocation in Radial Distribution Network: A comparative study","authors":"Ali Tarraq, Faissal Elmariami, Abdelaziz Belfqih, Touria Haidi, Naima Agouzoul, Rabiaa Gadal","doi":"10.1109/ISCV54655.2022.9806131","DOIUrl":"https://doi.org/10.1109/ISCV54655.2022.9806131","url":null,"abstract":"Due to the increasing electricity demand, the distribution network is becoming more and more uncontrollable and subject to higher power losses. To cope with this problem, the optimal integration of distributed generators (DGs) is proving to be efficient and sustainable. In this context, this paper investigates the minimization of active losses and the improvement of voltage profile through the integration of Multiple DGs in the IEEE 33-bus radial distribution system (RDS). The study aims to determine the optimal locations and sizes of l to 7 DGs to be integrated, in the case of unity power factor (UPF-DG) and optimal power factor (OPF-DG). The results are evaluated in a comparative study between three meta-heuristic optimization methods, namely Improved Cuckoo Search Algorithm (ICCSA), Improved Grey Wolf optimizer (IGWO), and a Chaotic-based Neural Network Algorithm (CNNA). In summary, CNNA outperforms the other algorithms mentioned above by increasing the problem dimension. Indeed, the total active loss reduction can reach 9S.27% by integrating seven OPF-DGs. On the opposite, poor results are generated by ICCSA.","PeriodicalId":426665,"journal":{"name":"2022 International Conference on Intelligent Systems and Computer Vision (ISCV)","volume":"57 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":"115710491","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

Big Data and public finance sector 大数据与公共财政

2022 International Conference on Intelligent Systems and Computer Vision (ISCV) Pub Date : 2022-05-18 DOI: 10.1109/ISCV54655.2022.9806125

Nabil Ourdani, Mohamed Chrayah

引用次数: 0

A New Financial Group Lending Based on Smart Contracts: An Agent-based Simulation 基于智能合约的新型金融集团借贷:基于代理的模拟

2022 International Conference on Intelligent Systems and Computer Vision (ISCV) Pub Date : 2022-05-18 DOI: 10.1109/ISCV54655.2022.9806082

Yahya Hanine, Mohamed El Moktar Malick, M. Tkiouat, Younes Lahrichi, Youssef Lamrani Alaoui

引用次数: 0

SASHA: A Shift-Add Segmented Hybrid Approximated Multiplier for Image Processing 一种用于图像处理的Shift-Add分段混合近似乘法器

2022 International Conference on Intelligent Systems and Computer Vision (ISCV) Pub Date : 2022-05-18 DOI: 10.1109/ISCV54655.2022.9806097

Sabeeh Abdul Rehman, Z. Jeffrey, Yichuang Sun, Oluyomi Simpson

引用次数: 0

Analysis and Classification of Skin Cancer Based on Deep Learning Approach 基于深度学习方法的皮肤癌分析与分类

2022 International Conference on Intelligent Systems and Computer Vision (ISCV) Pub Date : 2022-05-18 DOI: 10.1109/ISCV54655.2022.9806087

Y. Filali, H. E. Khoukhi, M. A. Sabri, A. Aarab

引用次数: 2

Machine Learning Based Methods for Arabic Duplicate Question Detection 基于机器学习的阿拉伯语重复问题检测方法

2022 International Conference on Intelligent Systems and Computer Vision (ISCV) Pub Date : 2022-05-18 DOI: 10.1109/ISCV54655.2022.9806071

Mohamed Zouitni, Alami Hamza, Said Lafkiar, Nabil Burmani, Mohammed Taleb, Noureddine En-Nahnahi

{"title":"Machine Learning Based Methods for Arabic Duplicate Question Detection","authors":"Mohamed Zouitni, Alami Hamza, Said Lafkiar, Nabil Burmani, Mohammed Taleb, Noureddine En-Nahnahi","doi":"10.1109/ISCV54655.2022.9806071","DOIUrl":"https://doi.org/10.1109/ISCV54655.2022.9806071","url":null,"abstract":"Incorporating a duplicate question detection system can be beneficial for various systems such as community forums or question answering systems. Detecting question that have already an answer improves the user experience by reducing the search time and returning the correct answer. In this paper, we construct several methods for Arabic duplicate question detection based on machine learning. First, the pre-processing step is applied to clean and normalize questions. Next, we use Term Frequency Inverse Document Frequency (TF-IDF), Word2Vec, and FastText methods to map questions from their textual format into a vector space. Then, we trained various shallow learning methods (SVM, XGBoost, Random Forest, Logistic Regression) and deep learning methods (CNN, RNN, LSTM, GRU) with the objective of detecting if a pair of questions is duplicate or not. Various experiments were conducted to evaluate the performances of our models. The results obtained show that the deep learning model based on GRU with FastText representation performed better compared to the other models.","PeriodicalId":426665,"journal":{"name":"2022 International Conference on Intelligent Systems and Computer Vision (ISCV)","volume":"37 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":"127496782","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

Comparative Study of latest CNN based Optical Flow Estimation 基于CNN的最新光流估计方法的比较研究

2022 International Conference on Intelligent Systems and Computer Vision (ISCV) Pub Date : 2022-05-18 DOI: 10.1109/ISCV54655.2022.9806070

Anis Ammar, Amani Chebbah, Hana Ben Fredj, C. Souani

{"title":"Comparative Study of latest CNN based Optical Flow Estimation","authors":"Anis Ammar, Amani Chebbah, Hana Ben Fredj, C. Souani","doi":"10.1109/ISCV54655.2022.9806070","DOIUrl":"https://doi.org/10.1109/ISCV54655.2022.9806070","url":null,"abstract":"Deep learning is continuously evolving and making significant advances in several applications. Nevertheless, it has a remarkable influence in the field of image processing. Recently, deep learning has hit the motion estimation area well. Optical flow estimation is a mature and ever-growing field of research. It can be considered a multidisciplinary field. However, it is not easy to get dataset suitable for deep learning of its models. While they have made fundamental contributions, However, it is unclear how to generate more data and generalize it to live scene videos. In this paper, we have tried off extensive analyses and categorized various deep learning-based optical flow estimation techniques. Lately hybrid methods have been very successful. Despite their high performance, even they have performed the state of the art of certain datasets, most bibliographic studies haven’t taken into account these methods. For this, we have added a comparative section of these hybrid algorithms to this study. While describing the set of datasets commonly used by the scientific community, we’ve identified the differences and the correspondences between deep methods and conventional methods. We hope that this extensive research will be a fundamental resource for researchers in the field of image processing and help them better understand and use methods using motion estimation.","PeriodicalId":426665,"journal":{"name":"2022 International Conference on Intelligent Systems and Computer Vision (ISCV)","volume":"20 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":"134127717","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

Image-Based Visual Servoing Techniques for Robot Control 基于图像的机器人视觉伺服控制技术

2022 International Conference on Intelligent Systems and Computer Vision (ISCV) Pub Date : 2022-05-18 DOI: 10.1109/ISCV54655.2022.9806078

Mohamed Kmich, H. Karmouni, Inssaf Harrade, A. Daoui, M. Sayyouri

引用次数: 1

OneDetect: A Federated Learning Architecture for Global Soft Biometrics Prediction OneDetect:一种用于全局软生物特征预测的联邦学习架构

2022 International Conference on Intelligent Systems and Computer Vision (ISCV) Pub Date : 2022-05-18 DOI: 10.1109/ISCV54655.2022.9806101

Bilal Hassan, E. Izquierdo

{"title":"OneDetect: A Federated Learning Architecture for Global Soft Biometrics Prediction","authors":"Bilal Hassan, E. Izquierdo","doi":"10.1109/ISCV54655.2022.9806101","DOIUrl":"https://doi.org/10.1109/ISCV54655.2022.9806101","url":null,"abstract":"Following the amount of threats and increased number of terrorism reported over the years, the development of security technologies is becoming a crucial subject, in recent era. Moreover, the paradigm of security technologies is shifting from concept of multiple representations to one and unique identity for each individual - like there are several different identity verification systems exist in the world, developed by different countries. On the other hand, security is a broad subject with its applications at open places like markets and roads and verification during online sessions, etc., however, border control is one of the most important point. Right now, biometrics like finger prints and facial scans are most widely used technology, termed as traditional or non-intrusive biometrics. To provide a single representation based unique identity verification system, we are going to propose a federated learning architecture called OneDetect, which uses intrusive features from human body to predict three most common global soft biometrics, e.g., gender, age and ethnicity. The purpose of using federated architecture is to ensure privacy of data from each client, i.e., country or region, while developing a more generalized verification system where each verified instance improves the verification system overall. Additionally, the use of global soft biometrics in such kind of architecture will provide seamless recognition whether, it is an airport, seaport or any public place. To achieve this goal, we recorded our own dataset called MMV Pedestrian dataset, in an airport like walking corridor and EfficientNetB3 architecture is used for training and prediction by each client.","PeriodicalId":426665,"journal":{"name":"2022 International Conference on Intelligent Systems and Computer Vision (ISCV)","volume":"12 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":"132659856","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: 3

Is Arabic text categorization a solved task? 阿拉伯语文本分类是一个解决的任务吗?

2022 International Conference on Intelligent Systems and Computer Vision (ISCV) Pub Date : 2022-05-18 DOI: 10.1109/ISCV54655.2022.9806076

Anoual El Kah, Imad Zeroual

{"title":"Is Arabic text categorization a solved task?","authors":"Anoual El Kah, Imad Zeroual","doi":"10.1109/ISCV54655.2022.9806076","DOIUrl":"https://doi.org/10.1109/ISCV54655.2022.9806076","url":null,"abstract":"The amount of Arabic content on the Internet is being proliferated day by day, scoring the highest growth among the top online languages during the last two decades. Arabic is the fourth most used language on the world wide web, making producing reliable data mining applications and information retrieval engines significantly challenging. Therefore, Arabic text categorization has gained the attention of different researchers from various fields. As a result, considerable research works have addressed Arabic text categorization. Some of these research works reported accuracy rates that were very close to 100%. Due to that, one can only think if Arabic text categorization is a solved task or is still under-studied? To answer this question, we screened 262 related papers based on eight former surveys and reviews, following the PRISMA-ScR guidelines. Then, we focused on top-ranked results that are over 95%. In this paper, we present the outcomes of our investigation by addressing several research questions regarding the datasets, the preprocessing techniques, the dimensionality reduction methods, and the classifiers that have been used.","PeriodicalId":426665,"journal":{"name":"2022 International Conference on Intelligent Systems and Computer Vision (ISCV)","volume":"33 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":"117339683","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