Inteligencia Artif.最新文献_第3页

A CP-based approach for mining sequential patterns with quantities 一种基于cp的方法，用于挖掘带有数量的顺序模式

Inteligencia Artif. Pub Date : 2023-03-13 DOI: 10.4114/intartif.vol26iss71pp1-12

Amina Kemmar, Chahira Touati, Yahia Lebbah

引用次数: 0

Person Re-Identification by Siamese Network 暹罗网络的人物再识别

Inteligencia Artif. Pub Date : 2023-03-13 DOI: 10.4114/intartif.vol26iss71pp25-33

Newlin Shebiah Russel, S. Arivazhagan, S. G. Amrith, S. Adarsh

引用次数: 0

A Novel Approach for Diagnosing Neuro-Developmental Disorders using Artificial Intelligence 利用人工智能诊断神经发育障碍的新方法

Inteligencia Artif. Pub Date : 2023-03-13 DOI: 10.4114/intartif.vol26iss71pp13-24

P. Vashisht, A. Jatain

{"title":"A Novel Approach for Diagnosing Neuro-Developmental Disorders using Artificial Intelligence","authors":"P. Vashisht, A. Jatain","doi":"10.4114/intartif.vol26iss71pp13-24","DOIUrl":"https://doi.org/10.4114/intartif.vol26iss71pp13-24","url":null,"abstract":"Artificial Intelligence (AI) has been rapidly advancing especially in the field of medicine. One of the highly considerable medical fields in the world today is that of neurodevelopment and diagnosing any disorders pertaining to the same can be overwhelming. Considering the fact that neurodevelopment plays a significant role in the growth and nourishment of a child, the former sentence is an irony as parents wouldn’t wish for their children to possess reduced capabilities in comparison to other children of the same age. In fact, testing the metal growth of a child is a tedious task which involves visiting the doctor each time and spending a lot of time. The proposition of this paper overcomes the above--mentioned hassles by utilizing computer aided techniques for identifying neurodevelopmental disorder. The proposed framework has its foundation over mathematical and Deep Learning (DL) models which helps in the diagnosis of four varied neurodevelopmental disorders which often tend to occur in the early phases of a child’s life. The application put forward here would suggest suitable remedies and strategies to parents and teachers which they can adopt to help their child recover from the illness.","PeriodicalId":176050,"journal":{"name":"Inteligencia Artif.","volume":"26 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-03-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134628406","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

Towards Misbehavior Intelligent Detection Using Guided Machine Learning in Vehicular Ad-hoc Networks (VANET) 基于引导机器学习的车辆自组织网络(VANET)不当行为智能检测

Inteligencia Artif. Pub Date : 2022-12-31 DOI: 10.4114/intartif.vol25iss70pp138-154

Omessaad Slama, B. Alaya, S. Zidi

{"title":"Towards Misbehavior Intelligent Detection Using Guided Machine Learning in Vehicular Ad-hoc Networks (VANET)","authors":"Omessaad Slama, B. Alaya, S. Zidi","doi":"10.4114/intartif.vol25iss70pp138-154","DOIUrl":"https://doi.org/10.4114/intartif.vol25iss70pp138-154","url":null,"abstract":"C-ITS (Cooperative Intelligent Transport Systems) est une nouvelle technologie qui contribue à la réduction des accidents de la circulation et à l'amélioration de la sécurité routière. VANET ( V ehicular A d hoc Networks) sont un système STI basé sur la communication inter-véhicules par la transmission de messages de sécurité de base (BSM), qui sont vulnérables à une variété de comportements inappropriés. Pour résoudre ce défi, nous avons développé dans cet article un système de détection de mauvais comportement (MDS) basé sur une approche d'apprentissage automatisé pour identifier et catégoriser les messages de mauvais comportement délivrés par un véhicule sur les VANET à l'aide de la base de données d'extension VeReMi. Cette étude examine différents types de classification : dans la classification binaire, toutes sortes d'inconduites ont été regroupées en une seule catégorie « inconduite » ; cependant, dans la classification multi-classes pour trois classes, la mauvaise conduite a été divisée en deux classes : les attaques et les fautes. Le classificateur a des problèmes substantiels lors de l'apprentissage à partir de données déséquilibrées wLorsque vous travaillez avec des problèmes multi-classes, cela devient considérablement plus complexe. Les relations entre les catégories ne sont plus bien définies et il est facile de perdre en efficacité dans une classe tout en s'améliorant dans une autre. En conséquence, les résultats ne sont pas cohérents dans l'approche d'apprentissage classique pour la classification multi-classes lors de la classification des comportements répréhensibles dans différents types de classes de comportements répréhensibles. Pour résoudre ce problème, nous avons développé une approche nouvelle et puissante appelée \"Approche d'apprentissage guidé pour la classification multi-classes\" pour réduire le nombre de classes en combinant des comportements inappropriés comparables en un seul. Selon les résultats, le classificateur Random Forest surpasse les autres classificateurs.","PeriodicalId":176050,"journal":{"name":"Inteligencia Artif.","volume":"15 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133904530","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

Trends in Research on Artificial Intelligence in Anesthesia: A VOSviewer -Based Bibliometric Analysis 麻醉中人工智能的研究趋势:基于VOSviewer的文献计量分析

Inteligencia Artif. Pub Date : 2022-12-29 DOI: 10.4114/intartif.vol25iss70pp126-137

M. Cascella, Francesco Perri, A. Ottaiano, A. Cuomo, S. Wirz, S. Coluccia

{"title":"Trends in Research on Artificial Intelligence in Anesthesia: A VOSviewer -Based Bibliometric Analysis","authors":"M. Cascella, Francesco Perri, A. Ottaiano, A. Cuomo, S. Wirz, S. Coluccia","doi":"10.4114/intartif.vol25iss70pp126-137","DOIUrl":"https://doi.org/10.4114/intartif.vol25iss70pp126-137","url":null,"abstract":"Background: The scientific literature on Artificial Intelligence (AI) in anesthesia is rapidly growing. Considering that applications of AI strategies can offer paramount support in clinical decision processes, it is crucial to delineate the research features. Bibliometric analyses can provide an overview of research tendencies useful for supplementary investigations in a research field. Methods: The comprehensive literature about AI in anesthesia was checked in the Web of Science (WOS) core collection. Year of publication, journal metrics including impact factor and quartile, title, document type, topic, and article metric (citations) were extracted. The software tool VOSviewer (version 1.6.17) was implemented for the co-occurrence of keywords and the co-citation analyses, and for evaluating research networks (countries and institutions). Results: Altogether, 288 documents were retrieved from the WOS and 154 articles were included in the analysis. The number of articles increased from 4 articles in 2017 to 37 in 2021. Only 34 were observational investigations and 7 RCTs. The most relevant topic is “anesthesia management”. The research network for countries and institutions shows severe gaps. Conclusion: Research on AI in anesthesia is rapidly developing. Further clinical studies are needed. Although different topics are addressed, scientific collaborations must be implemented.","PeriodicalId":176050,"journal":{"name":"Inteligencia Artif.","volume":"45 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115478749","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

Autonomous UAV object Avoidance with Floyd-warshall differential evolution approach 基于Floyd-warshall差分进化方法的自主无人机目标回避

Inteligencia Artif. Pub Date : 2022-12-05 DOI: 10.4114/intartif.vol25iss70pp77-94

Guruprasad Yk, N. NageswaragupthaM.

{"title":"Autonomous UAV object Avoidance with Floyd-warshall differential evolution approach","authors":"Guruprasad Yk, N. NageswaragupthaM.","doi":"10.4114/intartif.vol25iss70pp77-94","DOIUrl":"https://doi.org/10.4114/intartif.vol25iss70pp77-94","url":null,"abstract":"Unmanned Aerial Vehicles (UAVs) are recently focused with significant research attention from commercial to military industries. Due to its wide range of applications such as traffic monitoring, surveillance, aerial photograph and rescue mission, many research studies were conducted related to UAV development. UAV are commonly called as ‘drones’ used to suit dull, dangerous and dirty missions that can be suited by manned aircraft. UAV can be controlled either remotely or using automation approaches so that it can be travelled into predefined path. To make the autonomous UAV, the most complex issue that is faced by UAV is obstacle / object avoidance. Obstacle detection and avoidance are important for UAV and it is the complex problem to solve due to the payload restriction. This will limit the sensor count mounted on the vehicle. Radar was used to find the distance between the object and vehicle. This can help to detect and track the moving objects speed and direction towards the vehicle. This paper considered the object avoidance problem as path planning problem. There were many path planning methods related to UAV which formulates the path planning as an optimization problem to avoid the obstacles. With the consideration, this paper proposed an efficient and optimal approach called Floyd Warshall- Differential evolution (FWDE) approach to detect the frontal obstacles of UAV. Finally, statistical analysis of the simulated environment reveals that the proposed evolutionary method can efficiently avoid both static and dynamic objects for UAVs. This efficient avoidance algorithm for UAV can be experimented with simulation environment with three kinds of scenarios having different number of cells. The obtained accuracy and recall value of the proposed system is 95.21% and 91.56%.","PeriodicalId":176050,"journal":{"name":"Inteligencia Artif.","volume":"51 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126489468","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

Defective sewing stitch semantic segmentation using DeeplabV3+ and EfficientNet 使用DeeplabV3+和EfficientNet进行有缺陷的缝纫缝线语义分割

Inteligencia Artif. Pub Date : 2022-11-24 DOI: 10.4114/intartif.vol25iss70pp64-76

Quoc Toan Nguyen

{"title":"Defective sewing stitch semantic segmentation using DeeplabV3+ and EfficientNet","authors":"Quoc Toan Nguyen","doi":"10.4114/intartif.vol25iss70pp64-76","DOIUrl":"https://doi.org/10.4114/intartif.vol25iss70pp64-76","url":null,"abstract":"Defective stitch inspection is an essential part of garment manufacturing quality assurance. Traditional mechanical defect detection systems are effective, but they are usually customized with handcrafted features that must be operated by a human. Deep learning approaches have recently demonstrated exceptional performance in a wide range of computer vision applications. The requirement for precise detail evaluation, combined with the small size of the patterns, undoubtedly increases the difficulty of identification. Therefore, image segmentation (semantic segmentation) was employed for this task. It is identified as a vital research topic in the field of computer vision, being indispensable in a wide range of real-world applications. Semantic segmentation is a method of labeling each pixel in an image. This is in direct contrast to classification, which assigns a single label to the entire image. And multiple objects of the same class are defined as a single entity. DeepLabV3+ architecture, with encoder-decoder architecture, is the proposed technique. EfficientNet models (B0-B2) were applied as encoders for experimental processes. The encoder is utilized to encode feature maps from the input image. The encoder's significant information is used by the decoder for upsampling and reconstruction of output. Finally, the best model is DeeplabV3+ with EfficientNetB1 which can classify segmented defective sewing stitches with superior performance (MeanIoU: 94.14%).","PeriodicalId":176050,"journal":{"name":"Inteligencia Artif.","volume":"75 6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125999411","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

Fast segmentation of point clouds using a convolutional neural network for helping visually impaired people find the closest traversable region 利用卷积神经网络快速分割点云，帮助视障人士找到最近的可穿越区域

Inteligencia Artif. Pub Date : 2022-11-23 DOI: 10.4114/intartif.vol25iss70pp50-63

Paúl Tinizaray, Wilbert G. Aguilar, J. Lucio

引用次数: 1

Feature Selection: Binary Harris Hawk Optimizer Based Biomedical Datasets 特征选择:基于生物医学数据集的二进制Harris Hawk优化器

Inteligencia Artif. Pub Date : 2022-11-09 DOI: 10.4114/intartif.vol25iss70pp33-49

Hadeel Tariq Ibrahim, Wamidh Jalil Mazher, Enas Mahmood Jassim

{"title":"Feature Selection: Binary Harris Hawk Optimizer Based Biomedical Datasets","authors":"Hadeel Tariq Ibrahim, Wamidh Jalil Mazher, Enas Mahmood Jassim","doi":"10.4114/intartif.vol25iss70pp33-49","DOIUrl":"https://doi.org/10.4114/intartif.vol25iss70pp33-49","url":null,"abstract":"Feature selection (FS) is an essential preprocessing step in utmost solutions for the high-dimensional problem to reduce the number of features by deleting irrelevant and redundant data that preserve a suitable grade of classification accuracy. Feature selection can be treated as an optimization problem. Heuristic optimization algorithms are hopeful approaches to solve feature selection problems because of their difficulty, especially in high-dimensional data. Binary Harris hawk optimization (BHHO) is one of the lately suggested metaheuristic algorithms that has been demonstrated to be used more efficiently in facing some optimization problems. Support vector machines (SVMs) are a vital technique that are employed competently to resolve classification issues. We modified the BHHO algorithm with SVM classifier to solve the feature selection issue. This study suggests BHHO-FS to fix the feature selection problem in biomedical datasets. We ran the proposed approach BHHO-FS on real biomedical datasets with 17 types of cancer for Iraqi patients in 2010-2012. The experimental results demonstrate the supremacy of the proposed BHHO-FS in terms of three performance metrics: feature selection accuracy, runtime and number of selected features compared to four other state-of-art algorithms: Fire Fly (FF) algorithm, Genetic Algorithm (GA), Grasshopper Optimization Algorithm (GOA) and Particle Swarm Algorithm (PSO). Comparative experiments designate the importance of the proposed approach in comparison with the other four mentioned algorithms. The implementation of the proposed BHHO-FS approach on 17 datasets for different types of cancers reveals 99.967% average accuracy.","PeriodicalId":176050,"journal":{"name":"Inteligencia Artif.","volume":"45 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126577969","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

Ontology-Based Traffic Accident Information Extraction on Twitter In Indonesia 基于本体的印尼Twitter交通事故信息提取

Inteligencia Artif. Pub Date : 2022-09-25 DOI: 10.4114/intartif.vol25iss70pp1-12

Nur Aini Rakhmawati, Yasin Awwab, Ahmad Choirun Najib, A. Irsyad

{"title":"Ontology-Based Traffic Accident Information Extraction on Twitter In Indonesia","authors":"Nur Aini Rakhmawati, Yasin Awwab, Ahmad Choirun Najib, A. Irsyad","doi":"10.4114/intartif.vol25iss70pp1-12","DOIUrl":"https://doi.org/10.4114/intartif.vol25iss70pp1-12","url":null,"abstract":"Traffic accidents become one of the events that often occur in Indonesia. From the three-monthly report by the Indonesian National Police Traffic Police, there are about 25,000 traffic accidents. Many social media users, especially Twitter, share information about traffic accidents. Twitter has various information regarding traffic accidents. Therefore, this study aims to process and map information about traffic accidents contained on Twitter in Indonesia language. We use the domain ontology and Named-Entity Recognition for the data extraction process. Named-Entity Recognition is used for obtaining keywords from a tweet based on class categories such as actor, time, location, and information on the cause of the accident. This research generates a Named Entity Recognition (NER) model that can provide a reasonably accurate level of accuracy. Also, we create an ontology that can categorize the causes of traffic accidents based on the Directorate General of the Land Transportation Office, Indonesia. We found that the traffic accidents are generally caused by inadequate vehicle conditions with the main problem in the vehicle caused by brake failure, while environmental factors rarely cause traffic accidents. Moreover, the vehicle is the subclass that mostly appears in the tweets, where car is the most popular actor, followed by truck and motorcycle.","PeriodicalId":176050,"journal":{"name":"Inteligencia Artif.","volume":"23 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-09-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125735150","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