Aloísio Vieira, L. Neto, Elias Paulino Medeiros, Filipe Maciel de Moura, Jose Wally, Mendonça Menezes, S. Jagatheesaperumal, Victor Hugo, C. Albuquerque
Operation Carro Pipa (OCP) is a federal government action in Brazil with the objective of distributing drinking water to regions severely affected by long periods of drought and low rainfall using trucks. The region served has continental dimensions, covering an area of 688,064 km² and supplying 1703 cities and approximately 5.2 million people. Because it is a large-scale action that involves a lot of public resources, it is essential that all OCP activities are recorded in a safe, complete, and standardized way. This information can be used for potential benefits, audits, and analysis to propose improvements and ensure the provision of this essential service to society. To achieve this, this work analyzes data recorded from the OCP service offered in a Brazilian state and presents computational solutions that can improve the monitoring, registration, storage, and processing of the data generated by this action.
{"title":"Analyzing the Carro Pipa Operation with Geointelligence Techniques","authors":"Aloísio Vieira, L. Neto, Elias Paulino Medeiros, Filipe Maciel de Moura, Jose Wally, Mendonça Menezes, S. Jagatheesaperumal, Victor Hugo, C. Albuquerque","doi":"10.33969/ais.2023050101","DOIUrl":"https://doi.org/10.33969/ais.2023050101","url":null,"abstract":"Operation Carro Pipa (OCP) is a federal government action in Brazil with the objective of distributing drinking water to regions severely affected by long periods of drought and low rainfall using trucks. The region served has continental dimensions, covering an area of 688,064 km² and supplying 1703 cities and approximately 5.2 million people. Because it is a large-scale action that involves a lot of public resources, it is essential that all OCP activities are recorded in a safe, complete, and standardized way. This information can be used for potential benefits, audits, and analysis to propose improvements and ensure the provision of this essential service to society. To achieve this, this work analyzes data recorded from the OCP service offered in a Brazilian state and presents computational solutions that can improve the monitoring, registration, storage, and processing of the data generated by this action.","PeriodicalId":273028,"journal":{"name":"Journal of Artificial Intelligence and Systems","volume":"80 3 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131320970","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}
Georgines Jacobsen Rasoarinirina, Harimino Andriamalala Rajaonarisoa, I. P. Ramahazosoa, A. Ratiarison, Georgines Jacobsen, Rasoarinirina, Harimino Andriamalala, I. P. Rajaonarisoa, Ramahazosoa
This work objective is to determine the parameters of the fuzzy inference system model that best models the Mascarenes anticyclone trajectory. Our study area extends from 20°E to 110°E longitude and from 15°S to 50°S latitude. We start from the atmospheric pressure reanalysis data in grid point to determine the Mascarene anticyclone center. This center is none other than the center of the last closed Anticyclone's isobar. The monthly climatological mean value of the center coordinates are the data to be modeled by fuzzy inference system. The considered model parameters are the partitions number of the discourse universe and the model order. After evaluating the deviation between the input data and the simulated data, the minimum deviation is obtained with order model 2 by using 50 numbers of partitions.
{"title":"Fuzzy Inference System Modelling of the Mascarenes Anticyclone center Trajectory","authors":"Georgines Jacobsen Rasoarinirina, Harimino Andriamalala Rajaonarisoa, I. P. Ramahazosoa, A. Ratiarison, Georgines Jacobsen, Rasoarinirina, Harimino Andriamalala, I. P. Rajaonarisoa, Ramahazosoa","doi":"10.33969/ais.2023050103","DOIUrl":"https://doi.org/10.33969/ais.2023050103","url":null,"abstract":"This work objective is to determine the parameters of the fuzzy inference system model that best models the Mascarenes anticyclone trajectory. Our study area extends from 20°E to 110°E longitude and from 15°S to 50°S latitude. We start from the atmospheric pressure reanalysis data in grid point to determine the Mascarene anticyclone center. This center is none other than the center of the last closed Anticyclone's isobar. The monthly climatological mean value of the center coordinates are the data to be modeled by fuzzy inference system. The considered model parameters are the partitions number of the discourse universe and the model order. After evaluating the deviation between the input data and the simulated data, the minimum deviation is obtained with order model 2 by using 50 numbers of partitions.","PeriodicalId":273028,"journal":{"name":"Journal of Artificial Intelligence and Systems","volume":"59 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116129053","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}
The importance of data has increased in the last century and these days it is an essential resource for any human activity as well as a vital component for our society. The use of AI is a major improvement in handling these data, the amount of which is becoming enormous. In a regulatory perspective, AI applications have an impact on the social and economic structure and the rights and values on which it is based upon. This paper is a crucial step on the path of building a consensus on the legal hypostasis of AI. It is our belief that unforeseeable and ground-breaking AI applications can be regulatorily tackled with respect to energy law.
{"title":"The Legalhood of Artificial Intelligence: AI Applications as Energy Services","authors":"Lambrini Seremeti, I. Kougias","doi":"10.33969/AIS.2021.31006","DOIUrl":"https://doi.org/10.33969/AIS.2021.31006","url":null,"abstract":"The importance of data has increased in the last century and these days it is an essential resource for any human activity as well as a vital component for our society. The use of AI is a major improvement in handling these data, the amount of which is becoming enormous. In a regulatory perspective, AI applications have an impact on the social and economic structure and the rights and values on which it is based upon. This paper is a crucial step on the path of building a consensus on the legal hypostasis of AI. It is our belief that unforeseeable and ground-breaking AI applications can be regulatorily tackled with respect to energy law.","PeriodicalId":273028,"journal":{"name":"Journal of Artificial Intelligence and Systems","volume":"70 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122665938","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}
Deep Learning algorithms (DL) have been applied in different domains such as computer vision, image detection, robotics and speech processing, in most cases, DL demonstrated better performance than the conventional machine learning algorithms (shallow algorithms). The artificial intelligence research community has leveraged the robustness of the DL because of their ability to process large data size and handle variations in biometric data such as aging or expression problem. Particularly, DL research in automatic fingerprint recognition system (AFRS) is gaining momentum starting from the last decade in the area of fingerprint pre-processing, fingerprints quality enhancement, fingerprint feature extraction, security of fingerprint and performance improvement of AFRS. However, there are limited studies that address the application of DL to model fingerprint biometric for different tasks in the fingerprint recognition process. To bridge this gap, this paper presents a systematic literature review and an insightful meta-data analysis of a decade applications of DL in AFRS. Discussion on proposed model’s tasks, state of the art study, dataset, and training architecture are presented. The Convolutional Neural Networks models were the most saturated models in developing fingerprint biometrics authentication. The study revealed different roles of the DL in training architecture of the models: feature extractor, classifier and end-to-end learning. The review highlights open research challenges and present new perspective for solving the challenges in the future. The author believed that this paper will guide researchers in propose novel fingerprint authentication scheme.
{"title":"Deep Learning Algorithms based Fingerprint Authentication: Systematic Literature Review","authors":"H. Chiroma","doi":"10.33969/ais.2021.31010","DOIUrl":"https://doi.org/10.33969/ais.2021.31010","url":null,"abstract":"Deep Learning algorithms (DL) have been applied in different domains such as computer vision, image detection, robotics and speech processing, in most cases, DL demonstrated better performance than the conventional machine learning algorithms (shallow algorithms). The artificial intelligence research community has leveraged the robustness of the DL because of their ability to process large data size and handle variations in biometric data such as aging or expression problem. Particularly, DL research in automatic fingerprint recognition system (AFRS) is gaining momentum starting from the last decade in the area of fingerprint pre-processing, fingerprints quality enhancement, fingerprint feature extraction, security of fingerprint and performance improvement of AFRS. However, there are limited studies that address the application of DL to model fingerprint biometric for different tasks in the fingerprint recognition process. To bridge this gap, this paper presents a systematic literature review and an insightful meta-data analysis of a decade applications of DL in AFRS. Discussion on proposed model’s tasks, state of the art study, dataset, and training architecture are presented. The Convolutional Neural Networks models were the most saturated models in developing fingerprint biometrics authentication. The study revealed different roles of the DL in training architecture of the models: feature extractor, classifier and end-to-end learning. The review highlights open research challenges and present new perspective for solving the challenges in the future. The author believed that this paper will guide researchers in propose novel fingerprint authentication scheme.","PeriodicalId":273028,"journal":{"name":"Journal of Artificial Intelligence and Systems","volume":"71 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127171287","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}
The objective of this work is to present an improved inventory system with fuzzy constraints dealing with two warehouses system-own and rented. In the present model, we analyze the system under the consideration of two warehouses and without shortages with the assumptions of the linear demand function (increasing function of time). Generally, in today’s business scenario for sessional products, some constraints like storage cost, deteriorating cost, and ordering cost change with their original values. Therefore, these constraints cannot be assumed to be constant in that situation. Depending on these facts that we handle these costs as a triangular fuzzy number and hence apply the signed distance technique to solve the corresponding problem. The key objective of this work is to determine the optimal inventory level, and inventory time schedule to a minimum of the whole inventory cost. The proposed model is demonstrated with two numerical examples to observe the behavior of constraints with system cost and compare their performance with and without fuzzy environment.
{"title":"An Improved Fuzzy Inventory Model Under Two Warehouses","authors":"A. Malik, Harish Garg","doi":"10.33969/ais.2021.31008","DOIUrl":"https://doi.org/10.33969/ais.2021.31008","url":null,"abstract":"The objective of this work is to present an improved inventory system with fuzzy constraints dealing with two warehouses system-own and rented. In the present model, we analyze the system under the consideration of two warehouses and without shortages with the assumptions of the linear demand function (increasing function of time). Generally, in today’s business scenario for sessional products, some constraints like storage cost, deteriorating cost, and ordering cost change with their original values. Therefore, these constraints cannot be assumed to be constant in that situation. Depending on these facts that we handle these costs as a triangular fuzzy number and hence apply the signed distance technique to solve the corresponding problem. The key objective of this work is to determine the optimal inventory level, and inventory time schedule to a minimum of the whole inventory cost. The proposed model is demonstrated with two numerical examples to observe the behavior of constraints with system cost and compare their performance with and without fuzzy environment.","PeriodicalId":273028,"journal":{"name":"Journal of Artificial Intelligence and Systems","volume":"16 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129434708","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}
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