Inteligencia Artificial

Journal Information
ISSN / EISSN : 1137-3601 / 1988-3064
Total articles ≅ 558
Current Coverage
SCOPUS
COMPENDEX
DOAJ
ESCI
Archived in
EBSCO
SHERPA/ROMEO
Filter:

Latest articles in this journal

Jorge E Camargo, Rigoberto Sáenz
Inteligencia Artificial, Volume 24, pp 1-20; https://doi.org/10.4114/intartif.vol24iss68pp1-20

Abstract:
We want to measure the impact of the curriculum learning technique on a reinforcement training setup, several experiments were designed with different training curriculums adapted for the video game chosen as a case study. Then all were executed on a selected game simulation platform, using two reinforcement learning algorithms, and using the mean cumulative reward as a performance measure. Results suggest that curriculum learning has a significant impact on the training process, increasing training times in some cases, and decreasing them up to 40% percent in some other cases.
Yaming Cao, Zhen Yang, Chen Gao
Inteligencia Artificial, Volume 24, pp 21-32; https://doi.org/10.4114/intartif.vol24iss68pp21-32

Abstract:
Convolutional neural networks (CNNs) have shown strong learning capabilities in computer vision tasks such as classification and detection. Especially with the introduction of excellent detection models such as YOLO (V1, V2 and V3) and Faster R-CNN, CNNs have greatly improved detection efficiency and accuracy. However, due to the special angle of view, small size, few features, and complicated background, CNNs that performs well in the ground perspective dataset, fails to reach a good detection accuracy in the remote sensing image dataset. To this end, based on the YOLO V3 model, we used feature maps of different depths as detection outputs to explore the reasons for the poor detection rate of small targets in remote sensing images by deep neural networks. We also analyzed the effect of neural network depth on small target detection, and found that the excessive deep semantic information of neural network has little effect on small target detection. Finally, the verification on the VEDAI dataset shows, that the fusion of shallow feature maps with precise location information and deep feature maps with rich semantics in the CNNs can effectively improve the accuracy of small target detection in remote sensing images.
D. Gonzalez-Calvo, R.M. Aguilar, C. Criado-Hernandez, L.A. Gonzalez-Mendoza
Inteligencia Artificial, Volume 24, pp 53-71; https://doi.org/10.4114/intartif.vol24iss68pp53-71

Abstract:
The planning of industrial maintenance associated with the production of electricity is vital, as it yields a current and future snapshot of an industrial component in order to optimize the human, technical and economic resources of the installation. This study focuses on the degradation due to fouling of a gas turbine in the Canary Islands, and analyzes fouling levels over time based on the operating regime and local meteorological variables. In particular, we study the relationship between degradation and the suspended dust that originates in the Sahara Desert. To this end, we use a computational procedure that relies on a set of artificial neural networks to build an ensemble, using a cross-validated committees approach, to yield the compressor efficiency. The use of trained models makes it possible to know in advance how the local fouling of an industrial rotating component will evolve, which is useful for maintenance planning and for calculating the relative importance of the variables that make up the system
Moussa Demba
Inteligencia Artificial, Volume 24, pp 37-52; https://doi.org/10.4114/intartif.vol24iss68pp37-52

Abstract:
In relational databases, it is essential to know all minimal keys since the concept of database normaliza-tion is based on keys and functional dependencies of a relation schema. Existing algorithms for determining keysor computing the closure of arbitrary sets of attributes are generally time-consuming. In this paper we present anefficient algorithm, called KeyFinder, for solving the key-finding problem. We also propose a more direct methodfor computing the closure of a set of attributes. KeyFinder is based on a powerful proof procedure for findingkeys called tableaux. Experimental results show that KeyFinder outperforms its predecessors in terms of searchspace and execution time.
Gildã¡sio Lecchi Cravo, Dayan De Castro Bissoli, Andrã© Renato Sales Amaral
Inteligencia Artificial, Volume 24, pp 51-70; https://doi.org/10.4114/intartif.vol24iss67pp51-70

Abstract:
O problema de layout em linha dupla (DRLP) consiste em determinar a localização de facilidades ao longo de ambos os lados de um corredor central, tendo como objetivo, a minimização da soma ponderada das distâncias entre todos os pares de facilidades. Como facilidades podem ser máquinas, centros de trabalho, células de manufatura, departamentos de um edifício e robôs em sistemas de manufatura. Esse trabalho propõe uma abordagem puramente heurística, baseada na meta-heurística Otimização do Enxame de Partículas (PSO). Para validar o algoritmo proposto, o mesmo foi submetido a testes computacionais com cinquenta e uma instâncias, incluindo instâncias consideradas de grande porte e os resultados encontrados mostram o PSO proposto como uma excelente abordagem para o DRLP, melhorado tendo os valores conhecidos para diversas instâncias disponíveis na literatura.
Varsha Bhole, Arun Kumar
Inteligencia Artificial, Volume 24, pp 102-120; https://doi.org/10.4114/intartif.vol24iss67pp102-120

Abstract:
Shelf-life prediction for fruits based on the visual inspection and with RGB imaging through external features becomes more pervasive in agriculture and food business. In the proposed architecture, to enhance the accuracy with low computational costs we focus on two challenging tasks of shelf life (remaining useful life) prediction: 1) detecting the intrinsic features like internal defects, bruises, texture, and color of the fruits; and 2) classification of fruits according to their remaining useful life. To accomplish these tasks, we use the thermal imaging technique as a baseline which is used as non-destructive approach to find the intrinsic values of fruits in terms of temperature parameter. Further to improve the classification tasks, we combine it with a transfer learning approach to forecast the shelf life of fruits. For this study, we have chosen „Kesar? (Mangifera Indica Linn cv. Kesar) mangoes and for the purpose of classification, our designed dataset images are categorized into 19 classes viz. RUL-1 (Remaining Useful Life-1) to RUL-18 (Remaining Useful Life-18) and No-Life as after harvesting, the storage span of „Kesar? is near about 19 days. A comparative analysis using SqueezeNet, ShuffleNet, and MobileNetv2 (which are prominent CNN based lightweight models) has been performed in this study. The empirical results show a highest achievable accuracy of 98.15±0.44% with an almost a double speedup in training the entire process by using thermal images.
Hicham Deghbouch, Fatima Debbat
Inteligencia Artificial, Volume 24, pp 18-35; https://doi.org/10.4114/intartif.vol24iss67pp18-35

Abstract:
This work addresses the deployment problem in Wireless Sensor Networks (WSNs) by hybridizing two metaheuristics, namely the Bees Algorithm (BA) and the Grasshopper Optimization Algorithm (GOA). The BA is an optimization algorithm that demonstrated promising results in solving many engineering problems. However, the local search process of BA lacks efficient exploitation due to the random assignment of search agents inside the neighborhoods, which weakens the algorithm’s accuracy and results in slow convergence especially when solving higher dimension problems. To alleviate this shortcoming, this paper proposes a hybrid algorithm that utilizes the strength of the GOA to enhance the exploitation phase of the BA. To prove the effectiveness of the proposed algorithm, it is applied for WSNs deployment optimization with various deployment settings. Results demonstrate that the proposed hybrid algorithm can optimize the deployment of WSN and outperforms the state-of-the-art algorithms in terms of coverage, overlapping area, average moving distance, and energy consumption.
Flávio Arthur O. Santos, Thiago Dias Bispo, Hendrik Teixeira Macedo, Cleber Zanchettin
Inteligencia Artificial, Volume 24, pp 1-17; https://doi.org/10.4114/intartif.vol24iss67pp1-17

Abstract:
Natural language processing systems have attracted much interest of the industry. This branch of study is composed of some applications such as machine translation, sentiment analysis, named entity recognition, question and answer, and others. Word embeddings (i.e., continuous word representations) are an essential module for those applications generally used as word representation to machine learning models. Some popular methods to train word embeddings are GloVe and Word2Vec. They achieve good word representations, despite limitations: both ignore morphological information of the words and consider only one representation vector for each word. This approach implies the word embeddings does not consider different word contexts properly and are unaware of its inner structure. To mitigate this problem, the other word embeddings method FastText represents each word as a bag of characters n-grams. Hence, a continuous vector describes each n-gram, and the final word representation is the sum of its characters n-grams vectors. Nevertheless, the use of all n-grams character of a word is a poor approach since some n-grams have no semantic relation with their words and increase the amount of potentially useless information. This approach also increase the training phase time. In this work, we propose a new method for training word embeddings, and its goal is to replace the FastText bag of character n-grams for a bag of word morphemes through the morphological analysis of the word. Thus, words with similar context and morphemes are represented by vectors close to each other. To evaluate our new approach, we performed intrinsic evaluations considering 15 different tasks, and the results show a competitive performance compared to FastText. Moreover, the proposed model is $40\%$ faster than FastText in the training phase. We also outperform the baseline approaches in extrinsic evaluations through Hate speech detection and NER tasks using different scenarios.
Gerardo Ernesto Rolong Agudelo, Carlos Enrique Montenegro Marin, Paulo Alonso Gaona-Garcia
Inteligencia Artificial, Volume 24, pp 121-128; https://doi.org/10.4114/intartif.vol24iss67pp121-128

Abstract:
In the world and some countries like Colombia, the number of missing person is a phenome very worrying and growing, every year, thousands of people are reported missing all over the world, the fact that this keeps happening might indicate that there are still analyses that have not been done and tools that have not been considered in order to find patterns in the information of missing person. The present article presents a study of the way informatics and computational tools can be used to help find missing person and what patterns can be found in missing person datasets using as a study case open data about missing person in Colombia in 2017. The goal of this study is to review how computational tools like data mining and image analysis can be used to help find missing person and draw patterns in the available information about missing person. For this, first it will be review of the state of art of image analysis in real world applications was made in order to explore the possibilities when studying the photos of missing person, then a data mining process with data of missing person in Colombia was conducted to produce a set of decision rules that can explain the cause of the disappearance, as a result is generated decision rules algorithm suggest links between socioeconomic stratification, age, gender and specific locations of Colombia and the missing person reports. In conclusion, this work reviews what information about missing person is available publicly and what analysis can me made with them, showing that data mining and face recognition can be useful tools to extract patterns and identify patterns in missing person data.
Amin Rezaeipanah, Rahmad Syah, Siswi Wulandari, A Arbansyah
Inteligencia Artificial, Volume 24, pp 147-156; https://doi.org/10.4114/intartif.vol24iss67pp147-156

Abstract:
Nowadays, breast cancer is one of the leading causes of death women in the worldwide. If breast cancer is detected at the beginning stage, it can ensure long-term survival. Numerous methods have been proposed for the early prediction of this cancer, however, efforts are still ongoing given the importance of the problem. Artificial Neural Networks (ANN) have been established as some of the most dominant machine learning algorithms, where they are very popular for prediction and classification work. In this paper, an Intelligent Ensemble Classification method based on Multi-Layer Perceptron neural network (IEC-MLP) is proposed for breast cancer diagnosis. The proposed method is split into two stages, parameters optimization and ensemble classification. In the first stage, the MLP Neural Network (MLP-NN) parameters, including optimal features, hidden layers, hidden nodes and weights, are optimized with an Evolutionary Algorithm (EA) for maximize the classification accuracy. In the second stage, an ensemble classification algorithm of MLP-NN is applied to classify the patient with optimized parameters. Our proposed IEC-MLP method which can not only help to reduce the complexity of MLP-NN and effectively selection the optimal feature subset, but it can also obtain the minimum misclassification cost. The classification results were evaluated using the IEC-MLP for different breast cancer datasets and the prediction results obtained were very promising (98.74% accuracy on the WBCD dataset). Meanwhile, the proposed method outperforms the GAANN and CAFS algorithms and other state-of-the-art classifiers. In addition, IEC-MLP could also be applied to other cancer diagnosis.
Back to Top Top