Inteligencia Artificial

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ISSN / EISSN : 1137-3601 / 1988-3064
Total articles ≅ 541
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Gildã¡sio Lecchi Cravo, Dayan De Castro Bissoli, Andrã© Renato Sales Amaral
Inteligencia Artificial, Volume 24, pp 51-70; doi:10.4114/intartif.vol24iss67pp51-70

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.
Hicham Deghbouch, Fatima Debbat
Inteligencia Artificial, Volume 24, pp 18-35; doi:10.4114/intartif.vol24iss67pp18-35

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; doi:10.4114/intartif.vol24iss67pp1-17

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.
Jean Phelipe De Oliveira Lima, Carlos Maurí­cio Seródio Figueiredo
Inteligencia Artificial, Volume 24, pp 40-50; doi:10.4114/intartif.vol24iss67pp40-50

In modern smart cities, there is a quest for the highest level of integration and automation service. In the surveillance sector, one of the main challenges is to automate the analysis of videos in real-time to identify critical situations. This paper presents intelligent models based on Convolutional Neural Networks (in which the MobileNet, InceptionV3 and VGG16 networks had used), LSTM networks and feedforward networks for the task of classifying videos under the classes "Violence" and "Non-Violence", using for this the RLVS database. Different data representations held used according to the Temporal Fusion techniques. The best outcome achieved was Accuracy and F1-Score of 0.91, a higher result compared to those found in similar researches for works conducted on the same database.
Mariela Morveli Espinoza
Inteligencia Artificial, Volume 24, pp 36-39; doi:10.4114/intartif.vol24iss67pp36-39

Rhetorical arguments are used in negotiation dialogues when a proponent agent tries to persuade his opponent to accept a proposal more readily. When more than one argument is generated, the proponent must compare them in order to select the most adequate for his interests. A way of comparing them is by means of their strength values. Related work propose a calculation based only on the components of the rhetorical arguments, i.e., the importance of the opponent's goal and the certainty level of the beliefs that make up the argument. This work aims to propose a model for the calculation of the strength of rhetorical arguments, which is inspired on the pre-conditions of credibility and preferability stated by Guerini and Castelfranchi. Thus, we suggest the use of two new criteria to the strength calculation: the credibility of the proponent and the status of the opponent's goal in the goal processing cycle. The model is empirically evaluated and the results demonstrate that the proposed model is more efficient than previous works in terms of number of exchanged arguments and number of reached agreements.
UshaDevi G, Gokulnath Bv
Inteligencia Artificial, Volume 23, pp 136-154; doi:10.4114/intartif.vol23iss65pp136-154

The major agricultural products in India are rice, wheat, pulses, and spices. As our population is increasing rapidly the demand for agriculture products also increasing alarmingly. A huge amount of data are incremented from various field of agriculture. Analysis of this data helps in predicting the crop yield, analyzing soil quality, predicting disease in a plant, and how meteorological factor affects crop productivity. Crop protection plays a vital role in maintaining agriculture product. Pathogen, pest, weed, and animals are responsible for the productivity loss in agriculture product. Machine learning techniques like Random Forest, Bayesian Network, Decision Tree, Support Vector Machine etc. help in automatic detection of plant disease from visual symptoms in the plant. A survey of different existing machine learning techniques used for plant disease prediction was presented in this paper. Automatic detection of disease in plant helps in early diagnosis and prevention of disease which leads to an increase in agriculture productivity.
Suresh K, Karthik S, Hanumanthappa M
Inteligencia Artificial, Volume 23, pp 86-99; doi:10.4114/intartif.vol23iss65pp86-99

With the progressions in Information and Communication Technology (ICT), the innumerable electronic devices (like smart sensors) and several software applications can proffer notable contributions to the challenges that are existent in monitoring plants. In the prevailing work, the segmentation accuracy and classification accuracy of the Disease Monitoring System (DMS), is low. So, the system doesn't properly monitor the plant diseases. To overcome such drawbacks, this paper proposed an efficient monitoring system for paddy leaves based on big data mining. The proposed model comprises 5 phases: 1) Image acquisition, 2) segmentation, 3) Feature extraction, 4) Feature Selection along with 5) Classification Validation. Primarily, consider the paddy leaf image which is taken as of the dataset as the input. Then, execute image acquisition phase where 3 steps like, i) transmute RGB image to grey scale image, ii) Normalization for high intensity, and iii) preprocessing utilizing Alpha-trimmed mean filter (ATMF) through which the noises are eradicated and its nature is the hybrid of the mean as well as median filters, are performed. Next, segment the resulting image using Fuzzy C-Means (i.e. FCM) Clustering Algorithm. FCM segments the diseased portion in the paddy leaves. In the next phase, features are extorted, and then the resulted features are chosen by utilizing Multi-Verse Optimization (MVO) algorithm. After completing feature selection, the chosen features are classified utilizing ANFIS (Adaptive Neuro-Fuzzy Inference System). Experiential results contrasted with the former SVM classifier (Support Vector Machine) and the prevailing methods in respect of precision, recall, F-measure,sensitivity accuracy, and specificity. In accuracy level, the proposed one has 97.28% but the prevailing techniques only offer 91.2% for SVM classifier, 85.3% for KNN and 88.78% for ANN. Hence, this proposed DMS has more accurate detection and classification process than the other methods. The proposed DMS evinces better accuracy when contrasting with the prevailing methods.
Raul Cesar Alves, Josué Silva de Morais, Keiji Yamanaka
Inteligencia Artificial, Volume 23, pp 33-55; doi:10.4114/intartif.vol23iss65pp33-55

Indoor localization has been considered to be the most fundamental problem when it comes to providing a robot with autonomous capabilities. Although many algorithms and sensors have been proposed, none have proven to work perfectly under all situations. Also, in order to improve the localization quality, some approaches use expensive devices either mounted on the robots or attached to the environment that don't naturally belong to human environments. This paper presents a novel approach that combines the benefits of two localization techniques, WiFi and Kinect, into a single algorithm using low-cost sensors. It uses separate Particle Filters (PFs). The WiFi PF gives the global location of the robot using signals of Access Point devices from different parts of the environment while it bounds particles of the Kinect PF, which determines the robot's pose locally. Our algorithm also tackles the Initialization/Kidnapped Robot Problem by detecting divergence on WiFi signals, which starts a localization recovering process. Furthermore, new methods for WiFi mapping and localization are introduced.
José Daniel López-Cabrera, Luis Alberto López Rodríguez, Marlén Pérez-Díaz
Inteligencia Artificial, Volume 23, pp 56-66; doi:10.4114/intartif.vol23iss65pp56-66

Breast cancer is the most frequent in females. Mammography has proven to be the most effective method for the early detection of this type of cancer. Mammographic images are sometimes difficult to understand, due to the nature of the anomalies, the low contrast image and the composition of the mammary tissues, as well as various technological factors such as spatial resolution of the image or noise. Computer-aided diagnostic systems have been developed to increase the accuracy of mammographic examinations and be used by physicians as a second opinion in obtaining the final diagnosis, and thus reduce human errors. Convolutional neural networks are a current trend in computer vision tasks, due to the great performance they have achieved. The present investigation was based on this type of networks to classify into three classes, normal, benign and malignant tumour. Due to the fact that the miniMIAS database used has a low number of images, the transfer learning technique was applied to the Inception v3 pre-trained network. Two convolutional neural network architectures were implemented, obtaining in the architecture with three classes, 86.05% accuracy. On the other hand, in the architecture with two neural networks in series, an accuracy of 88.2% was reached.
Qing An, Xijiang Chen, Jupu Yuan
Inteligencia Artificial, Volume 23, pp 115-123; doi:10.4114/intartif.vol23iss65pp115-123

In order to meet the needs of high precision, high availability and high safety positioning for automatic driving, aiming at the technical difficulties of automatic driving positioning in the complex urban environment, an inertial navigation model suitable for the dynamic characteristics of vehicles is established, and a tight combination method of Beidou / inertial high precision positioning is proposed, which solves the problem of rapid accumulation of positioning errors in the weak signal environment of Beidou. The results show that when the Beidou signal is completely interrupted and the INS is combined tightly, the positioning accuracy and continuity are improved significantly, and the maximum error is less than 0.5m, which can realize the automatic driving high-precision continuous navigation and positioning in the complex urban environment.
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