Inteligencia Artificial 2024-06-01T18:54:00+02:00 Editor Open Journal Systems <p style="text-align: justify;"><span style="color: #000000;"><strong><em><a style="color: #003366; text-decoration: underline;" href="" target="_blank" rel="noopener">Inteligencia Artificial</a></em></strong><span id="result_box" class="" lang="en"> is an international open access journal promoted by <span class="">the Iberoamerican Society of</span> Artificial Intelligence (<a href="">IBERAMIA</a>). </span></span>Since 1997, the journal publishes high-quality original papers reporting theoretical or applied advances in all areas of Artificial Intelligence. <span style="color: rgba(0, 0, 0, 0.87); font-family: 'Noto Sans', -apple-system, BlinkMacSystemFont, 'Segoe UI', Roboto, Oxygen-Sans, Ubuntu, Cantarell, 'Helvetica Neue', sans-serif; font-size: 14px; font-style: normal; font-variant-ligatures: normal; font-variant-caps: normal; font-weight: 400; letter-spacing: normal; orphans: 2; text-align: justify; text-indent: 0px; text-transform: none; white-space: normal; widows: 2; word-spacing: 0px; -webkit-text-stroke-width: 0px; background-color: #ffffff; text-decoration-thickness: initial; text-decoration-style: initial; text-decoration-color: initial; display: inline !important; float: none;">There are no fees for subscription, publication nor editing tasks<span class="VIiyi" lang="en"><span class="JLqJ4b ChMk0b" data-language-for-alternatives="en" data-language-to-translate-into="es" data-phrase-index="0">.</span></span> <span class="VIiyi" lang="en"><span class="JLqJ4b ChMk0b" data-language-for-alternatives="en" data-language-to-translate-into="es" data-phrase-index="0">Articles can be written in English, Spanish or Portuguese and <a href="">will be subjected</a> to a double-blind peer review process.</span></span> <span class="VIiyi" lang="en"><span class="JLqJ4b ChMk0b" data-language-for-alternatives="en" data-language-to-translate-into="es" data-phrase-index="0">The journal is abstracted and indexed in several <a href="">data bases</a>. </span></span><br /></span></p> Accurate Price prediction by Double Deep Q-Network 2023-02-06T10:33:09+01:00 Mohammd Reza Feizi Derakhshi Bahram Lotfimanesh Omid Amani <p>For more than several decades, time series data have been in the center of attention for scholars to predict the future prices of the markets, the most fundamental and challenging of which has been the prediction of the price of the stock market. It is of great importance to note that the algorithms with the fewest errors in price predictions are more applicable. There have been more methods suggested for price prediction in the stock markets: time series data analysis, mathematical and statistical analysis, signal processing, pattern recognition and machine learning. One of the demerits of the aforementioned methods is failing to recognize sudden change of prices, in this regard, experiencing more errors is the consequence of such demerit. In this regard, to have the error solved, the DDQN algorithm, consisting of deep neural networks which includes LSTM-CNN layers, has been employed. Confronting price fluctuations, the agent has the privilege of having better performance by employing the advantages of LSTM-CNN layers. In this research, the algorithm has been carried out over Iranian Gold Market, including six various types of Gold, from 2009 to 2020. The results reveal the point that the given method is more precise in comparison with other suggested methods confronting sudden changes in prices.</p> 2024-05-17T00:00:00+02:00 Copyright (c) 2024 Iberamia & The Authors A Novel Deep Learning Model for Pancreas Segmentation: Pascal U-Net 2023-12-27T12:59:26+01:00 Ender Kurnaz Rahime Ceylan Mustafa Alper Bozkurt Hakan Cebeci Mustafa Koplay <p>A robust and reliable automated organ segmentation from abdomen images is a crucial problem in both quantitative imaging analysis and computer aided diagnosis. Especially, automatic pancreas segmentation from abdomen CT images is most challenging task which based on in two main aspects (1) high variability in anatomy (like as shape, size, etc.) and location across different patients (2) low contrast with neighboring tissues. Due to these reasons, achievement of high accuracies in pancreas segmentation is hard image segmentation problem. In this paper, we propose a novel deep learning model which is convolutional neural network-based model called Pascal U-Net for pancreas segmentation. Performance of the proposed model is evaluated on The Cancer Imaging Archive (TCIA) Pancreas CT database and abdomen CT dataset which is taken from Selcuk University Medicine Faculty Radiology Department. During the experimental studies, k-fold cross-validation method is used. Furthermore, results of the proposed model are compared with results of traditional U-Net. If results obtained by Pascal U-Net and traditional U-net for different batch size and fold number is compared, it can be seen that experiments on both datasets validate the effectiveness of Pascal U-Net model for pancreas segmentation.</p> 2024-05-17T00:00:00+02:00 Copyright (c) 2024 Iberamia & The Authors Age-Invariant Cross-Age Face Verification using Transfer Learning 2023-10-22T20:10:41+02:00 Newlin Shebiah Russel Arivazhagan Selvaraj Dhanya Devi S Dhivyarupini M <p>The integration of face verification technology has become indispensable in numerous safety and security software systems. Despite its promising results, the field of face verification encounters significant challenges due to age-related disparities. Human facial characteristics undergo substantial transformations over time, leading to diverse variations including changes in facial texture, morphology, facial hair, and eyeglass adoption. This study presents a pioneering methodology for cross-age face verification, utilizing advanced deep learning techniques to extract resilient and distinctive facial features that are less susceptible to age-related fluctuations. The feature extraction process combines handcrafted features like Local Binary Pattern/Histogram of Oriented Gradients with deep features from MobileNetV2 and VGG-16 networks. As the texture of the facial skin defines the age related characteristic the well-known texture feature extractors like LBP and HoG is preferred. These features are concatenated to achieve fusion, and subsequent layers fine-tune them. Experimental validation utilizing the Cross-Age Celebrity Dataset demonstrates remarkable efficacy, achieving an accuracy of 98.32%.</p> 2024-05-17T00:00:00+02:00 Copyright (c) 2024 Iberamia & The Authors FRESHNets: Highly Accurate and Efficient Food Freshness Assessment Based on Deep Convolutional Neural Networks 2024-01-22T13:09:42+01:00 Jorge Felix Martínez Pazos Jorge Gulín González David Batard Lorenzo Arturo Orellana García <p>Food freshness classification is a growing concern in the food industry, mainly to protect consumer health and prevent illness and poisoning from consuming spoiled food. Intending to take a significant step towards improving food safety and quality control measures in the industry, this study presents two models based on deep learning for the classification of fruit and vegetable freshness: a robust model and an efficient model. Models’ performance evaluation shows remarkable results; in terms of accuracy, the robust model and the efficient model achieved 97.6% and 94.0% respectively, while in terms of Area Under the Curve (AUC) score, both models achieved more than 99%, with the difference in inference time between each model over 844 images being 13 seconds.</p> 2024-05-17T00:00:00+02:00 Copyright (c) 2024 Iberamia & The Authors The Superiority of Fine-tuning over Full-training for the Efficient Diagnosis of COPD from CXR Images 2024-04-17T10:10:37+02:00 Victor Ikechukwu Agughasi <p>This research investigates the use of deep learning for diagnosing lung diseases like Chronic Obstructive Pulmonary Disease (COPD) using Chest X-rays (CXR). The study compares the impact of deep learning on improving these diagnoses by comparing the performance of models trained from the scratch with those enhanced through fine-tuning established architectures like InceptionV3, ResNet50, and VGG-19. The study revealed that fine-tuning pre-trained models offers significant benefits: faster convergence, improved stability, and increased accuracy. Data augmentation techniques were found to be particularly useful when dealing with limited or unbalanced datasets. A custom CNN model, Iyke-Net, showed promising results when fine-tuned. Interestingly, it was observed that models using grayscale images outperformed those using colour images in disease classification, suggesting that colour information might be less critical than previously thought for certain diagnostic procedures. The study emphasizes the importance of balancing model complexity with computational efficiency and diagnostic accuracy. It advocates for refining existing deep learning models for COPD diagnosis from CXR images, paving the way for further innovations in AI-enhanced medical diagnostics.</p> 2024-05-17T00:00:00+02:00 Copyright (c) 2024 Iberamia & The Authors Machine Learning-based Extrapolation of Crop Cultivation Cost 2024-01-22T13:15:58+01:00 Poonam Bari Lata Ragha <p>It is important to comprehend the relation between operational expenses such as labour, seed, irrigation, insecticides, fertilizers and manure costs necessary for the cultivation of crops. A precise cost for the cultivation of crops can offer vital information for agricultural decision-making. The main goal of the study is to compare machine learning (ML) techniques to measure relationships among operational cost characteristics for predicting crop cultivation costs before the start of the growing season using the dataset made available by the Ministry of Agriculture and Farmer Welfare of the Government of India. This paper describes various ML regression techniques, compares various learning algorithms as well as determines the most efficient regression algorithms based on the data set, the number of samples and attributes. The data set used for predicting the cost with 1680 instances includes varying costs for 14 different crops for 12 years (2010-2011 to 2021-2022). Ten different ML algorithms are considered and the crop cultivation cost is predicted. The evaluation results show that Random Forest (RF), Decision Tree (DT), Extended gradient boosting (XR) and K-Neighbours (KN) regression provide better performance in terms of coefficient of determination (R2), root mean square error (RMSE) and mean absolute error (MAE) rate while training and testing time. This study also compares different ML techniques and showed significant differences using the statistical analysis of variance (ANOVA) test. The optimal hyperparameters for the ML models are found using the gridsearchCV and randomizedsearchCV functions, which improves the model's capacity for generalisation.</p> 2024-05-17T00:00:00+02:00 Copyright (c) 2024 Iberamia & The Authors Binary Classification of Skin Cancer Images Using Pre-trained Networks with I-GWO 2024-03-26T14:41:37+01:00 Hadeer Hussein Ahmed Magdy Rehab F. Abdel-Kader Khaled Abd El Salam <p>One of the most prevalent forms of cancer worldwide is skin cancer. Determining disease characteristics necessitates a clinical evaluation of skin lesions, but this process is limited by long time horizons and a multiplicity of interpretations. Deep learning techniques have been created to help dermatologists with these issues as a higher patient survival rate depends on the early and precise detection of skin cancer. This research proposed a new approach for binary classification of dermoscopic images for skin cancer. The Improved Grey Wolf Optimizer (I-GWO) is used in this technique to fine-tune some hyperparameters’ values of various pre-trained deep learning networks to maximize results. SqueezeNet, ShuffleNet, AlexNet, ResNet-18, and DarkNet-19 are the pre-trained networks that were employed. We tested the MED-NODE and DermIS databases in our investigation. Concerning the MED-NODE and DermIS datasets, the proposed method's highest accuracy results are 100% and 97%, respectively.</p> 2024-05-26T00:00:00+02:00 Copyright (c) 2024 Iberamia & The Authors Uniqueness meets Semantics: A Novel Semantically Meaningful Bag-of-Words Approach for Matching Resumes to Job Profiles 2024-06-01T18:54:00+02:00 Seba Susan Muskan Sharma Gargi Choudhary <p>In an increasingly competitive world, automated screening of resumes of candidates is the need of the hour given the large numbers of such resumes in career portals on the World Wide Web. Resume classification is a subset of the document classification problem in which the keywords extracted from the resume play a significant role in determining the job profile. In this paper, we explore the novel combination of uniqueness in terms of the number of occurrences of a keyword in a resume class as compared to the other resume classes, and the concept of semantics by representing the filtered keywords using word embeddings that can be used to find semantic similarities between resume documents. The principle of maximum entropy partitioning is used to find the keywords unique to a particular class. The aim is to use semantic representations of only those keywords that occur more frequently in one class more than in any other class; these are then passed as input to a Bidirectional long short-term memory (LSTM) for classification. Our experiments on a benchmark dataset proves that the proposed approach outperforms the state of the art in text classification by a significant margin proving the efficacy of our approach.</p> 2024-06-09T00:00:00+02:00 Copyright (c) 2024 Iberamia & The Authors Leveraging Transfer Learning for Efficient Diagnosis of COPD Using CXR Images and Explainable AI Techniques 2023-11-01T13:24:17+01:00 Victor Ikechukwu Agughasi <p class="Abstract"><span lang="EN-GB">Chronic Obstructive Pulmonary Disease (COPD) is a predominant global health concern, ranking third in mortality rates, yet frequently remains undiagnosed until its advanced stages. Given its prevalence, the need for innovative and widely accessible diagnostic tools has never been more paramount. While spirometry tests serve as conventional diagnostic benchmarks, their reach remains limited, especially in regions with constrained medical resources. The presented research harnesses deep learning algorithms to facilitate early-stage COPD detection, specifically targeting Chest X-rays (CXRs). The clinically annotated VinDR-CXR dataset provides the primary foundation for model training, complemented by incorporating the ChestX-ray14 dataset for initial model pre-training. Such a dual-dataset strategy augments model generalization and adaptability. Among several explored Convolutional Neural Network (CNN) architectures, the Xception model emerges as a frontrunner. Through transfer learning methodologies, this model produces a noteworthy recall rate of 98.2%, markedly surpassing the metrics of the ResNet50 model. Recognizing the imperative for transparency in AI applications in medical imaging, the research integrates essential explainability approaches viz: Gradient Class Activation Mapping (Grad-CAM) and SHapley Additive exPlanations (SHAP). These techniques elucidate the AI’s decision-making process, offering invaluable visual and analytical insights for fostering trust among medical professionals. In essence, this study not only underscores the potential of integrating AI with medical imaging for COPD detection but also accentuates the pivotal role of transparency in AI-driven medical interventions.</span></p> 2024-06-12T00:00:00+02:00 Copyright (c) 2024 Iberamia & The Authors AEPIA (Asociación Española para la Inteligencia Artificial). 40 Aniversario (1984 – 2024) 2024-05-07T11:28:14+02:00 Felisa Verdejo Francisco Garijo Federico Barber Antonio Bahamonde Amparo Amparo Alonso Alicia Troncoso <p>En este año se cumplen los primeros cuarenta años de la Asociación Española para la Inteligencia Artificial (AEPIA), cuarenta años de marcha continuada por el camino de la Inteligencia Artificial. Desde aquel lejano 1984, en el que un grupo de investigadores pioneros, liderados por el profesor D. José Cuena, fundador y primer presidente de AEPIA, vieron la necesidad de unir a toda la comunidad científica y profesional para dar a conocer la IA, hasta nuestros días, muchas generaciones han contribuido a la historia de AEPIA construyendo una asociación con muchos logros y muchos retos aún por delante.</p> 2024-05-17T00:00:00+02:00 Copyright (c) 2024 Iberamia & The Authors