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This paper exploits various meta-heuristic optimization techniques to learn PID controller parameters for nonlinear systems. The nonlinear systems considered here are well known ball and beam, inverted pendulum, and robotic arm manipulator. The gain parameters of the controllers are optimized by using two categories of meta-heuristic optimization techniques—swarm-based grasshopper optimization algorithm and particle swarm optimization and human-based, i.e., teacher learning-based optimization. Mean square error has been used to measure the performance of various algorithms. Robustness of these algorithms is studied and compared using parameter perturbation and external disturbance. There are substantial improvements in the performance of these plants using the mentioned algorithms as shown in the simulation results. A detailed comparative analysis of these algorithms has also been done.
Vishal Srivastava; Smriti Srivastava; Gopal Chaudhary; Xiomarah Guzmán-Guzmán; Vicente García-Díaz. On Comparing the Performance of Swarm-Based Algorithms with Human-Based Algorithm for Nonlinear Systems. Arabian Journal for Science and Engineering 2021, 1 -16.
AMA StyleVishal Srivastava, Smriti Srivastava, Gopal Chaudhary, Xiomarah Guzmán-Guzmán, Vicente García-Díaz. On Comparing the Performance of Swarm-Based Algorithms with Human-Based Algorithm for Nonlinear Systems. Arabian Journal for Science and Engineering. 2021; ():1-16.
Chicago/Turabian StyleVishal Srivastava; Smriti Srivastava; Gopal Chaudhary; Xiomarah Guzmán-Guzmán; Vicente García-Díaz. 2021. "On Comparing the Performance of Swarm-Based Algorithms with Human-Based Algorithm for Nonlinear Systems." Arabian Journal for Science and Engineering , no. : 1-16.
In the digital era, innovations in business intelligence are critical to staying competitive and popular across the growing business trends. Businesses have begun to investigate the next stage of data analytics and business intelligence solutions. On the other hand, Customer Churn Prediction (CCP) is a crucial process in business decision making, which properly identifies the churn users and takes necessary steps for customer retention. churn and non-churn customers have resembling features. Therefore, this research work designs a dynamic CCP strategy for business intelligence using text analytics with metaheuristic optimization (CCPBI-TAMO) algorithm. In addition, the chaotic pigeon inspired optimization based feature selection (CPIO-FS) technique is employed for the feature selection process and reduces computation complexity. Besides, long short-term memory (LSTM) with stacked auto encoder (SAE) model is applied to classify the feature reduced data. In the LSTM-SAE model, the ability of SAE in the detection of compact features is integrated into the classification capability of the LSTM model. Finally, the sunflower optimization (SFO) hyperparameter tuning process takes place to further improve the CCP performance. A detailed simulation analysis is performed on the benchmark customer churn prediction dataset and the experimental values highlighted the superior performance of the proposed model over the other compared methods with the maximum accuracy of 95.56%, 93.44%, and 92.74% on the applied dataset 1-3 respectively.
Irina V. Pustokhina; Denis A. Pustokhin; Aswathy Rh; T. Jayasankar; C. Jeyalakshmi; Vicente García Díaz; K. Shankar. Dynamic customer churn prediction strategy for business intelligence using text analytics with evolutionary optimization algorithms. Information Processing & Management 2021, 58, 102706 .
AMA StyleIrina V. Pustokhina, Denis A. Pustokhin, Aswathy Rh, T. Jayasankar, C. Jeyalakshmi, Vicente García Díaz, K. Shankar. Dynamic customer churn prediction strategy for business intelligence using text analytics with evolutionary optimization algorithms. Information Processing & Management. 2021; 58 (6):102706.
Chicago/Turabian StyleIrina V. Pustokhina; Denis A. Pustokhin; Aswathy Rh; T. Jayasankar; C. Jeyalakshmi; Vicente García Díaz; K. Shankar. 2021. "Dynamic customer churn prediction strategy for business intelligence using text analytics with evolutionary optimization algorithms." Information Processing & Management 58, no. 6: 102706.
Energy consumption forecasting is essential for smart grid operations as it facilitates electricity demand management and utilities load planning. In this paper data analytics has been presented on the collected smart meter measurement and then predicting the energy consumption on a daily basis using (autoregressive integrated moving average) ARIMA, seasonal ARIMA (SARIMA) and long short-term memory (LSTM). The analysis tends to understand the different factors which influence energy consumption, and assist operators to make decisions accordingly. It is helpful in reducing the outage, and enhancing the situational awareness of power consumption on a daily basis of the smart meters. The relational factors are capable in lowering energy consumption, or rather contributing to the effective consumption of energy units. The parameters used for the result evaluation are various features of the weather features relation in terms of power consumption based on temperature, humidity, cloud cover, visibility, wind speed, UV index and dew point. The results indicate that the energy consumption has a high positive correlation with humidity and high negative correlation with temperature. (Dew point and UV index) and (Cloud cover and Visibility Display) have multicollinearity with temperature and humidity respectively, so, can be discarded. Pressure and Moon Phase have minimal correlation with energy consumption, so, it can also be discarded. Wind speed has low correlation with energy, but it does not show multicollinearity. So, it can be considered for further analysis. Overall LSTM found to be prominent in comparison to ARIMA and SARIMA with the average mean absolute error (MAE) of 0.23.
Ashutosh Kumar Dubey; Abhishek Kumar; Vicente García-Díaz; Arpit Kumar Sharma; Kishan Kanhaiya. Study and analysis of SARIMA and LSTM in forecasting time series data. Sustainable Energy Technologies and Assessments 2021, 47, 101474 .
AMA StyleAshutosh Kumar Dubey, Abhishek Kumar, Vicente García-Díaz, Arpit Kumar Sharma, Kishan Kanhaiya. Study and analysis of SARIMA and LSTM in forecasting time series data. Sustainable Energy Technologies and Assessments. 2021; 47 ():101474.
Chicago/Turabian StyleAshutosh Kumar Dubey; Abhishek Kumar; Vicente García-Díaz; Arpit Kumar Sharma; Kishan Kanhaiya. 2021. "Study and analysis of SARIMA and LSTM in forecasting time series data." Sustainable Energy Technologies and Assessments 47, no. : 101474.
Due to the fast development of medical imaging technologies, medical image analysis has entered the period of big data for proper disease diagnosis. At the same time, intracerebral hemorrhage (ICH) becomes a serious disease which affects the injury of blood vessels in the brain regions. This paper presents an artificial intelligence and big data analytics-based ICH e-diagnosis (AIBDA-ICH) model using CT images. The presented model utilizes IoMT devices for data acquisition process. The presented AIBDA-ICH model involves graph cut-based segmentation model for identifying the affected regions in the CT images. To manage big data, Hadoop Ecosystem and its elements are mainly used. In addition, capsule network (CapsNet) model is applied as a feature extractor to derive a useful set of feature vectors. Finally, the presented AIBDA-ICH model makes use of the fuzzy deep neural network (FDNN) model to carry out classification process. For validating the superior performance of the AIBDA-ICH method, an extensive set of simulations were performed and the outcomes are examined under diverse aspects. The experimental values pointed out the improved e-diagnostic performance of the AIBDA-ICH model over the other compared methods with the precision and accuracy of 94.96% and 98.59%, respectively.
Romany F. Mansour; José Escorcia-Gutierrez; Margarita Gamarra; Vicente García Díaz; Deepak Gupta; Sachin Kumar. Artificial intelligence with big data analytics-based brain intracranial hemorrhage e-diagnosis using CT images. Neural Computing and Applications 2021, 1 -13.
AMA StyleRomany F. Mansour, José Escorcia-Gutierrez, Margarita Gamarra, Vicente García Díaz, Deepak Gupta, Sachin Kumar. Artificial intelligence with big data analytics-based brain intracranial hemorrhage e-diagnosis using CT images. Neural Computing and Applications. 2021; ():1-13.
Chicago/Turabian StyleRomany F. Mansour; José Escorcia-Gutierrez; Margarita Gamarra; Vicente García Díaz; Deepak Gupta; Sachin Kumar. 2021. "Artificial intelligence with big data analytics-based brain intracranial hemorrhage e-diagnosis using CT images." Neural Computing and Applications , no. : 1-13.
This paper investigates the automated medical learning and proposes hybrid intelligent framework, called Hybrid Automated Medical Learning (HAML). The goal is the efficient combination of several intelligent components in order to automatically learn the medical data. Multi agents system is proposed by using distributed deep learning, and knowledge graph for learning medical data. The distributed deep learning is used for efficient learning of the different agents in the system, where the knowledge graph is used for dealing with heterogeneous medical data. To demonstrate the usefulness and accuracy of the HAML framework, intensive simulations on medical data were conducted. A wide range of experiments were conducted to verify the efficiency of the proposed system. Three case studies are discussed in this research, the first case study is related to process mining, and more precisely on the ability of HAML to detect relevant patterns from event medical data. The second case study is related to smart building, and the ability of HAML to recognize the different activities of the patients. The third one is related to medical image retrieval, and the ability of HAML to find the most relevant medical images according to the image query. The results show that the developed HAML achieves good performance compared to the most up-to-date medical learning models regarding both the computational and cost the quality of returned solutions.
Asma Belhadi; Youcef Djenouri; Vicente Garcia Diaz; Essam H. Houssein; Jerry Chun‐Wei Lin. Hybrid intelligent framework for automated medical learning. Expert Systems 2021, 1 .
AMA StyleAsma Belhadi, Youcef Djenouri, Vicente Garcia Diaz, Essam H. Houssein, Jerry Chun‐Wei Lin. Hybrid intelligent framework for automated medical learning. Expert Systems. 2021; ():1.
Chicago/Turabian StyleAsma Belhadi; Youcef Djenouri; Vicente Garcia Diaz; Essam H. Houssein; Jerry Chun‐Wei Lin. 2021. "Hybrid intelligent framework for automated medical learning." Expert Systems , no. : 1.
The recent advancements in Internet of Things (IoT), cloud computing, and Artificial Intelligence (AI) transformed the conventional healthcare system into smart healthcare. By incorporating key technologies such as IoT and AI, medical services can be improved. The convergence of IoT and AI offers different opportunities in healthcare sector. In this view, the current research article presents a new AI and IoT convergence-based disease diagnosis model for smart healthcare system. The major goal of this article is to design a disease diagnosis model for heart disease and diabetes using AI and IoT convergence techniques. The presented model encompasses different stages namely, data acquisition, preprocessing, classification, and parameter tuning. IoT devices such as wearables and sensors permit seamless data collection while AI techniques utilize the data in disease diagnosis. The proposed method uses Crow Search Optimization algorithm-based Cascaded Long Short Term Memory (CSO-CLSTM) model for disease diagnosis. In order to achieve better classification of the medical data, CSO is applied to tune both ‘weights’ and ‘bias’ parameters of CLSTM model. Besides, isolation Forest (iForest) technique is employed in this research work to remove the outliers. The application of CSO helps in considerable improvement in the diagnostic outcomes of CLSTM model. The performance of CSO-LSTM model was validated using healthcare data. During the experimentation, the presented CSO-LSTM model accomplished the maximum accuracies of 96.16% and 97.26% in diagnosing heart disease and diabetes respectively. Therefore, the proposed CSO-LSTM model can be employed as an appropriate disease diagnosis tool for smart healthcare systems.
Romany Fouad Mansour; Adnen El Amraoui; Issam Nouaouri; Vicente Garcia Diaz; Deepak Gupta; Sachin Kumar. Artificial Intelligence and Internet of Things Enabled Disease Diagnosis Model for Smart Healthcare Systems. IEEE Access 2021, 9, 45137 -45146.
AMA StyleRomany Fouad Mansour, Adnen El Amraoui, Issam Nouaouri, Vicente Garcia Diaz, Deepak Gupta, Sachin Kumar. Artificial Intelligence and Internet of Things Enabled Disease Diagnosis Model for Smart Healthcare Systems. IEEE Access. 2021; 9 ():45137-45146.
Chicago/Turabian StyleRomany Fouad Mansour; Adnen El Amraoui; Issam Nouaouri; Vicente Garcia Diaz; Deepak Gupta; Sachin Kumar. 2021. "Artificial Intelligence and Internet of Things Enabled Disease Diagnosis Model for Smart Healthcare Systems." IEEE Access 9, no. : 45137-45146.
Social media generate a massive amount of information each day. This information is usually generated by people and may be used for many studies. Social media mining is a growing discipline inside data mining, and the results have proven to be quite revealing. Getting the data to work with might seem to be the easiest task in the process, but it can be very challenging for people without programming knowledge. Thus, there are numerous ways to extract information from social media, such as to use the network API, a Web scraper or specialized libraries. Using a straightforward and a cross social media query language we can hide the complexity of those mechanisms and gather information in a more efficient and easier way. This is because many social media share common elements, so we can create and unify queries to search, find and extract information from those platforms. In this paper, we propose a domain-specific query language, specially designed to allow developers or domain experts to extract data from different social media. With this language we unify and simplify the mechanisms of data extraction from social networks such as Twitter and Facebook.
Xiomarah Guzmán-Guzmán; Edward Rolando Núñez-Valdez; Raysa Vásquez-Reynoso; Angel Asencio; Vicente García-Díaz. SWQL: A new domain-specific language for mining the social Web. Science of Computer Programming 2021, 207, 102642 .
AMA StyleXiomarah Guzmán-Guzmán, Edward Rolando Núñez-Valdez, Raysa Vásquez-Reynoso, Angel Asencio, Vicente García-Díaz. SWQL: A new domain-specific language for mining the social Web. Science of Computer Programming. 2021; 207 ():102642.
Chicago/Turabian StyleXiomarah Guzmán-Guzmán; Edward Rolando Núñez-Valdez; Raysa Vásquez-Reynoso; Angel Asencio; Vicente García-Díaz. 2021. "SWQL: A new domain-specific language for mining the social Web." Science of Computer Programming 207, no. : 102642.
It is always difficult and challenge to obtain suitable trading signals for the desired securities in financial markets. The popular way to deal with it is through the use of trading strategies (TSs) made up of technical or fundamental indicators. Due to the different properties of TSs, an algorithm was proposed to find trading signals by obtaining the group trading strategy portfolio (GTSP), which is composed of strategy groups that can be employed to generate various TS portfolios (TSP) instead of a single TS. The stop-loss and take-profit points (SLTP) are widely utilized by shareholders to avoid massive losses. However, the appropriate SLTP is hard to set by users. Therefore, in this paper, the algorithm, namely GTSP-SLTP algorithm, is proposed to not only obtain a reliable GTSP but also find appropriate SLTP using the grouping genetic algorithm. A chromosome is encoded by the generated SLTP and GTSP along with the weights for strategy groups that are the SLTP, grouping, weight, and strategy parts. To assess the goodness of a chromosome, the evaluation function that consists of the group balance, weight balance, risk factor, and profit factor, is employed. Genetic operators are then performed to produce new solutions for next population. The genetic process is performed iteratively until the stop conditions have achieved. Last but not the least, empirical experiments were conducted on three financial datasets with different trends and a case study is also given to reveal the effectiveness and robustness of the designed GTSP-SLTP algorithm.
Chun-Hao Chen; Yu-Hsuan Chen; Vicente Garcia Diaz; Jerry Chun-Wei Lin. An intelligent trading mechanism based on the group trading strategy portfolio to reduce massive loss by the grouping genetic algorithm. Electronic Commerce Research 2021, 1 -40.
AMA StyleChun-Hao Chen, Yu-Hsuan Chen, Vicente Garcia Diaz, Jerry Chun-Wei Lin. An intelligent trading mechanism based on the group trading strategy portfolio to reduce massive loss by the grouping genetic algorithm. Electronic Commerce Research. 2021; ():1-40.
Chicago/Turabian StyleChun-Hao Chen; Yu-Hsuan Chen; Vicente Garcia Diaz; Jerry Chun-Wei Lin. 2021. "An intelligent trading mechanism based on the group trading strategy portfolio to reduce massive loss by the grouping genetic algorithm." Electronic Commerce Research , no. : 1-40.
Monte Carlo Tree Search is one of the main search methods studied presently. It has demonstrated its efficiency in the resolution of many games such as Go or Settlers of Catan and other different problems. There are several optimizations of Monte Carlo, but most of them need heuristics or some domain language at some point, making very difficult its application to other problems. We propose a general and optimized implementation of Monte Carlo Tree Search using neural networks without extra knowledge of the problem. As an example of our proposal, we made use of the Dots and Boxes game. We tested it against other Monte Carlo system which implements specific knowledge for this problem. Our approach improves accuracy, reaching a winning rate of 81% over previous research but the generalization penalizes performance.
Alba Cotarelo; Vicente García-Díaz; Edward Núñez-Valdez; Cristian González García; Alberto Gómez; Jerry Chun-Wei Lin. Improving Monte Carlo Tree Search with Artificial Neural Networks without Heuristics. Applied Sciences 2021, 11, 2056 .
AMA StyleAlba Cotarelo, Vicente García-Díaz, Edward Núñez-Valdez, Cristian González García, Alberto Gómez, Jerry Chun-Wei Lin. Improving Monte Carlo Tree Search with Artificial Neural Networks without Heuristics. Applied Sciences. 2021; 11 (5):2056.
Chicago/Turabian StyleAlba Cotarelo; Vicente García-Díaz; Edward Núñez-Valdez; Cristian González García; Alberto Gómez; Jerry Chun-Wei Lin. 2021. "Improving Monte Carlo Tree Search with Artificial Neural Networks without Heuristics." Applied Sciences 11, no. 5: 2056.
Life insurance is an agreement between an insured and an insurer, where the insurer pays out a sum of money either on a specific period or the death of the insured. Now a day, People can buy a policy through an online platform. There are a lot of insurance companies available in the market, and each company has various policies. Selecting the best insurance company for purchasing an online term plan is a very complex problem. People may confuse to choose the best insurance company for buying an online term. It is a multi-criteria decision making (MCDM) problem, and the problem consists of different criteria and various alternatives. Here in this paper, a model has been proposed to solve this decision-making problem. In this model, a fuzzy multi-criteria decision-making approach combined with technique for order preference by similarity to ideal solution (TOPSIS) and it has been applied to rank the different insurance companies based on online term plans. The experimental results show that the life insurance corporation of India (LIC) gets the top rank out of 12 companies for purchasing an online term plan. A sensitivity analysis has been performed to validate the proposed model.
Chinmaya Ranjan Pattnaik; Sachi Nandan Mohanty; Sarita Mohanty; Jyotir Moy Chatterjee; Biswajit Jana; Vicente Garcia Diaz. A fuzzy multi-criteria decision-making method for purchasing life insurance in India. Bulletin of Electrical Engineering and Informatics 2021, 10, 344 -356.
AMA StyleChinmaya Ranjan Pattnaik, Sachi Nandan Mohanty, Sarita Mohanty, Jyotir Moy Chatterjee, Biswajit Jana, Vicente Garcia Diaz. A fuzzy multi-criteria decision-making method for purchasing life insurance in India. Bulletin of Electrical Engineering and Informatics. 2021; 10 (1):344-356.
Chicago/Turabian StyleChinmaya Ranjan Pattnaik; Sachi Nandan Mohanty; Sarita Mohanty; Jyotir Moy Chatterjee; Biswajit Jana; Vicente Garcia Diaz. 2021. "A fuzzy multi-criteria decision-making method for purchasing life insurance in India." Bulletin of Electrical Engineering and Informatics 10, no. 1: 344-356.
Currently, we have around us many Smart Objects. With the use of these objects, we can obtain benefits in our daily lives, as well as recommendations and help when we travel. Alternatively, we may increase and improve our industrial processes through the automation of certain tasks. Notwithstanding, we need to use specific software or to develop our own applications. Nevertheless, the main problem arises when we need to develop our own application because we need to save money, or in other cases, the existing applications are not adapted to us. In this case, it is possible that we need to learn new things, the money will then be spent, and such a process is likely to involve problems related to the Software Crisis. So, the main motivation is to create an environment which can reuse the previous knowledge and help people without knowledge about programming to create Smart Objects. Then, the research question of this paper is the following: Could we enable the creation of Smart Objects in an easy and efficient way for people who do not have programming skills? As a possible solution, we have developed a graphic Domain-Specific Language using the Midgar platform. In order to validate our proposal, we make an evaluation split into different phases; the first one consisted in measuring data obtained from participants when they were performing a specific task, and the second one consisted of a survey to collect their opinions about our proposal. Moreover, we also did a comparison of the measured data between two graphical editors and two different participant profiles according to their knowledge about Smart Objects.
Cristian Gonzalez Garcia; Daniel Meana-Llorian; Vicente Garcia-Diaz; Andres Camilo Jimenez; John Petearson Anzola. Midgar: Creation of a Graphic Domain-Specific Language to Generate Smart Objects for Internet of Things Scenarios Using Model-Driven Engineering. IEEE Access 2020, 8, 141872 -141894.
AMA StyleCristian Gonzalez Garcia, Daniel Meana-Llorian, Vicente Garcia-Diaz, Andres Camilo Jimenez, John Petearson Anzola. Midgar: Creation of a Graphic Domain-Specific Language to Generate Smart Objects for Internet of Things Scenarios Using Model-Driven Engineering. IEEE Access. 2020; 8 (99):141872-141894.
Chicago/Turabian StyleCristian Gonzalez Garcia; Daniel Meana-Llorian; Vicente Garcia-Diaz; Andres Camilo Jimenez; John Petearson Anzola. 2020. "Midgar: Creation of a Graphic Domain-Specific Language to Generate Smart Objects for Internet of Things Scenarios Using Model-Driven Engineering." IEEE Access 8, no. 99: 141872-141894.
Email services nowadays are flooded by spam and phishing attacks. Email service providers build their own email filters to protect the final users from such attacks resulting in an overall better experience. However, the attacks come mainly from the same problem in the email protocols, i.e., the lack of authentication mechanism. In this work, we attempt to minimize some of the most common problems identified in email services such as spam, phishing, spoofing, lack of encryption, repudiation and centralization by implementing a smart contract over the Ethereum protocol. The proposal involves a decentralized system, a smart contract, a file system and two applications used as proof of concept.
José Chamadoira González; Vicente García-Díaz; Edward Rolando Núñez-Valdez; Alberto Gómez Gómez; Rubén González Crespo. Replacing email protocols with blockchain-based smart contracts. Cluster Computing 2020, 23, 1795 -1801.
AMA StyleJosé Chamadoira González, Vicente García-Díaz, Edward Rolando Núñez-Valdez, Alberto Gómez Gómez, Rubén González Crespo. Replacing email protocols with blockchain-based smart contracts. Cluster Computing. 2020; 23 (3):1795-1801.
Chicago/Turabian StyleJosé Chamadoira González; Vicente García-Díaz; Edward Rolando Núñez-Valdez; Alberto Gómez Gómez; Rubén González Crespo. 2020. "Replacing email protocols with blockchain-based smart contracts." Cluster Computing 23, no. 3: 1795-1801.
Big data and artificial intelligence are currently two of the most important and trending pieces for innovation and predictive analytics in healthcare, leading the digital healthcare transformation. Keralty organization is already working on developing an intelligent big data analytic platform based on machine learning and data integration principles. We discuss how this platform is the new pillar for the organization to improve population health management, value-based care, and new upcoming challenges in healthcare. The benefits of using this new data platform for community and population health include better healthcare outcomes, improvement of clinical operations, reducing costs of care, and generation of accurate medical information. Several machine learning algorithms implemented by the authors can use the large standardized datasets integrated into the platform to improve the effectiveness of public health interventions, improving diagnosis, and clinical decision support. The data integrated into the platform come from Electronic Health Records (EHR), Hospital Information Systems (HIS), Radiology Information Systems (RIS), and Laboratory Information Systems (LIS), as well as data generated by public health platforms, mobile data, social media, and clinical web portals. This massive volume of data is integrated using big data techniques for storage, retrieval, processing, and transformation. This paper presents the design of a digital health platform in a healthcare organization in Colombia to integrate operational, clinical, and business data repositories with advanced analytics to improve the decision-making process for population health management.
Fernando López-Martínez; Edward Rolando Núñez-Valdez; Vicente García-Díaz; Zoran Bursac. A Case Study for a Big Data and Machine Learning Platform to Improve Medical Decision Support in Population Health Management. Algorithms 2020, 13, 102 .
AMA StyleFernando López-Martínez, Edward Rolando Núñez-Valdez, Vicente García-Díaz, Zoran Bursac. A Case Study for a Big Data and Machine Learning Platform to Improve Medical Decision Support in Population Health Management. Algorithms. 2020; 13 (4):102.
Chicago/Turabian StyleFernando López-Martínez; Edward Rolando Núñez-Valdez; Vicente García-Díaz; Zoran Bursac. 2020. "A Case Study for a Big Data and Machine Learning Platform to Improve Medical Decision Support in Population Health Management." Algorithms 13, no. 4: 102.
Vicente García-Díaz; Edward R. Núñez-Valdez; Vijender Kumar Solanki; Carlos E. Montenegro-Marin. Novel Advances in the Development of Machine Learning Solutions for Scientific Programming. Scientific Programming 2019, 2019, 1 -2.
AMA StyleVicente García-Díaz, Edward R. Núñez-Valdez, Vijender Kumar Solanki, Carlos E. Montenegro-Marin. Novel Advances in the Development of Machine Learning Solutions for Scientific Programming. Scientific Programming. 2019; 2019 ():1-2.
Chicago/Turabian StyleVicente García-Díaz; Edward R. Núñez-Valdez; Vijender Kumar Solanki; Carlos E. Montenegro-Marin. 2019. "Novel Advances in the Development of Machine Learning Solutions for Scientific Programming." Scientific Programming 2019, no. : 1-2.
The purpose of this study is to develop a non-invasive neural network classification model for early neonatal sepsis detection. Early neonatal sepsis is a public health issue and one of the leading causes of complications and deaths in neonatal intensive care units. The data used in this study is from Crecer’s Hospital center in Cartagena-Colombia. An imbalanced dataset of 555 neonates with (66%) of negative cases and (34%) of positive cases was used for this study. The study results show a sensitivity of 80.32%, a specificity of 90.4%, precision on the positive predicted value of 83.1% in the test sample and a calculated area under the curve of 92.5% (95% Confidence Interval [91.4-93.06]). This neural network model can be used as a smart system’s inference engine to support the detection of neonatal sepsis in neonatal intensive care units.
Fernando López-Martínez; Edward Rolando Núñez-Valdez; Jaime Lorduy Gomez; Vicente García-Díaz. A neural network approach to predict early neonatal sepsis. Computers & Electrical Engineering 2019, 76, 379 -388.
AMA StyleFernando López-Martínez, Edward Rolando Núñez-Valdez, Jaime Lorduy Gomez, Vicente García-Díaz. A neural network approach to predict early neonatal sepsis. Computers & Electrical Engineering. 2019; 76 ():379-388.
Chicago/Turabian StyleFernando López-Martínez; Edward Rolando Núñez-Valdez; Jaime Lorduy Gomez; Vicente García-Díaz. 2019. "A neural network approach to predict early neonatal sepsis." Computers & Electrical Engineering 76, no. : 379-388.
Cristian Gonzalez Garcia; Edward Núñez-Valdez; Vicente García-Díaz; Cristina Pelayo G-Bustelo; Juan Manuel Cueva-Lovelle. A Review of Artificial Intelligence in the Internet of Things. International Journal of Interactive Multimedia and Artificial Intelligence 2019, 5, 1 .
AMA StyleCristian Gonzalez Garcia, Edward Núñez-Valdez, Vicente García-Díaz, Cristina Pelayo G-Bustelo, Juan Manuel Cueva-Lovelle. A Review of Artificial Intelligence in the Internet of Things. International Journal of Interactive Multimedia and Artificial Intelligence. 2019; 5 (4):1.
Chicago/Turabian StyleCristian Gonzalez Garcia; Edward Núñez-Valdez; Vicente García-Díaz; Cristina Pelayo G-Bustelo; Juan Manuel Cueva-Lovelle. 2019. "A Review of Artificial Intelligence in the Internet of Things." International Journal of Interactive Multimedia and Artificial Intelligence 5, no. 4: 1.
This research presents a prediction model to evaluate the association between gender, race, BMI, age, smoking, kidney disease and diabetes using logistic regression. Data were collected from NHANES datasets from 2007 to 2016. An unbalanced sampling dataset of 19.709 with (83%) non-hypertensive individuals and (17%) hypertensive individuals. Some risk factors were categorized, and indicator variables were created to transform the continuous variables to a binary form to have consistent predictors with the outcome. The results show a sensitivity of 77%, a specificity of 68%, precision on the positive predicted value of 32% in the test sample and a calculated AUC of 73% (95% CI[0.70 - 0.76]). The model also confirms that individuals with obesity, age range between 71 and 80 years old, race non-Hispanic black and male have higher odds of having hypertension. Diabetes, kidney disease and smoking habits do not affect odds of the outcome. In clinical practice, this model can be used to inform patients and guide population health management in detecting patients with high probability of developing a cardiovascular disease. The proposed Logistic regression method can be used as an expert system’s inference engine to support the experts in the cardiovascular disease field to provide problem analysis for patients in risk of developing hypertension.
Fernando López-Martínez; Aron Schwarcz.Md; Edward Rolando Núñez-Valdez; Vicente García-Díaz. Machine learning classification analysis for a hypertensive population as a function of several risk factors. Expert Systems with Applications 2018, 110, 206 -215.
AMA StyleFernando López-Martínez, Aron Schwarcz.Md, Edward Rolando Núñez-Valdez, Vicente García-Díaz. Machine learning classification analysis for a hypertensive population as a function of several risk factors. Expert Systems with Applications. 2018; 110 ():206-215.
Chicago/Turabian StyleFernando López-Martínez; Aron Schwarcz.Md; Edward Rolando Núñez-Valdez; Vicente García-Díaz. 2018. "Machine learning classification analysis for a hypertensive population as a function of several risk factors." Expert Systems with Applications 110, no. : 206-215.
The creation of computer and videogames is a challenging and multidisciplinary endeavor, requiring different approaches to integrate different disciplines while keeping relatively low development costs. In this context, domain specific languages (DSLs) are increasingly becoming a valid tool, allowing nonprogrammers to participate in the development process. In this work, we focus on a DSL developed within the Gade4all project, focused on defining the behaviors of non-player characters, proposing a design which allows the participation of users with a lack of programming knowledge to define behavior and interaction complex patterns of in-game enemies in a simple and straightforward way. This preferable to other approaches to defining opponent AIs in games, such as machine learning techniques (which produce advanced AI opponents that do not behave as humans) or rigid rule systems created by programmers rather than behavioral experts. The approach has been tested by comparing the creation of well-known AI patterns using T2GAME with popular game editors, and resulted in significantly reduced development times, in addition to being more approachable for non-programmers such as behavioral psychologists.
Ismael Posada Trobo; Vicente García Díaz; Jordán Pascual Espada; Rubén González Crespo; Pablo Moreno-Ger. Rapid modeling of human-defined AI behavior patterns in games. Journal of Ambient Intelligence and Humanized Computing 2018, 10, 2683 -2692.
AMA StyleIsmael Posada Trobo, Vicente García Díaz, Jordán Pascual Espada, Rubén González Crespo, Pablo Moreno-Ger. Rapid modeling of human-defined AI behavior patterns in games. Journal of Ambient Intelligence and Humanized Computing. 2018; 10 (7):2683-2692.
Chicago/Turabian StyleIsmael Posada Trobo; Vicente García Díaz; Jordán Pascual Espada; Rubén González Crespo; Pablo Moreno-Ger. 2018. "Rapid modeling of human-defined AI behavior patterns in games." Journal of Ambient Intelligence and Humanized Computing 10, no. 7: 2683-2692.
The application of artificial vision to the field of instrumentation and measurement, known as vision-based measurement, is rapidly expanding due to recent developments in the Internet of Things (IoT). In this article, a model for obtaining instrument measurements from analog or digital instruments with no Internet connectivity using digital processing of images and the Tesseract architecture is demonstrated and implemented in a Raspberry Pi. The model’s implementation is used to create an interoperability instrument based on artificial vision that transforms the indicator’s visual information into digital data. The model is evaluated by capturing images of the indicator or instrument at 30, 60, 90, 120, and 150 degrees, with a 30 cm distance from a camera connected to a Raspberry Pi. The influence of the image’s capture angle is analyzed in the proposed model, extending the model’s evaluation into other industrial indicators of measurement.
Edwin Bojaca; John Anzola; Andrés Jiménez; Vicente García-Díaz. A vision-based measurement model for instruments without internet connectivity. Computers & Electrical Engineering 2018, 71, 533 -545.
AMA StyleEdwin Bojaca, John Anzola, Andrés Jiménez, Vicente García-Díaz. A vision-based measurement model for instruments without internet connectivity. Computers & Electrical Engineering. 2018; 71 ():533-545.
Chicago/Turabian StyleEdwin Bojaca; John Anzola; Andrés Jiménez; Vicente García-Díaz. 2018. "A vision-based measurement model for instruments without internet connectivity." Computers & Electrical Engineering 71, no. : 533-545.
Planning tasks performed by a robotic agent require previous access to a map of the environment and the position where the agent is located. This creates a problem when the agent is placed in a new environment. To solve it, the RA must execute the task known as Simultaneous Location and Mapping (SLAM) which locates the agent in the new environment while generating the map at the same time, geometrically or topologically. One of the big problems in SLAM is the amount of memory required for the RA to store the details of the environment map. In addition, environment data capture needs a robust processing unit to handle data representation, which in turn is reflected in a bigger RA unit with higher energy use and production costs. This article presents a design for a system capable of a decentralized implementation of SLAM that is based on the use of a system comprised of wireless agents capable of storing and distributing the map as it is being generated by the RA. The proposed system was validated in an environment with a surface area of 25 m 2 , in which it was capable of generating the topological map online, and without relying on external units connected to the system.
Andrés C. Jiménez; Vicente García-Díaz; Rubén González Crespo; Sandro Bolaños. Decentralized Online Simultaneous Localization and Mapping for Multi-Agent Systems. Sensors 2018, 18, 2612 .
AMA StyleAndrés C. Jiménez, Vicente García-Díaz, Rubén González Crespo, Sandro Bolaños. Decentralized Online Simultaneous Localization and Mapping for Multi-Agent Systems. Sensors. 2018; 18 (8):2612.
Chicago/Turabian StyleAndrés C. Jiménez; Vicente García-Díaz; Rubén González Crespo; Sandro Bolaños. 2018. "Decentralized Online Simultaneous Localization and Mapping for Multi-Agent Systems." Sensors 18, no. 8: 2612.