Neil Hernández-Gress

• PhD
• Managing Director at Tecnológico de Monterrey

Current research on the career satisfaction of graduates limits educational institutions in devising methods to attain high career satisfaction. Thus, this study aims to use data science models to understand and predict career satisfaction based on information collected from surveys of university alumni. Five machine learning (ML) algorithms were u...

Nowadays, well-designed user documentation plays a relevant role in the development and delivery of high-quality software products. It minimizes software maintenance costs either for developers and managers by following quality standards during software life-cycle process. In this research work it is proposed an assessment approach consisting of a...

Determining what causes field equipment malfunction and predicting when those malfunctions will occur can save large amounts of money for corporations that are capital-intensive. To avert equipment downtime, field equipment maintenance departments must be adequately resourced. Herein, we demonstrate the efficacy of machine learning to determine tim...

This study shows the significant features predicting graduates’ job levels, particularly high-level positions. Moreover, it shows that data science methodologies can accurately predict graduate outcomes. The dataset used to analyze graduate outcomes was derived from a private educational institution survey. The original dataset contains information...

Deep learning architectures lead the state-of-the-art in several computer vision, natural language processing, and reinforcement learning tasks due to their ability to extract multi-level representations without human engineering. The model’s performance is affected by the amount of labeled data used in training. Hence, novel approaches like self-s...

Background This paper explores machine learning algorithms and approaches for predicting alum income to obtain insights on the strongest predictors and a ‘high’ earners’ class. Methods It examines the alum sample data obtained from a survey from a multicampus Mexican private university. Survey results include 17,898 and 12,275 observations before...

Technical analysis aims to predict market movement by examining historical data through statistical procedures. Nevertheless, it is sensitive to the parameter it is working with. An optimization problem is defined to tune technical analysis parameters by minimizing an error metric for stock return prediction. Differential Evolution is a metaheurist...

Background: This paper explores different machine learning algorithms and approaches for predicting alum income to obtain insights on the strongest predictors for income and a ‘high’ earners’ class. Methods: The study examines the alum sample data obtained from a survey from Tecnologico de Monterrey, a multicampus Mexican private university, and an...

The understanding of occupancy patterns has been identified as a key contributor to achieve improvements in energy efficiency in buildings since occupancy information can benefit different systems, such as HVAC (Heating, Ventilation, and Air Conditioners), lighting, security, and emergency. This has meant that in the past decade, researchers have f...

Parking block regions host dangerous behaviors that can be detected from a surveillance camera perspective. However, these regions are often occluded, subject to ground bumpiness or steep slopes, and thus they are hard to segment. Firstly, the paper proposes a pyramidal solution that takes advantage of satellite views of the same scene, based on a...

Typically, women are scored with a lower financial risk than men. However, the understanding of variables and indicators that lead to such results, are not fully understood. Furthermore, the stochastic nature of the data makes it difficult to generate a suitable profile to offer an adequate financial portfolio to the women segment. As the amount, v...

Smart Mobility seeks to meet urban requirements within a city and solve the urban mobility problems, one of them is related with vehicular traffic. The anomalous vehicular traffic is an unexpected change in the day-to-day vehicular traffic caused by different reasons, such as an accident, an event, road works or a natural disaster. An early detecti...

Organizations are turning to predictive analytics to help solve difficult problems and uncover new opportunities. Nowadays, the processes are saturated in data, which must be used properly to generate the necessary key information in the decision making process. Although there are several useful techniques to process and analyze data, the main valu...

In the current world, sports produce considerable data such as players skills, game results, season matches, leagues management, etc. The big challenge in sports science is to analyze this data to gain a competitive advantage. The analysis can be done using several techniques and statistical methods in order to produce valuable information. The pro...

The prevalence of type 2 Diabetes Mellitus (T2DM) has reached critical proportions globally over the past few years. Diabetes can cause devastating personal suffering and its treatment represents a major economic burden for every country around the world. To property guide effective actions and measures, the present study aims to examine the profil...

Molecular Docking faces problems related to Curse of dimensionality, due to the fact that it analyzes data with high dimensionality and few samples. (Ligand-Based Virtual Screening) conducts studies of docking among molecules using common attributes registered in data bases. This branch of Molecular Docking, uses Optimization methods and Machine le...

Financial fraud and money laundering represent an important concern both nationally and worldwide due to the huge amounts of monetary losses they imply. Besides human inspection, Data Science (DS) has proved so far to be a useful tool in order to fight these activities by automatic means. Nevertheless, this approach to the problem is still in early...

Type 2 Diabetes Mellitus has reached epidemic proportions in the past few years worldwide. However, significant disparities exist in its prevalence and trends. The aim of this paper is to discuss a review of the risk socio-demographic factors studied and the profile obtained for type 2 diabetes mellitus patients, as well as addressing the principal...

Resumen. La cantidad de información que uno o más usuarios de In-ternet generan para la Web Semántica está incrementando diariamente. Por esto, es necesario desarrollar herramientas que nos permitan mos-trar esta información de una manera rápida, simple y fácil de entender. De acuerdo con esta premisa, hemos desarrollado una herramienta de visualiz...

This manuscript is focused on the efficiency analysis of Artificial Neural Networks (ANN) that belongs to the third generation, which are Spiking Neural Networks (SNN) and Support Vector Machine (SVM). The main issue of scientific community have been to improve the efficiency of ANN. So, we applied architecture GPU (Graphical Processing Unit) from...

This work presents a comparative analysis of specific, rather than general, mathematical programming implementation techniques of the quadratic optimization problem (QP) based on Support Vector Machines (SVM) learning process. Considering the Karush-Kuhn-Tucker (KKT) optimality conditions, we present a strategy of implementation of the SVM-QP follo...

The replacement problem can be modeled as a finite, irreducible, homogeneous Markov Chain. In our proposal, we modeled the problem using a Markov decision process and then, the instance is optimized using linear programming. Our goal is to analyze the sensitivity and robustness of the optimal solution across the perturbation of the optimal basis (...

This manuscript is focused on some applications of method Spikeprop of Spiking Neural Networks (SNN) using an especific hardware for parallel programming in order to measure the eficience. So, we are interested on pattern recognition and clustering, that are the main problems to solve for Artificial Neural Networks (ANN). As a result, we are going...

A fuzzy based hyperheuristic system is used for Genetic Aglorithm self adaption. A fuzzy Takagi-Sugeno Inference System is used as High level Heuristic and the GA is used as Low-level heuristic. The framework allows to the system to automatically adjust their own parameters without the need for manual adjustment. The fuzzy system to handle uncertai...

In this paper we study the performance of Spiking Neural Networks (SNN)and Support Vector Machine(SVM) by using a GPU, model GeForce 6400M. Respect to applications of SNN, the methodology may beused for clustering, classification of databases, odor, speech and image recognition..In case ofmethodology SVM, is typically applied for clustering, regres...

In this paper we study the improvement in the perfo rmance of Artificial Neural Networks (ANN) by using parallel programming in GPU or FPGA archit ectures. It is well known that ANN can be parallelized according to particular characteris tics of the training algorithm. We discuss both approaches: the software (GPU) and the Hardwar e (FPGA). Diff...

People interact with systems and applications through several devices and are willing to share information about preferences, interests and characteristics. Social networking profiles, data from advanced sensors attached to personal gadgets, and semantic web technologies such as FOAF and microformats are valuable sources of personal information tha...

Recently, many researchers are trying to automate the automatic production of ontologies, realizing extraction of the information contained in databases using reverse engineering, however, because of the continuous evolution of standards in relational databases, not all the characteristics presented by modern databases managers are still supported....

In this article we show how to find evidence of incomplete or fractured processes in non-structured reports of known business processes, by means of rules, patterns and detection of cause-effect relationships. A priori classifications and probabilities of process activities are used as inputs for the analysis and rules detection. In this method we...

This paper presents an ontology driven multi-agent system that uses a negotiation process for decision-support in a Bank Queue. The system assists queue client assignment based on the client profile and the cashiers' workload in order to guarantee a minimum time response in client attention. The multi-agent system has a direct positive impact in th...

This paper describes the implementation of an intelligent tutoring system dedicated to teaching probability and statistics at the preparatory school (or high school) in Mexico. The system solution was used as a desktop computer and adapted to carry a mobile environment for the implementation of mobile learning or m-learning. The system complies wit...

This paper presents a strategy to optimize the learning phase of the Support Vector Machines algorithm (SVM). The SVM algorithm is widely used in solving different tasks like classification, regression, density estimation and clustering problems. However, the algorithm presents important disadvantages when learning large scale problems. Training a...

This paper presents a particular methodology to monitor the level of vigilance of an operator in the context of a complex human-machine system. The apparatus consider the instrumentation of a car with several sensors and HMI equipment, developed within the European project SENSATION. We present the SENSATION sensors used in this experiment, the met...

This paper describes the implementation of an intelligent tutoring system dedicated to teaching probability and statistics at the preparatory school (or high school) in Mexico. The system solution was used as a desktop computer and adapted to carry a mobile environment for the implementation of mobile learning or m-learning. The system complies wit...

The identification of different heart diseases plays an important role in medical applications since it is becoming a growing problem. In order to decrease the number of deaths, it is important to consider warning signs, and knowing how to respond quickly and properly when it occurs. In this paper we propose the use of Fuzzy Support Vector Clusteri...

In recent years, the importance of the construction of fuzzy models from measured data has increased. Nevertheless, the complexity of real-life process is characterized by nonlinear and non-stationary dynamics, leaving so much classical identification techniques out of choice. In this paper, we present a comparison of Support Vector Machines (SVMs)...

This paper presents a comparison of different initialization algorithms joint with decomposition methods, in order to reduce the training time of Support Vector Machines (SVMs). Training a SVM involves the solution of a quadratic optimization problem (QP).The QP problem is very resource consuming (computational time and computational memory), becau...

The Support Vector Machine (SVM) is a well known method used for classification, regression and density estimation. Training a SVM consists in solving a Quadratic Programming (QP) problem. The QP problem is very resource consuming (computational time and computational memory), because the quadratic form is dense and the memory requirements grow squ...

This paper presents a study of the Quadratic optimization Problem (QP) lying on the learning process of Support Vector Machines (SVM). Taking the Karush-Kuhn-Tucker (KKT) optimality conditions, we present the strategy of implementation of the SVM-QP following two classical approaches: i) active set, also divided in primal and dual spaces, methods a...

We present a comparison between different warning strategies for onboard rumble strips in order to prevent road departure events. We compare time-to-lane-crossing (TLC) approach and some implementations of variable rumble strip (VRBS). The framework of this study is the AWAKE European research project and the PREDIT French research program. The val...

In recent years, the importance of the construction of fuzzy models from measured data has increased. Nevertheless, the complexity of real-life process is characterized by nonlinear and non-stationary dynamics, leaving so much classical identification techniques out of choice. In this paper, we present a new fuzzy clustering algorithm for the param...

In this paper, we introduce an original advanced driver assistance system, in order to tackle the Hypovigilance diagnosis problem. Some constraints of the system are: 1) the supervision and monitoring of the driver-vehicle-environment system is a complex problem due to particularities such as stochastic, personal and dynamic; 2) The solution must b...

Apart from the driving behavioural change that can be the direct consequence of operating a car phone, phone-use related behaviour may also be a threat to traffic safety. Making notes or looking up telephone numbers while driving are examples of such behaviour. In a driving simulator experiment, 20 drivers drove in two conditions under normal drivi...

The objective of this paper is to present detection results, produced from experiments in a Dual Mode Dual Polarity (DMDP) antenna structure, using a general detection methodology based on Statistics and Artificial Neural Networks (ANNs), in order to detect possible abnormalities within the structure. The antenna is supposed in three states (health...

This paper describes the results from validation of a new hybrid primary safety system intended to detect automatically low levels of driver vigilance and atypical driving maneuvers which are likely to cause an accident. This multiple sensor vehicle borne system has been optimized by the use of factorial analysis in association with Generalized Gau...

This paper describes the results from validation of a new hybrid primary safety system intended to detect automatically low levels of driver vigilance and atypical driving maneuvers which are likely to cause an accident. This multiple sensor vehicle-borne system has been optimized by the use of factonal analysis in association with Generalized Gaus...

We show the importance of a filtering step for real-time on-board applications such as the driver impairment detection system. Sampling on-board a vehicle produces noisy signals representing errors for the data processing module. Digital filtering is applied to signals from different sources including: lateral position, steering wheel angle and veh...

In this work, the authors present a statistical analysis applied to different sensors on the driver's impairment detection problem. Their goal is to get a minimal number of discriminating sensors to match the requirements of an industrial prototype. The signals coming from these group of sensors are used to create artificial variables based on seve...

In this paper, the diagnostic part of the SAVE system is described. It is composed of three main diagnostic subsystems 1) behavioural diagnosis, 2) physical diagnosis and 3) critical diagnosis. Each subsystem uses different sensors on-board the vehicle and the information is placed in a hierarchical order and mixed to obtain a general diagnosis. A...

Among binary unit-based constructive algorithms, the Sequential Learning is particularly interesting for many reasons, the most significant one being its ability to treat real valued inputs without preprocessing. However, due to the construction process, the classical algorithms derived from the Perceptron cannot be used for learning each unit of t...

In this paper, a new hybrid system is proposed involving Artificial Neural Networks (ANNs) and an optimization method to generate Multidimensional Fuzzy Sets (MFS). Usually, fuzzy systems designed for pattern recognition and control use one-dimensional orthogonal reference fuzzy sets such as triangular or trapezoidal ones. These systems suffer from...

This paper deals with two extensions of the Sequential Learning algorithm which are capable of learning any classification problem. Binary or real valued inputs can be processed and several classes coded as desired with an arbitrary number of outputs. Moreover, unlike many other constructive methods, the networks obtained rely on a conventional str...

In this paper an overview of a new constructive algorithm is proposed. Most commonly binary-unit based constructive algorithms are faced with 3 major drawbacks : binary inputs, only one output and a complex connectivity. The proposed algorithm aims to overcome these problems. An extension to multiple outputs of the Sequential Learning algorithm in...

Once the in board smart multisensor danger detection copilot system defined (1992). LAAS-CNRS has investigated different methodological approaches to realise the diagnosis. Three complementary directions have been identified : a. The following of the driving mistakes in terms of trafiic regulations (1992); b. The evaluation time of the reactivity c...