Data mining and neural networks to determine the financial market prediction

Predicting stock market movements has been a complex task for years by gaining the increasing interest of researchers and investors present all around the world. These have tried to get ahead of the way in order to know the levels of return and thus reduce the risk they face in investments [1]. Capital markets are areas of fundamental importance for the development of economies and their good management that favors the transition from savings to investment through the purchase and sale of shares [2]. These actions are so important that they are influenced by economic, social, political, and cultural variables. Therefore, it is reasonable to consider the value of an action in an instant not as a deterministic variable but as a random variable, considering its temporal trajectory as a stochastic process

A deep learning-based hybrid model for recommendation generation and ranking

A recommender system plays a vital role in information filtering and retrieval, and its application is omnipresent in many domains. There are some drawbacks such as the cold-start and the data sparsity problems which affect the performance of the recommender model. Various studies help with drastically improving the performance of recommender systems via unique methods, such as the traditional way of performing matrix factorization (MF) and also applying deep learning (DL) techniques in recent years. By using DL in the recommender system, we can overcome the difficulties of collaborative filtering. DL now focuses mainly on modeling content descriptions, but those models ignore the main factor of user–item interaction. In the proposed hybrid Bayesian stacked auto-denoising encoder (HBSADE) model, it recognizes the latent interests of the user and analyzes contextual reviews that are performed through the MF method. The objective of the model is to identify the user’s point of interest, recommending products/services based on the user’s latent interests. The proposed two-stage novel hybrid deep learning-based collaborative filtering method explores the user’s point of interest, captures the communications between items and users and provides better recommendations in a personalized way. We used a multilayer neural network to manipulate the nonlinearities between the user and item communication from data. Experiments were to prove that our HBSADE outperforms existing methodologies over Amazon-b and Book-Crossing datasets

Vehicle flow prediction through probabilistic modeling

Within the area of wireless and mobile communications, ad hoc vehicular networks have generated the interest of different organizations, which has generated a topic of study and analysis for the increase of applications, devices, technology integration, security, standards, and quality of service in different areas (Zhu et al. in IEEE Trans Veh Technol 64(4):1607–1619, [1]) and (Tian et al. in A self-adaptive V2V communication system with DSRC, pp 1528–1532, [2]). This study on vehicle networks shows a great deal of opportunity and motivation to deepen the aspects that involve it, which have emerged due to the advance of wireless technologies, as well as research in the automotive industry. This allows the development of networks with spontaneous topologies with vehicles in constant movement in several simulations (Mir and Filali in LTE and IEEE 802.11p for vehicular networking: a performance evaluation, pp 1–15, [3]), with reliable vehicle flows, through the share of traffic information, considering that continuous mobility is an essential characteristic of a VANET vehicle network, which can have short changes in terms of groups of vehicles in the same direction (Lokhande and Khamitkar, 9(12):30–33, [4]). The following paper uses a road scenario called VANET to obtain a predictive characterization of vehicle flow using a probabilistic model

Optimizing street mobility through a netlogo simulation environment

The routes and streets make it possible to drive and travel through the cities, but unfortunately traffic and particularly congestion leads to drivers losing time while traveling from one place to another, because of the time it takes to transit on the roads, in addition to waiting times by traffic lights. This research introduces the extension of an agent-oriented system aimed at reducing driver waiting times at a street intersection. The simulation environment was implemented in NetLogo, which allowed comparison of the impact of Smart traffic light use versus a fixed-time traffic light

Segmentation of sales for a mobile phone service through CART classification tree algorithm

The work consisted of detailing the CRISP-DM method in order to identify optimal groups of customers who are more likely to migrate from a prepaid to postpaid option in order to formulate an improvement plan for in call management by sorting the database. Classification models were applied to analyze the characteristics generated by the purchase of the different services. The CART Classification Tree algorithm. As a result, groups differentiated by probabilities of sales success (migrate from a prepaid to postpaid plan) were found, segments that reflect particular needs and characteristics to design marketing actions focused on the objective of increasing the effectiveness rate, contact information, and sales increase

Improving the effectiveness of energy savings measures at companies by means of a new baseline adjustment strategy

This paper discusses a strategy for establishing an energy consumption baseline for the effects of defining and applying new strategies to improve the effectiveness of energy savings measures. Through this analysis, the energy baseline is adjusted to the dynamics of a typical operation, reducing uncertainty about operating data when it is not possible to determine that a given energy consumption level is typical. The strategy enables focusing efforts on the points in the operation with greatest impact on energy efficiency as a function of frequency of operation

Management model for the logistics and competitiveness of SMEs in the city of Barranquilla

In Colombia, small and medium-sized enterprises (SMEs) are the most powerful engines of economic development, since they generate a high volume of jobs in the national territory, while diversifying productive, commercial, and service activities. In order to propose a model for logistics management as a component for the competitiveness of SMEs, a qualitative study is proposed that will allow us to collect from a structured review of recent literature, identifying the elements to be taken into account for logistics management in order to provide timely support and open space for continuous improvement. The proposed model is structured in six main blocks: characterization of processes according to the activity of the SME, external factors of influence, internal factors of influence, feeding of information, monitoring and control of operations, and feedback between support areas. The above elements have been analyzed and suggested taking into account the specific aspects of SMEs in the country, and taking into account the peculiarities of these small productive cells

Performance evaluation of a hybrid vehicle and sensor network to prevent traffic accidents

In recent years, wireless networks have become a widespread communication technology as well as a research challenge. Many contributions have been made on ad hoc networks, such as wireless sensor networks (WSNs) and vehicular ad hoc networks (VANETs). Recently, the number of cars on our streets, roads, and highways has been increasing, giving rise to a great interest in vehicular communication technologies. This paper presents an hybrid sensor and vehicular network (HSVN) platform, as well as the description and evaluation of a communication protocol between VANETs and WSNs using a network simulator for its evaluation

Effect on the demand and stock returns: cross-sectional of Big Data and time-series analysis

For reducing the degree of uncertainty caused by constant change in the environment, large, medium or small, private or public organizations must support their decisions in something more than experience or intuition; they must be supported by the development of accurate and reliable forecasts in order to meet the needs in the organization planning tasks. This case study presents a growing company dedicated to the storage of perishable products and incorporates time series forecasting techniques to estimate the volume of storage to foresee the requirements of additional facilities, personnel and materials needed for product mobility

Optimization of flow shop scheduling through a hybrid genetic algorithm for manufacturing companies

A task scheduling problem is a process of assigning tasks to a limited set of resources available in a time interval, where certain criteria are optimized. In this way, the sequencing of tasks is directly associated with the executability and optimality of a preset plan and can be found in a wide range of applications, such as: programming flight dispatch at airports, programming production lines in a factory, programming of surgeries in a hospital, repair of equipment or machinery in a workshop, among others. The objective of this study is to analyze the effect of the inclusion of several restrictions that negatively influence the production programming in a real manufacturing environment. For this purpose, an efficient Genetic Algorithm combined with a Local Search of Variable Neighborhood for problems of n tasks and m machines is introduced, minimizing the time of total completion of the tasks. The computational experiments carried out on a set of problem instances with different sizes of complexity show that the proposed hybrid metaheuristics achieves high quality solutions compared to the reported optimal cases