ACS Applied Computer Science

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Applied Computer Science Volume 16, Number 1, 2020

A DEEP LEARNING MODEL FOR ELECTRICITY DEMAND FORECASTING BASED ON A TROPICAL DATA

Electricity demand forecasting is a term used for prediction of users’ consumption on the grid ahead of actual demand. It is very important to all power stakeholders across levels. The power players employ electricity demand forecasting for sundry purposes. Moreover, the government’s policy on its market deregulation has greatly amplified its essence. Despite numerous studies on the subject using certain classical approaches, there exists an opportunity for exploration of more sophisticated methods such as the deep learning (DL) techniques. Successful researches about DL applications to com¬puter vision, speech recognition, and acoustic computing problems are motivation. However, such researches are not sufficiently exploited for electricity demand forecasting using DL methods. In this paper, we considered specific DL techniques (LSTM, CNN, and MLP) to short-term load fore¬casting problems, using tropical institutional data obtained from a Transmission Company. We also test how accurate are predictions across the techniques. Our results relatively revealed models appropriateness for the problem.

  • APA 6th style
Adewuyi, S. A., Aina, S., & Oluwaranti, A. I. (2020). A deep learning model for electricity demand forecasting based on a tropical data. Applied Computer Science, 16(1), 5-17. doi:10.23743/acs-2020-01
  • Chicago style
Adewuyi, Saheed A., Segun Aina, and Adeniran I. Oluwaranti. "A Deep Learning Model for Electricity Demand Forecasting Based on a Tropical Data." Applied Computer Science 16, no. 1 (2020): 5-17.
  • IEEE style
S. A. Adewuyi, S. Aina, and A. I. Oluwaranti, "A deep learning model for electricity demand forecasting based on a tropical data," Applied Computer Science, vol. 16, no. 1, pp. 5-17, 2020, doi: 10.23743/acs-2020-01
  • Vancouver style
Adewuyi SA, Aina S, Oluwaranti AI. A deep learning model for electricity demand forecasting based on a tropical data. Applied Computer Science. 2020;16(1):5-17.

A NOVEL PROFILE’S SELECTION ALGORITHM USING AI

In order to better understand the job requirements, recruitment processes, and hiring processes it is needed to know the people skills. For a recruiter this entails analyzing and comparing the curricula of each available candidate and determining the most appropriate candidate that the activities that are required by the position. This process must be carried in the shortest length of time possible. In this paper, an algorithm is proposed to identify those candidates, either workers or college graduates.

  • APA 6th style
Bello, M., Luna, A., Bonilla, E., Hernandez, C., Pedroza, B., & Portilla, A. (2020). A novel profile’s selection algorithm using AI. Applied Computer Science, 16(1), 18-32. doi:10.23743/acs-2020-02
  • Chicago style
Bello, Mario, Alejandra Luna, Edmundo Bonilla, Crispin Hernandez, Blanca Pedroza, and Alberto Portilla. "A Novel Profile’s Selection Algorithm Using Ai." Applied Computer Science 16, no. 1 (2020): 18-32.
  • IEEE style
M. Bello, A. Luna, E. Bonilla, C. Hernandez, B. Pedroza, and A. Portilla, "A novel profile’s selection algorithm using AI," Applied Computer Science, vol. 16, no. 1, pp. 18-32, 2020, doi: 10.23743/acs-2020-02.
  • Vancouver style
Adewuyi SA, Aina S, Oluwaranti AI. A deep learning model for electricity demand forecasting based on a tropical data. Applied Computer Science. 2020;16(1):5-17.

MODELING TRANSMISSION MECHANISMS WITH DETERMINATION OF EFFICIENCY

Continuing previous studies, reviewed by modeling transmission mechanisms with the definition of one of the major criteria of transmission efficiency – the efficiency of the gearing, which depends on the geometry and kinematics of the working surfaces of the engagement and position of the contact point on the engaging surface in the object of the simulation model the designed transmission. 

  • APA 6th style
Ratov, D., & Lyfar, V. (2020). Modeling transmission mechanisms with determination of efficiency. Applied Computer Science, 16(1), 33-40. doi:10.23743/acs-2020-03
  • Chicago style
Ratov, Denis, and Vladimir Lyfar. "Modeling Transmission Mechanisms with Determination of Efficiency." Applied Computer Science 16, no. 1 (2020): 33-40.
  • IEEE style
D. Ratov and V. Lyfar, "Modeling transmission mechanisms with determination of efficiency," Applied Computer Science, vol. 16, no. 1, pp. 33-40, 2020, doi: 10.23743/acs-2020-03.
  • Vancouver style
Ratov D, Lyfar V. Modeling transmission mechanisms with determination of efficiency. Applied Computer Science. 2020;16(1):33-40.

UNSUPERVISED DYNAMIC TOPIC MODEL FOR EXTRACTING ADVERSE DRUG REACTION FROM HEALTH FORUMS

The relationship between drug and its side effects has been outlined in two websites: Sider and WebMD. The aim of this study was to find the association between drug and its side effects. We compared the reports of typical users of a web site called: "Ask a patient" website with reported drug side effects in reference sites such as Sider and WebMD. In addition, the typical users' comments on highly-commented drugs (Neurotic drugs, Anti-Pregnancy drugs and Gastrointestinal drugs) were analyzed, using deep learning method. To this end, typical users' comments on drugs' side effects, during last decades, were collected from the website “Ask a patient”. Then, the data on drugs were classified based on deep learning model (HAN) and the drugs' side effect. And the main topics of side effects for each group of drugs were identified and reported, through Sider and WebMD websites. Our model demonstrates its ability to accurately describe and label side effects in a temporal text corpus by a deep learning classifier which is shown to be an effective method to precisely discover the association between drugs and their side effects. Moreover, this model has the capability to immediately locate information in reference sites to recognize the side effect of new drugs, applicable for drug companies. This study suggests that the sensitivity of internet users and the diverse scientific findings are for the benefit of dis¬tinct detection of adverse effects of drugs, and deep learning would facilitate it.

  • APA 6th style
Eslami, B., Habibzadeh Motlagh, M., Rezaei, Z., Eslami, M., & Amin Amini, M. (2020). Unsupervised dynamic topic model for extracting adverse drug reaction from health forums. Applied Computer Science, 16(1), 41-59. doi:10.23743/acs-2020-04
  • Chicago style
Eslami, Behnaz, Mehdi Habibzadeh Motlagh, Zahra Rezaei, Mohammad Eslami, and Mohammad Amin Amini. "Unsupervised Dynamic Topic Model for Extracting Adverse Drug Reaction from Health Forums." Applied Computer Science 16, no. 1 (2020): 41-59.
  • IEEE style
B. Eslami, M. Habibzadeh Motlagh, Z. Rezaei, M. Eslami, and M. Amin Amini, "Unsupervised dynamic topic model for extracting adverse drug reaction from health forums," Applied Computer Science, vol. 16, no. 1, pp. 41-59, 2020, doi: 10.23743/acs-2020-04.
  • Vancouver style
Eslami B, Habibzadeh Motlagh M, Rezaei Z, Eslami M, Amin Amini M. Unsupervised dynamic topic model for extracting adverse drug reaction from health forums. Applied Computer Science. 2020;16(1):41-59.

FOOD DELIVERY BASED ON PSO ALGORITHM AND GOOGLE MAPS

This article presents a solution to deal with the optimization of delivery routes problem for a mobile application focused on the restaurant sector, by using a bioinspired algorithm (PSO) to minimize delivery costs, maximize a greater number of deliveries and recommend an optional route for food delivery. Different computational experiments are carried out by using Google Maps (API) for showing the best delivery route. The results obtained are very promising for offering a good delivery service.

  • APA 6th style
Soto, S., Bonilla, E., Portilla, A., Hernández, J. C., Atriano, O., & Quintero, P.M. (2020). Food delivery based on PSO algorithm and Google maps. Applied Computer Science, 16(1), 60-72. doi:10.23743/acs-2020-05
  • Chicago style
Soto, Sergio, Edmundo Bonilla, Alberto Portilla, Jose C. Hernández, Oscar Atriano, and Perfecto M. Quintero. "Food Delivery Based on Pso Algorithm and Google Maps." Applied Computer Science 16, no. 1 (2020): 60-72.
  • IEEE style
S. Soto, E. Bonilla, A. Portilla, J. C. Hernández, O. Atriano, and P. M. Quintero, "Food delivery based on PSO algorithm and Google maps," Applied Computer Science, vol. 16, no. 1, pp. 60-72, 2020, doi: 10.23743/acs-2020-05.
  • Vancouver style
Soto S, Bonilla E, Portilla A, Hernández JC, Atriano O, Quintero PM. Food delivery based on PSO algorithm and Google maps. Applied Computer Science. 2020;16(1):60-72.

SOFTWARE FOR RECOGNITION OF CAR NUMBER PLATE

The purpose of this paper is to design and implement an automatic number plate recognition system. The system has still images as the input, and extracts a string corresponding to the plate number, which is used to obtain the output user data from a suitable database. The system extracts data from a license plate and automatically reads it with no prior assumption of background made. License plate extraction is based on plate features, such as texture, and all characters segmented from the plate are passed individually to a character recognition stage for reading. The string output is then used to query a relational database to obtain the desired user data. This particular paper utilizes the intersection of a hat filtered image and a texture mask as the means of locating the number plate within the image. The accuracy of location of the number plate with an image set of 100 images is 68%.

  • APA 6th style
Abdulhamid, M., & Kinyua, N. (2020). Software for recognition of car number plate. Applied Computer Science, 16(1), 73-84. doi:10.23743/acs-2020-06
  • Chicago style
Abdulhamid, Mohanad, and Njagi Kinyua. "Software for Recognition of Car Number Plate." Applied Computer Science 16, no. 1 (2020): 73-84.
  • IEEE style
M. Abdulhamid and N. Kinyua, "Software for recognition of car number plate," Applied Computer Science, vol. 16, no. 1, pp. 73-84, 2020, doi: 10.23743/acs-2020-06.
  • Vancouver style
Abdulhamid M, Kinyua N. Software for recognition of car number plate. Applied Computer Science. 2020;16(1):73-84.

INFORMATION MODEL OF SYSTEM OF SUPPORT OF DECISION MAKING DURING MANAGEMENT OF IT COMPANIES

An information model has been carried out, with the help of which it is possible to implement methods that ensure the growth of competitiveness of IT companies. Growth conditions for companies provide mergers and acquisitions (M&A). The analysis of the data obtained as a result of the P&L financial report is mainly based on current indicators and can be partially used to prolong economic indicators for a certain (most often limited) period. The authors propose using methods for assessing stochastic indicators of IT development processes based on the solution of a number of problems: (1) Development of models to assess the impact of indicators in the analysis of the financial condition of companies; (2) Creation of an information model and methods for processing current stochastic data and assessing the probability of the implementation of negative and positive outcomes.

  • APA 6th style
Tatarchenko, Y., Lyfar, V., & Tatarchenko, H. (2020). Information model of system of support of decision making during management of it companies. Applied Computer Science, 16(1), 85-94. doi:10.23743/acs-2020-07
  • Chicago style
Tatarchenko, Yehor, Volodymyr Lyfar, and Halyna Tatarchenko. "Information Model of System of Support of Decision Making During Management of It Companies." Applied Computer Science 16, no. 1 (2020): 85-94.
  • IEEE style
Y. Tatarchenko, V. Lyfar, and H. Tatarchenko, "Information model of system of support of decision making during management of it companies," Applied Computer Science, vol. 16, no. 1, pp. 85-94, 2020, doi: 10.23743/acs-2020-07.
  • Vancouver style
Tatarchenko Y, Lyfar V, Tatarchenko H. Information model of system of support of decision making during management of it companies. Applied Computer Science. 2020;16(1):85-94.

REMOTE HEALTH MONITORING: FALL DETECTION

Falling is a serious health issue among the elderly population; it can result in critical injuries like hip fractures. Immobilization caused by injury or unconsciousness means that the victim cannot summon help themselves. With elderly who live alone, not being found for hours after a fall is quite common and drastically increases the significance of fall-induced injuries. With an aging Baby Boomer population, the incidence of falls will only rise in the next few decades. The objective of this paper is to design and create a fall detection system. The system consists of a monitoring device that links wirelessly with a laptop. The device is able to accurately distinguish between fall and non-fall.

  • APA 6th style
Abdulhamid, M., & Peter, D. (2020). Remote health monitoring: fall detection. Applied Computer Science, 16(1), 95-102. doi:10.23743/acs-2020-08
  • Chicago style
Abdulhamid, Mohanad, and Deng Peter. "Remote Health Monitoring: Fall Detection." Applied Computer Science 16, no. 1 (2020): 95-102.
  • IEEE style
M. Abdulhamid and D. Peter, "Remote health monitoring: fall detection," Applied Computer Science, vol. 16, no. 1, pp. 95-102, 2020, doi: 10.23743/acs-2020-08.
  • Vancouver style
Abdulhamid M, Peter D. Remote health monitoring: fall detection. Applied Computer Science. 2020;16(1):95-102.