Forecasting model selection through out-of-sample rolling horizon weighted errors

Demand forecasting is an essential process for any firm whether it is a supplier, manufacturer or retailer. A large number of research works about time series forecast techniques exists in the literature, and there are many time series forecasting tools. In many cases, however, selecting the best time series forecasting model for each time series to be dealt with is still a complex problem. In this paper, a new automatic selection procedure of time series forecasting models is proposed. The selection criterion has been tested using the set of monthly time series of the M3 Competition and two basic forecasting models obtaining interesting results. This selection criterion has been implemented in a forecasting expert system and applied to a real case, a firm that produces steel products for construction, which automatically performs monthly forecasts on tens of thousands of time series. As result, the firm has increased the level of success in its demand forecasts. © 2011 Elsevier Ltd. All rights reserved.Poler Escoto, R.; Mula, J. (2011). Forecasting model selection through out-of-sample rolling horizon weighted errors. Expert Systems with Applications. 38(12):14778-14785. doi:10.1016/j.eswa.2011.05.072S1477814785381

Improvements on the bees algorithm for continuous optimisation problems

This work focuses on the improvements of the Bees Algorithm in order to enhance the algorithm’s performance especially in terms of convergence rate. For the first enhancement, a pseudo-gradient Bees Algorithm (PG-BA) compares the fitness as well as the position of previous and current bees so that the best bees in each patch are appropriately guided towards a better search direction after each consecutive cycle. This method eliminates the need to differentiate the objective function which is unlike the typical gradient search method. The improved algorithm is subjected to several numerical benchmark test functions as well as the training of neural network. The results from the experiments are then compared to the standard variant of the Bees Algorithm and other swarm intelligence procedures. The data analysis generally confirmed that the PG-BA is effective at speeding up the convergence time to optimum. Next, an approach to avoid the formation of overlapping patches is proposed. The Patch Overlap Avoidance Bees Algorithm (POA-BA) is designed to avoid redundancy in search area especially if the site is deemed unprofitable. This method is quite similar to Tabu Search (TS) with the POA-BA forbids the exact exploitation of previously visited solutions along with their corresponding neighbourhood. Patches are not allowed to intersect not just in the next generation but also in the current cycle. This reduces the number of patches materialise in the same peak (maximisation) or valley (minimisation) which ensures a thorough search of the problem landscape as bees are distributed around the scaled down area. The same benchmark problems as PG-BA were applied against this modified strategy to a reasonable success. Finally, the Bees Algorithm is revised to have the capability of locating all of the global optimum as well as the substantial local peaks in a single run. These multi-solutions of comparable fitness offers some alternatives for the decision makers to choose from. The patches are formed only if the bees are the fittest from different peaks by using a hill-valley mechanism in this so called Extended Bees Algorithm (EBA). This permits the maintenance of diversified solutions throughout the search process in addition to minimising the chances of getting trap. This version is proven beneficial when tested with numerous multimodal optimisation problems

Hybrid approaches to optimization and machine learning methods: a systematic literature review

Notably, real problems are increasingly complex and require sophisticated models and algorithms capable of quickly dealing with large data sets and finding optimal solutions. However, there is no perfect method or algorithm; all of them have some limitations that can be mitigated or eliminated by combining the skills of different methodologies. In this way, it is expected to develop hybrid algorithms that can take advantage of the potential and particularities of each method (optimization and machine learning) to integrate methodologies and make them more efficient. This paper presents an extensive systematic and bibliometric literature review on hybrid methods involving optimization and machine learning techniques for clustering and classification. It aims to identify the potential of methods and algorithms to overcome the difficulties of one or both methodologies when combined. After the description of optimization and machine learning methods, a numerical overview of the works published since 1970 is presented. Moreover, an in-depth state-of-art review over the last three years is presented. Furthermore, a SWOT analysis of the ten most cited algorithms of the collected database is performed, investigating the strengths and weaknesses of the pure algorithms and detaching the opportunities and threats that have been explored with hybrid methods. Thus, with this investigation, it was possible to highlight the most notable works and discoveries involving hybrid methods in terms of clustering and classification and also point out the difficulties of the pure methods and algorithms that can be strengthened through the inspirations of other methodologies; they are hybrid methods.Open access funding provided by FCT|FCCN (b-on). This work has been supported by FCT— Fundação para a Ciência e Tecnologia within the R &D Units Project Scope: UIDB/00319/2020. Beatriz Flamia Azevedo is supported by FCT Grant Reference SFRH/BD/07427/2021 The authors are grateful to the Foundation for Science and Technology (FCT, Portugal) for financial support through national funds FCT/ MCTES (PIDDAC) to CeDRI (UIDB/05757/2020 and UIDP/05757/2020) and SusTEC (LA/P/0007/2021).info:eu-repo/semantics/publishedVersio

Intelligent optimisation of analogue circuits using particle swarm optimisation, genetic programming and genetic folding

This thesis was submitted for the degree of Doctor of Philosophy and awarded by Brunel University London.This research presents various intelligent optimisation methods which are: genetic algorithm (GA), particle swarm optimisation (PSO), artificial bee colony algorithm (ABCA), firefly algorithm (FA) and bacterial foraging optimisation (BFO). It attempts to minimise analogue electronic filter and amplifier circuits, taking a cascode amplifier design as a case study, and utilising the above-mentioned intelligent optimisation algorithms with the aim of determining the best among them to be used. Small signal analysis (SSA) conversion of the cascode circuit is performed while mesh analysis is applied to transform the circuit to matrices form. Computer programmes are developed in Matlab using the above mentioned intelligent optimisation algorithms to minimise the cascode amplifier circuit. The objective function is based on input resistance, output resistance, power consumption, gain, upperfrequency band and lower frequency band. The cascode circuit result presented, applied the above-mentioned existing intelligent optimisation algorithms to optimise the same circuit and compared the techniques with the one using Nelder-Mead and the original circuit simulated in PSpice. Four circuit element types (resistors, capacitors, transistors and operational amplifier (op-amp)) are targeted using the optimisation techniques and subsequently compared to the initial circuit. The PSO based optimised result has proven to be best followed by that of GA optimised technique regarding power consumption reduction and frequency response. This work modifies symbolic circuit analysis in Matlab (MSCAM) tool which utilises Netlist from PSpice or from simulation to generate matrices. These matrices are used for optimisation or to compute circuit parameters. The tool is modified to handle both active and passive elements such as inductors, resistors, capacitors, transistors and op-amps. The transistors are transformed into SSA and op-amp use the SSA that is easy to implement in programming. Results are presented to illustrate the potential of the algorithm. Results are compared to PSpice simulation and the approach handled larger matrices dimensions compared to that of existing symbolic circuit analysis in Matlab tool (SCAM). The SCAM formed matrices by adding additional rows and columns due to how the algorithm was developed which takes more computer resources and limit its performance. Next to this, this work attempts to reduce component count in high-pass, low-pass, and all- pass active filters. Also, it uses a lower order filter to realise same results as higher order filter regarding frequency response curve. The optimisers applied are GA, PSO (the best two methods among them) and Nelder-Mead (the worst method) are used subsequently for the filters optimisation. The filters are converted into their SSA while nodal analysis is applied to transform the circuit to matrices form. High-pass, low-pass, and all- pass active filters results are presented to demonstrate the effectiveness of the technique. Results presented have shown that with a computer code, a lower order op-amp filter can be applied to realise the same results as that of a higher order one. Furthermore, PSO can realise the best results regarding frequency response for the three results, followed by GA whereas Nelder- Mead has the worst results. Furthermore, this research introduced genetic folding (GF), MSCAM, and automatically simulated Netlist into existing genetic programming (GP), which is a new contribution in this work, which enhances the development of independent Matlab toolbox for the evolution of passive and active filter circuits. The active filter circuit evolution especially when operational amplifier is involved as a component is of it first kind in circuit evolution. In the work, only one software package is used instead of combining PSpice and Matlab in electronic circuit simulation. This saves the elapsed time for moving the simulation between the two platforms and reduces the cost of subscription. The evolving circuit from GP using Matlab simulation is automatically transformed into a symbolic Netlist also by Matlab simulation. The Netlist is fed into MSCAM; where MSCAM uses it to generate matrices for the simulation. The matrices enhance frequency response analysis of low-pass, high-pass, band-pass, band-stop of active and passive filter circuits. After the circuit evolution using the developed GP, PSO is then applied to optimise some of the circuits. The algorithm is tested with twelve different circuits (five examples of the active filter, four examples of passive filter circuits and three examples of transistor amplifier circuits) and the results presented have shown that the algorithm is efficient regarding design.Tertiary Education Trust Fund (TETFUND) through University of Calabar, Nigeria

Enhancing the bees algorithm using the traplining metaphor

This work aims to improve the performance of the Bees Algorithm (BA), particularly in terms of simplicity, accuracy, and convergence. Three improvements were made in this study as a result of bees’ traplining behaviour. The first improvement was the parameter reduction of the Bees Algorithm. This strategy recruits and assigns worker bees to exploit and explore all patches. Both searching processes are assigned using the Triangular Distribution Random Number Generator. The most promising patches have more workers and are subject to more exploitation than the less productive patches. This technique reduced the original parameters into two parameters. The results show that the Bi-BA is just as efficient as the basic BA, although it has fewer parameters. Following that, another improvement was proposed to increase the diversification performance of the Combinatorial Bees Algorithm (CBA). The technique employs a novel constructive heuristic that considers the distance and the turning angle of the bees’ flight. When foraging for honey, bees generally avoid making a sharp turn. By including this turning angle as the second consideration, it can control CBA’s initial solution diversity. Third, the CBA is strengthened to enable an intensification strategy that avoids falling into a local optima trap. The approach is based on the behaviour of bees when confronted with threats. They will keep away from re-visiting those flowers during the next bout for reasons like predators, rivals, or honey run out. The approach will remove temporarily threatened flowers from the whole tour, eliminating the sharp turn, and reintroduces them again to the habitual tour’s nearest edge. The technique could effectively achieve an equilibrium between exploration and exploitation mechanisms. The results show that the strategy is very competitive compared to other population-based nature-inspired algorithms. Finally, the enhanced Bees Algorithms are demonstrated on two real-world engineering problems, namely, Printed Circuit Board insertion sequencing and vehicles routing problem

Моделювання, керування та інформаційні технології

Aniksuhyn A., Zhyvolovych O. Generalized solvability and optimal control for an integro-differential equation of a hyperbolic type 8 Babudzhan R., Isaienkov K., Krasii D., Melkonian R., Vodka O., Zadorozhniy I. Collection and processing of bearing vibration data for their technical condition classification by machine learning methods 10 Bardan A., Bihun Y. Computer modeling of differential games . 16 Beridze Z., Shavadze Ju., Imnaishvili G., Geladze M. Concept and functions of building a private network (VPN) 19 Bomba A., Klymiuk Y. Computer prediction of technological modes of rapid cone shaped adsorption filters with automated discharge of part of heat from separation surfaces in filtering model 21 Boyko N., Dypko O. Analysis of machine learning methods using spam filtering 25 Boyko N., Kulchytska O. Analysis of tumor classification algorithms for breast cancer prediction by machine learning methods 29 Denysov S., Semenov V., Vedel Ya. A novel adaptive method for operator inclusions 33 Didmanidze M., Chachanidze G., Didmanidze T. Modern trends in unemployment . 36 Bagrationi I., Zaslavski V., Didmanidze I., Yamkova O. Ethics of information technology in the context of a global worldview . 38 Didmanidze D., Zoidze K., Akhvlediani N., Tsitskishvili G., Samnidze N., Diasamidze M. Use of computer teaching systems in the learning process . 42 Dobrydnyk Yu., Khrystyuk A. Analysis of the elevator as an object of automation 44 Gamzayev R., Shkoda B. Development and investigation of adaptive micro-service architecture for messaging software systems . 46 Gayev Ye. Student' own discoveries in information theory curriculum 50 Didmanidze I., Geladze D., Motskobili Ia, Akhvlediani D., Koridze L. Follow digitally by using a blog . 52 Kirpichnikov A., Khrystyuk A. Automatic apiary care system 54 Kunytskyi S., Ivanchuk N. Mathematical modeling of water purification in a bioplato filter 56 Kyrylych V., Milchenko O. Optimal control of a hyperbolic system that describes Slutsky demand . 58 6 Makaradze N., Nakashidze-Makharadze T., Zaslavski V., Gurgenidze M., Samnidze N., Diasamidze M. Challenges of using computer-based educational technologies in higher education 60 Mamenko P., Zinchenko S., Nosov P., Kyrychenko K., Popovych I., Nahrybelnyi Ya., Kobets V. Research of divergence trajectory with a given risk of ships collisions . 64 Mateichuk V., Zinchenko S., Tovstokoryi O., Nosov P., Nahrybelnyi Ya., Popovych I., Kobets V. Automatic vessel control in stormy conditions 68 Petrivskyi Ya., Petrivskyi V., Bychkov O., Pyzh O. Some features of creating a computer vision system 72 Poliakov V. Calculation of organic substrate decomposition in biofilm and bioreactor-filter taking into account its limitation and inhibition 75 Poliakov V. Mathematical modeling of suspension filtration on a rapid filter at an unregulated rate 78 Prokip V. On the semi-scalar equivalence of polynomial matrices 80 Pysarchuk O., Mironov Y. A proposal of algorithm for automated chromosomal abnormality detection . 83 Rybak O., Tarasenko S. Sperner’s Theorem . 87 Sandrakov G., Hulianytskyi A., Semenov V. Modeling of filtration processes in periodic porous media 90 Stepanets O., Mariiash Yu. Optimal control of the blowing mode parameters during basic oxygen furnace steelmaking process . 94 Stepanchenko O., Shostak L., Kozhushko O., Moshynskyi V., Martyniuk P. Modelling soil organic carbon turnover with assimilation of satellite soil moisture data 97 Vinnychenko D., Nazarova N., Vinnychenko I. The dependence of the deviation of the output stabilized current of the resonant power supply during frequency control in the systems of materials pulse processing 100 Voloshchuk V., Nekrashevych O., Gikalo P. Exergy analysis of a reversible chiller 105 Шарко О., Петрушенко Н., Мосін М., Шарко М., Василенко Н., Белоусов А. Інформаційно-керуючі системи та технології оцінки ступеня підготовленості підприємств до інноваційної діяльності за допомогою ланцюгів Маркова . 107 Барановський С., Бомба А., Прищепа О. Модифікація моделі інфекційного захворювання для урахування дифузійних збурень в умовах логістичної динаміки 110 Бомба А., Бойчура М., Мічута О. Ідентифікація параметрів структури ґрунтових криволінійних масивів числовими методами квазіконформних відображень . 112 Василець К. Метод оцінювання невизначеності вимірювання електроенергії вузлом комерційного обліку 114 Волощук В., Некрашевич О., Гікало П. Доцільність застосування критеріїв ексергетичного аналізу для оцінювання ефективності об'єктів теплоенергетики . 117 Гудь В. Математичне моделювання енергетичної ефективності постійних магнітів в циліндричних магнітних системах . 120 Демидюк М. Параметрична оптимізація циклічних транспортних операцій маніпуляторів з активними і пасивними приводами 122 Клепач М., Клепач М. Вейвлет аналіз температурних трендів днища скловарної печі 125 Козирєв С. Керування високовольтним імпульсним розрядом в екзотермічному середовищі . 127 Очко О., Аврука І. Безпечне збереження конфіденційної інформації на серверах . 131 Реут Д., Древецький В., Матус С. Застосування комп’ютерного зору для автоматичного вимірювання швидкості рідин з тонкодисперсними домішками 133 Сафоник А., Грицюк І. Розроблення інформаційної системи для спектрофотометричного аналізу . 135 Ткачук В. Квантовий генетичний алгоритм та його реалізація на квантовому компютері 137 Цвєткова Т. Комп’ютерна візуалізація гідродинамічного поля в області зкриволінійними межами 140 Шпортько О., Бомба А., Шпортько Л. Пристосування словникових методів компресії до прогресуючого ієрархічного стиснення зображень без втрат . 142 Сафоник А., Таргоній І. Розробка системи керування напруженістю магнітного поля для процесу знезалізнення технологічних вод . 14

2015 Oklahoma Research Day Full Program

This document contains all abstracts from the 2015 Oklahoma Research Day held at Northeastern State University

11th International Conference on Business, Technology and Innovation 2022

Welcome to IC – UBT 2022 UBT Annual International Conference is the 11th international interdisciplinary peer reviewed conference which publishes works of the scientists as well as practitioners in the area where UBT is active in Education, Research and Development. The UBT aims to implement an integrated strategy to establish itself as an internationally competitive, research-intensive university, committed to the transfer of knowledge and the provision of a world-class education to the most talented students from all background. The main perspective of the conference is to connect the scientists and practitioners from different disciplines in the same place and make them be aware of the recent advancements in different research fields, and provide them with a unique forum to share their experiences. It is also the place to support the new academic staff for doing research and publish their work in international standard level. This conference consists of sub conferences in different fields like: Security Studies Sport, Health and Society Psychology Political Science Pharmaceutical and Natural Sciences Mechatronics, System Engineering and Robotics Medicine and Nursing Modern Music, Digital Production and Management Management, Business and Economics Language and Culture Law Journalism, Media and Communication Information Systems and Security Integrated Design Energy Efficiency Engineering Education and Development Dental Sciences Computer Science and Communication Engineering Civil Engineering, Infrastructure and Environment Architecture and Spatial Planning Agriculture, Food Science and Technology Art and Digital Media This conference is the major scientific event of the UBT. It is organizing annually and always in cooperation with the partner universities from the region and Europe. We have to thank all Authors, partners, sponsors and also the conference organizing team making this event a real international scientific event. Edmond Hajrizi, President of UBT UBT – Higher Education Institutio

Social work with airports passengers

Social work at the airport is in to offer to passengers social services. The main methodological position is that people are under stress, which characterized by a particular set of characteristics in appearance and behavior. In such circumstances passenger attracts in his actions some attention. Only person whom he trusts can help him with the documents or psychologically