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İki amaçlı tesis yerleşimi problemi için bir yinelemeli yerel arama algoritması
(2021) Avcı, Mustafa
Tesis yerleşimi, modern üretim sistemlerinde karşılaşılan en önemli sorunlardan biridir. Bu çalışmada hem nicel hem de nitel hedeflerin birleştirildiği iki amaçlı bir tesis yerleşimiproblemi(İA-TYP) ele alınmaktadır. Burada nicel amaç toplam malzeme taşıma maliyetinin en aza indirgenmesidir. Nitel amacımız ise toplam yakınlık derecelendirme puanlarının maksimize edilmesidir. Problemi çözmek için Değişken Komşuluklu İniş (DKİ) algoritmasının bir saptırma mekanizması ile birleştirildiği bir Yinelemeli Yerel Arama (YYA) algoritması önerilmiştir. Önerilen algoritmanın performansı önceki çalışmalarda sunulan çözüm algoritmalarının elde ettikleri çözümler baz alınarakdeğerlendirilmiştir. Hesaplama sonuçları, önerilen algoritmanın İA-TYP örneklerine yüksek kaliteli çözümler üretebildiğini göstermektedir.
A comparative analysis of constraint-handling mechanisms for solving engineering design problems
(2021) Gölcük, İlker
Optimization problems have numerous real-life applications in science and engineering. The engineering design problems are usually subject to various constraints. Although many state-of-the-art metaheuristic optimization algorithms have been developed during the last decades, these algorithms require additional constraint-handling mechanisms to cope with constrained optimization problems. Therefore, selecting a suitable constraint-handling mechanism requires extensive trial-and-error experiments, which is time-consuming and demanding. In this study, a comparative analysis of the eight constraint handling mechanisms is carried out, guiding decision-makers in their optimization practices. The constraint-handling techniques are used along with the Whale Optimization Algorithm (WOA), and 19 real-life mechanical design problems, which are also part of the CEC2020 benchmark suite, are tested in the experimental analysis. The nonparametric statistical analysis incorporating Nemenyi and Holm post-hoc procedures shows that the inverse tangent constraint-handling and eclectic penalty methods exhibit high performance in real-life mechanical design problems.
Categorization of customer complaints in food industry using machine learning approaches
(2022) Kılınç, Deniz; Doğan, Onur; Bozyiğit, Fatma
Customer feedback is one of the most critical parameters that determine the market dynamics of product development. In this direction, analyzing product-related complaints helps sellers to identify the quality characteristics and consumer focus. There have been many studies conducted on the design of Machine Learning (ML) systems to address the causes of customer dissatisfaction. However, most of the research has been particularly performed on English. This paper contributes to developing an accurate categorization of customer complaints about package food products, written in Turkish. Accordingly, various ML algorithms using TF-IDF and word2vec feature representation strategies were performed to determine the category of complaints. Corresponding results of Linear Regression (LR), Naive Bayes (NB), k Nearest Neighbour (kNN), Support Vector Machine (SVM), Random Forest (RF), and Extreme Gradient Boosting (XGBoost) classifiers were provided in related sections. Experimental results show that the best-performing method is XGBoost with TF-IDF weighting scheme and it achieves %86 F-measure score. The other considerable point is word2vec based ML classifiers show poor performance in terms of F-measure compared to the TF-IDF term weighting scheme. It is also observed that each experimented TF-IDF based ML algorithm gives a more successful prediction performance on the optimal subsets of features selected by the Chi Square (CH2) method. Performing CH2 on TF-IDF features increases the F-measure score from 86% to 88% in XGBoost.
Process mining methodology for digital processes under smart campus concept
(2022) Cengiz, Esra; Doğan, Onur
Digital transformation affects universities as well as many industries. Universities are increasingly using various digital resources and systems to manage their knowledge. The smart campus, on the other hand, supports informed decision-making by integrating these resources and systems. Process mining provides real insights for digital transformation, allowing processes to be examined more transparently. This study aims to examine the proposed project implementation processes related to the smart university with the process mining methodology. For this purpose, 32 completed projects submitted to İzmir Bakırçay University Scientific Research Projects Coordinatorship (BAPK) with the proposed methodology adapted from Deming's continuous improvement cycle were examined. The data are taken from two different pages in the project automation system. According to the research findings, Projects are grouped into three categories: Guided Projects (GDM, 5 projects), Graduate Thesis Projects (TEZ, 5 projects), and Career Start Support Projects (KBP, 22 projects). 40.6% (13 projects) of the applications went directly to the project review stage, while 19 (59.4%) needed procedural correction. Considering the time from the creation of the application of 32 projects to the signing of the contract, it is seen that the arithmetic average of the cycle time is 15.1 weeks, and the median average is 52.5 days. The notable difference between arithmetic and median mean is that very few projects are of long duration. Procedural adjustments affect project evaluation cycle time by an additional 14 days. The carelessness or lack of knowledge of the applicants extends the cycle time of the process from 15 days to 53 days. The total duration of unnecessary waiting time in the process is 17 days. This study primarily proposes that non-digital processes should be digitized as soon as possible.
Müşteri memnuniyetinin süreç odaklı değerlendirilmesi: Bir çağrı merkezinde süreç madenciliği uygulaması
(2021) Doğan, Onur; Ayyar, Başak; Çağıl, Gültekin
Firmaların varlıklarını sürdürebilmeleri için müşteri odaklı bir yaklaşım sergilemeleri büyük avantaj sağlamaktadır. Müşteriler artık kendisine sunulanı değil, beklentilerini karşılayan ürün veya hizmeti satın almaktadır. Bu sebepten dolayı firmalar müşterileri dinlemek, onların ihtiyaç ve beklentilerini karşılamak amacıyla çeşitli stratejiler izlemektedirler. Bu stratejik birimlerden birisi de firma ile müşteri arasında iletişimi sağlayan çağrı merkezleridir. Firmalar çağrı merkezleri ile müşterilerin sorunlarını çözerek memnuniyet düzeyini arttırmaya çalışırlar. Bununla birlikte, müşteri memnuniyetinin yönetilebilir, ölçülebilir ve karşılaştırılabilir olması için memnuniyet anketi uygulamaktadırlar. Bu çalışmanın amacı, müşteri memnuniyet anketlerinin de göz önüne alındığı süreç madenciliği yardımıyla müşteri memnuniyetinin değerlendirilmesidir. Firmanın IT sisteminde tutulan çağrı merkezi bölümüne ait 11 günlük (18 – 28 Mart 2019) tarihleri arasındaki 42185 olay günlüğü (event log) süreç madenciliği için kullanılmıştır. Öncelikle memnuniyet anketine katılan ve katılmayan müşterilerin sistem içindeki akışı çıkarılmıştır. Ardından ankete katılan müşteriler arasında 5 (çok memnunum) puan verenlerin ve diğer müşterilerin süreçleri incelenmiştir. Çalışmada ulaşılan sonuçlardan biri, çağrı sırasında bekletilen müşterilerin daha yüksek puan vermesidir. Bu sonuç müşterilerin bekletilmekten ziyade ihtiyacının karşılanmasına daha çok önem verdiğini göstermektedir. Bu açıdan çalışma, firmanın süreç odaklı yönetimine, süreç iyileştirme çalışmalarına, strateji çalışmalarına yol gösterebilecek özelliktedir.
Enterprise resource planning selection using fuzzy entropy-based fuzzy MOORA method: case study in a bearing company
(2022) Durmuş, Beyzanur; Akpınar, Muhammet Enes
Seçim problemleri işletmeler açısından sıklıkla karşılaşılan ve karar vermesi zor olan problem tiplerindendir. Zor problem olmasının sebebi birçok kriter ve alternatifin aynı anda dikkate alınması gerektiği içindir. Bu problemlerin çözümü için genellikle çok kriterli karar verme yaklaşımları kullanılmaktadır. Seçim problemleri hayatın her aşamasında karşılaştığı için çok fazla çeşitlilik gösterebilmektedir. Bu çal ışmada bir işletmenin kurumsal kaynak planlaması (KKP) seçim süreci ele al ınmıştır. Yeni bir yazılım satın almak isteyen i şletmenin satın alma departmanı birçok kriter ve alternatif yazılım belirlemiştir. Bu kriterlerin en uygun düzeyde karşılandığı alternatif yazılımın seçilmesi planlanmıştır. Bu problemin çözümü için kriter ağırlıkların belirlenmesi aşamasında bulanık Entropi yöntemi kullanılmıştır. Yazılım alternatiflerinin değerlendirilmesi sürecinde bulanık Oran Analiziyle Çok Amaçlı Optimizasyon (MOORA) yöntemi kullanılmış ve yazılımlardan en uygun olanına karar verilmiştir. Çalışma sonucunda belirlenen üç yazılım sisteminden en uygun olanın üçüncü yazılım sistemi olduğu görülmüştür.
The effect of brand-relationship quality on positive word-of-mouth intention
(IEOM Society, 2021) Akın Mahmut Selami; Doğan Onur
Measurement and management of consumer relationships with brands are critical issue that marketers have focused on for years. The quality of the relationship between the consumer and brand affects behavioral intention in favor of the brand. This research aims to reveal the effect of consumer-brand relationship quality (BRQ) on positive word-of-mouth (WoM) intention. The BRQ components were applied on the sample of private university students in Istanbul, Turkey. The questionnaire form with 5-point Likert scale was distributed and 199 valid surveys were obtained. Cronbach's Alfa was examined to internal consistency of the scales, and exploratory factor analysis was conducted to assess constructive validity. Multiple regression analysis resulted in love/passion, commitment, and self-connection, which are the dimensions of BRQ, have a positive WoM intention. On the other hand, the intimacy factor has no significant effect. © IEOM Society International.
Applications of machine learning approaches to combat COVID-19: A survey
(Elsevier, 2022) Tiwari Sanju Mishra; Doğan Onur; Jabbar M. Akhil; Shandilya Shishir K; Ortiz-Rodríguez Fernando; Bajpai Sailesh; Banerjee Sourav
Machine learning (ML) and artificial intelligence (AI) approaches are prominent and well established in the field of health-care informatics. Because they have a more productive ability to predict, they are successfully applied in several health-care applications. ML approaches are needed thanks to the unsatisfactory experience of the novel virus, considerable ambiguity, complicated social circumstances, and inadequate accessible data. Several approaches have been applied as a tool to combat and protect against the new diseases. The COVID-19 outbreak has rapid growth, so it is not easy to predict the patients and resources within a specified time. ML is a strong approach in the fighting against the pandemic such as COVID-19. It is found significant to predict the susceptible, infected, recovered, or exposed persons and can assist the control strategies to block the spread of infections. This study critically examines the appropriateness and contribution of AI/ML methods on COVID-19 datasets, enhancing the understanding to apply these methods for quick analysis and verification of pandemic databases. © 2022 Elsevier Inc. All rights reserved.
Machine learning applications for Fraud Detection in finance sector
(Springer, 2022) Taşer, Pelin Yıldırım; Bozyiğit, Fatma
Due to advances in information technology, instantaneous accessibility to financial services through digital channels has increased. Although digital platforms’ usage makes an individual’s life more comfortable, it may also cause some critical consequences like financial fraud which causes critical losses for companies in the industrial sector, investors, and governments. Identification of frauds can be challenging task for a human because it may be necessary to analyse high volume data during long time periods. An alternative is to use financial data as a fraud detection tool to automatically classify fraudulent activities. Currently, there are many practical solutions for automatically detect frauds in the finance domain. In this chapter, we examined on three different fraud types (bank fraud, insurance fraud, and corporate fraud) in finance sector and reviewed the studies using machine learning methods to detect financial fraud in a detailed manner. The findings from this review show that most commonly applied algorithms for financial fraud detection are Decision Tree, Support Vector Machine (SVM), Artificial Neural Networks (ANN), and Random Forest and most of machine learning-based studies were performed in bank fraud field. This chapter also reveals that deep learning and ensemble-based machine learning applications has been frequently preferred in recent years to improve detection performance of the frauds in finance sector. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
Prioritizing occupational safety risks with fuzzy FUCOM and fuzzy graph theory-matrix approach
(Gazi Univ, Fac Engineering Architecture, 2023) Gölcük, İlker; Durmaz, Esra Duygu; Şahin, Ramazan
In this study, a new failure mode and effects analysis (FMEA) model is proposed for evaluating occupational safety risks. In the classical FMEA, risk priority numbers (RPNs) are calculated by multiplying the risk scores of the occurrence, severity, and detectability. However, RPN numbers generated by classical FMEA have been the subject of severe criticism in the literature. To overcome the drawbacks of the classical FMEA, this study proposes a new Multiple Attribute Decision Making (MADM) model. The proposed risk evaluation model combines the full consistency method (FUCOM) and graph theory-matrix approach (GTMA) under a fuzzy environment. The risk scores of failure models and the weights of risk factors have been obtained using the fuzzy FUCOM method. On the other hand, the RPN value of each failure mode is calculated by utilizing fuzzy GTMA. Fuzzy GTMA considers all possible dependencies among risk factors, which in turn produces more accurate rankings. The fuzzy judgements of the decision makers are aggregated by using the least squares distance method. The proposed model is implemented in a real-life case study and the failure modes are ranked.
Multi-liquid repellent, fluorine-free, heat stable SLIPS via layer-by-layer assembly
(Elsevier, 2022) Kasapgil, Esra; Erbil, H. Yıldırım; Şakir, İlke Anaç
Slippery liquid-infused porous surfaces (SLIPS) are an emerging class of bio-inspired materials attracting re-searchers due to their good anti-fouling, anti-icing, and self-cleaning performance. The fluorinated lubricants which are used in the preparation of multi-liquid repellent SLIPS are not suitable for practical applications because of the lubricant losses by evaporation and water drop cloaking and also they cause environmental and health problems. In this study, layer-by-layer (LbL) assembly technique via a capping approach is used to obtain PDMS-based SLIPSs. Amino terminated PDMS was used as a homofunctional polymer layer and three different co-polymers of anhydride [poly(ethylene-alt-maleic anhydride) PEMA, poly(isobutylene-alt-maleic anhydride) PIMA and poly(maleic anhydride-alt-1-octadecene) PMAO] were used as the capping layers to give LbL layers having advancing contact angles between 115 and 120? with the change of the type of anhydride copolymer used and the constituent concentration. Silicone oil was used as the lubricant giving a fluorine-free LbL-SLIPS having a contact angle hysteresis as low as 5-6 repelling water and various other liquids such as glycerol, ketchup and soy sauce by preserving the low contact angle hysteresis for long durations. Moreover, LbL-SLIPS samples also had good heat resistance (to 80 ?C for 8 days) and chemical stability against acids and bases and were durable under the flow of continuous water droplets for a period of 4 h without any damage to SLIPS properties. The best performing LbL-SLIPS sample was prepared using the PEMA capping layer where the decrease in silicone oil thickness and in-crease in contact angle hystresis were found to be lowest under continuous water droplet flow after 4 h.
Interval type-2 fuzzy development of FUCOM and activity relationship charts along with MARCOS for facilities layout evaluation
(Elsevier, 2022) Gölcük, İlker; Durmaz, Esra Duygu; Şahin, Ramazan
This paper presents a new interval type-2 fuzzy (IT2F) multiple attribute decision making (MADM) model for evaluating facility layout alternatives. To this end, The Full Consistency Method (FUCOM) is extended to IT2F sets, and the mathematical model of IT2F-FUCOM is proposed. IT2F-FUCOM method requires a manageable number of pairwise comparisons and can deal with imprecision and uncertainty. Furthermore, the Activity Relationship Charts (ARCs), which are powerful instruments to articulate preferences regarding closeness between departments in the facility design, are also modeled through IT2F sets. The proposed IT2F-ARCs are incorporated into the decision hierarchy as a cost-type criterion, a novel integration between ARCs and the MADM is provided. Finally, the state-of-the-art IT2F MADM method of Measurement Alternatives and Ranking according to the Compromise Solution (MARCOS) is utilized to rank the alternatives. A real-life case study is conducted in order to demonstrate the applicability of the proposed model. (C) 2022 Elsevier B.V. All rights reserved.
Impact of epidemic outbreaks (COVID-19) on global supply chains: A case of trade between Turkey and China
(Elsevier Science Inc, 2023) Kazançogğu, Yiğit; Ekinci, Esra; Mangla, Sachin Kumar; Sezer, Mürüvvet Deniz; Özbiltekin-Pala, Melisa
COVID-19 has negative impacts on supply chain operations between countries. The novelty of the study is to evaluate the sectoral effects of COVID-19 on global supply chains in the example of Turkey and China, considering detailed parameters, thanks to the developed System Dynamics (SD) model. During COVID-19 spread, most of the countries decided long period of lockdowns which impacted the production and supply chains. This had also caused decrease in capacity utilizations and industrial productions in many countries which resulted with imbalance of maritime trade between countries that increased the freight costs. In this study, cause and effect relations of trade parameters, supply chain parameters, demographic data and logistics data on disruptions of global supply chains have been depicted for specifically Turkey and China since China is the biggest importer of Turkey. Due to this disruption, mainly exports from Turkey to China has been impacted in food, chemical and mining sectors. This study is helpful to plan in which sectors; the actions should be taken by the government bodies or managers. Based on findings of this study, new policies such as onshore activities should consider to overcome the logistics and supply chain disruptions in global supply chains. This study has been presented beneficial implications for the government, policymakers and academia.
Fuzzy association rule mining approach to identify e-commerce product association considering sales amount
(Springer Heidelberg, 2022) Doğan, Onur; Kem, Furkan Can; Öztayşi, Başar
Online stores assist customers in buying the desired products online. Great competition in the e-commerce sector necessitates technology development. Many e-commerce systems not only present products but also offer similar products to increase online customer interest. Due to high product variety, analyzing products sold together similar to a recommendation system is a must. This study methodologically improves the traditional association rule mining (ARM) method by adding fuzzy set theory. Besides, it extends the ARM by considering not only items sold but also sales amounts. Fuzzy association rule mining (FARM) with the Apriori algorithm can catch the customers' choice from historical transaction data. It discovers fuzzy association rules from an e-commerce company to display similar products to customers according to their needs in amount. The experimental result shows that the proposed FARM approach produces much information about e-commerce sales for decision-makers. Furthermore, the FARM method eliminates some traditional rules considering their sales amount and can produce some rules different from ARM.
An improved arithmetic optimization algorithm for training feedforward neural networks under dynamic environments
(Elsevier, 2023) Gölcük, İlker; Özsoydan, Fehmi Burçin; Durmaz, Esra Duygu
This paper proposes an improved Arithmetic Optimization Algorithm (AOA) to train artificial neural networks (ANNs) under dynamic environments. Despite many successful applications of metaheuristic training of ANNs, these studies assume static environments, which might not be realistic in real-world nonstationary processes. In this study, the training of ANNs is modeled as a dynamic optimization problem, and the proposed AOA is used to optimize connection weights and biases of the ANN under the presence of concept drift. The proposed method is designed to work for classification tasks. The performance of the proposed algorithm has been tested on twelve dynamic classification problems. Comparative analysis with state-of-the-art metaheuristic optimization algorithms has been provided. The superiority of the compared algorithms has been verified using nonparametric statistical tests. The results show that the improved AOA outperforms compared algorithms in training ANNs under dynamic environments. The findings demonstrate the potential of improved AOA for dynamic data-driven applications.(c) 2023 Elsevier B.V. All rights reserved.
A hyper-heuristic based reinforcement-learning algorithm to train feedforward neural networks
(Elsevier - Division Reed Elsevier India Pvt Ltd, 2022) Özsoydan, Fehmi Burçin; Gölcük, İlker
Artificial Neural Networks (ANNs) offer unique opportunities in numerous research fields. Due to their remarkable generalization capabilities, they have grabbed attention in solving challenging problems such as classification, function approximation, pattern recognition and image processing that can be quite complex to model mathematically in practice. One of the most vital issues regarding the ANNs is the training process. The aim at this stage is to find the optimum values of ANN parameters such as weights and biases, which indeed embed the whole information of the network. Traditional gradient-descent -based training methods include various algorithms, of which the backpropagation is one of the best-known. Such methods have been shown to exhibit outstanding results, however, they are known have two major theoretical and computational limitations, which are slow convergence speed and possible local minima issues. For this purpose, numerous stochastic search algorithms and heuristic methods have been individually used to train ANNs. However, methods, bringing diverse features of different optimiz-ers together are still lacking in the related literature. In this regard, this paper aims to develop a training algorithm operating based on a hyper-heuristic (HH) framework, which indeed resembles reinforcement learning-based machine learning algorithm. The proposed method is used to train Feed-forward Neural Networks, which are specific forms of ANNs. The proposed HH employs individual metaheuristic algo-rithms such as Particle Swarm Optimization (PSO), Differential Evolution (DE) Algorithm and Flower Pollination Algorithm (FPA) as low-level heuristics. Based on a feedback mechanism, the proposed HH learns throughout epochs and encourages or discourages the related metaheuristic. Thus, due its stochas-tic nature, HH attempts to avoid local minima, while utilizing promising regions in search space more conveniently by increasing the probability of invoking relatively more promising heuristics during train-ing. The proposed method is tested in both function approximation and classification problems, which have been adopted from UCI machine learning repository and existing literature. According to the com-prehensive experimental study and statistically verified results, which point out significant improve-ments, the developed HH based training algorithm can achieve significantly superior results to some of the compared optimizers.(c) 2022 Karabuk University. Publishing services by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).
A hybridisation of linear programming and genetic algorithm to solve the capacitated facility location problem
(Taylor & Francis Ltd, 2022) Özsoydan, Fehmi Burçin; Gölcük, İlker
This paper introduces a cooperative approach of a swarm intelligence algorithm and a linear programming solver to solve the capacitated facility location problem (CFLP). Given a set of potential locations to open facilities, the aim in CFLP is to find the minimum cost, which is the sum of facility opening costs and transportation costs. The developed solution strategy decomposes CFLP into two sub-problems. The former sub-problem has a binary domain. Although most of the swarm intelligence algorithms employ additional procedures such as sigmoid function to deal with binary domains, the proposed algorithm does not require for such methods. An adaptive mutation operator enhances this algorithm. The aim of the latter sub-problem is to generate a policy that optimally assigns customers to the opened facilities. In this regard, the generated binary vectors by the proposed algorithm are passed to a solver to optimise the generated linear model. Commonly used instances available in the literature are solved by the proposed strategy. Comprehensive experimental study includes comparisons with the sate-of-the-art. According to the statistically verified results, the proposed strategy is found as promising in solving CFLP.
Shuttle bus service routing: A systematic literature review
(Pamukkale Univ, 2022) Peker, Gaye; Tursel Eliiyi, Deniz
This paper aims to provide a comprehensive literature mapping for shuttle bus routing in different functional application areas. For this purpose, articles which were published in the last 20 years were systematically reviewed. Selected papers related to the topic were classified by their publication year, type of publication, solution approach, application area, usage of mathematical model and objective function. The existing articles were reviewed based on their functional application areas of employee/personnel, patient/hospital, students, elderly/disabled, and airport shuttles. The results of our analysis indicate that there has been an increasing movement in the usage of shuttle bus routing models through the recent years. This study therefore identifies this increasing attention to the topic as well as recent trends, and highlights research opportunities and literature gaps for future research.
Rethinking customer analytics: The impact of artificial intelligence
(Springer International Publishing Ag, 2022) Pişirgen, Ali; Hızıroğlu, Abdulkadir; Doğan, Onur
With the triggering effect of Covid-19 pandemic, the role of digitalization has become a strategic target and expedited the digital transformation process. World's direction to the digital future has therefore shaped the use of new-age technologies, such as internet of things, artificial intelligence (AI), machine learning and blockchain. In response to this evolvement of new-age technologies, a noticeable shift from data-driven analysis to technology-oriented applications has occurred, particularly addressing the significance of analytics and AI. These rapid advancements of AI applications influence the use of customer analytics whilst enhancing the importance both for the general understanding and individual behavior of customers, within the scope of customer analytics. Considering the embeddedness of these technologies on practical applications, this study acknowledges the high-impact role and power of AI. In this regard, the study concentrates AI applications from the perspectives of customer analytics. Furthermore, the task of AI, the level of intelligence of AI applications and how the information from customer analytics is obtained and exploited by these applications are discussed. Focusing on the practical case applications, the study suggests a taxonomical structure of AI and customer analytics.
Understanding patient activity patterns in smart homes with process mining
(Springer International Publishing Ag, 2022) Doğan, Onur; Akkol, Ekin; Oluçoğlu, Müge
Especially in people over 50 years of age, sedentary lifestyle can cause muscle loss called sarcopenia. Inactivity causes undesirable outcomes such as excessive weight gain and muscle loss. Weight gain can lead to a variety of problems, including deteriorating of the musculoskeletal system, joint problems, and sleep problems. In order to provide better service, it can be beneficial to understand human behavior in terms of health services. Process mining, which can be considered a part of knowledge graphs, is a crucial methodology for process improvement since it offers a model of the process that can be analyzed and optimized. This study uses process mining approaches to examine data from three patient that were collected using indoor location sensors, allowing the collection of flows of human behavior in the home. The analyses indicated how much time was spent by the patients of the house in each room during the day as well as how frequently they occurred. The movement of patients from room to room was observed daily and subjected to a variety of analyses. With the help of user pathways, lengths of stay in the rooms, and frequency of presence, it has been possible to reveal the details of daily human behavior. Inferences about the habits of the participants were revealed day by day.

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