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Öğe The Determination of the Developments of Beehives via Artificial Neural Networks(Univ Osijek, Tech Fac, 2018) Bayir, Raif; Albayrak, AhmetHoneybees provide great benefits for people both with the foods they produce and as a pollinator. It is known that they pass the whole year with the foods they collect in spring and summer months. Beekeepers also benefit from the honey produced in these periods. Whether a beehive works adequately or not and its status of development can be understood through the observations by beekeepers. In this study, an Arduino-supported neural network model was developed in order to obtain information about the general situations of beehives. The three-input and three-output neural networks were embedded in a board after the training and testing stage. While temperature, humidity, and weight refer to inputs, good situation, stable situation, and bad situation represent outputs. The real-time model has an accuracy of 99.84%.Öğe Development and evaluation of a web-based intelligent decision support system for migratory beekeepers in Turkey to follow nectar resources(Taylor & Francis Ltd, 2021) Albayrak, Ahmet; Duran, Fecir; Bayir, RaifIn honey production, high yields can be achieved with migratory beekeeping. Migratory beekeepers complete the honey production season by moving their colonies to areas with high nectar flow. Traditionally migratory beekeepers decide where to go based on their previous experience. Nowadays, given the rapidly changing climatic conditions, it is seen that the regions to be visited should be decided with more qualified information rather than experience. In this study, a web-based information system was developed which provides qualified information about the regions to be visited by migrant beekeepers. The information system takes into account the nectar flow and climatic conditions in the regions to be visited. These two important factors were evaluated using fuzzy cognitive maps (FCM) and an intelligent decision support system (IDSS) was developed. The cognitive map was analysed statistically in the conceptual modelling stage of FCM and it was found that it explained 82.3% of beekeeping potential according to multiple linear regression. Using IDSS, migratory beekeepers can learn the honey yield (good, average, and bad) they can obtain from the regions they will visit. The information system was also compared with the measurement results made with the wireless sensor network (WSN) and the migratory beekeeper information for 2017. As a result, it was found that IDSS operates with 79.8% accuracy.Öğe Development of a Mecanum-Wheeled Mobile Robot for Dynamic- and Static-Obstacle Avoidance Based on Laser Range Sensor(Korean Inst Intelligent Systems, 2020) Matli, Musa; Albayrak, Ahmet; Bayir, RaifThis study aims to present an idea about the practical consequences of using mobile robots with Mecanum wheels. For mobile robots, an approach is proposed to avoid obstacles without location and map information. This approach is presented using a series of developed solutions. This article shares the process on how a set of discussed conceptual methodologies can be applied as well as their practical results. This method is provided using fuzzy logic and gap tracking. LIDAR is used to recognize obstacles around the mobile robot. By using the LIDAR, the robot detects gaps around it and moves according to fuzzy logic. The fuzzy logic consists of three inputs, an output, and 45 rules. The first of the membership functions represents the membership function that replaces the obstacle. The second membership function calculates the distance to the obstacle. The final login membership function is used to determine the angle between the obstacle and robot view. The output membership function represents the membership function that moves the robot. The results are analyzed under three different scenarios with five different experiments for each scenario. The results show that the mobile robot can avoid obstacles without location and map information. We believe that the proposed method can be used in mobile robots such as guard and service robots.Öğe Development of Information System for Efficient Use of Nectar Resources and Increase Honey Yield per Colony(Univ Putra Malaysia Press, 2019) Albayrak, Ahmet; Bayir, RaifIn this study, for 5.1 million bee colonies and nearly 42 thousand migratory beekeepers in Turkey, an information system is recommended that determines the areas where the honey season will pass taking into account the flowering periods of plants. Migratory beekeepers produce honey by following the flowering periods of nectar sources. Bee colonies should be placed in the optimum number in areas with nectar sources. Less colony settlement has a negative impact on agricultural production. Colony condensation also adversely affects the honey yield of bee colonies per hive. In this study focuses on the optimal number of colonies in the nectar region. In the first stage, 81 provinces in Turkey were analyzed in terms of nectar resources and meteorological conditions which are the major sources of honey production. This evaluation used fuzzy cognitive maps. As a result of the evaluation, 33 provinces were identified as the most suitable provinces in terms of nectar sources and meteorological conditions. In the second phase of the study, a new approach has been proposed for migratory beekeepers to pass the nectar flow season at maximum efficiency and to use nectar resources at maximum level. This approach is based on the placement of bee colonies, considering the potential of the bee farming of the regions and the number of bee colonies subjected to migratory beekeeping. One of the advantages of this approach is that it will maximize honey yield per colony for migratory beekeepers. Another advantage of this system is that the distribution of bee colonies according to the number of plants in the region will be positive in terms of quality and quantity of agricultural production.Öğe Development of intelligent decision support system using fuzzy cognitive maps for migratory beekeepers(Tubitak Scientific & Technological Research Council Turkey, 2018) Albayrak, Ahmet; Duran, Fecir; Bayir, RaifThis study presents the development of an intelligent information system using fuzzy cognitive maps that provides information to migratory beekeepers about the nectar flow and climate conditions in the regions they will visit. Beekeeping is an agricultural activity essentially focused on honey production. High honey yields in beekeeping can be achieved through migratory beekeeping. Migratory beekeepers complete the honey production season by carrying their hives to regions with high nectar flow. Beekeepers decide on the regions they will visit based on their previous experiences. In this study, a software-based system that provides information to the beekeepers about the honey yield in the regions they will visit has been developed. It is an intelligent information system developed using fuzzy cognitive maps that helps the beekeepers in choosing the region they will visit.Öğe Development of intelligent decision support system using fuzzy cognitive maps for migratory beekeepers(2018) Albayrak, Ahmet; Bayır, Raif; Duran, FecirThis study presents the development of an intelligent information system using fuzzy cognitive maps thatprovides information to migratory beekeepers about the nectar flow and climate conditions in the regions they will visit.Beekeeping is an agricultural activity essentially focused on honey production. High honey yields in beekeeping canbe achieved through migratory beekeeping. Migratory beekeepers complete the honey production season by carryingtheir hives to regions with high nectar flow. Beekeepers decide on the regions they will visit based on their previousexperiences. In this study, a software-based system that provides information to the beekeepers about the honey yieldin the regions they will visit has been developed. It is an intelligent information system developed using fuzzy cognitivemaps that helps the beekeepers in choosing the region they will visitÖğe Kablosuz algılayıcı ağlar ile bal arısı kolonilerinin izlenerek arıcılık potansiyelinin tahmini için zeki karar destek sistemi geliştirilmesi(Karabük Üniversitesi, 2018) Albayrak, Ahmet; Bayır, RaifBu çalışmada, ülkemizde önemli bir tarımsal faaliyet olan gezgin arıcılığın modernizasyonu ve teknoloji ile entegre edilmesi üzerine odaklanılmaktadır. Gezgin arıcılar nektar ve polen taşıyan bitkilerin çiçeklenme dönemlerini tecrübeye dayalı ve niteliksel olarak takip etmektedirler. Bu durum çiçeklenme dönemlerinde arılar için nektar ve polen kaynağı haline gelen bitkilerin öznel olarak değerlendirilmesi nedeniyle bal üretimini çoğunlukla olumsuz etkilemektedir. Bu çalışmada öncelikle bitkilerdeki nektar akışının tespiti için ölçeklenebilir, düşük maliyetli ve az enerji harcayan kablosuz algılayıcı ağ (KAA) kurulumu ve nektar akış tespiti yapılmıştır. Deneysel çalışmalar kapsamında, Trabzon ili Dernekpazarı bölgesinde yoğun miktarda nektar ya da polen barındıran bitkilerin çiçeklenme dönemlerine göre nektar akışının değişimi bir ay boyunca gözlenmiştir. Deneysel çalışma iki adet deney iki adet kontrol kovanı olmak üzere dört adet arı kovanı (kolonisi) ile gerçekleştirilmiştir. Gerçekleştirilen nektar akışı ve bal üretimi (arıcılık potansiyeli) tahmini niceliksel ölçme aracı ile uzman paydaş görüşleri değerlendirilerek arıcıların bal üretimini arttırmak için kullanabileceği dinamik bir karar destek sistemi (KDS) geliştirilmiştir. Arı yetiştiricisi internet üzerinden sisteme girip gitmek istediği bölge ile ilgili; ne kadar bal üretebileceğini, bölgenin bal verimi açısından (iyi, orta, kötü) sınıflandırmasını, ne tür bal üreteceğini ve bölge için en uygun yükselti ve zaman dilimlerini öğrenebilmektedir. KDS'nin bu çıktıları bölgenin arıcılık potansiyelini de vermektedir. KDS, bulanık bilişsel haritalar (BBH) kullanılarak geliştirilmiştir. BBH'nın kavramsal modelleme aşamasında bilişsel harita istatistiksel olarak analiz edilmiştir. Doğrulama amacıyla gerçekleştirilen çoklu doğrusal regresyon sonucunda bilişsel haritanın %82,3 oranında problemi (bal üretimi- arıcılık potansiyeli) tanıdığı/açıkladığı görülmüştür. Karar destek sistemi lineer olmayan Hebbian öğrenme algoritması ile entegre edilerek zeki karar destek sistemi (ZKDS) haline getirilmiştir. ZKDS, KAA ile bir ay boyunca nektar akış ölçümü yapılan bölgeden elde edilen bilgiler ile karşılaştırılmıştır. ZKDS, KAA ile test edildiğinde %79,8'lik doğrulukta çalıştığı görülmektedir. Ayrıca ZKDS 2017 yılında üç farklı bölgede arı kolonilerini konaklatmış olan bir gezgin arıcının elde ettiği kovan başına bal üretimi ile de karşılaştırılmıştır. Gezgin arıcı bilgileri ile ZKDS karşılaştırması sonucuna göre, %81,7'lik doğruluk oranı elde edilmiştir.Öğe Modeling of migratory beekeeper behaviors with machine learning approach using meteorological and environmental variables: The case of Turkey(Elsevier, 2021) Albayrak, Ahmet; Ceven, Suleyman; Bayir, RaifIn this study, migratory beekeeping behavior, which is an important form of beekeeping, has been modeled. Modeling was performed in conditions of Turkey. Modeling was made by considering food sources (nectar / pollen) and meteorological variables (temperature, humidity, number of rainy days, number of cloudy days and sunshine duration) for Turkey in which migratory beekeeping carried out in a different form than in developed countries. The main output in migratory beekeeping is honey production. Considering honey production, modeling has been made with the food sources and meteorological variables that have the greatest effect on honey production. Since the data set developed for modeling consists of relatively few samples, the ensemble learning approach was preferred from the machine learning approaches. Random Forest and Decision Tree algorithms, which are among the ensemble learning techniques, were used. As a result, the migratory beekeeping behavior was correctly classified at a rate of 92%. As a result of classification of Turkey's 81 provinces in five different categories, it was concluded that 33 provinces are suitable for migratory beekeeping at different times of the year. These 33 provinces are regions in the good and very good categories. In the next stage, thematic maps were produced for migratory beekeepers. Maps were produced for each month of the year. Thus, a guidance and information system has been obtained for migratory beekeepers.Öğe The monitoring of nectar flow period of honey bees using wireless sensor networks(Sage Publications Inc, 2016) Bayir, Raif; Albayrak, AhmetHoney bees are extremely important creatures to humans that are able to pollinate flowers and produce products such as honey, bee pollen, and royal jelly. Honey is the primary product of beekeeping, and the practice of migratory beekeeping is the most profitable beekeeping method carried out by monitoring the nectar sources. Monitoring these nectar sources provides knowledge about the bloom periods of plant species. Using a wireless sensor network, this study aims to monitor the nectar flow in the region that the migratory beekeepers plan to visit. The wireless sensor network developed was tested in a region with a known nectar flow period from June to July. During the test period, the hive weight and ambient temperature and humidity were constantly measured. The real-time data were available via a website. The nectar flow is determined based on the changes in hive weight and these measurements provide the beekeepers with information about the instantaneous, hourly, and daily nectar flow in the specified region.Öğe Real-time range estimation in electric vehicles using fuzzy logic classifier(Pergamon-Elsevier Science Ltd, 2020) Ceven, Suleyman; Albayrak, Ahmet; Bayir, RaifNowadays, many scientists and companies in the automotive sector in the world are undertaking many important studies on electric vehicle technologies. For the electric vehicle to function as desired, the subsystems of the vehicle must be monitored and the parameters related to the vehicle must be kept in the most efficient range. Efficient use of these systems in electric vehicle will increase the vehicle range, as well as ensure the long life of the components used in the vehicle subsystems. Today, problem areas such as calculating the range of electric vehicles and battery state of charge have not yet been sufficiently standardized. The aim of this study is to make a range estimation in electric vehicle with fuzzy logic classifier which has been successfully applied in various problem areas. The fuzzy logic classifier is designed for range estimation, which is one of the most important research areas of electric vehicles today. In the Mamdani type fuzzy logic approach, dynamic vehicle parameters are taken into consideration. The fuzzy logic classifier considers the battery parameters of the vehicle and the power consumed instantly. In the prediction system, the power spent on the vehicle and the battery charge status are selected as inputs. The developed system was evaluated with three different test scenarios on the same track. These tests were conducted with no load (driver only), half load (driver + one person) and fully load (driver + three persons). The fuzzy logic classifier system determines in real-time how far electric vehicle can travel. (C) 2020 Elsevier Ltd. All rights reserved.Öğe Traffic accident severity prediction with ensemble learning methods(Pergamon-Elsevier Science Ltd, 2024) Ceven, Sueleyman; Albayrak, AhmetIn this study, decision tree-based models are proposed for classification of traffic accident severity. Traffic accident severity is classified into three categories. The data set used in the study belongs to the province of Kayseri, Turkey. The data consists of urban traffic accident reports (23074 accidents) between 2013 and 2021. There are 39 variables in the data set. As a result of data preprocessing, 15 variables that are meaningful and can be used for the model in the data set were determined. Since the input variables of the model mainly contain categorical data, they were coded with pseudo-coding and a total of 93 input variables were obtained. In the studies, ensemble learning methods such as Random Forest, AdaBoost and MLP methods were used. F1 scores of these methods were found to be 91.72%, 91.27% and 88.95%, respectively. Feature importance levels were calculated for 15 variables used in the model. Gini index and decision trees were used while calculating the importance of the features. Driver fault (0.64) was found to have the most effect on traffic accident severity. This study focuses especially on urban traffic accidents. Urban traffic is crowded in terms of both vehicles and pedestrians. As a result of this, according to the findings obtained in this study, traffic accidents occurred mostly at the intersections with crowded urban areas.Öğe Uzman sistem denetimli arı kovanı tasarımı(Karabük Üniversitesi, 2011) Albayrak, Ahmet; Bayır, RaifBu çalışmada, zayıf ve bakıma muhtaç arı kolonilerinin yaşam ortamlarını iyileştirmek ve koloninin güçlenmesini sağlamak amacıyla uzman sistem denetimli arı kovanı tasarlanmış ve üretilmiştir. Bu amaçla tasarlanmış arı kovanı; ısıtıcı çerçeve, otomatik şerbetlik sistemi, ağırlık ölçümü sistemi, sıcaklık ve nem ölçümü sistemi ve güneş panellerinden oluşmaktadır. Sıcaklık algılayıcıları ile ölçülen kovan içi sıcaklığı uzman sistemler ile ideal sıcaklık değerlerine göre ayarlanmaktadır. Bu sayede kışın ve ilkbaharda zayıf arı kolonilerinin soğuk nedeniyle ölümlerinin önüne geçilmesi hedeflenmektedir. Kovan içindeki nem oranı algılanarak kovan üzerine yerleştirilen nem alıcı ile istenilen değere uzman sistemle ayarlanmaktır. Ayrıca yük hücresi ile günlük bal üretimi ve kovan ağırlığı ölçülmektedir. Bu sayede zayıf koloniye gece saatlerinde verilmesi gereken şerbet otomatik olarak verilmektedir. Kovan üzerine yerleştirilmiş olan güneş panelleri ile elektronik sistemin enerjisi sağlanmaktadır. Besleme gerilimi doğru akım (DA) olduğu için arılara herhangi bir zarar vermemektedir.Kovandan ölçülen veriler kablosuz olarak bilgisayara gönderilmekte ve kaydedilmektedir. Uzman sistem yanında PID (Oransal Integral Türev) ve bulanık mantığın (BM) arı kovanlarında kontrol amaçlı kullanılabilirliği test edilmiştir. Uzman sistem denetimli arı kovanı zayıf ve bakıma muhtaç bal arıları için bir arı kuvözü niteliği taşımaktadır. Bu sistemin sayesinde zayıf koloniler yeniden kazanılmaktadır. Gezici arıcılık yapan üreticilerin taşındıkları bölgenin bal verimini ve çevre şartlarını rahatlıkla izleyip arılar için uygun olup olmadığını bu sistemle rahatlıkla öğrenebilmesi amaçlanmıştır.
