Browsing by Author "Akbaş, Ayhan"

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A Comparative Analysis of Passenger Flow Forecasting in Trams Using Machine Learning Algorithms
(2024) Akbaş, Ayhan; Dedeturk, Beyhan Adanur; Dedeturk, Bilge Kagan; 01. Abdullah Gül University; 02. 04. Bilgisayar Mühendisliği; 02. Mühendislik Fakültesi
Forecasting tram passenger flow is an important part of the intelligent transportation system since it helps with resource allocation, network design, and frequency setting. Due to varying destinations and departure times, it is difficult to notice large fluctuations, non-linearity, and periodicity of tram passenger flows. In this paper, the first-order difference technique is used to eliminate seasonal structure from the time series data and the performance of different machine learning algorithms on passenger flow forecasting in trams is evaluated. Furthermore, the impact of the Covid-19 pandemic on forecasting success is examined. For this purpose, the tram data of Kayseri Transportation Inc. for the years 2018-2021 are used. Different estimation models including Linear Regression, Support Vector Regression, Random Forest, Artificial Neural Network, Convolutional Neural Network, and LongTerm Short Memory are applied and the time series forecasting performances of the models are evaluated with MAPE and R2 metrics.
Comparative Performance Analysis of Arima, Prophet and Holt-Winters Forecasting Methods on European Covid-19 Data
(2022) Bakir-gungor, Burcu; Ersöz, Nur Şebnem; Şahan, Pınar Güner; Akbaş, Ayhan; 01. Abdullah Gül University; 02. 04. Bilgisayar Mühendisliği; 02. Mühendislik Fakültesi
COVID-19 son yılların en bulaşıcı hastalığıdır ve dünyanın her yerinde salgına neden olmuştur. Daha önce yüzlerce olan ölüm oranı önce binlere, sonra milyonlara yükselmiştir. Ocak 2020'den beri birçok bilim insanı, hükümetlerin hastanelerde yeterli düzenlemeleri yapabilmesi ve ölüm oranını azaltılabilmesi için COVID-19’un yayılımını anlamaya ve tahminlemeye çalışıyor. Bu araştırma makalesi, Avrupa’daki COVID-19 hastalık epidemiyolojisi için tahminler yapmak amacıyla, ARIMA, Prophet ve Holt Winters Üstel Düzeltme yöntemlerinin performans karşılaştırmasını sunmaktadır. Veri seti olarak, Dünya Sağlık Örgütü (DSÖ)'nün toplayıp kategorize ettiği, Avrupa ülkelerinin 2020 ile 2022 yılları arasındaki COVID-19 vaka verileri kullanılmıştır. Elde edilen sonuçlar, Holt-Winters Üstel Düzeltme (RMSE: 0.20, MAE: 0.17) yönteminin, ARIMA ve Prophet tahmin yöntemlerinden daha iyi performans gösterdiğini belirtmektedir.
Document Classification With Contextually Enriched Word Embeddings
(2024) Akbaş, Ayhan; Mahmood, Raad; Bakal, Mehmet; 01. Abdullah Gül University; 02. 04. Bilgisayar Mühendisliği; 02. Mühendislik Fakültesi
The text classification task has a wide range of application domains for distinct purposes, such as the classification of articles, social media posts, and sentiments. As a natural language processing application, machine learning and deep learning techniques are intensively utilized in solving such challenges. One common approach is employing the discriminative word features comprising Bag-of-Words and n-grams to conduct text classification experiments. The other powerful approach is exploiting neural network-based (specifically deep learning models) through either sentence, word, or character levels. In this study, we proposed a novel approach to classify documents with contextually enriched word embeddings powered by the neighbor words accessible through the trigram word series. In the experiments, a well-known web of science dataset is exploited to demonstrate the novelty of the models. Consequently, we built various models constructed with and without the proposed approach to monitor the models' performances. The experimental models showed that the proposed neighborhood-based word embedding enrichment has decent potential to use in further studies.
Machine Learning Based Early Prediction of Type 2 Diabetes: A New Hybrid Feature Selection Approach Using Correlation Matrix With Heatmap and SFS
(2022) Buyrukoglu, Selim; Akbaş, Ayhan; 01. Abdullah Gül University
A new hybrid machine learning method for the prediction of type 2 diabetes is introduced and explained in detail. Also, outcomes are compared with similar researches. Early prediction of diabetes is crucial to take necessary measures (i.e. changing eating habits, patient weight control etc.), to defer the emergence of diabetes and to reduce the death rate to some extent and ease medical care professionals’ decision-making in preventing and managing diabetes mellitus. The purpose of this study is the creation of a new hybrid feature selection approach combination of Correlation Matrix with Heatmap and Sequential forward selection (SFS) to reveal the most effective features in the detection of diabetes. A diabetes data set with 520 instances and seven features were studied with the application of the proposed hybrid feature selection approach. The evaluation of the selected optimal features was measured by applying Support Vector Machines(SVM), Random Forest(RF), and Artificial Neural Networks(ANN) classifiers. Five evaluation metrics, namely, Accuracy, F-measure, Precision, Recall, and AUC showed the best performance with ANN (99.1%), F-measure (99.1%), Precision (99.3%), Recall (99.1%), and AUC (99.2%). Our proposed hybrid feature selection model provided a more promising performance with ANN compared to other machine learning algorithms.