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Yayın Analysis and Modeling of Cyber Security Precautions(Ieee, 2021) Durmuş, Omer; Varol, AsafTaking security measures against cyber-attacks has become a necessity for the whole world. Determining the most appropriate measures and their effective implementation form the basis of cyber security for all key institutions. This study, aims to demonstrate the appropriate analysis of cyber-attacks and analysis output models that facilitate fast and effective interventions. An understandable scenario was created by applying statistical calculation methods to the analysis output models and thanks to this work a new model was provided for an effective and significant cyber security intervention. Studies have been carried out on data sets belonging to 2020 and covering many cyber-attacks, having a sampling space for network, software and different hardware attacks, and the structured output of the data analysis has been modeled with SPSS.Yayın ANN-polynomial-Fourier series modeling and Monte Carlo forecasting of tourism data(Wiley, 2022) Danbatta, Salim Jibrin; Varol, AsafModeling and forecasting of tourism data have received attention in the past decades. Turkey is one of the countries that benefit significantly from the tourism industry. Several time-series models have been recommended to best describe tourist arrivals to Turkey. However, in the 21st century, the world experiences great uncertainty in most possible event outcomes. These uncertainties are very difficult to account for. We proposed a hybrid artificial neural network (ANN)-polynomial-Fourier method to model the number of foreign visitors to Turkey from January 2004 to December 2020. The proposed model performance before and during the COVID-19 pandemic is evaluated separately. We evaluate the model performance by comparing with results from Danbatta and Varol (2021, ), Fourier series, and ARIMA models. To account for prediction uncertainties, we ran 300 Monte Carlo simulations within +/- 2 sigma from the model regression curve. According to the result outcomes, the proposed ANN-polynomial-Fourier has proven worthy to be considered a candidate model for the Turkish tourism data. The multistep ahead forecast suggests a 10.22% increase in the monthly foreign visitors' arrivals to Turkey in the year 2021.Yayın Blockchain-based IoT: An Overview(Ieee, 2021) Raza, Muhammad Raheel; Varol, Asaf; Hussain, WalayatThe Internet of Things (IoT) has revolutionized the human world by transforming ordinary everyday objects into smart devices. These autonomous devices have reshaped our lives. The emerging technology is expanding day-by-day with the increasing need for smart devices as so the issues are also increasing w.r.t security, data reliability, maintenance and authentication. On the other hand, another innovative technology- Blockchain- has transformed our financial world by introducing sophisticated security. An integrated Blockchain-IoT system can resolve the problems they face individually and serve the technological world better. The paper provides a comprehensive study of both technologies by highlighting their features and challenges. The article further critically analyses existing approaches that discussed various issue about IoT and Blockchain.Yayın Comparing interservice communications of microservices for e-commerce industry(2022) Gördesli, Mustafa; Varol, AsafMost of the e-commerce companies are using microservices rather than monolithic architecture. Using microservices gives the ability of scalability and better fault tolerance. On the other hand, using microservices has some challenges, one of them is that communication between microservices must be efficient and robust. In this work, we focused on this challenge, analyzed the performance of communication techniques, and identified their advantages and disadvantages. We compared synchronous REST architecture and asynchronous event driven architecture.Yayın Comparing Interservice Communications of Microservices for E-Commerce Industry(Ieee, 2022) Gordesli, Mustafa; Varol, AsafMost of the e-commerce companies are using microservices rather than monolithic architecture. Using microservices gives the ability of scalability and better fault tolerance. On the other hand, using microservices has some challenges, one of them is that communication between microservices must be efficient and robust. In this work, we focused on this challenge, analyzed the performance of communication techniques, and identified their advantages and disadvantages. We compared synchronous REST architecture and asynchronous event driven architecture.Yayın Detection of network anomalies with machine learning methods(2022) Kara, İhsan Rıza; Varol, AsafThe present study, aimed to detect cyber-attacks, and unexpected access requests on devices in the telecommunication networks, enabling the necessary measures to be taken early. With K-Nearest Neighbors (KNN) and Naive Bayes machine learning methods, predicted whether the raw data packets contain cyber-attack according to different properties of these packets using the UNSW-NB15 dataset. KNN algorithms with different K values and the Naive Bayes method were compared according to accuracy rates and the results were given in the table. As a result, changes in accuracy rates were observed according to different k neighbor values in the KNN algorithm. Higher accuracy rates than Naive Bayes were achieved in the models created with the KNN algorithm.Yayın Digital Currency Price Analysis Via Deep Forecasting Approaches for Business Risk Mitigation(Ieee, 2021) Raza, Muhammad Raheel; Varol, AsafBitcoin, the most well-known of all the cryptocurrencies, have attracted a lot of attention thus far, and their prices have been quite volatile. While some research employ traditional statistical and econometric methods to discover the factors that drive Bitcoin prices, experimenting on the development of prediction models to be utilized as decision support aids in investment approaches is uncommon. The sudden rise and fall of cryptocurrency rates affects the economies and future perspectives of various businesses. In order to minimize business risks, to track the differences and avoid serious economic loss, prediction of daily digital currency rates becomes a crucial task. Our study performs a comparative analysis of Bitcoin price prediction utilizing efficient neural network techniques such as LSTM and GRU. A better RNN-based approach is derived as a result of the study. This approach will assist to facilitate a secure environment for businesses and to alarms to carryout risk management tasks for business risk mitigation purposes.Yayın Forecasting Foreign Visitors Arrivals Using Hybrid Model and Monte Carlo Simulation(World Scientific Publ Co Pte Ltd, 2022) Danbatta, Salim Jibrin; Varol, AsafThe tourism industry is one of the important revenue sectors in today's world. Millions of visits are made monthly to different countries across the planet. Some countries host more tourists than others, depending on the availability of factors that would fascinate visitors. Tourism demand can be affected by different factors, which may include government policies, insecurity, political motive, etc. Being an important sector, policymakers/governments are keen on models that would provide an insight into the inherent dynamics of tourism in their country. Especially in forecasting future tourist arrivals, as it will greatly assist in decision making. Several tourism demand models have been presented in the literature. The best practice is to have a model that would account for uncertainty in estimations. In this paper, an ANN-Polynomial-Fourier series model is implemented to capture and forecast tourist data for Turkey, Japan, Malaysia, and Singapore. The proposed model is a combination of the artificial neural network (ANN), polynomial fitting (poly), and Fourier series fitting (Fourier). The proposed model is designed to capture the data trend component using the polynomial fitting, the data seasonal component using the Fourier series fitting, and other data anomalies using the artificial neural network. Multistep ahead forecasting is made for each of the studied tourist data, and estimation uncertainties are covered by generating multiple forecast paths (Monte Carlo forecast). According to estimations, Turkey will expect a 10.22% increase in 2021 compared to the tourist arrivals it received in 2020. Japan is expected to have a 92.42% decrease in 2021 compared to the tourist arrivals it received in 2020. Malaysia is also expected to have a 54.81% decrease in 2021 when compared to the number of tourists it received in 2020. Finally, Singapore will expect a 70.55% decrease in 2021 compared to the number of tourists it received in 2020.Yayın Forecasting monthly rainfall using hybrid time-series models and Monte Carlo simulation amidst security challenges: a case study of five districts from northern Nigeria(Springer, 2024) Danbatta, Salim Jibrin; Muhammad, Ahmad; Varol, Asaf; Abdurrahaman, Daha TijjaniNigeria's agricultural sector relies heavily on rainfall, but insecurity in various regions poses significant challenges. This study aims to address this issue by identifying secure, rain-rich areas in northern Nigeria to support sustainable agriculture. Two models, one integrating classical statistical methods (polynomial and Fourier series fittings) and another using a hybrid approach (artificial neural networks, polynomial, and Fourier series fittings), were employed to analyze historical rainfall data from 1981 to 2021 in the selected districts (Kano, Zaria, Bida, Nguru, and Yelwa) known for their rainfall levels and security stability. The study demonstrates that the machine learning-classical hybrid model outperforms existing models, including the classical-classical hybrid and benchmark models like Iwok's (2016) model, Fourier series, and SARIMA models. Multi-step ahead forecasting with this hybrid model reveals potential changes in rainfall patterns. Notably, Kano, Zaria, Bida, and Yelwa are expected to experience increased rainfall from 2022 to 2026, while Nguru may initially witness decreased rainfall, with improvement in the final year (2026). In conclusion, this study introduces an effective approach for rainfall modeling and forecasting, facilitating the identification of secure agricultural regions in northern Nigeria. These findings carry implications for crop production and agricultural development, contributing to climate resilience efforts and assisting stakeholders in strategic decision-making for regional agricultural investments.Yayın An Improved Transfer Learning-Based Model for Malaria Detection using Blood Smear of Microscopic Cell Images(Ieee, 2021) Bilyaminu, Muhammad; Varol, AsafBecause of insufficient medical specialists in some parts of the African and Asian continents, malaria patients' mortality rates have increased over the years. Since the people of regions generally suffer from malaria diseases, computer-aided detection (CAD) technology is required to decrease the number of casualties and reduce the waiting time for consulting by a Malaria specialist. This study shows the potential of transfer learning, a method of Deep Learning (DL) to classify the smeared blood of microscopic malaria cell images to determine whether it is parasitized or uninfected. This classification of malaria cell images will enhance the workflow of health practitioners at the frontline, especially microscopists, and provides them with a valuable alternative for malaria detection based on microscopic cell images. Although many technological advancements and evaluation techniques for identifying the infection exist, a microscopist at regions with limited resources faces challenges in improving diagnostic accuracy. We compared and evaluated a type of pre-trained CNN models, such as ResNet-50 and our appended Resnet-50+KNN. The experiment shows that our new model has the excellent capability and can perform better on malarial microscopic cell image classification with a higher accuracy rate of 98%.Yayın Köşe Yazıları "İletişim"(T.C. Maltepe Üniversitesi, 2021) Varol, Asaf; Mumcu, TanselBu kitap, 1997-1999 yılları arasında BT/Haber Gazetesi’nde ve 2000-2020 yılları arasında ise Günışığı Gazetesi’nde yayımlanan ve sadece iletişim alanını kapsayan köşe yazılarımın bir araya getirilmesi ile oluşturulmuştur. İletişim alanındaki çalışmalara, 1991 yılında Fırat Üniversitesi bünyesinde yayın yapmaya başlayan ve ilk yerel üniversite televizyonu olan Fırat TV’yi kurarak başlamıştım. 2000-2004 yılları arasında Fırat Üniversitesi İletişim Fakültesi’nin kurucu dekanlığı görevine atandım ve o yıldan beri İletişim Fakültesi Dekanları Konseyi (İLDEK) Yürütme Kurulunun tek Daimi Üyesi olarak görevimi hâlen sürdürmekteyim. Bu sayede iletişim fakültelerini ilgilendiren gelişmelerin önemli bir bölümünün içerisinde bizzat bulundum. İletişim Fakülteleri Dekanları ile birlikte iletişim eğitiminin sorunlarını inceledik, analizler yaptık, bu fakültelerin akreditasyon süreçlerine girmesi çalışmalarını başlattık, uluslararası düzeyde etkinlikler düzenledik ve öneri niteliğinde kararlar alarak ilgili kurum ve kuruluşlara ilettik. Kitap, iletişim alanındaki kronolojik gelişmelerin dizgesidir. İletişim alanında eğitim gören öğrencilerin, uzmanların ve eğiticilerin bu eserden istifade edeceklerine inanıyorum. En önemlisi de gelecek nesiller, ellerinde belgesel nitelikli bu kitabı kaynak olarak kullanabileceklerdir. İletişim kurabilme becerisi, sorunların çözümsüzlüğünün bertaraf edilmesinin en büyük anahtarıdır.Yayın Köşe yazıları "iletişim"(Maltepe Üniversitesi, 2021) Varol, Asaf; Mumcu, Tansel; Karasar, Şahin; Karataş, SerdarBu kitap, 1997-1999 yılları arasında BT/Haber Gazetesi’nde ve 2000-2020 yılları arasında ise Günışığı Gazetesi’nde yayımlanan ve sadece iletişim alanını kapsayan köşe yazılarımın bir araya getirilmesi ile oluşturulmuştur. İletişim alanındaki çalışmalara, 1991 yılında Fırat Üniversitesi bünyesinde yayın yapmaya başlayan ve ilk yerel üniversite televizyonu olan Fırat TV’yi kurarak başlamıştım. 2000-2004 yılları arasında Fırat Üniversitesi İletişim Fakültesi’nin kurucu dekanlığı görevine atandım ve o yıldan beri İletişim Fakültesi Dekanları Konseyi (İLDEK) Yürütme Kurulunun tek Daimi Üyesi olarak görevimi hâlen sürdürmekteyim. Bu sayede iletişim fakültelerini ilgilendiren gelişmelerin önemli bir bölümünün içerisinde bizzat bulundum. İletişim Fakülteleri Dekanları ile birlikte iletişim eğitiminin sorunlarını inceledik, analizler yaptık, bu fakültelerin akreditasyon süreçlerine girmesi çalışmalarını başlattık, uluslararası düzeyde etkinlikler düzenledik ve öneri niteliğinde kararlar alarak ilgili kurum ve kuruluşlara ilettik. Kitap, iletişim alanındaki kronolojik gelişmelerin dizgesidir. İletişim alanında eğitim gören öğrencilerin, uzmanların ve eğiticilerin bu eserden istifade edeceklerine inanıyorum. En önemlisi de gelecek nesiller, ellerinde belgesel nitelikli bu kitabı kaynak olarak kullanabileceklerdir. İletişim kurabilme becerisi, sorunların çözümsüzlüğünün bertaraf edilmesinin en büyük anahtarıdır.Yayın Liderlikte karar verme ikilemi.(Maltepe Üniversitesi, 2021) Varol, AsafAmerikan kamu yönetimi disiplininde 1960'ların sonunda ortaya çıkan "Yeni Kamu Yönetimi Hareketi”, geleneksel kamu yönetimi kavramının yetersiz olduğu inancına dayanıyordu. Köklerini Dwight Waldo’dan almıştı, ancak H. George Frederickson yeni hareketin çerçevesini çizdi. Teorilerini “Kamu Yönetimi Teorisi Primer” kitabında toplayan Frederickson, "Yeni Kamu Yönetimi Hareketi”nin kurucusu kabul edildi.Yayın Modeling and Forecasting of Tourism Time Series Data using ANN-Fourier Series Model and Monte Carlo Simulation(Ieee, 2021) Danbatta, Salim Jibrin; Varol, AsafTourism is counted as one of the most sensitive sectors to crises such as the COVID-19 pandemic. By the first quarter of 2020, it brought the foreign visitors' travels to a sudden and unexpected halt. This has negatively affected the tourism sector. Due to the perishable nature of the tourism industry products, many researchers are calling for urgent development and implementation of a rescue plan that will help in predicting the future number of foreign visitors. In this paper, we proposed an approach to modeling and forecasting a tourism time-series data that have both trend and seasonality. This approach is a combination of the Fourier series and artificial neural network methods to capture the seasonality and trend components in data. We applied this method to the monthly foreign visitors to Turkey dataset. We studied the data for the periods before, and during the COVID-19 pandemic. To account for uncertainties in the model prediction during the COVID-19 pandemic, we employed the Monte Carlo simulation method. We run 100 Monte Carlo simulations within +/- 2 sigma from the model curve. The mean of these 100 Monte Carlo simulation paths is computed and used for presenting the Monte Carlo forecast result values of the data. To test the feasibility of this approach, we compared the model predictions with some other existing models in the literature. In each case, the model has demonstrated a decent prediction and outperformed the benchmarked models. The proposed model produces a statistically good fit and acceptable result that can be used to forecast other tourism-related attributes.Yayın Monte Carlo forecasting of time series data using Polynomial-Fourier series model(World Scientific Publ Co Pte Ltd, 2021) Danbatta, Salim Jibrin; Varol, AsafThe perishable nature of tourism products and services makes forecasting an important tool for tourism planning, especially in the current COVID-19 pandemic time. The forecast assists tourism organizations in decision-making regarding resource allocations to avoid shortcomings. This study is motivated by the need to model periodic time series with linear and nonlinear trends. A hybrid Polynomial-Fourier series model that uses the combination of polynomial and Fourier fittings to capture and forecast time series data was proposed. The proposed model is applied to monthly foreign visitors to Turkey from January 2014 to August 2020 dataset and diagnostic checks show that the proposed model produces a statistically good fit. To improve the model forecast, a Monte Carlo simulation scheme with 100 simulation paths is applied to the model residue. The mean of the 100 simulation paths within +/- 2 sigma bounds from the model curve was taken and found to give statistically acceptable results.Yayın The necessity of emotion recognition from speech signals for natural and effective human-robot interaction in society 5.0(2022) Sönmez, Yeşim Ülgen; Varol, AsafThe history of humanity has reached Industry 4.0 that aims to the integration of information technologies and especially artificial intelligence with all life-sustaining mechanisms in the 21st century, and consecutively, the transformation of Society 5.0 has begun. Society 5.0 means a smart society in which humans share life with physical robots and software robots as well as smart devices based on augmented reality. Industry 4.0 contains main structures such as the internet of things, big data analytics, digital transformation, cyber-physical systems, artificial intelligence, and business processes optimization. It is impossible to consider the machines to be without emotions and emotional intelligence within the transformation of smart tools and artificial intelligence, in addition, while it is planned to give most of the commands with voice and speaking, it became more important to develop algorithms that can detect emotions. In the smart society, new and rapid methods are needed for speech recognition, emotion recognition, and speech emotion recognition areas to maximize human-computer (HCI) or human-robot interaction (HRI) and collaboration. In this study, speech recognition and speech emotion recognition studies in robot technology are investigated and developments are revealed.Yayın A novel deep feature extraction engineering for subtypes of breast cancer diagnosis: a transfer learning approach(2022) Muhammad, Bilyaminu; Özkaynak, Fatih; Varol, Asaf; Tuncer, TürkerFeature extraction from histological images is a challenging part of computer-aided detection of breast cancer. For this research, we present a novel technique for deep feature extraction for breast cancer diagnosis subtypes based on a transfer learning approach using the BreaKhis dataset. This approach consists of five phases: feature extraction, concatenation, transformation, selection, and classification. In the first phase, nineteen pre-trained convolutional neural networks were used as feature extractors to extract features from the input images. A Support Vector Machine was used at the feature extraction phase to calculate the misclassification rate of each feature generated by the pre-trained networks used. The feature extraction results showed that the two networks achieved the highest accuracy on the dataset and outperformed the other networks. The two networks considered were selected and connected to create the DRNet model, combining the pretrained networks ResNet50 and DenseNet201. The extracted features were decomposed into five sub-hand low-level features using a multilevel discrete wavelet transform in the transformation phase. An iterative neighborhood component analyzer was used to select the minimum number of features needed in the classification phase. A cubic support vector machine was used as a classifier in the final phase. Average classification accuracy of 98.61%, 98.04%, 97.68%, and 97.71% for the 40×, 100×, 200×, and 400× magnification levels, respectively, was achieved.Yayın Performance analysis of deep approaches on airbnb sentiment reviews(2022) Raza, Muhammad Raheel; Hussain, Walayat; Varol, AsafConsumer reviews in the Airbnb marketplace are one of the key attributes to measure the quality of services and the main determinant of consumer rentals decisions. Such feedback can impact both a new and repeated consumer's choice decision. The way to manage poor reviews can help to save or damage the host's reputation. Sentiment analysis enables an Airbnb host to get an insight into the business, pinpoint degradation of the specific component of compound services and assist in managing it proactively. Multiple Deep Learning algorithms have been used for Natural Language Processing (NLP). For optimal sentiment management in the Airbnb marketplace, it is crucial to identify the right algorithm. The paper uses multiple Deep Learning algorithms to identify different aspects of guest reviews and analyze their accuracies. The paper uses four accuracy measurement benchmarks – Precision, Recall, F1-score and Support to analyze results. The analysis shows that the GRU method achieves the best results with the highest classification metrics values as compared to RNN and LSTM.Yayın Prediction of Arrhythmia with Machine Learning Algorithms(Ieee, 2021) Gürsoy, Güneş; Varol, AsafThe present study uses the age, sex, diabetes mellitus, and arrhythmia data of patients from the datasets presented in an existing study to predict arrhythmia with machine learning algorithms, K-Nearest Neighbors (KNN), and Naive Bayes methods. The outputs are schematically presented, and the conclusions related to the Bayes theorem and KNN algorithms are compared. In the case of increasing the value of neighboring k in the KNN method, it is seen that the accuracy rate approaches the result obtained from the Naive Bayes method.Yayın Risks of Digital Transformation: Review of Machine Learning Algorithms in Credit Card Fraud Detection(Ieee, 2021) Gürsoy, Güneş; Varol, AsafIn addition to the advantages of the digital world, there are also disadvantages, which can harm people. With the spread of credit cards with the digital transformation, banks have become the targets of malicious hackers. In this study, firstly, information about artificial intelligence and digital transformation is given. In related studies, some machine learning methods such as Random Forest, Naive Bayes, K-Nearest Neighbor, Logistic Regression, Support Vector Machines, Decision Tree, Artificial Neural Networks, Multilayer Perceptron and Ensemble Learning have been used to detect credit card fraud and their algorithm performance has been demonstrated.
