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Öğe An anomaly detection study for the smart home environment(IEEE, 2022) Bilgin, Mehmet Erhan; Kilinc, H.Hakan; Zaim, Abdul HalimUnusual sensor data in smart homes may herald different problems based on sensor errors, security vulnerabilities, activity and behavior changes. This study focuses on detecting anomalies and unusual situations in 7 different sensor data in a house. For this, a model created with a combination of unsupervised and supervised machine learning algorithms is used. The sensor data are labeled using Isolation Forest which is one of the unsupervised algorithms. Then, the data is trained with the supervised algorithms Decision Tree, Extra Trees, Random Forest and XGBoost classification algorithms. Anomaly decisions are made with an accuracy of over 99 percent.Öğe A Comparative Study on Energy Harvesting Battery-Free LoRaWAN Sensor Networks(Istanbul University, 2022) Riva, Cengiz; Zaim, Abdul HalimSensor networks are emerging in many applications with the availability of the spreading IoT (Internet of Things) technologies. These applications having sensor networks generally do not require big data transfers, but instead, they need to send a small amount of data from time to time. Long-range wide area network (LoRaWAN) is a very good solution for this kind of communication since LoRaWAN offers long-range transmission with deep-sleep mechanism between the transmissions. However, these networks must be reliable, and from the point of energy supply for a long period of time, they should be sustainable. Since the nature of sensor-based IoT devices is being small and placable anywhere, they are generally battery-powered. With the usage of batteries, many disadvantages including limited power, cost of replacement of the dead battery, especially in rural areas, environmental concerns, etc. arise. Therefore, energy harvesting from different sources depending on the application and location of the sensor network may eliminate these problems. In this study, firstly, we focused on how the energy is consumed by the LoRAWAN communication systems by analyzing the power consumption during the transmit phase and providing necessary formulas. Secondly, by looking at the formulas, a possible optimization is recommended on the power consumption in LoRAWAN systems in order to have longer battery life. Thirdly, we have searched and analyzed energy harvesting methods and applications used by the researchers in recent years, as well as elaborating power consumptions, in typical microcontroller-controlled sensor nodes with LoRaWAN communication ability in order to provide comparative information on energy-harvesting battery-less LoRAWAN nodes.Öğe Cyberbullying detection through deep learning: A case study of Turkish celebrities on Twitter(IOS Press BV, 2023) Karadağ, Bulut; Akbulut, Akhan; Zaim, Abdul HalimOne of the ways that celebs maintain their fame in the modern era is by posting updates and photos to social media platforms like Twitter, Instagram, and Facebook. Comments left on their posts, however, expose them to cyberbullying. Cyberbullying, as a form of electronic device-based harassment, negatively impacts the lives of individuals. Thirty famous people from the fields of acting, art, music, politics, sports, and writing were chosen for this research. These notable figures include the top five Twitter followers of Turkey in each demographic. Between December 2019 and December 2020, comment responses for each celebrity were collated. Using the Deep Learning model, we were able to detect abuse content with an accuracy of 89%. Additionally, the percentage of celebrities exposed to cyberbullying by group was presented.Öğe Identification of phantom movements with an ensemble learning approach(Elsevier Ltd, 2022) Akbulut, Akhan; Gungor, Feray; Tarakci, Ela; Aydin, Muhammed Ali; Zaim, Abdul Halim; Catal, CagatayPhantom limb pain after amputation is a debilitating condition that negatively affects activities of daily life and the quality of life of amputees. Most amputees are able to control the movement of the missing limb, which is called the phantom limb movement. Recognition of these movements is crucial for both technology-based amputee rehabilitation and prosthetic control. The aim of the current study is to classify and recognize the phantom movements in four different amputation levels of the upper and lower extremities. In the current study, we utilized ensemble learning algorithms for the recognition and classification of phantom movements of the different amputation levels of the upper and lower extremity. In this context, sEMG signals obtained from 38 amputees and 25 healthy individuals were collected and the dataset was created. Studies of processing sEMG signals in amputees are rather limited, and studies are generally on the classification of upper extremity and hand movements. Our study demonstrated that the ensemble learning-based models resulted in higher accuracy in the detection of phantom movements. The ensemble learning-based approaches outperformed the SVM, Decision tree, and kNN methods. The accuracy of the movement pattern recognition in healthy people was up to 96.33%, this was at most 79.16% in amputees.Öğe IoT Malware Detection Based on OPCODE Purification(IUC University Press, AVES, 2023) Gülataş, İbrahim; Kılınç, Hacı Hakan; Aydın, Muhammed Ali; Zaim, Abdul HalimMalware threat for Internet of Things (IoT) devices is increasing day by day. The constrained nature of IoT devices makes it impossible to apply high-resource-demand ing anti-malware tools for these devices. Therefore there is an enormous need for lightweight and efficient anti-malware solutions for IoT devices. In this study, machine learning-based malware detection is performed using purified OPCODE analysis for IoT devices with MIPS architecture. The proposed methodology reduced the runtime of IoT malware detection up to 7.2 times without reducing the accuracy ratio.Öğe The proposal of a fuzzy AHP-based model for the efficiency analysis of technoparks(2019) Tepe, Serap; Zaim, Abdul HalimIn the paper, it is proposed a model to evaluate the efficiency of technopark by performing an activity analysis on the productivity of the technopark structure, aiming to contribute to the innovative movement and sustainable development goals, which may result in huge amounts of increase in added-value of Turkey’s technological development. The initial point of the paper is to define technopark structure and activities, to determine and eliminate uncertainties concerning structure and operation. Technoparks are investigated based upon their management, firms & incubation firms, R&D activities, and cooperation level among university-industry. In the paper, a fuzzy analytic hierarchy process method is used. The paper includes four technoparks that operate in Istanbul. By applying the developed model, the results of the performance evaluation are reached and the results are interpreted. It is thought that the findings obtained from this research will be beneficial for all stakeholders related to technoparks.Öğe A Review on Blockchain Applications in Fintech Ecosystem(Institute of Electrical and Electronics Engineers Inc., 2022) Karadağ, Bulut; Akbulut, Akhan; Zaim, Abdul HalimThe term fintech started to become popular from the 90s. With the rapid development of technology and the widespread use of the internet, Fintech has become a sector in itself, especially since 2004. Within the framework of Fintech, there have been a number of advances in ATM, credit cards, debit cards, mobile transactions, internet banking and digital banking infrastructure and transactions. The emergence of Bitcoin in 2008 caused us to hear the term blockchain frequently, and the path of blockchain technology intersected with fintech. The decentralization of the blockchain, thanks to its distributed ledger structure, made it possible to make Bitcoin transfers without intermediaries. After Bitcoin, the emergence of crypto assets like Ethereum opened the way for these transactions to be programmable as an infrastructure. Programmable blockchain infrastructures have started to be used not only in financial transactions, but also in sectors such as health, supply chain, education and insurance. There are different academic studies on applications related to these sectors. However, there is no such a review that includes them all together for finance. In this study, blockchain applications in the fintech ecosystem were investigated and included in a single study. In particular, it was explained in which business it was used and in which business there was a market volume. In addition, possible future blockchain applications were also mentioned.Öğe Siber güvenlikte klavye davranış analizi(İstanbul Ticaret Üniversitesi, 2022) Akşit, Nurgül; Aydın, Muhammed Ali; Zaim, Abdul Halim2019 yılında Çin'de ortaya çıkan ve tüm dünyayı etkisi altına alan Covid-19 salgını ile bilgi sistemleri üzerinde değişen çalışma koşullarını daha güvenli bir ortam haline getirme ihtiyacı artmıştır. Bu ihtiyaç araştırmacıları bilgi sistemlerini kullanan kişinin gerçek kişi olduğuna dair doğrulama sistemi geliştirmeye itmiştir. Geliştirilen Klavye Davranış Analizi programı ile her biri farklı alışkanlıklara sahip kullanıcıların verileri toplanmakta ve belirlenen örnekler derin öğrenme ile yapay zekada kullanmak üzere analiz edilmektedir. Bu analizlerin sonuçları, bilgisayarları ele geçiren kötü niyetli saldırganlar tarafından kullanıldığında kimlik doğrulama yöntemi ile tespitinin yapılması konusunda literatüre katkı sağlamaktadır. Çoklu kimlik doğrulama, kullanıcıların sahip oldukları kimliklerinin farklı kombinasyonlar ile bilgi sistemlerinde onaylanma yöntemidir. Çoklu kimlik doğrulamanın yönü, tekli kimlik doğrulama ile atlatılabilecek sistem açıklıklarının güvenliğini sağlamaktır. Bu çalışmanın amacı, iyi bir derin öğrenme yöntemi ile kullanıcıların klavye davranış analizlerini çıkarmak ve bilgi sistemlerine girişlerde kimlik doğrulaması yapmaktır.Öğe Sinyalize Bir Kavşakta Oluşan Trafik Akımının Kuyruk Teorisi ile Performansının İncelenmesi(2020) Güneş, Fatih; Bayraklı, Selim; Zaim, Abdul HalimYapılan bu araştırmada özellikle şehir merkezlerinde trafik yoğunluğunu azaltma amaçlı konumlandırılan sinyalize kavşakların, çalışma prensipleri ve araç akışını düzenleyici yaklaşımları ele alınmıştır. Araç gecikmeleri, sinyal sürelerinin hesaplama yöntemleri ve performans ölçütlerinin çıkarılması ile ilgili analitik yaklaşımlar incelenmiştir. İstanbul Güngören’de seçilen bir kavşaktan loop algılayıcılar ile elde edilen gerçek veriler kullanılarak performansının ortaya çıkarılması amaçlanmıştır. Seçilen bu kavşaktan elde edilen veriler ile yapılan çalışmada iki haftalık veri üzerinden günün en sık araç akışının olduğu üç zaman dilimi incelenmiştir. Kuyruk teorisi, bekleme hattı problemlerinde sıklıkla başvurulan yöntemlerin başında gelmektedir. Kuyruk teorisi modelleri, kendi içinde sisteme gelen bireylerin veya bizim uygulamamızda araçların, geliş, ayrılış ve servis disiplini gibi karakteristiklerine göre farklı notasyon ve hesaplama yöntemlerine sahip olabilmektedir. Kuyruk modellerinin ihtiyaç duyduğu dağılımlar için genellikle varsayımlar yapılmakta ve trafik mühendisliği araştırmalarında gelen akımın rastgele olduğu varsayılmaktadır. Araştırmada araçların gelişleri poisson, gelişler arası sürenin ise üstel dağılıma uyduğu kabul edilmiştir. Pilot olarak seçilen bu kavşaktaki veriler M/M/1 kuyruk modeli ile incelenmiş ve sinyalize bir kavşaktaki bağlı kolların kuyruk uzunlukları, sistemde geçirilen zaman, araç başı servis süreleri, ortalama bekleme süreleri gibi ölçütler ortaya çıkarılmıştır. Uygulanan senaryoda kavşak bağlantı kollarının her biri bir kuyruk olarak değerlendirilmiş ve hesaplamalarda her kolun performansı ayrı değerlendirilmiştir. Ortaya çıkan sonuçlara göre günün belli saat dilimlerinde sinyalize sistemlerin optimize çalışmadığı ve kuyruk uzunluklarını azaltmada yeterli olmadığı sonucuna ulaşılmıştır. Günün bu saat dilimlerinde kavşak kollarına tanınan yeşil sürelerin veya kolların çalışma sıralarının iyileştirmeye ihtiyaç duyduğu söylenebilir. Elde edilen kuyruk uzunlukları veya ortalama bir aracın sistemde kaybettiği zaman dikkate alınarak kavşak modellemesi veya süre dağılımları gözden geçirilmelidir.Öğe Software Log Classification in Telecommunication Industry(IEEE, 2022) Ülkü, Onur; Gözüacik, Necip; Tanberk, Senem; Aydm, Muhammed Ali; Zaim, Abdul HalimSoftware system admins depend on log data for understanding system beliavior, monitoring anomalies, tracking software bugs, and malfunctioning detection. Log analysis based on machine learning techniques enables to transform of raw logs into meaningful information that helps the DevOps team and administrators to solve problems. AI ensures to group similar logs together and keeps periodic logs more organized and sorted, allowing us to get to where we need to look faster. In this paper, we present a log classification system on log data generated by VoIP (Voice over Internet Protocol) soft-switch product. In this way, we targeted to detect the problem, direct it to the relevant department, allocate resources, and solve software bugs faster and more efficiently. Machine learning algorithms such as Linear Classifiers, Support Vector Machines, Decision Tree, Random Forest, Boosting, K-Nearest Neighbors, and Multilayer Perceptron are used for log classification.Öğe Stacking-based ensemble learning for remaining useful life estimation(Springer, 2023) Ture, Begum Ay; Akbulut, Akhan; Zaim, Abdul Halim; Catal, CagatayExcessive and untimely maintenance prompts economic losses and unnecessary workload. Therefore, predictive maintenance models are developed to estimate the right time for maintenance. In this study, predictive models that estimate the remaining useful life of turbofan engines have been developed using deep learning algorithms on NASA’s turbofan engine degradation simulation dataset. Before equipment failure, the proposed model presents an estimated timeline for maintenance. The experimental studies demonstrated that the stacking ensemble learning and the convolutional neural network (CNN) methods are superior to the other investigated methods. While the convolution neural network (CNN) method was superior to the other investigated methods with an accuracy of 93.93%, the stacking ensemble learning method provided the best result with an accuracy of 95.72%.Öğe Techniques for apply predictive maintenance and remaining useful life: A systematic mapping study(Bilecik Şeyh Edebali Üniversitesi, 2021) Ay Türe, Begüm; Akbulut, Akhan; Zaim, Abdul HalimWith prognostic activities, it is possible to predict the remaining useful life (RUL) of industrial systems with high accuracy by following the current health status of devices. In this study, we have collected 199 articles on predictive maintenance and remaining useful life. The aim of our systematic mapping study is to determine which techniques and methods are used in the areas of predictive maintenance and remaining useful life. Another thing we aim is to give an idea about the main subject to the researchers who will work in this field. We created our article repository by searching databases such as IEEE and Science Direct with certain criteria and classified the articles we obtained. By applying the necessary inclusion and exclusion criteria in the article pool we collected, the most appropriate articles were determined and our study was carried out through these articles. When we focused on the results, it was learned that the SupportVector Machine algorithm is the most preferred predictive maintenance method. Most studies aimed at evaluating the performance and calculating the accuracy of the results used the Root Mean Square Error algorithm. In our study, every method and algorithm included in the articles are discussed. The articles were examined together with the goals and questions we determined, and results were obtained. The obtained results are explained and shown graphically in the article. According to the results, it is seen that the topics of predictive maintenance and remaining useful lifetime provide functionality and financial gain to the environment they are used in. Our study was concluded by light on many questions about the application of predictive maintenance.
