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Yayın A robust Gradient boosting model based on SMOTE and NEAR MISS methods for intrusion detection in imbalanced data sets(Işık Üniversitesi, 2022-01-18) Arık, Ahmet Okan; Çavdaroğlu Akkoç, Gülsüm Çiğdem; Işık Üniversitesi, Lisansüstü Eğitim Enstitüsü, Enformasyon Teknolojileri Yüksek Lisans ProgramıNovel technologies cause many security vulnerabilities and zero-day attack risks. Intrusion Detection Systems (IDS) are developed to protect computer networks from threats and attacks. Many challenging problems need to be solved in existing methods. The class imbalance problem is one of the most difficult problems of IDS, and it reduces the detection rate performance of the classifiers. The highest IDS detection rate in the literature is 96.54%. This thesis proposes a new model called ROGONG-IDS (Robust Gradient Boosting) based on Gradient Boosting. ROGONGIDS model uses Synthetic Minority Over-Sampling Technique (SMOTE) and Near Miss methods to handle class imbalance. Three different gradient boosting-based classification algorithms (GBM, LightGBM, XGBoost) were compared. The performance of the proposed model on multiclass classification has been verified in the UNSW-NB15 dataset. It reached the highest attack detection rate and F1 score in the literature with a 97.30% detection rate and 97.65% F1 score. ROGONG-IDS provides a robust, efficient solution for IDS built on datasets with the imbalanced class distribution. It outperforms state-of-the-art and traditional intrusion detection methods.Yayın Rule based entity-relationship diagram modelling(Işık Üniversitesi, 2022-02-07) Ulusoy, Oğuzhan; Ekin, Emine; Işık Üniversitesi, Lisansüstü Eğitim Enstitüsü, Bilgisayar Mühendisliği Yüksek Lisans ProgramıModern society needs to use database system since they involve many activities that are related to database interaction directly. In this study, entity-relationship modeling using Natural Language Processing techniques is presented for the English language. Natural Language Processing refers to the capability of understanding human languages naturally, like Turkish and English, using computational power. To make this possible, combination of linguistics and current Machine Learning systems are used together. Entity-Relationship diagrams ensure to plan or trace relational databases in different fields. In the beginning, all details of a standard database management and its components have been studied. Heuristic rules which indicate the relation between human language and database components have been defined. According to the defined heuristic rules previously, an event-based pipeline has been constructed. A full text has been analyzed and processed every word at this pipeline using Natural Language Processing techniques.Yayın Co-training using prosodic, lexical and morphological information for automatic sentence segmentation of Turkish spoken language(Işık Üniversitesi, 2018-01-15) Dalva, Doğan; Güz, Ümit; Işık Üniversitesi, Fen Bilimleri Enstitüsü, Elektronik Mühendisliği Doktora ProgramıSentence segmentation of speech aims detecting sentence boundaries in a stream of words output by the speech recognizer. Sentence segmentation is a preliminary step toward speech understanding. It is of particular importance for speech related applications, as most of the further processing steps; such as parsing, machine translation and information extraction, assume the presence of sentence boundaries. Typically, statistical methods require a huge amount of manually labeled data, which is time and labor consuming process to prepare. In this work, novel multiview semi-supervised learning strategies for the solution of sentence segmentation problem are proposed. The aim of this work is to and effective semi-supervised machine learning strategies when only a small set of sentence boundary labeled data is available. This work proposes three-view co-training and committee-based strategies incorporating with agreement, disagreement and self-combined strategies using lexical, morphological and prosodic information, and investigates performance of the proposed learning strategies against baseline, self-training and co-training. The experimental results show that the proposed learning strategies highly improve the sentence segmentation problem, since data sets can be represented by three redundantly suffcient and disjoint feature sets.Yayın Fingertip electrocardiogram and speech signal based biometric recognition system(Işık Üniversitesi, 2021-12-27) Güven, Gökhan; Güz, Ümit; Gürkan, Hakan; Işık Üniversitesi, Lisansüstü Eğitim Enstitüsü, Elektronik Mühendisliği Doktora ProgramıFingertip electrocardiogram and speech signal based biometric recognition system In this research work, we presented a one-dimensional CNN-based person identification system which depends on the combination of both speech and ECG modalities to improve the overall performance compared to traditional systems. The proposed method has two approach: one is to develop combination of textindependent speech and fingertip ECG fusion system, the other one is to develop a robust rejection algorithm to prevent unauthorized access to the fusion system. In addition to the system robustness, we have developed an ECG spike and inconsistent beats removing algorithm, which detect and remove the problems caused by either portable fingertip ECG devices or movements of the patients. First approach has been tested on 30, 45, 60, 75 and 90 people which were taken from LibriSpeech Corpus database and combination of both CYBHi and our private fingertip ECG database. The 3-fold cross validation test setup has been conducted while system working time was set to 10 seconds. In the first experiment, we achieved 90.22% accuracy rate for 90 people for ECG based system. For the speech based system, 97.94% accuracy rate has achieved for 90 people. For the combination of both system, 99.92% accuracy rate has been achieved. For the second approach, 90 people for ECG and Speech database were being used as genuine class, 26 people as imposter class, and after the performance evaluation in optimum rejection thresholds, 71.08% accuracy rate for imposters rejection and 71.05% accuracy rate for genuine recognition has achieved for ECG based system. For the speech based system, imposter class were 87.82% accurately rejected while genuine classes were 86.48% accurately identified. The combination of both system has achieved 91.68% accuracy for genuine identification rate whereas 96.05% accuracy for imposter rejection.Yayın Learning to rank(Işık Üniversitesi, 2011-04-28) Kılıç, Yasin Ozan; Yıldız, Olcay Taner; Işık Üniversitesi, Fen Bilimleri Enstitüsü, Bilgisayar Mühendisliği Yüksek Lisans ProgramıThe web has grown so rapidly in the last decade and it brought the need for proper ranking. Learning to rank (LTR) is the collection of machine learning technolo- gies that construct a ranking model using training data. The model can sort documents according to their degrees of relevance or preference. In this thesis, we introduce LTR technologies and divide them into three ap- proaches: the point-wise, pair-wise and list-wise. We review the theoritical aspects of each category and introduce the representative algorithms of them. We also introduce a new LTR method GRwC which uses classifîcation and graph algorithms. We reduce the ranking problem to a two class classifîcation problem and apply KNN algorithm on a modified LTR dataset. We compared it with the popular ranking algorithm RankingSVM. Experiments on the well-known ranking datasets show that our proposed method gives slightly worse results than RankingSVM.Yayın Word sense disambiguation, named entity recognition, and shallow parsing tasks for Turkish(Işık Üniversitesi, 2019-04-02) Topsakal, Ozan; Yıldız, Olcay Taner; Işık Üniversitesi, Fen Bilimleri Enstitüsü, Bilgisayar Mühendisliği Yüksek Lisans ProgramıPeople interactions are based on sentences. The process of understanding sentences is thru converging, parsing the words and making sense of words. The ultimate goal of Natural Language Processing is to understand the meaning of sentences. There are three main areas that are the topics of this thesis, namely, Named Entity Recognition, Shallow Parsing, and Word Sense Disambiguation. The Natural Language Processing algorithms that learn entities, like person, location, time etc. are called Named Entity Recognition algorithms. Parsing sentences is one of the biggest challenges in Natural Language Processing. Since time efficiency and accuracy are inversely proportional with each other, one of the best ideas is to use shallow parsing algorithms to deal with this challenge. Many of words have more than one meaning. Recognizing the correct meaning that is used in a sentence is a difficult problem. In Word Sense Disambiguation literature there are lots of algorithms that can help to solve this problem. This thesis tries to find solutions to these three challenges by applying machine learning trained algorithms. Experiments are done on a dataset, containing 9,557 sentences.Yayın Machine learning-driven adaptive modulation for VLC-enabled medical body sensor networks(Iran University of Science and Technology, 2024-12) Rizi, Reza Bayat; Forouzan, Amir R.; Miramirkhani, Farshad; Sabahi, Mohamad F.Visible Light Communication, a key optical wireless technology, offers reliable, high-bandwidth, and secure communication, making it a promising solution for a variety of applications. Despite its many advantages, optical wireless communication faces challenges in medical environments due to fluctuating signal strength caused by patient movement. Smart transmitter structures can improve system performance by adjusting system parameters to the fluctuating channel conditions. The purpose of this research is to examine how adaptive modulation performs in a medical body sensor network system that uses visible light communication. The analysis focuses on various medical situations and investigates machine learning algorithms. The study compares adaptive modulation based on supervised learning with that based on reinforcement learning. The findings indicate that both approaches greatly improve spectral efficiency, emphasizing the significance of implementing link adaptation in visible light communication-based medical body sensor networks. The use of the Q-learning algorithm in adaptive modulation enables real-time training and enables the system to adjust to the changing environment without any prior knowledge about the environment. A remarkable improvement is observed for photodetectors on the shoulder and wrist since they experience more DC gain.Yayın ANN activation function estimators for homomorphic encrypted inference(Institute of Electrical and Electronics Engineers Inc., 2025-06-13) Harb, Mhd Raja Abou; Çeliktaş, BarışHomomorphic Encryption (HE) enables secure computations on encrypted data, facilitating machine learning inference in sensitive environments such as healthcare and finance. However, efficiently handling non-linear activation functions, specifically Sigmoid and Tanh, remains a significant computational challenge for encrypted inference using Artificial Neural Networks (ANNs). This study introduces a lightweight, ANN-based estimator designed to accurately approximate activation functions under homomorphic encryption. Unlike traditional polynomial and piecewise linear approximations, the proposed ANN estimators achieve superior accuracy with lower computational overhead associated with bootstrapping or high-degree polynomial techniques. These estimators are trained on plaintext data and seamlessly integrated into encrypted inference pipelines, significantly outperforming conventional methods. Experimental evaluations demonstrate notable improvements, with ANN estimators enhancing accuracy by approximately 2% for Sigmoid and up to 73% for Tanh functions, improving F1-scores by approximately 2% for Sigmoid and up to 88% for Tanh, and markedly reducing Mean Square Error (MSE) by up to 96% compared to polynomial approximations. The ANN estimator achieves an accuracy of 97.70% and an AUC of 0.9997 when integrated into a CNN architecture on the MNIST dataset, and an accuracy of 85.25% with an AUC of 0.9459 on the UCI Heart Disease dataset during ciphertext inference. These results underscore the estimator’s practical effectiveness and computational feasibility, making it suitable for secure and efficient ANN inference in encrypted environments.Yayın Enabling 5G and 6G technologies through millimeter-wave and VLC integration for enhanced remote health monitoring systems(Işık Üniversitesi, Lisansüstü Eğitim Enstitüsü, 2025-07-01) Dalloul, Ahmed Hany Assad; Miramirkhani, Farshad; Işık Üniversitesi, Lisansüstü Eğitim Enstitüsü, Elektronik Mühendisliği Yüksek Lisans Programı; Işık University, School of Graduate Studies, Electronics Engineering M.S. ProgramThis thesis examines the pivotal role of wireless networks in healthcare, emphasizing the need for high-performance technologies like 5G and emerging 6G to enable efficient data transfer between medical devices such as sensors and remote monitoring equipment. We delve into the current research landscape surrounding 5G mmWave technology in remote health monitoring systems, focusing on its applications, main challenges, and future trends. We explore the wireless connectivity requirements of reconfigurable hybrid optical-radio-based Medical Body Sensor Networks (MBSNs), proposing an extension of conventional MBSNs to more flexible and generic solutions. This thesis introduces a comprehensive literature review across diverse domains including antenna design, small implantable antennas, on-body wearable solutions, and adaptable detection and imaging systems. Our research further investigates methodological approaches in monitoring systems, analyzing channel characteristics, advancements in wireless capsule endoscopy, and sensing and imaging techniques. Additionally, we explore how 6G's framework integrates Visible Light Communication (VLC) in healthcare, demonstrating how VLC-enabled MBSNs can revolutionize remote patient monitoring and real-time health data transmission by accurately estimating VLC channel parameters, such as channel DC gain and RMS delay spread. We introduce a sophisticated ray tracing technique and ML-based algorithm to model channels and estimate path loss and RMS delay spread within different hospital settings such as ICU ward and family-type patient room. The detailed results of the hospital scenarios are listed using various machine learning algorithms such as LSTM, GRU, RNN, Linear Regression SVR, and KNN. The estimation was illustrated and detailed comprehensively by choosing the best-performing ML technique.Yayın Turkish sentiment analysis: a comprehensive review(Yildiz Technical University, 2024-08) Altınel Girgin, Ayşe Berna; Gümüşçekiçci, Gizem; Birdemir, Nuri CanSentiment analysis (SA) is a very popular research topic in the text mining field. SA is the process of textual mining in which the meaning of a text is detected and extracted. One of the key aspects of SA is to analyze the body of a text to determine its polarity to understand the opinions it expresses. Substantial amounts of data are produced by online resources such as social media sites, blogs, news sites, etc. Due to this reason, it is impossible to process all of this data without automated systems, which has contributed to the rise in popularity of SA in recent years. SA is considered to be extremely essential, mostly due to its ability to analyze mass opinions. SA, and Natural Language Processing (NLP) in particular, has become an overwhelmingly popular topic as social media usage has increased. The data collected from social media has sourced numerous different SA studies due to being versatile and accessible to the masses. This survey presents a comprehensive study categorizing past and present studies by their employed methodologies and levels of sentiment. In this survey, Turkish SA studies were categorized under three sections. These are Dictionary-based, Machine Learning-based, and Hybrid-based. Researchers can discover, compare, and analyze properties of different Turkish SA studies reviewed in this survey, as well as obtain information on the public dataset and the dictionaries used in the studies. The main purpose of this study is to combine Turkish SA approaches and methods while briefly explaining its concepts. This survey uniquely categorizes a large number of related articles and visualizes their properties. To the best of our knowledge, there is no such comprehensive and up-to-date survey that strictly covers Turkish SA which mainly concerns analysis of sentiment levels. Furthermore, this survey contributes to the literature due to its unique property of being the first of its kind.
