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Hybrid Machine Learning Model To Predict Chronic Kidney Diseases Using Handcrafted Features For Early Health Rehabilitation, Amjad Rehman, Tanzila Saba, Haider Ali, Narmine Elhakim, Noor Ayesha
Hybrid Machine Learning Model To Predict Chronic Kidney Diseases Using Handcrafted Features For Early Health Rehabilitation, Amjad Rehman, Tanzila Saba, Haider Ali, Narmine Elhakim, Noor Ayesha
Turkish Journal of Electrical Engineering and Computer Sciences
Chronic kidney diseases proliferate due to hypertension, diabetes, anemia, obesity, smoking etc. Patients with such conditions are sometimes unaware of first symptoms, complicating disease diagnosis. This paper presents chronic kidney disease (CKD) prediction model to classify CKD patients from NCKD (Non-CKD). The proposed study has two main stages. First, we found the odds ratio through logistic regression and comparison test to identify early risk factors from kidneys? MRI and differentiate CKD from NCKD subjects. In stage 2, LR, LDA, MLP classifiers were applied to predict CKD and NCKD by extracting features from MRI. The odds ratio of blood glucose random …
Cognitive Digital Modelling For Hyperspectral Image Classification Using Transfer Learning Model, Mohammad Shabaz, Mukesh Soni
Cognitive Digital Modelling For Hyperspectral Image Classification Using Transfer Learning Model, Mohammad Shabaz, Mukesh Soni
Turkish Journal of Electrical Engineering and Computer Sciences
Deep convolutional neural networks can fully use the intrinsic relationship between features and improve the separability of hyperspectral images, which has received extensive in recent years. However, the need for a large number of labelled samples to train deep network models limits the application of such methods. The idea of transfer learning is introduced into remote sensing image classification to reduce the need for the number of labelled samples. In particular, the situation in which each class in the target picture only has one labelled sample is investigated. In the target domain, the number of training samples is enlarged by …
Classification Of Chronic Pain Using Fmri Data: Unveiling Brain Activity Patterns For Diagnosis, Rejula V, Anitha J, Belfin Robinson
Classification Of Chronic Pain Using Fmri Data: Unveiling Brain Activity Patterns For Diagnosis, Rejula V, Anitha J, Belfin Robinson
Turkish Journal of Electrical Engineering and Computer Sciences
Millions of people throughout the world suffer from the complicated and crippling condition of chronic pain. It can be brought on by several underlying disorders or injuries and is defined by chronic pain that lasts for a period exceeding three months. To better understand the brain processes behind pain and create prediction models for pain-related outcomes, machine learning is a potent technology that may be applied in Functional magnetic resonance imaging (fMRI) chronic pain research. Data (fMRI and T1-weighted images) from 76 participants has been included (30 chronic pain and 46 healthy controls). The raw data were preprocessed using fMRIprep …
Trcaptionnet: A Novel And Accurate Deep Turkish Image Captioning Model With Vision Transformer Based Image Encoders And Deep Linguistic Text Decoders, Serdar Yildiz, Abbas Memi̇ş, Songül Varli
Trcaptionnet: A Novel And Accurate Deep Turkish Image Captioning Model With Vision Transformer Based Image Encoders And Deep Linguistic Text Decoders, Serdar Yildiz, Abbas Memi̇ş, Songül Varli
Turkish Journal of Electrical Engineering and Computer Sciences
Image captioning is known as a fundamental computer vision task aiming to figure out and describe what is happening in an image or image region. Through an image captioning process, it is ensured to describe and define the actions and the relations of the objects within the images. In this manner, the contents of the images can be understood and interpreted automatically by visual computing systems. In this paper, we proposed the TRCaptionNet a novel deep learning-based Turkish image captioning (TIC) model for the automatic generation of Turkish captions. The model we propose essentially consists of a basic image encoder, …
Yolo And Lsh-Based Video Stream Analytics Landscape For Short-Term Traffic Density Surveillance At Road Networks, Lavanya K, Stuti Tiwari, Rahul Anand, Jude Hemanth
Yolo And Lsh-Based Video Stream Analytics Landscape For Short-Term Traffic Density Surveillance At Road Networks, Lavanya K, Stuti Tiwari, Rahul Anand, Jude Hemanth
Turkish Journal of Electrical Engineering and Computer Sciences
The duty of monitoring traffic during rush hour is difficult due to the fact that modern roadways are getting more crowded every day. The automated solutions that have already been created in this area are ineffective at processing enormous amounts of data in a short amount of time, leading to ineffectiveness and inconsistent results. The YOLO (you only look once) and LSH (locality sensitive hashing) algorithms are combined with the Kafka architecture in this study to create a method for assessing traffic density in real-time scenarios. Our concept, which is specifically designed for vehicular networks, predicts the traffic density in …
Deep Feature Extraction, Dimensionality Reduction, And Classification Of Medical Images Using Combined Deep Learning Architectures, Autoencoder, And Multiple Machine Learning Models, Ahmet Hi̇dayet Ki̇raz, Fatime Oumar Djibrillah, Mehmet Emi̇n Yüksel
Deep Feature Extraction, Dimensionality Reduction, And Classification Of Medical Images Using Combined Deep Learning Architectures, Autoencoder, And Multiple Machine Learning Models, Ahmet Hi̇dayet Ki̇raz, Fatime Oumar Djibrillah, Mehmet Emi̇n Yüksel
Turkish Journal of Electrical Engineering and Computer Sciences
Accurate analysis and classification of medical images are essential factors in clinical decision-making and patient care. A novel comparative approach for medical image classification is proposed in this study. This new approach involves several steps: deep feature extraction, which extracts the informative features from medical images; concatenation, which concatenates the extracted deep features to form a robust feature vector; dimensionality reduction with autoencoder, which reduces the dimensionality of the feature vector by transforming it into a different feature space with a lower dimension; and finally, these features obtained from all these steps were fed into multiple machine learning classifiers (SVM, …
Feature Distillation From Vision-Language Model For Semisupervised Action Classification, Asli Çeli̇k, Ayhan Küçükmani̇sa, Oğuzhan Urhan
Feature Distillation From Vision-Language Model For Semisupervised Action Classification, Asli Çeli̇k, Ayhan Küçükmani̇sa, Oğuzhan Urhan
Turkish Journal of Electrical Engineering and Computer Sciences
The training of supervised machine learning approaches is critically dependent on annotating large-scale datasets. Semisupervised learning approaches aim to achieve compatible performance with supervised methods using relatively less annotation without sacrificing good generalization capacity. In line with this objective, ways of leveraging unlabeled data have been the subject of intense research. However, semisupervised video action recognition has received relatively less attention compared to image domain implementations. Existing semisupervised video action recognition methods trained from scratch rely heavily on augmentation techniques, complex architectures, and/or the use of other modalities while distillation-based methods use models that have only been trained for 2D …
Cccd: Corner Detection And Curve Reconstruction For Improved 3d Surface Reconstruction From 2d Medical Images, Mriganka Sarmah, Arambam Neelima
Cccd: Corner Detection And Curve Reconstruction For Improved 3d Surface Reconstruction From 2d Medical Images, Mriganka Sarmah, Arambam Neelima
Turkish Journal of Electrical Engineering and Computer Sciences
The conventional approach to creating 3D surfaces from 2D medical images is the marching cube algorithm, but it often results in rough surfaces. On the other hand, B-spline curves and nonuniform rational B-splines (NURBSs) offer a smoother alternative for 3D surface reconstruction. However, NURBSs use control points (CTPs) to define the object shape and corners play an important role in defining the boundary shape as well. Thus, in order to fill the research gap in applying corner detection (CD) methods to generate the most favorable CTPs, in this paper corner points are identified to predict organ shape. However, CTPs must …
Focal Modulation Network For Lung Segmentation In Chest X-Ray Images, Şaban Öztürk, Tolga Çukur
Focal Modulation Network For Lung Segmentation In Chest X-Ray Images, Şaban Öztürk, Tolga Çukur
Turkish Journal of Electrical Engineering and Computer Sciences
Segmentation of lung regions is of key importance for the automatic analysis of Chest X-Ray (CXR) images, which have a vital role in the detection of various pulmonary diseases. Precise identification of lung regions is the basic prerequisite for disease diagnosis and treatment planning. However, achieving precise lung segmentation poses significant challenges due to factors such as variations in anatomical shape and size, the presence of strong edges at the rib cage and clavicle, and overlapping anatomical structures resulting from diverse diseases. Although commonly considered as the de-facto standard in medical image segmentation, the convolutional UNet architecture and its variants …
Infrared Imaging Segmentation Employing An Explainable Deep Neural Network, Xinfei Liao, Dan Wang, Zairan Li, Nilanjan Dey, Rs Simon, Fuqian Shi
Infrared Imaging Segmentation Employing An Explainable Deep Neural Network, Xinfei Liao, Dan Wang, Zairan Li, Nilanjan Dey, Rs Simon, Fuqian Shi
Turkish Journal of Electrical Engineering and Computer Sciences
Explainable AI (XAI) improved by a deep neural network (DNN) of a residual neural network (ResNet) and long short-term memory networks (LSTMs), termed XAIRL, is proposed for segmenting foot infrared imaging datasets. First, an infrared sensor imaging dataset is acquired by a foot infrared sensor imaging device and preprocessed. The infrared sensor image features are then defined and extracted with XAIRL being applied to segment the dataset. This paper compares and discusses our results with XAIRL. Evaluation indices are applied to perform various measurements for foot infrared image segmentation including accuracy, precision, recall, F1 score, intersection over union (IoU), Dice …
A Comprehensive Review Of Geometrical Thermodynamics: From Fluctuations To Black Holes, S. Mahmoudi, Kh. Jafarzade, Seyed Hossein Hendi
A Comprehensive Review Of Geometrical Thermodynamics: From Fluctuations To Black Holes, S. Mahmoudi, Kh. Jafarzade, Seyed Hossein Hendi
Turkish Journal of Physics
This paper presents a comprehensive review of geometrical thermodynamics, which employs geometric concepts to study the thermodynamic properties of physical systems. The review covers key topics such as thermodynamic fluctuation theory, proposed thermodynamic metrics in various coordinate systems, and thermodynamic curvature.Additionally, the paper discusses the geometrical approach to black hole thermodynamics and provides an overview of recent research in this field.
A Portable And Low-Cost Incubator System Enabling Real-Time Cell Imaging Based On A Smartphone, Ari̇f Engi̇n Çeti̇n
A Portable And Low-Cost Incubator System Enabling Real-Time Cell Imaging Based On A Smartphone, Ari̇f Engi̇n Çeti̇n
Turkish Journal of Physics
Incubators serve as essential tools for maintaining optimal temperature, humidity, and atmospheric gas composition for cell cultures. Their applications span across various fields like cell biology, microbiology, and molecular biology. However, in many cell-based studies, observing cellular changes often necessitates moving cells out of the incubator for microscope examination. Unfortunately, exposing cells to an environment that does not support healthy growth can undermine the reliability of test outcomes. In this article, we present a groundbreaking solution: a cost-effective, portable incubator system seamlessly integrated with a smartphone for real-time characterization tests. This innovation offers comparable imaging capabilities and maintains an environment …
Isotope And Hydrochemical Characteristics Of Thermal Waters Along The Active Fault Zone (Erzin-Hatay/Turkey) And Their Geothermal Potential, Di̇dem Yasi̇n, Gali̇p Yüce
Isotope And Hydrochemical Characteristics Of Thermal Waters Along The Active Fault Zone (Erzin-Hatay/Turkey) And Their Geothermal Potential, Di̇dem Yasi̇n, Gali̇p Yüce
Turkish Journal of Earth Sciences
Geochemical investigations carried out on thermal waters over the Erzin-Hatay area allowed the collection of a suite of 9 samples from natural springs and one well characterized by outlet temperatures in the range from 19.6 to 31.5 °C. All of the springs have slightly acidic pH (in the range of 6) but one sample was marked by a pH value >11 as a consequence of serpentinization processes. The water chemistry denotes water/rock interactions with either magmatic or carbonatic rocks in a water reservoir equilibrated at temperatures estimated to be in the range of 58-162 °C. The stable isotope composition of …
Density Functional Theory Calculations Of Equilibrium Oxygen Isotope Fractionation Between Borate Minerals And Aqueous Fluids, Hai-Zhen Wei, Martin Palmer, Jun-Lin Wang, Shao-Yong Jiang, Simon V. Hohl, Yuan-Feng Zhu, Chun Huan, Miao-Miao Zhang, Yeşi̇m Yücel Öztürk
Density Functional Theory Calculations Of Equilibrium Oxygen Isotope Fractionation Between Borate Minerals And Aqueous Fluids, Hai-Zhen Wei, Martin Palmer, Jun-Lin Wang, Shao-Yong Jiang, Simon V. Hohl, Yuan-Feng Zhu, Chun Huan, Miao-Miao Zhang, Yeşi̇m Yücel Öztürk
Turkish Journal of Earth Sciences
Borax, ulexite, and colemanite minerals are by far the most important economic source of boron and occur almost exclusively in nonmarine evaporite deposits. While much is known about the geological setting in which they are found, surprisingly little is known about the chemical and physical properties of the brines from which they are formed. Oxygen isotope studies have the potential to reveal important new information regarding borate formation, but unlike most other common oxygen-bearing salts precipitated from brines, there are no experimental data regarding the oxygen isotope fractionation factors between borates and brines. As a first attempt to address this …
A Review Of The Geothermal System Evolution And Distribution In The Central Anatolian Crystalline Complex (Türkiye), Mehmet Furkan Şener, Muhammed Zeynel Öztürk, Alper Baba
A Review Of The Geothermal System Evolution And Distribution In The Central Anatolian Crystalline Complex (Türkiye), Mehmet Furkan Şener, Muhammed Zeynel Öztürk, Alper Baba
Turkish Journal of Earth Sciences
Türkiye is located in the Mediterranean sector of the Alpine-Himalayan tectonic belt and is among the foremost seven countries in the world having an abundance of geothermal resources. The Central Anatolian Crystalline Complex (CACC) is one of the most important geothermal regions in Türkiye. This study aims to evaluate the geothermal system of CACC using the geological, structural, and hydrogeochemical properties that were obtained from previous studies. The present study investigated and evaluated the hydrogeochemical and isotopic properties of 762 water samples belonging to 45 different localities from 41 scientific studies. The result shows that CACC has different heat sources …
Dynamics Of A Giant Slow Landslide Complex Along The Coast Of The Aral Sea, Central Asia, Gökhan Aslan, Marcello De Michele, Daniel Raucoules, François Renard, John Dehls, Ivanna Penna, Reginald Hermanns, Zi̇yadi̇n Çakir
Dynamics Of A Giant Slow Landslide Complex Along The Coast Of The Aral Sea, Central Asia, Gökhan Aslan, Marcello De Michele, Daniel Raucoules, François Renard, John Dehls, Ivanna Penna, Reginald Hermanns, Zi̇yadi̇n Çakir
Turkish Journal of Earth Sciences
We report here a slow-moving landslide complex of the lateral spreading type revealed by Sentinel-1 interferometric timeseries analysis. Located along the western coast of the Aral Sea, with a >150-km length and 3-km width, a giant active landslide complex, slides with a constant velocity of up to 60 mm/year. Systematic subsidence up to 5 mm/year is also observed along narrow strips of terraces that appear to result from rotations of fault-bounded blocks. The landslide deformation velocity does not correlate with the annual variations of the water level in the southwestern lake of the Aral Sea during the observation period of …
Mineralogical-Petrographical Features, Geochemical Characteristics, And S Isotope Variability Of Pb-Zn Deposits In The Sakarya Fragment Of The Biga Peninsula (Nw Türkiye), Gökhan Demi̇rela, Si̇nan Akiska, Eli̇f Akiska
Mineralogical-Petrographical Features, Geochemical Characteristics, And S Isotope Variability Of Pb-Zn Deposits In The Sakarya Fragment Of The Biga Peninsula (Nw Türkiye), Gökhan Demi̇rela, Si̇nan Akiska, Eli̇f Akiska
Turkish Journal of Earth Sciences
The Biga Peninsula in northwestern Anatolia, a part of the Alpine-Himalayan orogenic belt, has a complex geology that was formed following the closure of the northern branch of the Neotethys. Intense volcanism and plutonism in the area from the Eocene to the Middle Miocene period caused several Pb-Zn-Cu±Ag±Au deposits to form. The geometry of the mineralizations is largely made up of polymetallic veins, manto-chimneys, and irregular replacement bodies. Ore-bearing and host rocks in the mineralization zones and the wall rocks outside the mineralization zones were compiled in this study. The most common minerals in the skarn zones are garnet, pyroxene, …
Using T-Distributed Stochastic Neighbor Embedding For Visualization And Segmentation Of 3d Point Clouds Of Plants, Heli̇n Dutağaci
Using T-Distributed Stochastic Neighbor Embedding For Visualization And Segmentation Of 3d Point Clouds Of Plants, Heli̇n Dutağaci
Turkish Journal of Electrical Engineering and Computer Sciences
In this work, the use of t-SNE is proposed to embed 3D point clouds of plants into 2D space for plant characterization. It is demonstrated that t-SNE operates as a practical tool to flatten and visualize a complete 3D plant model in 2D space. The perplexity parameter of t-SNE allows 2D rendering of plant structures at various organizational levels. Aside from the promise of serving as a visualization tool for plant scientists, t-SNE also provides a gateway for processing 3D point clouds of plants using their embedded counterparts in 2D. In this paper, simple methods were proposed to perform semantic …
Dynamic Deep Neural Network Inference Via Adaptive Channel Skipping, Meixia Zou, Xiuwen Li, Jinzheng Fang, Hong Wen, Weiwei Fang
Dynamic Deep Neural Network Inference Via Adaptive Channel Skipping, Meixia Zou, Xiuwen Li, Jinzheng Fang, Hong Wen, Weiwei Fang
Turkish Journal of Electrical Engineering and Computer Sciences
Deep neural networks have recently made remarkable achievements in computer vision applications. However, the high computational requirements needed to achieve accurate inference results can be a significant barrier to deploying DNNs on resource-constrained computing devices, such as those found in the Internet-of-things. In this work, we propose a fresh approach called adaptive channel skipping (ACS) that prioritizes the identification of the most suitable channels for skipping and implements an efficient skipping mechanism during inference. We begin with the development of a new gating network model, ACS-GN, which employs fine-grained channel-wise skipping to enable input-dependent inference and achieve a desirable balance …
Joint Intent Detection And Slot Filling For Turkish Natural Language Understanding, Osman Büyük
Joint Intent Detection And Slot Filling For Turkish Natural Language Understanding, Osman Büyük
Turkish Journal of Electrical Engineering and Computer Sciences
Intent detection and slot filling are two crucial subtasks of a text-based goal-oriented dialogue system. In a goal-oriented dialogue system, users interact with the system to complete a goal (or to fulfill their intent) and provide the necessary information (slot values) to achieve that goal. Therefore, a user?s text input includes information about the user?s intent and contains required slot values. Recently, joint models that simultaneously detect the intent and extract the slots are proposed to benefit from the interaction between the two tasks. The proposed methods are usually tested using benchmark data sets in English such as ATIS and …
Transforming Temporal-Dynamic Graphs Into Time-Series Data For Solving Event Detection Problems, Kutay Taşci, Fuat Akal
Transforming Temporal-Dynamic Graphs Into Time-Series Data For Solving Event Detection Problems, Kutay Taşci, Fuat Akal
Turkish Journal of Electrical Engineering and Computer Sciences
Event detection on temporal-dynamic graphs aims at detecting significant events based on deviations from the normal behavior of the graphs. With the widespread use of social media, many real-world events manifest as social media interactions, making them suitable for modeling as temporal-dynamic graphs. This paper presents a workflow for event detection on temporal-dynamic graphs using graph representation learning. Our workflow leverages generated embeddings of a temporal-dynamic graph to reframe the problem as an unsupervised time-series anomaly detection task. We evaluated our workflow on four distinct real-world social media datasets and compared our results with the related work. The results show …
Recognizing Handwritten Digits Using Spiking Neural Networks With Learning Algorithms Based On Sliding Mode Control Theory, Yeşi̇m Öni̇z, Mehmet Ayyildiz
Recognizing Handwritten Digits Using Spiking Neural Networks With Learning Algorithms Based On Sliding Mode Control Theory, Yeşi̇m Öni̇z, Mehmet Ayyildiz
Turkish Journal of Electrical Engineering and Computer Sciences
In this paper, a spiking neural network (SNN) has been proposed for recognizing the digits written on the LCD screen of an experimental setup. The convergence of the learning algorithm has been ensured by using sliding mode control (SMC) theory and the Lyapunov stability method for the adaptation of the network parameters. The spike response model (SRM) has been utilized in the design of the SNN. The performance of the proposed learning scheme has been evaluated both on the experimental data and on the MNIST dataset. The simulated and experimental results of the SNN structure have been compared with the …
A Machine Learning Approach For Dyslexia Detection Using Turkish Audio Records, Tuğberk Taş, Muhammed Abdullah Bülbül, Abas Haşi̇moğlu, Yavuz Meral, Yasi̇n Çalişkan, Gunay Budagova, Mücahi̇d Kutlu
A Machine Learning Approach For Dyslexia Detection Using Turkish Audio Records, Tuğberk Taş, Muhammed Abdullah Bülbül, Abas Haşi̇moğlu, Yavuz Meral, Yasi̇n Çalişkan, Gunay Budagova, Mücahi̇d Kutlu
Turkish Journal of Electrical Engineering and Computer Sciences
Dyslexia is a learning disorder, characterized by impairment in the ability to read, spell, and decode letters. It is vital to detect dyslexia in earlier stages to reduce its effects. However, diagnosing dyslexia is a time-consuming and costly process. In this paper, we propose a machine-learning model that predicts whether a Turkish-speaking child has dyslexia using his/her audio records. Therefore, our model can be easily used by smart phones and work as a warning system such that children who are likely to be dyslexic according to our model can seek an examination by experts. In order to train and evaluate, …
Direct Pore-Based Identification For Fingerprint Matching Process, Vedat Delican, Behçet Uğur Töreyi̇n, Ege Çeti̇n, Ayli̇n Yalçin Saribey
Direct Pore-Based Identification For Fingerprint Matching Process, Vedat Delican, Behçet Uğur Töreyi̇n, Ege Çeti̇n, Ayli̇n Yalçin Saribey
Turkish Journal of Electrical Engineering and Computer Sciences
Fingerprints are one of the most important scientific proof instruments in solving forensic cases. Identification in fingerprints consists of three levels based on the flow direction of the papillary lines at the first level, the minutiae points at the second level, and the pores at the third level. The inadequacy of existing imaging systems in detecting fingerprints and the lack of pore details at the desired level limit the widespread use of third-level identification. The fact that fingerprints with images based on pores in the unsolved database are not subjected to any evaluation criteria and remain in the database reveals …
Zircon Grain Shape Parameters From Ignimbrites Of The Central Anatolian Volcanic Province (Cavp) With Implications For Petrogenetic Processes, Lütfi̇ye Akin
Turkish Journal of Earth Sciences
Zircon morphology parameters reflect the physicochemical conditions during crystallization and can be modified by different processes. Zircon populations from Miocene-Pliocene ignimbrites of the Central Anatolian Volcanic Province (CAVP) were studied to reveal relations between the external morphology of zircons and petrogenetic processes. Descriptive grain shape parameters (e.g., minor and major axes, area, perimeter, aspect ratio, roundness, and circularity) were automatically measured from transmitted light images of zircons by a graphical application called AnalyZr. Principal component and cluster analysis were used to determine potential shape descriptors (perimeter, major and minor axis length, and maximum and minimum Feret) for characterizing grains from …
Rank Of Kozlu Formation Coals In The Zonguldak Basin: Implications For Coalbed Gas, Mehmet Namik Yalçin, Cemi̇l Seyi̇s, Sedat İnan
Rank Of Kozlu Formation Coals In The Zonguldak Basin: Implications For Coalbed Gas, Mehmet Namik Yalçin, Cemi̇l Seyi̇s, Sedat İnan
Turkish Journal of Earth Sciences
Carboniferous bituminous coals of the Zonguldak Basin have been mined for over a century. Due to underground mining activity, there have been several fatal incidents related to gas explosions. The gas content of the coals varies greatly in the basin mainly based on coal maturity (rank), increasing with increasing coal depth. In this study, we report a map for coal depth and coal rank in the Kozlu-Üzülmez-Karadon districts of Zonguldak Basin with hope the that it would aid coal gas exploration/exploitation and also coal gas degassing efforts for safer underground mining production in the Zonguldak Basin.
Stepwise Dynamic Nearest Neighbor (Sdnn): A New Algorithm For Classification, Deni̇z Karabaş, Derya Bi̇rant, Peli̇n Yildirim Taşer
Stepwise Dynamic Nearest Neighbor (Sdnn): A New Algorithm For Classification, Deni̇z Karabaş, Derya Bi̇rant, Peli̇n Yildirim Taşer
Turkish Journal of Electrical Engineering and Computer Sciences
Although the standard k-nearest neighbor (KNN) algorithm has been used widely for classification in many different fields, it suffers from various limitations that abate its classification ability, such as being influenced by the distribution of instances, ignoring distances between the test instance and its neighbors during classification, and building a single/weak learner. This paper proposes a novel algorithm, called stepwise dynamic nearest neighbor (SDNN), which can effectively handle these problems. Instead of using a fixed parameter k like KNN, it uses a dynamic neighborhood strategy according to the data distribution and implements a new voting mechanism, called stepwise voting. Experimental …
Cognitive Load Detection Using Ci-Ssa For Eeg Signal Decomposition And Nature-Inspired Feature Selection, Jammisetty Yedukondalu, Lakhan Dev Sharma
Cognitive Load Detection Using Ci-Ssa For Eeg Signal Decomposition And Nature-Inspired Feature Selection, Jammisetty Yedukondalu, Lakhan Dev Sharma
Turkish Journal of Electrical Engineering and Computer Sciences
Cognitive load detection is eminent during the mental assignment of neural activity because it indicates how the brain reacts to stimuli. The level of cognitive load experienced during mental arithmetic tasks can be determined using an electroencephalogram (EEG). The EEG data were collected from publicly available datasets, namely, mental arithmetic task (MAT) and simultaneous task workload (STEW). The first phase comprises decomposing the electroencephalogram (EEG) signal into intrinsic mode functions (IMFs) using circulant singular spectrum analysis (Ci-SSA). In the second phase, entropy-based features were evaluated using IMFs. After that, the extracted features were fed to nature-inspired feature selection algorithms: genetic …
Determination Of The Genetic Diversity, Population Structure, And Some Ecological Preferences Of The Endemic Muscari Adilii, Ali̇ Murat Keser, Ahmet Emre Yaprak, Gül Ni̇lhan Tuğ
Determination Of The Genetic Diversity, Population Structure, And Some Ecological Preferences Of The Endemic Muscari Adilii, Ali̇ Murat Keser, Ahmet Emre Yaprak, Gül Ni̇lhan Tuğ
Turkish Journal of Botany
The population structure and genetic diversity of M. adilii and some edaphic and climatic preferences of the species were investigated. Although the chemical characteristics such as pH, EC, and gypsum were similar for the three subpopulations, the physical qualities and lime content of the soil were different from each other. In terms of bioclimate, the Nallıhan station (Nallıhan Bird Sanctuary) has a lower annual average temperature and precipitation value, and monthly average values during the germination and flowering periods, than the Beypazarı station (Hırkatepe and Çoban Ahmet Fountain). Amplification with 16 ISSR markers produced 377 bands from 84 individuals belonging …