Search

Abstract The most cost-effective data collection method is electroencephalography (EEG), which obtains meaningful information about the brain. Therefore, EEG signal processing is crucial for neuroscience and machine learning (ML). Therefore, a new EEG stress dataset has been collected, and an explainable feature engineering (XFE) model has been proposed using the Directed Lobish (DLob) symbolic language. The first phase of this research is the data collection phase, and an EEG stress dataset was gathered from 310 participants. This collected stress dataset contains two classes: (i) stress and (ii) control. An XFE model has been presented to detect stress automatically. The presented XFE model has four main phases, and these are (i) channel transformer and quadruple transition pattern (QuadTPat)-based feature generation, (ii) feature selection deploying cumulative weighted neighborhood component analysis (CWNCA), (iii) explainable results creation with DLob and (iv) classification with t algorithm-based k-nearest neighbors (tkNN) classifier. The proposed XFE model generates a DLob string, and the explainable results were obtained using this string. Moreover, the presented XFE model attained 92.95% and 73.63% classification accuracy, deploying 10-fold and leave-one subject-out (LOSO) cross-validations (CV). According to the classification performances, the recommended QuadTPat-based XFE is a good model for EEG signal classification. Also, the presented QuadTPat-based XFE model is a good model for explainable artificial intelligence (XAI) since TTPat-based XFE is cooperating with the DLob.

Abstract The most cost-effective data collection method is electroencephalography (EEG) to obtain meaningful information about the brain. Therefore, EEG signal processing is very important for neuroscience and machine learning (ML). The primary objective of this research is to detect neonatal seizures and explain these seizures using the new version of Directed Lobish. This research uses a publicly available neonatal EEG signal dataset to get comparative results. In order to classify these EEG signals, an explainable feature engineering (EFE) model has been proposed. In this EFE model, there are four essential phases and these phases: (i) automaton and transformer-based feature extraction, (ii) feature selection deploying cumulative weight-based neighborhood component analysis (CWNCA), (iii) the Directed Lobish (DLob) and Causal Connectome Theory (CCT)-based explainable result generation and (iv) classification deploying t algorithm-based support vector machine (tSVM). In the first phase, we have used a channel transformer to get channel numbers and these values have been divided into three levels and these levels are named (1) high, (2) medium and (3) low. By utilizing these levels, we have created an automaton and this automaton has three nodes (each node defines each level). In the feature extraction phase, transition tables of these nodes has been extracted. Therefore, the proposed feature extraction function is termed Triple Nodes Automaton-based Transition table Pattern (TATPat). The used EEG signal dataset contains 19 channels and there are 9 (= 32) connection in the defined automaton. Thus, the presented TATPat extracts 3249 (= 19 × 19 × 9) features from each EEG segment. To choose the most informative features of these 3249 features, a new feature selector which is CWNCA has been applied. By cooperating findings of this feature selector and the presented DLob, the explainable results have been obtained. The last phase is the classification phase and to get high classification performance from this phase, an ensemble classifier (tSVM) has been presented and the classification results have been obtained using two validation techniques which are 10-fold cross-validation (CV) and leave-one subject-out (LOSO) CV. The proposed EFE model generates a DLob string and by using this string, the explainable results have been obtained. Moreover, the presented EFE model attained 99.15% and 76.37% classification accuracy deploying 10-fold and LOSO CVs respectively. According to the classification performances, the recommended TATPat-based EFE is a good model at EEG signal classification. Also, the presented TATPat-based EFE model is a good model for explainable artificial intelligence (XAI) since TTPat-based EFE is cooperating by the DLob.

Electrocardiography (ECG) signals are often referred to as the language of the heart and have been widely utilized for diagnosing various heart ailments, particularly arrhythmias. Consequently, numerous machine learning models have been employed to automatically detect heart disorders using ECG signals. In this research, the primary objective is to detect arrhythmias using a center-symmetric self-organized textual pattern. A novel feature engineering model has been introduced, which includes the following components: (i) multilevel feature extraction using the proposed center-symmetric self-organized textual pattern (CSSOTP), (ii) iterative neighborhood component analysis (INCA), and (iii) classification using k-nearest neighbors (kNN) with 10-fold cross-validation. During the feature extraction phase, a multilevel feature extraction model incorporating maximum absolute pooling (MAP) and the proposed CSSOTP was applied. The CSSOTP was designed to select the most appropriate pattern for the given data block. A large language model (LLM) was leveraged to generate text for creating patterns, and ChatGPT was used to assist in text generation. To identify the most informative features, the INCA feature selector was employed, and the selected features were subsequently classified using the kNN classifier. An impressive 96.20 % classification accuracy was achieved by the CSSOTP-based feature engineering model when tested on an ECG dataset containing 17 classes. Furthermore, comparisons with state-of-the-art feature engineering models were conducted, demonstrating that the proposed model has superior classification capabilities.

This study focuses on automatically detecting developmental hip dysplasia (DHD) using a novel feature engineering model, FlexiLBPHOG, inspired by the FlexiViT model. The model utilizes five patch types for feature extraction with local binary pattern (LBP) and histogram of oriented gradients (HOG) techniques. During feature extraction, five feature vectors are generated. In the next stage, three feature selection methods—Neighborhood Component Analysis (NCA), Chi-square (Chi2), and ReliefF (RF)—are used to select the top 500 features. Classification is performed using support vector machine (SVM) and k-nearest neighbors (kNN), resulting in 30 outcomes. Information fusion through iterative majority voting (IMV) and a greedy algorithm yields 58 outcomes, from which the best is selected. The FlexiLBPHOG model achieved a classification accuracy of 94.38% in detecting DHD in ultrasound images from a private dataset. The study confirms the effectiveness of the proposed model in image classification by integrating shallow image descriptors.

necessity has disrupted traditional norms and has affected all aspects of life. Undoubtedly, the education sector is one of the most significant areas affected by technology. As the population rate increases, the importance and necessity of education also increase. In response to this need for education, new educational models emerge based on the contemporary conditions. One of the critical factors that lead to the emergence of these new models is the COVID-19 outbreak that affected the entire world in March 2020. As a result of this epidemic, countries have adopted various methods in the field of education and training. Some countries suspended their educational activities, while others have continued the process through distance education. Distance education is a teaching method that relies heavily on the active use of information and information tools. The extensive use of distance education tools can attract the attention of malicious individuals who may seek to access these services directly or indirectly. This study aims to present the data and information obtained from analyzing the traffic information stored by Microsoft Teams and Skype or incoming and outgoing traffic information over the network in an isolated environment. The study was conducted on computers with the Windows 10 operating system and web client applications installed. One of the primary goals of this study is to assist researchers who will study distance education tools in the future.

Community Emotion Detection (CED) is important to public safety and sound forensics. It enables the analysis of collective emotions in dynamic environments such as public events, protests, and emergencies.This research presents a new, cost-effective, automatic, and self-organized classification model for CED. The major objective of this research is to show the effectiveness of signal processing in the CED research area. The largest known CED sound dataset was constructed for this study. This dataset was curated to investigate the presented automated CED model’s classification capability. The curated CED sound dataset consists of 5,051 three-second overlapping sound samples categorized as negative, neutral, and positive emotions. This dataset increases the model’s generalizability by reflecting diverse acoustic and emotional scenarios.A self-organized feature extraction function, called the Forward-Forward Pattern-Based Feature Generator (FF-Orbital), is introduced. The FF-Orbital autonomously selects the most suitable pattern from six predefined patterns. This eliminates the need for manual feature engineering. Additionally, a multi-level feature extraction method is enabled by integrating the Unbalanced Tree Multilevel Discrete Wavelet Transform (UTMDWT). This method generates frequency bands that provide extract spatial and frequency-domain features. Iterative Neighborhood Component Analysis (INCA) has selected the most informative features. INCA is a self-organized feature selector. Classification is then performed using a Bayesian-optimized Support Vector Machine (SVM).Tests were conducted using 10-fold cross-validation. The model achieved a classification accuracy of 98.81%. These results demonstrate the usability of the CED model and its effectiveness in digital forensics, public safety, and community-level sentiment analysis.This work makes a significant contribution to the CED research domain by providing a feature-engineering-based alternative to resource-intensive deep learning models. The results show that the proposed model is valuable for signal processing and sound forensics.

This work aims to develop a novel convolutional neural network (CNN) named ResNet50* to detect various gastrointestinal diseases using a new ResNet50*-based deep feature engineering model with endoscopy images. The novelty of this work is the development of ResNet50*, a new variant of the ResNet model, featuring convolution-based residual blocks and a pooling-based attention mechanism similar to PoolFormer. Using ResNet50*, a gastrointestinal image dataset was trained, and an explainable deep feature engineering (DFE) model was developed. This DFE model comprises four primary stages: (i) feature extraction, (ii) iterative feature selection, (iii) classification using shallow classifiers, and (iv) information fusion. The DFE model is self-organizing, producing 14 different outcomes (8 classifier-specific and 6 voted) and selecting the most effective result as the final decision. During feature extraction, heatmaps are identified using gradient-weighted class activation mapping (Grad-CAM) with features derived from these regions via the final global average pooling layer of the pretrained ResNet50*. Four iterative feature selectors are employed in the feature selection stage to obtain distinct feature vectors. The classifiers k-nearest neighbors (kNN) and support vector machine (SVM) are used to produce specific outcomes. Iterative majority voting is employed in the final stage to obtain voted outcomes using the top result determined by the greedy algorithm based on classification accuracy. The presented ResNet50* was trained on an augmented version of the Kvasir dataset, and its performance was tested using Kvasir, Kvasir version 2, and wireless capsule endoscopy (WCE) curated colon disease image datasets. Our proposed ResNet50* model demonstrated a classification accuracy of more than 92% for all three datasets and a remarkable 99.13% accuracy for the WCE dataset. These findings affirm the superior classification ability of the ResNet50* model and confirm the generalizability of the developed architecture, showing consistent performance across all three distinct datasets.

Background and Objectives: Electroencephalography (EEG) signals, often termed the letters of the brain, are one of the most cost-effective methods for gathering valuable information about brain activity. This study presents a new explainable feature engineering (XFE) model designed to classify EEG data for violence detection. The primary objective is to assess the classification capability of the proposed XFE model, which uses a next-generation feature extractor, and to obtain interpretable findings for EEG-based violence and stress detection. Materials and Methods: In this research, two distinct EEG signal datasets were used to obtain classification and explainable results. The recommended XFE model utilizes a channel-based minimum and maximum pattern (ChMinMaxPat) feature extraction function, which generates 15 distinct feature vectors from EEG data. Cumulative weight-based neighborhood component analysis (CWNCA) is employed to select the most informative features from these vectors. Classification is performed by applying an iterative and ensemble t-algorithm-based k-nearest neighbors (tkNN) classifier to each feature vector. Information fusion is achieved through iterative majority voting (IMV), which consolidates the 15 tkNN classification results. Finally, the Directed Lobish (DLob) symbolic language generates interpretable outputs by leveraging the identities of the selected features. Together, the tkNN classifier, IMV-based information fusion, and DLob-based explainable feature extraction transform the model into a self-organizing explainable feature engineering (SOXFE) framework. Results: The ChMinMaxPat-based model achieved over 70% accuracy on both datasets with leave-one-record-out (LORO) cross-validation (CV) and over 90% accuracy with 10-fold CV. For each dataset, 15 DLob strings were generated, providing explainable outputs based on these symbolic representations. Conclusions: The ChMinMaxPat-based SOXFE model demonstrates high classification accuracy and interpretability in detecting violence and stress from EEG signals. This model contributes to both feature engineering and neuroscience by enabling explainable EEG classification, underscoring the potential importance of EEG analysis in clinical and forensic applications.

Background: The primary objective of this research is to propose a new, simple, and effective feature extraction function and to investigate its classification ability using electrocardiogram (ECG) signals. Methods: In this research, we present a new and simple feature extraction function named the minimum and maximum pattern (MinMaxPat). In the proposed MinMaxPat, the signal is divided into overlapping blocks with a length of 16, and the indexes of the minimum and maximum values are identified. Then, using the computed indices, a feature map is calculated in base 16, and the histogram of the generated map is extracted to obtain the feature vector. The length of the generated feature vector is 256. To evaluate the classification ability of this feature extraction function, we present a new feature engineering model with three main phases: (i) feature extraction using MinMaxPat, (ii) cumulative weight-based iterative neighborhood component analysis (CWINCA)-based feature selection, and (iii) classification using a t-algorithm-based k-nearest neighbors (tkNN) classifier. Results: To obtain results, we applied the proposed MinMaxPat-based feature engineering model to a publicly available ECG fibromyalgia dataset. Using this dataset, three cases were analyzed, and the proposed MinMaxPat-based model achieved over 80% classification accuracy with both leave-one-record-out (LORO) cross-validation (CV) and 10-fold CV. Conclusions: These results clearly demonstrate that this simple model achieved high classification performance. Therefore, this model is surprisingly effective for ECG signal classification.

Background: Electrocardiography (ECG) signals are commonly used to detect cardiac disorders, with 12-lead ECGs being the standard method for acquiring these signals. The primary objective of this research is to propose a new feature engineering model that achieves both high classification accuracy and explainable results using ECG signals. To this end, a symbolic language, named Cardioish, has been introduced. Methods: In this research, two publicly available datasets were used: (i) a mental disorder classification dataset and (ii) a myocardial infarction (MI) dataset. These datasets contain ECG beats and include 4 and 11 classes, respectively. To obtain explainable results from these ECG signal datasets, a new explainable feature engineering (XFE) model has been proposed. The Cardioish-based XFE model consists of four main phases: (i) lead transformation and transition table feature extraction, (ii) iterative neighborhood component analysis (INCA) for feature selection, (iii) classification, and (iv) explainable results generation using the recommended Cardioish. In the feature extraction phase, the lead transformer converts ECG signals into lead indexes. To extract features from the transformed signals, a transition table-based feature extractor is applied, resulting in 144 features (12 × 12) from each ECG signal. In the feature selection phase, INCA is used to select the most informative features from the 144 generated, which are then classified using the k-nearest neighbors (kNN) classifier. The final phase is the explainable artificial intelligence (XAI) phase. In this phase, Cardioish symbols are created, forming a Cardioish sentence. By analyzing the extracted sentence, XAI results are obtained. Additionally, these results can be integrated into connectome theory for applications in cardiology. Results: The presented Cardioish-based XFE model achieved over 99% classification accuracy on both datasets. Moreover, the XAI results related to these disorders have been presented in this research. Conclusions: The recommended Cardioish-based XFE model achieved high classification performance for both datasets and provided explainable results. In this regard, our proposal paves a new way for ECG classification and interpretation.

IntroductionIt is widely acknowledged that coaches and physical education teachers play an important role in supporting holistic development in children and ensuring optimal performance in the training processes carried out to acquire fundamental movements and sport-specific basic skills. However, there is a need for further information on how both groups utilize and value different teaching methods during the training. The present study aims to examine the perceptions of coaches and physical education teachers regarding the use and value of teaching methods.MethodsThe “Coaches’ Instructional Methods Utilization Scale” for coaches and the “Physical Education Teachers’ Perception of Teaching Methods Scale” for physical education teachers were administered to 114 coaches and 115 physical education teachers voluntarily participating from three randomly selected provinces of Türkiye. The Cronbach Alpha values ranged between .89 and .93 for the “Coaches’ Instructional Methods Utilization Scale” and between .90 and .96 for the “Physical Education Teachers’ Perception of Teaching Methods Scale”. Descriptive statistics were used in research, t-tests in binary comparisons, One-Way ANOVA in multiple comparisons, and Tukey's test in determining the source of differences.ResultsSimilarities were observed in the most and least used methods by coaches and physical education teachers, as well as in their perceptions of the highest and lowest values for these methods. Additionally, coaches and physical education teachers exhibited similarities in their perceptions of value in terms of entertainment, learning, and motivation. It was observed that female physical education teachers had lower value perceptions among the levels of use of teaching methods and value perceptions according to gender. Comparing the usage levels and value perceptions of teaching methods by professional experience, significant differences were found in Exercise (B), Learner-Designed Individual Program (I), and Learner-initiated (J) methods for coaches, whereas no statistically significant difference was observed in value perceptions. Moreover, considering the physical education teachers, significant differences were found in Command (A), Self-Check (D), Guided Discovery (F), Problem-Solving: Single Solution (G) methods, and in value perceptions for the Exercise (B), Guided Discovery (F), Problem-Solving: Single Solution (G), Problem-Solving: Crating Different Paths (H), and Learner-initiated (J) values.

Electroencephalogram (EEG) signals contain information about the brain’s state as they reflect the brain’s functioning. However, the manual interpretation of EEG signals is tedious and time-consuming. Therefore, automatic EEG translation models need to be proposed using machine learning methods. In this study, we proposed an innovative method to achieve high classification performance with explainable results. We introduce channel-based transformation, a channel pattern (ChannelPat), the t algorithm, and Lobish (a symbolic language). By using channel-based transformation, EEG signals were encoded using the index of the channels. The proposed ChannelPat feature extractor encoded the transition between two channels and served as a histogram-based feature extractor. An iterative neighborhood component analysis (INCA) feature selector was employed to select the most informative features, and the selected features were fed into a new ensemble k-nearest neighbor (tkNN) classifier. To evaluate the classification capability of the proposed channel-based EEG language detection model, a new EEG language dataset comprising Arabic and Turkish was collected. Additionally, Lobish was introduced to obtain explainable outcomes from the proposed EEG language detection model. The proposed channel-based feature engineering model was applied to the collected EEG language dataset, achieving a classification accuracy of 98.59%. Lobish extracted meaningful information from the cortex of the brain for language detection.

Çalışmadaki amaç, ülke çapında 1932’de açılan halkevlerinin yayın faaliyetleri üzerinden, Van Halkevi’nin tek yayını olarak tespit edebildiğimiz, halk dershaneleri komitesi başkanı ve aynı zamanda Maarif Müdürü olan Mustafa Noyan tarafından kaleme alınan Van Tarihine Medhal Birinci Sardur’un Kitabeleri isimli eserini analiz etmek ve erken dönem Cumhuriyet arkeolojisine katkılarını değerlendirmektir. Halkevleri açıldıktan sonra fiziki şartları el verdiği müddetçe şube kolları ile çalışmalar yürütmüştür. Dil-edebiyat ve tarih şubesi ile kitapsaray ve yayın şubesi bünyesinde açıldığı halkevinde yayın faaliyetlerinden sorumlu olmuştur. Kemalist ideolojinin halk nezdinde yayılması ve devrim mantığının anlaşılması için halkevleri yayınladıkları gazete, dergi, kitap, broşürlerle kendi etkinlik alanlarını yaratmışlardır. Özellikle cumhuriyet rejiminin idealize ettiği aynı ülkü etrafında toplanmış ve kaynaşmış bir toplum oluşturulması noktasında çalışmalar yürüten şubeler tarih, edebiyat, folklor, halk bilimi konularında eserler ortaya çıkarmıştır. Söz konusu eserlerde temel prensip millî değerlerin ve kültürün genç nesillere öğretilmesidir. Bu süreç de şüphesiz, okuma-yazma etkinliği ile anlaşılabilecek ve aktarılacaktır. Çalışmada, ülke genelindeki halkevlerinin yayın faaliyetleri ile ne amaçladığını izah etmeye çalışmakla birlikte, 1940’lı yılların başında Van Kalesi/Urartular ile ilgili yüksek bir bilinç seviyesine ve çalışma azmine sahip Mustafa Noyan’ın söz konusu eserini akademik olarak incelemek hedeflenmektedir.

Technological advancements have recently propelled individuals, institutions, and organizations to conduct their business processes on information systems. However, keeping personal and corporate data on information systems has given rise to issues related to data security. The accessibility of data on information systems has made it vulnerable to theft and exploitation by malicious groups or individuals, thus posing a significant risk to data security. Consequently, the demand for data security has led to a new business sector offering various cybersecurity solutions to protect organizations' systems. This paper presents an analysis of the prevalent types of cyber attacks worldwide. The study aims to create a virtual environment with Windows and Linux systems in Forensic Informatics and Incident Response processes to apply frequently used cyber attack methods, develop defense mechanisms against these methods, and contribute to revealing the root cause by solving the incident pattern. Furthermore, this application demonstrates how manual techniques and open-source solutions, such as YARA, can be used to detect malware derivatives commonly found in Windows systems.

Nowadays, the forward-forward (FF) algorithm is very popular in the machine learning society, and it uses a square-based activation function. In this research, we inspired the FF algorithm and presented a new kernel for a local binary pattern named square-kernelled local binary pattern (SKLBP). By deploying the proposed one-dimensional SKLBP, a new feature engineering model has been presented. To measure the classification ability of the proposed SKLBP-based model, we have collected a new textural environmental sound classification (ESC) dataset. The collected dataset is a balanced dataset, and it contains 15 classes. There are 100 sounds in each class. Our proposed model has mimicked the deep learning structure. Therefore, it uses multileveled feature extraction methodology by using discrete wavelet transform. The features generated have been considered as input for the iterative feature selector. The chosen feature vector has been utilized as input of the k nearest neighbor classifier. The proposed SKLBP-based signal classification model reached 94% classification accuracy. In this aspect, we contributed to the ESC methodology by collecting the new textural ESC dataset and proposing the SKLBP-based ESC model.

Fibromyalgia is a chronic pain syndrome associated with sleep disturbances, which may manifest as altered electroencephalography and electrocardiography (ECG) signal alterations during sleep. We aimed to develop a lightweight machine learning model for diagnosing fibromyalgia using single-lead ECG signals recorded during sleep. We analyzed 139 single-lead ECGs recorded during Stage 2 and Sleep Stage 3 of 16 patients with fibromyalgia and 16 age and sex matched controls. ECG records were divided into 15-second segments: 3308 and 1783 in healthy vs fibromyalgia classes, respectively. Our model comprised (1) feature extraction that combined an 8-wavelet filter and 4-level multiple filters-based multilevel discrete wavelet transform signal decomposition with a novel local binary pattern (LBP)-like function, 3LBP, that generated multiple patterns (analogous to quantum superposition) for feature map value extraction (the optimal input-specific pattern was dynamically selected using a novel forward-forward algorithm); (2) feature selection using neighborhood component analysis and Chi-square functions; (3) classification with k-nearest neighbors and support vector machine classifiers using leave-one-record-out cross-validation; and (4) mode function-based iterative majority voting to generate voted results, from which the best model result was derived. Our model attained binary classification accuracies of 93.87% and 92.02% for Sleep Stage 2 and Sleep Stage 3, respectively. The observed outcomes and empirical evidence unequivocally demonstrate the efficacy of our proposed methodology in differentiating the electrocardiographic signatures of fibromyalgia patients from control subjects. The model exhibited self-organizational properties and computational efficiency, rendering it amenable to facile clinical integration.

Most emotion classification schemes to date have concentrated on individual inputs rather than crowd-level signals. In addressing this gap, we introduce Sound-based Community Emotion Recognition (SCED) as a fresh challenge in the machine learning domain. In this pursuit, we crafted the FF-BTP-based feature engineering model inspired by deep learning principles, specifically designed for discerning crowd sentiments. Our unique dataset was derived from 187 YouTube videos, summing up to 2733 segments each of 3 seconds (sampled at 44.1 KHz). These segments, capturing overlapping speech, ambient sounds, and more, were meticulously categorized into negative, neutral, and positive emotional content. Our architectural design fuses the BTP, a textural feature extractor, and an innovative handcrafted feature selector inspired by Hinton’s FF algorithm. This combination identifies the most salient feature vector using calculated mean square error. Further enhancements include the incorporation of a multilevel discrete wavelet transform for spatial and frequency domain feature extraction, and a sophisticated iterative neighborhood component analysis for feature selection, eventually employing a support vector machine for classification. On testing, our FF-BTP model showcased an impressive 97.22% classification accuracy across three categories using the SCED dataset. This handcrafted approach, although inspired by deep learning’s feature analysis depth, requires significantly lower computational resources and still delivers outstanding results. It holds promise for future SCED-centric applications.

Pulmonary hypertension (PH) is a chronic and progressive disease. We introduced a novel automated self-organized feature engineering architecture for PH detection, which was trained and refined using a new thoracic CT image dataset. This study’s dataset includes 807 transverse contrast-enhanced CT images from 313 patients, categorized into four groups: Group 1 with 20 mmHg $\le $ mean pulmonary artery pressure (mPAP) < 25 mmHg; Group 2 with 25 mmHg $\le $ mPAP $\le30$ mmHg; Group 3 where mPAP > 30 mmHg; and a control group with no PH. Our model consists of four primary stages: (i) generation of features based on combinations from nested patches, (ii) feature selection, (iii) classification and (iv) majority voting. CT images were segmented into nested patches, each being processed through pretrained EfficientNetB0 and DenseNet201 to derive four deep feature vectors, utilizing both the global average pooling and fully connected layers of these networks. These four extracted features underwent combinatorial operations, resulting in 15 feature vectors. Subsequently, these vectors were introduced to neighborhood component analysis, ReliefF, and Chi2 feature selectors. This process yielded 45 refined feature vectors with diminished data dimensions. These selected vectors were then processed through a support vector machine and k-nearest neighbors classifiers, producing 90 predictive vectors. By applying mode-based iterative majority voting to these vectors, an additional 88 voted prediction vectors were generated, leading to a total of 178 classifier-generated and voted prediction vectors. The optimal classification result was selected from these 178 vectors. With the use of 10-fold cross-validation, our model achieved a remarkable 97.27% overall accuracy for the 4-class classification on the study dataset. Owing to its reduced time complexity, this model is practical for CT-based PH screenings.

In February 2023, Turkey experienced a series of earthquakes that caused significant damage to buildings and affected many people. Detecting building damage quickly is crucial for helping earthquake victims, and we believe machine learning models offer a promising solution. In our research, we introduce a new, lightweight deep-learning model capable of accurately classifying damaged buildings in remote-sensing datasets.Our main goal is to create an automated damage detection system using a novel deep-learning model. We started by collecting a new dataset with two categories: damaged and undamaged buildings. Then, we developed a unique convolutional neural network (CNN) called the inception and concatenation residual (InCR) deep learning network, which incorporates concatenation-based residual blocks and inception blocks to improve performance.We trained our InCR model on the newly collected dataset and used it to extract features from images using global average pooling. To refine these features and select the most informative ones, we applied iterative neighborhood component analysis (INCA). Finally, we classified the refined features using commonly used shallow classifiers.To evaluate our method, we used tenfold cross-validation (10-fold CV) with eight classifiers. The results showed that all classifiers achieved classification accuracies higher than 98 %. This demonstrates that our proposed InCR model is a viable option for CNNs and can be used to create an accurate automated damage detection application.Our research presents a unique solution to the challenge of automated damage detection after earthquakes, showing promising results that highlight the potential of our approach.

Abstract Pain intensity classification using facial images is a challenging problem in computer vision research. This work proposed a patch and transfer learning-based model to classify various pain intensities using facial images. The input facial images were segmented into dynamic-sized horizontal patches or “shutter blinds”. A lightweight deep network DarkNet19 pre-trained on ImageNet1K was used to generate deep features from the shutter blinds and the undivided resized segmented input facial image. The most discriminative features were selected from these deep features using iterative neighborhood component analysis, which were then fed to a standard shallow fine k-nearest neighbor classifier for classification using tenfold cross-validation. The proposed shutter blinds-based model was trained and tested on datasets derived from two public databases—University of Northern British Columbia-McMaster Shoulder Pain Expression Archive Database and Denver Intensity of Spontaneous Facial Action Database—which both comprised four pain intensity classes that had been labeled by human experts using validated facial action coding system methodology. Our shutter blinds-based classification model attained more than 95% overall accuracy rates on both datasets. The excellent performance suggests that the automated pain intensity classification model can be deployed to assist doctors in the non-verbal detection of pain using facial images in various situations (e.g., non-communicative patients or during surgery). This system can facilitate timely detection and management of pain.

Strabon’un, içinden Iris’in aktığı ve verimli bir ova olarak tanımladığı Dazimonitis bugünkü Kazova’nın Antik Çağ’daki adıydı. Dazimonitis Hellenistik Dönem’in başlarında Pontos Krallığı’nın, bu dönemin sonlarına doğru ise Roma’nın sınırları içerisine dâhil edilse de belirtilen bu tarihi süreçlerde yarı-özerk statüye sahip Komana tapınak devletinin mülküydü. Ancak Dazimonitis’in, Pontos Krallığı ile Roma arasında yaşanan mücadelede bir rolü olduğu da bilinmektedir. Bu bağlamda III. Mithradates-Roma Savaşı sırasında, VI. Mithradates tarafından ovanın sunmuş olduğu erzak tedariki imkânı ortadan kaldırılarak Roma ordusu yıpratılmaya çalışılmıştı. Dazimonitis’in sadece bu amaca hizmet etmediğini, Kazova’nın güneyindeki dağlık kesimde yer alan ve Pazar ilçesini Artova üzerinden Sivas iline bağlayan stratejik bir yol üzerindeki bir tepede konuşlandırılmış ve şimdiye kadar herhangi bir bilimsel çalışmanın konusu olmamış olan Dereköy Kalesi kanıtlamaktadır. Kazova’nın üzerine kurulu olan Pazar ilçesinde 2019 yılında gerçekleştirdiğimiz arkeolojik yüzey araştırması sırasında inceleme fırsatını bulduğumuz Dereköy’de bulunan ve köyün adıyla anılan kale, basamaklı bir tünele sahip olmasıyla dikkatleri üzerine çekmektedir. Böylece Dazimonitis’te yeni bir Pontos Krallığı Kalesi keşfedilerek bu dönemdeki savunma sisteminin atar damarları olan kaleler ağının şimdiye kadar bilinmeyen bir parçası bölge tarihine kazandırılmıştır. Bu çalışmayla söz konusu kalenin Hellenistik Dönem’deki yeri, önemi ve işlevi açığa çıkartılmaya çalışılacaktır.

Background and Aim: In the era of deep learning, numerous models have emerged in the literature and various application domains. Transformer architectures, particularly, have gained popularity in deep learning, with diverse transformer-based computer vision algorithms. Attention convolutional neural networks (CNNs) have been introduced to enhance image classification capabilities. In this context, we propose a novel attention convolutional model with the primary objective of detecting bipolar disorder using optical coherence tomography (OCT) images. Materials and Methods: To facilitate our study, we curated a unique OCT image dataset, initially comprising two distinct cases. For the development of an automated OCT image detection system, we introduce a new attention convolutional neural network named “TurkerNeXt”. This proposed Attention TurkerNeXt encompasses four key modules: (i) the patchify stem block, (ii) the Attention TurkerNeXt block, (iii) the patchify downsampling block, and (iv) the output block. In line with the swin transformer, we employed a patchify operation in this study. The design of the attention block, Attention TurkerNeXt, draws inspiration from ConvNeXt, with an added shortcut operation to mitigate the vanishing gradient problem. The overall architecture is influenced by ResNet18. Results: The dataset comprises two distinctive cases: (i) top to bottom and (ii) left to right. Each case contains 987 training and 328 test images. Our newly proposed Attention TurkerNeXt achieved 100% test and validation accuracies for both cases. Conclusions: We curated a novel OCT dataset and introduced a new CNN, named TurkerNeXt in this research. Based on the research findings and classification results, our proposed TurkerNeXt model demonstrated excellent classification performance. This investigation distinctly underscores the potential of OCT images as a biomarker for bipolar disorder.

Background: Seismic signals record earthquakes and also noise from different sources. The influence of noise makes it difficult to interpret seismograph signals correctly. This study aims to develop a computationally lightweight, accurate, and explainable machine learning model for the automated detection of seismogram signals that could serve as an effective warning system for earthquake prediction. Material and method: We developed a handcrafted model for earthquake detection using a balanced dataset of 5001 earthquakes and 5001 non-earthquake signal samples. The model included multilevel feature extraction, selector-based feature selection, classification, and post-processing. Input signals were decomposed using tunable Q wave transform and fed to a statistical and textural feature extractor based on the most complicated lock pattern (MCLP). Four feature selectors were used to choose the most valuable features for the support vector machine classifier. Additionally, voted vectors were generated using iterative hard majority voting. Finally, the best model was chosen using a greedy algorithm. Results: The presented self-organized MCLP-based feature engineering model yielded 96.82% classification accuracy with 10-fold cross-validation using the seismic signal dataset. Conclusions: Our model attained high seismological signal detection performance comparable with more computationally expensive deep learning models. Our handcrafted explainable feature engineering model is computationally less expensive and can be easily implemented. Furthermore, we have introduced a competitive feature engineering model to the deep learning models for the seismic signal classification model.

Öğrenci öğrenme stillerinin çeşitliliğinin yanısıra öğrenmenin yapılandırmacı doğasının artan tanınırlığı ile birlikte öğretmenlerin farklı öğretim stilleri kullanma ihtiyacı vurgulanır iken öğretmenlerin çeşitli öğretim stillerini ve algılaması hakkında çok az şey bilinmektedir. Buna paralel olarak antrenörlerin öğretim stilleri kullanması ve algılaması hakkında çok daha az çalışmanın olduğu görülmektedir. Bu nedenle, bu çalışmada antrenörlerin ve beden eğitimi öğretmenlerinin öğretim stillerini kullanma düzeyleri ve stillere ilişkin değer algılarının, grup, cinsiyet, yaş, eğitim durumları değişkenlerine göre karşılaştırarak incelenmiştir. Edirne merkezde MEM’de çalışan 90 öğretmen ile GSİM’de çalışan 39 antrenör olmak üzere toplam 129 katılımcı, kolayda örneklem yöntemi ile seçilmiş ve gönüllük ilkesine göre katılmışlardır. Bulgulara göre en çok değer verilen stiller, komut ve alıştırma, en az değer verilen stiller ise kendi kendine öğretme ve öğrencinin başlatması olarak sıralanmıştır. En çok kullanılan stiller komut ve alıştırma, en az kullanılan stiller ise kendi kendine öğretme ve öğrencinin başlatması olarak sıralandığı görülmektedir. Stillerin öğrencilere “Eğlenme”, “Öğrenme” ve “Motivasyon” sağlama boyutlarında; motivasyon açısından komut, alıştırma ve eşli çalışma, öğrenme açısından komut, alıştırma ve katılım, eğlence açısından komut, alıştırma ve katılım stillerine en fazla değer verildiği görülmektedir. Bulgular grup, cinsiyet ve yaş değişkenlerine göre stillere ilişkin değer algıları ve kullandıkları öğretim stilleri ortalama puanlarının karşılaştırılması sonuçlarında anlamlı değişikler olduğu görülmüştür. Sonuç olarak, en çok kullanılan ve değer verilen stiller olarak komut ve alıştırma stillerinin seçilmesi, öğretmen ve antrenörün öğrenciler üzerinde kontrolün daha fazla arttırma istekleri, bu stillere yönelten temel sebep olarak görülebilir. Bu nedenle beden eğitimi öğretmenliği ve antrenörlük programında öğrenimlerini sürdüren öğretmen ve antrenör adaylarının öğrenim süreleri boyunca aldıkları farklı derslerde uygulama imkanı bulmaları için fırsatlar verilerek, diğer öğretim stillerinin stratejik kullanım alanlarının keşfetmeleri sağlanarak, deneyim kazanmaları ve mesleki gelişim programlarının bu doğrultuda hazırlanması önerilir.

Titanium alloys are widely used in the aerospace industry due to their good mechanical properties. Especially Ti6Al4V alloy is preferred in machine parts that require specific strength in aerospace. However, the tribological properties of the Ti6Al4V alloy are poor and need improvement. It is possible to increase the life of Ti6Al4V alloys and improve their surface properties with various heat treatment and surface coating techniques. For this purpose, in this study, plasma nitriding heat treatment was first applied to the samples prepared from Ti6Al4V alloy. Then, TiN coating was carried out by the magnetron sputtering method. Surface roughness and surface hardness were measured before and after coating. Wear tests were performed with dry friction and room temperature conditions on Ti6Al4V alloys using the sliding velocity and normal load as variable parameters. The wear track widths, the weight loss of the samples, and the abrasive disc were measured. The steady‐state sliding regime average friction coefficients were determined from the recorded friction forces depending on the sliding distance. Normal load ‐ wear track widths, normal load ‐ friction coefficient graphics were created. Load wear track width plots showed that the wear track widths on the TiN coated and uncoated samples increased with the load. The fact that the track width values of the uncoated samples with the same load and velocity values were higher than the TiN‐coated samples showed that the TiN‐coated samples had higher wear resistance. In addition, it was observed that the friction coefficient values in the uncoated samples under constant sliding speed were relatively higher than the TiN‐ coated samples.

The development and widespread use of computer systems has increased the need for secure storage of data. At the same time, the analysis of digital data storage devices is very important for forensic IT professionals who aim to access information to clarify the crime. File systems of disk drives use partition structures to securely store data and prevent problems such as corruption. In this study, deletion or corruption of partitions on commonly used DOS / Master Boot Record (MBR) configured hard disk drives are investigated by using forensic tools. In order to analyze hard disk drives, Forensic Tool Kit (FTK), Magnet AXIOM, Encase, Autopsy and The Sleuth Kit (TSK), which are widely used as commercial and open source, are analyzed by using a presented scenario. In the scenario, the primary partition and the extended partition are created using the DOS / MBR partitioning structure on the test disk. Test files are added to the sections and the sections are deleted. The digital forensics tools were tested on the presented scenario. According to the obtained results, TSK and Encase are successful tools for DOS / MBR structured HDD analysis. However, FTK, Magnet AXIOM and Autopsy could not achieve information detection on DOS/MBR structured disks. These results clearly demonstrated that crime data can be hidden in MBR structured HDD. To carve these data, the correct methodology should be selected.

Deucalione iodine is an essential mineral vital for the optimal function of the thyroid gland in human beings. Apart from being found in a variety of foods, it is a component of various medications, amiodarone and expectorants, iodine-based swabs used for skin cleaning before interventional and surgical procedures, and iodinated contrast media in medical settings. Jod-Basedow Phenomenon, also known as Jod-Basedow Syndrome or iodine-induced thyrotoxicosis, is a rare cause of thyrotoxicosis that characteristically refers to a paradoxical phenomenon in which large loads of exogenous iodine can cause hyperthyroidism. This phenomenon is mainly seen in populations already at risk for thyroid diseases, such as autoimmune thyroid diseases, prior thyroid surgical history, latent Graves' disease, and prior non-toxic diffuse or nodular goiter formation, or those with underlying kidney disease, such as chronic kidney disease and end-stage renal disorders, which can impact iodine excretion. Typically, it is recognized in those with the administration of a large iodine load, dietary supplement, iodinated contrast media used in conjunction with computed tomography scans, angiography, and various other imaging studies, iodinated antiseptic solutions and oral supplements, or a medication, mainly amiodarone, class III antiarrhythmic drug used in the treatment of recurrent severe ventricular arrhythmias, paroxysmal atrial tachycardia, atrial fibrillation and maintenance of sinus rhythm after cardioversion of atrial fibrillation. Prophylactic medical management of the phenomenon may include antithyroid drugs, methimazole, or perchlorate, to be considered for patients at risk for developing iodine-induced thyrotoxicosis that is scheduled for radio imaging modalities by using iodinated contrast media. Moreover, the preliminary management modality comprises cessation of iodine administration, avoiding further exposure, administration of beta-adrenergic antagonists, thionamides, corticosteroids, and rarely lithium. Prognosis is usually favorable, although a small percentage of cases could suffer permanent sequelae from iodine-induced thyrotoxicosis. Nevertheless, complications are remarkable including thyroid storm, permanent hyperthyroidism, atrial fibrillation, and fetal hypothyroidism with goiter development, in terms of ordering iodinated contrast media in pregnants. Jod-Basedow Phenomenon should not be overlooked and should be managed by an interprofessional healthcare team serving and officiating not only to treat it but also to concern themselves, actively in the relevant prophylaxis.

Background: Chest computed tomography (CT) has a high sensitivity for detecting COVID-19 lung involvement and is widely used for diagnosis and disease monitoring. We proposed a new image classification model, swin-textural, that combined swin-based patch division with textual feature extraction for automated diagnosis of COVID-19 on chest CT images. The main objective of this work is to evaluate the performance of the swin architecture in feature engineering. Material and method: We used a public dataset comprising 2167, 1247, and 757 (total 4171) transverse chest CT images belonging to 80, 80, and 50 (total 210) subjects with COVID-19, other non-COVID lung conditions, and normal lung findings. In our model, resized 420 × 420 input images were divided using uniform square patches of incremental dimensions, which yielded ten feature extraction layers. At each layer, local binary pattern and local phase quantization operations extracted textural features from individual patches as well as the undivided input image. Iterative neighborhood component analysis was used to select the most informative set of features to form ten selected feature vectors and also used to select the 11th vector from among the top selected feature vectors with accuracy >97.5%. The downstream kNN classifier calculated 11 prediction vectors. From these, iterative hard majority voting generated another nine voted prediction vectors. Finally, the best result among the twenty was determined using a greedy algorithm. Results: Swin-textural attained 98.71% three-class classification accuracy, outperforming published deep learning models trained on the same dataset. The model has linear time complexity. Conclusions: Our handcrafted computationally lightweight swin-textural model can detect COVID-19 accurately on chest CT images with low misclassification rates. The model can be implemented in hospitals for efficient automated screening of COVID-19 on chest CT images. Moreover, findings demonstrate that our presented swin-textural is a self-organized, highly accurate, and lightweight image classification model and is better than the compared deep learning models for this dataset.

Nowadays, environmental sound classification (ESC) has become one of the most studied research areas. Sound signals that are indistinguishable from the human auditory systems have been classified by computer-aided systems and machine learning methods. Therefore, ESC has been used in signal processing and sound forensics applications. A novel ESC type is presented in this paper, and it is named as vehicle interior sound classification (VISC). VISC is defined as one of the sub-branches of the ESC, and it is utilized as sound-based biometrics for vehicles. A hand-crafted feature-based VISC method is presented. The proposed method has multileveled feature generation by using maximum pooling and the proposed local quintet magnitude pattern (LQMP), feature selection with iterative neighborhood component analysis (INCA), and classification phases. A novel VISC dataset was collected from YouTube and the proposed LQMP and INCA based method applied to the collected sounds. The results denoted that following: the accuracy, F1-score, and geometric mean of the proposed LQMP and INCA based VISC method were calculated as 98.38%,98.23%, and 98.21% by using support vector machine classifier respectively. The contribution of the proposed VISC method is to denote that the vehicles can be classified by using sound.

Background: Ankylosing spondylitis (AS) is a chronic, painful, progressive disease usually seen in the spine. Traditional diagnostic methods have limitations in detecting the early stages of AS. The early diagnosis of AS can improve patients’ quality of life. This study aims to diagnose AS with a pre-trained hybrid model using magnetic resonance imaging (MRI). Materials and Methods: In this research, we collected a new MRI dataset comprising three cases. Furthermore, we introduced a novel deep feature engineering model. Within this model, we utilized three renowned pretrained convolutional neural networks (CNNs): DenseNet201, ResNet50, and ShuffleNet. Through these pretrained CNNs, deep features were generated using the transfer learning approach. For each pretrained network, two feature vectors were generated from an MRI. Three feature selectors were employed during the feature selection phase, amplifying the number of features from 6 to 18 (calculated as 6 × 3). The k-nearest neighbors (kNN) classifier was utilized in the classification phase to determine classification results. During the information phase, the iterative majority voting (IMV) algorithm was applied to secure voted results, and our model selected the output with the highest classification accuracy. In this manner, we have introduced a self-organized deep feature engineering model. Results: We have applied the presented model to the collected dataset. The proposed method yielded 99.80%, 99.60%, 100%, and 99.80% results for accuracy, recall, precision, and F1-score for the collected axial images dataset. The collected coronal image dataset yielded 99.45%, 99.20%, 99.70%, and 99.45% results for accuracy, recall, precision, and F1-score, respectively. As for contrast-enhanced images, accuracy of 95.62%, recall of 80.72%, precision of 94.24%, and an F1-score of 86.96% were attained. Conclusions: Based on the results, the proposed method for classifying AS disease has demonstrated successful outcomes using MRI. The model has been tested on three cases, and its consistently high classification performance across all cases underscores the model’s general robustness. Furthermore, the ability to diagnose AS disease using only axial images, without the need for contrast-enhanced MRI, represents a significant advancement in both healthcare and economic terms.

Amaç: Bu araştırmada, beden eğitimi öğretmenlerinin otizmli kaynaştırma/bütünleştirme öğrencisine yönelik his, tutum ve görüşlerinin incelenmesi amaçlanmıştır. Gereç ve Yöntemler: Araştırma örneklemini Edirne İl Milli Eğitim Müdürlüğüne bağlı özel/resmi okullarda öğretmenlik yapan beden eğitimi öğretmenleri oluşturmaktadır. Araştırma verilerinin belirlenmesi araştırmacı tarafından geliştirilen ‘‘Kişisel Bilgi Formu’’ ve Segal (2011) tarafından OSB olan öğrencilerin yerleştirilmesi ve kaynaştırılması amacıyla geliştirilen ve Ahmetoğlu, Burak amp; Acar (2017) tarafından Türkçe ’ye uyarlanan ‘‘Yerleştirme ve Hizmet Anketi’nin (PASS) dördüncü bölümü “Otizm Spektrum Bozukluğu Olan Kaynaştırma/Bütünleştirme Öğrencisine Yönelik His, Tutum ve Görüşler Ölçeği- OKHTÖ” kullanılmıştır. Araştırmada anket yolu ile sağlanan veriler SPSS 17 paket programı ile analiz elde edilmiş ve değerlendirilmiştir. Değişkenlerin normal dağılımdan gelme durumları araştırılırken Kolmogorov-Smirnov ve Shapiro Wilk’s testlerinden yararlanılmıştır. Araştırmanın katılımcı grupları arasındaki farklılıklar incelenirken değişkenlerin normal dağılıma uyum gösterdikleri durumlarda İlişkisiz (Bağımsız) Örneklem t - Testinden (Independent Sample t - Testi) yararlanılmıştır. Araştırma sonucunda; Test sonucunda ilgili değişkenler için elde edilen p değerlerinin 0,05’ten büyük olması durumunda verinin normal dağılıma uyduğu, küçük olması durumunda ise normal dağılıma uymadığı kabul edilmiştir. Bulgular: Beden eğitimi öğretmenleri hizmet yılı süresince özel eğitim gerektiren çocuklarla çalışma durumlarına göre ortalama ölçek puanları açısından istatistiksel olarak anlamlı bir farklılık olduğu (p lt;0,05), Öğretmenliği süresince özel eğitim gerektiren çocuklarla çalıştığını belirten katılımcılara ait ortalama ölçek puanı (96,11) iken özel eğitim gerektiren çocuklarla çalışmadığını belirten beden eğitimi öğretmenlerine ait ortalama ölçek puanından (103,52) anlamlı derecede düşüktür olduğu bulunmuştur. Sonuç: Beden eğitimi öğretmenlerinin; hizmet yılı ile özel eğitim gerektiren çocuklarla çalışma amp; çalışmama durumunun his, tutum ve görüşlerinde önemli derecede etkili olduğu söylenebilinir.

May 25, World Thyroid Day, WTD, was officially accepted at the Annual General Meeting of the European Thyroid Association (ETA) before the September 2007 Congress in Leipzig, Germany. Although the date also refers to the establishment day of ETA in 1965, the aim is to create awareness about thyroid diseases on this day. After World Thyroid Day was first celebrated by ETA, in 2010, the American Thyroid Association (ATA) declared its support for this day. In addition, on May 25-31, International Thyroid Awareness Week, ITAW, is celebrated worldwide with the mentorship of Thyroid Federation International. The "thyroid gland", commonly used terminology simply "thyroid", is an important endocrine organ localized in front of the neck, extending laterally on both sides. This butterfly-shaped and delicate (1) vital organ is responsible for many metabolic activities that are critical for the organism through the secretion of thyroid hormones. Therefore, "thyroid health" becomes extremely important. As such, thyroid gland disorders continue to affect all age groups globally and possess a wide spectrum of signs and symptoms. Among these, hypothyroidism, thyrotoxicosis with or without hyperthyroidism, goiter, congenital thyroid diseases, thyroiditis, retro/substernal goiter, follicular nodular thyroid disease with primary and secondary thyroid carcinomas are noteworthy. Family history, genetic and familial predisposition, radiation exposure to the head & neck and thorax region during the first two decades particularly in children and women (0-20 years of age), iodine deficiency, and some drugs such as amiodarone are known as the leading factors to the thyroid gland disorders. Nevertheless, the Public Health Update reported that more than 200 million people worldwide are facing thyroid disorders, and over 50% of them remain undiagnosed. To achieve an accurate diagnosis for disorders and also carcinomas with their metastasis and recurrences before histopathology and during postoperative follow-up of this small, delicate, and papillon vital organ that can affect the whole organism: (i) biochemical essays (fT3, fT4, tT3, tT4, TSH, Tg, antiTg, antiTPO, calcitonin, TSI/TRAb, US-guided-Tg/calcitonin washout); (ii) imaging modalities (sonography, strain, and shear wave elastography, SWE, scintigraphy and CT, MRI, 18-FDG PET/CT, and 131I-WBS, when necessary) and (iii) cytopathologic examinations have been utilized. However, thyroid and neck US and cytopathological examination, in particular, still maintain their importance today. Indeterminate cytology (Category III, IV, V, The Bethesda System for Reporting Thyroid Cytopathology, TBSRTC, 1st and 2nd ed.s) (2,3) in cytopathology and follicular nodular thyroid disease, in general, is one of the controversial topics which remains to be a significant topic in Thyroidology (4,5). The latest ATA guidelines, 2015 ATA Management Guidelines for Adult Patients with Thyroid Nodules and Differentiated Thyroid Cancer (6), recommended repeat FNA cytology, molecular tests with further surveillance or diagnostic surgery, considering worrisome sonographic and clinical features with patient preference, as novel approaches in Endocrine Surgery, Endocrine Pathology, and Thyroidology to resolve the challenging issue, Category III, TBSRTC (AUS/FLUS, atypia of undetermined/follicular lesion of undetermined significance), which is at the center of the aforementioned controversion (Recommendation 15A, Weak recommendation, Moderate-quality evidence; 15B, Strong recommendation, Low-quality evidence). Of note, some authors and authorities have advocated the potential need for separation in TBSRTC in terms of Category III, AUS/FLUS (7,8,9). We very recently also postulated the so-called subdivision concept in Category III, TBSRTC, id est, (i) Category IIIA: AUS/FLUS without nuclear atypia (AUS/FLUS wo NA), and (ii) Category IIIB: AUS/FLUS with nuclear atypia (AUS/FLUS w NA) within the possible forthcoming 3rd ed., the 202X TBSRTC (10,11). Mater artium necessitas. Therefore, a novel risk of malignancy, ROM, for each diagnostic category of possible forthcoming TBSRTC would selectively enrich the different management proposals in Thyroidology. As a matter of fact that this issue merits further investigation. Bene diagnoscitur bene curatur.. Dum vivimus servimus.

SARS-CoV-2 and Influenza-A can present similar symptoms. Computer-aided diagnosis can help facilitate screening for the two conditions, and may be especially relevant and useful in the current COVID-19 pandemic because seasonal Influenza-A infection can still occur. We have developed a novel text-based classification model for discriminating between the two conditions using protein sequences of varying lengths. We downloaded viral protein sequences of SARS-CoV-2 and Influenza-A with varying lengths (all 100 or greater) from the NCBI database and randomly selected 16,901 SARS-CoV-2 and 19,523 Influenza-A sequences to form a two-class study dataset. We used a new feature extraction function based on a unique pattern, HamletPat, generated from the text of Shakespeare’s Hamlet, and a signum function to extract local binary pattern-like bits from overlapping fixed-length (27) blocks of the protein sequences. The bits were converted to decimal map signals from which histograms were extracted and concatenated to form a final feature vector of length 1280. The iterative Chi-square function selected the 340 most discriminative features to feed to an SVM with a Gaussian kernel for classification. The model attained 99.92% and 99.87% classification accuracy rates using hold-out (75:25 split ratio) and five-fold cross-validations, respectively. The excellent performance of the lightweight, handcrafted HamletPat-based classification model suggests that it can be a valuable tool for screening protein sequences to discriminate between SARS-CoV-2 and Influenza-A infections.

Smart health is one of the most popular and important components of smart cities. It is a relatively new context-aware healthcare paradigm influenced by several fields of expertise, such as medical informatics, communications and electronics, bioengineering, ethics, to name a few. Smart health is used to improve healthcare by providing many services such as patient monitoring, early diagnosis of disease and so on. The artificial neural network (ANN), support vector machine (SVM) and deep learning models, especially the convolutional neural network (CNN), are the most commonly used machine learning approaches where they proved to be performance in most cases. Voice disorders are rapidly spreading especially with the development of medical diagnostic systems, although they are often underestimated. Smart health systems can be an easy and fast support to voice pathology detection. The identification of an algorithm that discriminates between pathological and healthy voices with more accuracy is needed to obtain a smart and precise mobile health system. The main contribution of this paper consists of proposing a multiclass-pathologic voice classification using a novel multileveled textural feature extraction with iterative feature selector. Our approach is a simple and efficient voice-based algorithm in which a multi-center and multi threshold based ternary pattern is used (MCMTTP). A more compact multileveled features are then obtained by sample-based discretization techniques and Neighborhood Component Analysis (NCA) is applied to select features iteratively. These features are finally integrated with MCMTTP to achieve an accurate voice-based features detection. Experimental results of six classifiers with three diagnostic diseases (frontal resection, cordectomy and spastic dysphonia) show that the fused features are more suitable for describing voice-based disease detection.

Objectives: Gastric intestinal metaplasia have traditionally been associated with gastric adenocarcinoma. Gastric intestinal metaplasia is usually related to the Helicobacter pylori infection, older ages, smoking history, consumption of strong spicy foods, socioeconomic status and presence of IL10-592 C/A. The purpose of the present research study was to evaluate the simple laboratory parameters in subjects with gastric intestinal metaplasia Findings: From May 2018 and October 2018, a total of 541, 281 male and 260 female, consecutive cases with gastric intestinal metaplasia with the mean age of 58.5±15 years had been enrolled retrospectively with exclusion of the cases with a severe underlying disease, including a gastric cancer and gastric resection. The gastroscopy with the antral biopsy had been performed for all the cases and the biopsy samples had been evaluated for the presence of gastric intestinal metaplasia by Hematoxylin and Eosin and Helicobacter pylori status by Giemsa.The chi-squared test and independent t test were used for comparison. The mean serum urea level detected as 34.2.±16.1 mg/dL in the gastric intestinal metaplasia and 31.2±13.1 mg/dL in the control (95% CI from 32,3 to 34,6; p=0.013), while the mean serum creatinin level 0.84±0.28 mg/dL in the gastric intestinal metaplasia and 0.80±0.26 mg/dL in the control (95% CI from 0,80 to 0,85; p=0.042). Gastric intestinal metaplasia was detected mostly in elderly and male, regarding the multiple logistic regression (p

Background:The goal is to evaluate the association between the topo-sonographic polarity of the thyroid nodules and the thyroid malignancy by analyzing its effect on The Bethesda System for Reporting Thyroid Cytopathology (TBSRTC), score of Strain Elastography (SE) for thyroid as Tsukuba Elasticity Score (TES), and histopathologic assessment. Material and Methods: A preliminary single-center retrospective study was carried out by including the documents of 641 consecutive eligible patients, possessing 770 thyroid nodules which undergone neck ultrasonography (US), Doppler US, SE, and US-guided-fine needle aspiration (FNA) during April 2011 to April 2017. The stiffness had been measured by TES of SE. The ability of the prediction of the malignancy by the polarity of 770 thyroid nodules considering the association between; i) TBSRTC and histopathology and ii) TES and histopathology had been evaluated. Results: Of the 770 thyroid nodules evaluated, 408 (53.0%) were located at the superior pole (Pol 1) while 362 (47.0%) were at the inferior pole (Pol 0) with 0.9046 AUC and 0.8171 AUC for the association between TBSRTC and histopathology and 0.9280 AUC and 0.7888 AUC for the association between TES and histopathology, respectively. However, those difference were not significant for Pol 1, topographically. Conclusion: The topograghic and sonographic polarity of the thyroid nodules may not be useful for estimating the thyroid malignancy by using the association between TBSRTC and histopathology with TES and histopathology. However, the association with Pol 1, the superior thyroid pole, was stronger though the difference was not significant.

Background: Sleep stage classification is a crucial process for the diagnosis of sleep or sleep-related diseases. Currently, this process is based on manual electroencephalogram (EEG) analysis, which is resource-intensive and error-prone. Various machine learning models have been recommended to standardize and automate the analysis process to address these problems. Materials and methods: The well-known cyclic alternating pattern (CAP) sleep dataset is used to train and test an L-tetrolet pattern-based sleep stage classification model in this research. By using this dataset, the following three cases are created, and they are: Insomnia, Normal, and Fused cases. For each of these cases, the machine learning model is tasked with identifying six sleep stages. The model is structured in terms of feature generation, feature selection, and classification. Feature generation is established with a new L-tetrolet (Tetris letter) function and multiple pooling decomposition for level creation. We fuse ReliefF and iterative neighborhood component analysis (INCA) feature selection using a threshold value. The hybrid and iterative feature selectors are named threshold selection-based ReliefF and INCA (TSRFINCA). The selected features are classified using a cubic support vector machine. Results: The presented L-tetrolet pattern and TSRFINCA-based sleep stage classification model yield 95.43%, 91.05%, and 92.31% accuracies for Insomnia, Normal dataset, and Fused cases, respectively. Conclusion: The recommended L-tetrolet pattern and TSRFINCA-based model push the envelope of current knowledge engineering by accurately classifying sleep stages even in the presence of sleep disorders.

Attention deficit hyperactivity disorder (ADHD) is a common neurodevelopmental condition worldwide. In this research, we used an ADHD electroencephalography (EEG) dataset containing more than 4000 EEG signals. Moreover, these EEGs are noisy signals. A new hand-modeled EEG classification model has been proposed to separate healthy versus ADHD individuals using the EEG signals. In this model, a new ternary motif pattern (TMP) has been incorporated. We have mimicked deep learning networks to create this hand-modeled classification method. The Tunable Q Wavelet Transform (TQWT) has been utilized to generate wavelet subbands. We applied the proposed TMP and statistics to construct informative features from both raw EEG signals and wavelet bands by generating TQWT. Herein, features have been generated by 18 subbands and the original EEG signal. Thus, this model is named TMP19. The most informative features have been chosen by deploying neighborhood component analysis (NCA), and the selected features have been classified using the k-nearest neighbor (kNN) classifier. The used ADHD EEG dataset has 14 channels. Thus, these three phases—(i) feature extraction with TQWT, TMP, and statistics; (ii) feature selection by deploying NCA; and (iii) classification with kNN—have been applied to each channel. Iterative hard majority voting (IHMV) has been applied to obtain a higher and more general classification response. Our model attained 95.57% and 77.93% classification accuracies by deploying 10-fold and leave one subject out (LOSO) cross-validations, respectively.

Isoniazid (INH) is one of the key molecules employed in the treatment of tuberculosis (TB), the most deadly infectious disease worldwide. However, the efficacy of this cornerstone drug has seriously decreased due to emerging INH-resistant strains of Mycobacterium tuberculosis (Mtb). In the present study, we aimed to chemically tailor INH to overcome this resistance. We obtained thirteen novel compounds by linking INH to in-house synthesized sulfonate esters via a hydrazone bridge (SIH1–SIH13). Following structural characterization by FTIR, 1H NMR, 13C NMR, and HRMS, all compounds were screened for their antitubercular activity against Mtb H37Rv strain and INH-resistant clinical isolates carrying katG and inhA mutations. Additionally, the cytotoxic effects of SIH1–SIH13 were assessed on three different healthy host cell lines; HEK293, IMR-90, and BEAS-2B. Based on the obtained data, the synthesized compounds appeared as attractive antimycobacterial drug candidates with low cytotoxicity. Moreover, the stability of the hydrazone moiety in the chemical structure of the final compounds was confirmed by using UV/Vis spectroscopy in both aqueous medium and DMSO. Subsequently, the compounds were tested for their inhibitory activities against enoyl acyl carrier protein reductase (InhA), the primary target enzyme of INH. Although most of the synthesized compounds are hosted by the InhA binding pocket, SIH1–SIH13 do not primarily show their antitubercular activities by direct InhA inhibition. Finally, in silico determination of important physicochemical parameters of the molecules showed that SIH1–SIH13 adhered to Lipinski’s rule of five. Overall, our study revealed a new strategy for modifying INH to cope with the emerging drug-resistant strains of Mtb.

Diabetic retinopathy (DR) is a common complication of diabetes that can lead to progressive vision loss. Regular surveillance with fundal photography, early diagnosis, and prompt intervention are paramount to reducing the incidence of DR-induced vision loss. However, manual interpretation of fundal photographs is subject to human error. In this study, a new method based on horizontal and vertical patch division was proposed for the automated classification of DR images on fundal photographs. The novel sides of this study are given as follows. We proposed a new non-fixed-size patch division model to obtain high classification results and collected a new fundus image dataset. Moreover, two datasets are used to test the model: a newly collected three-class (normal, non-proliferative DR, and proliferative DR) dataset comprising 2355 DR images and the established open-access five-class Asia Pacific Tele-Ophthalmology Society (APTOS) 2019 dataset comprising 3662 images. Two analysis scenarios, Case 1 and Case 2, with three (normal, non-proliferative DR, and proliferative DR) and five classes (normal, mild DR, moderate DR, severe DR, and proliferative DR), respectively, were derived from the APTOS 2019 dataset. These datasets and these cases have been used to demonstrate the general classification performance of our proposal. By applying transfer learning, the last fully connected and global average pooling layers of the DenseNet201 architecture were used to extract deep features from input DR images and each of the eight subdivided horizontal and vertical patches. The most discriminative features are then selected using neighborhood component analysis. These were fed as input to a standard shallow cubic support vector machine for classification. Our new DR dataset obtained 94.06% and 91.55% accuracy values for three-class classification with 80:20 hold-out validation and 10-fold cross-validation, respectively. As can be seen from steps of the proposed model, a new patch-based deep-feature engineering model has been proposed. The proposed deep-feature engineering model is a cognitive model, since it uses efficient methods in each phase. Similar excellent results were seen for three-class classification with the Case 1 dataset. In addition, the model attained 87.43% and 84.90% five-class classification accuracy rates using 80:20 hold-out validation and 10-fold cross-validation, respectively, on the Case 2 dataset, which outperformed prior DR classification studies based on the five-class APTOS 2019 dataset. Our model attained about >2% classification results compared to others. These findings demonstrate the accuracy and robustness of the proposed model for classification of DR images.

Multiple sclerosis (MS) is a chronic demyelinating condition characterized by plaques in the white matter of the central nervous system that can be detected using magnetic resonance imaging (MRI). Many deep learning models for automated MS detection based on MRI have been presented in the literature. We developed a computationally lightweight machine learning model for MS diagnosis using a novel handcrafted feature engineering approach. The study dataset comprised axial and sagittal brain MRI images that were prospectively acquired from 72 MS and 59 healthy subjects who attended the Ozal University Medical Faculty in 2021. The dataset was divided into three study subsets: axial images only (n = 1652), sagittal images only (n = 1775), and combined axial and sagittal images (n = 3427) of both MS and healthy classes. All images were resized to 224 × 224. Subsequently, the features were generated with a fixed-size patch-based (exemplar) feature extraction model based on local phase quantization (LPQ) with three-parameter settings. The resulting exemplar multiple parameters LPQ (ExMPLPQ) features were concatenated to form a large final feature vector. The top discriminative features were selected using iterative neighborhood component analysis (INCA). Finally, a k-nearest neighbor (kNN) algorithm, Fine kNN, was deployed to perform binary classification of the brain images into MS vs. healthy classes. The ExMPLPQ-based model attained 98.37%, 97.75%, and 98.22% binary classification accuracy rates for axial, sagittal, and hybrid datasets, respectively, using Fine kNN with 10-fold cross-validation. Furthermore, our model outperformed 19 established pre-trained deep learning models that were trained and tested with the same data. Unlike deep models, the ExMPLPQ-based model is computationally lightweight yet highly accurate. It has the potential to be implemented as an automated diagnostic tool to screen brain MRIs for white matter lesions in suspected MS patients.

Thyroidology has yet to declare the optimal needle size for thyroid fine-needle aspiration (FNA) cytology. In this respect, the researches on this issue remain restricted in English-language literature. Currently, Saraph and colleagues (1) evaluated dissimilarity between 23- and 25-gauge needles in terms of nondiagnostic/unsatisfactory rates of The Bethesda System for Reporting Thyroid Cytopathology (TBSRTC) after FNA applications. The authors had worked with a sum of 141 23- and 157 25-gauge needles and reported their nondiagnostic/unsatisfactory rates as 31.09% and 35.07%, respectively, with no statistical difference. However, Dong et al. (2) reported working with 240 consecutive nodules, had undergone US-guided FNA (US-FNA), and obtained the highest scores of sample quality with 25-gauge needles in thyroid FNA cytology, comparing with 22- and 23-gauge needles, very recently. The authors declared the nondiagnostic scores of 15.42%, 19.58%, and 14.17% for US-FNA with 22-, 23-, and 25-gauge needles, respectively (2). In addition, recently, Shumrick et al. (3) reported 148 thyroid nodules in 107 cases, had undergone US-FNA, revealing significantly less non-diagnostic cytology with 27-gauged needles, 11.3%, comparing 25-gauge needles, 24.7%. We recently have studied the possible impact of size cutoff of 10- and 15-mm for the thyroid nodules on three diagnostic tools; i) strain elastography (SE), ii) sonography-guided FNA cytology, and iii) histopathology. A sum of 425 cases with 500 thyroid nodules had undergone US-guided FNA (USFNA) with 27-gauge finest-needle by a single surgeon (I.S.), surgeon-performed US (SUS), with a local anesthetic agent (1-3 mL of 1% lidocaine with 1:100,000 epinephrine) based on American Thyroid Association (ATA) management guidelines for patients with thyroid nodules and differentiated thyroid cancer [i.e. low, intermediate, and high suspicion nodules]; 2015 ATA management guidelines for adult patients with thyroid nodules and differentiated thyroid cancer] for three years and two months. TBSRTC 1st ed. had been used for this period, for the mentioned considerable and novel ‘SUS based’ US-FNA terminology with the specific and also ‘ well-accepted and crucial’ size cut offs of 10- and 15- mm in Thyroidology revealing the FNA cytology outcomes with27-gauge fine needles, for Category I, per se, TBSRTC, 1st ed. were revealed as 9.0% (4). Moss et al.(5) notified that regular and coordinated thyroid FNA should be performed with smaller needle gauges, 24-27-gauges, without aspiration, routinely, in a systematic review and meta-analysis. Saraph et al. (1) specified the utilization of a 25-gauge needle did not produce a lower nondiagnostic rate, comparing the 23-gauge one. In addition, they pointed out that larger nodules might increase diagnostic rates, while the older and cystic nodules are prone to inadequate samples. We agree with Saraph and colleagues’(1) opinion about the limitations of nodule location, such as a posterior, more difficult to reach nodules, and sonography of composition (mixed and solid) and echogenicity which may be crucial parameters for this issue. Nonetheless, we postulate that the so-called needle gauge concept selectively enriches with finer needles. Additionally, we currently introduced the proposal of a novel terminology, “minimally invasive FNA (MIFNA)” and “minimally invasive thyroid FNA; Thyroid MIFNA (Thy MIFNA)”, including 27-gauge fine-needle with pre-procedural topical and local anesthetic agents administration, hope to contribute in Thyroidology (6). Of note, we might recommend opting for a 27-gauge finest needle in terms of US-FNA for a butterfly gland, the thyroid, with reasonable low rates of non-diagnostic/unsatisfactory cytology, TBSRTC, 1st and 2nd ed. and low severity of pain, as a matter of course. To this end, we also recommend: “think twice with larger needles for thyroid US-FNA”. This issue merits further investigation.

Recently, deep models have been very popular because they achieve excellent performance with many classification problems. Deep networks have high computational complexities and require specific hardware. To overcome this problem (without decreasing classification ability), a hand-modeled feature selection method is proposed in this paper. A new shape-based local feature extractor is presented which uses the geometric shape of the frustum. By using a frustum pattern, textural features are generated. Moreover, statistical features have been extracted in this model. Textures and statistics features are fused, and a hybrid feature extraction phase is obtained; these features are low-level. To generate high level features, tunable Q factor wavelet transform (TQWT) is used. The presented hybrid feature generator creates 154 feature vectors; hence, it is named Frustum154. In the multilevel feature creation phase, this model can select the appropriate feature vectors automatically and create the final feature vector by merging the appropriate feature vectors. Iterative neighborhood component analysis (INCA) chooses the best feature vector, and shallow classifiers are then used. Frustum154 has been tested on three basic hand-movement sEMG datasets. Hand-movement sEMG datasets are commonly used in biomedical engineering, but there are some problems in this area. The presented models generally required one dataset to achieve high classification ability. In this work, three sEMG datasets have been used to test the performance of Frustum154. The presented model is self-organized and selects the most informative subbands and features automatically. It achieved 98.89%, 94.94%, and 95.30% classification accuracies using shallow classifiers, indicating that Frustum154 can improve classification accuracy.

The fact that children have been acquainted with sports from their early years is an important determinant of their attitudes and behaviors in their future lives and constitutes the most basic part of a country's athletic resource in broadcast sports policies. Early childhood is important in supporting the development of children as well as in acquiring certain habits. Quality management of this time zone is also important. For this reason, in this research, it was aimed to examine the opinions and expectations of the parents of preschool children who are in pre-school education to direct their children to judo sports. The data are arranged in categories, interpreted as descriptive and frequency. As a result, according to priority order; it is important for the female parents that their children are healthy, develop their own discipline, learn sports culture and morality. Moreover, they want them to be psychologically good and healthy as well as expecting them to socialize, learn sports culture and morality, develop their self- discipline. Also, they are guiding their children to judo sports because they want to improve morality, social cohesion and self-discipline. The results obtained in Turkey Judo Federation and judo policies will be developed in preschool education for coaches, and will create significant resources and proposals for the strategy which has been developed for data.