Browsing by Author "Tuncer, Turker"

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Advancing Pulmonary Embolism Detection with Integrated Deep Learning Architectures
(Springer, 2025) Biret, Can Berk; Gurbuz, Sukru; Akbal, Erhan; Baygin, Mehmet; Ekingen, Evren; Derya, Serdar; Tuncer, Turker
The main aim of this study is to introduce a new hybrid deep learning model for biomedical image classification. We propose a novel convolutional neural network (CNN), named HybridNeXt, for detecting pulmonary embolism (PE) from computed tomography (CT) images. To evaluate the HybridNeXt model, we created a new dataset consisting of two classes: (1) PE and (2) control. The HybridNeXt architecture combines different advanced CNN blocks, including MobileNet, ResNet, ConvNeXt, and Swin Transformer. We specifically designed this model to combine the strengths of these well-known CNNs. The architecture also includes stem, downsampling, and output stages. By adjusting the parameters, we developed a lightweight version of HybridNeXt, suitable for clinical use. To further improve the classification performance and demonstrate transfer learning capability, we proposed a deep feature engineering (DFE) method using a multilevel discrete wavelet transform (MDWT). This DFE model has three main phases: (i) feature extraction from raw images and wavelet bands, (ii) feature selection using iterative neighborhood component analysis (INCA), and (iii) classification using a k-nearest neighbors (kNN) classifier. We first trained HybridNeXt on the training images, creating a pretrained HybridNeXt model. Then, using this pretrained model, we extracted features and applied the proposed DFE method for classification. The HybridNeXt model achieved a test accuracy of 90.14%, while our DFE model improved accuracy to 96.35%. Overall, the results confirm that our HybridNeXt architecture is highly accurate and effective for biomedical image classification. The presented HybridNeXt and HybridNeXt-based DFE methods can potentially be applied to other image classification tasks.
Automated Anxiety Detection Using Probabilistic Binary Pattern with ECG Signals
(Elsevier Ireland Ltd, 2024) Baygin, Mehmet; Barua, Prabal Datta; Dogan, Sengul; Tuncer, Turker; Hong, Tan Jen; March, Sonja; Acharya, U. Rajendra
Background and aim: Anxiety disorder is common; early diagnosis is crucial for management. Anxiety can induce physiological changes in the brain and heart. We aimed to develop an efficient and accurate handcrafted feature engineering model for automated anxiety detection using ECG signals. Materials and methods: We studied open-access electrocardiography (ECG) data of 19 subjects collected via wearable sensors while they were shown videos that might induce anxiety. Using the Hamilton Anxiety Rating Scale, subjects are categorized into normal, light anxiety, moderate anxiety, and severe anxiety groups. ECGs were divided into non-overlapping 4- (Case 1), 5- (Case 2), and 6-second (Case 3) segments for analysis. We proposed a self-organized dynamic pattern-based feature extraction function-probabilistic binary pattern (PBP)- in which patterns within the function were determined by the probabilities of the input signal-dependent values. This was combined with tunable q-factor wavelet transform to facilitate multileveled generation of feature vectors in both spatial and frequency domains. Neighborhood component analysis and Chi2 functions were used to select features and reduce data dimensionality. Shallow k-nearest neighbors and support vector machine classifiers were used to calculate four (=2 x 2) classifier-wise results per input signal. From the latter, novel selforganized combinational majority voting was applied to calculate an additional five voted results. The optimal final model outcome was chosen from among the nine (classifier-wise and voted) results using a greedy algorithm. Results: Our model achieved classification accuracies of over 98.5 % for all three cases. Ablation studies confirmed the incremental accuracy of PBP-based feature engineering over traditional local binary pattern feature extraction. Conclusions: The results demonstrated the feasibility and accuracy of our PBP-based feature engineering model for anxiety classification using ECG signals.
Automated Detection of Gastrointestinal Diseases Using Resnet50*-Based Explainable Deep Feature Engineering Model with Endoscopy Images
(MDPI, 2024) Cambay, Veysel Yusuf; Barua, Prabal Datta; Hafeez Baig, Abdul; Dogan, Sengul; Baygin, Mehmet; Tuncer, Turker; Acharya, U. R.
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.
An Automated Earthquake Classification Model Based on a New Butterfly Pattern Using Seismic Signals
(Pergamon-Elsevier Science Ltd, 2024) Ozkaya, Suat Gokhan; Baygin, Mehmet; Barua, Prabal Datta; Tuncer, Turker; Dogan, Sengul; Chakraborty, Subrata; Acharya, U. Rajendra
Background: Seismic signals are useful for earthquake detection and classification. Therefore, various artificial intelligence (AI) models have been used with seismic signals to develop automated earthquake detection systems. Our primary goal is to present an accurate feature engineering model for earthquake detection and classification using seismic signals.Material and model: We have used a public dataset in this work containing three categories: (1) noise, (2) P waves, and (3) S waves. P and S waves are used to define earthquakes. We have presented two applied use cases using this dataset: (i) earthquake detection and (ii) wave classification. In this work, a new textural feature extractor has been presented by using a graph pattern similar to a butterfly. Thus, this feature extraction function is named Butterfly pattern (BFPat). We have created a new feature engineering architecture by deploying BFPat, statistics, and wavelet packet decomposition (WPD) functions. The recommended BFPat and statistics have been applied to the wavelet bands created by WPD and the raw seismic signals. Multilevel features have been extracted from both frequency and space domains. The used dataset contains signals with three channels. Using these three channels, seven signals have been created. Seven feature vectors have been created from 7 input signals used in this study. The most meaningful/informative features from the generated feature set are then selected using the iterative neighborhood component analysis feature selector method. Seven chosen feature vectors have been considered as inputs of the two shallow classifiers: k nearest neighbors (kNN) and support vector machine (SVM). A total of 14 (=7 x 2) results have been obtained in the classification phase. A majority voting process was applied in the last phase to choose the best results and improve the classification performance.Results: We have presented two use cases for our new BFPat method in this work to obtain superior results. Our model reached an accuracy of 99.58% in detecting the earthquake detection and 93.13% accuracy in 3-class classifications of waves.Conclusions: Our recommended model has achieved over 90% classification performance for both cases. Also, we have presented the most valuable channel and combinations in our work. Our developed system is ready to be tested with a bigger database.
Automated Mental Arithmetic Performance Detection Using Quantum Pattern- and Triangle Pooling Techniques with EEG Signals
(Pergamon-Elsevier Science Ltd, 2023) Baygin, Nursena; Aydemir, Emrah; Barua, Prabal D.; Baygin, Mehmet; Doganm, Sengul; Tuncer, Turker; Acharya, U. Rajendra
Background: Electroencephalography (EEG) signals recorded during mental arithmetic tasks can be used to quantify mental performance. The classification of these input EEG signals can be automated using machine learning models. We aimed to develop an efficient handcrafted model that could accurately discriminate "bad counters" vs. "good counters" in mental arithmetic. Materials and method: We studied a public mental arithmetic task performance EEG dataset comprising 20-channel EEG signal segments recorded from 36 healthy right-handed subjects divided into two classes 10 "bad counters" and 26 "good counters". The original 60-second EEG samples are divided into 424 15-second segments (119 and 305 belonging to the "bad counters" and "good counters", respectively) to input into our model. Our model comprised a novel multilevel feature extraction method based on (1) four rhombuses lattice pattern, a new generation function for feature extraction that was inspired by the lattice structure in post-quantum cryptography; and (2) triangle pooling, a new distance-based pooling function for signal decomposition. These were combined with downstream feature selection using iterative neighborhood component analysis, channel-wise result classification using support vector machine with leave-one-subject-out (LOSO) and 10-fold) crossvalidations (CVs) to calculate prediction vectors, iterative majority voting to generate voted vectors, and greedy algorithm to obtain the best results. Results: The model attained 88.44% and 96.42% geometric means and accuracies of 93.40% and 97.88%, using LOSO and 10-fold CVs, respectively. Conclusions: Our model's >93% classification accuracies compared favorably against published literature. Importantly, the model has linear computational complexity, which enhances its ease of implementation.
Black-White Hole Pattern: An Investigation on the Automated Chronic Neuropathic Pain Detection Using Eeg Signals
(Springer, 2024) Tasci, Irem; Baygin, Mehmet; Barua, Prabal Datta; Hafeez-Baig, Abdul; Dogan, Sengul; Tuncer, Turker; Acharya, U. Rajendra
Electroencephalography (EEG) signals provide information about the brain activities, this study bridges neuroscience and machine learning by introducing an astronomy-inspired feature extraction model. In this work, we developed a novel feature extraction function, black-white hole pattern (BWHPat) which dynamically selects the most suitable pattern from 14 options. We developed BWHPat in a four-phase feature engineering model, involving multileveled feature extraction, feature selection, classification, and cortex map generation. Textural and statistical features are extracted in the first phase, while tunable q-factor wavelet transform (TQWT) aids in multileveled feature extraction. The second phase employs iterative neighborhood component analysis (INCA) for feature selection, and the k-nearest neighbors (kNN) classifier is applied for classification, yielding channel-specific results. A new cortex map generation model highlights the most active channels using median and intersection functions. Our BWHPat-driven model consistently achieved over 99% classification accuracy across three scenarios using the publicly available EEG pain dataset. Furthermore, a semantic cortex map precisely identifies pain-affected brain regions. This study signifies the contribution to EEG signal classification and neuroscience. The BWHPat pattern establishes a unique link between astronomy and feature extraction, enhancing the understanding of brain activities.
Deep Learning in Forensic Analysis: Optical Coherence Tomography Image Classification in Methamphetamine Detection
(Pergamon-Elsevier Science Ltd, 2025) Gurbuzer, Nilifer; Ozkaya, Alev Lazoglu; Yaylali, Elif Topdagi; Tozoglu, Elif Ozcan; Baygin, Mehmet; Tasci, Burak; Tuncer, Turker
Detecting drug addiction in forensic science traditionally relies on expensive and time-consuming laboratory tests. This study proposes a rapid, non-invasive approach that uses optical coherence tomography images combined with deep learning techniques to identify methamphetamine users. A novel convolutional neural network was developed, incorporating depthwise and pointwise convolutions, patchify-based downsampling, and inception blocks to improve feature extraction and classification accuracy. To further enhance model performance, we introduced a grid-based deep feature engineering model that extracts and selects discriminative features using iterative neighborhood component analysis. The proposed model achieved 91.02 % accuracy, surpassing the 88.57 % accuracy of Mobile Network version 2 on the same dataset. By integrating the grid-based feature engineering model, classification accuracy was further improved to 93.27 %, demonstrating a significant enhancement over traditional deep learning approaches. The dataset consisted of 2172 optical coherence tomography images collected from 54 methamphetamine users and 60 control subjects, ensuring a diverse and representative sample. This research marks the first application of optical coherence tomography imaging in drug addiction detection, bridging biomedical imaging and forensic science. By employing gradient-weighted class activation mapping visualization, we identified key retinal features that distinguish methamphetamine users from non-users, thereby making the model more interpretable and clinically relevant. Given its high accuracy, lightweight architecture, and non-invasive nature, the proposed method offers a promising forensic tool for rapid, artificial intelligence-driven drug addiction screening with potential real-world applicability in forensic investigations and healthcare.
Directed Lobish-Based Explainable Feature Engineering Model with Ttpat and Cwinca for Eeg Artifact Classification
(Elsevier, 2024) Tuncer, Turker; Dogan, Sengul; Baygin, Mehmet; Tasci, Irem; Mungen, Bulent; Tasci, Burak; Acharya, U. R.
Background and Objective: Electroencephalography (EEG) signals are crucial to decipher various brain activities. However, these EEG signals are subtle and contain various artifacts, which can happen due to various reasons. The main aim of this paper is to develop an explainable novel machine learning model that can identify the cause of these artifacts. Material and method: A new EEG signal dataset was collected to classify various types of artifacts. This dataset contains eight classes: seven are artifacts, and one is the EEG signal without artifacts. A novel feature engineering model has been proposed to classify these artifact classes automatically. This model contains three main steps: (i) feature generation with the proposed transition table pattern (TTPat), (ii) the proposed cumulative weight-based iterative neighborhood component analysis (CWINCA)-based feature selection, and (iii) classification using t algorithm-based k-nearest neighbors (tkNN). The novelty of this work is TTPat feature extractor and CWINCA feature selector. Channel-based transformation is performed using the proposed TTPat, which extracts 392 features from the transformed EEG signal. A novel CWINCA feature selector is proposed. The artifacts are classified using tkNN algorithm. Results: The proposed TTPat and CWINCA-based feature engineering model obtained a classification accuracy ranging from 66.39% to 97.69% for 30 cases. We presented the explainable results using a new symbolic language termed Directed Lobish. Conclusions: The results and findings demonstrated that the proposed explainable feature engineering (EFE) model is good at artifact detection and classification. Directed Lobish has been presented to obtain explainable results and is a new symbolic language.
Flexilpq: Automated Osteoid Osteoma Detection Using Computed Tomography
(Springer London Ltd, 2026) Key, Sefa; Agar, Anil; Sercek, Ilknur; Poyraz, Ahmet Kursad; Baygin, Mehmet; Dogan, Sengul; Tuncer, Turker
In this study, we introduce FlexiLPQ, an innovative image classification model developed as the feature-engineering counterpart of FlexiViT, and evaluate its performance on Osteoid Osteoma diagnosis. For this purpose, a new Osteoid Osteoma CT dataset was curated. Using this dataset, our goal was to design an automatic detection system capable of identifying Osteoid Osteoma with high accuracy. The proposed FlexiLPQ model operates through five main phases: (i) multi-patch feature extraction using local phase quantization (LPQ), (ii) feature selection via cumulative weighted iterative neighborhood component analysis (CWINCA), (iii) classification using a t-algorithm-based k-nearest neighbors (tkNN) classifier, (iv) iterative majority voting (IMV) to refine the decision, and (v) selection of the best final outcome. FlexiLPQ was applied to the curated CT dataset and achieved a 98.89% classification accuracy. Additionally, multiple patch sizes were incorporated, and their performance differences were analyzed. The results clearly show that FlexiLPQ is an effective and robust image classification framework, and it is well suited for biomedical imaging tasks such as Osteoid Osteoma detection.
Flower Automata Pattern-Based Discrimination of Fibromyalgia from Control Subjects Using Fusion of Sleep EEG and ECG Signals
(IEEE-Inst Electrical Electronics Engineers Inc, 2025) Barua, Prabal Datta; Kobayashi, Makiko; Dogan, Sengul; Baygin, Mehmet; Tuncer, Turker; Paul, Jose Kunnel; Acharya, U. R.
Electroencephalogram (EEG) and electrocardiogram (ECG) signals provide vital insights into brain and heart activity and are widely used in automated medical diagnostics. This study introduces a novel, multimodal fibromyalgia detection system developed by the fusion of EEG and ECG signals recorded during sleep stages 2 and 3. The novelty of the model is the use of dynamic and interpretable feature engineering framework comprising of two innovations: 1) Flower Automata Pattern (FAP) for self-organized pattern-based feature extraction, and 2) Attention-Driven Wavelet Transform and Absolute Maximum Pooling (ADWTAMP) method for signal decomposition and compression. Three feature selection strategies-Neighborhood Component Analysis (NCA), Chi2, and the intersection of NCA and Chi2 (NCAChi2) - are employed to generate robust feature vectors, which are classified using k-nearest neighbors (kNN) and support vector machine (SVM) under the leave-one-record-out cross-validation (LORO CV) scheme. The final decision is derived through an iterative voting and greedy fusion approach. The proposed model achieved classification accuracies of 99.36% and 98.37% for sleep stages 2 and 3, respectively. Key advantages of the model include its high accuracy, low computational requirements (CPU-only execution), and explainable architecture. To the best of our knowledge, this is the first multimodal automata-based classification framework designed for fibromyalgia detection.
INCR: Inception and Concatenation Residual Block-Based Deep Learning Network for Damaged Building Detection Using Remote Sensing Images
(Elsevier, 2023) Tasci, Burak; Acharya, Madhav R.; Baygin, Mehmet; Dogan, Sengul; Tuncer, Turker; Belhaouari, Samir Brahim
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.
Lattice 123 Pattern for Automated Alzheimer's Detection Using EEG Signal
(Springer, 2024) Dogan, Sengul; Barua, Prabal Datta; Baygin, Mehmet; Tuncer, Turker; Tan, Ru-San; Ciaccio, Edward J.; Acharya, U. Rajendra
This paper presents an innovative feature engineering framework based on lattice structures for the automated identification of Alzheimer's disease (AD) using electroencephalogram (EEG) signals. Inspired by the Shannon information entropy theorem, we apply a probabilistic function to create the novel Lattice123 pattern, generating two directed graphs with minimum and maximum distance-based kernels. Using these graphs and three kernel functions (signum, upper ternary, and lower ternary), we generate six feature vectors for each input signal block to extract textural features. Multilevel discrete wavelet transform (MDWT) was used to generate low-level wavelet subbands. Our proposed model mirrors deep learning approaches, facilitating feature extraction in frequency and spatial domains at various levels. We used iterative neighborhood component analysis to select the most discriminative features from the extracted vectors. An iterative hard majority voting and a greedy algorithm were used to generate voted vectors to select the optimal channel-wise and overall results. Our proposed model yielded a classification accuracy of more than 98% and a geometric mean of more than 96%. Our proposed Lattice123 pattern, dynamic graph generation, and MDWT-based multilevel feature extraction can detect AD accurately as the proposed pattern can extract subtle changes from the EEG signal accurately. Our prototype is ready to be validated using a large and diverse database.
Lobish: Symbolic Language for Interpreting Electroencephalogram Signals in Language Detection Using Channel-Based Transformation and Pattern
(MDPI, 2024) Tuncer, Turker; Dogan, Sengul; Tasci, Irem; Baygin, Mehmet; Barua, Prabal Datta; Acharya, U. Rajendra
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.
Mnpdensenet: Automated Monkeypox Detection Using Multiple Nested Patch Division and Pretrained Densenet201
(Springer, 2024) Demir, Fahrettin Burak; Baygin, Mehmet; Tuncer, Ilknur; Barua, Prabal Datta; Dogan, Sengul; Tuncer, Turker; Acharya, U. Rajendra
BackgroundMonkeypox is a viral disease caused by the monkeypox virus (MPV). A surge in monkeypox infection has been reported since early May 2022, and the outbreak has been classified as a global health emergency as the situation continues to worsen. Early and accurate detection of the disease is required to control its spread. Machine learning methods offer fast and accurate detection of COVID-19 from chest X-rays, and chest computed tomography (CT) images. Likewise, computer vision techniques can automatically detect monkeypoxes from digital images, videos, and other inputs.ObjectivesIn this paper, we propose an automated monkeypox detection model as the first step toward controlling its global spread.Materials and methodA new dataset comprising 910 open-source images classified into five categories (healthy, monkeypox, chickenpox, smallpox, and zoster zona) was created. A new deep feature engineering architecture was proposed, which contained the following components: (i) multiple nested patch division, (ii) deep feature extraction, (iii) multiple feature selection by deploying neighborhood component analysis (NCA), Chi2, and ReliefF selectors, (iv) classification using SVM with 10-fold cross-validation, (v) voted results generation by deploying iterative hard majority voting (IHMV) and (vi) selection of the best vector by a greedy algorithm.ResultsOur proposal attained a 91.87% classification accuracy on the collected dataset. This is the best result of our presented framework, which was automatically selected from 70 generated results.ConclusionsThe computed classification results and findings demonstrated that monkeypox could be successfully detected using our proposed automated model.
N-Bodypat: Investigation on the Dementia and Alzheimer's Disorder Detection Using EEG Signals
(Elsevier, 2024) Barua, Prabal Datta; Tuncer, Turker; Baygin, Mehmet; Dogan, Sengul; Acharya, U. Rajendra
The N-body problem is a remarkable research topic in physics. We propose a new feature extraction model inspired by the N-body trajectory and test its feature extraction capability. In the first part of the research, an open-access electroencephalogram (EEG) dataset is used to test the proposed method. This dataset has three classes, namely (i) Alzheimer's Disorder (AD), (ii) frontal dementia (FD), and (iii) control groups. In the second step of the study, the EEG signals were divided into segments of 15 s in length, which resulted in 4,661 EEG signals. In the third part of the study, the proposed new self-organized feature engineering (SOFE) model is used to classify the EEG signals automatically. For this SOFE, two novel methods were presented: (i) a dynamic feature extraction function using a graph of the N-Body orbital, termed N-BodyPat, and (ii) an attention pooling function. A multileveled and combinational feature extraction method was proposed by deploying both methods. A feature selection function using ReliefF and Neighborhood Component Analysis (RFNCA) was used to choose the most informative features. An ensemble k-nearest neighbors (EkNN) classifier was employed in the classification phase. Our proposed N-BodyPat generates seven feature vectors for each channel, and the utilized EEG signal dataset contains 19 channels. In this aspect,133 (=19 x 7) EkNN-based outcomes were created. To attain higher classification performance by employing these 133 EkNN-based outcomes, an iterative majority voting (IMV)based information fusion method was applied, and the most accurate outcomes were selected automatically. The recommended N-BodyPat-based SOFE achieved a classification accuracy of 99.64 %.
A Novel Approach Using Deep Belief Network Patterns and Attention Binary Decomposition for Automated Community Emotion Detection
(Elsevier Sci Ltd, 2026) Yildiz, Arif Metehan; Barua, Prabal Datta; Baygin, Mehmet; Dogan, Sengul; Tuncer, Turker; Salvi, Massimo; Acharya, U. R.
Context: Sound-based community emotion detection (SCED) estimates community emotion from environmental sounds. It has value for public safety and human-computer interaction. Current SCED models have limited adaptivity on complex audio and often need manual tuning. Objective: We aim to design an accurate and efficient automated SCED model for large-scale data. Methods: We propose a feature extraction framework that combines DBNPat feature generation with ATT-BP attention-driven binary compression. The framework adapts to signal characteristics with low computational cost. We also introduce a new dataset of 10,017 environmental sound clips (three seconds) with negative (n = 1,729), neutral (n = 6,154), and positive (n = 2,134) classes. Results: The proposed SCED model achieves 87.28% accuracy on three-class SCED. It yields 81.30% UAR, 84.71% precision, 82.97% F1, and 80.59% geometric mean on the imbalanced dataset. Conclusion: The model links classical feature design and deep pattern generation in one adaptive pipeline. It offers a practical solution for digital sound forensics and other ambient-audio systems that need fine emotion cues.
Novel Tiny Textural Motif Pattern-Based RNA Virus Protein Sequence Classification Model
(Pergamon-Elsevier Science Ltd, 2024) Erten, Mehmet; Aydemir, Emrah; Barua, Prabal Datta; Baygin, Mehmet; Dogan, Sengul; Tuncer, Turker; Acharya, U. Rajendra
Background: RNA viruses, including severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2), are important human pathogens. Sequencing of the proteins produced by RNA viruses is essential for understanding disease pathogenesis and may have diagnostic and therapeutic implications. We aimed to develop an accurate and computationally efficient handcrafted feature engineering model for classifying the protein sequences of six pathogenic RNA viruses: SARS-CoV-2, influenza A, influenza B, influenza C, human respirovirus 3, and human immunodeficiency virus (HIV)-1. The first five cause primary respiratory infections; the last has some functional similarity with SARS-CoV-2, justifying the need for diagnostic differentiation. Materials and method: We downloaded 14,787 protein sequences belonging to the six categories in FASTA format from the open-source National Center for Biotechnology Information database and transformed the sequences into numeric arrays. First, the signal was divided into overlapping blocks representing three amino acids. Tiny textural motif pattern, a new histogram-based feature extractor, was then applied to extract textural features using simple signum, lower, and upper ternary functions. 512 features were extracted for each protein sequence and fed to an iterative neighborhood component analysis function to select a study dataset-specific optimal number (34) of the most discriminative features for downstream classification using a shallow k-nearest neighbor classifier with 10-fold cross-validation. Novelties: An efficient linear time complexity is introduced for data classification, providing a robust classification approach, especially for complex datasets. Notably, this approach extends beyond the traditional binary classification focus, successfully distinguishing up to six distinct classes. Furthermore, a novel handcrafted feature extraction method is developed, significantly enhancing data analysis and yielding more precise results. Results: The model attained 99.71% overall 6-class classification accuracy in a data subset and 99.85% for binary classification of SARS-CoV-2 vs. HIV-1, outperforming a similar published model. Conclusions: Our simple model accurately classified the protein sequences of six pathogenic RNA viruses and can potentially be implemented in diagnostic applications to improve RNA virus disease screening.
Swin-Lbp: A Competitive Feature Engineering Model for Urine Sediment Classification
(Springer London Ltd, 2023) Erten, Mehmet; Barua, Prabal Datta; Tuncer, Ilknur; Dogan, Sengul; Baygin, Mehmet; Tuncer, Turker; Acharya, U. Rajendra
Automated urine sediment analysis has become an essential part of diagnosing, monitoring, and treating various diseases that affect the urinary tract and kidneys. However, manual analysis of urine sediment is time-consuming and prone to human bias, and hence there is a need for an automated urine sediment analysis systems using machine learning algorithms. In this work, we propose Swin-LBP, a handcrafted urine sediment classification model using the Swin transformer architecture and local binary pattern (LBP) technique to achieve high classification performance. The Swin-LBP model comprises five phases: preprocessing of input images using shifted windows-based patch division, six-layered LBP-based feature extraction, neighborhood component analysis-based feature selection, support vector machine-based calculation of six predicted vectors, and mode function-based majority voting of the six predicted vectors to generate four additional voted vectors. Our newly reconstructed urine sediment image dataset, consisting of 7 distinct classes, was utilized for training and testing our model. Our proposed model has several advantages over existing automated urinalysis systems. Firstly, we used a feature engineering model that enables high classification performance with linear complexity. This means that it can provide accurate results quickly and efficiently, making it an attractive alternative to time-consuming and biased manual urine sediment analysis. Additionally, our model outperformed existing deep learning models developed on the same source urine sediment image dataset, indicating its superiority in urine sediment classification. Our model achieved 92.60% accuracy for 7-class urine sediment classification, with an average precision of 92.05%. These results demonstrate that the proposed Swin-LBP model can provide a reliable and efficient solution for the diagnosis, surveillance, and therapeutic monitoring of various diseases affecting the kidneys and urinary tract. The proposed model's accuracy, speed, and efficiency make it an attractive option for clinical laboratories and healthcare facilities. In conclusion, the Swin-LBP model has the potential to revolutionize urine sediment analysis and improve patient outcomes in the diagnosis and treatment of urinary tract and kidney diseases.
TQCpat: Tree Quantum Circuit Pattern-Based Feature Engineering Model for Automated Arrhythmia Detection Using PPG Signals
(Springer, 2025) Gelen, Mehmet Ali; Tuncer, Turker; Baygin, Mehmet; Dogan, Sengul; Barua, Prabal Datta; Tan, Ru-San; Acharya, U. R.
Background and PurposeArrhythmia, which presents with irregular and/or fast/slow heartbeats, is associated with morbidity and mortality risks. Photoplethysmography (PPG) provides information on volume changes of blood flow and can be used to diagnose arrhythmia. In this work, we have proposed a novel, accurate, self-organized feature engineering model for arrhythmia detection using simple, cost-effective PPG signals.MethodWe have drawn inspiration from quantum circuits and employed a quantum-inspired feature extraction function /named the Tree Quantum Circuit Pattern (TQCPat). The proposed system consists of four main stages: (i) multilevel feature extraction using discrete wavelet transform (MDWT) and TQCPat, (ii) feature selection using Chi-squared (Chi2) and neighborhood component analysis (NCA), (iii) classification using k-nearest neighbors (kNN) and support vector machine (SVM) and (iv) information fusion.ResultsOur proposed TQCPat-based feature engineering model has yielded a classification accuracy of 91.30% using 46,827 PPG signals in classifying six classes with ten-fold cross-validation.ConclusionOur results show that the proposed TQCPat-based model is accurate for arrhythmia classification using PPG signals and can be tested with a large database and more arrhythmia classes.
Turkernextv2: An Innovative CNN Model for Knee Osteoarthritis Pressure Image Classification
(MDPI, 2025) Esmez, Omer; Deniz, Gulnihal; Bilek, Furkan; Gurger, Murat; Barua, Prabal Datta; Dogan, Sengul; Tuncer, Turker
Background/Objectives: Lightweight CNNs for medical imaging remain limited. We propose TurkerNeXtV2, a compact CNN that introduces two new blocks: a pooling-based attention with an inverted bottleneck (TNV2) and a hybrid downsampling module. These blocks improve stability and efficiency. The aim is to achieve transformer-level effectiveness while keeping the simplicity, low computational cost, and deployability of CNNs. Methods: The model was first pretrained on the Stable ImageNet-1k benchmark and then fine-tuned on a collected plantar-pressure OA dataset. We also evaluated the model on a public blood-cell image dataset. Performance was measured by accuracy, precision, recall, and F1-score. Inference time (images per second) was recorded on an RTX 5080 GPU. Grad-CAM was used for qualitative explainability. Results: During pretraining on Stable ImageNet-1k, the model reached a validation accuracy of 87.77%. On the OA test set, the model achieved 93.40% accuracy (95% CI: 91.3-95.2%) with balanced precision and recall above 90%. On the blood-cell dataset, the test accuracy was 98.52%. The average inference time was 0.0078 s per image (approximate to 128.8 images/s), which is comparable to strong CNN baselines and faster than the transformer baselines tested under the same settings. Conclusions: TurkerNeXtV2 delivers high accuracy with low computational cost. The pooling-based attention (TNV2) and the hybrid downsampling enable a lightweight yet effective design. The model is suitable for real-time and clinical use. Future work will include multi-center validation and broader tests across imaging modalities.