Kemal Polat

• Professor
• Professor at Bolu Abant İzzet Baysal University

Constructing a trajectory index can efficiently improve the performances of trajectory data processing, provide basic supports for trajectory data mining. With the constantly growing of trajectory data scale and increasing demands for trajectory retrieval efficiency and accuracy, the indexing methods have become more and more crucial. The indexing...

Traffic flow prediction plays a crucial role in the management and operation of urban transportation systems. While extensive research has been conducted on predictions for individual transportation modes, there is relatively limited research on joint prediction across different transportation modes. Furthermore, existing multimodal traffic joint m...

Alzheimer’s disease (AD) and Parkinson’s disease (PD) are two distinct types of degenerative neurologic disorders that impact brain function and movement in their own ways. Early detection of PD and AD is crucial for improving the patient’s quality of life. For this reason, in this study, we employed shallow learning (SL) and deep learning (DL) met...

All three contrast-enhanced (CE) phases (e.g., Arterial, Portal Venous, and Delay) are crucial for diagnosing liver tumors. However, acquiring all three phases is constrained due to contrast agents (CAs) risks, long imaging time, and strict imaging criteria. In this paper, we propose a novel Common-Unique Decomposition Driven Diffusion Model (CUDD-...

Estimating blood sugar levels is a critical task in effective diabetes management. This study focuses on leveraging the power of machine learning models such as CatBoost, XGBoost, and Extra Trees Regressor, along with explainable AI techniques like SHAP values and confusion matrices, to predict blood sugar levels using Photoplethysmography (PPG) si...

The Open Journal of Nano -2024- Volume 9, Issue 1

In this study, the evaluation of classification models with frequency and chaotic features was aimed for the classification of healthy individuals and Alzheimer’s patients using EEG signals. Morlet wavelet transform was employed for calculating EEG features to determine the characteristics in the frequency domain. Additionally, Lyapunov exponents w...

Currently, most traffic simulations require residents’ travel plans as input data; however, in real scenarios, it is difficult to obtain real residents’ travel behavior data for various reasons, such as a large amount of data and the protection of residents’ privacy. This study proposes a method combining a convolutional neural network (CNN) and a...

Fruit and vegetable freshness testing can improve the efficiency of agricultural product management, reduce resource waste and economic losses, and plays a vital role in increasing the added value of fruit and vegetable agricultural products. At present, the detection of fruit and vegetable freshness mainly relies on manual feature extraction combi...

With the growing exploration of marine resources, underwater image enhancement has gained significant attention. Recent advances in convolutional neural networks (CNN) have greatly impacted underwater image enhancement techniques. However, conventional CNN-based methods typically employ a single network structure, which may compromise robustness in...

Motivated by the adverse impact of light attenuation and scattering, which leads to color distortion and low contrast in underwater images, our study primarily focuses on enhancement techniques for these images using localized transmission feature analysis and global atmospheric light feature extraction. To this end, we propose a novel approach, na...

Background Alzheimer’s disease (AD) is a disease that manifests itself with a deterioration in all mental activities, daily activities, and behaviors, especially memory, due to the constantly increasing damage to some parts of the brain as people age. Detecting AD at an early stage is a significant challenge. Various diagnostic devices are used to...

Pneumonia is one of the leading diseases of child mortality in the world. The fastest imaging method for detecting pneumonia in chest X-rays. Examining X-ray images is carried out by expert radiologists. It is important to develop computer-aided diagnosis systems due to the difficulty of the images examined. In this study, DenseNet121, DenseNet169,...

Applications with mobile and sensing devices have already become ubiquitous. In most of these applications, trajectory data is continuously growing to huge volumes. Existing systems for big trajectory data organize trajectories at distributed block storage systems. Systems like DITA that use block storage (e.g., 128 MB each) are more efficient for...

Due to the complex underwater environment, underwater images exhibit different degradation characteristics, severely affecting their practical applications. Although underwater image enhancement networks with physical priors exist, the statistical priors are not applicable in extreme underwater scenes. Therefore, we propose ReX-Net, a reflectance-g...

In a smart grid, the main goals are to provide grid stability, improve power system performance and security, and reduce operations, system maintenance, and planning costs. The prediction stability of smart grid (SG) systems is essential in terms of power loss minimization and the importance of adequate energy policies. SG systems must accurately p...

The increasing abuse of facial manipulation methods, such as FaceSwap, Deepfakes etc., seriously threatens the authenticity of digital images/videos on the Internet. Therefore, it is of great importance to identify the facial videos to confirm the contents and avoid fake news or rumours. Many researchers have paid great attention to the detection o...

In a smart grid, the main goals are to provide grid stability, improve power system performance and security, and reduce operations, system maintenance, and planning costs. The prediction stability of smart grid (SG) systems is essential in terms of power loss minimization and the importance of adequate energy policies. SG systems must accurately p...

In this paper, we proposed a novel approach to diagnose and classify Parkinson's Disease (PD) using ensemble learning and 1D-PDCovNN, a novel deep learning technique. PD is a neurodegenerative disorder; early detection and correct classification are essential for better disease management. The primary aim of this study is to develop a robust approa...

This paper investigates new feature extraction and regression methods for predicting cuffless blood pressure from PPG signals. Cuffless blood pressure is a technology that measures blood pressure without needing a cuff. This technology can be used in various medical applications, including home health monitoring, clinical uses, and portable devices...

Brain tumor happens due to the instant and uncontrolled cell growth. It may lead to death if not cured at an early stage. In spite of several promising results and substantial efforts in this research area, the real challenge is to provide the accurate classification and segmentation. The key issue in brain tumor detection develops from the irregul...

This study uses machine learning to perform the hearing test (audiometry) processes autonomously with EEG signals. Sounds with different amplitudes and wavelengths given to the person tested in standard hearing tests are assigned randomly with the interface designed with MATLAB GUI. The person stated that he heard the random size sounds he listened...

Melanoma is known worldwide as a malignant tumor and the fastest-growing skin cancer type. It is a very life-threatening disease with a high mortality rate. Automatic melanoma detection improves the early detection of the disease and the survival rate. In accordance with this purpose, we presented a multi-task learning approach based on melanoma re...

The visual quality of images captured under sub-optimal lighting conditions, such as over and underexposure may benefit from improvement using fusion-based techniques. This paper presents the Caputo Differential Operator-based image fusion technique for image enhancement. To effect this enhancement, the proposed algorithm first decomposes the overe...

This study developed a novel melanoma diagnosis model from dermoscopy images using a novel hybrid model. Melanoma is the most dangerous and rarest type of skin cancer. It is seen because of the uncontrolled proliferation of melanocyte cells that give color to the skin. Dermoscopy is a critical auxiliary diagnostic method in the differentiation of p...

Atrial fibrillation (AF) is one of the clinic's most common arrhythmias with high morbidity and mortality. Developing an intelligent auxiliary diagnostic model of AF based on a body surface electrocardiogram (ECG) is necessary. Convolutional neural network (CNN) is one of the most commonly used models for AF recognition. However, typical CNN is not...

Skin cancer is one of the most widespread threats to human health worldwide. Therefore, early-stage recognition and detection of these diseases are crucial for patients' lives. Computer-aided methods can be used to solve this problem with high performance. We presented a wavelet transform-based deep residual neural network (WT-DRNNet) for skin lesi...

Automatic screening approaches can help diagnose Cardiovascular Disease (CVD) early, which is the leading source of mortality worldwide. Electrocardiogram (ECG/EKG)-based methods are frequently utilized to detect CVDs since they are a reliable and non-invasive tool. Due to this, Smart Cardiovascular Disease Detection System (SCDDS) has been offered...

Deep neural networks (DNNs) are vulnerable to adversarial attacks, in which a small perturbation to samples can cause misclassification. However, how to select important words for textual attack models is a big challenge. Therefore, in this paper, an innovative score-based attack model is proposed to solve the important words selection problem for...

A global patient-oriented approach quickly replaces the traditionally specialized healthcare model in smart health care. Several technological breakthroughs have facilitated this tremendous transition in healthcare. Presently, innovative healthcare programs use 4G communication protocols in the field. Intelligent connected healthcare systems must i...

Knowledge Graph Embedding (KGE)-enhanced recommender systems are effective in providing accurate and personalized recommendations in diverse application scenarios. However, such techniques that exploit entire embedded Knowledge Graph (KG) without data relevance approval constraints fail to stop noise penetration into the data. Additionally, approac...

Human action acknowledgment is an abundant and significant area for machine learning-based researchers due to the level of accuracy in identifying human actions. Due to the rapid growth of technologies in the machine and deep learning techniques, wireless sensors, handy Internet of Things (IoT) devices, and Wireless Fidelity (Wi-Fi), the activity r...

Segmentation of skin lesions plays a very important role in the early detection of skin cancer. However, indistinguishability due to various artifacts such as hair and contrast between normal skin and lesioned skin is an important challenge for specialist dermatologists. Computer-aided diagnostic systems using deep convolutional neural networks are...

Speech is one form of biometric that combines both physiological and behavioral features. It is beneficial for remote-access transactions over telecommunication networks. Presently, this task is the most challenging one for researchers. People’s mental status in the form of emotions is quite complex, and its complexity depends upon internal behavio...

Objective: Measurement and monitoring of blood pressure are of great importance for preventing diseases such as cardiovascular and stroke caused by hypertension. Therefore, there is a need for advanced artificial intelligence-based systolic and diastolic blood pressure systems with a new technological infrastructure with a noninvasive process. The...

Segmentation of skin lesions from dermoscopic images plays an essential role in the early detection of skin cancer. However, skin lesion segmentation is still challenging due to artifacts such as indistinguishability between skin lesion and normal skin, hair on the skin, and reflections in the obtained dermoscopy images. In this study, an edge atte...

Lung cancer has emerged as a major cause of death among all demographics worldwide, largely caused by a proliferation of smoking habits. However, early detection and diagnosis of lung cancer through technological improvements can save the lives of millions of individuals affected globally. Computerized tomography (CT) scan imaging is a proven and p...

Recommender Systems (RS) are established to deal with the preferences of users to enhance their experience and interest in innumerable online applications by streamlining the stress persuaded by the reception of excessive information through the recommendation methods. Although researches have put a lot of efforts in making recommendation processes...

Demodulating the modulated signals used in digital communication on the receiver side is necessary in terms of communication. The currently used systems are systems with a variety of hardware. These systems are used separately for each type of communication signal. A single algorithm facilitates the classification and subsequent demodulation of sig...

The extraction of informative features from medical images and the retrieving of similar images from data repositories is vital for clinical decision support systems. Unlike general tasks such as medical image classification and segmentation, retrieval is more reliable in terms of interpretability. However, this task is quite challenging due to the...

The sensing node clustering algorithm is a network topology control method that can effectively extend the lifetime of smart sensing systems (SSSMs). However, the traditional topology algorithms suffer from the excessively early death of cluster heads. Hence, the attention-shifting mechanism for energy consumption based on hybrid energy-efficient d...

Cybersecurity in information technology (IT) infrastructures is one of the most significant and complex issues of the digital era. Increases in network size and associated data have directly affected technological breakthroughs in the Internet and communication areas. Malware attacks are becoming increasingly sophisticated and hazardous as technolo...

Due to the proliferation of COVID-19, the world is in a terrible condition and human life is at risk. The SARS-CoV-2 virus had a significant impact on public health, social issues, and financial issues. Thousands of individuals are infected on a regular basis in India, which is one of the populations most seriously impacted by the pandemic. Despite...

Polycystic Ovary Syndrome (PCOS) is a hormonal disorder that affects a large percentage of women of reproductive age. PCOS causes imbalanced or delayed menstrual cycles and produces high levels of the male hormone. The ovaries may create a significant number of little fluid-filled sacs (follicles) yet fail to discharge eggs regularly. The actual ca...

The optical coherence tomography (OCT) is useful in viewing cross-sectional retinal images and detecting various forms of retinal disorders from those images. Image processing methods and computational algorithms underlying this paper try to detect the shadowing region beneath exudates automatically. is paper presents a novel method for detecting h...

Deep phenotyping is defined as learning about genotype-phenotype associations and the history of human illness by analyzing phenotypic anomalies. It is significant to investigate the association between phenotype and genotype. Machine learning approaches are good at predicting the associations between abnormal human phenotypes and genes. A novel fr...

Filtration to optimal exactness is mandatory since the options inundate the online world. Knowledge graph embedding is extraordinarily contributing to the recommendations, but the existing knowledge graph (KG)-based recommendation methods only exploit the correlations among the preferences and stand-alone entities, without bonding the cocurricular...

Radio frequency identification (RFID) technology has already demonstrated its use. RFID is used in many productions for different applications, for example, apparatus chasing, personal and vehicle access panels, logistics, baggage, and safety items in departmental stores. The main benefits of RFID are optimizing resources, quality customer service,...

This paper presents a prototype implementation of arrhythmia classification using Probabilistic neural network (PNN). Ar-rhythmia is an irregular heartbeat, resulting in severe heart problems if not diagnosed early. erefore, accurate and robust arrhythmia classification is a vital task for cardiac patients. e classification of ECG has been performe...

Facial nerve paralysis results in muscle weakness or complete paralysis on one side of the face. Patients suffer from difficulties in speech, mastication and emotional expression, impacting their quality of life by causing anxiety and depression. The emotional well-being of a facial nerve paralysis patient is usually followed up during and after tr...

"DCA-IoMT Dataset" is related to the paper entitled "DCA-IoMT: Knowledge Graph Embedding-enhanced Deep Collaborative Alerts-recommendation against COVID19" available at https://ieeexplore.ieee.org/document/9740420

A color fundus image is a photograph obtained using a fundus camera of the inner wall of the eyeball. In the image, doctors may see changes in the retinal vessels, which can be used to diagnose various dangerous disorders such as arteriosclerosis, some macular degeneration related to age, and glaucoma. To diagnose certain disorders as early as poss...

Deep neural networks, an emerging paradigm in deep learning, have proven to make feature extraction from remote sensing data easier. Deep learning has been shown to be capable of effectively classifying hyperspectral images (HSI). Deep convolutional neural networks (CNNs) are one of the most effective approaches for HSI classification. The deep lea...

In recent years, malware detection has become necessary to improve system performance and prevent programs from infecting your computer. Signature-based malware failed to detect most new organisms. This article presents the hybrid technique to automatically generate and classify malicious signatures. The hybrid method is called the ANFIS-SSA approa...

A tilt sensor is a device used to measure the tilt on many axes of a reference point. Tilt sensors measure the bending position according to gravity and are used in many applications. Slope sensors allow easy detection of direction or slope in the air. These tilt gauges have become increasingly popular and are being adapted for a growing number of...

Speaker recognition systems are widely used in various applications to identify a person by their voice; however, the high degree of variability in speech signals makes this a challenging task. Dealing with emotional variations is very difficult because emotions alter the voice characteristics of a person; thus, the acoustic features differ from th...

Speaker recognition systems are widely used in various applications to identify a person by their voice; however, the high degree of variability in speech signals makes this a challenging task. Dealing with emotional variations is very difficult because emotions alter the voice characteristics of a person; thus, the acoustic features differ from th...