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Articles 61 - 90 of 4540

Full-Text Articles in Physical Sciences and Mathematics

Lif-Sn Composite Modification Layer To Modify Garnet/Lithium Metal Interface, Wu Yang, Xue-Fan Zheng, Yu-Qi Wu, Zheng-Liang Gong Nov 2023

Lif-Sn Composite Modification Layer To Modify Garnet/Lithium Metal Interface, Wu Yang, Xue-Fan Zheng, Yu-Qi Wu, Zheng-Liang Gong

Journal of Electrochemistry

The growing demands for electric vehicles and consumer electronics; as well as the expanding renewable energy storage market; have promoted extensive research on energy storage technologies with low costhigh energy density and safety. Lithium (Li) metal and solid-state electrolytes are considered as important components for next-generation batteries because of their great potential for improvements in energy density and safety performance. Inorganic garnet-type solid electrolytes with high Li-ion conductivity (about 10-3 S·cm-1) and high shear modulus (55 GPa) are considered to be ideal solid-state electrolytes; however; the issue of Li dendrite growth still obstructs their practical application. Herein; …

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Constructing Carbon-Encapsulated Nifev-Based Electrocatalysts By Alkoxide-Based Self-Template Method For Oxygen Evolution Reaction, En-Hui Ma, Xu-Po Liu, Tao Shen, De-Li Wang Nov 2023

Constructing Carbon-Encapsulated Nifev-Based Electrocatalysts By Alkoxide-Based Self-Template Method For Oxygen Evolution Reaction, En-Hui Ma, Xu-Po Liu, Tao Shen, De-Li Wang

Journal of Electrochemistry

The development of green and sustainable water-splitting hydrogen production technology is beneficial to reducing the over-reliance on fossil fuels and realizing the strategic goal of "carbon neutral". As one of the half reactions for water splitting, oxygen evolution reaction has suffered the problems of sluggish four-electron transfer process and relatively slow reaction kinetics. Therefore, exploring efficient and stable catalysts for oxygen evolution reaction is of critical importance for water-splitting technology. Metal alkoxides are a series of compounds formed by the coordination function of metal ions with alcohol molecules. Metal alkoxides possess the double advantages of organic materials and inorganic materials, …

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Preparation And Lithium Storage Properties Of Carbon Confined Li3Vo4 Nano Materials, Jia-Qi Fan, Huan-Qiao Song, Jia-Ying An, Amantai A-Yi-Da-Na, Mo Chen Nov 2023

Preparation And Lithium Storage Properties Of Carbon Confined Li3Vo4 Nano Materials, Jia-Qi Fan, Huan-Qiao Song, Jia-Ying An, Amantai A-Yi-Da-Na, Mo Chen

Journal of Electrochemistry

Li3VO4, as a promising anode material for lithium ion batteries, has been widely studied because of its low and safe voltage, and large capacity. However, its poor electronic conductivity impedes the practical application of Li3VO4 particularly at high rates. In this paper, carbon confined Li3VO4 nano materials (Li3VO4/C) were synthesized by hydrothermal and solid-phase method, and for comparison, the Li3VO4 (N) nano materials without carbon confinement and Li3VO4 (B) materials were also synthesized by pure solid-phase method. The composition, structure, morphology and specific …

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Peg-Water Electrolyte For High-Performance Zinc Iodine Dual-Ion Batteries, Xiao-Feng Qu, Yu-Ting Tang, Xin-Cheng He, Jia-Sheng Zhou, Zi-Heng Tang, Wen-Hua Feng, Jun Liu Nov 2023

Peg-Water Electrolyte For High-Performance Zinc Iodine Dual-Ion Batteries, Xiao-Feng Qu, Yu-Ting Tang, Xin-Cheng He, Jia-Sheng Zhou, Zi-Heng Tang, Wen-Hua Feng, Jun Liu

Journal of Electrochemistry

Thanks to abundant resource and rapid redox reaction kinetics, iodine is regarded as promising positive materials inthe batteries. However, the shuttling effect due to the high solubility of iodine in the electrolyte makes the performance of battery poor. In this paper, polyethylene glycol (PEG400) and potassium iodide were added into zinc-ion aqueous electrolyte. PEG400 could complex with iodine to reduce the dissolution of iodine, therefore alleviating the formation of soluble triiodide (I3–) from iodine and iodide ions. Furthermore, this electrolyte was used in the battery with double carbon cloths as the current collectors, double separators and zinc …

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The Algorithm For The Design Of Fine Granular Substances’ Smart-Type Heat And Moisture Converters Based On Their Accuracy And Speed Criteria, Erkin Uljaev, Ali Abduakhatovich Abduraxmanov Oct 2023

The Algorithm For The Design Of Fine Granular Substances’ Smart-Type Heat And Moisture Converters Based On Their Accuracy And Speed Criteria, Erkin Uljaev, Ali Abduakhatovich Abduraxmanov

Chemical Technology, Control and Management

The paper describes a technique and algorithm allowing to perform parametric design of smart-type heat and moisture converters (hereinafter SHMC) of fine-grained dispersive materials based on their criteria of accuracy and speed. The proposed algorithm optimizes the process of design of smart-type switches and ensure optimal performance of the switches. The method of calculation and selection of optimal parameters of smart-type heat and moisture converters intended to be used in the measurement of parameters such as heat and humidity of fine dispersive substances are aimed at boosting two parameters, i.e., the accuracy and speed. Also, the design stages have been …

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Research Progress And Performance Improvement Strategies Of Hard Carbon Anode Materials For Sodium-Ion Batteries, Xiu-Ping Yin, Yu-Feng Zhao, Jiu-Jun Zhang Oct 2023

Research Progress And Performance Improvement Strategies Of Hard Carbon Anode Materials For Sodium-Ion Batteries, Xiu-Ping Yin, Yu-Feng Zhao, Jiu-Jun Zhang

Journal of Electrochemistry

This paper systematically summarizes the research progress of hard carbon anode materials in sodium ion batteries(SIBs) and the development of the corresponding sodium storage mechanism in recent years, and reviews the performance improvement strategies of hard carbon materials from the aspects of structural design and electrolyte regulation. The effects of the selection of precursors, carbonization temperature, pretreatment, pore formers, heteroatom doping, material compounding, electrolyte regulation and pre-sodiumization on the sodium storage performance of hard carbon anode materials are briefly described. This paper provides new insights into the design, synthesis and electrolyte
matching of high-performance and low-cost hard carbon materials, and …

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Surface Modifications Of Lini0.96Co0.02Mn0.02O2 With Tungsten Oxide And Phosphotungstic Acid, Gang Zhao, Zheng-Liang Gong, Yi-Xiao Li, Yong Yang Oct 2023

Surface Modifications Of Lini0.96Co0.02Mn0.02O2 With Tungsten Oxide And Phosphotungstic Acid, Gang Zhao, Zheng-Liang Gong, Yi-Xiao Li, Yong Yang

Journal of Electrochemistry

With the rapid development of electric vehicles, enormous demands are made for higher energy density, better cycling performance and lower cost of lithium-ion batteries (LIBs). As an important high capacity cathode material for LIBs, the high nickel layered oxide material LiNi0.8Co0.1Mn0.1O2(NCM811) can reach an energy density of 760 Wh·kg-1. The ultra-high nickel ternary positive electrode material (LiNi1-x-yCoxMnyO2, x ≥ 0.90) has a specific capacity of more than 210 mAh·g-1, and can realize higher energy density. Besides, an ultra-high nickel material …

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Preparation And Electrocatalytic Performance Of Feni-Cop/Nc Bifunctional Catalyst, Si-Miao Liu, Jing-Jiao Zhou, Shi-Jun Ji, Zhong-Sheng Wen Oct 2023

Preparation And Electrocatalytic Performance Of Feni-Cop/Nc Bifunctional Catalyst, Si-Miao Liu, Jing-Jiao Zhou, Shi-Jun Ji, Zhong-Sheng Wen

Journal of Electrochemistry

Rechargeable zinc-air batteries have gradually attracted much attention worldwide due to their high capacity, high energy density and low price. Oxygen reduction reaction (ORR) and oxygen evolution reaction (OER) correspond to the charging and discharging processes in rechargeable zinc-air battery, respectively. At present, commercial Pt/C and IrO2 catalysts hinder the large-scale application of zinc-air batteries due to low reserves, high prices and poor stability. Therefore, exploring high performance, low cost and high stability with dual functional catalysts is important for the development of rechargeable zinc-Air batteries. The metal-organic frameworks (MOFs) have high specific surface area, structural stability, good catalytic …

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Dynamic Mechanism Of Science Based Technological Innovation And Industrial Evolution—Take Semiconductor, Digital Computer And Radio Technologies As Examples, Yi Zhang, Qiang Yan Oct 2023

Dynamic Mechanism Of Science Based Technological Innovation And Industrial Evolution—Take Semiconductor, Digital Computer And Radio Technologies As Examples, Yi Zhang, Qiang Yan

Bulletin of Chinese Academy of Sciences (Chinese Version)

By studying the technological innovation and industrial development process of semiconductor, digital computer and radio, this study analyzes the path, conditions and force of science-based technological innovation and its industrialization, establishes a chain reaction model of large-scale technological innovation and diffusion, and compares it with market-based technological innovation. It is found that the large-scale aggregation of scientific research institutions and industrial laboratories accelerates the speed of technological innovation, and diffuses along two paths of scientific research institutions to enterprises and enterprises to enterprises, forming a chain reaction of large-scale technological innovation. Strategic demand is the basic driving force for the …

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Fortifying Iot Against Crimpling Cyber-Attacks: A Systematic Review, Usman Tariq, Irfan Ahmed, Muhammad Attique Khan, Ali Kashif Bashir Oct 2023

Fortifying Iot Against Crimpling Cyber-Attacks: A Systematic Review, Usman Tariq, Irfan Ahmed, Muhammad Attique Khan, Ali Kashif Bashir

Karbala International Journal of Modern Science

The rapid growth and increasing demand for Internet of Things (IoT) devices in our everyday lives create exciting opportunities for human involvement, data integration, and seamless automation. This fully interconnected ecosystem considerably impacts crucial aspects of our lives, such as transportation, healthcare, energy management, and urban infrastructure. However, alongside the immense benefits, the widespread adoption of IoT also brings a complex web of security threats that can influence society, policy, and infrastructure conditions. IoT devices are particularly vulnerable to security violations, and industrial routines face potentially damaging vulnerabilities. To ensure a trustworthy and robust security framework, it is crucial to …

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Smart Service Function Chain System For Dynamic Traffic Steering Using Reinforcement Learning (Chrl), Ahmed Nadhum, Ahmed Al-Saadi Oct 2023

Smart Service Function Chain System For Dynamic Traffic Steering Using Reinforcement Learning (Chrl), Ahmed Nadhum, Ahmed Al-Saadi

Karbala International Journal of Modern Science

The rapid development of the Internet and network services coupled with the growth of communication infrastructure necessitates the employment of intelligent systems. The complexity of the network is heightened by these systems, as they offer diverse services contingent on traffic type, user needs, and security considerations. In this context, a service function chain offers a toolkit to facilitate the management of intricate network systems. However, various traffic types require dynamic adaptation in the sets of function chains. The problem of optimizing the order of service functions in the chain must be solved using the proposed approach, along with balancing the …

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Feature Distillation From Vision-Language Model For Semisupervised Action Classification, Asli Çeli̇k, Ayhan Küçükmani̇sa, Oğuzhan Urhan Oct 2023

Feature Distillation From Vision-Language Model For Semisupervised Action Classification, Asli Çeli̇k, Ayhan Küçükmani̇sa, Oğuzhan Urhan

Turkish Journal of Electrical Engineering and Computer Sciences

The training of supervised machine learning approaches is critically dependent on annotating large-scale datasets. Semisupervised learning approaches aim to achieve compatible performance with supervised methods using relatively less annotation without sacrificing good generalization capacity. In line with this objective, ways of leveraging unlabeled data have been the subject of intense research. However, semisupervised video action recognition has received relatively less attention compared to image domain implementations. Existing semisupervised video action recognition methods trained from scratch rely heavily on augmentation techniques, complex architectures, and/or the use of other modalities while distillation-based methods use models that have only been trained for 2D …

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Multi-View Brain Tumor Segmentation (Mvbts): An Ensemble Of Planar And Triplanar Attention Unets, Snehal Rajput, Rupal Kapdi, Mehul Raval, Mohendra Roy Oct 2023

Multi-View Brain Tumor Segmentation (Mvbts): An Ensemble Of Planar And Triplanar Attention Unets, Snehal Rajput, Rupal Kapdi, Mehul Raval, Mohendra Roy

Turkish Journal of Electrical Engineering and Computer Sciences

3D UNet has achieved high brain tumor segmentation performance but requires high computation, large memory, abundant training data, and has limited interpretability. As an alternative, the paper explores using 2D triplanar (2.5D) processing, which allows images to be examined individually along axial, sagittal, and coronal planes or together. The individual plane captures spatial relationships, and combined planes capture contextual (depth) information. The paper proposes and analyzes an ensemble of uniplanar and triplanar UNets combined with channel and spatial attention for brain tumor segmentation. It investigates the significance of each plane and analyzes the impact of uniplanar and triplanar ensembles with …

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Focal Modulation Network For Lung Segmentation In Chest X-Ray Images, Şaban Öztürk, Tolga Çukur Oct 2023

Focal Modulation Network For Lung Segmentation In Chest X-Ray Images, Şaban Öztürk, Tolga Çukur

Turkish Journal of Electrical Engineering and Computer Sciences

Segmentation of lung regions is of key importance for the automatic analysis of Chest X-Ray (CXR) images, which have a vital role in the detection of various pulmonary diseases. Precise identification of lung regions is the basic prerequisite for disease diagnosis and treatment planning. However, achieving precise lung segmentation poses significant challenges due to factors such as variations in anatomical shape and size, the presence of strong edges at the rib cage and clavicle, and overlapping anatomical structures resulting from diverse diseases. Although commonly considered as the de-facto standard in medical image segmentation, the convolutional UNet architecture and its variants …

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Infrared Imaging Segmentation Employing An Explainable Deep Neural Network, Xinfei Liao, Dan Wang, Zairan Li, Nilanjan Dey, Rs Simon, Fuqian Shi Oct 2023

Infrared Imaging Segmentation Employing An Explainable Deep Neural Network, Xinfei Liao, Dan Wang, Zairan Li, Nilanjan Dey, Rs Simon, Fuqian Shi

Turkish Journal of Electrical Engineering and Computer Sciences

Explainable AI (XAI) improved by a deep neural network (DNN) of a residual neural network (ResNet) and long short-term memory networks (LSTMs), termed XAIRL, is proposed for segmenting foot infrared imaging datasets. First, an infrared sensor imaging dataset is acquired by a foot infrared sensor imaging device and preprocessed. The infrared sensor image features are then defined and extracted with XAIRL being applied to segment the dataset. This paper compares and discusses our results with XAIRL. Evaluation indices are applied to perform various measurements for foot infrared image segmentation including accuracy, precision, recall, F1 score, intersection over union (IoU), Dice …

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Classification Of Chronic Pain Using Fmri Data: Unveiling Brain Activity Patterns For Diagnosis, Rejula V, Anitha J, Belfin Robinson Oct 2023

Classification Of Chronic Pain Using Fmri Data: Unveiling Brain Activity Patterns For Diagnosis, Rejula V, Anitha J, Belfin Robinson

Turkish Journal of Electrical Engineering and Computer Sciences

Millions of people throughout the world suffer from the complicated and crippling condition of chronic pain. It can be brought on by several underlying disorders or injuries and is defined by chronic pain that lasts for a period exceeding three months. To better understand the brain processes behind pain and create prediction models for pain-related outcomes, machine learning is a potent technology that may be applied in Functional magnetic resonance imaging (fMRI) chronic pain research. Data (fMRI and T1-weighted images) from 76 participants has been included (30 chronic pain and 46 healthy controls). The raw data were preprocessed using fMRIprep …

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Deep Feature Extraction, Dimensionality Reduction, And Classification Of Medical Images Using Combined Deep Learning Architectures, Autoencoder, And Multiple Machine Learning Models, Ahmet Hi̇dayet Ki̇raz, Fatime Oumar Djibrillah, Mehmet Emi̇n Yüksel Oct 2023

Deep Feature Extraction, Dimensionality Reduction, And Classification Of Medical Images Using Combined Deep Learning Architectures, Autoencoder, And Multiple Machine Learning Models, Ahmet Hi̇dayet Ki̇raz, Fatime Oumar Djibrillah, Mehmet Emi̇n Yüksel

Turkish Journal of Electrical Engineering and Computer Sciences

Accurate analysis and classification of medical images are essential factors in clinical decision-making and patient care. A novel comparative approach for medical image classification is proposed in this study. This new approach involves several steps: deep feature extraction, which extracts the informative features from medical images; concatenation, which concatenates the extracted deep features to form a robust feature vector; dimensionality reduction with autoencoder, which reduces the dimensionality of the feature vector by transforming it into a different feature space with a lower dimension; and finally, these features obtained from all these steps were fed into multiple machine learning classifiers (SVM, …

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Cognitive Digital Modelling For Hyperspectral Image Classification Using Transfer Learning Model, Mohammad Shabaz, Mukesh Soni Oct 2023

Cognitive Digital Modelling For Hyperspectral Image Classification Using Transfer Learning Model, Mohammad Shabaz, Mukesh Soni

Turkish Journal of Electrical Engineering and Computer Sciences

Deep convolutional neural networks can fully use the intrinsic relationship between features and improve the separability of hyperspectral images, which has received extensive in recent years. However, the need for a large number of labelled samples to train deep network models limits the application of such methods. The idea of transfer learning is introduced into remote sensing image classification to reduce the need for the number of labelled samples. In particular, the situation in which each class in the target picture only has one labelled sample is investigated. In the target domain, the number of training samples is enlarged by …

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Trcaptionnet: A Novel And Accurate Deep Turkish Image Captioning Model With Vision Transformer Based Image Encoders And Deep Linguistic Text Decoders, Serdar Yildiz, Abbas Memi̇ş, Songül Varli Oct 2023

Trcaptionnet: A Novel And Accurate Deep Turkish Image Captioning Model With Vision Transformer Based Image Encoders And Deep Linguistic Text Decoders, Serdar Yildiz, Abbas Memi̇ş, Songül Varli

Turkish Journal of Electrical Engineering and Computer Sciences

Image captioning is known as a fundamental computer vision task aiming to figure out and describe what is happening in an image or image region. Through an image captioning process, it is ensured to describe and define the actions and the relations of the objects within the images. In this manner, the contents of the images can be understood and interpreted automatically by visual computing systems. In this paper, we proposed the TRCaptionNet a novel deep learning-based Turkish image captioning (TIC) model for the automatic generation of Turkish captions. The model we propose essentially consists of a basic image encoder, …

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Cccd: Corner Detection And Curve Reconstruction For Improved 3d Surface Reconstruction From 2d Medical Images, Mriganka Sarmah, Arambam Neelima Oct 2023

Cccd: Corner Detection And Curve Reconstruction For Improved 3d Surface Reconstruction From 2d Medical Images, Mriganka Sarmah, Arambam Neelima

Turkish Journal of Electrical Engineering and Computer Sciences

The conventional approach to creating 3D surfaces from 2D medical images is the marching cube algorithm, but it often results in rough surfaces. On the other hand, B-spline curves and nonuniform rational B-splines (NURBSs) offer a smoother alternative for 3D surface reconstruction. However, NURBSs use control points (CTPs) to define the object shape and corners play an important role in defining the boundary shape as well. Thus, in order to fill the research gap in applying corner detection (CD) methods to generate the most favorable CTPs, in this paper corner points are identified to predict organ shape. However, CTPs must …

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Hybrid Machine Learning Model To Predict Chronic Kidney Diseases Using Handcrafted Features For Early Health Rehabilitation, Amjad Rehman, Tanzila Saba, Haider Ali, Narmine Elhakim, Noor Ayesha Oct 2023

Hybrid Machine Learning Model To Predict Chronic Kidney Diseases Using Handcrafted Features For Early Health Rehabilitation, Amjad Rehman, Tanzila Saba, Haider Ali, Narmine Elhakim, Noor Ayesha

Turkish Journal of Electrical Engineering and Computer Sciences

Chronic kidney diseases proliferate due to hypertension, diabetes, anemia, obesity, smoking etc. Patients with such conditions are sometimes unaware of first symptoms, complicating disease diagnosis. This paper presents chronic kidney disease (CKD) prediction model to classify CKD patients from NCKD (Non-CKD). The proposed study has two main stages. First, we found the odds ratio through logistic regression and comparison test to identify early risk factors from kidneys? MRI and differentiate CKD from NCKD subjects. In stage 2, LR, LDA, MLP classifiers were applied to predict CKD and NCKD by extracting features from MRI. The odds ratio of blood glucose random …

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Enhancing Exploration-Exploitation In Harmony Search For Airborne Hyperspectral Imaging Band Selection (E3hs), Mohammed Abdulmajeed Moharram, Divya Meena Sundaram Oct 2023

Enhancing Exploration-Exploitation In Harmony Search For Airborne Hyperspectral Imaging Band Selection (E3hs), Mohammed Abdulmajeed Moharram, Divya Meena Sundaram

Turkish Journal of Electrical Engineering and Computer Sciences

Hyperspectral imaging has emerged as a prominent area of research in the field of remote sensing science. However, hyperspectral images (HSIs) pose a notable challenge due to the presence of numerous irrelevant and redundant spectral bands exhibiting high correlation. Therefore, it is necessary to enhance the classification performance for HSI processing by selecting the most relevant discriminative spectral bands. To this end, this paper introduces a metaheuristic search method called enhancing exploration-exploitation in harmony search (E3HS). The standard harmony search suffers from many weaknesses, such as premature convergence and falling easily into the local optimum. Consequently, E3HS was proposed to …

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A Unique Hybrid Domain Hand-Crafted Feature To Classify Colorectal Tissue Histopathological Images Using Multiheaded Cnn, Anurodh Kumar, Amit Vishwakarma, Varun Bajaj Oct 2023

A Unique Hybrid Domain Hand-Crafted Feature To Classify Colorectal Tissue Histopathological Images Using Multiheaded Cnn, Anurodh Kumar, Amit Vishwakarma, Varun Bajaj

Turkish Journal of Electrical Engineering and Computer Sciences

Early diagnosis of colorectal cancer lengthens human life and is helpful in efforts to cure the illness. Histopathological inspection is a routinely utilized technique to diagnose it. Visual assessment of histopathological images takes more investigation time, and the decision is based on the individual perceptions of clinicians. The existing methods for colorectal cancer classification use only spatial information. However, studies on the spectral domains of information are lacking in the literature. Therefore, the performance of the existing techniques is moderate. To improve the performance of colorectal cancer classification, this work proposes a unique hybrid domain hand-crafted feature formulated using scale-invariant …

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Yolo And Lsh-Based Video Stream Analytics Landscape For Short-Term Traffic Density Surveillance At Road Networks, Lavanya K, Stuti Tiwari, Rahul Anand, Jude Hemanth Oct 2023

Yolo And Lsh-Based Video Stream Analytics Landscape For Short-Term Traffic Density Surveillance At Road Networks, Lavanya K, Stuti Tiwari, Rahul Anand, Jude Hemanth

Turkish Journal of Electrical Engineering and Computer Sciences

The duty of monitoring traffic during rush hour is difficult due to the fact that modern roadways are getting more crowded every day. The automated solutions that have already been created in this area are ineffective at processing enormous amounts of data in a short amount of time, leading to ineffectiveness and inconsistent results. The YOLO (you only look once) and LSH (locality sensitive hashing) algorithms are combined with the Kafka architecture in this study to create a method for assessing traffic density in real-time scenarios. Our concept, which is specifically designed for vehicular networks, predicts the traffic density in …

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Stepwise Dynamic Nearest Neighbor (Sdnn): A New Algorithm For Classification, Deni̇z Karabaş, Derya Bi̇rant, Peli̇n Yildirim Taşer Sep 2023

Stepwise Dynamic Nearest Neighbor (Sdnn): A New Algorithm For Classification, Deni̇z Karabaş, Derya Bi̇rant, Peli̇n Yildirim Taşer

Turkish Journal of Electrical Engineering and Computer Sciences

Although the standard k-nearest neighbor (KNN) algorithm has been used widely for classification in many different fields, it suffers from various limitations that abate its classification ability, such as being influenced by the distribution of instances, ignoring distances between the test instance and its neighbors during classification, and building a single/weak learner. This paper proposes a novel algorithm, called stepwise dynamic nearest neighbor (SDNN), which can effectively handle these problems. Instead of using a fixed parameter k like KNN, it uses a dynamic neighborhood strategy according to the data distribution and implements a new voting mechanism, called stepwise voting. Experimental …

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Using T-Distributed Stochastic Neighbor Embedding For Visualization And Segmentation Of 3d Point Clouds Of Plants, Heli̇n Dutağaci Sep 2023

Using T-Distributed Stochastic Neighbor Embedding For Visualization And Segmentation Of 3d Point Clouds Of Plants, Heli̇n Dutağaci

Turkish Journal of Electrical Engineering and Computer Sciences

In this work, the use of t-SNE is proposed to embed 3D point clouds of plants into 2D space for plant characterization. It is demonstrated that t-SNE operates as a practical tool to flatten and visualize a complete 3D plant model in 2D space. The perplexity parameter of t-SNE allows 2D rendering of plant structures at various organizational levels. Aside from the promise of serving as a visualization tool for plant scientists, t-SNE also provides a gateway for processing 3D point clouds of plants using their embedded counterparts in 2D. In this paper, simple methods were proposed to perform semantic …

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Recognizing Handwritten Digits Using Spiking Neural Networks With Learning Algorithms Based On Sliding Mode Control Theory, Yeşi̇m Öni̇z, Mehmet Ayyildiz Sep 2023

Recognizing Handwritten Digits Using Spiking Neural Networks With Learning Algorithms Based On Sliding Mode Control Theory, Yeşi̇m Öni̇z, Mehmet Ayyildiz

Turkish Journal of Electrical Engineering and Computer Sciences

In this paper, a spiking neural network (SNN) has been proposed for recognizing the digits written on the LCD screen of an experimental setup. The convergence of the learning algorithm has been ensured by using sliding mode control (SMC) theory and the Lyapunov stability method for the adaptation of the network parameters. The spike response model (SRM) has been utilized in the design of the SNN. The performance of the proposed learning scheme has been evaluated both on the experimental data and on the MNIST dataset. The simulated and experimental results of the SNN structure have been compared with the …

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Transforming Temporal-Dynamic Graphs Into Time-Series Data For Solving Event Detection Problems, Kutay Taşci, Fuat Akal Sep 2023

Transforming Temporal-Dynamic Graphs Into Time-Series Data For Solving Event Detection Problems, Kutay Taşci, Fuat Akal

Turkish Journal of Electrical Engineering and Computer Sciences

Event detection on temporal-dynamic graphs aims at detecting significant events based on deviations from the normal behavior of the graphs. With the widespread use of social media, many real-world events manifest as social media interactions, making them suitable for modeling as temporal-dynamic graphs. This paper presents a workflow for event detection on temporal-dynamic graphs using graph representation learning. Our workflow leverages generated embeddings of a temporal-dynamic graph to reframe the problem as an unsupervised time-series anomaly detection task. We evaluated our workflow on four distinct real-world social media datasets and compared our results with the related work. The results show …

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A Machine Learning Approach For Dyslexia Detection Using Turkish Audio Records, Tuğberk Taş, Muhammed Abdullah Bülbül, Abas Haşi̇moğlu, Yavuz Meral, Yasi̇n Çalişkan, Gunay Budagova, Mücahi̇d Kutlu Sep 2023

A Machine Learning Approach For Dyslexia Detection Using Turkish Audio Records, Tuğberk Taş, Muhammed Abdullah Bülbül, Abas Haşi̇moğlu, Yavuz Meral, Yasi̇n Çalişkan, Gunay Budagova, Mücahi̇d Kutlu

Turkish Journal of Electrical Engineering and Computer Sciences

Dyslexia is a learning disorder, characterized by impairment in the ability to read, spell, and decode letters. It is vital to detect dyslexia in earlier stages to reduce its effects. However, diagnosing dyslexia is a time-consuming and costly process. In this paper, we propose a machine-learning model that predicts whether a Turkish-speaking child has dyslexia using his/her audio records. Therefore, our model can be easily used by smart phones and work as a warning system such that children who are likely to be dyslexic according to our model can seek an examination by experts. In order to train and evaluate, …

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Cognitive Load Detection Using Ci-Ssa For Eeg Signal Decomposition And Nature-Inspired Feature Selection, Jammisetty Yedukondalu, Lakhan Dev Sharma Sep 2023

Cognitive Load Detection Using Ci-Ssa For Eeg Signal Decomposition And Nature-Inspired Feature Selection, Jammisetty Yedukondalu, Lakhan Dev Sharma

Turkish Journal of Electrical Engineering and Computer Sciences

Cognitive load detection is eminent during the mental assignment of neural activity because it indicates how the brain reacts to stimuli. The level of cognitive load experienced during mental arithmetic tasks can be determined using an electroencephalogram (EEG). The EEG data were collected from publicly available datasets, namely, mental arithmetic task (MAT) and simultaneous task workload (STEW). The first phase comprises decomposing the electroencephalogram (EEG) signal into intrinsic mode functions (IMFs) using circulant singular spectrum analysis (Ci-SSA). In the second phase, entropy-based features were evaluated using IMFs. After that, the extracted features were fed to nature-inspired feature selection algorithms: genetic …

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