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Articles 1621 - 1650 of 7211
Full-Text Articles in Computer Engineering
A Hybrid Approach Based On Transfer And Ensemble Learning For Improvingperformances Of Deep Learning Models On Small Datasets, Tunç Gülteki̇n, Aybars Uğur
A Hybrid Approach Based On Transfer And Ensemble Learning For Improvingperformances Of Deep Learning Models On Small Datasets, Tunç Gülteki̇n, Aybars Uğur
Turkish Journal of Electrical Engineering and Computer Sciences
The need for high-volume data is one of the challenging requirements of the deep learning methods, and it makes it harder to apply deep learning algorithms to domains in which the data sources are limited, in other words, small. These domains may vary from medical diagnosis to satellite imaging. The performances of the deep learning methods on small datasets can be improved by the approaches such as data augmentation, ensembling, and transfer learning. In this study, we propose a new approach that utilizes transfer learning and ensemble methods to increase the accuracy rates of convolutional neural networks for classification tasks …
An Observer Based Temperature Estimation In Cooking Heterogeneous Mixtures:A Turkish Coffee Machine Application, Arda Dönerkayali, Türker Türker
An Observer Based Temperature Estimation In Cooking Heterogeneous Mixtures:A Turkish Coffee Machine Application, Arda Dönerkayali, Türker Türker
Turkish Journal of Electrical Engineering and Computer Sciences
A high-precision temperature information is required to follow the recipe in automatic cooking processes of heterogeneous liquids. Therefore, measurement equipment plays a crucial role in appliances developed for automatic cooking processes. However, it is difficult to obtain the temperature information in such appliances since the sensors cannot be located inside the heterogeneous liquid and the diffusion model is not precise in general. In this manner, a method is proposed to estimate the temperature of the heterogeneous mixture during the cooking process. This is achieved by the utilization of only one temperature sensor located at the outside wall of the cooking …
A Hybrid Numerical Model For Long-Range Electromagnetic Wave Propagation, Gül Yesa Altun, Özlem Özgün
A Hybrid Numerical Model For Long-Range Electromagnetic Wave Propagation, Gül Yesa Altun, Özlem Özgün
Turkish Journal of Electrical Engineering and Computer Sciences
A hybrid numerical model is presented for solving long range electromagnetic wave propagation problems involving objects on or above the ground surface by hybridizing the two-way split-step parabolic equation (2W-SSPE) method with the method of moments (MoM). The advantages of the proposed model are twofold: (i) It reduces the staircasing error in irregular terrain modeling, which usually occurs when the standard SSPE method is used alone. This is achieved by employing the MoM to more accurately obtain the scattered fields from slanted/curved surfaces. (ii) It enables the SSPE method to handle the problems involving objects above the Earth's surface, which …
Comparison Of Metaheuristic Optimization Algorithms With A New Modifieddeb Feasibility Constraint Handling Technique, Murat Erhan Çi̇men, Zeynep Gari̇p, Ali̇ Fuat Boz
Comparison Of Metaheuristic Optimization Algorithms With A New Modifieddeb Feasibility Constraint Handling Technique, Murat Erhan Çi̇men, Zeynep Gari̇p, Ali̇ Fuat Boz
Turkish Journal of Electrical Engineering and Computer Sciences
In this study, the modification of the Deb feasibility method is considered to solve the constrained optimization problems. In the developed modified Deb feasibility constraint method, the third rule in its procedure was revised in order to increase the performance of the Deb feasibility constraint handling method. The innovation in the method is based on generating a new individual by using both possible solutions that violate the constraints in the method used for solving the problem. In detail, discussions were given about the application and usefulness of six constrained handling techniques. Furthermore, genetic algorithm, particle swarm optimization, Harris hawks optimization, …
Opinion Dynamics Of Stubborn Agents Under The Presence Of A Troll Asdifferential Game, Aykut Yildiz, Ari̇f Bülent Özgüler
Opinion Dynamics Of Stubborn Agents Under The Presence Of A Troll Asdifferential Game, Aykut Yildiz, Ari̇f Bülent Özgüler
Turkish Journal of Electrical Engineering and Computer Sciences
The question of whether opinions of stubborn agents result in Nash equilibrium under the presence of troll is investigated in this study. The opinion dynamics is modelled as a differential game played by n agents during a finite time horizon. Two types of agents, ordinary agents and troll, are considered in this game. Troll is treated as a malicious stubborn content maker who disagrees with every other agent. On the other hand, ordinary agents maintain cooperative communication with other ordinary agents and they disagree with the troll. Under this scenario, explicit expressions of opinion trajectories are obtained by applying Pontryagin?s …
A Hybrid Convolutional Neural Network Approach For Feature Selection Anddisease Classification, Prajna Paramita Debata, Puspanjali Mohapatra
A Hybrid Convolutional Neural Network Approach For Feature Selection Anddisease Classification, Prajna Paramita Debata, Puspanjali Mohapatra
Turkish Journal of Electrical Engineering and Computer Sciences
: Many researchers have analyzed the high dimensional gene expression data for disease classification using several conventional and machine learning-based approaches, but still there exists some issues which make this task nontrivial. Due to the growing complexities of the unstructured data, the researchers focus on the deep learning approach, which is the latest form of machine learning algorithm. In the presented work, a kernel-based Fisher score (KFS) approach is implemented to extract the notable genes, and an improvised chaotic Jaya (CJaya) algorithm optimized convolutional neural network (CJaya-CNN) model is applied to classify high dimensional gene expression or microarray data. This …
Malignant Skin Melanoma Detection Using Image Augmentation By Oversamplingin Nonlinear Lower-Dimensional Embedding Manifold, Olusola Oluwakemi Abayomi-Alli, Robertas Damasevicius, Sanjay Misra, Rytis Maskeliunas, Adebayo Abayomi-Alli
Malignant Skin Melanoma Detection Using Image Augmentation By Oversamplingin Nonlinear Lower-Dimensional Embedding Manifold, Olusola Oluwakemi Abayomi-Alli, Robertas Damasevicius, Sanjay Misra, Rytis Maskeliunas, Adebayo Abayomi-Alli
Turkish Journal of Electrical Engineering and Computer Sciences
The continuous rise in skin cancer cases, especially in malignant melanoma, has resulted in a high mortality rate of the affected patients due to late detection. Some challenges affecting the success of skin cancer detection include small datasets or data scarcity problem, noisy data, imbalanced data, inconsistency in image sizes and resolutions, unavailability of data, reliability of labeled data (ground truth), and imbalance of skin cancer datasets. This study presents a novel data augmentation technique based on covariant Synthetic Minority Oversampling Technique (SMOTE) to address the data scarcity and class imbalance problem. We propose an improved data augmentation model for …
Brain Tumor Detection From Mri Images With Using Proposed Deep Learningmodel: The Partial Correlation-Based Channel Selection, Atinç Yilmaz
Brain Tumor Detection From Mri Images With Using Proposed Deep Learningmodel: The Partial Correlation-Based Channel Selection, Atinç Yilmaz
Turkish Journal of Electrical Engineering and Computer Sciences
A brain tumor is an abnormal growth of a mass or cell in the brain. Early diagnosis of the tumor significantly increases the chances of successful treatment. Artificial intelligence-based systems can detect the tumor in early stages. In this way, it could be possible to detect a tumor and resolve this problem that may endanger human life early. In the study, the partial correlation-based channel selection formula was presented that allowed the selection of the most prominent feature that differs from the other studies in the literature. Additionally, the multi-channel convolution structure was proposed for the feature network phase of …
A Deep Transfer Learning Based Model For Automatic Detection Of Covid-19from Chest X-Rays, Prateek Chhikara, Prakhar Gupta, Prabhjot Singh, Tarunpreet Bhatia
A Deep Transfer Learning Based Model For Automatic Detection Of Covid-19from Chest X-Rays, Prateek Chhikara, Prakhar Gupta, Prabhjot Singh, Tarunpreet Bhatia
Turkish Journal of Electrical Engineering and Computer Sciences
Deep learning in medical imaging has revolutionized the way we interpret medical data, as high computational devices' capabilities are far more than their creators. With the pandemic causing havoc for the second straight year, the findings in our paper will allow researchers worldwide to use and create state-of-the-art models to detect affected persons before it reaches the R number. The paper proposes an automated diagnostic tool using the deep learning models on chest x-rays as an input to reach a point where we surpass this pandemic (COVID-19 disease). A deep transfer learning-based model for automatic detection of COVID-19 from chest …
Attention-Based End-To-End Cnn Framework For Content-Based X-Ray Imageretrieval, Şaban Öztürk, Adi Alhudhaif, Kemal Polat
Attention-Based End-To-End Cnn Framework For Content-Based X-Ray Imageretrieval, Şaban Öztürk, Adi Alhudhaif, Kemal Polat
Turkish Journal of Electrical Engineering and Computer Sciences
The widespread use of medical imaging devices allows deep analysis of diseases. However, the task of examining medical images increases the burden of specialist doctors. Computer-assisted systems provide an effective management tool that enables these images to be analyzed automatically. Although these tools are used for various purposes, today, they are moving towards retrieval systems to access increasing data quickly. In hospitals, the need for content-based image retrieval systems is seriously evident in order to store all images effectively and access them quickly when necessary. In this study, an attention-based end-to-end convolutional neural network (CNN)framework that can provide effective access …
Classification Of P300 Based Brain Computer Interface Systems Using Longshort-Term Memory (Lstm) Neural Networks With Feature Fusion, Ali̇ Osman Selvi̇, Abdullah Feri̇koğlu, Derya Güzel
Classification Of P300 Based Brain Computer Interface Systems Using Longshort-Term Memory (Lstm) Neural Networks With Feature Fusion, Ali̇ Osman Selvi̇, Abdullah Feri̇koğlu, Derya Güzel
Turkish Journal of Electrical Engineering and Computer Sciences
Enabling to obtain brain activation signs, electroencephalography is currently used in many applications as a medical diagnostic method. Brain-computer interface (BCI) applications are developed to facilitate the lives of individuals who have not lost their brain functions yet have lost their motor and communication abilities. In this study, a BCI system is proposed to make classification using Bi-directional long short term memory (Bi-LSTM) neural networks. In the designed system, spectral entropy method including instantaneous frequency change of signal is used as feature fusion. In the study, electroencephalography (EEG) data of 10 participants are collected with Emotiv EPOC+ device using 2x2 …
Mri Based Genomic Analysis Of Glioma Using Three Pathway Deep Convolutionalneural Network For Idh Classification, Sonal Gore, Jayant Jagtap
Mri Based Genomic Analysis Of Glioma Using Three Pathway Deep Convolutionalneural Network For Idh Classification, Sonal Gore, Jayant Jagtap
Turkish Journal of Electrical Engineering and Computer Sciences
As per 2016 updates by World Health Organization (WHO) on cancer disease, gliomas are categorized and further treated based on genomic mutations. The imaging modalities support a complimentary but immediate noninvasive diagnosis of cancer based on genetic mutations. Our aim is to train a deep convolutional neural network for isocitrate dehydrogenase (IDH) genotyping of glioma by auto-extracting the most discriminative features from magnetic resonance imaging (MRI) volumes. MR imaging data of total 217 patients were obtained from The Cancer Imaging Archives (TCIA) of high and low-grade gliomas. A 3-pathway convolutional neural network was trained for IDH classification. The multipath neural …
Leukocyte Classification Based On Feature Selection Using Extra Trees Classifier: Atransfer Learning Approach, Diana Baby, Sujitha Juliet Devaraj, Jude Hemanth, Anishin Raj M M
Leukocyte Classification Based On Feature Selection Using Extra Trees Classifier: Atransfer Learning Approach, Diana Baby, Sujitha Juliet Devaraj, Jude Hemanth, Anishin Raj M M
Turkish Journal of Electrical Engineering and Computer Sciences
The criticality of investigating the white blood cell (WBC) count cannot be underestimated, as white blood cells are an important component of the body's defence system. From helping to diagnose hidden infections to insinuating the presence of comorbidities like immunodeficiency, an accurate white blood cell count can contribute significantly to shape a physician?s assessment. The manual process performed by the pathologists for the classification of WBCs is a time consuming and tedious task, which is further disadvantaged by a lack of accuracy. This study concentrates on the automatic detection and classification of WBC without data augmentation into four subtypes such …
Evolution Of Histopathological Breast Cancer Images Classification Using Stochasticdilated Residual Ghost Model, Ramgopal Kashyap
Evolution Of Histopathological Breast Cancer Images Classification Using Stochasticdilated Residual Ghost Model, Ramgopal Kashyap
Turkish Journal of Electrical Engineering and Computer Sciences
Breast cancer detection is a complex problem to solve, and it is a topic that is still being studied. Deep learning-based models aid medical science by helping to classify benign and malignant cancers and saving lives. Breast cancer histopathological image classification (BreakHis) and breast cancer histopathological annotation and diagnosis (BreCaHAD) datasets are used in the proposed model. The study led to the resolution of four essential issues: 1) Addresses the color divergence issue caused by strain normalization during image generation 2) Data augmentation uses several factors like as flip, rotation, shift, resize, and gamma value in order to overcome overfitting …
Medical Image Fusion With Convolutional Neural Network In Multiscaletransform Domain, Asan Abas, Hasan Erdi̇nç Koçer, Nurdan Baykan
Medical Image Fusion With Convolutional Neural Network In Multiscaletransform Domain, Asan Abas, Hasan Erdi̇nç Koçer, Nurdan Baykan
Turkish Journal of Electrical Engineering and Computer Sciences
Multimodal medical image fusion approaches have been commonly used to diagnose diseases and involve merging multiple images of different modes to achieve superior image quality and to reduce uncertainty and redundancy in order to increase the clinical applicability. In this paper, we proposed a new medical image fusion algorithm based on a convolutional neural network (CNN) to obtain a weight map for multiscale transform (curvelet/ non-subsampled shearlet transform) domains that enhance the textual and edge property. The aim of the method is achieving the best visualization and highest details in a single fused image without losing spectral and anatomical details. …
Deep Hyperparameter Transfer Learning For Diabetic Retinopathy Classification, Mahesh Patil, Satyadhyan Chickerur, Yeshwanth Kumar V S, Vijayalakshmi Bakale, Shantala Giraddi, Vivekanand Roodagi, Yashaswini Kulkarni
Deep Hyperparameter Transfer Learning For Diabetic Retinopathy Classification, Mahesh Patil, Satyadhyan Chickerur, Yeshwanth Kumar V S, Vijayalakshmi Bakale, Shantala Giraddi, Vivekanand Roodagi, Yashaswini Kulkarni
Turkish Journal of Electrical Engineering and Computer Sciences
The detection of diabetic retinopathy (DR) in millions of diabetic patients across the globe is a challenging problem. Diagnosis of retinopathy is a lengthy and tedious process, requiring a medical professional to assess the individual fundus images of a patient's retina. This process can be automated by applying deep learning (DL) technology given a huge dataset. The problems associated with DL are the unavailability of a large dataset and their higher training time. The DL model's best performance is achieved using set of optimal hyperparameters (OHPs) obtained by performing costly iterations of hyperparameter optimization (HPO). These problems can be addressed …
Detection Of Amyotrophic Lateral Sclerosis Disease By Variational Modedecomposition And Convolution Neural Network Methods From Event-Relatedpotential Signals, Fatma Lati̇foğlu, Firat Orhan Bulucu, Rami̇s İleri̇
Detection Of Amyotrophic Lateral Sclerosis Disease By Variational Modedecomposition And Convolution Neural Network Methods From Event-Relatedpotential Signals, Fatma Lati̇foğlu, Firat Orhan Bulucu, Rami̇s İleri̇
Turkish Journal of Electrical Engineering and Computer Sciences
Amyotrophic lateral sclerosis (ALS), also known as motor neuron disease, is a neurological disease that occurs as a result of damage to the nerves in the brain and restriction of muscle movements. Electroencephalography (EEG) is the most common method used in brain imaging to study neurological disorders. Diagnosis of neurological disorders such as ALS, Parkinson's, attention deficit hyperactivity disorder is important in biomedical studies. In recent years, deep learning (DL) models have been started to be applied in the literature for the diagnosis of these diseases. In this study, event-related potentials (ERPs) were obtained from EEG signals obtained as a …
Improved Cell Segmentation Using Deep Learning In Label-Free Optical Microscopyimages, Aydin Ayanzadeh, Özden Yalçin Özuysal, Devri̇m Pesen Okvur, Sevgi̇ Önal, Behçet Uğur Töreyi̇n, Devri̇m Ünay
Improved Cell Segmentation Using Deep Learning In Label-Free Optical Microscopyimages, Aydin Ayanzadeh, Özden Yalçin Özuysal, Devri̇m Pesen Okvur, Sevgi̇ Önal, Behçet Uğur Töreyi̇n, Devri̇m Ünay
Turkish Journal of Electrical Engineering and Computer Sciences
The recently popular deep neural networks (DNNs) have a significant effect on the improvement of segmentation accuracy from various perspectives, including robustness and completeness in comparison to conventional methods. We determined that the naive U-Net has some lacks in specific perspectives and there is high potential for further enhancements on the model. Therefore, we employed some modifications in different folds of the U-Net to overcome this problem. Based on the probable opportunity for improvement, we develop a novel architecture by using an alternative feature extractor in the encoder of U-Net and replacing the plain blocks with residual blocks in the …
Diagnosis Of Paroxysmal Atrial Fibrillation From Thirty-Minute Heart Ratevariability Data Using Convolutional Neural Networks, Murat Sürücü, Yalçin İşler, Resul Kara
Diagnosis Of Paroxysmal Atrial Fibrillation From Thirty-Minute Heart Ratevariability Data Using Convolutional Neural Networks, Murat Sürücü, Yalçin İşler, Resul Kara
Turkish Journal of Electrical Engineering and Computer Sciences
Paroxysmal atrial fibrillation (PAF) is the initial stage of atrial fibrillation, one of the most common arrhythmia types. PAF worsens with time and affects the patient?s life quality negatively. In this study, we aimed to diagnose PAF early, so patients can start taking precautions before this disease gets worse. We used the atrial fibrillation prediction database, an open data from Physionet and constructed our approach using convolutional neural networks. Heart rate variability (HRV) features are calculated from time-domain measures, frequency-domain measures using power spectral density estimations (fast Fourier transform, Lomb-Scargle, and Welch periodogram), time-frequencydomain measures using wavelet transform, and nonlinear …
Bibliometric Review On Image Based Plant Phenotyping, Shrikrishna Ulhas Kolhar, Jayant Jagtap
Bibliometric Review On Image Based Plant Phenotyping, Shrikrishna Ulhas Kolhar, Jayant Jagtap
Library Philosophy and Practice (e-journal)
Plant phenotyping is a quantitative description of structural, physiological and temporal traits of plants resulting from interaction of plant genotypes with the environment. A rapid development is in progress in the field of image-based plant phenotyping. Plant phenotyping has wide range of applications in plant breeding research, plant growth prediction, biotic and abiotic stress analysis, crop management and early disease detection. The main motive is to provide detailed bibliometric review in order to know the available literature and current research trends in the area of plant phenotyping using plant images. The bibliometric analysis is primarily based on Scopus, web of …
Treated Hfo2 Based Rram Devices With Ru, Tan, Tin As Top Electrode For In-Memory Computing Hardware, Yuvraj Dineshkumar Patel
Treated Hfo2 Based Rram Devices With Ru, Tan, Tin As Top Electrode For In-Memory Computing Hardware, Yuvraj Dineshkumar Patel
Theses
The scalability and power efficiency of the conventional CMOS technology is steadily coming to a halt due to increasing problems and challenges in fabrication technology. Many non-volatile memory devices have emerged recently to meet the scaling challenges. Memory devices such as RRAMs or ReRAM (Resistive Random-Access Memory) have proved to be a promising candidate for analog in memory computing applications related to inference and learning in artificial intelligence. A RRAM cell has a MIM (Metal insulator metal) structure that exhibits reversible resistive switching on application of positive or negative voltage. But detailed studies on the power consumption, repeatability and retention …
The Assessments Of Internet-Based Technology Use By University Academic Members, Mohammed Alghamdi
The Assessments Of Internet-Based Technology Use By University Academic Members, Mohammed Alghamdi
BAU Journal - Science and Technology
Social networking applications (SNAs), as technologies that rely on internet infrastructure, are widely used as platforms for user interaction, content creation and social communication. However, in recent years, SNAs have been used in the context of teaching and learning, and this aspect, as well as others, has attracted research attention. Limited research has investigated the motivations and barriers to teaching with SNAs in higher education, and so this study sought to examine the purposes, motivations and barriers associated with SNA use in education among academic members at Albaha University, Saudi Arabia. A quantitative research design involving a sample of academic …
Recent Advances And Machine Learning Techniques On Sickle Cell Disease, Noorh H. Alharbi, Rana O. Bameer, Shahad S. Geddan, Hajar M. Alharbi
Recent Advances And Machine Learning Techniques On Sickle Cell Disease, Noorh H. Alharbi, Rana O. Bameer, Shahad S. Geddan, Hajar M. Alharbi
Future Computing and Informatics Journal
Sickle cell disease is a severe hereditary disease caused by an abnormality of the red blood cells. The current therapeutic decision-making process applied to sickle cell disease includes monitoring a patient’s symptoms and complications and then adjusting the treatment accordingly. This process is time-consuming, which might result in serious consequences for patients’ lives and could lead to irreversible disease complications. Artificial intelligence, specifically machine learning, is a powerful technique that has been used to support medical decisions. This paper aims to review the recently developed machine learning models designed to interpret medical data regarding sickle cell disease. To propose an …
Twitter Analysis Based On Damage Detection And Geoparsing For Event Mapping Management, Yasmeen Ali, Khaled Bahnasy, Adel El-Mahdy
Twitter Analysis Based On Damage Detection And Geoparsing For Event Mapping Management, Yasmeen Ali, Khaled Bahnasy, Adel El-Mahdy
Future Computing and Informatics Journal
Background: Early event detection, monitor, and response can significantly decrease the impact of disasters. Lately, the usage of social media for detecting events has displayed hopeful results. Objectives: for event detection and mapping; the tweets will locate and monitor them on a map. This new approach uses grouped geoparsing then scoring for each tweet based on three spatial indicators. Method/Approach: Our approach uses a geoparsing technique to match a location in tweets to geographic locations of multiple-events tweets in Egypt country, administrative subdivision. Thus, additional geographic information acquired from the tweet itself to detect the actual locations that …
Conditional Generative Adversarial Network Demosaicing Strategy For Division Of Focal Plane Polarimeters, Garrett Sargent, Bradley M. Ratliff, Vijayan K. Asari
Conditional Generative Adversarial Network Demosaicing Strategy For Division Of Focal Plane Polarimeters, Garrett Sargent, Bradley M. Ratliff, Vijayan K. Asari
Electrical and Computer Engineering Faculty Publications
Division of focal plane (DoFP), or integrated microgrid polarimeters, typically consist of a 2 × 2 mosaic of linear polarization filters overlaid upon a focal plane array sensor and obtain temporally synchronized polarized intensity measurements across a scene, similar in concept to a Bayer color filter array camera. However, the resulting estimated polarimetric images suffer a loss in resolution and can be plagued by aliasing due to the spatially-modulated microgrid measurement strategy. Demosaicing strategies have been proposed that attempt to minimize these effects, but result in some level of residual artifacts. In this work we propose a conditional generative adversarial …
Influence Of The Shaping Process On The Tensile Properties Of Steel Reinforcement Bars Carbon Steel Grades Bjtp24 And Bjts40, Nuraziz Handika, Ridha Amalia Idhar, Jessica Sjah, Essy Arijoeni, Elly Tjahjono
Influence Of The Shaping Process On The Tensile Properties Of Steel Reinforcement Bars Carbon Steel Grades Bjtp24 And Bjts40, Nuraziz Handika, Ridha Amalia Idhar, Jessica Sjah, Essy Arijoeni, Elly Tjahjono
Makara Journal of Technology
According to the current applicable national standard in Indonesia, i.e. SNI 07 2529 1991, in addition to the limitation on the loading rate, the steel bar must be reduced, formed, or lathed, as part of the shaping process of samples. This study determined and compared the effect of the shaping process on the yield strength, ultimate strength, and percent elongation by conducting tensile tests of steel bar grade BJTS40, i.e. deformed bar type of steel, and grade BJTP24, i.e., plain bar type of steel. Three diameters of the deformed bar (BJTS40) and one diameter of the plain bar (BJTP24) were …
A Density Functional Theory Study On Using Montmorillonite To Reduce Air Pollution, Triati Dewi Kencana Wungu, Meqorry Yusfi, Suprijadi Suprijadi
A Density Functional Theory Study On Using Montmorillonite To Reduce Air Pollution, Triati Dewi Kencana Wungu, Meqorry Yusfi, Suprijadi Suprijadi
Makara Journal of Technology
In this study, density functional theory (DFT) method is used to investigate the possibility of using a smectite clay mineral called montmorillonite (MMT) in reducing heavy metals, such as Cd, through Cd adsorption. The mechanism of Cd adsorption in MMT is observed theoretically, and the tetrahedrally isomorphic substitution on the upper layer of MMT is considered to observe the role of Al and Fe in strengthening Cd adsorption. Two types of MMT are modeled in this study: Al-MMT and Fe-MMT. The Al-MMT means that Al substitutes one atom in the upper tetrahedral layer of MMT, while Fe-MMT means that Fe …
Ionic Conductiviy Of Alginate-Nh4cl Polymer Electrolyte, Nurhasniza Mamajan Khan, Noor Saadiah Mohd Ali, Ahmad Faizrin Ahmad Fuzlin, Ahmad Salihin Samsudin
Ionic Conductiviy Of Alginate-Nh4cl Polymer Electrolyte, Nurhasniza Mamajan Khan, Noor Saadiah Mohd Ali, Ahmad Faizrin Ahmad Fuzlin, Ahmad Salihin Samsudin
Makara Journal of Technology
This study aims to produce a solid biopolymer electrolyte (SBE) by doping ammonium chloride (NH4Cl) into alginate. Solution casting was used to prepare the alginate–NH4Cl SBE system. Electrical impedance spectroscopy was performed to analyze the electrical properties of the SBE under the applied frequency range of 50 Hz–1 MHz. The incorporation of 8 wt.% NH4Cl enhances the ionic conductivity of the SBE up to 3.18 × 10-7 S/cm at ambient room temperature. Fourier transform infrared spectroscopy shows that complexation occurs between the hydroxyl (-OH), carboxylate (COO-) and ether linkage (C-O-C) functional groups due to the formation of inter- and intra-molecular …
Use Of Viscoplastic Damper For Improving The Resistance Of Steel Frames To Blast Loading, Gholamreza Abdollahzadeh, Hadi Faghihmaleki, Hamed Hamidi Jamnani, Atefeh Ebrahimi Bardar
Use Of Viscoplastic Damper For Improving The Resistance Of Steel Frames To Blast Loading, Gholamreza Abdollahzadeh, Hadi Faghihmaleki, Hamed Hamidi Jamnani, Atefeh Ebrahimi Bardar
Makara Journal of Technology
In this paper, we evaluated the effect of viscoplastic dampers on the response of steel frames under blast loading. We used SAP2000 software and link elements to investigate the responses of nine-story steel frames with and without dampers. The proposed viscoplastic damper is a new type of viscous damper. The application of this damper is based on the availability of its constituent materials. The damper acts as a viscoelastic damper at low levels of vibration, but it acts as a combination of viscoelastic operator and metal-yielding device at extreme levels of vibration. With respect to the height of the structure, …
Influence Of Lithium Bromide On Electrical Properties In Bio-Based Polymer Electrolytes, Ahmad Faizrin Fuzlin, Bouchta Sahraoui, Ahmad Salihin Samsudin
Influence Of Lithium Bromide On Electrical Properties In Bio-Based Polymer Electrolytes, Ahmad Faizrin Fuzlin, Bouchta Sahraoui, Ahmad Salihin Samsudin
Makara Journal of Technology
This research presents the influence of lithium bromide (LiBr) on the electrical properties of alginate in bio-based polymer electrolytes (BBPEs) system. Bio-based alginate was prepared using the solution casting technique with various LiBr compositions. The ionic conductivity and electrical properties of the prepared BBPEs samples were studied using electrical impedance spectroscopy over a frequency range of 50 Hz–1 MHz. A maximum ionic conductivity of 7.46 × 10−5 S cm-1 was obtained for a sample containing 15 wt. % lithium bromide-doped alginate BBPEs at ambient temperature (303 K). The electrical analysis revealed that the most conductive sample based on alginate-LiBr BBPEs …