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Articles 1741 - 1770 of 10319
Full-Text Articles in Physical Sciences and Mathematics
Synthesis And Characterization Of Novel 4-Benzyloxyphenyl 4-[4-(N-Dodecyloxy)Benzoyloxy]Benzoate Liquid Crystal, Emi̇ne Balkanli, Fati̇h Çakar, Hale Ocak, Özlem Cankurtaran, Belkiz Bi̇lgi̇n Eran
Synthesis And Characterization Of Novel 4-Benzyloxyphenyl 4-[4-(N-Dodecyloxy)Benzoyloxy]Benzoate Liquid Crystal, Emi̇ne Balkanli, Fati̇h Çakar, Hale Ocak, Özlem Cankurtaran, Belkiz Bi̇lgi̇n Eran
Turkish Journal of Chemistry
Liquid crystal (LC) compound 4-Benzyloxyphenyl 4-[4-(n-dodecyloxy)benzoyloxy]benzoate (BDBB) was prepared and characterized. Inverse gas chromatography (IGC) was to be a beneficial analysis method for the research of thermodynamic characteristics of the new LC. Acetate and alcohol isomers were used to examine LC selectivity via the IGC technique at temperatures between 333.2 K and 483.2 K. The retention diagrams of n-heptane, n-octane, n-nonane, n-decane, undecane, dodecane, tridecane, n-butyl acetate, isobutyl acetate, ethyl acetate, n-propylbenzene, isopropylbenzene, ethylbenzene, chlorobenzene, and toluene on BDBB were plotted with temperatures of 483.2?493.2 K. Flory?Huggins interaction parameter and weight fraction activity coefficient at infinite dilution were researched for …
Spectrophotometric Analysis Of Stability Of Gold Nanoparticles During Catalytic Reduction Of 4-Nitrophenol, Farhat Saira, Samia Saleemi, Humaira Razzaq, Rumana Qureshi
Spectrophotometric Analysis Of Stability Of Gold Nanoparticles During Catalytic Reduction Of 4-Nitrophenol, Farhat Saira, Samia Saleemi, Humaira Razzaq, Rumana Qureshi
Turkish Journal of Chemistry
Spectrophotometric monitoring of 4-nitrophenol (4-NP) reduction by sodium borohydride (NaBH4) using gold nanoparticles (GNPs) as a catalyst has been extensively studied, but the stability of GNPs in terms of change in the surface plasmon resonance (SPR) at different temperatures has not been explored. In the present investigation, our aim was to evaluate the SPR stability of GNPs as a catalyst during the reduction of 4-NP at different elevated temperatures (i.e. 30-60 °C) and sodium borohydride concentrations. Sensitivity of this degradation process toward concentration of GNPs at a range of temperatures is also evaluated. The spectrophotometric results reveal that up to …
Investigation Of Olea Ferruginea Roylebark Extracts For Potential In Vitroantidiabetic And Anticancer Effects, Samra Liaqat, Muhammad Islam, Hamid Saeed, Mehwish Iqtedar, Azra Mehmood
Investigation Of Olea Ferruginea Roylebark Extracts For Potential In Vitroantidiabetic And Anticancer Effects, Samra Liaqat, Muhammad Islam, Hamid Saeed, Mehwish Iqtedar, Azra Mehmood
Turkish Journal of Chemistry
This study was conducted to investigate the physicochemical, phytochemical, in vitro antidiabetic and anticancer potential of Olea ferruginea R bark. After extraction using Soxhlet, in vitro antidiabetic and cytotoxic activity on human hepatocellular carcinoma (HepG2) cells was assessed by nonenzymatic glycosylation of hemoglobin assay, alpha-amylase inhibition assay, glucose uptake by yeast cells, and 3-[4,5-dimethylthiazol-2-yl]-2,5 diphenyl tetrazolium bromide assay, respectively, and gene expression via real-time polymerase chain reaction. Primary and secondary metabolites were present in the extractants; polyphenols (35.61 ± 0.03) and flavonoids (64.33 ± 0.35) in the chloroform; and polysaccharides in the ethanol (268.75 ± 0.34), and glycosaponins (78.01 ± …
A New Electrochemical Impedance Biosensor Based On Aromatic Thiol For Alpha-1 Antitrypsin Determination, Hülya Yağar, Hakki Mevlüt Özcan, Osman Mehmet
A New Electrochemical Impedance Biosensor Based On Aromatic Thiol For Alpha-1 Antitrypsin Determination, Hülya Yağar, Hakki Mevlüt Özcan, Osman Mehmet
Turkish Journal of Chemistry
Alpha-1 antitrypsin (A1AT) is one of the acute phase proteins which are synthesized in the liver. A1AT inhibits the activity of many proteases, but its main task is to protect the lungs from the attack of neutrophil elastase. In an autosomal hereditary disease known as alpha-1 antitrypsin deficiency, the A1AT level in blood serum decreases, increasing the risk of developing emphysema, liver apoptosis, and liver cancer. Thus, the detection of A1AT concentration in blood serum is very important. In this study, an impedimetric biosensor was developed, forming an SAM (self-assembled monolayer) with 4-mercaptophenylacetic acid (4MPA) on the surface of the …
Theoretical Investigation Of The Interaction Between The Metal Phthalocyanine [Mpc] A(M = Sc, Ti, And V; A = ?1, 0, And +1) Complexes And Formaldehyde, Nasim Hassani
Turkish Journal of Chemistry
Formaldehyde (FA, CH2O) is one of the toxic volatile organic compounds that cause harmful effects on the human body. In this work, the interaction of FA gas with metal phthalocyanine (MPc) molecules was studied by employing density functional theory calculations. A variety of [MPc] a (M = Sc, Ti, and V; a = -1, 0, and +1) complexes were studied, and the electronic properties, interaction energies, and charge transfer properties of all of the studied molecules were systematically discussed. Among the studied complexes, the Sc and Ti phthalocyanines were more reactive toward the adsorption of FA gas. Moreover, it was …
The Application Of Qnmr For The Determination Of Rosuvastatin In Tablet Form, Gökhan Di̇kmen, Okan Uslu
The Application Of Qnmr For The Determination Of Rosuvastatin In Tablet Form, Gökhan Di̇kmen, Okan Uslu
Turkish Journal of Chemistry
In this study, quantative nuclear magnetic resonance (qNMR) method was used to determine the content of rosuvastatin in tablet. Linearity, range, limit of detection (LOD), limit of quantification (LOQ), accuracy, and precision were determined in validation study of rosuvastatin. Furthermore, validation study of rosuvastatin was performed with high performance liquid chromatography (HPLC). Uncertainties of qNMR and HPLC methods were determined using per EURACHEM/CITAC Guide CG 4 (3th edition), quantifying uncertainty in analytical measurement. qNMR and HPLC methods were linear in the ranges of 0.10 - 5.00 mg/mL and 0.001 - 0.0995 mg/mL, respectively and these lineraties indicate very good linearity …
Heavy Metal Removal With Magnetic Coffee Grain, Sevgi̇ Asliyüce Çoban, Ivo Safarik, Adi̇l Deni̇zli̇
Heavy Metal Removal With Magnetic Coffee Grain, Sevgi̇ Asliyüce Çoban, Ivo Safarik, Adi̇l Deni̇zli̇
Turkish Journal of Chemistry
The presence of heavy metals in environmental waters having an important place in the industrial waste is a major threat to viability. Heavy metals are transported to humans through the ecological cycle, damaging many tissues and organs. In recent years, agricultural and food waste can be used to remove heavy metals. At the present study, magnetically modified coffee grains which are alternative to conventional particle systems were prepared and heavy metal removal performances were investigated. The coffee grains used were magnetically modified by contact with water-based magnetic fluid. Magnetically modified coffee grains were characterized by scanning electron microscopy (SEM), Brunauer-Emmett-Teller …
Sensitive Determination Of Hydrazine Using Poly(Phenolphthalein), Au Nanoparticles Andmultiwalled Carbon Nanotubes Modified Glassy Carbon Electrode, Müge Hati̇p, Süleyman Koçak, Zekerya Dursun
Sensitive Determination Of Hydrazine Using Poly(Phenolphthalein), Au Nanoparticles Andmultiwalled Carbon Nanotubes Modified Glassy Carbon Electrode, Müge Hati̇p, Süleyman Koçak, Zekerya Dursun
Turkish Journal of Chemistry
This study reports a detailed analysis of an electrode material containing poly(phenolphthalein), carbon nanotubes and gold nanoparticles which shows superior catalytic effect towards to hydrazine oxidation in Britton?Robinson buffer (pH 10.0). Glassy carbon electrode was modified by electropolymerization of phenolphthalein (PP) monomer (poly(PP)/GCE) and the multiwalled carbon nanotubes (MWCNTs) was dropped on the surface. This modified surface was electrodeposited with gold nanoparticles (AuNPs/CNT/poly(PP)/GCE). The fabricated electrode was analysed the determination of hydrazine using cyclic voltammetry, linear sweep voltammetry and amperometry. The peak potential of hydrazine oxidation on bare GCE, poly(PP)/GCE, CNT/GCE, CNT/ poly(PP)/GCE, and AuNPs/CNT/poly(PP)/GCE were observed at 596 mV, …
A Novel Method For Soc Estimation Of Li-Ion Batteries Using A Hybrid Machinelearning Technique, Eymen İpek, Murat Yilmaz
A Novel Method For Soc Estimation Of Li-Ion Batteries Using A Hybrid Machinelearning Technique, Eymen İpek, Murat Yilmaz
Turkish Journal of Electrical Engineering and Computer Sciences
The battery system is one of the key components of electric vehicles (EV) which has brought groundbreaking technologies. Since modern EVs have mostly Li-ion batteries, they need to be monitored and controlled to achieve safe and high-performance operation. Particularly, the battery management system (BMS) uses complex processing systems that perform measurements, estimation of the battery states, and protection of the system. State of charge (SOC) estimation is a major part of these processes which defines remaining capacity in the battery until the next charging operation as a proportion to the total battery capacity. Since SOC is not a parameter that …
Covid-19 Detection On Ibm Quantum Computer With Classical-Quantum Transferlearning, Erdi̇ Acar, İhsan Yilmaz
Covid-19 Detection On Ibm Quantum Computer With Classical-Quantum Transferlearning, Erdi̇ Acar, İhsan Yilmaz
Turkish Journal of Electrical Engineering and Computer Sciences
Diagnose the infected patient as soon as possible in the coronavirus 2019 (COVID-19) outbreak which is declared as a pandemic by the world health organization (WHO) is extremely important. Experts recommend CT imaging as a diagnostic tool because of the weak points of the nucleic acid amplification test (NAAT). In this study, the detection of COVID-19 from CT images, which give the most accurate response in a short time, was investigated in the classical computer and firstly in quantum computers. Using the quantum transfer learning method, we experimentally perform COVID-19 detection in different quantum real processors (IBMQx2, IBMQ-London and IBMQ-Rome) …
Development Of Computationally Efficient Biorthogonal Wavelets, Mehmet Cemi̇l Kale
Development Of Computationally Efficient Biorthogonal Wavelets, Mehmet Cemi̇l Kale
Turkish Journal of Electrical Engineering and Computer Sciences
Daubechies 5-tap/3-tap (Daub 5/3) wavelet and Kale 5-tap/3-tap (Kale 5/3) wavelet are computationally efficient wavelets which can be implemented by bitwise shifts and additions in the lifting scheme. In this work, presented is a formulation for computationally efficient wavelet prediction (P) and update (U) filters of two-channel lifting structures. Their subband decomposition scheme counterparts are also given. This research bases itself on the Daub 5/3 and Kale 5/3 wavelets and develops a formula for wavelets (which can be implemented with bitwise shifts and additions) that are derived from these two wavelets. The proposed wavelets are tried on 16 test images …
Time-Oriented Interactive Process Miner: A New Approach For Time Prediction, İsmai̇l Yürek, Derya Bi̇rant, Özlem Ece Yürek, Kökten Ulaş Bi̇rant
Time-Oriented Interactive Process Miner: A New Approach For Time Prediction, İsmai̇l Yürek, Derya Bi̇rant, Özlem Ece Yürek, Kökten Ulaş Bi̇rant
Turkish Journal of Electrical Engineering and Computer Sciences
Everyday information systems collect a different kind of process instances of a business flow. As time goes on, the size of the collected data builds up speedily and constitutes a huge amount of data. It is a very challenging task to obtain valuable information and features of processes from such big data. Considering in advance, the trend and different features of the ongoing process are essential. Especially, time management is crucial in designing and conducting business processes. In this article, a novel process miner algorithm is proposed for time prediction, named time-oriented İnteractive process miner (T-IPM), which predicts the remaining …
Abnormal Behavior Detection Using Sparse Representations Through Sequentialgeneralization Of K-Means, Ahlam Aldhamari, Rubita Sudirman, Nasrul Humaimi Mahmood
Abnormal Behavior Detection Using Sparse Representations Through Sequentialgeneralization Of K-Means, Ahlam Aldhamari, Rubita Sudirman, Nasrul Humaimi Mahmood
Turkish Journal of Electrical Engineering and Computer Sciences
The potential capability to automatically detect and classify human behavior as either normal or abnormal events is an important aspect in intelligent monitoring/surveillance systems. This study presents a new high-performance framework for detecting behavioral abnormalities in video streams by utilizing only the patterns for normal behaviors. In this paper, we used a hybrid descriptor, called a foreground optical flow energy (FGOFE), which makes use of two effective motion techniques in order to extract the most descriptive spatiotemporal features in video sequences. The FGOFE descriptor can effectively capture both weak and sudden incidents in a scene. The sequential generalization of k-means …
Characterization Of Different Crowd Behaviors Using Novel Deep Learningframework, Abdullah Jaman Alzahrani, Sultan Daud Khan
Characterization Of Different Crowd Behaviors Using Novel Deep Learningframework, Abdullah Jaman Alzahrani, Sultan Daud Khan
Turkish Journal of Electrical Engineering and Computer Sciences
Crowd behavior understanding is recognized as a complex problem due to unpredictable behavior of humans and complex interactions of individuals in groups. For crowd managers, it is crucial to understand the crowd dynamics to manage the crowd efficiently and effectively. Current practice of crowd management is based on manual analysis of the scene. Such manual analysis of the scene is a tedious job and usually prone to errors due to limited human capabilities. Therefore, the task of automatizing crowd analysis has received tremendous attention from the research community during the recent years. In this paper, we propose a deep model …
A New Method For Optimal Expansion Planning In Electrical Energy Distributionnetworks With Distributed Generation Resources Considering Uncertainties, Amir Masoud Mohaghegh, S Yaser Derakhshandeh, Abbas Kargar
A New Method For Optimal Expansion Planning In Electrical Energy Distributionnetworks With Distributed Generation Resources Considering Uncertainties, Amir Masoud Mohaghegh, S Yaser Derakhshandeh, Abbas Kargar
Turkish Journal of Electrical Engineering and Computer Sciences
The present study aims to introduce a robust model for distribution network expansion planning considering system uncertainties. The proposed method determines optimal size and placement of distributed generation resources, as well as installation and reinforcement of feeders and substations. This model is designed to minimize cost and to determine the best time for the installation of equipment in the expansion planning. In the proposed expansion planning, the fuzzy logic theory is employed to model uncertainties of loads and energy price. Also, since the proposed model is a nonlinear and nonconvex optimization problem, a tri-stage algorithm is developed to solve it. …
Speed-Sensorless Predictive Torque Controlled Induction Motor Drive Withfeed-Forward Control Of Load Torque For Electric Vehicle Applications, Emrah Zerdali̇, Ridvan Demi̇r
Speed-Sensorless Predictive Torque Controlled Induction Motor Drive Withfeed-Forward Control Of Load Torque For Electric Vehicle Applications, Emrah Zerdali̇, Ridvan Demi̇r
Turkish Journal of Electrical Engineering and Computer Sciences
Nowadays, the global trend is towards reducing CO2 emissions and one solution is to replace internal combustion vehicles with electric vehicles. To this end, electric drive system, the most crucial part of an electric vehicle, has gained importance and has become a major research field. The induction motor (IM) is one of the best candidates for electric vehicle applications due to its advantages such as having simple and robust design, its low cost maintenance requirements and the ability to operate in harsh environments. However, it has a highly nonlinear model with timevarying electrical and mechanical parameters making them difficult to …
Performance Evaluation Of A Power Allocation Algorithm Based On Dynamicblocklength Estimation For Urllc In The Multicarrier Downlink Noma Systems, Won Jae Ryu, Soo Young Shin
Performance Evaluation Of A Power Allocation Algorithm Based On Dynamicblocklength Estimation For Urllc In The Multicarrier Downlink Noma Systems, Won Jae Ryu, Soo Young Shin
Turkish Journal of Electrical Engineering and Computer Sciences
This study investigates a power allocation algorithm using blocklength estimation by the finite blocklength (FBL) regime in a multicarrier downlink nonorthogonal multiple access (NOMA) system for ultrareliable low latency communication (URLLC) that is one of the services in 5G networks, requiring exceedingly high reliability and low latency. As NOMA systems can boost the capacity and increase the spectrum efficiency, it can be considered as a solution for URLLC. A multicarrier downlink NOMA system using blocklength estimation based on the FBL regime is proposed for effective resource allocation in this study. The FBL is used to derive the equation for dynamic …
Comparison Of Risc-V And Transport Triggered Architectures For A Postquantumcryptography Application, Lati̇f Akçay, Siddika Berna Örs Yalçin
Comparison Of Risc-V And Transport Triggered Architectures For A Postquantumcryptography Application, Lati̇f Akçay, Siddika Berna Örs Yalçin
Turkish Journal of Electrical Engineering and Computer Sciences
Cryptography is one of the basic phenomena of security systems. However, some of the widely used publickey cryptography algorithms can be broken by using quantum computers. Therefore, many postquantum cryptography algorithms are proposed in recent years to handle this issue. NTRU (Nth degree truncated polynomial ring units) is one of the most important of these quantum-safe algorithms. Besides the importance of cryptography algorithms, the architecture where they are implemented is also essential. In this study, we developed an NTRU public key cryptosystem application and designed several processors to compare them in many aspects. We address two different architectures in this …
A Novel Fibonacci Hash Method For Protein Family Identification By Usingrecurrent Neural Networks, Talha Burak Alakuş, İbrahi̇m Türkoğlu
A Novel Fibonacci Hash Method For Protein Family Identification By Usingrecurrent Neural Networks, Talha Burak Alakuş, İbrahi̇m Türkoğlu
Turkish Journal of Electrical Engineering and Computer Sciences
Identification and classification of protein families are one of the most significant problem in bioinformatics and protein studies. It is essential to specify the family of a protein since proteins are highly used in smart drug therapies, protein functions, and, in some cases, phylogenetic trees. Some sequencing techniques provide researchers to identify the biological similarities of protein families and functions. Yet, determining these families with sequencing applications requires huge amount of time. Thus, a computer and artificial intelligence based classification system is needed to save time and avoid complexity in protein classification process. In order to designate the protein families …
A Novel Data Placement Strategy To Reduce Data Traffic During Run-Time, Sridevi Sridhar, Rhymend Uthariaraj Vaidyanathan
A Novel Data Placement Strategy To Reduce Data Traffic During Run-Time, Sridevi Sridhar, Rhymend Uthariaraj Vaidyanathan
Turkish Journal of Electrical Engineering and Computer Sciences
High impact scientific applications processed in distributed data centers often involve big data. To avoid the intolerable delays due to huge data movements across data centers during processing, the concept of moving tasks to data was introduced in the last decade. Even after the realization of this concept termed as data locality, the expected quality of service was not achieved. Later, data colocality was introduced where data groupings were identified and then data chunks were placed wisely. However, the aspect of the expected data traffic during run time is generally not considered while placing data. To identify the expected data …
Performance Improvement Of The Shunt Active Power Filter Using A Novel Adaptivefiltering Approach, Abderrezzaq Zoghbi, Daoud Berkani
Performance Improvement Of The Shunt Active Power Filter Using A Novel Adaptivefiltering Approach, Abderrezzaq Zoghbi, Daoud Berkani
Turkish Journal of Electrical Engineering and Computer Sciences
This paper introduces an efficient control approach to enhance harmonics mitigation performance of the shunt active power filter (SAPF). This approach is based on adaptive filters favored by their built-in automatic parameters adaptation capability. The proposed filter, which uses a variable leaky least mean square (VLLMS) adaptation, is applied with a modified instantaneous power PQ theory. This enhances its dynamic performance over the use of conventional time-invariant filters, and overcomes its limitations in the presence of nonsinusoidal voltage conditions. The studied SAPF model is simulated in MATLAB/SIMULINK with combinations of nonlinear and unbalanced loads. Simulation results indicate a significant improvement …
Robust Model Reference Adaptive Pi Controller Based Sliding Mode Control Forthree-Phase Grid Connected Photovoltaic Inverter, Mojtaba Moeti, Mehdi Asadi
Robust Model Reference Adaptive Pi Controller Based Sliding Mode Control Forthree-Phase Grid Connected Photovoltaic Inverter, Mojtaba Moeti, Mehdi Asadi
Turkish Journal of Electrical Engineering and Computer Sciences
In this paper, the design of a new robust model reference adaptive PI (MRAC-PI) current controller is proposed for a two-stage grid connected photovoltaic (PV) inverter. Perturb & observe (P&O) algorithm is implemented in the boost part in order to extract the maximum power from the PV array. Firstly, the current dynamics with considering the system uncertainties are written in dq frame and are simplified by employing a decoupling system. Then, the MRAC-PI controller is designed based on a sliding mode control (SMC) to improve the robustness of the controller under system uncertainties. The parameters of PI controller are tuned …
Neurofuzzy Robust Backstepping Based Mppt Control For Photovoltaic System, Kamran Ali, Laiq Khan, Qudrat Khan, Shafaat Ullah, Naghmash Ali
Neurofuzzy Robust Backstepping Based Mppt Control For Photovoltaic System, Kamran Ali, Laiq Khan, Qudrat Khan, Shafaat Ullah, Naghmash Ali
Turkish Journal of Electrical Engineering and Computer Sciences
Linear maximum power point tracking (MPPT) techniques are unable to achieve the desired performance and efficiency under wide variation in atmospheric conditions (temperature and irradiance) and consequently the maximum power point (MPP). Hence, the design and implementation of a nonlinear MPPT controller is essential to address the problems associated with the variations of the MPP. In this research article, a new nonlinear robust backstepping-based MPPT control technique is proposed for a standalone PV array connected to a dynamic load, and its performance comparison with existing backstepping, integral backstepping and conventional proportional integral derivative (PID) and perturb and observe (P&O) based …
A Counter Based Approach For Reducer Placement With Augmented Hadoop Rackawareness, Mir Wajahat Hussain, K Hemant Reddy, Diptendu Sinha Roy
A Counter Based Approach For Reducer Placement With Augmented Hadoop Rackawareness, Mir Wajahat Hussain, K Hemant Reddy, Diptendu Sinha Roy
Turkish Journal of Electrical Engineering and Computer Sciences
As the data-driven paradigm for intelligent systems design is gaining prominence, performance requirements have become very stringent, leading to numerous fine-tuned versions of Hadoop and its MapReduce programming model. However, very few researchers have investigated the effect of intelligent reducer placement on Hadoop's performance. This paper delves into this much ignored reducer placement phase for improving Hadoop's performance and proposes to spawn reduce phase of Hadoop tasks in an asynchronous fashion across nodes in a Hadoop cluster. The main contributions of this paper are: (i) to track when map phase of tasks are completed, (ii) to count the number of …
Risk-Averse Optimal Bidding Strategy For A Wind Energy Portfolio Managerincluding Ev Parking Lots For Imbalance Mitigation, Alper Çi̇çek, Ozan Erdi̇nç
Risk-Averse Optimal Bidding Strategy For A Wind Energy Portfolio Managerincluding Ev Parking Lots For Imbalance Mitigation, Alper Çi̇çek, Ozan Erdi̇nç
Turkish Journal of Electrical Engineering and Computer Sciences
In this study, an optimal bidding strategy for a wind energy portfolio manager (WEPM) including electricvehicle parking lots (EVPLs) that aims to maximize profits by trading in the day-ahead (DA) market and balancingmarket (BM) and through bilateral contracts, taking into account line capacities and risk management is proposed. Thementioned structure is modeled in mixed integer linear programming (MILP) framework, and the uncertainties regardingelectric vehicle (EV) behavior, electricity market data, wind power generation are captured via a stochastic approach. Todemonstrate the effectiveness of the model, several case studies are carried out considering Sweden and Turkey electricitymarket prices with different risk aversion …
Development Of Majority Vote Ensemble Feature Selection Algorithm Augmentedwith Rank Allocation To Enhance Turkish Text Categorization, Emi̇n Borandağ, Akin Özçi̇ft, Yeşi̇m Kaygusuz
Development Of Majority Vote Ensemble Feature Selection Algorithm Augmentedwith Rank Allocation To Enhance Turkish Text Categorization, Emi̇n Borandağ, Akin Özçi̇ft, Yeşi̇m Kaygusuz
Turkish Journal of Electrical Engineering and Computer Sciences
The increase in the number of texts as digital documents from numerous sources such as customer reviews,news, and social media has made text categorization crucial in order to be able to manage the enormous amount ofdata. The high dimensional nature of these texts requires a preliminary feature selection task to reduce the featurespace with a potential increase in the prediction accuracy. In this study, we developed an ensemble feature selectionmethod, namely majority vote rank allocation, was developed for Turkish text categorization purposes. The methoduses a majority voting ensemble strategy in combination with a rank allocation approach to combine weak filters …
Mismatch Error Shaping Of Dac Unit Elements In Multibit $\Delta$$\Sigma$ Modulators Using A Novel Unified Adc/Dac, Leila Sharifi, Omid Hashemipour
Mismatch Error Shaping Of Dac Unit Elements In Multibit $\Delta$$\Sigma$ Modulators Using A Novel Unified Adc/Dac, Leila Sharifi, Omid Hashemipour
Turkish Journal of Electrical Engineering and Computer Sciences
This paper presents a unified analog to digital converter (ADC) and digital to analog converter (DAC) for multibit $\Delta\Sigma$ modulators. The unified ADC/DAC circuit provides error shaping for mismatches between DAC unit elements. Hence, the dynamic element matching (DEM) circuit or digital calibration is not required resulting in the area and power saving as well as the elimination of the excess loop delay introduced by DEM circuit. Incorporating a 6-bit unified ADC/DAC, the $\Delta\Sigma$ modulator achieves 16.15-bit resolution utilizing only a second order loop filter and oversampling ratio of 40. The proposed modulator is simulated in a 65-nm CMOS process. …
Deep Learning Techniques Of Losses In Data Transmitted In Wirelesssensor Networks, Mevlüt Ersoy, Beki̇r Aksoy
Deep Learning Techniques Of Losses In Data Transmitted In Wirelesssensor Networks, Mevlüt Ersoy, Beki̇r Aksoy
Turkish Journal of Electrical Engineering and Computer Sciences
Wireless sensor network (WSN) systems are frequently used today as a result of rapid technological developments. Wireless sensor networks, which form the basis of the Internet of Things (IoT), have a wide range of use in theworld from education to health, and from military applications to home applications. It enables the data obtained fromthe sensors to be transferred between nodes with the help of end-to-end wireless protocols. In parallel with the increasingnumber of nodes in WSN, data tra?ic density also increases. Due to the limitations of the WSN network, lost packetrates also increase with increasing data tra?ic. In this study, …
Impact Of Image Segmentation Techniques On Celiac Disease Classification Usingscale Invariant Texture Descriptors For Standard Flexible Endoscopic Systems, Manarbek Saken, Munkhtsetseg Banzragch Yağci, Nejat Yumuşak
Impact Of Image Segmentation Techniques On Celiac Disease Classification Usingscale Invariant Texture Descriptors For Standard Flexible Endoscopic Systems, Manarbek Saken, Munkhtsetseg Banzragch Yağci, Nejat Yumuşak
Turkish Journal of Electrical Engineering and Computer Sciences
Celiac disease (CD) is quite common and is a proximal small bowel disease that develops as a permanentintolerance to gluten and other cereal proteins in cereals. It is considered as one of the most di?icult diseases to diagnose.Histopathological evidence of small bowel biopsies taken during endoscopy remains the gold standard for diagnosis.Therefore, computer-aided detection (CAD) systems in endoscopy are a newly emerging technology to enhance thediagnostic accuracy of the disease and to save time and manpower. For this reason, a hybrid machine learning methodshave been applied for the CAD of celiac disease. Firstly, a context-based optimal multilevel thresholding technique wasemployed …
Ensemble Learning Of Multiview Cnn Models For Survival Time Prediction Of Braintumor Patients Using Multimodal Mri Scans, Abdela Ahmed Mossa, Ulus Çevi̇k
Ensemble Learning Of Multiview Cnn Models For Survival Time Prediction Of Braintumor Patients Using Multimodal Mri Scans, Abdela Ahmed Mossa, Ulus Çevi̇k
Turkish Journal of Electrical Engineering and Computer Sciences
Brain tumors have been one of the most common life-threatening diseases for all mankind. There have beenhuge efforts dedicated to the development of medical imaging techniques and radiomics to diagnose tumor patients quicklyand e?iciently. One of the main aims is to ensure that preoperative overall survival time (OS) prediction is accurate.Recently, deep learning (DL) algorithms, and particularly convolutional neural networks (CNNs) achieved promisingperformances in almost all computer vision fields. CNNs demand large training datasets and high computational costs.However, curating large annotated medical datasets are difficult and resource-intensive. The performances of singlelearners are also unsatisfactory for small datasets. Thus, this study …