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Automatic Detection Of Depression Using A Cnn-Transformer Model Based On Low-Channel Eeg Data, Dongfang Yu, Penghai Li, Fei Su, Kun Wang, Shutong Duan, Ding Yuan
Automatic Detection Of Depression Using A Cnn-Transformer Model Based On Low-Channel Eeg Data, Dongfang Yu, Penghai Li, Fei Su, Kun Wang, Shutong Duan, Ding Yuan
Turkish Journal of Electrical Engineering and Computer Sciences
Background: Accurate and portable diagnostic tools for Major Depressive Disorder (MDD) remain a critical unmet need in clinical practice. Electroencephalography (EEG), with its noninvasiveness and high sensitivity, presents an ideal modality, yet traditional multi-channel EEG systems limit clinical convenience and practical deployment. Methods: We developed a novel CNN-Transformer hybrid model using only eight EEG channels selected from frontal regions to detect MDD. Minimal preprocessing steps (basic filtering) were applied to EEG data before the model automatically learned both local and global signal features through convolutional and self-attention mechanisms. We validated this method through comprehensive experiments, including within-dataset and cross-dataset evaluations, …
Electronically Tunable Cccii And Ota-Based Fractional-Order Meminductor Emulator And Its Application, Bhawna Aggarwal, Rupam Das, Shireesh Kumar Rai
Electronically Tunable Cccii And Ota-Based Fractional-Order Meminductor Emulator And Its Application, Bhawna Aggarwal, Rupam Das, Shireesh Kumar Rai
Turkish Journal of Electrical Engineering and Computer Sciences
In this paper, two fractional-order meminductor (FOMI) emulators have been realized using second-generation current-controlled conveyor (CCCII) and operational transconductance amplifier (OTA). In the first realization, two OTAs, one CCCII, one resistor, one fractional-order capacitor, and one integer-order capacitor are used. In the second configuration two CCCIIs, one OTA, two resistors, one fractional-order capacitor, and one integer-order capacitor are used. Pinched hysteresis loops have been achieved for a wide range of frequencies. The robustness of the proposed FOMI emulators has been verified through formation of pinched hysteresis loops under process, voltage, and temperature variations. Other characteristics, such as transient analysis, nonvolatility …
Fractional-Order Adaptive Terminal Sliding Mode Control For Synchronization Of Uncertain Chaotic Memristive Chua's Circuit, Dorukhan Asteki̇n, Fati̇h Adigüzel
Fractional-Order Adaptive Terminal Sliding Mode Control For Synchronization Of Uncertain Chaotic Memristive Chua's Circuit, Dorukhan Asteki̇n, Fati̇h Adigüzel
Turkish Journal of Electrical Engineering and Computer Sciences
Memristive-based chaotic dynamical systems exhibit chaotic behavior with signals of unpredictable complex ity, making it difficult to achieve full synchronization in master-slave systems. Therefore, an advanced synchronization method is required to overcome the chaotic model uncertainties and external disturbances. In this study, a fractional order terminal sliding mode synchronization is proposed for the uncertain memristive chaotic Chua’s circuit. After presenting the integer-order model of the chaotic memristive Chua’s circuit, a fractional-order sliding surface-based non linear synchronization structure, incorporating the fractional derivative operator, is designed. The convergence of the synchronization closed-loop system is proven using Lyapunov theory. To demonstrate its effectiveness …
Electromagnetic Simulations Of Antennas On Gpus For Machine Learning Applications, Murat Temi̇z, Vemund Bakken
Electromagnetic Simulations Of Antennas On Gpus For Machine Learning Applications, Murat Temi̇z, Vemund Bakken
Turkish Journal of Electrical Engineering and Computer Sciences
This study proposes an antenna simulation framework powered by graphics processing units (GPUs) based on an open-source electromagnetic (EM) simulation software (gprMax) for machine learning applications of antenna design and optimization. Furthermore, it compares the simulation results with those obtained through commercial EM software. The proposed software framework for machine learning and surrogate model applications will produce antenna data sets consisting of a large number of antenna simulation results using GPUs. Although machine learning methods can attain the optimum solutions for many problems, they are known to be data-hungry and require a great deal of samples for the training stage …
Optimization And Model Averaging Of Histogram-Based Place Cell Firing Rate Maps Using The Point Process Framework, Murat Okatan
Optimization And Model Averaging Of Histogram-Based Place Cell Firing Rate Maps Using The Point Process Framework, Murat Okatan
Turkish Journal of Electrical Engineering and Computer Sciences
The firing rate of hippocampal place cells depends on the spatial position of the organism in an environment. This position dependence is often quantified by constructing spike-in-location and time-in-location histograms, the ratio of which yields a firing rate map. The purpose of this study is to present a new method for optimizing the spatial resolution of histogram-based firing rate maps. It is pointed out that histogram-based firing rate maps are conditional intensity functions of inhomogeneous Poisson process models of neural spike trains, and, as such, they can be optimized through model selection within the point process framework. The point process …
Isar Imaging Of Drone Swarms At 77 Ghz, Remzi̇ye Büşra Çoruk, Ali̇ Kara, Eli̇f Aydin
Isar Imaging Of Drone Swarms At 77 Ghz, Remzi̇ye Büşra Çoruk, Ali̇ Kara, Eli̇f Aydin
Turkish Journal of Electrical Engineering and Computer Sciences
The proliferation of easily available, internet-purchased drones, coupled with the emergence of coordinated drone swarms, poses a significant security threat for airspace. Detecting these swarms is crucial to prevent potential accidents, criminal misuse, and airspace disruptions. This paper proposes a novel inverse synthetic aperture radar (ISAR) imaging technique for high-resolution reconstruction of drone swarms at 77 GHz millimeter wave (mmWave) frequency, offering a valuable tool for military and defense anti-drone systems. The key parameters affecting down-range and cross-range resolution (0.05 m), ultimately enabling the generation of detailed ISAR images are discussed. Here, we create diverse scenarios encompassing various swarm formations, …
Adaptive Backstepping Control With Real-Time Fuzzy Logic Parameter Selection Of A Field- Oriented Control-Based Permanent Magnet Synchronous Motor Driver, Fati̇h Bayir, Erkan Zergeroğlu
Adaptive Backstepping Control With Real-Time Fuzzy Logic Parameter Selection Of A Field- Oriented Control-Based Permanent Magnet Synchronous Motor Driver, Fati̇h Bayir, Erkan Zergeroğlu
Turkish Journal of Electrical Engineering and Computer Sciences
This study proposes an adaptive backstepping control approach integrated with a real-time fuzzy logic parameter selection algorithm to enhance the robustness and stability of a permanent magnet synchronous motor (PMSM) controller under parametric uncertainties and external disturbances. Although backstepping control performs well under varying disturbances, it must be supported by an adaptive control algorithm to effectively handle both variable disturbances and parameter uncertainties. Moreover, because the fixed parameters of the adaptive backstepping controller limit the dynamic performance of the velocity tracking loop, this study incorporates fuzzy logic control—a soft computing algorithm capable of real-time parameter adjustment—to achieve more robust outcomes. …
Excitation Of Synchronous Machine By Contactless Power Transfer - Review From The Perspective Of Electric Vehicles, Erhan Tuncel, Emi̇n Yildiriz
Excitation Of Synchronous Machine By Contactless Power Transfer - Review From The Perspective Of Electric Vehicles, Erhan Tuncel, Emi̇n Yildiriz
Turkish Journal of Electrical Engineering and Computer Sciences
Electrically excited synchronous machines (EESMs) are one of the best choices for propulsion motor appli cation in electric vehicles (EVs) due to their wide torque-speed characteristics. Moreover, the air gap flux density can be easily controlled by varying the excitation current. Despite these advantages, it is difficult to transfer the current required by the rotating excitation winding into the motor under conventional methods, so it is not widely used in EVs. In this study, the emerging literature on contactless power transfer methods is reviewed for applicability to an EESM that can operate as an EV propulsion motor. Design criteria such …
Rescon: Residual Consistency For Real-World Super-Resolution, Erdi̇ Saritaş, Hazim Kemal Ekenel
Rescon: Residual Consistency For Real-World Super-Resolution, Erdi̇ Saritaş, Hazim Kemal Ekenel
Turkish Journal of Electrical Engineering and Computer Sciences
Real-world super-resolution is a highly challenging problem in the field of computer vision. Besides enhancing image resolution and improving visual details, information loss due to complex real-world degradations is desired to be restored. One of the primary hardness of this problem is finding sufficiently large paired datasets for training. Researchers have developed techniques that generate synthetic low-resolution pairs using high-resolution images with a generative adversarial network-based degradation generator to address this issue. In these approaches, the degradation generator is trained by utilizing real-world low-resolution images as the target domain, generating a degraded low-resolution counterpart of the high-resolution input. However, in …
Magnetic Macro Pendulum Design And Real-Time Control Application: Simulation And Experiment, Hüseyi̇n Yildiz, Serdar Yilmaz, Yasemi̇n Poyraz Koçak, Erol Uzal
Magnetic Macro Pendulum Design And Real-Time Control Application: Simulation And Experiment, Hüseyi̇n Yildiz, Serdar Yilmaz, Yasemi̇n Poyraz Koçak, Erol Uzal
Turkish Journal of Electrical Engineering and Computer Sciences
Over the last decade, the number of studies in the field of magnetic micro robots has significantly increased due to expectations of performing microsurgery, drug delivery, and similar medical procedures. Magnetic micro robots have advantages over other types of micro robots in terms of having independent designs for rotor and stator structures. Magnetic micro robots can be controlled by magnetic fields and can be programmed to move in certain directions and to perform various functions. This paper implements the computer-aided real-time control of a single-arm micro-pendulum structure to (eventually) perform cell manipulation tasks. The mechanical structure, mathematical model, control circuit …
Vaccine Hesitancy In Türkiye: A Natural Language Processing Study On Social Media, Semi̇h Sari, Ulya Bayram
Vaccine Hesitancy In Türkiye: A Natural Language Processing Study On Social Media, Semi̇h Sari, Ulya Bayram
Turkish Journal of Electrical Engineering and Computer Sciences
Vaccine hesitancy is a significant public healthcare problem that is threatening everyone worldwide. Vaccine hesitancy has become more ingrained in Turkish society, mainly through social media. Unfortunately, reflections of this hesitancy are preventable deaths or permanent disabilities. Because of the uncontrolled spread of misinformation and disinformation on social media, Türkiye is facing a future health crisis. As a step towards preventing this crisis, our main objective is to use the power of artificial intelligence techniques on Turkish social media posts to detect antivaccine posts. Through this study, it will be possible to raise awareness about the importance of vaccines in …
A Hybrid Ahp-Dea Approach For Software Architecture Evaluation And Selection, Mehran Hajipoor, Homayun Motameni, Ali̇ Ebrahimnejad
A Hybrid Ahp-Dea Approach For Software Architecture Evaluation And Selection, Mehran Hajipoor, Homayun Motameni, Ali̇ Ebrahimnejad
Turkish Journal of Electrical Engineering and Computer Sciences
Decisions made during the software architecture stage significantly influence the success or failure of software projects. Software architects must navigate the complex task of selecting the most suitable architecture style while balancing stakeholders’ operational and nonoperational requirements. These requirements often involve diverse and conflicting quality attributes with varying priorities, as well as intricate interactions where some attributes positively or negatively influence others. Furthermore, different architectural styles exhibit varying levels of support for these quality attributes, adding another layer of complexity to the decision-making process. This study leverages the Analytic Hierarchy Process (AHP) and Data Envelopment Analysis (DEA) within a novel …
Enhanced Cuffless Blood Pressure Estimation Using Ecg And Ppg Signals: A Hybrid Approach With Windkessel, Arima, And Lstm, Piyush Mahajan, Amit Kaul
Enhanced Cuffless Blood Pressure Estimation Using Ecg And Ppg Signals: A Hybrid Approach With Windkessel, Arima, And Lstm, Piyush Mahajan, Amit Kaul
Turkish Journal of Electrical Engineering and Computer Sciences
Accurate blood pressure (BP) estimation is essential for the monitoring and management of cardiovascular diseases. This study presents a hybrid model for cuffless BP estimation using electrocardiogram (ECG) and photoplethysmogram (PPG) signals. The model incorporates features from time-domain, frequency-domain, and model-based approaches, including the Windkessel model, AutoRegressive Integrated Moving Average (ARIMA), and Long Short-Term Memory (LSTM) networks. To enhance performance, feature selection and reduction techniques such as Minimum Redundancy Maximum Relevance (MRMR) and autoencoders were employed. Additionally, model ensemble strategies, including average and weighted average modes, were utilized to combine the predictions of different models. The proposed method demonstrated superior …
Robust Sliding Mode Control For Precision Anesthesia Delivery: Addressing Interpatient Variability In Depth Of Hypnosis, Talha Sajjad, Adeel Iqbal, Ali Khaqan, Ali Nauman, Bilal Ijaz, Raja Ali Riaz
Robust Sliding Mode Control For Precision Anesthesia Delivery: Addressing Interpatient Variability In Depth Of Hypnosis, Talha Sajjad, Adeel Iqbal, Ali Khaqan, Ali Nauman, Bilal Ijaz, Raja Ali Riaz
Turkish Journal of Electrical Engineering and Computer Sciences
Anesthesia induction is a critical aspect of ensuring patients' health and safety during surgical procedures. The manual administration of anesthesia can lead to serious medical issues due to variability among patients, as well as the risks of overdosing or underdosing anesthetic drugs. While various linear control techniques have been implemented to enhance the safety of anesthesia infusion, non-linear control strategies have demonstrated superior results in achieving the desired level of hypnosis for surgical activities, primarily due to the significant parametric variations present in different patients.Given these challenges, there is a pressing need to develop an efficient anesthesia infusion system that …
Design And Optimization Of Low-Loss, High-Gain Metamaterial-Based Log-Periodic Dipole Array Antenna For Full Ka-Band Coverage, Mohamed El Moniar, Ahmed Abd El Hady, Yasser Ismail, Nihal Areed
Design And Optimization Of Low-Loss, High-Gain Metamaterial-Based Log-Periodic Dipole Array Antenna For Full Ka-Band Coverage, Mohamed El Moniar, Ahmed Abd El Hady, Yasser Ismail, Nihal Areed
Turkish Journal of Electrical Engineering and Computer Sciences
This work describes a microstrip log-periodic dipole array (MLPDA) antenna that uses metamaterials and operates across the whole Ka-band. The suggested MLPDA antenna layout provides a wide bandwidth with fewer dipole elements than traditional MLPDA antennas while maintaining the same resonance frequencies. To reduce size while covering a wide operational spectrum, the antenna design includes bending dipoles as radiating elements, as well as an incomplete ground plane. Furthermore, the proposed MLPDA antenna’s energy loss has been reduced while boosting its signal strength (gain) by inserting a metamaterial-based structure in front of it at a certain distance and on the same …
A Methodology For Nuclei Segmentation In H&E Images Using Efficientnetb7 Guided U-Net And Multiple Data Augmentation Strategies, Furkan Atlan, Emrah Hançer
A Methodology For Nuclei Segmentation In H&E Images Using Efficientnetb7 Guided U-Net And Multiple Data Augmentation Strategies, Furkan Atlan, Emrah Hançer
Turkish Journal of Electrical Engineering and Computer Sciences
Automating nuclei segmentation in Hematoxylin and Eosin (H&E) stained images is crucial for advancing computational pathology. Despite significant research, challenges such as overlapping nuclei, varying scanner resolutions, and diverse nuclear morphologies continue to hinder segmentation accuracy. In this paper, we propose a deep-learning based methodology that integrates multiple data augmentation strategies with a U-Net architecture enhanced by the EfficientNetB7 encoder. To enhance generalization, we train the model using a combined dataset from MoNuSeg2018, CPM-17, and CoNSeP, exposing it to diverse staining techniques and tissue types. We then evaluate its robustness on the unseen CryoNuSeg dataset, which consists of fully annotated …
Karcı Fractional Artificial Neural Networks (Karcıfann): A New Artificial Neural Networks Model Without Learning Rate And Its Problems, Meral Karakurt, Hülya Saygili, Ali̇ Karci
Karcı Fractional Artificial Neural Networks (Karcıfann): A New Artificial Neural Networks Model Without Learning Rate And Its Problems, Meral Karakurt, Hülya Saygili, Ali̇ Karci
Turkish Journal of Electrical Engineering and Computer Sciences
The learning rate parameter used in classical artificial neural networks (ANNs) designed with stochastic gradient descent causes problems such as failure to learn, getting stuck in local minima, memorization, and long training times (divergence problem). To address these issues, this paper proposes a novel ANN method that uses a fractional derivative instead of Newton’s derivative. This method is referred to as Karcı fractional ANN (KarcıFANN). In classical ANNs, the weight update is done by assigning the same constant value to the learning rate in each iteration or for a set number of iterations. In contrast, in KarcıFANNs, the weight update …
A Proposed Coding Algorithm With Ai-Assisted Restoration Capability, Parviz Gharehbagheri, Hamid Haj Seyyed Javadi
A Proposed Coding Algorithm With Ai-Assisted Restoration Capability, Parviz Gharehbagheri, Hamid Haj Seyyed Javadi
Turkish Journal of Electrical Engineering and Computer Sciences
This paper introduces an innovative encoding method based on the concept of "distancecodes" , which allows characters to appear randomly in different locations during the decoding process. Codeword-based algorithms decode and present their characters sequentially, which can hinder accurate reconstruction in the event of incomplete text decoding, as there is no intelligent or non-intelligent model available for continuing the text reconstruction.In contrast, our proposed algorithm allows for the non-sequential and scattered appearance of characters throughout the data during the decoding process. This enables AI algorithms to restore undecoded characters using contextual information such as decoded characters, adjacent complete and incomplete …
Optimizing Parameters For Efficient Computation With Fully Homomorphic Encryption Schemes, Cavi̇dan Yakupoğlu Karaağaç, Kurt Rohloff
Optimizing Parameters For Efficient Computation With Fully Homomorphic Encryption Schemes, Cavi̇dan Yakupoğlu Karaağaç, Kurt Rohloff
Turkish Journal of Electrical Engineering and Computer Sciences
In this study, we aim to provide a parameter selection approach for the BFVrns scheme, one of the prominent fully homomorphic encryption (FHE) schemes. Selecting parameters for lattice-based FHE schemes poses a practical challenge for both experts and nonexperts. To solve this problem, we introduce a hybrid approach that combines theoretical approach with experimental analysis. First, we employ regression analysis to examine the impact of parameters on both performance and security. The varying behavior of FHE parameters in terms of performance, security, and ciphertext expansion factor (CEF) makes parameter selection more challenging. To address this issue, we employ a multi-objective …
Decomposition Lstm With Dual Multi-Head Self-Attention For Wind Turbine Drivetrain State Forecasting, Haikun Jia, Huini Sun, Shuang Bai
Decomposition Lstm With Dual Multi-Head Self-Attention For Wind Turbine Drivetrain State Forecasting, Haikun Jia, Huini Sun, Shuang Bai
Turkish Journal of Electrical Engineering and Computer Sciences
Due to the clean and renewable nature of wind energy, accurate prediction of rotor loads and operating states for wind turbine units has become of paramount importance. Currently, traditional methods relying on expert analysis combined with instrument testing for qualitative reasoning are both time-consuming and labor-intensive, and their accuracy guarantees are limited. In response to wind farm data entailing the interweaving of data from multiple sources and the diverse interrelations across various features and time steps, this study introduces a method for predicting rotor loads and operating states. Initially, we employ an iterative multi-scale seasonal-trend decomposition block to capture latent …
Fuzzy-Virtual Inertia Control To Improve The Frequency Response Of Multi-Area Power Systems, Nourelhouda Djaraf, Yacine Daili, Abderrahim Zemmit, Abdelghani Harrag
Fuzzy-Virtual Inertia Control To Improve The Frequency Response Of Multi-Area Power Systems, Nourelhouda Djaraf, Yacine Daili, Abderrahim Zemmit, Abdelghani Harrag
Turkish Journal of Electrical Engineering and Computer Sciences
Virtual inertia control (VIC) is essential for power systems dominated by electronic devices to compensate for the lack of inertia and ensure frequency regulation. However, most existing VICs often focus solely on optimizing the virtual inertia parameter to adapt to the high penetration of renewable energy sources (RESs) without considering the damping factor. This oversight can lead to significant fluctuations and power mismatches, especially in interconnected systems where the coordination between MGs is sensitive and essential, and there is a risk of propagation of deviations between MGs, which makes the control more complex. To address these issues, this paper presents …
Enhancing Spatial-Temporal Video Prediction With Ts-Vq-Vae: A Novel Encoder-Processor-Decoder Approach, Mei Feng, Fan Li
Enhancing Spatial-Temporal Video Prediction With Ts-Vq-Vae: A Novel Encoder-Processor-Decoder Approach, Mei Feng, Fan Li
Turkish Journal of Electrical Engineering and Computer Sciences
Video prediction is a significant and actively researched area within the data science community. Its primary objective is to generate future video frames based on historical frames, finding applications in diverse domains such as human motion prediction, climate change analysis, and traffic flow forecasting. Traditional methods combine Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs) to capture complex correlations in spatial-temporal signals. Recent methods improve video prediction accuracy by introducing external information such as optical flow, semantic maps, and human pose data. However, these methods have limitations, such as not fully exploring the intermediate states of learning representations, overlooking …
Conversational Open-Domain Question Answering For Resource-Constrained Languages, Emrah Budur, Tunga Güngör
Conversational Open-Domain Question Answering For Resource-Constrained Languages, Emrah Budur, Tunga Güngör
Turkish Journal of Electrical Engineering and Computer Sciences
The growing interest in Conversational AI has led to the development of Conversational OpenQA systems as a crucial step for meeting users' information needs in real world scenarios. Conversational OpenQA systems enhance standard OpenQA performance by leveraging conversation history of the users. However, building effective Conversational OpenQA systems requires large-scale Conversational OpenQA datasets, often limited to the English language, hindering progress in low-resource languages. We present a robust Conversational OpenQA system enhanced by conversational context, designed for languages with limited resources and exemplified in our case study for Turkish. To address data limitations in a cost-effective way, we repurpose existing …
A Case Study Of Gray-Box Fuzzing With Byte- And Tree-Level Mutation Strategies In Xml-Based Applications For Exposing Security Vulnerabilities, Şerafetti̇n Şentürk, Vahi̇d Garousi, Nejat Yumuşak
A Case Study Of Gray-Box Fuzzing With Byte- And Tree-Level Mutation Strategies In Xml-Based Applications For Exposing Security Vulnerabilities, Şerafetti̇n Şentürk, Vahi̇d Garousi, Nejat Yumuşak
Turkish Journal of Electrical Engineering and Computer Sciences
Fuzzing is an automated process for detecting crashes and vulnerabilities in software system and it is classified as grammar- or mutation-based in terms of input generation. While the grammar-based fuzzing generates inputs from a specification and takes highly-structured inputs, mutation-based fuzzing generates inputs by modifying input files and abstract syntax trees randomly. There are not many case studies comparing the crash detection capabilities in the scope of mutation-based fuzzing. To add to the body of empirical evidence in this area, this case study compares fuzzing with different mutation strategies to evaluate their effectiveness in three aspects: fault detection effectiveness, fault …
Multikernel Embedded Fusion Unet (Mkef-Unet): A Robust Deep Learning Approach For Accurate Segmentation Of Chagas Parasites, Preet Kumar, Carlos Brito-Loeza, Lavdie Rada
Multikernel Embedded Fusion Unet (Mkef-Unet): A Robust Deep Learning Approach For Accurate Segmentation Of Chagas Parasites, Preet Kumar, Carlos Brito-Loeza, Lavdie Rada
Turkish Journal of Electrical Engineering and Computer Sciences
This paper introduces a novel approach for segmenting Chagas parasites on stained blood smear samples from mice during the acute phase of infection with Trypanosoma cruzi utilizing a U-Net-based deep learning model named multikernel embedded fusion UNet (MKEF-UNet). Our proposed model incorporates DenseNet-121 for feature extraction, a classifier module for predicting parasite information, and a segmentation decoder with multiscale feature fusion to generate precise segmentation results. Notably, the integration of the embedded vector module, multikernel convolutions with dilations, and advanced data augmentation techniques significantly enhance the model’s robustness and generalization capabilities. In extensive experiments on the Chagas dataset, MKEF-UNet achieves …
A Novel Mu-Metal Based Weak Magnetic Energy Harvester For Self-Powered Monitoring Of Power Grid Assets, Arsalan Habib Khawaja, Hassan Pervaiz, Dongsheng Cai, Jian Li, Qi Huang
A Novel Mu-Metal Based Weak Magnetic Energy Harvester For Self-Powered Monitoring Of Power Grid Assets, Arsalan Habib Khawaja, Hassan Pervaiz, Dongsheng Cai, Jian Li, Qi Huang
Turkish Journal of Electrical Engineering and Computer Sciences
This paper presents a novel magnetic field driven contactless energy harvester with improved flux concentration capabilities for potential utilization in Power system monitoring where stray magnetic field is abundant and readily available. The designed harvester employs multilayered Mu-Metal based cone shaped core to maximize magnetic flux density. To achieve the final design, this work investigates magnetic flux concentration ability of various geometries and material properties in magnetic flux conditions typical to overhead 11 kV power distribution circuits. Impact of layers in core-coil region of harvesting coil on magnetic flux concentration is evaluated by means of Finite Element analysis. Resultantly, the …
Advanced Prediction Of Events And Temporal Expressions In Medical Text Using The Jena Api: Integrating Ontologies And Deep Learning, Hafida Tiaiba, Lyazid Sabri, Okba Kazar
Advanced Prediction Of Events And Temporal Expressions In Medical Text Using The Jena Api: Integrating Ontologies And Deep Learning, Hafida Tiaiba, Lyazid Sabri, Okba Kazar
Turkish Journal of Electrical Engineering and Computer Sciences
The automatic recognition of medical concepts and temporal expressions in narrative clinical text enhances the utility of electronic health records (EHRs) and supports clinical decision-making and research. However, challenges arise due to the complexity of medical language, ambiguity of terms, and variability in expression. To address these issues, the use of medical ontologies significantly improves data management in healthcare. A novel approach integrates various medical ontologies covering drugs, symptoms, diseases, anatomy, disease drivers, and food, and with convolutional neural networks (CNNs) -including Standard, Transposed, and Separable convolution models (CONSEPTR)- to extract both medical events (e.g., clinical departments, treatments, problems) and …
Machine Learning Models Approach For The Quantitative Classification Of Ferricyanide Compound Using Electrochemical Detection With Cpe-Fe3o4nps, Süleyman Aşir, Nemah Abu Shama, Najya Maroof Saleem, Devri̇m Kayali, Kami̇l Di̇mi̇li̇ler
Machine Learning Models Approach For The Quantitative Classification Of Ferricyanide Compound Using Electrochemical Detection With Cpe-Fe3o4nps, Süleyman Aşir, Nemah Abu Shama, Najya Maroof Saleem, Devri̇m Kayali, Kami̇l Di̇mi̇li̇ler
Turkish Journal of Electrical Engineering and Computer Sciences
Most common electrochemical analysis techniques used to evaluate enzymes, proteins, and heavy metals over a wide potential range include electrochemical impedance EIS, differential pulse voltammetry DPV, and square wave voltammetry SQWV. Machine leaning algorithms MLA are employed to classify the Potassium ferricyaniyde K3Fe(CN)6 concentrations using a modified carbon paste electrode CPE embedded with iron (II, III) oxide (Fe3O4) NPs. The CV, DPV, and SQWV voltametric data collected from all K3Fe(CN)6 concentrations were used as input data to the machine learning algorithms. Signaling current of K3Fe(CN)6 concentrations improved at Fe3O4 modified with nanoparticles NPs CPE in a comparison with the unmodified …
Balancing Anarchy And Efficiency: Partial Team Formations And Learning In Potential Games, Muhammed Sayin
Balancing Anarchy And Efficiency: Partial Team Formations And Learning In Potential Games, Muhammed Sayin
Turkish Journal of Electrical Engineering and Computer Sciences
Non-cooperative multi-agent learning, focusing on individual rationality (anarchy), often falls short in achieving system-wide efficiency in potential games, a class of games with applications in decentralized control and optimization. On the other hand, cooperative approaches prioritize system efficiency but often via global coordination, which could be impractical, e.g., for large-scale and less controlled environments. To address this dilemma, we propose a novel framework that introduces partial team formations, allowing team members with shared objectives to coordinate their actions while maintaining team-wise rationality for improved system-wide efficiency without the burden of global coordination. We model such interactions as a multi-team game …
A Content-Based Recommender System For The Uav Caching In The Field Of Entertainment In Fog Computing, Elham Darbanian, Mohsen Nickray
A Content-Based Recommender System For The Uav Caching In The Field Of Entertainment In Fog Computing, Elham Darbanian, Mohsen Nickray
Turkish Journal of Electrical Engineering and Computer Sciences
The Unmanned Aerial Vehicle (UAV) can be used as good flying base stations to cache popular content and follow a user mobility pattern, to help them in a suitable services. Conventional edge caching algorithms often prioritize cache contents with higher popularity. Nevertheless, the cache capacity of mobile devices is restricted, and diverse clients may have expansive varieties in content inclination designs. In this manner, the performance and effectiveness of the cache will be so constrained without great strategies. The composition of recommender system and edge caching is considered as a new research topic, which is used to reduce cost and …