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Articles 4981 - 5010 of 25614

Full-Text Articles in Computer Engineering

Breast Cancer-Caps: A Breast Cancer Screening System Based On Capsule Network Utilizing The Multiview Breast Thermal Infrared Images, Devanshu Tiwari, Manish Dixit, Kamlesh Gupta Jul 2022

Breast Cancer-Caps: A Breast Cancer Screening System Based On Capsule Network Utilizing The Multiview Breast Thermal Infrared Images, Devanshu Tiwari, Manish Dixit, Kamlesh Gupta

Turkish Journal of Electrical Engineering and Computer Sciences

This paper proposed an accurate and fully automated breast cancer early screening system called the "Breast Cancer-Caps". The capsule network is used in this approach for the cancer detection in breast utilizing the thermal infrared images for the first time. This capsule network is trained with the help of Dynamic as well as Static breast thermal images dataset consisting of left, right, frontal views along with a new multiview thermal images. These multiview breast thermal images are fabricated by concatenating the conventional left, frontal and right view breast thermal images. The other current and popular deep transfer learning models such …

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Measuring The Rol Of Digital Engineering: It's A Journey, Not A Number, Tom Mcdermott, Kaitlin Henderson, Eileen Van Aken, Alejandro Salado, Joseph Bradley Jul 2022

Measuring The Rol Of Digital Engineering: It's A Journey, Not A Number, Tom Mcdermott, Kaitlin Henderson, Eileen Van Aken, Alejandro Salado, Joseph Bradley

Engineering Management & Systems Engineering Faculty Publications

Systems engineering as a discipline has long had difficulty providing quantifiable evidence of its value (Honour 2004); DE transformation provides an opportunity to better measure its value. Transitioning from a document-based to a model-based approach is expensive, and organizations want to know if the effort and cost to adopt MBSE is worth it.

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Non-Gaussian Analysis Of Herbarium Specimen Damageto Optimize Specimen Collection Management, Aris Yaman, Yulia Aris Kartika, Ariani Indrawati, Zaenal Akbar, Lindung P. Manik, Wita Wardani, Tutie Djarwaningsih, Taufik Mahendra, Dadan R. Saleh Jul 2022

Non-Gaussian Analysis Of Herbarium Specimen Damageto Optimize Specimen Collection Management, Aris Yaman, Yulia Aris Kartika, Ariani Indrawati, Zaenal Akbar, Lindung P. Manik, Wita Wardani, Tutie Djarwaningsih, Taufik Mahendra, Dadan R. Saleh

Knowledge Engineering and Data Science

Damage to specimen collections occurs in practically every herbarium across the world. Hence, some precautions must be taken, such as investigating the factors that cause specimen damage in their collections and evaluating their herbarium collection handling and usage policy. However, manual investigation of the causes of herbarium collection damage requires a lot of effort and time. Only a few studies have attempted to investigate the causes of herbarium collection damage. So far, the non-gaussian approach to detecting the causes of damage to herbarium specimens has not been studied before. This study attempted to explore the effect of species type, time, …

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Social Distancing Monitoring System Using Deep Learning, Amelia Ritahani Ismail, Nur Shairah Muhd Affendy, Asmarani Ahmad Puzi Jul 2022

Social Distancing Monitoring System Using Deep Learning, Amelia Ritahani Ismail, Nur Shairah Muhd Affendy, Asmarani Ahmad Puzi

Knowledge Engineering and Data Science

COVID-19 has been declared a pandemic in the world by 2020. One way to prevent COVID-19 disease, as the World Health Organization (WHO) suggests, is to keep a distance from other people. It is advised to stay at least 1 meter away from others, even if they do not appear to be sick. The reason is that people can also be the virus carrier without having any symptoms. Thus, many countries have enforced the rules of social distancing in their Standard Operating Procedure (SOP) to prevent the virus spread. Monitoring the social distance is challenging as this requires authorities to …

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Automatic 3d Cranial Landmark Positioning Based Onsurface Curvature Feature Using Machine Learning, Putu Hendra Suputra, Anggraini Dwi Sensusiati, Myrtati Dyah Artaria, Gijsbertus Jacob Verkerke, Eko Mulyanto Yuniarno, I Ketut Eddy Purnama Jul 2022

Automatic 3d Cranial Landmark Positioning Based Onsurface Curvature Feature Using Machine Learning, Putu Hendra Suputra, Anggraini Dwi Sensusiati, Myrtati Dyah Artaria, Gijsbertus Jacob Verkerke, Eko Mulyanto Yuniarno, I Ketut Eddy Purnama

Knowledge Engineering and Data Science

Cranial anthropometric reference points (landmarks) play an important role in craniofacial reconstruction and identification. Knowledge to detect the position of landmarks is critical. This work aims to locate landmarks automatically. Landmarks positioning using Surface Curvature Feature (SCF) is inspired by conventional methods of finding landmarks based on morphometrical features. Each cranial landmark has a unique shape. With the appropriate 3D descriptors, the computer can draw associations between shapes and landmarks using machine learning. The challenge in classification and detection in three-dimensional space is to determine the model and data representation. Using three-dimensional raw data in machine learning is a serious …

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The Effect Of Resampling On Classifier Performance: Anempirical Study, Utomo Pujianto, Muhammad Iqbal Akbar, Niendhitta Tamia Lassela, Deni Sutaji Jul 2022

The Effect Of Resampling On Classifier Performance: Anempirical Study, Utomo Pujianto, Muhammad Iqbal Akbar, Niendhitta Tamia Lassela, Deni Sutaji

Knowledge Engineering and Data Science

An imbalanced class on a dataset is a common classification problem. The effect of using imbalanced class datasets can cause a decrease in the performance of the classifier. Resampling is one of the solutions to this problem. This study used 100 datasets from 3 websites: UCI Machine Learning, Kaggle, and OpenML. Each dataset will go through 3 processing stages: the resampling process, the classification process, and the significance testing process between performance evaluation values of the combination of classifier and the resampling using paired t-test. The resampling used in the process is Random Undersampling, Random Oversampling, and SMOTE. The classifier …

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A Comparison Of Machine Learning Models To Prioritise Emailsusing Emotion Analysis For Customer Service Excellence, Mohammad Yasser Chuttur, Yashinee Parianen Jul 2022

A Comparison Of Machine Learning Models To Prioritise Emailsusing Emotion Analysis For Customer Service Excellence, Mohammad Yasser Chuttur, Yashinee Parianen

Knowledge Engineering and Data Science

There has been little research on machine learning for email prioritization for customer service excellence. To fill this gap, we propose and assess the efficacy of various machine learning techniques for classifying emails into three degrees of priority: high, low, and neutral, based on the emotions inherent in the email content. It is predicted that after emails are classified into those three categories, recipients will be able to respond to emails more efficiently and provide better customer service. We use the NRC Emotion Lexicon to construct a labeled email dataset of 517,401 messages for our proposal. Following that, we train …

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Fish Image Classification Using Transfer Learning Method Withadaptive Learning Rate, Rizka Suhana, Wayan Firdaus Mahmudy, Agung Setia Budi Jul 2022

Fish Image Classification Using Transfer Learning Method Withadaptive Learning Rate, Rizka Suhana, Wayan Firdaus Mahmudy, Agung Setia Budi

Knowledge Engineering and Data Science

The diversity of fish species in coral reef ecosystems is one of the indications in determining health in coral reef ecosystems. Many Indonesian Fisheries and Marine Research and Development Agency experts carefully classify fish images. A reliable technique for performing image classification is Convolutional Neural Network (CNN). Transfer learning appears and adopts part of CNN, namely the modified convolution layer. The paper aims to solve the fish classification problem using the pre-trained model of Mobilenet V2. The model has a low computational process and does not use too many memory resources when training image data. The research image data used …

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Human Facial Expressions Identification Using Convolutionalneural Network With Vgg16 Architecture, Luther Alexander Latumakulita, Sandy Laurentius Lumintang, Deiby Tineke Salaki, Steven R. Sentinuwo, Alwin Melkie Sambul, Noorul Islam Jul 2022

Human Facial Expressions Identification Using Convolutionalneural Network With Vgg16 Architecture, Luther Alexander Latumakulita, Sandy Laurentius Lumintang, Deiby Tineke Salaki, Steven R. Sentinuwo, Alwin Melkie Sambul, Noorul Islam

Knowledge Engineering and Data Science

The human facial expression identification system is essential in developing human interaction and technology. The development of Artificial Intelligence for monitoring human emotions can be helpful in the workplace. Commonly, there are six basic human expressions, namely anger, disgust, fear, happiness, sadness, and surprise, that the system can identify. This study aims to create a facial expression identification system based on basic human expressions using the Convolutional Neural Network (CNN) with a 16-layer VGG architecture. Two thousand one hundred thirty-seven facial expression images were selected from the FER2013, JAFFE, and MUG datasets. By implementing image augmentation and setting up the …

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Sentiment Analysis Of Amazon Product Reviews Usingsupervised Machine Learning Techniques, Naveed Sultan Jul 2022

Sentiment Analysis Of Amazon Product Reviews Usingsupervised Machine Learning Techniques, Naveed Sultan

Knowledge Engineering and Data Science

Today, everything is sold online, and many individuals can post reviews about different products to show feedback. Serves as feedback for businesses regarding buyer reviews, performance, product quality, and seller service. The project focuses on buyer opinions based on Mobile Phone reviews. Sentiment analysis is the function of analyzing all these data, obtaining opinions about these products and services that classify them as positive, negative, or neutral. This insight can help companies improve their products and help potential buyers make the right decisions. Once the preprocessing is classified on a trained dataset, these reviews must be preprocessed to remove unwanted …

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Kean University Students Preparing For Cybersecurity Certification Exams In Free Summer Program, Stanley Mierzwa Jul 2022

Kean University Students Preparing For Cybersecurity Certification Exams In Free Summer Program, Stanley Mierzwa

Center for Cybersecurity

Fifteen Kean University students and recent graduates this summer are studying for cybersecurity certifications in a virtual pilot program offered free of charge through the University’s Center for Cybersecurity.

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An Ontology For Cardiothoracic Surgical Education And Clinical Data Analytics, Maryam Panahiazar, Yorick Chern, Ramon Riojas, Omar S.Latif, Usha Lokala, Dexter Hadley, Amit Sheth, Ramin E.Beygui Jul 2022

An Ontology For Cardiothoracic Surgical Education And Clinical Data Analytics, Maryam Panahiazar, Yorick Chern, Ramon Riojas, Omar S.Latif, Usha Lokala, Dexter Hadley, Amit Sheth, Ramin E.Beygui

Faculty Publications

The development of an ontology facilitates the organization of the variety of concepts used to describe different terms in different resources. The proposed ontology will facilitate the study of cardiothoracic surgical education and data analytics in electronic medical records (EMR) with the standard vocabulary.

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One-Bit Algorithm Considerations For Sparse Pmcw Radar, Ethan Triplett Jul 2022

One-Bit Algorithm Considerations For Sparse Pmcw Radar, Ethan Triplett

Department of Electrical and Computer Engineering: Dissertations, Theses, and Student Research

Phase Modulated Continuous Wave (PMCW) radar an emerging technology for autonomous cars. It is more flexible than the current frequency modulated systems, offering better detection resolution, interference mitigation, and future development opportunities. The issue preventing PMCW adoption is the need for high sample-rate analog to digital converters (ADCs). Due to device limits, a large increase in cost and power consumption occurs for every added resolution bit for a given sampling rate. This thesis explores radar detection techniques for few-bit and 1-bit ADC measurements. 1-bit quantization typically results in poor amplitude estimation, which can limit detections if the target signals are …

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Finding Top-M Leading Records In Temporal Data, Yiyi Wang Jul 2022

Finding Top-M Leading Records In Temporal Data, Yiyi Wang

Dissertations and Theses Collection (Open Access)

A traditional top-k query retrieves the records that stand out at a certain point in time. On the other hand, a durable top-k query considers how long the records retain their supremacy, i.e., it reports those records that are consistently among the top-k in a given time interval. In this thesis, we introduce a new query to the family of durable top-k formulations. It finds the top-m leading records, i.e., those that rank among the top-k for the longest duration within the query interval. Practically, this query assesses the records based on how long …

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Comparative Study Of A Bidirectional Multi-Phase Multiinput Converter For Electric Vehicles, Furkan Akar, Murat Kale, Sebahatti̇n Yalçin, Gözde Taş Jul 2022

Comparative Study Of A Bidirectional Multi-Phase Multiinput Converter For Electric Vehicles, Furkan Akar, Murat Kale, Sebahatti̇n Yalçin, Gözde Taş

Turkish Journal of Electrical Engineering and Computer Sciences

Multiinput converters allow to create hybrid energy systems in electric vehicles with a reduced part count. In addition, interleaved structures help to build efficient converters with several possible benefits, such as low current ripple and high power density. This paper proposes utilizing a multiphase multiinput converter (MPMIC), which concentrates the aforementioned advantages and presents a comprehensive comparison with its single-phase version, called single phase multiinput converter (SPMIC). After analysing their steady-state characteristics, SPMIC and MPMIC are designed considering same conditions. Then, two laboratory prototypes rated at 2.5kW output power are implemented to validate the analysis. Finally, these prototypes are compared …

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Actor-Critic Reinforcement Learning For Bidding In Bilateral Negotiation, Furkan Arslan, Reyhan Aydoğan Jul 2022

Actor-Critic Reinforcement Learning For Bidding In Bilateral Negotiation, Furkan Arslan, Reyhan Aydoğan

Turkish Journal of Electrical Engineering and Computer Sciences

Designing an effective and intelligent bidding strategy is one of the most compelling research challenges in automated negotiation, where software agents negotiate with each other to find a mutual agreement when there is a conflict of interests. Instead of designing a hand-crafted decision-making module, this work proposes a novel bidding strategy adopting an actor-critic reinforcement learning approach, which learns what to offer in a bilateral negotiation. An entropy reinforcement learning framework called Soft Actor-Critic (SAC) is applied to the bidding problem, and a self-play approach is employed to train the model. Our model learns to produce the target utility of …

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On The Performance Analysis Of Flexible Pairing Between Uav And Gu In Noma, Man Hee Lee, Soo Young Shin Jul 2022

On The Performance Analysis Of Flexible Pairing Between Uav And Gu In Noma, Man Hee Lee, Soo Young Shin

Turkish Journal of Electrical Engineering and Computer Sciences

The wireless communications regarding unmanned aerial vehicles (UAVs) have been investigated for the usage of base stations (BS) to provide Internet access. This paper presents the usage of a UAV as a pairing user to enhance the sum capacity by flexible pairing in nonorthogonal multiple access (NOMA). In the proposed scheme, the UAVs and the ground users (GUs) get paired to promote the line-of-sight (LoS) characteristics. The performance of flexible pairing is presented in terms of sum capacity, outage probability, and throughput with the LoS path loss. Channel modeling is necessary to apply flexible pairing by utilizing the LoS characteristic …

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A Novel Hybrid Algorithm For Morphological Analysis: Artificial Neural-Net-Xmor, Ayla Kayabaş, Ahmet Ercan Topcu, Özkan Kiliç Jul 2022

A Novel Hybrid Algorithm For Morphological Analysis: Artificial Neural-Net-Xmor, Ayla Kayabaş, Ahmet Ercan Topcu, Özkan Kiliç

Turkish Journal of Electrical Engineering and Computer Sciences

In this study, we present a novel algorithm that combines a rule-based approach and an artificial neural network-based approach in morphological analysis. The usage of hybrid models including both techniques is evaluated for performance improvements. The proposed hybrid algorithm is based on the idea of the dynamic generation of an artificial neural network according to two-level phonological rules. In this study, the combination of linguistic parsing, a neural network-based error correction model, and statistical filtering is utilized to increase the coverage of pure morphological analysis. We experimented hybrid algorithm applying rule-based and long short-term memory-based (LSTM-based) techniques, and the results …

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Content Loss And Conditional Space Relationship In Conditional Generative Adversarial Networks, Enes Eken Jul 2022

Content Loss And Conditional Space Relationship In Conditional Generative Adversarial Networks, Enes Eken

Turkish Journal of Electrical Engineering and Computer Sciences

In the machine learning community, generative models, especially generative adversarial networks (GANs) continue to be an attractive yet challenging research topic. Right after the invention of GAN, many GAN models have been proposed by the researchers with the same goal: creating better images. The first and foremost feature that a GAN model should have is that creating realistic images that cannot be distinguished from genuine ones. A large portion of the GAN models proposed to this end have a common approach which can be defined as factoring the image generation process into multiple states for decomposing the difficult task into …

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Using A Static Naming Approach To Implement Remote Scope Promotion, Ayşe Yilmazer Jul 2022

Using A Static Naming Approach To Implement Remote Scope Promotion, Ayşe Yilmazer

Turkish Journal of Electrical Engineering and Computer Sciences

GPUs employ simple coherence mechanisms and require explicit use of costly synchronization operations for data integrity. Local-scoped synchronization can be utilized to lower the performance penalty of synchronization when sharing is within a subgroup of threads. Unfortunately, in asymmetric sharing (which is an important dynamic sharing pattern), it is necessary to use global-scoped synchronization due to possible accesses by remote sharers. Remote Scope Promotion (RSP) was introduced to take advantage of local-scoped synchronization at regular accesses while using scope promotion at occasional remote accesses. First implementation of RSP makes use of a simple approach that performs costly cache operations on …

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A Concept For Weighting Sentiment Phrase Using Deterministic Solution Of Algebraic Equations, Maryam Jalali, Morteza Zahedi, Abdolali Basiri Jul 2022

A Concept For Weighting Sentiment Phrase Using Deterministic Solution Of Algebraic Equations, Maryam Jalali, Morteza Zahedi, Abdolali Basiri

Turkish Journal of Electrical Engineering and Computer Sciences

Many text mining methods have used statistical information as text and language-independent procedures that are not deterministic. On the other hand, grammatical structure-based methods are limited to use in a certain language and text. We aim to suggest an algorithmic algebraic equation in a deterministic and nonprobabilistic way while maintaining the advantage of language independence. We propose a mathematical approach that transforms text and labels into a set of dumb equations. By solving the equations, each word is assigned a weight that can reflect the semantic information of that word, then we use the proposed algorithm to build a novel …

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Development Of A Hybrid System Based On Abc Algorithm For Selection Of Appropriate Parameters For Disease Diagnosis From Ecg Signals, Ersi̇n Ersoy, Gazi̇ Erkan Bostanci, Mehmet Serdar Güzel Jul 2022

Development Of A Hybrid System Based On Abc Algorithm For Selection Of Appropriate Parameters For Disease Diagnosis From Ecg Signals, Ersi̇n Ersoy, Gazi̇ Erkan Bostanci, Mehmet Serdar Güzel

Turkish Journal of Electrical Engineering and Computer Sciences

The number of people who die due to cardiovascular diseases is quite high. In our study, ECG (electrocar-diogram) signals were divided into segments and waves based on temporal boundaries. Signal similarity methods such as convolution, correlation, covariance, signal peak to noise ratio (PNRS), structural similarity index (SSIM), one of the basic statistical parameters, arithmetic mean and entropy were applied to each of these sections. In addition, a square error-based new approach was applied and the difference of the signs from the mean sign was taken and used as a feature vector. The obtained feature vectors are used in the artificial …

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Automatic Keyword Assignment System For Medical Research Articles Using Nearest-Neighbor Searches, Fati̇h Di̇lmaç, Adi̇l Alpkoçak Jul 2022

Automatic Keyword Assignment System For Medical Research Articles Using Nearest-Neighbor Searches, Fati̇h Di̇lmaç, Adi̇l Alpkoçak

Turkish Journal of Electrical Engineering and Computer Sciences

Assigning accurate keywords to research articles is increasingly important concern. Keywords should be selected meticulously to describe the article well since keywords play an important role in matching readers with research articles in order to reach a bigger audience. So, improper selection of keywords may result in less attraction to readers which results in degradation in its audience. Hence, we designed and developed an automatic keyword assignment system (AKAS) for research articles based on k-nearest neighbor (k-NN) and threshold-nearest neighbor (t-NN) accompanied with information retrieval systems (IRS), which is a corpus-based method by utilizing IRS using the Medline dataset in …

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Analysis Of Tissue Electrical Properties On Bio-Impedance Variation Of Upper Limps, Enver Salkim Jul 2022

Analysis Of Tissue Electrical Properties On Bio-Impedance Variation Of Upper Limps, Enver Salkim

Turkish Journal of Electrical Engineering and Computer Sciences

Upper limb loss has a significant impact on individual socioeconomic life. Human-machine interface (HMI) using surface electromyography (sEMG) establishes a link between the user and a hand prosthesis to recognize hand gestures and motions which allows the control of robotic machines and prostheses to perform dexterous tasks. Numerous methods aimed to enhance hand gesture and motion recognition toward an HMI. Bio-impedance analysis (BIA) is a noninvasive way of assessing body compositions and has been recently used for hand motion interpretation using `brute force? pattern recognition. The impedance variation in the body mostly depends on the precise stimulation using appropriate electrical …

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A New Automatic Bearing Fault Size Diagnosis Using Time-Frequency Images Of Cwt And Deep Transfer Learning Methods, Yilmaz Kaya, Fatma Kuncan, Hüseyi̇n Meti̇n Ertunç Jul 2022

A New Automatic Bearing Fault Size Diagnosis Using Time-Frequency Images Of Cwt And Deep Transfer Learning Methods, Yilmaz Kaya, Fatma Kuncan, Hüseyi̇n Meti̇n Ertunç

Turkish Journal of Electrical Engineering and Computer Sciences

Bearings are generally used as bearings or turning elements. Bearings are subjected to high loads and rapid speeds. Furthermore, metal-to-metal contact within the bearing makes it sensitive. In today?s machines, bearing failures disrupt the operation of the system or completely stop the system. Bearing failures that can occur can cause enormous damage to the entire system. Therefore, it is necessary to anticipate bearing failures and to carry out a regular diagnostic examination. Various systems have been developed for fault diagnosis. In recent years, deep transfer learning (DTL) methods are often preferred in current bearing diagnosis models, as they provide time …

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Degree-Based Random Walk Approach For Graph Embedding, Sarmad N. Mohammed, Semra Gündüç Jul 2022

Degree-Based Random Walk Approach For Graph Embedding, Sarmad N. Mohammed, Semra Gündüç

Turkish Journal of Electrical Engineering and Computer Sciences

Graph embedding, representing local and global neighbourhood information by numerical vectors, is a crucial part of the mathematical modeling of a wide range of real-world systems. Among the embedding algorithms, random walk-based algorithms have proven to be very successful. These algorithms collect information by creating numerous random walks with a predefined number of steps. Creating random walks is the most demanding part of the embedding process. The computation demand increases with the size of the network. Moreover, for real-world networks, considering all nodes on the same footing, the abundance of low-degree nodes creates an imbalanced data problem. In this work, …

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Generating Ad Creatives Using Deep Learning For Search Advertising, Kevser Nur Çoğalmiş, Ahmet Bulut Jul 2022

Generating Ad Creatives Using Deep Learning For Search Advertising, Kevser Nur Çoğalmiş, Ahmet Bulut

Turkish Journal of Electrical Engineering and Computer Sciences

We generated advertisement creatives programmatically using deep neural networks. A landing page contains relevant text data, which can be used for generating advertisement creatives, i.e. ads. We treated the ad generation task as a text summarization problem and built a sequence to sequence model. In order to assess the validity of our approach, we conducted experiments on four datasets. Our empirical results showed that our model generated relevant ads on a template-based dataset with moderate hyperparameters. Training the model with more content increased the performance of the model, which we attributed to rigorous hyperparameter tune-up. The choice of word embedding …

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Transmorph: A Transformer Based Morphological Disambiguator For Turkish, Hi̇lal Özer, Emi̇n Erkan Korkmaz Jul 2022

Transmorph: A Transformer Based Morphological Disambiguator For Turkish, Hi̇lal Özer, Emi̇n Erkan Korkmaz

Turkish Journal of Electrical Engineering and Computer Sciences

The agglutinative nature of the Turkish language has a complex morphological structure, and there are generally more than one parse for a given word. Before further processing, morphological disambiguation is required to determine the correct morphological analysis of a word. Morphological disambiguation is one of the first and crucial steps in natural language processing since its success determines later analyses. In our proposed morphological disambiguation method, we used a transformer-based sequence-to-sequence neural network architecture. Transformers are commonly used in various NLP tasks, and they produce state-of-the-art results in machine translation. However, to the best of our knowledge, transformer-based encoder-decoders have …

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Packet-Level And Ieee 802.11 Mac Frame-Level Analysis For Iot Device Identification, Rajarshi Roy Chowdhury, Azam Che Idris, Pg Emeroylariffion Abas Jul 2022

Packet-Level And Ieee 802.11 Mac Frame-Level Analysis For Iot Device Identification, Rajarshi Roy Chowdhury, Azam Che Idris, Pg Emeroylariffion Abas

Turkish Journal of Electrical Engineering and Computer Sciences

In cyberspace, a large number of Internet of Things (IoT) devices from different manufacturers with hetero-geneous functionalities are connected together. It is challenging to identify all these devices in an IoT ecosystem. The situation becomes even more complicated when the devices come from the same manufacturer and of similar types due to their analogous network communication behaviour. In this paper, a device fingerprinting (DFP) approach based on a set of combined features from packet-level and frame-level has been proposed. A large number of features has been studied, and consequently, a suitable subset of features has been selected according to gain-ratio …

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Prediction Of Broken Rotor Bar In Induction Motor Using Spectral Entropy Features And Tlbo Optimized Svm, Sudip Halder, Sunil Bhat, Bimal Dora Jul 2022

Prediction Of Broken Rotor Bar In Induction Motor Using Spectral Entropy Features And Tlbo Optimized Svm, Sudip Halder, Sunil Bhat, Bimal Dora

Turkish Journal of Electrical Engineering and Computer Sciences

The information of the fault frequency characteristics is of great importance for all associated fault diag nostics. This requires a high-resolution spectrum analysis to achieve efficient monitoring of machinery faults, especially while diagnosing rotor bar breakage under light load conditions, because the fault frequencies almost overlap with the fundamental. In this context, rather than looking for frequencies associated with rotor faults, several frequency bands are observed separately in terms of the entropy contained within these bands. First, the motor current signal has been divided into several frequency bands using the continuous wavelet transform (CWT), and the spectral entropy is calculated …

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