Open Access. Powered by Scholars. Published by Universities.®

Computer Engineering Commons™

Open Access. Powered by Scholars. Published by Universities.®

Computer Sciences

Institution
Keyword
Publication Year
Publication
Publication Type
File Type

Articles 2341 - 2370 of 13037

Full-Text Articles in Computer Engineering

Lpvit: A Transformer Based Model For Pcb Image Classification And Defect Detection, Kang An, Yanping Zhang Jan 2022

Lpvit: A Transformer Based Model For Pcb Image Classification And Defect Detection, Kang An, Yanping Zhang

Computer Science Faculty Scholarship

PCB (printed circuit board) is an extremely important component of all electronic products, which has greatly facilitated human life. Meanwhile, tons of PCBs in the waste streams become a waste of resources, which puts the recycling and reuse of PCBs in urgent need. In the manufacturing and recycling of electronic products, the classification of PCBs, recognition of sub-components, and defect detection have been the key technology. Traditional manual detection and classification are subjective and rely on individuals’ experience. With the development of artificial intelligence, lots of research efforts have been dedicated to the automated detection and recognition of PCBs. In …

Go to article

Optimized Cancer Detection On Various Magnified Histopathological Colon Imagesbased On Dwt Features And Fcm Clustering, Tina Babu, Tripty Singh, Deepa Gupta, Shahin Hameed Jan 2022

Optimized Cancer Detection On Various Magnified Histopathological Colon Imagesbased On Dwt Features And Fcm Clustering, Tina Babu, Tripty Singh, Deepa Gupta, Shahin Hameed

Turkish Journal of Electrical Engineering and Computer Sciences

Due to the morphological characteristics and other biological aspects in histopathological images, the computerized diagnosis of colon cancer in histopathology images has gained popularity. The images acquired using the histopathology microscope may differ for greater visibility by magnifications. This causes a change in morphological traits leading to intra and inter-observer variability. An automatic colon cancer diagnosis system for various magnification is therefore crucial. This work proposes a magnification independent segmentation approach based on the connected component area and double density dual tree DWT (discrete wavelet transform) coefficients are derived from the segmented region. The derived features are reduced further shortened …

Go to article

Distributed Wireless Sensor Node Localization Based On Penguin Searchoptimization, Md Al Shayokh, Soo Young Shin Jan 2022

Distributed Wireless Sensor Node Localization Based On Penguin Searchoptimization, Md Al Shayokh, Soo Young Shin

Turkish Journal of Electrical Engineering and Computer Sciences

Wireless sensor networks (WSNs) have become popular for sensing areas-of-interest and performing assigned tasks based on information on the location of sensor devices. Localization in WSNs is aimed at designating distinct geographical information to the inordinate nodes within a search area. Biologically inspired algorithms are being applied extensively in WSN localization to determine inordinate nodes more precisely while consuming minimal computation time. An optimization algorithm belonging to the metaheuristic class and named penguin search optimization (PeSOA) is presented in this paper. It utilizes the hunting approaches in a collaborative manner to determine the inordinate nodes within an area of interest. …

Go to article

Cnn Based Sensor Fusion Method For Real-Time Autonomous Robotics Systems, Berat Yildiz, Aki̇f Durdu, Ahmet Kayabaşi, Mehmet Duramaz Jan 2022

Cnn Based Sensor Fusion Method For Real-Time Autonomous Robotics Systems, Berat Yildiz, Aki̇f Durdu, Ahmet Kayabaşi, Mehmet Duramaz

Turkish Journal of Electrical Engineering and Computer Sciences

Autonomous robotic systems (ARS) serve in many areas of daily life. The sensors have critical importance for these systems. The sensor data obtained from the environment should be as accurate and reliable as possible and correctly interpreted by the autonomous robot. Since sensors have advantages and disadvantages over each other they should be used together to reduce errors. In this study, Convolutional Neural Network (CNN) based sensor fusion was applied to ARS to contribute the autonomous driving. In a real-time application, a camera and LIDAR sensor were tested with these networks. The novelty of this work is that the uniquely …

Go to article

Fft Enabled Ecc For Wsn Nodes Without Hardware Multiplier Support, Utku Gülen, Selçuk Baktir Jan 2022

Fft Enabled Ecc For Wsn Nodes Without Hardware Multiplier Support, Utku Gülen, Selçuk Baktir

Turkish Journal of Electrical Engineering and Computer Sciences

ECC is a popular cryptographic algorithm for key distribution in wireless sensor networks where power efficiency is desirable. A power efficient implementation of ECC without using hardware multiplier support was proposed earlier for wireless sensor nodes. The proposed implementation utilized the number theoretic transform to carry operands to the frequency domain, and conducted Montgomery multiplication, in addition to other finite field operations, in that domain. With this work, we perform in the frequency domain only polynomial multiplication and use the fast Fourier transform to carry operands between the time and frequency domains. Our ECC implementation over $GF((2^{13}-1)^{13})$ on the MSP430 …

Go to article

Using Vertical Areas In Finite Set Model Predictive Control Of A Three-Level Inverter Aimed At Computation Reduction, Alireza Jaafari, Alireza Davari, Cristian Garcia, Jose Rodriguez Jan 2022

Using Vertical Areas In Finite Set Model Predictive Control Of A Three-Level Inverter Aimed At Computation Reduction, Alireza Jaafari, Alireza Davari, Cristian Garcia, Jose Rodriguez

Turkish Journal of Electrical Engineering and Computer Sciences

In power electronics applications, finite set model predictive control (FS-MPC) has proven to be a viable strategy. However, due to the high processing power required, using this technology in multilevel converters is difficult. This strategy, which is based on predicting the behavior of the system for all conceivable states, has an issue with a numerous of possible switching states. A recent and useful strategy for dealing with the problem is the limiting of calculations based on triangle regions. Despite its success, this method has several limitations, including the computation required to locate the right triangle and the boundary modes. In …

Go to article

An Approach For Performance Prediction Of Saturated Brushed Permanent Magnetdirect Current (Dc) Motor From Physical Dimensions, Rasul Tarvirdilu, Reza Zeinali, Hulusi̇ Bülent Ertan Jan 2022

An Approach For Performance Prediction Of Saturated Brushed Permanent Magnetdirect Current (Dc) Motor From Physical Dimensions, Rasul Tarvirdilu, Reza Zeinali, Hulusi̇ Bülent Ertan

Turkish Journal of Electrical Engineering and Computer Sciences

An analytical approach for performance prediction of saturated brushed permanent magnet direct current (DC) motors is proposed in this paper. In case of a heavy saturation in the stator back core of electrical machines, some flux completes its path through the surrounding air, and the conventional equivalent circuit cannot be used anymore. This issue has not been addressed in the literature. The importance of considering the effect of the flux penetrating the surrounding air is shown in this paper using finite element simulations and experimental results, and an analytical approach is proposed to consider this effect on magnet operating point …

Go to article

Application Of Long Short-Term Memory (Lstm) Neural Network Based On Deeplearning For Electricity Energy Consumption Forecasting, Mehmet Bi̇lgi̇li̇, Ni̇yazi̇ Arslan, Ali̇i̇hsan Şekerteki̇n, Abdulkadi̇r Yaşar Jan 2022

Application Of Long Short-Term Memory (Lstm) Neural Network Based On Deeplearning For Electricity Energy Consumption Forecasting, Mehmet Bi̇lgi̇li̇, Ni̇yazi̇ Arslan, Ali̇i̇hsan Şekerteki̇n, Abdulkadi̇r Yaşar

Turkish Journal of Electrical Engineering and Computer Sciences

Electricity is the most substantial energy form that significantly affects the development of modern life, work efficiency, quality of life, production, and competitiveness of the society in the ever-growing global world. In this respect, forecasting accurate electricity energy consumption (EEC) is fairly essential for any country?s energy consumption planning and management regarding its growth. In this study, four time-series methods; long short-term memory (LSTM) neural network, adaptive neuro-fuzzy inference system (ANFIS) with subtractive clustering (SC), ANFIS with fuzzy cmeans (FCM), and ANFIS with grid partition (GP) were implemented for the short-term one-day ahead EEC prediction. Root mean square error (RMSE), …

Go to article

A Futuristic Approach To Generate Random Bit Sequence Using Dynamic Perturbedchaotic System, Sathya Krishnamoorthi, Premalatha Jayapaul, Vani Rajasekar, Rajesh Kumar Dhanaraj, Celestine Iwendi Jan 2022

A Futuristic Approach To Generate Random Bit Sequence Using Dynamic Perturbedchaotic System, Sathya Krishnamoorthi, Premalatha Jayapaul, Vani Rajasekar, Rajesh Kumar Dhanaraj, Celestine Iwendi

Turkish Journal of Electrical Engineering and Computer Sciences

Most of the web applications require security which in turn requires random numbers. Pseudo-random numbers are required with good statistical properties and efficiency. Use of chaotic map to dynamically perturb another chaotic map that generates the random bit output is introduced in this work. Perturbance is introduced to improvise the chaotic behaviour of a base map and increase the periodicity. PRNG with this architecture is devised to generate random bit sequence from initial keyspace. The statistical properties of newly constructed PRNG are tested with NIST SP 800-22 statistical test suite and were shown to have good randomness. To ensure its …

Go to article

Motion-Aware Vehicle Detection In Driving Videos, Mehmet Kiliçarslan, Tansu Temel Jan 2022

Motion-Aware Vehicle Detection In Driving Videos, Mehmet Kiliçarslan, Tansu Temel

Turkish Journal of Electrical Engineering and Computer Sciences

This paper focuses on vehicle detection based on motion features in driving videos. Long-term motion information can assist in driving scenarios since driving is a complicated and dynamic process. The proposed method is a deep learning based model which processes motion frame image. This image merges both spatial (frame) and temporal (motion) information. Hence, the model jointly detects vehicles and their motion from a single image. The trained model on Toyota Motor Europe Motorway Dataset reaches 83% mean average precision (mAP). Our experiments demonstrate that the proposed method has a higher mAP than a tracking-based model. The proposed method runs …

Go to article

A Novel Energy Consumption Model For Autonomous Mobile Robot, Gürkan Gürgöze, İbrahi̇m Türkoğlu Jan 2022

A Novel Energy Consumption Model For Autonomous Mobile Robot, Gürkan Gürgöze, İbrahi̇m Türkoğlu

Turkish Journal of Electrical Engineering and Computer Sciences

In this study, a novel predictive energy consumption model has been developed to facilitate the development of tasks based on efficient energy consumption strategies in mobile robot systems. For the proposed energy consumption model, an advanced mathematical system model that takes into account all parameters during the motion of the mobile robot is created. The parameters of inclination, load, dynamic friction, wheel slip and speed-torque saturation limit, which are often neglected in existing models, are especially used in our model. Thus, the effects of unexpected disruptors on energy consumption in the real world environment are also taken into account. As …

Go to article

Automatically Classifying Familiar Web Users From Eye-Tracking Data:A Machine Learning Approach, Meli̇h Öder, Şükrü Eraslan, Yeli̇z Yesi̇lada Jan 2022

Automatically Classifying Familiar Web Users From Eye-Tracking Data:A Machine Learning Approach, Meli̇h Öder, Şükrü Eraslan, Yeli̇z Yesi̇lada

Turkish Journal of Electrical Engineering and Computer Sciences

Eye-tracking studies typically collect enormous amount of data encoding rich information about user behaviours and characteristics on the web. Eye-tracking data has been proved to be useful for usability and accessibility testing and for developing adaptive systems. The main objective of our work is to mine eye-tracking data with machine learning algorithms to automatically detect users' characteristics. In this paper, we focus on exploring different machine learning algorithms to automatically classify whether users are familiar or not with a web page. We present our work with an eye-tracking data of 81 participants on six web pages. Our results show that …

Go to article

Stability Regions In Time Delayed Two-Area Lfc System Enhanced By Evs, Ausnain Naveed, Şahi̇n Sönmez, Saffet Ayasun Jan 2022

Stability Regions In Time Delayed Two-Area Lfc System Enhanced By Evs, Ausnain Naveed, Şahi̇n Sönmez, Saffet Ayasun

Turkish Journal of Electrical Engineering and Computer Sciences

With the extensive usage of open communication networks, time delays have become a great concern in load frequency control (LFC) systems since such inevitable large delays weaken the controller performance and even may lead to instabilities. Electric vehicles (EVs) have a potential tool in the frequency regulation. The integration of a large number of EVs via an aggregator amplifies the adverse effects of time delays on the stability and controller design of LFC systems. This paper investigates the impacts of the EVs aggregator with communication time delay on the stability. Primarily, a graphical method characterizing stability boundary locus is implemented. …

Go to article

Temporal Bagging: A New Method For Time-Based Ensemble Learning, Göksu Tüysüzoğlu, Derya Bi̇rant, Volkan Kiranoğlu Jan 2022

Temporal Bagging: A New Method For Time-Based Ensemble Learning, Göksu Tüysüzoğlu, Derya Bi̇rant, Volkan Kiranoğlu

Turkish Journal of Electrical Engineering and Computer Sciences

One of the main problems associated with the bagging technique in ensemble learning is its random sample selection in which all samples are treated with the same chance of being selected. However, in time-varying dynamic systems, the samples in the training set have not equal importance, where the recent samples contain more useful and accurate information than the former ones. To overcome this problem, this paper proposes a new time-based ensemble learning method, called temporal bagging (T-Bagging). The significant advantage of our method is that it assigns larger weights to more recent samples with respect to older ones, so it …

Go to article

Evaluating The English-Turkish Parallel Treebank For Machine Translation, Onur Görgün, Olcay Taner Yildiz Jan 2022

Evaluating The English-Turkish Parallel Treebank For Machine Translation, Onur Görgün, Olcay Taner Yildiz

Turkish Journal of Electrical Engineering and Computer Sciences

This study extends our initial efforts in building an English-Turkish parallel treebank corpus for statistical machine translation tasks. We manually generated parallel trees for about 17K sentences selected from the Penn Treebank corpus. English sentences vary in length: 15 to 50 tokens including punctuation. We constrained the translation of trees by (i) reordering of leaf nodes based on suffixation rules in Turkish, and (ii) gloss replacement. We aim to mimic human annotator?s behavior in real translation task. In order to fill the morphological and syntactic gap between languages, we do morphological annotation and disambiguation. We also apply our heuristics by …

Go to article

An Active Contour Model Using Matched Filter And Hessian Matrix For Retinalvessels Segmentation, Mahtab Shabani, Hossein Pourghassem Jan 2022

An Active Contour Model Using Matched Filter And Hessian Matrix For Retinalvessels Segmentation, Mahtab Shabani, Hossein Pourghassem

Turkish Journal of Electrical Engineering and Computer Sciences

Medical image analysis, especially of the retina, plays an important role in diagnostic decision support tools. The properties of retinal blood vessels are used for disease diagnoses such as diabetes, glaucoma, and hypertension. There are some challenges in the utilization of retinal blood vessel patterns such as low contrast and intensity inhomogeneities. Thus, an automatic algorithm for vessel extraction is required. Active contour is a strong method for edge extraction. However, it cannot extract thin vessels and ridges very well. In this research, we propose an improved active contour method that uses discrete wavelet transform for energy minimization to solve …

Go to article

Stressed Or Just Running? Differentiation Of Mental Stress And Physical Activityby Using Machine Learning, Yekta Sai̇d Can Jan 2022

Stressed Or Just Running? Differentiation Of Mental Stress And Physical Activityby Using Machine Learning, Yekta Sai̇d Can

Turkish Journal of Electrical Engineering and Computer Sciences

Recently, modern people have excessive stress in their daily lives. With the advances in physiological sensors and wearable technology, people?s physiological status can be tracked, and stress levels can be recognized for providing beneficial services. Smartwatches and smartbands constitute the majority of wearable devices. Although they have an excellent potential for physiological stress recognition, some crucial issues need to be addressed, such as the resemblance of physiological reaction to stress and physical activity, artifacts caused by movements and low data quality. This paper focused on examining and differentiating physiological responses to both stressors and physical activity. Physiological data are collected …

Go to article

An Effective Prediction Method For Network State Information In Sd-Wan, Erdal Akin, Ferdi̇ Saraç, Ömer Aslan Jan 2022

An Effective Prediction Method For Network State Information In Sd-Wan, Erdal Akin, Ferdi̇ Saraç, Ömer Aslan

Turkish Journal of Electrical Engineering and Computer Sciences

In a software-defined wide area network (SD-WAN), a logically centralized controller is responsible for computing and installing paths in order to transfer packets among geographically distributed locations and remote users. Accordingly, this would necessitate obtaining the global view and dynamic network state information (NSI) of the network. Therefore, the centralized controller periodically collects link-state information from each port of each switch at fixed time periods. While collecting NSI in short periods causes protocol overhead on the controller, collecting in longer periods leads to obtaining inaccurate NSI. In both cases, packet losses are inevitable, which is not preferred for quality of …

Go to article

Efficacy Of Reported Issue Times As A Means For Effort Estimation, Paul Phillip Maclean Jan 2022

Efficacy Of Reported Issue Times As A Means For Effort Estimation, Paul Phillip Maclean

Graduate Theses, Dissertations, and Problem Reports (ETD)

Software effort is a measure of manpower dedicated to developing and maintaining and software. Effort estimation can help project managers monitor their software, teams, and timelines. Conversely, improper effort estimation can result in budget overruns, delays, lost contracts, and accumulated Technical Debt (TD). Issue Tracking Systems (ITS) have become mainstream project management tools, with over 65,000 companies using Jira alone. ITS are an untapped resource for issue resolution effort research. Related work investigates issue effort for specific issue types, usually Bugs or similar. They model their developer-documented issue resolution times using features from the issues themselves. This thesis explores a …

Go to article

Learning Representations For Human Identification, Sinan Sabri Jan 2022

Learning Representations For Human Identification, Sinan Sabri

Graduate Theses, Dissertations, and Problem Reports (ETD)

Long-duration visual tracking of people requires the ability to link track snippets (a.k.a. tracklets) based on the identity of people. In lack of the availability of motion priors or hard biometrics (e.g., face, fingerprint, or iris), the common practice is to leverage soft biometrics for matching tracklets corresponding to the same person in different sightings. A common choice is to use the whole-body visual appearance of the person, as determined by the clothing, which is assumed to not change during tracking. The problem is challenging because distinct images of the same person may look very different, since no restrictions are …

Go to article

A Cloud Computing-Based Dashboard For The Visualization Of Motivational Interviewing Metrics, E Jinq Heng Jan 2022

A Cloud Computing-Based Dashboard For The Visualization Of Motivational Interviewing Metrics, E Jinq Heng

Browse all Theses and Dissertations

Motivational Interviewing (MI) is an evidence-based brief interventional technique that has been demonstrated to be effective in triggering behavior change in patients. To facilitate behavior change, healthcare practitioners adopt a nonconfrontational, empathetic dialogic style, a core component of MI. Despite its advantages, MI has been severely underutilized mainly due to the cognitive overload on the part of the MI dialogue evaluator, who has to assess MI dialogue in real-time and calculate MI characteristic metrics (number of open-ended questions, close-ended questions, reflection, and scale-based sentences) for immediate post-session evaluation both in MI training and clinical settings. To automate dialogue assessment and …

Go to article

Core Point Pixel-Level Localization By Fingerprint Features In Spatial Domain, Xueyi Ye, Yuzhong Shen, Maosheng Zeng, Yirui Liu, Huahua Chen, Zhijing Zhao Jan 2022

Core Point Pixel-Level Localization By Fingerprint Features In Spatial Domain, Xueyi Ye, Yuzhong Shen, Maosheng Zeng, Yirui Liu, Huahua Chen, Zhijing Zhao

Computational Modeling & Simulation Engineering Faculty Publications

Singular point detection is a primary step in fingerprint recognition, especially for fingerprint alignment and classification. But in present there are still some problems and challenges such as more false-positive singular points or inaccurate reference point localization. This paper proposes an accurate core point localization method based on spatial domain features of fingerprint images from a completely different viewpoint to improve the fingerprint core point displacement problem of singular point detection. The method first defines new fingerprint features, called furcation and confluence, to represent specific ridge/valley distribution in a core point area, and uses them to extract the innermost Curve …

Go to article

Using Long Short-Term Memory Networks To Make And Train Neural Network Based Pseudo Random Number Generator, Aditya Harshvardhan Jan 2022

Using Long Short-Term Memory Networks To Make And Train Neural Network Based Pseudo Random Number Generator, Aditya Harshvardhan

Electronic Theses and Dissertations

Neural Networks have been used in many decision-making models and been employed in computer vision, and natural language processing. Several works have also used Neural Networks for developing Pseudo-Random Number Generators [2, 4, 5, 7, 8]. However, despite great performance in the National Institute of Standards and Technology (NIST) statistical test suite for randomness, they fail to discuss how the complexity of a neural network affects such statistical results. This work introduces: 1) a series of new Long Short- Term Memory Network (LSTM) based and Fully Connected Neural Network (FCNN – baseline [2] + variations) Pseudo Random Number Generators (PRNG) …

Go to article

C2 Microservices Api: Ch4rl3sch4l3m4gn3, Thai H. Nguyễn Jan 2022

C2 Microservices Api: Ch4rl3sch4l3m4gn3, Thai H. Nguyễn

School of Computer Science & Engineering Undergraduate Publications

In the 21st century, cyber-based attackers such as advance persistent threats are leveraging bots in the form of botnets to conduct a plethora of cyber-attacks. While there are several social engineering techniques used to get targets to unknowingly download these bots, it is the command-and-control techniques advance persistent threats use to control their bots that is of critical interest to the author. In this research paper, the author aims to develop a command-and-control microservice application programming interface infrastructure to facilitate botnet command-and-control attack simulations. To achieve this the author will develop a simple bot skeletal framework, utilize the latest …

Go to article

An Analysis On Network Flow-Based Iot Botnet Detection Using Weka, Cian Porteous Jan 2022

An Analysis On Network Flow-Based Iot Botnet Detection Using Weka, Cian Porteous

Dissertations

Botnets pose a significant and growing risk to modern networks. Detection of botnets remains an important area of open research in order to prevent the proliferation of botnets and to mitigate the damage that can be caused by botnets that have already been established. Botnet detection can be broadly categorised into two main categories: signature-based detection and anomaly-based detection. This paper sets out to measure the accuracy, false-positive rate, and false-negative rate of four algorithms that are available in Weka for anomaly-based detection of a dataset of HTTP and IRC botnet data. The algorithms that were selected to detect botnets …

Go to article

Dark Patterns: Effect On Overall User Experience And Site Revisitation, Deon Soul Calawen Jan 2022

Dark Patterns: Effect On Overall User Experience And Site Revisitation, Deon Soul Calawen

Dissertations

Dark patterns are user interfaces purposefully designed to manipulate users into doing something they might not otherwise do for the benefit of an online service. This study investigates the impact of dark patterns on overall user experience and site revisitation in the context of airline websites. In order to assess potential dark pattern effects, two versions of the same airline website were compared: a dark version containing dark pattern elements and a bright version free of manipulative interfaces. User experience for both websites were assessed quantitatively through a survey containing a User Experience Questionnaire (UEQ) and a System Usability Scale …

Go to article

Evaluating The Performance Of Vision Transformer Architecture For Deepfake Image Classification, Devesan Govindasamy Jan 2022

Evaluating The Performance Of Vision Transformer Architecture For Deepfake Image Classification, Devesan Govindasamy

Dissertations

Deepfake classification has seen some impressive results lately, with the experimentation of various deep learning methodologies, researchers were able to design some state-of-the art techniques. This study attempts to use an existing technology “Transformers” in the field of Natural Language Processing (NLP) which has been a de-facto standard in text processing for the purposes of Computer Vision. Transformers use a mechanism called “self-attention”, which is different from CNN and LSTM. This study uses a novel technique that considers images as 16x16 words (Dosovitskiy et al., 2021) to train a deep neural network with “self-attention” blocks to detect deepfakes. It creates …

Go to article

กระบวนการและแบบจำลองสำหรับการคัดกรองเพื่อการจัดการคุณภาพข้อมูลในคราวด์ซอร์สซิงแพลตฟอร์ม, กฤตย์ กังวาลพงศ์พันธุ์ Jan 2022

กระบวนการและแบบจำลองสำหรับการคัดกรองเพื่อการจัดการคุณภาพข้อมูลในคราวด์ซอร์สซิงแพลตฟอร์ม, กฤตย์ กังวาลพงศ์พันธุ์

Chulalongkorn University Theses and Dissertations (Chula ETD)

การเก็บรวบรวมข้อมูลด้วยคราวด์ซอร์สซิงเป็นวิธีที่โดยทั่วไปมีความเร็วมากกว่า มีต้นทุนต่ำกว่า และมีความหลากหลายมากกว่าวิธีการเก็บรวบรวมข้อมูลแบบอื่น ๆ อย่างไรก็ตาม คราวด์ซอร์สซิงอาจเผชิญกับปัญหาคุณภาพ เช่น การติดป้ายกำกับผิดหรือการนำมาใช้ในทางที่ไม่เหมาะสม ดังนั้น กระบวนการควบคุมคุณภาพเป็นสิ่งที่จำเป็นสำหรับแพลตฟอร์มคราวด์ซอร์สซิง วิทยานิพนธ์นี้ศึกษาค้นคว้าอุปสรรคและวิธีการแก้ไขที่เป็นไปได้ในการจัดการคุณภาพของผู้ใช้งานแพลตฟอร์มคราวด์ซอร์สซิง ส่วนแรกเน้นวิธีการเพิ่มกระบวนการในคราวด์ซอร์สซิง โดยศึกษา 3 วิธี ได้แก่ 1. งานที่จำเป็นต้องทำก่อน 2. คำถามมาตรฐานแบบทองคำ และ 3. การทำซ้ำของข้อมูล พบว่างานที่จำเป็นต้องทำก่อนเป็นสิ่งจำเป็นเพื่อคัดกรองให้ได้ผู้ปฏิบัติงานที่มีคุณภาพสูง โดยควรเน้นไปที่ลักษณะเฉพาะและรายละเอียดของงาน คำถามที่ตรวจสอบความสอดคล้องระหว่างงานดีกว่าคำถามแบบชัดเจนในการตรวจสอบด้วยคำถามมาตรฐานทองคำ ผู้ตรวจสอบข้อมูลคนเดียวอาจนำไปสู่การปรับปรุงคุณภาพข้อมูลได้มากที่สุด ส่วนที่สองคือ การใช้แบบจำลองการเรียนรู้ของเครื่องที่ใช้ข้อมูลพฤติกรรมในการทำนายคุณภาพของข้อมูล ซึ่งวิธีนี้ยังช่วยคัดกรองข้อมูลคุณภาพต่ำออกไปได้โดยไม่เสียทรัพยากรเพิ่มเติม

Go to article

การใช้รถโดยสารประจำทางเป็นโหนดที่ขอบในเครือข่ายยานพาหนะ, ณัฐนนท์ มานพ Jan 2022

การใช้รถโดยสารประจำทางเป็นโหนดที่ขอบในเครือข่ายยานพาหนะ, ณัฐนนท์ มานพ

Chulalongkorn University Theses and Dissertations (Chula ETD)

การเติบโตของเครือข่ายไร้สายแบบแอดฮอกบนยานพาหนะได้ทำให้เกิดการพัฒนาแอปพลิเคชันบนยานพาหนะต่าง ๆ มากมาย เพื่อตอบรับสนองต่อการเติบโตนี้โครงร่างระบบการคำนวณแบบขอบบนยานพาหนะจึงถูกพัฒนาขึ้นเพื่อมุ่งเน้นไปที่การติดตั้งโหนดที่ขอบที่มักติดตั้งที่สถานีรับส่งสัญญาณข้างทาง อย่างไรก็ตามการติดตั้งสถานีในพื้นที่ขนาดใหญ่ต้องพิจารณาให้ครอบคลุมพื้นที่การให้บริการมากที่สุด งานวิจัยนี้จึงได้นำเสนอโครงร่างระบบใหม่ชื่อว่า Buses as an Infrastructure ซึ่งได้มีการใช้งานให้รถโดยสารประจำทางเป็นโหนดที่ขอบในการให้บริการทรัพยากรในการคำนวณและบริการอื่น ๆ แก่ผู้ใช้งาน โดยงานวิจัยนี้ได้มีการใช้ข้อได้เปรียบของระบบขนส่งสาธารณะที่มีอยู่แล้วเพื่อลดค่าใช้จ่ายในการติดตั้งโหนดที่ขอบแบบดั้งเดิม อีกทั้งงานวิจัยนี้ยังได้เสนอฮิวริสติกอัลกอรึทึมสำหรับการคำนวณหาการติดตั้งโหนดที่ขอบบนรถโดยสารประจำทางโดยให้ลำดับความสำคัญแก่จำนวนงานที่เกิดขึ้นคู่กับการใช้เทคนิคการเลือก N ลำดับสูงสุด โดยได้ทำการทดลองบนสภาพแวดล้อมจำลองและบนชุดข้อมูลจริง ผลการทดลองเมื่อเทียบกับรูปแบบที่โหนดที่ขอบติดตั้งอยู่กับสถานีรับส่งสัญญาณข้างทางแสดงให้เห็นว่าฮิวริสติกอัลกอรึทึมที่นำเสนอสามารถให้จำนวนยานพาหนะที่โหนดที่ขอบสามารถให้บริการได้สูงขึ้นกว่า 6.08% - 52.20% และสามารถให้จำนวนยานพาหนะที่โหนดที่ขอบสามารถให้บริการได้สูงขึ้น 15.23% เมื่อเทียบกับรูปแบบที่โหนดที่ขอบติดตั้งอยู่กับสถานีรับส่งสัญญาณข้างทางบนสภาพแวดล้อมจำลองและให้ผลรวมของระยะเวลาที่ติดต่อสื่อสารกันได้สูงขึ้น 54.24% เมื่อเทียบกับรูปแบบที่โหนดที่ขอบติดตั้งอยู่กับสถานีรับส่งสัญญาณข้างทางบนชุดข้อมูลจริง

Go to article

บทบัญญัติของกฎหมายที่ใช้บังคับในคดีละเมิด: การวิเคราะห์คำพิพากษาศาลไทยด้วยกระบวนการเรียนรู้เชิงลึก, ทวีศักดิ์ ชูศรี Jan 2022

บทบัญญัติของกฎหมายที่ใช้บังคับในคดีละเมิด: การวิเคราะห์คำพิพากษาศาลไทยด้วยกระบวนการเรียนรู้เชิงลึก, ทวีศักดิ์ ชูศรี

Chulalongkorn University Theses and Dissertations (Chula ETD)

การประมวลผลเอกสารทางกฎหมายโดยใช้การประมวลผลภาษาธรรมชาติเป็นงานวิจัยที่น่าสนใจในการศึกษาอย่างมาก เนื่องจากการกำหนดหมวดกฎหมายสำหรับคดีในศาลมักต้องมีการปรึกษาหารือกับทนายความซึ่งมีค่าบริการที่สูงมาก ทำให้ประชาชนทั่วไปไม่สามารถเข้าถึงการใช้บริการดังกล่าวได้ จึงเป็นจุดเริ่มต้นในการจัดทำงานวิจัยนี้ ด้วยการสร้างระบบที่สามารถดึงข้อมูลส่วนกฎหมายที่เหมาะสมตามข้อเท็จจริงที่เกี่ยวข้องของคดีในศาล เพื่อให้บรรลุงานวิจัยนี้ ทางคณะผู้วิจัยได้รวบรวมชุดข้อมูลที่ครอบคลุมของคดีในศาลฎีกาจากประเทศไทย รวมถึงดึงข้อเท็จจริงจากเอกสารของโจทก์และจำเลยโดยใช้การผสมผสานระหว่างการเรียนรู้ของเครื่องและระบบที่ใช้กฎเป็นพื้นฐานเพื่อการวิเคราะห์การประมวลผลภาษาธรรมชาติเพิ่มเติม แนวทางที่ของงานวิจัยนี้นำเสนอมุ่งเน้นไปที่ระบบค้นคืนมาตราที่เกี่ยวข้องด้วยข้อมูลชุดฝึกที่มีจำนวนน้อย โดยใช้ข้อเท็จจริงของโจทก์เป็นข้อมูลเข้า ซึ่งระบบนี้จะสามารถจัดการกับมาตราต่างๆ ของกฎหมาย รวมถึงส่วนที่ไม่ค่อยพบหรือไม่ได้อยู่ในชุดการฝึกอบรม โดยที่ระบบจะทำงานได้ดีกว่ามาตรฐานพื้นฐาน โดยสรุป การวิจัยนี้มีเป้าหมายเพื่อสร้างระบบดึงข้อมูลส่วนกฎหมายที่เหมาะสมตามข้อเท็จจริงที่เกี่ยวข้องของคดีในศาลที่เข้าถึงได้และแม่นยำมากขึ้น ทางคณะผู้วิจัยหวังว่าจะลดความจำเป็นในการปรึกษาทนายความที่มีค่าบริการที่สูง และมอบเครื่องมือที่มีค่าสำหรับผู้เชี่ยวชาญด้านกฎหมายและบุคคลที่เกี่ยวข้องในกระบวนการพิจารณาคดีในศาล

Go to article

First Prev Next Last