Computer Sciences Commons^™

Big Data Analytics Framework For Effective Higher Education Institutions, George Matto

Tanzania Journal of Engineering and Technology (TJET)

There has been an increased dependency on Information and Communication Technologies (ICTs) in undertaking various activities in Higher Education Institutions (HEIs) ecosystems. Because of that, huge volumes of data have increasingly been generated. There have been, for instance, considerable amounts of data generated through electronic platforms involved in students’ admission and registration process, students’ academic records management, teaching and learning data, curriculum related data, and several other administrative data. Analysis of data generated from these platforms stands to give students, lecturers, HEIs Management, policy makers and implementers, and other stakeholders useful insights that would help in improving HEIs’ effectiveness. Unfortunately, …

An Iot Based Worker Safety Helmet Using Cloud Computing Technology, Yuthriya Iddi Khamis, Mahmoud Alawi, Waheed Muhammad Sanya

Tanzania Journal of Engineering and Technology (TJET)

Miner safety and security is a major challenge around the world due to the exposure to toxic gases that are frequently released in underground mines. Miners' health is adversely affected primarily by toxic gases, which endanger the workers' lives. Furthermore, human sensory abilities do not detect these dangerous gases. As a result, this paper proposes a safety monitoring system that includes a temperature sensor, humidity sensor, and gas sensors to detect harmful gases and alert miners to those harmful gases using the smart helmet they wear. These gases are transmitted to the control station via the cloud using Internet of …

Learning From Machines: Insights In Forest Transpiration Using Machine Learning Methods, Morgan Thornwell

Dissertations and Theses

Machine learning has been used as a tool to model transpiration for individual sites, but few models are capable of generalizing to new locations without calibration to site data. Using the global SAPFLUXNET database, 95 tree sap flow data sites were grouped using three clustering strategies: by biome, by tree functional type, and through use of a k-means unsupervised clustering algorithm. Two supervised machine learning algorithms, a random forest algorithm and a neural network algorithm, were used to build machine learning models that predicted transpiration for each cluster. The performance and feature importance in each model were analyzed and compared …

Control And Planning For Mobile Manipulators Used In Large Scale Manufacturing Processes, Joshua T. Nguyen

LSU Master's Theses

Sanding operations in industry is one of the few manufacturing tasks that has yet to achieve automation. Sanding tasks require skilled operators that have developed a sense of when a work piece is sufficiently sanded. In order to achieve automation in sanding with robotic systems, this developed sense, or intelligence, that human operators have needs to be understood and implemented in order to achieve, at the minimum, the same quality of work. The system will also need to have the equivalent reach of a human operator and not be constrained to a single, small workspace. This thesis developed solutions for …

Control Implemented On Quantum Computers: Effects Of Noise, Nondeterminism, And Entanglement, Kip Nieman, Keshav Kasturi Rangan, Helen Durand

Chemical Engineering and Materials Science Faculty Research Publications

Quantum computing has advanced in recent years to the point that there are now some quantum computers and quantum simulators available to the public for use. In addition, quantum computing is beginning to receive attention within the process systems engineering community for directions such as machine learning and optimization. A logical next step for its evaluation within process systems engineering is for control, specifically for computing control actions to be applied to process systems. In this work, we provide some initial studies regarding the implementation of control on quantum computers, including the implementation of a single-input/single-output proportional control law on …

Improving The Factual Accuracy Of Abstractive Clinical Text Summarization Using Multi-Objective Optimization, Amanuel Alambo, Tanvi Banerjee, Krishnaprasad Thirunarayan, Mia Cajita

Computer Science and Engineering Faculty Publications

While there has been recent progress in abstractive summarization as applied to different domains including news articles, scientific articles, and blog posts, the application of these techniques to clinical text summarization has been limited. This is primarily due to the lack of large-scale training data and the messy/unstructured nature of clinical notes as opposed to other domains where massive training data come in structured or semi -structured form. Further, one of the least explored and critical components of clinical text summarization is factual accuracy of clinical summaries. This is specifically crucial in the healthcare domain, cardiology in particular, where an …

The Message Design Of Raiders Of The Lost Ark On The Atari 2600 & A Fan’S Map, Quick Start, And Strategy Guide, Miguel Ramlatchan, William I. Ramlatchan

Distance Learning Faculty & Staff Books

The message design and human performance technology in video games, especially early video games have always been fascinating to me. From an instructional design perspective, the capabilities of the technology of the classic game consoles required a careful balance of achievable objectives, cognitive task analysis, guided problem solving, and message design. Raiders on the Atari is an excellent example of this balance. It is an epic adventure game, spanning 13+ distinct areas, with an inventory of items, where those hard to find items had to be used by the player to solve problems during their quest (and who would have …

Consemblex: A Consensus-Based Transcriptome Assembly Approach That Extends Consemble And Improves Transcriptome Assembly, Richard Mwaba

School of Computing: Dissertations, Theses, and Student Research

An accurate transcriptome is essential to understanding biological systems enabling omics analyses such as gene expression, gene discovery, and gene-regulatory network construction. However, assembling an accurate transcriptome is challenging, especially for organisms without adequate reference genomes or transcriptomes. While several methods for transcriptome assembly with different approaches exist, it is still difficult to establish the most accurate methods. This thesis explores the different transcriptome assembly methods and compares their performances using simulated benchmark transcriptomes with varying complexity. We also introduce ConSemblEX to improve a consensus-based ensemble transcriptome assembler, ConSemble, in three main areas: we provide the ability to use any …

Circadiomics: Circadian Omic Web Portal, Muntaha Samad, Forest Agostinelli, Tomoki Sato, Kohei Shimaji, Pierre Baldi

Faculty Publications

Circadian rhythms are a foundational aspect of biology. These rhythms are found at the molecular level in every cell of every living organism and they play a fundamental role in homeostasis and a variety of physiological processes. As a result, biomedical research of circadian rhythms continues to expand at a rapid pace. To support this research, CircadiOmics (http://circadiomics.igb.uci.edu/) is the largest annotated repository and analytic web server for high-throughput omic (e.g. transcriptomic, metabolomic, proteomic) circadian time series experimental data. CircadiOmics contains over 290 experiments and over 100 million individual measurements, across >20 unique tissues/organs, and 11 different species. Users are …

A Monte Carlo Framework For Incremental Improvement Of Simulation Fidelity, Damian Lyons, James Finocchiaro, Misha Novitsky, Chris Korpela

Faculty Publications

Robot software developed in simulation often does not be- have as expected when deployed because the simulation does not sufficiently represent reality - this is sometimes called the `reality gap' problem. We propose a novel algorithm to address the reality gap by injecting real-world experience into the simulation. It is assumed that the robot program (control policy) is developed using simulation, but subsequently deployed on a real system, and that the program includes a performance objective monitor procedure with scalar output. The proposed approach collects simulation and real world observations and builds conditional probability functions. These are used to generate …

Spiking Neural Networks And Their Applications: A Review, Kashu Yamazaki, Viet-Khao Vo-Ho, Darshan Bulsara, Ngan Le

Computer Science and Computer Engineering Faculty Publications and Presentations

The past decade has witnessed the great success of deep neural networks in various domains. However, deep neural networks are very resource-intensive in terms of energy consumption, data requirements, and high computational costs. With the recent increasing need for the autonomy of machines in the real world, e.g., self-driving vehicles, drones, and collaborative robots, exploitation of deep neural networks in those applications has been actively investigated. In those applications, energy and computational efficiencies are especially important because of the need for real-time responses and the limited energy supply. A promising solution to these previously infeasible applications has recently been given …

Robust Sensor Design For The Novel Reduced Models Of The Mead-Marcus Sandwich Beam Equation, Ahmet Aydin

Masters Theses & Specialist Projects

Novel space-discretized Finite Differences-based model reductions are proposed for the partial differential equations (PDE) model of a multi-layer Mead-Marcus-type beam with (i) hinged-hinged and (ii) clamped-free boundary conditions. The PDE model describes transverse vibrations for a sandwich beam whose alternating outer elastic layers constrain viscoelastic core layers, which allow transverse shear. The major goal of this project is to design a single boundary sensor, placed at the tip of the beam, to control the overall dynamics on the beam.

For (i), it is first shown that the PDE model is exactly observable by the so-called nonharmonic Fourier series approach. However, …

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 …

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 …

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 …

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 …

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 …

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 …

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 …

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 …

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 …

Learning Term Weights By Overfitting Pairwise Ranking Loss, Ömer Şahi̇n, İlyas Çi̇çekli̇, Gönenç Ercan

Turkish Journal of Electrical Engineering and Computer Sciences

A search engine strikes a balance between effectiveness and efficiency to retrieve the best documents in a scalable way. Recent deep learning-based ranker methods are proving to be effective and improving the state-of-the-art in relevancy metrics. However, as opposed to index-based retrieval methods, neural rankers like bidirectional encoder representations from transformers (BERT) do not scale to large datasets. In this article, we propose a query term weighting method that can be used with a standard inverted index without modifying it. Query term weights are learned using relevant and irrelevant document pairs for each query, using a pairwise ranking loss. The …

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 …

Automated Question Generation And Question Answering From Turkish Texts, Fati̇h Çağatay Akyön, Ali̇ Devri̇m Eki̇n Çavuşoğlu, Cemi̇l Cengi̇z, Si̇nan Onur Altinuç, Alpteki̇n Temi̇zel

Turkish Journal of Electrical Engineering and Computer Sciences

While exam-style questions are a fundamental educational tool serving a variety of purposes, manual construction of questions is a complex process that requires training, experience and resources. Automatic question generation (QG) techniques can be utilized to satisfy the need for a continuous supply of new questions by streamlining their generation. However, compared to automatic question answering (QA), QG is a more challenging task. In this work, we fine-tune a multilingual T5 (mT5) transformer in a multitask setting for QA, QG and answer extraction tasks using Turkish QA datasets. To the best of our knowledge, this is the first academic work …

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 …

A Deep Learning Based System For Real-Time Detection And Sorting Of Earthworm Cocoons, Ali̇ Çeli̇k, Si̇nan Uğuz

Turkish Journal of Electrical Engineering and Computer Sciences

Vermicompost, created by earthworms after eating and digesting organic waste, plays an important role as an organic fertiliser in sustainable agriculture. In this study, a deep learning-based smart system was developed to separate earthworm cocoons used in the production of vermicompost from the compost and return it to production. In the first stage of the study, a dataset containing 1000 images of cocoons was created. The cocoons in each image were labeled and training was performed using a deep learning architecture, one-stage and two-stage models. The models were trained over 2000 epochs with a learning rate of 0.01. From the …

Design And Implementation Of A Bioinspired Leaf Shaped Hybrid Rectenna As A Green Energy Manufacturing Concept, Kayhan Çeli̇k, Erol Kurt

Turkish Journal of Electrical Engineering and Computer Sciences

In this communication, the novel low cost hybrid energy harvester combining rectifying antenna with the solar cell for feeding the low power energy systems are reported. The bioinspired leaf shaped monopole antenna is designed to work in the most used communication frequency bands such as GSM-1800, UMTS-2100, WIFI-2.45 and LTE-2.65 GHz for the energy harvesting purposes and microstrip low pass filter is also added on the feeding line for the second harmonic rejection for increasing the efficiency of the harvester. The solar cell is placed on the ground plane of the designed leaf shaped antenna for using volumetric space efficiently …

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 …

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 …

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 …