Engineering Commons^™

Discussion On Identification Standard Of Coal-Measure Graphite, Cao Daiyong, Wang Lu, Zhu Wenqing, Wu Guoqiang, Wei Yingchun, Ning Shuzheng, Wang Guixiang, Xiao Jincheng, Xu Xiang, Liu Kang

Coal Geology & Exploration

The identification and classification of coal-measure graphite are the precondition and basic work of mineral geological exploration, as well as resource exploitation and utilization. The preparation of identification standards of coal-measure graphite should follow the principles of science, systematicness, applicability and operability. Coal graphitization and coalification is a continuous progression plus jump process, and the characteristics of coal-measure graphite, including the step evolution of macromolecular structure and the heterogeneity of material composition, leads to the complexity of ore identification. In order to establish the scientific and practical identification standards, and from the study of metallogenic mechanism and the demand of …

Spam Detection Using Machine Learning And Deep Learning, Olubodunde Agboola

LSU Doctoral Dissertations

Text messages are essential these days; however, spam texts have contributed negatively to the success of this communication mode. The compromised authenticity of such messages has given rise to several security breaches. Using spam messages, malicious links have been sent to either harm the system or obtain information detrimental to the user. Spam SMS messages as well as emails have been used as media for attacks such as masquerading and smishing ( a phishing attack through text messaging), and this has threatened both the user and service providers. Therefore, given the waves of attacks, the need to identify and remove …

Diagnosis Of Osteoporosis From Radiographs Using Image Processing And Deep Learning Techniques, Anu Shaju Areeckal

Technical Collection

A low cost prescreening tool for early diagnosis of osteoporosis using metacarpal radiogrammetry and texture analysis is developed and validated using sample data of Indian and Swiss population. Third metacarpal bone shaft and distal radius are automatically segmented from hand and wrist radiographs using marker-controlled watershed segmentation and intensity profile. Significant features are selected using statistical analysis and trained using various classifiers to classify healthy subjects and those with low bone mass. The accuracy of the developed prescreening tool can be further improved by using deep learning techniques in combination with handcrafted texture features.

A low cost technique to measure …

Tutorial: Neuro-Symbolic Ai For Mental Healthcare, Kaushik Roy, Usha Lokala, Manas Gaur, Amit Sheth

Publications

Artificial Intelligence (AI) systems for mental healthcare (MHCare) have been ever-growing after realizing the importance of early interventions for patients with chronic mental health (MH) conditions. Social media (SocMedia) emerged as the go-to platform for supporting patients seeking MHCare. The creation of peer-support groups without social stigma has resulted in patients transitioning from clinical settings to SocMedia supported interactions for quick help. Researchers started exploring SocMedia content in search of cues that showcase correlation or causation between different MH conditions to design better interventional strategies. User-level Classification-based AI systems were designed to leverage diverse SocMedia data from various MH conditions, …

Learning Pavement Surface Condition Ratings Through Visual Cues Using A Deep Learning Classification Approach., Waqar Shahid Qureshi, David Power, Joseph Mchale, Brian Mulry, Kieran Feighan, Dympna O'Sullivan

Articles

Pavement surface condition rating is an essential part of road infrastructure maintenance and asset management, and it is performed manually by the data analyst. The manual rating requires cognitive skills built through training and experience, which is quantitatively challenging and timeconsuming. This paper first analyses the complexity of the current manual visual rating system. This paper then investigates the suitability and robustness of a state-of-the-art convolutional neural network (CNN) classifier to automate the pavement surface condition index (PSCI) system used to rate pavement surfaces in Ireland. The dataset contains 3735 images of flexible asphalt pavements from Irish urban and rural …

Analysis Of Patch And Sample Size Effects For 2d-3d Cnn Models Using Multiplatform Dataset: Hyperspectral Image Classification Of Rosis And Jilin-1 Gp01 Imagery, Taşkin Kavzoğlu, Eli̇f Özlem Yilmaz

Turkish Journal of Electrical Engineering and Computer Sciences

Modern hyperspectral sensors provide a huge volume of data at spectral and spatial domains with high redundancy, which requires robust methods for analysis. In this study, 2D and 3D CNN models were applied to hyperspectral image datasets (ROSIS and Jilin-1 GP01) using varying patch and sample sizes to determine their combined impacts on the performance of deep learning models. Differences in classification performances in relation to particle and sample sizes were statistically analysed using McNemar?s test. According to the findings, raising the patch and sample size enhances the performance of the 2D/3D CNN model and produces more accurate results in …

Classification Of Special Vehicles On The Basis Of Commodity Nomenklature Of Foreign Economic Activity, K M. Karimkulov, U R. Khamroev, G R. Khamroev

Technical science and innovation

This article classifies the types of special vehicles as a commodity due to various technical changes in the technical and specific parameters of special vehicles, insufficient accuracy of attachment and parameters of additional vehicles. detected.As a result of the growing demand for special vehicles, the attachment of additional parts (exterior and interior design changes) by manufacturing plants has led to the classification of this vehicle into another category.As a result of the analysis, we can see in the analysis that from January 1, 2019 to 2022, a total of 11 others (others) CN FEA on 8704 commodity groups imported 8364 …

Ml-Based Online Traffic Classification For Sdns, Mohammed Nsaif, Gergely Kovasznai, Mohammed Abboosh, Ali Malik, Ruairí De Fréin

Articles

Traffic classification is a crucial aspect for Software-Defined Networking functionalities. This paper is a part of an on-going project aiming at optimizing power consumption in the environment of software-defined datacenter networks. We have developed a novel routing strategy that can blindly balance between the power consumption and the quality of service for the incoming traffic flows. In this paper, we demonstrate how to classify the network traffic flows so that the quality of service of each flow-class can be guaranteed efficiently. This is achieved by creating a dataset that encompasses different types of network traffic such as video, VoIP, game …

Development And Optimization Of Classification Neural Networks For Disaster-Assessment Using Unmanned Aerial Vehicle Systems, Maria Isabel Gonzalez Bocanegra

Honors College Theses

This research focuses on increasing the classification accuracy of convolutional neural networks in an autonomous network of unmanned aerial vehicles for transportation disaster management. The autonomous network of UAVs will allow first responders to optimize their rescue plans by providing relevant information on inaccessible roads. The research seeks to explore different methods to optimize the architecture of convolutional networks for the multiclass classification of disaster-damaged roads.

Transformer Fault Event Detection And Classification Using Pmus, Yadunandan Paudel

Theses and Dissertations

Transformer is one of the most reliable components in an electric power system, however its failure has huge opportunity costs for an electric utility. In this work, we detect transformer electrical faults promptly and accurately classify the fault types using voltage/current data from Phasor Measurement Units. Our work can also eliminate uncertainties which are inherent in traditional transformer fault diagnostic techniques like dissolved gas analysis. In this thesis, first, possible causes of transformer failures are discussed, and four common transformer electrical faults are identified. Second, a comprehensive simulation model for electrical faults is developed. Third, fast and efficient abrupt change …

Automated Quality Control For In-Situ Water Temperature Sensors, Leah S. Richardson

All Graduate Theses and Dissertations, Spring 1920 to Summer 2023

The identification of data not representative of the target subject for outdoor (in-situ) environmental sensors (bad data) is a topic that has been explored in the past. Many tools (such as data filters and computer models) have succeeded in providing an end user with properly identified incorrect data over 95% of the time. However, with the continuous increase in the use of automated data collection, a simple indication of the bad data may no longer provide the end user with enough information to reduce the amount of time that must be spent for manual quality control. The purpose of this …

Modeling Metastasis In Breast Cancer Patients Using Ehr Data, The Area Deprivation Index (Adi), And Machine Learning Models, Vishesh Patel

McKelvey School of Engineering Theses & Dissertations

Applying machine learning and statistical analysis on traditionally informatics problems is a growing area of research that can result in clinicians being better-able to predict disease outcomes and create more personalized levels of care. In this study, several machine learning models are used to model the likelihood of metastasis in breast cancer patients using a mix of data from the electronic health record and socioeconomic information derived from the Area Deprivation Index (ADI). Metastasis is a late-stage disease progression in a cancer diagnosis where a tumor spreads from its initial development point to another part of the body. In breast …

Classification And Phenological Staging Of Crops From In Situ Image Sequences By Deep Learning, Uluğ Bayazit, Deni̇z Turgay Altilar, Ni̇lgün Güler Bayazit

Turkish Journal of Electrical Engineering and Computer Sciences

Accurate knowledge of crop type information is not only valuable for verifying the declaration of farmers to obtain subsidy or insurance for the grown crop, but also for generating crop type maps that serve a variety of purposes in land monitoring and policy. On the other hand, accurate knowledge of crop phenological stage can help farm personnel apply fertilization and irrigation regimes on a timely basis. Although deep learning based networks have been applied in the past to classify the type and predict the phenological stage of crops from in situ images of fields, more advanced deep learning based networks, …

Identification Of Intrusive Massifs In The Nurata Mineralized Zones Based On Satellite Images, Samariddin Rabbimkulov, Abdulla Almordonov, Akmal Asadov, Abdimutal Tangirov

Technical science and innovation

The article presents the results of processing space images using PCA, ISODATA, K-Means and similar methods, as well as analysis based on GIS technologies. As a result of automatic and visual interpretation of the obtained images, intrusive complexes, linear and ring structures scattered throughout the Nurata region were identified. In particular, the result obtained by X-ray diffraction analysis proved that the composition of the 2R4G1B and 2R4G3B channels is one of the most effective methods for separating the intrusive massifs distributed in the region. Given that isolated intrusive massifs, linear and ring structures are directly related to gold mining zones …

A Machine Learning Framework For Identifying Molecular Biomarkers From Transcriptomic Cancer Data, Md Abdullah Al Mamun

FIU Electronic Theses and Dissertations

Cancer is a complex molecular process due to abnormal changes in the genome, such as mutation and copy number variation, and epigenetic aberrations such as dysregulations of long non-coding RNA (lncRNA). These abnormal changes are reflected in transcriptome by turning oncogenes on and tumor suppressor genes off, which are considered cancer biomarkers.

However, transcriptomic data is high dimensional, and finding the best subset of genes (features) related to causing cancer is computationally challenging and expensive. Thus, developing a feature selection framework to discover molecular biomarkers for cancer is critical.

Traditional approaches for biomarker discovery calculate the fold change for each …

The Analysis And Optimization Of Cnn Hyperparameters With Fuzzy Tree Modelfor Image Classification, Kübra Uyar, Şaki̇r Taşdemi̇r, İlker Ali̇ Özkan

Turkish Journal of Electrical Engineering and Computer Sciences

The meaningful performance of convolutional neural network (CNN) has enabled the solution of various state-of-the-art problems. Although CNNs achieve satisfactory results in computer-vision problems, they still have some difficulties. As the designed CNN models are deepened to achieve much better accuracy, computational cost and complexity increase. It is significant to train CNNs with suitable topology and training hyperparameters that include initial learning rate, minibatch size, epoch number, filter size, number of filters, etc. because the initialization of hyperparameters affects classification results. On the other hand, it is not possible to make a definite inference for the hyperparameter initialization and there …

Exploring The Concept Of The Digital Educator During Covid-19, Fernando Jimenez, Gracia Sanchez, Jose Palma, Luis Miralles-Pechuán, Juan A. Botia

Articles

T In many machine learning classification problems, datasets are usually of high dimensionality and therefore require efficient and effective methods for identifying the relative importance of their attributes, eliminating the redundant and irrelevant ones. Due to the huge size of the search space of the possible solutions, the attribute subset evaluation feature selection methods are not very suitable, so in these scenarios feature ranking methods are used. Most of the feature ranking methods described in the literature are univariate methods, which do not detect interactions between factors. In this paper, we propose two new multivariate feature ranking methods based on …

Uncertainty Estimation In Classification Of Mgnt Using Radiogenomics For Glioblastoma Patients, W. Farzana, Z. A. Shboul, A. Temtam, K. M. Iftekharuddin

Electrical & Computer Engineering Faculty Publications

Glioblastoma Multiforme (GBM) is one of the most malignant brain tumors among all high-grade brain cancers. Temozolomide (TMZ) is the first-line chemotherapeutic regimen for glioblastoma patients. The methylation status of the O6-methylguanine-DNA-methyltransferase (MGMT) gene is a prognostic biomarker for tumor sensitivity to TMZ chemotherapy. However, the standardized procedure for assessing the methylation status of MGMT is an invasive surgical biopsy, and accuracy is susceptible to resection sample and heterogeneity of the tumor. Recently, radio-genomics which associates radiological image phenotype with genetic or molecular mutations has shown promise in the non-invasive assessment of radiotherapeutic treatment. This study proposes a machine-learning framework …

Machine Learning In Requirements Elicitation: A Literature Review, Cheligeer Cheligeer, Jingwei Huang, Guosong Wu, Nadia Bhuiyan, Yuan Xu, Yong Zeng

Engineering Management & Systems Engineering Faculty Publications

A growing trend in requirements elicitation is the use of machine learning (ML) techniques to automate the cumbersome requirement handling process. This literature review summarizes and analyzes studies that incorporate ML and natural language processing (NLP) into demand elicitation. We answer the following research questions: (1) What requirement elicitation activities are supported by ML? (2) What data sources are used to build ML-based requirement solutions? (3) What technologies, algorithms, and tools are used to build ML-based requirement elicitation? (4) How to construct an ML-based requirements elicitation method? (5) What are the available tools to support ML-based requirements elicitation methodology? Keywords …

Life-Cycle Assessment Of Non-Domestic Building Stocks: A Meta-Analysis Of Current Modelling Methods, Julian Bischof, Aidan Duffy

Articles

Building stock models (BSMs) are essential for simulating the contributions of regional and national building sectors to climate change under different policy scenarios, and for identifying pathways to climate change mitigation. To date, BSMs have focused on the operational life-cycle impacts of domestic dwellings; there has been less emphasis either on non-domestic buildings (NDBs) or full life-cycle analysis. This paper provides a first review of the theory and practice of NDB stock modelling which considers life-cycle energy, emissions and costs. A meta-analysis of the literature was undertaken involving a structured search of relevant articles in key scientific repositories. 98 in-scope …

Computer Vision Based Classification Of Fruits And Vegetables For Self-Checkout At Supermarkets, Khurram Hameed

Theses: Doctorates and Masters

The field of machine learning, and, in particular, methods to improve the capability of machines to perform a wider variety of generalised tasks are among the most rapidly growing research areas in today’s world. The current applications of machine learning and artificial intelligence can be divided into many significant fields namely computer vision, data sciences, real time analytics and Natural Language Processing (NLP). All these applications are being used to help computer based systems to operate more usefully in everyday contexts. Computer vision research is currently active in a wide range of areas such as the development of autonomous vehicles, …

Deep Learning Inspired Feature Engineering For Classifying Tremor Severity, Ahmed Al Taee, Seyedehmarzieh Hosseini, Rami N. Khushaba, Tanveer Zia, Chin-Teng Lin, Adel Al-Jumaily

Research outputs 2022 to 2026

Bio-signals pattern recognition systems can be impacted by several factors with a potential to limit their associated performance and clinical translation. Among these factors, selecting the optimum feature extraction method, that can effectively exploit the interaction between the temporal and spatial information, is the most prominent. Despite the potential of deep learning (DL) models for extracting temporal, spatial, or temporal-spatial information, they are typically restricted by their need for a large amount of training data. The deep wavelet scattering transform (WST) is a relatively recent advancement within the DL literature to replace expensive convolution neural networks models with computationally less …

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 …