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Full-Text Articles in Artificial Intelligence and Robotics

Operational Structural Damage Identification Using De-Noised Modal Feature In Machine Learning, Chen Shilei Feb 2021

Operational Structural Damage Identification Using De-Noised Modal Feature In Machine Learning, Chen Shilei

Student Works (2020-2029)

Structural damage can severely affect the safety and functionality of the structure and lead to economic loss. Vibration-based structural damage detection has raised continuous interest over the decades, as a non-destructive way to provide warnings and predict certain faults at early stages. Compared with conventional modal parameters such as the natural frequency and mode shape, the upstream modal data, namely the frequency response function (FRF), can be a better alternative, because it is rich in modal information and can be easily obtained. However, the FRF is usually measured through experimental modal analysis (EMA) when the test object is in shut-down …

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Improving Robot Darwinian Particle Swarm Optimization Using Quantum-Behaved Swarm Theory For Robot Exploration And Communication, Duaa Mehiar Feb 2021

Improving Robot Darwinian Particle Swarm Optimization Using Quantum-Behaved Swarm Theory For Robot Exploration And Communication, Duaa Mehiar

Student Works (2020-2029)

Despite the significance of the Robotic Darwinian Particle Swarm Optimization (RDPSO) algorithm on swarm-robot exploration and communication, there remain notable gaps such as premature and slow convergence, collisions between robots, and communication breaks and constraints. The quantum computing theory has several advantages that can improve the searching capabilities of PSO-based algorithms. However, there has yet an attempt to adopt quantum behaviour onto robot-based system such as the RDPSO. In this study, a new algorithm called the Quantum Robot Darwinian Particle Swarm Optimization (QRDPSO) is contributed with the hypothesis that quantum behaving particles can address the RDPSO main gaps; i.e. to …

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Deep Learning-Based Breast Cancer Detection And Classification Using Histopathology Images, Ghulam Murtaza Feb 2021

Deep Learning-Based Breast Cancer Detection And Classification Using Histopathology Images, Ghulam Murtaza

Student Works (2020-2029)

Cancer disease is drastically increasing worldwide over the past few years. Among all types of cancers in women, breast cancer (BrC) is the main cause of abnormal deaths. For a confident diagnosis of BrC, histopathology (Hp) images are usually suggested by the doctors. BrC detection is a diagnostic test for benign (non-cancerous) and malignant (cancerous) breast tumors (BrT). Once the BrT is diagnosed, then it needs to be classified for subtypes of benign and malignant to start specific treatment. Several studies developed BrC detection and classification models using Hp images. However, the existing models required high computational resources, long training …

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Fractal And Edge-Based Techniques For Kidney Enhancement And Segmentation On Magnetic Resonance Images (Mri), Alaá Rateb Mahmoud Al-Shamasneh Dec 2020

Fractal And Edge-Based Techniques For Kidney Enhancement And Segmentation On Magnetic Resonance Images (Mri), Alaá Rateb Mahmoud Al-Shamasneh

Student Works (2020-2029)

Recently, many rapid developments in digital medical imaging have made further contributions to healthcare systems. However, the segmentation of regions of interest in medical images plays a vital role in assisting doctors in their medical diagnoses and for the early detection of disease. Since health issues related to the kidneys are increasing exponentially, this thesis focused on developing methods for the segmentation of MRI images of the kidney. Kidney images frequently suffer from low contrast, low resolution and noise, and are blur. Hence, it is necessary to enhance the images in order to improve the segmentation. Therefore, the current thesis …

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An Enhancement Of Age And Gender Classification Accuracy With Hybrid Handcrafted And Deep Features Using Hierarchical Extreme Learning Machine, Mohammad Javidan Darugar Sep 2020

An Enhancement Of Age And Gender Classification Accuracy With Hybrid Handcrafted And Deep Features Using Hierarchical Extreme Learning Machine, Mohammad Javidan Darugar

Student Works (2020-2029)

Age and gender classification are some of the essential algorithms that have many use cases in our everyday life. For example, in robotics, field robots can interact with a human base on their gender in data analysis, to have statistics about age and gender of audiences in social events, YouTube video analysis, and many other applications. In this research, we have addressed limitations in deep neural networks, which by overcoming this limitation, we can gain better accuracy and performance. Our study has several other possible applications which are not limited only to age and gender classification. This dissertation is about …

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Solar Power Forecasting Using Wavelet Transform And Machine Learning Approaches, Abdullah Nor Azliana Sep 2020

Solar Power Forecasting Using Wavelet Transform And Machine Learning Approaches, Abdullah Nor Azliana

Student Works (2020-2029)

Generation of photovoltaic (PV) power is intermittent in nature and integration of PV system into the grid system causes an imbalanced power production and power demand. One of the efforts to reduce this problem is to forecast the generation of solar power in the PV system. Solar power forecasting requires the collection of solar power and meteorological data. Hence, this work collected solar power data and various meteorological data (global radiation, tilted radiation, temperature surrounding, humidity surrounding, PV module/ PV panel temperature and wind speed) from Universiti Teknikal Malaysia Melaka (UTeM). A pre-processing process is carried out to ensure that …

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Waste Cooking Oil Classification Using Artificial Intelligence Technology, Kar Sin Lau Aug 2020

Waste Cooking Oil Classification Using Artificial Intelligence Technology, Kar Sin Lau

Student Works (2020-2029)

Palm oil – one of the most common edible oil consumed in Malaysia. It is because Malaysia is one of the countries which supply palm oil to the global market and it is cheap to obtain for the consumer in Malaysia. Most of the Malaysian consume it via food preparation such as deep-frying and cooking. However, due to widely available for Malaysians, consumers also lacking awareness in dealing after using the edible oil. Most of the household consumers discard excess waste cooking oil (WCO) into sewage and with courtesy, some of them stored them in containers and sell to NGOs. …

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Data Mining For Structural Damage Identification Using Hybrid Artificial Neural Network Based Algorithm For Beam And Slab Girder, Gordan Meisam Aug 2020

Data Mining For Structural Damage Identification Using Hybrid Artificial Neural Network Based Algorithm For Beam And Slab Girder, Gordan Meisam

Student Works (2020-2029)

One of the approaches for structural health monitoring (SHM) consists of two major components, i.e. a network of sensors to collect the response data and an extraction method to obtain information on the structural health condition. Data mining (DM) is a novel data extraction technology which can employ for development of inverse analysis. Implementation of DM techniques in different areas of civil engineering has recently given very good results. However, application of DM in SHM is not used as much as expected, thus, many challenges are still ahead. Therefore, it is necessary to develop the applicability of DM in SHM. …

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Efficient Vehicle Routing Optimization For Autistic Users, Mohammed Shabalah Abdulrahman Hasan Jul 2020

Efficient Vehicle Routing Optimization For Autistic Users, Mohammed Shabalah Abdulrahman Hasan

Student Works (2020-2029)

In recent years, daily life without a vehicle would be impossible. As an inevitable result, the number of vehicles on the road increases day by day in various large cities around the world. The increased number of vehicles is a big concern because it causes a lot of traffic congestions, especially during peak hours. Besides, there has been a rapid rise of on-demand Ride-Hailing Services (RHSs), such as Grab, Uber, EzCab, and MyCar, etc. This allows passengers with smartphones to place trip requests and assign them to drivers according to requester’s location and drivers' availability. In consequence, efficient routing algorithms …

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Identification Of Microscopy Cell Images By Using Convolutional Neural Network Application, Ajis Norfatin Farisya Jul 2020

Identification Of Microscopy Cell Images By Using Convolutional Neural Network Application, Ajis Norfatin Farisya

Student Works (2020-2029)

Breast cancer has been the major factor of cancer death and the second main cause of women’s deaths in the world. The false positive results of this cancer cell detection during the screening test leads to false treatment and emotional disturbance of the patients. Thus, breast cancer cell lines (MCF7) is used as the microscopy image samples together with the Human Bone Osteosarcoma Epithelial Cells (U2OS), and Human Hepatocyte as control to study the effectiveness of convolutional neural network (CNN) as a method of image recognition. The objectives of this study are to determine the ability of convolutional neural network …

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Unsupervised Monocular Depth Estimation With Multi-Scale Structural Similarity Powered Loss Function, Kohan Ali Jul 2020

Unsupervised Monocular Depth Estimation With Multi-Scale Structural Similarity Powered Loss Function, Kohan Ali

Student Works (2020-2029)

Depth Estimation refers to a set of techniques and algorithms that aim to obtain a representation of spatial information of a scene. Nowadays specific hardware such as sensors, radars and multiple-view-recording cameras are being used in order to acquire depth data of a scene. Modern approaches use deep learning to address this task by trying to learn depth information in a supervised manner. However, this approach requires a large amount ground-truth data for a particular scene so that a model can be trained successfully. Also preparing ground-truth data for a range of environments is a challenging and expensive task to …

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Integrating Finance Dictionary In Lexicon-Based Approach With Machine Learning Algorithm To Analyse The Impact Of Opec News Sentiment On Financial Market, Ling Wu Jun 2020

Integrating Finance Dictionary In Lexicon-Based Approach With Machine Learning Algorithm To Analyse The Impact Of Opec News Sentiment On Financial Market, Ling Wu

Student Works (2020-2029)

Since last few decades, machine learning algorithm which trains computers to learn from experience, is one of the most rapidly developing techniques which settles in the intersection research field of statistics and computer science. This research aims to build a properly trained machine learning classifier to study the impact of Organization of Petroleum Exporting Countries (OPEC) news sentiment on stock prices of six Malaysian public listed companies (energy sector) in the main board of Bursa Malaysia. The data used in this research are collected during the period 2012-2017. To carry out the research, firstly, lexicon-based approach is used to analyze …

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Vision And Sensor-Based Signer-Independent Framework For Arabic Sign Language Recognition, Al-Shamayleh Ahmad Sami Abd Alkareem Jun 2020

Vision And Sensor-Based Signer-Independent Framework For Arabic Sign Language Recognition, Al-Shamayleh Ahmad Sami Abd Alkareem

Student Works (2020-2029)

Hearing and speech-impairment disability is widespread throughout the world. At present, 15 million people have this disability in the Arab world, and about 86% of them come from low- and middle-income countries. Meanwhile, sign language (SL) can be classified into standard Arabic sign language (ArSL) and local Arabic sign language (LArSL). ArSL is the formal standard and is the more acceptable SL in the Arab world; it is also considered as the medium of instructions for schools and universities as well as television news, shows and programmes. With the absence of usable ArSL recognition (ArSLR) platforms, hearing- and speech-impaired people …

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Partial Discharge Classification For Xlpe Cable Joints Using K Nearest Neighbors Algorithm, Mohd Salleh Muhammad Shairazi May 2020

Partial Discharge Classification For Xlpe Cable Joints Using K Nearest Neighbors Algorithm, Mohd Salleh Muhammad Shairazi

Student Works (2020-2029)

Due to excellent mechanical and electrical properties, cross-linked polyethylene (XLPE) cables are commonly used in the power industry. However, cable joints are the weakest part of XLPE cables and are susceptible to insulation failures. Cable joint insulation breakup can cause large losses for power companies. It is therefore necessary to evaluate the consistency of the insulation for early detection of insulation failure. It is known that there is a link between the partial discharge (PD) and the quality of the insulation. PD analysis is an important tool for assessing the quality of insulation in cable joints. In this study, XLPE …

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Classification Of Labour Pain Using Electroencephalogram Signal Based On Wavelet Method, Chong Yeh Sai May 2020

Classification Of Labour Pain Using Electroencephalogram Signal Based On Wavelet Method, Chong Yeh Sai

Student Works (2020-2029)

Electroencephalogram (EEG) is the recording of electrical activity of the cerebral cortex through electrodes placed on the scalp. EEG is used to acquire neurophysiological signals for application in clinical diagnosis and brain computer interface (BCI). However, in practical settings the EEG signals are often contaminated by signal artifacts known as the biological and environmental artifacts. These artifacts degrade EEG signals, thereby obstructing clinical diagnosis or BCI applications by distorting the observed power spectrum. Procedures for automated removal of EEG artifacts are frequently sought after in pre-processing and filtering of the EEG signals. In recent years, a combination of independent component …

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Energy-Efficient Data Transmission With Clustering And Compressive Sensing In Wireless Sensor Networks, Alagirisamy Mukil May 2020

Energy-Efficient Data Transmission With Clustering And Compressive Sensing In Wireless Sensor Networks, Alagirisamy Mukil

Student Works (2020-2029)

One of the most important application of wireless sensor network is environmental monitoring. The application involves lifetime of sensor nodes for longer duration associating its energy module. Wireless sensor nodes deployed in sensing field aggregate enormous amount of sensed data and transfer them to the sink. The inherent limitation of energy carried within the battery of sensor nodes fetches extreme difficulty to acquire adequate network lifetime, becoming a bottleneck in forwarding data to sink. Hence the motivation is to reduce the amount of data transfer and attain energy efficiency. This is achieved by clustering and compressive sensing techniques. First objective …

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Supervised Optimal Decision Machine Learning Approach To Class- And Method-Level Data Preprocessing Towards Effective Software Defect Prediction, Felix Ebubeogu Amarachukwu May 2020

Supervised Optimal Decision Machine Learning Approach To Class- And Method-Level Data Preprocessing Towards Effective Software Defect Prediction, Felix Ebubeogu Amarachukwu

Student Works (2020-2029)

Software defect prediction provides actionable outputs to software teams while contributing to industrial success. Therefore, predicting the number of defects in a new version of software at both the class and method levels is an important goal of defect prediction studies to assist software teams in optimizing their test efforts towards improving software quality. However, despite remarkable achievements in defect prediction, the quality of the data applied in defect prediction studies has been a major concern, with related quality issues leading to numerous contradictory findings in machine learning research. In addition, a demonstrated approach for predicting the number of defects …

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Classification Of Dividend News Based On The Movement Of The Share Market Prices Of Public Listed Companies In Bursa Malaysia, Vijaya Kumar Shubana May 2020

Classification Of Dividend News Based On The Movement Of The Share Market Prices Of Public Listed Companies In Bursa Malaysia, Vijaya Kumar Shubana

Student Works (2020-2029)

Stock market is naturally complex and plays a major role in towards the nation’s growth. However, the performance of a company in stock market varies due to many influences but not limited to economics, political and financial related news. This study attempts to classify the share market dividend news announcement in Bursa Malaysia based on the pattern of share market price. Samples including five hundred (500) observations of dividend news from forty-seven (47) listed companies in Bursa Malaysia during the period of 2000 to 2018 are used in this study. There are three (3) main objectives in this study which …

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A Semi-Automatic Integrated Framework For Non-English Sentiment Lexicons, Abdullah Kaity Mohammed Salem Apr 2020

A Semi-Automatic Integrated Framework For Non-English Sentiment Lexicons, Abdullah Kaity Mohammed Salem

Student Works (2020-2029)

There has been significant growth in social media networks in the last few years. Posting opinions and messages on social networking websites has become a popular activity on the Internet. The data sources are necessary for business intelligence and market analytics, as human opinions form a major indicator of human desires and behaviour. This has resulted in the development of a new study field called sentiment analysis. This includes the analysis, evaluation and interpretation of the opinions with the help of text mining and Natural Language Processing (NLP) processes, for identifying the text polarity, as positive, neutral or negative. It …

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Multi-Tier Classification Based On Sentiment, Type, Emotion And Purpose For Online Diabetes Community, Ratan Singh Wandeep Kaur Apr 2020

Multi-Tier Classification Based On Sentiment, Type, Emotion And Purpose For Online Diabetes Community, Ratan Singh Wandeep Kaur

Student Works (2020-2029)

The evolution of social media platforms has created a niche for users to increasingly turn to such sites in order to share and exchange health related information. Facebook being one of the largest social networking sites has only encouraged such exchange thus mounting to a sheer amount of data that is hidden within unstructured text. The aim of this research is to propose a multi-tier classification based on sentiment, type, emotion and purpose (STEP) to classify data collected from diabetes community within Facebook. There are three tiers within the proposed STEP framework namely type, purpose and sentiment (and emotion within …

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Sentiment Analysis For Airline Services On Twitter Using Deep Learning With Word Embedding, Nour El Daim El Khalifa Mawada Mohamed Feb 2020

Sentiment Analysis For Airline Services On Twitter Using Deep Learning With Word Embedding, Nour El Daim El Khalifa Mawada Mohamed

Student Works (2020-2029)

The use of social media platform in the airline industries have increased rapidly to allow analysis introduce the quality and performance of the services. The role of Sentiment Analysis (SA) is to classify people's opinions into different categories, such as positive and negative from text, using existing algorithms. However, existing approaches such as the Bag of Words (BOW) model is frequently used for text classification, where a document is mapped to a feature vector before the construction of the actual model, using machine learning techniques, like Logistical Regression and Support Vector algorithms. This problem has led to low accuracy in …

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Short-Term Electricity Price Forecasting In Deregulated Electricity Market Based On Enhanced Artificial Intelligence Techniques, Pourdaryaei Alireza Feb 2020

Short-Term Electricity Price Forecasting In Deregulated Electricity Market Based On Enhanced Artificial Intelligence Techniques, Pourdaryaei Alireza

Student Works (2020-2029)

Electricity price forecasting is considered as one of prime factors for operation, planning and scheduling of price-setter market participants. However, possessing time variant, non-linear and non-stationary behaviors make the electricity price a complex signal. The main challenge in this area is providing highly accurate and efficient day-ahead price forecasting. A suitable feature selection technique, which is able to model the interacting features and nonlinearities of the forecast processes, is still required although researches have been performed for day-ahead forecasting. In this research, a hybrid electricity price forecasting methodology is proposed using two-stage feature selection method and optimization using adaptive neuro-fuzzy …

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The Evolving Fuzzy Clustering Approach For Discriminating Neutron And Gamma-Ray Pulses, Shirkhorshidi Ali Seyed Jan 2020

The Evolving Fuzzy Clustering Approach For Discriminating Neutron And Gamma-Ray Pulses, Shirkhorshidi Ali Seyed

Student Works (2020-2029)

Having a significant amount of data is not useful unless the data can be processed for extracting knowledge and information. One of the elementary steps in crunching data is to break it down into groups. When the data is small and collected in a controlled manner, and when the training data is appropriately labelled, the trivial approach is to use supervised learning to perform the grouping. Supervised methods need training data and information about groups beforehand; however, in the current reality, with an avalanche of data, this information is not available. Nevertheless, the need for grouping data remains. Clustering, as …

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Feature Extraction And Description For Retinal Fundus Image Registration, Ramli Roziana Jan 2020

Feature Extraction And Description For Retinal Fundus Image Registration, Ramli Roziana

Student Works (2020-2029)

Retinal fundus image registration (RIR) is performed to align two or more fundus images. A general framework of a feature-based RIR technique comprises of preprocessing, feature extraction, feature descriptor, matching and estimating geometrical transformation. The RIR is mainly performed for super-resolution, image mosaicking and longitudinal study applications to assist diagnosis and monitoring retinal diseases. Registering image pair from these applications involve a combination of challenges such as overlapping area and rotation between images. The challenges of the overlapping area and rotation can be addressed at feature extraction and feature descriptor stages of the feature-based RIR technique, respectively. To address the …

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An Intra-Severity Classification And Adaptation Technique To Improve Dysarthric Speech Recognition Accuracy, Al-Qatab Bassam Ali Qasem Jan 2020

An Intra-Severity Classification And Adaptation Technique To Improve Dysarthric Speech Recognition Accuracy, Al-Qatab Bassam Ali Qasem

Student Works (2020-2029)

Dysarthria is a motor speech impairment at the neurological and/or muscular levels that caused difficulty in pronouncing words clearly. Automatic speech recognition (ASR) system is increasingly applied as assistive technology to aid an individual with physical disability particularly the speech impaired community such as dysarthria speakers. However, the development of an effective ASR system is hindered by the data sparsity, either in the coverage of the language or the size of the existing speech databases. The speaker adaptation (SA) technique is one of the solutions to overcome the data sparsity issue of ASR for dysarthric speakers. Our proposed method introduces …

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An Implementation Of Natural Language User Interface In Smart Home System., Ngoi Se Keng Nov 2010

An Implementation Of Natural Language User Interface In Smart Home System., Ngoi Se Keng

Student Works (2010-2019)

Smart Home System has facilitated the control of home appliances through computer system. However, the physical interactions between the user and existing graphical user interface such as pointing and clicking button could be a challenge to elderly, whom are generally having vision and motor impairment. This project specifically focuses on the development of a user interface that does not require physical interaction. In this regard, a prototype smart home system namely Natural Language Instruction Driven System (NLIDS) that employed natural language (speech) based user interface has been developed. The prototype system consists of three modules, which are Input & Response …

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Inversion Of 2d And 3d Dc Resistivity Imaging Data Forhigh Contrast Geophysical Regions Using Artificial Neuralnetworks., Ahmad Neyamadpour Jan 2010

Inversion Of 2d And 3d Dc Resistivity Imaging Data Forhigh Contrast Geophysical Regions Using Artificial Neuralnetworks., Ahmad Neyamadpour

Student Works (2010-2019)

In electrical resistivity imaging surveys, the field data along a profile are normally acquired as a subsurface distribution of apparent resistivity. One common method to obtain the true resistivity distribution is by inverting the apparent resistivity values. However, the inversion of DC resistivity imaging data is complex due to its non-linearity. This is especially true for regions with high resistivity contrast. For the complicated subsurface structure, especially when regions of high resistivity contrast exist, a conventional inversion technique based on least squares methods may not be able to invert the DC resistivity data with adequate accuracy. Therefore, in this study, …

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A Genetic Algorithm Solution To The Shortest Path Problem In Ospf And Mpls, Wee Jing Tee Wee Jing Jan 2002

A Genetic Algorithm Solution To The Shortest Path Problem In Ospf And Mpls, Wee Jing Tee Wee Jing

Student Works (2000-2009)

This project studies and explores the potential of using genetic algorithm to solve the shortest path problem in Open Shortest Path First (OSPF) and Multiprotocol Label Switching (MPLS). The most critical task for developing a genetic algorithm to the shortest path problem is to how to encode a path in a network. In this project, two genetic algorithm solutions arc developed for the above two problem domains, i.e. Previous-node-based Encoding to solve the shortest path problem in OSPF and Priority-based Encoding to solve the shortest path problem in MPLS. For each of the shortest path problem domains, the proposed solution …

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Level Set Segmentation Of Mr Images For Extraction Of Femur Bone And Tissues, Christina Shanti Nayagam Jan 2002

Level Set Segmentation Of Mr Images For Extraction Of Femur Bone And Tissues, Christina Shanti Nayagam

Student Works (2000-2009)

This research explores a potentially useful segmentation algorithm, known as the level set method. It is suitable for images obtained from the Magnetic Resonance Imaging (MRJ) modality, despite the fact that MR images have low contrast between bone and tissue. The level set method is a numerical technique designed to track the evolution of an interface. The fast marching version of the method is implemented for two-dimensional (2-D) and three-dimensional (3-D) segmentation in this research. Femur segmentation is the main thrust of this thesis, however brain and heart images are also presented. Pre-processing steps are first performed for the 2-0 …

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Intelligent Score Agent Web Monitoring & Retrieving System, Chong Wah Chan Jan 2002

Intelligent Score Agent Web Monitoring & Retrieving System, Chong Wah Chan

Student Works (2000-2009)

This project documentation is an exercise submitted to the Facult Computer Science and Information Technology, University of Malaya, as a report for the Final Year Project Level One, WXES3l81 and Final Year Project Level Two, WXES3182, which in turn serves as a partial fulfillment for the Bachelor or Computer Science degree. The system proposed is ScoreAgent, an intelligent agent which will monitor a particular soccer Web site. The site is providing the online result to surfers for the on going soccer matches. If the result section in the page has changed, ScoreAgent will report the result of a soccer match …

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