Papers by Keyword: Classification

Abstract: There is a great deal of uncertainty regarding the factors that influence their final year grade, which includes their entry qualification. This paper investigates the impact of entry qualification and pre-university CGPA on student performance at the university level. Entry qualifications are critical for educational institutions or educational providers to ensure the quality of the graduates. The goal of this study is to analyze and compare performance of Bachelor of Science (Industrial Statistics) with Honours (BWQ) students. Total of 54 students were selected form the Faculty of Applied Sciences and Technology (FAST), Universiti Tun Hussein Onn Malaysia (UTHM). The students are coming from Malaysian Higher School Certificate (STPM) and Malaysian Matriculation Programme. Paired t test and Z test were carried out to analyze the impact of pre-university’s CGPA and each semester’s GPA as well as impact of entry qualification towards their final year grade. Classification and Regression Tree (CART), K-Nearest Neighbors and Naïve Bayes were used to develop and predict the students’ performance. The findings show that there is no relation between the result obtained from previous semester towards the next semester. Meanwhile, students from STPM outperform Matriculation in terms of their GPA per semester, pre-university CGPA as well as their final CGPA. The K-Nearest Neighbors and Naïve Bayes models have been documented as the most efficient data mining techniques in predicting student performance with the highest percentage of accuracy of 100%.

Abstract: Lung sound analysis plays an important role in the assessment and diagnosis of respiratory conditions and diseases. It can provide valuable information about the functioning of the respiratory system, including the airways, lungs, and associated structures. By analyzing the characteristics of lung sounds, healthcare professionals can gain insights into the presence of abnormalities, such as airway obstructions, lung diseases, and respiratory infections. In this paper, a multiple channel model for processing and classifying abnormalities in lung sound is proposed, which utilize the characteristics of Mel spectrogram and the Empirical Mode Decomposition (EMD). Unlike previous research which directly convert the lung sound into scalogram or spectrogram, the pre-processing of the original audio signal is considered and focused in this paper. This pre-processing step includes denoising, resampling, padding and augmentation, which incredibly increase the quality of the input signal. Finally, the multiple channel is put into the VGG16 deep learning model to classify the abnormalities in lung sound, including wheezes, crackles, and both. The model is trained and tested on the benchmark ICBHI dataset. The proposed model has shown better performance when compared with the state-of-the-art researches.

Abstract: Cerebral stroke is the second leading cause of death and the third leading cause of death and disability in the world, and more than half of these patients have hand dysfunction, making hand rehabilitation an urgent challenge. In this study, a system for hand rehabilitation therapy for stroke patients was designed using novel human-computer interaction technology. The system combines a brain-computer interface, a deep learning algorithm and a rehabilitation glove, and designs an electroencephalogram (EEG) signal acquisition card and a rehabilitation glove to realise the application of motor imagery therapy to the active rehabilitation of patients' hands. On the brain-computer interface-based motor imagery experiments, the Long Short Term Memory (LSTM) recurrent neural network algorithm designed in this study achieves an average accuracy of 95.78% for the classification accuracy of mental tasks in seven motor imagery modes, which is important for the active rehabilitation of patients with hand function based on motor imagery-driven rehabilitation.

Abstract: The construction industry generates a significant amount of waste, posing challenges for efficient waste management and resource recovery. This paper presents a preliminary study on the use of lightweight computer vision (CV) algorithms for the automatic recognition of construction and demolition waste (CDW) materials. Utilizing image datasets acquired by drones, the study aims to develop strategies for distinguishing between individual CDW materials based on the mean intensity gradient, brightness, and relative representation of color channels. Results indicate that the proposed method can effectively recognize crucial CDW materials, paving the way for potential applications in industry and geodesy. Further research is needed to test additional materials and metrics to refine the method for practical implementation.

Abstract: For years, humanity has been progressing with the cost of harming the environment. And now one of the biggest change and solution being the introduction of electric vehicles. And the past few years’ electric vehicles had shown us it’s environmental and economic advantages, but distribution of the charging stations of these electric vehicles is crucial so that it could meet the needs of the users of these electric vehicles. Numerous attempts have been made to tackle this problem to find an optimize way to allocate the charging stations, but the traditional mathematical equation used are time consuming and suffers when put in new conditions such as different countries as the constants taken changes according to the places. But having the advantage of manipulating large data with the help of machine learning and applying data algorithms which adapts with different situations and bringing out hidden inferential we could take a new way of handling this problem. This paper consists of an exploration of computational ways, using machine learning algorithms to determine an optimal allocation of the electric vehicle’s charging stations in metropolitan cities and creating an interface for ease of use, also a thorough comparison with petrol pumps.

Abstract: Brain tumors are developed as a result of unregulated and fast cell proliferation. It may result in death if not treated in the early stages. The imaging technology used to diagnose brain tumors is known as magnetic resonance imaging (MRI). Early detection of brain tumors is critical in medical practise in order to determine whether the tumor will progress to malignancy. For picture categorization, deep learning is a useful and effective method. Deep learning has been widely used in a variety of sectors, including medical imaging, because its application does not necessitate the expertise of a subject matter expert, but does necessitate a large amount of data and a variety of data in order to produce accurate classification results. The deep learning technique for image categorization is the convolutional neural network (CNN).In this research work , two different models are used to categorize brain tumors and their results were evaluated using performance metrics like accuracy and precision and the results were impressive

Abstract: Agriculture improvement is a global economic issue and ongoing challenge in this covid-19 pandemic that is highly dependent on effectiveness. The recognition of the diseases in plant leaf performs a major role in the agriculture industry and city-side greenhouse farms approximate analysis of the leaf disease this article intends to integrate image processing techniques with the “convolutional neural network”, which is one of the deep learning approaches, to classify and detect plant leaf disease and publicly available plant the late data to help treat the leaf as early as possible, which controls the economic loss. This paper has a set that was used which consists of 10 classes of disease and three classes of a plant leaf, this research offers an effective method for detecting different diseases in plant leaf variations. The model was created to detect and recognize healthy plant kinds, such as tomato and potato, and pepper these three leaves will perform under the algorithm called a convolutional neural network. By modifying the parameters and changing the pulling combination, models that have been used to train and test these types of leaf sample images can be created. leaf disease recognition was based on these 10 different types of classes in three different species tomato, potato, and pepper the classification of sample images has reached diseases identification accuracy.

Abstract: Employee attrition rate in Tech industry has become dreadful day by day in all over the world. Meanwhile It has been noticed that churn (attrition) rate in IT industries is growing rapidly than expected especially during pandemic times. This is taken as a foremost issue by each tech industry, to analyze and adapt to the change. The main snag is that, the expenditure of recruiting on a new employee is foremost ineffective than retaining a company trained professional employee. Also retaining an employee will assure certain credibility and work culture of the company than the new employee. Also, the latter will be given access to training modules and code of conduct of the company with lots of Information Overload on a short span of time. It is essential to mention, not every organization has comprehensive training programs for their employees, especially the start-up tech firms, which focuses heavily on skilled workers with experience beforehand. This anonymity causes HR departments to scrutinize and tweak their actions according to current trend in the market. The major goal of this study is to make predictions whether the skillful employee will quit or continue further and predict the reason for quit using supervised classification and machine learning algorithms. Acquainting the human resource team to help them with the required analytics to make decisions based on machine learning.

Abstract: The detection and classification of SiC Epitaxial extended defects was refined to separate out defective areas that influence device characteristics. Die level defect localization along with defect area calculations were performed on millions of die across product groups. A clear impact of non-killer defects was observed, especially with increasing density and defective area in the die. Specifically, all types of stacking faults caused higher leakage, lower blocking voltage, and increases in ON resistance and threshold leakage. Furthermore, MOSFET devices were affected to a much larger extent than diode devices. Testing die with higher numbers of defects provides insight on device reliability. Analyzing devices with specific counts of BPDs let us quantify the amount of bipolar degradation caused drift by product/voltage classes.

Abstract: Breast cancer is ranked first as the most common cancer case affecting women in the world. Early detection of breast cancer can increase the chances of survival in patients. The role of the radiologist is necessary for the detection of breast cancer, and the radiologists often have limitations in conducting disease consultations with so many patients. The detection gives a subjective result because the process is based on the decision-making of the radiologists. In this work, we proposed a system to detect and classify breast cancer accurately to anticipate delays in patient handling and subjective result. We proposed a digital image processing method using mammograms to classify breast cancer into four categories based on tissue density, namely BI-RADS I, II, III, and IV. The main stages carried out in this research are images processing, feature extraction, data normalization, feature selection, classification, and parameter optimization. This method uses GLCM to extract texture features and two feature selection methods namely, RFE-RF and Chi-Square. The method was tested with various classifiers such as SVM, KNN, Random Forests, and Decision Trees. The hyper-parameters of the classifier were optimized using GridSearch. The final result is measure using accuracy. In this work, Random Forest with the RFE-RF gives the highest accuracy of 99.7%. Feature selection offers a significant impact on improving accuracy. The results of this work prove that our system can classify breast cancer with high accuracy. So that our system can solve problems to assist radiologists in screening mammograms and help make decisions to diagnose patients with breast cancer based on density.