Training of Convolutional Neural Network using Transfer Learning for Aedes Aegypti Larvae

The flavivirus epidemiology has reached an alarming rate which haunts the world population including Malaysia. World Health Organization has proposed and practised various methods of vector control through environmental management, chemical and biological orientations. However, from the listed control vectors, the most crucial part to be heeded are non-accessible places like water storage and artificial container. The objective of the study was to acquire and compare various accuracies and cross-entropy errors of the training sets within different learning rates in water storage tank environment which was essential for detection. This experiment performed transfer learning where Inception-V3 was implemented. About 534 images were trained to classify between Aedes Aegypti larvae and float valve within 3 different learning rates. For training accuracy and validation accuracy, learning rates were 0.1; 99.98%, 99.90% and 0.01; 99.91%, 99.77% and 0.001; 99.10%, 99.93%. Cross-entropy errors for training and validation for 0.1 were 0.0021, 0.0184 whereas for 0.01 were 0.0091, 0.0121 and 0.001; 0.0513, 0.0330. Various accuracies and cross-entropy errors of the training sets within the different learning rates were successfully acquired and compared

Classification of Tiles using Convolutional Neural Network

Tiles are one of the building materials with various types that can make a residence more elegant, attractive, and colorful. However, not all people know about the types of tiles and their advantages. Therefore, a Convolutional Neural Networks (CNN) based method is proposed to make it easier for people to accurately recognize tiles based on their types and know their advantages. The purpose of this paper is to classify the types of tiles using CNN which is based on VGG16 model. The proposed method classifies tiles into 6 classes, namely granite, limestone, marble, motifs, mosaics, and terrazzo. This research uses 186 training data, 96 validation data and 60 test data with image resolution of 224x224. Based on the experiments, the training process produces 100% of training accuracy and 94% of validation accuracy. The testing process achieves 98.33% accuracy which can be concluded that the proposed CNN model able to classify the types of tiles well

Klasifikasi Sentimen Ulasan Film Indonesia dengan Konversi Speech-to-Text (STT) Menggunakan Metode Convolutional Neural Network (CNN)

Ulasan film adalah sebuah opini yang bersifat subjektif. Ulasan film memiliki media yang bera-gam, seperti tulisan, audio, dan video. Ulasan film dapat diolah dengan menggunakan klasifikasi sentimen, agar u-capan seseorang terkait film dapat ditentukan sebagai sen-timen tertentu. Di masa sekarang, data memiliki berbagai bentuk, pemilihan jenis data yang lebih baik juga dapat mempengaruhi klasifikasi sentimen. Data video dapat di-konversi menjadi data teks dengan bantuan Speech-to-Text (STT). Data teks digunakan karena kata atau kalimat dapat dibedakan secara negatif atau positif. Data ulasan dikelom-pokkan berdasarkan aspek penilaian film dan klasifikasi sentimen dilakukan pada keseluruhan potongan ulasan serta di tiap aspek yang ada. Dengan menggunakan metode Convolutional Neural Network, didapatkan bahwa model klasifikasi sentimen tiap aspek memiliki nilai AUC lebih baik dibandingkan model klasifikasi sentimen dengan keseluruhan data

Text Classification of Public Feedbacks using Convolutional Neural Network Based on Differential Evolution Algorithm

Online feedback is an effective way of communication between government departments and citizens. However, the daily high number of public feedbacks has increased the burden on government administrators. The deep learning method is good at automatically analyzing and extracting deep features of data, and then improving the accuracy of classification prediction. In this study, we aim to use the text classification model to achieve the automatic classification of public feedbacks to reduce the work pressure of administrator. In particular, a convolutional neural network model combined with word embedding and optimized by differential evolution algorithm is adopted. At the same time, we compared it with seven common text classification models, and the results show that the model we explored has good classification performance under different evaluation metrics, including accuracy, precision, recall, and F1-score

Automated Rock Fracture Detection Algorithm with Convolutional Neural Networks

학위논문(석사)--서울대학교 대학원 :공과대학 에너지시스템공학부,2019. 8. 송재준.암반에 존재하는 균열과 절리는 강도, 탄성계수, 투수계수 등에 큰 영향을 미치기 때문에 이들을 잘 검출하는 것은 중요한 문제이다. 특히 사진측량법은 간단하고 경제성이 있으므로 이를 기반으로 하는 연구들이 이루어졌다. 그 중, 절리는 선형성을 띠기 때문에 비교적 인식이 평이하나, 균열은 비정형성을 보이기 때문에 인식이 상대적으로 어려워 관련 연구가 부족한 실정이다. 또한, 기존의 연구들은 균열 인식을 방해하는 다양한 노이즈가 없는 이미지를 주로 사용하였다. 그림자, 균열 사이의 충전물, 식생 등의 노이즈는 전통적인 알고리즘의 균열 인식 정확도를 낮추는 요인이나, 사진을 촬영한 현장의 환경에 따라 사진에 포함될 수 있다. 본 연구에서는 딥러닝 알고리즘의 일종인 합성곱 신경망을 사용하여 다양한 노이즈가 존재하는 이미지로부터 암석 균열을 자동으로 인식하는 알고리즘을 개발하였다. 딥러닝 기반 합성곱 신경망 알고리즘은 사람이 직접 시행착오를 통해 적절한 피처를 결정했던 전통적인 이미지 처리 방식과 달리 신경망이 스스로 이미지에서 적합한 피처를 추출하여 이용하기 때문에 인식 성능이 향상된다. 또한, 기존 연구들은 모델 개발에 사용한 균열 이미지와 같은 이미지로 테스트를 수행하였기 때문에 그로부터 개발된 모델은 그 특정 이미지에 대해서만 좋은 성능을 낼 수 있었다. 그러나, 본 연구에서는 테스트 과정에 완전히 새로운 균열 이미지를 사용함으로써 새로운 이미지에도 좋은 결과를 내는 것을 보였다. 종합적으로, 본 연구에서 개발된 알고리즘은 사진으로부터 신속하고 일관적으로 균열 인식을 할 수 있으며 다양한 비정형 균열 이미지들에 대해서도 높은 검출 성능을 보인다.Detection of rock joint and fracture is important because they have a huge influence on rock mass strength. Photogrammetry technique, especially, has been used for decades due to its simplicity and economic feasibility. Although joints are easy to detected since it has linearity, fractures has irregularity which leads to difficulties in detection and lack of relevant studies. Additionally, previous researches used photographs without various types of noise such as shadow, infill material and vegetation. These kinds of noise reduce the accuracy of conventional algorithms. However, it can be included in the photographs under certain circumstances. In this study, a new algorithm based on convolutional neural networks, which can detect rock fracture from rock images with many kinds of noise, is presented. Furthermore, previous models were evaluated with the same image used in model construction stage. The model performance, therefore, is guaranteed only for that specific data. On the contrary, new rock images are used when testing the model, which shows the data-independent performance of proposed model. As a result, the developed model in this study can detect rock fracture from photographs quickly and consistently, and demonstrate high performance for irregular fractures.목 차 1. 서론 1 2. 인공신경망 이론 5 2.1 완전연결 신경망 5 2.2 합성곱 신경망 8 3. 실험 방법 13 3.1 데이터 수집 14 3.2 데이터 가공 16 3.2.1 데이터 레이블링 16 3.2.2 데이터 분리 18 3.2.3 데이터 증강 20 3.2.4 데이터 전처리 23 3.3 합성곱 신경망 모델 구조 27 3.4 학습 상세 과정 30 3.5 후처리 과정 30 4. 결과 및 고찰 33 4.1 그림자, 검은 표면 등이 있는 사진 34 4.2 줄무늬 구조가 존재하는 사진 40 4.3 충전물이 존재하는 사진 43 4.4 긁힌 자국이 존재하는 사진 47 4.5 식생이 존재하는 사진 49 4.6 모델 성능 고찰 52 5. 결론 56 참고문헌 58Maste

The use of image processing to determine cell defects in polycrystalline solar modules

This research aims to use image processingtodetermine cell defects in polycrystalline solar modules. Image processing is a process of enhancing images for differentapplications. One domain that seems to not yet utilise the use of image processing, is photovoltaics. An increased use of fossil fuels is damaging the earth and a call to protect the earth has resulted in the emergence of pollutant-free technologies such as polycrystalline photovoltaic (PV) cells, which are connected to make up solar modules. However, defects often affect the performance of PV cells and consequently solar modules. Electroluminescence (EL) images are used to examine polycrystalline solar (PV) modules to determine if the modules are defective. The main research question that this research addressed is“How can an image processing technique be used to effectively identify defective polycrystalline PV cells from EL images of such cells?“. The experimental research methodology was used to address the main research question. The initial investigation into the problem revealed that certain sectors within industry, as well as the Physics Department at Nelson Mandela University(NMU), do not currently utiliseimage processing when examining EL images of solar modules. The current process is a tedious, manual process whereby solar modules are manually inspected. An analysis of the current processes enabled the identification of ways in which to automatically examine EL images of solar modules. An analysis of literatureprovided a better understanding of the different techniques that are used to examine solar modules, and it was identified how image processing can be applied to EL images. Further analysis of literatureprovided a better understanding of image processing and how image classification experiments using Deep Learning (DL) as an image processing technique can be used to address the main research question. The outcome of the experiments conducted in this research weredifferentadaptive models(LeNet, MobileNet, Xception)that can classify EL images of PV cellsaccording to known standardsused by the Physics Department at NMU. The known standards yielded four classes; normal, uncritical, critical and very critical, which were used for the classification of EL images of PV cells. The adaptive models were evaluated to obtain the precision, recall and F1–scoreof the models.The precession, recall, and F1–score were required to determine how effective the models were in identifying defective PV cells from EL images.The results indicated that an image processing technique canbe used to identify defective polycrystalline PV cells from EL images of such cells. However, further research needs to be conducted to improve the effectiveness of the adaptive models

Characterisation of Dynamic Process Systems by Use of Recurrence Texture Analysis

This thesis proposes a method to analyse the dynamic behaviour of process systems using sets of textural features extracted from distance matrices obtained from time series data. Algorithms based on the use of grey level co-occurrence matrices, wavelet transforms, local binary patterns, textons, and the pretrained convolutional neural networks (AlexNet and VGG16) were used to extract features. The method was demonstrated to effectively capture the dynamics of mineral process systems and could outperform competing approaches

Towards Secure and Intelligent Diagnosis: Deep Learning and Blockchain Technology for Computer-Aided Diagnosis Systems

Cancer is the second leading cause of death across the world after cardiovascular disease. The survival rate of patients with cancerous tissue can significantly decrease due to late-stage diagnosis. Nowadays, advancements of whole slide imaging scanners have resulted in a dramatic increase of patient data in the domain of digital pathology. Large-scale histopathology images need to be analyzed promptly for early cancer detection which is critical for improving patient's survival rate and treatment planning. Advances of medical image processing and deep learning methods have facilitated the extraction and analysis of high-level features from histopathological data that could assist in life-critical diagnosis and reduce the considerable healthcare cost associated with cancer. In clinical trials, due to the complexity and large variance of collected image data, developing computer-aided diagnosis systems to support quantitative medical image analysis is an area of active research. The first goal of this research is to automate the classification and segmentation process of cancerous regions in histopathology images of different cancer tissues by developing models using deep learning-based architectures. In this research, a framework with different modules is proposed, including (1) data pre-processing, (2) data augmentation, (3) feature extraction, and (4) deep learning architectures. Four validation studies were designed to conduct this research. (1) differentiating benign and malignant lesions in breast cancer (2) differentiating between immature leukemic blasts and normal cells in leukemia cancer (3) differentiating benign and malignant regions in lung cancer, and (4) differentiating benign and malignant regions in colorectal cancer. Training machine learning models, disease diagnosis, and treatment often requires collecting patients' medical data. Privacy and trusted authenticity concerns make data owners reluctant to share their personal and medical data. Motivated by the advantages of Blockchain technology in healthcare data sharing frameworks, the focus of the second part of this research is to integrate Blockchain technology in computer-aided diagnosis systems to address the problems of managing access control, authentication, provenance, and confidentiality of sensitive medical data. To do so, a hierarchical identity and attribute-based access control mechanism using smart contract and Ethereum Blockchain is proposed to securely process healthcare data without revealing sensitive information to an unauthorized party leveraging the trustworthiness of transactions in a collaborative healthcare environment. The proposed access control mechanism provides a solution to the challenges associated with centralized access control systems and ensures data transparency and traceability for secure data sharing, and data ownership

Enhancing remanufacturing automation using deep learning approach

In recent years, remanufacturing has significant interest from researchers and practitioners to improve efficiency through maximum value recovery of products at end-of-life (EoL). It is a process of returning used products, known as EoL products, to as-new condition with matching or higher warranty than the new products. However, these remanufacturing processes are complex and time-consuming to implement manually, causing reduced productivity and posing dangers to personnel. These challenges require automating the various remanufacturing process stages to achieve higher throughput, reduced lead time, cost and environmental impact while maximising economic gains. Besides, as highlighted by various research groups, there is currently a shortage of adequate remanufacturing-specific technologies to achieve full automation. -- This research explores automating remanufacturing processes to improve competitiveness by analysing and developing deep learning-based models for automating different stages of the remanufacturing processes. Analysing deep learning algorithms represents a viable option to investigate and develop technologies with capabilities to overcome the outlined challenges. Deep learning involves using artificial neural networks to learn high-level abstractions in data. Deep learning (DL) models are inspired by human brains and have produced state-of-the-art results in pattern recognition, object detection and other applications. The research further investigates the empirical data of torque converter components recorded from a remanufacturing facility in Glasgow, UK, using the in-case and cross-case analysis to evaluate the remanufacturing inspection, sorting, and process control applications. -- Nevertheless, the developed algorithm helped capture, pre-process, train, deploy and evaluate the performance of the respective processes. The experimental evaluation of the in-case and cross-case analysis using model prediction accuracy, misclassification rate, and model loss highlights that the developed models achieved a high prediction accuracy of above 99.9% across the sorting, inspection and process control applications. Furthermore, a low model loss between 3x10-3 and 1.3x10-5 was obtained alongside a misclassification rate that lies between 0.01% to 0.08% across the three applications investigated, thereby highlighting the capability of the developed deep learning algorithms to perform the sorting, process control and inspection in remanufacturing. The results demonstrate the viability of adopting deep learning-based algorithms in automating remanufacturing processes, achieving safer and more efficient remanufacturing. -- Finally, this research is unique because it is the first to investigate using deep learning and qualitative torque-converter image data for modelling remanufacturing sorting, inspection and process control applications. It also delivers a custom computational model that has the potential to enhance remanufacturing automation when utilised. The findings and publications also benefit both academics and industrial practitioners. Furthermore, the model is easily adaptable to other remanufacturing applications with minor modifications to enhance process efficiency in today's workplaces.In recent years, remanufacturing has significant interest from researchers and practitioners to improve efficiency through maximum value recovery of products at end-of-life (EoL). It is a process of returning used products, known as EoL products, to as-new condition with matching or higher warranty than the new products. However, these remanufacturing processes are complex and time-consuming to implement manually, causing reduced productivity and posing dangers to personnel. These challenges require automating the various remanufacturing process stages to achieve higher throughput, reduced lead time, cost and environmental impact while maximising economic gains. Besides, as highlighted by various research groups, there is currently a shortage of adequate remanufacturing-specific technologies to achieve full automation. -- This research explores automating remanufacturing processes to improve competitiveness by analysing and developing deep learning-based models for automating different stages of the remanufacturing processes. Analysing deep learning algorithms represents a viable option to investigate and develop technologies with capabilities to overcome the outlined challenges. Deep learning involves using artificial neural networks to learn high-level abstractions in data. Deep learning (DL) models are inspired by human brains and have produced state-of-the-art results in pattern recognition, object detection and other applications. The research further investigates the empirical data of torque converter components recorded from a remanufacturing facility in Glasgow, UK, using the in-case and cross-case analysis to evaluate the remanufacturing inspection, sorting, and process control applications. -- Nevertheless, the developed algorithm helped capture, pre-process, train, deploy and evaluate the performance of the respective processes. The experimental evaluation of the in-case and cross-case analysis using model prediction accuracy, misclassification rate, and model loss highlights that the developed models achieved a high prediction accuracy of above 99.9% across the sorting, inspection and process control applications. Furthermore, a low model loss between 3x10-3 and 1.3x10-5 was obtained alongside a misclassification rate that lies between 0.01% to 0.08% across the three applications investigated, thereby highlighting the capability of the developed deep learning algorithms to perform the sorting, process control and inspection in remanufacturing. The results demonstrate the viability of adopting deep learning-based algorithms in automating remanufacturing processes, achieving safer and more efficient remanufacturing. -- Finally, this research is unique because it is the first to investigate using deep learning and qualitative torque-converter image data for modelling remanufacturing sorting, inspection and process control applications. It also delivers a custom computational model that has the potential to enhance remanufacturing automation when utilised. The findings and publications also benefit both academics and industrial practitioners. Furthermore, the model is easily adaptable to other remanufacturing applications with minor modifications to enhance process efficiency in today's workplaces