Deep learning network optimization and hyperparameter tuning for seismic lithofacies classification

Recommending music based on a user’s music preference is a way to improve user listening experience. Finding the correlation between the user data (e.g., location, time of the day, music listening history, emotion, etc.) and the music is a challenging task. In this paper, we propose an emotion-aware personalized music recommendation system (EPMRS) to extract the correlation between the user data and the music. To achieve this correlation, we combine the outputs of two approaches: the deep convolutional neural networks (DCNN) approach and the weighted feature extraction (WFE) approach. The DCNN approach is used to extract the latent features from music data (e.g., audio signals and corresponding metadata) for classification. In the WFE approach, we generate the implicit user rating for music to extract the correlation between the user data and the music data. In the WFE approach, we use the term-frequency and inverse document frequency (TF-IDF) approach to generate the implicit user ratings for the music. Later, the EPMRS recommends songs to the user based on calculated implicit user rating for the music. We use the million songs dataset (MSD) to train the EPMRS. For performance comparison, we take the content similarity music recommendation system (CSMRS) as well as the personalized music recommendation system based on electroencephalography feedback (PMRSE) as the baseline systems. Experimental results show that the EPMRS produces better accuracy of music recommendations than the CSMRS and the PMRSE. Moreover, we build the Android and iOS APPs to get realistic data of user experience on the EPMRS. The collected feedback from anonymous users also show that the EPMRS sufficiently reflect their preference on music.

This paper proposes a deep convolutional neural network approach to detect Ice surface and bottom layers from radar imagery. Radar images are capable to penetrate the Ice surface and provide us with valuable information from the underlying layers of ice surface. In recent years, deep hierarchical learning techniques for object detection and segmentation greatly improved the performance of traditional techniques based on hand-crafted feature engineering. We designed a deep convolutional network to produce the images of surface and bottom ice boundary. Our network take advantage of undecimated wavelet transform to provide the higest level of information from radar images, as well as multilayer and multi-scale optimized architecture. In this work, radar images from 2009–2016 NASA Operation IceBridge Mission are used to train and test the network. Our network outperformed the state-of-the art accuracy.

When grown on agar surfaces, microbes can produce distinct multicellular spatial structures called colonies, which contain characteristic sizes, shapes, edges, textures, and degrees of opacity and color. For over one hundred years, researchers have used these morphology cues to classify bacteria and guide more targeted treatment of pathogens. Advances in genome sequencing technology have revolutionized our ability to classify bacterial isolates and while genomic methods are in the ascendancy, morphological characterization of bacterial species has made a resurgence due to increased computing capacities and widespread application of machine learning tools. In this paper, we revisit the topic of colony morphotype on the within-species scale and apply concepts from image processing, computer vision, and deep learning to a dataset of 69 environmental and clinical Pseudomonas aeruginosa strains. We find that colony morphology and complexity under common laboratory conditions is a robust, repeatable phenotype on the level of individual strains, and therefore forms a potential basis for strain classification. We then use a deep convolutional neural network approach with a combination of data augmentation and transfer learning to overcome the typical data starvation problem in biological applications of deep learning. Using a train/validation/test split, our results achieve an average validation accuracy of 92.9% and an average test accuracy of 90.7% for the classification of individual strains. These results indicate that bacterial strains have characteristic visual ‘fingerprints’ that can serve as the basis of classification on a sub-species level. Our work illustrates the potential of image-based classification of bacterial pathogens and highlights the potential to use similar approaches to predict medically relevant strain characteristics like antibiotic resistance and virulence from colony data.

Deep learning has emerged as a state-of-the-art learning technique across a wide range of applications, including image recognition, object detection and localisation, natural language processing, prediction and forecasting systems. With significant applicability, deep learning could be used in new and broader areas of applications, including remanufacturing. Remanufacturing is a process of taking used products through disassembly, inspection, cleaning, reconditioning, reassembly and testing to ascertain that their condition meets new products conditions with warranty. This process is complex and requires a good understanding of the respective stages for proper analysis. Inspection is a critical process in remanufacturing, which guarantees the quality of the remanufactured products. It is currently an expensive manual operation in the remanufacturing process that depends on operator expertise, in most cases. This research investigates the application of deep learning algorithms to inspection in remanufacturing, towards automating the inspection process. This paper presents a novel vision-based inspection system based on deep convolution neural network (DCNN) for eight types of defects, namely pitting, rust, cracks and other combination faults. The materials used for this feasibility study were 100 cm × 150 cm mild steel plate material, purchased locally, and captured using a USB webcam of 0.3 megapixels. The performance of this preliminary study indicates that the DCNN can classify with up to 100% accuracy on validation data and above 96% accuracy on a live video feed, by using 80% of the sample dataset for training and the remaining 20% for testing. Therefore, in the remanufacturing parts inspection, the DCNN approach has high potential as a method that could surpass the current technologies used in the design of inspection systems. This research is the first to apply deep learning techniques in remanufacturing inspection. The proposed method offers the potential to eliminate expert judgement in inspection, save cost, increase throughput and improve precision. This preliminary study demonstrates that deep learning techniques have the potential to revolutionise inspection in remanufacturing. This research offers valuable insight into these opportunities, serving as a starting point for future applications of deep learning algorithms to remanufacturing.

This paper presents innovative approaches to various aspects of sign language recognition and learning, catering to the diverse needs within this field. It introduces a system utilizing 2D image sampling and concatenation, trained with convolutional neural networks, achieving accurate and robust sign recognition even with low-cost cameras. Another system, SignQuiz, offers a cost-effective web-based solution for learning finger-spelled signs in Indian Sign Language (ISL), outperforming traditional printed mediums. Additionally, a dynamic hand gesture recognition system employing deep learning architectures demonstrates superior performance over existing methods, addressing challenges such as hand segmentation and sequence modeling. Furthermore, an efficient deep convolutional neural network approach for hand gesture recognition, including transfer learning, achieves high recognition rates across various datasets. Finally, a robust model for static sign recognition in ISL, utilizing CNNs and evaluating on a diverse dataset, attains exceptional accuracy and outperforms previous works. These contributions collectively advance the field of sign language recognition and learning, offering solutions that are accurate, cost-effective, and efficient, thereby facilitating better communication and interaction for the hearing-impaired community. Key Words: CNN, Sign language recognition, Hand gesture recognition, Machine learning, Transfer Learning, SVM.

While X-ray computed tomography (XCT) is pushed further into the micro- and nanoscale, the limitations of various tool components and object motion become more apparent. For high-resolution XCT, it is necessary but practically difficult to align these tool components with sub-micron precision. The aim is to develop a novel reconstruction methodology that considers unavoidable misalignment and object motion during the data acquisition in order to obtain high-quality three-dimensional images and that is applicable for data recovery from incomplete datasets. A reconstruction software empowered by sophisticated correction modules that autonomously estimates and compensates artefacts using gradient descent and deep learning algorithms has been developed and applied. For motion estimation, a novel computer vision methodology coupled with a deep convolutional neural network approach provides estimates for the object motion by tracking features throughout the adjacent projections. The model is trained using the forward projections of simulated phantoms that consist of several simple geometrical features such as sphere, triangle and rectangular. The feature maps extracted by a neural network are used to detect and to classify features done by a support vector machine. For missing data recovery, a novel deep convolutional neural network is used to infer high-quality reconstruction data from incomplete sets of projections. The forward and back projections of simulated geometric shapes from a range of angular ranges are used to train the model. The model is able to learn the angular dependency based on a limited angle coverage and to propose a new set of projections to suppress artefacts. High-quality three-dimensional images demonstrate that it is possible to effectively suppress artefacts caused by thermomechanical instability of tool components and objects resulting in motion, by center of rotation misalignment and by inaccuracy in the detector position without additional computational efforts. Data recovery from incomplete sets of projections result in directly corrected projections instead of suppressing artefacts in the final reconstructed images. The proposed methodology has been proven and is demonstrated for a ball bearing sample. The reconstruction results are compared to prior corrections and benchmarked with a commercially available reconstruction software. Compared to conventional approaches in XCT imaging and data analysis, the proposed methodology for the generation of high-quality three-dimensional X-ray images is fully autonomous. The methodology presented here has been proven for high-resolution micro-XCT and nano-XCT, however, is applicable for all length scales.

From the viewpoint of combating illegal logging and examining wood properties, there is a contemporary demand for a wood species identification system. Several nondestructive automatic identification systems have been developed, but there is room for improvement to construct a highly reliable model. The present study proposes cognitive spectroscopy that combines near infrared hyperspectral imaging (NIR-HSI) with a deep convolutional neural network approach. We defined "cognitive spectroscopy" as a protocol that extracts features from complex spectroscopic data and presents the best results without human intervention. Overall, 120 samples representing 38 hardwood species were scanned using an NIR-HSI camera. A deep learning prediction model was built based on the principal component (PC) images obtained from the PC scores of hyperspectral images (wavelength range: 1000-2200 nm at approximately 6.2 nm interval). The results showed that the accuracy of wood species identification based on 6PC (PC1-PC6) images was 90.5%, which was considerably higher than the accuracy of 56.0% obtained with conventional visible images.

Developing a treatment strategy that effectively prolongs the lives of people with brain tumors requires an accurate diagnosis of the condition. Therefore, improving the preoperative classification of meningiomas is a priority. Machine learning (ML) has made great strides thanks to the development of convolutional neural networks (CNNs) and computer-aided tumor detection systems. The deep convolutional layers automatically extract important and dependable information from the input space, in contrast to more traditional neural network layers. One recent and promising advancement in this field is ML. Still, there is a dearth of studies being carried out in this area. Therefore, starting with the analysis of magnetic resonance images, we have suggested in this research work a tried-and-tested and methodical strategy for real-time meningioma diagnosis by image segmentation using a very deep transfer learning CNN model or DNN model (VGG-16) with CUDA. Since the VGGNet CNN model has a greater level of accuracy than other deep CNN models like AlexNet, GoogleNet, etc., we have chosen to employ it. The VGG network that we have constructed with very small convolutional filters consists of 13 convolutional layers and 3 fully connected layers. Our VGGNet model takes in an sMRI FLAIR image input. The VGG's convolutional layers leverage a minimal receptive field, i.e., 3 × 3, the smallest possible size that still captures up/down and left/right. Moreover, there are also 1 × 1 convolution filters acting as a linear transformation of the input. This is followed by a ReLU unit. The convolution stride is fixed at 1 pixel to keep the spatial resolution preserved after convolution. All the hidden layers in our VGG network also use ReLU. A dataset consisting of 264 3D FLAIR sMRI image segments from three different classes (meningioma, tuberculoma, and normal) was employed. The number of epochs in the Sequential Model was set to 10. The Keras layers that we used were Dense, Dropout, Flatten, Batch Normalization, and ReLU. According to the simulation findings, our suggested model successfully classified all of the data in the dataset used, with a 99.0% overall accuracy. The performance metrics of the implemented model and confusion matrix for tumor classification indicate the model's high accuracy in brain tumor classification. The good outcomes demonstrate the possibility of our suggested method as a useful diagnostic tool, promoting better understanding, a prognostic tool for clinical outcomes, and an efficient brain tumor treatment planning tool. It was demonstrated that several performance metrics we computed using the confusion matrix of the previously used model were very good. Consequently, we think that the approach we have suggested is an important way to identify brain tumors.

We are developing a decision support system for treatment response assessment of bladder cancer in CT urography (CTU). Accurate segmentation of bladder cancer is a critical and challenging task. We previously developed a bladder cancer segmentation method using a deep learning convolutional neural network and level set (DL-CNN+LS) approach. In this study we investigated the application of a U-Net based deep learning (U-Net) model for bladder cancer segmentation. Our new U-Net method did not require the second-stage level set refinement, greatly simplifying the overall segmentation pipeline. The proposed U-Net model utilized a user-defined box to direct the attention of the U-Net to the lesion region by masking out the structured background outside the box. We trained and evaluated the performance of the U-Net segmentations by using hand-drawn 3D contours from a radiologist as reference standard. The segmentation accuracy was evaluated by the average volume intersection ratio (AVI), average percent volume error (AVE), average absolute volume error (AAVE), average minimum distance (AMD), and the Jaccard index (JI). On the validation set, the cropped U-Net achieved values of AVI = 65.5±20.2%, AVE = 3.7±44.4%, AAVE = 30.9±30.8%, AMD = 2.9±1.5 mm, and JI = 51.4±16.8%. The previous DL-CNN+LS approach achieved values of AVI = 36.2±28.4%, AVE = 51.1±39.3%, AAVE = 52.0±38.0%, AMD = 4.1±1.7 mm, and JI = 32.9±27.2%. On the independent test set, the cropped U-Net achieved values of AVI = 66.0±22.8%, AVE = -3.7±39.4%, AAVE = 28.1±27.3%, AMD = 4.5±3.3 mm, and JI = 49.2±20.0%. The DL-CNN+LS achieved values of AVI = 31.2±24.3%, AVE = 59.9±30.9%, AAVE = 60.8±29.0%, AMD = 5.5±2.2 mm, and JI = 27.9±20.8%, respectively. The results demonstrated that the U-Net model could achieve a higher accuracy than the previous DL-CNN+LS model while reducing the complexity of the segmentation pipeline.

Skin cancer is one of the most dangerous types of cancer due to its immediate appearance and the possibility of rapid spread. It arises from uncontrollably growing cells, rapidly dividing cells in one area of the body, invading other bodily tissues, and spreading throughout the body. Early detection helps prevent cancer progress from reaching critical levels, reducing the risk of complications and the need for more aggressive treatment options. Convolutional neural networks (CNNs) revolutionize skin cancer diagnosis by extracting intricate features from images, enabling an accurate classification of lesions. Their role extends to early detection, providing a powerful tool for dermatologists to identify abnormalities in their nascent stages, ultimately improving patient outcomes. This study proposes a novel deep convolutional neural network (DCNN) approach to classifying skin cancer lesions. The proposed DCNN model is evaluated using two unbalanced datasets, namely HAM10000 and ISIC-2019. The DCNN model is compared with other transfer learning models, including VGG16, VGG19, DenseNet121, DenseNet201, and MobileNetV2. Its performance is assessed using four widely used evaluation metrics: accuracy, recall, precision, F1-score, specificity, and AUC. The experimental results demonstrate that the proposed DCNN model outperforms other deep learning (DL) models that utilized these datasets. The proposed DCNN model achieved the highest accuracy with the HAM10000 and ISIC-2019 datasets, reaching 98.5%\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$98.5\\%$$\\end{document} and 97.1%\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$97.1\\%$$\\end{document}, respectively. These experimental results show how competitive and successful the DCNN model is in overcoming the problems caused by class imbalance and raising skin cancer classification accuracy. Furthermore, the proposed model demonstrates superior performance, particularly excelling in terms of accuracy, compared to other recent studies that utilize the same datasets, which highlights the robustness and effectiveness of the proposed DCNN.

Detecting major depressive disorder presence using passively-collected wearable movement data in a nationally-representative sample

Diabetic Retinopathy (DR) is eye disease caused through the retinal damage induced through long-term diabetes mellitus illness. In the initial stage, DR has minor vision difficulties or no symptoms but it outputs in vision loss if it is not treated early. However, overfitting occurs when a model learns noise or specific features from a small dataset which leading to poor generalization. To solve this issue, this research proposed an Otsu Multi-level Adaptive Thresholding with Deep Convolution Neural Network (OMAT+DCNN) approach for DR segmentation and classification. The Contrast Limited Adaptive Histogram Equalization (CLAHE) is used for preprocessing input image which enhances the contrast level and image visibility without amplifying noise. The OMAT is used for segmenting the affected regions and ResNet50 is used for extracting Deep Learning (DL) features. Then, relevant features are selected through Multi-Verse Optimizer (MVO) and DCNN is used for classification. The OMAT improves segmentation accuracy by considering multiple thresholds which adopts to local intensity variations within the image, even when the dataset is limited. DCNN capturing intricate patterns and spatial hierarchies within images and its deep architecture allows to learn complex representations which leads to enhance accuracy. The parameters like dice coefficient, Intersection over Union (IoU), Mean IoU (MIoU), precision, accuracy, f1-score and sensitivity are used for assessing performance. The OMAT+DCNN reaches 0.993 of accuracy when compared to existing techniques such as Convolutional Transformer Network (CTNet) and Residual Attention Network (RAN).

This work presents a novel multi-class detection approach for predicting car damage due to accidents or other reasons. The proposed prediction model intends to extract the class information to analyze the objects over the frames. Here, a novel masking-based deep convolutional neural network (MD-CNN) is proposed to capture the car parts’ regions and the classification process. The proposed model works well compared to the various existing approaches. The computation is initiated with the acquisition of damaged images, and pre-processing is performed for data labeling, which is further divided into training and testing samples. The features are internally analyzed by the proposed MD-CNN model, where the internal feature extraction process is a significant advantage with the classification process. The simulation is done in MATLAB 2020a environment where various performance metrics like accuracy, precision, [Formula: see text]-measure, recall and detection rate are evaluated and compared with conventional learning approaches. The model gives an average precision of 94.5% and [Formula: see text]1-score of 91% which is higher than other approaches. The proposed model establishes a better trade-off among the prevailing approaches with superior prediction accuracy.

In modern manufacturing the ability of retracing produced components is crucial for quality management and process optimization. Tracking is essential, especially for analyzing the influence of the production parameters on the final quality of the castings. In the iron foundry industry, common marking methods, such as a datamatrix code, cannot be used due to harsh environmental conditions and the rough surface of the cast parts. This work presents a new coding and reading system that guarantees unique marking in the casting process.The coding is built up over several beveled pins and is read out using an optical 2D handheld scanner. With a deep convolutional neural network approach of object detection and classification, a stable image processing algorithm is presented. With a first prototype a reading accuracy of 99.86% for each pin was achieved with an average scanning time of 0.43 s. The presented code is compatible with existing foundry processes, while the handheld scanner is intuitive and reliable. This allows immediate benefits for process optimization.

Tumor growth prediction, a highly challenging task, has long been viewed as a mathematical modeling problem, where the tumor growth pattern is personalized based on imaging and clinical data of a target patient. Though mathematical models yield promising results, their prediction accuracy may be limited by the absence of population trend data and personalized clinical characteristics. In this paper, we propose a statistical group learning approach to predict the tumor growth pattern that incorporates both the population trend and personalized data, in order to discover high-level features from multimodal imaging data. A deep convolutional neural network approach is developed to model the voxel-wise spatio-temporal tumor progression. The deep features are combined with the time intervals and the clinical factors to feed a process of feature selection. Our predictive model is pretrained on a group data set and personalized on the target patient data to estimate the future spatio-temporal progression of the patient's tumor. Multimodal imaging data at multiple time points are used in the learning, personalization and inference stages. Our method achieves a Dice coefficient of 86.8% +- 3.6% and RVD of 7.9% +- 5.4% on a pancreatic tumor data set, outperforming the DSC of 84.4% +- 4.0% and RVD 13.9% +- 9.8% obtained by a previous state-of-the-art model-based method.