A Survey of Deep Network Techniques All Classifiers Can Adopt

Artificial intelligence in retinal disease: clinical application, challenges, and future directions

Retinal diseases are a leading cause of blindness in developed countries, accounting for the largest share of visually impaired children, working-age adults (inherited retinal disease), and elderly individuals (age-related macular degeneration). These conditions need specialised clinicians to interpret multimodal retinal imaging, with diagnosis and intervention potentially delayed. With an increasing and ageing population, this is becoming a global health priority. One solution is the development of artificial intelligence (AI) software to facilitate rapid data processing. Herein, we review research offering decision support for the diagnosis, classification, monitoring, and treatment of retinal disease using AI. We have prioritised diabetic retinopathy, age-related macular degeneration, inherited retinal disease, and retinopathy of prematurity. There is cautious optimism that these algorithms will be integrated into routine clinical practice to facilitate access to vision-saving treatments, improve efficiency of healthcare systems, and assist clinicians in processing the ever-increasing volume of multimodal data, thereby also liberating time for doctor-patient interaction and co-development of personalised management plans.

Rapid Detection of SARS-CoV-2 Variants of Concern by Genomic Surveillance Techniques

This chapter describes the application of genomic, transcriptomic, proteomic, and metabolomic methods in the study of SARS-CoV-2 variants of concern. We also describe the important role of machine learning tools to identify the most significant biomarker signatures and discuss the latest point-of-care devices that can be used to translate these findings to the physician's office or to bedside care. The main emphasis is placed on increasing our diagnostic capacity and predictability of disease outcomes to guide the most appropriate treatment strategies.

Music Score Recognition Method Based on Deep Learning

In recent years, the recommendation application of artificial intelligence and deep music has gradually become a research hotspot. As a complex machine learning algorithm, deep learning can extract features with value laws through training samples. The rise of deep learning network will promote the development of artificial intelligence and also provide a new idea for music score recognition. In this paper, the improved deep learning algorithm is applied to the research of music score recognition. Based on the traditional neural network, the attention weight value improved convolutional neural network (CNN) and high execution efficiency deep belief network (DBN) are introduced to realize the feature extraction and intelligent recognition of music score. Taking the feature vector set extracted by CNN-DBN as input set, a feature learning algorithm based on CNN&DBN was established to extract music score. Experiments show that the proposed model in a variety of different types of polyphony music recognition showed more accurate recognition and good performance; the recognition rate of the improved algorithm applied to the soundtrack identification is as high as 98.4%, which is significantly better than those of other classic algorithms, proving that CNN&DBN can achieve better effect in music information retrieval. It provides data support for constructing knowledge graph in music field and indicates that deep learning has great research value in music retrieval field.

Automatic Classification of Melanoma Skin Cancer with Deep Convolutional Neural Networks

Melanoma skin cancer is one of the most dangerous types of skin cancer, which, if not diagnosed early, may lead to death. Therefore, an accurate diagnosis is needed to detect melanoma. Traditionally, a dermatologist utilizes a microscope to inspect and then provide a report on a biopsy for diagnosis; however, this diagnosis process is not easy and requires experience. Hence, there is a need to facilitate the diagnosis process while still yielding an accurate diagnosis. For this purpose, artificial intelligence techniques can assist the dermatologist in carrying out diagnosis. In this study, we considered the detection of melanoma through deep learning based on cutaneous image processing. For this purpose, we tested several convolutional neural network (CNN) architectures, including DenseNet201, MobileNetV2, ResNet50V2, ResNet152V2, Xception, VGG16, VGG19, and GoogleNet, and evaluated the associated deep learning models on graphical processing units (GPUs). A dataset consisting of 7146 images was processed using these models, and we compared the obtained results. The experimental results showed that GoogleNet can obtain the highest performance accuracy on both the training and test sets (74.91% and 76.08%, respectively).

Research on Real-Time Detection of Safety Harness Wearing of Workshop Personnel Based on YOLOv5 and OpenPose

Wearing safety harness is essential for workers when carrying out work. When posture of the workers in the workshop is complex, using real-time detection program to detect workers wearing safety harness is challenging, with a high false alarm rate. In order to solve this problem, we use object detection network YOLOv5 and human body posture estimation network OpenPose for the detection of safety harnesses. We collected video streams of workers wearing safety harnesses to create a dataset, and trained the YOLOv5 model for safety harness detection. The OpenPose algorithm was used to estimate human body posture. Firstly, the images containing different postures of workers were processed to obtain 18 skeletal key points of the human torso. Then, we analyzed the key point information and designed the judgment criterion for different postures. Finally, the real-time detection program combined the results of object detection and human body posture estimation to judge the safety harness wearing situation within the current screen and output the final detection results. The experimental results prove that the accuracy rate of the YOLOv5 model in recognizing the safety harness reaches 89%, and the detection method of this study can ensure that the detection program accurately recognizes safety harnesses, and at the same time reduces the false alarm rate of the output results, which has high application value.

A Novel Integration of IF-DEMATEL and TOPSIS for the Classifier Selection Problem in Assistive Technology Adoption for People with Dementia

The classifier selection problem in Assistive Technology Adoption refers to selecting the classification algorithms that have the best performance in predicting the adoption of technology, and is often addressed through measuring different single performance indicators. Satisfactory classifier selection can help in reducing time and costs involved in the technology adoption process. As there are multiple criteria from different domains and several candidate classification algorithms, the classifier selection process is now a problem that can be addressed using Multiple-Criteria Decision-Making (MCDM) methods. This paper proposes a novel approach to address the classifier selection problem by integrating Intuitionistic Fuzzy Sets (IFS), Decision Making Trial and Evaluation Laboratory (DEMATEL), and the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS). The step-by-step procedure behind this application is as follows. First, IF-DEMATEL was used for estimating the criteria and sub-criteria weights considering uncertainty. This method was also employed to evaluate the interrelations among classifier selection criteria. Finally, a modified TOPSIS was applied to generate an overall suitability index per classifier so that the most effective ones can be selected. The proposed approach was validated using a real-world case study concerning the adoption of a mobile-based reminding solution by People with Dementia (PwD). The outputs allow public health managers to accurately identify whether PwD can adopt an assistive technology which results in (i) reduced cost overruns due to wrong classification, (ii) improved quality of life of adopters, and (iii) rapid deployment of intervention alternatives for non-adopters.

Challenges of Multiplex Assays for COVID-19 Research: A Machine Learning Perspective

Multiplex assays that provide simultaneous measurement of multiple analytes in biological samples have now developed into widely used technologies in the study of diseases, drug discovery, and other medical areas. These approaches span multiple assay systems and can provide readouts of specific assay components with similar accuracy as the respective single assay measurements. Multiplexing allows the consumption of lower sample volumes, lower costs, and higher throughput compared with carrying out single assays. A number of recent studies have demonstrated the impact of multiplex assays in the study of the SARS-CoV-2 virus, the infectious agent responsible for the current COVID-19 pandemic. In this respect, machine learning techniques have proven to be highly valuable in capturing complex disease phenotypes and converting these insights into models which can be applied in real-world settings. This chapter gives an overview of opportunities and challenges of multiplexed biomarker analysis, with a focus on the use of machine learning aimed at identification of biological signatures for increasing our understanding of COVID-19 disease, and for improved diagnostics and prediction of disease outcomes.

Blending Colored and Depth CNN Pipelines in an Ensemble Learning Classification Approach for Warehouse Application Using Synthetic and Real Data

Electric companies face flow control and inventory obstacles such as reliability, outlays, and time-consuming tasks. Convolutional Neural Networks (CNNs) combined with computational vision approaches can process image classification in warehouse management applications to tackle this problem. This study uses synthetic and real images applied to CNNs to deal with classification of inventory items. The results are compared to seek the neural networks that better suit this application. The methodology consists of fine-tuning several CNNs on Red–Green–Blue (RBG) and Red–Green–Blue-Depth (RGB-D) synthetic and real datasets, using the best architecture of each domain in a blended ensemble approach. The proposed blended ensemble approach was not yet explored in such an application, using RGB and RGB-D data, from synthetic and real domains. The use of a synthetic dataset improved accuracy, precision, recall and f1-score in comparison with models trained only on the real domain. Moreover, the use of a blend of DenseNet and Resnet pipelines for colored and depth images proved to outperform accuracy, precision and f1-score performance indicators over single CNNs, achieving an accuracy measurement of 95.23%. The classification task is a real logistics engineering problem handled by computer vision and artificial intelligence, making full use of RGB and RGB-D images of synthetic and real domains, applied in an approach of blended CNN pipelines.