SPACT: A clustering-driven multi-modal framework for survival prediction using genomic and histopathology data.

Nowadays, machine learning (ML) has attained a high level of achievement in many contexts. Considering the significance of ML in medical and bioinformatics owing to its accuracy, many investigators discussed multiple solutions for developing the function of medical and bioinformatics challenges using deep learning (DL) techniques. The importance of DL in Internet of Things (IoT)-based bio- and medical informatics lies in its ability to analyze and interpret large amounts of complex and diverse data in real time, providing insights that can improve healthcare outcomes and increase efficiency in the healthcare industry. Several applications of DL in IoT-based bio- and medical informatics include diagnosis, treatment recommendation, clinical decision support, image analysis, wearable monitoring, and drug discovery. The review aims to comprehensively evaluate and synthesize the existing body of the literature on applying deep learning in the intersection of the IoT with bio- and medical informatics. In this paper, we categorized the most cutting-edge DL solutions for medical and bioinformatics issues into five categories based on the DL technique utilized: convolutional neural network, recurrent neural network, generative adversarial network, multilayer perception, and hybrid methods. A systematic literature review was applied to study each one in terms of effective properties, like the main idea, benefits, drawbacks, methods, simulation environment, and datasets. After that, cutting-edge research on DL approaches and applications for bioinformatics concerns was emphasized. In addition, several challenges that contributed to DL implementation for medical and bioinformatics have been addressed, which are predicted to motivate more studies to develop medical and bioinformatics research progressively. According to the findings, most articles are evaluated using features like accuracy, sensitivity, specificity, F-score, latency, adaptability, and scalability.

Humans have to respond to everyday tasks with goal-directed actions in complex and time-continuous environments. However, modeling human behavior in such environments has been challenging. Deep Q-networks (DQNs), an application of deep learning used in reinforcement learning (RL), enable the investigation of how humans transform high-dimensional, time-continuous visual stimuli into appropriate motor responses. While recent advances in DQNs have led to significant performance improvements, it has remained unclear whether these advancements translate into improved modeling of human behavior. Here, we recorded motor responses in human participants (N = 23) while playing three distinct arcade games. We used stimulus features generated by a DQN as predictors for human data by fitting the DQN’s response probabilities to human motor responses using a linear model. We hypothesized that advancements in RL models would lead to better prediction of human motor responses. Therefore, we used features from two recently developed DQN models (Ape-X and SEED) and a third baseline DQN to compare prediction accuracy. Compared to the baseline DQN, Ape-X and SEED involved additional structures, such as dueling and double Q-learning, and a long short-term memory, which considerably improved their performances when playing arcade games. Since the experimental tasks were time-continuous, we also analyzed the effect of temporal resolution on prediction accuracy by smoothing the model and human data to varying degrees. We found that all three models predict human behavior significantly above chance level. SEED, the most complex model, outperformed the others in prediction accuracy of human behavior across all three games. These results suggest that advances in deep RL can improve our capability to model human behavior in complex, time-continuous experimental tasks at a fine-grained temporal scale, thereby opening an interesting avenue for future research that complements the conventional experimental approach, characterized by its trial structure and use of low-dimensional stimuli.

Multiscale attentional residual neural network framework for remaining useful life prediction of bearings

Few-shot learning aims to obtain a better deep learning model through training with a smaller amount of data. In recent years, with the development and application of deep learning, deep learning has faced the problem of few-shot in many scenarios. Therefore, the study of few-shot learning will promote the application and deployment of deep learning in the industrial field. First of all, this survey specifically introduces two few-shot learning implementation methods: based on data augmentation and based on transfer learning. Then, this survey lists actual cases to illustrate the application of few-shot learning in industry. One case is about Few-shot parts surface defect detection based on metric learning. Another case is about poultry egg detection in few-shot based on meta learning. Finally, this survey summarizes the research achievements and research value of few-shot learning. Survey summarizes the problems and challenges faced by few-shot learning and the future development direction of few-shot learning.KeywordsFew-shot learningData augmentationTransfer learningMetric learningMeta learning

With the explosive growth of computer science and big data, quantitative finance is undergoing a huge shift from traditional econometrics to data-driven Artificial Intelligence (AI). This study aims to review the applications of Machine Learning (ML), Deep Learning (DL), and Reinforcement Learning (RL) in vital areas such as asset pricing, algorithmic trading and risk management. Research shows that Deep Neural Networks (DNNs) capture non-linear market patterns, Natural Language Processing (NLP) analyses unstructured data while providing superior sentiment signals compared to generic alternatives. Deep Reinforcement Learning (DRL) can effectively automate execution in dynamic market environments, which optimizes trading decisions. Similarly, Temporal Fusion Transformers (TFT) have emerged as a dominant architecture for multi-horizon time series forecasting, offering superior interpretability and accuracy over standard recurrent networks. These ML models greatly outperform traditional methods like CAPM and ARIMA in prediction accuracy and handling complex data. However, there are still challenges about the lack of interpretability ("black box") and overfitting. In the future, quantitative finance lies in combining "Explainable AI" (XAI).

Background: Artificial intelligence (AI) and deep learning (DL) are rapidly changing the field of diagnostics and imaging in cardiology, offering tools for automatic segmentation, quantification of changes, and risk stratification. These technologies have the potential to increase diagnostic accuracy, work efficiency, and individualization of patient care. Methods: This structured narrative review critically evaluates clinically validated applications of artificial intelligence (AI) and deep learning (DL) in cardiovascular medicine, focusing on imaging (echocardiography, coronary CT angiography, cardiac MRI, and ECG), risk stratification, and biomarker integration. A systematic literature search was conducted in PubMed for studies published between January 2015 and December 2026, supplemented by references from key articles. Original English-language studies reporting quantitative clinical outcomes were included, with 78 studies ultimately analyzed. Results: AI and DL models, including convolutional neural networks and transformers, achieved performance comparable to experts in cardiac imaging, myocardial perfusion assessment, valve defect detection, and coronary event prediction. Multimodal approaches improved diagnostic accuracy and reproducibility, while explainable AI enhanced transparency and clinical confidence. Deep learning also enabled faster image acquisition and processing without compromising precision. Conclusions: AI and DL have transformative potential in cardiology, offering fast, accurate, and scalable diagnostic tools. The integration of multimodal data, the validation of algorithms in prospective studies, and ensuring the transparency of models are key. Future research should focus on prospective, multicenter validations and the ethical and safe implementation of AI in everyday clinical practice.

Deep Learning (DL) applications in the Internet of Things (IoT) have garnered significant attention, with numerous studies exploring various models and implementations. Concurrently, the emerging field of the Internet of Behaviors (IoB) has captured broad interest, extending the capabilities of IoT through the synergy of DL. This transformative approach presents novel possibilities across diverse domains (e.g. healthcare, business, education, and insurance). In this survey, we uniquely contribute by elucidating the synergistic potential of integrating IoB with DL. Our literature review provides a state-of-the-art analysis of DL implementations within the IoB ecosystem, highlighting architectures, features, and advantages over traditional machine learning methods. Through a meticulous examination of findings, we reveal specific insights gained from the survey, and we discuss issues, challenges, and future research directions in applying DL in IoB applications. Our survey stands as a testament to achievements in this dynamic intersection and serves as a call to action for exploring uncharted territories.

In today’s complex and ever-changing world, Supply Chain Management (SCM) is increasingly becoming a cornerstone to any company to reckon with in this global era for all industries. The rapidly growing interest in the application of Deep Learning (a class of machine learning algorithms) in SCM, has urged the need for an up-to-date systematic review on the research development. The main purpose of this study is to provide a comprehensive vision by reviewing a set of 43 papers about applications of Deep Learning (DL) methods to the SCM, as well as the trends, perspectives, and potential research gaps. This review uses content analysis to answer three research questions namely: 1- What SCM problems have been solved by the use of DL techniques? 2- What DL algorithms have been used to solve these problems? 3- What alternative algorithms have been used to tackle the same problems? And do DL outperform these methods and through which evaluation metrics? This review also responds to this call by developing a conceptual framework in a value-adding perspective that provides a full picture of areas on where and how DL can be applied within the SCM context. This makes it easier to identify potential applications to corporations, in addition to potential future research areas to science. It might also provide businesses a competitive advantage over their competitors by allowing them to add value to their data by analyzing it quickly and precisely.

Chapter Seven - Application of few-shot semisupervised deep learning in organic matter content logging evaluation

It is a computational approach, which uses a deep learning model with an architecture similar to that of biological brain networks, that has been trained using vast amounts of data. Deep reinforcement learning (DRL) is another name for deep learning (DL). By eliminating the need for direct programming in the process, DL functions as a middleman between data collection and meaningful knowledge. It has outperformed the vast majority of classification algorithms and is capable of learning data representations for a broad range of functions by itself. Deep learning applications in cancer include classification, feature extraction, object identification, picture interpretation and translation, sensitively and appropriately, and image annotation and labeling. The goal of this chapter is to gain a better understanding of the possible function of DL and how it may be used more successfully in radiation oncology. With the expansion of DL, a wide range of studies that led to the improvement of radiation oncology were explored more thoroughly than before. This article discussed medical imaging, image segmentation utilizing computers to assist diagnosis, computer-aided detection, treatment planning and delivery, quality assurance, treatment response, and treatment delivery. The studies that utilized DL were classified and organized according to the kind of radiation treatment used. The most current scientific achievements were chosen, and the therapeutic value of their results was assessed. Therapists may profit from employing a deep learning model since it will provide them with more precise and accurate solutions to their problems. A statistical approach to cancer patient safety is obvious, but implementing these concepts would require social adjustments at both the academic and industrial levels, which will take time. The goal of this chapter was to make it as accessible as possible to both radiotherapy and deep learning in order to encourage new collaboration between the two groups in the development of specialized radiation technologies.

With the rapid development of high-throughput sequencing technology, massive microbial data has been accumulated. The understanding of the microbial data could help us to find the relationships between microbes and diseases. However, due to the high dimensionality, sparseness, and complexity of the data, traditional machine learning methods have insufficient learning and representational ability. Meanwhile, the rise of deep learning enables us to deal with these complex problems effectively. In this survey, we introduce the application of machine learning in microbial data analysis and focus on microbial classification and feature selection tasks. In particular, we discuss the current application and challenges of deep learning in microbial studies. Based on these discussions, we recommend that before using deep learning to conduct microbiome-wide association studies, it is essential to consider prior knowledge such as phylogeny, which would improve the accuracy and interpretability of the model.

Building a new power system with new energy as the main body is one of the most important measures to achieve “carbon peak and carbon neutral” in China, which also puts forward higher requirements on the data analysis and processing capacity of the current power system. In recent years, artificial intelligence algorithms represented by deep learning have provided strong support for the realization and development of new energy power system. The paper summarizes the basic characteristics of deep learning algorithm and its five mainstream models. From the aspects of image recognition, power load prediction, fault diagnosis and system optimization scheduling, the application of deep learning in new energy power system is deeply analyzed. The application prospect of deep learning in new energy power system is prospected in order to provide reference for the research and construction of new power system.

Despite technological advancements in echocardiography (echo) systems, effectively utilizing these machines and achieving accurate and timely interpretation of the resulting image data still pose significant challenges. Hence, many researchers have sought to overcome these challenges by leveraging artificial intelligence (AI), particularly through the application of deep learning (DL) techniques. In this study, we provide a thorough analysis of studies aimed at leveraging DL to reshape the field of echocardiography, with a focus on their clinical impact, challenges, and opportunities for advancement. These studies can be categorized into two main groups, each encompassing multiple tasks: data acquisition and quality enhancement, and intelligent echo data analysis. The latter group is the most extensively studied, with key tasks including annotated data generation, cardiac abnormality diagnosis, cardiac structure segmentation, etc. Through thorough analysis of the selected studies, we highlight the transformative impact of DL techniques on the automatic diagnosis and monitoring of cardiovascular diseases, as well as the underexplored challenges and potential solutions and research tasks. Moreover, we introduce heart anatomy and important clinical parameters, thereby providing researchers of DL algorithms with clinical procedures associated with evaluating blood vessels and cardiac function. Furthermore, details of the commonly adopted deep learning models and their implementation procedures are presented. By providing a multidisciplinary perspective, we believe that our work paves the way for more collaborative efforts in the field and establishes a foundation for future innovations in DL-driven echocardiography.

As the application of deep learning in intelligent transportation systems becomes increasingly widespread, the accuracy and reliability of traffic flow prediction have become crucial. However, existing deep learning methods are often complex in model design and lack intuitiveness, making it challenging to provide responsible explanations for traffic predictions. This paper references a responsible and reliable traffic flow prediction model (R2T-LLM) based on large language models (LLMs). This model captures complex spatiotemporal patterns and external factors by converting multimodal traffic data into natural language descriptions. By leveraging LLMs’ advanced understanding capabilities, R2T-LLM provides more transparent and interpretable predictions. It bridges the gap between technical performance and real-world application needs. While maintaining accuracy comparable to deep learning baselines, R2T-LLM offers intuitive and reliable prediction explanations. This paper also explores the spatiotemporal and input dependencies of conditional future traffic predictions and compares the model with other different approaches and types. Furthermore, it evaluates the potential of R2T-LLM in addressing challenges in large-scale urban traffic systems, highlighting its developmental and application prospects.

Stroke is a major global health problem that causes mortality and morbidity. Predicting the outcomes of stroke intervention can facilitate clinical decision-making and improve patient care. Engaging and developing deep learning techniques can help to analyse large and diverse medical data, including brain scans, medical reports, and other sensor information, such as EEG, ECG, EMG, and so on. Despite the common data standardisation challenge within the medical image analysis domain, the future of deep learning in stroke outcome prediction lies in using multimodal information, including final infarct data, to achieve better prediction of long-term functional outcomes. This article provides a broad review of recent advances and applications of deep learning in the prediction of stroke outcomes, including (i) the data and models used, (ii) the prediction tasks and measures of success, (iii) the current challenges and limitations, and (iv) future directions and potential benefits. This comprehensive review aims to provide researchers, clinicians, and policy makers with an up-to-date understanding of this rapidly evolving and promising field.