Fire detection presents considerable challenges due to the destructive and unpredictable characteristics of fires. These difficulties are amplified by small size low-resolution nature fire smoke targets in images captured from a distance, making it hard for models extract relevant features. To address this, we introduce novel method small-target named YOLOv7scb. This approach incorporates two key improvements YOLOv7 framework: use space-to-depth convolution (SPD-Conv) C3 modules, enhancing...

Long non-coding RNA (LncRNA) and microRNA (miRNA) are both RNAs that play significant regulatory roles in many life processes. There is cumulating evidence showing the interaction patterns between lncRNAs miRNAs highly related to cancer development, gene regulation, cellular metabolic process, etc. Contemporaneously, with rapid development of sequence technology, numerous novel have been found, which might help explore regulated patterns. However, increasing unknown interactions may hinder...

Several factors, including advances in computational algorithms, the availability of high-performance computing hardware, and assembly large community-based databases, have led to extensive application Artificial Intelligence (AI) biomedical domain for nearly 20 years. AI algorithms attained expert-level performance cancer research. However, only a few AI-based applications been approved use real world. Whether will eventually be capable replacing medical experts has hot topic. In this...

Body fluid proteome has been intensively studied as a primary source for disease biomarker discovery. Using advanced proteomics technologies, early research success resulted in increasingly accumulated proteins detected different body fluids, among which many are promising biomarkers. However, despite handful of small-scale and specific data resources, current is clearly lacking effort compiling published into centralized sustainable repository that can provide users with systematic analytic...

Human proteins that are secreted into different body fluids from various cells and tissues can be promising disease indicators. Modern proteomics research empowered by both qualitative quantitative profiling techniques has made great progress in protein discovery human fluids. However, due to the large number of diverse modifications present fluids, as well existing technical limits major platforms (e.g. mass spectrometry), discrepancies often generated experimental studies. As a result,...

Cerebrospinal fluid (CSF) proteins are very important because they can serve as biomarkers for central nervous system diseases. Although many CSF have been identified with wet experiments, the identification of is still a challenge. In this paper, we propose novel method to predict in based on protein features. A two-stage feature-selection employed remove irrelevant features and redundant The deep neural network bagging used construct model prediction proteins. experiment results...

Abstract Body fluid biomarkers are very important, because they can be detected in a non-invasive or minimally invasive way. The discovery of secreted proteins human body fluids is an essential step toward proteomic biomarker identification for diseases. Recently, many computational methods have been proposed to predict and achieved some success. However, most them based on manual negative dataset, which usually biased therefore limits the prediction performances. In this paper, we first...

Prediction of secreted proteins in human body fluids is essential since hold promise as disease biomarkers. Various approaches have been proposed to predict whether a protein into specific fluid by its sequence. However, there may be relationships between different when are these fluids. Current ignore directly, and therefore their performances limited. Here, we present MultiSec, an improved approach for discovery exploit via multi-task learning. Specifically, sampling-based balance strategy...

Fire risk prediction is crucial for urban firefighting deployment, as it can reduce the damage and fatalities caused by fires. Therefore, we propose an fire model, FIRE-CLA, to predict risks in areas. This model aids departments prioritizing inspections at specific locations, including commercial property areas, based on predicted different regions. FIRE-CLA calculates over 6,000 streets city, achieving a accuracy of up 90%. Additionally, presents locations through interactive map visualized...

In the field of diagnosing lung diseases, application neural networks (NNs) in image classification exhibits significant potential. However, NNs are considered "black boxes," making it difficult to discern their decision-making processes, thereby leading skepticism and concern regarding NNs. This compromises model reliability hampers intelligent medicine's development. To tackle this issue, we introduce Evolutionary Neural Architecture Search (EvoNAS). tasks, EvoNAS initially utilizes an...

In recent years, the rapid growth of video data posed challenges for storage and transmission. Video compression techniques provided a viable solution to this problem. study, we proposed bidirectional coding model named DeepBiVC, which was based on two-stage learning. Firstly, conducted preprocessing by segmenting flow into groups continuous image frames, with each group comprising five frames. Then, in first stage, developed an module invertible neural network (INN) compress last frames...

Research on body fluid proteomes has led to the discoveries of context-dependent proteomics profiles and numerous novel disease biomarkers. Common challenges remain with current technologies about how effectively handle large variety protein modifications in those fluids. To this end, computational efforts have shown early successes identifying biomarker proteins specific human diseases. In article, we first reviewed published methods topic then presented a new database system deep...

Cerebrospinal fluid (CSF) exists in the surrounding spaces of mammalian central nervous systems (CNS); therefore, there are numerous potential protein biomarkers associated with CNS disease CSF. Currently, approximately 4300 proteins have been identified CSF by profiling. However, due to diverse modifications, as well existing technical limits, large-scale identification is still considered a challenge. Inspired computational methods, this paper proposes deep learning framework, named...