Surface defect inspection is a key step to ensure the quality of the hot rolled steel surface. However, current advanced detection (DET) methods have high precision but low detection speed, which hinders the application of the detector in actual production. In this work, a real-time detection network (RDN) focusing on both speed and accuracy is proposed to solve the problem of steel surface defect detection. RDN takes ResNet-dcn, a modular encoding, and decoding network with light weights, as the basic convolutional architecture whose backbone is pretrained on ImageNet. To improve the detection accuracy, a skip layer connection module (SCM) and a pyramid feature fusion module (PFM) are involved into RDN. On the standard dataset NEU-DET, the proposed method can achieve the state-of-the-art recognition speed of 64 frames per second (FPS) and the mean average precision of 80.0% on a single GPU, which fully meets the requirements of the detection accuracy and speed in the actual production line.

The computational methods of protein-protein interaction sites prediction can effectively avoid the shortcomings of high cost and time in traditional experimental approaches. However, the serious class imbalance between interface and non-interface residues on the protein sequences limits the prediction performance of these methods. This work therefore proposed a new strategy, NearMiss-based under-sampling for unbalancing datasets and Random Forest classification (NM-RF), to predict protein interaction sites. Herein, the residues on protein sequences were represented by the PSSM-derived features, hydropathy index (HI) and relative solvent accessibility (RSA). In order to resolve the class imbalance problem, an under-sampling method based on NearMiss algorithm is adopted to remove some non-interface residues, and then the random forest algorithm is used to perform binary classification on the balanced feature datasets. Experiments show that the accuracy of NM-RF model reaches 87.6% and 84.3% on Dtestset72 and PDBtestset164 respectively, which demonstrate the effectiveness of the proposed NM-RF method in differentiating the interface or non-interface residues.

The task of drug-target interaction (DTI) prediction plays important roles in drug development. The experimental methods in DTIs are time-consuming, expensive and challenging. To solve these problems, machine learning-based methods are introduced, which are restricted by effective feature extraction and negative sampling. In this work, features with electrotopological state (E-state) fingerprints for drugs and amphiphilic pseudo amino acid composition (APAAC) for target proteins are tested. E-state fingerprints are extracted based on both molecular electronic and topological features with the same metric. APAAC is an extension of amino acid composition (AAC), which is calculated based on hydrophilic and hydrophobic characters to construct sequence order information. Using the combination of these feature pairs, the prediction model is established by support vector machines. In order to enhance the effectiveness of features, a distance-based negative sampling is proposed to obtain reliable negative samples. It is shown that the prediction results of area under curve for Receiver Operating Characteristic (AUC) are above 98.5% for all the three datasets in this work. The comparison of state-of-the-art methods demonstrates the effectiveness and efficiency of proposed method, which will be helpful for further drug development.

As a high mortality disease, cancer seriously affects people's life and well-being. Reliance on pathologists to assess disease progression from pathological images is inaccurate and burdensome. Computer aided diagnosis (CAD) system can effectively assist diagnosis and make more credible decisions. However, a large number of labeled medical images that contribute to improve the accuracy of machine learning algorithm, especially for deep learning in CAD, are difficult to collect. Therefore, in this work, an improved few-shot learning method is proposed for medical image recognition. In addition, to make full use of the limited feature information in one or more samples, a feature fusion strategy is involved in our model. On the dataset of BreakHis and skin lesions, the experimental results show that our model achieved the classification accuracy of 91.22% and 71.20% respectively when only 10 labeled samples are given, which is superior to other state-of-the-art methods.

Drug target interactions (DTIs) play a crucial role in drug discovery and development. The prediction of DTIs based on computational method can effectively assist the experimental techniques for DTIs identification, which are time-consuming and expensive. However, the current computational models suffer from low accuracy and high false positive rate in the prediction of DTIs, especially for datasets with extremely unbalanced sample categories. To accurately identify the interaction between drugs and target proteins, a variety of descriptors that fully show the characteristic information of drugs and targets are extracted and applied to the integrated method random forest (RF) in this work. Here, the random projection method is adopted to reduce the feature dimension such that simplify the model calculation. In addition, to balance the number of samples in different categories, a down sampling method NearMiss (NM) which can control the number of samples is used. Based on the gold standard datasets (nuclear receptors, ion channel, GPCRs and enzymes), the proposed method achieves the auROC of 92.26%, 98.21%, 97.65%, 99.33%, respectively. The experimental results show that the proposed method yields significantly higher performance than that of state-of-the-art methods in predicting drug target interaction.

Defect of insulators and vibration dampers on power transmission lines would affect the normal operation of power transmission. Defect detection of insulators and dampers always relies on resource-constrained terminal devices, such as unmanned aerial vehicles (UAVs). Existing defect detection algorithms for insulators and vibration dampers often encounter challenges, such as large parameter sizes and high computational complexity, which hinder their deployment on resource-constrained terminal devices. Furthermore, these components are typically small targets that are sparsely distributed and prone to background interference, negatively impacting detection accuracy. To address these issues, this article proposes an enhanced lightweight defect detection model based on YOLOv8n for insulators and vibration dampers. First, a new mobile bottleneck lite-effective convolution (MBLEConv) is designed to significantly enhance feature representation capabilities by strengthening global information modeling. Second, a novel bi-directional fusion feature network (HS-BiFusion) is developed, leveraging its unique bidirectional fusion mechanism to effectively enhance the integration of multiscale features. Finally, a new and effective detail-enhanced detection head (DET-LD) is constructed to strengthen detail feature extraction while maintaining lightweight characteristics. The experimental results demonstrate that the proposed model not only significantly reduces computational burden but also ensures detection accuracy, offering an efficient and reliable solution for intelligent inspection of transmission lines.