In this paper, we present a powerful underwater image dehazing technique that exploits two image characteristics—RGB color channels and image features. In using RGB color channels, each color channel is decomposed into two units based on the si...
Mathematical morphology is a fundamental tool based on order statistics for image processing, such as noise reduction, image enhancement and feature extraction, and is well-established for binary and grayscale images, whose pixels can be sorted by th...
Background/Objectives: Ultrasound (US) imaging plays a crucial role in the early detection and treatment of hepatocellular carcinoma (HCC). However, challenges such as speckle noise, low contrast, and diverse lesion morphology hinder its diagnostic a...
Internet of Things (IoT) devices bring us rich sensor data, such as images capturing the environment. One prominent approach to understanding and utilizing such data is image classification which can be effectively solved by deep learning (DL). Combi...
This paper provides new models of the attention-based random forests called LARF (leaf attention-based random forest). The first idea behind the models is to introduce a two-level attention, where one of the levels is the “leaf” attention...
On resource-constrained edge hardware, fixed-point Transformer inference is constrained by GEMM kernels and non-GEMM normalization paths. In LayerNorm and Softmax, division, inverse square root, and denominator reciprocal operations complicate fixed-...
With the growth of data in the real world, datasets often encounter the problem of long-tailed distribution of class sample sizes. In long-tailed image recognition, existing solutions usually adopt a class rebalancing strategy, such as reweighting ba...
The paper proposes three modeling techniques to improve the performance evaluation of the call center agent. The first technique is speech processing supported by an attention layer for the agent’s recorded calls. The speech comprises 65 features for...
In recent times, DFU (diabetic foot ulcer) has become a universal health problem that affects many diabetes patients severely. DFU requires immediate proper treatment to avert amputation. Clinical examination of DFU is a tedious process and complex i...
Low-power NPUs enable on-device LLM inference through efficient integer and fixed-point algebra, yet their lack of native exponential support makes Transformer softmax a critical performance bottleneck. Existing NPU kernels approximate
The advancement of machine learning in industrial applications has necessitated the development of tailored solutions to address specific challenges, particularly in multi-class classification tasks. This study delves into the customization of loss f...
To address the issue associated with poor accuracy rates for specific emitter identification (SEI) under low signal-to-noise ratio (SNR) conditions, where the single-dimension radar signal characteristics are severely affected by noise, we propose an...
Automatic Modulation Classification (AMC) is of paramount importance in wireless communication systems. Existing methods usually adopt a single category of neural network or stack different categories of networks in series, and rarely extract differe...
In recent times, several machine learning models have been built to aid in the prediction of diverse diseases and to minimize diagnostic errors made by clinicians. However, since most medical datasets seem to be imbalanced, conventional machine learn...
In this paper, a method of Synthetic Aperture Radar (SAR) image Automatic Target Recognition (ATR) based on Convolution Auto-encode (CAE) and Support Vector Machine (SVM) is proposed. Using SVM replaces the traditional softmax as the classifier of th...
For efficient and effective energy management, accurate energy consumption forecasting is required in energy management systems (EMSs). Recently, several artificial intelligence-based techniques have been proposed for accurate electric load forecasti...
Reliable medical-image classification requires assessment of accuracy, calibration, and uncertainty under image degradation. We analyze the unit-prior Evidential Mean Squared Error (EMSE) objective with masked Kullback–Leibler (KL) regularizati...
This paper mainly improves the visual geometry group network-16 (VGG-16), which is a classic convolutional neural network (CNN), to classify the surface defects on cement concrete bridges in an accurate manner. Specifically, the number of fully conne...
The capability for recognizing pedestrian semantic attributes, such as gender, clothes color and other semantic attributes is of practical significance in bank smart surveillance, intelligent transportation and so on. In order to recognize the key mu...
Accurate classification and efficient retrieval of histopathological images are essential for the diagnosis of lung adenocarcinoma (LUAD). Existing deep learning approaches for Content-Based Histopathological Image Retrieval (CBHIR) typically generat...
The purity of graphite often affects its application in different fields. In view of the low efficiency of manual recognition and the omission of features extracted by single convolution neural network, this paper proposes a method for identifying gr...
In this paper, a novel Pixel-Voxel network is proposed for dense 3D semantic mapping, which can perform dense 3D mapping while simultaneously recognizing and labelling the semantic category each point in the 3D map. In our approach, we fully leverage...
In view of the rolling bearing fault signal non-stationarity, strong noise can lead to low fault diagnosis accuracy. A Swin Transformer and generalized S Transform fault diagnosis method is proposed to solve the problems of difficult signal feature e...
As state-of-the-art deep neural networks are being deployed at the core level of increasingly large numbers of AI-based products and services, the incentive for “copying them” (i.e., their intellectual property, manifested through the kno...
Cassava is a typical staple food in the tropics, and cassava leaf disease can cause massive yield reductions in cassava, resulting in substantial economic losses and a lack of staple foods. However, the existing convolutional neural network (CNN) for...
Intelligent methods have long been researched in fault diagnosis. Traditionally, feature extraction and fault classification are separated, and this process is not completely intelligent. In addition, most traditional intelligent methods use an indiv...
When reading texts for text classification tasks, a large number of words are irrelevant, and in text classification tasks, the traditional self-attention mechanism has the problem of weight distribution limitations. Therefore, a text classification...
The physical features of fruit and vegetables make the task of vision-based classification of fruit and vegetables challenging. The classification of fruit and vegetables at a supermarket self-checkout poses even more challenges due to variable light...
Convolutional neural networks (CNNs) have made great achievements on computer vision tasks, especially the image classification. With the improvement of network structure and loss functions, the performance of image classification is getting higher a...
Despite the success of dichotomous sentiment analysis, it does not encompass the various emotional colors of users in reality, which can be more plentiful than a mere positive or negative association. Moreover, the complexity and imbalanced nature of...
Image denoising requires simultaneous suppression of stochastic corruption and preservation of local texture and large-scale structure. This study presents the Multi-scale Attention Denoising Network (MADNet), an encoder–decoder residual archit...
Today, there is growing interest in the automatic classification of a variety of tasks, such as weather forecasting, product recommendations, intrusion detection, and people recognition. “Mixture-of-experts” is a well-known classification...
Low-altitude wireless networks (LAWNs) require reliable multi-user communication together with accurate range and velocity sensing. Communication and sensing share the same transmit power and spatial degrees of freedom (DoF), and therefore joint beam...
In order to adequately characterize the visual characteristics of atmospheric visibility and overcome the disadvantages of the traditional atmospheric visibility measurement method with significant dependence on preset reference objects, high cost, a...
Creating an effective deep learning technique for accurately diagnosing leak signals across diverse environments is crucial for integrating artificial intelligence (AI) into the power plant industry. We propose an automatic weight redistribution ense...
Person Re-Identification (ReID) has witnessed tremendous improvements with the help of deep convolutional neural networks (CNN). Nevertheless, because different fields have their characteristics, most existing methods encounter the problem of poor ge...
To monitor the tool wear state of computerized numerical control (CNC) machining equipment in real time in a manufacturing workshop, this paper proposes a real-time monitoring method based on a fusion of a convolutional neural network (CNN) and a bid...
Urban lakes are essential for ecological balance and urban development. This study developed a comprehensive framework to evaluate the ecosystem health of urban lakes in China. Nineteen representative lakes from four lake zones were examined using th...
Multiclass classification in machine learning often faces significant challenges due to unbalanced datasets. This situation leads to biased predictions and reduced model performance. This research addresses this issue by proposing a novel approach th...
Interpretable feature-group fusion for tabular regression remains challenging because strong predictive models often lack explicit group-level attribution, complexity control, and attribution-stability guarantees. This paper proposes SHAP-Weighted Fe...
Lithology identification is an essential fact for delineating uranium-bearing sandstone bodies. A new method is provided to delineate sandstone bodies by a lithological automatic classification model using machine learning techniques, which could als...
One of the challenges in fine-grained classification is that subcategories with significant similarity are hard to be distinguished due to the equal treatment of all subcategories in existing algorithms. In order to solve this problem, a fine-grained...
Facial expression recognition (FER) is a challenging problem in the fields of pattern recognition and computer vision. The recent success of convolutional neural networks (CNNs) in object detection and object segmentation tasks has shown promise in b...
The great success of artificial intelligence (AI) calls for higher-performance computing accelerators, and optical neural networks (ONNs) with the advantages of high speed and low power consumption have become competitive candidates. However, most of...
This work presents a deep learning method for scene description. (1) Background: This method is part of a larger system, called BlindSys, that assists the visually impaired in an indoor environment. The method detects the presence of certain objects,...
The bidirectional power flows and time-varying characteristics generated by distributed photovoltaic integration into low-voltage distribution networks pose accuracy and fairness challenges to traditional line loss allocation methods. Existing method...
To address the issue of reduced gaze estimation accuracy caused by individual differences in different environments, this study proposes a novel gaze estimation algorithm based on attention mechanisms. Firstly, by constructing a facial feature extrac...
Crop leaf diseases cause 10–40% annual yield losses, yet timely field diagnosis remains difficult. Vision-language models (VLMs) lift recognition accuracy with rich textual descriptions, but multimodal pipelines are too slow for real-time field...
Decision strategies in multi-attribute decision-making are described as qualitatively distinct rules with no common quantitative scale for comparing them. This study proposes a unified framework that represents a decision strategy as a feature vector...
When a system must detect several related conditions over the same inputs, its designer can train a specialized classifier for each condition or one pooled model for all of them. Published comparisons of these designs contradict one another, and cert...
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