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Modern multi-layer perceptron (MLP) models have shown competitive results in learning visual representations without self-attention. However, existing MLP models are not good at capturing local details and lack prior knowledge of human body ...

• We present, to the best of our knowledge, the first MLP-Like architecture called GraphMLP for 3D human pose estimation. It combines the advantages of modern MLPs and GCNs, including globality, locality, and connectivity.
• The novel SG-...

Traffic of different applications when they are encrypted or passed through virtual private networks (VPN) tunnels become similar. Finding discriminating features to distinguish such traffic is very challenging. We propose an approach called ...

Multivariate time series (MTS) forecasting is extensively applied in real-world scenarios. Recent studies introduced the concept of channel independence, which has achieved significant results. However, this approach often misses the intricate ...

Modeling actor interaction relations is crucial for group activity recognition. Previous approaches often adopt a fixed paradigm that involves calculating an affinity matrix to model these interaction relations, yielding significant performance. ...

• We propose an MLP-based module for interaction relation modeling implicitly.
• Our module is designed by MLP entirely, simplifying the module’s structure.
• Our module is a universal module that can be used for other methods.

Consumer load profiling involves examining patterns of energy consumption using available data. With smart meter data available at (sub-)hourly intervals, it is possible to use the it to generate daily load profiles that capture the typical ...

Depth estimation can provide auxiliary information for scene perception. Generally, extensive textureless surfaces, such as walls and ceilings, exist in indoor environments, and they share similar scene and semantic content. Overly consistent ...

Various studies have emphasized the importance of identifying the optimal Trigger Timing (TT) for the trigger shot in In Vitro Fertilization (IVF), which is crucial for the successful maturation and release of oocytes, especially in minimal ...

• AI-optimized trigger timing significantly increases the number of useable blastocysts.
• Oocyte quality and quantity are crucial for successful blastocyst formation.
• Hyperparameter optimization in ANN model significantly improves ...

• A multi-layer perceptron model for straight-turn binary classification training using K-Fold cross validation is proposed.
• A convolutional neural network-based model is proposed to proform turning classification between Omega turn and ...

GPS technology is an indispensable technique in “precision agriculture”. In the field of path planning, the evaluation of planned trajectories is based on GPS data. However, due to the unstructured operating environment, the existing methods for ...

We consider a square non linear parametric equations system F(P,X) = 0 which is constituted of n non differential equations in the n unknowns { x 1 , … , x n } that are the components of X while P = { p 1 , … , p m } is a set of m parameters that ...

Human musculoskeletal recognition is essential for analyzing the physical condition of elderly individuals. However, many elderly individuals are concerned about the inconvenience during rehabilitation and therapy sessions. To address this issue, ...

Human epidermal growth factor receptor-2 (HER2) is critical in regulating cellular growth, development, and differentiation processes in normal cells. Breast cancer is the most common and lethal cancer among women worldwide, and about 25% of ...

Flexible job shop scheduling problem (FJSSP), as a variant of the job shop scheduling problem, has a larger solution space. Researchers are always looking for good methods to solve this problem. In recent years, the deep reinforcement learning (...

In recent years, vision Transformers and MLPs have demonstrated remarkable performance in image understanding tasks. However, their inherently dense computational operators, such as self-attention and token-mixing layers, pose significant ...

Human activity recognition (HAR) with wearable inertial sensors is a burgeoning field, propelled by advances in sensor technology. Deep learning methods for HAR have notably enhanced recognition accuracy in recent years. Nonetheless, the ...

Soil moisture monitoring is a significant aspect of environmental and agricultural studies, and Global Navigation Satellite System Interferometric Reflectometry (GNSS-IR) has emerged as a promising technology for this purpose. Traditionally, GNSS-...

Weakly supervised object localization (WSOL) is a challenging task that aims to localize objects in images using only image-level labels. Despite the widespread use of WSOL methods based on class activation mapping (CAM), such methods do not ...

The multi-layer perceptron (MLP) is a highly popular artificial neural network used for classification across various applications. Swarm intelligence algorithms, such as the grey wolf optimizer, battle royale optimizer, sine cosine algorithm, ...

• A reliable and efficient GWO variant for training MLP classifiers is proposed.
• The competing wolf leaders mechanism provides a flexible social leadership hierarchy.
• A population diversity-enhanced initiation method improves the ...

Exogenous variables, unrelated to the recommendation system itself, can significantly enhance its performance. Therefore, integrating these time-evolving exogenous variables into a time series and conducting time series predictions can maximize ...

Transformer-based trackers have demonstrated promising performance in visual object tracking tasks. Nevertheless, two drawbacks limited the potential performance improvement of transformer-based trackers. Firstly, the static receptive field of ...

• The FCA learns different granularity information in one layer.
• The CC-MLP, is developed to capture local interaction information across samples.
• Based on the FCA and the CC-MLP, a novel encoder and decoder are designed for VOT.

Sensor data, such as traffic flow monitoring data, constitutes a type of multimedia data. Forecasting sensor data holds significant potential for decision-making. And we can explore its patterns using time series forecasting methods. In the past ...