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In this paper, an example of an intelligent security system based on deep learning approach has been analyzed and described. Security is an important element in many aspects of human life. Depending on customer requirements, different levels of security may be required and implemented. Nowadays, artificial intelligence is becoming an indispensable part in many areas of human life and accordingly security systems are also based on the new achievements in this field. In this paper, an intelligent security system based on two methods for identifying persons, gait recognition and face recognition, was described. The two methods mentioned above can be used to give persons access to different buildings, facilities, institutions, etc. or to different indoor or outdoor areas. In light of the above, the aforementioned system uses face and gait recognition to identify persons who should be granted access to a facility or a specific area within the facility. Accordingly, two deep learning models for gait and face recognition have been developed and described. The analysis, the settings defined and the results obtained in relation to the models developed were also described. [ABSTRACT FROM AUTHOR]

Digital technologies sustain today's world. Every part of the world is working towards digital technologies, which none of us can eliminate. Enormous growth is achieved only by unexpected acceleration by digital technologies, including the Internet of Everything (IoE), Artificial Neural Networks (ANN), Machine Learning (ML), Internet of Things (IoT), Artificial Intelligence (AI), Deep Learning (DL), and many more. These technologies started occupying all the engineering sectors, including manufacturing. This paper focuses on tribology analysis related to manufacturing concerning various digital manufacturing technologies. The paper narration includes Tribology using digital technologies wherein the journals and patent landscape analysis abet them. In trend, Tribology utilizes all these technologies today and envisages its growth with the predominant technological invention in the border view. The survey of various literature reveals that only three digital technologies, including AI, ML, and ANN, are used by tribologists around the globe. Other Technologies like Evolutionary Algorithm (EA), Support Vector Machine (SVM), and Adaptive Neuro-Fuzzy Interference Systems (ANFIS) are not used predominantly. [ABSTRACT FROM AUTHOR]

The article discusses the use of model predictive control (MPC) for drone flight control in unknown dynamic environments. MPC is a practical method for drone flight control due to its robustness against modeling errors and uncertainties. However, it can degrade in performance when faced with unknown environmental dynamics. The paper introduces a parameterized MPC approach called hyMPC that adapts to uncertain environmental conditions. It uses a novel policy search framework to train a high-level Gaussian policy and neural network policies to provide real-time decision variables. The effectiveness of hyMPC is validated through numerical simulations, achieving a 100% success rate in drone flight tests. The text discusses a framework for maneuvering drones through swinging gates using reinforcement learning (RL) and model predictive control (MPC). The framework utilizes episode-based policy search methods to acquire parameterized motor primitives and skills for controlling the drone. The RL-driven MPC framework incorporates a multi-layer perceptron to predict the gate's future state and a hybrid MPC cost function for trajectory optimization. The policy parameters are updated using a reward function that evaluates the quality of sampled trajectories. The proposed framework is validated through numerical simulations. This document presents a novel framework for navigating drones through swinging gates with unknown dynamics. The framework combines real-time Model Predictive Control (MPC) and offline Reinforcement Learning (RL) techniques. The approach involves training a neural network to predict gate motions and using an MLP to guide trajectory planning. The framework divides the traversal task into gate-following and gate-traversing [Extracted from the article]

This paper examines the activation energy influence in third-grade nanoparticle flow model (TG-NPFM), which is nonlinear mixed convective flow over a spinning disk under the influence of heat sink/source as well as viscous dissipation by utilizing Bayesian Regulation Method with backpropagated Artificial Neural Networks (BRM-BPANN). Nonlinear thermal radiation is also involved in the considered flow dynamics to obtain the approximated numerical solutions. The nonlinear PDEs of TG-NPFM are then transformed into nonlinear ODEs by implementing the corresponding transformation. We solved these ODEs by Optimal Homotopy Analysis Method (OHAM) to explain the dataset used as a reference for BRM-BPANN for different scenarios of TG-NPFM. This reference dataset is then exported to MATLAB to compute the results. The outcomes of TG-NPFM are figured by adopting the procedures of testing, validation and training. Moreover, approximated solution is compared with standard solution and the efficacy examination of TG-NPFM is authenticated by the studies of MSE, error histogram and regression plots. These soft computation frameworks provide incentive to use an efficient and dependable alternative paradigm built on soft computing environments to solve problems by doing a descriptive analysis to mitigate the impacts of different physical features. It is a new implementation of intelligent computational system of artificial intelligence introduced by incorporating the solver BRM-BPANN for interpreting the TG-NPFM. The absolute error values lie between 10 − 8 to 10 − 5 , 10 − 1 0 to 10 − 5 , 10 − 8 to 10 − 4 , 10 − 9 to 10 − 3 , 10 − 9 to 10 − 5 , 10 − 9 to 10 − 3 , 10 − 9 to 10 − 3 and 10 − 9 to 10 − 3 , which show the reliability and accuracy of the technique. The convergence and precision of the algorithm can easily be seen through the results of performance, training state and fitness plot, along with the regression value of R = 1. [ABSTRACT FROM AUTHOR]

Enzymes are protein molecules that play a crucial role in various biological processes in living organisms. They function as catalysts in biological reactions such as digestion, metabolism, DNA replication and other physiological processes. Furthermore, enzymes are widely used in food production, pharmaceuticals and biofuel production. In these industries, they accelerate desired chemical reactions as biocatalysts. Therefore, applying computational methods and data-driven algorithms to predict enzyme properties is essential. Over the past decade, deep learning has made remarkable advancements in science and technology. Deep learning is a subset of machine learning algorithms that rely on artificial neural networks. These algorithms can be employed for supervised, semi-supervised and unsupervised learning. Here, to provide an update on the current literature, we provide an overview of various deep learning algorithms and recent advancements in their application to enzyme science. These applications can generally be categorized into diverse subjects: function prediction, enzyme kinetic parameters prediction, enzyme-substrate identification, condition optimization, thermophilic property prediction, enzyme catalytic site prediction and enzyme design. In conclusion, we discuss the convergence of enzyme science and deep learning, highlighting the potential opportunities and challenges. Artificial intelligence algorithms have diverse applications in enzyme science. In this review paper, we focus on deep learning algorithms and their implications on various tasks, such as enzyme design, kinetic parameters prediction, etc. [ABSTRACT FROM AUTHOR]

Monitoring and managing litter on the roadways is important not only for preserving the cleanliness and esthetic appeal of our cities but also for safeguarding the overall health and sanitary environment of citizens. With the development of artificial intelligence, it is now possible to design algorithms capable of autonomously assessing the sanitation status of roadways. In this paper, we propose a method for detecting garbage on roads as our solution to tackle the issue. The proposed model is a deep learning model with an attention mechanism introduced to detect garbage in real-time scenarios. Furthermore, the object recognition capability of the model is enhanced through transfer learning to mitigate the influence of irrelevant content during training. The refined network can detect rubbish on roads with higher efficiency than the existing models. Also, compared with other methods, it consumes less amount of data for training. The experimental results demonstrate the efficiency of our proposed model, with a performance boost of 7.62% over the state-of-the-art methods. [ABSTRACT FROM AUTHOR]

The recognition of steel microstructure images plays a crucial role in the metallographic analysis process. Although some progress has been made through the application of artificial intelligence algorithms, several challenges remain. First, existing algorithms exhibit weak nonlinear feature extraction capabilities and noticeable limitations. Second, they overlook the intrinsic noise and redundant interference present in microscopic images. To address these issues, this paper investigates the automatic recognition of metallographic tissues by leveraging residual structures in deep neural networks. An enhanced residual network model based on transfer learning is proposed, which utilizes the pre-trained weights from the ImageNet dataset to facilitate learning with small sample data. This network offers higher classification accuracy and higher F1 scores. In addition, a deep residual shrinkage network model based on an attention mechanism is proposed. This model incorporates an attention sub-network into the original residual module and employs a soft threshold function to eliminate redundant features, including noise. The proposed algorithms are evaluated against various convolutional neural networks using 20 types of metallographic test sets. The experimental results showed that both methods have high accuracy rates of 95% and 94.44%, respectively, and F1 scores of 0.9464 and 0.9419. While maintaining the complexity of the model, there has been a significant improvement in accuracy, and the models exhibit strong generalization capabilities. Our research contributes to enhancing production efficiency, strengthening quality control, and improving material performance through computer vision technology. [ABSTRACT FROM AUTHOR]

This article explores a particle fusion approach for distributed filtering and smoothing in unmanned systems. The authors propose a drop-in replacement for the pointwise product operation using the generalized H¿older's inequality, which allows for tighter approximations in particle-based distributions. They validate their approach through simulations and experiments with miniature autonomous blimps, showing improved tracking performance. The text also discusses the concepts of distributed filtering and smoothing, as well as the use of factor graphs and the Belief Propagation algorithm in the context of distributed autonomous vehicles. The proposed methods are demonstrated to be effective through simulation examples and experimental results. The authors suggest future work involving the use of machine learning models to handle higher-dimensional data. [Extracted from the article]

The given text discusses a road segmentation method called MSAN that uses multi-modal scale-aware attentions to segment roads in real-time for autonomous driving. The method combines RGB and depth information to improve prediction results and outperforms single modality models. The MSAN architecture consists of two attention modules, one for feature fusion across modalities and scales and another for scale-aware fusion. The experiments show that MSAN achieves high performance and efficiency on multiple datasets. However, the method relies heavily on labeled data, which limits its adaptability to different scenarios. Future research aims to reduce data dependency through few-shot learning strategies. [Extracted from the article]