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Protein structure expectation is a particularly mind boggling issue that it is frequently assaulted and disintegrated using four distinct levels and they are: 1-D forecast of under- lying highlights along the essential succession of amino acids sequences, 2-D forecast of spatial connections between the sequence of amino acids, 3-D forecast of a tertiary structure of protein and quaternary structure of protein. This paper also try to introduce some assessment tools for finding the accuracy of result from applying ML and DL tools. And try to analyses and compare various algorithms based on deep learning methods verses machine learning methods used for sequence prediction. This paper also examines the turn of events and utilization of concealed Markov model, uphold vector machines, Bayesian techniques, and grouping strategies. This investigation will be helpful in creating future strategies to improve the exactness of protein auxiliary structure expectation. In this paper, also introduce and summarize the problem of quantum essential elements of: (1) Variational auto-encoder (2) GAN, generative adversarial network (3) RNN, recurrent neural (4) CNN, convolutional neural networks protein structure prediction. Later on also summarizes the evolution of predictive algorithms for 1-4D structure of protein from Amino Acid Sequences and summarize the deep learning ideas to prediction of structure of protein and learned algorithms of the last decade. [ABSTRACT FROM AUTHOR]

In the process of calisthenics training, athletes' movements need to be monitored and analyzed. The traditional monitoring method has the problem of insufficient resolution, which limits the improvement of accuracy and reliability. In this paper, a high dimensional image super resolution model is developed by using infrared spectral imaging technology to improve the monitoring and simulation results in the process of aerobics training. In this paper, infrared spectral imaging technology combined with deep learning algorithm is used to design a high dimensional image super resolution model. Infrared spectral imaging data were collected during aerobics training, and the data were trained and learned to extract features and realize super-resolution reconstruction. Finally, the model is tested and verified to evaluate its monitoring and simulation effect in aerobics training. The experimental results show that the high-dimensional image super resolution model has good monitoring and simulation effects in aerobics training. By increasing the detail and clarity of the image, the model can provide more accurate motion analysis and motion suggestions, so as to help the trainer improve the movement skills and improve the training effect. [ABSTRACT FROM AUTHOR]

In response to the personalized needs of the tourism industry, intelligent tourism planning services should be adopted. Through artificial intelligence and Big data technology, users' travel preferences, time, budget and other information are analyzed and mined in depth to provide users with more suitable travel routes and services. Therefore, according to the demand of tourist path recommendation in cultural scenic spots, this paper proposes a light sensor positioning method based on deep learning algorithm. This paper expounds the advantages of using optical sensor positioning for path recommendation, and describes the specific steps and processes of the optical sensor positioning method based on deep learning algorithm in detail. Finally, the feasibility and effect of this method are verified by experiments and data analysis. The results show that the localization of light sensor based on deep learning algorithm has high accuracy and practicability in the travel path recommendation of cultural scenic spots. Therefore, this method can provide accurate and reasonable path recommendation for tourists in cultural scenic spots and improve tourism experience and benefits. With the continuous development of the tourism market and the changing needs of users, this personalized tourism route planning and recommendation system will be increasingly widely applied and promoted. [ABSTRACT FROM AUTHOR]

This paper presents the review of recent progress in photonic crystal fiber (PCF) and its applications. PCF possesses prominent potential for demonstrating various optical devices such as logic gates, combinational circuits, sensors, absorbers, etc. Recent development in PCFs incorporates the deep learning algorithms which deploy new functionalities and enhance the performance capacity in term of accuracy and optimization in comparison to conventional techniques based on Maxwell solver like finite element method (FEM), and plane wave expansion and finite difference time domain (FDTD) method. Thus, this review paper describes the overview of recent development along with historical background of PCFs and rigorous study on deep learning which incorporates neural network and machine learning algorithms like polynomial expansion method, gradient boost, and random forest. [ABSTRACT FROM AUTHOR]

A distributed computing model for channel bandwidth allocation and optimization can involve multiple components working together to efficiently allocate and optimize the available bandwidth in a distributed system. The efficient allocation of channel bandwidth in the distributed computing model is crucial for optimizing resource utilization and improving system performance. This paper, proposed the Imperialist Competitive Spline Interpolation (ICSI) scheme, which combines computational intelligence and deep learning techniques to address the challenge of channel bandwidth allocation. The ICSI scheme optimizes resource allocation by considering user requirements and resource availability, utilizing polynomial equations and spline interpolation. The Imperialist Competitive Optimization model evaluates and optimizes the available resources in the distributed environment. With the optimized resources spline interpolation is implemented for the computation of the available resources. Extensive simulations and performance analysis demonstrate the effectiveness of the ICSI scheme in terms of resource utilization, throughput, latency, fairness index, and energy efficiency. The ICSI model achieves the minimal waiting time of 3 ms and minimal latency of 6.4 m. Comparative analysis of the Round Robin scheme further confirms the superiority of the ICSI scheme in terms of task scheduling efficiency. The findings of this paper contribute to the advancement of distributed computing models for channel bandwidth allocation, offering a promising solution for optimizing resource allocation and improving system performance in modern computing environments. [ABSTRACT FROM AUTHOR]

The precise classification of kidneys and kidney tumors is of great guiding significance for the diagnosis and surgical planning of clinical doctors. Artificial segmentation of kidneys and renal tumors not only increases the workload of doctors, but also has a certain degree of subjectivity. Due to the varying morphology and pathological characteristics of various kidney tumors, conventional imaging analysis methods have certain limitations in their application. This study aims to propose a novel optical imaging device based on deep learning to achieve better results in kidney tumor image segmentation and recognition. The study collected a batch of image data on kidney tumors and trained the data using deep learning algorithms. After many experiments and optimization, a model with excellent performance in renal tumor segmentation and recognition was obtained. Compared with several existing methods, the method proposed in this paper has better segmentation accuracy for kidney tumors. This can not only avoid mutual interference between the network and the resolution network, but also improve the recognition efficiency between the two networks. In addition, the system can also provide reliable data feedback for clinical doctors, helping them continuously correct problems in subsequent visual examinations, thereby achieving higher diagnostic accuracy. The experimental results show that the neural network is effective in image segmentation and quantitative analysis, and can be applied in clinical practice. [ABSTRACT FROM AUTHOR]

Given that AI has been developing quickly in recent years, its usage has become increasingly important. It has had a significant influence on a variety of industries, including sports. Although not many experts are discussing it, the use of AI in sports has become commonplace. Predictive analytics has made it possible for many different kinds of athletic events to produce more precise outcomes and judgements. Making the game more difficult on and off the pitch is one of sports AI's main objectives. As a result, it is critical for sports firms to always stay current. As we analyse this document, our goal is to comprehend and research fresh, cutting-edge ways to apply artificial intelligence (AI) to the world of sports. Elite sports require objective evaluation of an athlete's performance to enable in-depth quantum photonics research. The shortcomings of manual performance analysis techniques are solved by the application of automatic detection as well as recognition of sport-specific motions. Inertial measurement unit (IMU) and/or computer vision data inputs were used in this work to recognise sport-specific movements. The goal of the study was to conduct a comprehensive evaluation of literature on ML and DL for these purposes. There was a multi-database search done. Included papers must to have examined a sport-specific movement and used machine learning or deep learning techniques to construct a model. [ABSTRACT FROM AUTHOR]

In many buildings with wooden structures, the service life and comfort of the floor will be affected by the walking load of the human body. The vibration effect generated by the human walking on the floor will affect the comfort that the floor brings to people. This article primarily focuses on the study of the correlation between floor vibration and human walking load in wooden buildings. It provides an analysis and summary of research findings from both domestic and international sources. Furthermore, the data collected is utilized to establish a model for estimating human walking load. The advent of information technology has witnessed significant transformative changes spurred by shifts in societal needs. Notably, the establishment of wireless sensor networks stands as a significant milestone within the field of information technology, highlighting advancements in science and technology. The evolution of wireless sensor networks has proven instrumental in various sectors within China, including but not limited to the military, environmental monitoring, biological research and medical systems, disaster monitoring, and energy allocation. This paper uses deep learning algorithms to analyze the node deployment problems of wireless sensor networks, summarizes a reasonable node deployment plan through specific research and practice, and uses collaborative deep learning methods to improve the operating efficiency of the network. In addition, this article also tested the vibration effect of wood structure buildings, analyzed different influencing factors, and provided a good reference point for the formulation of wood structure comfort standards. [ABSTRACT FROM AUTHOR]

Machine Learning is an important field of research in current trends. The extended field of machine learning is Deep Learning and is used for various research areas such as neural networks, image and signal processing, pattern recognition, etc. The handwritten digit recognition is an important task or process included in various applications such as car number plate recognition, staff identity number detection, etc. This paper proposed the design and analysis of various deep learning algorithms such as deep neural networks, convolutional neural networks, LeNet-5, AlexNet and MiniVGGNet for handwritten digit recognition using MNIST dataset. [ABSTRACT FROM AUTHOR]

There has been a rapid development of THz technology—sources, detectors and various THz imaging and sensing techniques. The THz technology demonstrates great potential as a modality for early, label free, non-ionizing and non-invasive detection of cancer. Some progressive technological development milestones have been achieved in this regard, however, to become clinically competitive and to provide the sought after real operational convenience, there is need for further research and development to overcome the existing challenges. This paper provides recent trends and perspectives through identification of existing challenges for the development of THz imaging and sensing systems that can evolve into actual medical modalities. We provide an overview of various aspects of THz technology, including techniques for imaging and sensing, mechanisms for THz image contrast and models for tissue dielectric responses to THz waves. The THz imaging application for detection of various cancers is briefed. The advantages of THz cancer imaging and sensing as well as the existing challenges are identified, with recommendations provided in contribution to future research. Further, some recent THz imaging and sensing developments such as the near-field methods to break the diffraction limit including waveguides, resonance and plasmonic metasurfaces are discussed. We emphasize the contribution of analytical algorithms that are based on machine learning, in particular, deep learning for the development of THz technology. [ABSTRACT FROM AUTHOR]

Numerical simulation and machine learning represent opposite approaches to computational analysis of the real world, deductive vs. inductive. However, both methods suffer from various uncertainties and even their combination often fails to link theory and reality. Focusing on GaN-based light-emitting diode (LED) design optimization, this paper evaluates examples of simulation-based machine learning from a physics point of view. Strategies are suggested for achieving more realistic predictions. [ABSTRACT FROM AUTHOR]

Classification of hyperspectral image (HSI) is widely used for the study of remotely sensed images. Convolutional Neural Networks (CNNs) are one of the most commonly chosen deep learning algorithms for visual data analysis. The HSI classification framework based on the CNN is presented in this paper. Since the imbalance between the high dimension of HSI input data and the limited amount of labeled training data would induce overfitting, current convolutional networks are fairly superficial for HSI classification. To stop the limited efficiency of feature learning, a new HSI classification network called Residual Spectral Spatial-Channel Attention Network (RSS-CAN) is proposed. By utilizing the "shortcut connection" framework, RSS-CAN can use deeper layers to extract more succinct and efficient features. Furthermore, attention mechanism is used to emphasize meaningful features. In addition, we revised an HSI dataset called Shandong Feicheng. The resolution and pixel quantity of this dataset are significantly greater. In order to check its variety, it has been contrasted with state-of-the-art approaches. Experimental results with widely used hyperspectral image datasets demonstrate that, our proposed method has achieved better performance in comparison with state-of-the-art classifiers and conventional deep learning-based classifiers. [ABSTRACT FROM AUTHOR]