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This paper simulates the relative performance of various artificial intelligence (AI) techniques when applied to nonlinear distortion compensation in wavelength division multiplexing (WDM) optical communication systems. These procedures are less complex than state-of-the-art compensation methods and do not necessitate prior knowledge about the properties of data in neighboring WDM channels, which can be practically challenging. In this study, Neural Networks (NNs) were integrated into both the transmitter and receiver sections of 3- and 5-channel WDM systems, and the resulting enhancement in performance (Q-factor) was assessed across varying levels of fiber nonlinearities. While the NN stage enhances the system performance, the improvement decreases as expected with the channel number and γ . Next, two-stage architectures that employ a transmitter side NN together with a classifier at the receiver side were modeled. For the systems examined in this paper simple decision tree structures, boosting, forests, extra trees, and multi-layer perceptron (MLP) classifiers all yielded enhanced system performance compared to simple chromatic dispersion compensation (CDC) with the only exception being Ada boosting which decreased the Q-factor for γ = 14 W - 1 k m - 1 . The outcomes of these investigations show that the most effective performance in highly nonlinear WDM systems is attained by employing two-stage systems, with the incorporation of random forest or extra tree AI methods at the receiver side yielding the highest results. [ABSTRACT FROM AUTHOR]

Machine Learning (ML) is a specialized domain within the broader science of Artificial Intelligence. Machine Learning facilitates the acquisition of knowledge by machines through the analysis of data. As a result, ML empowering them to generate predictions or make informed decisions. Machine learning has found extensive application across diverse healthcare domains, encompassing the areas of disease diagnosis, prognosis, treatment, and disease management. Nevertheless, the integration and implementation of machine learning models in healthcare face numerous challenges and limitations. These include issues related to data governance, data quality, legal considerations, ethical concerns, and interpretability. This article presents a thorough examination of diverse machine learning concepts and their potential implications in the healthcare domain. Furthermore, the paper explores various categories of machine learning models and examines the diverse range of algorithms that can be employed to address specific tasks. In addition, this paper presents a precis of various machine learning techniques and their respective applications in the field of healthcare, utilizing diverse datasets and performance metrics. Ultimately, this study examines the barriers and potential remedies surrounding the implementation and utilization of machine learning in the healthcare sector. Additionally, offers suggestions for future research avenues. [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]

As the number of logs generated by each node in the national high-performance computing environment continues to increase, manual analysis of abnormal logs has become slow and inaccurate. This makes it difficult to meet the requirements of daily system analysis. In response to this problem, this paper proposes a method for defining an abnormal log business model. Through the analysis of the exception logs of the multi-node system, this paper finds that the exception logs have a certain regularity and repeatability. Therefore, the abnormal logs can be detected by analyzing the log traffic pattern. This method uses machine learning algorithm to analyze log data in multi-node system, extract normal log traffic patterns, and use these patterns to detect abnormal behavior. Experimental results show that the proposed method can effectively detect abnormal log traffic patterns in multi-node systems, with high accuracy and robustness, and can detect and locate system faults in time, improving the reliability and stability of the system. This paper provides a new solution for log traffic pattern detection of multi-node system, which has a certain application prospect in the field of computer system monitoring, and can provide a certain reference for computer system monitoring and maintenance. [ABSTRACT FROM AUTHOR]

Basketball motion image recognition is a challenging task, and traditional methods often face many limitations and difficulties. The purpose of this study is to explore the application of near-infrared spectral imaging and artificial intelligence technology in basketball motion image recognition to improve the recognition accuracy and robustness. In this paper, the near infrared spectral data of basketball movement images are collected, and an artificial intelligence classification model is established based on these data. The experimental results show that the basketball motion image recognition method based on near-infrared spectral imaging and artificial intelligence technology has achieved significant improvement in accuracy and robustness. Compared with traditional methods, our model can still perform accurate classification stably under different lighting conditions. This study proves the potential application of near-infrared spectral imaging and artificial intelligence classification in basketball motion image recognition. This method has important practical application value. The method can be further explored and optimized in the future to adapt to a wider range of motion scenarios and task requirements. [ABSTRACT FROM AUTHOR]

Precision medicine techniques pinpoint the characteristics of people with uncommon treatment outcomes or distinct medical requirements. Artificial intelligence (AI) fuels the system's ability to think and learn, generates insights through complex computing and inference, and enhances clinical decision-making through enhanced intelligence. The main advantage of AI in sports medicine is its capacity for prediction. In order to forecast possible injuries, machine learning algorithms may examine enormous volumes of data, such as an athlete's training regimen, medical history, and performance indicators. A new area of study called photonic quantum information has emerged as a result of recent advancements in technology enabling the production, control, and detection of individual single photons. Realising single photon switches, creating photonic quantum circuits with specialised uses, and using new photonic states for optical metrology that goes beyond conventional optics are some examples of this advancement. Based on the author's previous and present efforts, the current state of photonic quantum information technology is reviewed in this review paper. Sports medicine professionals will need to have a basic working understanding of the strengths in the future, much way doctors presently need to understand the business of medicine. [ABSTRACT FROM AUTHOR]

This paper explores the transformative fusion of Quantum computing, Artificial Intelligence (AI), the Internet of Things (IoT), and advanced optical systems within smart city development. A significant innovation within this study is the concept of "optical IoT," wherein IoT relies on advanced optical technologies, including high-resolution cameras, LiDAR scanners, meters, sensors, and wearables, strategically distributed throughout urban environments for real-time image data acquisition. Traditional smart city models may rely on conventional data acquisition methods that are not real-time or high-resolution, leading to delayed and less accurate urban management decisions. Existing models might use disparate systems for monitoring and management, which can result in inefficient resource allocation and coordination, especially in critical situations like emergency response. In this research Advanced Smart City Architecture (ASCA) this integration empowers the system to excel in tasks such as semantic segmentation, enabling precise identification and categorization of urban elements. Quantum optical systems are employed in quantum-enhanced sensors, such as quantum-enhanced interferometers and atomic clocks. These sensors offer improved precision for measurements like distance, time, and acceleration. The ASCA approach equips city planners, administrators, and emergency responders with real-time urban monitoring and management capabilities. This dynamic system yields numerous advantages, including optimized resource allocation, enhanced traffic management, improved environmental quality, and swift emergency response capabilities. This research underscores the immense potential of ASCA in reshaping urban development and sustainability within smart cities. By harmonizing AI, IoT, and advanced optical systems, this paradigm shift enables smart cities to evolve into more efficient and resilient urban environments. These cities become finely attuned to the ever-evolving needs of their residents, ultimately fostering innovation and progress at an unprecedented scale. Proposed ASCA achieves an impressive 91.98% enhancement in sustainable smart city development when compared to these existing techniques. [ABSTRACT FROM AUTHOR]

Due to the advancement of high definition, 5G technologies, the Internet of Things, and Artificial Intelligence, the demand for optical networks has increased widely. Optical communication networks offer various metrics, including high transmission capacity, efficient anti-interference, minimum transmission loss and robustness, and so on, that offer opportunities for communication networks. To satisfy the optical network demands, effective network resource utilization is essential. Therefore, developing a tool with improved Quality of Transmission (QoT) accuracy in optical networks is necessary. Recently, Artificial intelligence (AI) approaches have provided various opportunities to resolve these issues and the deep learning (DL) algorithms offer improved performance over the conventional methods. This paper developed a novel DL-based cognitive QoT prediction model for Quantum optical communication networks. This proposed model predicts the QoT for the QoS (Quality of Service) setup using the DL model with the transmission computation. The proposed model utilized an optimized DL model called CNN-LSTM for the prediction process using the signal and link characteristics as input features. The DL model is trained using the transmission equations. The hyperparameters of the neural network are optimized using frog leap optimization to improve the predictive performance. The experimental results highlight the enhanced and improved version of the proposed model, and the results are compared with the conventional systems in terms of performance measures. [ABSTRACT FROM AUTHOR]

Traditional coaches need to rely on experience and intuition to adjust athletes' movements and postures, which has the problems of strong subjectivity and unstable effects. In this paper, an image optical processing method based on artificial intelligence is proposed to realize the adaptive adjustment of aerobics athletes. The research uses the image optical processing technology to collect and analyze the movement of athletes in the training process and extract the key movement parameters. Then, combined with artificial intelligence algorithm, the extracted action parameters are analyzed and compared, so as to achieve accurate evaluation and adjustment of athletes' actions. Finally, the adjustment plan is applied to the training of athletes, and through real-time monitoring and feedback mechanisms, the athletes' movements and postures are continuously adjusted and optimized. Through experiments and practical applications, the system can accurately determine the movements and postures of athletes, and provide corresponding adjustment plans based on the actual situation. Compared with traditional manual adjustments, the system has higher accuracy and stability, which can effectively improve the technical level and performance ability of athletes. [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]

This paper proposes to enhance the capabilities of a Technical Vision System (TVS) based on optical scanning and dynamic triangulation for 3D spatial coordinates measurements to determine and add to the record of its measurements the color of the objects coordinates scanned. Optoelectronics signals output from the TVS scanning aperture contain a huge amount of data that cannot be friendly or obvious to human understanding and interpretation. However, time domain characteristic parameters can be extracted from the optoelectronics signals, which can describe aspects of the optoelectronic signal and the 3D spatial coordinate to whom it belongs; that is, it is specific and punctual information from the area of the object under scanning. In this theoretical and experimental evaluation, the parameters obtained from optoelectronics signals have been used as feature inputs for supervised learning and as an approach to implement artificial intelligence to the TVS signal processing by a model able to predict in real time the color of the 3D spatial coordinates measured. [ABSTRACT FROM AUTHOR]

Traditional methods of automatic generation of recruitment text usually rely on a large number of data annotation and complex statistical algorithms, but these methods have certain limitations. In order to further optimize the processing power of neural network and improve its computational efficiency and stability, this paper aims to solve the shortcomings of convergence and local vulnerability in existing neural network algorithms, and make general improvements to them. Through optical detection and improved neural network algorithm, a more efficient method of automatic generation of artificial intelligence recruitment text is developed. Candidates' resumes are pre-processed with light detection technology to extract key information and filter out spam text. Then the processed text is input into the improved neural network model for training to improve the quality of recruitment text generation. The experimental results show that the proposed method has achieved significant improvement in the accuracy and readability of the automatic generation of recruitment texts, which can provide high quality recruitment texts for human resources departments, improve recruitment efficiency and reduce labor costs. This study provides new ideas and methods for further developing the application of artificial intelligence in the field of recruitment. [ABSTRACT FROM AUTHOR]

Artificial intelligence (AI) has quickly integrated into contemporary healthcare organizations, providing a variety of purposes, including identifying diseases, individualized therapy suggestions, and healthcare picture analytics. However, substantial security concerns are raised by the extensive use of AI in Healthcare, particularly regarding the reliability and confidentiality of private patient information. In this paper, we offer quantum photonic convolutional neural network (QP-CNN) to strengthen AI-based healthcare systems' security to overcome this crucial issue. To create an innovative convolutional neural network (CNN) architecture capable of handling the quantum computations that can endanger the safety of patient data, we analyze the special aspects of quantum photonics in this study. We explore the basic ideas behind quantum photonic computation and show how they can be used to develop strong cryptography methods for securing healthcare information during transport and preservation. Initially, we gather the dataset and preprocess the collected data using min–max normalization to remove duplicates and guarantee homogeneity. The subsequent stage involves utilizing principal component analysis (PCA) to extract pertinent features from the preprocessed data. We simulate trials with Python 3.11 software to assess the efficiency of the suggested algorithm. In terms of accuracy (82.67%), precision (82%), recall (75.84%), and F1-score (85%), specificity (95.67%), AUC (93.15%). Our results show that the QP-CNN technique outperforms other methods in effectiveness. Our suggested QP-CNN technique provides exciting outcomes for the security of an artificial intelligence-based healthcare system. [ABSTRACT FROM AUTHOR]