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The quality of medical images is crucial for accurately diagnosing and treating various diseases. However, current automated methods for assessing image quality are based on neural networks, which often focus solely on pixel distortion and overlook the significance of complex structures within the images. This study introduces a novel neural network model designed explicitly for automated image quality assessment that addresses pixel and semantic distortion. The model introduces an adaptive ranking mechanism enhanced with contrast sensitivity weighting to refine the detection of minor variances in similar images for pixel distortion assessment. More significantly, the model integrates a structure-aware learning module employing graph neural networks. This module is adept at deciphering the intricate relationships between an image's semantic structure and quality. When evaluated on two ultrasound imaging datasets, the proposed method outshines existing leading models in performance. Additionally, it boasts seamless integration into clinical workflows, enabling real-time image quality assessment, crucial for precise disease diagnosis and treatment. [ABSTRACT FROM AUTHOR]

Copyright of Application Research of Computers / Jisuanji Yingyong Yanjiu is the property of Application Research of Computers Edition and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

With the increasing awareness of environmental protection, the rotary hearth furnace system has emerged as a key technology that facilitates a win-win situation for both environmental protection and enterprise economic benefits. This is attributed to its high flexibility in raw material utilization, capability of directly supplying blast furnaces, low energy consumption, and high zinc removal rate. However, the complexity of the raw material proportioning process coupled with the rotary hearth furnace system's reliance on human labor results in a time-consuming and inefficient process. This paper innovatively introduces an intelligent formula method for proportioning raw materials based on online clustering algorithms and develops an intelligent batching system for rotary hearth furnaces. Firstly, the ingredients of raw materials undergo data preprocessing, which involves using the local outlier factor (LOF) method to detect any abnormal values, using Kalman filtering to smooth the data, and performing one-hot encoding to represent the different kinds of raw materials. Afterwards, the affinity propagation (AP) clustering method is used to evaluate past data on the ingredients of raw materials and their ratios. This analysis aims to extract information based on human experience with ratios and create a library of machine learning formulas. The incremental AP clustering algorithm is utilized to learn new ratio data and continuously update the machine learning formula library. To ensure that the formula meets the actual production performance requirements of the rotary hearth furnace, the machine learning formula is fine-tuned based on expert experience. The integration of machine learning and expert experience demonstrates good flexibility and satisfactory performance in the practical application of intelligent formulas for rotary hearth furnaces. An intelligent batching system is developed and executed at a steel plant in China. It shows an excellent user interface and significantly enhances batching efficiency and product quality. [ABSTRACT FROM AUTHOR]

Designing intelligent diagnosis of prostate diseases in intelligent medical assistance systems has gradually become a research hotspot. However, rectal ultrasound (TRUS) as the main diagnostic tool for prostate diseases remains a challenging issue. (1) Due to limited prostate TRUS imaging data, it is difficult to train a robust deep learning model. (2) In terms of visual features, ultrasound images of prostate cancer are similar to TRUS images of other tissues and organs, so it is difficult for a single neural network model to accurately learn the feature representation of the disease. To address the above problems, we first establish a high-quality dataset for prostate TRUS imaging, and then design multi teacher knowledge distillation to achieve accurate disease recognition. The experimental results show that, compared with knowledge distillation without a teacher model and a single teacher model, knowledge distillation using multiple teacher models can significantly improve the accuracy of prostate TRUS image cancer prediction. As the number of teacher models increases, the accuracy rate is further improved, which verifies the effectiveness of this method in intelligent systems. [ABSTRACT FROM AUTHOR]

Complex practical problems are distinguished by their subjective and dynamic character. Such problems never repeat exactly; therefore, the method for their solution must be built each time. In view of this, development of intelligent systems that are dedicated and have the capability of acquiring, reproducing, and using the knowledge, whose functions are estimated from the point of view of ensuring stability, openness, and real-time operation is urgent. In this work, understanding of intelligent systems and their openness is considered, as well as approaches to their design. [ABSTRACT FROM AUTHOR]

The problem of improving the efficiency of decision making based on cases (case-based reasoning) in real-time intelligent systems is considered. Methods for preprocessing and storing temporal data are discussed. A method and algorithms for structuring the case base are proposed. The main stages of the method are the generalization of properties of dynamic parameters, the formation of classes of similar situations associated with cases from the case base, and the construction of decision trees to enable efficient search for solutions in each class. The results of a machine experiment are presented. To implement the proposed approach, a temporal database created using the Neo4j NoSQL graph database management system is used. [ABSTRACT FROM AUTHOR]

Fatigued driving is a problem that every driver will face, and traffic accidents caused by drowsy driving often occur involuntarily. If there is a fatigue detection and warning system, it is generally believed that the occurrence of some incidents can be reduced. However, everyone's driving habits and methods may differ, so it is not easy to establish a suitable general detection system. If a customized intelligent fatigue detection system can be established, it may reduce unfortunate accidents. With its potential to mitigate unfortunate accidents, this study offers hope for a safer driving environment. Thus, on the one hand, this research hopes to integrate the information obtained from three different sensing devices (eye movement, finger pressure, and plantar pressure), which are chosen for their ability to provide comprehensive and reliable data on a driver's physical and mental state. On the other hand, it uses an autonomous learning architecture to integrate these three data types to build a customized fatigued driving detection system. This study used a system that simulated a car driving environment and then invited subjects to conduct tests on fixed driving routes. First, we demonstrated that the system established in this study could be used to learn and classify different driving clips. Then, we showed that it was possible to judge whether the driver was fatigued through a series of driving behaviors, such as lane drifting, sudden braking, and irregular acceleration, rather than a single momentary behavior. Finally, we tested the hypothesized situation in which drivers were experiencing three cases of different distractions. The results show that the entire system can establish a personal driving system through autonomous learning behavior and further detect whether fatigued driving abnormalities occur. [ABSTRACT FROM AUTHOR]

Existing control strategies, such as Real-time Optimization (RTO), Dynamic Real-time Optimization (DRTO), and Economic Model Predictive Control (EMPC) cannot enable optimal operation and control behavior in an optimal fashion. This work proposes a novel control strategy, named the efficiency-oriented model predictive control (MPC), which can fully realize the potential of the optimization margin to improve the global process performance of the whole system. The ideas of optimization margin and optimization efficiency are first proposed to measure the superiority of the control strategy. Our new efficiency-oriented MPC innovatively uses a nested optimization structure to optimize the optimization margin directly online. To realize the computation, a Periodic Approximation technique is proposed, and an Efficiency-Oriented MPC Type I is constructed based on the Periodic Approximation. In order to alleviate the strict constraint of Efficiency-Oriented MPC Type I, the zone-control-based optimization concept is used to construct an Efficiency-Oriented MPC Type II. These two well-designed efficiency-oriented controllers were compared with other control strategies over a Continuous Stirred Tank Reactor (CSTR) application. The simulation results show that the proposed control strategy can generate superior closed-loop process performance, for example, and the Efficiency-Oriented MPC Type I can obtain 7.11% higher profits than those of other control strategies; the effectiveness of the efficiency-oriented MPC was, thereby, demonstrated. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Xihua University (Natural Science Edition) is the property of Xihua University and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of GeSec: Revista de Gestao e Secretariado is the property of Sindicato das Secretarias e Secretarios do Estado de Sao Paulo (SINSESP) and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Background: Effective management of cancer pain critically depends on timely medication administration and adherence to precise medication guidelines. In the context of limited time and a busy healthcare environment, tailoring the optimal medication schedule for each patient with cancer pain presents a significant challenge for physicians and clinical pharmacists. Methods: To address this challenge, we conducted a comprehensive analysis of healthcare professionals’ needs in guiding cancer pain medication. By developing core features based on key user needs and continuously updating them, we have created the Universal Medication Schedule System (UMSS). We invited 20 physicians and pharmacists specializing in oncology or cancer pain to trial the system and assessed UMSS usage through distributed questionnaires. Results: We identified five key needs of healthcare professionals in cancer pain medication guidance. Based on these needs, we (1) constructed a comprehensive drug information database, including basic information for 1135 drugs, 130,590 drug interaction data entries, and 1409 individual medication timing constraints, and (2) developed a web-based system that provides essential reference information such as drug interactions and dietary restrictions. It can create medication schedules and provide medication education tailored to the patient’s daily routine. Participating evaluators unanimously agreed (100%) that the system aids in accurately assessing the risks of polypharmacy and quickly scheduling medication regimens. Conclusion: UMSS, by offering personalized medication schedule support, assists healthcare professionals in better managing patients’ medication treatment plans. However, further improvements are needed in the automation of database updates and maintenance, as well as in integrating it with electronic health records. [ABSTRACT FROM AUTHOR]

Due to the absence of more efficient diagnostic tools, the spread of mpox continues to be unchecked. Although related studies have demonstrated the high efficiency of deep learning models in diagnosing mpox, key aspects such as model inference speed and parameter size have always been overlooked. Herein, an ultrafast and ultralight network named Fast‐MpoxNet is proposed. Fast‐MpoxNet, with only 0.27 m parameters, can process input images at 68 frames per second (FPS) on the CPU. To detect subtle image differences and optimize model parameters better, Fast‐MpoxNet incorporates an attention‐based feature fusion module and a multiple auxiliary losses enhancement strategy. Experimental results indicate that Fast‐MpoxNet, utilizing transfer learning and data augmentation, produces 98.40% classification accuracy for four classes on the mpox dataset. Furthermore, its Recall for early‐stage mpox is 93.65%. Most importantly, an application system named Mpox‐AISM V2 is developed, suitable for both personal computers and smartphones. Mpox‐AISM V2 can rapidly and accurately diagnose mpox and can be easily deployed in various scenarios to offer the public real‐time mpox diagnosis services. This work has the potential to mitigate future mpox outbreaks and pave the way for developing real‐time diagnostic tools in the healthcare field. [ABSTRACT FROM AUTHOR]

Hand gesture recognition using a camera provides an intuitive and promising means of human-computer interaction and allows operators to execute commands and control machines with simple gestures. Research in hand gesture recognition-based control systems has garnered significant attention, yet the deploying of microcontrollers into this domain remains relatively insignificant. In this study, we propose a novel approach utilizing micro-hand gesture recognition built on micro-bottleneck Residual and micro-bottleneck Conv blocks. Our proposed model, comprises only 42K parameters, is optimized for size to facilitate seamless operation on resource-constrained hardware. Benchmarking conducted on STM32 microcontrollers showcases remarkable efficiency, with the model achieving an average prediction time of just 269ms, marking a 7× faster over the state-of-art model. Notably, despite its compact size and enhanced speed, our model maintains competitive performance result, achieving an accuracy of 99.6% on the ASL dataset and 92% on OUHANDS dataset. These findings underscore the potential for deploying advanced control methods on compact, costeffective devices, presenting promising avenues for future research and industrial applications. [ABSTRACT FROM AUTHOR]

Coal is the cheapest source of energy. It is much cheaper than nuclear and petroleum. However, spontaneous combustion is inherent in coal seams that lead to fires at their place of storage. Therefore, early detection is required to ensure safety. This article aims to develop an intelligent system for the classification of coal seams based on their spontaneous heating on the Internet of Things (IoT) paradigm. It utilizes an artificial intelligence (AI) approach by utilizing a two-stage neural network architecture to render an accurate and robust categorization of coal samples. Three commonly available proximate analysis parameters of the coal samples have been used as input to this system. These inherent parameters are found to be sufficient to classify coal samples into different categories according to their combustion susceptibility. Three publicly available datasets are involved in the assessment purpose. Our proposed method has outperformed previous approaches. The coal seams classification results agree with the real field experiences. [ABSTRACT FROM AUTHOR]

One of the most challenging scenarios for drivers of open-pit mines is driving in dense fog. As a result, drivers cannot see the road, which causes significant vehicle colloidal accidents. To overcome the problem, an intelligent vision enhancement system has been developed by real-time processing and integrating data from the vehicle-to-everything (V2X) module, global navigation satellite system (GNSS), radar, and infrared cameras to enhance the visibility range of the operative road to the driver and mitigate the chances of accidents. Final images after image processing and object detection are displayed using a dashboard screen in front of the driver for adaptive control and safe operation. Performance analysis of the visual computing algorithm has been carried out in a mine based on image quality parameters. Field-tested images' average contrast value, peak signal-to-noise ratio, structural similarity index, visual information fidelity, and universal quality index were 0.87, 19.36, 0.39, 0.82, and 0.56, respectively, and the calculated accurateness of the convolution neural network (CNN)-based object detection model was 90%. These values indicate the output of the image processing algorithm provided better-enhanced images in the foggy condition. With the help of image processing algorithms, the camera view is able to provide a clear vision of more than 30 m in dense fog. Further, using V2X technology, the augmented visibility range has been increased up to 200 m. The 3D augmented reality (AR) view and 2D navigation view provide other surrounding vehicle locations. Untagged objects like rocks or bunds are also detected and displayed using radar. HIGHLIGHTS: The system fuses IR cameras, V2X, GNSS, radar, & visual computing for safe driving Image processing algorithm fuses CLAHE and DCP methods for clear image output CNN-based object detection algorithm integrated with image processing software Provides multi-stage safety using camera, 2D navigation, & 3D virtual reality views Intelligent system inbuilt with software and advanced analytics for fleet management [ABSTRACT FROM AUTHOR]

Copyright of Machine Tool & Hydraulics is the property of Guangzhou Mechanical Engineering Research Institute (GMERI) and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The object of research is the process of automating systems for structuring data from several sources. The subject of the research is methods and algorithms for implementing a complete system for automated and parallel processing, validation and structuring of data. One of the most problematic areas is the merging of databases with different structures and several common fields into a generalized structure. The research was aimed at developing a system to increase the efficiency of automation of big data processing. As a result of the work, optimization methods were studied, the influence of their internal parameters on the operation of algorithms was analyzed, their main advantages and disadvantages were determined, and software was developed in which the corresponding methods were implemented. An algorithm for structuring data before processing has been obtained. Data structuring is achieved by performing the «mapping» operation. Mapping can take place by indexes of already cleaned data or using a defined dictionary with a given set of keys, which allows not to care about the sequence of storing values and their possible shift. The practical significance of the developed system lies in the improvement of methods of collecting and processing information for the purpose of its further validation, cleaning and accumulation in the following categories: geographic addresses and geo-coordinates, validation and automated addition of a mobile phone number to the international format, processing of car numbers (in modern and outdated format), VIN code of the engine and car brand, validation of urls of social networks, passport data and processing of personal data. Compared to similar methods for processing large volumes of data, the possibility of splitting the input file or stream into separate parts was used, the cleaned data from which is combined at the end of the system operation. Thanks to this, it is possible to process data whose size exceeds the available volume of the device’s RAM, and the method of working with loosely structured text files in CSV format has been improved. [ABSTRACT FROM AUTHOR]

Flexible sensors for food quality control are experiencing rapid development. The purpose of this study is to address the time‐consuming issues associated with traditional fruit grading methods by utilizing a homemade flexible impedance sensing system (FISS). A customized spiral slide system was innovatively designed in the study to simulate the grading process on a fruit assembly line with multimodal features for low power consumption and non‐destructive evaluation. FISS integrated components such as a camera, spiral slider, 3D printed slider, latex ball, flexible impedance electrodes, and back‐end measurement circuits and successfully achieved accurate food quality assessment by using a visual classification model for primary grading based on YOLOv5s‐CBAM and an impedance feature classification model for secondary grading of mangoes based on the coefficient of variation method and threshold coefficient method. With an impressive assessment accuracy of 97.07% and a classification speed of up to 3 s/piece, the system successfully classified mangoes into seven grades covering overripe, fully ripe, ripe, unevenly ripe, unripe, underripe, and rotten states. By eliminating the reliance on complex instruments and expensive equipment, FISS provides a cost‐effective alternative for food quality control, significantly reducing operational costs. Practical applications: The purpose of this study is to address the time‐consuming issues associated with traditional fruit grading methods by utilizing a homemade flexible impedance sensing system (FISS). A customized spiral slide system was innovatively designed in the study to simulate the grading process on a fruit assembly line with multimodal features for low power consumption and non‐destructive evaluation. FISS integrated components such as a camera, spiral slider, 3D printed slider, latex ball, flexible impedance electrodes, and back‐end measurement circuits and successfully achieved accurate food quality assessment by using a visual classification model for primary grading based on YOLOv5s‐CBAM and an impedance feature classification model for secondary grading of mangoes based on the coefficient of variation method and threshold coefficient method. With an impressive assessment accuracy of 97.07% and a classification speed of up to 3 s/piece, the system successfully classified mangoes into seven grades covering overripe, fully ripe, ripe, unevenly ripe, unripe, underripe, and rotten states. By eliminating the reliance on complex instruments and expensive equipment, FISS provides a cost‐effective alternative for food quality control, significantly reducing operational costs. [ABSTRACT FROM AUTHOR]

The supply chain functions as a complex web, interconnecting various stakeholders such as suppliers, manufacturers, wholesalers, retailers, and ultimately, end consumers. Central to effective supply chain management is the meticulous handling of inventory, a critical factor influencing both cost efficiency and service excellence throughout the entire network. The management of inventory within this context extends beyond the confines of individual enterprises, bearing significance across the entirety of the supply chain. Consequently, achieving optimal performance necessitates a cohesive, holistic approach to management, aligning with the overarching objectives of the system. Through dynamic data analysis of multiple types of power materials, a dynamic inventory control model for power materials is constructed to achieve optimal adjustment of inventory management. Ultimately, a multi-granularity inventory control method based on big data analysis of power warehousing is constructed, which effectively improves inventory management efficiency and reduces logistics management costs for enterprises. Through big data analysis of power material warehousing, the characteristics of power material demand are excavated, and a classification method for power material demand is constructed to achieve an overall inventory control strategy for power materials. The implementation results show that the controlled inventory can better meet the changing demand, thereby improving inventory management efficiency. The multi-granularity inventory control method based on big data mining of warehousing combines inventory and multiobjective optimization theories, proves the applicability and feasibility of the proposed method, effectively improves inventory management efficiency, reduces logistics management costs for enterprises, and provides practical guidance and decision-making reference for improving the intensive management level of power production and maintenance materials. [ABSTRACT FROM AUTHOR]

Copyright of Railway Construction Technology is the property of Railway Construction Technology Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Advanced sensor technology, especially those that incorporate artificial intelligence (AI), has been recognized as increasingly important in various contemporary applications, including navigation, automation, water under imaging, environmental monitoring, and robotics. Data-driven decision-making and higher efficiency have enabled more excellent infrastructure thanks to integrating AI with sensors. The agricultural sector is one such area that has seen significant promise from this technology using the Internet of Things (IoT) capabilities. This paper describes an intelligent system for monitoring and analyzing agricultural environmental conditions, including weather, soil, and crop health, that uses internet-connected sensors and equipment. This work makes two significant contributions. It first makes it possible to use sensors linked to the IoT to accurately monitor the environment remotely. Gathering and analyzing data over time may give us valuable insights into daily fluctuations and long-term patterns. The second benefit of AI integration is the remote control; it provides for essential activities like irrigation, pest management, and disease detection. The technology can optimize water usage by tracking plant development and health and adjusting watering schedules accordingly. Intelligent Control Systems (Matlab/Simulink Ver. 2022b) use a hybrid controller that combines fuzzy logic with standard PID control to get high-efficiency performance from water pumps. In addition to monitoring crops, smart cameras allow farmers to make real-time adjustments based on soil moisture and plant needs. Potentially revolutionizing contemporary agriculture, this revolutionary approach might boost production, sustainability, and efficiency. [ABSTRACT FROM AUTHOR]

In order to improve the reliability of Tibetan Thangka sales channels, this paper combines blockchain technology to construct an intelligent model that can be used for Tibetan Thangka sales. This paper uses cryptographic encryption technology to encrypt the uploaded data to prevent Thangka producers or logistics turnover parties from tampering with Thangka‐related information, and uses the timestamp in blockchain technology to realize the traceability of Thangka‐related information. In addition, this paper uses the timestamp technology in the blockchain system to generate a traceable, query and supervised blockchain, which runs through the whole process of thangka from planting to selling. Finally, a sales model of Tibet Thangka based on blockchain technology is designed, and system performance analysis is carried out through simulation experiments. The experimental research results verify that the designed sales model has a certain effect. [ABSTRACT FROM AUTHOR]

Copyright of Iraqi Journal of Science is the property of Republic of Iraq Ministry of Higher Education & Scientific Research (MOHESR) and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Water resources are essential for human beings and nowadays polluted water jeopardizes the human health. Toxic substances come from houses, industries and farm lands, dust mix with water causes water pollution. This pollution depreciates the quality of water and affects the human life. In this paper, our objective is to evaluate and supervise the physicochemical quality of the ground water, for the safety of human beings. The sample quality of 15 sites was used for measuring important parameters like pH, EC, Ca2+, Mg2+, Na+, K+, Cl−, SO42−, Also, NH4+ and NO3−, Fe2+ and HCO3−12 were considered for performance analysis. A soft computing component fuzzy logic system is used to design an intelligent system. The fuzzy logic system-based model measures groundwater quality status along with its sustainability. The results obtained from the model help the authorities, policy makers to plan proper policies for geochemical operation (water treatment process) and a foundation for observing the physicochemical quality of water in the area. [ABSTRACT FROM AUTHOR]

Copyright of Bearing is the property of Bearing Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

During an epidemic, the transportation of patients to emergency departments and the monitoring of their physiological parameters pose significant challenges in this critical scenario. Swift and efficient diagnosis has the potential to rescue the lives of these patients. The objective is accomplished through the utilization of deep learning to categorize information into emergencies, prioritizing its dispatch. In this article, we present a sophisticated emergency system that employs deep learning to swiftly transmit vital information from emergency patients to the hospital that can provide the highest quality healthcare for these individuals. The fusion method integrates data obtained and refined from patients' electronic medical records with data acquired by the wireless medical sensor network during the transportation phase. Subsequently, the process of choosing the parameters is employed as inputs to the learning model. The data gathered and educational outcomes, such as emergency notifications, are transmitted through Wi-Fi and 5G devices in our sophisticated system. The proposed contribution achieves a 98% accuracy with a runtime of 1.53 seconds. This discovery demonstrates the efficacy of our system, particularly in the context of epidemic situations such as COVID-19. [ABSTRACT FROM AUTHOR]

The object of this research is the processing of synthetic aperture radar (SAR) images using artificial intelligence. The subject of the study focuses on the utilization of artificial intelligence for the object detection on SAR images. The primary goal of this thesis is to investigate the principles of SAR operation, analyze various systems for detecting anomalous objects in soil, develop an intelligent system for processing SAR images, and evaluate the potential of the developed system for the classification of explosive objects. The research methods include the analysis of existing literature and programming in Python. The findings and materials from this thesis are recommended for use in the analysis of current underground anomaly detection systems, the potential application of artificial intelligence and machine learning in demining processes, and the examination of radar image processing methods. [ABSTRACT FROM AUTHOR]

The purpose of the study is to develop an approach to planning the trajectory of the manipulator robot using an intelligent system based on neural networks. For this purpose, the work considered the processes of planning and deploying the movement of the robot. The analysis of existing methods of planning the movement of manipulator robots and the review of intelligent control systems made it possible to obtain a complete picture of the current state of this issue. A system is proposed that can perceive the environment and control the movement of the robot by generating the correct control commands. For this, 3 tasks were solved, namely: analysis of the environment in order to determine its features, determination of the trajectory in order to neutralize the collision and determination of controlled influences for the executive authorities in order to implement the movement. The functionality and structure of the neural network for solving each of the tasks are proposed. [ABSTRACT FROM AUTHOR]

Heart disease is a complex disease, and many people around the world suffer from this disease. Due to the lack of a healthy lifestyle, it is the most common cause of death worldwide. Machine learning plays an important role in medical treatment. The goal of this research is to develop a machine learning model to help diagnose heart disease quickly and accurately. In this article, an effective and improved machine learning method is proposed to diagnose heart disease. We designed a novel and robust ensemble model that combines the top three classifiers, namely Random Forest, XGBoost and Gradient Boosting Machine, to effectively diagnose heart disease. We used an ensemble voting method to combine the results of the top three classifiers to improve the prediction of heart disease. We used a combined heart disease dataset containing five different datasets (Hungary, Statlog, Switzerland, VA Long Beach and Cleveland). Feature selection algorithms (Pearson Correlation, Univariate Feature Selection, Recursive Feature Elimination, Boruta Feature Selection, Random forest, and LightGBM) are used to select highly relevant features based on rankings to improve classification accuracy. The proposed ensemble model is designed using seven highly relevant features, and a comparison of machine learning algorithms and ensemble learning techniques is applied to the selected features. Different performance evaluation methods are used to evaluate the proposed model: accuracy, sensitivity, precision, F1-score, MCC, NPV and AUC. Results analysis shows that the ensemble model achieves excellent classification accuracy, sensitivity, and precision of 96.17%, 98.37%, and 94.53%. Our proposed model performs better than existing models and individual classifiers. The results show that the proposed ensemble method can effectively predict the risk of heart disease. [ABSTRACT FROM AUTHOR]

Long COVID is a condition that affects a significant proportion of patients who have had COVID-19. It is characterised by the persistence of associated symptoms after the acute phase of the illness has subsided. Although several studies have investigated the risk factors associated with long COVID, identifying which patients will experience long-term symptoms remains a complex task. Among the various symptoms, dyspnea is one of the most prominent due to its close association with the respiratory nature of COVID-19 and its disabling consequences. This work proposes a new intelligent clinical decision support system to predict dyspnea 12 months after a severe episode of COVID-19 based on the SeguiCovid database from the Álvaro Cunqueiro Hospital in Vigo (Galicia, Spain). The database is initially processed using a CART-type decision tree to identify the variables with the highest predictive power. Based on these variables, a cascade of expert systems has been defined with Mamdani-type fuzzy-inference engines. The rules for each system were generated using the Wang-Mendel automatic rule generation algorithm. At the output of the cascade, a risk indicator is obtained, which allows for the categorisation of patients into two groups: those with dyspnea and those without dyspnea at 12 months. This simplifies follow-up and the performance of studies aimed at those patients at risk. The system has produced satisfactory results in initial tests, supported by an AUC of 0.75, demonstrating the potential and usefulness of this tool in clinical practice. [ABSTRACT FROM AUTHOR]

In recent years, with the rapid development of China's technological level, internet business has also increased, resulting in a shortage of wireless spectrum resources. Therefore, relevant scholars have introduced device direct communication technology into communication networks. Traditional cellular networks improve utilization efficiency and system capacity by introducing device through technology, and can also reduce the transmission pressure of base stations. However, this solution allows cellular users and device through users to share resources simultaneously, and each user may cause interference before. When the interference is severe, users cannot use the communication system. Therefore, this article proposes a communication resource allocation algorithm based on a proportional algorithm, which can enable users to experience better usage effects and leverage the performance advantages of device direct communication technology. Intelligent manufacturing can reflect the industrial competitiveness of every country, and China also attaches great importance to the development of intelligent manufacturing and increases efforts to support intelligent manufacturing. The manufacturing industry of intelligent systems is carried out through the industrialization of computers, promoting system optimization and upgrading. This article studies and explores the clustering of industrial IoT and digital intelligent manufacturing, applying these technologies to wireless resource allocation, thereby promoting the development and improvement of intelligent systems. [ABSTRACT FROM AUTHOR]

The objective of this paper is to introduce a module that works with smart glasses that to detect and recognize traffic signs on the road. The developed module encompasses the detection and classification of traffic signs, thereby enhancing safety and convenience for users during their travels. Employing innovative recognition algorithms, the software distinguishes between various types of signs, including speed limits, hazard warnings, and directional indicators. Furthermore, the system prioritizes user convenience by providing an intuitive and easily com-prehensible interface. This facilitates quick and precise access to information about traffic signs for both drivers and pedestrians, irrespective of their technological experience. Our endeavor not only illustrates the functionality of the system but also underscores its significance in augmenting road user safety. This innovative approach reflects our commitment to advancing technologies that not only offer intelligent solutions but also simplify everyday tasks for individuals. The paper establishes the sustainability and efficacy of this prototype, thereby laying the groundwork for future development and potential large-scale implementation of the system. This presents an exciting opportunity to enhance the mobility and safety for road users through innovative technologies and intelligent solutions. [ABSTRACT FROM AUTHOR]

A secure and reliable intelligent multi-cloud resource scheduling system in cyberspace is especially important in some industry applications. However, this task has become exceedingly challenging due to the intricate nature of information, the variety of knowledge representations, the compatibility of diverse knowledge reasoning engines, and the numerous security threats found in cloud networks. In this paper, we applied the endogenous security theory to the multi-cloud resource scheduling intelligent system and presented a novel model of the system. The proposed model incorporates various knowledge representations and inference engines, resulting in a multi-cloud resource scheduling intelligent system that ensures endogenous security. In addition, we have devised a scheme for an intelligent system that schedules multi-cloud resources using dual-channels and has an endogenous security mechanism, which we have named Dynamic, Heterogeneous, and Redundant (DHR). Finally, we have used the multi-cloud resource scheduling intelligent run log database to carry out numerous experiments to validate the efficiency of the dual-channel redundant reasoning system with the endogenous security mechanism's DHR property. The results of the experiment demonstrated that the multi-cloud resource intelligent scheduling system model with an endogenous security mechanism was superior to the current single-channel inference system scheme in regards to security and reliability. [ABSTRACT FROM AUTHOR]

Cardiovascular disease is the leading cause of global mortality and responsible for millions of deaths annually. The mortality rate and overall consequences of cardiac disease can be reduced with early disease detection. However, conventional diagnostic methods encounter various challenges, including delayed treatment and misdiagnoses, which can impede the course of treatment and raise healthcare costs. The application of artificial intelligence (AI) techniques, especially machine learning (ML) algorithms, offers a promising pathway to address these challenges. This paper emphasizes the central role of machine learning in cardiac health and focuses on precise cardiovascular disease prediction. In particular, this paper is driven by the urgent need to fully utilize the potential of machine learning to enhance cardiovascular disease prediction. In light of the continued progress in machine learning and the growing public health implications of cardiovascular disease, this paper aims to offer a comprehensive analysis of the topic. This review paper encompasses a wide range of topics, including the types of cardiovascular disease, the significance of machine learning, feature selection, the evaluation of machine learning models, data collection & preprocessing, evaluation metrics for cardiovascular disease prediction, and the recent trends & suggestion for future works. In addition, this paper offers a holistic view of machine learning's role in cardiovascular disease prediction and public health. We believe that our comprehensive review will contribute significantly to the existing body of knowledge in this essential area. [ABSTRACT FROM AUTHOR]

Featured Application: An automatic honey bee classification system based on audio signals for tracking the frequency of workers and drones entering and leaving a hive. The recent advances in smart beekeeping focus on remote solutions for bee colony monitoring and applying machine learning techniques for automatic decision making. One of the main applications is a swarming alarm, allowing beekeepers to prevent the bee colony from leaving their hive. Swarming is a naturally occurring phenomenon, mainly during late spring and early summer, but it is extremely hard to predict its exact time since it is highly dependent on many factors, including weather. Prevention from swarming is the most effective way to keep bee colonies; however, it requires constant monitoring by the beekeeper. Drone bees do not survive the winter and they occur in colonies seasonally with a peak in late spring, which is associated with the creation of drone congregation areas, where mating with young queens takes place. The paper presents a method of early swarming mood detection based on the observation of drone bee activity near the entrance to a hive. Audio recordings are represented by Mel Frequency Cepstral Coefficients and their first and second derivatives. The study investigates which MFCC coefficients, selected by the Least Absolute Shrinkage and Selection Operator, are significant for the worker bee and drone bee classification task. The classification results, obtained by an autoencoder neural network, allow to improve the detection performance, achieving accuracy slightly above 95% for the chosen set of signal features, selected by the proposed method, compared to the standard set of MFCC coefficients with only up to 90% accuracy. [ABSTRACT FROM AUTHOR]

The aim of this review is to analyze robotic systems used in lower limb rehabilitation, focusing on signal acquisition, processing, and intelligent system applications. Literature research was done using keywords giving an initial result of 130 publications, to which inclusion and exclusion criteria were applied, giving 58 related publications found during the last ten years. The increasing development of intelligent systems applied to assistive or rehabilitation devices allows one to find new ways of robotic systems usage, in the coming future, in this area. The use of bibliometric analysis software shows the areas of greatest relevance in the conception of this type of system. Finally, from the state-of-the-art research conducted allows to show in this document the most relevant assistive or rehabilitation robotic systems, their main characteristics, their functionality, as well as future trends. [ABSTRACT FROM AUTHOR]

The purpose of the study is to develop an approach to planning the trajectory of a robot manipulator using an intelligent system based on neural networks. For this purpose, the work considered the processes of planning and deploying the movement of the robot. The analysis of existing methods of planning the movement of robot manipulators and the review of intelligent control systems provided a comprehensive picture of the current state of this issue. A system is proposed that can perceive the environment and controls the movement of the robot by generating correct control commands. For this purpose, 3 tasks were solved, namely, the analysis of the environment in order to determine its features, the determination of the trajectory in order to neutralize the collision, and the determination of controlled influences for the executive bodies in order to implement the movement. The functionality and structure of the neural network for solving each of the tasks is proposed. The proposed approach is compared with existing approaches on key parameters, such as the execution time of the planned movement and the time of calculating the movement trajectory. The results confirmed that the use of neural network to optimize the trajectory and dynamic prediction to avoid obstacles significantly increased the adaptability of the system to the changing conditions of the production environment, which opens up new opportunities for improving automated processes and providing optimal conditions for the functioning of manipulator robots in real-time. [ABSTRACT FROM AUTHOR]

The ability to learn and to comprehend is fundamental to a human being. In this article, authors intend to develop a system to enhance the cognitive skills specifically of intellectually challenged children, by stimulation techniques. Authors have analysed the components and concepts of the brain with the intellectually challenging situation at the cognitive level and then we move on to analyse the causes and followed by the stimulation techniques in improving the learning skills. Our method has been tested to evaluate the device. The system we have designed is called the Self-Involved Motivated Action Reward Technique (SMART) that can show improvement in the conditions of intellectually challenged children. The customised product that we developed in this system is elaborated with all the technical details. The stimulation caused by the SMART has caused intervention in the brain thereby increasing the efficiency of the brain. The surrounding environment and effort can change and shape the performance of the brain, whereas the number of neurons, their ability to make connections and to retain them, determines the quality of behaviour of the subject. This study demonstrates changes in cerebral blood flow patterns over a period of time during the execution of certain cognitive activities by people. Magnetic Resonance Imaging (MRI) and functional Magnetic Resonance Imaging (fMRI) are considered to be prohibitively expensive modalities. When used for the purpose of monitoring the progress of children with performance graph calculate up to 20, 40, and 60 days. [ABSTRACT FROM AUTHOR]

The implementation of physical fitness tests requires adequate facilities, so technology is needed to make it easier without having to provide facilities. The purpose of this study is to make it easier to get the results of a person's physical fitness level using age, gender, height and weight data through an intelligent system using the naïve Bayes algorithm without having to do a physical fitness test. This research is included in the Experimental research. The method used in this study used machine learning and classification with the naïve Bayes algorithm. Data analysis techniques use probability by using data tests and evaluations. The evaluation used uses accuracy. The population in this study was 100 college students. Training model scheme 98 and test 2 get an accuracy value when training is 100%, on testing an accuracy value of 50%. The best model is used as a reference in predicting new data, using 5 new data where 3 data already know the VO2Max value with the same prediction value and actual value, then 2 new data are not yet known VO2Max value, the 4th data gets a value of 44.2 and the 5th data gets a value of 33.2. The results of VO2Max testing using the naïve Bayes algorithm are declared accountable. Contribution to future research is to multiply research datasets to improve accuracy and improve user interface quality through development research. [ABSTRACT FROM AUTHOR]

In clutter scenes, one or several targets need to be obtained, which is hard for robot manipulation task. Especially, when the targets are flat objects like book, plates, due to limitation of common robot end-effectors, it will be more challenging. By employing pre-grasp operation like sliding, it becomes feasible to rearrange objects and shift the target towards table edge, enabling the robot to grasp it from a lateral perspective. In this paper, the proposed method transfers the task into a Parameterized Action Markov Decision Process to solve the problem, which is based on deep reinforcement learning. The mask images are taken as one of observations to the network for avoiding the impact of noise of original image. In order to improve data utilization, the policy network predicts the parameters for the sliding primitive of each object, which is weight-sharing, and then the Q-network selects the optimal execution target. Meanwhile, extra reward mechanism is adopted for improving the efficiency of task actions to cope with multiple targets. In addition, an adaptive policy scaling algorithm is proposed to improve the speed and adaptability of policy training. In both simulation and real system, our method achieves a higher task success rate and requires fewer actions to accomplish the flat multi-target sliding manipulation task within clutter scene, which verifies the effectiveness of ours. [ABSTRACT FROM AUTHOR]

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The environment of the epidemic is a life changer for everyone, and specifically for the educational system is progress. In order to overcome the challenges in the educational system, this study proposes a new intelligent approach to teaching and learning. Teaching plays a vital role in higher education and can be advanced by utilising certain tools and technologies such as internet enabled mobile applications, automated scheduling of courses, assessment etc. The Emergency Distance Learning Methodology (EDLM) is proposed to improve the teaching-learning process by implementing artificial intelligence. Comparing the proposed system with the existing qualitative response analysis methods, it is observed that the proposed system provides 98.56% accuracy over the existing models. This study aims to assess the optimisation of teaching and learning models in higher education. [ABSTRACT FROM AUTHOR]

The concept of a regular memory area for an intelligent system (IS) is considered. The formalized description of the memory of a separate IS component is based on an infinite saturated binary tree. Knowledge is stored in special memory subareas in the form of semantic hierarchies. This knowledge constitutes the memory content, represented by its semantic structure. This structure integrates knowledge generated and transformed by morphisms and evolutions of knowledge, which, in turn, are used to implement the IS goals. The system of knowledge morphisms and knowledge evolution is used for IS modeling. This system allows one to describe the areas of initial data and values for these morphisms and evolution using regular expressions. The family of these sets generalizes the system of classes of morphism domains. These classes are developed for the knowledge formalisms and knowledge processing flowcharts used. The application of regular expressions to describe memory structures of IS components makes it possible to construct high-level mathematical models for big and complex intelligent systems. These models allow one to develop distributed memory control schemes for knowledge processing flows and processes in intelligent systems. [ABSTRACT FROM AUTHOR]

As the demand for private vehicles rises, there has been a gradual increase in the number of motor vehicles on the roads, leading to a growing concern about addressing traffic safety. Currently, China's approach to assessing driver capabilities remains rooted in traditional, non-intelligent, and standardized evaluation methods based on examination subjects. The traditional model often falls short in providing constructive feedback on a driver's real-world vehicle handling abilities, as many of the examination subjects can be practiced in advance to achieve a mere passing result, which, undoubtedly, increases the likelihood of underqualified drivers on the road. To address the issues of the current examination-oriented driver evaluation system in China, we propose a road performance assessment model (RPAM) that assesses drivers comprehensively by evaluating their road environment perception and vehicle operation abilities based on an in-vehicle and out-vehicle perception system. The model leverages patterns of the driver's head posture, along with real-time information on the vehicle's behavior and the road conditions, to quantify various performance metrics related to reasonable operation processes. These metrics are then integrated to generate a holistic assessment of the driving capabilities. This paper ultimately conducted tests of the RPAM on one actual examination route in Beijing. Two drivers were randomly selected for the examination. The model successfully computed the overall ability scores for each driver, validating the effectiveness. [ABSTRACT FROM AUTHOR]

Farmed fish disease diagnosis is an important problem in the fish farming industry, affecting quality of production and financial losses. In this paper, we present a web-based intelligent system that tackles the problem of fish disease diagnosis. To this end, it uses multiple knowledge representation and reasoning methods: rule-based, case-based, weight-based, and voting. Knowledge, which concerns the diagnosis of sea bass diseases, was acquired from experts in the field and represented in the form of decision trees. The diagnostic process is performed in two stages: a general one and a specialized one. In the general stage, a level-based diagnosis is performed, where environmental parameters, external signs, and internal signs are successively examined, and the three most probable diseases are identified. In the specialized stage, which is optional, a specialized expert system is used for each of the resulting diseases, where additional parameters concerning laboratory tests (microbiological, microscopic, molecular, and chemical) are considered. The general stage is the most useful, given that it can be performed on-site in real-time, whereas the specialized one requires time-consuming lab tests. The system also provides explanations for its decisions. Evaluation of the general-stage diagnostic process showed a top-3 accuracy of 78.79% on expert test cases and 94% on an artificial dataset. [ABSTRACT FROM AUTHOR]

The paper presents the IACPaaS cloud platform, which is used for creating intelligent services based on ontologies, as well as the conceptual ideas and architecture underlying its development. The main features of the supported technologies for creating intelligent services of various types are described, as the experience of their use. The platform offers an evolved instrumental support for the development of all components of intelligent services. First of all, it was positioned as an environment for creating cloud systems with knowledge bases, and now it is considered to be a tool for software development based on ontologies with semantic representation. [ABSTRACT FROM AUTHOR]

A power shortage is a common case prevailing in today's power scenario. Load shedding occurs when the demand power is greater than the generation power; then, the excessive load is cut to avoid power shortage. Usually, load shedding will make use of complex circuitry and systems along with expensive materials. This necessitates the need for a simple and efficient solution that meets all the requirements. The intelligent system is created with the help of microcontrollers achieving real-time load shedding. The power limit in the system is obtained from the utility company. The measured value in the system is obtained from the sensor and the value is compared to the power limit. If it is within the limit, no action is taken. When the limit exceeds the calculated value, the power supply is cut off. Graded load shedding is achieved through the above-mentioned method. Through this graded load shedding method, the need for changing the existing infrastructure is removed and the existing system is made useful for a longer period of time. As this proposed design uses the simplest of components and technologies, the financial and technical capital that is required to make this is lower when compared to the existing technologies. [ABSTRACT FROM AUTHOR]

The penaeid shrimp farming industry is experiencing rapid growth. To reduce costs and labour, automation techniques such as counting and size estimation are increasingly being adopted. Feeding based on the degree of stomach fullness can significantly reduce food waste and water contamination. Therefore, we propose an intelligent shrimp farming system that includes shrimp detection, measurement of approximated shrimp length, shrimp quantity, and two methods for determining the degree of digestive tract fullness. We introduce AR-YOLOv5 (Angular Rotation YOLOv5) in the system to enhance both shrimp growth and the environmental sustainability of shrimp farming. Our experiments were conducted in a real shrimp farming environment. The length and quantity are estimated based on the bounding box, and the level of stomach fullness is approximated using the ratio of the shrimp´s digestive tract to its body size. In terms of detection performance, our proposed method achieves a precision rate of 97.70%, a recall rate of 91.42%, a mean average precision of 94.46%, and an F1-score of 95.42% using AR-YOLOv5. Furthermore, our stomach fullness determined method achieves an accuracy of 88.8%, a precision rate of 91.7%, a recall rate of 90.9%, and an F1-score of 91.3% in real shrimp farming environments. [ABSTRACT FROM AUTHOR]

The financial system has reached its pinnacle because of economic and social growth, which has propelled the financial sector into another era. Public and corporate financial investment operations have significantly risen in this climate, and they now play a significant part in and impact the efficient use of market money. This finance sector will be affected by high-risk occurrences because of the cohabitation of dangers and passions, which will cause order to become unstable and definite financial losses. An organization’s operational risk is a significant barrier to its growth. A bit of negligence could cause the business’s standing to erode rapidly. Increasing funding management and forecasting risks is essential for the successful development of companies, enhancing their competitiveness in the marketplace and minimizing negative effects. As a result, this study takes the idea of mobile edge computing. It creates an intelligent system that can forecast different risks throughout the financial investment process based on the operational knowledge of important investment platforms. The CNN-LSTM approach, based on knowledge graphs, is then used to forecast financial risks. The results are then thoroughly examined through tests, demonstrating that the methodology can accurately estimate the risk associated with financial investments. Finally, a plan for improving the system for predicting financial risk is put out. [ABSTRACT FROM AUTHOR]

Recognizing facial expressions plays a crucial role in various multimedia applications, such as human–computer interactions and the functioning of autonomous vehicles. This paper introduces a hybrid feature extraction network model to bolster the discriminative capacity of emotional features for multimedia applications. The proposed model comprises a convolutional neural network (CNN) and deep belief network (DBN) series. First, a spatial CNN network processed static facial images, followed by a temporal CNN network. The CNNs were fine-tuned based on facial expression recognition (FER) datasets. A deep belief network (DBN) model was then applied to integrate the segment-level spatial and temporal features. Deep fusion networks were jointly used to learn spatiotemporal features for discrimination purposes. Due to its generalization capabilities, we used a multi-class support vector machine classifier to classify the seven basic emotions in the proposed model. The proposed model exhibited 98.14% recognition performance for the JaFFE database, 95.29% for the KDEF database, and 98.86% for the RaFD database. It is shown that the proposed method is effective for all three databases, compared with the previous schemes for JAFFE, KDEF, and RaFD databases. [ABSTRACT FROM AUTHOR]