Application Of Intelligent Algorithms Research Articles

Application of intelligent algorithms in library resource malicious download detection system

Application of intelligent algorithms in library resource malicious download detection system

Study on the Effect of Post-Freezing Mechanical Properties of Polypropylene Fibre Concrete Based on BAS-BPNN

An indoor accelerated freezing and thawing test of polypropylene fibre-reinforced concrete in chloride and sulphate environments was conducted using the “fast-freezing method” with the objective of investigating the damage law of the post-freezing mechanical properties of hydraulic concrete structures and studying the effects of different mixing amounts of polypropylene fibres on the mechanical properties of concrete. Furthermore, in order to reduce the cost of concrete tests and shorten the time required for conducting concrete tests, a backpropagation neural network based on a Beetle Antenna Search algorithm (BAS-BPNN) was established to simulate and predict the mechanical properties of polypropylene fibre-reinforced concrete. The accuracy of the model was verified. The results indicate that the order of improvement in the macro-physical properties of concrete due to fibre doping is as follows: PPF1.2 exhibited the greatest improvement in macro-physical properties of concrete, followed by PPF0.9, PPF1.5, PPF0.6, and PC. When the freezing and thawing medium and the number of cycles are identical, all four assessment indexes (R2, RMSE, SI, MAPE) demonstrate that the four groups of polypropylene fibre concrete exhibit superior performance to the control group of ordinary concrete. This indicates that polypropylene fibre can enhance the mechanical properties and freezing resistance of the concrete matrix, delay the process of freezing and thawing damage to the matrix, and extend the lifespan of the matrix, yet cannot prevent the ultimate failure of the matrix. The application of intelligent algorithms to optimise the parameters of an artificial neural network model can enhance its capacity to generalise and predict the mechanical properties of concrete. In terms of the coefficient of determination (R2), the Beetle Antenna Search algorithm (0.9782) outperforms the Particle Swarm Optimization (PSO; 0.9676), the Genetic Algorithm (GA; 0.9645), and the backpropagation neural network (BPNN; 0.9460). The improved backpropagation neural network based on the Beetle Antenna Search algorithm not only avoids the trap of local optimality but also improves the model accuracy while further accelerating the convergence speed. This approach can address the complexity, non-linearity, and modelling difficulties encountered during the freezing process of concrete. Moreover, it offers relatively accurate prediction outcomes at a reduced cost in comparison to traditional experimental methodologies.

Financial big data management and intelligence based on computer intelligent algorithm

With the acceleration of China’s economic integration process, enterprises have gained greater advantages in the fierce market competition, and gradually formed the trend of grouping and large-scale. However, as the scale of the company increases, the establishment of a branch also causes many problems. For example, in order to obtain more benefits, the business performance of the company can generate false growth, resulting in financial and operational risks. This paper analyzed the current situation and needs of enterprise financial control from two aspects of theory and practice, combined with specific engineering projects, taking ZH Group as an example, according to the actual situation of the enterprise. The article first introduces the basic situation of the enterprise; Then, the financial control strategy was designed, and different modules were designed to achieve financial control; Afterwards, use a reverse neural network to evaluate the effectiveness of financial management and risk warning; Relying on particle swarm optimization algorithm to seek the optimal solution and applying it to financial management and risk warning, in order to improve the level of introspection and risk management in decision-making. Finally, the value of computer intelligence algorithms in financial big data management is evaluated by constructing a financial risk indicator system. Through the analysis of enterprise financial management, the total asset turnover rate of ZH Group decreased by 0.39 times in 5 years. After 5 years of adjustment of the company’s business, the company’s overall operational capabilities still needed to be improved, and the company’s comprehensive business capabilities also still needed to be improved. Therefore, the application of intelligent algorithms for financial control is very necessary.

Application of Intelligent Algorithms and Big Data Analysis in Film and Television Creation

With the rapid development of social media, people can access a large amount of data in a short period of time, and big data technology has emerged. With the vigorous development of cloud computing and big data, the method of mining audience interests through a large amount of data to guide film and television creation has attracted more and more attention from experts and scholars. In order to understand the current situation of film and television drama creation in China and provide suggestions for the shortcomings in the industry, this article mainly analyzes the application of intelligent algorithms for traffic prediction models and big data analysis in film and television creation. This intelligent algorithm can predict the potential audience of movies or TV dramas, helping producers and investors make decisions. This system utilizes artificial intelligence technology to select suitable actors for characters based on their matching degree and past work performance. This article applies intelligent algorithms to big data processing to improve the accuracy of data processing. This article explores the application of intelligent algorithms and big data analysis in film and television creation. Using machine learning algorithms to predict the potential audience of a movie or TV series based on historical data, providing decision-making basis for investors and producers. This system utilizes AI technology to select suitable actors for characters based on their matching degree with characters and past work performance, improving the scientific and accurate selection of roles. The application of these technologies helps to improve production efficiency and quality, reduce costs and risks, and inject new impetus into the sustainable development of the film and television industry.

Curriculum learning-based domain generalization for cross-domain fault diagnosis with category shift

Intelligent fault diagnosis has witnessed significant advancements in the preceding years. Domain generalization-based methods can effectively alleviate the domain shift problem and be employ for fault diagnosis in unknown domains. Apart from the problem of domain shift, another challenge arises from the incomplete label space of each source domain due to the difficulty of data acquisition. Category shift can have a significant impact on the subsequent application of intelligent algorithms. To confront this more challenging and practical problem, we begin by formulating the setting of domain generalization with category shift. This paper proposes a Curriculum Learning-based Domain Generalization method (CLDG) to tackle with the intricate problem. The basic network consists of a feature extractor, a mixup-based reciprocal point learning classifier for tackling the category shift between the source and target domains, and a conditional domain discriminator for addressing the domain shift. In addition, we construct a curriculum learning strategy that uses the knowledge of categories with high observation degree to assist in extracting domain invariant features of lower ones, dealing with the category shift between the source domains and improving the generalization ability of the categorical information. Extensive experimental results on two datasets provide evidence for the effectiveness and superiority of the proposed algorithm in classifying known and missing classes in each source domain, as well as identifying unobserved failure modes in unknown target domains.

Calculation of carbon emissions in wastewater treatment and its neutralization measures: A review

As the pursuit of “carbon neutrality” gains momentum, the emphasis on low-carbon solutions, emphasizing energy conservation and resource reuse, has introduced fresh challenges to conventional wastewater treatment approaches. Precisely evaluating carbon emissions in urban water supply and drainage systems, wastewater treatment plants, and establishing carbon-neutral operating models has become a pivotal concern in the future of wastewater treatment. Regrettably, limited research has been devoted to carbon accounting and the development of carbon-neutral strategies for wastewater treatment. In this review, to facilitate comprehensive carbon accounting, we initially recognizes direct and indirect carbon emission sources in the wastewater treatment process. We then provide an overview of several major carbon accounting methods and propose a carbon accounting framework. Furthermore, we advocate for a systemic perspective, highlighting that achieving carbon neutrality in wastewater treatment extends beyond the boundaries of wastewater treatment plants. We assess current technical measures both within and outside the plants that contribute to achieving carbon-neutral operations. Encouraging the application of intelligent algorithms for the multifaceted monitoring and control of wastewater treatment processes is paramount. Supporting resource and energy recycling is also essential, as is recognizing the benefits of synergistic wastewater treatment technologies. We advocate a systematic, multi-level planning approach that takes into account a wide range of factors. Our goal is to offer valuable insights and support for the practical implementation of water environment management within the framework of carbon neutrality, and to advance sustainable socio-economic development and contribute to a more environmentally responsible future.

Intelligent algorithm-based analysis of corporate financial decisions in the era of cloud accounting

Abstract The development of cloud computing technology has promoted a new information technology revolution, and the arrival of the cloud accounting era has brought new ideas to enterprise financial decision-making. This paper first introduces the improved swarm intelligence algorithm grasshopper optimization algorithm optimization, using reverse learning the Levy flight mechanism to improve the global search ability of the original grasshopper algorithm, using the Gaussian variation mechanism to increase the diversity of the population to achieve algorithm optimization. The IGOA algorithm is then used to optimize the internal parameters of KELM by dynamically adjusting the optimal parameters based on the training set data through a 5-fold cross-validation analysis to evaluate the external classification performance. Then the factors affecting the financial decision analysis and the financial decision process in the cloud accounting era are introduced, and finally, some problems in traditional enterprise decision methods and the impact of intelligent algorithms on enterprise financial decisions are analyzed. In terms of the existing problems, the main ones are: 83% of them think that the traditional enterprise decision-making methods need to be innovated, and 83% of them think there is a shortage of professionals with professional financial decision-making expertise. On the impact of intelligent algorithms on enterprise financial decision-making, 88% think that the decision analysis dimension has been broadened based on intelligent algorithms, and 85% think that the decision risk has achieved fine control. The development and application of intelligent algorithms and cloud accounting technology have an important and positive effect on the financial decision-making mode of enterprises.

DB-TASNet for disease diagnosis and lesion segmentation in medical images

Deep learning algorithms have been successfully used in the field of medical image analysis and have greatly improved application of intelligent algorithms to medical diagnosis. However, existing deep-learning-based diagnostic methods still suffer from several drawbacks: (1) In most medical image multi-tasking methods, focus segmentation and disease classification are often performed linearly, resulting in excessive reliance on the final results of focus segmentation. (2) The computational cost of the traditional attention mechanism for performing the segmentation task is very high and the convolutional architecture cannot be used to model long-distance dependencies, which in turn affects the segmentation accuracy. To address these issues, we propose a disease diagnosis and lesion segmentation model, Dual-Branch with Transformer Axial-attention Segmentation Net (DB-TASNet). DB-TASNet is built by the DenseNet-121 classification network and U-Net segmentation network improved using an axial-attention transformer model. Moreover, DB-TASNet also includes a lesion integration module to integrate segmentation results with the classification network in order to increase its attention to lesions and improve the diagnosis results. Experimental results on the Pneumothorax dataset provided by the Society for Imaging Informatics in Medicine (SIIM) show that the average AUC of the DB-TASNet classification task reaches 0.939, and the DICE coefficient of the segmentation task reaches 0.886. Such performance suggests that the proposed model may provide an efficient and effective diagnosis tool for medical personnel.

Design Optimization of Hydraulic Machinery Based on ISIGHT Software: A Review of Methods and Applications

Optimizing hydraulic machinery is a critical research area within the field of fluid mechanics, aiming to enhance product design efficiency and improve performance while reducing development time. The application of intelligent algorithms and combinatorial optimization strategies has become increasingly prevalent in this domain, providing a comprehensive understanding of optimization-related theoretical developments. Recently, the emergence of ISIGHT software as a new technology for software integration platforms has opened new avenues for optimization in hydraulic machinery. By leveraging intelligent algorithms and combinatorial optimization strategies, ISIGHT software provides a comprehensive framework for optimizing hydraulic machinery. This paper serves as an introduction to ISIGHT software, highlighting its advantages in addressing optimization problems. It presents a detailed examination of the process and technology involved in hydraulic machinery optimization based on ISIGHT software, along with its practical application. Furthermore, the paper summarizes the future development trends of ISIGHT software, offering engineers a theoretical foundation and reference for optimizing hydraulic machinery performance. Overall, this paper provides a valuable contribution to the field of hydraulic machinery optimization, showcasing the potential of ISIGHT software.

Globally Localized Multisource Domain Adaptation for Cross-Domain Fault Diagnosis With Category Shift.

Deep learning has demonstrated splendid performance in mechanical fault diagnosis on condition that source and target data are identically distributed. In engineering practice, however, the domain shift between source and target domains significantly limits the further application of intelligent algorithms. Despite various transfer techniques proposed, either they focus on single-source domain adaptation (SDA) or they utilize multisource domain globally or locally, which both cannot address the cross-domain diagnosis effectively, especially with category shift. To this end, we propose globally localized multisource DA for cross-domain fault diagnosis with category shift. Specifically, we construct a GlocalNet to fuse multisource information comprehensively, which consists of a feature generator and three classifiers. By optimizing the Wasserstein discrepancy of classifiers locally and accumulative higher order multisource moment globally, multisource DA is achieved from domain and class levels thus to reduce the shift on domain and category. To refine the classifier at sample level, a distilling strategy is presented. Finally, an adaptive weighting policy is employed for reliable result. To evaluate the effectiveness, the proposed method is compared with multiple methods on four bearing vibration datasets. Experimental results indicate the superiority and practicability of the proposed method for cross-domain fault diagnosis.

Application of machine learning algorithms in prognostics and health monitoring of electronic systems: A review

In the modern age of digitalization, electronics are fundamental to any engineering system. With the current strong focus on the Internet of Things (IoT), autonomous vehicles and Industry 4.0, reliable electronics are gaining crucial importance. Predicting the health of complex systems is able to avoid catastrophic failures. Prognostic and Health Monitoring (PHM) approaches are an important step toward trustable and reliable electronics. Nowadays, Artificial Intelligence (AI) and machine learning (ML) algorithms are integrated into PHM approaches, enabling complex fault diagnosis. In this contribution, we provide an overview of the application of intelligent algorithms in PHM of electronics in a systematic manner. The challenges of prognostics in electronics are provided and a detailed overview of the available PHM precursors for various electronic components and the associated selection process is given. Based on the literature review conducted, the main research challenges with ML algorithms in PHM are discussed along with performances of each model.

Review of Intelligent Algorithms for Breast Cancer Control: a Latin America Perspective

Breast cancer in women is a worldwide health problem that is one of the main causes of death. This situation is accentuated in Latin America and the Caribbean countries, where about 159 women die daily from this disease. The World Health Organization recommends focusing on Prevention and Early Detection of cancer to reduce mortality. However, this requires a great deal of information processing and analysis by experts, who require the support of technology to perform these tasks promptly. In recent years, the use of so-called intelligent algorithms has increased to support the fight against breast cancer. The authors summarized the studies published between January 2016 and June 2021, highlighting the current situation and opportunities for Latin America and the Caribbean. Studies were selected using the following terms: intelligent algorithms, assessment metrics, stages of breast cancer control addressed, data sources, data types, female population with breast cancer under study and the countries of the authors who have written articles on this subject. In this study, after applying the inclusion and exclusion criteria 226 articles were selected from a total of 1,105 articles found in the ACM digital library, IEEE Explore, Nature, PubMed, Scopus (Science Direct) and Springer Link databases. Publication between January 2016 and June 2021, breast cancer as main interest, algorithm and data type information, along with compliance with the general question were the inclusion criteria, while, being a research article, compliance with the three subqueries and availability, were the exclusion criteria. Using a spreadsheet as based tool to collect and analyze the data, the study found that the most used elements were: SVM, RF and DT algorithms; accuracy as assessment metric; public information sources; data on tumors (size and shape, among others); USA information sources; India as the country of the first authors who wrote the most articles of the selected papers; and Diagnosis and treatment as the most addressed stage of cancer control. Results in this review paper provide an overview of the application of intelligent algorithms against breast cancer. In this regard, the gaps that were detected are: the Prevention stage of cancer control has not been addressed with intelligent algorithms, and the Early Detection stage has been very little addressed; private data sources could be beneficial in this type of research, but the difficulty in accessing them is a barrier for researchers. In addition, although Latin America and the Caribbean have a significant death rate from breast cancer, patients in this region have not been the subject of study and the participation of researchers on the subject has been almost nonexistent. Finally, there seems to be a great opportunity to generate proposals based on intelligent algorithms with low cost and time to implement that could directly impact patient survival, improving the health systems of the countries in the region.

The application of intelligent algorithms in word discrimination

The application of intelligent algorithms in word discrimination

Artificial Intelligence-Oriented User Interface Design and Human Behavior Recognition based on Human–Computer Nature Interaction

This work is to explore the application of intelligent algorithms based on deep learning in human–computer interaction systems, hoping to promote the development of human–computer interaction systems in the field of behavior recognition. Firstly, the design scheme of the human–computer interaction system is presented, and the establishment of the robot visual positioning system is emphasized. Then, the fast-region convolutional neural networks (fast-RCNN) algorithm is introduced, and it is combined with deep convolutional residual network (ResNet101). A candidate region extraction algorithm based on ResNet and long short-term memory network is proposed, and a residual network (ResNet) for spatial context memory is proposed. Both algorithms are employed in human–computer interaction systems. Finally, the performance of the algorithm and the human–computer interaction system are analyzed and characterized. The results show that the proposed candidate region extraction algorithm can significantly reduce the loss value of training set and test set after training. In addition, the corresponding accuracy, recall, and F-value of the model are all above 0.98, which proves that the model has a good detection accuracy. Spatial context memory ResNet shows good accuracy in speech expression detection. The detection accuracy of single attribute, double attribute, and multi-attribute speech expression is above 89%, and the detection accuracy is good. In summary, the human–computer interaction system shows good performance in capturing target objects, even for unlabeled objects, the corresponding grasping success rate is 95%. Therefore, this work provides a theoretical basis and reference for the application of intelligent optimization algorithm in human–computer interaction system.

Investigation on the optimization strategy of phase change material thermal management system for lithium-ion battery

Due to the lack of detailed comparison and reliability validation of paraffin (PA)-based optimization methods for lithium-ion thermal management systems, in this work, PA was selected as a phase change material (PCM). Both expanded graphite (EG) and the fin were used to optimize the thermal management performance of the PA. Charging and discharging experiments under different optimization methods were conducted, and the thermal properties of both PCMs before and after cycling were recorded to assess reliability. The experimental results indicated that both PCMs had similar thermal properties and maintained good thermal management performance after cycling. PA/EG exhibited better thermal management performance than the PA/Fin due to its excellent thermal conductivity network. Higher ambient temperatures also reduced the thermal management performance of PCM and affected the optimization of EG and Fin in the meantime. PA/EG/Fin offered the best thermal management performance, achieving a maximum 35.5 % reduction in battery temperature and controlling the temperature increase of the battery even at extremely high temperatures. This work may serve as a reference for the design of thermal management systems for lithium-ion battery, providing a basis for the application of intelligent algorithms in the optimization of thermal management of PCMs.

Research on Intelligent Embedded Fan System Based on YOLOv2 Lightweight Algorithm.

With the development of artificial intelligence, the application of intelligent algorithms to low-power embedded chips has become a new research topic today. Based on this, this study optimizes the YOLOv2 algorithm by tailoring and successfully deploys it on the K210 chip to train the face object detection algorithm model separately. The intelligent fan with YOLOv2 model deployed in K210 chip can detect the target of the character and obtain the position and size of the character in the machine coordinates. Based on the obtained information of character coordinate position and size, the fan's turning Angle and the size of air supply are intelligently perceived. The experimental results show that the intelligent fan design method proposed here is a new embedded chip intelligent method of cutting and improving the YOLOv2 algorithm. It innovatively designed solo tracking, crowd tracking, and intelligent ranging algorithms, which perform well in human perception of solo tracking and crowd tracking and automatic air volume adjustment, improve the accuracy of air delivery and user comfort, and also provide good theoretical and practical support for the combination of AI and embedded in other fields.

Innovative Artistic Design of Nanomaterial Products Based on Intelligent Algorithm

At present, algorithm technology has achieved very rapid development, and it is more and more widely penetrated and applied in people’s production and life. Among them, product design based on intelligent algorithm has also been widely used. This technology plays an important role in promoting product innovation and design and contributes to the improvement and optimization of technology, showing very significant application benefits in the product design and development process of major enterprises. The application of intelligent algorithms in product design can not only make products more in line with customers’ usage habits, but also products blessed with intelligent algorithms often have more commercial value. Therefore, in order to enhance the added value of commodities, innovative design of products has become a must. Nanomaterials must have unique properties that are unmatched by general building materials, such as high surface activity, strong oxidation, and superparamagnetic. This article mainly studies design strategies from two aspects, namely the innovation of material properties and application methods, and introduces the properties of nanomaterials, their application status, and the existing problems. And it starts from the application of intelligent technology in nanomaterial products, innovative design, and development prospects, combined with actual cases, to find a feasible way for the development of innovative design and research of nanomaterial products. On this basis, it is proposed that the idea of expanding the design should reunderstand and reposition the product design of nanomaterials and make the design more acceptable to more consumers. New design ideas and methods such as adjusting material properties through intelligent algorithms provide certain assistance and support for the application of intelligent algorithms in product design innovation practice. Some completed application practices are also presented to illustrate the rationality of the application of intelligent algorithms in nanomaterial products. The results show that the crowd search algorithm has the highest optimization degree and has good stability and optimization effect, and its standard deviation is the lowest close to 0.

The Export Operation Mode and Optimization Strategy of Crossborder e-Commerce Enterprises Integrating Data Mining Algorithms

With the advent of the crossborder e-commerce (CE) boom, more and more traditional enterprises have transformed into the field of crossborder e-commerce. It has become a new outlet for enterprises in the international trade environment, and at the same time, it has some problems in the utilization of resources and the maximization of benefits. In response to this problem, it is very important to optimize the export operation model of crossborder e-commerce enterprises (CEE). With the development of intelligent algorithms, research on the application of intelligent algorithms to the economic field has gradually been carried out. Its characteristics and advantages are of great significance to the optimization of CEE export operation mode. The purpose of this paper is to study the CEE export operation mode and optimization strategy based on data mining algorithm (DMA). Through the analysis and research of DMA, it can be applied to the optimization of CEE export operation mode to cope with the world trade in the new environment. This paper explains the basic theory of DMA and CEE export operation mode. Its effect is experimentally analyzed, and the relevant theoretical formulas are used to explain. The results show that the comprehensive trade quality index of the CEE export operation mode integrating DMA is higher, and the adaptability is stronger. Compared with the traditional operating model, the difference between the two indices is 3.175 index points. It has guiding significance in terms of CEE export operation mode and optimization strategy.

Development and Optimization of the Internet of Things Network for Product Quality Monitoring

The purpose of the article is to analyze methods, approaches and means of Internet of Things (IoT) technology for product quality control. The objectives of the article are: analysis of dairy farm management problems, development of the structure of the IoT network for milk quality analysis, development of an algorithm for the operation of the IoT milk quality control network, optimization of IoT network protocols choice. Two types of management are considered for the functioning of a dairy farm: production and processes. To implement dairy farm management using 4.0 technology, the use of an IoT network is proposed, in which various communication protocols are considered. The structure of the IoT network of four-level dairy farm management is proposed, which includes analyzers, a gateway, a cloud platform and various applications for farmers and operators. A generalized algorithm for milk quality control on a farm based on the developed IoT network structure is presented, which includes receiving information from analyzers, transmitting it through a gateway to a cloud platform for storage and intelligent processing, displaying results through operator applications. The application of intelligent algorithms for farm management is also considered: data processing and training ‒ ML, knowledge processing ‒ DT, security ‒ FL. The process of selecting a suitable network protocol for monitoring a dairy farm is carried out using the hierarchy analysis method, which uses expert estimates corresponding to four network indicators: transmission speed, distance, frequency and security. The formulated approaches can be applied in various production processes to monitor product quality.

A study of student performance under English teaching using a decision tree algorithm

With the popularity of English, more and more attention has been paid to students' English learning. In order to understand student learning and make accurate predictions about student performance, this paper analyzed student performance under English teaching by using a decision tree algorithm, i.e. the C4.5 algorithm. The calculation process of the algorithm was simplified by the Taylor series, and an example was analyzed. The results showed that the running time of the improved C4.5 algorithm was improved by 22.86% compared with the C4.5 algorithm, the precision rate was above 75%, the recall rate was above 85%, and the F1-measure value was above 80%. The experimental results verified the effectiveness of the improved C4.5 method in studying student achievement. This work is beneficial to the further optimization of decision tree algorithms and provides some reference for the application of intelligent algorithms in the field of education.