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With the development of computer technology, automatic test paper generation systems have gradually become an effective tool for detecting and maintaining national machine security and protecting the rights and interests of workers. This article achieved multi-level oral scores for different types of questions through online scoring using artificial neural networks in recent years. Based on its specific situation and evaluation index requirements, an analysis module that is reasonable, efficient, and in line with the hierarchical structure and module requirements of national conditions has been designed to complete the research on automatic test paper generation technology, in order to help better manage and allocate human resources and improve production efficiency. Afterwards, this article conducted functional testing on the technical module. The test results showed that the scalability of the system was over 82%. [ABSTRACT FROM AUTHOR]

Abstract: There are a number of issues which are involved with organizing a conference. Among these issues, assigning conference-papers to reviewers is one of the most difficult tasks. Assigning conference-papers to reviewers is automatically the most crucial part. In this paper, we address this issue of paper-to-reviewer assignment, and we propose a method to model the reviewers, based on the matching degree between the reviewers and the papers by combining a preference-based approach and a topic-based approach. We explain the assignment algorithm and show the evaluation results in comparison with the Hungarian algorithm. [Copyright &y& Elsevier]

Abstract: Recommendation algorithms have been researched extensively to help people deal with abundance of information. In recent years, the incorporation of multiple relevance criteria has attracted increased interest. Such multi-criteria recommendation approaches are researched as a paradigm for building intelligent systems that can be tailored to multiple interest indicators of end-users – such as combinations of implicit and explicit interest indicators in the form of ratings or ratings on multiple relevance dimensions. Nevertheless, evaluation of these recommendation techniques in the context of real-life applications still remains rather limited. Previous studies dealing with the evaluation of recommender systems have outlined that the performance of such algorithms is often dependent on the dataset – and indicate the importance of carrying out careful testing and parameterization. Especially when looking at large scale datasets, it becomes very difficult to deploy evaluation methods that may help in assessing the effect that different system components have to the overall design. In this paper, we study how layered evaluation can be applied for the case of a multi-criteria recommendation service that we plan to deploy for paper recommendation using the Mendeley dataset. The paper introduces layered evaluation and suggests two experiments that may help assess the components of the envisaged system separately. [Copyright &y& Elsevier]

Abstract: The paper describes a system for executable papers for publishers enabling them to reuse content and to generate further advances of science and engineering. The executable algorithmic descriptions within a paper are presented in natural language sentences and basic code, thereby making long term compatibility absolute. Authors are required to use publicly numerical libraries on the Internet or references to publications with executable papers. As used by authors the system automatically creates a web of algorithmic knowledge on the Internet. Novelty of new algorithms in publications can be evaluated by automated tools available to authors, reviewers and readers of scientific papers published. [Copyright &y& Elsevier]

The smart car collects the signal through the infrared sensor, and obtains the gray map in front of the car, according to the gray map, the tracking is judged.Due to the complexity of the environment, many gray-scale maps are difficult to trace, and even cannot be judged, which brings great uncertainty to the operation of the smart car.The paper designs a method to improve the infrared tracking accuracy of smart cars.First, determine the level of contrast in this graph.By improving the brightness in the grayscale image, the gray signal around the tracking line is eliminated, and then the tracking line is highlighted by increasing the contrast.Therefore, it effectively overcomes the shortcomings of poor practicality of smart cars and improves the infrared tracking accuracy of smart cars. [ABSTRACT FROM AUTHOR]

The existing forensics systems are developing in a more and more perfect direction in terms of forensic steps, legal constraints, audit supervision, etc., but they are still not fully applicable to legal forensics. A general legal forensics system must have a complete forensics process to ensure the smooth progress of the forensics work. One of the purposes of legal forensics is to use legal means to bring criminals to justice. Only by ensuring the legality of the evidence can it be accurately identified case, and therefore also to ensure the legality of the evidence collection process. In order to design a more powerful legal forensics system, this paper introduces a data mining algorithm to extract valuable information from massive data through association rules to improve the efficiency of forensics. At the same time, this paper also proposes three coding schemes for the integrity of the evidence object, and compares the forensic effects of the three schemes. [ABSTRACT FROM AUTHOR]

With the development of artificial intelligence technology, it has become convenient for people to work, study and travel and face recognition is widely used in many fields, but also brings a lot of hidden dangers to security. Aiming at the problem of reduced accuracy of face detection algorithm caused by local occlusion factors, The paper proposes a partial occlusion face recognition method based on DCGAN face repair combined with ResNet. The experimental results show that the algorithm proposed in the paper has better face recognition effect when there is partial occlusion in the face. [ABSTRACT FROM AUTHOR]

In this paper, we construct an enterprise economic management sharing platform based on the feature recognition grid system, firstly, we use the fuzzy matching recognition algorithm to extract the grid features of economic management and map them into the fuzzy set of affiliation features, and then we extract the feature vectors from each grid to get the feature matrix, and finally, according to the similarity degree of the feature sequences, we set a threshold to give the recognition result. The results show that the recognition rate of the platform constructed in this paper reaches more than 99%, the classification accuracy is about 90% on average, and the optimization time is only about 19 s. It is verified that the method of this paper improves the accuracy of enterprise economic management, and has a positive role in promoting the healthy development of enterprises. [ABSTRACT FROM AUTHOR]

The large deformation of soft rock tunnel is an important problem to be solved urgently with the development of engineering technology. In order to improve the application effect of prestressed anchor cable adaptive active support system, this paper puts forward an optimization method based on adaptive genetic algorithm, reviews the research status of prestressed anchor support in soft rock tunnel, and analyzes and evaluates the existing methods. This paper introduces the construction process of AGA model, establishes the problem description model and genetic optimization algorithm selection model, and constructs the adaptive active support model of prestressed anchor cable. The model adopts an optimization algorithm with a total of 100 iterations. The Simple Genetic Algorithm and Parallel Genetic Algorithm are selected to evaluate the anchoring force, the control effect of surrounding rock displacement, the erosion of supporting structure and the construction time by comparative test. The test results show that AGA has a good effect on the anchoring force, the minimum anchoring force is 457 kN, and the displacement of surrounding rock is controlled within 1.21-1.59mm, which has a good influence on the quality of supporting structure and the construction completion time. Finally, the application of AGA model in parameter optimization of prestressed anchor cable in soft rock tunnel is summarized, and the future research direction is put forward. [ABSTRACT FROM AUTHOR]

The unstoppable trend of globalization has made the integration of voice recognition with science and technology increasingly close, and has called for the emergence of more powerful oral voice recognition systems. At present, most speech recognition research uses Hidden Markov Model-Gaussian Mixture Modell (HMM-GMM) and onnectionist Temporal Classification (CTC) end-to-end method, which has obvious limitations. Therefore, based on neural network, this paper introduces deep neural network posterior probability algorithm into speech recognition system, and constructs the spoken speech recognition system based on deep neural network posterior probability algorithm. The design principle of the constructed speech system is discussed and the performance analysis and effectiveness evaluation are carried out. The experimental results show that the performance of the neural network-based speech recognition system constructed in this paper has made a breakthrough compared with the traditional technology, and the Character Error Rate (CER) is only 4.6%, and the End-to-End Latency and Equal Error Rate (EER) have reached a low level. This study points out the future development direction of speech recognition and provides the impetus to promote its further development [ABSTRACT FROM AUTHOR]

A critical aspect of software engineering is Software fault prediction which aims to identify and prevent errors in software systems before their release which can cause failures or issues for its users. Various techniques and tools have been developed to detect software faults, including static code analysis, dynamic testing, and machine learning-based approaches. In past few years, the world has seen a growing interest in the use of ML models for predicting software faults, as they can effectively analyse high dimensional datasets and detect complex patterns which are difficult for human experts to detect. However, developing accurate and reliable software fault detection models requires careful selection of data, feature engineering, and model evaluation. This purpose of this paper is to present a comprehensive analysis of potential applications and future research directions in the field of software fault detection. The study emphasizes the importance of identifying and addressing software faults to ensure the reliability and efficiency of software systems. Additionally, the paper outlines various approaches and techniques that can be employed for effective software fault detection. [ABSTRACT FROM AUTHOR]

The paper discusses the possibility of increasing the effectiveness of strategic management of research and development activities in higher education institutions. To achieve this goal, the authors proposed an algorithm for selecting competencies, which allows for the distribution of university employees and students among research and development teams in accordance with their individual competencies for carrying out research and development work. The paper covers the problem statement of competence selection, an overview of natural language processing methods for algorithm development, problematic areas of the algorithm, and prospects for its implementation in university management. [ABSTRACT FROM AUTHOR]

At present, the threats to network security are also increasing, among which abnormal traffic detection is the key link to ensure network security. Traditional detection methods based on signature or threshold are often difficult to adapt to the increasingly complex network environment and new attack methods. Therefore, this paper optimizes and improves the data processing technology, proposes a network ATD method based on particle swarm optimization (PSO) algorithm, and explores in detail the traffic data collection and pre-processing, the feature recognition of abnormal traffic, the application of PSO algorithm, real-time monitoring and response mechanism. The results of two sets of simulation experiments are as follows: compared with the traditional model, the accuracy rate of ATD of the improved algorithm is increased by 7.2% on average, and the detection time is reduced by 7.35s on average. This method not only enhances the adaptability of the model to new attacks, but also improves the degree of automation of detection. [ABSTRACT FROM AUTHOR]

One of the most dangerous threats in WSNs is botnet attacks, in which attackers use mutual communications between IoT devices to launch large-scale malicious activities. In this regard, developments in the field of effective and reliable means of defence against this type of threat, in particular, reliable methods for detecting, identifying, and countering botnet attacks, are becoming increasingly important and relevant. This paper presents a comprehensive study that applies machine learning techniques, namely Random Forest and XGBoost, to identify botnet attacks on IoT effectively. These algorithms are analyzed, compared, and shown to be highly effective in detecting complex patterns indicative of botnet activity, thus achieving a significant improvement in IoT security. The conducted research aims to make a useful contribution to the problem of securing WSNs and IoT in general. The results of the study demonstrated high accuracy in detecting attacks with an accuracy of 99.18% for XGBoost and Random Forest showed an accuracy of 99.21%. Thus, it was shown that the significance of applying machine learning techniques such as Random Forest and XGBoost can be one of the key approaches in combating botnet attacks and securing the IoT. The results of the work emphasize the promising application of machine learning techniques for effective defense against cyber threats and highlight the importance of further. [ABSTRACT FROM AUTHOR]

Intellectual analysis of small and middle-sized datasets through machine learning tools presents challenges in various application domains. Existing methods fail to provide sufficient accuracy, and their utilization is accompanied by a range of issues during data analysis. This paper proposes the improvement of the input doubling method for middle-sized data analysis. The existing method employs an augmentation procedure where the augmented data sample increases quadratically. This imposes several limitations on the method's usage for middle-sized data analysis. The authors propose enhancing this method by introducing an additional clustering procedure during data augmentation. The training algorithms and application methods are described, and a visualization of the main steps of its operation is provided. Modeling is performed on two medium-sized datasets. Optimal parameters for the improved method are selected, demonstrating its high efficiency. Specifically, significant reductions in the volumes of augmented datasets (8-9 times for both datasets respectively) are achieved, accompanied by substantial reductions in the training procedure duration of the method (more than 100 and 260 times for both datasets respectively), while maintaining high accuracy. [ABSTRACT FROM AUTHOR]

In the modern era, industrial automation ofers significant competitive advantages. Replacing human labor with robots allows processes to be carried out much faster and more efficiently, with lower waste rates and the possibility of scheduling production 24/7. However, this also presents new challenges and opportunities in optimizing these automated processes. This paper proposes a hybrid algorithm BRKGA (Biased Random Key Genetic Algorithm) to optimize fexible job shop scheduling (FJSSP) problems in automated manufacturing systems. The algorithm is applied to a real-world case study in the Cinvestav Intelligent Manufacturing Laboratory, seeking to optimize the production of a four-piece product. The results demonstrate that the proposed algorithm outperforms previous results in terms of both, solution quality and computational efficiency. [ABSTRACT FROM AUTHOR]

Dataset distillation refers to the process of constructing a smaller dataset based on a larger dataset, so that the training model with the smaller dataset can obtain similar results to the larger dataset. In this paper, we first briefly review the basic methods and some recent advances in this field from the perspectives of meta-learning framework and data matching framework, including the backpropagation through time(BPTT), kernel ridge regression(KRR), decoupled techniques, gradient matching(DC), trajectory matching(MTT) and distribution matching(DM). The results of the plug-and-play approach in recent years are then presented. Finally, we discuss possible future developments from the perspectives of data distillation algorithms on large datasets, the relationship between compress ratio and information contained, more tasks and data modalities, and the use of generative models to improve the distillation results of datasets. [ABSTRACT FROM AUTHOR]

With the rapid development of e-commerce platforms, how to better identify and filter fake reviews has become an urgent issue for the healthy development of the e-commerce industry. However, traditional fake review identification methods cannot effectively solve this problem. They are easily affected by text changes, language differences, and context, and fake reviewers may take measures to blur their behavioral characteristics, making them difficult to detect by behavior-based algorithms. Large language models can capture the contextual relationships of the entire text through self-attention mechanisms, thereby understanding the overall meaning and emotional tendency of the review, which enables them to more effectively judge the authenticity of the review. In addition, large models have strong generalization ability and a certain degree of interpretability, which also makes them suitable for research on fake review text identification. Since fake reviews have an asymmetric distribution feature, this paper uses the RoBERTa model to extract information and combine it with the behavioral features in traditional research for model training. Compared with traditional methods, the accuracy rate is improved by nearly 3%. [ABSTRACT FROM AUTHOR]

In this paper, we explore how to use a Nvidia Jetson Nano and Python to create a system that detects weariness in a person's face using computer vision and machine learning techniques. The system captures the person's face in real time, preprocesses the picture to cut noise, then detects the face using Haar cascades. Next, using computer vision algorithms, characteristics associated with weariness, such as the eyes, are retrieved. These characteristics are then used to build a machine learning model that can predict weariness in the live stream feed. Lastly, using a graphical user interface, the findings are shown, and the system may be fine-tuned to increase accuracy. This device might be used in applications such as driver monitoring and traffic safety. [ABSTRACT FROM AUTHOR]

I n network information mining, people must measure the influence of important nodes in complex networks, otherwise it cannot be mined. However, traditional node influence measurement methods have defects and strong application limitations. Therefore, this paper proposes a multi-scale centrality algorithm under some traditional methods, and carries out related research. This paper introduces the basic concept and application advantages of multi-scale centrality algorithm, then constructs the algorithm model, and finally verifies the effectiveness of the algorithm model in the measurement of node influence in complex networks. After verification, the algorithm is effective and the measurement result is accurate. [ABSTRACT FROM AUTHOR]

With the construction of smart cities, smart ports are developing rapidly, providing favorable conditions for enhancing port competitiveness, promoting industrial upgrading, and economic transformation. With the continuous progress of society and science and technology, the safety risks of intelligent ports are also increasing. In order to better address these risks, it is necessary to establish a stable and efficient management system. This paper applies data mining and fuzzy control theory technologies for large-volume, high-precision real-time information collection, based on the research of the BP neural network algorithm. By establishing and analyzing a safety risk management system model based on the BP multilayer neuron network, it provides a rationality evaluation method and design concept. After designing the safety risk management system, this paper conducts a performance test on the system. The test results show that through optimization, the accuracy, generalization ability, and reliability of the model can be improved. Its accuracy is as high as 92% or more, fully demonstrating the system's reliability and superiority! [ABSTRACT FROM AUTHOR]

The rapid development of the Internet leads to the rapid growth of network information, we call it information explosion. The Internet is full of information, and it is difficult for users to find this information and useful knowledge of the ocean. The Web has become the world's largest information repository, and there is an urgent need for efficient access to the valuable knowledge of vast amounts of web information. The purpose of this paper is to study the process and algorithm analysis of text mining system based on artificial intelligence. This paper presents an algorithm of document feature acquisition based on genetic algorithm. Selecting suitable features is an important task in specific text classification and information retrieval. Finding appropriate feature vectors to represent the text will undoubtedly help with subsequent sorting and grouping. Based on the genetic algorithm of variable length chromosome, this paper improves the crossover, mutation and selection operations, and proposes an algorithm to obtain text feature vectors. This method has a wide range of applications and good results. [ABSTRACT FROM AUTHOR]

With the rapid growth of social economy, the requirements for resource utilization are becoming higher and higher. How to effectively solve the problem of energy shortage has become a hot topic. However, there are still many deficiencies in the current resource scheduling algorithm. For example, for different types of satellites with different needs and requirements, their performance indicators are different, and in the satellite communication system, the interaction between multiple nodes needs to be considered. Therefore, this paper studies the artificial intelligence algorithm, and proposes an improved particle complementary scheduling strategy for a single user in view of the shortcomings of the existing particle swarm optimization (PSO) algorithm. On this basis, this paper applies this method to evaluate the system performance and verify its effectiveness. The verification results show that the utilization efficiency of the satellite communication system resource scheduling algorithm is above 93%. [ABSTRACT FROM AUTHOR]

Big data has become the mainstream of information dissemination today. Big data plays an important role in the field of intelligent network monitoring. How to improve the hidden danger identification of intelligent network monitoring system and improve its accuracy is a hot issue to be solved. Therefore, in the context of big data, this paper uses intelligent analysis algorithm to identify and analyze the hidden dangers of intelligent network monitoring system. This paper mainly applies the methods of experimental setup, fuzzy analysis and data comparison to carry out relevant research on intelligent analysis of hidden danger identification of intelligent network monitoring system. The experimental data shows that the accuracy of the data obtained by the proposed method in the hidden danger identification is more than 50%, but less than 80%. Therefore, the application of background difference method needs further improvement. [ABSTRACT FROM AUTHOR]

One of the most dangerous threats in WSNs is botnet attacks, in which attackers use mutual communications between IoT devices to launch large-scale malicious activities. In this regard, developments in the field of effective and reliable means of defence against this type of threat, in particular, reliable methods for detecting, identifying, and countering botnet attacks, are becoming increasingly important and relevant. This paper presents a comprehensive study that applies machine learning techniques, namely Random Forest and XGBoost, to identify botnet attacks on IoT effectively. These algorithms are analyzed, compared, and shown to be highly effective in detecting complex patterns indicative of botnet activity, thus achieving a significant improvement in IoT security. The conducted research aims to make a useful contribution to the problem of securing WSNs and IoT in general. The results of the study demonstrated high accuracy in detecting attacks with an accuracy of 99.18% for XGBoost and Random Forest showed an accuracy of 99.21%. Thus, it was shown that the significance of applying machine learning techniques such as Random Forest and XGBoost can be one of the key approaches in combating botnet attacks and securing the IoT. The results of the work emphasize the promising application of machine learning techniques for effective defense against cyber threats and highlight the importance of further. [ABSTRACT FROM AUTHOR]

Intellectual analysis of small and middle-sized datasets through machine learning tools presents challenges in various application domains. Existing methods fail to provide sufficient accuracy, and their utilization is accompanied by a range of issues during data analysis. This paper proposes the improvement of the input doubling method for middle-sized data analysis. The existing method employs an augmentation procedure where the augmented data sample increases quadratically. This imposes several limitations on the method's usage for middle-sized data analysis. The authors propose enhancing this method by introducing an additional clustering procedure during data augmentation. The training algorithms and application methods are described, and a visualization of the main steps of its operation is provided. Modeling is performed on two medium-sized datasets. Optimal parameters for the improved method are selected, demonstrating its high efficiency. Specifically, significant reductions in the volumes of augmented datasets (8-9 times for both datasets respectively) are achieved, accompanied by substantial reductions in the training procedure duration of the method (more than 100 and 260 times for both datasets respectively), while maintaining high accuracy. [ABSTRACT FROM AUTHOR]

In the contemporary era, technology plays a significant role in our daily lives. Consequently, there is a growing emphasis on securing information transmitted across networks. To meet this need, numerous cryptographic systems have been developed to enhance security. The distinguishing factor among these systems lies in their capability to uphold fundamental criteria for information security, including authentication confidentiality and integrity. This paper introduces a method involving the use of DNA and lightweight cryptography[1-2-3]. This method utilizes for the encryption and decryption of data using DNA sequence and the SIMON cryptographic system—a symmetric encryption algorithm specifically designed for simplicity and efficiency. [ABSTRACT FROM AUTHOR]

Clustering and routing algorithms are critical to collecting and processing data sensed by nodes of wireless sensor networks deployed in various missions including environmental monitoring, tracking, and rescue operations. In this paper, a clustering algorithm is presented for wireless sensor networks with balanced cluster size resulting in energy efficiency. The algorithm is based on forming clusters using topology of the network and routing aggregated data from each cluster head to the base station. The cluster formation phase involves using tree-based traversals on the underlying sensor network graph. Using simulations, it is demonstrated that clusters of similar sizes are obtained for networks of varying sensor radii. It is demonstrated that the time to death of the first node of this clustering algorithm is superior to well-known clustering algorithm for random networks. [ABSTRACT FROM AUTHOR]

Medical images acquired at different institutions have different data distributions due to varying scanners, imaging protocols, or patient cohorts. Thus, normalizing samples before using them to train the Deep Learning (DL) models is an essential task. Scholars have used various normalization methods to normalize mammograms. However, which normalization method works best on mammograms and if it can help training generalize the DL model has not been analyzed in the literature. This paper presents a comparative analysis of commonly used normalization methods, such as scaling, Contrast Limited Adaptive Histogram Equalization (CLAHE), and Z-score normalization on mammograms for breast mass segmentation tasks. We trained the Attention Unet model on INbreast and the subset of the Cohort of Screen-Aged Women dataset (CSAW-S). We performed cross-dataset analysis on trained models to investigate how normalization methods helped train robust models to handle diverse datasets. Our results suggest that the normalizing method of input data affects the segmentation model's performance. We achieved the best scores with the Z-score normalization closely followed by CLAHE. However, further investigation suggests that the normalization method's performance may depend on the variability of mammograms in the dataset. Results of cross-dataset analysis suggest the importance of choosing the normalization method for consistent DL model performance. To our knowledge, this study represents the first systematic analysis of the performance of prevalent normalization techniques on a diverse dataset of mammogram images. The insights gained from our experiments can guide the selection of normalization methods for improved mass segmentation accuracy. [ABSTRACT FROM AUTHOR]

This paper aims to address two significant challenges of Deep Learning(DL) model generation, high computational cost involved during the training phase and data interpretability from high dimensional data. The computational cost is curbed using centralized as well as distributed matrix factorization technique known as CUR decomposition. CUR decomposition has the advantage of reducing the dimensions of the dataset without data transformation and minimal information loss. Though Singular Value De-composition(SVD) is the best matrix decomposition technique with least reconstruction error, it transforms the data and hence the original data cannot be interpreted from the decomposed matrices while applying DL technique. So CUR decomposition is leveraged to reduce the number of features in high-dimensional data while preserving the essential information. Extensive experimental analysis is conducted to evaluate and compare the performance of CUR and SVD techniques based on reconstruction error and time complexity in centralized as well as distributed setting. The results exhibit that CUR outperforms SVD in terms of computational efficiency, especially in a distributed setting where the decomposition time for CUR was a fraction compared to SVD. At the same time DL models generated from this reduced dataset exhibit comparable accuracy with the original high dimensional dataset as well as reduced dataset obtained through SVD. This work also enhances the accessibility of the CUR algorithm to users, for which an application is developed that enables users to execute the algorithm on any dataset. The user-friendly interface of the application facilitates the upload of datasets, configuration of desired decomposition rank, and execution of the algorithm with a single click. [ABSTRACT FROM AUTHOR]

Techniques for classifying data using data mining are now a day prevalent in agriculture. The method of classifying seeds involves grouping various seed varieties according to their morphological characteristics. To accomplish categorization of the typical Charotar region (generally comprising Anand and Kheda districts of the Gujarat State of India) Gujarat Wheat (GW) varieties (TRITICUM – AESTIVUM) viz. GW 273, GW 496, GW 322, LOK-1, and GDW 1255 (TRITICUM – DURUM), Weka Explorer was used. The features used are area, perimeter, solidity, aspect ratio, major and minor axis of seed kernel, Hue, Saturation, Value, and SF1 (empirical). Features reduction was done using Information Gain (IG) and its modified version Gain Ratio (GR). This paper compares performance of Tree based data mining algorithms in classifying wheat varieties. For classification we used purely tree-based machine learning algorithms viz. J48, Random Forest, Hoeffding Tree, Logistic Model Tree (LMT), and REPTree. LMT- logistics regression method gives higher accuracy 96.4% compared to other classifiers. Hoeffding Tree classifiers stood second with 96% accuracy. For validation 10-fold cross validation was used. By reducing the number of folds in cross validation performance of most algorithms decreased except J48. The percentage of correctly classified instance increased for all algorithms when features were selected by GR except for J48. [ABSTRACT FROM AUTHOR]

Product recommendation systems are an important aspect of retailing because of the improved shopping experience provided for customers. Due to the wide range of products offered by retailers, recommendation systems provide an optimal approach for displaying only relevant products to customers by forming associations that exist between products. Still, it is also important to understand the characteristics of customers connected to different product associations. Conventional approaches for product recommendation systems apply association algorithms and unsupervised classification of customers based on product ratings. However, it is not clear what demographic properties of customers are linked to which different product associations. This paper applies a hybrid system of machine learning (ML) association and clustering algorithms to implement a product recommendation system that shows associations that exist in products and unique customer profiles linked to these associations. The method described in this paper is evaluated with a case of a hygiene product retailer in Austria. [ABSTRACT FROM AUTHOR]

Graph mining is one of the significant tasks in the field of computer science. Most of the applications generate a vast amount of data which is represented with the help of a graph. Due to this graph representation, these applications have become complex and increased in size. Finding relevant information from that graph becomes a complicated task. For this community detection algorithms play a vital role in graph partitioning to retrieve relevant information. Finding communities in a graph reduces the complexity of the graph due to related data comes closer to forming a community. Many algorithms have been introduced in the last decade; the Clique percolation method (CPM) is the benchmark algorithm for finding an overlapping community. But in this method, some nodes remain unclassified, nodes that are not part of the clique. Paper proposed the clique-based Louvain algorithm(CBLA), which can classify the non-classified node (NCN) obtained after finding cliques in one of the communities by applying the Louvain algorithm. Louvain algorithm is used to classify the non-overlapped community, but with the help of cliques, it will also detect the overlapped nodes. This paper compared the proposed algorithm with four other benchmark algorithms. The proposed algorithm gives equal or enhanced performance among all compared algorithms. [ABSTRACT FROM AUTHOR]

Stock market trading is a major and predominant activity when one talks about the financial markets. With the inevitable uncertainty and volatility in the prices of the stocks, an investor keeps looking for ways to predict the future trends in order to dodge the losses and make the maximum possible profits. However, it cannot be denied that as of yet there is no such technique to predict the upcoming trends in the markets with complete accuracy, while multiple methods are being explored to improve the predictive performance of models to an extent as large as possible. With the advancement in Machine Learning (ML) and Deep Learning (DL) over the past few years, many algorithms are being deployed for stock price prediction. This paper researches 5 algorithms namely K-Nearest Neighbors, Linear Regression, Support Vector Regression, Decision Tree Regression, and Long Short-Term Memory for predicting stock prices of 12 leading companies of the Indian stock market. After exhaustive research of the various aspects related to the application of ML in stock market, a data extensive implementation has been carried out as a part of this research work wherein the stock price dataset of 12 companies over the last 7 years was collected and used. The paper also highlights some more efficient and robust techniques that are used to forecast trends in the stock market. In detail, the methodology followed, to acquire the results, has been talked about step-wise. Furthermore, a detailed comparative analysis of the performances of the aforementioned algorithms for stock price prediction has been carried out with the results displayed in a legible tabulated and graphical form to analyze them better. The conclusions from this novel, data comprehensive research work have been presented and it has been inferred that the DL algorithm outperforms all the other algorithms for stock price or time series prediction and provides results with extensive accuracy. [ABSTRACT FROM AUTHOR]

The evolution and civilization development lead us to the progress next important step - the creation of AGI. More "intelligent" tasks are successfully solved by machine learning methods using neural network algorithms. Not only the solved problems quantitative increase in the number and accuracy are important, but also a qualitative transition to the tasks of obtaining "truly" new knowledge and building "truly" new goals on their basis. This transition can serve as a criterion for determining AGI. The paper shows necessity of using existing knowledge to advance into the field of more and more diverse "truly" new knowledge. Also, the paper shows usefulness of implementing proposed ideas on a scalable hierarchical structure which has two main modes of operation: "intuition" and "thinking", and structure of vector control of these and other operation modes, similar to animal hormonal control. [ABSTRACT FROM AUTHOR]

This paper presents a formalization of the "following the leader" task and an implementation of the environment emulator based on that formalization for training a neural network model that solves this task. The emulator is based on the path planning algorithm that creates the leader route used in the training process of the neural network model. The paper presents a comparative evaluation of the speed of a number of path planning algorithms with the choice of the best one. In the created emulator, it is possible to set up an environment with a different number of obstacles, routes of different lengths and complexity, as well as set up the desired behavior of the agent following the leader. Due to the speed of the developed emulator, it allows training leader-following models based on reinforcement learning technology, the tuning process of which requires a large number of training iterations in various environments. [ABSTRACT FROM AUTHOR]

Graphs are the most complex data structure that involves heavy mathematical computations. Various real-time applications use the graph at storage, processing, object characterization, and behavior specification stages. The connectivity analysis, coverage building, and routing are the critical applications of graph evaluation. Various associated applications need the exploration of a graph with a practical and real-time outcome. The processing of larger graphs is a big challenge for the researcher. In this paper, a hybrid graph-based computation optimization method is designed to optimize the performance of graph processing in multi-core processors. The BFS algorithm is applied over the multiple processors to optimize the functional response. The multithreaded-BFS is implemented to reduce the time of graph processing and computation. The proposed algorithm is implemented in the OpenMP environment. The experimentation is done on two-core and four-core processors. The experiment is conducted on a 100 to 1000-node network. The traversing and graph coloring algorithms are implemented for the analysis. The comparative analysis is conducted on single-core and multi-core processors with DFS and BFS-based algorithms. The analysis results identified that the proposed algorithm reduced the processing time effectively. [ABSTRACT FROM AUTHOR]

In recent times, the use of autonomous mobile robots (AMRs) has increased in many industries. As more AMRs roam the same area, traffic management becomes essential to prevent congestion and deadlocks. In related work, traffic management is often achieved using sophisticated, centralised planning approaches, albeit this often suffers from scalability issues. This paper therefore explores an approach where multi-AMR traffic management is achieved with layered costmaps and a modified Dijkstra's algorithm. This keeps the global path planner in the individual AMRs, thus not suffering scalability issues. To achieve multi-AMR coordination, traffic lanes and restricted areas are added to the AMRs' global costmaps. Furthermore, the AMRs also use a modified Dijkstra's algorithm that supports implementation of traffic directions. A proof-of-concept solution is implemented in Robot Operating System 2 with Nav2 and Gazebo. The implemented solution was tested against a standard solution without any traffic management in three scenarios designed to provoke collisions. The results indicate that the implemented solution can prevent a set of collisions better than one without traffic management. [ABSTRACT FROM AUTHOR]

With the fast-moving deployment of IoT (Internet of Things) applications, a large amount of data is generated and the data is in need for processing to be able contribute to the user's requirements. All of the IoT applications require low latency and certain bandwidth to provide us with new features which are Time-Dependent. Thus, it would be advantageous to redesign the current IoT architecture by understanding the unique properties of communications and computational power to support intelligent processing of IoT data and its analysis. The paper is focused on topics such as characteristics of IoT data, trends in IoT network architecture, problems in IoT data analysis and their solutions. Thus, in particular, the paper is a discussion that Edge software-defined computing and CEP (Complex Event Processing) based fog computing as a architecture that promises to support the different requirements of IoT data analytics. And also, the paper is a discussion about how machine learning, deep learning are useful in removing data processing anomalies. The leader node election for data processing at cloud is also a time consuming task, the proposed genetic based Genetic based Leader Election Algorithm (GLEA) algorithm in the paper show promising results for the solution. [ABSTRACT FROM AUTHOR]

Now, we have entered the "internet plus" era, and all industries are making use of information technology to seek a longer-term development. For vegetable distribution, we must rely on the power of information technology. Faced with complex global optimization problems, traditional optimization algorithms have many performance limitations, such as complex solution process and easy to fall into local optimization, which can't meet the needs of practical applications. This paper studies the design and application of optimization algorithm for vegetable planting and distribution based on adaptive differential evolution algorithm. The method adopted in this paper is adaptive differential evolution algorithm. In the original differential evolution algorithm, there are three undetermined parameters: population size (NP), variation factor (F) and crossover probability (CR). In the process of evolution, NP takes a fixed value, while the key control parameters F and CR, which affect the performance of the algorithm, will adaptively take different values on [0,1], and the values of F and CR will be recalculated for each individual in each generation. Let F and CR automatically adjust and change in the process of evolution. The research in this paper shows that the algorithm in this paper is effective and suitable for application. [ABSTRACT FROM AUTHOR]

Clustering is one of the most common technologies in data mining. The size, dimension and sparsity of data are all different aspects that restrict clustering analysis. Clustering machine learning is called unsupervised learning, which is different from classification in that the data objects used in clustering analysis have no class marks, so they need to be calculated by the clustering learning algorithm, while the classified data objects need class marks. At present, most of the sparse data algorithms with high attribute dimensions are oriented to binary data, and there is no evaluation method of clustering results, which brings great limitations to their application. The BP neural network, RBF neural network and Elman neural network model with feedback function, which are mature and widely used in pest prediction models, are studied. The purpose of this paper is to study diseases and pests based on sparse clustering algorithm. In this paper, algorithm formulas, models and data graphs are established to study. From the research, it can be seen that the damage index of pests and diseases is very high, reaching about 50. 54%. Through the research in this paper, we can know that the future will lay a certain foundation for the research of diseases and pests. [ABSTRACT FROM AUTHOR]

In order to be able to extract useful information from MRI medical images, we need to carry out effective image segmentation of MRI medical image and use appropriate operators to detect the edge of the image. Firstly, this paper adopts a research method based on the characteristics of horizontal, vertical and diagonal differences, and then carries out comparative analysis on different operators from different perspectives such as frequency domain and velocity domain. After analyzing the results, it is concluded that: Canny operator can detect complete, continuous and detailed edges at a faster speed. This paper uses edge detector based on Canny operator as the training output of artificial neural network. By training artificial neural network. The optimized parameters were obtained, and subjective analysis and computational analysis were performed on the quality of edge detection results of MRI medical images based on different methods. The results show that compared with the single Sobel, Canny and other traditional operators, the edge information of MRI medical image obtained by the neural network-based edge detection method is more complete and its processing time is nearly 3 times faster than the traditional edge detection method. [ABSTRACT FROM AUTHOR]

The focus of this paper is to determine how the mechanisms of each algorithm contribute to search performance. swarm-based algorithms are composed of several mechanisms. This paper regards these independent mechanisms as modules. The modules constituting each algorithm are classified into three categories: (1) the common module: a collection of mechanisms that all swarm-based algorithms have in common; (2) the main module: the unique mechanisms of each algorithm that updates the solution by interacting with other solutions; and (3) the sub module: the unique mechanisms of each algorithm that do not directly improve the quality of the solution. Testing the algorithms combined into these modules reveals the contribution of each module to performance. The experiments are conducted for the three algorithms PSO, ABC, and CS to test the usefulness of this concept. The results of these experiments revealed the following implications: (a) the main module of ABC alone can have good performance; (b) the main module of CS does not perform well as a stand-alone; (c) the combination algorithm with the main module of PSO and the sub module of ABC can improve applicability. [ABSTRACT FROM AUTHOR]

Object detection techniques are the foundation for the artificial intelligence field. This research paper gives a brief overview of the You Only Look Once (YOLO) algorithm and its subsequent advanced versions. Through the analysis, we reach many remarks and insightful results. The results show the differences and similarities among the YOLO versions and between YOLO and Convolutional Neural Networks (CNNs). The central insight is the YOLO algorithm improvement is still ongoing.This article briefly describes the development process of the YOLO algorithm, summarizes the methods of target recognition and feature selection, and provides literature support for the targeted picture news and feature extraction in the financial and other fields. Besides, this paper contributes a lot to YOLO and other object detection literature. [ABSTRACT FROM AUTHOR]

Traditional recommendation algorithms have problems such as data sparseness and not paying attention to the diversity of recommendation results. In this paper, we use LDA to extract topics of comments about movies, and identify the emotional tendencies related to topics. As a result, we enrich user interest model and product feature model based on emotional tendencies to improve content-based recommendation algorithms. Most of prior work on applying sentiment classification to recommendation systems only consider the use of sentiment dictionaries to judge polarity, and adopt pattern matching methods to identify features. This paper uses BERT to train sentiment classification models and uses LDA to extract topics. The algorithm is run on the movie review database crawled from Douban, and the experimental result showed that the diversity of recommendation lists had been significantly improved. [ABSTRACT FROM AUTHOR]

Graph is a widely used data structure which can be regarded as a generalized measure metirc space. Since a graph not only includes points but also relation between points, the distance between two graphs is difficult to measure. In recent years, Gromov-Wasserstein discrepancy has been proposed as a pseudometirc on graphs which has a complete theoretical basis, but has few applications. Therefore, this paper reviews the basic ideas, applications and improvements of Gromov-Wasserstein. Since Gromov-Wasserstein discrepancy is a quadratic programming and difficult to calculate, this paper focuses on the iterative algorithm for solving this discrepancy. At the end, we look forward to the development of Gromov-Wasserstein discrepancy. [ABSTRACT FROM AUTHOR]

Particle Swarm Optimization (PSO) provides a metaheuristic optimization technique for real-life functions with complex behavior. Inspired by the metaphor of social behavior as exhibited by swarms operating in natural surroundings, the algorithm captures the Emergent Complexity demonstrated by individual components of large groups which work together using simple rules and create complex, yet efficient, adaptive, and self-organizing systems. The paper seeks to rigorously analyze the hyperparameters of the algorithm to find the relative importance of cognitive(personal) vis-à-vis social (swarm) parameters of the algorithm so as to gain a perfect understanding of the importance of exploitation versus exploration parameters thus enabling us to enhance the application efficiency of the algorithm. The paper further suggests and applies a rolling coefficient updating technique to the algorithm parameters so as to use a formal statistical procedure in the initialization and subsequent iterations for updating coefficients rather than leaving it on trial and error or on a practitioner's knowledge. Results suggest that the technique employed provides an enhancement in the efficiency of the algorithm by creating a dynamic Emergent Complexity environment. [ABSTRACT FROM AUTHOR]

This paper is devoted to the practical application of parallel sorting algorithms and parallel input-output methods for the problem of genome alignment. The paper considers different approaches to the implementation of such algorithms, taking into account the capabilities of high-performance systems. Main purpose of the work is to develop a genome sorting program, the efficiency of which significantly exceeds the efficiency of free software analogues. The genome sorting program is implemented for a supercomputer using the C++ language and the OpenMP and OpenMPI. The developed program demonstrates a significant increase in the speed of operation (up to 10 times) compared to free software analogues due to massive parallel data input and output. Different approaches for data input/output parallelization and data processing considered in the paper can be applied in other subject areas. [ABSTRACT FROM AUTHOR]

In this paper feasibility-preserving genetic operators for hybrid algorithms using TSP solvers for the Inventory Routing Problem (IRP) are studied. The IRP is a problem of jointly optimizing delivery schedules and routes for vehicles transporting products from a supplier to a number of retailers. This optimization problem is highly constrained, because limits on inventory levels as well as the vehicle capacity have to be taken into account. Moreover, the IRP is a generalization of the TSP and solving the TSP effectively is an important part of obtaining good solutions to the IRP. In this paper evolutionary algorithms are used for solving the IRP, but finding good routes is delegated to a state-of-the-art TSP solver. Therefore, genetic operators used in this paper focus on constructing the delivery schedule and not on optimizing the routes. In the experimental part of the paper an evolutionary algorithm using feasibility-preserving genetic operators is compared to the Infeasibility Driven Evolutionary Algorithm (IDEA) that uses the selective pressure to obtain feasible solutions. Presented results suggest that designing good feasibility-preserving genetic operators is important, because allowing the optimization algorithm to generate infeasible solutions and handling infeasibility in IDEA using the selective pressure leads to inferior results. [ABSTRACT FROM AUTHOR]

Block symmetric cryptographic algorithms are the essential and inalienable element of any information security system. The main component of block symmetric cryptographic algorithms is the S-box, which largely determines the speed and cryptographic quality of the entire cryptographic transformation. The rapid development of cryptanalysis methods, including the development of quantum computers, requires a more careful research of all possible representations of nonlinear elements of block symmetric cryptographic algorithms. In view of the fact that in addition to the mathematical apparatus of Boolean functions used today to estimate the cryptographic quality of S-boxes, a cryptanalyst can apply the mathematical apparatus of functions of many-valued logic, which makes relevant the task of researching and comparing the cryptographic quality of S-boxes of modern cryptographic algorithms, represented by functions of many-valued logic. This paper presents a research and comparative analysis of nonlinear transformations of the cryptographic algorithms AES, Kalyna, BelT, and Kuznechik when represented by functions of many-valued logic. It was found that only the nonlinear transformation of the BelT cryptographic algorithm is characterized by growth when represented by many-valued logic functions, while the Kalyna cryptographic algorithm demonstrates the greatest decrease in nonlinearity when represented by 16-functions among the cryptographic algorithms researched. The results obtained in the paper indicate a significant unused reserve of cryptographic quality, which can be used in the design of new cryptographic algorithms and their structural elements. [ABSTRACT FROM AUTHOR]