Computational Intelligence Technologies Research Articles

Advancements in Q‐learning meta‐heuristic optimization algorithms: A survey

AbstractThis paper reviews the integration of Q‐learning with meta‐heuristic algorithms (QLMA) over the last 20 years, highlighting its success in solving complex optimization problems. We focus on key aspects of QLMA, including parameter adaptation, operator selection, and balancing global exploration with local exploitation. QLMA has become a leading solution in industries like energy, power systems, and engineering, addressing a range of mathematical challenges. Looking forward, we suggest further exploration of meta‐heuristic integration, transfer learning strategies, and techniques to reduce state space.This article is categorized under: Technologies > Computational Intelligence Technologies > Artificial Intelligence

Natural Language Processing Pretraining Language Model for Computer Intelligent Recognition Technology

Computer intelligent recognition technology refers to the use of computer vision, Natural Language Processing (NLP), machine learning and other technologies to enable computers to recognize, analyze, understand and answer human language and behavior. The common applications of computer intelligent recognition technology include image recognition, NLP, face recognition, target tracking, and other fields. NLP is a field of computer science, which involves the interaction between computers and natural languages. NLP technology can be used to process, analyze and generate natural language data, such as text, voice and image. Common NLP technology applications include language translation, emotion analysis, text classification, speech recognition and question answering system. Language model is a machine learning model, which uses a large number of text data for training to learn language patterns and relationships in text data. Although the language model has made great progress in the past few years, it still faces some challenges, including: poor semantic understanding, confusion in multilingual processing, slow language processing and other shortcomings. Therefore, in order to optimize these shortcomings, this article would study the pre-training language model based on NLP technology, which aimed at using NLP technology to optimize and improve the performance of the language model, thus optimizing the computer intelligent recognition technology. The model had a higher language understanding ability and more accurate prediction ability. In addition, the model could learn language rules and structures by using a large number of corpus, so as to better understand natural language. Through experiments, it could be known that the data size and total computing time of the traditional Generative Pretrained Transformer-2 (GPT-2) language model were 10 GB and 97 hours respectively. The data size and total computing time of BERT (Bidirectional Encoder Representations from Transformer) were 12 GB and 86 hours respectively. The data size and total computing time of the pre-training language model based on NLP were 18 GB and 71 hours respectively. Obviously, the pre-training language model based on NLP had a larger data size and shorter computing time. The experimental results showed that the NLP technology could better optimize the language model and effectively improve its various capabilities. This article opened up a new development direction for computer intelligent recognition technology and provided excellent technical support for the development of language models.

Data traffic unloading method of internet of things based on mobile edge computing

The Industrial Internet of Things integrates modern technologies such as intelligent terminals, computer technology, and big data, achieving low cost and high applicability in industrial production processes, and improving industrial production efficiency. The offloading of IoT data traffic requires significant energy consumption due to limited mobile terminal device resources. For this reason, the author designs a method of IoT data traffic unloading based on mobile edge computing. The initialization simulation parameters include the number of mobile users, the number of base stations equipped with edge servers, fixed bandwidth, base station height, etc. Calculate the distance and channel power gain from each base station to mobile users, and optimize power allocation through algorithms such as simulated annealing and PSO. The binary PSO algorithm is used to maximize the welfare in edge computing. Finally, by comparing with local offloading, utilizing layered offloading methods, and collaborative computing offloading methods based on fiber wireless networks. Simulation resultsThe energy consumption of SAPA is generally higher than that of PSO, and with the increase of mobile users, the energy consumption of both algorithms shows a significant growth trend. Especially, SAPA's energy consumption is significantly higher than PSO when the number of users is 40 and 60. This indicates that in the mobile network environment, PSO algorithm has more advantages in energy consumption than SAPA, By using particle swarm optimization algorithm to further optimize energy consumption, it greatly saves energy consumption. In comparison to local execution, the proposed offloading method yields substantial energy savings of nearly 62.5 %. The maximum difference in energy consumption between the proposed method and the collaborative computing offloading method based on fiber optic wireless networks is 268 J, while the maximum difference compared to the layered offloading method is 150 J. These results highlight the strategy's ability to enhance the computational efficiency of high-priority tasks on edge servers, concurrently reducing latency and energy consumption for task completion. This underscores the effectiveness of the proposed offloading method in conserving energy and emphasizes its practical significance.

D2Former: Dual-Domain Transformer for Change Detection in VHR Remote Sensing Images

Computational intelligence technologies have been extensively applied for the interpretation of remote sensing imagery. Recently, the computational-intelligence-based Transformer change detection (CD) approach has attracted increasing attention. However, the current Transformer-based CD method can better capture global features, but there is no good solution for the loss of local detail information. For this reason, introducing semantic and frequency information from the perspective of a dual-domain can be beneficial for improving the representation of detailed features to improve CD performance. To overcome this limitation, a dual-domain Transformer (D2Former) is proposed for CD. Firstly, we adopt a semantic tokenizer to capture the semantic information, which promotes the enrichment and refinement of semantic change information in the Transformer. Secondly, a frequency tokenizer is introduced to acquire the frequency information of the features, which offers the proposed D2Former another aspect and dimension to enhance the ability to detect change information. Therefore, the proposed D2Former employs dual-domain tokenizers to acquire and fuse the feature representation with rich semantic and frequency information, which can refine the features to acquire more fine-grained CD ability. Extensive experiments on three CD benchmark datasets demonstrate that the proposed D2Former obviously outperforms some other existing approaches. The results present the competitive performance of our method on the WHU-CD, LEVIR-CD, and GZ-CD datasets, for which it achieved F1-Score metrics of 92.85%, 90.60%, and 87.02%, respectively.

Computer intelligent network security and preventive measures of internet of things devices

AbstractThe paper focused on researching and analyzing computer intelligence network security and preventive measures in the context of the IoT, aiming to improve the security coefficient of the IoT network and reduce IoT network security accidents through computer intelligence technology. Through experiments, we obtained data that demonstrated the effectiveness of computer intelligence in improving IoT security. In several groups of experiments, the maximum number of information leaks in the IoT network using computer intelligence within a month was 10 times smaller than the maximum number in traditional IoT networks, and the minimum number was 8 times smaller. This shows that computer intelligence can prevent information leakage in the IoT. Similarly, in several groups of experiments, the maximum number of data thefts in a month in the IoT network using computer intelligence was 15 times smaller than the maximum number in traditional IoT networks, and the minimum number was 16 times smaller. This demonstrates that computer intelligence can prevent data theft in the IoT. These findings confirm that computer intelligence can improve the security of the IoT network.

Development and Practical Applications of Computational Intelligence Technology

Computational intelligence (CI) uses applied computational methods for problem-solving inspired by the behavior of humans and animals. Biological systems are used to construct software to solve complex problems, and one type of such system is an artificial immune system (AIS), which imitates the immune system of a living body. AISs have been used to solve problems that require identification and learning, such as computer virus identification and removal, image identification, and function optimization problems. In the body’s immune system, a wide variety of cells work together to distinguish between the self and non-self and to eliminate the non-self. AISs enable learning and discrimination by imitating part or all of the mechanisms of a living body’s immune system. Certainly, some deep neural networks have exceptional performance that far surpasses that of humans in certain tasks, but to build such a network, a huge amount of data is first required. These networks are used in a wide range of applications, such as extracting knowledge from a large amount of data, learning from past actions, and creating the optimal solution (the optimization problem). A new technique for pre-training natural language processing (NLP) software ver.9.1by using transformers called Bidirectional Encoder Representations (BERT) builds on recent research in pre-training contextual representations, including Semi-Supervised Sequence Learning, Generative Pre-Training, ELMo (Embeddings from Language Models), which is a method for obtaining distributed representations that consider context, and ULMFit (Universal Language Model Fine-Tuning). BERT is a method that can address the issue of the need for large amounts of data, which is inherent in large-scale models, by using pre-learning with unlabeled data. An optimization problem involves “finding a solution that maximizes or minimizes an objective function under given constraints”. In recent years, machine learning approaches that consider pattern recognition as an optimization problem have become popular. This pattern recognition is an operation that associates patterns observed as spatial and temporal changes in signals with classes to which they belong. It involves identifying and retrieving predetermined features and rules from data; however, the features and rules here are not logical information, but are found in images, sounds, etc. Therefore, pattern recognition is generally conducted by supervised learning. Based on a new theory that deals with the process by which the immune system learns from past infection experiences, the clonal selection of immune cells can be viewed as a learning rule of reinforcement learning.

Intelligent computer technology and its application in environmental art design

Intelligent computer technology and its application in environmental art design

Semantic analysis and construction of English discourse based on neural network

Abstract Accurate recognition and analysis of English semantics is of great significance in the application field of computer and artificial intelligence technology. Traditional text analysis technology has poor real-time analysis accuracy and efficiency. This paper constructs an English semantic analysis model based on neural networks. By designing an English semantic framework that integrates LSTM and RNN and optimizing it on the basis of LSTM and RNN models, an English semantic analysis model with improved attention mechanisms and Bi-LSTM is constructed. By comparing the Bi-LSTM+Attention model proposed in this paper with other semantic analysis models, the classification accuracy of the model constructed in this paper reaches 98%. Through the practical application of the model to analyze the semantic coherence of English, the average score of the semantic coherence quality measured by the model is 15.76, and the average score of the manual judgment is 16.03, which indicates that the application of the semantic analysis model constructed in this paper is close to the effect of the manual judgment.

Retracted: The Construction and Realization of the Precise Funding Platform for Impoverished Students in Colleges and Universities Driven by Computer Intelligence Technology

Retracted: The Construction and Realization of the Precise Funding Platform for Impoverished Students in Colleges and Universities Driven by Computer Intelligence Technology

Implementasi Profil Pelajar Pancasila melalui Kegiatan Ekstrakurikuler Robotik dan Smart Farmer di SMAS Santo Tarcisius Dumai

Santo Tarcisius High School is the first driving school in Dumai City, so Santo Tarcisius High School has the responsibility to implement the Pancasila Student Profile for its students. One of the characteristics of the Pancasila Student Profile is creative, namely being able to modify and produce something original, meaningful, useful and impactful. To realize the Pancasila Student Profile, an effort is needed to provide knowledge and skills to students in an interesting way, one of which is through robotics and smart farmer extracurricular learning activities. Based on the results of discussions with the school principal, the problems faced in implementing robotics and smart farming extracurricular activities include 1) Students have never had an understanding of computer and robot-based intelligent farming system technology, 2) They do not yet have teachers who are competent in the field of robotics, 3) Limited costs purchasing robotic electronic equipment and installing smart farmers at schools. Based on the problems above, the aim of this activity is to realize the Pancasila Profile, especially in the IT sector, through education on making Line Follower robots and smart farming systems for hydroponic plants. The implementation method is by dividing students into several groups to make Line Follower robots. The number of students in this activity is 150 students/class XI SMA Santo Tarcisius. Success in Beginner Community Service (PMP) is to improve students' skills in making robots and intelligent systems which is measured through the final product results, pre test and post test questionnaires, the product results from this PMP are line follower robots and smart farmer systems, the conclusion of the PMP This is to increase efficiency, productivity and sustainability in the agricultural sector through the application of the latest technology.

Учет переменного характера расчетных нормативов плана формирования поездов с использованием технологий вычислительного интеллекта

Purpose: The train formation plan is the most important logistics tool for cargo transportation management. But the use of discrete values of design standards in its development — the quantity of car-hours for the accumulation of trains and savings from passing of cars through technical station without processing — do not always guarantee the optimal solution due to the objectively existing unevenness of operational work. It is precisely this factor that generates uncertainty, which is not inherently stochastic, and necessitates adjustments to the train formation plan throughout its lifecycle. In addition to fluctuations in car traffic, it causes a change in the values of the calculated standards. To manage such uncertainty, there are special methods, one of which is fuzzy logic. The article describes a way to account for changes in all calculation standards of the train formation plan. Methods: The methods of one of the technologies of computational artificial intelligence are used — fuzzy logic, fuzzy sets and fuzzy mathematics. Results: The dependence has been established on how changes in the calculated parameters of the train formation plan are affected by fluctuations in car flows on specific destinations. The formulas obtained on the basis of known analytical expressions allow us to determine the values of the standards of the formation plan without using auxiliary tables and graphs. Practical significance: The use of the obtained dependencies in the development of the formation plan will improve the accuracy of its calculation by taking into account fluctuations not only of car traffic, but also the values of the calculated standards depending on them.

Retracted: Research on Precision Cultivation of Digital Multimedia Crop Based on Predictive Computational Intelligence Technology

Retracted: Research on Precision Cultivation of Digital Multimedia Crop Based on Predictive Computational Intelligence Technology

Manipulator Smooth Control Method Based on LSTM-XGboost and Its Optimization Model Construction

With the rapid development of computer and artificial intelligence technology, robots have been widely used in assembly, sorting, and other work scenarios, gradually changing the human-oriented mechanical assembly line working mode. Traditional robot control methods often rely on application fields and mathematical models, and they cannot meet the emerging requirements of versatility and flexibility in many fields, such as intelligent manufacturing and customized production. Therefore, aiming at the relationship between the manipulator’s smooth control command parameters and the manipulator’s actual motion stability in the multi-step object sorting task, this paper proposes a method for predicting the stability of the manipulator based on Long Short-Term Memory Extreme Gradient Boosting. The acquisition signal of the manipulator vibration is segmented according to the action, and the boost model is used to learn the relationship between the control command parameters and the stability characteristic indexes. Next, the Extreme Gradient Boosting algorithm establishes a feature index-stationarity score prediction model. The minimum Mean Absolute Error predicted by the five indicators is 0.0024 so that the model can predict the manipulator’s motion stability level according to the manipulator’s command parameters.

Convolutional neural network-based recognition method for volleyball movements

With the development of network technology and computer intelligent monitoring technology, a large number of video data came into being. In view of the analysis of specific targets in video, the traditional artificial analysis method can not meet the existing needs. In volleyball teaching in college physical education, because each student has different movements, intelligent processing of video data has become a key issue. Through collecting and sorting out the relevant research results on behavior recognition, it is found that in the research of deep learning, the algorithm structure applied in this paper is representative, and its strong learning ability, especially compared with the traditional algorithm, can more accurately identify human movements, natural language processing and so on, but the research on volleyball action recognition is still less. Therefore, this paper constructs a data set, improves the convolution neural network model, subsequently, new models are constructed through the neural network structure to improve the accuracy of the nonlinear expression and optimize the content of the input data. In order to more accurately analyze the effectiveness of this algorithm, the new data are obtained by grouping the volleyball games in college sports courses. Compared with the original paper, the accuracy of the improved 3D network is improved by 3.3%–88.5%, and the complexity is reduced by 33.6%.

Signal enhancement in wireless sensor networks based on adaptive filters

Wireless sensor networks are widely used in communication, medical treatment, radar and detection. With the vigorous development of computer science and intelligent technology, wireless sensor networks are also constantly improving in the development. Sensor networks are prone to noise interference when input signals, which will affect the estimation accuracy of the network. In order to enhance the signal of sensor network and improve its accuracy, a distributed filtering algorithm based on fusion adaptive weighting is proposed. Before building the model, the experiment first studied the three traditional adaptive filtering algorithms, LMS, RLS and AP, as the basis for building the experimental model. Then, combined with the distributed characteristics of the sensor network, the attributes of the nodes and their influence in the network were considered in the experiment, and the importance and support of the nodes were linearly weighted to obtain the estimated certainty of each sensor node to the target. Finally, a fusion adaptive weighted distributed filtering algorithm is constructed in the experiment. The simulation experiment verifies that the constructed model can reduce the noise interference to a certain extent, which is conducive to the enhancement of its network signal, and its error estimation accuracy is also improved.

Optimization Method of Green Logistics Route Based on Computer Intelligent Technology

Optimization Method of Green Logistics Route Based on Computer Intelligent Technology

Research on the Application of Intelligent Computer Technology in Minority Traditional Residential Buildings

Research on the Application of Intelligent Computer Technology in Minority Traditional Residential Buildings

The Construction and Realization of the Precise Funding Platform for Impoverished Students in Colleges and Universities Driven by Computer Intelligence Technology

At present, the state’s financial aid for students from financially disadvantaged families is increasing, and the number of students receiving financial aid is also increasing substantially. Poor students have to run back and forth to submit various materials while busy with their own studies, which causes a lot of inconvenience to students and teachers. The development of the poverty-stricken student precision funding platform frees teachers from tedious and low-end work and engages in core affairs, which improves the overall efficiency of school affairs and the controllability of information. In this context, this paper uses computer intelligence technology to realize the construction of the precise funding platform for impoverished students in colleges and universities (CAU) and completes the following tasks: (1) the research status of precise funding for impoverished students in CAU at home and abroad is introduced. (2) The SSI frame structure diagram of the funding information management platform is designed and the specific function description and detailed design of the relevant modules are given. (3) The module function test and system function test of the platform designed in this paper are completed through experiments. The results show that the system is very stable, and the functions are perfectly realized.

Design of vehicle positioning system based on artificial intelligence cloud computing technology

AbstractWith the continuous progress of society, people's living consumption needs are also constantly changing. The traditional vehicle positioning technology can no longer meet the new positioning needs of travelers, so a more accurate positioning system is urgently needed. This paper aims to study a more accurate positioning system and proposes a vehicle positioning system based on radio frequency identification (RFID). After comparing several common positioning methods, the use of LANDMARC positioning algorithm makes the vehicle positioning more accurate. This paper has effectively integrated advanced computer and artificial intelligence technology and applies it to control and management and finally established a comprehensive traffic positioning system that can effectively alleviate traffic congestion, reduce traffic accidents and quickly deal with traffic emergencies. The experimental results have shown that when the vehicle runs at a speed of 40 km/h and the communication rate is 860 kpbs, the average positioning accuracy of the improved algorithm is doubled compared with the traditional algorithm, and the error is reduced by nearly one. Therefore, compared with the traditional positioning system and artificial intelligence, the RFID vehicle positioning system under cloud computing has more accurate positioning information and more timely and convenient services.

Machine Learning and AI Technologies for Smart Wearables

The recent progress in computational, communications, and artificial intelligence (AI) technologies, and the widespread availability of smartphones together with the growing trends in multimedia data and edge computation devices have led to new models and paradigms for wearable devices. This paper presents a comprehensive survey and classification of smart wearables and research prototypes using machine learning and AI technologies. The paper aims to survey these new paradigms for machine learning and AI for wearables from various technological perspectives which have emerged, including: (1) smart wearables empowered by machine learning and AI; (2) data collection architectures and information processing models for AI smart wearables; and (3) applications for AI smart wearables. The review covers a wide range of enabling technologies for AI and machine learning for wearables and research prototypes. The main findings of the review are that there are significant technical challenges for AI smart wearables in networking and communication aspects such as issues for routing and communication overheads, information processing and computational aspects such as issues for computational complexity and storage, and algorithmic and application-dependent aspects such as training and inference. The paper concludes with some future directions in the smart wearable market and potential research.