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As the operational landscape of enterprises grows increasingly complex and volatile, the significance of modularization in economic management has become even more pronounced. By segmenting the management system into distinct yet interdependent modules, enterprises are better equipped to adapt swiftly to market fluctuations, enabling the efficient allocation of resources and the enhancement of management efficacy. Enterprise risk management, a pivotal component of modular management, faces unprecedented challenges, with traditional risk assessment methodologies often failing to meet the stringent demands for precision and real-time responsiveness. To overcome these challenges, this paper proposes a novel GT-DQN framework, integrating Graph Neural Networks (GNNs), transformer, and Deep Q-Network (DQN) algorithms to facilitate risk assessment within enterprise economic management. The framework undertakes comprehensive modeling of enterprise financial data, market transaction records, macroeconomic indicators, and supply chain relationships via GNN, while the transformer captures dynamic shifts in time series data. Ultimately, DQN optimizes risk decision-making strategies within an evolving economic environment, thereby enhancing the accuracy and stability of risk assessments. Experimental results demonstrate that the GT-DQN framework developed in this study achieves a recognition accuracy of 90% on public datasets across three tiers of enterprise risk — high, medium, and low — providing a robust technical foundation for future risk prediction and analysis in the modular management of enterprise economies.

Personalized College English Learning offers customized education based on individual necessities and aims. Students enhance their linguistic ability efficiently over engaging sessions, specific curricula, and professional teachers. This study aims to progress a smart Artificial Intelligence (AI)-based adaptive learning method for enhancing personalized college English learning experience. Our proposed model provides an intelligent detecting device-enabled setting for learning English over big data analysis and machine learning based on a DM approach. Intelligent sensing strategies detention significant statistics from college English students and big data analysis examines the data to produce beneficial insights that increase real-time personalizing of English learning experiences. We suggest an innovative Starling Murmuration fine-tuned Dynamic Weighted Random Forest (SM-DWRF) system for classifying the data. Our model influences perceptions from collective behavior in starling flocks to dynamically alter feature weights in DWRF arrangement. It integrates fine-tuning mechanisms to adaptively weight features, developing classification accurateness and strength in adapted college English learning settings. We implemented our suggested model in Python software. In the assessment stage, we accurately measure the effectiveness of our proposed SM-DWRF model in identifying diverse features of English learning across various parameters. Our experimental findings incontestably showcase the greater performance of our model associated with conventional approaches in classifying content from multimodal statistics. Significantly, we perceive prominent enhancements in reliability and robustness, when acclimating to dynamic learning settings.

The emergence of Intelligent Education Cloud Platforms has revolutionized how educational resources are shared and utilized, enabling a more inclusive and adaptive approach to learning. In the context of college preschool education, the integration of distributed sharing and personalized recommendation systems addresses critical challenges in resource accessibility and learner engagement. Traditional methodologies often rely on static and generalized frameworks, lacking the flexibility to cater to diverse learning needs and the scalability for dynamic resource allocation. These limitations hinder their capacity to provide tailored educational pathways and real-time adaptability, which are essential in preschool education’s highly individualized context. To overcome these barriers, we propose a Knowledge-Adaptive Education Network (KAEN) augmented by the Adaptive Learning Pathway Strategy (ALPS), tailored for deployment on Intelligent Education Cloud Platforms. KAEN leverages graph-based knowledge representation, dynamic content alignment networks, and reinforcement learning to optimize resource recommendation and personalize learning experiences. ALPS complements this system by generating individualized learning pathways, integrating multi-modal content, and real-time feedback mechanisms to enhance engagement and educational outcomes. Experimental validation demonstrates significant improvements in resource utilization efficiency, learner engagement metrics, and adaptive content delivery quality. These findings underscore the potential of integrating AI-driven frameworks into Intelligent Education environments, offering scalable and effective solutions for preschool education resource sharing and personalization.

With the rapid evolution of educational technology, leveraging advanced methodologies for English instruction has become increasingly critical to addressing the growing demand for efficient and engaging language learning. Current approaches in English language teaching often fall short in personalization, adaptability, and learner engagement, primarily due to their static structure and limited integration of cognitive and technological advancements. To bridge this gap, we propose a novel framework grounded in deep neural networks to enhance English Audio Visual Oral (AVO) instruction, aligning with the thematic scope of computational advancements in education. This study introduces the Adaptive Cognitive Learning Model (ACLM), a pedagogical innovation designed to dynamically adjust teaching strategies to individual learner profiles by integrating real-time performance feedback, modular content delivery, and multimedia-assisted learning. The ACLM employs a systematic feedback loop and adaptive mechanisms to personalize learning pathways, addressing domain-specific challenges such as vocabulary acquisition, grammar comprehension, and conversational fluency. Experimental evaluations demonstrate that our method significantly improves learner outcomes in engagement, comprehension, and retention compared to traditional approaches. These findings underscore the potential of combining cognitive alignment with dynamic neural networks to establish scalable, personalized, and effective instructional strategies in English AVO education, contributing to advancements in computational language pedagogy.

This paper introduces the Successive Affine Learning (SAL) model for constructing deep neural networks (DNNs). Traditionally, a DNN is trained by solving a single non-convex optimization problem, which is often challenging due to its high non-convexity and large number of layers. To address this challenge, the Multi-Grade Deep Learning (MGDL) model was recently initiated by the author of this paper, inspired by the human education system. MGDL constructs a DNN progressively, training a sequence of shallow networks with fewer layers. However, it still requires solving multiple non-convex optimization problems. The proposed SAL model evolves from MGDL by leveraging the structure of DNNs, where each layer consists of an affine transformation followed by an activation function. In SAL, we train the affine transformation first by solving a quadratic/convex optimization problem, incorporating the activation function only after learning the weight matrix and bias vector for the current layer. In the context of function approximation, given a target function, the SAL model generates an expansion of the function with adaptive basis functions in the form of DNNs. We establish key theoretical results, including the Pythagorean identity and the Parseval identity for the system generated by SAL. Furthermore, we prove a convergence theorem, showing that the SAL process either terminates after a finite number of grades or ensures a strictly decreasing sequence of optimal error norms, converging to a limit as the number of grades approaches infinity. Finally, we present numerical experiments as a proof of concept, demonstrating that the SAL model significantly outperforms traditional deep learning approaches

The power communication network is the key to ensuring the safe operation of the distribution network, and how to quickly and accurately predict faults is a challenging task, especially considering the variable topology of the distribution network. To address this issue, common faults will be modeled and simulated to predict faults in power communication systems. In this study, considering the generalized Laplacian smoothing filters and the long sequence representation capability of the Transformer, an adaptive graph encoder based on historical performance graph embedding is proposed and used for fault prediction in power communication networks. The proposed method consists of two modules: (1) To better alleviate high-frequency noise in node features, a carefully designed Laplacian smoothing filter is first applied. (2) Adopting a transformer-based adaptive encoder to iteratively enhance filtering characteristics for better node embedding. The performance of the proposed method in fault prediction tasks is tested using a dataset collected from a real power communication network. The experimental results show that the proposed method consistently outperforms other fault prediction methods in terms of performance.

In this work, a basic cerebellar neural layer and a machine learning engine are embedded in a recurrent loop which avoids dealing with the motor error or distal error problem. The presented approach learns the motor control based on available sensor error estimates (position, velocity, and acceleration) without explicitly knowing the motor errors. The paper focuses on how to decompose the input into different components in order to facilitate the learning process using an automatic incremental learning model (locally weighted projection regression (LWPR) algorithm). LWPR incrementally learns the forward model of the robot arm and provides the cerebellar module with optimal pre-processed signals. We present a recurrent adaptive control architecture in which an adaptive feedback (AF) controller guarantees a precise, compliant, and stable control during the manipulation of objects. Therefore, this approach efficiently integrates a bio-inspired module (cerebellar circuitry) with a machine learning component (LWPR). The cerebellar-LWPR synergy makes the robot adaptable to changing conditions. We evaluate how this scheme scales for robot-arms of a high number of degrees of freedom (DOFs) using a simulated model of a robot arm of the new generation of light weight robots (LWRs).

The cerebellum, which is responsible for motor control and learning, has been suggested to act as a Smith predictor for compensation of time-delays by means of internal forward models. However, insights about how forward model predictions are integrated in the Smith predictor have not yet been unveiled. To fill this gap, a novel bio-inspired modular control architecture that merges a recurrent cerebellar-like loop for adaptive control and a Smith predictor controller is proposed. The goal is to provide accurate anticipatory corrections to the generation of the motor commands in spite of sensory delays and to validate the robustness of the proposed control method to input and physical dynamic changes. The outcome of the proposed architecture with other two control schemes that do not include the Smith control strategy or the cerebellar-like corrections are compared. The results obtained on four sets of experiments confirm that the cerebellum-like circuit provides more effective corrections when only the Smith strategy is adopted and that minor tuning in the parameters, fast adaptation and reproducible configuration are enabled.

Networks of neurons can perform computations that even modern computers find very difficult to simulate. Most of the existing artificial neurons and artificial neural networks are considered biologically unrealistic, nevertheless the practical success of the backpropagation algorithm and the powerful capabilities of feedforward neural networks have made neural computing very popular in several application areas. A challenging issue in this context is learning internal representations by adjusting the weights of the network connections. To this end, several first-order and second-order algorithms have been proposed in the literature. This paper provides an overview of approaches to backpropagation training, emphazing on first-order adaptive learning algorithms that build on the theory of nonlinear optimization, and proposes a framework for their analysis in the context of deterministic optimization.

Recurrent networks constitute an elegant way of increasing the capacity of feedforward networks to deal with complex data in the form of sequences of vectors. They are well known for their power to model temporal dependencies and process sequences for classification, recognition, and transduction. In this paper we propose an advanced nonmonotone Conjugate Gradient training algorithm for recurrent neural networks, which is equipped with an adaptive tuning strategy for both the nonmonotone learning horizon and the stepsize. Simulation results in sequence processing using three different recurrent architectures demonstrate that this modification of the Conjugate Gradient method is more effective than previous attempts.

We constructed a neural network of the hippocampus and proposed an adaptive learning rule of synapses to simulate the storing and retrieving processes of memory in the hippocampus by a mechanism of resonance. The hippocampus network consists of CA1, CA3 and DG, in particular, CA1 is a storage of memory, which receives inputs from both EC through perforant path (PP) and CA3 through Schaffer collaterals (SC). The stimulated results showed that the memory trace was unable to be encoded in CA1 when only a single subthreshold signal from EC or CA3 was inputted, of which the main reason might be lack of the resonance of the two signals. We calculated signal-to-noise ratio (SNR) of the network, and found it reached a peak value at appropriate SC connection strength, indicating that a typical stochastic resonance phenomenon appeared in PP signal detection. The inputs from EC and CA3 were able to enhance the memory representation in CA1, although still incomplete. We used a learning rule to modify synaptic weights by which the network could learn an external pattern. The hippocampus network tended to be stable after sufficient evolution. Some CA1 neurons show synchronized firings which are used to represent memory and are clearer than observed memory traces before learning. The model and results provide a good guidance to our understanding of the mechanism of the hippocampus memory.

Optimization is an important and decisive task in science. Many optimization problems in science are naturally too complicated and difficult to be modeled and solved by the conventional optimization methods such as mathematical programming problem solvers. Meta-heuristic algorithms that are inspired by nature have started a new era in computing theory to solve the optimization problems. The paper seeks to find an optimization algorithm that learns the expected quality of different places gradually and adapts its exploration-exploitation dilemma to the location of an individual. Using birds’ classical conditioning learning behavior, in this paper, a new particle swarm optimization algorithm has been introduced where particles can learn to perform a natural conditioning behavior towards an unconditioned stimulus. Particles are divided into multiple categories in the problem space and if any of them finds the diversity of its category to be low, it will try to go towards its best personal experience. But if the diversity among the particles of its category is high, it will try to be inclined to the global optimum of its category. We have also used the idea of birds’ sensitivity to the space in which they fly and we have tried to move the particles more quickly in improper spaces so that they would depart these spaces as fast as possible. On the contrary, we reduced the particles’ speed in valuable spaces in order to let them explore those places more. In the initial population, the algorithm has used the instinctive behavior of birds to provide a population based on the particles’ merits. The proposed method has been implemented in MATLAB and the results have been divided into several subpopulations or parts. The proposed method has been compared to the state-of-the-art methods. It has been shown that the proposed method is a consistent algorithm for solving the static optimization problems.

With the rapid explosion of the data streams from the applications, ensuring accurate data analysis is essential for effective real-time decision making. Nowadays, data stream applications often confront the missing values that affect the performance of the classification models. Several imputation models have adopted the deep learning algorithms for estimating the missing values; however, the lack of parameter and structure tuning in classification, degrade the performance for data imputation. This work presents the missing data imputation model using the adaptive deep incremental learning algorithm for streaming applications. The proposed approach incorporates two main processes: enhancing the deep incremental learning algorithm and enhancing deep incremental learning-based imputation. Initially, the proposed approach focuses on tuning the learning rate with both the Adaptive Moment Estimation (Adam) along with Stochastic Gradient Descent (SGD) optimizers and tuning the hidden neurons. Secondly, the proposed approach applies the enhanced deep incremental learning algorithm to estimate the imputed values in two steps: (i) imputation process to predict the missing values based on the temporal-proximity and (ii) generation of complete IoT dataset by imputing the missing values from both the predicted values. The experimental outcomes illustrate that the proposed imputation model effectively transforms the incomplete dataset into a complete dataset with minimal error.

As Internet usage becomes more popular over the world, e-learning system, e.g., online learning, employee training courses, e-books, etc. has been accepted globally. How to provide the optimum content for individualized learning becomes an important issue for e-learning system. In this work, a Computer-Assisted Learning Expert System (CAL-ES), based upon expert system methodology, is proposed to provide individualized teaching materials for learners in accordance with their learning aptitudes and evaluation results. According to the cycle of knowledge management in CAL system, which is similar to the cycle of general knowledge management, four mechanisms, i.e. Knowledge Representation, Knowledge Acquisition, Knowledge Organizer, and Knowledge Miner, are designed in CAL-ES. The Object Oriented Course Model (OOCM) is used as the presentation of knowledge in designing CAL system, and is used in CAL-ES. Knowledge Acquisition technology is applied to acquire knowledge from experts, including the knowledge for constructing Teaching Object, building Teaching Materials, and managing Teaching Resources. Knowledge Organizer is a directory service for efficiently managing teaching resources. Finally, Knowledge Miner is used to find implicit knowledge from historical records of the system using Data Mining technologies. These four mechanisms in CAL-ES provide complete knowledge system capability for a CAL system. In the last three years, we have been working on building an integrated CAL platform in the Virtual Mathematical High School project, and the prototype of CAL-ES will be introduced in this work.

The run-time context domain has much effect on the performance of practical corpus-based applications. Previous smoothing techniques, and class-based and similarity-based models cannot handle the dynamic status perfectly. In this paper, an adaptive learning algorithm is proposed for task adaptation to fit best the run-time context domain in the application of Chinese homophone disambiguation. It shows which objects are to be adjusted and how to adjust their probabilities by a neural network model. The resulting techniques are greatly simplified and robust. The experimental results demonstrate the effects of the learning algorithm from generic domain to specific domain. A methodology is also presented to show how these techniques can be extended to various language models and corpus-based applications.

We study the relation between the trading behavior of agents and volatility in toy markets of adaptive inductively rational agents. We show that excess volatility, in such simplified markets, arises as a consequence of (i) the neglect of market impact implicit in price taking behavior and of (ii) excessive reactivity of agents. These issues are dealt with in detail in the simple case without public information. We also derive, for the general case, the critical learning rate above which trading behavior leads to turbulent dynamics of the market.

This work describes the use of a weighted ensemble of neural network classifiers for adaptive learning. We train the neural networks by means of a quantum-inspired evolutionary algorithm (QIEA). The QIEA is also used to determine the best weights for each classifier belonging to the ensemble when a new block of data arrives. After running several simulations using two different datasets and performing two different analysis of the results, we show that the proposed algorithm, named neuro-evolutionary ensemble (NEVE), was able to learn the data set and to quickly respond to any drifts on the underlying data, indicating that our model can be a good alternative to address concept drift problems. We also compare the results obtained by our model with an existing algorithm, Learn++.NSE, in two different nonstationary scenarios.

As education field has become online due to covid in both schools and colleges, e-learning security has become an important issue. An e-Learning framework provides a collection of online services that are helpful for the learners, resource persons and others who are involved in enhancing the management and delivery of education to all sections of people. The two most important aspects of an e-Learning system are better search of learning resources and the secure authentication between the learner and the trainer. This chapter introduces two novel methods: (i) optimization of Learning Object (LO) search based on learners’ characteristics, (ii) secure authentication of trainers and learners using visual cryptography. Storage and delivery of optimal resources that are well suited for individual learner is always a challenging task. To find the best learning objects, an enhanced attribute-based Ant Colony Optimization (ACO) algorithm that provides flexibility for the learners based on learner characteristics is proposed. A novel visual cryptography-based technique with kite-based partition technique is designed to perform file sharing and blockchain-based secure authentication and verification of valid learners is proposed for the framework. Several measures like match ratio, relevancy factor, and heuristic values show the efficiency of the proposed ACO search technique in the context of an e-Learning framework.

This chapter discusses an artificial neural network (ANN) specifically designed for large-scale memory strorage and retriecal of information. The chapter discusses applications of ANN to retrieval, diagnosis, classification, prediction and decision problems. The network is based on Minsky's knowledge-lines theory of memory storage and retrieval. It employs arrays of SOM modules, such that the “k-lines” are implemented via link weights (address-correlations) that are being updated by learning.

This paper presents an adaptive approach to address a kind of adaptive learning for intelligent agent. We propose a knowledge processing cycle for intelligent agent to mimic the human's learning process. In this process we regard two important parts. We apply the assimilation process to learn the new information and the accommodation process when the new information has some conflict with agent's proper known. We use the JADE (Java Agent DEvelopment Framework) as our agent platform, and explain our approach through pursuit-evasion game.