Adaptive Intelligent Systems Research Articles

Intelligent Adaptive Real-Time Monitoring and Recognition System for Human Activities

Intelligent Adaptive Real-Time Monitoring and Recognition System for Human Activities

Thematic control and criteria-based assessment of foreign language writing skills using artificial intelligence technologies

Importance. Currently, there is a tendency that when teaching a foreign language, teachers are increasingly resorting to the use of artificial intelligence technologies to plan training sessions, generate educational content, as well as to conduct automated testing of formed communication skills. The monitoring of academic achievements and assessment is one of the key components of the organization of the educational process. Traditional methods of control and assessment require significant time and labor-intensive costs from the teacher, while artificial intelligence technologies make it possible to simplify and automate routine tasks: check tests and written papers, analyze them and identify mistakes, provide feedback. Thanks to the integration of natural language processing (NLP) technologies into chatbots and adaptive intelligent learning systems, it becomes possible on a daily basis to check texts created by students, evaluate them from the point of view of grammatical and lexical correctness, as well as identify stylistic and factual errors in real time. The purpose of this work is to test the applicability of artificial intelligence technologies for thematic control and criteria-based assessment of educational achievements using the example of productive and reproductive writing.Research Methods. In carrying out this study, the following groups of methods were used: theoretical methods aimed at familiarizing with scientific and methodological literature on the topic of research, analysis and classification of theoretical and methodological material for conducting thematic control and criterion assessment in foreign language classes, as well as empirical methods that allowed modeling pedagogical control and assessment processes using artificial intelligence technologies, observation, analysis and description of the results obtained.Definition of Concepts. The main concepts used in the study are “control of educational results” and its variety “thematic control”, “criterion assessment”.Results and Discussion. In the course of the study, various types of monitoring educational results using artificial intelligence technologies were considered: preliminary, current, intermediate, thematic, final. The choice of thematic control is due to the opportunity to trace its applicability within the framework of a single lesson in a foreign language and identify the main difficulties in using this form of control. The following criteria for evaluating writing skills are used to evaluate learning outcomes using artificial intelligence technologies: a) the structure of the written text; b) compliance with the main topic; c) coherence; d) relevance; e) grammatical correctness; f) lexical correctness; g) ethics of writing and stylistic correctness.Conclusion. Artificial intelligence technologies at the present stage have a high degree of adaptability and include a wide range of software and hardware solutions that allow for such important pedagogical procedures as monitoring educational achievements and evaluation in accordance with user-defined evaluation criteria. The obtained results are proposed to be used in research devoted to the study of modern methods of monitoring educational achievements in the methodology of teaching foreign languages using artificial intelligence technologies.

Адаптивная интеллектуальная обучающая система

<p>The goal of the work is to create a modern adaptive intelligent system using current machine learning technologies to automate a significant part of the teacher’s work. Existing intelligent systems, the purpose of which is to train students to work in various subject areas, currently have a set of various disadvantages, for example, the need to prepare educational material in a given format, which is sometimes a very labor-intensive task. In addition, in such systems there is a need to assess knowledge to correct the training plan for students, which requires various practical tasks for their formal presentation. In this case, practical assignments must be compiled by the course author, which can also be very labor-intensive. The novelty of the adaptive intelligent system presented in the work lies in the improvement of learning approaches using the latest machine learning methods. To help the teacher prepare educational material provides the ability to create video material automatically. This approach provides an opportunity for students to receive material not only in text form, but also in video format, without increasing the labor intensity on the part of the teacher. In addition, the teacher will be given the opportunity to manipulate versions of educational materials in accordance with the statistics provided by the system on student performance.</p>

Dynamic assessment approach for intelligent power distribution systems based on runtime verification with requirements updates

The study aims to address the challenge of dynamic assessment in power systems by proposing a design scheme for an intelligent adaptive power distribution system based on runtime verification. The system architecture is built upon cloud–edge-end collaboration, enabling comprehensive monitoring and precise management of the power grid through coordinated efforts across different levels. Specifically, the study employs the adaptive observer approach, allowing dynamic adjustments to observers to reflect updates in requirements and ensure system reliability. This method covers both structural and parametric adjustments to specifications, including updating time protection conditions, updating events, and adding or removing responses. The results demonstrate that with the implementation of adaptive observers, the system becomes more flexible in responding to changes, significantly enhancing its level of efficiency. By employing dynamically changing verification specifications, the system achieves real-time and flexible verification. This research provides technical support for the safe, efficient, and reliable operation of electrical power distribution systems.

Learner’s attention detection in connected smart classroom using internet of things and convolutional neural networks

Detecting learner attention is an essential part of learning assessment. Consequently, it becomes an essential requirement for adaptive intelligent teaching systems, to identify specific needs and anticipate orientations. In this article, we propose a new model of a connected smart classroom, based on the internet of things, artificial intelligence and machine learning to detect in real time learners' attention and marking their presence during the execution of a teacher-assisted pedagogical activity, as well as to adapt the most suitable learning objects to these learners. The proposed model is based on head position, gaze direction, yawning and eye-state analysis as facial landmarks detected by cameras connected via the Bluetooth low energy network and transmitted to a developed convolutional neural network. In addition, a series of experiments have been conducted to evaluate the performance and efficiency of the model developed. The findings demonstrate that the model developed can be used to precisely capture the status of learners in the classroom in terms of attention and identification. In this way, these interesting findings can be used to adapt teaching activities to the individual needs of learners, and to identify areas where they have difficulties and needs extra help.

Student Performance Prediction Using Machine Learning Algorithms

Education is crucial for a productive life and providing necessary resources. With the advent of technology like artificial intelligence, higher education institutions are incorporating technology into traditional teaching methods. Predicting academic success has gained interest in education as a strong academic record improves a university’s ranking and increases student employment opportunities. Modern learning institutions face challenges in analyzing performance, providing high-quality education, formulating strategies for evaluating students’ performance, and identifying future needs. E-learning is a rapidly growing and advanced form of education, where students enroll in online courses. Platforms like Intelligent Tutoring Systems (ITS), learning management systems (LMS), and massive open online courses (MOOC) use educational data mining (EDM) to develop automatic grading systems, recommenders, and adaptative systems. However, e-learning is still considered a challenging learning environment due to the lack of direct interaction between students and course instructors. Machine learning (ML) is used in developing adaptive intelligent systems that can perform complex tasks beyond human abilities. Some areas of applications of ML algorithms include cluster analysis, pattern recognition, image processing, natural language processing, and medical diagnostics. In this research work, K-means, a clustering data mining technique using Davies’ Bouldin method, obtains clusters to find important features affecting students’ performance. The study found that the SVM algorithm had the best prediction results after parameter adjustment, with a 96% accuracy rate. In this paper, the researchers have examined the functions of the Support Vector Machine, Decision Tree, naive Bayes, and KNN classifiers. The outcomes of parameter adjustment greatly increased the accuracy of the four prediction models. Naïve Bayes model’s prediction accuracy is the lowest when compared to other prediction methods, as it assumes a strong independent relationship between features.

The role of artificial intelligence in building an adaptive educational environment

Purpose: of the article is to study the influence of artificial intelligence on the construction of an adaptive educational environment that takes into account the positive and negative impact of artificial intelligence, as the core of digital technologies, on the well-being of participants in the educational process. Methods: the work uses a complex of theoretical methods, including axiomatic, formalization, abstraction, logical analysis, historical retrospection. The most signifi cant practical research methods include statistical, operationalization and evaluation, comparative analysis. Results: the article provides a statistical substantiation of the demand for artifi cial intelligence in the education. The current educational problems are shown, in solving which it is advisable to use artifi cial intelligence. The article describes the trends in the use of artificial intelligence in the Russian education. The interpretation of the adaptive educational environment is given and the possibilities of using artificial intelligence as a tool for its construction are shown. The logical sequence for constructing an adaptive intelligent teaching system is proposed. The analysis of the impact of digital technologies used in hybrid learning (including artifi cial intelligence) on the well-being of participants in the educational process was carried out. The need for a rational, balanced and cautious approach to the use of artifi cial intelligence in education is emphasized, which gives rise to numerous ethical problems, ignoring which can negatively aff ect the values of education. Conclusions and Relevance: in the context of digitalization of the economy and society, the key technology of which is artifi cial intelligence, education is facing technological challenges that force it to adapt to new operating conditions. The penetration of artifi cial intelligence into the field of education is the pattern of scientific and technological progress that cannot be resisted. The positive aspects and threats of using artificial intelligence in education should be critically assessed, and informed decisions should be made on this basis. Considering the specifics of the educational environment in which the foundation of personality is laid, and the need to preserve the values of education as a factor of cultural progress, priority in goal-setting and the meanings of education should remain with a person whose assistant can be artifi cial intelligence.

A new emotional intelligent adaptive system for controlling the proposed sensor-less induction motor drive with dual stator winding based on a ten-switch converter in low speed range

A dual stator winding induction motor (DSWIM) has two isolated three-phase windings in its stator, and its rotor is a usual squirrel cage type. In a sensor-less DSWIM drive, the standard and optimal operating mode in all operating speed ranges is the synchronous mode. Until now, researchers use the asynchronous mode to control it in the low speed range that is not the standard mode. But, this paper proposes a new intelligent model reference adaptive system (IMRAS) to control a DSWIM drive without the speed sensor based on a ten-switch converter in the low speed range. In the proposed intelligent sensor-less DSWIM drive, the rotor speed is estimated via an intelligent model as single and bi-objective functions. In the proposed IMRAS, the second objective increases the speed estimation accuracy at very low speeds. In this idea, the rotor speed error is used as data in the rotor speed estimation process. As a result, the convergence between estimated speed and reference speed in the proposed IMRAS is increased at zero and very low speeds. Also, in this paper, the power losses, the capital cost, and the number of switching elements in the converter are reduced via the concept of flux compensation and using a ten-switch converter. The intelligent proposed sensor-less DSWIM drive, unlike conventional methods, works in its standard operating mode (synchronous mode) and has a suitable performance in the low speed range. Hence, the converter power losses are significantly decreased. The suggested techniques are simulated in MATLAB software. The simulation of the proposed DSWIM drive system is performed under different scenarios of operating conditions and the obtained results approve the assumptions.

Research on the application of personalized recommendation

With the development of the Internet, people have more opportunities to be exposed to personalized recommendations, and the heat of related topics has increased. The purpose of this paper is to summarize the application principle and use of personalized recommendations by analyzing the relevant data, so that people can have a better understanding of personalized recommendations and make preparations for future related research. This paper first introduces the general development context of the personalized recommendation system and its uniqueness. The analysis finds that personalized recommendations can provide users with the most relevant and valuable information, goods, or services according to their interests, preferences, and behaviors. Secondly, this paper explores the application scenarios of personalized recommendation systems in different fields, including but not limited to video content, e-commerce, and online learning. In addition, this paper also lists the application of YouTube and Netflix in video content recommendation, as well as personalized recommendation services in e-commerce and online learning platforms. In particular, the rise of intelligent adaptive online learning systems and their adaptability in the field of education and training are analyzed in detail. Through this research, it can be found that a personalized recommendation system is very beneficial to people's production and life, but at present, personalized recommendation is still in the development stage, and there are many problems to be solved.

Adaptive plc intelligent control based on the integrated system of smart grid thermal energy conversion

During the construction of smart grids, due to technological limitations, traditional sequential control systems have been unable to meet the needs of grid operation, exposing problems such as single control methods and functions, low operational efficiency, inability to independently complete complex tasks, and poor anti-interference ability, which in turn affect the power supply quality and operational stability of the grid, and fail to truly achieve the reliability, safety, and reliability of smart grids Economic and efficient goals. For this purpose, the author describes an adaptive intelligent control method for an integrated biomass energy heat conversion system consisting of more than one suction biomass gasifier, biomass gas burner, and steam boiler. The system uses PLC as a tool and takes the boiler outlet steam pressure as the control parameter. Based on the changes in the boiler outlet steam pressure, it automatically adjusts the feed and gas production of the gasifier, and subsequently adjusts the air supply of the burner. Ultimately, it achieves the goal of automatically adjusting the boiler heating volume with changes in steam pressure. The integrated heating and adaptive intelligent control system based on biomass gasification has been reliably verified in industrial applications.

Intelligent Adaptive E-Learning Systems: Current Approaches, Architectures, and Applications

Considering the weakness of traditional e-learning, a multiple of e-learning systems attempt to get individualization into the process of learning by offering learner-centered instruction, the adaptive E-learning system(AES)is seen as one of the more famous models. An AES can tailor its response to different circumstances. Various types and platforms in E-Learning systems have been discussed and explained such as blended, adaptive, and educational e-learning. Architectures, functions, and challenges also illustrated in details in this paper. Mostly focused on the Adaptive e-learning concept and importance for the learners. AES concentrates on adaptively delivering learning materials. This paper discusses some studies that approved the importance of adaption in the e-learning system in the years (2018-2023), and introduced some challenges faced by this system.

Optimizing medical personnel speech recognition models using speech synthesis and reinforcement learning

Text-to-Speech synthesis (TTS) can be used to generate training data for building Automatic Speech Recognition models (ASR). Access to medical speech data is because it is sensitive data that is difficult to obtain for privacy reasons. Speech can be synthesized by mimicking different accents, dialects, and speaking styles in a medical language. Reinforcement Learning (RL), in the context of ASR, can be used to optimize a model. A model can be trained to minimize errors in speech-to-text transcription, especially for technical medical terminology. In this case, the “reward” to the RL model can be negatively proportional to the number of transcription errors. The paper presents a method and experimental study from which it is concluded that the combination of TTS and RL can enable the creation of a speech recognition model suited to the specific needs of medical personnel, helping to expand the training data and optimize the model to minimize transcription errors. The learning process used reward functions based on Mean Opinion Score (MOS), a subjective metric for assessing speech quality, and Word Error Rate (WER), which evaluates the quality of speech-to-text transcription. [The Polish National Center for Research and Development (NCBR) supported the project: “ADMEDVOICE- Adaptive intelligent speech processing system of medical personnel with the structuring of test results and support of therapeutic process,” no. INFOSTRATEG4/0003/2022.]

Educational Technology Innovation: AI-Integrated Learning System Design in AILS-Based Education

Information and Communication Technology (ICT) has seamlessly integrated itself into modern society, revolutionizing education through its innovations. One prominent innovation is the Artificial Intelligence (AI)-based learning system, promising an elevated quality of education. The AI-based learning system harnesses machine learning to tailor approaches for each student, monitoring their progress and providing pertinent feedback. A noteworthy example is the Intelligent Adaptive Learning System (AILS), which amalgamates Natural Language Processing (NLP) technology and 3D animation with the Google Search Engine. AILS aids comprehension via tablet devices, with teachers concurrently assuming a supportive role. AILS empowers students to autonomously acquire knowledge, accessing information through NLP and the Google Search Engine. The visualization of teachers through 3D animations enhances interactivity. Through AILS, students can learn from home or in-class, guided and mentored by teachers. AILS efficiently processes data, monitors progress, offers feedback, and identifies student needs. Teachers continue to facilitate profound learning and critical thinking skills. AILS fosters self-directed learning and lifelong skills. Furthermore, AILS augments the quality of the educational framework, integrating AI technology to realize student self-directedness in the Society 5.0 era.

Intelligent adaptive testing system

Intelligent adaptive testing system

A Study of Multimodal Intelligent Adaptive Learning System and Its Pattern of Promoting Learners’ Online Learning Engagement

A Study of Multimodal Intelligent Adaptive Learning System and Its Pattern of Promoting Learners’ Online Learning Engagement

Optimized PI Controller Based Reboost Luo Converter for Micro Grid Application

In the recent era, the significance of Renewable Energy (RE) sources in the process of power generation has attained considerable attention as it provides multiple beneficial impacts without harming the environment. The Photovoltaic (PV) and Doubly-Fed Induction Generator (DFIG) fed wind turbine are employed as hybrid power sources in this study. The output voltages of these sources are independently controlled by separate controllers in an optimal manner for effectively maximizing the overall performance of the system. The Reboost Luo converter is introduced in this work to maximize the output voltage of PV in an efficient manner whereas the Grey Wolf Optimization (GWO) tuned Proportional Integral (PI) is used to optimize the converter output. The wind turbine output is fed to rectifier that is controlled by a PI controller. Furthermore, a battery setup is utilized with bidirectional converter as a secondary power source to ensure continuous power supply to the grid. An intelligent Adaptive Neuro-Fuzzy Inference System (ANFIS) is implemented to regulate the power flow in two directions between the battery and Point of Common Coupling (PCC). The output of PV, Wind and Battery are combined at a single point and fed to the grid through a three phase inverter. The overall setup is validated through MATLAB Simulink and the performances are verified with the developed experimental prototype.

Towards an Adaptive e-Learning System Based on Deep Learner Profile, Machine Learning Approach, and Reinforcement Learning

Now-a-days, the great challenge of adaptive e-learning systems is to recommend an individualized learning scenario according to the specific needs of learners. Therefore, the perfect adaptive e-learning system is the one that is based on a deep learner profile to recommend the most appropriate learning objects for that learner. Yet, the majority of existing adaptive e-learning systems do not give high importance to the adequacy of the real learner profile and its update with the one taken into account in the learning path recommendation. In this paper, we proposed an intelligent adaptive e-learning system, based on machine learning and reinforcement learning. The objectives of this system are the creation of a deep profile of a given learner, via the implementation of K-means and linear regression, and the recommendation of adaptive learning paths according to this deep profile, by implementing the Q-learning algorithm. The proposed system is decomposed into three principal modules, data preprocessing module, learner deep profile creation module, and learning path recommendation module. These three modules interact with each other to provide a personalized adaptation according to the learner's deep profile. The results obtained indicate that taking into account the learner's deep profile improves the quality of learning for learners.

A Novel ANFIS Based SMC with Fractional Order PID Controller

Interacting The highest storage capacity of a circular tank makes it popular in process industries. Because of the varying surface area of the cross-sections of the tank, this two-tank level system has nonlinear characteristics. Controlling the flow rate of liquid is one of the most difficult challenges in the production process. This proposed effort is critical in preventing time delays and errors by managing the fluid level. Several scholars have explored and explored ways to reduce the problem of nonlinearity, but their techniques have not yielded better results. Different types of controllers with various techniques are implemented by the proposed system. Sliding Mode Controller (SMC) with Fractional Order PID Controller based on Intelligent Adaptive Neuro-Fuzzy Inference System (ANFIS) is a novel technique for liquid level regulation in an interconnected spherical tank system to avoid interferences and achieve better performance in comparison of rise time, settling time, and overshoot decrease. Evaluating the simulated results acquired by the controller yields the efficiency of the proposed system. The simulated results were produced using MATLAB 2018 and the FOMCON toolbox. Finally, the performance of the conventional controller (FOPID, PID-SMC) and proposed ANFIS based SMC-FOPID controllers are compared and analyzed the performance indices.

Three Algorithms for Grouping Students: A Bridge Between Personalized Tutoring System Data and Classroom Pedagogy

Computer-assisted instructional programs such as intelligent tutoring systems are often used to support blended learning practices in K-12 education, as they aim to meet individual student needs with personalized instruction. While these systems have been shown to be effective under certain conditions, they can be difficult to integrate into pedagogical practices. In this paper, we introduce three group formation algorithms that leverage learning data from the adaptive intelligent tutoring system ALEKS to support pedagogical and collaborative learning practices with ALEKS. Each grouping method was devised for different use cases, but they all utilize a fine-grained multidimensional view of student ability measured across several hundred skills in an academic course. As such, the grouping algorithms not only identify groups of students, but they also determine what areas of ALEKS content each group should focus on. We then evaluate each of the three methods against two alternative baseline methods, which were chosen for their plausibility of being used in practice—one that groups students randomly and one that groups students based on a unidimensional course score. To evaluate these methods, we establish a set of practical metrics based on what we anticipate teachers would care about in practice. Evaluations were performed by simulating mock groupings of students at different time periods for real ALEKS algebra classes that occurred between 2017 and 2019. We show that each devised method obtains more favorable results on the specified metrics than the alternative methods under each use-case. Moreover, we highlight examples where our methods lead to more nuanced groupings than grouping based on a unidimensional measure of ability.

An Adaptive Intelligent System Based on Energy‐Efficient Synaptic Resistor Circuits with Fast Real‐Time Learning

Unlike the human brain, which concurrently executes inference and learning algorithms in neural networks in real time, artificial intelligence (AI) systems usually execute inference algorithms and learning algorithms in series, which lack fast real‐time learning functionality, high computing energy efficiency, and adaptability in the complex, erratic real world. Herein, an intelligent system integrating a drone and a synaptic resistor (synstor) circuit that concurrently executes inference and reinforcement learning algorithms in real‐time is reported. Without any prior learning or programming, the conductance matrix of the synstor circuit is dynamically optimized in its real‐time learning processes, thus enabling the drone to adapt and fly toward its target positions in erratic aerodynamic environments. In learning experiments involving a drone driven by synstor circuits, humans, or computers, the real‐time learning by the synstor circuit is superior to the real‐time learning by humans and the cloud learning by computers, in terms of key benchmarks including adaptability, learning time, precision, power consumption, and energy efficiency. By circumventing the fundamental limitations in computers, synstor circuits open up new directions to establish AI systems with brain‐like fast real‐time learning functionality, high computing energy efficiency, and adaptability in complex, erratic real‐world environments for versatile applications.