Adaptive System Research Articles

Model for motivating learners with personalized learning objects in a hypermedia adaptive learning system

<span lang="EN-US">A number of weaknesses were demonstrated in the E-learning platforms during the Covid-19 pandemic despite the efforts invested. This has negatively influenced learners' motivation and consequently their performance. With the proliferation of technology and the revolution of information and communication technologies (ICT), learning objects have become new epitomes widely used, accessible, and implemented with educational resources and technological support. The integration of learning objects into E-learning has enhanced educational progress, but during critical periods, it is crucial to ensure pedagogical continuity and learner motivation. Based on this observation, we will propose architecture of a personalized learning object model in the context of an adaptive hypermedia learning system (AHS). The objective of our model is to increase the motivation factor which is a determining element in the success of E-learning, our model aims to improve the performance of the learners in order to avoid the abounding of learning and to promote the attendance of the learners. This will be useful later for any design or development of learning objects in hypermedia learning systems that are adaptive to the needs of the learners and in line with their preferences and profiles throughout the learning process offered by the system. </span>

Smart traffic forecasting: leveraging adaptive machine learning and big data analytics for traffic flow prediction

The issue of road traffic congestion has become increasingly apparent in modern times. With the rise of urbanization, technological advancements, and an increase in the number of vehicles on the road, almost all major cities are experiencing poor traffic environments and low road efficiency. To address this problem, researchers have turned to diverse data resources and focused on predicting traffic flow, a crucial issue in Intelligent Transportation Systems (ITS) that can help alleviate congestion. By analyzing data from correlated roads and vehicles, such as speed, density, and flow rate, it is possible to anticipate traffic congestion and patterns. This paper presents an adaptive traffic system that utilizes supervised machine learning and big data analytics to predict traffic flow. The system monitors and extracts relevant traffic flow data, analyzes and processes the data, and stores it to enhance the model's accuracy and effectiveness. A simulation was conducted by the authors to showcase the proposed solution. The outcomes of the study carry substantial implications for transportation systems, offering valuable insights for enhancing traffic flow management.

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.

Do domain-specific protein language models outperform general models on immunology-related tasks?

Deciphering the antigen recognition capabilities by T-cell and B-cell receptors (antibodies) is essential for advancing our understanding of adaptive immune system responses. In recent years, the development of protein language models (PLMs) has facilitated the development of bioinformatic pipelines where complex amino acid sequences are transformed into vectorized embeddings, which are then applied to a range of downstream analytical tasks. With their success, we have witnessed the emergence of domain-specific PLMs tailored to specific proteins, such as immune receptors. Domain-specific models are often assumed to possess enhanced representation capabilities for targeted applications, however, this assumption has not been thoroughly evaluated. In this manuscript, we assess the efficacy of both generalist and domain-specific transformer-based embeddings in characterizing B and T-cell receptors. Specifically, we assess the accuracy of models that leverage these embeddings to predict antigen specificity and elucidate the evolutionary changes that B cells undergo during an immune response. We demonstrate that the prevailing notion of domain-specific models outperforming general models requires a more nuanced examination. We also observe remarkable differences between generalist and domain-specific PLMs, not only in terms of performance but also in the manner they encode information. Finally, we observe that the choice of the size and the embedding layer in PLMs are essential model hyperparameters in different tasks. Overall, our analyzes reveal the promising potential of PLMs in modeling protein function while providing insights into their information-handling capabilities. We also discuss the crucial factors that should be taken into account when selecting a PLM tailored to a particular task.

Performance evaluation of BLDC motor drive mounted in aerial vehicle (drone) using adaptive neuro-fuzzy

The development of autonomous drones equipped with cameras and various sensors has paved the way for their application in agriculture and perimeter security. These aerial drones require specific power, acceleration, high torque, and efficiency to meet the demands of agricultural tasks, utilizing built-in brushless DC (BLDC) motors. However, a common challenge drone’s face is maintaining the desired speed for extended periods. Enhancing the performance of BLDC motors through advanced controllers is crucial to address this issue. This research paper proposes optimizing the size and speed of brushless DC motors for aerial vehicles using an adaptive fuzzy inference system and supervised learning techniques. When these drones carry loads, the BLDC motors must dynamically adjust the drone's speed. During this phase, the motors must control their speed and torque using artificial intelligence controllers like adaptive neuro-fuzzy inference systems (ANFIS) to enhance the drone's functionality, resilience, and safety. This research has conducted analyses focused on improving the performance of BLDC motors explicitly personalized for unmanned aerial vehicle (UAVs). The proposed method will be implemented using MATLAB/Simulink, expecting to significantly enhance the BLDC motor's performance compared to conventional controllers. Comparative analyses will be conducted between traditional and ANFIS controllers to validate the effectiveness of the proposed approach.

A modified sliding mode controller based on Fuzzy logic to control the longitudinal dynamics of the autonomous vehicle

This article delves into the intricate world of controlling the longitudinal dynamics of autonomous vehicles. In the first part, we studied two distinct controllers: the Super Twisting Sliding Mode Control and its modified version enriched with the integration of fuzzy logic, applied to the longitudinal dynamics of the autonomous automobile to follow a desired speed longitudinal profile, the two controllers are compared with a Neural Network-Based Non-singular Terminal Sliding-Mode Control, the system takes the throttle and brake as its inputs and delivers speed and acceleration as outputs. The overarching objective is to ensure that the controlled vehicle maintains a close and precise alignment with the desired speed profile. The second part of our research is dedicated to the development of adaptive cruise control systems and cruise control according to the safety conditions. This controller consists of two blocks low and upper controller, in upper controller the inputs are the speeds of the automobile in front and of the autonomous automobile itself, the safety distance, the measured distance. The output is the desired acceleration. The objective is to maintain a distance between the front vehicles, greater than or equal to the safety distance. For this, to achieve this task, we have implemented a control system known as the Proportional Integral Derivative (PID) controller in the adaptive cruise control system to control this system. In the low controller block, the same controllers used in the first part: the Super Twisting Sliding Mode Control and its modified version based on fuzzy logic, are applied, the system inputs are throttle and brake, and the outputs are speed and acceleration. This system is processed by MATLAB code, we obtained a better result with our proposed controller such that the maximum absolute speed error is equal to 0.0144 m/s in the first case of speed tracking, and to 0.006 m/s in the second case of the using adaptive cruise control, the illustrations below show the efficiency and robustness of these controllers.

Kernel density estimation of Tsalli’s entropy with applications in adaptive system training

Information theoretic learning plays a very important role in adaption learning systems. Many non-parametric entropy estimators have been proposed by the researchers. This work explores kernel density estimation based on Tsallis entropy. Firstly, it has been proved that for linearly independent samples and for equal samples, Tsallis-estimator is consistent for the PDF and minimum respectively. Also, it is investigated that Tsallis-estimator is smooth for differentiable, symmetric, and unimodal kernel function. Further, important properties of Tsallis-estimator such as scaling and invariance for both single and joint entropy estimation have been proved. The objective of the work is to understand the mathematics behind the underlying concept.

Application of AI in the Field of Education: Dynamic Course Generation

Abstract: This research article examines the evolving relationship between Artificial Intelligence (AI) and Education, exploring its impact and potential. It covers adaptive learning systems, intelligent tutoring platforms, and emerging technologies, analyzing real-world applications and addressing ethical and implementation challenges. By synthesizing research findings, it contributes to understanding AI's role in transforming learning paradigms

Feasibility of integrated numerical and Adaptive Neuro-Fuzzy Inference System models in predicting the thermophysical properties of unsteady coupled micropolar–Casson hybrid nanofluids

This article aims to probe postulated phenomena using a paired micropolar and Casson hybrid fluid over a rotating disk. The intension to design a numerical technique (Runge–Kutta fourth-order along with shooting technique) integrated Adaptive Neuro-Fuzzy Inference System (ANFIS) envisioned with a thermal and exponentially space-dependent heat source, nonlinear thermal radiation and entropy production is developed in this phase of work. To ensure that, a nonlinear partial differential equation set of equations has been transformed into an ordinary differential equation by using the proper self-similarity variables. The model's research results, with a few notable outliers, are mostly consistent with those from prior research that was merged into the dataset used to train the ANFIS model. With the impact of active factors, the results are esthetically exhibited for numerous profiles. This displays that with the rise in magnetic field and radiation, the velocity and temperature profiles increase sharply, resulting in a contradiction phenomenon with the decreasing electric field inputs. Also, tilting of vortex viscosity, spin gradient viscosity and microinertia density on the various microrotation components displays inclination. Moreover, ANFIS training was exploited to analyze the approximate solutions for specific scenarios, and the developed ANFIS was evaluated against a testing dataset to emphasize its performance. Due to their longer render, the nanoparticles exploited here are deemed suitable for use in bone implants, iodinated agents for blood imaging and red blood cell stimulation. Thus, the results of this study may be applied to therapeutic anemia therapies.

Firefighters in Action: How Strategic Improvisation Enables Agile and Creative Responsiveness in Complex Adaptive Systems

This study explored “how strategic improvisation (SI) enables agile and creative responsiveness in dynamic organizations recognized as complex adaptive systems.” We highlight “what” constitutes SI, “why” it is strategic and “how” it allows for agile and creative responsiveness. We advance the conceptualization of SI proposing it as “ an improvised deliberate practice that combines coupling unconventional ideas with emergency actions seeking new strategic patterns to indeterminacy.” This is the case of firefighters, with unique features, such as operating continuously in experimental conditions, exposure to failure, risk, and unpredictability, despite strict protocols to guide operations. Furthermore, agile and creative responsiveness in a complex system is the rule rather than the exception and, therefore, requires new adaptive managerial practices. Our findings provide the SI framework based on four main dimensions such as (i) resolvability; (ii) experimentation; (iii) interactivity; and (iv) sensemaking, composing a SI mechanism. Practical implications and future perspective were proposed.

Adaptive output feedback control system design for nonlinear systems via neural networks

AbstractAdaptive output feedback control based on output feedback exponential passivity (OFEP) has a simple structure and strong robustness in regard to disturbances and system uncertainties. However, it is difficult for most nonlinear systems to satisfy the conditions of OFEP. Thus, the introduction of a suitable parallel feedforward compensator (PFC) to construct an OFEP‐augmented system with the controlled system has been used, but the control output cannot achieve perfect tracking because of the output of the introduced PFC. As a solution, introducing a feedforward (FF) input to build a 2 degree of freedom (2‐DOF) is a simple and effective way to solve this problem. In this paper, we propose the design schemes for suitable PFC and FF input of nonlinear systems via the use of neural networks (NN), respectively. Besides, to cope with possibly present input disturbances, we also provide a method to achieve disturbance compensation based on NN to reduce their interference. Finally, the effectiveness of all proposed design schemes is confirmed through numerical simulations.

Crafting An AI-Powered Adaptive E-Learning Framework: Based on Kolb's Learning Style

In the contemporary educational realm, the convergence of pedagogical advancements with machine learning presents a profound opportunity for transformation. This in-depth exploration delves into the complexities of constructing an adaptive e-learning platform, seamlessly integrating Kolb's learning preferences with state-of-the-art machine learning techniques. At the core of this initiative lies the certification module, meticulously scrutinized to bolster its effectiveness in acknowledging student accomplishments. With a focus on personalized education driven by machine learning for error analysis, learner profiling, and content adaptation, this amalgamation reshapes the educational landscape, offering bespoke and dynamic learning journeys. By proposing an intelligent and dynamic adaptive learning system, this study addresses the constraints of passive and one-size-fits-all platforms, aiming to discern and furnish personalized learning environments tailored to the unique needs of individual learners within the hybrid teaching paradigm.

Multi-functional Inconel 625 micro-machining and process optimization using ANFIS

The primary objective of this study is to develop an adaptive neuro-fuzzy inference-based system (ANFIS) that assesses the ideal parameters and impact of the micro-electro-discharge machining (Micro-EDM) technique on surface roughness (SR). In this work multi-functional Inconel alloy specimens were machined using micro-elector-discharge machining (Micro-EDM) with zinc-coated copper electrodes in a graphite nano abrasive submersion medium. An ANFIS and Taguchi design of experiments (DOE) was employed to evaluate the Micro-EDM method. The study utilized Taguchi's L9 mixed orthogonal arrays (OA) to consider three process variables, namely open voltage (V), capacitance (C), and powder concentration (CP). ANOVA analysis was conducted to identify important process variables and evaluate their influence on surface properties. The experimental results showed that all approaches were effective in representing the process. Based on the results of the response surface methodology (RSM) and ANFIS predictions, the correlation coefficients (R2) of the experimental roughness were 0.995649 and the root mean square error (RMSE) value was 1.01226e-06, respectively. The results obtained indicate a strong alignment between the predicted and experimental values, effectively fulfilling the proposed objective. These findings hold significant potential for practical applications in the Micro-EDM industry, particularly in the machining of Inconel alloy.

Enhancing Surface Integrity and Quality through Roller Burnishing: A Comprehensive Review of Parameters Optimization, and Applications.

Surface integrity is a critical aspect in ensuring the quality of components or products, particularly in industries such as automotive, aerospace, and machinery where superior surface finish and prolonged functionality of cylindrical parts are essential. Burnishing, a chip-less cold working finishing process, enhances the surface finish of machined workpieces through plastic deformation. This review paper delves into various aspects of the burnishing process and its impact on surface properties, drawing insights from multiple research projects. It explores topics including residual stresses, surface roughness, microhardness, cooling strategies, and modeling techniques. Key findings underscore the influence of burnishing parameters such as speed, feed rate, penetration depth, and number of passes on surface roughness and microhardness. Moreover, the process induces compressive residual strains on the workpiece surface, with their magnitude varying based on operational parameters. The choice of cooling methods, including kerosene, minimum quantity lubrication (MQL), cryogenic cooling, and hybrid cooling, significantly affects surface integrity. Various modeling approaches, such as Artificial Neural Networks (ANN), Adaptive Neuro-Fuzzy Inference Systems (ANFIS), and Response Surface Methodology (RSM), aid in predicting and optimizing surface characteristics. Despite the extensive discussion on surface roughness, microhardness, residual stresses, cooling methods, and modeling, out-of-roundness in the burnishing process remains understudied and warrants further investigation. This review contributes valuable insights for enhancing surface qualities in manufacturing applications and deepening our understanding of the burnishing process.

The Efficacy of Artificial Intelligence-Enabled Adaptive Learning Systems From 2010 to 2022 on Learner Outcomes: A Meta-Analysis

The purpose of this research study was to examine the overall effect of adaptive learning systems deployed using artificial intelligence technology across a range of relevant variables (e.g., duration, student level, etc.). Following a systematic procedure, this meta-analysis examined literature from 18 academic databases and identified N = 45 independent studies utilizing AI-enabled adaptive learning. This meta-analysis examined the overall effect of AI-enabled adaptive learning systems on students’ cognitive learning outcomes when compared with non-adaptive learning interventions and found that they have a medium to large positive effect size ( g = 0.70). The effect was significantly moderated by publication type, origin of study, student classification level, student discipline, duration, and research design. In addition, all three adaptive sources (cognitive, affective, and behavioral) and adaptive targets (navigation and assessment) were significant moderators. The type of AI used in the adaptive engine did not moderate the effects. Implications for both practice and research are provided.

Computational Methods for Predicting Key Interactions in T Cell-Mediated Adaptive Immunity.

The adaptive immune system recognizes pathogen- and cancer-specific features and is endowed with memory, enabling it to respond quickly and efficiently to repeated encounters with the same antigens. T cells play a central role in the adaptive immune system by directly targeting intracellular pathogens and helping to activate B cells to secrete antibodies. Several fundamental protein interactions-including those between major histocompatibility complex (MHC) proteins and antigen-derived peptides as well as between T cell receptors and peptide-MHC complexes-underlie the ability of T cells to recognize antigens with great precision. Computational approaches to predict these interactions are increasingly being used for medically relevant applications, including vaccine design and prediction of patient response to cancer immunotherapies. We provide computational researchers with an accessible introduction to the adaptive immune system, review computational approaches to predict the key protein interactions underlying T cell-mediated adaptive immunity, and highlight remaining challenges.

Structure and genome editing of type I-B CRISPR-Cas

Type I CRISPR-Cas systems employ multi-subunit effector Cascade and helicase-nuclease Cas3 to target and degrade foreign nucleic acids, representing the most abundant RNA-guided adaptive immune systems in prokaryotes. Their ability to cause long fragment deletions have led to increasing interests in eukaryotic genome editing. While the Cascade structures of all other six type I systems have been determined, the structure of the most evolutionarily conserved type I-B Cascade is still missing. Here, we present two cryo-EM structures of the Synechocystis sp. PCC 6714 (Syn) type I-B Cascade, revealing the molecular mechanisms that underlie RNA-directed Cascade assembly, target DNA recognition, and local conformational changes of the effector complex upon R-loop formation. Remarkably, a loop of Cas5 directly intercalated into the major groove of the PAM and facilitated PAM recognition. We further characterized the genome editing profiles of this I-B Cascade-Cas3 in human CD3+ T cells using mRNA-mediated delivery, which led to unidirectional 4.5 kb deletion in TRAC locus and achieved an editing efficiency up to 41.2%. Our study provides the structural basis for understanding target DNA recognition by type I-B Cascade and lays foundation for harnessing this system for long range genome editing in human T cells.

Characteristics and immune functions of the endogenous CRISPR-Cas systems in myxobacteria.

Serving as an adaptive immune system, clustered regularly interspaced short palindromic repeats and their associated proteins (CRISPR-Cas) empower prokaryotes to fend off the intrusion of external genetic materials. Myxobacteria are a collective of swarming Gram-stain-negative predatory bacteria distinguished by intricate multicellular social behavior. An in-depth analysis of their intrinsic CRISPR-Cas systems is beneficial for our understanding of the survival strategies employed by host cells within their environmental niches. Moreover, the experimental findings presented in this study not only suggest the robust immune functions of CRISPR-Cas in myxobacteria but also their potential applications.

HDAC6 inhibition releases HR23B to activate proteasomes, expand the tumor immunopeptidome and amplify T-cell antimyeloma activity.

Proteasomes degrade intracellular proteins to generate antigenic peptides that are recognized by the adaptive immune system and promote anticancer immunity. However, tumors subvert the antigen presentation machinery to escape immunosurveillance. We hypothesized that proteasome activation could concomitantly increase antigen abundance and diversity in multiple myeloma (MM) cells. High-throughput screens revealed that histone deacetylase 6 (HDAC6) inhibitors activated proteasomes to unmask neoantigens and amplify the tumor-specific antigenic landscape. Treatment of patient CD138+ cells with HDAC6 inhibitors significantly promoted the anti-myeloma activity of autologous CD8+ T-cells. Pharmacologic blockade and genetic ablation of the HDAC6 ubiquitin-binding domain released HR23B, which shuttles ubiquitinylated cargo to proteasomes, while silencing HDAC6 or HR23B in MM cells abolished the effect of HDAC6 inhibitors on proteasomes, antigen presentation and T-cell cytotoxicity. Taken together, our results demonstrate the paradigm-shifting translational impact of proteasome activators to expand the myeloma immunopeptidome and have revealed novel, actionable antigenic targets for T-cell-directed immunotherapy.

Adaptive control method and experimental study of cone crusher based on aggregate online detection

The size of the discharge outlet of a cone crusher directly impacts the size of the aggregate produced. However, the discharge outlet is still adjusted manually, which has a significant error and affects production efficiency. For this reason, this study proposed an adaptive control method for cone crushers based on aggregate online detection. Firstly, the aggregate image was segmented using an instance segmentation model and the anchor and structure of the model were optimised. Then, this study proposed an evaluation method for quickly and accurately assessing the overall segmentation effect of network models. By comparing the results with those before optimisation, the accuracy of the optimised network model was improved from 0.923 to 0.940. Finally, an adaptive control experiment was conducted based on the online aggregate detection results. The experimental results showed that the discharge particle size distribution of the cone crusher becomes more stable after intelligent control is added, with the variance of the proportion of cumulative gradation at 15 mm decreased from 34.3 to 14.4. These results indicated that the developed adaptive control system effectively controls the fine processing of coarse aggregates and significantly improves the quality of aggregate crushing and processing.