Self-learning Model Research Articles

Massive Education in Prison Health in Brazil: A Look Beyond the Walls

Equal access to health initiatives and services under the principles of universal and comprehensive care remains a challenge in Brazil. The realization of public health policies is further intricate when one examines the health situation of people deprived of liberty. This study showcases the “Prison System: Beyond the Walls” educational pathway, available on the Virtual Learning Environment of the Brazilian National Health System (AVASUS). The action research methodological strategy guided the pathway development, emphasizing dialogic learning. The goal was to address the need for massive training on the topic of prison health, with the model focusing on engagement through spontaneous, non-mandatory participation in the pathway courses. The pathway comprised four modules, whose educational offerings were based on the self-learning model. Students were free to choose which courses to take and in what order, as there was no prerequisite for participating in modules. Hence, students could either take all the courses or only those with which they identify their learning needs, regardless of work demands or personal interests. Structuring the pathway through action research facilitated a massive, cohesive, and continuous training process. This approach expanded knowledge and established meaningful relationships among the related topics and the key players involved: health professionals, prison officers, and people deprived of liberty. Notably, the pathway courses have surpassed the 50,000-enrollment mark, spanning all five regions of Brazil. In this context, this article presents and discusses the development of the “Prison System: Beyond the Walls” pathway, emphasizing the massive improvement of health within Brazil’s prison system and highlighting the results achieved.

Validity of facial skin analysis pore detection: A comparative analysis.

Reliable, objective measures to assess facial characteristics would aid in the assessment of many dermatological treatments. Previous work utilized an iOS application-based artificial intelligence (AI) tool compared to the "gold standard" computer-based and a physician assessment on five skin metrics (British Journal of Dermatology, 2013, 169, 474). The AI tool had superior agreement for all skin metrics except pores and subsequently underwent an algorithm update for its pore detection system. This comparative analysis assessed the performance of the updated AI tool's pore scores across all Fitzpatrick skin phototypes to determine whether the AI tool more accurately represents a dermatologist's assessment of pores. Frontal facing photographs in uniform lighting conditions were taken of each participant. Percentile scores were generated by each of the four self-learning models of the updated AI tool. The pore percentile scores generated by the original and updated AI tool were used to rate "worse" pores among participant pairs. These ratings were compared to pore assessments performed by a "gold-standard" device and a board-certified dermatologist. Compared to the original pore detection tool and the computer-based program, models A and D had the highest concordance with the physician's pore assessments for Fitzpatrick skin phototypes III-IV and V-VI, respectively. The AI tool's pores detection update was successful in its ability to accurately detect pores on all Fitzpatrick skin types, improving on the performance of the AI prior to the update. Responsibly developed AI tools that can accurately and reliably detect skin metrics across diverse Fitzpatrick skin types can facilitate dermatologic evaluation, individualize treatment, and determine treatment response.

Synergistic insights: Exploring continuous learning and explainable AI in handwritten digit recognition

Deep Neural Networks achieve outstanding results; however, their reliance on a static environment with fixed data poses challenges in dynamic scenarios where data continuously evolves. Being capable of learning, adapting, and generalizing continually in a scalable, successful, and efficient manner is crucial for the sustainable development of AI systems. The classical solution of retraining the model using both old and new data is time-consuming and expensive. Continual Learning tackles the problem of learning new data distributions without the need for retraining from scratch. Furthermore, the task of recognizing unlabeled images using previously acquired knowledge becomes challenging, particularly when the new data needs to be incrementally annotated without starting the training process from scratch.To gain a deeper understanding of how “Black Box” neural networks make decisions, it is important to visualize components inside the model that affect the error rate throughout the decision-making process. The Continual Self-Learning model on label-less historical digits yields increasingly perceptive interpretations. This paper aims to establish a literature review of the latest advances in continual learning for computer vision tasks, to articulate catastrophic forgetting using Explainable Artificial Intelligence on both split MNIST and the historical digit dataset DIDA, and to shed light on important but still understudied topics.

Applying reinforcement learning to learn best net to rip and re-route in global routing

Physical designers typically employ heuristics to solve challenging problems in global routing. However, these heuristic solutions are not adaptable to the ever-changing fabrication demands, and the experience and creativity of designers can limit their effectiveness. Reinforcement learning (RL) is an effective method to tackle sequential optimization problems due to its ability to adapt and learn through trial and error. Hence, RL can create policies that can handle complex tasks. This work presents an RL framework for global routing that incorporates a self-learning model called RL-Ripper. The primary function of RL-Ripper is to identify the best nets that need to be ripped and rerouted in order to decrease the number of total short violations. In this work, we show that the proposed RL-Ripper framework’s approach can reduce the number of short violations for ISPD 2018 benchmarks when compared to the state-of-the-art global router CUGR. Moreover, RL-Ripper reduced the total number of short violations after the first iteration of detailed routing over the baseline while being on par with the wirelength, VIA, and runtime. The proposed framework’s major impact is providing a novel learning-based approach to global routing that can be replicated for newer technologies.

Manajemen Pembelajaran Al-Qur’an di RA Al-Akhyar Jakarta Selatan

Al-Qur’an education for young children is important, especially for the development of children’s character. This research focuses on Al-Qur’an learning management at RA Al Akhyar, Cilandak, South Jakarta. The employed method is qualitative. The research data is interviewing results with the school principal as primary source data, supplemented by observations of the Al-Qur’an learning management at RA Al-Akhyar during 2022/23 academic years in March to May 2023. The research results show Al-Qur’an learning management at RA Al Akhyar is conducted systematically, involving teachers as implementers and the teacher council in policy-making. The curriculum at RA Al Akhyar is dynamic. Planning involves the modification of self-learning models through: (1) the use of storytelling methods, (2) teachers as role models to instill Al-Qur’an knowledge and values. The learning is conducted in an effective 30-minute duration and implements the planned learning model. The learning process also involves parents in activities that require guidance. Learning evaluation is conducted systematically with daily and end-of-semester records of learning. The final assessment takes into consideration not only the child's abilities but also the child's mental maturity in Quranic learning.

Design and Experimental Assessment of Real-Time Anomaly Detection Techniques for Automotive Cybersecurity.

In recent decades, an exponential surge in technological advancements has significantly transformed various aspects of daily life. The proliferation of indispensable objects such as smartphones and computers underscores the pervasive influence of technology. This trend extends to the domains of the healthcare, automotive, and industrial sectors, with the emergence of remote-operating capabilities and self-learning models. Notably, the automotive industry has integrated numerous remote access points like Wi-Fi, USB, Bluetooth, 4G/5G, and OBD-II interfaces into vehicles, amplifying the exposure of the Controller Area Network (CAN) bus to external threats. With a recognition of the susceptibility of the CAN bus to external attacks, there is an urgent need to develop robust security systems that are capable of detecting potential intrusions and malfunctions. This study aims to leverage fingerprinting techniques and neural networks on cost-effective embedded systems to construct an anomaly detection system for identifying abnormal behavior in the CAN bus. The research is structured into three parts, encompassing the application of fingerprinting techniques for data acquisition and neural network training, the design of an anomaly detection algorithm based on neural network results, and the simulation of typical CAN attack scenarios. Additionally, a thermal test was conducted to evaluate the algorithm's resilience under varying temperatures.

Deep Ontology Alignment Using a Natural Language Processing Approach for Automatic M2M Translation in IIoT.

The technical capabilities of modern Industry 4.0 and Industry 5.0 are vast and growing exponentially daily. The present-day Industrial Internet of Things (IIoT) combines manifold underlying technologies that require real-time interconnection and communication among heterogeneous devices. Smart cities are established with sophisticated designs and control of seamless machine-to-machine (M2M) communication, to optimize resources, costs, performance, and energy distributions. All the sensory devices within a building interact to maintain a sustainable climate for residents and intuitively optimize the energy distribution to optimize energy production. However, this encompasses quite a few challenges for devices that lack a compatible and interoperable design. The conventional solutions are restricted to limited domains or rely on engineers designing and deploying translators for each pair of ontologies. This is a costly process in terms of engineering effort and computational resources. An issue persists that a new device with a different ontology must be integrated into an existing IoT network. We propose a self-learning model that can determine the taxonomy of devices given their ontological meta-data and structural information. The model finds matches between two distinct ontologies using a natural language processing (NLP) approach to learn linguistic contexts. Then, by visualizing the ontological network as a knowledge graph, it is possible to learn the structure of the meta-data and understand the device's message formulation. Finally, the model can align entities of ontological graphs that are similar in context and structure.Furthermore, the model performs dynamic M2M translation without requiring extra engineering or hardware resources.

Remaining Useful Life Estimation Using Fault to Failure Transformation in Process Systems

The remaining useful life (RUL) plays a significant role in the predictive maintenance of process systems. In recent years, data-driven approaches have been used to estimate the RUL. However, without a deeper understanding of process systems and their failure mechanisms, it is challenging to estimate the RUL for process systems. This demands an approach that integrates data with mechanistic understanding. This article proposes a model to estimate the RUL of process systems in real time by monitoring the system's fault condition. The fault is modeled using real-time observed data, while the progression of fault to failure is modeled using mechanistic understanding. These steps are carried out using a novel mixed model combining machine learning-based autonomous fault diagnosis with root cause analysis and a statistical degradation model. The autonomous RUL estimation method is then developed as a self-learning model. A fault dictionary is developed to store the fault patterns and failure margin for different failure conditions. The proposed approach is benchmarked using the Tennessee Eastman process.

Collaborative planning algorithm for incomplete navigation graphs

In a complex environment, in order to improve the efficiency of multi-AUV collaborative work, this paper proposes a new collaborative hunting algorithm based on the Bionic Neural Wave Network (BNWN) algorithm. This algorithm integrates search, tracking and capture of targets by multi-AUV under the incomplete navigation graphs. A search mechanism with memory tabu that to avoid redundant search and improve global search efficiency is designed. Then a real-time redistribution method is embedded into this algorithm to ensure the optimal matching state during the tracking and capture process. Further, multi-AUV track and capture prey based on energy consumption models and self-learning models, and recast time-varying navigation maps through the recognition mechanisms, under the whole collaborative operations. Simulation results have proved that the number of multi-AUV path turns is reduced and the execution time is shortened by an average of 81%. In this way, the optimal matching state is maintained, the repeated search and partial self-locking problems are overcome, and the efficient capture of intelligent prey is realized.

Developing a machine learning algorithm to predict probability of retear and functional outcomes in patients undergoing rotator cuff repair surgery: protocol for a retrospective, multicentre study

IntroductionThe effectiveness of rotator cuff tear repair surgery is influenced by multiple patient-related, pathology-centred and technical factors, which is thought to contribute to the reported retear rates between 17% and...

Low-Resource Neural Machine Translation Improvement Using Source-Side Monolingual Data

Despite the many proposals to solve the neural machine translation (NMT) problem of low-resource languages, it continues to be difficult. The issue becomes even more complicated when few resources cover only a single domain. In this paper, we discuss the applicability of a source-side monolingual dataset of low-resource languages to improve the NMT system for such languages. In our experiments, we used Wolaytta–English translation as a low-resource language. We discuss the use of self-learning and fine-tuning approaches to improve the NMT system for Wolaytta–English translation using both authentic and synthetic datasets. The self-learning approach showed +2.7 and +2.4 BLEU score improvements for Wolaytta–English and English–Wolaytta translations, respectively, over the best-performing baseline model. Further fine-tuning the best-performing self-learning model showed +1.2 and +0.6 BLEU score improvements for Wolaytta–English and English–Wolaytta translations, respectively. We reflect on our contributions and plan for the future of this difficult field of study.

Lateral semi-trailer truck control using a parameter self-learning MPC method in urban environment

This paper analyzes the lateral control technology of autonomous semi-trailer trucks. Existing researches on the lateral control algorithm of semi-trailer trucks focus on making the head-truck or trailer follow a track well while ignoring the motion characteristics during the turning process, leading to specific security issues. Meanwhile, it is difficult to cope with the complex and uncertain factors influencing lateral control effect, such as the curvature of the desired trajectory, the load, and the velocity of the semi-trailer truck. This paper proposes a parametric self-learning model predictive control (MPC) based on the Proximal Policy Optimization of One Step (OSPPO) method to solve these problems. After modeling the kinematics of the semi-trailer truck, a lateral motion controller for the relationship between the head-truck and trailer based on the MPC method is established. The traditional MPC method has difficulty in adapting to the changeable influencing factors. Thus, a deep reinforcement learning algorithm named OSPPO is introduced to improve the flexibility of the MPC method. OSPPO establishes the nonlinear mapping relationship between the critical parameter of the MPC method and the factors influencing control effect by self-learning, avoiding a large amount of labeled data for training. In simulations, Trucksim and Matlab were used to conduct co-simulation to verify the usefulness of the control method. The method was implemented on an autonomous semi-trailer truck for many outdoor scenes. The actual experimental results showed the validity and advantages of the proposed method.

Variational Autoencoders-BasedSelf-Learning Model for Tumor Identification and Impact Analysis from 2-D MRI Images.

Over the past few years, a tremendous change has occurred in computer-aided diagnosis (CAD) technology. The evolution of numerous medical imaging techniques has enhanced the accuracy of the preliminary analysis of several diseases. Magnetic resonance imaging (MRI) is a prevalent technology extensively used in evaluating the progress of the spread of malignant tissues or abnormalities in the human body. This article aims to automate a computationally efficient mechanism that can accurately identify the tumor from MRI images and can analyze the impact of the tumor. The proposed model is robust enough to classify the tumors with minimal training data. The generative variational autoencoder models are efficient in reconstructing the images identical to the original images, which are used in adequately training the model. The proposed self-learning algorithm can learn from the insights from the autogenerated images and the original images. Incorporating long short-term memory (LSTM) is faster processing of the high dimensional imaging data, making the radiologist's task and the practitioners more comfortable assessing the tumor's progress. Self-learning models need comparatively less data for the training, and the models are more resource efficient than the various state-of-art models. The efficiency of the proposed model has been assessed using various benchmark metrics, and the obtained results have exhibited an accuracy of 89.7%. The analysis of the progress of tumor growth is presented in the current study. The obtained accuracy is not pleasing in the healthcare domain, yet the model is reasonably fair in dealing with a smaller size dataset by making use of an image generation mechanism. The study would outline the role of an autoencoder in self-learning models. Future technologies may include sturdy feature engineering models and optimized activation functions that would yield a better result.

Generative Model of Autoencoders Self-Learning on Images Represented by Count Samples

Generative Model of Autoencoders Self-Learning on Images Represented by Count Samples

An ensemble dynamic self-learning model for multiscale carbon price forecasting

Precise carbon price forecasting can provide decision support for policy-makers and investors. However, due to the high non-stationarity and nonlinearity of carbon price series, it is difficult to get accurate forecasting results under volatile situations. To accommodate different scenarios, this paper proposes a dynamic self-learning integrating forecasting model to forecast the carbon price by considering external impact factors. The multi-dimensional time series is initially decomposed into different intrinsic mode functions simultaneously by the noise-assisted multivariate empirical mode decomposition method. After reconstructing the decomposed series into high-frequency, low-frequency, and trend modules, the extreme learning machine optimized by the cosine-based whale optimization algorithm is proposed to predict the carbon price. The dynamic relationships between the carbon price and impact factors are simulated by the sliding window structure, which improves the adaptability of the proposed model. The high prediction accuracy under different situations including extreme scenarios demonstrates the stability of the proposed model. A self-learning algorithm, which can automatically learn the evolving model structure and update model parameters, is designed to alleviate the underfitting/overfitting problem. The comparison results with existing models indicate the superiority of the proposed model.

Acceptability and feasibility of the World Health Organization's Caregiver Skills Training Programme (WHO CST) delivered via eLearning, videoconferencing, and in-person hybrid modalities in Hong Kong.

BackgroundLocal children with developmental disabilities were deprived of learning opportunities due to recent social and health incidents, resulting in elevating challenging behaviors and familial conflicts. This study explored the acceptability and feasibility of the World Health Organization's Caregiver Skills Training Programme (WHO CST) in alternative delivery modes under new normal and post COVID-19 period.MethodCST was delivered via eLearning (EL), videoconferencing (VC), and in-person hybrid (IP) modes to 34 parent-child dyads, being randomly assigned to modes of asynchronous non-interfering EL (n = 9), synchronous with online coaching VC (n = 7), synchronous with in-person coaching IP (n = 9) and Wait-list Control WLC (n = 9). Data from two standardized scales of General Health Questionnaire (GHQ-12) and Strengths and Difficulties Questionnaire (SDQ), and Post-session and Home Visit Feedback Form by Caregivers that included both structured and open-ended questions were collected before and after intervention. Both quantitative and qualitative approaches were used in studying the collected data.ResultsHigh levels of acceptability and feasibility of the training programme were supported by ratings on comprehensiveness and relevance, agreement with their personal values, duration, and usefulness. IP and VC groups yielded more positive changes than EL and WLC groups with 3, 16, 13, and −3% in General Health Questionnaire (GHQ-12), −13, −15, −6 and 0% in Difficulties-total, and 36.5, 35.5, 5.8 and 2.4% in Prosocial Scale at Strengths and Difficulties Questionnaire (SDQ) for EL, VC, IP, and WLC groups respectively from baseline to 12 weeks after intervention. Results from two standardized scales echoed with qualitative observations that the programme helped improve caregivers' well-being, child's communication, and behaviors across intervention groups.ConclusionsCurrent findings revealed that CST delivered in three alternative modes were acceptable and feasible, and yielded positive impacts toward both caregivers and children. In-person coaching, and skill-practicing sessions were effective in mitigating child's challenging behaviors while personal interaction, either face-to-face or virtual, is a significant factor in uplifting caregivers' well-being, whereas the self-learning model was appreciated by the busy caregivers. In clinical practice, needs and goals of families and the constraints of remote interventions at the settings should be balanced.

A Numerical Algorithm for Self-Learning Model Predictive Control in Servo Systems

Model predictive control (MPC) is one of the most effective methods of dealing with constrained control problems. Nevertheless, the uncertainty of the control system poses many problems in its performance optimization. For high-precision servo systems, friction is typically the main factor in uncertainty affecting the accuracy of the system. Our work focuses on stochastic systems with unknown parameters and proposes a model predictive control strategy with machine learning characteristics that utilizes pre-estimated information to reduce uncertainty. Within this model, the parameters are obtained using the estimator. The uncertainty caused by the parameter estimation error in the system is parameterized, serving as a learning control component to reduce future uncertainty. Then, the estimated parameters and the current state of the system are used to predict the future p-step state. The control sequence is calculated under the MPC’s rolling optimization mechanism. After the system output is obtained, the new parameter value at the next moment is re-estimated. Finally, MPC is carried out to realize the dual rolling optimization mechanism. In general, the proposed strategy optimizes the control objective while reducing the system uncertainty of the future parameter and achieving better system performance. Simulation results demonstrate the effectiveness of the algorithm.

Development of a Novel Object Detection System Based on Synthetic Data Generated from Unreal Game Engine

This paper presents a novel approach to training a real-world object detection system based on synthetic data utilizing state-of-the-art technologies. Training an object detection system can be challenging and time-consuming as machine learning requires substantial volumes of training data with associated metadata. Synthetic data can solve this by providing unlimited desired training data with automatic generation. However, the main challenge is creating a balanced dataset that closes the reality gap and generalizes well when deployed in the real world. A state-of-the-art game engine, Unreal Engine 4, was used to approach the challenge of generating a photorealistic dataset for deep learning model training. In addition, a comprehensive domain randomized environment was implemented to create a robust dataset that generalizes the training data well. The randomized environment was reinforced by adding high-dynamic-range image scenes. Finally, a modern neural network was used to train the object detection system, providing a robust framework for an adaptive and self-learning model. The final models were deployed in simulation and in the real world to evaluate the training. The results of this study show that it is possible to train a real-world object detection system on synthetic data. However, the models showcase a lot of potential for improvements regarding the stability and confidence of the inference results. In addition, the paper provides valuable insight into how the number of assets and training data influence the resulting model.

Static elastic modulus of rocks predicted through regression models and Artificial Neural Network

The statistical analysis of the main physical, mechanical and ultrasonic features of various sedimentary rock samples (limestone, calcarenite, marly calcisiltite and sandstone) is presented with the aim of finding reliable models to predict the static elastic modulus from known properties. Based on the relevance of the static elastic modulus Estat in engineering geological applications and on the increasing need of non-destructive procedures for its estimation, this paper attempts to establish prediction models by means of single and multiple regression approaches and Artificial Neural Network (ANN). While in the first two cases rock properties are plotted against each other to study their mutual dependence, even combining more than one independent variable, the Artificial Neural Network approach allows a self-learning model capable of predicting a target value from known input variables to be built. Results show that the static elastic modulus of tested rocks is mainly related to the rock Uniaxial Compressive Strength and it varies with respect to the seismic wave velocities (both compressional Vp and longitudinal Vs) and, consequently, to the dynamic elastic modulus Edyn calculated by ultrasonic tests. In particular, there is a numerical difference between the static and dynamic values of the elastic modulus, as the dynamic ones are generally higher than the corresponding static values. Satisfactory prediction equations were found by multiple regressions involving Vp, Edyn and the rock bulk density, thus providing useful statistical laws for the indirect calculation of Estat. The results arising from the ANN models are used herein to draft Estat prediction graphs from known variables, in the perspective of a practical utility for the quick and non-destructive estimation of the static elastic property of tested sedimentary rocks.

Automatic gear shift strategy for manual transmission of mine truck based on Bi-LSTM network

As mine trucks face severe and changeable driving conditions in mining area, it is important to formulate a general automatic gear shift strategy for reducing the intensity and difficulty of the work. Therefore, gear shift strategy performs a significant role in automatic transmission studies. However, it is difficult to devise an automatic shift strategy due to the fact that it relies on tremendous historical experiment data which is the highest classified commercial secret of the automobile manufacture. Since the artificial neural network can build self-learning model by inputting multiple parameters, adjusting weights and automatically outputting results, many automatic gear shift strategy studies are reported based on it. However, the gear shift prediction accuracy by traditional neural network is limited due to the non-existence of feedback connection, lacking of associative memory function and missing implicit connection between continuous data. Hence, this paper proposes a novel method based on Bi-directional Long Short-Term Memory (Bi-LSTM) network, which controls gear shifting with nine parameters and updates the traditional mine truck transmission system from manual to automatic. Truck status data while manual driving are captured through the Controller Area Network-bus (CAN-bus) and converted to the proper sequence data format. Some sequence data are used to train the Bi-LSTM network for gear prediction, while the rest is used for verification. In order to evaluate the proposed machine learning model performance, it is compared with the Back Propagation Neural Network (BPNN), Recurrent Neural Network (RNN) and Long-Short Term Memory (LSTM) networks with two and nine parameters, respectively. The experimental results demonstrate that machine learning networks with nine parameters reach higher gear shifting prediction accuracy than those with dual-parameter, except BPNN. In addition, the highest prediction accuracy is 95.8 %, which is achieved by the Bi-LSTM network with nine parameters. This proves that Bi-LSTM network outperforms the traditional neural networks, such as BPNN with 81.5 %, and other recurrent neural networks, such as RNN and LSTM with 87 % and 91.5 %, respectively. As for the average time consumption, the Bi-LSTM network spends 1.86 ms per prediction calculation which is longer than LSTM and RNN respectively having 1.58 ms and 1.06 ms. Thus, it still fits the requirement of real-time processing on mine truck industry. In general, the Bi-LSTM network shows its advantage in processing sequential data within limited time, and has the potential to be widely applied in the field of prediction based on data with temporal characteristic.