Learning Approach Research Articles

Rethinking how people learn: A holistic framework for effective learning design

Learning is an integral part of being human. How people learn has long been discussed, revealed in many learning theories, investigated in numerous studies, and demonstrated in extensive practices. The goal of this article is to rethink how people learn from four fundamental perspectives, that is, learning by interaction with content (C), learning by interaction with other people (O), learning by interaction with self (S), and learning by interaction with tasks or practices (T), so-called COST model. This framework offers a high-level view of human learning and the role of technology in human learning. Moreover, it serves as a guide for effective design of learning experiences, learning environments, and learning approaches, where technology has become a crucial component.

Adaptive fault detection for lithium-ion battery combining physical model-based observer and BiLSTMNN learning approach

Lithium-ion batteries possess one of the best energy-weight ratios, while safety and reliability are critical issues for its continued operation. Due to extreme fast-charging, huge application sizes and variable usage environment, the risk of soft battery faults, including soft internal short circuit (SISC), is increased and may evolve into severe accidents. Therefore, accurate early detection of lithium ion battery fault is imperative to guarantee the battery performance. Motivated by this fact, we proposed a real time fault detection framework for battery soft faults. Based on the Equivalent Circuit Model (ECM) and coupling thermal model, Extended Kalman Filter (EKF) observer is used for reliable monitoring of battery voltage and surface temperature. Inspired by uncertainty interference during the observation process, we explore an observed-based learning approach that combine physical model-based observer and Bidirectional Long Short-Term Memory neural networks (BiLSTMNN) to empower the observer with the ability to learn the uncertainties, which is efficient in distinguishing soft faults information and uncertainties from residual. Furthermore, the memory characteristics of BiLSTMNN are considered to improve the robustness of the detection system, including comprehensive training data and optimized input features. Finally, through the comparative analysis and the robustness evaluation experiments including various driving cycles and internal short circuits (ISC) faults test verity the good performance of the proposed method.

Negation handling for sentiment analysis task: approaches and performance analysis

Negation plays an essential role in sentiment analysis within natural language processing (NLP). Its integration involves two key aspects: identifying the scope of negation and incorporating this information into the sentiment model. Before delving into scope detection, the specific negation cue must be identified, with explicit and implicit negation cues being the two main types. Various methodologies, such as rule-based, machine learning, and hybrid approaches, address the negation scope detection challenge. Strategies for leveraging negation information in sentiment models encompass heuristic polarity modification, feature space augmentation, end-to-end approach, and hierarchical multi-task learning. Notably, there is a need for more studies addressing implicit negation cue detection, even within the state-of-the-art bidirectional encoder representation for transformers (BERT) approach. Some studies have employed reinforcement learning and hybrid techniques to address the implicit negation problem. Further exploration, particularly through a hybrid and multi-task learning approach, is warranted to make potential contributions to the nuanced challenges of handling negation in sentiment analysis, especially in complex sentence structures.

Aircraft routing using dynamic programming and reinforcement learning: A customer-centric approach

Over the past few decades, the airline industry has evolved into a sophisticated business, with conventional objectives such as operational efficiency, effective fleet assignment, flight punctuality, and equitable crew scheduling being the primary focus. Nevertheless, given the growing accessibility to customer feedback (either through public forums or private data), it is crucial to make business decisions that cater to the customers’ requirements in addition to conventional goals. Within aircraft operations, there is a series of decisions to be made, such as flight scheduling, fleet assignment, aircraft routing, and crew scheduling. After designating a fleet type to each flight leg, airlines ascertain the sequence of flight legs, or strings, to be operated by individual aircrafts, while complying with obligatory maintenance checks. This constitutes the aircraft routing problem.We introduce a novel approach to aircraft routing problem, by infusing traditional methodologies with insightful customer feedback to create a more responsive and customer-centric model. Unlike conventional techniques of constructing routes, in our work, we construct maintenance feasible strings. Traditional aircraft routing methodologies typically concentrate on minimizing propagated delay. However, by integrating customer feedback, more effective prioritization to minimize delays for flight legs with higher customer dissatisfaction can be achieved. We propose an optimization model that strives to minimize propagated delay while prioritizing flights based on customer feedback, thus resulting in customer-aware routes. To solve the proposed optimization model, we design an iterative alternating optimization scheme where feasible strings are first constructed from the pool of available flight legs using a string generation procedure, followed by solving an integer linear optimization problem over only the feasible set of strings obtained in the first stage. To construct feasible strings of flights at each iteration, we use two different methods. The first method is based on a dynamic programming approach which is useful for mean delay information. To handle practical cases where delays are uncertain, we propose a reinforcement learning (RL) approach to construct the strings. We provide experiments of our proposed methods on synthetic and real data sets and demonstrate the effectiveness of our RL model in comparison to dynamic programming based model. Our experiments affirm that routing decisions informed by customer feedback prioritize minimizing delays for specific flight legs, resulting in routes that are inherently customer-friendly.

State-of-the-RNArt: benchmarking current methods for RNA 3D structure prediction.

RNAs are essential molecules involved in numerous biological functions. Understanding RNA functions requires the knowledge of their 3D structures. Computational methods have been developed for over two decades to predict the 3D conformations from RNA sequences. These computational methods have been widely used and are usually categorised as either ab initio or template-based. The performances remain to be improved. Recently, the rise of deep learning has changed the sight of novel approaches. Deep learning methods are promising, but their adaptation to RNA 3D structure prediction remains difficult. In this paper, we give a brief review of the ab initio, template-based and novel deep learning approaches. We highlight the different available tools and provide a benchmark on nine methods using the RNA-Puzzles dataset. We provide an online dashboard that shows the predictions made by benchmarked methods, freely available on the EvryRNA platform: https://evryrna.ibisc.univ-evry.fr/evryrna/state_of_the_rnart/.

Framework for multiple person identification using YOLOv8 detector: a transfer learning approach

Video surveillance extensively uses person detection and tracking technology based on video. The majority of person detection and classification techniques currently in use encounter challenges in video sequences brought on by occlusion, ambient lighting, and variations in human facial position. This paper proposed an effective person identification and classification system based on deep learning, which comprises a you only look once at version 8 (YOLOv8) detection and classification model, to classify human faces in video sequences accurately. This work proposes a new staff-detection and classification (S-DEC) dataset for comprehensive performance evaluation. visual tracker benchmark (VTB) standard database is used for performance comparison with the proposed S-DEC dataset. The proposed technique achieved 98.67% precision accuracy. For the S-DEC dataset, the system gave 94.67% accuracy in identifying facial images from a video sequence of 38 people addressing the pose variation occlusion challenge. Earlier methods used to provide approximately 85% to 90% results taking more execution time. Many existing techniques were successful in detecting people only-identification of the detected person has been done in limited papers. The proposed method uses the cross-stage partial connections (CSPDarknet53) model, integrated with YOLOv8, to achieve faster results. The proposed framework took 35 minutes to train a deep learning model. A testing time of 2 minutes ensured that the proposed framework outplayed other existing methodologies and successfully identified extra information about the detected person.

Smart irrigation with crop recommendation using machine learning approach

Increasing crop yield with sustainable growth is the primary requirement for farmers with a growing population. Effective management and conservation of depleting natural resources is a priority task. Decrease in manpower due to migrating population has forced automation in agriculture. In this work, an automatic water irrigation and an effective crop recommendation system is proposed. Gypsum blocks based soil sensor is used to measure dielectric permittivity associated with the tested soil. The water-potential present in soil, along with potassium (K), nitrogen (N), phosphorus (P), potential of hydrogen (pH) helps to quantify the soil nutrients available and the suitable crop that can be considered for harvesting in a specified demography and environment. Sensory data indicating soil quality obtained is used to recommend crops by utilizing machine learning approaches. Telegram application is linked to the recommendation model to assist decision making and to ensure farmer-friendliness by sending notifications periodically.

Redefining brain tumor segmentation: a cutting-edge convolutional neural networks-transfer learning approach

Medical image analysis has witnessed significant advancements with deep learning techniques. In the domain of brain tumor segmentation, the ability to precisely delineate tumor boundaries from magnetic resonance imaging (MRI) scans holds profound implications for diagnosis. This study presents an ensemble convolutional neural network (CNN) with transfer learning, integrating the state-of-the-art Deeplabv3+ architecture with the ResNet18 backbone. The model is rigorously trained and evaluated, exhibiting remarkable performance metrics, including an impressive global accuracy of 99.286%, a high-class accuracy of 82.191%, a mean intersection over union (IoU) of 79.900%, a weighted IoU of 98.620%, and a Boundary F1 (BF) score of 83.303%. Notably, a detailed comparative analysis with existing methods showcases the superiority of our proposed model. These findings underscore the model’s competence in precise brain tumor localization, underscoring its potential to revolutionize medical image analysis and enhance healthcare outcomes. This research paves the way for future exploration and optimization of advanced CNN models in medical imaging, emphasizing addressing false positives and resource efficiency.

Machine learning approaches for predicting postpartum hemorrhage: a comprehensive systematic literature review

Postpartum hemorrhage (PPH) represents a significant threat to maternal health, particularly in developing countries, where it remains a leading cause of maternal mortality. Unfortunately, only 60% of pregnant women at high risk for PPH are identified, leaving 40% undetected until they experience PPH. To address this critical issue and ensure timely intervention, leveraging rapidly advancing technology with machine learning (ML) methodologies for maternal health prediction is imperative. This review synthesizes findings from 43 selected research articles, highlighting the predominant ML techniques employed in PPH prediction. Among these, logistic regression (LR), extreme gradient boosting (XGB), random forest (RF), and decision tree (DT) emerge as the most frequently utilized methods. By harnessing the power of ML, we aim to foster technological advancements in the healthcare sector, with a particular focus on maternal health and ultimately contribute to the reduction of maternal mortality rates worldwide.

Leveraging physics-based and explainable machine learning approaches to quantify the relative contributions of rain and air pollutants to wet deposition

A quantitative understanding of the roles of rainfall and pollutant concentrations in wet deposition is important because they critically influence terrestrial and aquatic ecosystems. However, their relative contributions to wet deposition, which vary across regions, have not yet been identified. We propose two methods that quantitatively separate the contributions of rain and pollutant concentrations to wet deposition: one is based on simplified equations describing the wet scavenging of pollutants and the other is based on random forest models employing SHapley Additive exPlanations. Three-dimensional long-term air quality simulations from 2003 to 2019 are used as inputs for both the physics-based and machine learning models. Remarkably, the results drawn from the explainable machine learning model are consistent with those from the physics-based approach: overall, rain is a more important limiting factor than pollutant concentrations and the relative contribution of rain is larger than that of pollutants by up to a factor of 3–4 in polluted regions. In polluted regions, pollutant concentrations can remain relatively high even in the presence of precipitation owing to continuous and intense emissions; therefore, wet deposition is limited by rainfall. The contribution of rainfall is larger by 1.5–2.5 than that of pollutant concentrations in regions even with low emissions and this considerably large role of rain suggests that regional or transboundary pollutant transport plays a key role in modulating wet deposition. However, in very remote regions, once the rainfall amount exceeds a certain value, rainfall no longer contributes to increasing wet deposition because atmospheric pollutants are readily removed by rain. So, the contributions of the two factors are comparable in pristine regions. Our results can serve as a basis for explaining interannual variations in wet deposition and for future projections of wet deposition under emission control plans and climate change scenarios across regions.

A multimodal machine learning approach to generate news articles from geo-tagged images

Classical machine learning algorithms typically operate on unimodal data and hence it can analyze and make predictions based on data from a single source (modality). Whereas multimodal machine learning algorithm, learns from information across multiple modalities, such as text, images, audio, and sensor data. The paper leverages the functionalities of multimodal machine learning (ML) application for generating text from images. The proposed work presents an innovative multimodal algorithm that automates the creation of news articles from geo-tagged images by leveraging cutting-edge developments in machine learning, image captioning, and advanced text generation technologies. Employing a multimodal approach that integrates machine learning and transformer algorithms, such as visual geometry group network16 (VGGNet16), convolutional neural network (CNN) and a long short-term memory (LSTM) based system, the algorithm initiates by extracting the location from exchangeable image file format (Exif) data from the image. The features are extracted from the image and corresponding news headline is generated. The headlines are used for generating a comprehensive article with contemporary large language model (LLM). Further, the algorithm generates the news article big-science large open-science open-access multilingual language model (BLOOM). The algorithm was tested on real time photographs as well as images from the internet. In both the cases the news articles generated were validated with ROUGE and BULE score. The proposed work is found to be successful attempt in journalism field.

Resource-efficient federated learning over IoAT for rice leaf disease classification

Rice is an important staple food in Asia. It is produced and consumed in large quantities. It contributes to 15% of protein intake and 21% of total per capita energy intake in the region, underscoring its important role as a primary global food source. Conversely, rice plants are heavily affected by bacterial, fungal and other microbial diseases, resulting in reduced plant health and crop yields and posing a major challenge for rice farmers. Manual diagnosis of these diseases is particularly problematic in regions with a shortage of agricultural experts. Farmers with insufficient experience sometimes incorrectly identify these diseases by hand. Recent advances in deep learning (DL) models offer a promising solution through automatic image recognition systems that can be very helpful in accurately identifying these diseases. This manuscript presents a resource-efficient federated learning IoAT (Internet of Agriculture Things) approach for rice leaf disease classification. This approach incorporates two key strategies, namely federated transfer learning and feature extraction, and evaluates their performance on various metrics such as accuracy, loss, precision, recall, AUC, and resource-related parameters such as GPU memory consumption, virtual memory, CPU and GPU process utilization. The dataset used in the study includes 5932 images of rice leaf diseases categorized as bacterial leaf blight, blast, brown spot and tungro. The research investigates the application of federated transfer learning and federated feature extraction techniques to classify rice leaf disease images. It performs a comparative analysis of their performance and resource utilization. EfficientNetB3, with an impressive validation accuracy of 99%, is identified as the base model for the federated learning (FL) environment. Furthermore, we implement FL using both transfer learning and feature extraction methods and compare their results on a number of performance and resource related parameters. It is shown that the federated feature extraction approach has lower GPU memory and process utilization compared to the federated transfer learning approach and is thus more resource efficient. Extracting features before model training results in a lightweight model, which is especially beneficial for FL, IoAT devices that require efficient execution on edge devices. Therefore, the proposed approach is presented as a lightweight, privacy-friendly and resource-efficient solution for rice leaf disease classification.

Sustainability insights on learning-based approaches in precision agriculture in internet-of-things

Precision agriculture (PA) is meant to automate the complete agricultural processes with the sole target of enhanced crop yield with reduced cost of operation. However, deployment of PA in internet of things (IoT) based architecture demands solutions towards addressing various challenges where most are related to proper and precise predictive management of agricultural data. In this perspective, it is noted that learning-based approaches have made some contributory success towards addressing different variants of issues in PA; however, such methods suffer from certain loopholes, primarily related to the non-inclusion of practical constraints of IoT infrastructure in PA and lack of emphasis towards bridging the trade-off between higher accuracy and computational burden that is eventually associated with this. This paper contributes towards highlighting the strengths and weaknesses of recent learning approaches and contributes towards novel findings.

Power Control of Hybrid Grid-Connected Renewable Energy System using Machine Learning

This article addresses the crucial challenge of maintaining a reliable power supply in integrated electric systems that combine solar power and energy storage. It focuses on optimizing key parameters for remote photovoltaic, wind, and battery energy storage within the grid, with a primary goal of sustaining 24-hour load demand. The study introduces a comprehensive approach that integrates fuel price dynamics and battery depletion costs into the optimization model for hybrid grids. This approach seeks a delicate balance between sustainable energy usage and cost-effectiveness. To achieve this balance, the research employs genetic algorithms, a robust computational technique. These algorithms play a vital role in calculating discharge-charge cycles and assessing battery health, addressing concerns about battery longevity and performance. Additionally, the study explores a machine learning framework, specifically nested learning, to further enhance the optimization process. Nested learning allows for the development of a sophisticated target function that considers various grid operation parameters and constraints. This machine learning approach enhances the system's ability to dynamically monitor power consumption and generation. Within this machine learning framework, the TD Lambda learning algorithm takes a prominent role in identifying the optimal function. This algorithm is well-known for its efficiency, particularly in non-Markovian scenarios. Its quick convergence enhances the overall performance of the hybrid grid, ensuring efficiency in changing conditions. The research also evaluates the impact of weight factors on various aspects, including battery charging status, algorithmic decisions, and their consequences on optimal energy pricing within the hybrid grid. This comprehensive assessment deepens the understanding of how these weight factors influence grid performance and economic viability.

Enhancing Driver Safety: Deep Learning Approach for Drowsiness Detection and Accident Prevention

Road safety concerns have spurred the development of innovative technologies aimed at reducing accidents, particularly those caused by driver fatigue. The main scope of the driver drowsiness detection system is to minimize road accidents caused by fatigue or sleepiness of drivers. This system leverages deep learning and computer vision, employing a Raspberry Pi camera to monitor facial expressions, including yawning, to assess the driver's alertness. Upon detecting signs of drowsiness, such as prolonged eye closure or altered facial expressions, the system triggers a buzzer alert by analyzing the Eye Aspect Ratio (EAR) and Mouth Aspect Ratio (MAR) ratios. Integrated within an embedded system, it utilizes frontal face detection algorithms and Haar cascade classifiers to localize key facial features in real-time, facilitating efficient monitoring for signs of fatigue or distraction. Additionally, it issues vibration alerts if the seat belt is not fastened. The seat belt remainder will be helpful to ensure the safety of the driver, reduces accidents and monitors the driver's heart rate, triggering a PANIC message if irregularities are detected. At traffic signals, the system automatically reduces vehicle speed using a vibration motor. In the unfortunate event of an accident, the system initiates speed reduction and promptly notifies registered contacts through the Blynk application, thereby significantly reducing accidents caused by drowsy driving.

Expressway Traffic Incident Detection Using a Deep Learning Approach Based on Spatiotemporal Features with Multilevel Fusion

Expressway Traffic Incident Detection Using a Deep Learning Approach Based on Spatiotemporal Features with Multilevel Fusion

Enhanced Deep Learning Approach for Multi-class Segmentation of Carotid Bifurcation in Digital Subtraction Angiography Roadmaps

Enhanced Deep Learning Approach for Multi-class Segmentation of Carotid Bifurcation in Digital Subtraction Angiography Roadmaps

Distributed denial of service attacks classification system using features selection and ensemble techniques

Distributed denial-of-service (DDoS) attacks are expanding threat to online services and websites. These attacks overwhelm targets with traffic from multiple sources to exhaust resources and make services unavailable. The frequency of DDoS attacks exhibits an ongoing upward trajectory over time. This persistent escalation highlights the need for effective countermeasures. While machine learning approaches have been extensively investigated for binary classification of DDoS attacks, multi-class classification has received comparatively less examination in the literature despite its greater practical utility. In this paper, we propose an intrusion detection system for detecting and classifying DDoS attacks, based on two main axes: feature selection for selecting the best relevant features and ensemble learning technique for improving performance by combining weak learners. The proposed model has been trained and evaluated on the CICDDoS2019 dataset. Experimental evaluation demonstrates improved performance using a subset of 16 relevant features identified, with a test accuracy of 82.35% attained for discriminating between the 12 classes represented in the dataset. By aggregating attacks sharing common characteristics resulting in 7 classes, the approach achieves surpassing 97% accuracy. Additionally, a binary classification delineating benign and DDoS attacks attain 99.90% accuracy.

Heat stroke prediction: a perspective from the internet of things and machine learning approach

With the increasing occurrence of heat-related illnesses due to rising temperatures worldwide, there is a need for effective detection and prediction systems to mitigate the risks. Heat stroke, a life-threatening condition occurs when the body’s temperature exceeds 104 degrees Fahrenheit (40 degrees Celsius). It can happen due to prolonged exposure to temperatures. When the body struggles to cool itself down adequately. The internet of things (IoT) and machine learning (ML) are two advancing technologies that have the potential to revolutionize industries and enhance our lives in numerous ways. Currently, monitoring devices are primarily used to diagnose when individuals suffering from heatstroke are at the location. This paper delves into the exploration of utilizing the IoT and ML algorithms to predict heat strokes. It reviews existing studies in this field, focusing on how IoT has been deployed and the application of machine learning techniques. The research aims to define the integration of IoT devices and ML algorithms that has a great potential to detect and predict heat-related illnesses such as heat stroke at an early stage.

A real-time deep learning approach for inferring intracranial pressure from routinely measured extracranial waveforms in the intensive care unit

A real-time deep learning approach for inferring intracranial pressure from routinely measured extracranial waveforms in the intensive care unit