Traditional Learning Approaches Research Articles

Exploring copper corrosion inhibition in value added neem biodiesel: Evaluation by traditional and machine learning approaches

Neem seed biodiesel is an eco-friendly fuel, however, to minimize its corrosion on copper metal used in engine parts, ethyl acetate extract of Syzygium aromaticum flowers was tested as an inhibitor. Copper corrosion evaluation in biodiesel revealed an inhibition efficiency of 97.96 %. Corrosion at the operating temperature of diesel engine was predicted by machine learning - quadratic regression. Thermodynamics and spectral evaluations bring to light the adsorption of inhibitors on the metal surface for inhibiting the corrosion of copper metal immersed in biodiesel. Surface morphological studies revealed the protective nature of the copper metal corrosion. Quadratic Surface Vector Machine (SVM) algorithm classified the corrosion classes as low, medium, and high with an overall accuracy value of 94.6 %. The percentage of non-corroded area of copper metal specimen in biodiesel medium with the said inhibitor as estimated with quadratic SVM is 97.193 %. The research findings suggest that the ethyl acetate extract of S. aromaticum flowers has the potential to perform as an effective corrosion inhibitor for copper in biodiesel medium and revealed the potential of quadratic SVM machine learning algorithm in corrosion estimation and classification.

Unsupervised Transfer Learning Across Different Data Modalities for Bearing's Speed Identification

Recent advancements in transfer learning have revolutionized predictive maintenance, enabling cross-domain generalization for components with varying characteristics and operating under different conditions. While traditional transfer learning approaches require labeled data in both source and target domains, unsupervised transfer learning strives for a more cost-effective alternative for which only labels are available in the source domain. This study investigates adversarial transfer learning between two different sensor modalities: vibration and acoustic. The goal is to enable bearing monitoring using microphones, which are, in general terms, cheaper and easier to deploy than vibration sensors; and without the need to label data in the target domain. The research goal is to identify the operating speed of a bearing testbed. The source domain data correspond to vibration measurements taken from an attached sensor, while the target domain uses a microphone array at distance. Artificial Neural Networks are used as the base architecture. Transferability is assessed with two unsupervised adversarial learning techniques: gradient reversal and deep correlation alignment. Their performance is compared to traditional supervised transfer learning via fine-tuning. Experimental results demonstrate that gradient reversal outperforms deep correlation alignment and is able to achieve results similar to those obtained with supervised transfer learning. These findings highlight the feasibility of speed identification using a microphone array and establish a baseline for future condition monitoring research with such sensors. Conclusion. These findings highlight the feasibility of speed identification using a microphone array and establish a baseline for future condition monitoring research with such sensors.

Application of the Self Organized Learning Environment Learning Model in Mathematics Learning Arithmetic Sequence Material

The objective of this study is to analyze the disparities in the mean learning achievements between students who utilize the SOLE learning model and those who employ the direct learning model for arithmetic sequence material at Tondano State Vocational School. The research employed a quasi-experimental design with a Posttest Only Control Group Design. The participants in this study consisted of students from class X OTKP 1, who were assigned to the experimental group, and students from class X OTKP 2, who were assigned to the control group. The data gathering method involved administering a technical learning outcomes test at the conclusion of the class (Posttest). The data analysis technique employed in this research utilizes tests on data that has undergone normality and homogeneity testing. The t test calculations yielded a t_count value of 2.736, which exceeded the critical t_table value of 1.685. Therefore, the null hypothesis (H_0) was rejected, and the alternative hypothesis (H_1) was accepted. Therefore, it can be inferred that there is a disparity in the mean learning results of students in the Self-Organized Learning Environment (SOLE) instructional approach and the traditional direct learning approach when it comes to arithmetic sequence material.

DeGAN - Decomposition-based unified anomaly detection in static networks

Graph anomaly detection aims to identify anomalous occurrences in networks. However, this is more challenging than the traditional anomaly detection problem because anomalies in graphs can manifest in three different forms: anomalous nodes, anomalous edges, and anomalous sub-graphs. It is crucial to detect all these anomaly types within a single framework to provide a unified solution to the graph anomaly detection task. The main objective of this study is to propose a model that is capable of detecting all static graph anomalies in a single architecture across various domains. In this paper, we introduce DeGAN (Decomposition-based unified Graph ANomaly detection), a novel framework for unified graph anomaly detection in static networks. DeGAN combines two deep learning concepts with graph decomposition to identify anomalous graph objects: a graph neural network and an adversarial autoencoder. DeGAN is featured with its capability to detect anomalies in a single process, and adopting graph decomposition has improved performance compared to the traditional adversarial learning approach. DeGAN is evaluated with six real-world datasets to demonstrate that our framework can work in multiple domains. Experimental results demonstrate that DeGAN is capable of detecting anomalous nodes, edges, and sub-graphs within a single model. Additionally, the effectiveness of the sub-components of DeGAN has been demonstrated through experimentation. Even though DeGAN is proposed for plain graphs, it can be extended to attributed and dynamic graphs.

Implementation of Project-Based Learning in English Learning to Improve Students’ Vocabulary Mastery

This research was carried out based on the many students facing difficulties in expanding and enriching their vocabulary. Traditional learning approaches that are often used in teaching vocabulary are often monotonous and less attractive to students, which can lead to boredom and helplessness. For this reason, this study aims to improve students' vocabulary mastery skills using Project-Based Learning. This research is a classroom action research conducted in two cycles. The subjects of this study were students in class 7E at SMPN 1 Getasan for the 2022/2023 academic year. The results of the data analysis in this study, that PjBL in learning material part of body and physical appearance can improve students' vocabulary mastery. This can be proven by the increased understanding of students' vocabulary from 5.4% to 81% in the first cycle and 100% in the second cycle. Learning using PjBL also increases the enthusiasm of students with evidence that in making projects students explore their knowledge more and contemplate their understanding with the various media they are interested in.

An explainable dual-mode convolutional neural network for multivariate time series classification

Multivariate time series classification (MTSC) is a crucial machine learning problem prevalent across various real-life domains. Traditional deep learning approaches with high accuracy in MTSC are often criticized for their “black box” nature, offering no insight into their operational mechanisms or decision-making processes. Explainable artificial intelligence (XAI) becomes a key idea for dealing with such limitations in decision-sensitive areas. While some researchers have explored the interpretability of MTSC, the majority have focused on elucidating the relationship between variables over time, neglecting the intricate connections among different variables. To address the interpretability issue in MTSC, we introduce an explainable dual-mode convolutional neural network (XDM-CNN) designed specifically for MTSC. The proposed XDM-CNN framework comprises two modules: a classification module and an explanation module. The classification module can ensure exceptional classification performance by combining both one-dimensional and two-dimensional convolutional neural networks, while the explanation module can mine the underlying logical relationships among variables during the classification process by utilizing a synergy of visualization and quantification techniques. We validate our approach using 26 datasets sourced from the University of East Anglia (UEA) archive. The experimental results demonstrate that XDM-CNN not only exhibits excellent performance in classification accuracy, but also has strong explainability. By combining visual and numerical explanation, the hidden logical relationships among multivariate time series are explored and interpreted, providing human users with a decidable basis for classification decisions.

Implementation Paper on Automated 2D Image to 3D Model Generation

With the rise of digital world, traditional learning methods, while time-tested, often fall short in meeting the dynamic educational needs of today’s learners. They are constrained by several drawbacks that impede effective comprehension and engagement. This abstract sheds light on the limitations of traditional learning approaches and introduces how the integration of 3D model reconstruction can mitigate these challenges, thereby enhancing the learning experience. We proposed deep Conventional techniques, which create a virtual view from a reference picture, use two distinct steps: depth image-based rendering (DIBR) with estimated depth and depth (or disparity) estimation for the reference image and based on these steps we can create 3D model. Which allows educators to create immersive and interactive learning environments that engage students on a profound level. By converting abstract concepts into tangible and visually stimulating representations, 3D models bridge the gap between theory and practice, making complex subjects more accessible. Keywords: 2D Image, 3D Model, CNN, DIBR

A generation-storage coordination dispatch strategy for power system based on causal reinforcement learning

In the backdrop of global energy transformation, power systems integrating high proportions of renewable energy sources are facing unprecedented challenges in operational stability and dispatch efficiency. To address these challenges, this study introduces a generation-storage coordination real-time dispatch strategy based on Causal Power System Dynamic Reinforcement Learning (CPSDRL). Diverging from traditional reinforcement learning approaches, CPSDRL innovatively incorporates causal inference within the state prediction model – the crux of model-based reinforcement learning – thereby establishing the Power Causal Dynamic Model (PCDM). Assisted by the prior knowledge of power systems, the model significantly enhances prediction accuracy and reliability through a two-stage training process. Utilizing PCDM, this study further applies a direct policy search algorithm to optimize the real-time dispatch strategy. Experimental results indicate that the proposed method improves the stability of generation-storage coordination real-time dispatch and exhibits competitive advantages in sample efficiency and computational speed, compared to traditional model-based and model-free reinforcement learning algorithms. This method is expected to enhance the practicality and adaptability of causal reinforcement learning techniques in power system scheduling and control.

Need for education of psychiatric evaluation of offenders with mental disorders: A questionnaire survey for Japanese designated psychiatrists.

The management of offenders with mental disorders has been a significant concern in forensic psychiatry. In Japan, the introduction of the Medical Treatment and Supervision Act in 2005 addressed the issue. However, numerous psychiatric patients at risk of violence still find themselves subject to the administrative involuntary hospitalization (AIH) scheme, which lacks clarity and updated standards. To explore current as well as optimized learning strategies for risk assessment in AIH decision making. We conducted a questionnaire survey among designated psychiatrists to explore their experiences and expectations regarding training methods for psychiatric assessments of offenders with mental disorders. The findings of this study's survey suggest a prevalent reliance on traditional learning approaches such as oral education and on-the-job training. This underscores the pressing need for structured training protocols in AIH consultations. Moreover, feedback derived from inpatient treatment experiences is identified as a crucial element for enhancing risk assessment skills.

Development of interactive learning multimedia based on Somatic, Auditory, Visual, Intellectual (SAVI) approach assisted by Articulate Storyline 3 on circular motion material

There are limited learning approaches and laboratory facilities regarding developing interactive learning multimedia for circular motion material. Therefore, this research aims to develop interactive multimedia learning based on the Somatic, Auditory, Visual, and Intellectual (SAVI) approach assisted by Articulate Storyline 3 on circular motion material. This research is a type of research and development of the ADDIE model. The respondents involved were 31 students in class XI MIPA B and XI MIPA C. Experts validated the product from media, material, and language aspects. Product validity was analyzed using the Aiken V technique. The Aiken V index scores were 0.89, 0.92, and 0.89 for assessing media, material, and language aspects. The practicality assessment resulted in an average of 96%, which shows high practicality in the learning context. In conclusion, this SAVI-based interactive multimedia has been validated by experts and is well received by students. So, it has proven to be very suitable for effective learning at Darussalam Rajapolah Integrated High School, Tasikmalaya. This research implies that the development of interactive learning multimedia based on the Somatic, Auditory, Visual, and Intellectual (SAVI) approach assisted by Articulate Storyline 3 on circular motion material can be a solution to overcome the limitations of traditional learning approaches and laboratory facilities in teaching the concept of circular motion.

Survey on the convergence of learning techniques for precise pattern identification in fingerprint analysis

Fingerprint analysis has been an essential component of forensic science and biometric authentication systems. The precise recognition of fingerprint patterns is critical for solving crimes, confirming identities and assuring the security of numerous applications. In recent years, the convergence of learning approaches, particularly machine learning and deep learning, has played an essential part in improving the accuracy and efficiency of fingerprint pattern recognition. In this paper, we discuss recent advancements in physiological-based biometric multimodalities, with a particular focus on the field of precise pattern identification in fingerprint analysis. Additionally, we assume the task of summarizing and examining a range of physiological-based biometric modalities, encompassing both traditional and deep learning approaches. A detailed review of several biometric measurements across many modalities is presented, encompassing various phases, including preprocessing, feature extraction and classification, which are thoroughly discussed. We provide a comprehensive analysis of the challenges and future developments associated with conventional and deep learning methodologies. The objective is to enable investigators to recognize these problems. A review is conducted to evaluate the standard and deep learning approaches employed in different physiological-based biometric systems. The comparative analysis of this review suggests that more advancement is necessary for the development of a reliable physiological-based approach to enhance and optimize the functionality of the fingerprint system.

Evaluation of traditional and machine learning approaches for modeling volatile fatty acid concentrations in anaerobic digestion of sludge: potential and challenges.

This study evaluates models for predicting volatile fatty acid (VFA) concentrations in sludge processing, ranging from classical statistical methods (Gaussian and Surge) to diverse machine learning algorithms (MLAs) such as Decision Tree, XGBoost, CatBoost, LightGBM, Multiple linear regression (MLR), Support vector regression (SVR), AdaBoost, and GradientBoosting. Anaerobic bio-methane potential tests were carried out using domestic wastewater treatment primary and secondary sludge. The tests were monitored over 40days for variations in pH and VFA concentrations under different experimental conditions. The data observed was compared to predictions from the Gaussian and Surge models, and the MLAs. Based on correlation analysis using basic statistics and regression, the Gaussian model appears to be a consistent performer, with high R2 values and low RMSE, favoring precision in forecasting VFA concentrations. The Surge model, on the other hand, albeit having a high R2, has high prediction errors, especially in dynamic VFA concentration settings. Among the MLAs, Decision Tree and XGBoost excel at predicting complicated patterns, albeit with overfitting issues. This study provides insights underlining the need for context-specific considerations when selecting models for accurate VFA forecasts. Real-time data monitoring and collaborative data sharing are required to improve the reliability of VFA prediction models in AD processes, opening the way for breakthroughs in environmental sustainability and bioprocessing applications.

SustainableTransformation of Undergraduate Engineering Education in China through Sino-Australian Cooperation: A Case Study on Electro-Mechanical System

China’s undergraduate engineering education is facing two significant challenges: passive learning and limited cross-cultural communication. In response, active learning methods such as project-based learning (PBL) and Sino-foreign cooperative education emerge as promising solutions. However, despite their potential, PBL’s application remains constrained, and many Sino-foreign cooperative programs persist with traditional passive learning approaches. How to adeptly integrate the two, namely realizing active learning in Sino-foreign cooperative education, is the primary objective of this study. To this end, a sustainable strategy for cross-cultural transfer and the adaptation of active learning is proposed. Initially, a PBL-based active learning pedagogy is tailored for a course within a Sino-Australian cooperative undergraduate program. This approach integrates hands-on activities, teamwork, and the flipped classroom method. Moreover, to ensure implementation quality, an assessment method reliant on continuous student survey feedback is suggested. Subsequently, through the statistical analysis of students’ survey data as well as final grades, both quantitative and qualitative results affirm the efficacy of the proposed strategy.

Deep Transfer Learning Using Real-World Image Features for Medical Image Classification, with a Case Study on Pneumonia X-ray Images.

Deep learning has profoundly influenced various domains, particularly medical image analysis. Traditional transfer learning approaches in this field rely on models pretrained on domain-specific medical datasets, which limits their generalizability and accessibility. In this study, we propose a novel framework called real-world feature transfer learning, which utilizes backbone models initially trained on large-scale general-purpose datasets such as ImageNet. We evaluate the effectiveness and robustness of this approach compared to models trained from scratch, focusing on the task of classifying pneumonia in X-ray images. Our experiments, which included converting grayscale images to RGB format, demonstrate that real-world-feature transfer learning consistently outperforms conventional training approaches across various performance metrics. This advancement has the potential to accelerate deep learning applications in medical imaging by leveraging the rich feature representations learned from general-purpose pretrained models. The proposed methodology overcomes the limitations of domain-specific pretrained models, thereby enabling accelerated innovation in medical diagnostics and healthcare. From a mathematical perspective, we formalize the concept of real-world feature transfer learning and provide a rigorous mathematical formulation of the problem. Our experimental results provide empirical evidence supporting the effectiveness of this approach, laying the foundation for further theoretical analysis and exploration. This work contributes to the broader understanding of feature transferability across domains and has significant implications for the development of accurate and efficient models for medical image analysis, even in resource-constrained settings.

An Optimized Intelligent Deep Network for Herbal Leaf Classification

In recent times, a variety of industries have made extensive use of image processing techniques for tasks including segmentation and classification. However, the traditional image processing and ensemble learning approaches face challenges in feature selection and classification. To overcome the demerits of the conventional image processing and boosting algorithm, a novel hybrid Chimp-based Boltzmann Prediction Network (CbBPN) was developed in this article. The presented work was designed and verified in MATLAB software with the herbal leaf dataset. In the model development, the pre-processing and feature extraction module is responsible for extracting valuable features that are pertinent to the classification process. Furthermore, the chimp fitness function increases the classification rate by removing unwanted elements during the classification stage. Additionally, the developed model uses the matching operation to specify the types of the leaf. Furthermore, a case study was created to explain the ways the suggested approach operates. Moreover, a comparison of the projected findings with the existing categorization approaches validates the effectiveness of the constructed model. The comparative analysis shows that the new methods outperformed previously available ones in terms of output.

Auditory Learner

Auditory Learner

A Comparative Study of Traditional and Quiz Team-Type Cooperative Learning Approaches on Students' Academic Performance in Geography

This Classroom Action Research conducted at Madrasah Aliyah Negeri Asahan, Asahan Regency, in the academic year 2018/2019, investigates the efficacy of the Quiz Team Type Cooperative Learning Model (QTCLM) on enhancing student engagement and academic performance in Anthroposphere studies within class XI-IPS2. Specifically, it aims to assess: (1) the variation in student learning activities, and (2) the improvement in student learning outcomes following the QTCLM implementation. The research, adopting a Class Action Research (CAR) methodology, involved the entirety of the XI-IPS2 class, comprising 28 students. Data were amassed utilizing indirect communication techniques and systematic observation, subsequently analyzed through descriptive quantitative methods. Findings reveal a notable escalation in student learning activities from an initial 56.84% in Cycle I to 69.99% in Cycle II post-QTCLM application. Concurrently, student learning outcomes exhibited a significant rise from 71% in Cycle I to 87% in Cycle II. This improvement indicates that the objectives of classical completion have been satisfactorily achieved. The results corroborate the action hypothesis posited at the outset of the study: Implementing the QTCLM in teaching Anthroposphere material in class XI-IPS2 at MAN Asahan effectively enhances student participation and academic achievement, ensuring a minimum of 85% classical completeness. This research underscores the potential of interactive and cooperative learning strategies in fostering a more engaging and fruitful educational environment.

Designing A Distance Learning System for English Reading by Applying Multimodal Discourse Analysis Theory

Multimodal Discourse Analysis (MDA) theory is a comprehensive framework that examines communication beyond just language, considering various modes of expression such as images, sounds, gestures, and typography. This theory recognizes that communication is inherently multimodal, and meaning is conveyed through the interplay of different modes. MDA analyzes how these modes interact within texts and contexts to create meaning, emphasizing the importance of visual, auditory, and spatial elements alongside linguistic features. By examining multimodal texts, MDA provides insights into how meaning is constructed, negotiated, and understood in diverse communicative contexts, from advertising campaigns to political speeches. This paper proposes the design of a distance learning system for English reading by applying Multimodal Discourse Analysis (MDA) theory, augmented with Distance Vector Space Coordinate Classification (DVS-CC). The system aims to provide an immersive and interactive learning experience by integrating various modes of communication, including text, images, audio, and video. Through simulated experiments and empirical validations, the effectiveness of the DVS-CC-enhanced distance learning system is evaluated. Results demonstrate significant improvements in student engagement, comprehension, and learning outcomes compared to traditional distance learning approaches. For example, students using the DVS-CC-enhanced system achieved an average score increase of 25% in reading proficiency assessments. Additionally, the system's adaptive learning features enabled personalized instruction tailored to each student's learning style and pace. These findings underscore the potential of applying MDA theory with DVS-CC in designing effective distance learning systems for English reading, fostering enhanced learning experiences in remote educational settings.

Ultimate pose estimation: A comparative study

AbstractPose estimation is a computer vision task used to detect and estimate the pose of a person or an object in images or videos. It has some challenges that can leverage advances in computer vision research and others that require efficient solutions. In this paper, we provide a preliminary review of the state‐of‐the‐art in pose estimation, including both traditional and deep learning approaches. Also, we implement and compare the performance of Hand Pose Estimation (HandPE), which uses PoseNet architecture for hand sign problems, for an ASL dataset by using different optimizers based on 10 common evaluation metrics on different datasets. Also, we discuss some related future research directions in the field of pose estimation and explore new architectures for pose estimation types. After applying the PoseNet model, the experiment results showed that the accuracy achieved was 99.9%, 89%, 97%, 79%, and 99% for the ASL alphabet, HARPET, Yoga, Animal, and Head datasets, comparing those with common optimizers and evaluation metrics on different dataset.

The Impact of English-Speaking Films and Songs on English Language Acquisition

This study investigates the effectiveness of immersion and entertainment in the process of language acquisition, with a specific focus on the utilisation of English films and songs. The statement emphasises the educational value of films, which utilise visual clues and storytelling to facilitate comprehension. It also specifies genres that are particularly helpful for English language learning. An investigation of popular English songs explores the impact of rhythm, repetition, and personal involvement in language acquisition. The cultural observations offered by these media types emphasise the significance of cultural environment in the process of acquiring knowledge. The text explores the challenges associated with relying only on entertainment for language acquisition and emphasises the importance of incorporating traditional learning approaches. The resource provides practical advice for learners and presents case examples of successful language acquisition via films and music, offering a thorough perspective on this language learning strategy.