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127 articles for “problem-based learning”
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Empowering Minds: Fostering Critical Thinking through Mathematics Teaching
Abstract: In academic and professional realms, critical thinking emerges as a paramount skill, yet numerous students encounter challenges in honing it effectively. The realm of mathematics education presents a unique platform to foster critical thinking abilities, as it compels students to tackle intricate problems using logical reasoning and evidence-based methodologies. This research paper comprehensively reviews the correlation between critical thinking and mathematics teaching, providing exemplars of impactful teaching strategies that effectively …
Published in Research and Reviews: A Journal of Neuroscience · Vol. 13, Issue 3, 2023 · pp. 19–25 Read article
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Studies of Mathematics: A Review of Research Trends, Themes and Implications
Abstract: This review examines contemporary research trends, thematic developments, and emerging implications within the field of mathematics education and mathematical studies. Drawing on a synthesis of recent scholarly literature, it explores how mathematics as both a discipline and a pedagogical practice continues to evolve in response to technological advancements, interdisciplinary applications, and changing educational paradigms. Major research trends reveal a growing emphasis on problem-based learning, mathematical modeling, and the integration of …
Published in Recent Trends in Mathematics · Vol. 2, Issue 2, 2025 · pp. 19–24 Read article
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Machine Learning-based Peer-to-Peer Platform for Precision Agriculture in Crop Growth and Disease Monitoring
Abstract: Farmer suicides are a significant problem in India due to various circumstances. One of the main problems is the financial side of managing and growing crops while still trying to make a profit. This study proposes a decentralized platform for buying and selling agricultural produce by connecting farmers with individuals interested in investing in their fields and continuous monitoring of quality and crop health using IoT, Blockchain, and Machine Learning …
Published in Journal of Computer Technology & Applications · Vol. 13, Issue 3, 2022 · pp. 26–39 Read article
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Automated Machine Learning System for Model Selection and Hyperparameter Optimization
Abstract: The proliferation of machine learning applications in various scientific and industrial domains has given rise to an urgent need for developing principled, automated techniques for optimal architecture selection and hyperparameter tuning for machine learning models without human expert intervention. In this paper, we introduce the Automated Machine Learning System for Model selection and hyperparameter Optimization (AMLSMO)—a state-of-the-art, all-encompassing AutoML system that combines the power of meta-learning-based warm-starting, Bayesian Optimization with …
Published in Recent Trends in Mathematics · Vol. 3, Issue 2, 2026 · pp. 15–23 Read article
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A Study and Performance Evaluation of Evolutionary Optimization Techniques for Multi-objective Master Production Scheduling Problems
Abstract: Master production schedule (MPS) can effectively and efficiently synchronize the operations in any organization. MPS, which is posed as one of the multi-objective parameter optimization problems, is a plan that determines optimal values of products to be produced. For many engineering optimization problems, more competitive and optimal solutions can be obtained by using Heuristic evolutionary optimization algorithms. Among these, two main algorithms considered here are the differential evolution (DE) whose …
Published in Journal of Production Research & Management · Vol. 3, Issue 2, 2013 · pp. 12–22 Read article
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Comparison of Knowledge Perceived by Final Year Dental Students for a Lesson on the Apical Barrier in the Premature Root by Clinical Case Demonstration as Compared to the Didactic Teaching Method
Abstract: The present dental education is using didactic teaching method, which is teacher-centered with minimal or no active participation from the students. Case-based learning (CBL) is defined as learning that is based upon description of a patient’s problems, analysis and interpretation of all the relevant data obtained from history, examination and investigations and planning for further management of patient. The goal of CBL is to prepare students for clinical practice, by …
Published in Research and Reviews: A Journal of Dentistry · Vol. 15, Issue 2, 2024 · pp. 1–7 Read article
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Linear Programming for Profit Optimization in Small-Scale Manufacturing: A Python-Based Simplex and Machine Learning Approach
Abstract: Profit maximization under resource constraints is a classic challenge. Small manufacturers face tight margins and scarce capital every day. This paper tackles that problem using four Python-based methods. The case study is Bintang Bakery in Bandar Lampung, Indonesia. The bakery makes three bread types and faces 18 resource constraints. Data comes from Anggoro et al. Methods tested include LP revised simplex, Differential Evolution, PSO, and ANN Surrogate. General-purpose scipy minimizers …
Published in Research & Reviews : Journal of Statistics · Vol. 15, Issue 2, 2026 · pp. 01–11 Read article
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Comparative Performance Analysis of Swarm and Intelligent Evolutionary Techniques for Optimal Design of Distribution Transformer
Abstract: This paper addresses the method of optimal design of a three phase distribution transformer using Genetic Algorithms (GA), Particle Swarm Optimization (PSO) and Teaching-Learning-Based-Optimization Algorithm (TLBO). The design and analysis programs have been developed for constrained optimal design with cost as the objective function. The active part cost of the transformer has been minimized keeping in view BEE (Bureau of Energy Efficiency) standards and constraints. A design example on a …
Published in Trends in Electrical Engineering · Vol. 5, Issue 3, 2015 · pp. 46–58 Read article
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A Comprehensive Review on Brain Tumour Classification through Deep Learning Utilizing Convolutional Neural Networks
Abstract: Abstract- Convolutional neural networks (CNNs) constitute a widely used deep learning approach that has frequently been applied to the problem of brain tumor diagnosis. Such techniques still face some critical challenges in moving towards clinic application. Brain tumours are classified using a biopsy, which is not normally done before conclusive brain surgery. The enhancement of this technology by machine learning could aid radiologists in tumour detection without the use of …
Published in Research and Reviews: Journal of Oncology and Hematology · Vol. 12, Issue 3, 2023 · pp. 24–29 Read article
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Deep Learning -Based Dental Issue Detection
Abstract: Dentistry is vital for preserving oral health, a key component of overall wellness. Early identification of dental issues is crucial for effective treatment and avoiding further complications. Conventional approaches to diagnosing dental problems typically depend on physical examinations and visual assessments by skilled professionals, which can be both time-intensive and influenced by individual judgment.In recent years, the application of deep learning algorithms has demonstrated significant potential in automating and enhancing …
Published in Research and Reviews: A Journal of Dentistry · Vol. 16, Issue 1, 2025 · pp. 18–23 Read article
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Network Intrusion Detection System using Machine Learning and Deep Learning Approach
Abstract: Networks play a significant part in today’s world; fast internet and communication industries result in vast network size and data expansion. Furthermore, attackers aiming to launch various cyberattacks inside the system cannot be neglected. An IDS keeps track of the network’s software and hardware security to preserve its privacy, integrity, and accessibility. Despite the significant efforts of the researchers, current IDS continue to confront challenges in terms of accuracy rate, …
Published in Journal Of Network security · Vol. 10, Issue 1, 2022 · pp. 7–34 Read article
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Hybrid Approach for Community Detection Using Deep Learning Techniques
Abstract: Community detection in complex networks is a fundamental problem with applications across diverse domains, ranging from social networks to biological systems and beyond. Traditional methods based on graph theory have been widely used for identifying communities within networks. However, the intricate and evolving nature of modern networks demands more sophisticated approaches. This research work proposes a hybrid approach that combines the strengths of deep learning techniques with traditional community detection …
Published in Recent Trends in Electronics Communication Systems · Vol. 11, Issue 3, 2024 · pp. 18–26 Read article
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Integrate AI and IoT to Develop Sustainable Polymer Structural Materials Processing Optimization: Enabled Monitoring Strategies for Performance and Lifecycle Assessment
Abstract: The need for long-lasting structural polymer materials that are both environmentally friendly and highly mechanically effective is driving demand for these materials as the industrial sector continues to grow. Optimizing processes, saving energy, detecting faults, and monitoring structures are all hindered by conventional polymer manufacture. This study suggests an AI-IoT system for environmentally friendly production of structural polymer materials to get around these problems. Tools for evaluating system performance and …
Published in Journal of Polymer & Composites · Vol. 14, Issue 4, 2026 · pp. 169–192 Read article
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Bias Detection and Accuracy Enhancement in Voice-based Banking Authentication Using Deep Learning
Abstract: Biometric systems have become an integral part of how many people access banking services today, and voice verification systems can be a secure and easy-to-use source of banking authentication that does not require any physical contact with the bank or any other person. From the security perspective, these systems would normally provide an effective means of identifying an individual but frequently exhibit bias with respect to demographics such as the …
Published in International Journal of Information Security Engineering · Vol. 4, Issue 2, 2026 Read article
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Comparative Analysis of Supervised Learning Algorithms
Abstract: Supervised learning is a fundamental and widely used branch of machine learning in which models are trained on labeled datasets, meaning that each input is associated with a known output. Supervised learning algorithms develop predictive capability by understanding the mapping between input variables and corresponding output labels, enabling them to accurately forecast outcomes for previously unseen data. Due to this capability, supervised learning has found extensive applications across diverse domains …
Published in Journal of Computer Technology & Applications · Vol. 17, Issue 1, 2026 · pp. 25–30 Read article
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Trust Based Deep Learning Model for Women Information Security Improvement in LBS
Abstract: The pervasiveness of mobile devices equipped with positioning proficiencies has managed to the emergence of frequent location-based applications and services. A huge fraction of the information sought is related to the current women position. This comprises queries for nearby medical services, specialized stores, social activities and groups, and others. In general, location-based service (LBS) operators are expected to be trusted parties that preserve the user’s privacy. Due to the sensitive …
Published in Journal Of Network security · Vol. 7, Issue 1, 2019 · pp. 18–23 Read article
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Research on Adversarial Disturbance Based on Meteorological Time Series Data
Abstract: When the deep learning model is used to predict time series data, it is easy to be adversarially attacked. The time series data is sensitive to the abnormal disturbance and has strict requirements on the disturbance amount. To solve these problems, we propose to generate adversarial time series by adding disturbance terms to the original time series, and design an adversarial attack algorithm based on the importance measure (AAIM in …
Published in Journal of Industrial Safety Engineering · Vol. 9, Issue 3, 2022 · pp. 1–19 Read article
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A Study on Science Teachers’ Perception of Newly Developed Science Workbooks and Their Effectiveness in Fostering Scientific Attitude in Learners
Abstract: This study examines science teachers’ perceptions of newly developed science workbooks and evaluates their effectiveness in fostering scientific attitude among learners. Using a structured opinionnaire, the research gathered comprehensive feedback from teachers across multiple grade levels regarding the clarity, relevance, and pedagogical value of the workbooks. Analysis of opinionnaire responses revealed strong teacher agreement regarding the workbook’s clarity, activity-based structure, and alignment with curriculum goals. Teachers reported noticeable improvements in …
Published in International Journal of Education Sciences · Vol. 2, Issue 2, 2025 · pp. 82–90 Read article
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Economic Load Dispatch with PEV Using Evolutionary Techniques
Abstract: In this paper, the Economic Load Dispatch (ELD) problem is solved by using four different evolutionary techniques Social Spider Algorithm (SSA), Particle Swarm Optimization (PSO), and Teaching Learning Based Optimization (TLBO) with PEV on 20-unit thermal generation station. The most recent Self-Learning Teaching Learning Based Optimization (SL-TLBO) is introduced, including a weighting factor w for adjusting the learning range. A comprehensive study demonstrates that the unique algorithm has the potential …
Published in Journal of Thermal Engineering and Applications · Vol. 10, Issue 3, 2023 · pp. 22–33 Read article
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Efficient Masked Face Recognition Methods using Deep Learning
Abstract: Covid-19 questioned not only people's health but alsoconventional scientific systems. Due to face masks that were made mandatory to wear, the existing cognitive systems failed to perform in real-time scenarios. The demand to develop face recognition systems that detect people even when they wore masks was naturallyhigh. Deep learning techniques help to solve this problem, working efficiently in detecting user face features and comparing them with a known image database. …
Published in Journal of Instrumentation Technology & Innovations · Vol. 13, Issue 2, 2023 · pp. 1–10 Read article