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A Neural Network Framework for Classifying Educational Content Aligned with Learning Outcomes

Procedia computer science 2025年 257卷 529-536页

作者： Adel Alkhalil Department of Software Engineering College of Computer Science and Engineering University of Ha’il Ha’il 81481 Saudi Arabia

Rapid growth of digital educational content necessitates efficient and accurate methods for organizing and mapping resources to ensure well-alignment with targeted learning outcomes, academic standards, and competency frameworks. Traditional text classification approaches, including rule-based and classical machine learning techniques, often fail to address the semantic diversity and scalability demands of modern educational systems. This study investigates the application of neural networks for text classification to automate the mapping of educational content into predefined categories. Leveraging state-of-the-art architectures such as Long Short-Term Memory (LSTM) networks, and transformers like BERT, we present an architecture of a systematic Classification of educational materials. We discuss the implications of this work for adaptive learning environments, emphasizing the potential of neural networks to enhance the efficiency and scalability of content mapping. This study contributes to the growing body of research in artificial intelligence for education and sets the stage for further exploration into multilingual and domain-specific content classification methods.

关键词： Neural Networks Text Classification Educational Content Curriculum Mapping Transformer Models Learning Outcomes

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Blockchain-Based Anomaly Detection and Intrusion Prevention in IoT Networks 8th

Blockchain-Based Anomaly Detection and Intrusion Prevention ...

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8th International Conference on Internet of Things and Connected Technologies, ICIoTCT 2023

作者： Ganesh Kumar, G. Kanakaprabha, S. Venu Gopal, Gaddam Shaik, Riaz Senthil Kumar, T. Udhaya Kumar, T. Department of Information Technology Hindusthan Institute of Technology Tamilnadu Coimbatore641032 India Department of Computer Science and Engineering Rathinam Technical Campus Anna University Coimbatore India B V Raju Institute of Technology Telangana Narsapur Medak502313 India Department of Internet of Things KL Deemed to be University Vijayawada India Department of Information Technology Hindusthan Institute of Technology Tamilnadu Coimbatore India Department of Computer Science and Engineering Hindusthan Institute of Technology Coimbatore India

ISBN: (纸本)9789819757855

The increasing adoption of IoT devices has raised significant concerns about security and privacy. This paper proposes a blockchain-based approach for anomaly detection and intrusion prevention in IoT networks. The methodology combines blockchain technology with anomaly detection algorithms to enhance the security and trustworthiness of IoT systems. Through simulations and experiments using real-world IoT datasets, the effectiveness of the approach is evaluated. The results demonstrate improved accuracy in detecting anomalies and reduced false positives compared to traditional methods. The blockchain-based solution also provides tamper-proof and decentralized data storage, ensuring the integrity and availability of IoT network information. This research highlights the potential of blockchain technology as a robust solution for enhancing anomaly detection and intrusion prevention in IoT networks, addressing critical security challenges in the IoT ecosystem. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Network intrusion

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Automatic Image Unfolding and Stitching Framework for Esophageal Lining Video Based on Density-Weighted Feature Matching

Automatic Image Unfolding and Stitching Framework for Esopha...

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Medical Imaging 2025: Image Processing

作者： Li, Muyang Xiong, Juming Deng, Ruining Yao, Tianyuan Tyree, Regina N. Hiremath, Girish Huo, Yuankai Department of Computer Science Vanderbilt University NashvilleTN United States Department of Electrical and Computer Engineering Vanderbilt University NashvilleTN United States Division of Pediatric Gastroenterology Hepatology and Nutrition Vanderbilt University Medical Center NashvilleTN United States

ISBN: (纸本)9781510685901

Endoscopy is a crucial tool for diagnosing the gastrointestinal tract, but its effectiveness is often limited by a narrow field of view and the dynamic nature of the internal environment, especially in the esophagus, where complex and repetitive patterns make image stitching challenging. This paper introduces a novel automatic image unfolding and stitching framework tailored for esophageal videos captured during endoscopy. The method combines feature matching algorithms, including LoFTR, SIFT, and ORB, to create a feature filtering pool and employs a Density-Weighted Homography Optimization (DWHO) algorithm to enhance stitching accuracy. By merging consecutive frames, the framework generates a detailed panoramic view of the esophagus, enabling thorough and accurate visual analysis. Experimental results show the framework achieves low Root Mean Square Error (RMSE) and high Structural Similarity Index (SSIM) across extensive video sequences, demonstrating its potential for clinical use and improving the quality and continuity of endoscopic visual data. © 2025 SPIE.

关键词： Endoscopy

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Intermittent-Friendly Neural Architecture Search: Demystifying Accuracy and Overhead Trade-Offs

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IEEE Transactions on computer-Aided Design of Integrated Circuits and Systems 2025年

作者： Mendis, Hashan Roshantha Yen, Chih-Hsuan Kang, Chih-Kai Hsiu, Pi-Cheng Taipei11529 Taiwan National Taiwan University Department of Computer Science and Information Engineering Taipei10617 Taiwan Augentix Inc Taipei104427 Taiwan National Chi Nan University Department of Computer Science and Engineering Nantou54561 Taiwan National Taiwan University College of Electrical Engineering and Computer Science Taipei10617 Taiwan

The fusion of tiny energy harvesting devices with deep neural networks (DNN) optimized for intermittent execution is vital for sustainable intelligent applications at the edge. However, current intermittent-aware neural architecture search (NAS) frameworks overlook the inherent intermittency management overhead (IMO) of DNNs, leading to under-performance upon deployment. Moreover, we observe that straightforward IMO minimization within NAS may degrade solution accuracy. This work explores the relationship between DNN architectural characteristics, IMO, and accuracy, uncovering the varying sensitivity towards IMO across different DNN characteristics. Inspired by our insights, we present two guidelines for leveraging IMO sensitivity in NAS. Firstly, the overall architecture search space can be reduced to exclude parameters with low IMO sensitivity, and secondly, network blocks with high IMO sensitivity can be primarily focused during the search, facilitating the discovery of highly accurate networks with low IMO. We incorporate these guidelines into TiNAS, which integrates cutting-edge tiny NAS and intermittent-aware NAS frameworks. Evaluations are conducted across various datasets and latency requirements, as well as deployment experiments on a Texas Instruments device under different intermittent power profiles. Compared to two variants, one minimizing IMO and the other disregarding IMO, TiNAS respectively achieves up to 38% higher accuracy and 33% lower IMO, with greater improvements for larger datasets. Its deployed solutions also achieve up to a 1.33 times inference speedup, especially under fluctuating power conditions © 1982-2012 IEEE.

关键词： Deep neural networks

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Hybrid Approach of TabNet and Transformer-XGBoost for Predicting Traffic Flow in Smart Cities 1

Hybrid Approach of TabNet and Transformer-XGBoost for Predic...

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1st IEEE International Conference on Smart and Sustainable Developments in Electrical engineering, SSDEE 2025

作者： Hole, Shreyas Rajendra Kolluru, Vinothkumar Salotagi, Shreekant Challagundla, Yagnesh Mungara, Sudeep Naik, Mude Nagarjuna Bangalore India Stevens Institute of Technology Department of Data Science HobokenNJ07030 United States Dayananda Sagar University Computer Science & Engineering Bangalore India Herbert Wertheim College of Engineering University of Florida Department of Computer Science GainesvilleFL32611 United States Stevens Institute of Technology Masters in Information Systems HobokenNJ07030 United States

ISBN: (纸本)9798331542108

A smart city's transportation system can be en-hanced if there is an accurate prediction of the traffic flow which develops in an area as time goes on. This article investigates the relative accuracy of predicting traffic flow at target intersections in a city, taking advantage of two models: Tab Net and a hybrid Transformer-XGBoost. Several metrics including Mean Squared Error (MSE) and Coefficient of determination (R2 score) among others were employed to evaluate the models employed. A sequential deep learning model known as Tab Net triumphing in the competition with MSE, RMSE, and MAE being 34.48, 5.87, and 3.73 respectively, and R2 score as high as 0.917, although performing well while enhancing identification of features such as year and junction. On the other hand, while reporting similar R2 score as low as 0.184, the Transformer-XGBoost hybrid model had structuring limitations where complex interdependencies and time variations were involved recording MSE, RMSE and MAE as 338.52, 18.40 and 13.24 respectively. The graphs of residuals and QQ plots served to uncover that the hybrid model failed making a right prediction during high traffic volume days, as general tabnet results shown in the final graphs are more stable. Following investigation of the models in this study, the researchers suggest implementing TabNet as a better option for traffic management explaining its high accuracy and providing valuable guidance for cities. This would result in improvement of the traffic flow prediction, which is highly needed for effective urban mobility solutions. © 2025 IEEE.

关键词： Smart city

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A Multilingual Mental Health Support Chatbot with Regional Language Capabilities Based On the AMER Model

A Multilingual Mental Health Support Chatbot with Regional L...

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Data science, Agents & Artificial Intelligence (ICDSAAI), International Conference on

作者： Amirthavarshini H Senthil Kumar G Vibhav Krashan Chaurasiya Kirubakaran N Balaraman Sundarambal Department of Computer Science and Business Systems Chennai Institute of Technology Chennai India Department of Computer Science and Engineering Oriental Institute of Science and Technology Bhopal India

ISBN: (数字)9798331537555

ISBN: (纸本)9798331537562

Despite that, access to mental health care is still limited in many areas, particularly where there are differences in language and cultural considerations prove challenging. Many available solutions poorly respond to the needs for inclusive and personalized support in local languages. We present the development of our advanced multilingual mental health support chatbot, AMER: Advanced Multilingual Emotional Response Model with Regional Language Capabilities. Using sophisticated NLP, emotion detection, and speech technologies, the chatbot aims to provide empathetic, relevant, and interactive support for the users in their desired languages. This model is responsible for the emotion prediction and classification; AMER runs through six advanced algorithms: sentiment analysis, emotion detection, multilingual support, emoji recognition, personalized recommendation, and speech emotion recognition. To make sure that it recognizes emotions and processes language accurately, the system is trained on datasets such as GoEmotions, Common Voice, and IndicCorp to accommodate ten different languages, including as many local languages (especially in Indian context). Thanks to its modular design, it can process user inputs, evaluate emotions, and produce tailored, context-sensitive outputs. Emotion detection accuracy at baseline testing was 87%, while language processing accuracy was greater than 92%, boosting user engagement by 76% in relation to standard text-based systems. Future improvements will center around multimodal features like expression and tone analysis, as well as integration with teletherapy platforms. The improvements follow the goal of making the chatbot more accessible and better performing for more users, specifically targeting underserved communities worldwide to broaden the accessibility of mental health support.

关键词： Emotion recognition Sentiment analysis Accuracy Mental health Speech recognition Predictive models Chatbots Multilingual Standards Testing

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A Novel Heart Disease Monitoring and Prediction Using Machine Learning Algorithm 8th

A Novel Heart Disease Monitoring and Prediction Using Machin...

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8th International Conference on Internet of Things and Connected Technologies, ICIoTCT 2023

作者： Senbagavalli, M. Karpagalakshmi, R.C. Sumathi, D. Lenin, J. Prasad, G.R.K. Manikandan, A. Department of Computer Science and Information Technology Alliance College of Engineering and Design Alliance University Bangalore India Department of ECE Koneru Lakshmaiah Education Foundation Vaddeswaram India Kattankulathur Chennai603203 India

ISBN: (纸本)9789819757855

Background: Continuous monitoring of patient health statistics becomes a difficult task in hospitals. Manually, it is difficult to monitor the health of the patients in the hospital continuously. Older and unconscious older people in particular need to be monitored regularly, and their relatives want to be informed about their health at every time. We, therefore, propose a revolutionary system that easily computerizes this task. Our device provides an intelligent device for monitoring patient status, which uses sensors to track hospital patients’ health status and informs their relatives in the event of a problem via the Internet. Our system uses temperature, glucose levels and heart rate detection to track health. This task has been proposed to improve the monitoring system by using the Internet of Things (IoT) for hospital applications. Result: The suggested system was developed by MAX30100, LM35, ultrasonic sensor and nodeMCU connected to the Internet. BLYNK IoT Android app has already been used to send the notification via Android Application. The remote healthcare monitoring system is also proposed with cloud service and data analytics as to the aiding features. The readings are captured by mobile phone, which acts as the graphical user interface to get the status of the patient’s health. The implemented hardware results illustrate that the suggested model can continuously observe the physiologic parameters and save lives promptly. Conclusion: ML classification algorithms like Regression tree, SVM, and RF analysis for prediction accuracy of patient health data. The proposed RF algorithm’s simulation results provide minimum prediction error and high accuracy compared with the existing Regression tree and SVM algorithm. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Ultrasonic sensors

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AI-based robots in industrialized building manufacturing

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Frontiers of engineering Management 2025年第1期12卷 59-85页

作者： Mengjun WANG Jiannan CAI Da HU Yuqing HU Zhu HAN Shuai LI Department of Civil and Environmental Engineering The University of TennesseeKnoxvilleTN 37996USA Department of Construction Science The University of Texas at San AntonioSan AntonioTX 78249USA Department of Civil and Environmental Engineering Kennesaw State UniversityMariettaGA 30060USA Department of Architectural Engineering Pennsylvania State UniversityUniversity ParkPA 16802USA Department of Electrical and Computer Engineering University of HoustonHoustonTX 77004USA

Industrialized buildings,characterized by off-site manufacturing and on-site installation,offer notable improvements in efficiency,cost-effectiveness,and material *** transition from traditional construction methods not only accelerates building processes but also enhances working efficiencies *** its widespread adoption,the performance of industrialized building manufacturing(IBM)can still be optimized,particularly in enhancing time efficiency and reducing *** paper explores the integration of Artificial Intelligence(AI)and robotics at IBM to improve efficiency,cost-effectiveness,and material use in off-site *** a narrative literature review,this study systematically categorizes AI-based Robots(AIRs)applications into four critical stages—Cognition,Communication,Control,and Collab-oration and Coordination,and then investigates their appli-cation in the factory assembly process for industrialized buildings,which is structured into distinct stages:compo-nent preparation,sub-assembly,main assembly,finishing tasks,and quality *** stage,from positioning components to the integration of larger modules and subsequent quality inspection,often involves robots or human-robot collaboration to enhance precision and *** examining research from 2014 to 2024,the review highlights the significant improvements AI-based robots have introduced to the construction sector,identifies existing challenges,and outlines future research *** comprehensive analysis aims to establish more effi-cient,precise,and tailored construction processes,paving the way for advanced IBM.

关键词： industrialized building off-site assembly automated factory assembly AI applications in construction robot manufacturing

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A Meta-Model to Support Compatibility Testing of Cross-Browser Web Application

A Meta-Model to Support Compatibility Testing of Cross-Brows...

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International Conference on Advancements in Computational sciences (ICACS)

作者： Mishal Muneer Uzair Rasheed Muhammad Majid Hussain Department of Computer Science & IT The University of Lahore Lahore Pakistan Department of Computer and Software Engineering College of Electrical and Mechanical Engineering NUST Islamabad Pakistan

ISBN: (数字)9798331533038

ISBN: (纸本)9798331533045

Ensuring the user interface (UI) compatibility of web applications across diverse client-side configurations, including various operating systems and browsers, is a significant challenge due to the extensive range of possible combinations. Traditional tools often struggle to address this complexity, leading to visual inconsistencies and malfunctions. To improve compatibility testing, we propose a meta-model that initiates the process with a checklist covering critical configurations across different browsers. This checklist is then translated into Interaction Flow Modeling Language (IFML) constructs, enabling a model-driven approach to compatibility assessment. By leveraging this checklist-IFML integration, we systematically generate test cases that target compatibility issues more effectively. Our approach is validated through the Laptop-Web Case Study, where we assess compatibility by comparing checklist items with IFML constructs within the case study’s domain model. The results show that our approach enhances the accuracy and efficiency of compatibility testing, addressing a core gap in current methods. This research introduces a structured, model-driven method for compatibility testing, providing a more reliable framework for identifying and mitigating UI compatibility issues in web applications across varying client-side configurations.

关键词： Visualization Translation Flow production systems Computational modeling Operating systems User interfaces Reliability engineering Browsers Complexity theory Testing

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Real-World Implementation of Offline Model-Free Reinforcement Learning for Thermostat Control

Real-World Implementation of Offline Model-Free Reinforcemen...

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2025 IEEE International Conference on Mechatronics, ICM 2025

作者： Ghane, Sara Elmaz, Furkan Jacobs, Stef Huybrechts, Thomas Verhaert, Ivan Mercelis, Siegfried Mannens, Erik University of Antwerp - imec IDLab - Faculty of Applied Engineering Antwerp Belgium University of Antwerp EMIB Faculty of Applied Engineering Antwerp Belgium University of Antwerp - imec IDLab Department of Computer Science Antwerp Belgium

ISBN: (纸本)9798331533892

This study introduces a model-free, offline Reinforcement Learning (RL) approach for optimizing the thermostat control in heating systems. Specifically, historical data from a real-world building was used to train the RL agent that is based on the Dueling Double Deep Q-Network (DQN) algorithm. The RL agent's objective is to balance thermal comfort with energy savings, by controlling indoor temperature set points. Traditional rule-based thermostat control methods often depend on fixed schedules and lack adaptability to varying conditions, resulting in energy inefficiencies. Training the RL model offline eliminates the requirement for live interaction, thereby preventing potential disruptions to occupant comfort. During evaluation, the RL model achieved an 18.66% reduction in energy use compared to a rule-based controller, while maintaining recommended indoor temperature levels. © 2025 IEEE.

关键词： Reinforcement learning

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