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Enhancing Educational Video Discovery Using Advanced Latent Semantic Analysis

Procedia computer Science 2025年 252卷 784-795页

作者： B. Sindhu A. Bhaskar G. Yugesh S. Reshma B. Rohit Assistant Professor Department of Computer Science and Engineering Godavari Global University Rajamahendravaram -533296 Andhra Pradesh India

The rapid growth of online educational videos has created abundant learning resources, but also has challenges in discovering high-quality, relevant content. This project mainly focuses on video-based content analysis and providing high-quality learning sources for scholars, easing the best possible way to discover accurate resources. Existing Systems often rely on round-robin, random selection which fails to grasp semantics and linguistic complexity. The proposed system focuses on content-based analytics using NLP, where the extraction of transcripts from videos, perform relevance assessment which maps content to concept and extract insights using Latent Semantic Analysis which effectively captures underlying structures in the transcript. Complexity Assessment measures readability metrics using the Flesch-Kincaid Grade Level and SMOG Index. Created a web-based interface for easy access to educational videos, with built-in analytics to assess relevance. The insights are shared with scholars to refine content and better meet user requirements.

关键词： Latent Semantic Analysis NLP(Natural Language Processing) SMOG(Simple Measure of Gobbledygook) Index Topic Modelling TF-IDF (Term frequency-inverse document frequency) Content-based filtering

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Design and Simulation of Microstrip Patch Antennas with Textile Dielectrics for Body Sensor Networks (BSNs) and Wearable IoT Applications

Design and Simulation of Microstrip Patch Antennas with Text...

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International Conference on Advances in Data Science and Computing Technologies, ASDC 2022

作者： Mahamuni, Chaitanya Vijaykumar Assistant Professor Department of Computer Engineering Fr. Conceicao Rodrigues Institute of Technology University of Mumbai Navi Mumbai Maharashtra Vashi India

ISBN: (纸本)9789819936557

Wireless Body Area Networks (WBANs) connect sensors, i.e., independent nodes or computing devices located in the clothes, on the human body, or under a person’s skin, expanding over the whole body and connected through wireless networks. WBANs find a wide range of applications in the medical sector, like implanting Body Sensor Networks (BSNs) for cancer screening, cardiovascular (CVD) monitoring, remote health monitoring systems, and telemedicine. They are also used for non-medical applications like sports and fitness monitoring, physical state monitoring, biometrics, etc. A textile antenna uses wearable, durable, and flexible textiles as either dielectric substrates or conductive materials. Microstrip patch antennas are widely used in WBAN and IoT applications due to their numerous advantages, like high gain, directivity, and ease of construction. In addition, these antennas have other safety considerations, e.g., they well isolate the radiating element from the body to meet the rated Specific Absorption Rate (SAR). In this paper, microstrip patch antennas with different types of textile dielectrics and conducting materials have been designed and simulated in MATLAB r2020a. According to the simulation results, textile antennas are suited for use with GSM, GPS, WLAN, and other technologies operating in the Industry Scientific Medical (ISM) band of frequencies. Furthermore, a comparative analysis of the results shows that the microstrip antenna with the flectron patch and the polyurethane dielectric gives the highest gain of 26.96 and an efficiency of 99%. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Efficiency

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Characterization and Micro Structural Analysis of Concrete Using Industrial By-Products and Incinerated Ash

Characterization and Micro Structural Analysis of Concrete U...

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International Conference on Recent Trends in Infrastructural Development and Sustainable Materials, IC-RTIDSM 2023

作者： Dambhare, Pranita S. Dhale, S.A. Das, A. Tajne, K.M. Yadav, A. Ph.D Research Scholar Department of Electronics and Computer Science R T M Nagpur University Nagpur India Principal & Professor Priyadarshini College of Engineering Nagpur India Assistant Professor Priyadarshini College of Engineering Nagpur India Associate Professor Government College of Engineering Nagpur India Assistant Professor Yashwantrao Chavan College of Engineering Nagpur440033 India

ISBN: (纸本)9783031821325

For the last vicennial, scientists have been focusing attention on mitigating the risks of environmental contamination brought on by the manufacturing of industrial waste and cementitious materials. Finding efficient waste to effectively incorporate into concrete production is the most feasible approach. Incinerated ash, an industrial by-product, offers unique qualities and long-term advantages that make it suitable as a building material. As a result, the characteristics of waste material are thoroughly covered in this paper to assess how effective it is as filler or cement substitute in concrete. This study examines the impact of replacing silica fume in concrete with incinerated ash on the material's strength and penetration capabilities. To achieve this, four concrete mixes containing four different percentages of silica fume (6, 8, 10, and 20%) and incinerated ash (4, 6, 8, and 10%) were prepared, along with a control concrete mix. It was noted that the control-mix concrete was easier to work than the waste-mix concrete. These investigations verified that 4% incinerated ash replacement combined with 8% silica fume results in improved mechanical properties and elevated pozzolanic reactivity. In the meanwhile, further study is required, particularly in the area of durability details, to assess the comprehensive performance of concrete made from industrial and incinerated ash. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Concrete mixtures

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Plastic Waste Management Strategies: A Sustainable Approach

Plastic Waste Management Strategies: A Sustainable Approach

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National Conference on Technological Advancements in Waste Management: Challenges and Opportunities, TAWMCO 2023

作者： Pandey, Srijan Singh, Sudheer Kumar Singh, Ranjana Scholar Department of Economics National P. G. College UP Lucknow India Associate Professor Department of Computer Science and Engineering Galgotias University UP Greater Noida India Assistant Professor Department of Economics National P. G. College UP Lucknow India

ISBN: (纸本)9789819760237

Plastic pollution has become a global environmental crisis, with detrimental impacts on ecosystems, economy, and human health. To address this challenge, effective plastic waste management strategies are essential. This research paper focuses on the issue of plastic waste and the management strategies to be adopted in India. The paper emphasizes the necessity of innovative waste management techniques to tackle this problem on a larger scale. Despite notable efforts to tackle the problem in India, an alarming plastic waste approximately 3.8 million tons remains uncollected or improperly disposed of each year, which contributes to pollution. Drawing on existing literature and case studies, the paper examines the environmental, economic, and social dimensions of plastic waste management. The findings underscore the importance of holistic and collaborative efforts involving government policies, industry initiatives, public awareness campaigns, and technological innovations to achieve a circular economy and mitigate the adverse effects of plastic pollution. Key strategies include the implementation of extended producer responsibility (EPR) laws, the promotion of circular economy principles, investment in recycling infrastructure, public awareness campaigns, and international cooperation. The paper offers a set of recommendations that are based on solid evidence and propose a more sustainable and environmentally friendly approach. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Circular economy

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Comparative Evaluation of Deep Learning Models in Alzheimer’s Disease Diagnosis

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Procedia computer Science 2025年 258卷 2352-2361页

作者： Leena Arya Yogesh Kumar Sharma Smitha Nayak Janaki Sivakumar Assistant Professor Computing Department Muscat College Oman Associate Professor Programme leader Computer security and forensics Department of Computer Science and Creative Technologies Global College of Engineering and Technology Muscat Oman

Alzheimer’s Disease (AD) is an ever-evolving neurodegenerative confusion that influences the neurons of the mind in a manner that hinders the patients’ ability to solve problems and remember issues and that requires timely and proper identification to enable appropriate management and treatment. Based on MRI medical images, deep learning models have become one of the most effective approaches for analyzing the signs of the early stages of AD. This research, therefore, carries out a comparative analysis of five deep learning models such as Convolutional Neural Networks (CNNs) like AlexNet, VGGNet, and ResNet, as well as Long Short-Term Memory (LSTM) networks and 3D Convolutional Neural Networks (3D-CNNs) in the finding of Alzheimer’s Sickness from medical imaging data. Each model’s arrangement parameters, for example, layer configurations, learning rate, batch size, optimizer, number of epochs, loss function, applying techniques for regularization, activation functions, and the input dimensions, using pre-trained weights, were carefully initialized to yield a proper comparison between the models. Diagnostic performances were evaluated utilizing precision, responsiveness(recall), specificity, and region under the receiver operating characteristic curve (AUC-ROC). ResNet outperformed other profound learning models in diagnosing Alzheimer’s Disease from MRI scans with a precision of 0.91 and AUC-ROC of 0.95. At the same time, VGGNet, AlexNet, LSTM, and 3D-CNNs demonstrated varying strengths and limitations in balancing accuracy, computational efficiency, and feature extraction.

关键词： Artificial Intelligence (AI) Cognitive Impairment Data Augmentation Diagnosis Accuracy Medical Imaging

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Quantum-Accelerated Hyperparameter Tuning for Dynamic NLP Models

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Procedia computer Science 2024年 235卷 506-519页

作者： Ravikumar S Arockia Raj Y R. Babu Vijay K R. Ramani Associate Professor Department of Computer Science and Engineering Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Chennai Tamilnadu India 600062 Assistant Professor Department of Computer Science and Engineering PSNA College of Engineering and Technology Dindigul India Assistant Professor Department of Computational Intelligence Faculty of College of Engineering and Technology SRM Institute of Science and Technology Chennai Tamilnadu India 603203 Assistant Professor Department of Computer Science and Engineering Rajalakshmi Engineering College Chennai Tamilnadu India 602117 Associate Professor Department of Computer Science and Engineering P.S.R. Engineering College Sivakasi Tamilnadu India 626140

In the rapidly evolving field of natural language processing (NLP), performance optimization of large-scale NLP models is crucial. Through the application of Quantum-Accelerated Hyperparameter Tuning (QAHT), this abstract introduces a novel approach to addressing this issue. Our proposed framework leverages quantum computing capabilities to dynamically optimize NLP model hyperparameters in real-time, catering to the ever-changing character of textual data streams. Traditional hyperparameter optimisation methods usually rely on laborious grid searches or random exploration, which may not be suitable for dynamic NLP jobs. Contrarily, QAHT uses Quantum Neural Network (QNN) architectures that have been specially designed for hyperparameter optimisation. These QNNs improve performance and efficacy by dynamically modifying and improving model configurations. This abstract discusses the key elements of the QAHT architecture, including real-time model deployment, adaptive learning, and continuous data stream processing. In addition to speeding up the hyperparameter optimisation process, QAHT makes sure that NLP models are still flexible and responsive to shifts in the sentiment of the data and its distribution. This method has applications beyond NLP since it provides a foundation for effectively optimising machine learning models in complex, real-time situations. As quantum computing develops, QAHT represents a promising future in machine learning, where quantum-enhanced capabilities satisfy the needs of contemporary data-driven applications.

关键词： Hyperparameter optimization Quantum computing Natural language processing (NLP) Quantum Neural Networks (QNN) Real-time processing Adaptive learning

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Heart Rate Anomaly Detection in Healthcare Using Elliptic Envelope and Local Forest

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Procedia computer Science 2025年 258卷 1677-1687页

作者： Chandralekha E S. Vinodhini Kandasamy V Rama P Assistant Professor Department of Computer Science and Engineering Vel Tech Rangarajan Dr.Sagunthala R&D Institute of Science and Technology Chennai Tamilnadu India-600062 Assistant Professor Department of Information Technology Velammal Engineering College Chennai Tamilnadu India-600062 Assistant Professor Panimalar Engineering College Chennai Tamilnadu India – 600123 Assistant Professor Department of Computing Technologies College of Engineering and Technology Faculty of Engineering and Technology SRM Institute of Science and Technology Chennai Tamilnadu India-603203

Medical professionals use Anomaly Detection(AD) to identify patients with potential health problems in the heart rate data (HRD), an essential metric related to cardiovascular health. In this research, the most effective AD techniques were identified in detecting unusual variations of the heart rate pattern. Synthetic HRD data with anomalous rates of 15%, 20%, and 25% was generated and standardized it using min-max scaling for consistency among the datasets. Normal HR was based on function with a mean of 70 and a standard deviation of 5 while anomalous values were generated with a mean of 120 and a standard deviation of 10. Among the evaluated models (Elliptic Envelope, one-class SVM, Local Outlier Factor, and Isolation Forest), the Elliptic Envelope (EE) and Isolation Forest (IF) models obtain the best scores in terms of recall and precision – 0.92 and 0.89, respectively. Overall, the results of this research indicate that equally robust AD models should be implemented within clinical systems and wearable devices that serve as a digital space for early cardiac risk identification, prompting adequate intervention and, as a result, health improvement.

关键词： Anomaly Detection Heart Rate Data Isolation Forest Local Outlier Factor One-Class SVM Elliptic Envelope

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Detection and Prediction of Bond Degradation for Piezo Impedance-Based Structural Health Monitoring (PISHM) Using Hybrid Deep Learning Model 4th

Detection and Prediction of Bond Degradation for Piezo Imped...

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4th Structural Integrity Conference and Exhibition, SICE 2022

作者： Parida, Lukesh Moharana, Sumedha Giri, Sourav Kumar Research Scholar Department of Civil Engineering Shiv Nadar Institute of Eminence Dadri201314 India Postdoctoral Researcher Indian Institute of Technology Bombay Mumbai India Associate Professor Department of Civil Engineering Shiv Nadar Institute of Eminence Dadri201314 India Assistant Professor School of Computer Engineering KIIT Deemed to be University Odisha Patia Bhubaneswar756001 India

ISBN: (纸本)9789819763665

In past years, sensor-based health diagnostics have shown significant potential for electromechanical impedance (EMI) techniques with many practical implementations. In order to enhance the effectiveness of health monitoring systems, it is crucial to examine the bonding interface between the piezoelectric sensor (PZT) and the host structure. A promising approach to achieve this goal is the utilization of Sn–Ag bonding, which offers improved resistance to moisture, chemicals, and temperature compared to traditional epoxy adhesive bonding. The Sn–Ag bond layer presents a novel attachment method to the structural design, which may ultimately enhance the sensitivity of the health monitoring system. This study used EMI techniques to instrument a unique Sn–Ag alloy-based bond and compared it against an epoxy adhesive bond for better sensing and actuation. Later, both the bonds were cured, and the bond performances were analyzed through conductance and susceptance signatures. Moreover, three different bond layer damage conditions, i.e., incipient, moderate, and severe, were chosen to check the performance of the bond layer through admittance signatures. The EMI techniques rely on pristine data, which presents significant constraints when applied to pre-existing structures. Furthermore, the experimental results were used to predict baseline data through deep learning classifications, i.e., convolutional neural network (CNN), long short-term memory (LSTM), and CNN-LSTM hybrid model for all the bond layer damage conditions. The experimental results were compared against the chosen deep learning model for futuristic prediction. The efficiency of all the deep learning models is quantified using performance metrics like accuracy and R2 score. The present study demonstrates that the hybrid model yields superior performance compared to the other two models for predicting Sn–Ag and adhesive bond layer damage. The proposed framework aims to replace traditional optimization algorithm

关键词： Structural health monitoring

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A novel random finite element model for holistically modeling of the frost effects on soils and cold region pavements

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Journal of Infrastructure Preservation and Resilience 2022年第1期3卷 1-14页

作者： Dong, Shaoyang Jiang, Yusheng Yu, Xiong Graduate Research Assistant Department of Civil and Environmental Engineering Case Western Reserve University ClevelandOH United States Opal J. and Richard A. Vanderhoof Professor and Chair Department of Civil and Environmental Engineering Professor of Electrical Engineering and Computer Science Case Western Reserve University ClevelandOH United States

This paper describes the development of a random finite element model (RFEM) that allows holistic simulation of frozen soil behaviors, including the effects of phase transition and the consequent internal stress and volume changes. The performance of the model is firstly validated with laboratory experiments. The model is implemented to simulate the effects of frost action on pavement. The coupled thermal-mechanical actions including the mechanical responses of subgrade soils subjected to freezing temperature and its effects on the pavement structure are analyzed. The results show that the frost action and expansion of ice lenses change the interaction modes between pavement layers. This leads to increase of stress and deformation in the pavement layer. Methods to mitigate the effects of frost heave are analyzed with this model. The simulation results indicate that the detrimental effects of frost heave on the pavement structure can be mitigated by increasing the thickness of base layer, use of thermal insulation layer or improve drainage in the subgrade layer. The RFEM combines the advantages of discrete element model (DEM) in holistically describing the microstructure effects and in the finite element model (FEM) in terms of computational efficiency. This allows to focus research on understanding the behaviors of individual soils phase and their interfacial interactions. © The Author(s) 2022.

关键词： Pavements

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Investigation of Security Attacks in IoMT Devices and Federated Learning as a Mitigation Strategy

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Procedia computer Science 2025年 258卷 3426-3435页

作者： Kiruthika M T Poongodi Assistant Professor Department of Computational Intelligence School of Computing SRM Institute of Science and Technology Kattankulathur Tamilnadu 603203 India Professor Department of Computer Science and Engineering School of Engineering Dayananda Sagar University Bangalore Karnataka India

Internet of Medical Things (IoMT) is transforming the healthcare business with an increasing number of wearables, gadgets, and sensing devices. As a result, several patients are under daily monitoring for their cardio health, blood glucose level, blood pressure, etc. The data generated by these devices are very voluminous, and there is an increasing need for safeguarding these sensitive data in order to protect them from malicious attacks. This paper suggests to incorporate Federated learning model to IoMT as a mitigative measure. It is apt to induct FL into IoMT as it operates on a collaborative environment which do not allow transfer of the raw data. Patients’ data are stored at the client end itself and only model updates are shared to the server. In this way, the data and network attacks can be reduced to a great extent. This work proposes an integrated architecture where the layers of FL and IoMT can be knotted up for ensuring privacy and security. This proposed architecture model, further elaborates a distributed working environment. This paper illustrates how FL helps in securing the data produced by the different IoMT devices through the proposed architecture. The challenges involved in the deployment of FL in the framework of IoMT along with potential research directions are discussed further in this research.

关键词： : Federated Learning IoMT Security Internet of Medical Things IoMT Data Attacks Optimization

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