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Learner’s intention analysis to mitigate the cold start problem in personalized learning recommendation systems

Multimedia Tools and applications 2024年 1-55页

作者： Pal, Saurabh Pramanik, Pijush Kanti Dutta Choudhury, Prasenjit Department of Computer Science and Engineering School of Engineering Brainware University Kolkata India School of Computer Applications and Technology Galgotias University Uttar Pradesh Greater Noida India Department of Computer Science & amp Engineering National Institute of Technology Durgapur West Bengal India

Understanding the learner’s requirements and status is important for recommending relevant and appropriate learning materials to the learner in personalized learning. For this purpose, the learning recommendation system must include the learner’s contextual and behavioral information. However, in the case of cold start problems, the system has no prior information about a new user. Moreover, the system cannot acquire the required information instantly because the automated and instantaneous acquisition of this information is not trivial. This hinders the personalized recommendation for a new learner. Engaging the learner in a questionnaire session may allow the learning system to acquire most of the required information. However, this makes the learner uninterested and disengaged with the system. To address this, we propose a recommendation system for personalized learning that mitigates the cold start problem by acquiring important information by analyzing the learner’s questions submitted to the learning system. By analyzing the meta-information of a question, we identified the learner’s exact requirements, domain and topic knowledge, and expected knowledge gain from the learning material. Ontology, BiLSTM, and context mapping are used for learner modelling and topic classification, and identifying suitable learning materials, respectively. Furthermore, MCDM techniques are used for ranking the learning materials. Our experimental assessment and validation suggest that the information acquired based on our proposed approach is sufficient for understanding a new learner’s needs and suitability and, accordingly, for recommending suitable personalized learning materials. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Recommender systems

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Impact of Hyper Parameter Optimization Over Performance Improvement of CNN Models 5

Impact of Hyper Parameter Optimization Over Performance Impr...

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5th International Conference on Sustainable Communication Networks and Application, ICSCNA 2024

作者： Ganesh Kumar, M. Karthiga Devi, K. Nesarani, A. Department of Computer Applications Krishnankoil India Department of Computer Science and Engineering Andra Pradesh India

ISBN: (数字)9798331530013

ISBN: (纸本)9798331530013

With the use of Convolutional neural Networks (CNN) in medical image processing, researchers focus on improving the accuracy of CNN model in numerous ways. One such concepts booms with optimizing the hyper-parameters of CNN models. Discovering the optimal value of hyper-parameters was a time consuming task and available optimizing techniques fails to provide the boundary of optimal hyper-parameter values. This research article was concerned with optimizing the hyper-parameter during transfer learning focusing the impact of optimal hyper-parameter value and optimization methods on CNN models. In this research we focused on selection of optimal hyper-parameter value and performed a comparative analysis of the optimization methods. Three Optimization algorithms such as Grid Search, Random Search and Bayesian Optimization were used and evaluated with accuracy metrics. It was observed that Bayesian optimization yields best CNN model with accuracy of 85.4% over other algorithms. © 2024 IEEE.

关键词： Convolutional neural networks

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A hybrid encryption for secure data deduplication the cloud

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International Journal of Cloud Computing 2023年第2-4期12卷 295-307页

作者： Ebinazer, Silambarasan Elkana Savarimuthu, Nickolas Bhanu, S. Mary Saira Department of Computer Science and Engineering Cyber Security IIIT Kottayam India Department of Computer Applications National Institute of Technology Tiruchirappalli India Department of Computer Science and Engineering National Institute of Technology Tiruchirappalli India

Cloud computing (CC) is a cost-effective platform for users to store their data on the internet rather than investing in additional devices for storage. Data deduplication (DD) defines a process of eliminating redundant data in the cloud by leaving a single copy. This paper provides a hybrid encryption scheme using genetic algorithm (GA) and attribute-based encryption (ABE) to ensure secure data transmission in the cloud and preventing the data deduplication by using message locked encryption (MLE). The hybrid encryption scheme provides a secure data transmission in the cloud. From the simulation output, the time requirement is reduced up to 94% and consumes 43% bandwidth compared with existing works. The proposed method tends to be quite effective in reducing storage and bandwidth costs. Copyright © 2023 Inderscience Enterprises Ltd.

关键词： Genetic algorithms

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Lane Detection for Autonomous Vehicles with Canny Edge Detection and General Filter Convolutional Neural Network 18

Lane Detection for Autonomous Vehicles with Canny Edge Detec...

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18th INDIAcom;11th International Conference on Computing for Sustainable Global Development, INDIACom 2024

作者： Kishor, S. Nair, Rekha R. Babu, Tina Sindhu, S. Vishnu Vilashini, S. Department of Computer Applications Mangalore India Alliance University Department of Computer Science and Engineering Bengaluru India Dayananda Sagar University School of Engineering Department of Computer Applications Bengaluru India

ISBN: (纸本)9789380544519

Lane detection has been a complex issue that has garnered the attention of the computer vision community for many years. It is a crucial element for self-driving cars and computer vision in general. Lane detection is used to define the path for autonomous vehicles and prevent the risk of drifting into another lane. So proposed a study to develop a method that can detect lane lines in real-time using the OpenCV library and computer vision concepts. To accomplish this, identify the white markings on both sides of the lane. The proposed work focuses on identifying the road lane lines that autonomous cars must adhere to, ensuring that they do not cross into other lanes or drive in the opposite direction, which could lead to accidents. The performance of the work assessed using actual road images and videos captured by the car's front-mounted camera. So proposing a basic and easy algorithm for tacking and detecting lanes. The lanes have been detected through all competitions based on data from the camera, which is carried by algorithms. The proposed work implementing image processing techniques to capture the exact and accurate lane ways and hough transformation techniques. © 2024 Bharati Vidyapeeth, New Delhi.

关键词： Arduino UNO Canny Detection DHT11 Hough Transformation LDR sensor

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Chi-Square Target Encoding for Categorical Data Representation: A Real-World Sensor Data Case Study

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SN computer science 2025年第3期6卷 1-22页

作者： Anitha, M. Savarimuthu, Nickolas Bhanu, S. Mary Saira Department of Computer Applications National Institute of Technology Trichy India Department of Computer Science and Engineering National Institute of Technology Trichy India

Feature engineering is critical for improving machine learning performance (ML), especially when handling categorical data. Traditional encoding methods, such as one-hot and label encoding, often result in challenges like high dimensionality and loss of category significance. To address these limitations, this study introduces a novel Chi-Square Target Encoding (CSTE) approach, which transforms categorical variables into numerical representations by leveraging the chi-square statistic to evaluate the association between categories and the target variable, preserving information effectively. Unlike conventional techniques, CSTE uses Chi-square test values to generate target-based encoded representations, ensuring reliable transformation without information loss. Comprehensive empirical evaluations were conducted on various datasets for binary classification, showcasing CSTE’s superiority over methods like regularized target encoding, basic target encoding, and fuzzification. A case study on real-world sensor data further validated its efficiency and scalability for large-scale data-driven applications. The proposed CSTE method achieved an average increment rate of 3.9447%, surpassing one-hot and fuzzification (1.0430%) and regularized target encoding (1.8308%). Classification outcomes demonstrated F1-scores exceeding 0.90 and AUC values nearing 0.99 across diverse datasets, highlighting its robustness. Furthermore, the reduced dimensionality significantly enhanced inference time while maintaining high accuracy. The CSTE method offers a robust framework for categorical data transformation, addressing limitations of traditional encoding techniques. It improves the interpretability and efficiency of categorical data representation, boosting ML performance. This innovative approach is well-suited for applications across various domains involving categorical data. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2025.

关键词： Categorical data encoding Chi- Square statistics Data distribution preservation Feature engineering Machine learning Target encoding

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An Effective Hybrid Model of ELM and Enhanced GWO for Estimating Compressive Strength of Metakaolin-Contained Cemented Materials

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computer Modeling in engineering & sciences 2024年第5期139卷 1521-1555页

作者： Abidhan Bardhan Raushan Kumar Singh Mohammed Alatiyyah Sulaiman Abdullah Alateyah Department of Civil Engineering National Institute of Technology PatnaPatnaIndia Department of Computer Engineering and Applications GLA UniversityMathuraIndia Department of Computer Science College of Sciences and Humanities-AflajPrince Sattam Bin Abdulaziz UniversityAl-KharjSaudi Arabia Department of Computer Science College of Science and ArtsQassim UniversityUnaizahSaudi Arabia

This research proposes a highly effective soft computing paradigm for estimating the compressive strength(CS)of metakaolin-contained cemented *** proposed approach is a combination of an enhanced grey wolf optimizer(EGWO)and an extreme learning machine(ELM).EGWO is an augmented form of the classic grey wolf optimizer(GWO).Compared to standard GWO,EGWO has a better hunting mechanism and produces an optimal *** EGWO was used to optimize the ELM structure and a hybrid model,ELM-EGWO,was *** train and validate the proposed ELM-EGWO model,a sum of 361 experimental results featuring five influencing factors was *** on sensitivity analysis,three distinct cases of influencing parameters were considered to investigate the effect of influencing factors on predictive *** consequences show that the constructed ELM-EGWO achieved the most accurate precision in both training(RMSE=0.0959)and testing(RMSE=0.0912)*** outcomes of the ELM-EGWO are significantly superior to those of deep neural networks(DNN),k-nearest neighbors(KNN),long short-term memory(LSTM),and other hybrid ELMs constructed with GWO,particle swarm optimization(PSO),harris hawks optimization(HHO),salp swarm algorithm(SSA),marine predators algorithm(MPA),and colony predation algorithm(CPA).The overall results demonstrate that the newly suggested ELM-EGWO has the potential to estimate the CS of metakaolin-contained cemented materials with a high degree of precision and robustness.

关键词： Metakaolin-contained cemented materials compressive strength extreme learning machine grey wolf optimizer swarm intelligence uncertainty analysis artificial intelligence

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A comparative analysis of using Deep Learning and Machine Learning technologies for intrusion detection for effective network traffic analysis 7

A comparative analysis of using Deep Learning and Machine Le...

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7th International Conference on Circuit Power and Computing Technologies, ICCPCT 2024

作者： Divya, S.B. Mary, S. Prince Department of Computer Science and Engineering Sathyabhama Institute of Science & Technology Chennai India Mangalam College of Engineering Department of Computer Applications India

ISBN: (纸本)9798350372816

The broad utilization of interconnectivity and interoperability of processing frameworks have turned into an irreplaceable need to improve our everyday activities. All the while, it opens a way to exploitable weaknesses that work out positively past human control capacity. The weaknesses consider network protection instruments fundamental to accepting correspondence trade. Secure correspondence requires safety efforts to battle the dangers and necessities headways to safety efforts that counter advancing security dangers. So, emerging technologies like Deep Learning/ Machine Learning (DL/ML) come in handy for integration and better prediction. So, in this paper, we depict various deep learning/ML strategies used in detecting the imbalance in network traffic. The document also includes research that has been proposed on the topic of intrusion detection in network traffic over the previous four years (2020-2024) by a variety of research specialists. The main achievements of the security measures are recognized in this review together with their quantitative and qualitative success indicators. Additionally, as a guide for upcoming discoveries, this research study examines the major discoveries and the rationale for the lessons discovered. © 2024 IEEE.

关键词： Network intrusion

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CNN-DBO: Dung Beetle Optimization Based Urban Area Change Detection Using Satellite Images

CNN-DBO: Dung Beetle Optimization Based Urban Area Change De...

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2025 IEEE International Conference on Computational, Communication and Information Technology, ICCCIT 2025

作者： Pujari, Jambukeshwar Wasim, Javed Tripathi, Aprna Mangalayatan University Department of Computer Engineering and Applications Aligarh20221385 India Mangalayatan University Department of Computer Engineering and Applications Aligarh India Manipal University Department of Data Science Engineering Jaipur India

ISBN: (纸本)9798331512965

In recent years, Geographic Information Systems (GIS) have garnered a significant deal of interest for their ability to detect changes in metropolitan areas. One of the uses of change detection in satellite photographs is in the field of urban planning. Other applications include environmental monitoring and remote surveillance. A number of different approaches have been suggested in recent times for the purpose of automatically identifying alterations in the land cover based on GIS photographs. However, due to poor texture, cluttered context, and considerable object scale disparities, the convolutional neural network (CNN) method is only partially successful in capturing the context from geographic information systems (GISs). This is despite the fact that it remains an effective method for detecting change in urban environments. The article presents the construction of a CNN-based change detection model using the utilization of Dung Beetle Optimization (DBO). The DBO algorithm is used to adjust the loss parameter in order to increase the overall performance. The approach that was proposed makes use of the Onera satellite data set and displays an overall accuracy of 97.50% percent. In light of this, it appears that the strategy that is proposed is both very efficient and effective. © 2025 IEEE.

关键词： Convolutional neural networks

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Parkinson’s disease Severity Detection Based on Fused features with tuned Residual Deep Convolutional Neural Network

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Multimedia Tools and applications 2025年第14期84卷 13111-13131页

作者： Vishwakarma, Gagan Nandanwar, Amit Kumar Thakur, Ghanshyam Singh Computer Science & Engineering Indian Institute of Information Technology MP Bhopal India Computer Science & Engineering Maulana Azad National Institute of Technology MP Bhopal India Department of Computer Applications Maulana Azad National Institute of Technology MP Bhopal India

Parkinson's disease (PD) diagnosis involves the assessment of a variety of motor and non-motor symptoms. To accurately diagnose PD, it is necessary to differentiate its symptoms from those of other conditions. During the diagnosis process, clinicians must consider gait abnormalities as one of the key symptoms. However, relying solely on clinical judgment for the diagnosis of PD may have limitations. In this work, we have proposed a Multikernel SVM (MK-SVM) based Parkinson's severity analysis method. Initially, an adaptive median filter is used to reduce the noise in the gait image. Then, to extract the features, we propose a dual-layer method that employs stacked autoencoders (SAEs) for local feature extraction and residual deep convolution neural networks (RDCNN) for global feature extraction. The hyper-parameters from the RDCNN are optimized using the Enhanced walrus optimization algorithm (EWO). In the final step, we use MK-SVM to classify PD according to severity. This model is evaluated using the GAIT-IT and GAIT-IST datasets. With the help of Python, we evaluated the proposed model in terms of precision, accuracy, recall, specificity, and F-scores, and the model achieved a maximum accuracy of 97.3%. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Feature extraction

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AdamW-RSO DBN: AdamW-Rat Swarm Optimized deep belief network for software vulnerability detection

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Multimedia Tools and applications 2024年 1-23页

作者： Shaji, Lijin R, Suji Pramila Department of Computer Applications Noorul Islam Centre for Higher Education Tamil Nadu Thuckalay629180 India Department of Computer Science and Engineering Mar Baselios Institute of Technology and Science Kerala Kothamangalam686693 India

During the software development phase, vulnerability detection needs huge interest to make it less vulnerable and secure. Every time, the vulnerable software provokes hackers to make malicious activities and interrupts the software operation which results in millions in financial losses to the software firm. To minimize the losses, there are reliable and effectual vulnerability detection systems developed by security communities aspiring to recognize software vulnerabilities earlier in the development or testing stage. One such system is deep learning which can detect security vulnerabilities in the software. This work develops an optimized deep-learning system for detecting software vulnerabilities. Feature fusion approach is developed using canonical correlation analysis and Deep Residual Network (DRN) so that all the features in the input open-source projects are utilized without negligence. Moreover, an algorithm, AdamW- Rat Swarm Optimizer (AdamW-RSO), is proposed by combining AdamW and RSO, to train the DBN that is adopted to perform vulnerability detection. Finally, the detection performance is examined by utilizing metrics, such as recall, precision, and F-measure on a function-level vulnerable dataset. The test results reveal that the AdamW-RSO-based DBN has obtained superior detection performance. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Network intrusion

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