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检索条件"机构=School of Computer Science& Engineering KLE Technological University"

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Student Internship Experiences and Learning Outcomes - A Qualitative Study 13

Student Internship Experiences and Learning Outcomes - A Qua...

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13th IEEE International Conference on Teaching, Assessment and Learning for engineering, TALE 2024

作者： Hegade, Prakash Shettar, Ashok School of Computer Science and Engineering KLE Technological University Hubli India Pro Chancellor KLE Technological University Hubli India

ISBN: (纸本)9798350376234

In engineering program tenure, most students get an opportunity to intern in a company and experience the professional life before they actually earn a degree and be employed. This practice helps them to be prepared with the professional etiquettes and helps them bridge the academic and professional lifecycle. The purpose of this work is two-fold. The work attempts to understand how students perceive the relevance of their academic coursework to their internship experiences and the recommendations that can be made to optimize the course structure design and learning outcomes to align with the key elements of the internships. The research questions are formulated on the grounds of identified purpose. With a pragmatic philosophical assumption and reflective practitioner model as conceptual framework, qualitative research method was adapted. With two cycles of coding process: descriptive, vivo and focused coding were used for data analysis. Data was collected using semi-structured interviews and survey forms. Self-selection was used for sampling and a total of 29 participants were part of the study. Students who participated in the interview were final year students of [name removed]. Themes were generated using the process that can guide the institutes to re-write the learning outcomes. Gaps were identified in the process and an analysis was carried out on how students find industry different from academics. The work brings out the differences in academic and professional settings in identified five themes. While the academics need to draft learning outcomes with professional objectives starting from lower semesters, industries need to collaborate in designing them as well. The work presents five themes on the areas of process of learning, skill set, technology and application of core courses in professional practices. © 2024 IEEE.

关键词： academics industry institutes internship experiences learning outcomes student

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Implementation of Basic Mathematical Operations on Openpower-ISA of Libresoc 12th

Implementation of Basic Mathematical Operations on Openpower...

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12th International Conference on Soft Computing for Problem Solving, SocProS 2023

作者： Bilal, Maliha Simeen Chickerur, Satyadhyan Giraddi, Shantala School of Computer Science and Engineering KLE Technological University Karnataka Hubballi580031 India Centre of High-performance Computing School of Computer Science and Engineering KLE Technological University Karnataka Hubballi580031 India School of Computer Science and Engineering KLE Technological University Karnataka Hubballi580031 India

ISBN: (纸本)9789819731794

In this era, it is observed that electronic system gadgets are developed every day with the most upcoming features which are beneficial and convenient for the user. SoCs, Softcores, and Softprocessors play a vital role in developing such systems. Softcore is the digital circuitry that is implemented using logic synthesis. One such Softcore used by authors here is Libresoc. This Softcore precisely focuses on designing a SoC which contains all the functionalities of 3D GPU, CPU, and VPU. The main reason for choosing Libresoc as the base softcore is that it uses Openpower-ISA 3.0. The main objective of the proposed solution is to implement basic mathematical operations such as Addition, Subtraction, Multiplication, and Division on the decoder test cases of Openpower-ISA. This solution can act as the base for the further development of SoCs with high-end features. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： System-on-chip

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A Hybrid Deep Learning Model with Consensus-Based Feature Selection for DDoS Attacks Detection in SDN 4

A Hybrid Deep Learning Model with Consensus-Based Feature Se...

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4th International Conference on Evolutionary Computing and Mobile Sustainable Networks, ICECMSN 2024

作者： Kachavimath, Amit V. Narayan, D.G. Department of Master of Computer Applications KLE Technological University Hubballi580031 India School of Computer Science and Engineering KLE Technological University Hubballi580031 India

Software-Defined Networking (SDN) increases network flexibility by decoupling network control from hardware. However, this also makes networks more vulnerable to Distributed Denial of Service (DDoS) attacks, that can severely disrupt operations. Existing detection methods typically focus on specific DDoS attacks, highlighting the necessity for more comprehensive detection strategies. Our proposed methodology presents a resilient technique for detecting DDoS attacks by employing a hybrid deep learning approach. Utilizing the InSDN dataset tailored for SDN environments, we employ an advanced feature selection process based on a consensus approach to identify the best eight features, enhancing detection accuracy and efficiency. Cross entropy is utilized at the control plane to detect anomalous activity by computing the entropy between source IP (Internet Protocol) and destination IP data to identify DDoS attacks. Our proposed model integrates Long Short-Term Memory (LSTM) and Convolutional Neural Networks (CNN) networks, achieving a detection accuracy of 99.9%. The CNN provides efficient spatial feature extraction, while LSTM captures temporal dependencies, enhancing the model's capability to detect complex attack patterns. We also implement a comprehensive evaluation framework, including metrics such as model loss, model accuracy, and confusion matrix. © 2025 The Authors. Published by Elsevier B.V.

关键词： Feature Selection

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An Efficient DDoS Attack Detection in SDN using Multi-Feature Selection and Ensemble Learning 4

An Efficient DDoS Attack Detection in SDN using Multi-Featur...

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4th International Conference on Evolutionary Computing and Mobile Sustainable Networks, ICECMSN 2024

Software-defined networking (SDN) has significantly enhanced network agility by decoupling network control from hardware devices. This architectural shift exposes SDN infrastructures to increased risks from Distributed Denial of Service (DDoS) attacks, which can severely disrupt network services and render them unavailable to legitimate users. Despite the existence of various techniques for detecting specific types of DDoS attacks, there remains a need for more comprehensive work capable of accurately identifying multiple attack categories. We have addressed the gap by proposing a robust DDoS attack detection method in the SDN. We utilize advanced machine learning (ML) algorithms to analyze the InSDN dataset, employing advanced feature extraction techniques to improve detection accuracy and efficiency. Three feature extraction methods, Se-lectKBest, ANOVA F-value scores, and feature importance scores from RandomForest Classifier, were used to select the best ten features from 81. This feature selection, combined with ensemble learning, achieved an accuracy of 99.9%. The Receiver Operating Characteristic (ROC) curve, confusion matrix, and K-fold cross-validation were used to evaluate the performance of the proposed model. © 2025 The Authors. Published by Elsevier B.V.

关键词： Adversarial machine learning

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A Deep Learning Approach for Accurate Environmental Sounds Analysis 5

A Deep Learning Approach for Accurate Environmental Sounds A...

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5th IEEE International Conference for Emerging Technology, INCET 2024

作者： Kademani, Vinay Anmol, A. Patil, Priyadarshini Meena, S.M. School of Computer Science and Engineering KLE Technological University Karnataka Hubli India

ISBN: (纸本)9798350361155

This study presents a novel approach to enhance the classification clarity of ambient sounds as an input data type for this study we employed a data collection of ten classes that are in the audio *** preprocessing of the audio data is to be done before entering it into our machine learning model this entails transforming audio signals into appropriate formats such as tonnetz, mfccs and spectrograms in our study we have employed cnn to normalise the data and predict the ambient sound using machine learning methods the initial conv1d layer which is activated by relu has 32 filters and a kernel size of 3 there are 64 filters in the second conv1d layer a kernel size of as well as ReLU initiation. A MaxPooling1D layer with a pool size of two comes after each convolutional layer to help minimise the spatial dimensions of the data intended for one-dimensional input data, perhaps for a classification task with ten classes. Convolutional and pooling layers are used to extract hierarchical features, and fully linked layers are then used for classification. Class probabilities are provided via the output layer's softmax activation, while the dropout layer is present to improve generalisation. The above mentioned machine learning models enable us to classify ambient sounds with 91.12% accuracy, exceeding the current CNN-based models' 84% accuracy. his study presents a novel approach to enhance the classification clarity of ambient sounds as an input data type for this study we employed a data collection of ten classes that are in the audio *** preprocessing of the audio data is to be done before entering it into our machine learning model this entails transforming audio signals into appropriate formats such as tonnetz, mfccs and spectrograms in our study we have employed cnn to normalise the data and predict the ambient sound using machine learning methods the initial conv1d layer which is activated by relu has 32 filters and a kernel size of 3 there are 64 filte

关键词： Chemical activation

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Classification and Determination of Parameters for 3D-SEM Images using Deep Learning and the Novel Algorithm CompEx 15

Classification and Determination of Parameters for 3D-SEM Im...

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15th International Conference on Computing Communication and Networking Technologies, ICCCNT 2024

作者： Patil, Ranispoorti Ravindra Jayalaxmi, G.N. Choudapur, V.H. Baligar, Vishwanth P. Kle Technological University School of Computer Science & Engineering Karnataka Hubballi580031 India Kle Technological University School of Advanced Science Karnataka Hubballi580031 India Kle Technological University School of Computer Science Karnataka Hubballi580031 India

ISBN: (纸本)9798350370249

With an emphasis on Field-Emission Scanning Electron Microscope (FE-SEM) images, our goal is to enhance, classify, and segment 3D images acquired using a Scanning Electron Microscope (SEM). Based on the kind and concentration of doping present in the samples, these images are divided into three groups: Nd (Neodymium) doped cobalt chromite, La (Lanthanum) doped cobalt chromite and cobalt chromite in pure form. Our dataset includes ninety-three photos taken at different magnifications, offering a wide variety for additional processing and analysis. The effectiveness and appeal of the images are improved by the use of augmentation techniques. We use a new technique and pre-existing classical Deep Learning (DL) models to classify SEM pictures into different classes. While the 3D Residual Networks (3D ResNet) model achieves 40% average testing accuracy, the 3D Convolutional Neural Networks (CNN) model shows 50% accuracy. But the categorization procedure is complicated by the similarities and complexity seen in each class of images as well as the inclusion of different magnification levels. We provide a unique algorithm 'CompEx' (Integrated Image compression and Feature extraction), a approach based on modular arithmetic to to identify unique characteristics from the pictures and to overcome these difficulties. In addition, we evaluate Cobalt Chromite samples and assess factors like porosity, particle size, and distribution using a variety of computer vision and image processing techniques. © 2024 IEEE.

关键词： Convolutional neural networks

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Enhancing Learning and Engagement in computer Organization and Architecture through Hands-On Activity 10

Enhancing Learning and Engagement in Computer Organization a...

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10th Research in engineering Education Symposium: Connecting Research-Policy-Practice for Transforming engineering Education, REES 2024

作者： Nayak, Aruna S. Hiremath, Namrata D. Umadevi, F.M. Patil, Preeti B. School of Computer Science & Engineering KLE Technological University Hubballi India Centre for Engineering Education Research KLE Technological University Hubballi India

ISBN: (纸本)9781713893813

Context The educational phenomenon being studied in this paper is the integration of hands-on activities in the teaching and learning of computer Organization and Architecture (COA), a foundational subject in computer science and engineering. The paper aims to explore how incorporating hands-on experiences can enhance the learning process, reinforce complex concepts and develop problem-solving and critical thinking skills. Purpose or Goal Given the intricate nature of COA, which encompasses a multitude of theoretical concepts and models, it becomes imperative to provide students with immersive hands-on activities that serve to enhance their comprehension of these complexities. Moreover, active engagement in practical exercises not only enriches the learning trajectory but also imparts students with invaluable tangible knowledge. This effectively narrows the gap between theoretical understanding and practical application. Methods The study compared two student groups: one focused on individual processor modules called as control group (CG) and the other, on designing a complete processor which was the experimental group (EG). Both groups aimed to develop critical thinking, problem-solving, teamwork, communication and technical skills. The research assessed skill development using rubrics and employed statistical analysis to compare the two groups. This paper details the EG's activity and skill attainment. In this hands-on activity, approximately 250 students in EG were divided into teams and tasked with designing and simulating a processor datapath based on specific design requirements (Hamacher, V. C., Vranesic, Z. G., Zaky, S. G., Vransic, Z., & Zakay, S. (1996), computer organization, McGraw-Hill). The activity emphasized effective instructional strategies, teamwork and scaffolded learning experiences, offering step-by-step guidance and opportunities for exploration and experimentation. Pre- and post-activity conceptual assessments were conducted to measure studen

关键词： Students

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Cycle-Consistent Generative Adversarial Network Based Approach for Denoising CT Scan Images 4

Cycle-Consistent Generative Adversarial Network Based Approa...

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4th International Conference on Evolutionary Computing and Mobile Sustainable Networks, ICECMSN 2024

作者： Kuraning, Vidya Giraddi, Shantala Baligar, Vishwanath P. School of Computer Science and Engineering KLE Technological University Karnataka Hubballi580031 India

Computed Tomography (CT) plays a pivotal role in detecting and observing various medical conditions. Although low-dose CT scans are frequently chosen to minimize radiation exposure, the image quality is degraded and substantial noise is produced. Important details may be obscured by the added noise. It is challenging for conventional image denoising techniques to strike a compromise between minimizing noise and keeping image details. Hence, minimizing radiation exposure while maintaining image quality makes denoising low-dose CT images a critical challenge in medical imaging. This study investigates the application of CycleGAN, a deep learning-based model, for this purpose. CycleGAN model makes use of a U-net based generator and PatchGAN based discriminator. A conventional UNet model and a Gaussian filter were compared with the CycleGAN model. Structural Similarity Index (SSIM) and peak signal-to-noise ratio (PSNR) measures were utilized to assess the models' functioning in terms of quality of image and structural similarity. The CycleGAN model performed better than the UNet and Gaussian filters, according to the results, striking an improved harmony between detail preservation and noise reduction. The results indicate that CycleGAN is a promising low-dose CT image denoising technique that can be used to improve diagnostic accuracy while exposing users to as little radiation as possible. © 2025 The Authors. Published by Elsevier B.V.

关键词： Medical imaging

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DefogNet: A Residual Network for Removal of Fog Using Weighted Combination Loss 4th

DefogNet: A Residual Network for Removal of Fog Using Weight...

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4th International Conference on Recent Trends in Machine Learning, IoT, Smart Cities, and Applications, ICMISC 2023

作者： Kulkarni, Uday Chikkamath, Satish Patil, Vinod Raichur, Ankit Siddesha, M. Math, Supreet School of Computer Science Engineering KLE Technological University Hubballi Hubballi India School of Electronics and Communication Engineering KLE Technological University Hubballi Hubballi India

ISBN: (纸本)9789819994410

Fog is a common atmospheric phenomenon that significantly affects outdoor images and videos, causing reduced visibility and image quality. Various image processing techniques have been proposed for fog removal, but most of them require prior knowledge about the scene or complex models, which can be computationally expensive. In this paper, we propose a deep learning-based technique called DefogNet for removing fog from images. DefogNet is a residual network that uses a weighted combination loss function to generate visually pleasing and perceptually accurate results. The network is trained end-to-end using a dataset of foggy and clear images, and it can efficiently restore the visibility of images without requiring any prior knowledge of the scene. Experimental results show that the proposed method outperforms state-of-the-art methods in terms of both quantitative metrics and visual quality. The proposed approach can be useful in various applications, including surveillance, self-driving cars, and outdoor photography. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Fog

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A Computational Approach to Classify Empathy Expressed in Text-Based Student Reflections 13

A Computational Approach to Classify Empathy Expressed in Te...

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13th IEEE International Conference on Teaching, Assessment and Learning for engineering, TALE 2024

作者： Dyavappanavar, Sanjana Amashi, Radhika Vijayalakshmi, M. Kle Technological University School of Computer Science and Engineering Hubballi India Kle Technological University Centre for Engineering Education Research Hubballi India

ISBN: (纸本)9798350376234

Empathy is the ability to understand and share the feelings or perspectives of others [1]. Empathy is a complex psychological and emotional concept that involves understanding and sharing the feelings, thoughts, and perspectives of others. In the realm of engineering education, understanding and fostering empathy among students is paramount for promoting social and emotional learning [2]. While there are studies in literature, where researchers and educators are trying new pedagogies to foster empathy in engineering students, it is often not reported on how can empathy be identified from their artifacts. Therefore, this study aims to use the existing RoBERTa model (application of natural language processing) for predicting the type of empathy exhibited by students on real-world problems in the large corpus of 550 reflective essays. This study addresses the following research question:How does the proposed model help in predicting the 3 levels of empathy in student's reflections?The anticipated outcome of this study is to find three distinct categories of empathy: cognitive, affective, and prosocial behavior, and classify students' written reflections to align with these categories. By harnessing the power of advanced language models, we aim to automate the classification of empathetic responses, shedding light on the empathetic inclinations of students. The results hold promise for educators, offering insights into students' empathetic responses to real-world issues, thereby enabling the design of interventions and engineering curricular enhancements. As engineering education continues to emphasize empathy as a vital skill, this research presents a forward-looking approach to assess the empathetic responses of students in the modern educational landscape. © 2024 IEEE.

关键词： Students

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