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Impact of Learning Style based Teaching on Students Performance in a Formal Methods Course for computer Scientists 11

Impact of Learning Style based Teaching on Students Performa...

引用

11th IEEE International Conference on Teaching, Assessment and Learning for Engineering, TALE 2022

作者： Schorr, Ruth Frankfurt University of Applied Sciences Dept. of Computer Science and Engineering Frankfurt Germany

ISBN: (纸本)9781665491174

Teaching a formal methods course can be challenging. Students perceive formal methods as difficult, too mathematical and practical irrelevant. Students show low motivation for the subject, they just want to pass the course with minimal effort. Grades in mandatory formal methods courses are typically below average. In order to address the problems, we analyzed the learning styles of our students. In this work-inprogress paper, we present the changes made to our teaching framework and their impact on student performance. This paper is an experience report on improving performance of master students in formal methods at Frankfurt University of Applied sciences. © 2022 IEEE.

关键词： Formal methods

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A Predictive Study of Feature Encoding and Selection Methods on PTM Lysine Glutarylation Site Allocation 4

A Predictive Study of Feature Encoding and Selection Methods...

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4th International Conference on Electrical, computer and Telecommunication Engineering, ICECTE 2022

作者： Fahad, Md. Salahuddin Mondal, Md. Nazrul Islam Era, Anika Tabassum Rajshahi University of Engineering and Technology Dept. of Computer Science and Engineering Rajshahi Bangladesh

ISBN: (纸本)9798350320541

The post-translational modification, Lysine Glutarylation is an active outcome of protein acylation. To determine the functional behaviors at the molecular level, it is required to have a proper visualization of the translation sites of the PTM. In this regard, machine learning algorithms and methodologies play an inevitable role. A wide range of conventions in machine learning is constantly being used to determine the PTM sites. However, in this study, the incorporated feature encoding methods are CKSAAP (Composition of K-Spaced Amino Acid Pairs) and PseKRAAC (Pseudo K-tuple Reduced Amino Acid Composition). Specifically, the main intention of the study is to trial two feature extraction methods with two feature selection processes (RFECV and mRMR), so that better performance is gained regardless of other feature screening methods. Consequently, the sequence received, is likely to have more related information regarding the specified PTM. The approach implemented has 4 comparative combinations that are determined with the AUC (Area Under Curve). One of the conclusions is the combined prediction of CKSAAP and RFECV which derives an exceptional remark. Whereas, the merged case of PseKRAAC and RFECV through a noteworthy output of 0.908 AUC. The notable portion is that, when the RFECV is used in both cases (CKSAAP and PseKRAAC), the chances to have an outperformed prediction is achieved as RFECV extracts optimal features by cross val-idations. However, the mRMR selection method for CKSAAP results in 0.779 AUC, and PseKRAAC results in 0.786 AUC. So, the comparative study depicts that RFECV is more impactful than mRMR and more precisely, the achievable accuracy is for the combination of PseKRAAC and RFECV. © 2022 IEEE.

关键词： Amino acids

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FedPruNet: Federated Learning Using Pruning Neural Network

FedPruNet: Federated Learning Using Pruning Neural Network

引用

2022 IEEE Region 10 Symposium, TENSYMP 2022

作者： Gowtham, L Annappa, B Sachin, D N National Institute of Technology Karnataka Dept. of Computer Science and Engineering Mangaluru India

ISBN: (数字)9781665466585

ISBN: (纸本)9781665466585

Federated Learning (FL) is a distributed form of training the machine learning and deep learning models on the data spread over heterogeneous edge devices. The global model at the server learns by aggregating local models sent by the edge devices, maintaining data privacy, and lowering communication costs by just communicating model updates. The edge devices on which the model gets trained usually will have limitations towards power resource, storage, computations to train the model. This paper address the computation overhead issue on the edge devices by presenting a new method named FedPruNet, which trains the model in edge devices using the neural network model pruning method. The proposed method successfully reduced the computation overhead on edge devices by pruning the model. Experimental results show that for the fixed number of communication rounds, the model parameters are pruned up to 41.35% and 65% on MNIST and CIFAR-10 datasets, respectively, without compromising the accuracy compared to training FL edge devices without pruning. © 2022 IEEE.

关键词： Edge computing

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Indian Currency Recognition:A Lightweight Transfer Learning Approach for Visually Impaired 7

Indian Currency Recognition:A Lightweight Transfer Learning ...

引用

7th International Conference on Parallel, Distributed and Grid Computing, PDGC 2022

作者： Saini, Poonam Goyal, Ankit Pokhrel, Bishwash Singh, Jaspreet Kumar, Neetish Punjab Engineering College Dept. of Computer Science and Engg. Chandigarh India

ISBN: (纸本)9781665454018

The knowledge of artificial intelligence, especially, machine learning has been widely used to handle various real-world applications and related societal challenges. One of the crucial issues faced by visually impaired people is to correctly recognize paper currency and its denomination easily. The paper proposes a convolutional neural network-based currency recognition system applied to the Indian Currency. The basic features include data pre-processing to filter out the old currency notes and incorrect labels, followed by a pre-trained model selection. The proposed model is trained using transfer learning, and afterward, a custom layer is added to recognize currency notes. Further, a fully connected customized neural network architecture has been designed. The model undergoes fine tuning of essential hyperparameters. The proposed model classifies Indian banknotes with more reliable accuracy (99.5%), which is even flexible to different image orientations (flipped, folded, out of frame, etc.) as well as empty backgrounds for a real-time application. © 2022 IEEE.

关键词： Network architecture

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Impact of Demographics on Automated Criminal Tendency Detection from Facial Images

Impact of Demographics on Automated Criminal Tendency Detect...

引用

2022 International Conference on Frontiers of Information Technology, FIT 2022

作者： Mushtaq, Negarish Ali, Khizer Moetesum, Momina Siddiqi, Imran Bahria University Center of Excellence in Ai Dept. of Computer Science Islamabad Pakistan

ISBN: (纸本)9798350345933

An individual's face can provide important insight about his personal traits like age, psychology, health, ethnicity, emotions, kinship, and much more. The biometric potentials of facial images make them an ideal tool for various forensic inferences. One such interesting area of research is the detection of criminal tendencies in people from their facial images. Several studies have proposed machine and deep learning-based solutions for this purpose. However, to the best of our knowledge, none have explicitly analyzed the impact of demographic attributes on the performance of such systems. In this paper, we provide an in-dept. analysis to measure the impact of three important demographic properties i.e. age, gender, and ethnicity on facial image-based criminality detection systems. For this purpose, a balanced dataset is prepared as there was no such dataset available with age, race, and gender splits. The performance of various convolutional neural network architectures (VGG-16, VGG-19, and FaceNet) is evaluated to assess their potential in perceiving criminal tendencies. Based on the outstanding performance of FaceNet, it is selected to measure the impact of different demographic groups in detecting criminal tendencies from facial images. The analysis presented in this study can prove vital for the development of robust and unbiased systems that can provide reliable proactive solutions for the security of all communities. © 2022 IEEE.

关键词： Convolutional neural networks

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Credit Card Fraud Detection Utilizing Machine Learning, Deep Learning, and Bayesian Networks

Credit Card Fraud Detection Utilizing Machine Learning, Deep...

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2025 International Conference on Artificial Intelligence and Data Engineering, AIDE 2025

作者： Reddy, Arra Keerthan Vardhan Rapeti, Harsha Reddy, Doduguru Amarnath Srinivas, M. Computer Science Engineering Amrita Vishwa Vidyapeetham Amaravati India Amrita Vishwa Vidyapeetham Dept. of Computer Science Amaravati India

ISBN: (纸本)9798331527518

Detecting credit card fraud is a critical challenge in the modern financial landscape, where robust and efficient solutions are essential to mitigate losses and safeguard consumers. This research evaluates and contrasts three primary approaches to this issue: Machine Learning (ML), Deep Learning (DL), and Bayesian Networks (BN). Using publicly available datasets, several ML techniques, including Logistic Regression, Support Vector Machines (SVM), Random Forests, Decision Trees, and XGBoost, were assessed alongside DL models such as Multi-Layer Perceptron (MLP) and Deep Neural Networks (DNNs). Additionally, a Bayesian Network was employed to analyze causal relationships between fraud-related features, offering an interpretable probabilistic framework. While ML and DL approaches achieved high performance regarding accuracy, precision, and recall, the Bayesian Network distinguished itself by providing enhanced interpretability and reliability. This study highlights the complementary strengths of these methods and the potential for their integration to develop adaptive and efficient fraud detection systems © 2025 IEEE.

关键词： Deep neural networks

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Data Analytics in Wind Turbine Generators for Improving Efficiency

Data Analytics in Wind Turbine Generators for Improving Effi...

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2022 International Conference on Sustainable Computing and Data Communication Systems, ICSCDS 2022

作者： Sankineni, Preethi Vahitha Grace, Angeline Holyah M. Juliet, Sujitha Lincy Esther, U. Joanna, J. Karunya Institute of Technology and Sciences Dept. of Computer Science and Engineering Coimbatore India

ISBN: (纸本)9781665478847

Wind energy being a notable and eligible source, has the possibility for bringing out energy in a very constant and sustainable manner. However, wind energy does include numerous challenges like, the halted asset of wind plants, early investment costs, and the strain in discovering areas of wind efficiency. The major objective for proposing this work is to determine the power efficiency of wind turbines, which also aids in the formulation of a proposal to reduce wind turbine maintenance costs. During this research, data analysis of turbine generators is performed on day-to-day wind speed info using machine learning and deep learning algorithms. A way is put forward to support deep learning and machine learning algorithms which can predict different values of power reliably. Hence, the execution of machine and deep learning algorithms are analyzed. For forecasting for a longer term, these algorithms may be used for wind generation rate with historical relation to wind speed info. Moreover, the application of deep and machine learning-based models is place distinct to that of model-trained places. This data analysis demonstrates that in unspecified geographies of wind plants, these sets of algorithms could be successfully implied by utilizing the base location model. The entire project focuses on wind turbine generators and includes the use of data visualization of data analytics to analyze the data and detect the factors that influence wind power generation. With the support of previous data output, wind power is anticipated using both machine learning and deep learning models, where different datasets are used for training and testing. This adds to the uniqueness of this work. © 2022 IEEE.

关键词： Wind power

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Comparing Interpolations Using Standard and Normalized Radial Basis Functions 16

Comparing Interpolations Using Standard and Normalized Radia...

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16th IEEE International Scientific Conference on Informatics, Informatics 2022

作者： Kacerekova, Zuzana University of West Bohemia Dept. of Computer Science and Engineering Pilsen Czech Republic

ISBN: (纸本)9798350310344

Radial basis functions (RBFs) are commonly used in solving partial differential equations, interpolation and ap-proximation of scalar and vector data, image reconstruction, geographic information systems, and machine learning. In this contribution, the properties of interpolations using standard and normalized RBFs are compared, and the impact of an added polynomial term is analyzed using various samplings of input functions. Experimental results across a range of tested kernel functions and shape parameters are provided. © 2022 IEEE.

关键词： Interpolation

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Runtime Detection of Time-Delay Security Attack in System-An-Chip 15

Runtime Detection of Time-Delay Security Attack in System-An...

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15th IEEE/ACM International Workshop on Network on Chip Architectures, NoCArc 2022

作者： Rajan, Manju Choksey, Mayank Jose, John Indian Institute of Technology Dept. of Computer Science and Engineering Assam Guwahati India

ISBN: (数字)9781665455893

ISBN: (纸本)9781665455893

Soft real-Time applications, including multimedia, gaming, and smart appliances, rely on specific architectural characteristics to deliver output in a time-constrained fashion. Any violation of application deadlines can lower the Quality-of-Service (QoS). The data sets associated with these applications are distributed over cores that communicate via Network-on-Chip (NoC) in multi-core systems. Accordingly, the response time of such applications depends on the worst-case latency of request/reply packets. A malicious implant such as Hardware Trojan (HT) that initiates a delay-of-service attack can tamper with the system performance. We model an HT that mounts a time-delay attack in the system by violating the path selection strategy used by the adaptive NoC router. Our analysis shows that once activated, the proposed HT increases the packet latency by 17% and degrades the system performance (IPC) by 18% over the Baseline. Furthermore, we propose an HT detection framework that uses packet traffic analysis and path monitoring to localise the HT. Experiment results show that the proposed detection framework exhibits 4.8% less power consumption and 6.4% less area than the existing technique. © 2022 IEEE.

关键词： Network-on-chip

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HCB Machine Learning Approach For Movie Recommendation System 6

HCB Machine Learning Approach For Movie Recommendation Syste...

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6th International Conference on Intelligent Computing and Control Systems, ICICCS 2022

作者： Gourammolla, Samheeta Gokila, S. Hindustan Insititute of Technology and Science Dept. of Computer Application Chennai603103 India

ISBN: (纸本)9781665410359

The Recommendation System (RS) is a very powerful technique which supports the user to derive decision quickly. The RS is essential in any business model like B2B, B2C and C2C. The user wants to get the quality and essential information suitable to perception in an entertainment which falls on C2C category. The recommendation system can achieve this by filtering the essential choices from the large amount of rating of given by other users. The Hybrid Collaborative-Based (HCB) recommendation system in proposed work is experimented. The proposed experimental results on movie rating and review rating with movie lens data set shows high result than SVD, KNN and Co-Clustering Machine Learning (ML) algorithms. © 2022 IEEE.

关键词： Collaborative filtering

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