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TRANSNATIONAL COLLABORATION ON LIFELONG LEARNING BETWEEN HIGHER ENGINEERING EDUCATION INSTITUTIONS: A UNIVERSITY PERSPECTIVE 51

TRANSNATIONAL COLLABORATION ON LIFELONG LEARNING BETWEEN HIG...

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51st Annual Conference of the European Society for Engineering Education, SEFI 2023

作者： Bennedsen, J. Øien, G. Dept. of Electro- and Computer Engineering Aarhus University Aarhus Denmark Center for Science and Engineering Education Development NTNU Trondheim Norway

ISBN: (纸本)9782873520267

Lifelong learning (LLL) is in focus in all European countries. Workforce upskilling and reskilling are seen as central elements in ensuring national competitiveness. Universities are main players in this effort but often find it difficult to find sustainable models for LLL activities, in terms of e.g., economy, student intake, and academic resources. Collaboration between universities can be one possible way forward to overcome such obstacles, and given the enhanced post-Covid digitalization is also increasingly made possible, even across borders. However, many universities also find such collaboration challenging, e.g., due to outdated legislation, lacking financial predictability, lacking academic capacity, or other factors. Studies done by the authors indicate that universities' perspectives are seldom present in the literature when barriers and enablers for LLL participation are analysed. This motivates us to particularly consider a university perspective here. This paper analyses responses to a questionnaire sent to 28 Nordic and Baltic universities, collecting information about successes, opportunities, and barriers for formal (i.e., ECTS-awarding) university-level LLL with professional content within engineering and technology. The respondents were management representatives representing an institutional view and having good knowledge of the institution's LLL offer (e.g., further education centre managers and LLL coordinators). 19 institutions answered, mostly with free text. Our analysis is done following constructivist grounded theory using an open and focused coding approach. The main aim is to identify the main barriers and success factors seen by the universities for upscaling LLL activities, and subsequently to suggest strategies for alleviating barriers and facilitating success factors. © 2023 SEFI 2023 - 51st Annual Conference of the European Society for Engineering Education: Engineering Education for Sustainability, Proceedings. All Rights Reserved.

关键词： Engineering education

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A Compressive Study on Detection Accuracy Model for DoS Attack in SDN Using Ensemble Learning Techniques 7

A Compressive Study on Detection Accuracy Model for DoS Atta...

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7th International Symposium on Innovative Approaches in Smart Technologies, ISAS 2023

作者： Pusarla, Sindhu Ghugar, Umashankar Ozseven, Turgut Dewangan, Bhupesh Kumar Choudhury, Tanupriya Patni, Jagdish Chandra GITAM School of Technology Department of Computer Science Engineering Visakhapatnam530045 India OP Jindal University Department of Computer Science and Engineering Chhattisgarh Raigarh India Tokat Gaziosmanpaşa University Department of Computer Engineering Tokat Turkey CSE Dept. Pune India Nagpur Campus Pune India

ISBN: (纸本)9798350383065

The possible networking architecture known as a 'Software-defined Network' (SDN) separates the information and management layers and offers polarized control over the network. This new approach considers responsibilities and empowers network administrators to electronically assign, manage, adjust, and monitor clan behaviour. One important advantage of SDN is its polarising power, which may occasionally cause a serious breach. The snitcher will have access to the complete framework if he is successful in getting to the controller's core. The regulators are utterly powerless to combat Distributed Denial of Service (DDoS) attacks, which wear down the model and make the administrators of the regulations inaccessible. It's crucial to identify potential dangers in the controllers early on. As a result, many algorithms and processes. © 2023 IEEE.

关键词： Denial-of-service attack

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Comparing Deep Reinforcement Learning Methods for HVAC System Optimization with Forecast Parameters Over Various Time Periods

Comparing Deep Reinforcement Learning Methods for HVAC Syste...

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Interdisciplinary Approaches in Technology and Management for Social Innovation (IATMSI), IEEE Conference on

作者： Prema Arokia Mary G Hema M S S Bama Nageswara Guptha Dept. of Computer Science and Engineering Sri Venkateshwara College of Engineering Bengaluru India Dept. of Computer Science and Engineering RV Institute of Technology and Management Bengaluru India

ISBN: (数字)9798331521691

ISBN: (纸本)9798331521707

Buildings are significant contributors to global energy consumption. Maintaining comfortable indoor temperatures while reducing energy consumption are conflicting objectives. Deep Reinforcement Learning (DRL) is a promising area of research for building Heating, Ventilation and Air Conditioning (HVAC) system optimization. In this study an open-source framework Building Optimization Testing Framework (BOPTEST), which is a virtual testbed that help comparison different control strategies for evaluation of DRL control methods is used. A Proportional-Integral (PI) controller is used to benchmark the DRL methods. A single zone residential building of 192 m 2 with a radial heating system and a heat pump in a climate zone with high heating requirement with dynamic electricity prices with prices varying every 15 min based on demand is chosen for implementing different control strategies. On comparing Deep Deterministic Policy Gradient (DDPG), Proximal Policy Optimization (PPO) and Twin Delay DDPG (TD3) based DRL controllers and the baseline controller, the DDPG based controller reduced energy consumption by 97.3 % and operating cost by 17.7 % during the peak heating period with reference to baseline method. Then on analyzing the impact of inclusion of forecast parameters occupancy, solar irradiance, and electricity prices over the period 3, 6 and 12 hours in DDPG based controller. The prediction for 3 hours gave the greatest reduction in thermal discomfort of 99.7 % and prediction for 12 hours gave maximum reduction in cost by 30.4 % but resulted in only 82% reduction in thermal comfort when compared with baseline method indicating that longer prediction horizon is not necessarily results in better performance.

关键词： Energy consumption HVAC Costs PI control Heat pumps Electricity Buildings Deep reinforcement learning Optimization Resistance heating

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Bayesian-Driven Deep Learning and Machine Learning Approaches for Handwritten Word Recognition

Bayesian-Driven Deep Learning and Machine Learning Approache...

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Artificial Intelligence and Data Engineering (AIDE), International Conference on

作者： Medisetty Dinesh Lingamaneni Sagar Pallapu Moksha Pappula Baladhitya M. Srinivas Computer Science Engineering Amrita Vishwa Vidyapeetham Amaravati India Dept. of Computer Science Amrita Vishwa Vidyapeetham Amaravati India

ISBN: (数字)9798331527518

ISBN: (纸本)9798331527525

Identifying handwritten words poses a complicated problem owing to the variances in handwriting styles and the possible noise and distortions present in the data. The performance of used deep learning and traditional machine learning handwriting recognition methods for recognizing handwritten words is studied in this paper and the advantages of using deep learning are discussed. In particular, neural network models such as CNN and 2DLSTM networks had a great work in this area. In contrast, we detect whole handwritten words without the need of segments into standalone characters. The CNNs are used for feature extraction, the Bidirectional LSTMs for sequence prediction and output is decoded via a CTC layer. The model was evaluated over the IAM words dataset from the IAM handwriting database, achieving 87% accuracy and a character error rate of 7.77%. In addition, we experimented with ML techniques, including SVM, RF has got an accuracy of 72% and 80% accuracies, and a Bayesian network of 61% accuracy, to provide a comparative analysis.

关键词： Deep learning Support vector machines Radio frequency Handwriting recognition Accuracy Error analysis Neural networks Feature extraction Bayes methods 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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Artificial Intelligence and Data Engineering (AIDE), International Conference on

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

ISBN: (数字)9798331527518

ISBN: (纸本)9798331527525

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

关键词： Deep learning Support vector machines Logistic regression Accuracy Credit cards Probabilistic logic Bayes methods Fraud Ensemble learning Random forests

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Cultural Heritage Copyright Protection: A blind and robust watermarking technique for heritage images 7

Cultural Heritage Copyright Protection: A blind and robust w...

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7th IEEE International conference for Convergence in Technology, I2CT 2022

作者： Mushtaq, Subreena Mehraj, Samrah Parah, Shabir A. Giri, Kaiser J. Sheikh, Javaid A. University of Kashmir Dept. of Electronics and Instrumentation Technology Srinagar India Islamic University of Science and Technology Dept. of Computer Science Pulwama India

ISBN: (纸本)9781665421683

The Cultural Heritage (CH) media, which include photographs, audio, and videos, are the valuable asset of a region, state, or even a nation. This not only provides them with information about their ancestors, but also portrays their way of life, artistic approach, and a variety of other historical and archaeological principles. The protection of the heritage media is one of the main concerns in today's world where cyber-crime is on the rise and unauthorized users can easily access the precious heritage media. For copyright protection of the CH images, this paper presents a robust blind watermarking algorithm for color images based on Integer Wavelet Transform (IWT). The watermark is embedded in the Cb component of the YCbCr color space. The IWT divides the Cb component into four sub-bands, among those four sub-bands, the LL sub-band being highly robust is selected for embedding the watermark by employing inter pixel-differencing procedure. Arnold transform has also been used to increase the security of the watermark. From the experimental results, it is evident that the proposed scheme is highly robust to various types of singular as well as hybrid attacks. The proposed scheme offers the mean Peak-Signal-to-Noise-Ratio (PSNR) of 46.12dB for 100 cultural images. Also, Normalized Correlation (NC) values are close to 1 which not only shows better robustness of the scheme but also guarantees its imperceptibility. © 2022 IEEE.

关键词： Copyrights

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Walking-age estimator based on gait parameters 12

Walking-age estimator based on gait parameters

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12th IEEE Global Conference on Consumer Electronics, GCCE 2023

作者： Kuroda, Tomohito Okamoto, Shogo Akiyama, Yasuhiro Tokyo Metropolitan University Department of Computer Science Hino Japan Shinshu University Dept. Mechanical Engineering and Robotics Ueda Japan

ISBN: (纸本)9798350340181

Human gait motions differ depending on age. We estimated peoples' age using kernel regression analysis with reported height and weight and representative gait parameters as explanatory variables. The samples were Japanese women aged in their 20s-70s represented in an open database. The estimated ages exhibited a correlation coefficient of 0.65 with the actual ages, and the mean absolute error was 17.7 years. Estimation errors were significantly greater for older adults than for young people. Given that the method involves gait parameters that can be measured using wearable devices, such as inertial measurement units, this technique provides an accessible way for people to independently monitor their physical health in daily life. © 2023 IEEE.

关键词： Regression analysis

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Identification of Candidate Genes for Colorectal Cancer Using Machine Learning and Bioinformatics Approaches

Identification of Candidate Genes for Colorectal Cancer Usin...

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International Conference on Electrical, computer and Communication Engineering (ECCE)

作者： Humayan Kabir Rana Boshir Ahmed Dept. of Computer Science and Engineering Rajshahi University of Engineering & Technology (RUET) Rajshahi Bangladesh Dept. of Computer Science and Engineering Green University of Bangladesh Dhaka Bangladesh

ISBN: (数字)9798350357509

ISBN: (纸本)9798350357516

Colorectal cancer (CRC) stands as the second most prevalent cause of cancer deaths, and its incidence is rising over time. Identifying key genes is essential for diagnosing and developing effective therapeutic strategies for cancer. Several studies were conducted to determine the candidate genes of CRC but this is still not sufficient and further research is needed in this area. Thus, we aimed to identify the key candidate genes of CRC using The Cancer Genome Atlas (TCGA) dataset, applying the Kruskal-Wallis test and Bonferroni correction machine learning techniques. We successfully identified 9 candidate genes, including CALB2, GRP, KRAS, MLH1, NPR3, PPFIA4, SOX11, STAC2, and TRPA1, from 20518 genes of CRC using our model. In addition, we used bioinformatics frameworks to identify signaling pathways, gene ontological pathways, and PPI networks that reflect the functions of these candidate genes. We found 9 significant signaling pathways, 12 ontological pathways, and 3 hub genes for CRC. The diagnostic effectiveness of the candidate genes was evaluated through the receiver operating characteristic (ROC) analysis, and all candidate genes showed good performance according to area under the curve (AUC) values. Notably, the gene MLH1 demonstrated the highest AUC of .91. In fine, the findings of this study may play a role in disease management and offer a foundation for further laboratory investigations to uncover potential therapeutic targets for CRC treatment.

关键词： Proteins Electric potential Biological system modeling Genomics Machine learning Colorectal cancer Receivers Bioinformatics

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Enhancing Grade Prediction via Course Attribute-Aware Student Similarity in Collaborative Filtering 35th

Enhancing Grade Prediction via Course Attribute-Aware Studen...

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35th International Florida Artificial Intelligence Research Society Conference, FLAIRS-35 2022

作者： Park, Young Dept. of Computer Science & Information Systems Bradley University IL United States

Collaborative filtering has been used in predicting personalized students’ grades in courses at higher educational institutions. The traditional collaborative filtering through student-to-student correlation is based on similarity between students with respect to their grades in all courses that they have taken. However, courses are not completely independent each other. Given a course, the grades of its prerequisite, corequisite and similar courses will affect the grade of the course more than the grades of other unrelated courses will. © 2022 by the authors. All rights reserved.

关键词： Collaborative filtering

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Multinomial Naive Bayes Based Machine Learning Analysis of Twitter Sentiment 2

Multinomial Naive Bayes Based Machine Learning Analysis of T...

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2nd International Conference on Edge Computing and Applications, ICECAA 2023

作者： Saravanan, U.K.Balaji Vijay, M. Shreedhar, T. Rajasekar, G. Yashwanth, R. Shakthipriya, P. Sona College of Technology Information Technology Salem India Vmkv Enginerring College Dept. of Computer Science and Engineering Salem India

ISBN: (纸本)9798350347579

Sentiment analysis identifies and categorizes thoughts and feelings expressed in the source text. Social media uses tweets, status updates, and blog columns to generate sensitive data. SA on user-generated data helps understand crowd opinion. Due to slang and spelling errors, Twitter sentiment analysis (TSA) is much more complex than general sentiment analysis. Every day, millions of tweets are generated on various topics. Machine learning algorithms can be used to solve this problem-the proposed machine learning algorithm includes preprocessing, weight calculation, feature extraction, and classification. First, we collected a Kaggle dataset from Twitter sentiment analysis. In the second step, the data preprocessing steps mainly include stop words, emoji, and repeated characters in words and URLs due to their low importance. Then, we used tokenization, normalization, stemming, and lemmatization to achieve better results. In the third step, we computed weights using word2vec to represent the tweets. In the fourth step, we use a feature extraction method consisting of TF-IDF and word embedding to check based on word and noun counts. Finally, TSA can be used to explore sentiment using Multinomial Naïve Bayes (MNB) and Maximum Entropy classification. The proposed machine learning algorithm achieves better results with TSA in terms of recognition accuracy, recall, precision and F-measure. © 2023 IEEE.

关键词： Sentiment analysis

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