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Cloud computing: Bridging the link between industry and the classroom

international Journal of Electrical engineering and Education 2022年第4期59卷 329-349页

作者： Al-Mousa, Amjed Department of Computer Engineering Princess Sumaya University for Technology Amman Jordan

With the rapid expansion of cloud computing usage and applications, the need to prepare graduates with high caliber cloud computing skills is becoming a necessity. This paper discusses the challenges of teaching cloud computing, and how to overcome these to equip the graduates with modern industry-standard skills. The paper reports on the experience of achieving this goal by employing the cultural–historical activity learning theory in teaching this course. This was achieved by introducing a variety of activities in the course such as: up-to-date technical content, a hands-on projects, technical presentations, and a certification program. The paper assesses the success of the experience by analyzing the feedback of the students twice;at the end of the course, and several years afterward when they have enrolled in the job market. The results clearly show the importance of having practical and hands-on courses that would keep students engaged, at the same time it is preparing them for the job market. The results also showed that the students appreciation for the course have increased after they have started working. Specifically, two-thirds of the students believe that the course was helpful in getting their current job. © The Author(s) 2019.

关键词： Students

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Sensitive Content Detection in Social Networks Using Deep learning Models and Explainability Techniques 9

Sensitive Content Detection in Social Networks Using Deep Le...

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9th IEEE/ACIS international conference on Big Data, Cloud computing, and Data Science, BCD 2024

作者： Perikos, Isidoros University of Patras Computer Engineering and Informatics Department Patras Greece University of Peloponnese Computer Technology Institute and Press 'Diophantus' Electrical and Computer Engineering Dept. Patras Greece

ISBN: (纸本)9798350394191

In the dynamic landscape of online social networks, recognizing sensitive content is essential for safeguarding user privacy, fostering inclusivity, and enhancing diversity awareness. Building on prior research, this study explores new dimensions and methodologies for detecting sensitive content. We examine the temporal evolution of sensitive content, revealing how patterns shift over time, and address cross-linguistic challenges, emphasizing cultural and contextual nuances in detection. We employ advanced machine learning techniques, including deep learning models and BERT that improve the accuracy and robustness of the detection procedure. In the experimental study, BERT transformer reported the best performance in detecting sensitive content in text. Additionally, we incorporate explainability techniques such as LIME and SHAP to provide deeper insights into the model's decision-making processes, ensuring predictions are interpretable and reliable. Our work enhances the theoretical framework of sensitive content detection in social networks and provide methods that are accurate and scalable and can facilitate the creation of user-centric interaction that prioritize privacy and user experience. © 2024 IEEE.

关键词： Adversarial machine learning

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An Effective Personality Recognition Model Design using Generative Artificial Intelligence based learning Principles 1

An Effective Personality Recognition Model Design using Gene...

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1st IEEE international conference on computing and Data Science, ICCDS 2024

作者： Jenifa, G. Padmapriya, K. Sevanthi, P. Karthika, K. Pandi, V. Samuthira Arumugam, Deepak Andhra Pradesh India RMD Engineering College Department of Computer Science and Engineering Chennai India Department of AI & DS Tamil Nadu Chennai India Prince Shri Venkateshwara Padmavathy Engineering College Department of Civil Engineering Tamil Nadu Chennai India Centre for Advanced Wireless Integrated Technology Chennai Institute of Technology Tamil Nadu Chennai India Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Department of Electrical and Electronics Engineering Chennai India

ISBN: (纸本)9798350365337

Personality recognition from textual data plays a crucial role in various applications such as social media analysis, recommendation systems, and personalized marketing. In this study, we propose an effective personality recognition model leveraging Generative Artificial Intelligence based learning Principles (GAILP), which combines DenseNet feature extraction from user profile images with text-based features extracted using Natural Language Processing (NLP) techniques. The model is implemented in Python, utilizing popular libraries such as TensorFlow, Scikit-learn, and NLTK. We evaluate the performance of the proposed model on a Myers-Briggs Personality Type Dataset obtained from Kaggle, containing self-reported MBTI types and associated textual data. Experimental results demonstrate the effectiveness of the GAILP model, achieving an accuracy of over 97% in personality recognition tasks. Our findings highlight the importance of integrating visual and textual cues for accurate personality recognition and underscore the potential of GAILP-based approaches in this domain. © 2024 IEEE.

关键词： Support vector machines

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An in-Depth Analysis for Machine-Vision-Based Mango Leaf Diseases Recognition 6

An in-Depth Analysis for Machine-Vision-Based Mango Leaf Dis...

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6th international conference on Mobile computing and Sustainable Informatics, ICMCSI 2025

作者： Das, Prothoy Rahman, Md. Ataur Habib, Md. Tarek Khan, Md. Abbas Ali Islam, Mohammad Monirul Department of Computer Science and Engineering Daffodil International University Dhaka Bangladesh Department of Computer Science and Engineering Independent University Bangladesh Dhaka Bangladesh

ISBN: (纸本)9798331522667

Many leaf diseases that affect crop health cause severe mango farming concerns. This study employed deep learning techniques to analyze mango leaf disease categorization comprehensively. This study looks at the classification of five different mango leaf diseases using convolutional neural networks (CNNs): powdery mildew, dieback, gall midge, anthracnose, and healthy leaves. A dataset of 661 raw images was first gathered, and these raw images were augmented to create a final dataset of 2678 images. The study examines how preprocessing methods affect classification accuracy using five CNN models before and after data preprocessing. The outcomes show notable gains in accuracy following preprocessing, highlighting its critical function in improving model performance. Additionally, a new custom model is proposed and evaluated, demonstrating its effectiveness in reaching an impressive accuracy of 98.50%. This comparative study shows that the proposed model outperforms current CNN architectures, indicating that it has the potential to classify mango leaf diseases accurately. Our research aims to highlight the importance of preprocessing techniques and creating customized deep-learning models by presenting an effective method for automated mango leaf disease identification. © 2025 IEEE.

关键词： Fruits

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Comprehensive and Comparative Analysis between Transfer learning and Custom Built VGG and CNN-SVM Models for Wildfire Detection 6

Comprehensive and Comparative Analysis between Transfer Lear...

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6th international conference on Intelligent computing in Data Sciences, ICDS 2024

作者： Jonnalagadda, Aditya V. Hashim, Hashim A. Harris, Andrew Carleton University Department of Mechanical and Aerospace Engineering OttawaONKIS-5B6 Canada

ISBN: (纸本)9798350351200

Contemporary Artificial Intelligence (AI) and Machine learning (ML) research places a significant emphasis on transfer learning, showcasing its transformative potential in enhancing model performance across diverse domains. This paper examines the efficiency and effectiveness of transfer learning in the context of wildfire detection. Three purpose-built models-Visual Geometry Group (VGG)-7, VGG-10, and Convolutional Neural Network (CNN)-Support Vector Machine(SVM) CNN-SVM-Are rigorously compared with three pretrained models-VGG-16, VGG-19, and Residual Neural Network (ResNet) ResNet101. We trained and evaluated these models using a dataset that captures the complexities of wildfires, incorporating variables such as varying lighting conditions, time of day, and diverse terrains. The objective is to discern how transfer learning performs against models trained from scratch in addressing the intricacies of the wildfire detection problem. By assessing the performance metrics, including accuracy, precision, recall, and F1 score, a comprehensive understanding of the advantages and disadvantages of transfer learning in this specific domain is obtained. This study contributes valuable insights to the ongoing discourse, guiding future directions in AI and ML research. © 2024 IEEE.

关键词： Convolutional neural networks

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A Wearable System for Diagnosing and Monitoring the Intensity of Stuttering 6

A Wearable System for Diagnosing and Monitoring the Intensit...

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2024 international conference on Smart Systems and Technologies

作者： Susac, Petar Gudan, Nikola Kuk, Petra Salamun, Karla Dzapo, Hrvoje Univ Zagreb Fac Elect Engn & Comp Zagreb Croatia Univ Zagreb Fac Educ & Rehabil Sci Zagreb Croatia

ISBN: (纸本)9798350386394;9798350386400

We present a battery-powered wearable system that is able to identify the three basic types of speech disfluencies found in people who stutter: blocks, prolongations, and repetitions. Such a system could be used to aid speech pathology clinicians by performing automated diagnosis of stuttering or monitoring the progress of speech therapy, tasks that are currently time-consuming and produce potentially unreliable results. The system uses a deep learning model trained on the SEP-28k dataset and deployed on a microcontroller. It performs speech audio acquisition and model inference in real time and stores the inference results to non-volatile memory. Once stored, the results can be further analyzed on a PC and presented to the clinician. Our deep learning model achieved a classification accuracy of 65%, 71%, and 64% for blocks, prolongations, and repetitions, respectively. We discuss the possible applications of this system in speech disorder diagnosis and therapy as well as potential improvements.

关键词： audio deep learning embedded systems speech stuttering wearable

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Comparison of Machine learning and Quantum Machine learning for Breast Cancer Detection

Comparison of Machine Learning and Quantum Machine Learning ...

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2024 international conference on Smart Systems for Applications in Electrical Sciences, ICSSES 2024

作者： Desai, Usha Kola, Kalyan Sundar Nikhitha, S. Nithin, G. Prudhvi Raj, G. Karthik, G. S.E.A. College of Engineering & Technology Dept. of Electronics and Communication Engineering Bengaluru India SR University Dept. of Electronics and Communication Engineering Telangana Warangal India

ISBN: (纸本)9798350364040

Yearly the number of breast cancer subjects are rising exponentially. In fact, breast cancer is very deadly, and all over the world the losses are increasing. It is very important to have an improved system to predict and diagnose the cancer effectively, which helps the subjects to have a better quality of life. In fact, it is crucial to have an accurate prediction system to improve the treatment and chances of survival. Moreover, earlystage detection, high accuracy of prediction and diagnosis is required at appropriate time. This paper aims to analyze the Machine learning (ML), Deep learning (DL) and Quantum Machine learning (QML) techniques to diagnose the breast cancer subjects. The QML is an emerging field which transforms the input data onto the qubits and processing is performed using the quantum gates and operators. Quantum computing technology enhances the security of data transmission, hence has great application in communication and health care data processing. In this work, Logistic Regression (LR) and SVM (Linear) achieved accuracy of 9 7. 9%, CNN 9 8. 6% and QSVC 9 3% to classify 569 samples of normal and cancerous binary classes. Less accuracy shows noise in the quantum computing, which can be improved by optimization of rotation of specific gates. © 2024 IEEE.

关键词： Recurrent neural networks

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Insider threat detection of ransomware using AutoML

Insider threat detection of ransomware using AutoML

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international conference on Artificial Intelligence, Blockchain, computing and Security, ICABCS 2023

作者： Bhuvaneswari, R. Kumar, Enaganti Karun Padmasini, Annadanam Varma, K. V. Priyanka Department of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Chennai India

ISBN: (纸本)9781032669663

Insider threats are one of the biggest issues that modern-day organizations and many large-scale companies face. The inside threats are caused by the insiders who are authorized individuals, also may have proper access to sensitive and secret information, and may be aware of the weaknesses in the implemented systems and operational procedures. And, it is confirmed that these insider threats have already shown their great damaging power in securing important information and because of this, organizations require more proactive and modern-day solutions to support existing cybersecurity tools in order to detect and prevent them. Conventional interruption recognition frameworks neglect to be powerful in insider threats because of the absence of a wide range of information for insider ways of behaving. All things considered;a more complex and standard technique is expected to have a more profound comprehension of the ways that insiders speak with the information framework. Ransomware hackers appear to be adopting new methods in response to tightening security. Automated machine learning has been utilised in this work for detection of ransomware and we also included the ensembling method for a few algorithms that are suitable for the given dataset. © 2024 The Author(s).

关键词： Machine learning

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ENCODING UNITIG-LEVEL ASSEMBLY GRAPHS WITH HETEROPHILOUS CONSTRAINTS FOR METAGENOMIC CONTIGS BINNING 12

ENCODING UNITIG-LEVEL ASSEMBLY GRAPHS WITH HETEROPHILOUS CON...

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12th international conference on learning Representations, ICLR 2024

作者： Xue, Hansheng Mallawaarachchi, Vijini Xie, Lexing Rajan, Vaibhav School of Computing Australian National University Canberra Australia School of Computing National University of Singapore Singapore College of Science and Engineering Flinders University Adelaide Australia

Metagenomics studies genomic material derived from mixed microbial communities in diverse environments, holding considerable significance for both human health and environmental sustainability. Metagenomic binning refers to the clustering of genomic subsequences obtained from high-throughput DNA sequencing into distinct bins, each representing a constituent organism within the community. Mainstream binning methods primarily rely on sequence features such as composition and abundance, making them unable to effectively handle sequences shorter than 1,000 bp and inherent noise within sequences. Several binning tools have emerged, aiming to enhance binning outcomes by using the assembly graph generated by assemblers, which encodes valuable overlapping information among genomic sequences. However, existing assembly graph-based binners mainly focus on simplified contig-level assembly graphs that are recreated from assembler's original graphs, unitig-level assembly graphs. The simplification reduces the resolution of the connectivity information in original graphs. In this paper, we design a novel binning tool named UNITIGBIN, which leverages representation learning on unitig-level assembly graphs while adhering to heterophilous constraints imposed by single-copy marker genes, ensuring that constrained contigs cannot be grouped together. Extensive experiments conducted on synthetic and real datasets demonstrate that UNITIGBIN significantly surpasses state-of-the-art binning tools. © 2024 12th international conference on learning Representations, ICLR 2024. All rights reserved.

关键词： Graphic methods

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Fault Detection and Classification for DC Microgrid using Binary Classification Models 5

Fault Detection and Classification for DC Microgrid using Bi...

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5th international conference on Control, Communication and computing, ICCC 2023

作者： Amal Krishna, T.S. Hari Kumar, R. College of Engineering Department of Electrical Engineering Kerala Trivandrum India

ISBN: (纸本)9798350334128

This paper presents a current and current derivative based fault detection and fault classification scheme. Current and current derivatives are taken and is processed through a binary classification based machine learning(ML) algorithm. A comparative study is done between binary classification models like Decision Tree, Naive Bayes and KNN algorithms. MATLAB/SIMULINK is used to simulate a 380 V DC Microgrid(DCMG). Fault classification is done for pole-pole(PP) and pole-ground(PG) faults under different fault resistances and varying load conditions. © 2023 IEEE.

关键词： Poles

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