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TEACHING MACHINE LEARNING TO PROGRAMMING NOVICES – AN ACTION-ORIENTED DIDACTIC CONCEPT 32

TEACHING MACHINE LEARNING TO PROGRAMMING NOVICES – AN ACTIO...

32nd Interdisciplinary Information Management Talks: Changes to ICT, Management, and Business Processes through AI, IDIMT 2024

作者： Tkáč, Michal Sieber, Jakub Meyer, Anne Kuhlmann, Lara Brueggenolte, Matthias Rinciog, Alexandru Henke, Michael Schweidtmann, Artur M. Gao, Qinghe Theisen, Maximilian F. El Shawi, Radwa Faculty of Business Economy University of Economics Bratislava Slovakia Faculty of Mechanical Engineering Karlsruhe Institute of Technology Germany Faculty for Mechanical Engineering TU Dortmund University Germany Department of Chemical Engineering Delft University of Technology Netherlands Institute of Computer Science University of Tartu Estonia

ISBN: (纸本)9783991515272

Machine Learning (ML) techniques are encountered nowadays across disciplines, from social sciences, through natural sciences to engineering. However, teaching ML is a daunting task. Aside from the methodological complexity of ML algorithms, both with respect to theory and implementation, the interdisciplinary and empirical nature of the field need to be taken into consideration. This paper introduces the MachineLearnAthon format, an innovative didactic concept designed to be inclusive for students of different disciplines with heterogeneous levels of mathematics, programming, and domain expertise. The format is grounded in a systematic literature review and the didactic principles action orientation, constructivism, and problem orientation. At the heart of the concept lie ML challenges, which make use of industrial data sets to solve real-world problems. Micro-lectures enable students to learn about ML concepts and algorithms, and associated risks. They cover the entire ML pipeline, promoting data literacy and practical skills, from data preparation, through deployment, to evaluation. © IDIMT *** rights reserved.

关键词： Adversarial machine learning

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Implications of the forthcoming forensic sciences standard ISO/IEC 21043 for forensic biometrics 12

Implications of the forthcoming forensic sciences standard I...

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12th International Workshop on Biometrics and Forensics, IWBF 2024

作者： Meuwly, Didier University of Twente Faculty of Electrical Engineering Mathematics and Computer Science Enschede Netherlands

ISBN: (纸本)9798350354478

The forthcoming forensic sciences standard ISO/IEC 21043 is a methodological and technical standard, currently at the stage of Draft International Standard. When adopted, it will apply to all forensic disciplines, including forensic biometrics. This standard has been designed by forensic, legal, and standardisation professionals to ensure that forthcoming forensic services, products, and systems are safe, reliable, and consistently perform as intended. The aim of this contribution is to present to the forensic biometric community some of the implications of adopting ISO/IEC 21043 for the forensic biometric *** an introduction (§ I) and a review of the existing international standards applied in forensic science (§ II), the five-part structure of ISO/IEC 21043 is described: 1. Terms and definitions, 2. Recovery, 3. Analysis, 4. Interpretation and 5. Reporting, (§ III). Then a selection of requirements and recommendations of this five-part standard are highlighted, and their implication for forensic biometric research, development, implementation, and practice is discussed (§ IV).The conclusion is that the adoption and use of ISO/IEC 21043 will help forensic laboratories to reach accreditation and to enhance their existing quality management systems. It will also make it easier for forensic biometric researchers to develop, deliver, and implement more relevant, more reliable, and safer services, products, and systems to the forensic biometric examiners performing casework (§ V). © 2024 IEEE.

关键词： Forensic science

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Artificial Intelligence for Alzheimer's Disease Diagnosis: A Review of Recent Advancements, Obstacles, and Future Prospects

Artificial Intelligence for Alzheimer's Disease Diagnosis: A...

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2025 IEEE International Students' Conference on Electrical, Electronics and computer science, SCEECS 2025

作者： Jain, Ashima Kumar, Bijendra Netaji Subhas University of Technology Department of Computer Science and Engineering Delhi India

ISBN: (纸本)9798331529833

Alzheimer's disease (AD) is a common chronic ailment that predominantly affects elderly people. AD is a neurological condition that causes a steady deterioration in cognitive function. There is no cure for AD;however, early detection and timely treatment can slow its progression. The development and implementation of artificial intelligence (AI) technology in medicine has greatly improved the accuracy and precision of the detection and diagnosis of AD. Machine learning (ML) and deep learning (DL) models have grown in popularity in recent years, particularly for medical image analysis. The application of ML and DL approaches to diagnose AD has grown significantly in recent years. This study reviews numerous studies from 2020 to 2024 that used ML and DL to detect AD. It gives a complete review of the ML/DL models used in these studies, as well as their limitations and future prospects. © 2025 IEEE.

关键词： Adversarial machine learning

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Malicious Code Detection Based on Generative Adversarial Model 6th

Malicious Code Detection Based on Generative Adversarial Mo...

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6th International Conference on Machine Learning for Cyber Security, ML4CS 2024

作者： Zhang, Jinzhihao Yang, Jia Zhou, Weiqi School of Computer Science and Engineering Hubei University of Technology Wuhan430000 China

ISBN: (纸本)9789819645657

With the increasing diversity and complexity of malicious code, the threats posed by malicious code are continuously growing, making malicious code detection increasingly challenging. Currently, researchers typically extract malware features and use deep-learning models for malware detection. Using image-based features for classification can improve the accuracy and efficiency of malware detection. However, traditional deep learning-based malware detection faces challenges such as data imbalance, insufficient data volume, and malware obfuscation. We propose to augment the malicious code dataset using Generative Adversarial Network (GAN) technology to improve the accuracy and efficiency of malicious code detection. First, we converted malicious software binary files into grayscale images. We used Generative Adversarial Network (GAN), Wasserstein Generative Adversarial Network (WGAN), and Wasserstein Generative Adversarial Networks-Gradient Penalty (WGAN-GP) models to augment the grayscale image dataset of malicious code. Subsequently, we obtained three augmented datasets: w1, w2, and w3. These datasets were then fed into the ResNet50 neural network model for training. Experimental results indicate that using the GAN model for data augmentation improved ResNet’s recognition accuracy by approximately 10%. When using the WGAN model for data augmentation, ResNet’s recognition accuracy increased by 2%. Similarly, using the WGAN-GP model for data augmentation also increased ResNet’s recognition accuracy by 2%. Among the three models, the GAN model demonstrated the strongest improvement effect. We demonstrate that the ResNet50 network with GAN models can significantly improve malware code detection. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Generative adversarial networks

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Double Auction Strategies for Peer-to-Peer Energy Trading in Smart Grids: A Game Theoretic and Blockchain Approach 6

Double Auction Strategies for Peer-to-Peer Energy Trading in...

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6th International Conference on Energy, Power and Environment, ICEPE 2024

作者： Naik, Madhusudan Pradhan, Nihar Ranjan Pratap Singh, Akhilendra National Institute of Technology Meghalaya Department of Computer Science and Engineering Shillong India VIT-AP University School of Computer Science and Engineering Amaravati India

ISBN: (纸本)9798350379297

The concept of using blockchain technology for peer-to-peer energy trading (P2PET) in smart grids, particularly employing a double auction-based game theoretic method, is a fascinating application of both blockchain and economic principles. This paper deals with a blockchain-enabled framework for P2PET in a smart grid by using a double auction-based game theory approach. The proposed permissioned blockchain network was developed using Hyperledger Fabric, Minifab and designed by configuring the *** file. Three smart contracts, or Chaincode namely Double Auction Order, CreateP2PEnergy, and Matching, have been written using Golang and deployed into the blockchain network, thereby creating automatic energy trading transactions between energy producers and consumers. The client communication, such as consumer queries, user input, and producer trading concepts, was designed using the Postman API. Additionally, the double auction framework's performance has been measured by Hyperledger Caliper. Analysis shows that when 1000 transactions were initiated in each round by clients using Hyperledger Caliper and REST API services, the Proof of Elapsed Time (PoET) mechanism successfully processed 946 transactions, in contrast to the 627 transactions managed by Proof of Work (PoW). The outcomes from multiple rounds of testing underscore the enhanced performance of this framework. In summary, the combination of blockchain technology, P2P energy trading, smart grids, and game theory offers a promising approach to revolutionizing the energy sector. By decentralizing energy trading, empowering consumers, and leveraging market mechanisms, this approach has the potential to enhance efficiency, promote renewable energy adoption, and foster a more sustainable energy ecosystem. © 2024 IEEE.

关键词： Blockchain

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Opinion Analysis and Text Pattern Detection from Social Media Data Using Integrated Machine Learning Approach 2

Opinion Analysis and Text Pattern Detection from Social Medi...

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2nd International Conference on Emerging Trends in Information technology and engineering, ic-ETITE 2024

作者： Abdullah, A. Sheik Mishra, Utkarsh Bala Adithyan, G. Sriram, B. School of Computer Science and Engineering Vellore Institute of Technology Chennai India

ISBN: (纸本)9798350328202

Opinion analysis using social media data has become essential for corporations, politics, and social research because it provides insightful information about the public's attitudes toward various issues. In the realm of e-commerce, understanding consumer preferences and emerging trends is crucial for businesses to adapt and thrive in a competitive market. This paper focuses on trend detection techniques in fashion e-commerce, aiming to analyze market successes and failures. By harnessing social media data, the objective is to equip retailers with predictive tools, enabling them to anticipate trends, optimize logistics, and meet consumer demands effectively. The study utilizes Python and sentiment analysis tools, including the Vader Polarity Score, to evaluate social media content from platforms like Flipkart and Amazon. The research identifies trending keywords and assesses their relevance in the market by analyzing product names, ratings, reviews, and sentiment scores. This research empowers fashion retailers to make informed decisions by leveraging social media data and sentiment analysis. The findings spotlight current market trends and provide a framework for anticipating future consumer preferences. Implementing such techniques can revolutionize e-commerce strategies, enhance product offerings and ensure sustained market relevance. © 2024 IEEE.

关键词： Sentiment analysis

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Comprehensive Analysis of Attention Deficit/Hyperactivity Disorder using Machine Learning and Deep Learning Algorithms 1

Comprehensive Analysis of Attention Deficit/Hyperactivity Di...

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1st International Conference on Intelligent Systems and Computational Networks, ICISCN 2025

作者： Sindhu, T.N. Sujatha, S.R. Sri Siddhartha Institute of Technology Department of Computer Science and Engineering Tumakuru India

ISBN: (纸本)9798331529246

Attention deficit/hyperactivity disorder (ADHD) is a neurodevelopmental disorder that affects children from childhood to adulthood. The symptoms of ADHD include attention deficit, impulsive behaviour, hyperactivity, and early diagnosis and treatment are imperative, especially for children as it continues into adulthood. ADHD detection is generally carried out using the existing methods such as Machine Learning (ML) and Deep Learning (DL) methods. The ML methods such as Linear Support Vector Machine (SVM), K-Nearest Neighbors (KNN), Linear Discriminative Analysis (LDA), Linear Regression (LR), Random Forest (RF), and Least Square SVM (LS-SVM) are used for the detection and analysis of ADHD. The existing DL methods such as Deep Convolutional Neural Network (DCNN), Graph Recurrent Unit (GRU), Convolutional Long Short-Term Memory (ConvLSTM), and Bidirectional LSTM are also employed for the detection of ADHD. The ML and DL algorithms enhance ADHD detection and diagnosis by automatically determining large patterns in data, thereby offering higher accuracy. The performance of the existing techniques is evaluated in terms of evaluation metrics: accuracy, precision, sensitivity, and F1-score to assess their effectiveness. © 2025 IEEE.

关键词： Adversarial machine learning

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An improved pulse coupled neural networks model for semantic IoT

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Digital Communications and Networks 2024年第3期10卷 557-567页

作者： Rong Ma Zhen Zhang Yide Ma Xiping Hu Edith C.H.Ngai Victor C.M.Leung School of Information Science and Engineering Lanzhou UniversityLanzhou 730000China School of Medical Technology Beijing Institute of TechnologyBeijing100081China Department of Electrical and Electronic Engineer The University of Hong KongHong Kong 999077China College of Computer Science and Software Engineering Shenzhen UniversityShenzhen 518060China Department of Electrical and Computer Engineering the University of British ColumbiaVancouverBC V6T 1Z4Canada

In recent years,the Internet of Things(IoT)has gradually developed applications such as collecting sensory data and building intelligent services,which has led to an explosion in mobile data ***,with the rapid development of artificial intelligence,semantic communication has attracted great attention as a new communication ***,for IoT devices,however,processing image information efficiently in real time is an essential task for the rapid transmission of semantic *** the increase of model parameters in deep learning methods,the model inference time in sensor devices continues to *** contrast,the Pulse Coupled Neural Network(PCNN)has fewer parameters,making it more suitable for processing real-time scene tasks such as image segmentation,which lays the foundation for real-time,effective,and accurate image ***,the parameters of PCNN are determined by trial and error,which limits its *** overcome this limitation,an Improved Pulse Coupled Neural Networks(IPCNN)model is proposed in this *** IPCNN constructs the connection between the static properties of the input image and the dynamic properties of the neurons,and all its parameters are set adaptively,which avoids the inconvenience of manual setting in traditional methods and improves the adaptability of parameters to different types of *** segmentation results demonstrate the validity and efficiency of the proposed self-adaptive parameter setting method of IPCNN on the gray images and natural images from the Matlab and Berkeley Segmentation *** IPCNN method achieves a better segmentation result without training,providing a new solution for the real-time transmission of image semantic information.

关键词： Internet of things(IoT) Semantic information Real-time application Improved pulse coupled neural network Image segmentation

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Advanced EEG-Based Classification of Periodic Leg Movements and Bruxism Using Complexity and Entropy Features 2

Advanced EEG-Based Classification of Periodic Leg Movements ...

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2nd International Conference on Machine Learning and Data engineering, ICMLDE 2023

作者： S., Tiwari D., Arora V., Nagar Department of Computer Science and Engineering Amity University Lucknow Campus India Department of Computer Science and Engineering Pranveer Singh Institute of Technology Kanpur India

Periodic leg movements (PLM) and bruxism are prevalent sleep disorders that significantly impact sleep quality and overall health. Accurate and timely detection of these disorders is pivotal for effective treatment and improved patient well-being. In this research paper, we present a novel approach for the identification of Periodic Leg Movements (PLM) and bruxism. Our methodology leverages sophisticated complexity and entropy features extracted from electroencephalogram (EEG) signals recorded across various sleep stages. The implementation involves a diverse set of artificial intelligence (AI) classifiers, encompassing K-nearest neighbours (KNN), artificial neural networks, support vector machines (SVM) with radial basis function, SVM with polynomial kernel, and SVM with sigmoid kernel. These classifiers are employed to categorize EEG signals into three distinct classes: PLM, bruxism, or normal sleep patterns. To underscore the efficacy and superiority of our proposed approach, we conduct a thorough comparison with existing publications in the field. Our approach consistently outperforms previous methods and attains remarkable accuracies of 98.74% for PLM and 94.74% for bruxism, particularly when utilizing the SVM Sigmoid classifier. Furthermore, our study highlights the potential of EEG signals in elucidating alterations in brain activity associated with PLM and bruxism, underscoring the reliability of complexity and entropy features as robust biomarkers for these sleep disorders. Our research significantly advances the contemporary pinnacle in EEG-based detection of Periodic Leg Movements (PLM) and bruxism signifies a significant advancement, holding potential for impactful clinical applications in the realm of sleep disorder diagnosis and treatment. © 2024 Elsevier B.V.. All rights reserved.

关键词： Support vector machines

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A Review of Machine Learning and Deep Learning algorithms for Chronic Kidney Disease Prediction 2

A Review of Machine Learning and Deep Learning algorithms fo...

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2nd DMIHER International Conference on Artificial Intelligence in Healthcare, Education and Industry, IDICAIEI 2024

作者： Nagre, Pratik Verma, Prateek Kumar, Praveen Gavankar, Nitin L. Datta Meghe Institute of Higher Education and Research Faculty of Engineering and Technology Department of Artificial Intelligence and Machine Learning Sawangi Maharashtra Wardha442001 India Datta Meghe Institute of Higher Education and Research Faculty of Engineering and Technology Department of Computer Science and Medical Engineering Sawangi Maharashtra Wardha442001 India Walchand College of Engineering Department of Computer Science and Engineering Maharashtra Sangli416415 India

ISBN: (纸本)9798331528713

Chronic Kidney Disease (CKD) is a common disease worldwide that is manifested by slow progressive loss of kidney function that can result in complications and kidney failure. That is why conventional approaches to CKD diagnosis and treatment have certain difficulties: late identification of the disease, inaccuracy of the diagnosis, and high costs for the therapy. The new approaches for solving these problems are opened with the help of Machine Learning (ML) and deep learning (DL). These technologies can help existing information sets in large volumes in diagnosing, predicting, and in early detection of diseases to enhance the well-being of the patients. However, the outcome comes with a handful of constraints, including the quality of the data used in developing the models, the ability to interpret results from the generated model, and the complexity of the computation process. The following section describes the various ML and DL algorithms used for CKD, their strengths, weaknesses, and the possibility of implementing these technologies in clinical settings for better CKD outcomes. © 2024 IEEE.

关键词： Adversarial machine learning

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