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2025 CHI Conference on Human Factors in Computing Systems, CHI EA 2025

作者： MacKenzie, Scott Roig-Maimó, Maria Francesca Mas-Sansó, Ramon Department of Electrical Engineering and Computer Science York University TorontoON Canada Department of Mathematics and Computer Science University of the Balearic Islands Palma Spain

ISBN: (纸本)9798400713958

Most attendees at CHI conferences will agree that an experiment (user study) is the hallmark of good research in human-computer interaction. But what constitutes an experiment? And how does one go from an experiment to a CHI paper? This course will teach how to pose testable research questions, how to make and measure observations, and how to design and conduct an HCI experiment. Specifically, attendees will participate in a real experiment to gain experience as both an investigator and as a participant. The second session covers the statistical tools typically used to analyze data. Most notably, attendees will learn how to organize experiment results and write a CHI paper. © 2025 Copyright held by the owner/author(s).

关键词： Design of experiments

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Comparative Analysis of Diverse Architectures for Accurate Blood Cancer Cell Classification

Comparative Analysis of Diverse Architectures for Accurate B...

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2024 International Conference on computer, electrical and Communication engineering, ICCECE 2024

作者： Md. Muttakin, G.K. Yesmin, Farhana Alam, Syeda Sadia Ashraf, Md Sadi Akuthota, Vishwanath Quader, Montaser Abdul Rajshahi University of Engineering and Technology Department of Electrical and Computer Engineering India Metropolitan University Department of Computer Science and Engineering United Kingdom Brac University Department of Computer Science and Engineering Bangladesh Drpinnacle Hyderabad India Green University of Bangladesh Department of Computer Science and Engineering Bangladesh

ISBN: (纸本)9798350386479

Blood cancer cell diagnosis is crucial in medical diagnostics. It demands accurate classification of blood cell images. Proper classification of blood cancer cells is fundamental for accurately diagnosing the specific type and subtype of blood cancer as well as efficient treatment planning. It provides specific information, helping healthcare professionals predict the prognosis, survival rates, and the potential for disease recurrence. On this basis, deep learning models have demonstrated remarkable performance. In this paper, we have introduced a comprehensive research into blood cancer cell classification, employing a diverse dataset encompassing various blood cell types. We explore the effectiveness of VGG19 with batch normalization, Vision Transformer (ViT), Ensemble Adversarial Inception-ResNetV2, DenseNet201, and ResNeXt50 architectures in this challenging task. In order to enhance the model performance, we integrate essential data preprocessing techniques, including resizing, cropping, and normalization. Additionally, novel data augmentation strategies, such as random cropping, and flipping are introduced to augment the training dataset and improve model generalization. Remarkably, VGG19 with batch normalization has shown tremendous success by achieving 99.86% accuracy. Moreover, the DenseNet201 model has performed brilliantly by achieving an accuracy of 98.55%. The ResNeXt50 model shows excellent performance with an accuracy of 98.31%. The Vision Transformer (ViT) achieves a solid accuracy of 98.91%. Lastly, Ensemble Adversarial Inception-ResNet V2 also performed no-tably achieving 96.38%. In this context, VGG19 with batch normalization is able to show more excellent performance than other models by achieving the highest accuracy. © 2024 IEEE.

关键词： Cancer cells

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Unsupervised Learning for Insider Threat Prediction: A Behavioral Analysis Approach 17

Unsupervised Learning for Insider Threat Prediction: A Behav...

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17th International Conference on Security of Information and Networks, SIN 2024

作者： Mehmood, Rahat Singh, Priyanka Jeffery, Zoe School of Physics Engineering and Computer Science University of Hertfordshire Hatfield United Kingdom School of Electrical Engineering and Computer Science The University of Queensland Brisbane Australia

ISBN: (纸本)9798331509736

Most of the devastating cyber-attacks are caused by insiders with access privileges inside an organization. The main reason of insider attacks being more effective is that they don't have many security barriers before they get into the critical resources of the system. Different machine learning techniques have been previously utilized to identify insider threats within cy-bersecurity domain whereas research done in predicting insider attacks is not significant. Moreover, machine learning models used for prediction and detection face a critical limitation as they require training on labeled datasets, rendering them less effective for real-time data streams which lack threat presence indicators. This work presents an unsupervised machine learning approach that predicts insider threat using behavior analysis for real-time threat data. Patterns are identified in user behavior, to make predictions about benign and malicious insiders. Features are selected by analyzing activities performed. Selected features are utilized to feed machine learning model which extracts anomalous behavior among users, using anomalies in their activity patterns followed by learning methods for threat detection. A dataset that contains selected features from CERT r4.2 is used to make predictions. The performance of Isolation Forest (iForest) is compared with other algorithms of the same category including One-class SVM, Local Outlier Factor (LOF) and DBSCAN to evaluate the new approach. The iForest shows the best performance accuracy 80 percent and recall 84.2 percent. © 2024 IEEE.

关键词： Adversarial machine learning

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Forecasting Bitcoin Prices Using Deep Learning for Consumer-Centric Industrial Applications

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IEEE Transactions on Consumer Electronics 2024年第1期70卷 1351-1358页

作者： Roy, Pradeep Kumar Kumar, Abhinav Singh, Ashish Sangaiah, Arun Kumar Indian Institute of Information Technology Surat Department of Computer Science and Engineering Surat394190 India Motilal Nehru National Institute of Technology Allahabad Department of Computer Science and Engineering Prayagraj211004 India KIIT Deemed to Be University School of Computer Engineering Bhubaneswar751024 India National Yunlin University of Science and Technology International Graduate Institute of AI Douliou64002 Taiwan Lebanese American University Department of Electrical and Computer Engineering Byblos1102-2801 Lebanon

As cryptocurrencies become more popular as investment vehicles, bitcoin draws interest from businesses, consumers, and computer scientists all across the world. Bitcoin is a computer file stored in digital wallet applications where each transaction is secured using strong cryptographic algorithms. It was challenging to forecast the future price of bitcoin due to its nonlinearity and extreme volatility. Several recent classic parametric models have been found with limited accuracy. To address the limitations and fill the existing research gaps, there is a need for a good prediction model which will provide the desired accuracy in the case of uncertainty and dynamism. This research suggested a deep learning-based framework for predicting and forecasting Bitcoin price. The research will be helpful for worldwide consumers and industries to take their decision on whether to invest or not. The research utilizes Yahoo! finance dataset for the period of 01-03-2016 to 26-02-2021 having 1828 samples. The experimental outcomes of the proposed Long Short-Term Memory (LSTM) model outperformed similar deep learning models by securing minimum loss and confirming that it can be used for future price prediction of the cryptocurrencies, which is helpful for the buyer to take their decision. © 1975-2011 IEEE.

关键词： Hidden Markov models

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International Journal of computers and Applications 2023年第5期45卷 379-390页

作者： Liu, J. Shao, H.J. Deng, X. Jiang, Y.T. School of Computer Jiangsu University of Science and Technology Zhenjiang China Department of Electrical and Computer Engineering University of Nevada Las VegasNV United States

The paper discusses the high computational costs associated with convolutional neural networks (CNNs) in real-world applications due to their complex structure, primarily in hidden layers. To overcome this issue, the paper proposes a novel channel pruning technique that leverages the correlation topology of feature maps generated by each CNNs layer to construct a network with fewer nodes, reducing computational costs significantly. Redundant channels exhibit a high degree of topological similarity and tend to increase as the number of network layers rises. Removing the channel corresponding to highly correlated feature maps allows retrieval of the ‘base’ set of characteristics needed by subsequent layers. The proposed channel pruning technique provides a promising approach to reducing the computational costs of deep convolutional neural networks while maintaining high performance levels. By designing a network structure optimized for specific input data types, the method results in more efficient and effective machine learning models. The pruning operation requires fine-tuning to optimize network performance, and experiments using X-ray, chest CT, and MNIST images show that the pruned network can eliminate approximately 80% of redundant channels with minimal performance deterioration (maintaining original CNNs performance at 99.2%). © 2023 Informa UK Limited, trading as Taylor & Francis Group.

关键词： Topology

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Fusing Superpixel Graph Propagation and Positional Convolutions for Small Object Detection in Computed Tomography Scan

Fusing Superpixel Graph Propagation and Positional Convoluti...

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2024 International Conference on Microelectronics, ICM 2024

作者： Modak, Sudipta Abdel-Raheem, Esam Rueda, Luis University of Windsor Department of Electrical and Computer Engineering Canada School of Computer Science University of Windsor WindsorON Canada

ISBN: (纸本)9798350379396

Small object detection in radiological images has been a key challenge in the field of medical diagnosis for the last decade. Radiological modalities such as computed tomography scan imaging are often used to evaluate the condition of a patient. These modalities can capture anomalies from various parts of the body which are then analyzed by radiologists to identify tumors, stones, nodules, etc. However, it becomes a strenuous task for any radiologist to accurately identify tiny abnormalities contained within the radiological images. In this paper, a two-step lightweight computer-aided anomaly detection method is proposed that is suitable for small object detection in computed tomography scan images. The proposed method leverages the power of uniform superpixel generation, graph propagation, and positional convolutions to detect anomalies in computed tomography scan images with great accuracy. Furthermore, experiments conducted on imaging datasets from two distinct organs of the body that is the lungs and the kidneys, show the effectiveness of the proposed method in terms of small object detection in medical imaging. © 2024 IEEE.

关键词： computerized tomography

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Kinmen Wind Lion Face Generation Based on the Deep Convolutional Generative Adversarial Network 13

Kinmen Wind Lion Face Generation Based on the Deep Convoluti...

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13th IEEE Global Conference on Consumer Electronic, GCCE 2024

作者： Wu, Chia-Huan Zhao, Yu-Xiang Chou, Cheng-Hou Hsieh, Yi-Zeng National Taiwan University of Science and Technology Department of Electrical and Computer Engineering Taiwan National Quemoy University Department of Computer Science and Information Engineering Taiwan

ISBN: (纸本)9798350355079

Under the advancements of science and technology at present, artificial intelligence has become widely applied in daily life. Hence, deep learning has attracted much attention in recent years and has been widely used in recognition, classification and generation. The results generated by generative adversarial networks have become increasingly realistic and are now being applied to human face generation and reconstruction. Among the various technologies, the deep convolutional generative adversarial network (DCGAN) is more widely used. Based on the trained DCGAN of human and cat faces, this paper collected Kinmen wind lion data by YOLOv3 and crawler technology and transferred the data to the DCGAN of wind lions through network-based transfer learning to generate complete wind lion faces. The experimental process consisted of four parts. The wind lion dataset was directly used to train the DCGAN in the first two parts, and the DCGAN of the human and cat faces was used for transfer learning in the second two parts. According to the results, the training effect of human face-based transfer learning was the best and was the closest to Kinmen's wind lions. © 2024 IEEE.

关键词： Generative adversarial networks

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FAU Hack-a-Thons: An Introduction to Computational and Hardware, Logistical Skills, and Intense Training for Outcomes-Based Learning for Developing Internet of Things Products

FAU Hack-a-Thons: An Introduction to Computational and Hardw...

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2024 ASEE Annual Conference and Exposition

作者： Weinthal, Charles Perry Jaramillo, David Department of Computer & Electrical Engineering & Computer Science Florida Atlantic University Boca RatonFL United States

In preparation for the upcoming FAU Hack-a-Thon, we have implemented extensive support structures to ensure that all participating teams are thoroughly prepared for the competition. This preparation includes the provision of online support from a variety of programming experts from IBM and experienced Hack-a-Thon participants. This support is accessible 24/7 in the three weeks leading up to the event, aimed at ensuring all teams are up to speed and ready to participate. Our proactive approach focuses on encouraging teams to engage deeply with the training materials, consistently reminding them of their capability to successfully compete with adequate preparation. During the event, teams are expected to utilize the skills they have honed in areas such as teamwork, programming, and hardware development to create innovative Internet of Things (IoT) products. The support continues throughout the 24-hour event, with resources readily available to assist teams in overcoming any technical challenges they encounter. This setup aims to simulate real-world problem-solving scenarios that participants are likely to face in their professional careers, thus enhancing their experience and skills in a practical, hands-on manner. The Hack-a-Thon not only focuses on technical skills development but also emphasizes the cultivation of soft skills such as communication, teamwork, and time management. By the end of the event, participants are expected to present their projects to a panel of industry experts and faculty, demonstrating not only their technical prowess but also their ability to communicate their ideas effectively. This comprehensive approach ensures that participants leave the Hack-a-Thon with enhanced skills, increased confidence, and a significant competitive edge in the job market, as many past participants have noted the value of this experience during job interviews. © American Society for engineering Education, 2024.

关键词： Personnel training

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Revolutionizing Requirements Elicitation: Deep Learning-Based Classification of Functional and Non-Functional Requirements 5

Revolutionizing Requirements Elicitation: Deep Learning-Base...

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5th IEEE International Conference on Pattern Recognition and Machine Learning, PRML 2024

作者： Hanna, Grace Boyar, Nathan Garay, Nathan Maleki, Mina University of Detroit Mercy Dept. of Electrical and Computer Engineering and Computer Science DetroitMI United States

ISBN: (纸本)9798350355925

The requirements elicitation phase in the software development life cycle (SDLC) is both critical and challenging, especially in the context of big data and rapid technological advancement. Traditional approaches like workshops and proto-typing, while useful, often struggle to keep pace with the massive data volumes and rapidly changing user demands characteristic of modern technology. This paper introduces a data-driven approach that utilizes deep learning (DL) and natural language processing (NLP) to enhance the requirements elicitation process by extracting requirements and classifying them into functional and non-functional categories. Our research involves a deep neural network (DNN) trained on a large dataset of transcriptions from client/user stories. This DNN can identify whether a specific text represents a functional requirement, a non-functional requirement, or neither. Our approach shows a marked improvement over previous methods, with a 33% increase in accuracy and an 18% increase in the F1 score. These results indicate the capability for deep learning techniques to play a vital role in elicitation. © 2024 IEEE.

关键词： Software design

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Lung Cancer Relapse Prediction Using Self-Organizing Maps: A Clustering-Based Approach for Early Detection

Lung Cancer Relapse Prediction Using Self-Organizing Maps: A...

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2025 International Conference on Ambient Intelligence in Health Care, ICAIHC 2025

作者： Samanta, Indu Sekhar Nayak, Subrat Kumar Rout, Pravat Kumar Department of Computer Science and Engineering Bhubaneswar India Department of Electrical and Electronics Engineering Bhubaneswar India

ISBN: (纸本)9798331530259

Lung cancer relapse is very often found to affect patient prognosis and treatment outcomes potentially and may lead to more challenging management and poorer survival rates. Early prediction of relapse is necessary for timely intervention and personalized treatment plans and to handle the key effects of relapse on prognosis and treatment, such as prognosis and survival, treatment outcomes, impact on treatment strategies, psychosocial effects, and potential for clinical trials. With this motivation, this paper proposed Self-Organizing Maps (SOMs) to predict lung cancer relapse. The SOM technique is considered for predicting lung cancer relapse due to its dimensionality reduction, ability to recognize and cluster patterns, visual way to interpret complex medical data operation without the need for labelled training data, capability to handle non-linear relationships, adaptability to evolving data, and noise tolerance. It is investigated that this unsupervised learning technique clusters patients based on their clinical and pathological features, providing a valuable tool for identifying relapse-prone patients. The results reflect that the proposed approach attains high prediction accuracy by leveraging the clustering capabilities of SOMs and effectively classifying patients into relapse and non-relapse categories. It can be concluded that the SOMs have the potential to enhance early detection and personalized treatment of lung cancer relapse. © 2025 IEEE.

关键词： Personalized medicine

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