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2024 Innovations in Intelligent Systems and Applications Conference, ASYU 2024

作者： Gulsoy, Muhammed Kokach, Vael Kocaçınar, Büşra Akbulut, Fatma Patlar Department of Computer Engineering Istanbul Kültür University Istanbul Turkey Department of Software Engineering Istanbul Kültür University Istanbul Turkey

ISBN: (纸本)9798350379433

This study proposes an AI-driven framework for generating coherent narratives and corresponding visuals tailored for children's stories. We produce textual material by using Long Short-Term Memory (LSTM) models, and visually appealing images by using Generative Adversarial Networks (GANs) and fine-tuned Stable Diffusion Models. Furthermore, we investigate various prefix-based story starting points to increase the variety and interest of the story. Our method includes a significant innovation: the Stable Diffusion model is fine-tuned for image generation, enabling the production of high-quality, contextually relevant images from particular story segments. We show by experiments that the Stable Diffusion model can be fine-tuned to greatly improve the quality and relevance of the produced images, and that the layered LSTM model with extended training produces the best text generation performance. Using these advanced models together improved the storytelling experience by fusing imaginative AI with rich, narrative-driven imagery. © 2024 IEEE.

关键词： Generative adversarial networks

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UVM Based Verification Framework for Deep Learning Hardware Accelerator: Case Study 4

UVM Based Verification Framework for Deep Learning Hardware ...

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4th IEEE International Conference on Electrical, computer, and Energy Technologies, ICECET 2024

作者： Aboudeif, Randa Awaad, Tasneem A. Abdelsalam, Mohamed Ismail, Yehea The American University in Cairo Siemens Digital Industries Software Electronics and Communications Engineering Department Cairo Egypt Ain Shams University Siemens Digital Industries Software Department of Computer and Systems Engineering Faculty of Engineering Cairo Egypt Siemens Digital Industries Software Cairo Egypt The American University in Cairo Electronics and Communications Engineering Department Cairo Egypt

ISBN: (纸本)9798350395914

Hardware Verification of Deep Learning Accelerators (DLAs) has become critically important for testing the reliability and trustworthiness of Learning Enabled Autonomous Systems (LEAS). In this paper, we introduce a scalable, reusable, and efficient hardware verification framework for DLAs using the Universal verification methodology (UVM). The verification environment is focused on testing the inference functionality of the perception module in LEAS for different DLA designs. Moreover, the verification environment is portable across simulation and hardware-assisted verification platforms for emulation and FPGA prototyping. To assess our proposed UVM design methodology, we have applied it to the Nvidia Deep Learning Accelerator (NVDLA), an open-source core for DLAs as a case study. © 2024 IEEE.

关键词： CNN Deep Learning Accelerators NVDLA UVM veri-fication

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Alzheimer Disease Detection Studies: Perspective on Multi-Modal Data

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Yearbook of Medical Informatics 2025年第1期33卷 266-276页

作者： Dehghani, Farzaneh Derafshi, Reihaneh Lin, Joanna Bayat, Sayeh Bento, Mariana Biomedical Engineering Department University of Calgary Canada Computer Science Department University of Calgary Canada Geomatics Engineering Department University of Calgary Canada Hotchkiss Brain Institute Cumming School of Medicine University of Calgary Canada Electrical and Software Engineering Department University of Calgary Canada

OBJECTIVES: Alzheimer's Disease (AD) is one of the most common neurodegenerative diseases, resulting in progressive cognitive decline, and so accurate and timely AD diagnosis is of critical importance. To this end, various medical technologies and computer-aided diagnosis (CAD), ranging from biosensors and raw signals to medical imaging, have been used to provide information about the state of AD. In this survey, we aim to provide a review on CAD systems for automated AD detection, focusing on different data types: namely, signals and sensors, medical imaging, and electronic medical records (EMR). METHODS: We explored the literature on automated AD detection from 2022-2023. Specifically, we focused on various data resources and reviewed several preprocessing and learning methodologies applied to each data type, as well as evaluation metrics for model performance evaluation. Further, we focused on challenges, future perspectives, and recommendations regarding automated AD diagnosis. RESULTS: Compared to other modalities, medical imaging was the most common data type. The prominent modality was Magnetic Resonance Imaging (MRI). In contrast, studies based on EMR data type were marginal because EMR is mostly used for AD prediction rather than detection. Several challenges were identified: data scarcity and bias, imbalanced datasets, missing information, anonymization, lack of standardization, and explainability. CONCLUSION: Despite recent developments in automated AD detection, improving the trustworthiness and performance of these models, and combining different data types will improve usability and reliability of CAD tools for early AD detection in the clinical practice. The Author(s). This is an open access article published by Thieme under the terms of the Creative Commons Attribution License, permitting unrestricted use, distribution, and reproduction so long as the original work is properly cited. (https://***/licenses/by/4.0/).

关键词： Alzheimer’s Disease computer-aided Diagnosis Electronic Medical Records Medical Imaging Signals and Sensors

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Generative Diffusion Model-Based Deep Learning Framework for Remaining Useful Life Prediction

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IEEE Internet of Things Journal 2025年第11期12卷 18431-18434页

作者： Ha, Sangjun Sung, Mingyu Saeed, Faisal Yun, Sangseok Kim, Il-Min Kang, Jae-Mo Kyungpook National University Department of Artificial Intelligence Daegu41566 Korea Republic of Shenzhen University College of Computer Science and Software Engineering Shenzhen518060 China Pukyong National University Department of Information and Communications Engineering Busan48513 Korea Republic of Queen's University Department of Electrical and Computer Engineering KingstonONK7L 3N6 Canada

In this letter, we propose a novel and high-performing deep learning framework for remaining useful life (RUL) prediction, called RUL-Diff, by leveraging a generative diffusion model. It is composed of two modules that are connected in tandem: (i) a feature extractor corresponding to the encoder part of our customized U-Net and (ii) a RUL predictor constructed by a multi-layer perceptron. We further devise an effective two-stage training methodology for the proposed RUL-Diff, in which the feature extractor is initially pre-trained for high-quality feature learning, and then, is re-trained jointly with the RUL predictor for accurate RUL prediction. Extensive experimental results on NASA Commercial Modular Aero-Propulsion System Simulation (C-MAPSS) datasets demonstrate the superiority and effectiveness of the proposed scheme. © 2014 IEEE.

关键词： Prediction models

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Understanding the Impact of Unexpected Cobot Movements on Human Stress Levels: A Time Series Classification Task

Understanding the Impact of Unexpected Cobot Movements on Hu...

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2024 Workshop on Embracing Human-Aware AI in Industry 5.0, HAII5.0 2024

作者： Shilon, Lior Shevtsov, Vladyslav Schillreff, Nadia Rother, Anne Mark, Shlomo Spiliopoulou, Myra Department of Software Engineering Sami Shamoon College of Engineering Ashdod Israel Faculty of Computer Science Otto-von-Guericke-University Magdeburg Germany

This study investigates the impact of unexpected stimuli on participants’ stress levels during human-robot interactions (HRI). We designed an experiment, where a cobot performs writing tasks, as well as some unexpected actions, intended to surprise the human observer. During this experiment, we are continuously monitoring physiological responses through Electrodermal Activity (EDA) and Electrocardiogram (ECG) sensors. Our goal was to capture the relationship between surprising actions by the cobot and indicators of change in the human stress levels. We employed time series classification techniques, namely ROCKET, Shapelet Transform and WEASEL, along with Support Vector Machine (SVM) analysis on temporal segments. This approach enabled us to develop a binary classification model on stress levels. Our research contributes to the design of AI systems that can detect changes in the stress levels of humans who interact with cobots that perform unexpected actions. Thus, we work towards the improvement of safety protocols in HRI. © 2024 Copyright for this paper by its authors.

关键词： Collaborative robots

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A Novel Color Image Watermarking Method with Adaptive Scaling Factor Using Similarity-Based Edge Region

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computer Systems Science & engineering 2023年第10期47卷 55-77页

作者： Kali Gurkahraman Rukiye Karakis Hidayet Takci Faculty of Engineering Department of Computer EngineeringSivas Cumhuriyet UniversitySivas58140Turkey Faculty of Technology Department of Software EngineeringSivas Cumhuriyet UniversitySivas58140Turkey

This study aimed to deal with three challenges:robustness,imperceptibility,and capacity in the image watermarking *** reach a high capacity,a novel similarity-based edge detection algorithm was developed that finds more edge points than traditional *** colored watermark image was created by inserting a randomly generated message on the edge points detected by this *** ensure robustness and imperceptibility,watermark and cover images were combined in the high-frequency subbands using Discrete Wavelet Transform and Singular Value *** the watermarking stage,the watermark image was weighted by the adaptive scaling factor calculated by the standard deviation of the similarity *** to the results,the proposed edge-based color image watermarking technique has achieved high payload capacity,imperceptibility,and robustness to all *** addition,the highest performance values were obtained against rotation attack,to which sufficient robustness has not been reached in the related studies.

关键词： Image watermarking edge detection discrete wavelet transform singular value decomposition adaptive scaling factor

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Turkish Sign Language Recognition Using a Fine-Tuned Pretrained Model 2nd

Turkish Sign Language Recognition Using a Fine-Tuned Pretra...

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2nd International Conference on Advanced engineering, Technology and Applications, ICAETA 2023

作者： Ozgul, Gizem Derdiyok, Şeyma Akbulut, Fatma Patlar Department of Computer Engineering Istanbul Kültür University Istanbul Turkey Department of Software Engineering Istanbul Kültür University Istanbul Turkey

ISBN: (纸本)9783031509193

Many members of society rely on sign language because it provides them with an alternative means of communication. Hand shape, motion profile, and the relative positioning of the hand, face, and other body components all contribute to the uniqueness of each sign throughout sign languages. Therefore, the field of computer vision dedicated to the study of visual sign language identification is a particularly challenging one. In recent years, many models have been suggested by various researchers, with deep learning approaches greatly improving upon them. In this study, we employ a fine-tuned CNN that has been presented for sign language recognition based on visual input, and it was trained using a dataset that included 2062 images. When it comes to sign language recognition, it might be difficult to achieve the levels of high accuracy that are sought when using systems that are based on machine learning. This is due to the fact that there are not enough datasets that have been annotated. Therefore, the goal of the study is to improve the performance of the model by transferring knowledge. In the dataset that was utilized for the research, there are images of 10 different numbers ranging from 0 to 9, and as a result of the testing, the sign was detected with a level of accuracy that was equal to 98% using the VGG16 pre-trained model. © 2024, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Convolutional Neural Networks (CNN) Sign language Transfer learning

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A Novel Lightweight IoT Intrusion Detection Model Based on Self-knowledge Distillation

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IEEE Internet of Things Journal 2025年第11期12卷 16912-16930页

作者： Wang, Zhendong Zhou, Renqiang Yang, Shuxin He, Daojing Chan, Sammy Jiangxi University of Science and Technology School of Information Engineering Ganzhou341000 China Nanchang University Software Engineering Institute Nanchang330047 China Harbin Institute of Technology School of Computer Science and Technology Shenzhen518055 China City University of Hong Kong Department of Electrical Engineering Hong Kong

The Internet of Things (IoT) environment contains many different types of devices, each with different functionalities, communication protocols, and security capabilities, which makes the IoT a complex challenge for security protection. Therefore, network intrusion detection (NID) is needed to detect intrusions in the network to secure the IoT. In recent years, deep learning-based intrusion detection systems have achieved excellent results, but they tend to require high computational resources and storage space, which is not feasible for most IoT devices. In this paper, we propose a lightweight intrusion detection model based on self-knowledge distillation, namely TBCLNN, which improves the detection accuracy while also reducing the number of model parameters and computational cost. Specifically, we use the binary Harris Hawk optimization algorithm for dimensionality reduction of traffic features. We use lightweight convolution, such as Tied Block Convolution (TBC), to design lightweight neural network models with residual and inverse residual structures. Moreover, we propose an improved self-knowledge distillation loss function to solve the sample imbalance problem and compensate for the performance degradation caused by lightweight neural networks. The multiclassification accuracy of our proposed method exceeds 99% on all three publicly available IoT datasets. The experimental results show that our method has a small model size and requires only low computational resources, making it suitable for resource-constrained IoT intrusion detection. © 2014 IEEE.

关键词： Network intrusion

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PaddyShield: A CNN-Based Rice Leaf Diseases Detection and Management System 8

PaddyShield: A CNN-Based Rice Leaf Diseases Detection and Ma...

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8th SLAAI - International Conference on Artificial Intelligence, SLAAI-ICAI 2024

作者： Chathuranga, Shalika Subhashini, Pavithra Ekanayake, Jeeva NSBM Green University Department of Software Engineering Colombo Sri Lanka The Open University of Sri Lanka Electrical and Computer Engineering Department Nawala Sri Lanka

ISBN: (纸本)9798331509170

Sri Lankan paddy farmers face numerous challenges due to limited access to resources and knowledge of pests and diseases effectively. This often leads to wasting money on pesticides without addressing the problem, affecting crop yield. Existing systems excel at swift disease detection but do not acknowledge farmers in preventing and mitigating diseases. Farmers expect information on the diseases and suitable mechanisms to get rid of the diseases. Further, we conducted a pre-survey with paddy farmers in the Anuradhapura district. We understood the importance of acknowledging the pests and diseases and the methods to mitigate them. Therefore, we developed a 'PaddyShield' system to identify diseases and provide comprehensive disease details on prevention mechanisms for the identified diseases through rice leaf. The 'PaddyShield' system was implemented after employing the sophisticated Convolutional Neural Network (CNN) as the linchpin for precise disease detection. The system's strength lies in its adept handling of a diverse dataset encompassing various paddy leaf diseases, ensuring accuracy (95%) in early disease identification. Through rigorous testing, the system indisputably highlights its superiority over existing solutions. Accessibility is prioritized through a user-friendly Flaskpowered interface. Overcoming hurdles like data preprocessing and model complexity with inventive solutions, the system achieves seamless backend-frontend integration. Optimal GPU resource utilization further accentuates its efficiency. © 2024 IEEE.

关键词： Convolutional neural networks

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Hybrid Deep Learning Framework for Stock Price Prediction Incorporating Technical and Macroeconomic Indicators 9

Hybrid Deep Learning Framework for Stock Price Prediction In...

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9th International Conference on computer Science and engineering, UBMK 2024

作者： Turan, Ali Can Kokach, Vael Kocaçinar, Büşra Şengel, Öznur Akbulut, Fatma Patlar Istanbul Kültür University Department of Computer Engineering Istanbul Turkey Istanbul Kültür University Department of Software Engineering Istanbul Turkey

ISBN: (纸本)9798350365887

The complicated interaction of several elements makes accurate stock price prediction still difficult. This study suggests a hybrid deep learning model to improve stock market prediction by integrating Bidirectional Long Short-Term Memory (LSTM) networks with Convolutional Neural Networks (CNNs). Incorporating both technical and macroeconomic variables helps the model to adequately identify complex trends and interconnections inside the financial data. While the bidirectional LSTM models temporal dependencies, the CNN component shines in retrieving pertinent features from the input data. Experimental findings show that the suggested model beats conventional approaches, therefore offering traders and investors important new perspectives. © 2024 IEEE.

关键词： Prediction models

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