Threats Detection in the Internet of Things Using Convolutional neural networks, long short-term memory, and gated recurrent units

Bajao, Naomi A.; Sarucam, Jae-an

doi:10.58496/mjcs/2023/005

Cited by 8 publications

(7 citation statements)

References 12 publications

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“…The essential characteristic of the gates of GRU is keeping the information for a long ago without forgetting it over time as well as it doesn't remove irrelevant information with prediction. So, it can solve vanishing problems with better and more efficient performance than LSTM [49].…”

Section: Gru and Bigrumentioning

confidence: 99%

Developing Hybrid CNN-GRU Arrhythmia Prediction Models Using Fast Fourier Transform on Imbalanced ECG Datasets

Oleiwi,

AlShemmary,

Al-Augby

2024

MMEP

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There are many methods to diagnose heart disease; the most effective way is to analyze electrocardiogram (ECG) signals. Generally, the automatic classification techniques based on ECG analysis consist of three steps: data preprocessing, feature extraction, and classification. This study designed eight hybrid model architectures using several types of deep neural networks, including Convolution Neural Network (CNN), Gated Recurrent Unit (GRU), and Bidirectional GRU (Bi-GRU), four of them without Fast Fourier Transform (FFT) and the rest using FFT. Firstly, the MIT-BIH arrhythmia database is cleaned using the wavelet (WT) thresholding method that separates the combined noise and signal frequencies, making it ideal for processing nonstationary ECG signals. Additionally, the imbalance problem in this database was addressed using the synthetic minority over-sampling technique (SMOTE), which is more suitable for medical data than random synthesis methods. Secondly, hybrid models FFT-CNN, FFT-GRU, FFT-CNN-GRU, and FFT-CNN-Bi-GRU are constructed using the new proposed architecture by concatenating resultant features from two paths, the first path using ECG in the time domain and the second path using the resultant spectrum of ECG from FFT as input. A comparative study of the performance of all models was created in terms of accuracy, training time, number of trainable parameters, and robustness against noise. The results show that the proposed CNN, GRU, CNN-GRU, and CNN-Bi-GRU models without WT and FFT achieved 90%, 93%, 95%, and 96% accuracies, while the proposed FFT-CNN, FFT-GRU, FFT-CNN-GRU, and FFT-CNN-Bi-GRU models achieved 97%, 95%, 96%, and 97% accuracies with WT. So, the proposed FFT-CNN model was the best, with less training time and parameters than other models, which significantly impacts designing a high-efficiency model with less complexity for a practical medical diagnosis system. On the other hand, using FFT improved all models' performance, accuracy and robustness against noise.

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Section: Gru and Bigrumentioning

confidence: 99%

Developing Hybrid CNN-GRU Arrhythmia Prediction Models Using Fast Fourier Transform on Imbalanced ECG Datasets

Oleiwi,

AlShemmary,

Al-Augby

2024

MMEP

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“…It encompasses a range of techniques and methods that enable computers to exhibit intelligent behavior, such as problem-solving and decision-making, which were previously thought to be the exclusive domain of human beings. AI algorithms and systems can analyze and interpret vast amounts of data, learn from patterns, and improve their performance over time, making them increasingly capable of handling complex tasks and making accurate predictions [38][39][40][41][42]. AI finds applications in a wide range of fields, from healthcare and finance to transportation and manufacturing [43][44][45][46][47].…”

Section: Chatgpt and Ethicsmentioning

confidence: 99%

ChatGPT and the Future of Academic Integrity in the Artificial Intelligence Era: A New Frontier

M. Mijwil,

Hiran,

Doshi

et al. 2023

AJEST

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ChatGPT is a state-of-the-art language model developed by OpenAI. It is part of the GPT (Generative Pre-trained Transformer) series, which are designed to generate human-like language output based on large amounts of input data. ChatGPT is one of the largest and most advanced language models to date, with 175 billion parameters. The article aims to examine the impact of artificial intelligence tools and techniques on academic research and their potential implications for academic ethics. In particular, this article will focus on the practices of ChatGPT in generating scientific research within the context of academic ethics. ChatGPT is a powerful tool that can generate text in different formats, conduct literature searches, and suggest titles for the created text. However, using ChatGPT to select topics with a low similarity score in similarity checkers may lead to ethical violations. This article finds that the use of artificial intelligence applications in academic research may raise concerns about academic ethics, and the limited availability of technologies that can detect such violations poses a significant challenge to academic writing.

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“…A typical CNN consists of multiple layers, including convolutional layers, pooling layers, and fully connected layers. The computation and memory requirements of CNNs [7] can be significant, particularly for real-time applications. Therefore, there is a growing interest in developing hardware accelerators that can efficiently process CNNs.…”

Section: Cnns and Fpga-based Pipeliningmentioning

confidence: 99%

Convolutional Neural Networks using FPGA-based Pipelining

Ali¹,

Ali

2023

ijcsm

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In order to speed up convolutional neural networks (CNNs), this study gives a complete overview of the use of FPGA-based pipelining for hardware acceleration of CNNs. These days, most people use convolutional neural networks (CNNs) to perform computer vision tasks like picture categorization and object recognition. The processing and memory demands of CNNs, however, can be excessive, especially for real-time applications. In order to speed up CNNs, FPGA-based pipelining has emerged as a viable option thanks to its parallel processing capabilities and low power consumption. The examination describes the fundamentals of FPGA-based pipelining and the basic structure of convolutional neural networks (CNNs). The current best practises for developing pipelined accelerators for CNNs on FPGAs are then reviewed, covering topics like partitioning and pipelining. Area and power limits, memory needs, and latency considerations are only some of the difficulties and trade-offs discussed in the article. In addition, the survey evaluates and contrasts the various pipelined FPGA accelerators for CNNs in terms of performance, energy consumption, and resource utilisation. Future directions and potential research areas are also discussed in the paper, such as the use of approximate computing techniques, the integration of reconfigurable architectures with emerging memory technologies, and the exploration of hybrid architectures that combine FPGAs and other hardware accelerators. This survey was created to aid researchers and practitioners in developing efficient and effective hardware accelerators for neural networks by providing a thorough overview of current trends and issues in FPGA-based pipelining for CNNs.

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Threats Detection in the Internet of Things Using Convolutional neural networks, long short-term memory, and gated recurrent units

Cited by 8 publications

References 12 publications

Developing Hybrid CNN-GRU Arrhythmia Prediction Models Using Fast Fourier Transform on Imbalanced ECG Datasets

Developing Hybrid CNN-GRU Arrhythmia Prediction Models Using Fast Fourier Transform on Imbalanced ECG Datasets

ChatGPT and the Future of Academic Integrity in the Artificial Intelligence Era: A New Frontier

Convolutional Neural Networks using FPGA-based Pipelining

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