Search Results - (( data distribution sensor algorithm ) OR ( data distribution learning algorithm ))

Machine learning in botda fibre sensor for distributed temperature measurement by Nur Dalilla binti Nordin

“…To make the sensor more reliable, temperature data must be collected over the length of the cable, or distributed data rather than point data. …”

Algorithm enhancement for host-based intrusion detection system using discriminant analysis by Dahlan, Dahliyusmanto

“…Misuse detection algorithms model know attack behavior. They compare sensor data to attack patterns learned from the training data. …”
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Supervised deep learning algorithms for process fault detection and diagnosis under different temporal subsequence length of process data by Terence Chia Yi Kai, Agus Saptoro, Zulfan Adi Putra, King Hann Lim, Wan Sieng Yeo, Jaka Sunarso

“…Deep learning algorithms were widely used among all the data-driven algorithms. …”
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A systematic literature review on outlier detection in wireless sensor networks by Safaei, Mahmood, Asadi, Shahla, Driss, Maha, Boulila, Wadii, Alsaeedi, Abdullah, Chizari, Hassan, Abdullah, Rusli, Safaei, Mitra

“…Therefore, an efficient local/distributed data processing algorithm is needed to ensure: (1) the extraction of precise and reliable values from noisy readings; (2) the detection of anomalies from data reported by sensors; and (3) the identification of outlier sensors in a WSN. …”
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A Divide-and-Distribute Approach to Single-Cycle Learning HGN Network for Pattern Recognition by Muhamad Amin , Anang Hudaya, Khan, Asad I.

“…Distributed Hierarchical Graph Neuron (DHGN) is a single-cycle learning distributed pattern recognition algorithm, which reduces the computational complexity of existing pattern recognition algorithms by distributing the recognition process into smaller clusters. …”
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Improving Prediction Accuracy and Extraction Precision of Frequency Shift from Low-SNR Brillouin Gain Spectra in Distributed Structural Health Monitoring by Nordin N.D., Abdullah F., Zan M.S.D., Bakar A.A.A., Krivosheev A.I., Barkov F.L., Konstantinov Y.A.

“…Brillouin scattering; Concretes; Curve fitting; Data handling; Extraction; Fiber optic sensors; Fiber optics; Learning algorithms; Machine learning; Structural health monitoring; BOTDA; Brillouin frequency shift extraction; Brillouin frequency shifts; Brillouin gain spectrum; Correlation techniques; Distributed fiber-optic sensors; Frequency shift; Generalized linear model; Low signal-to-noise ratio; Prediction accuracy; Signal to noise ratio; algorithm; fiber optics; noise; signal noise ratio; Algorithms; Fiber Optic Technology; Noise; Signal-To-Noise Ratio…”

A study on advanced statistical analysis for network anomaly detection by Ngadi, Md. Asri, Idris, Mohd. Yazid, Abdullah, Abd. Hanan

“…Misuse detection algorithms model know attack behavior. They compare sensor data to attack patterns learned from the training data. …”
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Coordinate-Descent Adaptation over Hamiltonian Multi-Agent Networks by Azam Khalili, Vahid Vahidpour, Amir Rastegarnia, Ali Farzamnia, Teo, Kenneth Tze Kin, Saeid Sanei

“…The incremental least-mean-square (ILMS) algorithm is a useful method to perform distributed adaptation and learning in Hamiltonian networks. …”
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Water quality monitoring using machine learning and IoT: a review by Hasan, Tahsin Fuad, Kabbashi, Nassereldeen Ahmed, Saleh, Tanveer, Alam, Md. Zahangir, Abd Wahab, Mohd Firdaus, Nour, Abdurahman Hamid

“…ML algorithms can analyze large volumes of water quality data, enabling data-centric approaches to designing, supervising, simulating, assessing, and refining various water treatment and management systems. …”
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Class binarization with self-adaptive algorithm to improve human activity recognition by Zainudin, Muhammad Noorazlan Shah

“…Two publicly activity datasets are used; Wireless Sensor Data Mining (WISDM) and Physical Activity Monitoring for Aging People (PAMAP2). …”
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The Vision of 5G and Cell-Free Communication Networks in Malaysia by Mahdi M.N., Mohamed K.S., Ahmad A.R., Subhi M.A.

Deep learning in distributed denial-ofservice attacks detection method for Internet of Things networks by Mohammed Aswad, Firas, Saleh Ahmed,, Ali Mohammed, Ali Majeed Alhammadi, Nafea, Ahmad Khalaf, Bashar, A. Mostafa, Salama

“…Subsequently, this study proposes combining three deep learning algorithms, namely recurrent neural network (RNN), long short-term memory (LSTM)-RNN, and convolutional neural network (CNN), to build a bidirectional CNN-BiLSTM DDoS detection model. …”
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Deep learning in distributed denial-ofservice attacks detection method for Internet of Things networks by Mohammed Aswad, Firas, Ahmed, Ali Mohammed Saleh, Majeed Alhammadi, Nafea Ali, Ahmad Khalaf, Bashar, A. Mostafa, Salama

“…Subsequently, this study proposes combining three deep learning algorithms, namely recurrent neural network (RNN), long short-term memory (LSTM)-RNN, and convolutional neural network (CNN), to build a bidirectional CNN-BiLSTM DDoS detection model. …”
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Deep learning in distributed denial-ofservice attacks detection method for Internet of Things networks by Mohammed Aswad, Firas, Saleh Ahmed, Ali Mohammed, Ali Majeed Alhammadi, Nafea, Ahmad Khalaf, Bashar, A. Mostafa, Salama

“…Subsequently, this study proposes combining three deep learning algorithms, namely recurrent neural network (RNN), long short-term memory (LSTM)-RNN, and convolutional neural network (CNN), to build a bidirectional CNN-BiLSTM DDoS detection model. …”
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Deep learning in distributed denial-ofservice attacks detection method for Internet of Things networks by Firas Mohammed Aswad, Firas Mohammed Aswad, Ali Mohammed Saleh Ahmed, Ali Mohammed Saleh Ahmed, Nafea Ali Majeed Alhammadi, Nafea Ali Majeed Alhammadi, Bashar Ahmad Khalaf, Bashar Ahmad Khalaf, Salama A. Mostafa, Salama A. Mostafa

“…Subsequently, this study proposes combining three deep learning algorithms, namely recurrent neural network (RNN), long short-term memory (LSTM)-RNN, and convolutional neural network (CNN), to build a bidirectional CNN-BiLSTM DDoS detection model. …”
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Deep learning in distributed denial-ofservice attacks detection method for Internet of Things networks by Mohammed Aswad, Firas, Ahmed, Ali Mohammed Saleh, Ali Majeed Alhammadi, Nafea, Bashar Ahmad Khalaf, Bashar Ahmad Khalaf, Salama A. Mostafa, Salama A. Mostafa

“…Subsequently, this study proposes combining three deep learning algorithms, namely recurrent neural network (RNN), long short-term memory (LSTM)-RNN, and convolutional neural network (CNN), to build a bidirectional CNN-BiLSTM DDoS detection model. …”
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Deep learning in distributed denial-ofservice attacks detection method for Internet of Things networks by Firas Mohammed Aswad, Firas Mohammed Aswad, Ali Mohammed Saleh Ahmed, Ali Mohammed Saleh Ahmed, Nafea Ali Majeed Alhammadi, Nafea Ali Majeed Alhammadi, Bashar Ahmad Khalaf, Bashar Ahmad Khalaf, Salama A. Mostafa, Salama A. Mostafa

“…Subsequently, this study proposes combining three deep learning algorithms, namely recurrent neural network (RNN), long short-term memory (LSTM)-RNN, and convolutional neural network (CNN), to build a bidirectional CNN-BiLSTM DDoS detection model. …”
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Deep learning in distributed denial-ofservice attacks detection method for Internet of Things networks by Firas Mohammed Aswad, Firas Mohammed Aswad, Ali Mohammed Saleh Ahmed, Ali Mohammed Saleh Ahmed, Nafea Ali Majeed Alhammadi, Nafea Ali Majeed Alhammadi, Bashar Ahmad Khalaf, Bashar Ahmad Khalaf, Salama A. Mostafa, Salama A. Mostafa

“…Subsequently, this study proposes combining three deep learning algorithms, namely recurrent neural network (RNN), long short-term memory (LSTM)-RNN, and convolutional neural network (CNN), to build a bidirectional CNN-BiLSTM DDoS detection model. …”
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Deep learning in distributed denial-ofservice attacks detection method for Internet of Things networks by Mohammed Aswad, Firas, Mohammed Saleh Ahmed, Ali, Ali Majeed Alhammadi,, Nafea, Ahmad Khalaf, Bashar, Mostafa, Salama A.

“…Subsequently, this study proposes combining three deep learning algorithms, namely recurrent neural network (RNN), long short-term memory (LSTM)-RNN, and convolutional neural network (CNN), to build a bidirectional CNN-BiLSTM DDoS detection model. …”
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Deep learning in distributed denial-ofservice attacks detection method for Internet of Things networks by Mohammed Aswad, Firas, Ahmed, Ali Mohammed Saleh, Ali Majeed Alhammadi, Nafea Ali Majeed Alhammadi, Khalaf, Bashar Ahmad, Mostafa, Salama A.

“…Subsequently, this study proposes combining three deep learning algorithms, namely recurrent neural network (RNN), long short-term memory (LSTM)-RNN, and convolutional neural network (CNN), to build a bidirectional CNN-BiLSTM DDoS detection model. …”
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