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Optimisation of fed-batch fermentation process using deep reinforcement learning by Chai, Wan Ying

“…Fed-batch fermentation process has always been a challenge for optimisation because it is highly non-linear and complex. Deep reinforcement learning is a self-learning algorithm through trial and error and experience, without any prior knowledge. …”
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Realization of a Hybrid Locally Connected Extreme Learning Machine with DeepID for Face Verification by Wong S.Y., Yap K.S., Zhai Q., Li X.

“…Backpropagation algorithms; Computation theory; Deep learning; Face recognition; Iterative methods; Knowledge acquisition; Machine learning; Neural networks; Biological learning; Convolutional neural network; DeepID; Extreme learning machine; Face Verification; Fast implementation; Feature mapping; Labeled faces in the wilds (LFW); Learning algorithms…”

Adam optimization algorithm for wide and deep neural network by Mohd Jais, Imran Khan, Ismail, Amelia Ritahani, Syed, Qamrun Nisa

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Development of deep reinforcement learning based resource allocation techniques in cloud radio access network by Amjad, Iqbal

“…A step towards long network performance optimization is theterm use of deep reinforcement learning (DRL), which can learn the best policy via interaction with the environment. …”
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A Reinforced Active Learning Algorithm for Semantic Segmentation in Complex Imaging by Usmani, U.A., Watada, J., Jaafar, J., Aziz, I.A., Roy, A.

“…We propose a new reinforced active learning strategy based on a deep reinforcement learning algorithm. …”
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Lung Nodules Classification Using Convolutional Neural Network with Transfer Learning by Abdulrazak Yahya, Saleh, Ros Ameera, Rosdi

“…With the implementation of deep learning, more deep knowledge can be gathered and help healthcare workers to know more about a patient’s disease. …”
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Change vulnerability forecasting using deep learning algorithm for Southeast Asia by Amelia Ritahani, Ismail, Nur ‘Atikah, Mohd Ali, Junaida, Sulaiman

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A hybrid deep CNN model for fast class-incremental food classification / Aymen Taher Ahmed al-Ashwal by Aymen Taher , Ahmed al-Ashwal

“…Lastly, the incremental learning algorithm ABACOC is used to classify each feature of food classes. …”
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Evaluation of Convolutional Neural Network based on Dental Images for Age Estimation by Alkaabi S., Yussof S., Al-Mulla S.

Lithium-ion Battery State of Charge Estimation Method Using Optimized Deep Recurrent Neural Network Algorithm by Lipu M.S.H., Hannan M.A., Hussain A., Saad M.H.M., Ayob A., Muttaqi K.M.

Intrusion detection system for the internet of things based on blockchain and multi-agent systems by Liang, Chao, Shanmugam, Bharanidharan, Azam, Sami, Karim, Asif, Islam, Ashraful, Zamani, Mazdak, Kavianpour, Sanaz, Idris, Norbik Bashah

“…The results demonstrate the efficiency of deep learning algorithms when detecting attacks from the transport layer. …”
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AUTOMATED PLANT DISEASE DETECTION USING DEEP LEARNING ON MOBILE PLATFORM by Vera Ruth, Rewcastle

“…This would be difficult and costly for farmers to employ a professional horticulturist or rely on knowledge gained through experience. Therefore, this project aims at developing a mobile applicatiJ:;m which is equipped with deep learning algorithm to enable the detection and identification of a disease for a particular plant. …”
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Convolutional neural networks and deep belief networks for analysing imbalanced class issue in handwritten dataset by Amri, A’inur A’fifah, Ismail, Amelia Ritahani, Zarir, Abdullah Ahmad

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Research on the construction of an efficient and lightweight online detection method for tiny surface defects through model compression and knowledge distillation by Chen, Qipeng, Xiong, Qiaoqiao, Huang, Haisong, Tang, Saihong, Liu, Zhenghong

“…In response to the current issues of poor real-time performance, high computational costs, and excessive memory usage of object detection algorithms based on deep convolutional neural networks in embedded devices, a method for improving deep convolutional neural networks based on model compression and knowledge distillation is proposed. …”
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Effect of different modalities of facial images for diagnosis of ASD by deep neural network by Rashid, Muhammad Mahbubur, Alam, Mohammad Shafiul, Haque, M A, Ali, Mohammad Yeakub, Yvette, Susiapan

“…This research aims to explore the potential of various facial image types in diagnosing Autism Spectrum Disorder (ASD) through the application of deep learning neural networks. It delves into how deep learning algorithms perform with different facial image modalities, especially 2D and 3D, while addressing specific challenges associated with each. …”
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Stress Classification using Deep Learning with 1D Convolutional Neural Networks by Abdulrazak Yahya, Saleh, Khai Xian, Lau

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Ensemble-based machine learning algorithms for classifying breast tissue based on electrical impedance spectroscopy by Rahman, Sam Matiur, Ali, Md. Asraf, Altwijri, Omar, Alqahtani, Mahdi, Ahmed, Nasim, Ahamed, Nizam Uddin

“…The application of artificial intelligence techniques (e.g., machine learning and deep learning) in medical science is becoming increasingly important for intelligently transforming all available information into valuable knowledge. …”
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Enhancing battery state of charge estimation through hybrid integration of barnacles mating optimizer with deep learning by Zuriani, Mustaffa, Mohd Herwan, Sulaiman

“…In response to the growing importance of SoC estimation, this study introduces a hybrid approach called the Barnacles Mating Optimizer with Deep Learning (BMO-DL) for SoC of Nissan Leaf batteries. …”
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Automated model selection for corporation credit risk assessment using machine learning / Zulkifli Halim by Halim, Zulkifli

“…The global trend in the CCRA study shows that implementing machine learning and deep learning techniques is expanding rapidly. …”
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Herbal plant image classification using transfer learning and fine-tuning deep learning model by Khalid, Fatimah, Romle, Amirul Azuani

“…Transfer learning is an algorithm that learns to recognize image features in one domain and having the capability to generalize the learnt knowledge to a new domain with a smaller dataset. …”
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