Search Results - (( based cognitive learning algorithm ) OR ( java implication based algorithm ))

An energy-efficient spectrum-aware reinforcement learning-based clustering algorithm for cognitive radio sensor networks by Mustapha, Ibrahim, Mohd Ali, Borhanuddin, A. Rasid, Mohd Fadlee, Sali, Aduwati, Mohamad, Hafizal

“…In this paper, we propose a reinforcement learning-based spectrum-aware clustering algorithm that allows a member node to learn the energy and cooperative sensing costs for neighboring clusters to achieve an optimal solution. …”
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Assessment of cognitive load using multimedia learning and resting states with deep learning perspective by Qayyum, A., Faye, I., Malik, A.S., Mazher, M.

“…The deep learning algorithm based on Pre-trained convolutional neural network (CNN) networks have been used as a transfer learning for the classification of rest and cognitive states and also assessed the cognitive load using brain waves particularly alpha wave. …”
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A reinforcement learning-based energy-efficient spectrum-aware clustering algorithm for cognitive radio wireless sensor network by Mustapha, Ibrahim

“…Simulation results show convergence, learning and adaptability of the RL based algorithms to dynamic environment toward achieving the optimal solutions. …”
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An energy efficient reinforcement learning based cooperative channel sensing for cognitive radio sensor networks by Mustapha, Ibrahim, Mohd Ali, Borhanuddin, Sali, Aduwati, A. Rasid, Mohd Fadlee, Mohamad, Hafizal

“…This may be inefficient in energy constraint devices networks such as Cognitive Radio Wireless Sensor Network (CR-WSN). In this paper, we propose a Reinforcement Learning based clustered Cooperative Channel Sensing (RL-CCS) that learns channels’ dynamic behaviors in terms of channel availability, sensing energy cost, and channel impairment to achieve optimal sensing sequence and optimal set of channels. …”
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Cognitive benefits of employing multiple AI voices as specialist virtual tutors in a multimedia learning environment by Liew, Tze Wei, Tan, Su Mae, Chan, Tak Jie, Tian, Yang, Ahmad, Faizan

“…To address this gap, we draw on multimedia learning and cognitive models to investigate the effects of using multiple AI voices as specialist virtual tutors for distinct programming algorithm subtopics on cognitive load and learning outcomes. …”
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Bridging Mayer’s cognitive theory of multimedia learning and computational thinking in tackling the cognitive load issues among young digital natives : a conceptual framework by Wan Nor Ashiqin Wan Ali, Wan Ahmad Jaafar Wan Yahaya

“…CT refers to the capacity of learners to systematically tackle unstructured tasks focused on four computing concepts such as decomposition, abstraction, pattern recognition, and algorithmic thinking. The purpose of this paper is to study the relationship between CT and Mayer’s Cognitive Theory of Multimedia Learning (CTML) on the cognitive load of the learners. …”
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A novel scheme for spectrum prediction in cognitive radio networks / Mehdi Askari and Rezvan Dastanian by Askari, Mehdi, Dastanian, Rezvan

“…An efficient spectrum prediction model is presented to improve the spectrum utilization in cognitive radio network. In this model, a novel improved version of Teaching-Learning-Based-Optimization algorithm, also referred to iTLBO algorithm, is proposed to train a feed forward artificial neural network (ANN). …”
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Cooperative spectrum sensing based on machine learning in cognitive radio vehicular network / Mohammad Asif Hossain by Mohammad Asif , Hossain

“…The selection would be made based on the hybrid machine learning (ML) algorithm. …”
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Cognitive knowledge-based model for adaptive feedback: A case in physics / Andrew Thomas Bimba by Andrew Thomas, Bimba

“…In a computer-based learning environment, feedback is considered as one of the most effective factors which influence learning. …”
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A cognitive mapping approach in real-time haptic rendering interaction for improved spatial learning ability among autistic people / Kesavan Krishnan by Kesavan , Krishnan

“…Meanwhile, experimental evaluation of the application was conducted in four different groups to measure the efficiency and performance of the application; experimental evaluation based on navigation algorithms, experimental evaluation based on haptic sensory sensitivity, experimental evaluation based on real-time haptic rendering interaction and experimental evaluation based on the performance of autistic people with using the application in spatial learning and cognitive mapping. …”
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Evolutionary multi-objective optimization of autonomous mobile robots in neural-based cognition for behavioural robustness by Chin, Kim On, Teo, Jason Tze Wi, Azali Saudi

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Nature-Inspired cognitive evolution to play Ms. Pac-Man by Tse, Guan Tan, Jason Teo, Patricia Anthony

“…The focus of this research is to explore the hybridization of nature-inspired computation methods for optimization of neural network-based cognition in video games, in this case the combination of a neural network with an evolutionary algorithm. …”
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Exploring brain structure differences and male/female classification: a machine learning study on Huffaz and non-Huffaz by Sapuan, Abdul Halim, Abdul Majid, Zafri Azran, Mohd Tamrin, Mohd Izzuddin, Che Azemin, Mohd Zulfaezal

“…This study investigates the potential of machine learning (ML) to classify sex based on brain structural features, incorporating the unique perspective of cognitive training through Quran memorization. …”
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Fusion of moment invariant method and deep learning algorithm for COVID-19 classification by Ervin Gubin Moung, Chong, Joon Hou, Maisarah Mohd Sufian, Mohd Hanafi Ahmad Hijazi, Jamal Ahmad Dargham, Sigeru Omatu

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Visualisasi pohon sintaksis berasaskan model dan algoritma sintaks ayat bahasa Melayu by Yusnita, Muhamad Noor

“…Analysis on those results indicated positive scores on the model-based product specifically on usefulness, ease of use, ease of learning, subjective satisfaction, and cognitive measures. …”
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Task-state EEG signal classification for spatial cognitive evaluation based on multiscale high-density convolutional neural network by Wen, Dong, Li, Rou, Tang, Hao, Liu, Yijun, Wan, Xianglong, Dong, Xianling, Saripan, M. Iqbal, Lan, Xifa, Song, Haiqing, Zhou, Yanhong

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Comparing deep learning CNN method with traditional MRI-based hippocampal segmentation and volumetry for early Alzheimer’s disease diagnosis across diverse populations by Ibrahim, Nur Shahidatul Nabila, Suppiah, Subapriya, Ibrahim, Buhari, Mohad Azmi, Nur Hafizah, Seriramulu, Vengkatha Priya, Mohamad, Mazlyfarina, Hanafi, Marsyita, Mohammad Sallehuddin, Hakimah, Omar Sharif, Nurallysha Najwa, Razali, Rizah Mazzuin, Harrun, Noor Harzana

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The implications for ahybrid detection technique against malicious sqlattacks on web applications by Bahjat Arif, Sarajaldeen Akram, Wani, Sharyar

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Identifying the mechanism to forecast the progression of Alzheimer’s disease from mild cognitive impairment using deep learning by Hiu, Theresa Wei Xin

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Diagnostic power of resting-state fMRI for detection of network connectivity in Alzheimer's disease and mild cognitive impairment: a systematic review by Ibrahim, Buhari, Suppiah, Subapriya, Ibrahim, Normala, Mohamad, Mazlyfarina, Abu Hassan, Hasyma, Syed Nasser, Nisha, Saripan, M. Iqbal

“…Nevertheless, various concerns over the homogeneity of data based on patient selection, scanner effects, and the variable usage of classifiers and algorithms pose a challenge for ML-based image interpretation of rs-fMRI datasets to become a mainstream option for diagnosing AD and predicting the conversion of HC/MCI to AD.…”
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