Search Results - (( java implication based algorithm ) OR ( knowledge sensing learning algorithm ))

Development of self-learning algorithm for autonomous system utilizing reinforcement learning and unsupervised weightless neural network / Yusman Yusof by Yusof, Yusman

“…These proves that without prior knowledge, the hybrid AI algorithm can self-learn. …”
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Ontology enrichment with causation relations by Amaal Saleh Hassan, Al Hashimy, Narayanan, Kulathuramaiyer

“…Ontology learning is considered a potential approach that can help to reduce the bottleneck of knowledge acquisition. …”
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Arcing Faults Detection in Switchgear with Extreme Learning Machine by Ishak S., Koh S.P., Tan J.D., Tiong S.K., Chen C.P., Yaw C.T.

Adaptive approach in handling human inactivity in computer power management by Candrawati, Ria, Hashim, Nor Laily

“…This study introduces Control, Learn and Knowledge model that adapts the Monitor, Analyze, Planning, Execute control loop integrates with Q Learning algorithm to learn human inactivity period to minimize the computer power consumption.An experiment to evaluate this model was conducted using three case studies with same activities. …”
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A Survey Of Supervised Machine Learning In Wireless Sensor Network: A Power Management Perspective by Ul haq, Riaz, Norrozila, Sulaiman, Muhammad, Alam

“…Machine learning algorithms are iteration based algorithms, as the new knowledge is based on the previous predicted /calculated knowledge which helps to decrease errors in order to increase efficiency. …”
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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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Path Following Using A Learning Neural Network by NHH , Mohamad Hanif

“…The thesis differs from [2] in a sense that different types of neural controller are established to achieve a better path following accuracy. …”
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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

“…This research also aims to present a reliable autonomous algorithm that minimizes the issues with localization and navigation skills among autistic people in a HBVE to improve their wayfinding and spatial knowledge. …”
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A new modern scheme for solving fractal–fractional differential equations based on deep feedforward neural network with multiple hidden layer by Admon, Mohd Rashid, Senu, Norazak, Ahmadian, Ali, Majid, Zanariah Abdul, Salahshour, Soheil

“…Thus, this research aims to extend the application of ANN to solve FFDE with power law kernel in Caputo sense (FFDEPC) by develop a vectorized algorithm based on deep feedforward neural network that consists of multiple hidden layer (DFNN-2H) with Adam optimization. …”
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Digital Quran With Storage Optimization Through Duplication Handling And Compressed Sparse Matrix Method by Ashraf Saleh Mohammad Alomoush

Physiological signals as predictors of mental workload: Evaluating single classifier and ensemble learning models by Nailul, Izzah, Sutarto, Auditya Purwandini, Hendi, Ade, Ainiyah, Maslakhatul, Muhammad Nubli, Abdul Wahab

“…A comprehensive evaluation was conducted on several ML algorithms, including both single (Support Vector Machine – SVM, and Naïve Bayes) and ensemble learning (Gradient Boost and AdaBoost) classiers and incorporating selected features and validation approaches. …”
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A review on spatial technologies for enhancing malaria control: concepts, tools, and challenges by Rayner Alfred, Joe Henry Obit

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Propositional satisfiability method in rough classification modeling for data mining by Abu Bakar, Azuraliza

“…This model is expected to be able to represent the knowledge of the system efficiently.…”
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Machine-learning approach using thermal and synthetic aperture radar data for classification of oil palm trees with basal stem rot disease by Che Hashim, Izrahayu

“…As the ALOS PALSAR-2 image was evaluated with dual-polarization (HH and HV), each digitized point has two distinct backscatter data with four severity levels (T0 to T3). The machine learning algorithm consistently performs well when presented with a well-balanced dataset. …”
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