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Comparison of malware detection model using supervised machine learning algorithms / Syamir Mohd Shahirudin by Mohd Shahirudin, Syamir

“…The Windows malware dataset has been trained and tested by these three machine learning algorithms to get the percentage detection accuracy. …”
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Using streaming data algorithm for intrusion detection on the vehicular controller area network by Sharmin, Shaila, Mansor, Hafizah, Abdul Kadir, Andi Fitriah, Abdul Aziz, Normaziah

“…Results of experiments that compare the attack detection performance of iForestASD and iForest show that CAN traffic stream demonstrates insignificant concept drift and the detection model does not benefit from being retrained with a sliding window of latest CAN traffic, as in iForestASD. The size of the training sample is, however, found to be an important consideration - a model trained with only 30 s of CAN traffic always yields better detection performance than a model trained with a larger window of CAN traffic.…”
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Integrating finance dictionary in lexicon-based approach with machine learning algorithm to analyse the impact of OPEC news sentiment on financial market / Wu Ling by Wu, Ling

“…Since last few decades, machine learning algorithm which trains computers to learn from experience, is one of the most rapidly developing techniques which settles in the intersection research field of statistics and computer science. …”
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Face recognition and identification system (FaceRec) / Khew Jye Huei by Khew , Jye Huei

“…It describes the implementation and functions of a working system that performs the recognition and identification of human faces using the implement algorithms. Java is use as programming language to develop the Face Rec system and Microsoft Access as the database to store and access the fa e images for the training set. …”
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Object tracing from synthetic fluid spray through instance segmentation by Md Refat Khan, Pathan

“…A divide and conquer technique using cropping window extraction was employed with nine window sizes to reduce object density, resulting in a significant increase in object count. …”
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Comparison of supervised machine learning algorithms for malware detection / Mohd Faris Mohd Fuzi ... [et al.] by Mohd Fuzi, Mohd Faris, Mohd Shahirudin, Syamir, Abd Halim, Iman Hazwam, Jamaluddin, Muhammad Nabil Fikri

“…This study was solely concerned with the Windows malware dataset. The malware classification was determined by testing and training the supervised ML algorithms using the extracted features from the malware dataset. …”
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Snake detection system using convolutional neural network / Muhammad Danial Ahmad Tarmizi by Ahmad Tarmizi, Muhammad Danial

“…The algorithm is built using Tensorflow software. The development of the project is based on Waterfall methodology. …”
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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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DRIFT ANALYSIS ON NEURAL NETWORK MODEL OF HEAT EXCHANGER FOULING by M., Ramasamy, A., Shahid, H., Zabiri

“…This paper proposes the use of information criteria for tracking the model prediction accuracy and provides an algorithm for retraining the model. A heat exchanger in a refinery Crude Preheat Train (CPT) has been used as a case study. …”
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Comparative Analysis Using Bayesian Approach To Neural Network Of Translational Initiation Sites In Alternative Polymorphic Contex by Herman, Nanna Suryana, Husin, Nurul Arneida, Hussin, Burairah

“…The objectives of this paper are to develop useful algorithms and to build a new classification model for the case study.The first approach of neural network includes training on algorithms of Resilient Backpropagation,Scaled Conjugate Gradient Backpropagation and Levenberg-Marquardt.The outputs are used in comparison with Bayesian Neural Network for efficiency comparison.The results showed that Resilient Backpropagation have the consistency in all measurement but performs less in accuracy.In second approach,the Bayesian Classifier_01 outperforms the Resilient Backpropagation by successfully increasing the overall prediction accuracy by 16.0%.The Bayesian Classifier_02 is built to improve the accuracy by adding new features of chemical properties as selected by the Information Gain Ratio method,and increasing the length of the window sequence to 201.The result shows that the built model successfully increases the accuracy by 96.0%.In comparison,the Bayesian model outperforms Tikole and Sankararamakrishnan (2008) by increasing the sensitivity by 10% and specificity by 26%. …”
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Engine fault diagnosis using probabilistic neural network by Sheng, Zhu, Min, Keng Tan, Ka, Renee Yin Chin, Bih, Lii Chua, Xiaoxi, Hao, Tze, Kenneth Kin Teo

“…The proposed PNN is trained using the collected engine fault data from experiment and the probability density of PNN is determined based on the Parzen window estimation method. …”
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Digital Quran With Storage Optimization Through Duplication Handling And Compressed Sparse Matrix Method by Ashraf Saleh Mohammad Alomoush

Interactive learning package for artificial neural network (Demonstration Module) / Camellia Mohd Kamal by Camellia , Mohd Kamal

“…For Perception there will be the Description Neuron Model, Perceptron Basic Architecture and perceptron Algorithm with one example of solved problem. For the Feed Forward, Recurrent and Self Organizing Map Networks there are the Neuron Model, Basic Architecture and Training Algorithm. …”
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Online teleoperation of writing manipulator through graphics processing unit based accelerated stereo vision by Abu Raid, Fadi Imad Osman

“…These benefits however are challenged by the high computational requirements of the algorithms used. In this thesis, a framework is developed to enable the use of stereo vision in realtime teleoperation of a manipulator robot for the task of writing. …”
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Optimization of Microbial Electrolysis Cell for Sago Mill Wastewater Derived Biohydrogen via Modeling and Artificial Neural Network by Mohamad Afiq, Mohd Asrul

“…The review of optimization studies in the literature contributes to the development of a flowchart of an advanced optimization strategy that serves as a guide for the methodology of the study. …”
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