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Brain Machine Interface Controlled Robot Chair by Hema Chengalvarayan, Radhakrishnamurthy

“…Brain Machine Interface Controlled Robot Chair: Brain Machine Interface is a device that links the human brain directly to devices such as computer, wheelchairs and prosthetic arms. …”
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Brain machine interface based wheelchair control with minimal subject training by Hema, Chengalvarayan Radhakrishnamurthy, Paulraj, Murugesapandian, Sazali, Yaacob, Abdul Hamid, Adom, Ramachandran, Nagarajan

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EEG-Based Brain-Machine Interface (BMI) for Controlling Mobile Robots: The Trend of Prior Studies by Murali Krishnan, Muralindran Mariappan

“…In this paper, we review the background, feature extraction and classification algorithms used to design the Electroencephalography (EEG) based Brain-Machine Interface (BMI) to control the mobile robots.…”
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Improving classification of EEG signals for a four-state brain machine interface by Hema, Chengalvarayan Radhakrishnamurthy, Paulraj, Murugesa Pandiyan, Assoc. Prof. Dr., Abdul Hamid, Adom, Prof. Dr.

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Automatic email classification system / Phang Siew Ting by Phang , Siew Ting

“…Automatic Email Classification System is an email reader tool that implements machine learning algorithm in email classification, manipulated by a Graphical User Interface. …”
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Fuzzy based classification of EEG mental tasks for a brain machine interface by Hema, Chengalvarayan Radhakrishnamurthy, Paulraj, Murugesapandian, Ramachandran, Nagarajan, Sazali, Yaacob, Abdul Hamid, Adom

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A new mobile botnet classification based on permission and API calls by Yusof M., Madihah Mohd Saudi, Farida Hazwani Mohd Ridzuan

Detection of eye movements based on EEG signals and the SAX algorithm by Shanmuga, P. M. M., Lau, Sian Lun *, Jou, Chichang.

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A New Mobile Botnet Classification based on Permission and API Calls by Yusof, M, Saudi, MM, Ridzuan, F

“…As a result, 16 permissions and 31 API calls that are most related with mobile botnet have been extracted using feature selection and later classified and tested using machine learning algorithms. The experimental result shows that the Random Forest Algorithm has achieved the highest detection accuracy of 99.4% with the lowest false positive rate of 16.1% as compared to other machine learning algorithms. …”

Personalized grocery' basket recommendation using content-based filtering algorithm / Fatin Nadhirah Azland by Azland, Fatin Nadhirah

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Restaurant locator using Djikstra Algorithm / Mohamad Aliff Hakimi Lukman Hakim by Lukman Hakim, Mohamad Aliff Hakimi

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Development of machine learning-based algorithm to determine the condition in transformer oil by Mohsen Al-Katheri, Hussein Hasan

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NLP- based for providing mental health support in mobile application / Muhammad Amirul Roslan by Roslan, Muhammad Amirul

“…Future enhancements, such as advanced machine learning algorithms and user interface improvements, are proposed to further enhance functionality. …”
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Online food ordering application using Boyer-Moore algorithm / Muhammad Zulhilmie Dzulkifli by Dzulkifli, Muhammad Zulhilmie

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Comparison of static and dynamic neural network classifiers for brain-machine interfaces / Hema C.R. ...[et al.] by C.R., Hema, M.P., Paulraj, Yaacob, S., Adom, A.H., Nagarajan, R.

“…Neural network classifiers are one among the popular modes in the design of brain machine interface (BMI). In this study two novel dynamic neural network classifier designs for a four-state BMI are presented. …”
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The shortest route of the potential companies for internship using Dijkstra’s Algorithm / Mohamad Khairul Ikhwan Mohd Radzi by Mohd Radzi, Mohamad Khairul Ikhwan

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Heavy Transportation Shortest Route using Dijkstra’s algorithm (HETRO) / Nurul Aqilah Ahmad Nezer by Ahmad Nezer, Nurul Aqilah

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A machine learning approach to predict the activity of smart home inhabitant by Marufuzzaman M., Tumbraegel T., Rahman L.F., Sidek L.M.

Students’ attitude towards video-based learning: machine learning analysis with rapid software / Abdullah Sani Abd Rahman ... [et al.] by Abd Rahman, Abdullah Sani, Meutia, Rita, Hamid, Yusnaliza, Abdul Rahman, Rahayu

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Diagnosis and treatment recommender system for myocardial infarction using decision tree and Support Vector Machines (SVM) / Wan Marzuqiamrin Wan Mansor by Wan Mansor, Wan Marzuqiamrin

“…This project presents the development process of the prototype for diagnosis and treatment recommender system for myocardial infarction using decision tree and support vector machine (SVM) algorithms. Healthcare professionals can benefit from this prototype system that uses ECG images for myocardial infarction diagnosis while recommending proper treatments based on patient clinical information. …”
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