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BFEDroid: A Feature Selection Technique to Detect Malware in Android Apps Using Machine Learning by Chimeleze C., Jamil N., Ismail R., Lam K.-Y., Teh J.S., Samual J., Akachukwu Okeke C.

“…Android (operating system); Android malware; Classification (of information); Feature Selection; Learning systems; Mobile security; Android apps; Classification models; Feature weight; Features selection; Machine learning algorithms; Machine-learning; Malware detection; Malwares; Memory usage; Selection techniques; Learning algorithms…”

Automated feature selection using boruta algorithm to detect mobile malware by Che Akmal, Che Yahaya, Ahmad Firdaus, Zainal Abidin, Salwana, Mohamad, Ernawan, Ferda, Mohd Faizal, Ab Razak

“…This research proposed automated feature selection using Boruta algorithm to detect the malware. The proposed method adopts machine learning prediction and optimizes the selecting features in order to reduce the model of machine learning complexity. …”
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RSA Encryption & Decryption using JAVA by Ramli, Marliyana

“…The implementation of this project will be based on Rapid Application Design Methodology (RAD) and will be more focusing on research and finding, ideas and the implementation of the algorithm, and finally running and testing the algorithm. …”
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Study of machine learning in computer vision using Raspberry Pi by Chong, Kah Luo

Feature selection in intrusion detection, state of the art: A review by Rais, H.M., Mehmood, T.

“…These input features give information to the learning algorithms which used in intrusion detection system in the form of the detection method. …”
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Application of machine learning and artificial intelligence in detecting SQL injection attacks by Md Sultan, Abu Bakar, Agiliga, Nwabudike Augustine, Osman, Mohd Hafeez Bin, Sharif, Khaironi Yatim

“…More recently, cyber-attacks have also been on the rise and SQL injection attacks are some of major threats to data security. AI and machine learning have come a long way, however their usage in cybersecurity is still somewhat nascent. …”
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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

Intrusion detection on the in-vehicle network using machine learning by Sharmin, Shaila, Mansor, Hafizah

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A COLLABORATIVE FRAMEWORK FOR ANDROID MALWARE IDENTIFICATION USING DYNAMIC ANALYSIS by Thayaaleni, Rajandran

“…The machine learning algorithm is used to train and test datasets with the percentage of 70% which is 140 samples from malware and benign applications and 30% of total datasets which is 60 samples from malware and benign applications respectively. …”
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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. …”

A wheelchair sitting posture detection system using pressure sensors by Mohamad Yusoff, Muhammad Annuar Alhadi, Azmi, Nur Liyana, Nordin, Nor Hidayati Diyana

“…Three classification algorithms based on the supervised learning of machine learning, such as support vector machine (SVM), random forest (RF),and decision tree (DT) were used to classify the postures which produced an accuracy of 95.44%, 98.72%,and 98.80%, respectively. …”
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Comparison of Search Algorithms in Javanese-Indonesian Dictionary Application by Yana Aditia, Gerhana, Nur, Lukman, Arief Fatchul, Huda, Cecep Nurul, Alam, Undang, Syaripudin, Devi, Novitasari

“…Performance Testing is used to test the performance of algorithm implementations in applications. …”
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A novel framework for potato leaf disease detection using an efficient deep learning model by Mahum, R., Munir, H., Mughal, Z.-U.-N., Awais, M., Sher Khan, F., Saqlain, M., Mahamad, S., Tlili, I.

“…Various experiments have been performed to confirm that our proposed algorithm is more consistent and proficient to detect and classify potato leaves diseases than existing models. …”
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A novel framework for potato leaf disease detection using an efficient deep learning model by Mahum, R., Munir, H., Mughal, Z.-U.-N., Awais, M., Sher Khan, F., Saqlain, M., Mahamad, S., Tlili, I.

“…Various experiments have been performed to confirm that our proposed algorithm is more consistent and proficient to detect and classify potato leaves diseases than existing models. …”
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Fraud detection in shipping industry based on location using machine learning comparison techniques by Ganesan Subramaniam, Mr.

“…Results from this study indicates the usage of k-NN is proven to be most effective algorithm in terms of performance and accuracy required by the shipping industry fraud detection.…”

Beat: heart monitoring system by Shamsolnizam, Afif Fanshuri, Basri, Iskandar Zulkarnain, Zakaria, Noor Azura, Tajuddin, Taniza, Suryady, Zeldi

“…Machine learning algorithms were also developed by our team to detect abnormalities in heart rate. …”
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Machine learning-based anomaly detection in NFV: a comprehensive survey by Sehar Zehra, Ummay Faseeha, Hassan Jamil Syed, Fahad Samad, Ashraf Osman Ibrahim Elsayed, Anas W. Abulfaraj, Wamda Nagmeldin

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Sedentary sitting posture recognition with YOLOv3 algorithm / Muhammad Amin Mohamad Razali by Mohamad Razali, Muhammad Amin

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Hand gesture recognition for autism diagnosis using Support Vector Machine (SVM) Algorithm / Muhammad Asyraf Mohamad Zain by Mohamad Zain, Muhammad Asyraf

“…To counter this problem, a system has been proposed to detect the hand gesture using one of the machine learning technique which is Support Vector Machine (SVM) Algorithm. …”
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Brain tumor image segmentation using deep learning approach by Darshan, Suresh

“…Deep learning algorithm is able to provide good tumor segmentation results compared to other conventional segmentation algorithms as it learns from the labeled brain MRIs to predict the location of tumor region and consequently segment the tumor. …”
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