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A study on the performances of danger theory and negative selection algorithms for mobile spam detection by Sulaiman N.F., Jali M.Z., Abdullah Z.H., Ismail S.

Secure mobile AES encryptor (SMAE) by Satia, Anbalagan

“…The reason the AES encryption algorithm selected is because it is considered as one of the most secure encryption algorithm. …”
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Sentiment analysis using clonal selection algorithm for Twitter’s data / Fatimah Mamat by Mamat, Fatimah

“…The sentiment analysis using clonal selection algorithm for twitter’s data system was developed to achieve the main objective which is to classify the twitter’s messages according three sentiments which are positive, negative and neutral. …”
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Incremental learning for large-scale stream data and its application to cybersecurity by Ali, Siti Hajar Aminah

“…As many human currently depend on technologies to assist with daily tasks, there are more and more applications which have been developed to be fit in one small gadget such as smart phone and tablet. …”
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Deep Learning-Driven Mobility And Utility-Based Resource Management In Mm-Wave Enable Ultradense Heterogeneous Networks by Ukasyah bin Mahamod

Framework for pedestrian walking behaviour recognition to minimize road accident by Hashim Kareem, Zahraa

“…The results indicate the following: (1) From 262 samples, 66.80% and 48.10% of respondents use mobile phones for calling and chatting, respectively. (2) 263 samples of participants are obtained and analysed, and 90 features are extracted from each sample. (3) 100% classification accuracy are obtained for each class (normal walking, calling, chatting, and running) using the grid optimiser method in machine learning. (4) The precision of classification using Euclidean algorithm for normal walking and calling is 70%. …”
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Framework for pedestrian walking behaviour recognition to minimize road accident by Hashim Kareem, Zahraa

“…The results indicate the following: (1) From 262 samples, 66.80% and 48.10% of respondents use mobile phones for calling and chatting, respectively. (2) 263 samples of participants are obtained and analysed, and 90 features are extracted from each sample. (3) 100% classification accuracy are obtained for each class (normal walking, calling, chatting, and running) using the grid optimiser method in machine learning. (4) The precision of classification using Euclidean algorithm for normal walking and calling is 70%. …”
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Enhancement Of Aodv Routing Protocol In Masnets by Jambli, M.N., Wan Mohd Shuhaimi, W.B., Lenando, H., Abdullah, J., Mohamad Suhaili, S.

“…The rapid development of wireless communication technologies and portable mobile devices such as laptops, PDAs, smart phones and wireless sensors brings the best out of mobile computing particularly mobile ad-hoc and sensor networks. …”
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