Search Results - (( java implementation level algorithm ) OR ( training program machine algorithm ))

AUTOMATED MODEL GENERATION OF FSM AND NUSMV MODEL FROM RSA JAVA SOURCE CODE FOR MODEL CHECKING by SALLEH AL-HUMAIKANI, MOHAMMED ABDULQAWI

“…The encryption algorithms are playing an important part in the protection level for data. …”
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Development of Machine Learning Algorithm for Acquiring Machining Data in Turning Process by Ali Al-Assadi, Hayder M. A.

“…Artificial Neural Network (ANN) was selected from Machine Learning Algorithms to be the learning algorithm. …”
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Evolution Performance of Symbolic Radial Basis Function Neural Network by Using Evolutionary Algorithms by Alzaeemi, Shehab Abdulhabib, Tay, Kim Gaik, Huong, Audrey, Sathasivam, Saratha, Majahar Ali, Majid Khan

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Implementation of (AES) Advanced Encryption Standard algorithm in communication application by Moh, Heng Huong

“…Internet communication has become more common in this modern world recently, and one of the important algorithms used is ABS algorithm. However, most of the users have inadequate knowledge and understanding regard to this algorithm implementation in the communication field, as well as the level of security and accuracy will be questioned by the users because of the necessary to maintain the confidentiality of particular data transferred. …”
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Implementation of locust inspired scheduling algorithm with huge number of servers for energy efficiency in a cloud datacenter by Azhar, Nur Huwaina

“…It does not consider the LACE algorithm implemented in huge number of server in one Cloud datacenter. …”
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A Model for Evaluation of Cryptography Algorithm on UUM Portal by Norliana, Abdul Majid

“…Level one is the development of userID and password, level two involve the insertion of the testing parameter speed coding. …”
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Impact learning: A learning method from feature's impact and competition by Prottasha, Nusrat Jahan, Murad, Saydul Akbar, Abu Jafar, Md Muzahid, Rana, Masud, Kowsher, Md, Adhikary, Apurba, Biswas, Sujit, Bairagi, Anupam Kumar

“…Machine learning algorithms build a model from sample data, called training data, to make predictions or judgments without being explicitly programmed to do so. …”
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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

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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. The test results show that the Boyer Moore and Knuth Morris Pratt algorithms have an accuracy rate of 100%, and the Horspool algorithm 85.3%. …”
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An improved RSA cryptosystem based on thread and CRT / Saheed Yakub Kayode and Gbolagade Kazeem Alagbe by Yakub Kayode, Saheed, Kazeem Alagbe, Gbolagade

“…We use a parallel technique that divides RSA power process into seperate threads and employs the use of Chinese Remainder Theorem (CRT) to decrease the time required for both encryption and decryption operation. Java programming language is used to implement the algorithm. …”
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Kernel methods and support vector machines for handwriting recognition by Ahmad A.R., Khalid M., Yusof R.

“…This paper presents a review of kernel methods in machine learning. The support vector machine (SVM) as one of the methods in machine learning to make use of kernels is first discussed with the intention of applying it to handwriting recognition. …”

Impact learning : A learning method from feature’s impact and competition by Prottasha, Nusrat Jahan, Murad, Saydul Akbar, Abu Jafar, Md Muzahid, Rana, Masud, Kowsher, Md, Adhikary, Apurba, Biswas, Sujit, Bairagi, Anupam Kumar

“…Machine learning algorithms build a model from sample data, called training data, to make predictions or judgments without being explicitly programmed to do so. …”
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Current applications of machine learning in dentistry by Ghazali, Ahmad Badruddin, Reduwan, Nor Hidayah, Ibrahim, Roliana

“…The process is similar to a human expert that can learn by repeated training (Hung et al., 2019). The quality of the output depends on the quality of data used to train and validate the algorithm (Rowe, 2019). …”
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Measuring GPU-accelerated parallel SVM performance using large datasets for multi-class machine learning problem by Sulaiman M.A.H.B., Suliman A., Ahmad A.R.

Comparison of algorithm Support Vector Machine and C4.5 for identification of pests and diseases in chili plants by M, Irfan, N, Lukman, A. A, Alfauzi, J, Jumadi

“…In this study comparing the performance classification techniques of Support Vector Machine (SVM) and C4.5 algorithms. The attributes used consist of Leaves, Stems, and Fruits. …”
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Design and Implementation of Data-at-Rest Encryption for Hadoop by Wan Nor Shuhadah, Wan Nik, Mohamad Afendee, Mohamed, Zarina, Mohamad, Siti Hanisah, Kamaruzaman

“…Therefore, the AES encryption algorithm has been implemented in HDFS to ensure the security of data stored in HDFS. …”
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Design and Implementation of Data-at-Rest Encryption for Hadoop by Wan Nor Shuhadah, Wan Nik, Mohamad Afendee, Mohamed, Zarina, Mohamad, Siti Hanisah, Kamaruzaman

“…Therefore, the AES encryption algorithm has been implemented in HDFS to ensure the security of data stored in HDFS. …”
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Impact learning: A learning method from feature’s impact and competition by Prottasha, Nusrat Jahan, Murad, Saydul Akbar, Muzahid, Abu Jafar Md, Rana, Masud, Kowsher, Md, Adhikary, Apurba, Biswas, Sujit, Bairagi, Anupam Kumar

“…Machine learning algorithms build a model from sample data, called training data, to make predictions or judgments without being explicitly programmed to do so. …”
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Poverty Classification of Central Perak Population Using Machine Learning by P.Rajendran, Kumaran

“…In this study, back propagation algorithm and other machine learning algorithm will be used to build models via anaconda using python programming language that can classify each poor household appropriate their poverty status. …”
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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. …”