Search Results - (( java implementation level algorithm ) OR ( learning process setting algorithm ))
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AUTOMATED MODEL GENERATION OF FSM AND NUSMV MODEL FROM RSA JAVA SOURCE CODE FOR MODEL CHECKING
Published 2021“…The encryption algorithms are playing an important part in the protection level for data. …”
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Thesis -
2
Implementation of (AES) Advanced Encryption Standard algorithm in communication application
Published 2014“…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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Undergraduates Project Papers -
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Implementation of locust inspired scheduling algorithm with huge number of servers for energy efficiency in a cloud datacenter
Published 2019“…It does not consider the LACE algorithm implemented in huge number of server in one Cloud datacenter. …”
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4
A Model for Evaluation of Cryptography Algorithm on UUM Portal
Published 2004“…Level one is the development of userID and password, level two involve the insertion of the testing parameter speed coding. …”
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5
Comparison of Search Algorithms in Javanese-Indonesian Dictionary Application
Published 2020“…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
Published 2017“…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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7
Design and Implementation of Data-at-Rest Encryption for Hadoop
Published 2017“…Therefore, the AES encryption algorithm has been implemented in HDFS to ensure the security of data stored in HDFS. …”
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Conference or Workshop Item -
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Design and Implementation of Data-at-Rest Encryption for Hadoop
Published 2017“…Therefore, the AES encryption algorithm has been implemented in HDFS to ensure the security of data stored in HDFS. …”
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Conference or Workshop Item -
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Development of Machine Learning Algorithm for Acquiring Machining Data in Turning Process
Published 2004“…Artificial Neural Network (ANN) was selected from Machine Learning Algorithms to be the learning algorithm. …”
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PREDICTING THE PRICE OF COTTON USING RNN AND LSTM
Published 2020“…The data will then be separated into training set and testing set and will be feed to the machine learning algorithm to find the pattern and try to do prediction. …”
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Final Year Project -
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Deep learning metaphor detection with emotion-cognition association
Published 2022“…However, even the sets that are not useful separately is proven to be very useful after the combination process.…”
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Conference or Workshop Item -
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Predicting Customer Buying Decisions for Online Shopping with Unbalanced Data Set
Published 2022“…Weka, a data mining tool, provides the facility to classify the data set with different machine learning algorithms. …”
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Final Year Project / Dissertation / Thesis -
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Enhancement processing time and accuracy training via significant parameters in the batch BP algorithm
Published 2020“…The average accuracy training is 0.9909 and average processing time improved of dynamic algorithm is 430 times faster than the BBP algorithm. …”
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Article -
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Data Analysis and Machine Learning Algorithms Evaluation for Bioliq AI-based Predictive Tool
Published 2019“…This final year project identified relevant parameters through literature research, analysis and expert interview, and evaluated different machine learning algorithms and identified linear regression as the most applicable and efficient with its R-square of 0.8015, qualifying it to be used for the development of a hybrid model for the AI-based tool for predictive process optimization for chemical plants.…”
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Final Year Project -
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Context-driven satire detection with deep learning
Published 2022“…This shows that each of the feature sets are significant. Finally, the combined feature sets undergoes the classification using well-known machine learning classification algorithms. …”
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Performance comparison of different machine learning algorithms on a time-series of covid-19 data: A case study for Saudi Arabia
Published 2021“…Prediction of total cases and total deaths are obtained by taking previous 14 days of time series data as the input to the machine learning algorithms developed in this paper. This study can be helpful in analysing the capabilities of machine learning methodologies for time-series data-sets as well as helping governments in the decision making process for mitigation of the pandemic. …”
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Performance comparison of different machine learning algorithms on a time-series of covid-19 data: A case study for Saudi Arabia
Published 2021“…Prediction of total cases and total deaths are obtained by taking previous 14 days of time series data as the input to the machine learning algorithms developed in this paper. This study can be helpful in analysing the capabilities of machine learning methodologies for time-series data-sets as well as helping governments in the decision making process for mitigation of the pandemic. …”
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Performance evaluation for different intrusion detection system algorithms using machine learning
Published 2018“…The objectives of this project is to evaluate the performance of various intrusion detection algorithms based on machine learning. The algorithms considered are the Naive Bays Algorithm, Decision Tree Algorithm and Hybrid Algorithm for different datasets. …”
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Thesis -
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Deep learning algorithms for personalized services and enhanced user experience in libraries
Published 2023“…The integration of deep learning (DL) algorithms in library settings engenders a multitude of challenges and complexities, encompassing unintended ramifications, ethical quandaries, a dearth of specialized literature elucidating DL in library contexts, the intricacies of dataset selection and human intervention, and the inherent limitations when juxtaposed with the remarkable cognitive capabilities of the human brain. …”
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