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Mohammed, Amin Salih
- Privacy Preserving Data Mining using Threshold Based Fuzzy C-Means Clustering
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Authors
Affiliations
1 Department of Information Technology, Lebanese French University, Erbil, IQ
1 Department of Information Technology, Lebanese French University, Erbil, IQ
Source
ICTACT Journal on Soft Computing, Vol 9, No 1 (2018), Pagination: 1813-1816Abstract
Privacy preserving is critical in the field of where data mining is transformed into cooperative task among individuals. In data mining, clustering algorithms are most skilled and frequently used frameworks. In this paper, we propose a privacy-preserving threshold clustering that uses code based technique with threshold estimation for sharing of secret data in privacy-preserving mechanism. The process includes code based methodology which enables the information to be partitioned into numerous shares and handled independently at various servers. The proposed method takes less number of iterations in comparison with existing methods that does not require any trust among the clients or servers. The paper additionally provides experimental results on security and computational efficiency of proposed method.Keywords
Privacy Preserving, Data Mining, Threshold Cryptography, Fuzzy C-Means Clustering, Vandermonde Matrix, Secure Multiparty Computation.
References
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- Detection of Accurate Facial Detection using Hybrid Deep Convolutional Recurrent Neural Network
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Authors
Affiliations
1 Department of Information Technology, Lebanese French University, IQ
1 Department of Information Technology, Lebanese French University, IQ
Source
ICTACT Journal on Soft Computing, Vol 9, No SP 2 (2019), Pagination: 1844-1850Abstract
Facial Landmark discovery is an imperative issue in numerous PC vision applications about appearances. It is extremely testing as human faces in wild conditions regularly present expansive varieties fit as a fiddle because of various stances, impediments or demeanors. Profound neural systems have been connected to take in the guide from face pictures to confront shapes. To the best of our insight, Recurrent Neural Network (RNN) has not been utilized in this issue yet. In this paper, we propose a technique which uses RNN and Deep Neural Network (DNN) to take in the face shape. To start with, we design a system utilizing Convolutional Neural Network (CNN) to get the underlying Landmark estimation of appearances. At that point, we utilize feed-forward neural systems for neighborhood look where a segment based seeking technique is investigated. By utilizing LSTM- CNN-RNN, the underlying estimation is more dependable which makes the accompanying segment based pursuit doable and exact. Tests demonstrate that the worldwide system utilizing CNN-LSTM-RNN shows signs of improvement results than past systems in the two recordings and single picture. Our technique beats the cutting edge calculations particularly regarding fine estimation of Landmark spots.Keywords
Facial landmark, Deep Neural Network, Recurrent Neural Network, Convolutional Neural Network.References
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