Papers by Mohammad Ishrat
Advances in systems analysis, software engineering, and high performance computing book series, Feb 23, 2024
Advances in systems analysis, software engineering, and high performance computing book series, Feb 23, 2024
2023 3rd International Conference on Pervasive Computing and Social Networking (ICPCSN)
Advances in human and social aspects of technology book series, Jun 16, 2023
2023 4th International Conference on Smart Electronics and Communication (ICOSEC)
Advances in intelligent systems and computing, 2023
2022 International Conference on Inventive Computation Technologies (ICICT), Jul 20, 2022
Data Science and Management
2023 International Conference on Sustainable Computing and Smart Systems (ICSCSS)
2023 International Conference on Sustainable Computing and Smart Systems (ICSCSS)
IEEE Access
Ongoing researches on multiple view data are showing competitive behavior in the machine learning... more Ongoing researches on multiple view data are showing competitive behavior in the machine learning field. Multi-view clustering has gained widespread acceptance for managing multi-view data and improves clustering efficiency. Large dimensionality in data from various views has recently drawn a lot of interest from researchers. How to efficiently learns the appropriate lower dimensional subspace which can manage the valuable information from the diverse views is challenging and considerable issue. To concentrate on the mentioned issue, we asserted a novel clustering approach for multiple view data through low-rank representation. We consider the importance of each view by assigning the weight control factor. We combine consensus representation with the degree of disagreement among lower rank matrices. The single objective function unifies all factors. Furthermore, we give the efficient solution to update the variable and to optimized the objective function through the Augmented Lagrange's Multiplier strategy. Real-world datasets are utilized in this study to exemplify the efficiency of the introduced technique, and it is contemplated to preceding algorithms to demonstrate its superiority. INDEX TERMS Low-rank representation, spectral clustering, weighted multi-view data, sparse constraints.
2022 19th International Computer Conference on Wavelet Active Media Technology and Information Processing (ICCWAMTIP)
Wireless Communications and Mobile Computing
In a sensor network, packet transmission is easy, but achieving an effective routing path is diff... more In a sensor network, packet transmission is easy, but achieving an effective routing path is difficult. The packet information is modified by the intruder node. Initial node capacity is not monitored, so it does not filter out the individual status of each and every node in the routing path. It causes a network that utilizes more energy and minimum packet delivery ratio. This work has implemented the enhanced centrum path allotment-based shielded communication (ECSC) scheme to achieve the shielded packet broadcasting from the sender node to the destination node in the network environment. The quality of packet transmission is improved by using the spatial uniqueness node selection algorithm. It is designed to select the routing node based on its uniqueness; priority-based communication is carried out by the uniqueness process. It improves the packet delivery ratio and network lifetime. It also minimizes packet drop rate and end-to-end delay.
IEEE Access
Clustering of multi-view data has got broad consideration of the researchers. Multi-view data is ... more Clustering of multi-view data has got broad consideration of the researchers. Multi-view data is composed through different domain which shows the consistent and complementary behavior. The existing studies did not draw attention of over-fitting and sparsity among the diverse view, which is the considerable issue for getting the unique consensus knowledge from these complementary data. Herein article, a multi-view clustering approach is recommended to provide the consensus solution from the multiview data. To accomplish this task, we exploit non-negative matrix factorized method to generate a cost function. Further, manifold learning model is used to build the graph through the nearest neighbor strategy, which is effective to save the geometrical design for data and feature matrix. Furthermore, the over-fitting problem, sparsity is handled through adaption of frobenious norm, and L 1-norm on basis and coefficient matrices. The whole formulation is done through the mathematical function, which is optimized through the iterative updating strategy to get the optimal solution. The computational experiment is carried on the available datasets to exhibits that the proposed strategy beats the current methodologies in terms of clustering execution. INDEX TERMS Non-negative matrix factorization, multi-view data, manifold structure, nearest neighbor.
2022 International Conference on Inventive Computation Technologies (ICICT)
The run-time verification of security properties (Integrity, Availability and Confidentiality) re... more The run-time verification of security properties (Integrity, Availability and Confidentiality) received increased attention from researchers. In particular a security property that relate to information that is made available by end users is achievable only to a limited degree using static and dynamic verification techniques. The more sensitive the information, such as banking data, government intelligence or military information being processed by software, the more important it is to ensure the confidentiality of this information. This paper aims to compare the static and dynamic methodology for run time monitoring. This paper will help to carried out the actual performance of these two different methodologies and their performance in different conditions. The objective of this paper is to find the better solution for run time monitoring using static or dynamic analysis.
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Papers by Mohammad Ishrat