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Smartphones play an extensive position withinside most company investigations into cyber intrusions. Investigations from the Law Enforcement additionally contain cellular gadgets sporting crucial proof that could assist carry the case to its logical conclusion. Smartphones are utilized in several crook justice contexts. The company presents mobile telephones to lots of its personnel as a part of their task duties, and the BOYD (carry your very own tool) idea is broadly adopted, permitting customers to get admission to workplace applications (like Outlook, Skype, etc.) the usage of their very own transportable gadgets. Based on forensic proof from research starting from conventional crimes (homicide, robbery, kidnapping, etc.) to technological crimes like cybersecurity incidents on company-huge cellular facilities, unlawful get admission to or exchanges, or facts theft, the cybercrime inquiry unit can be required to capture the cellular gadgets and ship them to FLS for added analysis. The chain of custody high-quality practices for securing mobile telephones, iPads, company cellular telephones, and BYOD gadgets in a manner that protects the integrity of the proof and is consequently suited in courtroom docket are in short defined on this paper. This paper's chain of custody technique was created especially for cellular platforms which require specific application of mind and use of tools in the reconstruction of the scene of crime as well as management of the evidence due to the variety of operating systems and nature of dynamic evidence which mobile devices have. The necessities of the commercial enterprise and regulation enforcement agencies that might use the record for normal cellular tool investigations in India have been taken into consideration while it was being developed. This paper has been written retaining in view the necessities of each enterprise in addition to the Law Enforcement Agencies to apply it for his or her everyday cybersecurity investigations concerning cellular gadgets. [ABSTRACT FROM AUTHOR]

The inter-class face classification problem is more reasonable than the intra-class classification problem. To address this issue, we have carried out empirical research on classifying Indian people to their geographical regions. This work aimed to construct a computational classification model for classifying Indian regional face images acquired from south and east regions of India, referring to human vision. We have created an Automated Human Intelligence System (AHIS) to evaluate human visual capabilities. Analysis of AHIS response showed that face shape is a discriminative feature among the other facial features. We have developed a modified convolutional neural network to characterize the human vision response to improve face classification accuracy. The proposed model achieved mean F1 and Matthew Correlation Coefficient (MCC) of 0.92 and 0.84, respectively, on the validation set, outperforming the traditional Convolutional Neural Network (CNN). The CNN-Contoured Face (CNN-FC) model is developed to train contoured face images to investigate the influence of face shape. Finally, to cross-validate the accuracy of these models, the traditional CNN model is trained on the same dataset. With an accuracy of 92.98%, the Modified-CNN (M-CNN) model has demonstrated that the proposed method could facilitate the tangible impact in intra-classification problems. A novel Indian regional face dataset is created for supporting this supervised classification work, and it will be available to the research community. [ABSTRACT FROM AUTHOR]