The Internet of Things (IoT) and the Android operating system have made cutting-edge technology accessible to the general public. These are affordable, easy-to-use, and open-source technology. Android devices connect to different IoT devices such as IoT-enabled cameras, Alexa powered by Amazon, and various other sensors. Due to the escalated growth of Android devices, users are facing cybercrime through their Android devices. This article aims to provide a comprehensive study of the IoT and Android systems. This article classifies different attacks on IoT and Android devices and mitigation strategies proposed by different researchers. The article emphasizes the role of the developer in secure application design. This article attempts to provide a relative analysis of several malware detection methods in the different environments of attacks. This study expands the awareness of certain application-hardening strategies applicable to IoT devices and Android applications and devices. This study will help domain experts and researchers to gain knowledge of IoT systems and Android systems from a security point of view and provide insight into how to design more efficient, robust, and comprehensive solutions. This article discusses different attack vectors and mitigation strategies available to both developers and in the open domain. Certain guidelines are also suggested for application and platform developers, as well as application databases (Google play store), to limit the risk of attack, and users can form their own defense with knowledge regarding keeping hardware and software updated and securing their system with a strong password.

An accurate identification of objects from the acquisition system depends on the clear segmentation and classification of remote sensing images. With the limited financial resources and the high intra-class variations, the earlier proposed algorithms failed to handle the sub-optimal dataset. The building of an efficient training set iteratively in active learning (AL) approaches improves classification performance. The heuristics-based AL provides better results with the inheritance of contextual information and the robustness to noise variations. The uncertainty exists pixel variations make the heuristics-based AL fail to handle the remote sensing image classification. Previously, we focused on the extraction of clear textural pattern information by using the extended differential pattern-based relevance vector machine (EDP-AL). This paper extends that work into the novel pixel-certainty activity learning (PCAL) based on the information about textural patterns obtained from the extended differential pattern (EDP). Initially, distributed intensity filtering (DIF) is used to eliminate noise from the image, and then histogram equalization (HE) is used to improve the image quality. The EDP is used to merge and classify different labels for each image sample, and this algorithm expresses the textural information. The PCAL technique is used to classify the HSI patterns that are important in remote sensing applications using this pattern collection. Pavia University and Indian Pines (IP) are the datasets used to validate the performance of the proposed PCAL (PU). The ability of PCAL to accurately categorize land cover types is demonstrated by a comparison of the proposed PCAL with existing algorithms in terms of classification accuracy and the Kappa coefficient.

Histogram Equalization (HE) is one of the most popular techniques for this purpose. Most histogram equalization techniques, including Contrast Limited Adaptive Histogram Equalization (CLAHE) and Local Contrast Modification CLAHE (LCM CLAHE), use a fixed block size technique for feature enhancement. Due to this, all these state of art techniques are used to give poor denoising performance after feature enhancement. In this paper, a deep learning based new approach, namely Dynamic Block Size Technique (DBST), is used to improve image denoising. In this approach, we use the Categorical Subjective Image Quality (CSIQ) image set, an image database generally used for preprocessing of images. The results obtained from experiments show better performance for different important parameters (used by state of art techniques). The work is novel in the preprocessing of images because in this work, we classify the image depending upon the image features for selecting appropriate block sizes dynamically during preprocessing. Proposed work outperforms in terms of PSNR, MSE, NRMSE, SSIM and SYNTROPY. The average respective values are 18.92, 863.86, 0.25, 0.81 and 19.35 and are better in comparison of CLAHE and LCM CLAHE.

The social network is an interconnected online web of people spanning across the digital space for sharing information, reflecting opinions, and building societal trends. This necessitates quantification of relative prominence of an individual in the online community. The significance of a node is measured by centrality metrics, with different indicators accentuating different characteristics of the network. Though there are a myriad of centrality indicators however, it would be interesting to explore which centrality indicators are highly correlated. The literature survey shows that extensive study is still required to establish the network-specific choice of centrality measures for defining node importance. In our work, we have analysed different classes of networks by calculating various centrality indicators. We have computed the pair-wise correlation of the centrality indicators for each network. This is followed by setting threshold value to find the strongly associated centrality indictors. These indicators are further partitioned into clusters, which are validated by comparing the top 50 ranked nodes based on the strongly correlated centrality measures. We have proposed technique to find the similarity in clustering of centrality indictors for each category of networks. Also, we have explored the similarity of centrality indices irrespective of the network class for generalization. Our findings have established that each member of a cluster of correlated centralities is capable of serving as a suitable indicator for detecting the influential nodes based on the structural properties of a specific class of network. Furthermore, we employed association rule mining, a machine learning approach, to uncover frequently co-occurring centrality indicators that exhibit consistent patterns across networks. This AI-driven analysis provides deeper insight into the interdependencies among centrality measures, facilitating more efficient and interpretable network analysis.