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Recent days, many researchers have concentrated on the development in salient object detection which is important in numerous computer vision applications. Nevertheless, the main confront is the competent SOD employing the still images. Hence, this work presents the SOD method employing the developed Improved-Deer Hunting Optimization (Improved-DHO) approach. The approach experiences 3 steps that include saliency mapping, keyframe extraction, contourlet mapping, subsequently, a combination of attained outputs by exploiting the optimal coefficients. Initially, extracted frames are given to saliency as well as contourlet mapping concurrently to decide each pixel quality. Subsequently, the outcomes attained from the contourlet mapping and saliency mapping are combined by exploiting the random coefficients in order to attain the last consequence which is used to recognize the salient objects. Moreover, the developed Improved-DHO is used to select the optimal coefficients in order to detect salient objects. The investigational analysis of developed Improved-DHO based on the performance measures exposes that the developed Improved-DHO obtained a maximum accuracy, specificity, and sensitivity.

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<p>Target detection in hyperspectral imagery is a complex process due to many factors. Exploiting the hyperspectral image<br />for analysis is very challenging due to large information and low spatial resolution. However, hyperspectral target<br />detection has numerous applications. Hence, it is important to pursue research in target detection. In this paper, a<br />comparative study of target detection algorithms for hyperspectral imagery is presented along with scope for future<br />research. A comparative study behind the hyperspectral imaging is detailed. Also, various challenges involved in<br />exploring the hyperspectral data are discussed.</p>

The world strives to exploit the fourth revolution in technology, including the great developments such as the Internet of Things (IoT) to improve life. IoT technology is used in many areas of life from industry health and environment to personal life. Agriculture is, therefore, one of the key sectors of life that have benefited from modern technology such as the use of drones for crop assessment, irrigation, monitoring and mapping, big data in crop analysis for companies and countries. The Internet of Things was not far off as it was introduced to monitor plant irrigation. Increased awareness of the environment and the exploitation of small and semi-enclosed areas in agriculture and turning them into green areas. The IoT technology converts this process into intelligent and dynamic based on wireless sensing with the help of the device programmed by the Arduino. This data is collected in Power BI what to help in the future development of the device and analysis of the data. The device senses soil moisture and temperature and connects it to its cloud platform for optimal management of future planting. The device senses soil moisture and temperature and connects it to its cloud power BI platform for optimal management of future planting. This technology helps reduce agricultural costs and labor. Therefore, this paper proposes an intelligent system with the help of the Internet of Things in the management of smart farming in apartment building with the help of Arduino.