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Now a days, Remote Sensing (RS) techniques are used for earth observation and for detection of soil types with high accuracy and better reliability. This technique provides perspective view of spatial resolution and aids in instantaneous measurement of soil's minerals and its characteristics. There are a few challenges that is present in soil classification using image enhancement such as, locating and plotting soil boundaries, slopes, hazardous areas, drainage condition, land use, vegetation etc. There are some traditional approaches which involves few drawbacks such as, manual involvement which results in inaccuracy due to human interference, time consuming, inconsistent prediction etc. To overcome these draw backs and to improve the predictive analysis of soil characteristics, we propose a Hybrid Deep Learning improved BAT optimization algorithm (HDIB) for soil classification using remote sensing hyperspectral features. In HDIB, we propose a spontaneous BAT optimization algorithm for feature extraction of both spectral-spatial features by choosing pure pixels from the Hyper Spectral (HS) image. Spectral-spatial vector as training illustrations is attained by merging spatial and spectral vector by means of priority stacking methodology. Then, a recurring Deep Learning (DL) Neural Network (NN) is used for classifying the HS images, considering the datasets of Pavia University, Salinas and Tamil Nadu Hill Scene, which in turn improves the reliability of classification. Finally, the performance of the proposed HDIB based soil classifier is compared and analyzed with existing methodologies like Single Layer Perceptron (SLP), Convolutional Neural Networks (CNN) and Deep Metric Learning (DML) and it shows an improved classification accuracy of 99.87%, 98.34% and 99.9% for Tamil Nadu Hills dataset, Pavia University and Salinas scene datasets respectively. [ABSTRACT FROM AUTHOR]

Due to the advanced developments in communication technologies, Internet of vehicles and vehicular adhoc networks (VANET) offers numerous opportunities for effectively managing transportation problems. On the other, the cloud environment needs to disseminate the emergency message to the vehicles which are consistently distributed on the roadway so that every vehicle gets the messages from closer vehicles in a straightforward way. To resolve this issue, clustering and routing techniques can be designed using computational intelligence approaches. With this motivation, this paper presents a new type-2 fuzzy sets based clustering with metaheuristic optimization based routing (T2FSCMOR) technique for secure communication in VANET. The T2FSC-MOR technique aims to elect CHs and optimal routes for secure intercluster data transmission in VANET. The proposed model involves T2FSC technique for the selection of CHs and construction of clusters. The T2FSC technique uses different parameters namely traveling speed (TS), link quality (LQ), trust factor (TF), inter-vehicle distance (IVD), and neighboring node count (NCC). The inclusion of trust factor helps to select the proper cluster heads (CHs) for secure data dissemination process. Moreover, trust aware seagull optimization based routing (TASGOR) approach was derived for the optimal selection of routes in VANET. In order to validate the enhanced performance of proposed technique, the set of simulations take place and the outcomes are examined interms of different measures. The experimental outcomes highlighted the improved performance of the proposed model over the other state of art techniques with a higher throughput of 98%. [ABSTRACT FROM AUTHOR]

Remote sensing (RS) is a very reliable and effective way to monitor the environment and landscape changes. In today's world topographic maps are very important in science, research, planning and management. It is quite possible to detect the changes based on RS data which is obtained at two different times. In this paper, we propose an optimal technique that handles problems like urban green space destruction and detection of crop stress assessment. Firstly, the optimal preprocessing is performed on the given RS dataset, for image enhancement using geometric correction and image registration. Secondly, we propose the improved cat swarm optimization algorithm to optimize the greenery region with the help of vegetation index parameters like Normalized Difference Built-up Index (NDBI) & Normalized Difference Vegetation Index (NDVI). Thirdly, we use Conditional Principal Component Analysis (PCA) to reduce dimension of a response matrix & retain the dominant information to identify key vegetation indices and the classification of crops. Then, an optimal decision maker-based post classification method is introduced to differentiate area changes based on the overlay of two or more classified images. From the simulation results we observed and conclude that the performance of proposed crop classification, crop stress and yield assessments performed very effective compared to existing methods in terms of F-Measure, recall, precision & accuracy. [ABSTRACT FROM AUTHOR]

A comparative study on effectiveness of topical ocular medications, latanoprost and timolol, in intraocular pressure (IOP) reduction was done. IOP was obtained from primary open-angle glaucoma (POAG) and also from ocular hypertensive patients before commencement of therapy and after commencement of therapy on 2nd and 4th months. Other than IOP, central corneal thickness (CCT) parameter was also used. CCT is a predictive parameter in the development of glaucoma from ocular hypertension. There is a greater risk of developing POAG if CCT is 555 µm or less compared corneal thickness of more than 588 µm. The mean IOP before commencement of treatment was above 22 mm Hg in both the groups. By 2nd and 4th months after the commencement of therapy, mean IOP showed a significant improvement in both the groups especially in latanoprost administered compared to the timolol administered group. Increase in CCT by the 4th was also observed which is normal when IOP range becomes normalized. The study concluded that topical latanoprost 0.005% was more effective in reducing IOP than timolol maleate 0.5% twice daily. [ABSTRACT FROM AUTHOR]