In recent decades, big data analysis has become the most important research topic. Hence, big data security offers Cloud application security and monitoring to host highly sensitive data to support Cloud platforms. However, the privacy and security of big data has become an emerging issue that restricts the organization to utilize Cloud services. The existing privacy preserving approaches showed several drawbacks such as a lack of data privacy and accurate data analysis, a lack of efficiency of performance, and completely rely on third party. In order to overcome such an issue, the Triple Data Encryption Standard (TDES) methodology is proposed to provide security for big data in the Cloud environment. The proposed TDES methodology provides a relatively simpler technique by increasing the sizes of keys in Data Encryption Standard (DES) to protect against attacks and defend the privacy of data. The experimental results showed that the proposed TDES method is effective in providing security and privacy to big healthcare data in the Cloud environment. The proposed TDES methodology showed less encryption and decryption time compared to the existing Intelligent Framework for Healthcare Data Security (IFHDS) method.

Task scheduling is an emerging challenge in cloud platforms and is considered a critical application utilized by the cloud service providers and end users. The main challenge faced by the task scheduler is to identify the optimal resources for the input task. In this research, a Sine Cosine-based Elephant Herding Optimization (SCEHO) algorithm is incorporated with the Improved Particle Swarm Optimization (IPSO) algorithm for enhancing the task scheduling behavior by utilizing parameters like load balancing and resource allocation. The conventional EHO and PSO algorithms are improved utilizing a sine cosine-based clan-updating operator and human group optimizer that improve the algorithm’s exploration and exploitation abilities and avoid being trapped in the local optima problem. The efficacy of the SCEHO-IPSO algorithm is analyzed by using performance measures like cost, execution time, makespan, latency, and memory storage. The numerical investigation indicates that the SCEHO-IPSO algorithm has a minimum memory storage of 309 kb, a latency of 1510 ms, and an execution time of 612 ms on the Kafka platform, and the obtained results reveal that the SCEHO-IPSO algorithm outperformed other conventional optimization algorithms. The SCEHO-IPSO algorithm converges faster than the other algorithms in the large search spaces, and it is appropriate for large scheduling issues.

The spotlight of our discussion here is on developing a model for detecting snakes present in the agricultural fields using cameras and machine learning algorithms. The idea is to construct a machine learning model to identify the snakes, which will be trained on a vast set of images of snakes. These images will be carefully selected to include the snake patterns, skin color of different species of snakes (extraction of the unique features). This is done to train the model properly to identify different types of snakes that the farmer encounters in the fields. Most famers are unaware and have a need for a way to detect these snakes so that they can either avoid them or get rid of them. Hence, this system can be implemented to prevent thousands of deaths and can help famers alleviate the repercussions of snake bites. Mistakes in recognizing potentially harmful animal species based only on visual cues are major contributors to the high death toll from venomous animal attacks. Since they spend so much time in the fields, where rice and wheat are cultivated, farmers are at a higher risk of being bitten by a snake than the general population. Because of their ignorance, illiterate farmers are more inclined to believe in superstitions, which can lead to their untimely deaths from snakebites despite medical intervention. Animals that would normally pose no threat to humans are responsible for the deaths of thousands of people every year because of environmental factors. However, because it is hard for humans to recognize these dangers, a new design paradigm has been developed to make it simpler. Researchers in the field of animal biology can use it to search for endangered species. Predators may enter gardens and green areas like tea and coffee plantations. There is not yet a plan in place to implement automated sorting for discovering distinctions. By applying the suggested framework to photographs of potentially dangerous animals, several factors useful for studying animal organization may be easily identified. For the classifier to run in real-time, it is suggested that YOLO be utilized for double processing within the framework. Furthermore, the YOLO technique enables quick animal recognition in a similar fashion. First, the YOLO method will be used to determine whether a snake is present in the proposed work. Here, 87 percent precision is also reached in the detection of snakes.

Images are frequently affected because of blurring, data loss occurred by sampling and noise occurrence. The images are getting blurred because of object movement in the scenario, atmospheric misrepresentations and optical aberrations. The main objective of image restoration is to evaluate the original image from the corrupted data. To overcome this issue, the Multi-Objective Reptile Search Algorithm is proposed for performing an effective Image Deblurring and Restoration (MORSA-IDR). The proposed MORSA is used in two different processes such as threshold and kernel parameter calculation. In that, threshold values are used for detecting and replacing the noisy pixel removal using Deep Residual Network (DRN), and estimation of kernel is performed for deblurring the images. The main objective of the proposed MORSA-IDR is to enhance the process of deblurring for recovering low-level contextual information. The MORSA-IDR is evaluated using Peak SignalNoise Ratio (PSNR) and structural similarity index (SSIM). The existing research such as Enhanced Local Maximum Intensity (ELMI) prior and Deep Unrolling for Blind Deblurring (DUBLID) are used to evaluate the MORSA-IDR.The PSNR of MORSA-IDR for image 6 is 30.98 dB which is high when compared to the ELMI and DUBLID.