No abstract

The advancements in the technologies, the revolution in the business procedures and the entailment to modify the operation in the warehousing as the result of the accumulating orders along with the complications involved in it, and the shortage in the management skills has paved way for the emergence of the smart ware housing. More over as the warehousing takes a vital role in the supply chain and prevails as the key feature in the logistics, smart ware housing is very much necessitated to enhance the organization management and success. The application of the artificial intelligence in the warehousing operations enhances the potentials of the warehousing functioning in the logistics, management and the co-ordination. The application of the artificial intelligence in the warehousing to make it a smart environment for the automated logistics is proposed in the paper. The paper concentrates on the automated storage and the retrieval using the internet of things, artificial intelligence and the cloud computing to have an any time access of the stock available in the warehouse.

Nowadays to increase the efficiency, consistency and the quality of the organizations and to further extend the business world wide the digitization is followed in processing, storing and conveying the information. This in turn has also caused huge set of data flow paving way for the data recovery services. The cloud computing with the massive storage capabilities have become a predominantly used paradigm for data storage and recovery due to its on demand network access, elasticity, flexibility and pay as you go. Moreover to secure the information that is stored the information’s are fragmented and stored. However this fragmentation process often occurs in the form of dispersed and scattered packages lacking proper order heightening the time and minimizing the efficiency of the recovery and information collection. To bring down the restoration time and enhance its efficiency the proposed method in the paper tries to reduce the fragmentation by minimizing the number of dispersed and scattered packages for this the paper utilizes the Hybridized Historical aware algorithm (HHAR) along with the cache aware filter to gather the historical information’s associated with the back-up system and the identify the out of order containers respectively. Further the every data package is protected applying the advanced encryption standard by producing a key to authenticate the access of the data. The proposed model is simulated using the network simulator-II and the results obtained shows that the recovery time is enhanced by 95% and the restore performance is improved by 94.3%.

Rice (Oryza sativa) is India’s major crop. India has the most land dedicated to rice agriculture, which includes both brown and white rice. Rice cultivation creates jobs and contributes significantly to the stability of the gross domestic product (GDP). Recognizing infection or disease using plant images is a hot study topic in agriculture and the modern computer era. This study paper provides an overview of numerous methodologies and analyses key characteristics of various classifiers and strategies used to detect rice illnesses. Papers from the last decade are thoroughly examined, covering studies on several rice plant diseases, and a survey based on essential aspects is presented. The survey aims to differentiate between approaches based on the classifier utilized. The survey provides information on the many strategies used to identify rice plant disease. Furthermore, model for detecting rice disease using enhanced convolutional neural network (CNN) is proposed. Deep neural networks have had a lot of success with picture categorization challenges. We show how deep neural networks may be utilized for plant disease recognition in the context of image classification in this research. Finally, this paper compares the existing approaches based on their accuracy.

The applications that are capable of identifying the tangible products and the movement of those product has attained a predominant position in the field of robotics. The complexity in identifying the changes going around is very high on the indoor environments that are too messy. Segregating things and sorting objects in such messy environment becomes even more tedious and challenging for the people with visual disorders. To subdue these issues and enable the blind and the visually challenged to be aware of the changes or the tangible objects they come across in the indoor environment, the proffered method in the paper devise a recognition aid that is empowered with the deep learning neural networks. The usual conventional-CNN is refurbished by upgrading the components to achieve a better accuracy in recognizing. The images based on different scenes of the indoor environment gathered under different circumstance where used as training and the testing dataset for the proffered model and the accuracy and the recognition rate for the training as well as testing was examined.