Pest Detection and Classification in Peanut Crops Using CNN, MFO, and EViTA Algorithms

The growth of vision transformer (ViT) methods have been quite enormous since its features provide efficient outcome in image classification, and identification. Inspired of this beneficial, this paper propose an Enhanced vision transformer architecture (EViTA) model for pest identification, segmentation, and classification. The as of late found that, compare to machine learning, Convolutional neural network algorithms the ViT has providing trusted results on image classification. Motivated by this, in this paper, we concentrate on the best way to learn dual barch segment representations in ViT models for image arrangement. Based upon its features, here propose a double layer transformer encoder to integrate pest image segments of various sizes of pest images to create more grounded image highlights. The current study uses, three pest datasets that affects peanut crops such as Aphids (IP102 Dataset), Wireworm (IP102 Dataset), and Gram Caterpillar collected from public available repository. Our methodology processes small segment and huge segment of tokens with two separate parts of various computational intricacy also, these tokens are then combined simply by consideration numerous times to complete one another. The taken datasets’ are preprocessed utilizing the characteristic by using moth flame optimization (MFO), and flatten the images by using linear projector methodology to enhance the missing quality in pest images, and afterward normalization methods are executed to switch it over completely in to mathematical arrangement. This processed information is standardized further utilizing the self attention in StandardScaler procedures are carried out for choosing the ideal highlights in the dataset accordingly having huge effect towards affecting pest image predictions. These ideal highlights are at last taken care of into the EViTA model and the outcomes created are considered in contrast to the cutting edge models which at last legitimize the predominance of the proposed EViTA+PCA+MFO model in pest image prediction with high accuracy rate. Broad trials show that our methodology performs better compared to or on standard with a few simultaneous deals with vision transformer, notwithstanding productive CNN models

Cognitive fuzzy-based behavioral learning system for augmenting the automated multi-issue negotiation in the e-commerce Applications

Evolution of agent-based technology presents behavioral learning and sustainable negotiation challenges in e-commerce applications. In particular, the challenge of designing the negotiation strategy to incorporate sustainability in e-commerce business that can leverage the agent to reach its objectives by increasing the negotiation coordination and cooperation with the opponent agents. Therefore, the proposed research introduces the negotiation strategy sustainable solution using a cognitive fuzzy-based behavioral learning system which can change the preferences of negotiating agents according to human psychological characteristics. It will mimic the attitudes of human risk, patience and regret during the course of bilateral negotiation and also change the preference structures according to the fuzzy logic rules. As a result, the proposed negotiation strategy makes significant improvements on various parameters such as utility value, success rate, total negotiation time, and communication overhead while changing the negotiation rounds from 50 to 500. Since this system leverages the negotiation strategy of the agent by taking appropriate decisions to reach better agreement based on the interest, belief and psychological characteristics of negotiating opponents. Moreover, the usage of negotiation in the cloud-based platform can leverage the e-commerce applications to handle as many requests as possible due to its dynamic elasticity

Smart Water Resource Management Using Artificial Intelligence—A Review

Water management is one of the crucial topics discussed in most of the international forums. Water harvesting and recycling are the major requirements to meet the global upcoming demand of the water crisis, which is prevalent. To achieve this, we need more emphasis on water management techniques that are applied across various categories of the applications. Keeping in mind the population density index, there is a dire need to implement intelligent water management mechanisms for effective distribution, conservation and to maintain the water quality standards for various purposes. The prescribed work discusses about few major areas of applications that are required for efficient water management. Those are recent trends in wastewater recycle, water distribution, rainwater harvesting and irrigation management using various Artificial Intelligence (AI) models. The data acquired for these applications are purely unique and also differs by type. Hence, there is a dire need to use a model or algorithm that can be applied to provide solutions across all these applications. Artificial Intelligence (AI) and Deep Learning (DL) techniques along with the Internet of things (IoT) framework can facilitate in designing a smart water management system for sustainable water usage from natural resources. This work surveys various water management techniques and the use of AI/DL along with the IoT network and case studies, sample statistical analysis to develop an efficient water management framework