Facial expression is a way of non-verbal communication. A person depicts his/her feelings through facial expressions. In computer systems facial expressions help in verification, identification and authentication. One popular use of facial expression recognition is automatic feedback capture from customers upon reacting to a particular product. Effective recognition technology is in high demand by the common users of today's gadgets and technologies. Facial expression recognition technique is broadly classified into two techniques: Feature based techniques and Model based techniques. The key contribution of this article is that we have analyzed latest state of the art techniques in Feature based techniques and Model based techniques. These techniques are analyzed using various standard public face databases: GEMEP-FERA, BU-3DFE, CK+, Bosphorous, MMI, JAFFE, LFW, FERET, CMU-PIE, Georgia tech, AR, eNTERFACE 05 and FRGC. From our analysis we found that for Feature based Curvelet approach performed on FRGCv2 database gave an excellent 97.83% recognition rate and Model based textured 3D video technique performed on BU-4DFE database gave an excellent 94.34 % recognition rate.

The agriculture plays very important role as it helps to accomplish the need of food among people. The production in agriculture consequentially contributes to the economy of every country. The grain crops rice, wheat, maize, and legumes are suffering a lot due to some viral, bacterial, and fungal diseases. The pest and variety of diseases can bring a heavy loss to the global economy. The monitoring of crops health and identification of diseases at early days is very challenging and emerging task in agriculture. So, it is very important to prevent crops from fatal diseases in the early stage, but the manual process of disease discovery can lead to erroneous magnitude of pesticides. The trouble is figure out by automate discovery of diseases and supplication of relevant medication on time. It is very necessary to find out accurate disease to overcome heavy loss to economy. From the few decades, to detect disease correctly, the process of detection become automate using emerging technologies and techniques using computer vision, machine learning and image processing. This article presents the extensive literature on existing methodologies utilized for recognition and classification of leaves diseases. The studies addresses that there is still many limitations and challenges find in different phases in plant disease detection system. The presented research also highlights the pros and cons of different techniques that help out the researchers for contribution in future.

Human life has been made easier and more comfortable thanks to technological advancements. Predictive analytics is a revolutionary technique that utilizes a significant amount of historical data to create predictions about the future. Its goal is to analyze specific data in order to forecast the future and identify the risks connected with a certain decision. Using data-driven predictive models, decisions that were the product of several mathematical computations can be made more quickly and accurately. Banking, education, healthcare, entertainment, and other industries employ technologies to create difficult decisions and forecast future trends. The goal of predictive analytics is to make accurate and cost-effective predictions. The data required for the analysis comes from a variety of sources and will be in a structured, semi-structured, or unstructured format. The classification of a large volume of data during the data analytics process is a tough challenge. The purpose of classification is to turn accessible data into knowledge that will be useful in future research. It is possible to learn from the training data set using machine learning, and the knowledge gathered this way can be applied to effective decision-making. Classification algorithms examine at the training data and use that knowledge to categorize the test data. To maximize their profitability, organizations acquire experts in critical decision-making. Using human intelligence to make key decisions is costly, dangerous, and timeconsuming. As a result, predictive analytics is getting lots of attention these days. It makes the most out of available data in order to make better and more informed decisions. It can be used to discover different patterns and relationships in data in order to forecast future events. Data analysis delivers useful insights and reliably identifies potential hazards. The predictive model and attributes chosen for analysis determine the accuracy of the prediction. The use of an incorrect model and erroneous data can be catastrophic for an organization. Artificial intelligence, cloud computing, machine learning, and other emergent technologies are used to collect, store, and analyze data effectively. The quality of the data acquired and the models employed for analysis are both important factors in forecasting. To analyze the data and make predictions, many supervised learning approaches can be applied. The authors of this paper attempt to provide a thorough overview of the many supervised learning approaches prevalent in machine learning. They also attempt to investigate several application areas in which these strategies are employed to aid decision-making.

Deepfake image manipulation has achieved great attention in the previous year’s owing to brings solemn challenges from the public self-confidence. Forgery detection in face imaging has made considerable developments in detecting manipulated images. However, there is still a need for an efficient deepfake detection approach in complex background environments. This paper applies the state-of-the-art quantum transfer learning approach for classifying deepfake images from original face images. The proposed model comprises classical pre-trained ResNet-18 and quantum neural network layers that provide efficient features extraction to learn the different patterns of the deepfake face images. The proposed model is validated on a real-world deepfake dataset created using commercial software. An accuracy of 96.1 % was obtained.