The birth of mass production started in the early 1900s. The manufacturing industries were transformed from mechanization to digitalization with the help of Information and Communication Technology (ICT). Now, the advancement of ICT and the Internet of Things has enabled smart manufacturing or Industry 4.0. Industry 4.0 refers to the various technologies that are transforming the way we work in manufacturing industries such as Internet of Things, cloud, big data, AI, robotics, blockchain, autonomous vehicles, enterprise software, etc. Additionally, the Industry 4.0 concept refers to new production patterns involving new technologies, manufacturing factors, and workforce organization. It changes the production process and creates a highly efficient production system that reduces production costs and improves product quality. The concept of Industry 4.0 is relatively new; there is high uncertainty, lack of knowledge and limited publication about the performance measurement and quality management with respect to Industry 4.0. Conversely, manufacturing companies are still struggling to understand the variety of Industry 4.0 technologies. Industrial standards are used to measure performance and manage the quality of the product and services. In order to fill this gap, our study focuses on how the manufacturing industries use different industrial standards to measure performance and manage the quality of the product and services. This paper reviews the current methods, industrial standards, key performance indicators (KPIs) used for performance measurement systems in data-driven Industry 4.0, and the case studies to understand how smart manufacturing companies are taking advantage of Industry 4.0. Furthermore, this article discusses the digitalization of quality called Quality 4.0, research challenges and opportunities in data-driven Industry 4.0 are discussed.

Artificial Neural Network (ANN) are one of the recently explored advanced technologies which shows promise in the area of medical. In this paper, Radial Basis Function is used to predict the medical prescription of heart disease. This work includes the detailed information about the patient's symptoms and preprocessing was done. The trainee doctors can also use this web based tool for diagnosis and appropriate medical prescription of heart disease using radial basis function. About 300 patient's data were collected from Sahara Hospital, Aurangabad under the supervision of heart specialist. The radial basis function is applied to heart disease data for prediction of medical prescription of heart disease. Results obtained show that radial basis function can be successfully used for prescribing the medicines for heart disease. The role of effective diagnosis and the advantages of data training on neural networks-based automatic medical diagnosis system are suggested by the outcomes.

This paper presents an experimental evaluation of the effectiveness of various techniques based upon moment invariants (Hu, 1961). The features that have been extracted are based on moments, image partition, principal component axes (PCA), correlation coefficient and perturbed moments. In image partition method, the image is divided into four parts with three different ways. The principal component axes (PCA) method has been used to balance the distribution of pixels in different regions of the image. Correlation coefficient provides dependencies of different moments on each other. In perturbed moment method, moment invariants are computed by small perturbation in image and information is extracted from the perturbation. All techniques have been applied on 2000 handwritten Devanagari numerals. The Gaussian distribution function has been adopted for classification. The success rate can be enhanced to 92% using image partition method

Modi was very useful script in the kingdoms of medieval Maharashtra. In the reign of the great Maratha-Chhatrapati Shivaji and also in the reign of Peshwas, this script was widely incorporated in ruling the state. This script is very similar to the shorthand. At that time, it was used in Maharashtra to prepare the documents such as Property matters, Donation of Land (Dan-Patra), Land Revenue, Military orders (Farman), Justice, Religious matters, Jahagir vatan, Judiciary, etc. In this research, Analysis of the MODI script is studied. Identification and Recognition of the handwritten MODI characters are performed. The database of handwritten samples is prepared by using ANESP program. The original manuscript of MODI script is collected and it is preprocessed. Moment Invariant and Affine Moment Invariant are the two techniques which are used for extracting the features from the handwritten segregated samples. Identification and recognition is done using machine learning technique. Among the variants of machine learning techniques, support vector machine is used as a classifier. Linear kernel function is applied in this support vector machine while performing classification. This system provides good recognition rate for the handwritten samples of MODI script. This research will be helpful to the researchers to enlighten the uncovered part of history.

The palm-print verification system is based on two modes, viz, Enrollment mode and Recognition mode. In the enrollment mode, the palm-print features are acquired from the sensor and stored in a database along with the person's identity for the recognition of his/her identity. In the recognition mode, the palm-print features are re-acquired from the sensor and compared against the stored data to determine the user identity. In the pre-processing stage, two segmentation processes are proposed to extract the region of interest (ROI) of palm. The first skin-color segmentation is used to extract the hand image from the background. The second region of interest of the palm is segmented by using the valley detection algorithm. The Discrete Wavelet Transform (DWT) and Discrete Cosine Transform (DCT) are applied for the purpose of extracting the features. Further, the Sobel Operator and Local Binary Pattern (LBP) are used for increasing the number of features. The mean and standard deviation of DWT, DCT and LBP are computed. Twenty hand-scanned images and ten samples of CASIA palmprint database were used in this experiment. The performance of the proposed system is found to be satisfactory.