Many machine learning softwares are available which help the researchers to accomplish various tasks. These software packages have various conventional algorithms which perform well if the training and test data are independent and identically distributed. However, this might not be the case in the real world. The training data may not be available at one time. In the case of neural networks, the architecture has to be retrained with new data that are made available subsequently. In this paper, we present a novel training algorithm which can avoid complete retraining of any neural network architecture meant for visual pattern recognition. To show the utility of the algorithm, we have investigated the performance of convolutional neural network (CNN) architecture for a face recognition task under transfer learning. The proposed training algorithm may be used for enhancing the utility of machine learning software by providing researchers with an approach that can reduce the training time under transfer learning.

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Face and facial components are the most significant pieces of evidence used in forensic applications. Component-based recognition and age-invariant face recognition are the key factors in forensic face recognition. A major challenge during a forensic investigation is the association of facial components with a relevant face and this paper addresses this concern using the proposed transfer learning methodology. Research has been carried out treating facial components and the face as individual and separate entities. However, they share common auxiliary information which is exploited to bring the face and facial components in a common subspace in the proposed method. This facilitates transfer of knowledge gained from a face and classification of facial components of a person using the same subspace. Stability and invariant features of the facial components catalyze a solution for the problem of recognition. Convolutional neural network (CNN) is used to extract the features of the biometrics and then they are given to a regularization framework for reducing the probability distribution difference between them. The system is trained using the face and facial components and then tested using either the full-face or individual facial components. For the transfer, the proposed Fisher linear discriminant analysis and locality preserving projection, a convolutional neural network-based algorithm gives 91% and 90% accuracy, respectively, which outperforms the Histogram of Gradient and Gabor methods for predicting an association.

Vitamins are essential nutrients that aid in metabolism, cell growth, and the appropriate functioning of other biomolecules. They are required for the proper functioning of various systems in human body. Both vitamin shortage and excess can pave the way for a variety of illnesses. They enter the body via food and supplements eaten, making it critical to measure the vitamin concentrations in food, medicines, and biological fluids. The concentrations of these vitamins are determined using a variety of techniques. The performance measure of the techniques like selectivity, sensitivity, and limit of detection is crucial in their utilization. Among the many techniques of determination, electrochemical sensing and optical sensing have garnered widespread interest because of their potential to improve performance. Additionally, the introduction of innovative materials has added a lot of benefits to sensing. The aim of this article is to summarize significant work toward recent improvements in electrochemical and optical methods for detecting different vitamins. Additionally, it attempts to assess the gaps in vitamin sensing in order to encourage researchers to fill such gaps that will benefit the community.