Face recognition from image or video is a popular topic in biometrics research. It has many important practical applications, like surveillance and access control. It is concerned with the problem of correctly identifying facial images and assigning them to persons in a database. This paper proposes an efficient face recognition method based on Scale Invariant Feature Transform (SIFT) for feature extraction and using Levenberg-Marquardt Backpropagation (LMBP) neural network for classification. In this proposed method, we assign the extracted SIFT features of the face images as input vectors to our neural network instead of using just the raw data as the input. Experiments performed on the Yale face database show that the facial images can be recognized by the proposed face identification method efficiently. Also, the traditional face recognition algorithms are compared with the proposed algorithm to show its effectiveness.

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AbstractDiabetic retinopathy is a microvascular complication in diabetes that affects eyes and is responsible for most visual impairment in diabetic patients. Diabetic retinopathy affects up to 80% of those who have had diabetes for 20 years or more. At least 90% of new cases could be reduced with proper treatment and monitoring of the eyes. The longer a person has diabetes, the higher his or her chances of developing diabetic retinopathy. Hence, it compels need for its prevention and cure. There is an increasing interest in natural products in pharmacotherapy as the chemical diversity of natural products has better matches than the diversity of synthetic compounds. The current review summarises the potential of leading traditional herbs like Azadirachta indica, Ginkgo biloba, Anisodus tanguticus, Pinus pinaster, Salvia miltiorrhiza, Stephania tetrandra and Gymnema sylvestre in the management and potential reversal of DR-related pathogenesis. It also discusses the probable mechanism of actions, which are based on epidemiological, in-vitro and in-vivo studies carried out within past few years. Graphical Abstract.

Stroke is one of the leading causes of long-term disability worldwide. Present techniques employed for rehabilitation of victims suffering from partial paralysis or loss of function, such as mirror therapy, require substantial amount of resources, which may not be readily available. In traditional mirror therapy, patients place a mirror beside the functional limb, blocking their view of the affected limb, creating the illusion that both the limbs are working properly, which enhances recovery by enlisting direct simulation. This paper proposes an alternate robot based concept, named Wear-A-BAN, where the rehabilitative task will be carried out by a normal articulated industrial robot. During the proposed rehabilitative procedure, the patients are made to wear a smart sleeve on the functional limb. Movement of this limb is monitored in real-time, by wireless Body-Area Network (BAN) sensors placed inside the sleeve, and copied over the sagittal plane to the affected limb.This procedure results in considerable savings in terms of money and personnel, as even though this procedure does not make the rehabilitation process autonomous, but one therapist can monitor various patients at a time. The industrial robot used is suitable for this purpose due to safety aspects naturally existing in the robot, is relatively cheap in price, and allows comprehensive 3-D motions of the limb. Also, unlike traditional therapy, this procedure allows actual movement of the affected limb. The sensors can also be used for other applications, such as gaming and daily life personal activity monitoring.