Image processing techniques play an important role with various images such as rice grain identification, wheat, fruits, medical, vehicle, and digital text images in image acquisition, image preprocessing, clustering, segmentation, and classification techniques. In the application of object detection and classification images, preprocessing and segmentation techniques are used. This paper delves into the specifics of automated segmentation processes, focusing on rice variety identification and classification images. The aim is to talk about the issues that come up when segmenting digital images and the relative merits and drawbacks of the various methods for preprocessing and segmenting images that are currently available. In this paper, we propose a hybrid approach of Preprocessing and Segmentation techniques to develop an automatic rice variety identification system.

Rice is regarded as main staple food for around 80% of the Southeast Asia population alone. As most nations achieving selfcompetency in rice production, better quality rice is of utmost priority of consumer. It is very tedious work for consumers to analyze the good quality and rice grading in the market. Rice grains' quality determination is judged visually by human inspectors following manual process which is neither objective in nature nor effective due to non-reliability of results occurring as a result of inexperienced inspectors or human errors. So need for an automatic rice quality grading system arises which can eliminate the inadequacies of manual quality grading process. In this paper, Different techniques of machine learning, deep learning and image processing considering morphological, color, shape, textural as well as other features of rice are analyzed to review the current research scenario in automatic quality grading process. Various procedures and methods are considered for the review purpose to analyze the quality of rice grains on the basis of different features of rice.

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Transcranial direct current stimulation (TDCS) is a neuromodulatory device that is used for its ability to enhance cognitive and behavioral performance. Human studies suggest that TDCS modulates cortical excitability during stimulation by nonsynaptic changes of the cells, along with evidence that the after-effects of TDCS are driven by synaptic modification. TDCS represents a potential intervention to enhance cognition across clinical populations, including mild cognitive impairment among psychological and neurological disorders. Studies suggest that TDCS might be helpful in treating depression with appropriate current, size of electrodes, and employment of montages. TDCS opens a new perspective in treating major depressive disorder (MDD) because of its ability to modulate cortical excitability and induce long-lasting effects.

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