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There are an increased number of patients and elderly people who are alone worldwide. These people are often treated in –home and at times enter into a critical situation that may require help. The facial expressions are widely researched and employed to classify people emotional states. Such facial expressions have already been studied to determine the emotional health of an individual which, in turn, can be used as an important symptom for the diagnosis of various dis-eases such as: schizophrenia, depression, autism, bipolar disorder. Capturing facial expressions over a certain period of time can give an idea of to what extent the patient is feeling pain and can enable nurse/family members to decide the feelings of the patient and to provide necessary assistance. This research work describes the design of a system for continuous monitoring of patients/elderly people through person identification and emotion recognition to provide healthcare in home environments in an automatic way through an IoT infrastructure without human intervention. Using smart camera the patient’s images are captured continuously and transmitted to the decision maker (laptop/desktop) for person identification. Once the patient is identified, he/she is monitored continuously for emotion recognition through facial expressions and the detected emotions are stored in the cloud. When abnormal condition is detected, an alert message is sent to the care taker/nurse.

E-health, Remote Monitoring, Person Identification, Emotion Recognition, Internet of Things

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G. Santhi, "Enhanced Healthcare Provisioning through Emotion Recognition," International Journal of Computer Sciences and Engineering, Vol.8, Issue.5, pp.182-186, 2020.

The rate of computer power steadily doubles every 13 months, with this face detection and recognition has transcended from an esoteric to a popular area of research in computer vision and one of the better and successful applications of image analysis and algorithm based understanding. Because of the intrinsic nature of the problem, computer vision is not only a computer science area of research, but also the object of neuro- scientific and psychological studies, mainly because of the general opinion that advances in computer image processing and understanding research will provide insights into how our brain works and vice-versa. Considering the general curiosity and interest in the matter, we propose to create and develop a facial recognition based attendance management system, using Intel’s open source computer vision project, OpenCV and Microsoft’s .NET framework. The paper describes how to take student’s attendance using face recognition. The face recognition is implemented with the help of Local Binary Patterns Histogram (LBPH) algorithm. The system will recognize the face of the student and saves the response in database automatically

Automatic, Database, Face Recognition, LBPH

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Shivani Duggi, Khajabani S., Katha Harshitha, N.B. Shaguftha, M. Nagaraj, "Face Recognition using OpenCV," International Journal of Computer Sciences and Engineering, Vol.8, Issue.5, pp.187-191, 2020.

Uncontrolled growth of the urban population in developing countries in recent years has made solid waste management a crucial issue. Very often, a considerable amount of total expenditures is spent on the gathering of solid waste by city authorities. Optimization of the routing system for the gathering and transport of solid waste thus constitutes a crucial component of an efficient solid waste management system. This paper describes an attempt to design and develop an appropriate storage, collection and disposal plan for Kalyani, West Bengal State (India). A GIS optimal routing model is proposed to determine the location of dustbins in the Kalyani City, where citizens can look up for dustbins with capacity and type and can dump the garbage in it. The model uses dustbin type, dustbin location in the form of latitude and longitude and dustbin managing organization. The proposed model can be used by citizens to find the dustbins and it will promote cleanliness in the society.

waste, Geographic Information System, maps, waste management

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Aquib Jawed, Anik Mandal, Sudipta Sahana, "Solid Waste Management using GIS," International Journal of Computer Sciences and Engineering, Vol.8, Issue.5, pp.192-195, 2020.

The Enhanced-Super Resolution Generative Adversarial Networks is an enhancement of Super-Resolution Generative Adversarial Networks by tweaking the model architecture to achieve high resolution. ISRGAN aims to further improve the quality of the image produced by the model by utilizing specially trained instances to upscale different portions of the image by enhancing each portion of the image by a model that is specially trained for such certain objects or classes. The idea is to divide and conquer the super-resolution problem utilizing the specialized models to up-scale sub-problems and improving the quality of generated images. Firstly image is passed through the Object detection phase which utilizes the Yolov3 structure to identify different classes present in the object, each class is then given to a generator that is specialized in that domain to further improve the quality. For the objects having unidentified classes or the base background image, we will have a generalized generator which will be trained on a combination of different domains. Also, to reduce the hardware requirement and improve the efficiency, we developed a way to split the images into sub-images to be enhanced individually and combined together to obtain the final image. These small images are in the form of squares which are enhanced and with the help of specialized generators and base models it is intended to convert low-resolution images into higher resolution models by up-scaling them to 4 times.

Super Resolution, Generative Adversarial Networks, Image enhancement, Upscaling

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Yash Bansal, Vishal Sharma, Siddharth Singh, Vanshika Bhatt, Pankaj Agarwal, "Imagenics Super-Resolution Generative Adversarial Networks (ISRGAN)," International Journal of Computer Sciences and Engineering, Vol.8, Issue.5, pp.196-200, 2020.