Volume-06 , Special Issue-08 , Oct - 2018 Go Back

IoT wearable devices allow non– invading uninterrupted observing of physiological constraints that assist in constant monitoring of fitness and capability of health. Wearable devices are in forms of belts and wrist-bands. IoT applications are used as an application for health and lifestyle monitoring systems and wearable electronics. IoT has come across into numerous technological fields in a faster way. IoT wearable medical devices are emerging to help the affected patients suffering from chronic diseases. Healthcare monitoring, earlier diagnose, personalized treatment is a capable approach of wearable devices. Medical wearable device is a supportable, maintainable and cost-effective advancement in the field of IT. This paper provides the survey on the futures of wearable IoT in healthcare applications.

Devices healthcare, monitoring, Internet

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S. Sapna, "A Survey on Wearable Internet of Things (IoT) Devices and its Benefits", International Journal of Computer Sciences and Engineering, Vol.06, Issue.08, pp.103-105, 2018.

Computer Aided Diagnosis (CAD) is available for automatic detection of suspicious colorectal polyps in the CT images of the colon. These schemes help the radiologist to identify the location of the polyps in an efficient and accurate manner. A detailed survey was made on the different CAD scheme proposed by different authors for the detection of different categories of polyps. The different CAD schemes was implemented by incorporating some modification in the segmentation phase such as automatic colon segmentation or vary the identification of features in the feature extraction phase in the classical polyp detection system for the identification of polyps. Their performances were measured by two parameters sensitivity and specificity. Thus the ultimate aim of the authors was to improve the sensitivity and decrease the chance of missing sessile and flat polyps, with acceptable false-positive rates.

Computer Aided Detection, Virtual Colonoscopy, Polyps, Polyp detection

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K. Gayathri Devi, C. Makesh, "A Survey on the State of Art Techniques for the Identification of Polyps for Colorectal Cancer", International Journal of Computer Sciences and Engineering, Vol.06, Issue.08, pp.106-113, 2018.

This researching Fingerprint based identification is one of the most mature & proven technique compared to the biometric technique. Fingerprints are classified as rolled, plain & latent fingerprints. However, even today, a fully automated latent fingerprint matching system has not been developed. However, even today, a fully automated latent fingerprint matching system has not been developed. Matching latent fingerprints over rolled or plain fingerprints is difficult task. The reason behind this is nothing but presence of noise and non-linear distortion in latent prints. Matching latent fingerprints over rolled or plain fingerprints is difficult task. An CIESs should be designed as a criminal investigation decision support system (CIDSS), a reasoning process based on production rules. we using image processing tool box for identify the criminals finger print ,If one decides to use only one year of crime data for detailed analysis then an analyst must spend 1.5 million minutes. As Per Regional Crime Analysis Program (RECAP), If one decides to use crime data for detailed analysis then an analyst.

We also implemented a Pass Matrix prototype on Android and carried out real user we identify the criminals within short period using finger print scanner, If we found new finger print, we will add the finger print register immediately as well as matching finger print to some criminal, This method is used to identify the vehicles easily

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M. Chandran, B. Vinothramkumar, "Crime Intelligent Security Control Robot Investigation System", International Journal of Computer Sciences and Engineering, Vol.06, Issue.08, pp.114-124, 2018.

Melanoma is a category of cancer that develops from the pigment-containing cells recognized as melanocytes. Melanomas usually ensue in the fur but may arise in the jaws, guts or ogle. This paper tends to two distinct frameworks for identification of skin growth in dermoscopy pictures. The primary framework utilizes worldwide strategies and the second framework utilizes neighborhood highlights and the classifier. Henceforth, melanoma is effortlessly to distinguish utilizing with help of worldwide strategies and neighborhood highlights. Skin Disease prediction has become important in a variety of applications such as health insurance, tailored health communication and public health. Due to the costs for dermatologists to monitor every patient, there is a need for an computerized system to evaluate a patient’s risk of melanoma using images of their skin lesions captured using a standard digital camera. The traditional diagnosis technique aims at improving the quality of existing diagnostic systems by proposing advanced feature selection and classification methods.RBF neural network derives classification. For this classification (RBF neural network)this paper proposed new learning method using K means clustering. This paper focuses on the detection of skin lesion as a literature survey.

Dermoscopy, Melanoma , Neural network, Clustering, Classification

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K. Thenmozhi, "Melanoma Skin Cancer Detection Using Improved RBF Classifier", International Journal of Computer Sciences and Engineering, Vol.06, Issue.08, pp.125-132, 2018.