在ternational Conference on Computing, Analytics and Security Trends

Abstract:Diabetic retinopathy is when damage occurs to the retina due to diabetes, which affects up to 80 percent of all patients who have had diabetes for 10 years or more. The expertise and equipment required are often lacking in areas where diabetic retinopathy detection is most needed. Most of the work in the field of diabetic retinopathy has been based on disease detection or manual extraction of features, but this paper aims at automatic diagnosis of the disease into its different stages using deep learning. This paper presents the design and implementation of GPU accelerated deep convolutional neural networks to automatically diagnose and thereby classify high-resolution retinal images into 5 stages of the disease based on severity. The single model accuracy of the convolutional neural networks presented in this paper is 0.386 on a quadratic weighted kappa metric and ensembling of three such similar models resulted in a score of 0.3996.

Abstract:Development of an automated system for identifying and classifying different diseases of the contaminated plants is an emerging research area in precision agriculture. Identification of the diseases is the key to prevent qualitative and quantitative loss of agricultural yields. Rice (Oryza Sative) is one of the essential crops in India and losses due to the diseases badly impact the economy. Manual detection of the diseases is very difficult and not accurate. This creates a need for Image processing techniques which will help in accurate and timely detection of the diseases and overcome the limitations of the human vision. A new technique to diagnose and classify rice diseases has been proposed in this paper. Four diseases namely rice bacterial blight, rice blast, rice brown spot and rice sheath rot have been identified and classified. Different features like shape, the color of a diseased portion of the leaf have been extracted by developing an algorithm. All the extracted features have been combined as per the diseases and diseases have been classified using Minimum Distance Classifier (MDC) and k-Nearest Neighbor classifier (k-NN). The performance of the proposed technique has been evaluated with the help of 115 rice leaf images of four diseases and 70 percent image data has been used for training the classifier and 30 percent has been used for testing. Classification accuracy has been calculated for each disease using both classifiers. The overall accuracy achieved by using k-NN and MDC is 87.02 percent and 89.23 percent respectively.

Abstract:物联网技术th(物联网)是最近的一个at permits the users to connect anywhere, anytime, anyplace and to anyone. In this paper, the various medical services of IoT such as Ambient Assisted Living (AAL), Internet of m-health, community healthcare, indirect emergency healthcare and embedded gateway configuration are surveyed. Further, the applications of IoT in sensing the glucose level, ECG monitoring, blood pressure monitoring, wheelchair management, medication management and rehabilitation system are analyzed. The analysis results show that the use of IoT in the medical field increases the quality of life, user experience, patient outcomes and real-time disease management. The introduction of medical IoT is not without security challenges. Hence, the security threats such as confidentiality, authentication, privacy, access control, trust, and policy enforcement are analyzed. The presence of these threats affect the performance of IoT, thus, the cryptographic algorithms like Advanced Encryption Standard (AES), Data Encryption Standard (DES) and Rivest-Shamir-Adleman (RSA) are used. The investigation on these techniques proves that the RSA provides better security than the AES and DES algorithms.

Abstract:This paper presents a facial expression recognition framework which infers the emotional states in real-time, thereby enabling the computers to interact more intelligently with people. The proposed method determines the face as well as the facial landmark points, extracts discriminating features from suitable facial regions, and classifies the expressions in real-time from live webcam feed. The speed of the system is improved by the appropriate combination of the detection and tracking algorithms. Further, instead of the whole face, histogram of oriented gradients (HOG) features are extracted from the active facial patches which makes the system robust against the scale and pose variations. The feature vectors are further fed to a support vector machine (SVM) classifier to classify into neutral or six universal expressions. Experimental results show an accuracy of 95% with 5 folds cross-validation in extended Cohn-Kanade (CK+) dataset.

Abstract:The indoor location-based services has become attractive with the fast improvement of indoor position estimation and with the spread of smart phones. Bluetooth Low Energy is one of the most recent advancements for IoT and particularly appropriate for ultra-low power sensors running on Complementary Metal-Oxide Semiconductor coin cell batteries. Bluetooth Low Energy is effective option for indoor positioning frameworks which offers sensible exactness and minimal effort arrangement. An estimation strategy utilizing Bluetooth low energy labels turns out to be most enhancing and promising among different procedures for assessing area on the grounds that a most of advanced mobile phones uses Bluetooth. The BLE tags transmit advertisement packets intermittently, which incorporate one of universally unique identifier, a major and a minor value only identified with every tag. At the point when a cellphone gets the advertisement packet, the Smartphone Indoor Positioning Application initiates the scanning by turning on Bluetooth and detects a BLE tags. In this paper, received signal strength of BLE tags and transmitting power is measured first so as to take position of BLE tag for indoor situating. BLE CY8CKIT-042 is utilized as a BLE tag to arrange the BLE segment. The figured parameter such as RSSI and transmitting power is used to accesses the distance determination between smartphone and BLE tags.