K. K. Thyagharajan

Cervical cancer is a main public health concern, particularly in low-and middle-income countries. A certain amount of cervical cancer is caused by lifestyle choices. The Human Papillomavirus (HPV) is the sexually transmitted infection that causes the most cervical cancers. But only persistent HPV infections cause pre-cancer and cancer to proceed. Risk-dependent prediction approach aid to stratify female with a higher risk of developing cervical cancer with screened them on a priority basis. An Effective Method for Predicting Cervical Cancer Risk Using Semantic-Preserved Generative Adversarial Networks to Enhance Prediction Accuracy and Model Efficiency (PCC-SPGAN-PAM) is proposed. Initially, data are collected from the UCI dataset. Then collected data are fed in to pre-processing phase. Trusted-Based Distributed Set Membership Filtering (TDSF) is used for data cleaning and missing value is restored for target tracking. Then, the pre-processed data are fed into Quaternion Offset Linear Canonical Transform (QOLCT) to extract the features, including the number of sexual partners, age, deliveries, sexually transmitted infections, tobacco usage, and age at first sexual intercourse. The extracted features are fed into SPGAN for predicting the risk of cervical cancer as positive or negative. PCC-SPGAN-PAM has been developed to detect the risk of cervical cancer dependent on one’s lifestyle choices. The PCC-SPGAN-PAM is selected to arrive at an accuracy of 99.23%. It is effective to relate risk factors and PCC that aid ineffectual prevention with management of cervical cancer. It is implemented in Python. Performance metrics are analyzed with existing techniques like Design with Development of Efficient Risk Prediction Method for Cervical Cancer (ERP-CC), cervical cancer prediction utilizing stacked ensemble approach with SMOTE-RFERF (CCP-SEA), ensemble classification method for cervical cancer prediction utilizing behavioral risk factors (EC-CCP-BRF) techniques, respectively.

This research proposes a unique approach to human action recognition using mobile sensor data and a computationally efficient 1D Convolutional Neural Network (1 This research proposes a unique approach to human action recognition using mobile sensor data and a computationally efficient 1D Convolutional Neural Network (1

Aim. Analyse the diabetes mellitus (DM) of a person through the facial skin region using vision diabetology. Diabetes mellitus is caused by persistent high blood glucose levels and related complications, which show variation in facial skin regions due to reduced blood flow in the facial arteries. Materials and Method. In this study, 200 facial images of diabetes patients with skin conditions such as Bells palsy, rubeosis faciei, scleroderma, and vitiligo were collected from existing face videos. Moreover, face images are collected from diabetic persons in India. Viola Jones face-detecting algorithm extracts face skin regions from a diabetic persons face image in video frames. The affected skin area on the diabetic persons face is detected using HSV colour model segmentation. The proposed multiwavelet transform convolutional neural network (MWTCNN) extracts the features for diabetic measurement from up- and downfacial scaled images of diabetic persons. Results. The existing deep learning models are compared with the proposed MWTCNN model, which provides the highest accuracy of 98.3. Conclusion. The facial skin region-based diabetic measurement avoids pricking of the serum and is used for continuous glucose monitoring.

KSII Transactions on Internet and Information Systems TIIS Paper Details

The demand for increased data rate with improved QoS for real-time data traffic is ever increasing in the present day wireless environment. The scheduling schemes available in the literature incur lot of scheduling overhead at the eNodeB. Therefore, this work recommends an energy efficient, QoS-aware scheduler with reduced scheduling complexity at the eNodeB, for transmission of delay sensitive data. The scheduling problem is composed as a gain of weighted transmission rates of all possible combinations of various resources required by the channel for transmitting data. An improved greedy algorithm at the eNodeB, has been developed to allocate the resources dynamically to the user equipments (UEs) for the transmission of real-time data. The input video frames to the algorithm are compressed using discrete wavelet transform. The results of this research work show that the proposed scheduling algorithm greatly improves the coverage of the cell edge users. The performance of this greedy scheduler is compared with other two notable schedulers in the literature namely LOG rule and EXP-rule. This scheduling algorithm outperforms the other schemes in terms of QoS parameters for real-time data transmission.

Several applications such as object recognition and face recognition are established with the progress of smart devices and computer technology, to assist human-computer interaction (HCI). In HCI, Hand-held object recognition hasamain role. This approach helps the computer to realise the user’s intentions and also meetsthe user requirements. Hand as an organ which is considered as a direct and natural way of communication for humans. The Hand-held Object Recognition (HHOR) assigns a label for the object which is heldin hand this could help machines in understanding the environment and the intention of the people. However, it has not been well studied in the community.So, in this paper, we proposed system for recognizing such activities happening between hands and faces in real time. The interaction events (e.g. eating, phoning and smoking) between hands and faces are analysed using the event analysis approach. Ratio histogram is used for obtaining the essential colour bins for detecting the desired objects via re-projection method. For object tracking and feature extraction, a code book method is used. To recognize various human-object interaction events, the dynamic and multiplicity contexts of event are modelled together. Finally, atwo stage cascaded CNN classifiers for the recognition is implemented as this technology improves the performance of object recognition.To make fair comparisons, six methods were compared in this paper based on the HMDB dataset. This system is effective and can be performed in real time because an exhaustive search process to find possible interaction pairs in the huge space of all possible event parameters is not involved. Experimental results have proved the superiority of our proposed system to analyse different human behaviours and events between hands and a face.

Facial expression is a standout amongst the most imperative features of human emotion recognition. For demonstrating the emotional states facial expressions are utilized by the people. In any case, recognition of facial expressions has persisted a testing and intriguing issue with regards to PC vision. Recognizing the Micro-Facial expression in video sequence is the main objective of the proposed approach. For efficient recognition, the proposed method utilizes the optimal convolution neural network. Here the proposed method considering the input dataset is the CK+ dataset. At first, by means of Adaptive median filtering preprocessing is performed in the input image. From the preprocessed output, the extracted features are Geometric features, Histogram of Oriented Gradients features and Local binary pattern features. The novelty of the proposed method is, with the help of Modified Lion Optimization (MLO) algorithm, the optimal features are selected from the extracted features. In a shorter computational time it has the benefits of rapidly focalizing and effectively acknowledging with the aim of getting an overall arrangement or idea. Finally the recognition is done by Convolution Neural network (CNN). Then the performance of the proposed MFEOCNN method is analyzed in terms of false measures and recognition accuracy. This kind of emotion recognition is mainly used in medicine, marketing, E-learning, entertainment, law and monitoring. From the simulation, we know that the proposed approach achieves maximum recognition accuracy of 99.2% with minimum Mean Absolute Error (MAE) value. This results are compared with the existing for Micro-Facial Expression Based Deep-Rooted Learning (MFEDRL), Convolutional Neural Network with Lion Optimization (CNN+LO) and Convolutional Neural Network (CNN) without optimization. The simulation of the proposed method is done in the working platform of MATLAB.

with the fast growth of digital technology and user friendly image editing tools, duplicating the contents of digital images becomes easier. The main aim of a digital image forensic is to determine the originality of digital images. Copy move forgery is one of the common methods to forge the digital content. In this paper we introduce a passive method to identify the copy move forgery. It decomposes the image and extracts the moments as feature vector to detect the forgery. The experimental results are studied to demonstrate that the proposed technique detects the forgery and classify the related forged images as clusters based on hierarchical method by BIRCH algorithm. The authentic image even after the post processing operations done on images is identified. Computer vision related with automatic analysis and innovating the useful information from digital information is implemented. It is mainly applied in image understanding.