In recent years there has been a growing interest in autism spectrum individuals in the expression and understanding of emotions. The objective of this work is through a literature review: a) to illustrate the emotional development and education of individuals on the spectrum b) to present the findings of investigations c) to present and raise key concerns about the emotional intelligence of children spectrum of autism (d) raise questions about the development of educational methods aimed at enhancing the emotional development of individuals in the autism spectrum and thereby the development of  social feelings their maternal skills.

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Autism, Expression, and Understanding of Emotions: Literature Review

In recent years, RFID technology has been widely used in 
various sectors, such as in-education, transportation, agriculture, animal 
husbandry, store sales and other sectors. RFID utilization in education is 
student attendance monitoring system, by using Internet of Things (IoT) 
and Cloud technology, it will produce a real time attendance monitoring 
system that can be accessed by various parties, such as lecturer, campus 
administration and parents. With this monitoring system if there are 
students who are not present can be immediately discovered and can be 
taken immediate action and the learning process can run smoothly.

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RFID and IOT for Attendance Monitoring System

Objectives: The proposed work is to classify breast cancer with few attributes. Reducing the attributes reduces the time, so that the patient need not wait for result for a long time. For classification, the user friendly environment is created. The user can enter the details of the patient such as Clumpthickness, Uniformity in cell size etc., and the result is classified as benign or malignant. Statistical analysis: Variable selection is done by one of the variable reduction algorithm called Linear Discriminant Analysis (LDA). LDA is one of the statistical method. The dataset is passed to LDA function repeatedly and the combination of variables which gave the good accuracy is selected. The variables that are selected by using LDA are used in classifying breast cancer. Findings: This application is created to find whether the given record is benign or malignant tumor. In this proposed work, the dataset from UCI repository for breast cancer detection is used. There are many other works done for finding breast cancer risk, diagnosing breast cancer etc., and there may be at least ten variables used for classification which may be time consuming. But in this proposed work, only four are used and it gave the accuracy of up to 96%. Hence this may be the first step or idea for detecting breast

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An Automated Technique using Gaussian Naïve Bayes Classifier to Classify Breast Cancer

Attendance monitoring system is an essential element in all organizations and is considered as an integral part of efficient organizational information systems. Most of the time it so happens that if any stakeholder wants to meet concerned in charge employees they need to take an appointment and then consult them. In situations where meeting becomes inevitable there is no system available to trace them with in the vicinity of the organization. The current research has been undertaken to address this issue, which is to know whereabouts of in charge employees during organizational timings. The proposed system works on RFID technology where in, as the concerned employees enters in the organization vicinity, his RFID will be validated with a check from the organizational database, if he/she is indeed an employee of the respective organization he/she will be allowed to enter into workspace and his attendance will be updated in the respective organizational database. Their status will be displayed on the screens available outside the workspace; apart from it the details will also be updated on cloud and the stakeholders willing to meet their respective personnel can easily see from the respective web-portal and can make an appointment based on their availability.

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Secure Smart and Remote Multipurpose Attendance Monitoring System

Three-dimensional (3D) point clouds are important for many applications, including object tracking and 3D scene reconstruction. Point clouds are usually obtained from laser scanners, but their high cost impedes the widespread adoption of this technology. We propose a method to generate the 3D point cloud corresponding to a single red–green–blue (RGB) image. The method retrieves high-quality 3D data from two-dimensional (2D) images captured by conventional cameras, which are generally less expensive. The proposed method comprises two stages. First, a generative adversarial network generates a depth image estimation from a single RGB image. Then, the 3D point cloud is calculated from the depth image. The estimation relies on the parameters of the depth camera employed to generate the training data. The experimental results verify that the proposed method provides high-quality 3D point clouds from single 2D images. Moreover, the method does not require a PC with outstanding computational resources, further reducing implementation costs, as only a moderate-capacity graphics processing unit can efficiently handle the calculations.

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Generative Adversarial Network-Based Method for Transforming Single RGB Image Into 3D Point Cloud

If we talk about the current scenario of our education system than we found that we have lot of technologies to use but still we are following the traditional system. We if we talk about the attendance system in universities and schools, lecturers did that work manually. Lecturers took the attendance and update it manually in the database. If we talk about the technology than we found that there are lot of tools to use and reduce the burden of lectures. Using RFID is the one example of that. We if combine the RFID and IOT (Internet of Things) than we can do it automatically and there is no need to do it by lectures. Here we are planning to use the Cloud as storage for better performance. Using IOT and Cloud we can access it from anywhere and anytime which will provide us the better proficiency and flexibility.

An automatic attendance monitoring system using RFID and IOT using Cloud

In recent times Autism Spectrum Disorder (ASD) is picking up its force quicker than at any other time. Distinguishing autism characteristics through screening tests is over the top expensive and tedious. Screening of the same is a challenging task, and classification must be conducted with great care. Machine Learning (ML) can perform great in the classification of this problem. Most researchers have utilized the ML strategy to characterize patients and typical controls, among which support vector machines (SVM) are broadly utilized. Even though several studies have been done utilizing various methods, these investigations didn't give any complete decision about anticipating autism qualities regarding distinctive age groups. Accordingly, this paper plans to locate the best technique for ASD classi-fication out of SVM, K-nearest neighbor (KNN), Random Forest (RF), Naive Bayes (NB), Stochastic gradient descent (SGD), Adaptive boosting (AdaBoost), and CN2 Rule Induction using 4 ASD datasets taken from UCI ML repository. The classification accuracy (CA) we acquired after experimentation is as follows: in the case of the adult dataset SGD gives 99.7%, in the adolescent dataset RF gives 97.2%, in the child dataset SGD gives 99.6%, in the toddler dataset Ada-Boost gives 99.8%. Autism spectrum quotients (AQs) varied among several sce-narios for toddlers, adults, adolescents, and children that include positive predic-tive value for the scaling purpose. AQ questions referred to topics about attention to detail, attention switching, communication, imagination, and social skills.

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A Machine Learning Way to Classify Autism Spectrum Disorder

Ciphertext-Policy Attribute based Encryption (CP-ABE) is a widely adopted access control technique in cloud computing. It offers “one to many user access services” which forms the basis of the point to multipoint communication systems. In such type of technique, the number of user attributes increases with the increasing number of data users, which complicates the process of user attribute management. The paper defines a scalable and efficient CP-ABE scheme that groups users in a relative order depending upon their common attributes and stores it in the form of user attribute relationship table over the cloud server. As the users and their attributes are stored in a relative order on the cloud server, the process of user attribute management is made easier. The experimental results show that the proposed system has better efficiency and performance measure than existing techniques comparatively.

Scalable and efficient attribute based encryption scheme for point to multi-point communication in cloud computing

Background/Objectives: The goal of screening exams for breast cancer is to find cancer before they start to cause symptoms. The objective of this paper is to propose a diagnosis tool to detect breast cancer at the earlier stage. Methods/ Statistical Analysis: The proposed diagnosis tool for detecting breast cancer involves the various processes which includes wavelets. The diagnosis tool is proposed to execute multiscalar contrast enhancement at different wavelet scales. The process is continued with segmentation techniques which combine information of the core image structure and the boundaries. The approach confirms the use of a single algorithm for image processing and detects both microclassification and masses in the breast image. Findings: An algorithm has been proposed for the enhancement and denoising of an image with the help of processing through wavelets. Moreover, in case of microcalcification, a general method is described. The function is to execute multiscalar contrast enhancement at different wavelets scales. On the other hand mass detection means a segmentation of image where mathematical morphology is mixed with dyadic wavelet information. Segmentation technique is divided in two different groups: 1. Discrete contour model and 2. Region growing. The proposed system was able to identify the masses and micro classification without any biopsy and other process. In this paper we have proposed a technique that introduces segmentation combining efficiently both the information of the core of image structure and the boundaries. This approach confirms the use of a single algorithm for image processing and detects both microcalcification and masses. Application/Improvements: The proposed diagnosis tool is also used for enhancing tiny features which can be still accurate in cancer detection. Further the algorithm can be used to classify the breast image as malignant or benign.

An Approach for Detecting Breast Cancer using Wavelet Transforms

The recent development with respect to Information and Communication Technology (ICT) has a very high impact on the social well-being, economic-growth as well as national security. The ICT includes all the recent technologies like computers, mobile-devices and networks. This also includes few people who have the intent to attack maliciously and they are generally called as network intruders, cybercriminals, etc. Confronting these detrimental cyber activities has become the highest priority internationally and hence the focused research area. For this kind of confront, anomaly detection plays a major role. This is an important task in data analysis which helps in detecting these kinds of intrusions. It helps in identifying the abnormal patterns in various domains like finance, computer networks, human behaviour, gene expression etc. This paper focuses on detecting the abnormalities in the telecommunication domain using the Call Detail Records (CDR). The abnormalities are identified using the clustering techniques namely k-means clustering, hierarchical clustering and PAM clustering. The results obtained are discussed and the clustering technique which is suited better in identifying the anomaly accurately is suggested.

S. L. Aarthy

Papers

An automatic attendance monitoring system using RFID and IOT using Cloud

A Machine Learning Way to Classify Autism Spectrum Disorder

Scalable and efficient attribute based encryption scheme for point to multi-point communication in cloud computing

An Approach for Detecting Breast Cancer using Wavelet Transforms

Big Data Analysis for Anomaly Detection in Telecommunication Using Clustering Techniques