Secure Data Sharing Platform for Portable Social Networks with Power Saving Operation

Several subscribing and content sharing services are largely personalized with the growing use of mobile social media technology. The end user privacy in terms of social relationships, interests and identities as well as shared content confidentiality are some of the privacy concerns in such services. The content is provided with fine-grained access control with the help of attribute-based encryption (ABE) in existing work. Decryption of privacy preserving content suffers high consumption of energy and data leakage to unauthorized people is faced when mobile social networks share privacy preserving data. In the mobile social networks, a secure proxy decryption model with enhanced publishing and subscribing scheme is presented in this paper as a solution to the aforementioned issues. The user credentials and data confidentiality are protected by access control techniques that work on privacy preserving in a self-contained manner. Keyword search based public-key encryption with ciphertext policy attribute-based encryption is used in this model. At the end users, ciphertext decryption is performed to reduce the energy consumption by the secure proxy decryption scheme. The effectiveness and efficiency of the privacy preservation model is observed from the experimental results.

Review of Internet of Wearable Things and Healthcare based Computational Devices

Wearable computing have variety of applications in healthcare ranging from muscle disorders to neurocognitive disorders, Alzheimer’s disease, Parkinson’s disease, and psychological diseases, such as cardiovascular diseases, hypertension and so on. Different types of wearable computing devices are used, for example, bio fluidic-place on wearables, textile-place on wearables, and skin-place on wearables including tattoo place on wearables. In drug delivery systems, the wearable computing systems have shown promising developments, increasing its use in personalized healthcare. Wearable contain experiments, which need to be addressed before their consumerist as a fully customized healthcare system. Distinct types of wearable computing devices currently used in healthcare field are reviewed in this paper. Based on various factors, the paper provides an extensive classification of wearable computing devices. Additionally, limitations, current challenges and future perspective in health care is reviewed.

Design of Deep Learning Algorithm for IoT Application by Image based Recognition

The Internet of Things (IoT) is an ecosystem comprised of multiple devices and connections, a large number of users, and a massive amount of data. Deep learning is especially suited for these scenarios due to its appropriateness for "big data" difficulties and future concerns. Nonetheless, guaranteeing security and privacy has emerged as a critical challenge for IoT administration. In many recent cases, deep learning algorithms have proven to be increasingly efficient in performing security assessments for IoT devices without resorting to handcrafted rules. This research work integrates principal component analysis (PCA) for feature extraction with superior performance. Besides, the primary objective of this research work is to gather a comprehensive survey data on the types of IoT deployments, along with security and privacy challenges with good recognition rate. The deep learning method is performed through PCA feature extraction for improving the accuracy of the process. Our other primary goal in this study paper is to achieve a high recognition rate for IoT based image recognition. The CNN approach was trained and evaluated on the IoT image dataset for performance evaluation using multiple methodologies. The initial step would be to investigate the application of deep learning for IoT image acquisition. Additionally, when it comes to IoT image registering, the usefulness of the deep learning method has been evaluated for increasing the appropriateness of image recognition with good testing accuracy. The research discoveries on the application of deep learning in the Internet of Things (IoT) system are summarized in an image-based identification method that introduces a variety of appropriate criteria.

Big Data Analytics for Improved Risk Management and Customer Segregation in Banking Applications

While the phrase Big Data analytics is not only applicable for a certain realm of technology, diverse business segments like banking also benefit from the use of advanced mathematical and statistical models like predictive analysis, artificial intelligence, and data mining. If it is a query that is data volume generated in a bank or any financial institution is huge, it is absolutely a yes. As per the recent survey, it is observed that banks worldwide aren't just concentrating on improving the asset quality and fulfilling regulatory compliance but on the lookout for a digital convergence strategy to reach customers effectively in delivering services and products. As most of the data generated in internet banking and ATM transactions are unstructured accounting around for 2.5 quintillion bytes useful for fraud detection, risk management, and customer satisfaction, the use of trending Big Data Analytics methodology can be used to tackle the challenges and competition among banks. There are surplus advantages of Big Data strategy in the banking field and in this paper, we have made an analysis over Big Data Analytics on banking applications and their related concepts.

Analysis of Serverless Computing Techniques in Cloud Software Framework

This paper describes briefly about the concept of serverless cloud computing model, its usage in IT industries and its benefits. In the traditional model the developer is responsible for resource allocation, managing servers and owning of servers, and it included three models based upon the service such as IaaS, PaaS and SaaS. In IaaS (Infrastructure as a Service) the content storage and accessing of network is carried out by the cloud provider, SaaS (Software as a Service) here different software’s are provided to the user as a service, PaaS (Platform as a Service), the developer gets access to certain services for carrying out organizing process and run it accordingly. In serverless cloud computing, the developer need not worry about owning, management, and maintenance of servers as it is carried out by the cloud service provider. Hence by using this model, the time that is needed for a system to reach the market is very much reduced and is cost effective. Serverless architecture includes three categories namely, AWS Lambda, Azure, and Google cloud. It also includes certain challenges such as it cannot be used in the case where a process takes longer time to run and it is discussed below in this paper.

Finding an optimal distance of social distancing for COVID 19

Social distancing is a non-pharmaceutical infection prevention and control approach that is now being utilized in the COVID-19 scenario to avoid or restrict the transmission of illness in a community. As a consequence, the disease transmission, as well as the morbidity and mortality associated with it are reduced. The deadly coronavirus will circulate if the distance between the two persons in each site is used. However, coronavirus exposure must be avoided at all costs. The distance varies due to different nations' political rules and the conditions of their medical embassy. The WHO established a social distance of 1 to 2 metres as the standard. This research work has developed a computational method for estimating the impact of coronavirus based on various social distancing metrics. Generally, in COVID – 19 situations, social distance ranging from long to extremely long can be a good strategy. The adoption of extremely small social distance is a harmful approach to the pandemic. This calculation can be done by using deep learning based on crowd image identification. The proposed work has been utilized to find the optimal social distancing for COVID – 19 and it is identified as 1.89 meter. The purpose of the proposed experiment is to compare the different types of deep learning based image recognition algorithms in a crowded environment. The performance can be measured with various metrics such as accuracy, precision, recall, and true detection rate.

Hybrid Framework on Automatic Detection and Recognition of Traffic Display board Signs

Automatically identifying traffic signs is a challenging and time-consuming process. As the academic community pays more attention to traditional algorithms for vision-based detection, tracking, and classification, three main criteria drive the investigation, they are detection, tracking, and classification. It is capable of performing detection and identification operations to minimize traffic accidents and move towards autonomous cars. A novel method proposed in this paper is based on moment invariants and neural networks for performing detection and recognition with classification, and it also includes automatic detection and identification of traffic signs and traffic board text that uses colour segmentation. Aside from the proposed structure, it is also required to identify the potential graphic road marking with text. This research article contains two algorithms, which are used to accurately classify the board text. The detection through image segmentation and recognition can be done by using the CNN algorithm. Finally, the classification is performed by the SVM framework. Therefore, the proposed framework will be very accurate and reliable with high efficiency, which has been proven in many big dataset applications. The proposed algorithm is tested with various datasets and provided good identification rate compared to the traditional algorithm.

A Novel Signal Processing Based Driver Drowsiness Detection System

Drowsiness is a major cause of vehicle collisions and it most of the cases it may cause traffic accidents. This condition necessitates the need to develop a drowsiness detection system. Generally, the degree of sleep may be assessed by the number of eye blinks, yawning, gripping power on the steering wheel, and so on. These methods simply compute the actions of the driver. Henceforth, this research work proposes a Brain Computer Interface (BCI) technology to evaluate the mental state of brain by utilizing the EEG signals. Brain signal analysis is the main process involved in this project. Depending on the mental state of the drivers, the neurons pattern differs. Different electric brain signals will be produced in every neurons pattern. The attention level of brain signal varies from general state when the driver is sleeping mentally with eyes open. Various frequency and amplitude of EEG based brain signal are collected by using a brain wave sensor and the attention level is analyzed by using a level splitter section to which the brain signals are made into packets and transmitted through a medium. Level splitter section (LSS) figures out the driver’s state and provides a drowsiness alarm and retains the vehicle in a self-controlled mode until the driver wakes up. Additionally, this research work will provide an alert to the users and control the vehicle by employing the proposed model.

Hyperspectral Image Processing in Internet of Things model using Clustering Algorithm

With the advent of technology, several domains have b on Internet of Things (IoT). The hyper spectral sensors present in earth observation system sends hyper spectral images (HSIs) to the cloud for further processing. Artificial intelligence (AI) models are used to analyse data in edge servers, resulting in a faster response time and reduced cost. Hyperspectral images and other high-dimensional image data may be analysed by using a core AI model called subspace clustering. The existing subspace clustering algorithms are easily affected by noise since they are constructed based on a single model. The representation coefficient matrix connectivity and sparsity is hardly balanced. In this paper, connectivity and sparsity factors are considered while proposing the subspace clustering algorithm with post-process strategy. A non-dominated sorting algorithm is used for that selection of close neighbours that are defined as neighbours with high coefficient and common neighbours. Further, pruning of useless, incorrect or reserved connections based on the coefficients between the close and sample neighbours are performed. Lastly, inter and intra subspace connections are reserved by the post-process strategy. In the field of IoT and image recognition, the conventional techniques are compared with the proposed post-processing strategies to verify its effectiveness and universality. The clustering accuracy may be improved in the IoT environment while processing the noise data using the proposed strategy as observed in the experimental results.