Patient's Feedback Platform for Quality of Services via “Free Text Analysis” in Healthcare Industry

Data analysis of social media posting continues to offer a huge variety of information about the health situation faced by an individual. Social networking or social media websites provide us a wealth of information generated by users in a variety of domains, that generated information are unstructured and unlabeled and are not captured in an exceedingly systematic manner, as info generated is not humanly possible to process due to its size. One traditional way of collecting patients experience is by conducting surveys and questionnaires, as these methods ask fixed questions and are expensive to administer. In this paper, a patient feedback platform (PFP) using free text sentiment analysis is developed to computationally identify and categorize the polarity expressed in a piece of text. Six machine learning latest algorithms have been used as key evaluation for evaluating accuracy of the developed (PFP) model. Results achieved have shown 88 % accuracy on the basis of which it is recommended that developed (PFP) patient feedback platform could be used to improve E-health care services indeed

Expert System for Handling Congestion in Opportunistic Network

Opportunistic networks are one of the most popular categories of mobile Adhoc networks. These types of networks usually have to deal with intermittent disconnected path from source to the destination most of the time therefor suffers from numerous key challenges for successful custody transfer to be done between encounter nodes. There are numerous challenges faced due to such disconnectivity within the network but one of the key challenges is that which intermediate node will be selected as custody transfer and till how much time custodian node can carry messages as it will have storage constraints until a destination node is found. In this research study the same problem has been addressed via proposing a rule based expert system that will have better coordination among intermediate nodes for the transmission with less drop and better delivery ratio. Proposed technique has been tested and validated via ONE simulator and compared with MaxProp protocol

Particle Swarm Based Hyper-Heuristic For Tackling Real World Examinations Scheduling Problem

Abstract: Examinations scheduling is highly significant institutes to finalize the academic events of a semester. The is largely noticed by researchers of various domains as well. In this research work, PSO is involved to manage the overall hyper-heuristic solving method. The particle contains an ordered set of low level heuristics, each implement one by one on identical tentative solutions so the effectiveness of their order placement and selection mechanism can be measured by PSO. Consequently, each new generation gets converged set of particles. The partial but improved result promotes and furnishes to successive process whereas the substandard may be discarded. The research work has produced quite satisfactory and applicable results at end

Bayesian Analysis of Inverted Kumaraswamy Mixture Model with Application to Burning Velocity of Chemicals

Burning velocity of different chemicals is estimated using a model from mixed population considering inverted Kumaraswamy (IKum) distribution for component parts. Two estimation techniques maximum likelihood estimation (MLE) and Bayesian analysis are applied for estimation purposes. BEs of a mixture model are obtained using gamma, inverse beta prior, and uniform prior distribution with two loss functions. Hyperparameters are determined through the empirical Bayesian method. An extensive simulation study is also a part of the study which is used to foresee the characteristics of the presented model. Application of the IKum mixture model is presented through a real dataset. We observed from the results that Linex loss performed better than squared error loss as it resulted in lower risks. And similarly gamma prior is preferred over other priors

A Subjective Logical Framework-Based Trust Model for Wormhole Attack Detection and Mitigation in Low-Power and Lossy (RPL) IoT-Networks

The increasing use of wireless communication and IoT devices has raised concerns about security, particularly with regard to attacks on the Routing Protocol for Low-Power and Lossy Networks (RPL), such as the wormhole attack. In this study, the authors have used the trust concept called PCC-RPL (Parental Change Control RPL) over communicating nodes on IoT networks which prevents unsolicited parent changes by utilizing the trust concept. The aim of this study is to make the RPL protocol more secure by using a Subjective Logic Framework-based trust model to detect and mitigate a wormhole attack. The study evaluates the trust-based designed framework known as SLF-RPL (Subjective Logical Framework-Routing Protocol for Low-Power and Lossy Networks) over various key parameters, i.e., low energy consumption, packet loss ratio and attack detection rate. The achieved results were conducted using a Contiki OS-based Cooja Network simulator with 30, 60, and 90 nodes with respect to a 1:10 malicious node ratio and compared with the existing PCC-RPL protocol. The results show that the proposed SLF-RPL framework demonstrates higher efficiency (0.0504 J to 0.0728 J out of 1 J) than PCC-RPL (0.065 J to 0.0963 J out of 1 J) in terms of energy consumption at the node level, a decreased packet loss ratio of 16% at the node level, and an increased attack detection rate at network level from 0.42 to 0.55 in comparison with PCC-RPL

A Lightweight Post-Quantum Lattice-Based RSA for Secure Communications

Conventional RSA algorithm, being a basis for several proposed cryptosystems, has remarkable security laps with respect to confidentiality and integrity over the internet which can be compromised by state-of-the-art attacks, especially, for different types of data generation, transmission, and analysis by IoT applications. This security threat hindrance is considered to be a hard problem to solve on classical computers. However, bringing quantum mechanics into account, the concept no longer holds true. So, this calls out for the modification of the conventional pre-quantum RSA algorithm into a secure post-quantum cryptographic-based RSA technique. In this research, we propose a post-quantum lattice-based RSA (LB-RSA) for IoT-based cloud applications to secure the shared data and information. The proposed work is validated by implementing it in 60-dimensions. The key size is about .152\times 10^{5}$ -bits and generation time is 0.8 hours. Furthermore, it has been tested with AVISPA, which confirms security in the presence of an intruder. Moreover, the proposed LB-RSA technique is compared with the existing state-of-the-art techniques. The empirical results advocate that the proposed lattice-based variant is not only safe but beats counterparts in terms of secured data sharing

A Survey on Modern Cloud Computing Security over Smart City Networks: Threats, Vulnerabilities, Consequences, Countermeasures, and Challenges

Cloud Computing (CC) is a promising technology due to its pervasive features, such as online storage, high scalability, and seamless accessibility, in that it plays an important role in reduction of the capital cost and workforce, which attracts organizations to conduct their businesses and financial activities over the cloud. Even though CC is a great innovation in the aspect of computing with ease of access, it also has some drawbacks. With the increase of cloud usage, security issues are proportional to the increase. To address these, there has been much work done in this domain, whereas research work considering the growing constrained applications provided by the Internet of Things (IoT) and smart city networks are still lacking. In this survey, we provide a comprehensive security analysis of CC-enabled IoT and present state-of-the-art in the research area. Finally, future research work and possible areas of implementation and consideration are given to discuss open issues

Convolutional Neural Network Based Vehicle Classification in Adverse Illuminous Conditions for Intelligent Transportation Systems

In step with rapid advancements in computer vision, vehicle classification demonstrates a considerable potential to reshape intelligent transportation systems. In the last couple of decades, image processing and pattern recognition-based vehicle classification systems have been used to improve the effectiveness of automated highway toll collection and traffic monitoring systems. However, these methods are trained on limited handcrafted features extracted from small datasets, which do not cater the real-time road traffic conditions. Deep learning-based classification systems have been proposed to incorporate the above-mentioned issues in traditional methods. However, convolutional neural networks require piles of data including noise, weather, and illumination factors to ensure robustness in real-time applications. Moreover, there is no generalized dataset available to validate the efficacy of vehicle classification systems. To overcome these issues, we propose a convolutional neural network-based vehicle classification system to improve robustness of vehicle classification in real-time applications. We present a vehicle dataset comprising of 10,000 images categorized into six-common vehicle classes considering adverse illuminous conditions to achieve robustness in real-time vehicle classification systems. Initially, pretrained AlexNet, GoogleNet, Inception-v3, VGG, and ResNet are fine-tuned on self-constructed vehicle dataset to evaluate their performance in terms of accuracy and convergence. Based on better performance, ResNet architecture is further improved by adding a new classification block in the network. To ensure generalization, we fine-tuned the network on the public VeRi dataset containing 50,000 images, which have been categorized into six vehicle classes. Finally, a comparison study has been carried out between the proposed and existing vehicle classification methods to evaluate the effectiveness of the proposed vehicle classification system. Consequently, our proposed system achieved 99.68%, 99.65%, and 99.56% accuracy, precision, and F1-score on our self-constructed dataset