Diabetic retinopathy is a diabetes complication that effects eyes. It disrupts the vasculature of the sensitive tissue present at the back of the eye. If this complication is untreated it may lead to blindness. The aim of this work is to train a model that efficiently predicts diabetic retinopathy. Machine learning techniques like Decision tree, Random forest, Adaptive boosting and Bagging are used as primary algorithms to train predictive models. An algorithm namely ‘Support Vector Machine using Gaussian kernel for retinopathy prediction’ is proposed in this work. The proposed algorithm is compared with the primary algorithms based on five evaluation metrics namely accuracy, Youden’s J index, concordance, Somers’ D statistic and balanced accuracy. From the results obtained the proposed algorithm obtained better values for all considered evaluation metrics. Thus the use of SVM with Gaussian kernel is proposed to be used for prediction of diabetic retinopathy.

Semantic Relatedness computation has been a fundamental as well as an essential step for domains like Information Retrieval, Natural Language Processing, Semantic Web, etc. Many techniques for Semantic Relatedness calculation in a single domain have been proposed. However, these techniques give inappropriate results for the massive multidomain dataset because they provide a relation between concepts across different domains, which are not related to each other. Their similarities should be minimized. In this paper, a novel method, "modified Balanced Mutual Information(MBMI)," to calculate the semantic relatedness of multidomain data has been proposed. In this proposed method, to get semantic relatedness, concepts are extracted, followed by a fuzzy vector from a given corpus. A comparison of the proposed method with other existing methods has been performed. We used medical and computer science articles as our dataset. The proposed method shows better results for multidomain data.

This paper proposes a self-controllable super-resolution adaptation algorithm in drone platforms. The drone platforms are generally used for surveillance in target network areas. Thus, super-resolution algorithms which are for enhancing surveillance video quality are essential. In surveillance drone platforms, generating video streams obtained by CCTV cameras is not static, because the cameras record the video when abnormal objects are detected. The generation of streams is not predictable, therefore, this unpredictable situation can be harmful to reliable surveillance monitoring. To handle this problem, the proposed algorithm designs superresolution adaptation. With the proposed algorithm, the shallow model which is fast and low-performance will be used if the stream queue is near overflow. On the other hand, the deep model which is highperformance and slow will be used if the queue is idle to improve the performance of super-resolution.

Dyslexia is a medical disorder due to which children have difficulties in learning and reproducing the learnt concepts. In this context children are mostly considered to be not interested or negligent towards their studies. According to dyslexia association of India 10-15 % of children enrolled in schools suffer from some type of Dyslexia. Awareness on Learning difficulties and detection is a complicated. As detection of LD requires diverse features it requires proper guidance and intervention. The issues with explicit learning difficulties in kids have been a reason for worry to parents and educators. Its challenging for the teachers and educationalists to differentiate students with LD and other students. This paper mainly analysis of the paper titled “Diagnosis of Dyslexia using Computing Analysis“, Electroencephalogram (EEG) as a tool is used for understanding of brain process and related functions, Number of factors related to Dyslexia and “Power spectral density” is extracted using Gibson test for brain skills. And to identify differences in brain processing using EEG Technology in kids with dyslexia and non-dyslexic. Data sets are generated classifying Dyslexic and non dyslexic and were analysed using the K-means, Fuzzy and ANN classifiers. then the results obtained from these classifiers differentiate between the three different groups (dyslexic, non-dyslexic and disordered). And the next paper “Spatial Blockchain-based Secure Mass Screening Framework for Children with Dyslexia”, this paper was mainly focused in detection of symptoms of dyslexia at an age of 8-11 years, so that children can be assisted with various assisting tools and take technologic support so that children can take part in regular schools. This paper is based on Mobile Edge Computing, cloud computing.

Resource allocation is one of the major concern in cloud computing model. When several problems exists in rendering a useful resource allocator. In this research, a self adaptive resource allocation frame work based on machine learning is proposed for modelling and analysing the problem of multi-dimensional cloud resource. This novel self-adaptive resource allocation architecture consists of three stages, QoS prediction model, Improved Bat Algorithm (IBA) and Energy Efficient Model (EEM). The first one, the QoS prediction model, which depends on the same scale of system’s past events data, can attain a comparable accuracy with regard to QoS prediction. Secondly, an Energy Efficient Model, which is based on Modified Clonal Selection Algorithm (MCSA) is introduced for minimizing the energy depletion. Thirdly, a runtime decision-making algorithm that depends on improved bat algorithm can rapidly decide on a suitable function for resource allocation.

The control strategy for wind turbine is very important an equipage that diverts the kinetic energy of the wind to electrical power. The rotation speed control of wind turbine is necessary for highest degree energy capture and pitch angle advanced control planning depending on traditional PID controller and fuzzy logic adaptive PID controller. Input variables of fuzzy logic controller (FLC) are selected to give smooth control of pitch angle which in turns will adjust the speed of wind turbine at reference level. A very small adaptation in pitch angle has influence on the ancestry of obtainable energy, torque and output power of the grid. A modeling of self-excitation induction generator (SEIG) 1KW wind turbine is achieved by Matlab/Simulink package and all modeling equations are studied. The effect of pitch angle on speed of the wind turbine is studied and a comparison between traditional PID controller and fuzzy logic adaptive PID controller was made.

Internet of Medical Things (IOMT) or healthcare Internet of Thing (IOT) is a collection of medical devices and applications that connect the various healthcare systems based on IT through online computer networks. The devices are connected the output Wi-Fi allowing the M-M (machine to machine) communication through IOMT. IOMT provides various applications including remote patient monitoring (RPM), wearable fitness bands (WFB), hospital beds that are sensor equipped and many-more. IOMT allows communication of medical devices without the intervention of human. The widespread deployment of IOMT faces challenges of security, privacy, connectivity as well as compatibility. IOMT based systems suffer from various security breaches and hacking attacks. Traditional security measures of login/password do not compliment IOT based systems. So this research chapter proposes a novel Biometric Based Internet of Medical Things (BBIOMT) technology that is unique and spoof free. The BBIOMT approach eliminates shortcomings of traditional password schemes and offer a much superior authentication solution.

INTRODUCTION: Many organizations of health care have recognized that quality of service can be improved by maintaining e-records of patient’s reports, medical histories, surgeries recordings, etc. over the multimedia cloud servers. But, the data breach is always a matter of stake for an organization as well as for the patients.

OBJECTIVES: This work has considered a qualitative scenario related to multimedia e-content management for a multiforte hospital’s cloud server.

METHODS: An End-to-End Encryption with the cryptographic algorithm is applied along with an access control framework for secure transmission and storage of e-records.

RESULTS: The results are presented in the form of bar graphs for the time taken to perform encryption/decryption of various media files and combo graphs for different scenarios of access control.

CONCLUSION: This can provide prevention from many attacks/threats, ensures authentication and privacy along with limiting the server usage and cost.

INTRODUCTION: Individuals with visual impairments face a variety of challenges in their daily lives, from daily activities to physical world navigation. One of the biggest challenges is the ability to travel around safely and independently. This challenge is complicated and stressed to the visually impaired as the inability to perform obstacles or ground plane checking will result in severe injury or even death.

OBJECTIVES: This work aims to prove the outdoor performance of the developed solution in detecting and recognising the frontal ground plane conditions.

METHODS: The proposed model uses a LiDAR module as a distance-measuring tool to perform ground plane checking.

RESULTS: In the selected outdoor path-based scenarios, the ground plane checking system succeeded in achieving an overall recognition rate of 93.10%, with an overall false positive rate of 2.72% and average false negative rate of 4.25%.

CONCLUSION: Overall, the findings showed the ability of the proposed model to provide effective frontal ground plane checking for the visually impaired.

The research covers the issues of complexity in formalization of complex control objects using conventional methods and algorithms, considers different approaches to modeling of these objects, as well as reviews the features of different methods for synthesis of intelligent control systems using multi-cascade fuzzy control. It also represents a tracking drive model based on two-stage system. The structural solution for such an intelligent system includes four fuzzy controllers, three of which use Mamdani inference algorithms, and the fourth one represents an intelligent switching device with Sugeno inference algorithm. The article demonstrates the use of multi-cascade control technology for implementation of spacial membership functions.