Search

This paper discusses different techniques and specialized procedures which can be used to effectively protect data from the owner to the cloud and then to the user. The next step involves categorizing the data using three encryption parameters provided by the user, which are Integrity, Availability, and Confidentiality (IAC). The data is secured through various methods such as SSL and MAC protocols to ensure data integrity checks, searchable encryption, and splitting the data into three parts for cloud storage. Dividing the data into three portions not only enhances security but also facilitates easier access. Access to the encrypted data requires the user to provide the login information and password of the owner. This paper also studies critical security issues like unauthorized servers, brute force attacks, threats from cloud service providers, and loss of user identity and password.

As a result of the new coronavirus outbreak's global expansion and the respiratory diseases it causes in people, COVID-19 has become a major global pandemic. The only way to stop this spread, according to the World Health Organization, is to increase testing and isolate those who are sick. In the meantime, the clinical testing that is now being used is time-consuming. Systems for remote diagnosis may be useful in this situation. The healthcare industry generates a large quantity of data, which we process using certain machine learning algorithms to identify the presence of illness. Several IoT-enabled sensors are accessible to detect the patient's entire information about a specific person's behavior, human anatomy, and physiology. The information gathered by the sensors is sent to the internet and linked to a cloud server. Physicians may access patient records stored on the web server and preserve them there, giving them access to the information from anywhere on the globe. Any unexpected change in a patient's data, while they are using the healthcare system, will unavoidably result in the patient's data being immediately uploaded to the appropriate doctor. In rural and distant places, this kind of healthcare system would be most beneficial. We proposed a novel butterfly-optimized multitemporal support vector machine (BO-MTSVM) approach to overcome the aforementioned problems. The suggested technique performs better than other current methods in COVID monitoring, according to simulation data.

The power take off device used in Oscillating Water Column (OWC) type wave energy converters is mainly a bidirectional turbine: either an impulse turbine or a reaction (Wells) turbine. Both these turbines have been studied numerically and experimentally to understand the influence of different parameters. However, an experimental analysis is more realistic in nature compared to the actual operation of the turbine. Such an experimental facility needs a system to create a bidirectional airflow similar to what happens in an OWC chamber. In this chapter, the experimental facility at the Wave Energy & Fluids Engineering Lab, IIT Madras, India, has been discussed in detail. The experimental procedure and the required instrumentations are explained meticulously. Finally, two case studies are presented: one with the impulse turbine and another with Wells turbine tested under sinusoidal flow conditions. The tests were carried out for no-load condition as well as with different resistive loading. The significant results from the experiments are mentioned with detailed explanation.

Energy from the ocean is one of the least exploited renewable and sustainable energy sources, and possesses an enormous amount of untapped energy. The harnessing of energy from the ocean can be accomplished by several methods such as salinity and temperature gradient, waves, currents, and tidal variations. Amidst all, the ocean waves are a highly promising form of energy. Ocean waves have a tremendous amount of energy that can be harvested to meet the increasing energy demand. There are several challenges in wave energy conversion, which include the corrosive environment, the constraints of the converters, untuned device performance for the incoming waves, and irregularities in waves and air velocity. These challenges affect the overall system performance and decrease the overall conversion efficiency. The solution for many of these problems is to tune the device according to the wave climate and a controlled operation of the system. As waves differ in height and period, an optimal control method can help improve the efficiency, performance, and power absorption of a wave energy converter (WEC). This chapter presents a review of various control schemes applied to wave energy devices to achieve higher efficiency. The actual efficiency of WEC in real-time is very less compared to theoretical efficiency which is obtained around 70–80%. This large efficiency gap is due to various stages involved in the wave energy conversion process. To achieve good overall efficiency an optimal design and control techniques need to be applied at different stages of WEC.

Aim: A study on effectiveness and success factors of educational inhaler technique intervention in asthma and COPD. Objectives: To identify the percentage of common errors done by people who use inhalers. To identify the risk factors associated with asthma and COPD. Method: Data was collected from the subject’s face to face interview in a community setup and the lung capacities were measured using spirometer and peak flow meter; the values were noted and the subjects were asked to demonstrate their inhaler technique using placebo inhalers; numbers of correct and incorrect steps were noted. After a period of one month; the subjects were taken to follow up and measured the lung capacities and checked the inhaler steps; number of correct and incorrect steps were noted. Results: A total of 572 samples were collected and 500 were followed up, in which 300 had Asthma and 200 had COPD. Out of which 189 were female and 311 were male. Low education level was the single most important factor leading to incorrect technique. Formal training resulted in a statistically significant increase in the percentage of correct techniques for all the devices. For pMDI (54.01% vs. 91.03%, *p=0.005 before & after training respectively). For pMDI+Spacer (52.75% vs. 94.85%, *p=0.007 before & after training respectively). For Accuhaler (54.07% vs. 91.27%, *p=0.017 before & after training respectively). For Rotahaler (63.94 vs. 98.64%. *p=0.029 before & after training respectively). For Nebuliser (56.97% vs. 91.88%, *p=0.001 before & after training respectively. Conclusion: Proper education to patients on correct usage may not only improve control of the symptoms of the disease but might also allow dose reduction in long term. The number of subjects doing correct steps gradually increased after the pharmacist led intervention. Thus, proper counselling for the inhaler use in patients must be encouraged as it is directly linked to the quality of life of patient. Keywords: Asthma and COPD control inhaler devices, inhaler technique, pharmacist intervention, inhaler education.

Wave energy converters (WECs) face difficulties such as low operating range, low power output and, fluctuating power. A seamless synchronization among WEC components is required to get a better performance. For control system studies, the model should capture all the necessary dynamics involved in each conversion stages, however the interlinked complexity in each subsystem increases the computation time. This article presents a reduced order wave-to-wire (WTW) model of an oscillating water column (OWC) based WEC. The approach involves modeling of hydrodynamic and aerodynamic coupling of the capture chamber, aerodynamic and thermodynamic coupling inside the capture chamber, aerodynamic and rotor dynamic coupling in air turbine; and rotor dynamics and generator dynamics in the turbine generator coupling. The result shows that the model retains its fundamental dynamics and reduces the number of unknowns to describe the state space. The model indicates the correlation of each variable represented in the state space. The model predicted power output for different sea state. It also shows that the accuracy and the efficiency of the model are acceptable for OWC-WEC control system studies. The present model can be used as a time domain tool to design an effective control system for OWC device for different sea states, and the overall device performance can be improved significantly.

Marine energy is a major source of renewable energy and yet remains untapped. As the ocean covers about two-thirds of the Earth’s surface, it contains a large amount of energy. Energy can be extracted from the sea exploiting several phenomena, such as salinity gradient (energy from the difference in the salt concentration between seawater and fresh water), temperature gradient, waves, and ocean currents. Among these, ocean waves have tremendous potential as a source of renewable energy. The vast amount of of energy from the waves could meet an important part of the energy demand for humankind. Wave energy is enormous, reliable, and has higher energy density compared to wind power and solar power, and thus offers a good correlation between resource and demand. Waves also offer multiple locations for energy harvesting from the shoreline to deep water. There has been worldwide interest in harnessing this immense power from the ocean that can play a major part to solve our energy problems.

With the rapid growth of e-commerce, online product and service monitoring is becoming more and more established as an important source of information for both sellers and customers. Emotional surveys and comments for online review analysis are gaining more and more attention as such studies help use information from online reviews for potential economic impacts. Twitter is a widely used social networking site and a trusted source of public opinion. The success of the film can be predicted by analyzing the tweets and researching the impact of the film. This report discusses the application of emotional analysis and in-depth machine learning methods to understand the relationship between online movie reviews, and this story is used to generate revenue at the movie box office. In this paper, this work present a Intelligent Extensive Information Rich Transfer Network (IEIRTN). It is modeled with information from sentences (i.e., reviews) and aspects simultaneously. First, IEIRTN extract all aspects of the sentence. After obtaining the aspects, it utilize all data in the source domain and the target domain for training Multiview Light Semi Supervised Convolution Neural Network (MLSSCNN) classifier. To understand the predictive performance of this approach several performance metrics are used. The experimental result shows that the MLSSCNN offers a superior predictive effect than other classifier.

Emotional information in film commentary is very important for emotional analysis. An emotional analysis that focuses on classifying opinions into positive and negative classes according to an emotional glossary is a study. Most existing research focuses on word synthesis and user evaluation, while users' attitudes toward feedback are ignored. To consider this point, this paper uses an emotional analysis and in-depth learning approach to examine the relationship between online film reviews, and this point is used for movie box revenue efficiency. In this paper, this work present a 11 different types of Feature Dictionary. It is modeled with information from sentences (i.e., reviews) and aspects simultaneously. First, Feature Dictionary is created with all aspects of the sentence. After obtaining the aspects, it utilize all data in the source domain and the target domain for training Multiview Light Semi Supervised Convolution Neural Network (MLSSCNN) classifier. To understand the predictive performance of this approach several performance metrics are used. The experimental result shows that the MLSSCNN provides a superior predictive effect than other classifier.

With the advancement of data and communications technology, social media platforms and small news blogs serve as significant sources of data. In a small blogging forum, people can share their opinions, complaints, feelings and behaviors about the topic, current problems, and products. Emotional examination is an significant examination area in natural language processing that intends to target the emotion of the source material. Twitter is a well-liked stage where people around the globe can interrelate through user-produced messages. Data received from Twitter can give out as a primary source for many applications, together with event recognition, news recommendations as well as emergency supervision. In the categorization of emotions, recognition of suitable sub feature set acts an significant role. LIWC (Linguistic Inquiry and Word Count) is a research program for text examination to retrieve psychometric features from text documents. In this article this work present a psychometric method called the intelligent high performance automatic sentiment analysis model (IHPASAM) for Twitter emotion analysis. In this scheme, this work employed two main types of LIWC (linguistic processes along with psychological) as feature sets. To discover the predictive efficiency of dissimilar feature engineering systems, five supervised learning techniques (Naïve Bayes, logistic regression, k-nearest neighbor algorithm, support vector machines as well as convolution neural network) along with proposed Intelligent Deep Convolution Neural Network (IDCNN) are employed. Investigational outcome show that the ensemble feature sets provides a superior predictive efficiency than the individual set.

The Students of class 12 in India choose a program from a pool of programs offered by higher education institutions. It is proven that students end up choosing inappropriate programs as they lack proper guidance in program selection. As there are multiple program options for students after class 12, this research work is towards the recommendation of appropriate programs for the students by using collaborative filtering algorithms. This research work creates an implicit rating matrix using the academic information of the students. The implicit rating matrix is created by using the domain knowledge of the application. Augmenting the domain knowledge with different types of collaborative filtering like user-based and item-based using implicit rating matrix shows an improvement in the performance of the recommender systems.

The interest in wave energy converters (WECs) is increasing, the study of grid connection of WEC along with the control system has become inevitable. WEC such as an oscillating water column (OWC) device involves conversions in various physical domains, thus a model describing the conversions at each stage and coupling between them should be accurate yet simple enough to reduce the computation time involved. The already existing models do not include all the components of wave to wire conversion. This paper presents a wave to wire model for control system studies. The model reduction technique is used to create a dynamically equivalent model for any large systems have more interconnecting stages. The dynamics involved in conversion stages are hydrodynamic and aerodynamic coupling at the capture chamber, aerodynamic and thermodynamic coupling inside the capture chamber, aerodynamic and rotor dynamic coupling in air turbine; and rotor dynamics and generator dynamics in the turbine generator coupling. Thus, a wave to wire model is represented to capture all the dynamics involved. It is observed that the model retains its fundamental physics, improves the computation time and reduces the number of unknowns to describe the state-space of OWC system. The accuracy and efficiency of the model is investigated through various static and dynamic analyses and found acceptable for OWC-WEC control system studies.

An oscillating water column (OWC) wave energy device can use a bidirectional flow impulse turbine for energy extraction. The airflow velocity profiles during inhalation and exhalation of the OWC are asymmetric, and the flowrate at exhalation is higher. Hence, this article presents an optimized turbine to work for asymmetrical airflow conditions. The rotor blade setting angle (γ) was made asymmetric with a non-zero setting angle, and the guide vane shape was optimized. The unsteady Reynolds-averaged Navier-Stokes equations (URANS) were solved to get the objective function values for the Latin hypercube sampling (LHS) generated design space. A pseudo-sinusoidal velocity profile replicating the flow inside the OWC device was used for the transient simulations. A weighted average surrogate model generated the initial population for a hybrid genetic algorithm to produce optimum designs. The effect of turbine induced damping and starting characteristics were analyzed under irregular flow conditions obtained from the Pierson–Moskowitz wave spectrum. The optimized turbine resulted in better starting and running characteristics. Also, the mean efficiency of the optimized turbine was increased relatively by 9.5%.

Internet of things is rapidly expanding its reach into a multitude of real-world applications putting an onus on using automation to manage devices and processes. Real-world processes are event dri...

Applications such as street lights, parking meters, traffic light and congestion sensors, safety cameras, air quality sensors, weather sensors, remote personal healthcare monitors, and acoustic detection define the new world of “Smart Environments” that we live in. The technologies driving these applications are customized to the environment in which they run. The backhauling infrastructure to support each application is as diverse as the application itself. An abstraction which hides the complexities of such disparate systems presenting a unified view is desired. Integrated management platform aids in comprehensive management of the infrastructure. “Smartness” also means it is ready to use by just plug and play. A plug-in based implementation supports multiple vendor devices while a unified northbound controller ensures minimal vendor lock for the applications running on the top.

Learning Analytics is a process to analyze the learners which improves the educational performance. Learning Analytics also helps the higher educational institutions to improve the educational practices and techniques. This paper provides a detailed survey of the current research activities conducted in the education system. The review of Learning Analytics is based on some of existing Learning Analytics applications, purpose of the Learning Analytics in the education system and the type of students. The review based on various tools, techniques and data collection methods used to implement the Learning analytics is discussed in this paper. KeywordsAnalytics, tools, techniques

Data mining is the way of extracting the useful information, patterns from large volume of information by using various techniques. It is a powerful technology with great potential to help businesses to make full use of the available data for competitive advantages. This paper discusses various machine learning techniques and the detailed processes of Knowledge Discovery in Databases (KDD). This study also focus on various DM/ML approaches such as Classification, Clustering and Regression and discuss different types of each approach with its advantages and disadvantages.

Internet of Things (IoT) encompasses numerous old and new technologies under its umbrella; which means there is a huge diversity in types of devices, protocols and mechanisms that are to be supported. Things by themselves need to inter-network and collaborate to provide the necessary level of service expected out of them, working in isolation does not help in many cases. Interoperability issues have been attempted to be solved in broadly five different ways — publishing standards, reference architectures and frameworks, defining protocols and media-type standards and by using abstract interface definition languages and semantic ontologies. An interoperable environment requires a well-established data-model to provide properties, behavior and message formats and a framework to support selecting the right protocol along with an ontology indicating what the data means based on the context. Application layer is the primary interface for interacting with the systems and services; hence it makes it important to provide an interoperable environment at this layer enabling dynamic composition and rapid development of new services. IoT requires a common construct that is adaptive, expressive and dynamic in nature abstracting out the communications, behavior and models that allows diverse set of device stacks to interoperate. Such constructs enables device vendors to publish changes systematically and incrementally without affecting the advances in the rapid development of applications.

This paper deals with the efficiency maximization of a wave energy converter (WEC). The WEC is an oscillating water column (OWC) device and drives a permanent magnet synchronous generator (PMSG) through a bidirectional flow impulse-turbine. The converter faces challenges such as large peak-to-average power ratio, low overall efficiency, and inefficient energy absorption for regular and irregular sea states. In this context, a higher order sliding mode controller (HOSMC) was proposed, and its gains were optimized to control through the best efficiency point tracking (BEPT) of the turbine. The flow through the turbine-passage was simulated by the computational fluid dynamics (CFD) technique, and the BEPT characteristics were obtained. An adaptive inertia-weight particle-swarm algorithm and a grouped grey-wolf algorithm were used for optimization. The Optimized HOSMC reduced chattering, minimized the reaching time and improved the mean efficiency by about 67% compared to the uncontrolled cases. In addition, the relative improvement of the mean efficiency was at least 4.8% compared to conventional controllers. The controller reduced the peak-to-average power ratio of at least 35.6% relative to the uncontrolled case of the turbine under different sea states.

This work deals with unsteady analysis and control of a bi-directional impulse turbine equipped in oscillating water column (OWC) wave energy converter (WEC). The Unsteady Reynolds-averaged Navier-Stokes equations (URANS) were solved to get the turbine characteristics and the maximum efficiency tracking for different axial velocities. The grouped grey wolf optimization (GGWO) algorithm finds the optimal gains of proportional-integral-derivative (PID) controllers of a permanent magnet synchronous generator (PMSG), such that a maximum efficiency tracking can be achieved. The URANS analysis show that the best efficiency point (BEP) occurs at a flow coefficient of 1.25. The GGWO based controller increases efficiency by 61.5% and 4% compared to the uncontrolled and conventionally controlled case, respectively and limits the peak-to-average power ratio by 38.6% compared to the uncontrolled case.

Server consolidation of virtual machines is very much essential in a cloud environment for energy conservation and cost cutting .Consolidation can be achieved through live migration of virtual machines. We propose a modified bin packing algorithm for Server Consolidation that avoids unnecessary migrations and minimizes the instantiation of new physical servers. We implement ideas from the First Fit algorithm (decreasing strategy) for live migration of virtual machines. We have simulated our algorithm using java with multiple test cases. The proposed system consequently results in Server Consolidation through minimal migrations.

Education for a person plays an anchor role in shaping an individual’s career. In order to achieve success in the academic path, care should be taken in choosing an appropriate course for the learners. This research work is based on the framework to design a course advisory system in an efficient way. The design approach is based on overlapping of learning analytics, academic analytics, and personalized systems. This approach provides an efficient way to build course advisory system. Also, mapping of course advisory systems into the reference model of learning analytics is discussed in this paper. Course advisory system is considered as enhanced personalized system. The challenges involved in the implementation of course advisory system is also elaborated in this paper.

In recent years the industrial application of advanced control techniques for the process industries has become more demanding, mainly due to the increasing complexity of the processes themselves as well as to enhanced requirements in terms of product quality and environmental factors. Therefore the process industries require more reliable, accurate, robust, efficient and flexible control systems for the operation of process plant. In order to fulfill the above requirements there is a continuing need for research on improved forms of control. In Industries, pH of a solution plays a major role in the output and efficiency of the system. Deviation in pH can affect health of humans and plants. World Health Organization has issued an order that pH value of metro water should be maintained at 7 which is a neutral value. Nowadays in industries pH value is maintained only using manual methods. This method cannot be determined as error less and accurate. This is one of the most important reasons that an automatic pH controller should be developed and used in industries. During the manual pH control there many possibilities for error to occur which in turn may affect many life forms. This automatic pH control method is fast, error free and accurate.

Recommender system (RS)s are widely used in different walks of life. This research work is to explore the usage of RS in the field of education. This review is performed in five dimensions which includes, Purpose of RS in Education, various techniques to build RS, input parameters used in design of RS, type of students involved in design of RS and Modelling strategies for RS to represent the data. The outcome of the research work is to facilitate the efficient design of the recommender system in education which will help the students by generating the appropriate recommendations.

Social Networking Sites (SNS) is a buzz word in today’s world due to its enormous growth, customer base and usage. The main focus of this paper is to present an insight into impact of SNS usage on the minds of youth population. SNS has created a fourth world without boundaries. A platform for people to connect and share on 24/7/365. It is a common phenomenon across the world where growing citizens (i.e. youth population) are the biggest users and consumers of SNS. This platform has provided the youth a golden opportunity in exchanging knowledge, finding employment and social quotient among them. Increased participation in issues of social importance, providing quick help for the needy are other positive effects of SNS among youth population. On the other side it has created new issues to society to solve. Privacy has taken beating due to overexposure to social media. Participation in chats and discussions in subjects of least importance is killing the valuable creative time among youth. Recent studies has also pointed out that SNS has created change of character, lose in concentration and spike in psychological disorders.

While Virtualization has gained popularity in recent years; data centers reap the benefits of resource utilization through virtual machine migration. We present a virtual machine placement algorithm in a resource constrained environment .The first part of the algorithm deals with virtual machine migration. In the second part we implement a virtual machine swapping model that improves the complexity of finding the right neighborhood for swapping virtual machines that are running between two servers. The aim of our approach is to increase the resource utilization and reduce the resource wastage. We have evaluated the applicability of our models through experimentation and proved that our algorithm leads to efficient resource allocation.