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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.

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.

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.

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

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.

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.