Volume 9 | Issue 6

 Abstract

Health is indeed the true wealth of the people. The health management and promotion have gained significance in modern times in the context of Sustainable Development Goals. The process of health management becomes incomplete in the absence of health communication. It basically includes information, education and communication of health centric policies, programmes and allied measures. Health communication is an important component of health management in modern times. Health communication is the practice of communicating promotional health information in the form of education, training, orientation and campaigns which benefit the mankind. The contemporary relevance of health communication is emphasized in this article based on qualitative research methodology.

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 Keywords

Health Communication, Healthy Lifestyle, Health Management, Attitudes and Behaviours, Healthcare Professionals

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 Abstract

The 21st century is striving hard for cultivation of plants in a controlled manner, But because of lacking of land availability and scarcity of water. In traditional farming, farmer has to visit the farming land regularly to measure the various environmental parameters such as temperature, humidity, light intensity and soil moisture to cultivate the right crops at right time in right soil. Even though this traditional farming system have been used for years, the system is hectic and fail to prove high productivity rate as farmer usually unable to measure all the parameter accurately. Therefore there is need to optimize the system in a way that is affordable and efficiently conserves water. Manually controlling the water is a tedious process and inefficient due to requirement of a person to control. Working in such a manner sometimes it leads to damage of plant in the absence of that person. The main problem is it depends on weather conditions, sometimes which leads to complete damage of plants. Therefore, there is a need to come up with a system which overcomes the problems of manual systems. A system which reduces manual control and would efficiently worked in a control manner. This is done by using Automatic greenhouse monitoring and controlling. It replaces the direct supervision of the human. In this paper the different papers have been reviewed and developed the proposed system based on the limitation in the present monitoring system. Greenhouse is a building where plants are grown in a controlled manner. We created a total model of smart greenhouse for implementing our system. It consists of simple but efficient algorithm, which happens to manage all the aspects.

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Arduino ATmega328, Atmospheric sensors, DC motor controlled sprinkler system. Solar panel.

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Face recognition is a difficult field within the scope of biometric authentication since it is reliant on the uniqueness of biological qualities or qualities of an individual. Face recognition systems are subject to a variety of threats and hence their security is critical. Face spoofing is a way of gaining unauthorized access to a system by impersonating a legitimate user using a photo, video clip, or 3D mask as a replacement for another person's face. 2D and 3D spoof attacks are the two types of spoof attacks. To prevent all of these scams, there is a need for security solution that can combined with current using Biometric technology. This paper helps to detects fraud by using the image of the user's identity as a form of authentication. For training 72% and for testing 28% of total dataset are used. Tools used for this application are sypder, keras, Numpy and Tensorflow.

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 Keywords

Face Spoof, Face Biometrics, Face recognition

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 Abstract

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Malnutrition, Deep learning, Convolution Neural Network

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This Concrete is the most widely used man-made construction material in the world, and is second only to water as the most utilized substance on the plant. It is obtained by mixing cementing materials, water and aggregates, and sometimes admixtures, in required proportions. The mixture when placed in forms and allowed to cure hardens into a rock-like mass known as concrete. Concrete is composed principally of aggregates, Portland cement, and water, and many contain other cementitious materials and/or chemical admixtures. It will contain some amount of entrapped air and may also contain purposely entrained air obtained by use of admixture or air- entraining cement. Chemical admixtures are frequently used to accelerate, retard, improve workability, reduce mixing water requirements, increase strength, or alter other properties of the concrete. In this work the graphene was used with different proportions

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Compressive Strength, Split Tensile Strength, Graphene

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Covid even though has created havoc but has presented many business sectors a great opportunity for who were able to acclimatize to work from home (WFH) practices. Such crisis mandates not only the business but employees doing WFH also need to fine-tune this model with learning how to carry it out appropriately. This WFH model has both opportunities and threats on both the ends i.e., business and employees. While business can draw a lot of benefits in terms of widening talent pool, saving costs etc. Also, when employees are working from home, they are working with the same business having same responsibilities, deliverables and need to meet the targets with no change in timelines. Employees working from home have the opportunity to work in a stress free environment with good health and wellbeing. However, the challenges are also faced by both the ends and these challenges need to be mitigated in order to make the WFH truly successful.

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Remote work, work from home, WFH

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 Abstract

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Python, Machine Learning, Convolutional Neural Network

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Word affiliation, uncovering mental portrayals and associations of humans, has been generally concentrated in brain science. However, the size of accessible acquainted signal reaction words is harshly limited because of the customary physically gathering approach. In the meantime, with the huge accomplishment in (NLP) technique tasks, an extremely huge measure of plain messages can be effectively obtained. This proposes a knowledge about discovering affiliation words from the content corpus rather than physically assortment. This paper moves toward proposing a profound learning-based structure for programmed affiliation word extraction as a unique endeavor. The Framework comprises two phases of affiliation word identification and machine affiliation network development. Specifically, consideration instrument-based Reading Comprehension (RC) calculation is investigated to discover important affiliation words consequently. To approve the worth of the extricated affiliation words, the connection coefficient between semantic similitudes of a machine and human affiliation words is presented as a viable estimation for assessing affiliation consistency. The investigation further confirms that the machine affiliation words are for the most part steady with human affiliation words as for semantic likeness, which features the promising usage of the machine affiliation words, later on, explores both brain science and NLP.

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Text Extraction, NLP Technique, Deep Learning, Transcript, Word Affiliation

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Over the years, an explosive growth in the number of items in the catalogue of e-commerce businesses, such as Amazon, Netflix, Pandora, etc., have warranted the development of recommender systems to guide consumers towards their desired products based on their preferences and tastes. Some of the popular approaches for building recommender systems, for mining user derived input datasets, are: content-based systems, collaborative filtering, latent-factor systems using Singular Value Decomposition (SVD), and Restricted Boltzmann Machines (RBM). In this project, user-user collaborative filtering, item-item collaborative filtering, content based recommendation, SVD, and neural networks were chosen for implementation in Python to predict the user ratings of unwatched movies for each user, and their performances were evaluated and compared.

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Recommender systems, collaborative filtering, neural network, machine learning, RMSE

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In 2019, Corona virus Disease (COVID-19) spread seriously in more than 180 countries, causing a global crisis. The lack of immunity to COVID-19 in the absence of aggressive treatment increases the vulnerability of the population. Due to the lack of vaccines, social distance (suspicious activity against COVID-19) is the only way to combat this pandemic. Motivated by this concept, this article describes a deep learning-based framework for automating social distance tasks with surveillance videos. The generated boundary box helps identify clusters or groups of people that meet the neighborhood properties calculated using the pair wise vectorization method.

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suspicious activity, deep learning

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With the widespread use of credit cards, fraud has become a major problem in the credit card industry. As a result of theft, businesses and banks are reluctant to disclose the amount of money lost. Another issue with calculating credit card fraud losses is that it can only estimate the number of frauds that have been detected. It becomes harder to monitor the behavior and pattern of such transactions. Therefore, the assessment of fraud is necessary on a regular basis. The technologies like machine-learning, data-mining, and other artificial technologies are used to resolve this. In this regard, enhancing effective fraud detection using machine-learning methods is critical for empowering fraud investigators with these losses. This research paper explores how several Machine Learning models are being used to diagnose suspicious transactions. Machine learning contributes to the critical function of detecting credit card fraud during transactions. The dataset sampling methodology, selection of variables, and methods used for identification always have a massive influence on fraud detection efficiency. This system provides the necessary characteristics for monitoring both illegal and legal transactions. Classification Techniques like Decision Tree, K-nearest Neighbors algorithm, Logistic Regression, Gaussian Naive Bayes, and Artificial Neural Networks are demonstrated to identify fraudulent transactions. By training these techniques and evaluating them on various factors such as precision, accuracy, recall, and visualized the ROC curve. Based on the criteria of different methods, the best approach for detecting credit card fraud is chosen. As opposed to other algorithms, this paper shows that Decision Tree is the best optimal approach and can be used to detect further frauds

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Artificial Neural Networks; Decision Tree; K-Nearest Neighbors; Logistic regression, Gaussian Naive Bayes.

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Forensic Investigation

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Theme of Love and Revenge in the Novel Wuthering Heights

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With the advent of Information Technology (IT) and Data Sciences, businesses now have the opportunity to better understand and forecast consumer demands using quantitative methods. The use of Big Data to better understand business processes and results is an evolving IT trend that has piqued the interest of researchers and practitioners. Big Data systems have the potential to assist businesses in better understanding complicated business relationships by delivering knowledge that was previously unavailable. Previous research has shown that providing an effective supply chain gives a producer a competitive advantage over its competitors, how data from online marketplaces or e-commerce allows manufacturers to better understand product demands is a field that has not been thoroughly researched by previous researchers. This paper provides a comprehensive survey about the ongoing researches done in this field by reviewing the articles of the other computer scientists. This paper paves a better way for the upcoming researchers in this field.

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Web scraping, Neural Network, Locating files in websites, online sale promotion strategies.

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The most important part of a city is the people who live in it. Therefore, wellness and health are indispensable for the Smart Cities concept. In this paper, we introduce an approach that combines genetic research with environmental monitoring in real time by using an integrated system of IT and IOT that helps to understand better and prevent diseases, improving the quality of life of the citizens. We are working to develop a virtual platform that creates and establishes correlations between the genetic information of the user and their environment. The principal advantages of this project are to allow health care providers, governments and citizens to make smarter decisions oriented to health care, implementing Health Information Technologies (HIT) and improving the concept of Mobile Health (mHealth), all of these based on the Quadruple Helix Innovation model.

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genetics, HIT, smart health, predictive modelling, smart cities

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Raspberry PI , Ultrasonic Sensor, Wireless Camera , LCD Display.

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Purpose of this study is to find out about the occupational stress among the dual career families working in the IT sector, Bangalore. Revised Occupational Stress Inventory (OSI-R) scale was employed for collecting the data of 600 samples from IT professionals. It was a cross sectional design with snow ball technique to collect data for the study. Findings are analysed using Statistical package for Social Science (SPSS.16). Occupational Stress Inventory has 140 items, with three main sections and 4 subscales or stressors. Three main sections are Occupational Roles, Personal Strain and Personal Resources. Findings were analysed with descriptive analysis using SPSS.16. T- scores were generated after generating Z- scores. Significant stressors in the Occupational Roles are, Role Boundary, Physical Environment, Personal/psychological Strain, Vocational Strain, Psychological Strain, Interpersonal Strain. In the Personal Resources, Self-Care, Social Support and Rational/Cognitive coping Resources have indicated significant lack among dual career families. Dual career families need coping skills such as physical exercise, reliance on God, meditating the Scriptures, music therapy to reduce or remove these stressors and maximise their marital satisfaction.

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Occupational Stress Among Dual Career IT Families: A Cross Sectional Study

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 Abstract

हिंदी शोध की उपलब्धियां एवं सीमाएं

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हिंदी शोध की उपलब्धियां एवं सीमाएं

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The efforts required in achieving the desired output can be effectively and economically be decreased by the implementation of better designs. A system and method for actuating a hatch door. Ammunitions are commonly transported to battlefields within canisters to avoid environmental exposure and cushion against vibrational damage. A vertical launcher system (Canister) that consists of a hatch door at the top of the hold of the vertical launcher system. This door mechanism enables both opening and closing of the door. For quick opening and closing of hatch door a four-bar mechanism has been designed. A crank-rocker inversion of four-bar mechanism is designed for opening and closing of hatch door of canister. The hatch door opening closing mechanism is simple and easy in operation. The significance and purpose of this work is to provide smooth and quick opening and closing of hatch door during a launch system and also ensure to safeguard the article stored inside the canister launching system.

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kinematic,dynamic,hatch quick door mechanism,hatch quick opening closing mechanism,KINEMATIC AND DYNAMIC ANALYSIS OF HATCH QUICK OPENING/CLOSING MECHANISM FOR CANISTER,

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ABSTRACT: High liquid chromatography (HPLC) performance is an important quality and quantitative method used to measure pharmaceutical and environmental samples. It is the most flexible, safest, most reliable, and fastest chromatographic method for quality control of drug components. High-performance liquid chromatography (HPLC; formerly referred to as high-pressure liquid chromatography) is a technique in analytical chemistry used to separate, identify, and quantify each component in a mixture

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High performance liquid chromatography, instrumentation, elution, applications, mobile phase

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