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PERFORMANCE ANALYSIS OF COVID 19 IN CLOUD COMPUTING ENVIRONMENTS
The COVID-19 pandemic has highlighted the urgent need for efficient and scalable solutions for analyzing large volumes of open research datasets. To address this challenge, we propose a cloud-based platform for mining and analyzing COVID-19 open research datasets using natural language processing (NLP), machine learning, and data visualization techniques. The proposed system comprises several components, including data ingestion, data preprocessing, text classification, in sights and visualization, and cloud computing infrastructure. Our system leverages cloud-based storage services, such as Amazon S3, and cloud computing infrastructure, such as Amazon EC2, to process large volumes of data and perform complex computations. The main contributions of this paper include a detailed description of the proposed system architecture, the data ingestion and preprocessing techniques used, the text classification algorithms employed, and the insights and visualization techniques used to generate meaningful patterns and trends in the data. We believe that our proposed system has the potential to significantly impact COVID-19 research and support the development of new treatments, vaccines, and public health policies.
SEMANTIC ANALYSIS OF LONG ANSWERS
The inspiration for this research paper was the natural bias in university paper checking. When a paper is checked it is either checked by a professor who teaches the subject or someone who has no knowledge of the subject. When checked by the latter type, the answers cannot be appropriately marked unless obviously highlighted. This paper aims to check long answers without human intervention using artificial intelligence and regular expressions. It checks student or examinee written digital form answer by comparing it to an answer key which is to be provided by the exam host. The proposed methodology allows doing so by combining two techniques to get a faster and more accurate system to check long answers. The long answers will be evaluated by breaking them to simplest form of sentences and then encoding them to high density vectors using a Deep Averaging Network (DAN) to analyses the semantic similarity of the examinees answer to the provided answer key. This system does not look for only keywords in the content of the answer but looks at the sentence as a whole and if it evaluates similarly to the content in the answer key. This research relies on the availability of an answer key to check answers and does not check the relevance of content written by the examinee, meaning as long as examinee writes points mentioned in the answer key, he/she will be marked correct. This system of evaluation doesn't cut marks for wrong point (meaning no negative marking).
DESIGN & DEVELOPMENT OF ROBUST MACHINE LEARNING DESIGNS UTILIZING AI CALCULATIONS TO CONTROL ADVERSIAL SETTINGS
Numerous offices are currently utilizing AI calculations to settle on high-stake choices. Deciding the correct choice unequivocally depends on the accuracy of the info information. This reality gives enticing motivations to lawbreakers to attempt to mislead AI calculations by controlling the information that is encouraged to the calculations. Then, conventional AI calculations are not intended to be protected when going up against startling data sources. In this exposition, we address the issue of antagonistic AI; i.e., we will likely form safe AI calculations that are hearty within the sight of loud or adversarial controlled information. Ill-disposed AI will be additionally testing when the ideal yield has a mind boggling structure. In this paper, a sign cannot concentrate is on antagonistic AI for anticipating organized yields. To start with, we build up another calculation that dependably performs aggregate classification, which is an organized expectation issue. Our learning strategy is efficient and is defined as a raised quadratic program. This procedure verifies the expectation calculation in both the nearness and the nonappearance of an enemy. Next, we explore the issue of parameter learning for hearty, organized forecast models. This strategy builds regularization capacities dependent on the impediments of the foe. In this exposition, we demonstrate that strength to antagonistic control of information is proportionate to some regularization for huge edge organized expectation, and the other way around. A customary enemy consistently either does not have enough computational capacity to structure a definitive ideal assault, or it doesn't have sufficient data about the student's model to do as such. In this manner, it frequently endeavors to apply numerous irregular changes to the contribution to an expectation of making a leap forward. This reality suggests that on the off chance that we limit the normal misfortune work under antagonistic clamor, we will acquire power against unremarkable enemies. Dropout preparing takes after such a commotion infusion situation. We infer a regularization technique for huge edge parameter learning dependent on the dropout system. We stretch out dropout regularization to non-straight parts in a few unique ways. Experimental assessments demonstrate that our procedures reliably beat the baselines on various datasets. This exploration work incorporates recently distributed and unpublished coauthored material.
DEVELOPMENT OF MURINE MONOCLONAL ANTIBODIES TO PNEUMOCOCCAL POLYSACCHARIDE ANTIGEN
Streptococcus pneumoniae is a major cause of mortality in infants, children and the elderly. Despite the availability of excellent antimicrobial therapy and adequate care systems, respiratory diseases and invasive infections caused by pneumococci still comprise a major health problem. Thus to overcome this, murine monoclonal antibodies were developed using pneumococcal polysaccharide as the antigen. Polyclonal MAb was developed against different serotypes namely 4, 6B, 9V, 14, 18C, 19F, 23F. Hybridoma cell line developed against Serotype 4 showed maximum fluorescence absorbance units when detected, using Rapid multiplex assay. Single cell cloning against Serotype 4 hybridoma cell line was done by Limiting dilution. A stable single hybridoma cell line was developed producing Monoclonal antibody against pneumococcal polysaccharide Type 4 serotype. The expression of the murine monoclonal antibody was assayed using SDS- PAGE (Reducing). Presence of heavy chain at 50kDa and light chain at 25kDa in reducing condition confirmed presence of MAb.
RAINFALL PREDICTION USING REGRESSION AND MULTIPLE ALGORITHMS
Rainfall is very important aspect for farmers in day-to-day agriculture and also very important in prospect to Indian economical growth. As now a day we see there is daily climate change in our India as there no stable season any month the rainfall occurs in India which also cause damage to the farming industry as the crop seeds gets damaged. In the proposed study the future rainfall is predicted using Linear Regression Technique as we also compared various model such as SVM, Random Forest Regression, Neural Network model so we obtained better results in Linear Regression so we further consider this model for future prediction of rainfall.
FUTURE OF INTERNET OF EVERYTHING (IOE)
In the world, the man matters, not the machine, people need to be care, not data. There is transition from information technology to human technology. The answer is internet of everything (IoE). The internet of everything (IoE) is a concept that extends the internet of things (IOT) by encompassing the machine-to-machine (M2M) communication, machine-to-people (M2P) and technology-assisted people-to-people (P2P) with expended digital features. It includes different types of devices, appliances and items that are connected to the global internet. The internet of everything (IoE) comprises four components, namely, people, process, data and things to make the networked connections more relevant and valuable. These components enable us to create new capabilities, wide experiences and economic opportunity for individuals and businesses. IoE connects people in more relevant and valuable ways, deliver the right information to the right people and/or machine at the right time, leverages data into more useful information for decision making and connect physical devices and objects with each other via the internet of things (IoT). The IoE includes user-generated communications and interactions associated with the global internet. The IoE is 5G technology that include evolution, elasticity, capacity, speed and agility. In this research work, we discuss the potentiality of IoE technology that includes the seamless interconnections and autonomous coordination among people, data and process through the global internet infrastructure. We will highlight the various applications of IoE in the area of privacy and security. Security and privacy are the major challenge in today world. There are various challenges like software and hardware compatibility, wired and wireless infrastructure, data analysis, data mining, big data and many other things that need to be solves globally. We will discuss the security aspects of IoE that ensure the securing the environment, the infrastructure and the process. Data collection is also the major challenge at the network edge. It includes multitude of endpoint devices and sensors that automatically collect, analyze and transmit the data on the massive scale and at very fast rate. Internet of everything (IoE) will have a huge impact on individuals and business in the next few years. It will be a radial shift to think that how we live, solve the problem, create value, secure environment to keep people, data, process and things under one umbrella. It will include devices and objects at the massive scale. The network capabilities will create the new experiences, capabilities and economic opportunities for individuals, businesses and governments. In short, the internet of everything (IoE) changes only one thing, which is everything.
A NOVEL PROPOSED FUSION METHOD
In this paper, two proposed fusion methods will be proposed for merging important information from more than one source for the purpose of either making final image with high quality compared to the input images or making final image holding more information that captured in the input images depending on some important extracted features from the input images. The first method merge images depending on edges information in each pixel by applying one order prewitt method for edge detection then calculating the ratio related to each image for this position then calculating new pixel value for this place in the output image, while the second proposed method working by estimating texture unit number value in each pixel in the input images and merging pixels by applying proposed techniques for the purpose of calculating output pixel which reflect good degree of the proposed feature with accepted compatible degree with neighbouring eight pixels. The results of the proposed methods checked with some suitable metrics for measuring the quality of the output images compared with the standard methods like maximum, minimum, ratio, wavelet,… and the result of the proposed method reflect good and perfect quality for the output images and this result can be expected because fusion operation performed according to effected visible features that making fused images with good quality.
FORMULATION AND OPTIMIZATION OF CHITOSAN MICROSPHERE OF SALBUTAMOL SULPHATE ON BASIS OF AMOUNT OF CROSS LINKING AGENT AND ON BASIS OF CROSS LINKING TIME FOR THE TREATMENT OF NOCTURNAL ASTHMA
The process that occur periodically in an organism in conjunction with and often in response to periodic changes in environmental condition Biological rhythm within a single day is termed as circadian rhythm. Chronobiology is the technical study of biologic rhythms and their fundamental mechanisms4. The term "Chrono" pertains to time and "Biology" is the science of life, thus, chronobiology concerns with the observation of every metabolic event goes through rhythmic changes in time that can be measured from seconds to seasons. Chronotherapeutics is the synchronization of medication levels in time with reference to need, taking into account biologic rhythms in the pathophysiology of medical conditions, and/or rhythm dependencies in patient tolerance for given chemical interventions. In this study design and characterize an oral, pulsatile drug delivery system of Salbutamol Sulphate intended to approximate the chronobiology of asthma, proposed for colonic targeting. It is a chronopharmaceutical approach for the better treatment of nocturnal asthma. The ultimate aim of this proposal is to design a new Chronotherapeutics system for salbutamol sulphate with high potential benefits in treating nocturnal asthma. Based on the concept that at the pH of colon various polymers release the drug due to their solubility at specified pH, a pH dependent pulsatile drug delivery system was designed.
SMART TROLLEY USING IOT
Smart shopping trolley system helps customers while shopping. The trolley has an automatic billing system, and it follows the movement commands provided by the user by using hand gesture. An android application is installed in customers mobile. The products purchased by the customer and their cost will be automatically updated to the android application through internet and that bill will automatically send to the billing counter system in the shop. This helps customer by replacing tedious process of pulling and pushing the trolley and saves time spent in the billing counter. The Automated Shopping Trolley is a Smart Trolley which integrates with ultrasonic sensor, Arduino, Wi-Fi module, weigh sensor in it. The scanned items by the customer using mobile app which will automatically log into the shopping cart and thereby can generate bill for items selected. These modules are integrated into an IoT system and are tested to satisfy the functionality.
FIR FILTER DESIGN WITH INERTIA WEIGHT AND COMPRESSION FACTOR APPROACH USING ADVANCED OPTIMIZATION TECHNIQUES
This paper includes the design of Finite impulse response filter with inertia weight and compression factor approach. The objective function of filter design involves accurate control of various parameters of frequency spectrum and is thus highly non-uniform, non-linear, non-differentiable and multimodal in nature. Classical optimization methods cannot converge to solutions. Because they have disadvantages such as highly sensitive to starting points, frequent convergence to local optimum solution or divergence or revisiting the same solution, requirement of continuous and differentiable objective cost function, requirement of the piecewise linear cost approximation and problem of convergence and algorithm. Evolutionary optimization methods for the design of optimal digital filters have better control of parameters and the highest stop band attenuation.
AIRBNB SOFTWARE ARCHITECTURE MODELING USING UML EXTENSIONS
Airbnb is a California based company that operates an online marketplace for lodging and homestays for vacation rentals, and tourism activities. Modeling a prototype of the application with all the integral features intact would help us understand the core working and success of the company. The Unified Modeling Language (UML) is a language for the specification, visualization and documentation of object-oriented software systems. In order to understand the features of the miniature Airbnb, we try to model the system using UML diagrams. In this study, the proposed extension to UML covers aspects of use case diagram, sequence diagram, activity diagram and class diagram of the miniature Airbnb model at various levels and perspectives.
CHALLENGES AND POTENTIAL SOLUTIONS OF MODERN HEALTH CARE SYSTEM USING INTERNET OF THINGS
Health is one of the sustainable development areas in all of the countries. The Internet of Things (IoT) is a process of interlinked computing devices, mechanical and digital machines, objects, animals or people that are provided with unique identifiers. Internet of Things (IoT) rises as a powerful domain where embedded devices and sensors can connect and exchange information over the Internet. Among all the application IOT (Internet of Things) has grease the wheels around the world and has gained a lot of popularity due to its efficiency and great performance, researchers are working very hard in order to convert nearly about each and every device with IOT. In this research paper we have explained the role of IOT technology in healthcare system. This technology has become the ultimate building blocks in the development of healthcare monitoring system. So in this paper we also highlighted the implementation of IOT in healthcare monitoring systems and the standard model for application in future internet of things healthcare system.
DESIGN AND SIMULATION OF LOW POWER, HIGH GAIN AND HIGH BANDWIDTH CMOS FOLDED CASCODE OTA USING RECYCLING AND gm/ID TECHNIQUE
This paper includes the design of Enhanced Recycling folded cascode Operational Transconductance Amplifier (ERFC OTA) in both strong inversion and moderate inversion. The primary motivation for the design is from standard folded cascade (FC) OTA. The biased current sources are not contributing to the Transconductance (Gm) of the OTA in the usual FC OTA. In the Recycling FC OTA, the currents are recycled and the biased current sources are made to contribute to the Gm of the OTA and so the Gm is increased. Gm is further enhanced in Improved RFC OTA by creating one more high impedance dc path to the RFC architecture. The design is implemented in 90 nm UMC CMOS technology.
ADAPTIVE SECURE AND DYNAMIC ENCRYPTED CLOUD DATA USING ELLIPTIC CURVE CRYPTOGRAPHY
Cloud computing is technology based on the internet. Computing actually took place on a computer that is not the one currently being used, mostly remote. Data obtained during this process is stored by remote servers and processed by them. Cloud encryption is required because, when it is distributed via the Internet and other computer networks, its main purpose is to safeguard and protect sensitive information. Providers of cloud storage encrypt data and transfer the user’s encryption keys. When required, these keys are used to securely decrypt data. Many studies have argued a variety of cryptographic techniques to preserve the privacy and security of user data stored in the cloud. In this paper, to encrypt the data, an effective privacy protection scheme called Elliptic Curve Cryptography (ECC) is proposed. Uploading encrypted files and indexes to a cloud server will reduce the computing cost of encryption and decryption. It will then allow users to use the hash conflict feature to create trap door and send it to the cloud service provider to search for matched ciphertext. Experiments show that, by using this process, files can be encrypted, decrypted, and retrieved more easily compared to conventional methods. It can also maintain data protection and decrease the overhead for communication.
SOIL ANALYSIS AND CROP FERTILITY PREDICTION USING MACHINE LEARNING
Soil is a critical part of successful agriculture and is the source of the nutrients that we use to grow crops. There are different types of soil and there are different properties of each soil. On these different properties, several types of crops grow. We need to know the properties and characteristics of various soil types to understand which crops sow in certain soil types. Machine Learning allows the user to feed a computer algorithm on an immense amount of data and have the computer analyze, make data-driven recommendations and decisions based to analyze the input data. Machine Learning techniques are used to model this process. Machine Learning has come into the picture with the big data technologies and high-performance computing that create new opportunities for data-intensive science in the multi-disciplinary agri-technology domain. In this paper, we have proposed a model that can find whether the soil is fertile or not, Sowing crop seed on fertile soil, and at last predicting the crop yield on different soil features. According to prediction, it can be suggested and recommended which crops grow more. Various Machine Learning algorithms such as Support Vector Machine (SVM), Random Forest, Naive Bayes, Linear Regression, Multilayer perceptron (MLP), and ANN are used for soil classification and crop yield. Test results show that the proposed ANN method follows a deep learning architecture which means it has several layers for input and output are connected to achieve better accuracy than numerous existing methods.
PREDICTION OF BREAST CANCER USING MACHINE LEARNING
In order to support and supervise patients, the key detection and estimation of cancer type should establish a compulsion in the cancer research. Many research teams from the biomedical and bioinformatics fields have been advised to learn and evaluate the use of machine learning (ML) methods because of the relevance of classifying cancer patients into high or low risk clusters. To predict breast cancer, the logistic regression method and many classifiers have been proposed to generate profound predictions about breast cancer data in a new environment. This paper discusses the various approaches to data mining using classification to create deep predictions that can be applied to Breast Cancer data. In addition, by testing datasets on different classifiers, this analysis predicts the best model that delivers high efficiency. In this paper, the UCI machine learning repository has 699 instances with 11 attributes collected from the Breast cancer dataset. First, the data set is pre-processed, visualized and fed to different classifiers such as Logistic Regression, Support Vector Classifier, K-Nearest Neighbour, Decision Tree and Random Forest. 10-fold cross validation is implemented and testing is carried out in order to create and validate new models. Effective analysis shows that Logistic Regression generates the deep predictions of all classifiers and obtains the best model delivering strong and precise outcomes, followed by other methods: Support Vector Classifier, K-Nearest Neighbour, Decision Tree and Random Forest. Most models were less reliable compared to the approach of logistic regression.
SOIL ANALYSIS AND CROP FERTILITY PREDICTION USING MACHINE LEARNING
Soil is a critical part of successful agriculture and is the source of the nutrients that we use to grow crops. There are different types of soil and there are different properties of each soil. On these different properties, several types of crops grow. We need to know the properties and characteristics of various soil types to understand which crops sow in certain soil types. Machine Learning allows the user to feed a computer algorithm on an immense amount of data and have the computer analyze, make data-driven recommendations and decisions based to analyze the input data. Machine Learning techniques are used to model this process. Machine Learning has come into the picture with the big data technologies and high-performance computing that create new opportunities for data-intensive science in the multi-disciplinary agri-technology domain. In this paper, we have proposed a model that can find whether the soil is fertile or not, Sowing crop seed on fertile soil, and at last predicting the crop yield on different soil features. According to prediction, it can be suggested and recommended which crops grow more. Various Machine Learning algorithms such as Support Vector Machine (SVM), Random Forest, Naive Bayes, Linear Regression, Multilayer perceptron (MLP), and ANN are used for soil classification and crop yield. Test results show that the proposed ANN method follows a deep learning architecture which means it has several layers for input and output are connected to achieve better accuracy than numerous existing methods.
SASI DUKA (SAL): GRIEVING FAMILY RITUALS AS GRIEF MENTORING AND PSYCHO-COUNSELING IN TANAS VILLAGE, PAPUA
This study aims to explore the culture, Sasi Duka as the mentoring and counseling in Tanas village in Papua. This research was conducted in one of the local communities in Papua, namely in the Tanas village. In this study, the theory that was used was the concept of mentoring and counseling which could be seen in the values, traditional rituals of local communities, which were local wisdom of the community, actually was able to be a power to help the bereaved family, because of the death of a loved one. This ritual was solidarity with the whole community in feeling the grief of their bereaved siblings, the Sasi Duka ritual was done for forty days, so that when it was finished the family has gone through a period of sadness and was able to relate normally as social beings.
CHYAWANPRASH: A TRADITIONAL INDIAN BIOACTIVE HERBAL MEDICINAL FORMULATION TO BOOST IMMUNITY AND RESTORE YOUTHFULNESS
Chyawanprash is an Ayurvedic dietary health supplement used for boosting immunity and help in making a person feel young. Chyawanprash is one of the anti-ageing supplements, which is purely herbal in nature. It has Amla as its main ingredient, which is a powerful antioxidant. Master Charaka is the first to mention this herbal medicine. Ayurveda recommends the use of herbal supplements to help the increase immunity, restore drained reserves of life force and to preserve strength, stamina, and vitality, while stalling the course of aging. There are many benefits of chyawanprash. It is useful in cough and cold. It helps in the rejuvenation of the aged and also in proper nourishment of young ones. It is effectively used in the treatments of throat infections. Chyawanprash is formulated by processing around 50 medicinal herbs, minerals sugar, honey, ghee, Indian gooseberry jam, sesame oil, berries and spices. Chyawanprash market has seen a sharp rise of 30 to 40 percent in the period of rising cases of coronavirus. This article focused on immunity boosting herbal medicine in COVID-19 pandemic.
IMPORTANCE OF MEDICINAL PLANTS AND HERBS AS AN IMMUNITY BOOSTER FOR PANDEMIC COVID-19
The whole world facing COVID-19 pandemic, so there has been a lot of interest in ways to boosting our immune system, and thus build a first line of defence against the deadly virus. Since ancient times, medicinal plants, herbs and spices were well known for their medicinal properties. Therefore, the medicinal plants and herbs playing a critical role to boosting our immunity during the COVID-19 pandemic. it is also very important to consume supplements in the form of immune nutrients such as vitamin A, C, E, D, B-complex, Zinc and copper that will support your body to fight against the pathogens. This paper presents an analysis of popular immune-boosting medicinal plants and herbs.