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CARBON NANOFIBER - REINFORCED MULTIFUNCTIONAL SHEET INTEGRATED WITH PIEZOELECTRIC CRYSTAL: AN OVERVIEW OF STRUCTURAL BEHAVIOR ANALYSIS METHODS
Most of the phenomena in life, such as heat transfer, wave propagation, thermoelectricity, sheet materials, multifunctional material properties, etc., can be expressed through mathematical equations or partial differential equations (PDEs). However, with complex differential equations, it is difficult or impossible to find solutions, especially differential equations for complex problems such as solid-liquid interactions, mechanical-thermal-electrical environments, and functional material plate environments in the multiphysics environment, etc. Since then, many numerical methods have been researched and developed to find approximate solutions to partial differential equations. Today, the finite element method (FEM) is the most widely used and effective among the commonly used numerical methods. However, with the continuous emergence of new complex problems in science and technology, especially problems in the multiphysics environment in sheet-material structures, the finite element method has revealed new challenges. There are certain limitations related to the element technique and the weak form discretization of the problem of sheet structure with many different degrees of freedom, significantly affecting the accuracy and efficiency of calculations. Therefore, it is always important to propose improvements to the traditional finite element method and the optimal method algorithm to meet the increasing requirements in the behavior analysis of sheet materials. This research direction is always topical and has received the attention of scientists around the world for many decades.
SALARY ESTIMATOR: A LITERATURE REVIEW
The economic liberalization of Indian markets in early 90s boosted the economic growth of the nation in various sectors over the next two decades. One such sector that has seen a massive growth in this time is Information Technology (IT). The IT industry has played a very crucial role in transforming India from a slow moving economy to one of the largest exporters of IT services. This growth created a huge demand in the labour markets for skilled labour, which in turn made engineering one of the top choices of study after high school over the years. In addition, the earning potential and an opportunity to contribute to technology advancements after engineering, makes it a popular choice of study. These growth dynamics along with the diversified education and labour markets demands gives insight into the factors affecting the employment outcomes of engineering students. This research study focuses on studying the key salary determinants for entry-level engineering graduates in India Labour Markets .The research examined the impact of demographics, academic performance, personality traits and standardized test scores on the starting salary. The research findings indicated that the academic performance in school and college, college reputation, school affiliation and engineering major are key predictors for starting salary. The findings also revealed that Cognitive skills English and Quantitative ability along with a desire to do a task well are significant contributors to the starting salary of engineering graduates in Indian Labor Markets.
INDUSTRIAL B2B BIG DATA
Big data is a field that treats ways to analyze, systematically extract information from, or otherwise deal with data sets that are too large or complex to be dealt with by traditional data-processing application software &for buying and selling of personal and consumer data. The use of big data analytics in managing B2B customer relationships and examines the effects of big data analytics on customer relationship performance and sales growth using a multi-industry dataset from B2B firms. The study finds that the use of customer big data significantly fosters sales growth and enhances the customer relationship performance. However, the latter effect is stronger for firms which have an analytics culture which supports marketing analytics, whereas the former effect remains unchanged regardless of the analytics culture. The study empirically confirms that customer big data analytics improves customer relationship performance and sales growth in B2B firms.
QUESTION ANSWERING SYSTEM FOR HOSPITALITY DOMAIN USING TRANSFORMER-BASED LANGUAGE MODELS
Recent research demonstrates significant success on a wide range of Natural Language Processing (NLP) tasks by utilizing Transformer architectures. Question answering (QA) is an important aspect of the NLP task. The systems enable users to ask a question in natural language and receive an answer accordingly. Most questions in the hospitality industry are content-based, with the expected response being accurate data rather than”yes” or ”no.” Therefore, it requires the system to understand the semantics of the questions and return relevant answers. Despite several advancements in transformer-based models for QA, we are interested in evaluating how it performs with unlabeled data using a pre-trained model, which could also define-tune. This project aims to develop a Question-Answering system for the hospitality domain, in which text will have hospitality content, and the user will be able to ask a question about them. We use an Attention mechanism to train a span-based model that predicts the position of the start and end tokens in a paragraph. By using the model, the users can directly type in their questions in the interactive user interface and receive the response. The data set for this study is created using response templates from the existing dialogue system. We use the Stanford Question and Answer (SQuAD 2.0) data structure to form the dataset, which is mostly used for QA models. During phase1, we evaluate the pre-trained QA models BERT, ROBERTa, and DistilBERT to predict answers and measure the results using Exact Match(EM) and ROUGE-LF1-Score. In Phase 2 of the project, we fine-tune the QA models and their hyper-parameters by training the model with hospitality data sets, and the results are compared. The fine-tuned ROBERTa models achieved the maximum of ROUGE-L F1-Score and EM of 71.39 and 52.17, respectively, which is a relatively 4% increase in F1-Score and 8.7% increase in EM score compared to the pre-trained model. The results of this project will be used to improve the efficiency of the dialogue system in the hospitality industry.
WIRELESS SENSOR NETWORK: KEY SECURITY ISSUE AND CHALLENGES
Wireless sensor networks (WSNs) are interconnected sensor nodes that communicate wirelessly to collect data about the surrounding environment. A typical sensor node is made up of different components to perform the task of sensing, processing and transmitting data. WSNs are used for many applications in diverse forms from indoor deployment to outdoor deployment. The basic requirement of every application is to use the secured network. Providing security to the sensor network is a very challenging issue along with saving its energy. Many security threats may affect the functioning of these networks. Because of their low cost and adaptability, wireless sensor networks are widely used in civil, military, and commercial fields and other fields. However, since the sensor node in the calculation of the capacity, battery capacity, and storage capacity are restricted by the limitations and inherent characteristics of the sensor networks, compared to traditional networks, which makes wireless sensor networks face more security threats. This paper summarized the various security issues and security threats in WSNs and solutions. It identifies various possible attacks at different layers of the communication protocol stack in a typical WSN and presents their possible countermeasures. A brief discussion on the future direction of research in WSN security is also included.
PREDICTING CANCER AND HEART DISEASE AT EARLY STAGES USING MACHINE LEARNING
People today suffer from a variety of diseases as a result of their living habits and their surroundings. As a result, predicting sickness at an early stage becomes a critical responsibility. Machine learning has been demonstrated to be useful in assisting with decision-making and prediction from enormous amounts of data generated by the health sector. As a result, machine learning's use in medical diagnostics is rapidly rising. Heart disease is one of the leading causes of death in the modern world. Predicting and detecting cardiac disease has always been a difficult issue for medical professionals. To treat cardiac disorders, hospitals and other clinics are offering costly medicines and operations. As a result, anticipating cardiac disease in its early stages will be beneficial to people all over the world, allowing them to take the essential steps before it becomes serious. Cancer has been described as a diverse illness with numerous subgroups. Early detection and prognosis of a cancer type has become a necessity in cancer research since it can help with patient clinical treatment. The health industry nowadays plays a key part in curing patient’s problems, thus this is also a way for the health sector to enlighten users. It is useful for the user if he or she does not want to go to the hospital or any other clinic, because by simply entering the symptoms and other relevant information, the user can learn about the disease they are suffering from. The health industry can also benefit from this system by simply asking the user for symptoms and entering them into the system, and in a matter of seconds, they can tell the exact and, to a degree, accurate diseases.
THE NUMERICAL STUDY OF SURFACE PLASMON POLARITON AT THE SINGLE INTERFACE BETWEEN METAL AND VEGETABLE OILS DIELECTRICS
We report the numerical studies of the properties of surface plasmon polaritons which are generated at the single interface between metal and dielectric. In this numerical work, we used parameters representing natural dielectrics. Using Maxwell equations and also the continuity of the fields at the interface between metal and dielectrics, the dispersion relation of the surface plasmon polaritons were derived. The dispersion relations were solved by taking parameters for metal appropriate for gold. For natural dielectric, we used parameters which are correspond to castor oil, coconut oil and sun flower oil with the range of dielectric constants were from tens to sixties. The results showed that the lower part of dispersion curves of surface plasmon polaritons approach SPP frequencies. We found that the highest surface plasmon frequency was resulted by using crude palm oil (CPO) as liquid dielectric. The usage of sunflower oil as dielectric lead to the lowest surface plasmon polaritons frequency.
PREDICTION OF CARDIOVASCULAR DISEASE BY MACHINE LEARNING AND IOT
Heart disease is one of the most significant causes of mortality in the world today. Prediction of cardiovascular disease is a critical challenge in the area of clinical data analysis. Machine learning (ML) has been shown to be effective in assisting in making decisions and predictions from the large quantity of data produced by the healthcare industry. We have also seen ML techniques being used in recent developments in different areas of the Internet of Things (IoT). Various studies give only a glimpse into predicting heart disease with ML techniques. In this paper, we propose a novel method that aims at finding significant features by applying machine learning techniques resulting in improving the accuracy in the prediction of cardiovascular disease. The prediction model is introduced with different combinations of features and several known classification techniques. We produce an enhanced performance level through the prediction model for heart disease with the hybrid random forest with a linear model (HRFLM). Based on the sensors connected with the human body the ML algorithm forecast the future health status.
SENTIMENT ANALYSIS OF COVID-19 VACCINE TWEETS
In the last decade, social media has emerged as the largest centralized source of opinions, expressions, blogs and micro-blogs, news, and other information. It has presented a great opportunity for the researchers, industries, and governments to understand the behavior of their customers and constituents to better align their products and services with their customers’ and citizens’ requirements. Among the social media sources, Twitter is a unique source in that data (microblogs) is unstructured and is available for free. Twitter is used widely across the globe and its microblog concept lends itself to analyze the underlying sentiment. A recent debate has been on the COVID vaccines – whether the potential benefits outweigh the side effects. Currently, there are many vaccines available with different claimed efficacy against the virus. The varying efficacies of these vaccines have attracted a public discourse. This research aims to analyze COVID-19 vaccines related tweets to better understand the pattern of public sentiments and opinions about the vaccines with respect to their side effects, potency, availability, and efficacy. The tweets are categorized and analyzed based on their polarity and subjectivity towards the vaccines. To perform the classification of tweets based on aspects, machine learning techniques such as Logistic Regression (LR), Naïve Bayes (MNB), and Support Vector Machine (SVM) along with deep learning technique Long Term Short Memory (LSTM) are used. All these classification algorithms are then compared and evaluated on the measures of precision, recall, accuracy score and F1-score. Apart from categorization and classification, topic modelling method LDA is used to extract the topics based on their similarity and frequency that can sum up the sentiment of common public towards whole process of COVID vaccine. Based on 60,000 tweets between 1-March-2021 to 31-May-2021, overall public sentiment for vaccine indicated a positive trend. Analyzing aspects of vaccines, efficacy of vaccines has turned out to be the most positive aspect which has encouraged people to advocate for vaccines. With evaluation and comparison of model’s performance, bidirectional LSTM with 92% accuracy has outperformed all the machine learning algorithms. Among all the machine learning algorithms based on different vectorization techniques, Logistic Regression achieved the highest accuracy of 73% with count vectorizer while SVM and MNB got accuracy of 64% only. Topic modeling with LDA for whole dataset with 60,000 tweets, yielded 4optimum number of topics with coherence score of 0.625. Most of the topics had common theme of availability, efficacy, and side effect.
METHODS FOR CLASSIFICATION OF IMBALANCED DATA: A REVIEW
Imbalance in dataset enforces numerous challenges to implementing data analytics in all existing real-world applications using machine learning. Data imbalance occurs when the sample size from a class is very small or large than another class. The performance of predicted models is greatly affected when the dataset is highly imbalanced and the sample size increases. Overall, Imbalanced training data have a major negative impact on performance. Leading machine learning techniques combat the imbalanced dataset by focusing on avoiding the minority class and reducing the inaccuracy for the majority class. This article presents a review of different approaches to classifying imbalanced dataset and their application areas.
QUANTUM COMPUTING: A REVIEW ON BIG DATA ANALYTICS AND DATA SECURITY
The world's most difficult problem during the preceding decade was the big data problem. The big data challenge refers to the fact that data is growing at a much faster rate than computational rates. People, as well as virtually all organizations and scientific institutions, are keeping a rising amount of data as the cost of storage falls every day. Large amounts of data are generated by social activities, scientific inquiries, biological discoveries, and sensor devices. Big data is beneficial to society and economy, but it also poses challenges to scientific communities. Traditional tools, machine learning algorithms, and methodologies cannot handle, manage, or analyze large amounts of data. Quantum computing, in the realm of Big Data, allows businesses to collect and analyze large volumes of data quickly using quantum algorithms. Separate data sets may be detected, analyzed, integrated, and diagnosed with far greater ease. To find patterns, all of the items of a large database can be studied at the same time. As a result, it may be years before quantum computing makes its way into most businesses or becomes a common data analytics tool. Quantum computing will still be a relatively new technology in 2021. Machine learning algorithms are currently improving thanks to breakthroughs in quantum computing technology. There's still a lot to learn about quantum computing's capabilities and the implications of such a strong technology.
A REVIEW ON SENTIMENT ANALYSIS
Sentiment analysis is an technique which identifies text from different kind of image and dataset. It also characterized the data from Twitter, Facebook and YouTube. Sentiment analysis is a part of machine learning technique which find the trends of data and predict future trend. This paper shows different kind of machine learning and lexicon analysis methodology in the current trend and also we compare different kind of algorithms in sentiment analysis in different dataset.
CLASS IMBALANCE PROBLEM IN DATA SCIENCE: REVIEW
In last few years there are many changes and evolution has been done on the classification of data. Application area of technology increases then the size of data also increases. Classification of data becomes difficult because of imbalance nature and unbounded size of data. Class imbalance problem becomes the greatest issue in the data mining. Imbalance problem occurs where one of the two classes having more sample than the other classes. The most of the algorithms are focusing on the classification of major sample while ignoring minority sample. The minority samples are those samples that rarely occur but are very important. There are different methods available for the classification of imbalance data set which is divided into three categories i.e. the algorithmic approach, feature selection approach and the data processing approach. These approaches have their own advantages and disadvantages. In this paper a systematic study of each process is defined which gives the right direction for research in class imbalance problem.
SELF NANOEMULSIFYING DRUG DELIVERY SYSTEM: AN UPDATED REVIEW
Self- nano emulsifying drug delivery system (SNEDDS) is getting popularity for enhancing the solubility of hydrophobic drugs. SNEDDS are employed to solve issues regarding low bioavailability of poorly soluble and greatly permeable compounds. The SNEDDS are isotropic mixtures of solvents, co-solvents, surfactants and oil. The chief characteristic of SNEDDS is their capability to make acceptable Microemulsions or oil‐in‐water (o/w) emulsions with mild agitation. SNEDDS comprise a less content of lipid whereas greater amount of lipophobic surfactants and co-surfactants. Usually SNEDDS prepared as liquid, but several methods have been developed for converting liquid SNEEDS into solid forms, extrusion, and melting, spray-drying, adsorption onto porous carriers freeze drying. Due to vast development of this technology, SNEDDS will assist novel applications in drug delivery and resolve difficulties related with the poorly soluble drug delivery.
FLOATING MICROSPHERE AS GASTRO RETENTIVE DRUG DELIVERY SYSTEM: AN UPDATED REVIEW
Floating drug delivery system is anticipated to float in long lasting way when come in contact with the gastric contents and subsequently to improve the drugs bioavailability. The different floating formulations which include powders, tablets, pills, capsules, granules and films are presently under research and development. Floating microspheres (FMs) are particularly getting importance because of their varied applicability for drug targeting to stomach. These gastro retentive FMs are systems having less density have the potential to float and distributed evenly through the entire gastric fluid, lessen the chances of gastric emptying and ensure the drug release for longer period of time. The FMs also enhances the patient compliance by diminishing the frequency of dosage. By employing FMs, significant therapeutic effect could be attained from drugs having short half-life. In this review we will discuss fundamentals of floating microspheres along with recent applications of floating microspheres.
STABILITY AND BIODISTRIBUTION STUDY OF TARAXACUM OFFICINALE NIOSOMES
Recent progress in the study of such plants has resulted in isolation of about different phytoconstituents from plants. Among them 110 species of different plant families are mentioned their specific parts having protective action against certain liver disorder. Over the past several years, great advances have been made on development of novel drug delivery system (NDDS) for plant actives and extracts. The variety of novel herbal formulations like polymeric nanoparticles, microspheres, transferosomes and ethosomes has been reported using bioactive and plant extracts. The biodistribution pattern says that the dandelion loaded niosomes minimized the toxicity associated with plain dandelion solution. Vesicular systems not only help in targeting the drug the liver, but also help in providing controlled parenteral delivery by preventing metabolism of the drug from the enzymes present at the hepatic cells. Longer circulation of the niosomes showed that, incorporation of dandelion into the niosomes helped to increase the stability of dandelion by preventing it from chemical and enzymatic degradation. vesicles consisting of one or more surfactant bilayers enclosing aqueous spaces called niosomes have been considered of particular interest as they offer several advantages over liposomes with respect to chemical stability, lower cost and availability of materials. Lyophilized niosomal formulation (Proniosomes) were stable when stored at both refrigerated and room temperature.
ARTIFICIAL INTELLIGENCE AND INTERNET OF THING BASED SMART DEFENSE HELMET
Military Helmet and Surveillance System is a concept model of an IOT based wearable device for military purposes. This idea can fulfill several use cases in the military. MHSS is likewise an upgraded adaptation of Battlefield Management System – BMS (a framework intended to incorporate data securing and preparing to improve order and control of a military unit).These framework additionally underpins the popular movement (Integrated Knowledge based Command and Control), a framework intended to incorporate data securing and preparing to upgrade order and control of a military unit. MHSS makes it easy for the soldiers to know several parameters such as their fighting direction, surrounding conditions, sending messages to base station, etc. it provides a simple to use interface . They can get assistance from the base through the wearable helmet device and at the same time a log is created about those parameters which is the main function of Military soldiers and Surveillance System.
BENEFITS OF USING SMART GAS MONITORING SYSTEMS IN OIL AND GAS INDUSTRY USING INDUSTRIAL INTERNET OF THINGS
The industrial Internet of Things (IIoT) is the use of Internet of Things technologies to transform manufacturing and industrial processes. Also known as “industry 4.0”, Industrial IoT integrates big data and machine learning technologies to harness the sensor data, M2M communication and Automation technologies. The world has been rapidly adapting smart technologies and connecting everything, which implies everything. To provide the greatest quality service to its consumers, many establishments such as hotels, canteens, and enterprises use flammable gases such as LPG, carbon dioxide, ammonia, and others. The usage of these gases has undoubtedly made technologies smarter, but it also comes with a risk aspect that poses a threat to human life. As a result, safety becomes a major concern. As a result of this element, a gas detection system was deemed necessary in such accident-prone areas, allowing for continuous monitoring of any form of leakage, regardless of human senses.
IMPLEMENTATION OF WEARABLE DEVICE FOR MONITORING ALZHEIMER’S PATIENTS
Thousands of persons with Alzheimer's disease and other cognitive problems go missing every year. As a result, the person with dementia must frequently keep track of their current location. As a result, a dependable and affordable monitoring system is required to improve the quality of life of these individuals and their families. Individuals and their caretakers must be constantly monitored and cared for, as well as intervene and provide support, putting a great financial and human load on them. Interconnected sensing technologies, such as wearable devices and Internet of Things (IoT) devices, offer a potential solution for objective, dependable, and remote monitoring, assessment, and support throughout one's lifetime, with the help of the environment. This suggested wearable device would combine GPS systems with a microcontroller to provide real-time monitoring of Alzheimer's sufferers when they leave their homes, ensuring their safety. The fall is detected, the caregiver is alerted, and emergency care can be given in the event of a patient collapsing. On the screen, there are also simple routine reminders.
AN IOT BASED BANK LOCKER SECURITY SYSTEM
This paper will focused on effective recognizing and controlling system for Bank locker room which is fully self determining. In cases of robberies, it’s commonly happen that the banned entrance in the locker room area which can be detected by our security system. If the robbery take place the banks are not be capable to recognize the robber due to absence of the proof by using the current human operated security system. Development of various sensors has enabled systems to have preventive and corrective measures in this regard significantly. In order to deliver a concrete security solution for critically important and confidential documents and goods, we proposed an Automated Safety Vault with Double Layered Defense Mechanism. The solution comprised of an Electronic Lock driven by password verification and a Biometric authentication for users using a Finger print scanning and sensing tool. Both of these two layers ensured the authenticity of the user by preventing any unauthorized access to the Vault. The system was then implemented in a prototype scope for testing and validation of the proposals. The implemented system and testing data showed that the Automated Safety Vault with all its security features had successful operation. The specification of the whole system as well as the results is observed and verified.