Prevalence and pattern of anaemia and correlation with booking status in a new Medical College in Haryana, India

Background: Anaemia is the commonest medical disorder in pregnancy. It is a leading cause of maternal morbidity and mortality.  This study analyses the   prevalence and pattern of anaemia and correlates it with booking status in a   new medical college in rural Haryana.Methods: This retrospective study was conducted to analyze prevalence, severity and morphology of anaemia in hospitalized pregnant patients at the time of labour in a new medical college in rural Haryana.  Antenatal booking status was correlated with haemoglobin levels and severity. The study was conducted over a period of six months from Nov ’18 to April’19.Results: 390 singleton labour patients at or near term with no other known medical complications were evaluated. Prevalence of anaemia in the centre serving as a referral with onsite blood bank facilities was as high as 79.7 %. 47.9%   of patients did not have even a single antenatal visit. 50.8% had microcytic hypochromic anaemia followed by 32.3% who had normal morphological picture; dimorphic was 14% and macrocytic 2.8%. The prevalence in booked patients was 78.91% compared to 80.1% in unbooked.Conclusions: Anaemia continues to be a major challenge to the obstetric services despite targeted efforts by the government and various organizations to provide free prophylaxis. Iron deficiency or nutritional anaemia is the commonest.  However, booking visits, counselling and free distribution of iron tablets doesn’t ensure that the patient is protected from anaemia. This raises concerns about compliance and hence effectiveness of oral iron therapy. An aggressive strategy for diagnosis, prophylaxis and treatment of anaemia as well as a method to ensure compliance must be developed

Learning-Based Routing in Cognitive Networks

Intelligent Routing can influence the overall performance of a communication network’s throughput and efficiency. Routing strategies is required to adapt to changing network loads and different topologies. Learning from the network environment, in order to optimally adapt the network settings, is an essential requirement for providing efficient communication services in such environments. Cognitive networks are capable of learning and reasoning. They can energetically adapt to varying network conditions in order to optimize end-to-end performance and utilize network resources. In this paper we will focus machine learning in routing scheme that includes routing awareness, a routing reconfiguration

Flow-Based Rules Generation for Intrusion Detection System using Machine Learning Approach

Rapid increase in internet users also brought new ways of privacy and security exploitation. Intrusion is one of such attacks in which an authorized user can access system resources and is major concern for cyber security community. Although AV and firewall companies work hard to cope with this kind of attacks and generate signatures for such exploits but still, they are lagging behind badly in this race. This research proposes an approach to ease the task of rules generationby making use of machine learning for this purpose. We used 17 network features to train a random forest classifier and this trained classifier is then translated into rules which can easily be integrated with most commonly used firewalls like snort and suricata etc. This work targets five kind of attacks: brute force, denial of service, HTTP DoS, infiltrate from inside and SSH brute force. Separate rules are generated for each kind of attack. As not every generated rule contributes toward detection that's why an evaluation mechanism is also used which selects the best rule on the basis of precision and f-measure values. Generated rules for some attacks have 100% precision with detection rate of more than 99% which represents effectiveness of this approach on traditional firewalls. As our proposed system translates trained classifier model into set of rules for firewalls so it is not only effective for rules generation but also give machine learning characteristics to traditional firewall to some extent.&nbsp

Identifying Optimal Parameters And Their Impact For Predicting Credit Card Defaulters Using Machine-Learning Algorithms

Data mining and Machine learning are the emerging technologies that are rapidly spreading in every field of life due to their beneficial aspects. The financial sector also makes use of these technologies. Many research studies regarding banking data analysis have been performed using machine learning techniques. These research studies also have many Problems as the main focus of these studies was to achieve high accuracy and some of them only perform comparative analysis of different classifier's performance. Another major drawback of these studies was that they do not identify any optimal parameters and their impact. In this research, we have identified optimal parameters. These parameters are valuable for performing the credit scoring process and might also be used to predict credit card defaulters. We also find their impact on the results. We have used feature selection and classification techniques to identify optimal parameters and their impact on credit card defaulters identification. We have introduced three classifiers which are Kstar, SMO and Multilayer perceptron and repeat the process of classification and feature selection for every classifier. First, we apply feature selection techniques to our dataset with each classifier to find out possible optimal parameters and In the next phase, we use classification to find the impact of possible optimal parameters and proved our findings. In each round of classification, we have used different parameters available in the dataset every time we include and exclude some parameters and noted the results of each run of classification with each classifier and in this way, we identify the optimal parameters and their impact on the results Whereas we also analyze the performance of classifiers. To perform this research study, we use the “credit card defaults” dataset which we obtained from UCI Machine learning online repository. We use two feature selection techniques that include ranker approach and evolutionary search method and after that, we also apply classification techniques on the dataset. This research can help to reduce the complexities of the credit scoring process. Through this study, we identify up to six optimal parameters and also find their impact on the performance of classifiers. Further We also identify that multilayer perceptron was the best performing classifier out of three. This research work can also be extended to other fields in the future where we use this mechanism to find out optimal parameters and their impact can help us to predict the  results.  &nbsp

Isolation of 4,5- O

There is a continual need to develop novel and effective melanogenesis inhibitors for the prevention of hyperpigmentation disorders. The plant Artemisia capillaris Thunberg (Oriental Wormwood) was screened for antipigmentation activity using murine cultured cells (B16-F10 malignant melanocytes). Activity-based fractionation using HPLC and NMR analyses identified the compound 4,5-O-dicaffeoylquinic acid as an active component in this plant. 4,5-O-Dicaffeoylquinic acid significantly reduced melanin synthesis and tyrosinase activity in a dose-dependent manner in the melanocytes. In addition, 4,5-O-dicaffeoylquinic acid treatment reduced the expression of tyrosinase-related protein-1. Significantly, we could validate the antipigmentation activity of this compound in vivo, using a zebrafish model. Moreover, 4,5-O-dicaffeoylquinic acid did not show toxicity in this animal model. Our discovery of 4,5-O-dicaffeoylquinic acid as an inhibitor of pigmentation that is active in vivo shows that this compound can be developed as an active component for formulations to treat pigmentation disorders

Usability Evaluation of Online Educational Applications in COVID-19

COVID-19 is a pandemic faced by almost every country in the world, this has resulted in health crisis. Due to COVID-19, all the countries around the world have decided to close all educational institutes to prevent this pandemic. Educational institutes have taken every possible measure to minimize the impact of the closure of schools and introduce the concept of an online education system which is not only a massive shock for parents but it also affects the children's learning process and social life. The educational applications (Apps) are very important, because they offer more opportunities for development and growth to society. In this pandemic situation, educational Apps like Zoom, HEC LMS, Google Classroom, and Skype, etc. are the need of the hour when everything goes online. In this paper, the usability features of online educational Apps are thoroughly discussed including the effectiveness and usability for students. Using the results obtained from the survey, this paper observes the student's perspective of usefulness of online educational Apps in student’s learning process of different age groups. It also analyzes the easiness for students to understand, interact and use these Apps

A Concurrence Study on Interoperability Issues in IoT and Decision Making Based Model on Data and Services being used during Inter-Operability

The Internet-of-Things (IoT) has become an important topic among researchers owing to its potential to change the way we live and use smart devices. In recent years, many research work found in the world are interrelated and convey via the existing web structure which makes a worldwide system called IoT. This study focused on the significant improvement of answers for a wider scope of gadgets and the Internet of Things IoT stages in recent years. In any case, each arrangement gives its very own IoT framework, gadgets, APIs, and information configurations promoting interoperability issues. These issues are the outcome of numerous basic issues, difficulty to create IoT application uncovering cross-stage, and additionally cross-space, trouble in connecting non-interoperable IoT gadgets to various IoT stages, what's more, eventually averts the development of IoT innovation at an enormous scale. To authorize consistent data sharing between various IoT vendors, endeavors by a few academia, industrial, and institutional groups have accelerated to support IoT interoperability. This paper plays out a far-reaching study on the cutting-edge answers for encouraging interoperability between various IoT stages. Likewise, the key difficulties in this theme are introduced

Forecasting of Intellectual Capital by Measuring Innovation Using Adaptive Neuro-Fuzzy Inference System

Purpose – The aim of every organization is to achieve its set goals and objectives as well as secure competitive advantage over its competitors. However, these cannot be achieved or actualized if staff or workers act independently and do not share ideas. Today prominent businesses are becoming more aware that the knowledge of their employees is one of their primary assets. Sometimes organizational decisions cannot be effectively made with information alone; there is need for knowledge application. An effective Knowledge Management System can give a company the competitive edge it needs to be successful, and, for that reason, knowledge Management projects should be high priority. This means that for any organization to be competitive in today’s global world there is need for combination or pooling together of ideas by employees in order to achieve teamwork; this is in support of the saying that ‘two good heads are better than one’. Due to the advent of the knowledge-based economy and the developments in activity nature of the companies at international level, intellectual capital is taken to be one of the fundamental pillars of the companies for achieving efficiency. The aim of this study is to predict the amount and effectiveness of intellectual capital or intangible assets on the basis of innovation ability of the companies using an integrated artificial neural networks fuzzy logic analysis approach in order to cope with future challenges of strategic management. Design/methodology/approach – This paper suggests some guidelines for setting up the development of valuation approach based on application and adaption of selected financial and non-financial indicators by means of artificial neural networks and fuzzy logic. The artificial neural network model is highly accurate in predicting intellectual capital of the companies. This research paper presents the construction and design of Hybrid Application using Neural Network and Fuzzy Logic. This proposed system uses a simplified algorithmic design approach with wide range of input and output membership functions. In this research a hybrid Neuro-Fuzzy systems modelling methodology is developed and applied to an empirical data set in order to determine the hidden fuzzy if-then rules. Furthermore, the proposed methodology is a valuable tool for successful knowledge management. Findings – The findings show the opinion of that the complexity of development has been improved by expansion in the amount of knowledge available to organizations. Future research should contain of high degree of study to analytically examine the successful project knowledge management in different types of plans, companies and commences. Learning comes through creating and applying knowledge, whilst learning increases an individual's and organization's knowledge asset. Both learning and knowledge management feed off the same root: learning, improved capacity to perform work tasks, ability to make effective decisions, predict future parameters on the basis of some certain parameters and positively impact the world around us. Challenges – Identification and evaluation of the significant factors that create and determine enterprise value in industry is based on complex calculations involving many variables. Regardless of this reason, existing business valuation methods for such companies have to be improved with taking into account a numerous qualitative and even additional quantitative factors.Therefore, economic experts and scientists in the field of business valuation are confronted with new challenges in determination of appropriate approaches that should be able to eliminate the disadvantages of existing valuation methods. The environment in which businesses operate is ever changing. The market has become global and the technological advancement has changed the way business is done. The resulting impact of globalization is fierce competition that has altered the business landscape. Firms are increasingly employing various techniques in order to remain relevant and competitive. Since decision making is considered as the management main elements and sometimes equivalent to management itself, it is essential that researchers pay a specific attention to this field because if decisions are made in an optimized and effective form in an organization. This work is motivated by the need for a model that addresses the study of Knowledge in specific environments such as Business and Management, where several situations are very difficult to be analyze in conventional ways and therefore is insufficient in describing the complications of represent a realistic social phenomena and their social actors. Distributed Agency methodology will be used that requires the use of all available computational techniques and interdisciplinary theories as an approach to describe the interactions between agents in the development of social phenomena. Data Mining and Neuro-Fuzzy System are also used as part of the methodology to discover and assign rules on agents that represent real-world companies and employees. Practical implications – Today most organizations have discovered that advanced trainings can be considered as the key asset for modern organizations. This study presents a forecasting model that predicts intangible assets on the basis of innovation performance in organizational training using widely applied innovation criteria. The research focused on criteria, such as organization culture, ability to respond to organizational technology changes, relationship with other organizations in the training process and the use of high technology in education. The adaptive neuro-fuzzy inference systems (ANFIS) approach has been used to verify the proposed model. It is possible to predict innovation performance and it can also adjust allocated resources to organizational training system for its innovation objectives with this method. Originality/value – A great deal of work has been published over the past decade on the application of neural networks in diverse fields. This paper presents a model that measure and forecasts the intangible assets by the development of an Adaptive Neural Network with Fuzzy Inference system (ANFIS), using data that concern human capital, organizational support and innovativeness. The results indicate that fuzzy neural networks could be an efficient system that is easy to apply in order to accurately measure and forecast the intangible assets by measuring innovation capabilities of an organization or firm

Isolation and Characterization of Isofraxidin 7- O

Abnormalities in skin pigmentation can produce disorders such as albinism or melasma. There is a research need to discover novel compounds that safely and effectively regulate pigmentation. To identify novel modulators of pigmentation, we attempted to purify compounds from a bioactive fraction of the Korean medicinal plant Artemisia capillaris Thunberg. The novel compound isofraxidin 7-O-(6′-O-p-coumaroyl)-β-glucopyranoside (compound 1) was isolated and its pigmentation activity was characterized in mammalian melanocytes. Compound 1 stimulated melanin accumulation and increased tyrosinase activity, which regulates melanin synthesis. Moreover, compound 1 increased the expression of tyrosinase and the key melanogenesis regulator microphthalmia-associated transcription factor (MITF) in melanocytes. Compared to the parent compound, isofraxidin, compound 1 produced greater effects on these pigmentation parameters. To validate compound 1 as a novel hyperpigmentation agent in vivo, we utilized the zebrafish vertebrate model. Zebrafish treated with compound 1 showed higher melanogenesis and increased tyrosinase activity. Compound 1 treated embryos had no developmental defects and displayed normal cardiac function, indicating that this compound enhanced pigmentation without producing toxicity. In summary, our results describe the characterization of novel natural product compound 1 and its bioactivity as a pigmentation enhancer, demonstrating its potential as a therapeutic to treat hypopigmentation disorders