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BACKGROUND: The nutrition screening tool determines whether or not a patient is at risk of malnutrition. Many organizations contributed to the form’s design by researching various nutritional screening tools. OBJECTIVE: The purpose of this study is to analyse the different screening forms for the hospitalised geriatric patients to understand the nutritional status as well as the comorbidities. In addition, the study intends to identify the essential parameters from three nutrition screening forms-MUST,NRS-2002,MNA. The primary outcome is to identify the essential parameters from the screening form and calculating the nutrition risk score is considered a secondary outcome. METHODS: The research was done on geriatric patients who were admitted to the hospital from November 2019 to March 2020, suffering from comorbidities. The study comprises 192 hospitalized patients with an average age of 74 years including both genders. The patients’ data was collected by a certified dietician to understand the essential parameters of these nutrition screening forms. The Pearson correlation test is used to find the correlation value between the screening form parameters. Based on the analysis, features with a correlation value greater than 0.5 with the nutrition score are considered a significant parameter for the research. RESULTS: The analysis of the nutrition screening strategy demonstrates the different significant parameters of the above three screening forms. The experimentation shows, the significant parameters are the Body Mass Index, impaired nutrition status, the severity of disease, mobility, mode of feeding, nutritional status, and weight loss score. CONCLUSIONS: The research is performed to understand the correlation between the screening form parameters MNA, MUST, NRS2002 using a non-invasive method and analyze the score in patients to improve health rates by using nutritional risk therapy and nutritional care for such patients. Nutrition therapy can be applied to patients suffering from malnutrition and at risk of malnutrition to improve the health of the patient.

For cancer prediction, the prognostic stage is the main factor that helps medical experts to decide the optimal treatment for a patient. Specialists study prognostic stage information from medical reports, often in an unstructured form, and take a larger review time. The main objective of this study is to suggest a generic clinical decision-unifying staging method to extract the most reliable prognostic stage information of breast cancer from medical records of various health institutions. Additional prognostic elements should be extracted from medical reports to identify the cancer stage for getting an exact measure of cancer and improving care quality. This study has collected 465 pathological and clinical reports of breast cancer sufferers from India's reputed medical institutions. The unstructured records were found distinct from each institute. Anatomic and biologic factors are extracted from medical records using the natural language processing, machine learning and rule-based method for prognostic stage detection. This study has extracted anatomic stage, grade, estrogen receptor (ER), progesterone receptor (PR), and human epidermal growth factor receptor 2 (HER2) from medical reports with high accuracy and predicted prognostic stage for both regions. The prognostic stage prediction's average accuracy is found 92% and 82% in rural and urban areas, respectively. It was essential to combine biological and anatomical elements under a single prognostic staging method. A generic clinical decision-unifying staging method for prognostic stage detection with great accuracy in various institutions of different regional areas suggests that the proposed research improves the prognosis of breast cancer.

Anatomic stage of cancer is a key factor in cancer prognosis and provides vital information in almost all types of cancer. The anatomic stage used to represent stage of cancer which shows attributes of tumor - T, number of affected lymph node -N and distant metastasis - M. Experts retrieve the anatomic stage information from clinical and pathological reports and most of these are unstructured. The main aim of the research study is to propose generalized decision-making tool for most accurate anatomic stage extraction from medical reports of different health organizations. Most previous work considered single health organization for research; extracted only TNM value, did not identify anatomic stage from TNM value; used pathology reports only. This research addresses the extraction of anatomic stage from Breast Cancer medical reports using Natural Language Processing because most of these reports are unstructured and require text processing to extract stage information from them. To detect most accurate anatomic stage, TNM information will be extracted from medical reports using NLP, machine learning technique and rule-based methodology. Average T, N and M accuracy for the rural region is 98%, 94% and 99% respectively whereas for urban region, average accuracy is 95%, 88% and 98% respectively. The average stage accuracy for rural and urban region is 93% and 85% respectively. 87% average stage accuracy achieved in both regions. Anatomic stage extraction with high accuracy in different health organizations of different regions shows that the proposed study is promising and generalized for most accurate anatomic stage extraction.

Chatbot is a popular application used to converse with a computer-based system in an interactive and amicable manner. It enables a person to interact with the vast amount of knowledge in an easier and cost-effective way. Chatbots are able to answer the questions using its knowledge base just like a human expert, the only exception is that it takes mere minutes or even seconds for a chatbot to learn as opposed to the human’s ability to acquire the same knowledge over a period of months or years. The learning rate depends on the algorithm applied, processing speed of the system and the amount of data to train the model. This particular characteristic of quick learning proves valuable in all application domains and more so in the health services. In this paper, we propose a health chatbot developed using deep learning techniques of complex neural networks. The model is trained on a Knowledge Base which consists of health queries and its answers. These question-answer pairs were obtained manually from WebMD, which is one of the leading health service websites on the internet. An accuracy of 0.97% and loss of 0.02%, in terms of correct answer prediction for a given query, was obtained while training the deep learning model.

Requirement Engineering (RE) plays an integral role throughout the process of software development. Requirement identification and prioritisation are the foremost phases of the RE process. Latest RE research work uses Machine Learning (ML) algorithms to tackle RE problems such as identifying requirements and assigning priorities to requirements, which have given better results than that of traditional natural language processing methods. An adequate understanding of these ML methods, however, is still lacking. The aim of this study is to understand which of the ML algorithms is likely to classify and prioritise the requirements efficiently and how they can be evaluated. It is observed that the current approaches are having constraints of scalability and complexity. Different methods used for the text preprocessing of requirements from SRS and user reviews are also proposed. 6 different ML algorithms and 6 different prioritisation algorithms, which are most common methods, are found. The most popular performance parameters used are accuracy, precision and recall. The limitations of these ML approaches are irrespective of dependency of requirements, priorities are assigned to requirements, the results with respect to scalability and speed is inferior.

The COVID-19 infection caused by Novel Corona Virus has been declared a pandemic and a public health emergency of international concern. Infections caused by Corona Virus have been previously recognized in people and is known to cause Middle East Respiratory Syndrome (MERS) and Severe Acute Respiratory Syndrome (SARS). Unlike the earlier infections, COVID19 spreads alarmingly and the experience and volume of the scientific knowledge on the virus is small and lacks substantiation. To manage this crisis, Artificial intelligence (AI) promises to play a key role in understanding and addressing the COVID-19 crisis. It tends to be valuable to identify the infection, analyse it, treat it and also predict the stages of infection. Artificial intelligence algorithms can be applied to make diagnosis of COVID-19 and stepping up research and therapy. The paper explains a detailed flowchart of COVID-19 patient and discusses the use of AI at various stages. The preliminary contribution of the paper is in identifying the stages where the use of Artificial Intelligence and its allied fields can help in managing COVID-19 patient and paves a road for systematic research in future.

For cancer prediction, the prognostic stage is the main factor that helps medical experts to decide the optimal treatment for a patient. Specialists study prognostic stage information from medical reports, often in an unstructured form, and take a larger review time. The main objective of this study is to suggest a generic clinical decision-unifying staging method to extract the most reliable prognostic stage information of breast cancer from medical records of various health institutions. Additional prognostic elements should be extracted from medical reports to identify the cancer stage for getting an exact measure of cancer and improving care quality. This study has collected 465 pathological and clinical reports of breast cancer sufferers from India's reputed medical institutions. The unstructured records were found distinct from each institute. Anatomic and biologic factors are extracted from medical records using the natural language processing, machine learning and rule-based method for prognostic stage detection. This study has extracted anatomic stage, grade, estrogen receptor (ER), progesterone receptor (PR), and human epidermal growth factor receptor 2 (HER2) from medical reports with high accuracy and predicted prognostic stage for both regions. The prognostic stage prediction's average accuracy is found 92% and 82% in rural and urban areas, respectively. It was essential to combine biological and anatomical elements under a single prognostic staging method. A generic clinical decision-unifying staging method for prognostic stage detection with great accuracy in various institutions of different regional areas suggests that the proposed research improves the prognosis of breast cancer.

Software Development Lifecycle (SDLC) is a systematic process used to achieve high quality software that meets customer requirements. During SDLC requirements, engineering plays an important role. Prioritisation helps to focus on the most important requirements in terms of importance, cost, penalty, time and risk. Stakeholders (users, developers) of the software product identify requirements. The two major activities of requirement engineering process are requirements classification and requirements prioritisation. Sometimes requirement mentioned by stakeholder can be of both types, i.e. functional and non-functional. So it is challenging to classify requirements separately in two different categories. There are many fundamental prioritisation techniques available to prioritise software requirements. In this paper, we have compared existing requirements prioritisation techniques based on ease of use, speed, scalability and accuracy. Our literature study suggests that the appropriate requirements prioritisation technique has to be selected that can help software developer to minimise the risk, penalty. In automating various tasks of software engineering, machine learning (ML) has shown useful positive impact. This paper discusses the various algorithms used to classify and prioritise the software requirements. The results in terms of performance, scalability and accuracy from different studies are contradictory in nature due to variations in research methodologies and the type of dataset used. Based on the literature survey conducted, we propose a new architecture that will use both types of datasets, i.e. Software Requirement Specifications (SRS) and user text reviews to create a generalised model. Our proposed architecture will attempt to extract features which can be used to train the model using ML algorithms. The ML algorithms for classifying and prioritising software requirements will be developed and assessed based on performance, scalability and accuracy.

The object-oriented programming is widely adopted in recent software developments. The development of a well-designed software system is needed that reduces software maintenance costs. On the other hand, the internal structure of the software system is deteriorating due to prolong maintenance operations. In such cases, restructuring is one of the strategies to strengthen the system’s overall internal structure without changing its external behavior. Another restructuring strategy is to use refactoring on the current system. Code refactoring is an effective approach to software development to improve the internal structure of the program. Through refactoring, the quality of the program can be enhanced by maintenance and improvement in reliability. Code refactoring is done without any modification in its features. Cohesion is used to assess a software system’s design quality and is the main pillar of object-oriented software development in good software design. Using software metrics, the quality of object-oriented classes that require code refactoring is assessed. This work proposes the need for refactoring and focuses on exploring how to use object-oriented metrics as guidance where code refactoring may be used. We present object-oriented software metric, i.e., cohesion metric and analyzes the need of metric for restructuring.

It is a big challenge to assess and monitor the well-being of geriatric people due to the rise in the average population and keep track of their health status on a daily basis. Nutrition screening is a method used to find the nutrition risk in patients that can be improved by nutritional therapy. Nutrition screening strategy helps to evaluate the nutrition risk of patients which can be improved by nutritional therapy. There are different types of screening forms identified based on age group and hospital settings. The primary objective of the paper is to study the screening forms MUST, NRS-2002, and MNA to identify the correlation between the form parameters using statistical methods. The purpose of this study is to design a system to understand the nutritional status of the patient and identify patients that will be benefited from nutrition therapy. A classification algorithm of data mining is proposed to identify patients with comorbidities and hence suffering from malnutrition. It will help physicians to take the daily decision-making activities to prevent the events before occurring. The patients’ health care can be monitored by the use of devices like mobile applications by observing patients’ nutrition intake. The developed system helps to keep track of the geriatric patient’s health and improve the method of how dietician or practitioner delivers care when the patient is monitored remotely.

Producing a well-designed software system is always required, as a result of that largely maintenance cost of software is reduced and reusability of the software is improved. On the other hand, the internal structure of software system is deteriorating because of prolonged maintenance activities. In these circumstances, one technique to get the better overall internal structure of the system without changing its external behavior is restructuring. One technique to carry out restructuring is to use refactoring on the existing source code. Code refactoring is a strong approach in software development in turn to enhance the internal structure of the software without changing its external behavior. The purpose of refactoring is to increase the cohesion of existing code and reducing coupling in the existing code of software system. Different methods have been proposed to increase the cohesion of the system. Clustering is one of the techniques used for improving the cohesion of the system. Clustering, a data mining technique, is used for grouping things based on the characteristics, so that it can be used to overhaul the class structure of the software system. Clustering algorithms are used to find the refactoring opportunity. In this paper, we propose a new method for software restructuring using clustering with multiple refactoring methods. The proposed method utilizes multiple refactoring methods to increase the cohesion of the part with no change in the meaning of software system. The benefit of the proposed method is likely to increase the effectiveness of the software system by improving its cohesion.

Cancer staging system is the most important prognostic factor and performs key role in most of the cancer subtypes. It helps to interpret patient’s prognosis and helps to decide treatment option. It is also used by cancer registry to analyze cancer statistics, existing cases and death rate. Cancer staging system is also known as TNM staging system which includes factors T—attributes of tumor, N—involvement of lymph node and M—distant metastasis. Pathological reports of cancer patients include information related to cancer stage. Most of the pathological digital reports are in unstructured form. Specialists have to read all reports thoroughly for detection of cancer stage. This is often tough process and takes lot of time. Tumor registrars of cancer registry perform manual cancer stage data collection required for cancer registry which may cause accidental human mistake and degrades data of cancer registry. This study proposes automated extraction of TNM cancer staging from free text pathological reports of breast cancer patients. To find out most accurate stage of cancer, pTNM will be extracted from pathological reports using combination of pattern matching and rule-based technique. The main goal of proposed work is to make generalized system for breast cancer stage detection by collecting reports from different institutions. Because different hospitals have different ways of describing staging information, report format, structure and style of medical terms representation and it is difficult task in natural language processing.

Pregnancy-induced hypertension (PIH) is a foremost reason for disease and death in maternal, fetal, and neonatal babies. Women having PIH are at greater risk of intrauterine growth retardation in fetuses, premature delivery of a baby, and intrauterine death. Machine Learning has been widely used in an array of applications in the healthcare domain for analyzing data. The aim of this study by the authors is to predict the PIH levels using supervised learning algorithms with an aim to prevent PIH-related complications. The study works on a data set of about 100 pregnant women between the age group of 18–32. The data set uses 19 predictor variables like body surface area (BSA), pulse rate (PR), systolic blood pressure (SBP), and diastolic blood pressure (DBP). SBP and DBP variables are considered to predict the PIH level of the pregnant woman. This work shows that the accuracy achieved by the use of decision tree (90%) is better than that of support vector machine (86.667%) and logistics regression (83.334%) algorithms used in earlier work.

Document classification particularly in biomedical research plays a vital role in extracting knowledge from medical literature, journal, article and report. To extract meaningful information such as signs, symptoms, diagnoses and treatments of any disease by classification, the context needs to be considered. The need to automatically extract key information from medical text has been widely accepted and it has been proven that search based approaches are limited in their ability. This paper presents a novel method of information identification for a particular disease using Gaussian Naive Bayes and feature weighting approach that is then classified by the context. It is useful to enhance the effectiveness of analytics by considering the importance of the term as well as the probability of every feature of the disease during classification. Experimental results show that our method upgrades performance of classification system and is an improvement from traditional classification system.

Data analysis plays a significant role in handling a large amount of data in the healthcare. The previous medical researches based on handling and assimilate a huge amount of hospital data instead of prediction. Due to an enormous amount of data growth in the biomedical and healthcare field the accurate analysis of medical data becomes propitious for earlier detection of disease and patient care. However, the accuracy decreases when the medical data is partially missing. To overcome the problem of missing medical data, we perform data cleaning and imputation to transform the incomplete data to complete data. We are working on heart disease prediction on the basis of the dataset with help of Naive bayes and KNN algorithm. To extend this work, we propose the disease risk prediction using structured data. We use convolutional neural network based unimodel disease risk prediction algorithm. The prediction accuracy of CNN-UDRP algorithm reaches more than 65%. Moreover, this system answers the question related to disease which people face in their life.

The NUTRIC score (Nutrition Risk in the Critically Ill) is a method designed to calculate the risk of critically ill patients developing adverse conditions that is likely be improved by optimal nutritional therapy. The objective of this study is to validate the NUTRIC score for aggressive nutrition therapy and identify critically ill patients that are most likely to benefit from macronutrients with an aim to improving mortality rate. A logistic model is proposed to identify the relationship between the observed parameters of NUTRIC score like age, APACHE II score, SOFA score and number of co-morbidities using statistical methods and further authenticate it. The relationship between these parameters is often aggravated in the ICU due to the hypermetabolic nature of critical illness. This care can be extended by use of IoT devices which may be wearable for monitoring patients health. It will help not only to keep track patient's health, but to improve how the physicians deliver care as well. New technology innovations can be leveraged in nutritional health care context to monitor patients remotely.

Testing a product has become one of the most important tasks for any organization (Be it small scale or large scale). Without testing the product, it is not delivered to the customer. Testing is an ongoing activity from the beginning of a product’s development. A performance testing suite shall be developed using Apache JMeter for the purpose of testing a product. To perform performance testing on client- and server-type softwares, a 100% pure Java application named Apache JMeter is used. Apache JMeter is not a browser, it works at protocol level. Static and dynamic resources performance testing can be done using JMeter. A high level performance testing suite will be developed in capturing aspects of performance at UI and System level. Developing the testing suite helps in saving the time and cost of the organization. The discussion follows and describes benefits of performance testing and the performance testing suite.

User satisfaction is recognized as an important contributor to the success of software applications. It is subjective and influenced by several factors that are linked to the non-functional requirements. Although non-functional requirements provide good criteria for selection of web service in Service-Oriented Architecture, specifying them during the discovery process is a difficult task. Dealing with the non-functional requirements that have an inherent ability to conflict is a complex task and modeling them can facilitate analyzing them. The aim of our research work is to propose a framework to improve design stage during web service selection so as to produce minimal conflicts and dependencies between requirements. We argue that the design structure that best supports and embodies the user constraints is the one that best meets a user's needs. Our framework is proposed after a detailed study of the issues and challenges identified by published literature by researchers working on non-functional requirement To exemplify our work we consider a Remote Patient Monitoring System scenario and propose a complete framework that will take the user requirements as input and analyze how they interact using graph transformation rules. The output matrix of dependencies can help the designer to select the most desirable design solution. We make use of simple natural language processing techniques, graph transformation and the aspect oriented paradigm. By integrating the framework into the design phase, it is possible to limit the impact of changes and also to understand in advance the likely interdependencies. conflicts.

searching methods have advanced the increased risk of privacy disclosure makes it important to protect privacy of user during data publishing. Many of the algorithms used for the data de-identification are not efficient because resulted dataset can easily linked with the public database and it reveals the users identity. One of the method uses for protecting the privacy of user is to apply anonymization algorithms. TDS and TDR using generalization of method to anonymized the dataset. Major drawback as these algorithm is they requires a manually generated domain hierarchy taxonomy for every quasi-identifier in the data set on which k- anonymity has to be performed. Therefore, in this paper we propose new approach which will makes use of suppression based k-anonymization method to allow data publisher to de- identify datasets and in this method only certain attributes from record are suppressed based on values other attributes. As suppression method is used in algorithm, it does not required manually created taxonomy tree of quasi-identifiers. We applied this algorithm on 3 different data sets to evaluate its accuracy as compared to other k-anonymity generalization algorithms. It is found that predicative performance of this algorithm is better than existing generalization methods. This method is expected to provide privacy and accuracy measures to data publishers.

The process of generating UML Diagrams from natural language specification is a highly challenging task. This paper proposes a method and tool to facilitate the requirements analysis process and extract UML diagrams from textual requirements using natural language processing (NLP) and Domain Ontology techniques. Requirements engineers analyze requirements manually to understand the scope of the system. The time spent on the analysis and the low quality of human analysis justifies the need of a tool for better understanding of the system. “Requirement analysis to Provide Instant Diagrams (RAPID)” is a desktop tool to assist requirements analysts and Software Engineering students to analyze textual requirements, finding core concepts and its relationships, and extraction UML diagrams. The evaluation of RAPID system is in the process and will be conducted through two forms of evaluation, experimental and expert evaluation.

Markup Language (XML) is a simple text format which was designed to describe data using custom tags. The use of custom tags makes XML extremely flexible and enables it to not only describe structured data like information from a table of relational database but also semi-structured data. An XML document is self-describing which has made it a standard means of data exchange between applications and for use in configuration files of enterprise applications. The increasing preference to store and transmit data in the XML format has led to a need for searching these data stores for information. Query languages like Xpath and XQuery are used to retrieve information from xml document. But these query languages are complex for non expert user to learn. Keyword search allows such user to retrieve information without knowledge of complex query language. In this paper we proposed an algorithm for relevance ranking of nodes which retrieved as result by considering keyword ambiguity and intension of user. General Terms Information Retrieval.

Substantial accumulations of computerized photos are consistently made. Various collections of digital photos like in the area of technology, private organization, etc. are structured because of the result of digitizing existing accumulations of simple pictures, charts, outlines, canvases, and archives. For the most part, the main strategy for investigating these accumulations was by perusing or indexing of index words, Digital photo databases begin the best approach to searching based on content. In this paper, we have shown a strategy that has no past information about the picture inside the database, yet retrieval is carried out considering the content data of the pictures prone to be called as content based on image retrieval. Here we are attempting to enhance the image retrieval framework for more exactness and efficiency by utilizing Radial base Function neural system. This arrangement with multilayer feed forward system recognition. By utilizing these procedures we can get into know the exact relevant image gave by the client according to the query. The Scale Invariant Feature Transform (SIFT) is a standout amongst the most nearby feature detector and descriptors which is utilized as a part of the vast majority of the vision programming. In this paper in regards to CBIR framework we can use SIFT algorithm to concentrate the nearby feature of the pictures. The retrieval method has been implemented on two CBIR systems, one using multiple features and the other using only RBG feature. The results obtained are positive and we have obtained higher precision (81.93%) using multiple features than that of using only RGB (41.18%). Experiment demonstrates that the proposed system get better results of retrieval system.

Aspect-oriented modeling (AOM) has been developed to modularize crosscutting concerns appropriately in UML models. In software engineering, aspects are concerns that cut across multiple modules. In requirements modeling, we analyze interactions and potential inconsistencies. We use UML to model requirements in a use case driven approach. During requirements specification a structural model of the problem domain is captured with a class diagram. Use cases refined by activities are the join points to compose crosscutting concerns. Graph transformation systems provide analysis support for detecting potential conflicts and dependencies between rule-based transformations.

Association rule mining is used to uncover closely related item sets in transactions for deciding business policies. Apriori algorithm is widely adopted is association rule mining for generating closely related item sets. Traditional apriori algorithm is space and time consuming since it requires repeated scanning of whole transaction database. In this paper we propose improved apriori algorithm based on compressed transaction database. Transaction database is compressed based on the consequence of interest.

As more and more web services that provide the same functionality are developed, it is a major issue to consider not only the functional requirements but also the non functional requirement (NFR) during web service discovery process. The NFR define the Quality of Service (QoS) of the system. Much work has been done on web service discovery process that considers the functional requirements as selection criteria. However not many service discovery strategies consider the QoS properties. On the basis of our study and survey, we advocate that the best performance is achieved by use of both functional and NFR properties. We believe that the user NFR should be expressed explicitly to aid in selecting a web service that best fulfils them. To support this claim we present a detailed survey of web service discovery systems that makes use of QoS factors. Paper also highlights the importance of ontology to represent the non functional requirements in web service discovery. We emphasize how Ontology if built and refined by domain experts can be used for web service discovery process with the purpose of reuse and improved design.

The necessity of finding right skilled people, sharing resource and limitation on cost has made distributed software development indispensable. In a distributed development project, but are working collaboratively toward the outcome. Such offshore service providers follow the traditional process models. Agile practices promote development iterations, open collaboration, and process adaptability throughout the life cycle of the project. Adapting these practices in a distributed environment can help distributed development tackle the challenges of cultural incompatibility, leadership struggle and lack of trust. This paper describes the benefits of using Agile process and Scrum the iterative incremental process in Distributed Software development, and proposes two team structures for its implementation.