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Predictive analytics for cardiac arrhythmia using machine intelligence
Myocardial Infarction (MI) is the primary cause of death worldwide. MI occurs when a plaque buildup in the inner surface of the coronary artery suddenly ruptures and prevents the blood flow. A heart attack is medically termed as MI. It is the irreversible damage caused by the prolonged ischemia. Ischemia is nothing but the heart organ doesn t get enough blood and oxygen which is also termed as coronary artery disease or coronary heart disease. The heart gets damaged if it has not received enough blood or oxygen. In connection to the damage of the heart, arrhythmia would occur. Arrhythmia is the problem based on the heart rhythm or rate of the heartbeat. Tachycardia, when there is a fast beat in the heart. Bradycardia, when the heart beats too slow. The common type of arrhythmia is atrial fibrillation. The great concern is that the patient who has arrhythmia has to be treated immediately. They lose consciousness in a few minutes when the heart is not pumping enough blood mainly to the brain. Death occurs when the patient is not given emergency treatment. newline Treatment which is included in the emergency is defibrillation and Cardiopulmonary Resuscitation (CPR). CPR is an emergency procedure which is combined with the chest compressions. It is through artificial ventilation which gives manual effort, preserves the brain functions until further treatment for the restoration of spontaneous blood circulation. The common symptoms of sudden cardiac death are chest pain, shortness of breath, severe wheezing, irregular heartbeats, fainting etc. newlineHeart Scar tissue which is not like heart muscle. It doesn t contrast like the normal heart muscle. Heart muscles get damaged for the heart attack patient based on the time of the treatment. The damage of the heart is based on the blockage of the artery. Arrhythmia can be predicted based on the volume of the scar region in the heart. Arrhythmia patients are treated by fixing Implantable Cardioverter Defibrillator (ICD). -
A Study on defective colouring of graphs
If different technology represents distinct colours that are to be located on some geographical region which can be represented as vertices of a graph, then the proper colouring is obtained when no two technology of same type share a common edge between the vertices they are placed on. The minimum number of technology required for such a colouring of a graph is the chromatic number of the graph. However, if the available technology are less than that of the minimum required, then the question arises on how to place the technology on the vertices of a graph in such a way that there is a minimum adjacency between the technology of same type. The solution for this problem can be attained by defining certain rules for the properness of colouring in which a few thresholds are tolerated. We know that, in a proper colouring every colour class is an independent set. If the available colours to colour a graph is less than that of the chromatic number of graphs, then a threshold that can be tolerated is permitting few colour classes to be non-independent set. An edge uv is said to be a monochromatic edge or bad edge if the colours assigned to both u and v are the same. A near proper colouring of graphs is a colouring that minimises the number of monochromatic edges by permitting few colour classes to have adjacency between the elements in it. The minimum number of monochromatic edges obtained from near proper colouring is called near defect number, denoted by B_k (G). A and#948;^((k))-colouring of graph G is a near proper colouring of G consisting of k given colours, where 1and#8804;kand#8804;and#967;(G)-1, which minimises the number of monochromatic edges by permitting at most one colour class to have adjacency among the vertices in it. The and#948;^((k))-defect number is the minimum number of monochromatic edges obtained from a and#948;^((k))-colouring of graphs and it is denoted by b_k(G). The study concerned is the further work on a near proper colouring and a and#948;^((k))-colouring of graphs. -
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Prognosis of kidney disease on ultrasound images using machine learning
Kidney diseases can affect the ability to clean the blood, filter extra water out of your blood. The kidneys failure will affect the control over blood pressure and sugar level. It can also affect red blood cell production and vitamin D metabolism which is very important for bone health. When your kidneys are damaged, waste products and fluid can build up in the body. This is harmful to the health. This damages the kidney function, can get worse over time, and when the kidneys stop working completely, this is called kidney failure or end-stage renal disease. Not all patients with kidney disease progress to kidney failure. This disease has emerged as one of the most prominent reasons of death and suffering in this century. Recent studies states that, kidney disease affects most of the population and over two million people require kidney replacement. To help prevent Chronic Kidney Diseases and lower the risk for kidney failure, control risk factors for CKD, get tested yearly, make lifestyle changes, take medicine as needed. The detection of kidney abnormalities at their early stages helps to avoid the impairment of newlinekidney. The US imaging is considered as preliminary diagnostic tool in finding various kidney diseases in the clinical imaging field. This is one of the commonly used imaging modalities due to the inexpensiveness and non-ionization nature. The presence of noise in US images, degrade newlinethe quality and clarity of the images. Also, the heterogeneous structure of kidney, makes it very difficult to detect and measure the size of stones and cysts. Hence, an automatic kidney disease detection system is highly in demand. The proposed model can assist the radiologist in accurate abnormality detection. The proposed model includes different phases such as, pre-processing, features extraction, classification and newlinesegmentation. The pre-processing phase include cropping and noise removal. Further, the GLCM and intensity-based features are extracted for the classification of abnormal kidney images.
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Prognosis of Kidney Disease on Ultrasound Images Using Machine Learning
Kidney diseases can affect the ability to clean the blood, filter extra water out of your blood. The kidneys failure will affect the control over blood pressure and sugar level. It can also affect red blood cell production and vitamin D metabolism which is very important for bone health. When your kidneys are damaged, waste products and fluid can build up in the body. This is harmful to the health. This damages the kidney function, can get worse over time, and when the kidneys stop working completely, this is called kidney failure or end-stage renal disease. Not all patients with kidney disease progress to kidney failure. This disease has emerged as one of the most prominent reasons of death and suffering in this century. Recent studies states that, kidney disease affects most of the population and over two million people require kidney replacement. To help prevent Chronic Kidney Diseases and lower the risk for kidney failure, control risk factors for CKD, get tested yearly, make lifestyle changes, take medicine as needed. The detection of kidney abnormalities at their early stages helps to avoid the impairment of newlinekidney. The US imaging is considered as preliminary diagnostic tool in finding various kidney diseases in the clinical imaging field. This is one of the commonly used imaging modalities due to the inexpensiveness and non-ionization nature. The presence of noise in US images, degrade newlinethe quality and clarity of the images. Also, the heterogeneous structure of kidney, makes it very difficult to detect and measure the size of stones and cysts. Hence, an automatic kidney disease detection system is highly in demand. The proposed model can assist the radiologist in accurate abnormality detection. The proposed model includes different phases such as, pre-processing, features extraction, classification and newlinesegmentation. The pre-processing phase include cropping and noise removal. Further, the GLCM and intensity-based features are extracted for the classification of abnormal kidney images. -
Preparation and Application of Nanoparticles and Core-Shell Nanoparticles of Transition Metals
There is an increasing need for the development of environmentally viable, economically effective, highly active and renewable catalytic systems for the various applications in the industrial field. The demand for the decolorisaion of synthetic dyes using bioremediation methods has been in a decreased mode due to its lower decomposition rates. Hence in recent years the decomposition of these organic colorants considered to be a worldwide need. The term nanocatalysis has gained huge importance in recent years due to its selectivity, higher activity, and productivity compared to their bulk materials. The nanosize, shape, and large surface to volume ratio provide unique properties to the nanomaterials. The principles of green chemistry are mainly relies on the development of catalytic systems that work similar to nature. Nanocatalyst combines both homogeneous and heterogeneous catalysis and provides rapid and selective chemical transformations with high yield and easier separation of catalyst at the end of reaction. In this work we have synthesized various metal doped magnetite nanoparticles (Ferrite nanoparticles-NdMxFe3-xO4 where M (Mn, Co, Cu, Ni)) by precipitation and hydrothermal method. The one objective of this work was to check the photocatalytic application of prepared ferrite nanoparticles for the heterogeneous photo-Fenton degradation of MB and Rh B dyes. Each dye was degraded separately under visible light and dark with the assistance of neutral pH and H2O2, in order to shows the improved activity of catalysts under visible light. The degradation experiments using the photo-Fenton systems (Fe2+/H2O2/Visible light) suggested that, the highest degradation rate was 97% for MB and 81% for Rh B within 4h and the used catalyst was NdFe3-xCuxO4, a good photo-Fenton catalyst. . We have also tried the synthesis of core- shell nanoparticles using NdFe3O4 nanoparticles with the help of polyethylene glycol as dispersing agent. The synthesized samples were characterized by various techniques like, XRD, XPS, SEM and TEM. -
Classification and Retrieval of Research Classification and Retrieval of Research Papers: A Semantic Hierarchical Approach
"Classification and Retrieval of Research papers: A Semantic Hierarchical Approach" demonstrates an effective and efficient technique for classification of Research documents pertaining to Computer Science. The explosion in the number of documents and research publications in electronic form and the need to perform a semantic search for their retrieval has been the incentive for this research. The popularity and the widespread use of electronic documents and publications, has necessitated the development of an efficient document archival and retrieval mechanism. Categorizing journal papers by assigning them relevant and meaningful classes, predicting the latent concept or the topic of research, based on the relevant terms and assigning the appropriate Classification labels is the objective of this thesis. This thesis takes a semantic approach and applies the text mining techniques in a hierarchical manner in order to classify the documents. The use of a lexicon containing domain specific terms (DSL) adds a semantic dimension to classification and document retrieval. The Concept Prediction based on Term Relevance (CPTR) technique demonstrates a semantic model for assigning concepts or topics to papers. This Thesis proposes a conceptual framework for organizing and classifying the research papers pertaining to Computer Science. The efficacy of the proposed concepts is demonstrated with the help of Classification experiments. Classification experiments reveal that the DSL technique of training works efficiently when categorization is based on keywords. The CPTR technique, on the other hand, shows very high accuracy; when the classification is based on the contents of the document. Both these techniques lend a semantic dimension to classification. Narrowing down the scope of search at each level of hierarchy enables time efficient retrieval and access of the goal documents. The hierarchical interface for Document Retrieval enables retrieval of the target documents by gradually restricting the scope of search at each level of hierarchy This work comprises of two main components. 1. The Framework for Hierarchical Classification. 2. The Hierarchical Interface for Document Retrieval. Two distinct techniques for classification are proposed in this thesis. These include 1. The use of Domain Specific Lexicon (DSL) which is comparable to a Domain Specific Ontology. 2. The Concept Prediction Based on Term Relevance (CPTR) technique These techniques lend a semantic dimension to classification. Keywords: Text Mining, Classification, Document Retrieval, Hierarchical, Domain specific lexicon (DSL), Probabilistic Latent Semantic Analysis (PLSA) , Concept Prediction based on Term frequency (CPTR) -
Multi-component condesation mediated synthesis of bioactive heterocyclic compounds
Aromatic heterocycles constitute the most diverse family of organic compounds. Moreover, aromatic heterocycles are widely used for the synthesis of dyes and polymeric materials of high value. The development of selective reactions that utilize easily available and abundant precursors for the efficient synthesis of heterocyclic compounds is a long-standing goal of chemical research. Despite the centrality of its role in a number of important research areas, including medicinal chemistry, total synthesis, and materials science, a general, selective, step-economical, and step-efficient synthesis of heterocycles is still needed. newlinePyrano[2,3-c]pyrazole derivatives have been synthesised by a one-pot multicomponent condensation of different aldehydes, dialdehydes, and ketones with malononitrile, ethyl acetoacetate, hydrazine hydrate (or phenylhydrazine) in the presence of magnetic nano-[CoFe2O4] catalyst under ultrasonic irradiation. The catalyst can be retrieved using an external magnet and used repeatedly. A practical, scalable method for obtaining various pyranopyrazoles has been demonstrated. The extraordinary catalytic role of the various catalyst has been discovered in the processes, which reveals a possible character of enhancing reaction rates and stabilising the intermediates during the course of the reactions. -
Business model of IPL: Role of product placement in creating it as a brand /
Product Placement is a new innovative and modern form of advertising and promotional mechanism in which the marketers brand their goods by placing it in sports & entertainment programs. Brand Placement when collaborated with a well respected athlete or is strategically placed on the field at a game has helped many brands draw much needed exposure to their product offerings. The modes of product placement have transformed over the years, from the traditional practice of inserting ten or thirty seconds commercials to direct placement of the brand during live game or match intervals. Brand placement has the power to persuade a consumer or to strengthen the loyalty towards a brand. -
Social media and crisis communication the new age mantra? /
The topic Social Media and Crisis communication ?? The new age mantra? is indeed one where immense amount of research is possible. Simply because social media today has become one of the most rapidly growing means of communication. The researcher through this research aims to find out if Public relations professionals use this medium extensively while communicating crisis. The theory used behind this research is ??uses and gratification theory. The research begins with the theoretical aspect of social media and crisis communication. The researcher has taken examples of a few ground breaking crisis that have taken place in the consumer world and cited their examples where in prominent use of social media has made people aware of the crisis and it is the same social media that has eventually regained peoples faith back into the brand. -
Graphene and graphene enhanced nanomaterials from biological precursors synthesis characterization and proliferant applications
Graphene family materials with non-photocatalytic biocidal properties are highly sought after in the field of biomedicine and nanobiotechnology. But the applications of graphene-based materials were often hampered by their high production cost, low yield, non-renewable precursors, harmful processing newlinetechniques, etc. In this context, this study presented the successful usage of biomass materials as sustainable feedstock for the production of graphene derivatives. Five raw materials of biological origin namely, coconut shell, wood, sugarcane bagasse, Colocasia esculenta leaves and Nelumbo nucifera leaves, were investigated. The graphitized forms of the above materials were newlineused as precursors for the graphene nanomaterial synthesis. They were chemically oxidized and functionalized with tin oxide nanoparticles to form the composite. Nano-systems obtained using an identical chemical route from a universal source of carbon nanomaterials, namely carbon black, were also newlinestudied for the purpose of validation and comparison. The synthesis protocols adopted for the preparation of graphene-based materials were devoid of hazardous reducing agents or byproducts. The products obtained after each stage of treatment were characterized with the help of various spectroscopic and microscopic techniques. newlineEven though structural properties of all the precursors appeared to be broadly the same, a variation in their morphology and defect density was discerned. Various analyses revealed the formation of graphene oxide domains with distinct dimensions after the oxidative treatment. An increase in defect newlinedensity was also observed due to the intercalation of oxygen groups to the carbon layers. Post composite formation, a distribution of ultrafine tin oxide newlinenanoparticles on the graphene surface was observed. The distribution of oxygen newlinefunctionalities on the carbon backbone were found to play a major role in governing the dispersal of tin oxide particles during the nanocomposite formation. -
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Economic Burden and Productivity Loss of Employees with Lifestyle Diseases in Sedentary Occupations During Pandemic
Over the past few decades, the prevalence of Lifestyle Diseases or Non-Communicable Diseases (NCDs) have increased. There has been an increasing concern about these lifestyle diseases, with hypertension acting as the most prevalent lifestyle disease in the populace. It further exaggerates the issue as its prevalence increases exposure to other lifestyle diseases such as Diabetes and Cardiovascular Diseases (CVD). With health being an important component of human capital, the presence of lifestyle diseases has an economic impact on the individual and the organisation. The presence of an illness reduces the productivity level delivered by the individual to work, resulting in productivity loss. Apart from impacting an employee's productivity, the prevalence of lifestyle diseases incurs a significant monetary expense in the form of healthcare required to manage them. This monetary expense is called an economic burden or out-of-pocket expenditure. On these grounds, the current study examines the economic burden and impact on the productivity of employees suffering from lifestyle diseases (Hypertension, Diabetes and CVD) working in sedentary occupations. With lifestyle diseases majorly influenced by the lifestyle patterns of an individual, employees working in a sedentary occupation are at greater exposure to lifestyle diseases and hence were selected as the target population. A cross-sectional study was conducted among 426 employees of sedentary occupations in the Delhi-NCR region. The economic burden has been measured as a sum of the direct and indirect costs of the diseases incurred in a year. Using the estimates of economic burden, Catastrophic Healthcare Expenditure (CHE) was measured at different threshold levels. The study has also evaluated productivity loss through presenteeism and absenteeism approaches. An attempt was made to examine the relationship between the economic burden 7 and productivity loss through presenteeism and absenteeism approaches. The result of the study shows a significant share of the economic burden for lifestyle diseases and their comorbidities. CHE was highest at the 40% threshold level. The level of disparity in catastrophe among lower and high-income individuals was also highest at the 40% threshold level. Further statistical results show a high cost of absenteeism due to lifestyle diseases compared to presenteeism and found that economic burden has a strong positive relationship with absenteeism and presenteeism. Overall, the study concludes that lifestyle disease incurs a substantial economic burden and CHE for employees working in sedentary occupations. The estimate for the same increases if multiple lifestyle diseases are present. Further, the impact of catastrophe is more for low-income than high-income individuals due to the limited availability of resources to manage the health issue. Apart from causing monetary expense, the presence of lifestyle diseases also causes a high cost of absenteeism and presenteeism, increasing the economic cost of managing lifestyle diseases. -
Investigations on Affective Computing to Improve Classroom Engagement Analysis in Higher Education by Deep Learning
The learning and teaching experience can be improved by using approaches that are not obtrusive to perform a comprehensive student engagement analysis throughout the classroom. In these modern times, when courses are conducted online, it is vital to accurately measure the levels of participation that each individual student has. It is crucial and essential to provide assistance to educators so that they may annotate and comprehend the signifcant learning rate of the students. A system that can perceive data and transpire it into information automates the learning and teaching experience in a classroom. In this study, videos are collected from online and ofand#64258;ine classes that have one single student per frame or many students per frame and are analysed for emotions and behavioural engagement through a multimodal system. newlineLarge amounts of video data processing call for an increase in the hardware resources newlineas well as the time required for processing images. This is particularly true in a newlineclassroom setting, where there are a large number of frames to analyse each and every minute in order to handle classroom involvement detection. Hierarchical Video newlineSummarization is used as a preliminary step on the videos to detect important frames newlinethat have the sum of all the information in the local neighborhood. These key frames newlineserve as important information units that provide details of facial emotions and behavioural aspects. The local maxima estimation based on the frst derivative provides summative information about the local neighborhood. The key frames serve newlineas an input for face detection and emotional analysis. In this research, the method newlinecan perform video summarization on a varied category of videos and with different newlineresolutions. Face detection in a temporal environment have not been trivial. Though there are methods that can identify multiple faces with varied sizes in a frame, it is still a current research topic to address false localization of faces in a frame. -
An Analysis of the effectiveness of HIV and AIDS policy implementation in teacher training colleges in zimbabwe
The study analyzed the effectiveness of implementation of the HIV and AIDS Policy for teachers colleges. The policy was enacted by the Ministry of Higher and Tertiary Education in 2004 and implemented by teacher training colleges in Zimbabwe. Teacher training colleges were a ready captive audience for the young people with the potential to newlineinfluence behaviour change among student teachers, with a snowball effect to pupils in primary and secondary schools. Despite the newlinedevelopment of the policy, incidences of sexually transmitted infections continued to increase. The objectives of the study were to determine to what extent the HIV and AIDS Policy has been operationalized and its newlineeffects on the sexual and reproductive health of students. Literature reviewed explored the HIV and AIDS policy implementation and quality assurance practices in teaching of HIV and AIDS in the colleges. Effective implementation was discussed within the context of the reproductive health challenges faced by students and access to quality newlineservices. Pragmatism philosophical paradigm was used for the study, since it combines quantitative and qualitative research techniques, methods and approaches into a single study. The population, sample and sampling procedure for the study were defined. Selection of the colleges was newlinepurposive and stratified according to location (urban or rural); administrative authorities (government and church-related colleges). While research questions guided the collection of qualitative data from newlineinterviews and focus group discussions for college authorities and stakeholders, hypotheses guided quantitative data collected from questionnaires completed by male and female third year students and Lecturers. Key informants were selected based on their role in policy implementation at the colleges. Validity and reliability of both qualitative newlineand quantitative data collecting instruments was assured. -
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A psychoanalytical study of the nature and appearance of character analysing 6 songs each of Jim Morrison, Kurt Cobain and Janis Joplin (members of the famous club 27) /
To understand how character could be understood by analysing the greatest works of the above artists. This research is to find a possible connection to show that the songs written by the artist is not just a form of expression but also to show the world who they are, and their ideologies and most importantly what they feel about themselves. -
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Impact of Behavioural Biases, Emotional Intelligence and Financial Literacy on Financial Behaviour
Behavioural finance is the integration of finance and psychology. Finance pertains to the administration of finances by an individual, while psychology involves the examination of the mind and human conduct. Behavioural finance provides a reasonable explanation for the abnormalities observed in financial markets and the irrational decisions made by investors. The foundation of behavioural finance lies in the irrational choices made by individuals, which elucidates the reasons for investors frequently encountering different biases, emotional filters, and a lack of knowledge when making financial decisions. The primary challenges stem from three critical deficiencies: the investors' inability to express their cognitive and emotional biases, insufficient financial knowledge, and an inability to regulate emotions during financial decision-making. This study investigates the influence of investors' financial literacy, emotional intelligence, and behavioural bias on their financial conduct. Studies have shown that behavioural biases negatively impact financial behaviour. Financial literacy and emotional intelligence have been recognised as critical elements that significantly impact the financial behaviour of salaried class investors.