Big Data Clustering Algorithm and Strategies

In current digital era extensive volume ofdata is being generated at an enormous rate. The data are large, complex and information rich. In order to obtain valuable insights from the massive volume and variety of data, efficient and effective tools are needed. Clustering algorithms have emerged as a machine learning tool to accurately analyze such massive volume of data. Clustering is an unsupervised learning technique which groups data objects in such a way that objects in the same group are more similar as much as possible and data objects in different groups are dissimilar. But, traditional algorithm cannot cope up with huge amount of data. Therefore efficient clustering algorithms are needed to analyze such a big data within a reasonable time. In this paper we have discussed some theoretical overview and comparison of various clustering techniques used for analyzing big data

Retroperitoneal pelvic schwannoma in pregnancy: a case report

Solitary nerve sheath tumor such as Benign schwannomas arising in the pelvic retro peritoneum is infrequently reported. Retroperitoneal location accounts for 0.3-3.2% of primary schwannomas. We report a case of benign retroperitoneal pelvic schwannoma in pregnancy that was incidentally diagnosed when it presented with Preterm premature rupture of membranes and mechanical obstruction for labour. She underwent caesarean section and delivered a healthy baby. She was evaluated in the postoperative period by computerized tomography (CT) imaging studies and CT guided fine needle aspiration cytology (FNAC) was not diagnostic. Complete surgical excision of the tumor was achieved in the postpartum period. The adjacent vascular and urinary channels sustained no injuries and she had no neurologic deficit. Histology revealed spindle cell neoplasm composed of interlacing fascicles and sheets of spindle cell with focal areas of nuclear palisading and thick walled blood vessels. Immunohistochemistry was positive for S 100 suggesting schwannoma. Retroperitoneal location of schwannomas is rare and surgery is curative. Prognosis is good, since recurrence is rare.

Comparative study of maternal and perinatal outcome of abruptio placenta in normotensive and hypertensive patients

OBJECTIVE: The objective of this study was to compare the maternal and perinatal outcome of hypertensive and normotensive women experiencing abruptio placenta. Our hypothesis is that hypertensive woman have a less favourable maternal and perinatal outcome than do normotensive women. STUDY DESIGN: Women with the diagnosis of abruptio placenta delivered between January 2010 to June 2011, composed the study group (226 cases). Out of 226 cases 126 cases were normotensive women with abruption placenta and 100 cases were hypertensive women with abruption placenta. Maternal data were collected including maternal age, parity, gestational age at admission, socio economic status, antenatal visits, past history of placental abruption, current obstetric complications( like Chronic hypertension, pre eclampsia, prelabour rupture of membrane, polyhydramnios) mode of delivery, abruption grade, maternal complications including hypovolemic shock, DIC, renal failure. Neonatal outcome data were recorded including gestational age at devliery, birth weight, sex of the baby, apgar score at 1 minute and 5minute, live born or still birth, neonatal death. RESULTS: The incidence of abruption in this study is 1.2%, in normotensive patients it is0.75%, in hypertensive patients it is 5.23%. Hypertensive women were no more likely to be delivered before 37 weeks, have neonates weighing < 2500grams or to be delivered by LSCS. There is no difference in the grade of abruption, maternal complications like ARF,DIC , shock, maternal mortality between normotensive cases & hypertensive cases. The perinatal mortality is 66.8% ,the Perinatal mortality rate in normotensive patients was – 65.07%, Perinatal mortality rate in hypertensive patients was - 70%. CONCLUSION: In this present study hypertensive women experiences 7 fold increased incidence of abruption but the overall maternal and perinatal outcome was not significantly different from that of normotensive women experiencing abruptio placenta

Design of Static Segment Adder for Approximating Computing Applications

The digital VLSI design needs to attain high performance with desired reliability range. The high performance involves low power, area efficiency and high speed. This paper proposes a design of High speed energy efficient static segment adder (SSA) to enhance the overall performance based on approximation technique. Static segmentation includes both accurate and inaccurate part. The normal full adder performs accurate part and the carry select adder is used for inaccurate part. By using static segmentation the approximate computation is done. Approximate computing is a computation which generates “good enough” result rather than totally accurate result. Image processing is accomplished using SSA design. In this process 99.4% whole computational accuracy for 16 bit addition and also for 8 bit addition can be achieved

Geographical Mapping and Socio-Demographic Analysis of Out-Patient at A Tertiary Hospital in Chennai

There is a lack of comprehensive geographical mapping and socio-demographic analysis of outpatients at a Tertiary Hospital in Chennai. This knowledge gap hinders the understanding of distribution patterns and socio-demographic characteristics of patients visiting the hospital from different geographic locations. The study seeks to enhance healthcare delivery by identifying specific health needs, catchment zones, and areas for improvement in healthcare services. Therefore, the study aims to assess the geographical distribution of outpatients attending the institution, to identify catchment zones and investigate changes in the pattern of geographic distribution of outpatients and to assess the socio-demographic characteristics of outpatients. Geographical data, including addresses, and patient demographic data such as age and gender are collected from the Hospital's Electronic Health Records (EHR) department. The sampling technique employed is an entire population approach, where data is collected and analyzed from every patient attending the outpatient department. MS Excel, Power BI, and ArcGIS are used for data analysis. A total of 40,90,460 patients visited the Outpatient department from 2018 to 2022. Female patients accounted for approximately 57.96% of the total patient visits. Patients between the ages of 31-64 years are the most frequent visitors. General medicine is the most visited department, followed by general surgery and obstetrics &amp; gynecology. The geographical distribution analysis identified Chennai, Kancheepuram, Tiruvallur, and Vellore as major catchment zones. There is a need for targeted outreach programs and resource allocation to improve healthcare services. The results emphasize the importance of tailoring healthcare to the specific needs of female patients and middle-aged adults. Strengthening the general medicine department and optimizing resource allocation based on patient demand can enhance service delivery. Continuous monitoring and analysis of patient data are essential for adapting healthcare strategies to evolving patient demographics and needs

Prognóstico de exploração no Chat GPT com ética de inteligência artificial

Natural language processing innovations in the past few decades have made it feasible to synthesis and comprehend coherent text in a variety of ways, turning theoretical techniques into practical implementations. Both report summarizing software and sectors like content writers have been significantly impacted by the extensive Language-model. A huge language model, however, could show evidence of social prejudice, giving moral as well as environmental hazards from negligence, according to observations. Therefore, it is necessary to develop comprehensive guidelines for responsible LLM (Large Language Models). Despite the fact that numerous empirical investigations show that sophisticated large language models has very few ethical difficulties, there isn't a thorough investigation and consumers study of the legality of present large language model use. We use a qualitative study method on OpenAI's ChatGPT3 to solution-focus the real-world ethical risks in current large language models in order to further guide ongoing efforts on responsibly constructing ethical large language models. We carefully review ChatGPT3 from the four perspectives of bias and robustness. According to our stated opinions, we objectively benchmark ChatGPT3 on a number of sample datasets. In this work, it was found that a substantial fraction of principled problems are not solved by the current benchmarks; therefore new case examples were provided to support this. Additionally discussed were the importance of the findings regarding ChatGPT3's AI ethics, potential problems in the future, and helpful design considerations for big language models. This study may provide some guidance for future investigations into and mitigation of the ethical risks offered by technology in large Language Models applications.Las innovaciones en el procesamiento del lenguaje natural en las últimas décadas han hecho posible sintetizar y comprender textos coherentes en una variedad de formas, transformando las técnicas teóricas en implementaciones prácticas. Ambos informan que el software extenso y las industrias como la de los creadores de contenido se han visto significativamente afectadas por el modelo de lenguaje extensivo. Sin embargo, un modelo de lenguaje enorme podría mostrar evidencia de sesgo social, dando riesgos morales y ambientales por negligencia, según las observaciones. Por lo tanto, es necesario desarrollar lineamientos completos para los LLM (Modelos de Lenguaje Grandes) responsables. A pesar de que numerosas investigaciones empíricas muestran que los modelos sofisticados de lenguaje amplio tienen muy pocas dificultades éticas, no existe una investigación exhaustiva y un estudio del consumidor sobre la legalidad del uso actual de modelos de lenguaje amplio. Usamos un método de estudio cualitativo en ChatGPT3 de OpenAI para enfocarnos en resolver los riesgos éticos del mundo real en los modelos actuales de lenguaje amplio para guiar aún más los esfuerzos en curso en la construcción responsable de modelos éticos de lenguaje amplio. Analizamos cuidadosamente ChatGPT3 desde las cuatro perspectivas de sesgo y robustez. De acuerdo con nuestras opiniones expresadas, comparamos ChatGPT3 objetivamente en múltiples conjuntos de datos de muestra. En este trabajo se encontró que una fracción sustancial de los problemas de principios no son resueltos por los marcos actuales; por lo tanto, se han proporcionado nuevos ejemplos de casos para respaldar esto. Además, se discutió la importancia de los hallazgos sobre la ética de la IA de ChatGPT3, los problemas potenciales en el futuro y las consideraciones de diseño útiles para modelos de lenguaje grandes. Este estudio puede proporcionar algunas pautas para futuras investigaciones y mitigación de los riesgos éticos que ofrece la tecnología en grandes aplicaciones de Language Models.As inovações de processamento de linguagem natural nas últimas décadas tornaram possível sintetizar e compreender textos coerentes de várias maneiras, transformando técnicas teóricas em implementações práticas. Ambos relatam que softwares resumidos e setores como criadores de conteúdo foram significativamente afetados pelo extenso modelo de linguagem. Um enorme modelo de linguagem, no entanto, poderia mostrar evidências de preconceito social, dando riscos morais e ambientais por negligência, de acordo com as observações. Portanto, é necessário desenvolver diretrizes abrangentes para LLM (Large Language Models) responsáveis. Apesar do fato de numerosas investigações empíricas mostrarem que modelos sofisticados de linguagem ampla têm muito poucas dificuldades éticas, não há uma investigação completa e estudo de consumidores sobre a legalidade do uso atual de modelos de linguagem ampla. Usamos um método de estudo qualitativo no ChatGPT3 da OpenAI para focar na solução os riscos éticos do mundo real nos atuais modelos de linguagem ampla, a fim de orientar ainda mais os esforços contínuos na construção responsável de modelos éticos de linguagem ampla. Analisamos cuidadosamente o ChatGPT3 a partir das quatro perspectivas de viés e robustez. De acordo com nossas opiniões declaradas, comparamos objetivamente o ChatGPT3 em vários conjuntos de dados de amostra. Neste trabalho, constatou-se que uma fração substancial dos problemas de princípios não é resolvida pelos referenciais atuais; portanto, novos exemplos de casos foram fornecidos para apoiar isso. Além disso, foram discutidas a importância das descobertas sobre a ética de IA do ChatGPT3, possíveis problemas no futuro e considerações de design úteis para grandes modelos de linguagem. Este estudo pode fornecer algumas orientações para futuras investigações e mitigação dos riscos éticos oferecidos pela tecnologia em grandes aplicações de Modelos de Linguagem

Knowledge and attitude of medical students towards bioethics- A cross sectional study from a medical college in northern Tamil Nadu

Studies have shown that a significant proportion of healthcare professionals were unaware of the universally recognized bioethical principles. The study was conducted to assess the knowledge and attitude towards bioethics among undergraduate medical students of a Medical College and also to find out the association of knowledge and attitude towards bioethics with other factors. This was a Cross Sectional study conducted at a medical college of Chengalpattu district between April to September of 2019. Study participants included medical undergraduate students from second academic year to fourth academic year of the medical college who had clinical exposure. Data was collected from a total of 224 subjects using a pretested, self-administered questionnaire. 89.3% of the respondents had poor knowledge about medical ethics.  The prevalence of good+excellent knowledge about bioethics was highest among 17-19 years age group (95.8%) and least among &gt;22 (60%) years age group, highest among those with &lt;12 months of clinical exposure (100%) and least among those with 25-36 months of exposure (57.8%) and both these associations were found to be statistically significant by Chi square test. (P= 0.048 and &lt;0.001 respectively). Majority of the subjects (&gt;58%) had a favourable attitude towards the correct ethical practices with respect to most of the issues (11/15). However, majority of them (&gt;53.1%) also had a favorable attitude towards certain issues (4/15) which are debatable. The most preferred sources for learning about medical ethics were seminars (81.7%), clinical discussions (78.1%) and lectures (57.1%). Majority of the subjects had poor knowledge about bioethics. The knowledge was better among students of earlier years of course compared to those in the later part. Majority of the subjects had a favorable attitude towards the correct ethical practices in most of the cases. The most preferred sources for learning about medical ethics were seminars, clinical discussions and lecture