Role of Manasik Bhava in etiopathogenesis of Essential Hypertension

As man has entered in 21st century with modernization in each and every walk of life, he has also paid for it by living in several stressful psychological conditions. The response to the psychological conditions varies person to person because each has different psychic and bodily constitution. However, these stressors play certain role in the development, progression, prognosis as well as management of the disease. This stressful life-style affects one’s mind and homeostasis of body by several psychosomatic mechanisms and causes many psychosomatic disorders. The ‘Uccharaktachapa’ (Essential hypertension) is one of such diseases. Hypertension is most prevalent cause for cerebrovascular and cardiovascular disorders, causing high rate of mortality and morbidity. So, hypertension is gaining more and more attention globally. Due to its high prevalence in our country, India is known as Nation of Hypertension. Hypertension is also known as silent killer of mankind because most sufferers (85%) are asymptomatic and as per available reports, in more than 95% cases of hypertension under lying cause is not found. Such patients are said to have Essential Hypertension (EHT)

Deep Learning Unveils Hidden Insights: Advancing Brain Tumor Diagnosis

Timely detection and treatment are crucial in managing brain tumors, a severe medical condition. MRI is a commonly used diagnostic tool to detect brain tumors. However, because of the complex structure of the brain and the wide range of tumors sizes and forms, MRI scan interpretation can be time-consuming and error-prone. The automated detection and segmentation of brain tumors has shown encouraging results with to recent developments in DL techniques. We suggest a CNN-RNNs and GANs based DL technique for brain tumor identification in this paper. Transfer learning and data augmentation techniques are used in the suggested method to train the CNN on a sizable dataset of MRI images labelled with tumor areas. The suggested strategy, according to experimental findings, is more accurate than the most advanced techniques now available for finding brain tumors. The suggested strategy has the potential to help radiologists identify brain tumors quickly and reliably, improving patient outcomes.

Congestion Articulation Control Using Machine Learning Technique

Congestion is the most serious issue in both Adhoc mobile networking and regular road traffic systems. The definition of a vehicle is changing as the automotive industry advances. Nowadays, all automobiles are outfitted with the most up-to-date sensors and communication capabilities. Mobile Ad Hoc Network that avoids traffic jams and articulation issues while also saving time by receiving direction from the GPS system on the shortest path using various algorithms. It also provides information on road safety and where to go. It repeatedly recalculates the shortest way using multiple algorithms to ensure that the user does not become stuck and stranded in traffic. From the point of view of research, this paper defines the architecture and protocols. However, VANETs are a subset of MANETs and constitute the future of Intelligent Transportation Systems. The development of big data, the latest sensors and probing vehicle data, as well as the widespread use of machine learning technologies, has given articulation control measurement in the traffic congestion area a completely new and different direction. By examining multiple traffic metrics. With machine learning, it is straightforward to forecast traffic congestion. This study is based on traffic congestion forecasting in real-time. This paper presents a summary of recent research conducted using various AI approaches and machine learning models

Artificial Intelligence Accelerated Transformation in The Healthcare Industry

The healthcare industry was a pioneer in the deployment of artificial intelligence (AI) technology. Due to the nature of the services and the vulnerability of a sizable portion of end users, there has been a significant amount of research and discussion on the concept of artificial intelligence. A mixed-method approach has been used to pinpoint the components of moral AI in the healthcare sector and look into how it affects value creation and market performance. Since AI technology is still developing in India, analysis is conducted in an Indian context. The understanding of how various AI components supported healthcare organisations and deliver better patient-centered care and evidence-based medicine was aided by these in-depth studies and analyses of the patient perspective

Unleashing the Power of Multi-Agent Deep Learning: Cyber-Attack Detection in IoT

Detecting botnet and malware cyber-attacks is a critical task in ensuring the security of computer networks. Traditional methods for identifying such attacks often involve static rules and signatures, which can be easily evaded by attackers. Dl is a subdivision of ML, has shown promise in enhancing the accuracy of detecting botnets and malware by analyzing large amounts of network traffic data and identifying patterns that are difficult to detect with traditional methods. In order to identify abnormal traffic patterns that can be a sign of botnet or malware activity, deep learning models can be taught to learn the intricate interactions and correlations between various network traffic parameters, such as packet size, time intervals, and protocol headers. The models can also be trained to detect anomalies in network traffic, which could indicate the presence of unknown malware. The threat of malware and botnet assaults has increased in frequency with the growth of the IoT. In this research, we offer a unique LSTM and GAN-based method for identifying such attacks. We utilise our model to categorise incoming traffic as either benign or malicious using a dataset of network traffic data from various IoT devices. Our findings show how well our method works by attaining high accuracy in identifying botnet and malware cyberattacks in IoT networks. This study makes a contribution to the creation of stronger and more effective security systems for shielding IoT devices from online dangers.  One of the major advantages of using deep learning for botnet and malware detection is its ability to adapt to new and previously unknown attack patterns, making it a useful tool in the fight against constantly evolving cyber threats. However, DL models require large quantity of labeled data for training, and their performance can be affected by the quality and quantity of the data used.  Deep learning holds great potential for improving the accuracy and effectiveness of botnet and malware detection, and its continued development and application could lead to significant advancements in the field of cybersecurity

Dynamic Data Scaling Techniques for Streaming Machine Learning

This research delves into innovative dynamic data scaling techniques designed for streaming machine learning environments. In the realm of real-time data streams, conventional static scaling methods may encounter challenges in adapting to evolving data distributions. To overcome this hurdle, our study explores dynamic scaling approaches capable of adjusting and optimizing scaling parameters dynamically as the characteristics of incoming data shift over time. The objective is to augment the performance and adaptability of machine learning models in streaming scenarios by ensuring that the scaling process remains responsive to changing patterns in the data. Through empirical evaluations and comparative analyses, the study aims to showcase the efficacy of the proposed dynamic data scaling techniques in enhancing predictive accuracy and sustaining model relevance in dynamic and fast-paced streaming environments. This research contributes to the advancement of scalable and adaptive machine learning methodologies, particularly in applications where timely and accurate insights from streaming data are crucial

A LITERARY STUDY ON NIDANAPANCHAKA OF PANDU ROGA

Pandu means pallor. In this disease there is predominance of paleness all over the body. due to its similarity it can be co-related with modern disease anaemia. Pandu Roga is explained by almost all of our Acharyas. This article is based on Nidanapanchaka of Pandu Roga from Ayurvedic texts as Charak samhita, Susruta samhita, Astanga hrudaya etc with all commentaries. Rasavaha and Raktavaha srotas are chiefly involved in pathogenesis of Pandu Roga. The changing lifestyle of human being by means of Ahara and Vihar plays a major role in manifestation of various diseases. Pandu Roga is also one of them. Our faulty dietary habits and lifestyle produces Ama which further causes Agnimandya and ultimately Amayukta Ahararasa produced. It hampers Rasa Dhatu utpatti and manifests Pandu Roga. Aggravated Pitta is responsible for the production of Posaka (nutrient portion) from the Rasadhatu as a result depletion of Rakta takes place. The detail knowledge of Nidanapanchaka and types of Pandu Roga will help in its diagnosis and management in this modern era also

Leveraging Sentiment Analysis for Twitter Data to Uncover User Opinions and Emotions

Huge amounts of emotion are expressed on social media in the form of tweets, blogs, and updates to posts, statuses, etc. Twitter, one of the most well-known microblogging platforms, is used in this essay. Twitter is a social networking site that enables users to post status updates and other brief messages with a maximum character count of 280. Twitter sentiment analysis is the application of sentiment analysis to Twitter data (tweets) in order to derive user sentiments and opinions. Due to the extensive usage, we intend to reflect the mood of the general people by examining the thoughts conveyed in the tweets. Numerous applications require the analysis of public opinion, including businesses attempting to gauge the market response to their products, the prediction of political outcomes, and the analysis of socioeconomic phenomena like stock exchange. Sentiment classification attempts to estimate the sentiment polarity of user updates automatically. So, in order to categorize a tweet as good or negative, we need a model that can accurately discern sarcasm from the lexical meaning of the text. The main objective is to create a practical classifier that can accurately classify the sentiment of twitter streams relating to GST and Tax. Python is used to carry out the suggested algorithm

Stopped Flow Kinetics of MnII Catalysed Periodate Oxidation of 2, 3- dimethylaniline - Evaluation of Stability Constant of the Ternary Intermediate Complex

The formation of ternary intermediate unstable complex during the oxidation of aromatic amines by periodate ion catalysed by MnII has been proposed in case of some anilines. This paper is the first report on stopped-flow kinetic study and evaluation of stability constant of ternary complex forming in the MnII - catalysed periodate oxidation of 2, 3-dimethylaniline (D) in acetone-water medium. Stop-flow spectrophotometric method was used to study the ternary complex formation and to determine its stability constant. The stop-flow trace shows the reaction to occur in two steps. The first step, which is presumably the formation of ternary complex, is relatively fast while the second stage is relatively quite slow. The stability constant evaluated for D - MnII - IO4- ternary complex by determining equilibrium absorbance is (2.2 ± 1.0) × 105. Kinetics of ternary complex formation was defined by the rate law(A) under pseudo first order conditions. ln{[C2]eq / ( [C2]eq -[C2])} = kobs . t (A) where, kobs is the pseudo first order rate constant, [C2] is concentration of ternary complex at given time t, and [C2]eq is the equilibrium concentration of ternary complex. © 2015 BCREC UNDIP. All rights reserve

Analysis of caesarean sections according to modified Robson’s ten group classification system at a tertiary care centre in Western India

Background: The increasing trends for Caesarean section (CS) in India and worldwide have been a cause of concern. The aim is to compare and analyse CS rates across the globe, WHO recommends the Robson’s ten group classification system (TGCS). This will help to target appropriate group of women for reduction of overall CS rates.Methods: This was a retrospective study design using hospital records for women delivered in December 2017. Data was entered and analysed using excel 2007 and presented using modified Robson’s ten group classification system.Results: Out of total 650 women delivered during the study period, 184 (28.3%) delivered by CS. Group 1 and group 2 included a total of 49.53% women in the present study. The CS rates varied from 100% in group 5 (previous CS), group 7 (breech, multiparous) and group 9 (abnormal lie) to as low as 0.9% in group 3. The present study highlights that group 5 i.e. women with previous CS, contributed maximum (37%) to the overall surgical deliveries with group 2 being the second largest contributor (21%).Conclusions: The findings of the study indicate that group 5-women with prior CS and group 2-women with induced labour contributed maximum to overall CS rates. TOLAC should be a routine and not optional. Simultaneously Judicious selection of women for induction, strict implementation of induction protocols to decrease the cases of failed inductions will also reduce primary CS. To monitor the CS rates and take appropriate actions it is recommended that Robson’s TGCS be used continuously in all health institutions