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This Focuses on the development of an expert system utilizing artificial intelligent neural networks. Expert systems are computer-based programs designed to mimic human expertise in a specific domain. By integrating advanced neural network algorithms, this research aims to enhance the capabilities of expert systems and improve their accuracy and efficiency. Artificial intelligent neural networks are powerful tools that can learn from data, recognize patterns, and make intelligent decisions. By leveraging the capabilities of neural networks, the developed expert system can analyze complex information, extract relevant features, and provide expert-level recommendations or solutions. The integration of artificial intelligence techniques in expert systems enables them to adapt and learn from new data, making them more robust and capable of handling dynamic environments. This interdisciplinary approach combines expertise from computer science, machine learning, and domain-specific knowledge to develop a cutting-edge system. The outcome of this research has significant implications across various domains, including healthcare, finance, engineering, and more. The developed expert system can assist professionals in decision-making, problem-solving, and optimizing complex processes. Ultimately, this study contributes to the advancement of artificial intelligence technologies and their practical applications in expert systems.

This study conducts a comparative analysis of selected physical fitness parameters between individuals in aerobic and yoga groups. The research aims to assess and compare the physical fitness levels of participants engaged in aerobic exercise and yoga practice. Various fitness parameters, such as cardiovascular endurance, flexibility, strength, and body composition, are evaluated to determine the differences between the two groups. By examining these factors, valuable insights can be gained regarding the effects of aerobic exercise and yoga on physical fitness. The findings from this study will provide valuable information for individuals and fitness professionals seeking to understand and compare the benefits and outcomes of aerobic exercise and yoga in improving physical fitness. Additionally, this research can guide the development of tailored exercise programs that leverage the unique strengths of both aerobic exercise and yoga for enhancing overall physical fitness. Ultimately, this study contributes to the existing knowledge on the comparative effects of different exercise modalities and assists individuals in making informed decisions about their exercise choices based on their specific fitness goals and preferences.This comparative study examines the physical fitness levels of individuals in aerobic and yoga groups. The research focuses on assessing and comparing various physical fitness parameters, including cardiovascular endurance, flexibility, strength, and body composition. By analyzing these factors, the study aims to provide insights into the differential effects of aerobic exercise and yoga on physical fitness. The findings will be valuable for individuals and fitness professionals looking to understand and compare the benefits and outcomes of these two exercise modalities. The research may help inform the development of targeted exercise programs that leverage the specific strengths of aerobic exercise and yoga to enhance overall physical fitness. Ultimately, this study aims to contribute to the existing body of knowledge on the comparative effects of different exercise approaches, enabling individuals to make informed decisions based on their specific fitness goals and preferences.

This econometric analysis focuses on the relationship between asymmetric information and financial markets. The study aims to examine how the presence of asymmetric information affects market dynamics and outcomes. By utilizing advanced statistical techniques and econometric modeling, the research investigates the impact of information asymmetry on various financial market variables.The study analyzes the role of information asymmetry in influencing market efficiency, price formation, trading volume, and investor behavior. It explores how differences in information between market participants lead to market inefficiencies, such as mispricing and suboptimal trading strategies. Additionally, the research investigates the implications of asymmetric information for market liquidity, volatility, and the overall stability of financial markets.Through the econometric analysis, the study provides empirical evidence and insights into the effects of asymmetric information on financial markets. It aims to contribute to the existing literature by shedding light on the mechanisms through which information asymmetry influences market dynamics and outcomes. The findings have practical implications for investors, financial institutions, and policymakers, helping to enhance market transparency, investor protection, and the efficiency of financial markets.Overall, this econometric analysis delves into the relationship between asymmetric information and financial markets. By employing rigorous statistical techniques, it aims to understand the impact of information asymmetry on market variables, providing valuable insights for market participants and stakeholders. The research ultimately seeks to contribute to the understanding of market dynamics and inform strategies to mitigate the adverse effects of information asymmetry in financial markets.

This study delves into the effect of different intensity training on the aerobic and anaerobic efficiency of Kho Kho players. The objective is to understand how varying training intensities impact the overall efficiency and performance of these athletes. Kho Kho, a traditional Indian sport, requires rapid changes in direction, agility, and speed, making aerobic and anaerobic capacities crucial for success.The research methodology involves a sample of Kho Kho players who undergo different intensity training protocols. These protocols may include high-intensity interval training, moderate continuous training, and low-intensity recovery training. The participants' aerobic and anaerobic efficiency is measured through various performance indicators, such as VO2 max (maximum oxygen consumption) and lactate threshold.By examining the effects of different intensity training, the researchers aim to identify the optimal training approach for enhancing the aerobic and anaerobic capacities of Kho Kho players. This knowledge can contribute to the development of targeted training programs that enhance the players' overall efficiency and performance on the field.The findings of this study have significant implications for coaches, trainers, and athletes involved in Kho Kho. Understanding the specific impacts of varying training intensities can help in designing personalized training regimens tailored to individual players' needs. Moreover, it can aid in developing periodized training plans that maximize the players' potential and minimize the risk of injuries.Ultimately, this research aims to contribute to the advancement of training methodologies in Kho Kho and optimize the athletic performance of players through evidence-based approaches. The results will not only benefit Kho Kho players but also have potential applications in other sports that require similar aerobic and anaerobic capabilities.

This critical analysis delves into the intricate dynamics of stock price behavior in the Indian stock market, with a specific emphasis on banking companies. By employing meticulous research and comprehensive data analysis, this study aims to uncover the underlying factors influencing the stock prices of banking companies in India.The analysis incorporates a wide range of quantitative and qualitative methodologies to gain a deeper understanding of the stock market behavior. It examines key variables such as market trends, economic indicators, regulatory policies, and financial performance metrics specific to the banking sector. By scrutinizing these factors, the study aims to identify patterns, correlations, and anomalies in stock price movements.Furthermore, this analysis emphasizes the significance of banking companies within the Indian stock market. Given their integral role in the economy, the performance and behavior of banking stocks can have profound implications for investors, financial institutions, and the overall market stability. By examining this sector, the analysis sheds light on the broader trends and characteristics of the Indian stock market as a whole.Overall, this critical analysis aims to provide valuable insights into the stock price behavior of banking companies in the Indian stock market. By critically evaluating the underlying factors and conducting a thorough examination, this study contributes to the understanding of the dynamics and trends within the banking sector, enabling investors and market participants to make informed decisions.

This study investigates the impact of futures trading on stock market volatility and hedging efficiency, focusing on the S&P CNX Nifty index and select stocks in India. By conducting a comprehensive analysis, this research aims to examine the relationship between futures trading activity and its influence on market volatility and the effectiveness of hedging strategies.The study utilizes empirical methods to evaluate the effects of futures trading on stock market volatility. It analyzes the S&P CNX Nifty index, which represents the broader market, and specific individual stocks to understand how futures trading impacts price fluctuations and overall market stability.Furthermore, the research assesses the hedging efficiency of futures contracts as risk management tools. It examines whether investors can effectively hedge their positions and reduce portfolio risk through futures trading. By evaluating the effectiveness of hedging strategies in the context of the Indian stock market, this study provides valuable insights for market participants.Overall, this study delves into the impact of futures trading on stock market volatility and hedging efficiency in India. By examining the S&P CNX Nifty index and select stocks, it aims to shed light on the relationship between futures trading and market dynamics. The findings contribute to the understanding of risk management practices and assist investors in making informed decisions related to hedging strategies in the Indian stock market.

Music education encompasses a range of activities and programs that aim to teach individuals about music, develop their musical skills, and foster an appreciation for music. It can be offered at various levels, including early childhood, primary and secondary education, and higher education.In schools, music education typically includes both theoretical and practical components. Students learn about music theory, history, composition, and music appreciation, as well as engage in practical activities such as singing, playing musical instruments, and participating in ensembles or choirs. The curriculum may also cover topics like music technology, improvisation, and music performance.Music education programs can vary in their structure and approach. Some schools have dedicated music teachers who deliver music lessons as part of the regular curriculum, while others may offer extracurricular music programs or partner with external music organizations. In some cases, specialized music schools or conservatories provide intensive music education for students with a particular interest or talent in music.The availability and quality of music education can depend on factors such as the educational policies of a country or region, the funding allocated to music programs, the expertise of music teachers, and community support for music education initiatives. Some educational systems prioritize music education and ensure its inclusion in the curriculum, while others may face challenges due to limited resources or competing educational priorities.Music education has been recognized for its numerous benefits. It helps develop cognitive skills, such as critical thinking, problem-solving, and pattern recognition. It also enhances creativity, self-expression, and communication skills. Additionally, music education can contribute to social and emotional development, teamwork, cultural understanding, and personal fulfillment.The status of music education may evolve over time, influenced by educational policies, societal values, technological advancements, and shifts in funding priorities. It is important for communities and policymakers to recognize the value of music education and support initiatives that ensure its accessibility and quality for all learners.

This description focuses on the utilization of Carnatic music characteristics for music content identification. Carnatic music, a classical music tradition of South India, possesses unique characteristics that can be leveraged for the identification and analysis of music content.By exploiting the distinct elements and patterns found in Carnatic music, researchers and music experts can develop methods and algorithms for identifying and categorizing music content. These methods may include analyzing melodic structures, rhythmic patterns, scales, ornaments, and other characteristic features that are specific to Carnatic music.The application of Carnatic music characteristics in music content identification has several advantages. It enables more accurate and precise identification of Carnatic music compositions, helping to differentiate them from other music genres and styles. Additionally, it facilitates the organization and categorization of Carnatic music in digital libraries, making it easier for enthusiasts, researchers, and musicians to explore and access specific compositions.Furthermore, the exploitation of Carnatic music characteristics can contribute to the preservation and documentation of this rich musical heritage. By analyzing and identifying Carnatic music content, valuable insights can be gained into the historical context, lineage, and evolution of specific compositions and musical traditions.Overall, the utilization of Carnatic music characteristics for music content identification offers a specialized approach to accurately recognize and categorize Carnatic music compositions. It aids in organizing digital libraries, preserving cultural heritage, and enhancing the accessibility and understanding of this distinctive musical tradition.

Machine learning-based heart disease diagnosis is a rapidly evolving area of research that aims to improve the accuracy and efficiency of cardiovascular disease diagnosis using artificial intelligence (AI) algorithms. The use of machine learning models trained on medical imaging and electronic health record (EHR) data has shown promising results in predicting and diagnosing heart disease, as well as identifying risk factors and potential treatments. Predictive models can extract relevant features from EHR data and medical images to identify patterns and predict future outcomes. The use of deep learning algorithms and expert systems can further improve the sensitivity, specificity, and accuracy of heart disease diagnosis. The potential benefits of machine learning-based heart disease diagnosis include improving clinical decision-making, providing personalized treatment plans, and reducing healthcare costs. Additionally, machine learning-based diagnosis has the potential to improve the speed and accuracy of diagnosis, leading to improved patient outcomes. Overall, machine learning-based heart disease diagnosis is an important area of research with significant potential for improving cardiovascular health and patient care.

Ear biometrics is an emerging area of research that aims to develop accurate and reliable systems for identifying individuals based on the unique features of their ears. Soft computing techniques, such as machine learning, artificial intelligence, fuzzy logic, and neural networks, are increasingly being used in ear biometrics to extract and analyze ear features, patterns, and shapes. The use of soft computing techniques has led to the development of more accurate and robust ear recognition systems, with applications in security, authentication, and identification. The use of deep learning algorithms, coupled with large datasets of ear images, has shown promising results in improving the accuracy of ear biometrics, as well as reducing the impact of noise and other variations. Applications of soft computing in ear biometrics have important implications for privacy, as ear recognition systems can potentially be used for mass surveillance. Therefore, the development of accurate and reliable ear biometrics systems must be balanced with ethical considerations and legal regulations. Overall, the use of soft computing techniques in ear biometrics is an important area of research with significant potential for improving security and identification in various domains.

An empirical study and analysis of heart disease prediction involves using data analysis techniques to identify patterns and risk factors associated with cardiovascular disease. This approach utilizes machine learning algorithms to classify patients based on their likelihood of developing heart disease.The study involves collecting data on risk factors such as age, gender, family history, blood pressure, cholesterol levels, smoking, and diabetes. Feature selection techniques are used to identify the most important risk factors, and a classification model is trained using these factors. The accuracy of the model is evaluated using metrics such as sensitivity, specificity, and AUC.This empirical study and analysis has several advantages, including the ability to identify new risk factors associated with heart disease, improved accuracy in predicting cardiovascular risk, and the potential to develop more personalized prevention and treatment strategies. This approach has the potential to improve medical decision-making and reduce the burden of heart disease on individuals and society.

Kidney problems can be caused by a variety of risk factors, including medical conditions, lifestyle habits, and environmental factors. Diabetes and hypertension are the leading causes of kidney disease, with age, obesity, and family history also being important risk factors.Other risk factors for kidney problems include smoking, cardiovascular disease, high cholesterol, autoimmune diseases, urinary tract infections, kidney stones, chronic dehydration, use of certain medications, excessive alcohol consumption, exposure to toxins or pollutants, poor diet, lack of exercise, sleep apnea, stress, and certain gender and race/ethnicity.Understanding and managing risk factors is important in preventing kidney problems. Strategies to reduce risk include managing medical conditions such as diabetes and hypertension, maintaining a healthy weight, not smoking, reducing exposure to toxins and pollutants, eating a healthy diet and getting regular exercise. By addressing and reducing risk factors, individuals can take steps to protect their kidney health.

Chronic kidney disease (CKD) is a progressive and irreversible condition that affects the kidneys' ability to filter waste products from the blood. It can lead to serious complications such as heart disease, stroke, and kidney failure. Early detection of CKD is crucial in order to slow or halt the progression of the disease, improve outcomes and reduce the risk of complications.To know about the early detection of CKD, first it is important to know the anatomy of kidney, anatomy of nephron.

Cardiovascular disorder severity analysis in magnetic resonance images (MRI) involves using machine learning techniques to analyze MRI images and assess the severity of cardiovascular disorders. This approach utilizes deep learning algorithms, such as convolutional neural networks (CNN), for image analysis, segmentation, and feature extraction.The severity analysis involves quantifying the extent and location of damaged tissues, narrowing of blood vessels, and other pathological changes related to cardiovascular disorders. This analysis can aid in the diagnosis, prognosis, and treatment planning of patients with cardiovascular disorders.This method has several advantages, including the ability to detect subtle changes in MRI images that may be missed by human observers, the potential to provide more accurate and objective measures of disease severity, and the ability to integrate data from electronic health records and other sources.Overall, this approach has the potential to improve medical decision-making and provide more personalized care for patients with cardiovascular disorders, thus helping to reduce the burden of these conditions on individuals and society.

A heart disease risk model is a tool that can be used to identify individuals who are at increased risk for developing cardiovascular diseases. These risk models incorporate a range of factors, including medical history, lifestyle factors, biomarkers, genetic factors, and medical imaging. The use of predictive modeling and artificial intelligence (AI) algorithms to analyze electronic health record (EHR) data has shown promising results in identifying individuals at increased risk for heart disease, as well as improving clinical decision-making and precision medicine. The sensitivity and specificity of heart disease risk models can be improved by incorporating a wider range of data sources and more advanced machine learning techniques. The development of effective heart disease risk models has important implications for healthcare, enabling healthcare providers to identify patients who are at increased risk for cardiovascular diseases, and to develop personalized treatment plans to mitigate this risk. Overall, heart disease risk models are an important area of research with significant potential for improving population health and chronic disease management.

Heart disease forecasting in healthcare is an important area of research that aims to predict and prevent cardiovascular diseases. The use of machine learning and artificial intelligence techniques to analyze large amounts of electronic health record (EHR) data has shown promising results in identifying risk factors, early detection, and treatment planning. The goal of heart disease forecasting is to improve clinical decision-making, reduce costs, and improve patient outcomes. Medical imaging and biomarkers also play a critical role in predicting heart disease, and researchers are exploring new ways to integrate these data sources with machine learning models. The use of precision medicine in heart disease forecasting can help personalize treatment plans for patients, based on their individual risk factors and genetic profiles. Heart disease forecasting has important implications for population health and chronic disease management, providing healthcare providers with a powerful tool to prevent and manage cardiovascular diseases, which remain a leading cause of death worldwide.

A reliable and accurate heart disease prediction system uses machine learning algorithms to predict the likelihood of heart disease based on a set of risk factors. This system utilizes decision tree, Naive Bayes, random forest, and support vector machine algorithms to analyze patient data and identify patterns that are indicative of cardiovascular disease.Feature selection techniques are used to identify the most important risk factors, which may include age, gender, family history, blood pressure, cholesterol levels, smoking, and diabetes. The accuracy of the model is evaluated using metrics such as sensitivity, specificity, and AUC.This system has several advantages, including improved accuracy in predicting heart disease risk, the ability to identify patients at high risk for cardiovascular disease, and the potential to integrate data from electronic health records and other sources. This approach has the potential to improve medical decision-making, provide more personalized care for patients, and reduce the burden of heart disease on individuals and society.

Heart disease is a leading cause of death worldwide, and early prediction is crucial for effective prevention and management. A novel cluster and rank-based method for prediction of heart disease involves using machine learning algorithms to cluster patients based on similar risk factors and rank them based on their likelihood of developing cardiovascular disease.This method utilizes feature selection techniques to identify the most important risk factors and uses a classification model to predict the risk of heart disease based on these factors. The accuracy of the model is evaluated using metrics such as sensitivity, specificity, and AUC.This approach has several advantages, including improved accuracy in predicting heart disease risk, the ability to identify subgroups of patients with similar risk profiles, and the potential to integrate data from electronic health records and other sources.

The field of genetics has undergone a revolution in recent years, thanks to the development of powerful tools such as CRISPR-Cas9, gene editing, and genome engineering. These tools allow scientists to study genes in unprecedented detail, and to modify them in ways that were previously impossible. This has opened up new possibilities for precision medicine, gene therapy, and the treatment of genetic diseases.Genetic engineering and modification have also been used to study the function of genes and to elucidate their role in various biological processes. Gene regulation and expression, transcription factors, and RNA interference are some of the key areas of research in genetics. Epigenetics, which refers to changes in gene expression that do not involve alterations to the DNA sequence, is another important area of study.Overall, genetics has enormous potential to improve our understanding of biology and to revolutionize healthcare. As the field continues to evolve, we can expect to see more exciting discoveries and breakthroughs in the years ahead

Heart disease is a significant public health concern globally, and timely and accurate diagnosis is critical for effective treatment. Data classification techniques have been extensively used to identify potential heart disease patients using various medical data attributes such as age, blood pressure, cholesterol levels, and family history. However, the performance of these classification algorithms largely depends on the selection of appropriate features and the tuning of algorithm parameters.Hybrid optimization techniques offer a promising solution to improve the performance of heart disease classification models. These techniques combine the advantages of multiple optimization algorithms, such as genetic algorithms, particle swarm optimization, and simulated annealing, to overcome their individual limitations and achieve optimal results.The hybrid optimization technique can be used to optimize feature selection and hyperparameter tuning, resulting in improved classification accuracy and reduced computational time. Moreover, hybrid optimization techniques can handle complex data distributions, which is particularly relevant in the case of heart disease diagnosis, where data patterns can be highly nonlinear.In summary, the application of hybrid optimization techniques can significantly improve heart disease classification models' accuracy, making them more effective in identifying potential patients and aiding in timely medical interventions.

This research work focuses on developing and evaluating nanoparticles containing allopurinol for the management of uric acid nephrolithiasis. The study formulated three different nanoparticles - magnetic nanoparticles, bovine serum albumin (BSA) nanoparticles, and chitosan nanoparticles - using different methods and characterized them based on particle size, polydispersity index, zeta potential, entrapment efficiency, FTIR spectra, DSC thermograms, XRD patterns, TEM images, and in vitro drug release. The in vivo targeting potential of the nanoparticles was evaluated in mice, and the effectiveness of the nanoparticles was tested in managing hyperuricemic nephrolithiasis by measuring pH and uric acid levels in urine and serum samples of mice. Histological examination of kidney samples was also performed to confirm the outcomes. The results showed that the nanoparticles prepared from low MW chitosan polymer exhibited maximum kidney targeting potential and efficacy in managing hyperuricemia-related nephrolithiasis. This research could potentially lead to the development of a targeted therapy for uric acid nephrolithiasis.

Secure multimodal biometric authentication is a process of using multiple biometric traits to authenticate a user's identity. This approach offers increased security by combining the strengths of different biometric authentication techniques, such as fingerprint recognition, iris recognition, face recognition, voice recognition, and behavioral biometrics. By combining multiple biometric traits, the risk of false positives and false negatives can be reduced, providing a more reliable and secure authentication process.Machine learning and artificial intelligence algorithms can be used to develop secure multimodal biometric authentication systems that can adapt to changing user behavior and environmental conditions. Deep learning techniques can also be used to enhance the accuracy and efficiency of biometric recognition.Cryptography plays a vital role in securing the biometric data and ensuring the privacy of the users. The biometric data should be encrypted before transmission, and the encryption keys must be securely stored and managed.Overall, secure multimodal biometric authentication can provide a reliable and secure authentication process for user identification and access control. The combination of different biometric traits and machine learning algorithms can enhance the accuracy and efficiency of the authentication process, ensuring the privacy and security of the users

Fetal dissection of human lungs is a process of analyzing the structure and development of lungs in the fetus. The study of the morphology of the lungs is important to understand the development and function of these vital organs. Fetal dissection involves the careful removal of the lungs from the fetus and examining their size, shape, and structure. It provides valuable information about the development of the airways, blood vessels, and other components of the lungs. Morphological analysis of the lungs involves the observation and measurement of various features, including the size and shape of the lobes, bronchi, and alveoli. This information can be used to understand the changes that occur during fetal development, as well as to identify abnormalities that may lead to respiratory disorders. Fetal dissection of human lungs is a crucial step in understanding the development and function of the respiratory system and can provide important insights into the diagnosis and treatment of respiratory diseases.

Lung cancer is a major cause of cancer-related deaths worldwide. Machine learning techniques have shown promising results in the early detection and prediction of lung cancer. However, high-dimensional data, such as gene expression profiles, can introduce noise and decrease the classification accuracy of machine learning models. Feature selection techniques can alleviate this issue by identifying the most relevant and informative features, leading to better model performance.Optimized feature selection techniques can enhance the prediction accuracy of lung cancer using machine learning algorithms. Support vector machines, random forest, and artificial neural networks are commonly used algorithms for lung cancer prediction. By optimizing feature selection, these models can be trained with the most informative features, reducing overfitting and improving classification accuracy.Cross-validation techniques can also be used to evaluate the performance of feature selection and machine learning algorithms. The integration of optimized feature selection with machine learning techniques can provide an accurate and reliable lung cancer prediction model, which has the potential to improve early detection and precision medicine for lung cancer patients.Overall, optimized feature selection for enhancing lung cancer prediction using machine learning techniques is a promising approach to improving patient outcomes and reducing the global burden of lung cancer.

Lung cancer is one of the leading causes of cancer-related deaths worldwide, and early diagnosis is crucial for effective treatment. Exhaled breath analysis is a promising non-invasive approach for the early detection of lung cancer. The analysis of volatile organic compounds (VOCs) in exhaled breath can provide valuable information on the presence of cancer biomarkers.Nanomaterial-based chemical sensors have emerged as a potential solution for the accurate detection of VOCs in exhaled breath. These sensors can detect VOCs with high sensitivity and selectivity, and their small size and low power consumption make them suitable for point-of-care diagnosis.The combination of nanomaterial-based chemical sensors with machine learning and artificial intelligence algorithms can enhance the accuracy and reliability of lung cancer diagnosis. These algorithms can identify patterns in the VOCs data that are characteristic of lung cancer, leading to improved diagnostic accuracy and personalized treatment options.Overall, the use of nanomaterial-based chemical sensors for exhaled breath analysis offers a promising approach for the early detection of lung cancer. This approach has the potential to revolutionize cancer diagnosis and improve patient outcomes through precision medicine.

According to most organizations, the biggest drivers to cloud are elasticity and agility.In other words, it allows you to instantly provision and de-provision resources based on the needs of the business. You no longer have to build the church for Sunday. Once in the cloud, approximately 80% of companies report receiving bills two to three times what they expected. The truth is that while the promise of cloud is that you only pay for what you use, the reality is that you pay for what you provision. The gap between consumption and allocation is what causes the large and unexpected bills.Cost isn't the only challenge. While most organizations report cost being their biggest problem in managing a public cloud environment, you cannot truly separate performance from cost - the two are tightly coupled. If an organization was optimizing for cost alone, moving all applications to the smallest instance type would be the way to go, but no one is willing to take the performance hit.In the cloud, more than ever, cost and performance are tied together.Digital transformation and a rush to the cloud are placing enterprise IT teams under tremendous pressure. While cloud addresses an old pain point - that infrastructure supply is static while application demand is dynamic - matching demand with supply in real-time across multiple metrics and dimensions requires more decisions than any human being can make.Hybrid cloud estates are unbelievably complex. There are millions of configuration options for EC2 instances alone, AWS has 212 additional products and services and Microsoft lists over 600 Azure services (as of May 2020). This is simply too much complexity for the average IT team to manage and, as a result, many organizations that kicked off digital transformation initiatives with high hopes end up watching innovation grind to a halt while the IT team struggles just to keep the lights on.