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Etat : New.

Paperback. Etat : new. Paperback. 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. Shipping may be from our Sydney, NSW warehouse or from our UK or US warehouse, depending on stock availability.

Taschenbuch. Etat : Neu. Neuware - 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.

Taschenbuch. Etat : Neu. Neuware - 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.

Etat : Gut. Zustand: Gut | Sprache: Englisch | Produktart: Bücher | Internet has grown rapidly due to growth of computer technology, computer net-works and network communication technology during the recent decades. Rapid increase and tremendous growth in computer network and Internet communica-tion has increased the growth of security threats for computer networks (Kruegel et al., 2004). Everyday new vulnerabilities are exposed and attacks are occurred frequently in Internet. It makes the computer networks environment insecure and more vulnerable day by day (Ehlert et al., 2010). These threats and vulnerabilities can interrupt and in¿uences the performance of personal, social, government and organizational operations and functions (Olusola et al., 2013). Due to this, security of computer networks has become an important issue and essential component in modern computer systems (Singh, 2004). Network security threat can take place from external intruders as well as internal users in the form of anomalous behav-ior and misuse (Anderson, 1980). Network security is a mechanism of protection from external and internal threats in order to ensure security of network com-munications (Holm, 2012). It protects network environment against unauthorized access to vital information, Denial of Service (DoS) attacks, alteration and demo-lition of data and information, and information loss. There are various protection mechanisms available like authentication and access control mechanisms, periph-eral protection mechanisms but these mechanisms are not helpful against internal intrusions. Therefore, there is an immense need for additional level of integral protection such as Network Intrusion Detection and Prevention System (NIDPS) against network intrusions. Network intrusion can arise in network träc that emerge as normal (Kruegel et al., 2004; Gollmann, 2006). NIDPS compliments the security mechanism by attempting to detect anomalous behavior and misuse and preventing from them.

Etat : Hervorragend. Zustand: Hervorragend | Sprache: Englisch | Produktart: Bücher | 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.

Etat : Sehr gut. Zustand: Sehr gut | Sprache: Englisch | Produktart: Bücher | 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.

Etat : Hervorragend. Zustand: Hervorragend | Sprache: Englisch | Produktart: Bücher | 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.

Etat : Hervorragend. Zustand: Hervorragend | Sprache: Englisch | Produktart: Bücher | Grid computing is emerging as a promising solution to some of the most vexing challenges facing e-Health. It also offers a powerful tool for other areas of healthcare such as drug discovery, as well as economic and weather forecasting, earthquake analysis, etc. In all these domains, grid computing easily outclasses traditional IT systems, in terms of its ability to cope cost effectively with their massive demands on computer-processing power, and the intensity of real-time data throughputs. Grid Computing and e-Health Whether one speaks of business travellers and tourists outside their home countries, the growing number of elderly with chronic diseases who cannot easily visit hospitals, or urgent, complex cases requiring consultations with a range of specialists, e-health means real-time, secure acquisition and access to extremely vast volumes of data from anywhere, at anytime. From a pacemaker to an electronic health record, grid computing is seen as one of the ways to address these, as well as a wide array of associated challenges. Both the robustness and the in-built fault tolerance of grids juxtaposes directly with the demand for 'always live' healthcare applications. Other than access to distributed databases, the rapid data mining capabilities of grids are seen as enabling tools for a variety of epidemiological and biomedical applications. Grid computing is also proving itself in bioinformatics research, and has several proponents who swear about its advantages in terms of new drug design/discovery as well as personal medicine (or i-health). At the moment, there are several e-health grid computing initiatives underway in Europe, at different stages - from research through pilot projects to implementation.

Etat : Hervorragend. Zustand: Hervorragend | Sprache: Englisch | Produktart: Bücher | 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.

Etat : Sehr gut. Zustand: Sehr gut | Sprache: Englisch | Produktart: Bücher | 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.

Etat : Sehr gut. Zustand: Sehr gut | Sprache: Englisch | Produktart: Bücher | The demand for cloud computing increases day by day and this also increases the need for Security for the same. Several researchers focused on the objective to provide security to the cloud platform but till now no one ensures it. So, similar to the same focus firstly the study of different methods like Encryption, authentication, and many others discussed in this work and then based on the recent analysis some of the methods were selected in the work to propose a novel approach. This work is done in three different phases which involved (a) Cryptography based Frameworks, (b) Proposed Fragmentation Framework, (c) Proposed Biometric-based Framework. To analyse the performance of all these phases, five different datasets are prepared with the combination of different types of data files like Image, Audio, and Video. In this, the data size varies from 100 to 300 files and the maximum size of data is 289810KB. The performance is analysed on every phase based on different parameters depending on the phase. In phase 1, three cloud environments are generated where different security algorithms are added and performance is analysed based on Time, Throughput, and size of Data after Encryption. These three environments use Diffie-Hellman, proxy reencryption, HASBE and Blowfish, RSA with ECC algorithms, and results are calculated for all these algorithms. After analysis, it is clear that the performance of cloud environment-3 in phase 1 is better in terms of Time, Throughput, and size of Data. In this environment, Keyword Encryption and Hybrid Cryptographic Algorithm are used which works based on RSA with ECC Algorithm. Though this scheme provides end to end security in a cloud environment yet, it can be forged. So, to enhance the framework different researchers followed fragmentation-based techniques in which data is first divided into small portions called fragments and then after encryption, it will be uploaded on different cloud servers to protect data against various forgeries. Different researchers also performed well on the same but none of them focus on the data loss due to fragmentation. As data is of different types so, the same fragmentation will be one cause of the data loss. So, to cope up with this issue a new FragSecure framework is proposed in the second phase of this work. In this framework, the type of data uploaded on the cloud server plays its major role and use for fragmentation.

Etat : Hervorragend. Zustand: Hervorragend | Sprache: Englisch | Produktart: Bücher | 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.

Etat : Hervorragend. Zustand: Hervorragend | Sprache: Englisch | Produktart: Bücher | 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.

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