Cloud computing has emerged as one of the most groundbreaking technologies to have redefined the bounds of conventional computing techniques. It has ushered in a paradigm shift and pushed the frontiers of how computing assets, inclusive of infrastructure resources, software, and applications can be used, adopted, and purchased. The economic benefits or rather the fundamental economic shift offered by cloud computing in reducing capital expenditure and converting it to operational expenditure has been a primary motivating factor for early adopters. However, despite its inherent advantages that include better access and control, there exist several reservations around cloud computing that have impeded its growth. The control, elasticity, and ease of use that cloud computing is associated with also engender many security issues. Security is considered to be the topmost hurdle out of the nine identified challenges of cloud computing as underlined by the study conducted by the International Data Corporation. It therefore follows that an exceedingly secure system is essential for the safeguarding of an organizational entity, its resources, and assets. In this article, it is our endeavor to offer insights into the implementation of a novel architecture that can deliver an enhanced degree of security for outsourcing information in a cloud computing environment while involving numerous independent cloud providers. The framework comprises of dual encryption and data fragmentation techniques that envision the secure distribution of information in a multicloud environment. The various concerns surrounding this area, specifically, the challenges of integrity, security, confidentiality, and authentication have been addressed. All simulations and scrutiny have been accomplished on an Oracle virtual machine Virtual‐Box and a Fog environment on an Ubuntu 16.04 platform. Extensive safety measures and performance analysis that take into account diverse parameters, especially execution time, integrity, throughput, entropy, transfer rate, and delay demonstrate that our projected proposal is vastly proficient and satisfies the security prerequisites of secure data sharing and can efficiently withstand security attacks.

The use of machine learning algorithms for facial expression recognition and patient monitoring is a growing area of research interest. In this study, we present a technique for facial expression recognition based on deep learning algorithm: convolutional neural network (ConvNet). Data were collected from the FER2013 dataset that contains samples of seven universal facial expressions for training. The results show that the presented technique improves facial expression recognition accuracy without encoding several layers of CNN that lead to a computationally costly model. This study proffers solutions to the issues of high computational cost due to the implementation of facial expression recognition by providing a model close to the accuracy of the state-of-the-art model. The study concludes that deep l\earning-enabled facial expression recognition techniques enhance accuracy, better facial recognition, and interpretation of facial expressions and features that promote efficiency and prediction in the health sector.

Medicinal services experts experience significant levels of word-related worry because of their working conditions. Subsequently, the point of this study is to build up a model that spotlights human services experts in order to break down the impact that activity requests, control, social help, and acknowledgment have on the probability that a specialist will experience pressure. The authors have beforehand presented a technique for pitch highlight identification utilizing a convolutional neural network (CNN) that yields great execution utilizing low-level acoustic descriptors alone, with no express span data. This paper utilizes this model for different pitch complement and lexical pressure discovery errands at the word and syllable level on the DIRNDL German radio news corpus. This research demonstrates that data on word or syllable span is encoded in the elevated level CNN include portrayal via preparing a direct relapse model on these highlights to foresee term.

Machine learning algorithms are excellent techniques to develop prediction models to enhance response and efficiency in the health sector. It is the greatest approach to avoid the spread of hepatitis C, especially injecting drugs, is to avoid these behaviors. Treatments for hepatitis C can cure most patients within 8 to 12 weeks, so being tested is critical. After examining multiple types of machine learning approaches to construct the classification models, we built an AI-based ensemble model for predicting Hepatitis C disease in patients with the capacity to predict advanced fibrosis by integrating clinical data and blood biomarkers. The dataset included a variety of factors related to Hepatitis C disease. The training data set was subjected to three machine-learning approaches and the validated data was then used to evaluate the ensemble learning-based prediction model. The results demonstrated that the proposed ensemble learning model has been observed ad more accurate compared to the existing Machine learning algorithms. The Multi-layer perceptron (MLP) technique was the most precise learning approach (94.1% accuracy). The Bayesian network was the second-most accurate learning algorithm (94.47% accuracy). The accuracy improved to the level of 95.59%. Hepatitis C has a significant frequency globally, and the disease's development can result in irreparable damage to the liver, as well as death. As a result, utilizing AI-based ensemble learning model for its prediction is advantageous in curbing the risks and improving treatment outcome. The study demonstrated that the use of ensemble model presents more precision or accuracy in predicting Hepatitis C disease instead of using individual algorithms. It also shows how an AI-based ensemble model could be used to diagnose Hepatitis C disease with greater accuracy.

Breast cancer is one of the leading causes of untimely deaths among women in various countries across the world. This can be attributed to many factors including late detection which often increase its severity. Thus, detecting the disease early would help mitigate its mortality rate and other risks associated with it. This study developed a hybrid machine learning model for timely prediction of breast cancer to help combat the disease. The dataset from Kaggle was adopted to predict the breast tumor growth and sizes using random tree classification, logistic regression, XBoost tree and multilayer perceptron on the dataset. The implementation of these machine learning algorithms and visualization of the results was done using Python. The results achieved a high accuracy (99.65%) on training and testing datasets which is far better than traditional means. The predictive model has good potential to enhance early detection and diagnosis of breast cancer and improvement of treatment outcome. It could also assist patients to timely deal with their condition or life patterns to support their recovery or survival.

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Cloud Computing is observed as the greatest paradigm change in Information technology. Data outsourcing is an inventive representation with the intention of trustworthy storage and proficient query execution to customers. Data stored on the cloud is showing great attention. However, the security issues allied with data storage over the cloud is a chief daunting cause for potential adopters. Hence the focus is to find techniques that will offer more security. Many diseases fighting organizations are working together io implement cloud as a data sharing vehicle. It is obligatory to build up innovative solutions with the intention of amalgamate diverse approaches in order to generate flexible and adaptable systems, particularly for achieving elevated levels of utilization of developed algorithms. In this document, we suggest an innovative model based on fragmentation, secret sharing and encryption for medical databases which will divide the data amongst several cloud service providers. We develop a systematic structure exploiting the sensitive nature of information and results in enhanced security level. A database for medical system is represented as Entity association and Relational model. A cloud based model is proposed to offer secure patient centric right to access PHR in a competent way. The simulation results implemented in NetBeans Java for performance evaluation of existing cryptographic techniques are shown. Our security model is evaluated using CrypTool 1.4.30 considering the entropy of algorithms. The future work includes development of a computerized system retrieving, storing and maintaining data efficiently and quickly.