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Crowdsourcing is now one of the most important and transformative paradigms, with great success in a variety of application tasks. Crowdsourcing obtains knowledge and information to solve cognitive or intelligence-intensive tasks from an evolving group of participants via the Internet. Unfortunately, providing a hard privacy guarantee and query optimization is incompatible when a higher task acceptance rate needs to be accomplished and this case is common in most existing crowdsourcing solutions. The state of art systems suffered from different complexities such as lack of crowdsourcing optimization techniques, increased cost, latency, security, and scalability issues. In this paper, we have proposed a crowdsourcing model to optimize the cost and latency, issues that occur while query optimization using the Moth Flame and Tunicate Swarm Algorithm (MF-TSA). The TSA algorithm is added to the MF algorithm to enhance its exploitation capability and yield fast convergence. The data privacy concerns of the worker and the requestor are addressed using homomorphic encryption that simultaneously enhances the efficiency of the crowdsourcing framework. The main aim of this work is to optimize the cost and latency for query plan selection along with security. Initially, the homomorphic encryption model is used to encrypt the data. In query design, two kinds of crowd-controlled administrators, that is, Crowd Powered Selection (CSelect) and Crowd Powered Join (CJoin) are connected for assessing query. The proposed framework utilizes MF-TSA to optimize the selection and join queries with low cost and latency. Finally, the experimental results demonstrate better query optimization performance than other existing algorithms such as sequential, parallel, andcrowdsourcing, moth flame-based tunicate swarm algorithm, query, select and join queries INTRODUCTIONRecently, large-scale user representations of extensive collaboration have been made available through the Internet via various types of technologies and mechanisms. The collection of text documents and data source connections is an important part of the World Wide Web (WWW). The emerging crowdsourcing platform has been authorized to collect and distribute human computing for a wide range of tasks. 1 To generate an accurate and efficient solution, crowdsourcing targets a massive audience. The consensus is shared between the crowdsourcing system and implementations, such as employees who participate and delegate tasks that differ. When it comes to tasks such as answering questions, some workers are smarter and faster than others. 2,3

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Alzheimer's Disease (AD) is referred to as one of the highest non-unusual neurodegenerative disorders that inflict eternal harm to the memory-associated brain cells and wonder skills. There is a 99.6 percent failure rate in clinical trials of Alzheimer's disease pills, perhaps due to the fact that AD sufferers cannot be without early-stage complications. This observation analyzed machine learning knowledge of strategies to use empirical statistics to forecast the progression of AD in the years of fate. Diagnosis of AD is often difficult, particularly at an early stage in the disease system, due to the degree of mild cognitive impairment (MCI). However, it is at this point where treatment is much more likely to be successful, so there will be great benefits in enhancing the diagnosis process. Research in this area aims to identify the most complex mechanisms directly related to changes in AD. Various imaging methods are used to diagnose AD, and image modes play a key role in the diagnosis of AD. This paper uses a Positron Emission Tomography (PET) image to detect AD early. The PET image is often used to know how organs and tissues function in the human body. This research study analyses prediction approaches using various kinds of machine learning algorithms to solve AD diagnostic problems. Artificial Neural Networks are one of the many algorithms. Modern research has shown that deep learning is a proficient technique for solving numerous problems of image recognition, but most of these published approaches owe their performance to training on a very large number of data samples.