Pulmonary disease is a kind of disease that affects the lungs and other parts of the respiratory system and is mainly caused by smoking, asbestos, secondhand smoke, and other forms of air pollutants. Several types of pulmonary diseases are emphysema, fibrosis, pneumothorax, asthma, lung cancer, chronic obstructive pulmonary disease (COPD), and so on. Pulmonary diseases are otherwise known as lung disorders and respiratory diseases. Pulmonary diseases are predicted by several methods using x-ray images and CT scan images. Some of the recent works predict only a specific disease, and optimal prediction is not yet achieved. Hence, we proposed a novel method known as African vulture optimization (AVO) algorithm-based weighted support vector machine approach (w-SVM). The proposed method in this article predicts emphysema, fibrosis, pneumothorax, and normal kinds from the NIH chest x-ray dataset. The X-images are preprocessed after data acquisition to obtain the desired size and to remove undesirable noise. The preprocessed images are then sent into the SVM for feature extraction, and the AVO is used to improve the SVM so that a kernel function may be obtained. The proposed w-SVM effectively predicts the emphysema, fibrosis, pneumothorax, and normal classes from the dataset. The experimental analyses are conducted and compared with existing works and concluded that the proposed work outperforms other approaches in terms of accuracy, sensitivity, specificity, and Matthews's correlation coefficient, prediction time, and modeling time.

Pulmonary disease diagnosis using African vulture optimized weighted support vector machine approach

Crowdsourcing is an environment where a group of users collaborates together to exchange information and to find answers for complex problems (queries). Query optimization is the task of selecting the best query strategy with less cost associated with it. The crowdsourcing cost can be determined by selecting the best plan from the set of options available and the best plan considerably reduce the cost for the inquiry configuration. As one of the center tasks in information recovery, the investigation of top‐k queries with crowdsourcing, to be specific group empowered top k inquiries is depicted. This issue is defined with three key variables, latency, money related expense, and nature of answers. The fundamental point is to plan a novel system that limits financial cost when the latency is compelled. In this article, we used a heuristic search algorithm named as Evolutionary Fuzzy‐based Gravitational Search algorithm (EFGSA) that produces an optimal query feature selection results with minimizing cost and latency. EFGSA‐based crowdsourcing framework gives a better balance between latency and cost while generating query plans. The performance analysis of proposed EFSGA for optimal query plan is evaluated in terms of running time, accuracy, monetary cost, and so on. From the experimental results, the proposed method achieved better results than other methods in our cost and latency model.

Evolutionary Fuzzy-based gravitational search algorithm for query optimization in crowdsourcing system to minimize cost and latency

This article is mainly to study the realization of travel recommendations for different users through deep learning under global information management. The personalized travel route recommendation is realized by establishing personalized travel dynamic interest (PTDR) algorithm and distributed lock manager (DLM) model. It is hoped that this model can provide more complete data information of tourist destinations on the basis of the past, and can also meet the needs of users. The innovation of this article is to compare and analyze with a large number of baseline algorithms, highlighting the superiority of this model in personalized travel recommendation. In addition, the model incorporates the topic factor features, geographic factor features, and user preference features to make the data more in line with user needs and improve the efficiency and applicability of the model. It is hoped that the plan proposed in this article can help users make choices of tourist destinations more conveniently.

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Research on the Realization of Travel Recommendations for Different Users Through Deep Learning Under Global Information Management

Crowdsourcing is a procedure for demonstrating data outsourcing to a wide range of individual workers rather than considering a unique entity or a company. Crowdsourcing has made different kinds of chances for some trying issues by utilizing human knowledge. In order to attain an optimal global assignment technique, it is necessary to gather information regarding the location of the entire workers. There occur few security issues during information gathering that causes severe threat to all workers. To overcome the concerns based on privacy-preserving, this paper proposes a privacy-preserving model based on Fuzzy with the Black widow and Spider Monkey Optimization (BW–SMO). The fuzzy can be used to cluster the query solution. To optimize the query selection, we exploited the Black widow optimization algorithm incorporated with the Spider Monkey optimization algorithm. The parameters of both algorithms are controlled by the Fuzzy logic controller. Thus, our proposed frameworks of Fuzzy with BW–SMO effectively solve optimizing selection and join queries with low cost, latency, and securing data. The proposed model will be compared with the existing models to show the system's effectiveness.

Fuzzy with black widow and spider monkey optimization for privacy-preserving-based crowdsourcing system

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, and CrowdOp.

An efficient privacy-preserving model based on OMFTSA for query optimization in crowdsourcing

Generally, human and machine-based query operations can be modified with the use of crowdsourcing. Location-based queries are classified into range and k-nearest neighbor (KNN) queries. Space and point of interest (POI) information can be obtained from both range and KNN queries. In this paper, we expose the trust stage computation of range and KNN query answers with the help of the whale optimization algorithm (WOA). The system chooses either parallel or serial processing, and the experiments are carried out using real-time crowdsourcing. The effectiveness of the proposed concept is evaluated through various consequences such as gang dimension, POI information, space information, and range and KNN query consequences. Each of these effects produces an optimal and reliable result. Finally, the computation time and communication overhead performance of serial and parallel processing are analyzed by examining consequences and production of optimal outcomes.

Optimal processing of nearest-neighbor user queries in crowdsourcing based on the whale optimization algorithm

Crowdsourcing is developing as a conveyed critical thinking and business creation in recent years. The expression “crowdsourcing” was authored by Jeff Howe in 2006. From that point forward, a great deal of work in Crowdsourcing has concentrated on various parts of publicly supporting, for example, computational procedures and performance analysis. Declarative crowdsourcing frameworks help diminish the complexities and conceal them from users and manages the weight of the crowd. Crowdsourcing has been a critical perspective with regards to locate a specific information in a database. Crowdsourcing gives an amazing platform to execute inquiries that require progressively human talents, insight and investigation rather than simply counterfeit canny computers, which use picture acknowledgment, information filtration and tagging. Crowd optimization realizes how to adjust among cost and latency and accordingly query optimization targets are increasingly effective. CROWDOPT for upgrading three sorts of questions: selectionquires, join quiries and complex quires. In this paper, we give the outline of the survey of Crowdsourcing worldview which are arranged by the Crowdsourcing operators and datasets. In view of this study we sketch the vital components that essential to be estimated to improve Crowdsourced data management.

Cohort of Crowdsourcıng – Survey

The problem of query optimization with declarative crowdsourcing systems [1] has been well studied. There exist some approaches discussed toward the problem of query optimization, but still, the methods suffer to achieve higher performance in query optimization. To improve the performance of query optimization, object access frequency (OAF) measure-based horizontal–vertical-information-access-time–cost (HVIATC)-based approach is presented in this paper. The HVIATC method receives the crowdsource on the specific query submitted. The query evaluator receives the crowdsource, and for each source is received, the query evaluator extracts the fields and verifies the completeness of the query toward the requirement. Then, the optimizer computes the access frequency of each source on different objects of the data set. Similarly, the method computes the horizontal information cost, horizontal access cost. Similarly, the cost of vertical features is estimated. The cost on time feature has been estimated with each query recommended by the crowdsourcing. In conclusion, the method computes the query sequence weight for each recommendation. Based on computed weight measure, a single sequence has been selected. The proposed algorithm improves the performance of optimization in relational databases. The method also reduces the time complexity of optimization and execution.

N. Bhaskar

Papers

Optimal processing of nearest-neighbor user queries in crowdsourcing based on the whale optimization algorithm

Evolutionary Fuzzy-based gravitational search algorithm for query optimization in crowdsourcing system to minimize cost and latency

Fuzzy with black widow and spider monkey optimization for privacy-preserving-based crowdsourcing system

Cohort of Crowdsourcıng – Survey

HVIATC: Ontology-Based Efficient Query Optimization for Declarative Crowdsourcing System Using OAF Measures