Optimized Gabor Feature Extraction for Mass Classification Using Cuckoo Search for Big Data E-Healthcare

Khan, Salabat; Khan, Amir; Maqsood, Muazzam; Aadil, Farhan; Ghazanfar, Mustansar Ali

doi:10.1007/s10723-018-9459-x

Cited by 44 publications

(14 citation statements)

References 25 publications

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“…With the recent advances in big data technologies, new machine learning techniques for precision medicine (Njølstad et al 2019 ; Van Den Berg et al 2019 ), the life sciences, and clinical data analysis (Ho et al 2019 ; Palanisamy and Thirunavukarasu 2019 ; Ngiam and Khor 2019 ) are continuously developed and extended in medical data science to achieve a better understanding of diseases. The field of medical data science covers different areas such as prediction of response to treatment in personalized medicine (Abul-Husn and Kenny 2019 ; Suwinski 2019 ), biomarker detection (Zhang et al 2019 ; Fitzgerald 2020 ), tumor classification (Khan et al 2019 ; Lin and Berger 2020 ), COVID detection and classification (Wang et al 2020 ; Bragazzi et al 2020 ), and the understanding of gene interactions (Shukla and Muhuri 2019 ). When it comes to big data, central processing techniques may not be enough to process these medical data and get the required information on time correctly.…”

Section: Introductionmentioning

confidence: 99%

RETRACTED ARTICLE: A topic-based hierarchical publish/subscribe messaging middleware for COVID-19 detection in X-ray image and its metadata

Eken

2020

Soft Comput

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Putting real-time medical data processing applications into practice comes with some challenges such as scalability and performance. Processing medical images from different collaborators is an example of such applications, in which chest X-ray data are processed to extract knowledge. It is not easy to process data and get the required information in real time using central processing techniques when data get very large in size. In this paper, real-time data are filtered and forwarded to the right processing node by using the proposed topic-based hierarchical publish/subscribe messaging middleware in the distributed scalable network of collaborating computation nodes instead of classical approaches of centralized computation. This enables processing streaming medical data in near real time and makes a warning system possible. End users have the capability of filtering/searching. The returned search results can be images (COVID-19 or non-COVID-19) and their meta-data are gender and age. Here, COVID-19 is detected using a novel capsule network-based model from chest X-ray images. This middleware allows for a smaller search space as well as shorter times for obtaining search results. Keywords Capsule networks Á Topic-based hierarchical publish/subscribe Á COVID-19 detection Á Hybrid intelligence Á X-ray images Á Medical data management Communicated by Valentina E. Balas.

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Section: Introductionmentioning

confidence: 99%

RETRACTED ARTICLE: A topic-based hierarchical publish/subscribe messaging middleware for COVID-19 detection in X-ray image and its metadata

Eken

2020

Soft Comput

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“…Algorithms include particle such as PSO, ACO, Gray wolf optimization (GWO), artificial bee colony (ABC), owl optimization algorithm (OOA), Falcon optimization algorithm (FOA), cuckoo search algorithm (CSA), and firefly algorithm (FA). Many researchers around the world have been benefited from the diversity in swarm-based algorithms, which are applied to solve complex optimization problems in various fields such as test scheduling problems, 23,24 engineering optimization problems, 10,11,[25][26][27][28] heat exchangers problems, [29][30][31] neural network parameter optimization, 32,33 health-care, 34,35 real-time object tracking, 36,37 protein detection, 38,39 task scheduling in cloud computing, 40,41 and clustering for wireless sensor networks. 42,43 The third category of algorithms that use physical or chemical systems, typically simulate physical phenomena occurring in nature like Newton's gravitational law, quantum mechanics, and universe theory.…”

Section: Literature Reviewmentioning

confidence: 99%

A new competitive multiverse optimization technique for solving single‐objective and multiobjective problems

Benmessahel

Xie

Chellal

2020

Engineering Reports

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The development of useful algorithms for solving global optimization problems has recently drawing the research community's attention. A number of optimization algorithms have been suggested, which mimic a particular biological process or imitate natural evolution. In this work, a novel population‐based optimization technique is proposed, the so‐called competitive multiverse optimizer (CMVO) for solving global optimization problems. This novel method is fundamentally inspired by the multiverse optimizer algorithm (MVO) but with a different framework. Our basic idea is to inject a pairwise competition mechanism between universes and adopts a novel update strategy that make universes learn from the winner. In CMVO, the mechanism of updating universes is not confined to the optimal universe but rather uses a bicompetitive scheme at each generation in which the universe that loses the competition learns from the winner, unlike in MVO in which all universes learn from the optimal universe. The main idea of this work is to raise the exploration rate for the search space by performing pairwise competition and improve the exploitation ability through learning from the winner. This work also presents a multi‐objective version of CMVO called MOCMVO with a simple structure to show the rapid convergence toward the Pareto front. The performance of our proposed method in single‐objective optimization is demonstrated on a large number of mathematical problems of standard benchmark. The results obtained confirm the superior overall performance of CMVO in terms of the quality of obtained solutions, computational efficiency, and convergence speed relative to many of the state‐of‐the‐art metaheuristic algorithms. In multiobjective optimization, the proposed MOCMVO is evaluated and compared to other multiobjective algorithms using 10 multiobjective benchmarks involving five unconstrained, three constrained, and two engineering design problems. The experimental results of the proposed method in multiobjective optimization demonstrate the competitiveness of the proposed method in term of quantitative and qualitative measures.

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“…There have been a lot of evaluation metrics used for foreign exchange prediction but mean absolute error (MAE) and root mean squared error (RMSE) are the most common metrics [54][55][56][57][58]. These metrics can be evaluated as explained below.…”

Section: Evaluation Metricsmentioning

confidence: 99%

An Intelligent Event-Sentiment-Based Daily Foreign Exchange Rate Forecasting System

et al. 2019

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Financial time series analysis is an important research area that can predict various economic indicators such as the foreign currency exchange rate. In this paper, a deep-learning-based model is proposed to forecast the foreign exchange rate. Since the currency market is volatile and susceptible to ongoing social and political events, the proposed model incorporates event sentiments to accurately predict the exchange rate. Moreover, as the currency market is heavily dependent upon highly volatile factors such as gold and crude oil prices, we considered these sensitive factors for exchange rate forecasting. The validity of the model is tested over three currency exchange rates, which are Pak Rupee to US dollar (PKR/USD), British pound sterling to US dollar (GBP/USD), and Hong Kong Dollar to US dollar (HKD/USD). The study also shows the importance of incorporating investor sentiment of local and foreign macro-level events for accurate forecasting of the exchange rate. We processed approximately 5.9 million tweets to extract major events’ sentiment. The results show that this deep-learning-based model is a better predictor of foreign currency exchange rate in comparison with statistical techniques normally employed for prediction. The results present evidence that the exchange rate of all the three countries is more exposed to events happening in the US.

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Optimized Gabor Feature Extraction for Mass Classification Using Cuckoo Search for Big Data E-Healthcare

Cited by 44 publications

References 25 publications

RETRACTED ARTICLE: A topic-based hierarchical publish/subscribe messaging middleware for COVID-19 detection in X-ray image and its metadata

RETRACTED ARTICLE: A topic-based hierarchical publish/subscribe messaging middleware for COVID-19 detection in X-ray image and its metadata

A new competitive multiverse optimization technique for solving single‐objective and multiobjective problems

An Intelligent Event-Sentiment-Based Daily Foreign Exchange Rate Forecasting System

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