This study presents a novelmethod to detect themedical application based on Quantum Computing(QC)and a few Machine Learning(ML)*** has a primary advantage i.e.,it uses the impact of quantum parallelism to provide the ...
详细信息
This study presents a novelmethod to detect themedical application based on Quantum Computing(QC)and a few Machine Learning(ML)*** has a primary advantage i.e.,it uses the impact of quantum parallelism to provide the consequences of prime factorization issue in a matter of ***,this model is suggested for medical application only by recent researchers.A novel strategy i.e.,Quantum KernelMethod(QKM)is proposed in this paper for data *** this QKM process,Linear Tunicate Swarm Algorithm(LTSA),the optimization technique is used to calculate the loss function initially and is aimed at medical *** output of optimization is either 0 or 1 i.e.,odd or even in *** this output value,the data is identified according to the ***,the method also reduces time,saves cost and improves the efficiency by feature selection process i.e.,Filter *** the features are extracted,QKM is deployed as a classification model,while the loss function is minimized by *** motivation of the minimal objective is to remain ***,some computations can be performed more efficiently by the proposed *** testing,the test data was evaluated by minimal loss *** outcomes were assessed in terms of accuracy,computational time,and so *** this,databases like Lymphography,Dermatology,and Arrhythmia were used.
This paper presents a novel hybrid ensemble approach for classification in medicaldatabases. The proposed approach is formulated to cluster extracted features from medicaldatabases into soft clusters using unsupervi...
详细信息
This paper presents a novel hybrid ensemble approach for classification in medicaldatabases. The proposed approach is formulated to cluster extracted features from medicaldatabases into soft clusters using unsupervised learning strategies and fuse the decisions using parallel data fusion techniques. The idea is to observe associations in the features and fuse the decisions made by learning algorithms to find the strong clusters which can make impact on overall classification accuracy. The novel techniques such as parallel neural-based strong clusters fusion and parallel neural network based data fusion are proposed that allow integration of various clustering algorithms for hybrid ensemble approach. The proposed approach has been implemented and evaluated on the benchmark databases such as Digital database for Screening Mammograms, Wisconsin Breast Cancer, and Pima Indian Diabetics. A comparative performance analysis of the proposed approach with other existing approaches for knowledge extraction and classification is presented. The experimental results demonstrate the effectiveness of the proposed approach in terms of improved classification accuracy on benchmark medicaldatabases.
Colorectal cancer is one of the most commonly diagnosed cancers and it develops in the colon region of large *** histopathologist generally investigates the colon biopsy at the time of colonoscopy or *** detection of ...
详细信息
Colorectal cancer is one of the most commonly diagnosed cancers and it develops in the colon region of large *** histopathologist generally investigates the colon biopsy at the time of colonoscopy or *** detection of colorectal cancer is helpful to maintain the concept of accumulating cancer *** medical practices,histopathological investigation of tissue specimens generally takes place in a conventional way,whereas automated tools that use Artificial Intelligence(AI)techniques can produce effective results in disease detection *** this background,the current study presents an Automated AI-empowered Colorectal Cancer Detection and classification(AAI-CCDC)*** proposed AAICCDC technique focuses on the examination of histopathological images to diagnose colorectal ***,AAI-CCDC technique performs preprocessing in three levels such as gray scale transformation,Median Filtering(MF)-based noise removal,and contrast *** addition,Nadam optimizer with EfficientNet model is also utilized to produce meaningful feature ***,Glowworm Swarm Optimization(GSO)with Stacked Gated Recurrent Unit(SGRU)model is used for the detection and classification of colorectal *** proposed AAI-CCDC technique was experimentally validated using benchmark dataset and the experimental results established the supremacy of the proposed AAI-CCDC technique over conventional approaches.
The immense amount of data managed during the diagnosis process overwhelms, by far, the clinicians' processing capabilities. Artificial intelligence methods like Multi -Layer Perceptrons come to help by providing ...
详细信息
The immense amount of data managed during the diagnosis process overwhelms, by far, the clinicians' processing capabilities. Artificial intelligence methods like Multi -Layer Perceptrons come to help by providing a second opinion based on powerful and reliable data processing. Unfortunately, these methods often suffer from problems related to their training methods, which can lead to poor performance. Metaheuristics are promising training alternatives because of their stochastic and general-purpose nature. This work introduces a new training method based on metaheuristics, called Genetic Ant Lion Optimiser. It includes new features for dealing with the convergence problems of the original Ant Lion Optimiser and integrates a novel crossover operator for avoiding stagnation. Experiments compare our proposal against 31 state-of-the-art algorithms, over 20 different medicaldatasets. classification quality metrics reflect that our approach attains a robust and efficient behaviour with the majority of the datasets, obtaining highlighted results, such as an accuracy of 1.0 with the kidney dataset (bi-class) and 0.943 with the lung -cancer dataset (multi -class). Besides, it reaches adequate convergence rates and reasonable time consumption.
Traditionally, supervised machine learning methods are the first choice for tasks involving classification of data. This study provides a non-conventional hybrid alternative technique (pEAC) that blends the Possibilis...
详细信息
Traditionally, supervised machine learning methods are the first choice for tasks involving classification of data. This study provides a non-conventional hybrid alternative technique (pEAC) that blends the Possibilistic Fuzzy CMeans (PFCM) as base cluster generating algorithm into the 'standard' Evidence Accumulation Clustering (EAC) clustering method. The PFCM coalesces the separate properties of the Possibilistic C-Means (PCM) and Fuzzy C-Means (FCM) algorithms into a sophisticated clustering algorithm. Notwithstanding the tremendous capabilities offered by this hybrid technique, in terms of structure, it resembles the hEAC and fEAC ensemble clustering techniques that are realised by integrating the K-Means and FCM clustering algorithms into the EAC technique. To validate the new technique's effectiveness, its performance on both synthetic and real medicaldatasets was evaluated alongside individual runs of well-known clustering methods, other unsupervised ensemble clustering techniques and some supervised machine learning methods. Our results show that the proposed pEAC technique outperformed the individual runs of the clustering methods and other unsupervised ensemble techniques in terms accuracy for the diagnosis of hepatitis, cardiovascular, breast cancer, and diabetes ailments that were used in the experiments. Remarkably, compared alongside selected supervised machine learning classification models, our proposed pEAC ensemble technique exhibits better diagnosing accuracy for the two breast cancer datasets that were used, which suggests that even at the cost of none labelling of data, the proposed technique offers efficient medical data classification.
In today's medical world, the patient's data with symptoms and diseases are expanding rapidly, so that analysis of all factors with updated knowledge about symptoms and corresponding new treatment is merely no...
详细信息
In today's medical world, the patient's data with symptoms and diseases are expanding rapidly, so that analysis of all factors with updated knowledge about symptoms and corresponding new treatment is merely not possible by medical experts. Hence, the essential for an intelligent system to reflect the different issues and recognize an appropriate model between the different parameters is evident. In recent decades, rough set theory (RST) has been broadly applied in various fields such as medicine, business, education, engineering and multimedia. In this study, a hybrid intelligent system that combines rough set (RST) and bijective soft set theory (BISO) to build a robust classifier model is proposed. The aim of the hybrid system is to exploit the advantages of the constituent components while eliminating their limitations. The resulting approach is thus able to handle data inconsistency in datasets through rough sets, while obtaining high classification accuracy based on prediction using bijective soft sets. Toward estimating the performance of the hybrid rough-bijective soft set (RBISO)-based classification approach, six benchmark medicaldatasets (Wisconsin breast cancer, liver disorder, hepatitis, Pima Indian diabetes, echocardiogram data and thyroid gland) from the UCI repository of machine learning databases are utilized. Experimental results, based on evaluation in terms of sensitivity, specificity and accuracy, are compared with other well-known classification methods, and the proposed algorithm provides an effective method for medical data classification.
In this paper, we present the application of a Multi-Agent Classifier System (MACS) to medical data classification tasks. The MACS model comprises a number of Fuzzy Min-Max (FMM) neural network classifiers as its agen...
详细信息
ISBN:
(数字)9789814585422
ISBN:
(纸本)9789814585422;9789814585415
In this paper, we present the application of a Multi-Agent Classifier System (MACS) to medical data classification tasks. The MACS model comprises a number of Fuzzy Min-Max (FMM) neural network classifiers as its agents. A trust measurement method is used to integrate the predictions from multiple agents, in order to improve the overall performance of the MACS model. An auction procedure based on the sealed bid is adopted for the MACS model in determining the winning agent. The effectiveness of the MACS model is evaluated using the Wisconsin Breast Cancer (WBC) benchmark problem and a real-world heart disease diagnosis problem. The results demonstrate that stable results are produced by the MACS model in undertaking medical data classification tasks.
暂无评论