Soft Computing

Today, biomedical technology is essential for the early diagnosis and effective management of a wide range of illnesses, from mild to fatal. Brain tumours, a mass multiplication of abnormal brain cells, are one of the worst diseases. Early detection and treatment can save a person's life by preventing the spread of abnormal cells. To provide an efficient diagnosis technique, the healthcare sector has recently undergone significant improvement. Finding a specific image categorization method based on tumour cell areas is crucial in the medical industry. After choosing the tumour site, the segmentation and classification processes are carried out. In a shorter amount of time, the identification-based method aids in both the restriction of the image's region and the identification of its border. Automatic brain tumour classification is hard because the area around the brain tumour is so different regarding geography and structure. This paper makes a case for the automatic detection of brain tumours using deep neural network classification. MRI-based image segmentation for fuzzy-based cell detection concerning image extraction by utilizing deep learning model. The results show that the performance levels of the suggested model are superior to those of the existing models.

Humans have the ability to quickly identify their own environment, understand, judge and analyze it, which is one of the important reasons why human beings can survive in nature for a long time and gradually develop it into a prosperous society today. The key to human's ability to perceive and understand the environment lies in the ability to accurately find and identify objects, understand and describe visual scenes, and even express emotions on this basis. And if computers can realize automatic and accurate image recognition, and even understand the semantics of images correctly, it will surely improve and facilitate human life. Based on this, the author integrated and optimized the computer image recognition system and applied it in this paper. The core technology of the system is to improve the image algorithm, which can complete the training, testing and classification of target images. The experimental data are available. Choosing this algorithm to improve the learning and training of the data generated by the original image processing is more effective than directly training the original image.

Databases obtained from different search engines, market data, patients’ symptoms and behaviours, etc., are some common examples of set-valued data, in which a set of values are correlated with a single entity. In real-world data deluge, various irrelevant attributes lower the ability of experts both in speed and in predictive accuracy due to high dimension and insignificant information, respectively. Attribute selection is the concept of selecting those attributes that ideally are necessary as well as sufficient to better describe the target knowledge. Rough set-based approaches can handle uncertainty available in the real-valued information systems after the discretization process. In this paper, we introduce a novel approach for attribute selection in set-valued information system based on tolerance rough set theory. The fuzzy tolerance relation between two objects using a similarity threshold is defined. We find reducts based on the degree of dependency method for selecting best subsets of attributes in order to obtain higher knowledge from the information system. Analogous results of rough set theory are established in case of the proposed method for validation. Moreover, we present a greedy algorithm along with some illustrative examples to clearly demonstrate our approach without checking for each pair of attributes in set-valued decision systems. Examples for calculating reduct of an incomplete information system are also given by using the proposed approach. Comparisons are performed between the proposed approach and fuzzy rough-assisted attribute selection on a real benchmark dataset as well as with three existing approaches for attribute selection on six real benchmark datasets to show the supremacy of proposed work.

The world has been greatly affected by increased utilization of mobile methods as well as smart devices in field of health. Health professionals are increasingly utilizing these technologies' advantages, resulting in a significant improvement in clinical health care. For this purpose, machine learning (ML) as well as Internet of Things can be utilized effectively. This study aims to propose a novel data analysis method for a health monitoring system based on ML. Goal of research is to create a ML-based smart health monitoring method. It helps the doctors keep an eye on patients from a distance as well as take periodic actions if they need to. Utilizing wearable sensors, a set of five parameters—the electrocardiogram, pulse rate, pressure, temperature, and position detection—have been identified. Kernelized component vector neural networks are used to choose the features in the input data. Then, a sparse attention-based convolutional neural network with a structural search algorithm was used to classify the selected features. For a variety of datasets, the proposed technique attained validation accuracy 95%, training accuracy 92%, RMSE 52%, F-measure 53%, and sensitivity 77%.

We present a category equivalent to that of semi-Nelson algebras. The objects in this category are pairs consisting of a semi-Heyting algebra and one of its filters. The filters must contain all the dense elements of the semi-Heyting algebra and satisfy an additional technical condition. We also show that the category of dually hemimorphic semi-Nelson algebras is equivalent to that of dually hemimorphic semi-Heyting algebras.