Search

Growing older is a phenomenon that is associated with increasingly complex health situations as a result of the coexistence of several chronic diseases. As a result, there is a downward tendency in both older people and their caretakers’ quality of life, which frequently results in frailty. There are numerous solutions available to treat the issue, which primarily affects older people. The basic and most popular imaging method for predicting cognitive impairment is magnetic resonance imaging. Furthermore, few of the earlier models had a definite level of accuracy when diagnosing the condition. Further, there is a critical need to put in place a stronger, more reliable approach to precise prediction. When compared to other procedures, using magnetic resonance images to predict cognitive decline is the safest and most straightforward. The advanced concept for a better optimized strategy to predict cognitive impairment at an early stage is presented in this research. The hybrid krill herd and grey wolf optimization method is offered as a solution to address the challenges in locating the impacted area. In a short amount of time, a significant number of MRI images are analyzed, and the results show a more precise or higher rate of recognition. [ABSTRACT FROM AUTHOR]

This paper improves the current situation of English language teaching from a technological perspective. Fuzzy that is a method of having local information to project the whole. Soft computing is a kind of optimization relative to traditional should computing, emphasizing the uncertainty and imprecision of things, and with the development of modern society, soft computing can solve many problems in production practice. Scenario teaching method is an important asset to enrich classroom content and mobilize students' learning enthusiasm. Situational teaching is the sum use of preparation before class to improvisation in the classroom. It has been proved that using scenario teaching in the process of English teaching can improve students' English ability. However, this traditional situational teaching method has problems such as incomplete evaluation index and poor teaching effect. To solve this problem, we constructed a new situational teaching method and a fuzzy evaluation system based on fuzzy sets and soft computing technology, and verified through experiments that both the new situational teaching method and the fuzzy evaluation system have good effects. At present, the popularity of situational English teaching is low, the overall level of English teaching is not high, fill-in-the-blank teaching is predominant, spoken English is weak, the cost of situational teaching is high, and teachers' willingness to implement it is low. To improve these problems, based on the research results above we propose the following four recommendations to improve the quality of contextualized teaching, solve the problems of contextualized teaching, and reduce teachers' pressure to prepare lessons. Finally, we make recommendations based on the current situation of the development of scenario-based teaching. (1) Build situational teaching in many ways. (2) Improve the quality of teachers' English. (3) Develop individualized improvement programs for students. (4) Promote the reform of teaching tools. [ABSTRACT FROM AUTHOR]

The continuous emergence of new social media has promoted the interaction of data in life. In this context, big data technology has gradually become a type of technology with special value in the field of education. It realizes business decision-making through the collection and analysis of specific types of data. The application of technology to the field of education can realize the analysis of students' learning needs and classroom teaching efficiency, while the application of big data technology to piano teaching can strengthen the combination of piano teaching and technology, and at the same time provide more data analysis and decision support for the whole process of piano teaching. Soft computing simplifies the calculation process by combining a variety of sophisticated algorithms. Soft computing is similar to human brain processing and perception. It can complete multi-variable complex calculations through low-cost and uncertain processes. With the continuous integration, the variables generated in the teaching process gradually increase, and the application of soft computing technology can effectively solve the complex calculation problems caused by many variables in the analysis of piano teaching. This paper first analyzes the basic theory of big data, soft computing and piano informatization teaching, and then obtains the problems existing in the current piano informatization teaching through questionnaire survey and analysis, and designs piano informatization, teaching innovation model based on big data and soft computing technology respectively. Through the post-investigation of the piano informatization teaching institutions combined with technical strategies, it is found that the degree of match between the piano informatization teaching content of the institution and the students' learning needs, the evaluation accuracy of piano teachers' teaching effects, and the students' piano learning performance have improved. [ABSTRACT FROM AUTHOR]

Mobile Adhoc Networks (MANET) in modern research have many optimal energy conservation mechanisms that can be deployed easily and in a faster manner. The routing approaches associated with energy consumption play a dominant role in routing the data packets between the mobile sensor nodes within the range of optimization. However, major challenges associated with energy consumption in MANETs include reduced lifetime of sensor nodes, poor coverage, and throughput. Most methods tend to reduce the interference of data while traversing between the sensor nodes and increase the capacity of the network. This results in delays while transmitting the packets across the network, and this may result in failure of packets being transmitted. To resolve this issue, in this paper, we propose an ant colony optimization combined with a flower pollination algorithm for minimal energy consumption and throughput maximisation in MANETs. This hybrid meta-heuristic model resolves the issues, including delays, poor coverage, and reduced network lifetime. This hybrid model uses the estimation of neighbourhood distance among the nodes for optimal placement of nodes for effective location. The estimation of location is found using a flower pollination algorithm with a levy flight mechanism. The estimation is carried out in a hyper sphere model that helps in finding the coverage area of the sensor nodes. Depending upon the estimation of neighbourhood distance among the sensor nodes, the consumption of energy among the sensor nodes in MANETs is reduced. The simulation was conducted between the proposed hybrid approach and conventional soft computing heuristics, where the results show that the proposed model achieves a higher rate of energy conservation and reduces delay than other methods. [ABSTRACT FROM AUTHOR]

As the population increases day by day, there has been a tremendous increment of ownership over two-wheeler and four-wheeler systems. Thereby usage of public transports system decreases than private vehicles system. In this scenario, this study focused on the significant factors in the mode choice behaviour of commuters from the extensive household survey conducted by Thiruvarur City to obtain detailed information on the modal split pattern analysis. The questionnaire of 4857 respondents was used in the Multinomial Logit Model using the Statistical Packages for Social Science (SPSS) tool to predict the mode choice behaviour of commuters. A total of seven explanatory variables were extracted from the responses; the Multinomial logit model was calibrated to fit the data, and the chi-square test was used to measure the goodness of fit. Apart from the MLM model, commuter behaviour prediction analysis is also made using soft computing technologies, which involves multi-linear regression and neuro-fuzzy model, keeping uncertainties involved in commuter behaviour to avoid collecting household data. It was found that two-wheeler commuters are more in numbers for their travel choice as cost minimizing than four-wheelers, public transport system commuters. Consequently, the developed model predicts the behaviour of commuters in a precise manner that closely matches the actual conduct. [ABSTRACT FROM AUTHOR]

Interval complex neutrosophic soft sets (I-CNSSs) refers to interval neutrosophic soft sets (I-NSSs) featuring three two-dimensional independent membership functions accordingly (falsity, indeterminacy, as well as uncertainty interval). A relation is a tool that helps in describing consistency and agreement between objects. Throughout this paper, we insert and discuss the interval complex neutrosophic soft relation (simply denoted by I-CNSR), a novel soft computing technique used to examine the interaction degree among corresponding models known as I-CNSSs. We present the definition of the Cartesian product of I-CNSSs followed by the definition of I-CNSR. Furthermore, the definitions and some theorems and properties related to the composition, inverse, and complement of I-CNSR are provided. The notions of symmetric, reflexive, transitive, and equivalent of I-CNSRs are proposed, and the algebraic properties of these concepts are verified. Furthermore, we demonstrate the relevance of our notion to real-world situations by offering a suggested method for solving a decision-making issue in the field of economics. Ultimately, an analysis is made between the current relationships and the proposed model to determine the model's significance. [ABSTRACT FROM AUTHOR]

COVID-19 is a contagious respiratory illness that can be passed from person to person. Because it affects the lungs, damages blood arteries, and causes cardiac problems, COVID-19 must be diagnosed quickly. The reverse transcriptase polymerase chain reaction (RT-PCR) is a method for detecting COVID-19, but it is time consuming and labor expensive, as well as putting the person collecting the sample in danger. As a result, clinicians prefer to use CT scan and Xray images. COVID-19 classification can be done manually, however AI makes the process go faster. AI approaches include image processing, machine learning, and deep learning. An AI model is required to diagnose COVID-19, and a dataset is necessary to train that model. A dataset consists of the information from which the model is trained. This paper consists of the review of different image processing, machine learning and deep learning papers proposed by different researchers. As well as models based on deep learning and pretrained model using gradient boosting algorithm The goal of this paper is to provide information for future researchers to work with. [ABSTRACT FROM AUTHOR]

Soft Computing (SC) techniques are oriented towards the analysis and design of intelligent systems covering an interdisciplinary methodological framework. Soft computing as it was defined by Professor Lotfi Zadeh is a collection of methodologies that cope with the main disadvantage of the conventional (hard) computing the poor performances when working in uncertain conditions. [Extracted from the article]