Artificial Intelligence with uncertainty
Lincy Jacquline M; Sudha N
Abstract
Problem Statement: Chronic nephritic sickness is another name for chronic kidney disease (CKD). Numerous complications, such as elevated blood levels, anemia, weak bones, and nerve damage, constitute a problem. It is usually possible to prevent chronic uropathy from getting worse by early identification ...
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Problem Statement: Chronic nephritic sickness is another name for chronic kidney disease (CKD). Numerous complications, such as elevated blood levels, anemia, weak bones, and nerve damage, constitute a problem. It is usually possible to prevent chronic uropathy from getting worse by early identification and treatment. Methodology: To circumvent these problems, current research has presented the fruit fly optimization algorithm (FFOA) and effective multi-kernel support vector machine (MKSVM) for illness classification. Finding best features from a collection is usually done using FFOA. Main findings/Contributions: MKSVM categorizes medical data using chosen dataset criteria. The accuracy of classifier will be impacted by any range variations in data obtained for this study. MKSVM continues to yield more incorrectly classified findings. To resolve those problems introduces a pre-processing step based on min max normalization to normalize scale of input CKD data values. Then significant features will be selected utilizing Improved FFOA (IFFOA). The selected features will be clustered using Weighted Fuzzy C means clustering (WFCM) to predict the class label of the data sample to reduce the misclassification results. Finally, CKD classification will be performed using the Enhanced Adaptive Neuro Fuzzy Inference System (EANFIS) as normal or abnormal. Conclusions: The suggested strategy efficacy is demonstrated by findings in fields of recall, accuracy, precision, and f-measure.
Artificial Intelligence with uncertainty
Yinghao Li; Jawis M N
Abstract
In an effort to fulfil the requirements of China's quality education policy, several Chinese institutions and colleges have recently included badminton as an optional sport. Examining current issues in the field, the paper argues that badminton instruction in Chinese higher education needs improvement. ...
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In an effort to fulfil the requirements of China's quality education policy, several Chinese institutions and colleges have recently included badminton as an optional sport. Examining current issues in the field, the paper argues that badminton instruction in Chinese higher education needs improvement. It then proposes new approaches to teaching the sport in the classroom, including ideas for lesson plans, instructional strategies, and pedagogical techniques. Concerning badminton education in higher education, it outlines a strategy to address the issues. In order to overcome the obstacles of fuzzy assisted virtual reality badminton instruction, teachers should think about their own lesson planning and delivery processes, as well as their Badminton Students’ perceptions of physical education programmers. To teach badminton in universities, the article proposes VR-ITM, or fuzzy assisted virtual reality assisted interactive teaching techniques using neural network. This study aims to examine the advantages and disadvantages of utilizing fuzzy assisted virtual reality (VR) to teach badminton in PE classes, as well as the difficulties and solutions that teachers have found for these issues through the use of a neural network. This study investigates the potential benefits of incorporating virtual reality technology into the physical education curriculum and uses VR-ITM, which stands for virtual reality based interactive teaching methods, to teach badminton at college locations. Incorporating badminton into university curricula as a means of encouraging students to lead healthier lifestyles is the primary focus of this study. In addition to fostering Badminton Students’ professional skills, universities should emphasise the importance of Badminton Students’ physical well-being in comparison to the conventional approaches used by the control group to teach badminton, the neural network-based intelligent teaching system performs better.
Artificial Intelligence with uncertainty
Chunyan Xing
Abstract
Management models in education have recently emerged with plans to make school administration more effective and efficient. Higher education (HE), a postsecondary education, leads to academic degrees. An object class having membership grades that run along a continuum is called a fuzzy set. When tested ...
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Management models in education have recently emerged with plans to make school administration more effective and efficient. Higher education (HE), a postsecondary education, leads to academic degrees. An object class having membership grades that run along a continuum is called a fuzzy set. When tested in online classrooms with abnormal data, this method's effectiveness exceeded that of the intelligent education system. The challenging characteristics of such higher education using fuzzy sets are the students' low family income, a complicated network, and skill development due to the low quality of education. Block structure has been developed based on higher education in a fuzzy sets system for students in terms of low family income, complicated networks, and skill development due to the low quality of education. Hence, in this research, Double Deep Q-Learning network-enabled Multi-Criteria Decision-Making (D2QLN-MCDM) technologies have improved students' higher education with fuzzy sets. It has been used to design, develop, and verify students' higher education in fuzzy sets. The workforce tasked with integrating digital technology into HE have a profound effect on students' learning experiences. HE institutions will need experienced individuals with varied digital knowledge to manage and integrate these technologies effectively. The experimental analysis of D2QLN-MCDM outperforms fuzzy sets using the student's HE regarding precision (99.4), accuracy (90.4%), Recall ratio (97.5%), and specificity (93.9%).
Artificial Intelligence with uncertainty
Mehmet Karahan; Furkan Lacinkaya; Kaan Erdonmez; Eren Deniz Eminagaoglu; Cosku Kasnakoglu
Abstract
In recent years, development of the machine learning algorithms has led to the creation of intelligent surveillance systems. Thanks to the machine learning, it is possible to perform intelligent surveillance by recognizing people's facial features, classifying their age and gender, and detecting objects ...
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In recent years, development of the machine learning algorithms has led to the creation of intelligent surveillance systems. Thanks to the machine learning, it is possible to perform intelligent surveillance by recognizing people's facial features, classifying their age and gender, and detecting objects around instead of ordinary surveillance. In this study, a novel algorithm has been developed that classifies people's age and gender with a high accuracy rate. In addition, a novel object recognition algorithm has been developed that detects objects quickly and with high accuracy. In this study, age and gender classification was made based on the facial features of people using Convolutional Neural Network (CNN) architecture. Secondly, object detection was performed using different machine learning algorithms and the performance of the different machine learning algorithms was compared in terms of median average precision and inference time. The accuracy of the age and gender classification algorithm was tested using the Adience dataset and the results were graphed. The experimental results show that age and gender classification algorithms successfully classify people's age and gender. Then, the performances of object detection algorithms were tested using the COCO dataset and the results were presented in graphics. The experimental results stress that machine learning algorithms can successfully detect objects.
