Accrual and Dismemberment of Brain Tumours Using Fuzzy Interface and Grey Textures for Image Disproportion.

A neurological disorder is a problem with the neural system of the body, as a brain tumor is one of the deadliest neurological conditions and it requires an early and effective detection procedure. The existing detection and diagnosis methods for image evaluation are based on the judgment of the radiologist and neurospecialist, where a risk of human mistakes can be found. Therefore, a new flanged method and methodology for detecting brain tumors using magnetic resonance imaging and the artificial neural network (ANN) technique are applied. The research is based on an artificial neural network-based behavioral examination of neurological disorders. In this study, an artificial neural network is used to detect a brain tumor as early as possible. The current work develops an effective approach for detecting cancer from a given brain MRI and recognizing the retrieved data for further use. To obtain the desired result, the following three procedures are used: preprocessing, feature extraction, training, and detection or classification. A Gaussian filter is also incorporated to eliminate noise from the image, and for texture feature extraction, GLCM is considered in this study. Further entropy, contrast, energy, homogeneity, and other GLCM texture properties of tumor categorization are measured using the ANFIS approach, which determines if the tumor is normal, benign, or malignant. Future research will focus on applying advanced texture analysis to classify brain tumors into distinct classes in order to improve the accuracy of brain tumor diagnosis. In the future, MRI brain imaging will be used to classify metastatic brain tumors.

Human Intelligence Analysis through Perception of AI in Teaching and Learning.

Instructional practices have undergone a drastic change as a result of the development of new educational technology. Artificial intelligence (AI) as a teaching and learning technology will be examined in this theoretical review study. To enhance the quality of teaching and learning, the use of artificial intelligence approaches is being studied. Artificial intelligence integration in educational institutions has been addressed, though. Students' assistance, teaching, learning, and administration are also addressed in the discussion of students' adoption of artificial intelligence. Artificial intelligence has the potential to revolutionize our social interactions and generate new teaching and learning methods that may be evaluated in a variety of contexts. New educational technology can help students and teachers better accomplish and manage their educational objectives. Artificial intelligence algorithms are used in a hybrid teaching mode in this work to examine students' attributes and introduce predictions of future learning success. The teaching process may be carried out in a more efficient manner using the hybrid mode. Educators and scientists alike will benefit from artificial intelligence algorithms that may be used to extract useful information from the vast amounts of data collected on human behavior.

Implementation of Whale Optimization for Budding Healthiness of Fishes with Preprocessing Approach.

This article examines distinctive techniques for monitoring the condition of fishes in underwater and also provides tranquil procedures after catching the fishes. Once the fishes are hooked, two different techniques that are explicitly designed for smoking and drying are implemented for saving the time of fish suppliers. Existing methods do not focus on the optimization algorithms for solving this issue. When considering the optimization problem, the solution is adequate for any number of inputs at time t. For this combined new flanged technique, a precise system model has been designed and incorporated with a set of rules using contention protocols. In addition, the designed system is also instigated with a whale optimization algorithm that is having sufficient capability to test the different parameters of assimilated sensing devices using different sensors. Further to test the effectiveness of the proposed method, an online monitoring system has been presented that can monitor and in turn provides the consequences using a simulation model for better understanding. Moreover, after examining the simulation results under three different scenarios, it has been observed that the proposed method provides an enhancement in real-time monitoring systems for an average of 78%.

Incorporating EBO-HSIC with SVM for Gene Selection Associated with Cervical Cancer Classification.

Microarray technology is utilized by the biologists, in order to compute the expression levels of thousands of genes. Cervical cancer classification utilizing gene expression data depends upon conventional supervised learning methods, wherein only labeled data could be used for learning. The previous methodologies had problem with appropriate feature selection as well as accurateness of classification outcomes. So, the entire performance of the cancer classification is decreased meaningfully. With the aim of overcoming the aforesaid problems, Enhanced Bat Optimization Algorithm with Hilbert-Schmidt Independence Criterion (EBO-HSIC) and Support Vector Machine (SVM) algorithm is presented in this research for identifying the specific genes from the gene expression dataset that belongs to cancer microarray. This proposed system contains phases of instance normalization, module detection, gene selection and classification. By Fuzzy C Means (FCM) algorithm, the normalization is performed for eliminating the inappropriate features from the gene dataset. Meanwhile, for effective feature selection, the EBO algorithm is used for producing more appropriate features via improved objective function values. For determining a subset of the most informative genes utilizing a rapid as well as scalable bat algorithm, this proposed method focuses on measuring the dependence amid Differentially Expressed Genes (DEGs) as well as the gene significance. The algorithm is dependent upon the HSIC and was partially enthused by EBO. With the help of SVM classifier, these gene features are categorized very precisely. Experimentation outcomes demonstrate that the presented EBO with SVM algorithm confirms a clear-cut classification performance for the given gene expression datasets. Hence the result provides higher performance by launching EBO with SVM algorithm to obtain greater accuracy, recall, precision, f-measure and less time complexity more willingly than the previous techniques.

Prediction of Novel Drugs and Diseases for Hepatocellular Carcinoma Based on Multi-Source Simulated Annealing Based Random Walk.

Computational techniques for foreseeing drug-disease associations by means of incorporating gene expression as well as biological network give high intuitions to the composite associations amongst targets, drugs, disease genes in addition to the diseases at a system level. Hepatocellular Carcinoma (HCC) is a malevolent tumor containing a greater rate of sickness as well as mortality. In the present work, an Integrative framework is presented with the aim of resolving this problem, for identifying new Drugs for HCC dependent upon Multi-Source Random Walk (PD-MRW), in which score the complete drugs by means of building the drug-drug similarity network. On the other hand, the collection of clinical phenotypes as well as drug side effects in combination with patient-specific genetic info. As a result, the formation of disease-drug networks that denotes the prescriptions, which are allotted to treat those diseases that are not concentrated by means of PD-MRW model. With the aim of overcoming this issue, this research offers an integrative framework for foreseeing new drugs as well as diseases for HCC dependent upon Multi-Source Simulated Annealing based Random Walk (PDD-MSSARW). Primarily, build a Gene-Gene Weighted Interaction Network (GWIN), dependent upon the gene expression as well as protein interaction network. After that, construct a drug-drug similarity network, dependent upon multi-source random walk in GWIN, disease-drug similarity network with the help of Similarity Weighted Bipartite Graph Network (SWBGN) that is build up in which the nodes are drugs as well as association among one node to another node that explains the disease diagnoses. Lastly, dependent upon the known drugs for HCC, score the entire drugs in the similarity networks. The sturdiness of the likelihoods, their overlap with those stated in Comparative Toxicogenomics Database (CTD) as well as kinds of literature, and their enhanced KEGG pathway illustrate PDD-MSSARW method be capable of efficiently find out novel drug signs.