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The authors have crafted a comprehensive three step blueprint for planned marketing preparation that harnesses the manifold advantages of artificial intelligence (AI). This framework encompasses three facets of AI: motorized AI for automating boringadvertising tasks, rational AI for data analysis leading to choice making and examining human interactions and emotions. AI plays a pivotal role in this context, involving a strategic integration of AI across various facets, such as market research, strategy development (including subdivision, directing, and positioning, commonly known as STP), and the execution of actionable plans. In the marketing study phase, powered AI handles data collection, rational AI drives market analysis, and feeling AI fosters a deep understanding of customer behavior. Moving into the marketing strategy (STP) phase, mechanical AI assists in segment credit, rational AI provides segment targeting recommendations, and feeling AI contributes to resonating with the positioned segments. Transitioning to the advertisingact stage, motorized AI streamlines standardization processes, thinking AI enhances personalization, and sensation AI enriches efforts towards renationalization. [ABSTRACT FROM AUTHOR]

The proper segmentation of brain tumors is essential in medical image analysis field. Convolutional neural networks (CNNs), which make up the majority of deep learning approaches, have demonstrated encouraging results in automating this process. This research paper presents a novel two-step approach for brain segmenting and detecting tumors. In the first step, we employ a DenseNet121 model with transfer learning, fine-tuning the pre-trained weights obtained from the Image Net dataset, to recognize tumors in the brain. The second step involves utilizing a ResUnet model, which combines the strengths of ResNet50 and Unet models, for accurate segmentation. To validate our approach, we conduct experiments on "The Cancer Imaging account (TCIA)'s dataset "The Cancer Genome Atlas (TCGA)" and compare the results with other models of deep learning such as K-means clustering and VGG16. Our experimental findings demonstrate that our proposed approach achieves superior accuracy and efficiency compared to the alternative models. [ABSTRACT FROM AUTHOR]

Diabetes is linked to an increased susceptibility to various health complications, encompassing heart disease, kidney issues, strokes, ocular impairments, and nerve afflictions. The current healthcare practice revolves around the execution of multiple diagnostic tests to procure the requisite information for diagnosing diabetes and consequently devising treatment plans based on these diagnoses. The healthcare sector gains significantly from applying Big Data Analytics, offering the capability to dissect vast and intricate datasets to unveil concealed patterns, insights, and knowledge that can ultimately facilitate precise predictive outcomes. In this research endeavour, we focus on the comprehensive analysis of the Pima Indian diabetes database sourced from the UCI repository. Our primary objective is to fashion a robust and efficient model for predicting and diagnosing diabetes. To enhance the accuracy of our model, we integrate the bootstrapping resampling technique and meticulously assess the performance of a range of machine learning algorithms. These encompass the Random Forest, Decision Trees, K-Nearest Neighbours (KNN), Support Vector Machine (SVM), and Artificial Neural Network (ANN). Through these exhaustive analyses, we aspire to pinpoint the algorithm that offers the utmost prediction accuracy for diabetes diagnosis. [ABSTRACT FROM AUTHOR]

Several fuzzy time-series forecasting (FTSF) models have been created throughout the years to deal with the indeterminism and uncertainty in time-series data. In this work, the inherited uncertainty of a time series dataset is represented with three distinct memberships namely, truth, false and indeterminacy, and the approach is known as neutrosophic set (NS) theory. Also, the performance of the forecasting models are highly dependent on the appropriate selection of the number of divisions of universe of discourse (UOD). So, the study adopts a completely data-driven partitioning technique to obtain the optimal number of intervals thus making the model more reliable. The forecasting is made based on a simple arithmetic rule using the score function of the neutrosophic elements which makes the model user-friendly and avoids the complicated computations. To demonstrate the usefulness and applicability of the proposed forecasting approach, it is applied to the University of Alabama time series dataset. The model's performance is assessed using Root mean square error (RMSE), and its low value reflects the model's superiority over other previously existing methodologies. [ABSTRACT FROM AUTHOR]

Football is a famous sport played around the world that involves various actions from defending to attacking the ball to score goals. Various rules like half-side, No hands, Throw-ins Direct and Indirect kicks, etc. are followed. In a game, players might get yellow-cards and red-cards which serves as warning and elimination from that particular game. There are-many factors like shots, possession, pass accuracy, yellow and red cards, etc. that decides which team wins in a match. In the data that we worked on,the Rating is calculated from the parameters which are mentioned. Machine learning regression algorithms have been applied to predict the Rating feature. The goal of this paper is to know which regression algorithm performs better to predict the Rating variable. Evaluation metrics like Root mean square error (RMSE), Mean absolute error (MAE), and R square (R2) are used to identify the best algorithm among the applied algorithms. [ABSTRACT FROM AUTHOR]

District-based health care development and guidelines necessitate grouping affected areas due to novel corona virus (COVID-19) into regions of similar severity. In this study, the grouping is focused on districts in the state Gujarat, India in the context of COVID-19 infections. The state government, health ministry, doctors etc. are facing lots of difficulties how to identify regions and to group them depending upon their similarities. Data mining is one of the techniques which deal with a process to uncover hidden features of big data. In the current study, we apply cluster analysis, one of the data mining techniques for the purpose. The main objective of the results of clustering in this study is to develop and to optimize monitoring techniques in various regions of the state Gujarat to a level of severity of the infections. And thus the study will be very valuable for the policy makers, the state government, district governments, health care professionals who are delivering services to hospitals, doctors, the police and others involved in understanding seriousness of the spread of novel corona virus (COVID-19) so that they could plan at a district level need such as number of doctors, nurses, beds, ventilators, testing kits, masks and other facilities. Such study will also be helpful in reducing total number of infected persons, daily new cases as well as in increasing the number of recovered patients so that number of deceased persons could be reduced. [ABSTRACT FROM AUTHOR]

Fuzzy Time Series (FTS) techniques are gaining popularity among researchers. Fuzzification, fuzzy relation determination, and defuzzification are the three steps of fuzzy time series operations. Generally, research is focused on these stages and how to improve them. This study proposes a new clustering algorithm which partition the dataset into group by determining both the shape and the number of the clusters, and these clusters centers are used to partition the discourse. The proposed clustering algorithm is a non-parametric technique since, it uses a concept of "epsilon radius neighbours". Also, we used the computational method to forecast the data where the weights of the forecasting parameter is optimized using the Grey-Wolf Optimization (GWO) method. Because GWO has a unique quality of striking a balance between exploitation and exploration, incorporating it into the computational method aids the model's convergence. The model's suitability was evaluated using data from the University of Alabama's enrollment. The predicting accuracy of the recommended model was proved to be superior than the other models in the context of average forecasting and root mean square error. The suggested FTS forecasting method's validity is also tested using a tracking signal (TS). [ABSTRACT FROM AUTHOR]