INTRODUCTION: A stroke, a sudden interruption of blood flow to the brain, is a leading cause of disability and death. Early diagnosis is paramount for minimizing brain damage and maximizing treatment effectiveness.OBJECTIVES: Traditional diagnostic methods can be time-consuming and have limited Accuracy. METHODS: This study investigates the efficacy of various machine-learning models for stroke prediction. Specifically, it compares established models like K-Nearest Neighbor, Artificial Neural Network, Long Short Term Memory (LSTM), and stacked LSTM with a newly proposed Transformer Convolutional Neural Network (TCNN) architecture, which fuses Transformer and Convolutional neural network (CNN) models.RESULTS: The TCNN demonstrates significant promise, achieving a superior accuracy of 98% when optimized with the AMSGrad optimizer.CONCLUSION: These findings suggest that the TCNN architecture has the potential to revolutionize stroke prediction accuracy compared to existing methods, potentially leading to improved patient outcomes.

The need for information is gradually shifting from text to images due to the technology’s growth and increase in digital images. It is quite challenging for people to find similar color images. To obtain similarity matching, the color of the image needs to be identified. This paper aims at various clustering techniques to identify the color of the digital image. Though many clustering techniques exist, this paper focuses on Fuzzy c-Means, Mean-Shift, and a hybrid technique that amalgamates the agglomerative hierarchies and k-Means, known as hKmeans to cluster the intensity of the image. Applying evaluation metrics of Mean Squared Error, Root Mean Squared Error, Mean Absolute Error, Homogeneity, Completeness, V-Score, and Peak signal-to-noise ratio it is proven that the results obtained demonstrate the good performance of the proposed technique. Then the color histogram is applied to identify the color and differentiate the color distribution on the original and clustered image.

Urbanization has continued to increase over the years, thus forcing cities to deal with various challenges such as availability and allocation of resources, infrastructure and environmental degradation. This survey paper also aims at reviewing the historic shift to the incorporation of Artificial Intelligence (AI) and predictive analytics in urban planning to come up with a better improved way of decision making as well as improving the systems that are in the urban setting. Applying artificial intelligence such as ML and data analytics allow the urban planners to provide predictions of needs that can be arising with regards to transport, energy use, and disasters based on historical data as well as real-time data acquired in smart cities. With the focus on current applications, research approaches, and future perspectives of AI and predictive analytics applications this survey wants to contribute to the further understanding of how innovative solutions can build cities for the future that are resilient, efficient, and citizen centric.

Data analytics, which is critical in today's decision-making, is driving the rapidly changing corporate environment. Its management, growth, and strategic objective transformation are explored in this article. Strategic choices, innovation, and competitiveness are enabled by data analytics. Big data values huge data kinds, recognises patterns, anticipates trends, and real-time possibilities, decision-making and helping companies grow. For market leadership, predictive analytics improves operational efficiency and resource allocation by forecasting trends, client demands, and simplifying processes. AI and ML automate activities, find patterns, and propose ways to enhance judgements, revolutionising industry and e-commerce. Data-driven marketing improves expenditure, engagement, and campaigns. Responsible data use and transparency address privacy and ethics. Examples demonstrate how data analytics supports innovation, solves business problems, and provides outcomes. Finally, data analytics integration trends, challenges, and possibilities emphasise the need for a data-centric culture.