This research paper explores the impact of artificial intelligence (AI) on business strategy and decision-making processes. Artificial intelligence refers to the development of computer systems that can perform tasks that typically require human intelligence, such as speech recognition, problem-solving, and learning. As AI technologies continue to advance rapidly, organizations are increasingly integrating AI into their business strategies to gain a competitive advantage. The study examines the integration of AI into business strategies and its effects on decision-making processes. Additionally, it investigates the potential benefits and challenges associated with the adoption of AI in business contexts. Through a comprehensive literature review and analysis, this research provides insights into the current state of AI implementation in businesses. The findings indicate that AI has the potential to revolutionize business strategies and decision-making processes by enhancing efficiency, effectiveness, and innovation. However, challenges related to ethical considerations, data privacy, and the need for human oversight must be addressed to ensure responsible and effective AI adoption. The study concludes with recommendations for organizations to effectively leverage AI in their business strategies and decision-making processes. Understanding the impact of AI on business strategy and decision-making empowers organizations to harness the transformative potential of AI and navigate the dynamic and complex business landscape.

The identification of suitable feature subsets from High-Dimensional Low-Sample-Size (HDLSS) data is of paramount importance because this dataset often contains numerous redundant and irrelevant features, leading to poor classification performance. However, the selection of an optimal feature subset from a vast feature space creates a significant computational challenge. In the domain of HDLSS data, conventional feature selection methods often face challenges in achieving a balance between reducing the number of features and preserving high classification accuracy. Addressing these issues, the study introduces an effective framework that employs a filter and wrapper-based strategy specifically designed to address the classification challenges inherent in HDLSS data. The framework adopts a multi-step approach where ensemble feature selection integrates five filter ranking approaches: Chi-square ( $\chi ^{2}$ ), Gini index (GI), F-score, Mutual Information (MI), and Symmetric uncertainty (SU) to identify the top-ranking features. In the subsequent stage, a wrapper-based search method is utilized, which employs the Differential Evaluation (DE) metaheuristic algorithm as the search strategy. The fitness of feature subsets during this search is assessed based on a weighted combination of the error rate of the Support Vector Machine (SVM) classifier and the ratio of feature cardinality. The datasets, after undergoing dimensionality reduction, are then utilized to construct classification models using SVM, K-Nearest Neighbors (KNN), and Logistic Regression (LR). The approach was evaluated on 13 HDLSS datasets to assess its efficacy in selecting appropriate feature subsets and improving Classification Accuracy (ACC) analog with Area Under the Curve (AUC). Results show that the proposed ensemble with wrapper-based approach produces a smaller number of features (ranging between 2 and 9 for all datasets), while maintaining a commendable average AUC and ACC (between 98% and 100%). The comparative analysis reveals that the proposed method surpasses both ensemble feature selection and non-feature selection approaches in terms of feature reduction and ACC. Additionally, when compared to various other state-of-the-art methods, this approach demonstrates commendable performance.

The rapid advancement of artificial intelligence (AI) and machine learning (ML) technologies has revolutionized numerous industries, including the field of digital marketing. This research paper explores the impact of AI and ML in digital marketing strategies and their implications for businesses. The paper begins by providing an overview of AI and ML, highlighting their capabilities and potential applications in digital marketing. It then delves into the various ways in which AI and ML have transformed key aspects of digital marketing, including customer segmentation, personalized targeting, content creation, and customer experience optimization. Furthermore, the paper discusses the benefits and challenges associated with incorporating AI and ML in digital marketing strategies. Additionally, it addresses the ethical considerations and potential biases that may arise from the use of AI and ML in digital marketing. Artificial intelligence is the present day era which is used to perform person tasks using computers in addition to robots. There are many every day responsibilities that can be without problems performed with the aid of computers and robots in preference to human beings. Artificial intelligence has made easy to carry out obligations which requires human brains by means of the use of green pc structures. This helps to save a whole lot of time and efforts taken by humans as it may be finished at a quicker fee using excessive tech pc. There are numerous categories of synthetic intelligence which implements various factors. The intention and goal of artificial intelligence is to carry out wondering, information gathering, arranging, gaining knowledge of, regular language handling, decision making, etc. This paper will give an explanation for the types of synthetic intelligence, its advantages in addition to challenges. AI analysts have used extensive categories of important questioning approaches which include mathematical and statistical computations, formal rationale, neural network agencies, and strategies and monetary components. Synthetic intelligence also works on improving the principles of software engineering, mind storming strategies, semantics, reasoning, and numerous clinical domain names It also discusses the evolving role of marketers in this new AI-driven landscape and the skills required to leverage these technologies effectively. Finally, the paper concludes by emphasizing the significance of AI and ML in shaping the future of digital marketing. It highlights the need for businesses to adapt and embrace these technologies to stay competitive in an increasingly data-driven and customer-centric environment. In summary, this research paper provides insights into the transformative impact of AI and ML in digital marketing strategies. It offers valuable perspectives on the opportunities and challenges that arise with the integration of these technologies, ultimately emphasizing their vital role in shaping the future of digital marketing practices.

Objective: This study aims to address the challenge of privacy-preserving Alzheimer’s disease classification using federated learning across various data distributions, focusing on real-world applicability. The goal is to improve the efficiency of classification by minimizing communication rounds between clients and the central server. Methods: The proposed approach leverages two key strategies: increasing parallelism by utilizing more clients in each communication round and increasing computation per client during the intervals between rounds. To reflect real-world scenarios, data is divided into three distributions: identical and independently distributed, non-identical and independently distributed equal, and non-identical and independently distributed unequal. The impact of extreme quantity distribution skew is also examined. A convolutional neural network is used to evaluate the performance across these setups. Results: The empirical study demonstrates that the proposed federated learning approach achieves a maximum accuracy of 84.75%, a precision of 86%, a recall of 85%, and an F1-score of 84%. Increasing the number of local epochs improves classification performance and reduces communication needs. The experiments show that federated learning is effective in handling heterogeneous datasets when all clients participate in each round of training. However, the results also indicate that extreme quantity distribution skew negatively impacts classification performance. Conclusions: The study confirms that federated learning is a viable solution for Alzheimer’s disease classification while preserving data privacy. Increasing local computation and client participation enhances classification performance, though extreme distribution imbalances present a challenge. Further investigation is needed to address these limitations in real-world scenarios.

Disease samples are naturally fewer than healthy samples which introduces bias in the training of machine learning (ML) models. Current study focuses in learning discriminating patterns between cesarean and non-cesarean phenomena based on a dataset consisting of 161 features of total 692 cesarean and 5465 non-cesarean samples which comes as four folds based on four different hospitals (hospital A, B, C and D). The dataset is noisy, contains missing values, features are at different scales and above all, 161 features are quite a large in number and risks containing unnecessary information with respect to learning to separate the C-section class from non-cesarean.This study introduced a data pre-processing pipeline, resolving issues with data imbalance, handling missing values, identifying and deleting outliers, etc. A novel ensemble model is proposed which is able to consistently perform better irrespective of data volumes (data fold A, A+B, A+B+C and A+B+C+D) and pre-processing pipeline and achieved 96-99% accuracy across data volumes. Finally, the proposed model’s decision-making was explained in terms of prominent features where higher values of features like Episiotomy, age of women and Fetal intrapartum pH accounts for causing C-section.

Understanding and analyzing the search intent of a user semantically based on their input query has emerged as an intriguing challenge in recent years. It suffers from small-scale human-labeled training data that produce a very poor hypothesis of rare words. The majority of data portals employ keyword-driven search functionality to explore content within their repositories. However, the keyword-based search cannot identify the users’ search intent accurately. Integrating a query-understandable framework into keyword search engines has the potential to enhance their performance, bridging the gap in interpreting the user’s search intent more effectively. In this study, we have proposed a novel approach that focuses on spatial and temporal information, phrase detection, and semantic similarity recognition to detect the user’s intent from the search query. We have used the n-gram probabilistic language model for phrase detection. Furthermore, we propose a probability-aware gated mechanism for RoBERTa (Robustly Optimized Bidirectional Encoder Representations from Transformers Approach) embeddings to semantically detect the user’s intent. We analyze and compare the performance of the proposed scheme with the existing state-of-the-art schemes. Furthermore, a detailed case study has been conducted to validate the model’s proficiency in semantic analysis, emphasizing its adaptability and potential for real-world applications where nuanced intent understanding is crucial. The experimental result demonstrates that our proposed system can significantly improve the accuracy for detecting the users’ search intent as well as the quality of classification during search.