The Role of AI-Enhanced Personalization in Customer Experiences

The purpose of this research study is to investigate how AI-driven-personalization chatbots and virtual assistants might improve customer experiences across different types of businesses. It investigates if artificial intelligence is able to cater goods, services, and marketing to the preferences of customers. The areas of retail and hospitality, together with finance, are the primary emphasis of this article. This study investigates the ways in which artificial intelligence can improve retail virtual shopping assistants and product recommendations. This article investigates the use of artificial intelligence (AI) chatbots in the hotel industry to give individualized booking experiences and recommendations. This study investigates the ways in which artificial intelligence-driven communications and individualized financial advice can improve customer service. Through the use of case studies and data analysis, the author of this study analyzes the practical uses of AI-powered personalization as well as the benefits to the customer experience. The findings are an attempt to illustrate that AI is capable of personalizing their experiences and engaging customers across a variety of industries

Advancements in Early Detection of Lung Cancer in Public Health: A Comprehensive Study Utilizing Machine Learning Algorithms and Predictive Models

Lung cancer stands as the leading cause of death in the United States, attributed to factors such as the spontaneous growth of malignant tumors in the lungs that can metastasize to other parts of the body, posing severe threats. Notably, smoking emerges as a predominant external factor contributing to lung problems and ultimately leading to lung cancer. Nevertheless, early detection presents a pivotal strategy for preventing this lethal disease. Leveraging machine learning, we aspire to develop robust algorithms capable of predicting lung cancer at its nascent stage. Such a model could prove instrumental in aiding physicians in making informed decisions during the diagnostic process, determining whether a patient necessitates an intensive or standard level of diagnosis. This approach holds the potential to significantly reduce treatment costs, as physicians can tailor the treatment plan based on accurate predictions, thereby avoiding unnecessary and costly interventions. Our goal is to establish a sustainable model that accurately predicts the disease, and our findings reveal that XGBoost outperformed other models, achieving an impressive accuracy level of 96.92%. In comparison, LightGBM, AdaBoost, Logistic Regression, and Support Vector Machine achieved accuracies of 93.50%, 92.32%, 67.41%, and 88.02%, respectively

Harmonizing Macro-Financial Factors and Twitter Sentiment Analysis in Forecasting Stock Market Trends

The surge in generative artificial intelligence technologies, exemplified by systems such as ChatGPT, has sparked widespread interest and discourse prominently observed on social media platforms like Twitter. This paper delves into the inquiry of whether sentiment expressed in tweets discussing advancements in AI can forecast day-to-day fluctuations in stock prices of associated companies. Our investigation involves the analysis of tweets containing hashtags related to ChatGPT within the timeframe of December 2022 to March 2023. Leveraging natural language processing techniques, we extract features, including positive/negative sentiment scores, from the collected tweets. A range of classifier machine learning models, encompassing gradient boosting, decision trees and random forests, are employed to train on tweet sentiments and associated features for the prediction of stock price movements among key companies, such as Microsoft and OpenAI. These models undergo training and testing phases utilizing an empirical dataset gathered during the stipulated timeframe. Our preliminary findings reveal intriguing indications suggesting a plausible correlation between public sentiment reflected in Twitter discussions surrounding ChatGPT and generative AI and the subsequent impact on market valuation and trading activities concerning pertinent companies, gauged through stock prices. This study aims to forecast bullish or bearish trends in the stock market by leveraging sentiment analysis derived from an extensive dataset comprising 500,000 tweets. In conjunction with this sentiment analysis derived from Twitter, we incorporate control variables encompassing macroeconomic indicators, Twitter uncertainty index and stock market data for several prominent companies

Deep Learning in Stock Market Forecasting: Comparative Analysis of Neural Network Architectures Across NSE and NYSE

This research explores the application of four deep learning architectures—Multilayer Perceptron (MLP), Recurrent Neural Networks (RNN), Long Short-Term Memory (LSTM), and Convolutional Neural Networks (CNN)—in predicting stock prices using historical data. Focusing on day-wise closing prices from the National Stock Exchange (NSE) of India and the New York Stock Exchange (NYSE), the study trains the neural network on NSE data and tests it on both NSE and NYSE stocks. Surprisingly, the CNN model outperforms the others, successfully predicting NYSE stock prices despite being trained on NSE data. Comparative analysis against the ARIMA model underscores the superior performance of neural networks, emphasizing their potential in forecasting stock market trends. This research sheds light on the shared underlying dynamics between distinct markets and demonstrates the efficacy of deep learning architectures in stock price prediction

Comparing Machine Learning Techniques for Detecting Chronic Kidney Disease in Early Stage

In medical care, side effect trial and error processes are utilized for the discovery of hidden reasons for ailments and the determination of conditions. In our exploration, we used a crossbreed strategy to refine our optimal model, improving the Pearson relationship for highlight choice purposes. The underlying stage included the choice of ideal models through a careful survey of the current writing. Hence, our proposed half-and-half model incorporated a blend of these models. The base classifiers utilized included XGBoost, Arbitrary Woods, Strategic Relapse, AdaBoost, and the Crossover model classifiers, while the Meta classifier was the Irregular Timberland classifier. The essential target of this examination was to evaluate the best AI grouping techniques and decide the best classifier concerning accuracy. This approach resolved the issue of overfitting and accomplished the most elevated level of exactness. The essential focal point of the assessment was precision, and we introduced a far-reaching examination of the significant writing in even configuration. To carry out our methodology, we used four top-performing AI models and fostered another model named "half and half," utilizing the UCI Persistent Kidney Disappointment dataset for prescient purposes. In our experiment, we found out that the AI model XGBoost classifier gains almost 94% accuracy, a random forest gains 93% accuracy, Logistic Regression about 90% accuracy, AdaBoost gains 91% accuracy, and our proposed new model named hybrid gains the highest 95% accuracy, and performance of Hybrid model is best on this equivalent dataset. Various noticeable AI models have been utilized to foresee the event of persistent kidney disappointment (CKF). These models incorporate Naïve Bayes, Random Forest, Decision Tree, Support Vector Machine, K-nearest neighbor, LDA (Linear Discriminant Analysis), GB (Gradient Boosting), and neural networks. In our examination, we explicitly used XGBoost, AdaBoost, Logistic Regression, Random Forest, and Hybrid models with the equivalent dataset of highlights to analyze their accuracy scores

Transforming Breast Cancer Identification: An In-Depth Examination of Advanced Machine Learning Models Applied to Histopathological Images

Breast cancer stands as one of the most prevalent and perilous forms of cancer affecting both women and men. The detection and treatment of breast cancer benefit significantly from histopathological images, which carry crucial phenotypic information. To enhance accuracy in breast cancer detection, Deep Neural Networks (DNNs) are commonly utilized. Our research delves into the analysis of pre-trained deep transfer learning models, including ResNet50, ResNet101, VGG16, and VGG19, for identifying breast cancer using a dataset comprising 2453 histopathology images. The dataset categorizes images into two groups: those featuring invasive ductal carcinoma (IDC) and those without IDC. Through our analysis of transfer learning models, we observed that ResNet50 outperformed the other models, achieving impressive metrics such as accuracy rates of 92.2%, Area under Curve (AUC) rates of 91.0%, recall rates of 95.7%, and a minimal loss of 3.5%

Revolutionizing Retail: A Hybrid Machine Learning Approach for Precision Demand Forecasting and Strategic Decision-Making in Global Commerce

A thorough comparison of several machine learning methods is provided in this paper, including gradient boosting, AdaBoost, Random Forest (RF), XGBoost, Artificial Neural Network (ANN), and a unique hybrid framework (RF-XGBoost-LR). The assessment investigates their efficacy in real-time sales data analysis using key performance metrics like Mean Absolute Error (MAE), Mean Squared Error (MSE), and R2 score. The study introduces the hybrid model RF-XGBoost-LR, leveraging both bagging and boosting methodologies to address the limitations of individual models. Notably, Random Forest and XGBoost are scrutinized for their strengths and weaknesses, with the hybrid model strategically combining their merits. Results demonstrate the superior performance of the proposed hybrid model in terms of accuracy and robustness, showcasing potential applications in supply chain studies and demand forecasting. The findings highlight the significance of industry-specific customization and emphasize the potential for improved decision-making, marketing strategies, inventory management, and customer satisfaction through precise demand forecasting