Personalized Recommendations Research Articles

A framework for generating recommendations based on trust in an informal e-learning environment

Rapid advancement in information technology promotes the growth of new online learning communities in an e-learning environment that overloads information and data sharing. When a new learner asks a question, how a system recommends the answer is the problem of the learner’s cold start. In this article, our contributions are: (i) We proposed a Trust-aware Deep Neural Recommendation (TDNR) framework that addresses learner cold-start issues in informal e-learning by modeling complex nonlinear relationships. (ii) We utilized latent Dirichlet allocation for tag modeling, assigning tag categories to newly posted questions and ranking experts related to specific tags for active questioners based on hub and authority scores. (iii) We enhanced recommendation accuracy in the TDNR model by introducing a degree of trust between questioners and responders. (iv) We incorporated the questioner-responder relational graph, derived from structural preference information, into our proposed model. We evaluated the proposed model on the Stack Overflow dataset using mean absolute precision (MAP), root mean squared error (RMSE), and F-measure metrics. Our significant findings are that TDNR is a hybrid approach that provides more accurate recommendations compared to rating-based and social-trust-based approaches, the proposed model can facilitate the formation of informal e-learning communities, and experiments show that TDNR outperforms the competing methods by an improved margin. The model’s robustness, demonstrated by superior MAE, RMSE, and F-measure metrics, makes it a reliable solution for addressing information overload and user sparsity in Stack Overflow. By accurately modeling complex relationships and incorporating trust degrees, TDNR provides more relevant and personalized recommendations, even in cold-start scenarios. This enhances user experience by facilitating the formation of supportive learning communities and ensuring new learners receive accurate recommendations.

Revolutionising Skincare Through Airtificial Intelligence

Abstract: Background: Over the last 10years there has been a drastic shift in the development of computer-based systems, artificial intelligence, algorithms and technology in the world of dermatology be it cosmetic or even retail production of products sold to customers with high-tech technology and innovative means to build their client base through customer delight. Artificial Intelligence is particularly helpful in recognizing the type of skin customers have and suggesting solutions which help customers pick from the range of options given to them. Most e-commerce platforms have begun using such AI driven soft wear to help customers while they are in the comfort of their homes to choose from a range of cosmetic options right from lipsticks, concealers, foundation, eyeshadows, shades of tins, serums etc. Given that every person has a different skin type, these applications ensure 100 percent personalized suggestions. This new trend is sending shock waves across the retail sector around the world attracting a massive customer base to adapting to AI Derma logy and retail. The intelligent software used by sever companies not only benefits the customer, but also helps companies with their operational efficiency as they can use AI algorithms to predict and forecast the inventory to help reduce over production and excessive stock production minimizing product wastage and improving sustainably.AI is booming in both clinical diagnosis as well as giant companies such as Loreal and Olay who are dedicated to providing customers with advanced tools like skin analyzers or apps based on predefined rules which help AI-powered tools analyze skin conditions, age, and personal skincare history to provide highly personalized product recommendations. In clinical diagnosis AI offers valuable insight to the patient’s skin and condition be it cancer or other skin diseases making judgments to assist the doctors and dermatologist in making informed decisions regarding the line of treatment for the patient in question. This includes screening, diagnosis, treatments and predictions of the treatment outcomes. These new innovative and robust AI systems leaving customers feeling confident and have not begun relying on AI driven treatments and procedures

A novel explainable machine learning-based healthy ageing scale

BackgroundAgeing is one of the most important challenges in our society. Evaluating how one is ageing is important in many aspects, from giving personalized recommendations to providing insight for long-term care eligibility. Machine learning can be utilized for that purpose, however, user reservations towards “black-box” predictions call for increased transparency and explainability of results. This study aimed to explore the potential of developing a machine learning-based healthy ageing scale that provides explainable results that could be trusted and understood by informal carers.MethodsIn this study, we used data from 696 older adults collected via personal field interviews as part of independent research. Explanatory factor analysis was used to find candidate healthy ageing aspects. For visualization of key aspects, a web annotation application was developed. Key aspects were selected by gerontologists who later used web annotation applications to evaluate healthy ageing for each older adult on a Likert scale. Logistic Regression, Decision Tree Classifier, Random Forest, KNN, SVM and XGBoost were used for multi-classification machine learning. AUC OvO, AUC OvR, F1, Precision and Recall were used for evaluation. Finally, SHAP was applied to best model predictions to make them explainable.ResultsThe experimental results show that human annotations of healthy ageing could be modelled using machine learning where among several algorithms XGBoost showed superior performance. The use of XGBoost resulted in 0.92 macro-averaged AuC OvO and 0.76 macro-averaged F1. SHAP was applied to generate local explanations for predictions and shows how each feature is influencing the prediction.ConclusionThe resulting explainable predictions make a step toward practical scale implementation into decision support systems. The development of such a decision support system that would incorporate an explainable model could reduce user reluctance towards the utilization of AI in healthcare and provide explainable and trusted insights to informal carers or healthcare providers as a basis to shape tangible actions for improving ageing. Furthermore, the cooperation with gerontology specialists throughout the process also indicates expert knowledge as integrated into the model.

Future of knowledge management in investment banking: Role of personal intelligent assistants

Purpose: The study’s objective focuses on investigating the involvement of Personal Intelligent Assistants (PIAs) in the Knowledge Management Process (KMP) in Investment Banking Companies leading to Industrial Revolution 5.0 leading to effective Organizational Knowledge Management. Design/Methodology: A Self-administered Survey Questionnaire was circulated to 695 employees of Investment Banking Companies operating in Bangalore, Mumbai, Delhi, Hyderabad, Chennai, and Pune using the Cluster Sampling method. The Covariance-based Structural Equation Modelling (CB-SEM) and Gradient Boosting Regression technique of Machine Learning were used to validate the hypothesis through JASP V.18 Software. Knowledge Creation, Knowledge Sharing, Knowledge Retrieval, Knowledge Application, and Organizational Knowledge Management are the crucial constructs considered in the study. Findings: The results revealed that Knowledge Application is the most influencing factor in effective organizational Knowledge management among the Investment Banks followed by Knowledge Sharing. It also emphasizes that they have a weak Knowledge retrieval process and minimal efforts taken to create knowledge within these banks. Implications: The PIAs can facilitate effective Data Analysis and research in managing vast data eliminating the repeated tasks in portfolio reconciliation and offering personalized recommendations to manage portfolios. It enables in compliance, risk management, client relationship management, real-time monitoring and leveraged decision-making through predictive analysis.

The Broad Impact of Contemporary Digital Marketing Strategies on Consumer Behavior

This research aims to explore the extensive impact of contemporary digital marketing strategies on consumer behavior. By analyzing the successful case of Alibaba’s Double Eleven event, it details how multi-channel reach, personalized marketing, and ad content influence consumer behavior. The case analysis reveals that Alibaba effectively increased consumer participation and purchase behavior during the promotional period and the event day through widespread media promotion, diverse interactive activities, and precise personalized recommendations using digital technology. The results demonstrate the significant impact of digital marketing strategies on consumer behavior. However, this research is limited by the recent data used, indicating certain constraints. Challenges such as privacy concerns and data security still need to be addressed. Future research should further explore the varying effects of digital marketing strategies across different cultural backgrounds and emerging technologies.

Deep neural network provides personalized treatment recommendations for de novo metastatic breast cancer patients

Deep neural network provides personalized treatment recommendations for <i>de novo</i> metastatic breast cancer patients

Comorbidity neural network model for personalised recommendations in cardiometabolic risk factor management: towards early precision medicine in routine clinical practice

Abstract Background and Aims Hypertension and hypercholesterolaemia often develop in tandem to confer a risk of cardiovascular disease that may be more than additive. The present study explored early comorbid therapeutic intervention, i.e., beyond individual disease silos, while minimising data bias to provide clinical decision support tools that are portable across clinics. Methods A neural network was created to reproduce UK national clinical guidelines with an accuracy higher than 95%. Relying on real-world evidence from the UK primary care Clinical Practice Research Datalink electronic health record resource, a transfer learning methodology was then applied to achieve recommended risk factor goal attainment after two years. Shapley values derived from game theory were used to define similar digital twin cohorts, and to evaluate individualised recommendations that deviated from national clinical guidelines by rejecting a no-benefit hypothesis retrospectively in a classical proportion test. The methodology was ported to an independent hold-out dataset with anonymized records from six primary care clinics in the South of England. Results The adult patient population (n=469,496, mean+SD age 61+11yr; M 56% vs F 44%; primary 84% vs secondary prevention 16%) included 101,255 diagnosed for hypertension and 112,180 for cholesterol management. While a majority of the 52,504 therapeutic decisions in a test set agree with single morbidity guidelines, for 4639 deviations from the guidance, a significant minority was identified for whom alternative therapy was recommended. Rejecting a no-clinical benefit hypothesis at the 95% confidence level in a retrospective analysis, 1367 decisions showed statistically significant better outcomes over guidelines, for example by escalating anti-hypertensive therapies for patients receiving statin therapy (1085) or by complementing anti-hypertensive therapy with a statin (158). Similar findings were obtained when tested in a hold-out dataset with 13,725 decisions for 6531 patients. Conclusions The results support the contention that machine learning can identify patient subgroups for whom comorbid intervention deviating from clinical guidelines may be appropriate. A novel aspect of the study is the demonstration of clinically portable and actionable application of machine learning in the context of comorbid cardiometabolic diseases. The results represent practical steps towards personalised precision medicine for cardiovascular comorbidity in routine clinical practice.

Job Title Predictor System

In this paper, we propose a job title recommendation system using a combination of Natural Language Processing (NLP) techniques and machine learning. We implement a TF-IDF Vectorizer and cosine similarity to recommend jobs based on user inputs like skills, experience, industry, and role category. The system was built using Python and integrated into a user-friendly interface using Streamlit, enabling personalized recommendations. We evaluate the accuracy of recommendations and discuss potential improvements

Bibliometric and Content Analysis of Articles on Artificial Intelligence in Healthcare

The use of artificial intelligence in the healthcare sector is becoming widespread for reasons such as analyzing digital patient data, including it in decision-making processes, improving the quality of healthcare services, and providing cost, time, and access advantages. This study aims to evaluate published articles on bibliometric indicators and the use of artificial intelligence in the healthcare sector and examine the content of the most cited articles. Articles about artificial intelligence in the health sector in the Web of Science database were included in the study using the criteria of “keyword, publication year, and publication language”. The research covers 2,680 articles published in English by 14,195 authors from 106 countries in 1084 journals between 2020-2024. 4,671 different keywords were used in the published articles. The country that published the most was “USA”, the journal was “Journal of Medical Internet Research”, the author was “Meng Ji”, and the most cited author was “Weihua Li”. The 55 most cited (≥50) articles focused on themes related to “diagnosis of COVID-19 disease”, “diagnosis of diseases”, “detection and classification of cancerous cells”, “identification of disease risk factors and disease prediction”, “prediction of treatment outcomes”, “prediction of disease course”, “personalized treatment recommendations”, “decision-making processes”, “ethical considerations, risks, and responsibilities”. With the COVID-19 pandemic, it is seen that the number of articles on artificial intelligence in the healthcare sector has increased exponentially. In the research, articles related to artificial intelligence in the health sector were examined, and a framework was created for researchers by revealing the most publishing countries, journals, authors, most cited authors, and keywords that were used the most.

Early, Individualized Recommendations for Hospitalized Patients With Acute Kidney Injury

Acute kidney injury (AKI) is a common complication during hospitalization and is associated with adverse outcomes. To evaluate whether diagnostic and therapeutic recommendations sent by a kidney action team through the electronic health record improve outcomes among patients hospitalized with AKI compared with usual care. Randomized clinical trial conducted at 7 hospitals in 2 health systems: in New Haven, Bridgeport, New London, and Waterbury, Connecticut, and Westerly, Rhode Island; and in Baltimore, Maryland. Hospitalized patients with AKI were randomized between October 29, 2021, and February 8, 2024. Final follow-up occurred February 22, 2024. An alert about AKI was sent to the kidney action team, consisting of a study physician and study pharmacist, which sent personalized recommendations through the electronic health record in 5 major categories (diagnostic testing, volume, potassium, acid base, and medications) within 1 hour of AKI detection. The note was immediately visible to anyone with access to the electronic health record. Randomization to the intervention or usual care occurred after the recommendations were generated, but the note was only delivered to clinicians of patients randomized to the intervention group. The primary outcome was a composite outcome consisting of AKI progression to a higher stage of AKI, dialysis, or mortality occurring while the patient remained hospitalized and within 14 days from randomization. Of the 4003 patients randomized (median age, 72 years [IQR, 61-81 years), 1874 (47%) were female and 931 (23%) were Black patients. The kidney action team made 14 539 recommendations, with a median of 3 (IQR, 2-5) per patient. The primary outcome occurred in 19.8% of the intervention group and in 18.4% in the usual care group (difference, 1.4%, 95% CI, -1.1% to 3.8,% P = .28). Of 6 secondary outcomes, only 1 secondary outcome, rates of recommendation implementation, significantly differed between the 2 groups: 2459 of 7270 recommendations (33.8%) were implemented in the intervention group and 1766 of 7269 undelivered recommendations (24.3%) were implemented in the usual care group within 24 hours (difference, 9.5%; 95% CI, 8.1% to 11.0%). Among patients hospitalized with AKI, recommendations from a kidney action team did not significantly reduce the composite outcome of worsening AKI stage, dialysis, or mortality, despite a higher rate of recommendation implementation in the intervention group than in the usual care group. ClinicalTrials.gov Identifier: NCT04040296.

Designing Privacy-Protecting System with Visual Masking Based on Investigation of Privacy Concerns in Virtual Screen Sharing Environments

Virtual meeting tools, such as Zoom, can sometimes lead to inadvertently sharing private information through screen sharing features. We conducted fundamental investigations on what concerns and strategies users have for protecting their privacy. The results indicate that while most users take actions to protect their privacy before or during meetings, a significant number of users also reported experiences with inadvertent information sharing. We also found that users avoid sharing not only personal data, such as usernames, but also information that could reveal their interests and activities, such as browsing histories or personalized recommendations. Thus, we propose a system that automatically occludes areas that users do not want to share to facilitate the management of privacy during screen sharing. We conducted a data collection experiment to construct a deep learning model to detect the areas that users do not want to share with others. We implemented our system by using this model to protect information in real time during screen sharing in virtual meeting tools.

The Digital Transformation in Health: How AI Can Improve the Performance of Health Systems

ABSTRACT Mobile health has the potential to revolutionize health care delivery and patient engagement. In this work, we discuss how integrating Artificial Intelligence into digital health applications focused on supply chain operation, patient management, and capacity building, among other use cases, can improve the health system and public health performance. We present the Causal Foundry Artificial Intelligence and Reinforcement Learning platform, which allows the delivery of adaptive interventions whose impact can be optimized through experimentation and real-time monitoring. The system can integrate multiple data sources and digital health applications. The flexibility of this platform to connect to various mobile health applications and digital devices, and to send personalized recommendations based on past data and predictions, can significantly improve the impact of digital tools on health system outcomes. The potential for resource-poor settings, where the impact of this approach on health outcomes could be decisive, is discussed. This framework is similarly applicable to improving efficiency in health systems where scarcity is not an issue.

Internet of Things and Distributed Computing Systems in Business Models

The integration of the Internet of Things (IoT) and Distributed Computing Systems (DCS) is transforming business models across industries. IoT devices allow immediate monitoring of equipment and processes, mitigating lost time and enhancing efficiency. In this case, manufacturing companies use IoT sensors to monitor machinery, predict failures, and schedule maintenance. Also, automation via IoT reduces manual intervention, resulting in boosted productivity in smart factories and automated supply chains. IoT devices generate this vast amount of data, which businesses analyze to gain insights into customer behavior, operational inefficiencies, and market trends. In turn, Distributed Computing Systems process this data, providing actionable insights and enabling advanced analytics and machine learning for future trend predictions. While, IoT facilitates personalized products and services by collecting data on customer preferences and usage patterns, enhancing satisfaction and loyalty, IoT devices support new customer interactions, like wearable health devices, and enable subscription-based and pay-per-use models in transportation and utilities. Conversely, real-time monitoring enhances security, as distributed systems quickly respond to threats, ensuring operational safety. It also aids regulatory compliance by providing accurate operational data. In this way, this study, through a Bibliometric Literature Review (LRSB) of 91 screened pieces of literature, aims at ascertaining to what extent the aforementioned capacities, overall, enhance business models, in terms of efficiency and effectiveness. The study concludes that those systems altogether leverage businesses, promoting competitive edge, continuous innovation, and adaptability to market dynamics. In particular, overall, the integration of both IoT and Distributed Systems in business models augments its numerous advantages: it develops smart infrastructures e.g., smart grids; edge computing that allows data processing closer to the data source e.g., autonomous vehicles; predictive analytics, by helping businesses anticipate issues e.g., to foresee equipment failures; personalized services e.g., through e-commerce platforms of personalized recommendations to users; enhanced security, while reducing the risk of centralized attacks e.g., blockchain technology, in how IoT and Distributed Computing Systems altogether impact business models. Future research avenues are suggested.

A Survey on Intent-aware Recommender Systems

Many modern online services feature personalized recommendations. A central challenge when providing such recommendations is that the reason why an individual user accesses the service may change from visit to visit or even during an ongoing usage session. To be effective, a recommender system should therefore aim to take the users’ probable intent of using the service at a certain point in time into account. In recent years, researchers have thus started to address this challenge by incorporating intent-awareness into recommender systems. Correspondingly, a number of technical approaches were put forward, including diversification techniques, intent prediction models or latent intent modeling approaches. In this paper, we survey and categorize existing approaches to building the next generation of Intent-Aware Recommender Systems (IARS). Based on an analysis of current evaluation practices, we outline open gaps and possible future directions in this area, which in particular include the consideration of additional interaction signals and contextual information to further improve the effectiveness of such systems.

Performance Evaluation on E-Commerce Recommender System based on KNN, SVD, CoClustering and Ensemble Approaches

E-commerce recommender systems (RS) nowadays are essential for promoting products. These systems are expected to offer personalized recommendations for users based on the user preference. This can be achieved by employing cutting-edge technology such as artificial intelligence (AI) and machine learning (ML). Tailored recommendations for users can boost user experience in using the application and hence increase income as well as the reputation of a company. The purpose of this study is to investigate popular ML methods for e-commerce recommendation and study the potential of ensemble methods to combine the strengths of individual approaches. These recommendations are derived from a multitude of factors, including users' prior purchases, browsing history, demographic information, and others. To forecast the interests and preferences of users, several techniques are chosen to be investigated in this study, which include Singular Value Decomposition (SVD), k-Nearest Neighbor Baseline (KNN Baseline) and CoClustering. In addition, several evaluation metrics including the fraction of concordant pairs (FCP), mean absolute error (MAE), root mean square error (RMSE) and normalized discounted cumulative gain (NDCG) will be used to assess how well different techniques work. To provide a better understanding, the outcomes produced in this study will be incorporated into a graphical user interface (GUI).

Performance Evaluation on E-Commerce Recommender System based on KNN, SVD, CoClustering and Ensemble Approaches

E-commerce recommender systems (RS) nowadays are essential for promoting products. These systems are expected to offer personalized recommendations for users based on the user preference. This can be achieved by employing cutting-edge technology such as artificial intelligence (AI) and machine learning (ML). Tailored recommendations for users can boost user experience in using the application and hence increase income as well as the reputation of a company. The purpose of this study is to investigate popular ML methods for e-commerce recommendation and study the potential of ensemble methods to combine the strengths of individual approaches. These recommendations are derived from a multitude of factors, including users' prior purchases, browsing history, demographic information, and others. To forecast the interests and preferences of users, several techniques are chosen to be investigated in this study, which include Singular Value Decomposition (SVD), k-Nearest Neighbor Baseline (KNN Baseline) and CoClustering. In addition, several evaluation metrics including the fraction of concordant pairs (FCP), mean absolute error (MAE), root mean square error (RMSE) and normalized discounted cumulative gain (NDCG) will be used to assess how well different techniques work. To provide a better understanding, the outcomes produced in this study will be incorporated into a graphical user interface (GUI).

From Sales Strategies to Information Ecosystems: The Role of Personalized Recommendation

The paper is about the application of personalized recommendations on online shopping applications to improve sales success and the spread of fake news on social media platforms. First, the paper introduces a personalized recommendation system's concept and working principle, then emphasizes its importance in improving user experience and sales efficiency. Then it analyzes the information cocoon effect that personalized recommendation may bring, it is users only see the content that conforms to their interests and views, which intensifies the single and misleading information. This is followed by a discussion of how companies can use personalized advertisement recommendations to improve sales success.Then through deep learning and search, personalization systems can efficiently provide personalized product recommendations to users, but there are still opportunities to improve their effectiveness, such as improving network effects among relevant users. Therefore, it points out a new method to improve the sales success rate of online shopping applications. Then discusses the role of social networks in the spread of fake news. Finally, through the network diagram, the phenomenon of information-closed loop between different groups is demonstrated, and the information transmission gap that may be caused by personalized recommendations is explained.

Personalised decision support in the management of patients with musculoskeletal pain in primary physiotherapy care: a cluster randomised controlled trial (the SupportPrim project).

We developed the SupportPrim PT clinical decision support system (CDSS) using the artificial intelligence method case-based reasoning to support personalised musculoskeletal pain management. The aim of this study was to evaluate the effectiveness of the CDSS for patients in physiotherapy practice. A cluster randomised controlled trial was conducted in primary care in Norway. We randomised 44 physiotherapists to (1) use the CDSS alongside usual care or (2) usual care alone. The CDSS provided personalised treatment recommendations based on a case base of 105 patients with positive outcomes. During the trial, the case-based reasoning system did not have an active learning capability; therefore, the case base size remained the same throughout the study. We included 724 patients presenting with neck, shoulder, back, hip, knee, or complex pain (CDSS; n = 358, usual care; n = 366). Primary outcomes were assessed with multilevel logistic regression using self-reported Global Perceived Effect (GPE) and Patient-Specific Functional Scale (PSFS). At 12 weeks, 165/298 (55.4%) patients in the intervention group and 176/321 (54.8%) in the control group reported improvement in GPE (odds ratio, 1.18; confidence interval, 0.50-2.78). For PSFS, 173/290 (59.7%) patients in the intervention group and 218/310 (70.3%) in the control group reported clinically important improvement in function (odds ratio, 0.41; confidence interval, 0.20-0.85). No significant between-group differences were found for GPE. For PSFS, there was a significant difference favouring the control group, but this was less than the prespecified difference of 15%. We identified several study limitations and recommend further investigation into artificial intelligence applications for managing musculoskeletal pain.

Can We Effectively Allocate Time for Work and Life and Be Happy? The Empirical Evidence from Self-Employed Persons in Lithuania

There is not enough attention paid to the analysis of time allocation in the context of happiness. Perhaps it is because happiness economics is an expanding interdisciplinary field. So, recommendations for self-employed persons in Lithuania how daily time should be effectively allocated for life (sleep, personal care, study, housework, family care, leisure, travel and other activities) and work in order to be happy still remain an open question. In this paper the specific recommendations about it are provided for that case. So, this paper analyses two phenomena, i.e. time allocation and happiness. The main attention is given to self-employed persons in Lithuania during 2019 years. Data collection (time diary, survey, interview) and optimization methods are used to identify how daily time should be effectively allocated for two main areas (work and life) in order for self-employed persons to be happy. Having mathematical modeling of time allocation and happiness accomplished, it has been found that 36.1% of day time should be allocated for work and the rest time of the day (63.9%) should be allocated for life. The person’s levels of job satisfaction, life satisfaction and happiness should be equal to 9 points (on a ten-point scale). In this case, monthly net income would be 1001 euros and desirable monthly net income 1101 euros. The number of children should be two. The scientific and practical uniqueness of this article lies in the creation of a mathematical model that can determine how to effectively allocate work time and life time in order for a person to be happy. Although the mathematical model constructed in this paper is based on Lithuanian data, it is not country specific.

NUTRIFLEX

The NUTRIFLEX project is a comprehensive AI-powered application designed to provide personalized nutrition and health management solutions. The goal of the project is to assist individuals in optimizing their diet based on their unique biological and emotional needs, including gut health, mood, and overall well-being. By combining artificial intelligence (AI) and user input, NUTRIFLEX offers tailored meal plans, calorie predictions, mood-enhancing food recommendations, and a gamified approach to motivate users to maintain a healthy lifestyle. The problem addressed by NUTRIFLEX is the growing challenge people face in maintaining a balanced and healthy diet. Modern lifestyles, characterized by fast-paced routines and unhealthy food choices, contribute to poor physical and mental health. Many individuals struggle to find the right balance between their nutritional intake and their health goals, whether it's weight loss, muscle gain, or general wellness. Furthermore, gut health, which plays a critical role in overall health, is often neglected in conventional meal planning applications. Mood fluctuations also significantly impact people's food choices, leading to stress eating or energy dips. NUTRIFLEX solves these problems by offering a holistic, AI-driven solution that adapts to users' unique needs. By using machine learning models, NUTRIFLEX predicts the calorie content of meals and creates personalized meal plans based on user-specific data such as Basal Metabolic Rate (BMR) and Total Energy Expenditure (TEE). The application also provides mood-based meal suggestions, helping users manage their emotional state through food choices that boost energy, focus, and relaxation. One of the most innovative features of NUTRIFLEX is its gut health functionality. By allowing users to input microbiome data, the app generates personalized meal recommendations designed to improve gut health, which in turn enhances overall physical and mental well-being. This focus on gut health distinguishes NUTRIFLEX from traditional meal planners. In addition, NUTRIFLEX integrates gamification to keep users engaged and motivated. Users can set health goals, track their progress, and earn rewards, making the health journey enjoyable and sustainable.