Variety Of Data Sources Research Articles

Multiple Disease Prediction System Using ML

Abstract: The increasing prevalence of diverse diseases presents a challenge to global healthcare systems, underscoring the need for innovative and efficient methods for early detection and preventive measures. This paper explores the application of machine learning algorithms in multiple disease prediction to enhance diagnostic accuracy and enable timely intervention. Leveraging diverse health-related data sources, including medical records and genomic information, comprehensive predictive models are developed. A multi-faceted machine learning approach integrates support vector machines, decision trees, neural networks, and ensemble learning methods to analyze complex data patterns. Feature selection and dimensionality reduction techniques optimize model performance and interpretability. The development of a predictive system involves essential steps such as data collection, preprocessing, and model training, followed by evaluation using metrics like accuracy and recall. Integration of Flask for web application development facilitates user interaction and prediction functionality. Deployment, testing, debugging, and ongoing maintenance ensure system efficiency and compliance with regulatory requirements for healthcare data security and privacy.

Data analytics for predicting disease outbreaks: A review of models and tools

The burgeoning field of data analytics has emerged as a pivotal force in the realm of public health, particularly in the context of predicting and mitigating disease outbreaks. This comprehensive review delves into the diverse landscape of models and tools employed in data analytics for disease outbreak prediction. With a focus on synthesizing existing knowledge, the paper aims to provide a nuanced understanding of the strengths, limitations, and future directions within this dynamic field. The review begins with an exploration of various models utilized for disease outbreak prediction, ranging from statistical approaches to machine learning models and epidemiological frameworks. Each model category is scrutinized for its efficacy in capturing the complexities inherent in infectious disease dynamics. Simultaneously, the paper investigates the array of tools and technologies leveraged in disease outbreak prediction, encompassing Geographic Information Systems (GIS), data visualization tools, and big data analytics platforms. A critical aspect of the review lies in the examination of diverse data sources contributing to predictive analytics. Epidemiological data, environmental factors, and the burgeoning influence of social media and web data are dissected for their roles in enhancing the accuracy and timeliness of outbreak predictions. Amidst the promises of data analytics, the paper navigates the challenges inherent in predicting disease outbreaks. Issues of data quality and availability, model complexity, interpretability, and ethical considerations are dissected, providing a holistic view of the hurdles that practitioners encounter. Drawing upon case studies and real-world applications, the review showcases instances where data analytics has proven successful in predicting disease outbreaks, shedding light on both triumphs and setbacks. The implications for public health, lessons learned from challenges, and the evolving role of data analytics in shaping global health preparedness are thoroughly discussed. As the paper unfolds, it illuminates future trends and innovations in the field, foreseeing the integration of advanced technologies, global collaboration for information sharing, and the adaptation of predictive analytics for emerging diseases. The review culminates in a comprehensive conclusion, summarizing key findings and emphasizing the potential transformative impact of data analytics on the landscape of disease outbreak prediction.

ECG Arrhythmia Classification using Deep Learning

An electrocardiogram (ECG) is a painless, noninvasive way to help diagnose numerous common heart problems. ECG plays an important role in diagnosing various Cardiac ailments. In recent years, Deep learning techniques have shown remarkable promise in achieving accurate and automated ECG arrhythmia classification. The primary goal of the system is to develop a robust and accurate system for the automated detection and classification of arrhythmias in electrocardiogram (ECG) data. By leveraging state-of-the-art techniques such as Convolutional Neural Networks (CNNs), we analyze pattern recognition within ECG signals to detect arrhythmias. Furthermore, we address the challenge of dataset scarcity by augmenting the data through nine different image cropping methods during the training phase. The implementation of techniques like Batch Normalization and data augmentation will further enhance the model's adaptability to diverse data sources, making it an invaluable tool for healthcare professionals. The CNN will be trained and tested using the ECG Dataset obtained from the MIT-BIH Database and from it, seven types of signals of arrhythmia will be classified. These seven signals are Premature Ventricular contractions (PVC), Paced beat (PAB), Right bundle branch block beat (RBB), Left bundle branch block beat (LBB), Atrial premature contraction (APC), Ventricular escape beat (VEB) and Normal beat. This system bridges the gap between advanced technology and healthcare, offering a transformative approach to ECG arrhythmia classification that has the potential to significantly improve patient outcomes and reduce the burden of manual diagnosis

Unified data integration and record identification framework for diverse data sources in healthcare demographics

Challenges of bridging data from different sources with diverse data formats are faced by organizations in the modern data management environment. Problems with disparate data sources leading to different formats and inconsistencies mean it can be challenging to get the right matching of data records, especially when information errors such as typos are present. The current lack of a standard pattern for data integration and record identification presents a major problem in ensuring the accurate identification of individual records across disparate sources. The variations in data formats and the abundance of errors, such as typographical mistakes in names, dates of birth, and gender, add to the complexity of this problem. Organizations face the challenge of ensuring the correctness and consistency of data across multiple datasets without a formalized methodology.

Realizing the Promise of Artificial Intelligence in Hepatocellular Carcinoma through Opportunities and Recommendations for Responsible Translation

This study aims to provide an overview of the current state-of-the-art applications of artificial intelligence (AI) and machine learning in the management of hepatocellular carcinoma (HCC), and to explore future directions for continued progress in this emerging field. This study is a comprehensive literature review that synthesizes recent findings and advancements in the application of AI and machine learning techniques across various aspects of HCC care, including screening and early detection, diagnosis and staging, prognostic modeling, treatment planning, interventional guidance, and monitoring of treatment response. The review draws upon a wide range of published research studies, focusing on the integration of AI and machine learning with diverse data sources, such as medical imaging, clinical data, genomics, and other multimodal information. The results demonstrate that AI-based systems have shown promise in improving the accuracy and efficiency of HCC screening, diagnosis, and tumor characterization compared to traditional methods. Machine learning models integrating clinical, imaging, and genomic data have outperformed conventional staging systems in predicting survival and recurrence risk. AI-based recommendation systems have the potential to optimize personalized therapy selection, while augmented reality techniques can guide interventional procedures in real-time. Moreover, longitudinal application of AI may enhance the assessment of treatment response and recurrence monitoring. Despite these promising findings, the review highlights the need for rigorous multicenter prospective validation studies, standardized multimodal datasets, and thoughtful consideration of ethical implications before widespread clinical implementation of AI technologies in HCC management.

Ontology-Driven Architecture for Managing Environmental, Social, and Governance Metrics

The burgeoning significance of environmental, social, and governance (ESG) metrics in realms such as investment decision making, corporate reporting, and risk management underscores the imperative for a robust, comprehensive solution capable of effectively capturing, representing, and analysing the multifaceted and intricate ESG data landscape. Facing the challenge of aligning with diverse standards and utilising complex datasets, organisations require robust systems for the integration of ESG metrics with traditional financial reporting. Amidst this, the evolving regulatory landscape and the demand for transparency and stakeholder engagement present significant challenges, given the lack of standardized ESG metrics in certain areas. Recently, the use of ontology-driven architectures has gained attention for their ability to encapsulate domain knowledge and facilitate integration with decision-support systems. This paper proposes a knowledge graph in the ESG metric domain to assist corporations in cataloguing and navigating ESG reporting requirements, standards, and associated data. Employing a design science methodology, we developed an ontology that serves as both a conceptual foundation and a semantic layer, fostering the creation of an interoperable ESG Metrics Knowledge Graph (ESGMKG) and its integration within operational layers. This ontology-driven approach promises seamless integration with diverse ESG data sources and reporting frameworks, while addressing the critical challenges of metric selection, alignment, and data verification, supporting the dynamic nature of ESG metrics. The utility and effectiveness of the proposed ontology were demonstrated through a case study centred on the International Financial Reporting Standards (IFRS) framework that is widely used within the banking industry.

Computer-aided resilience: Advanced techniques for disaster management in smart urban environments

This research paper explores innovative contributions to the field of disaster management in smart urban environments, with a particular focus on integrating advanced computer-aided techniques, specifically GRU-CNN. Three key contributions are highlighted: (1) the development of dynamic risk assessment algorithms utilizing GRU-CNN for real-time analysis and predictive modeling, enabling proactive disaster mitigation; (2) the establishment of an integrated sensor network infrastructure for early warning systems, leveraging various sensors and GRU-CNN-based data analytics to detect and respond to potential disasters at their nascent stages; and (3) the implementation of human-centric resilience planning, utilizing GRU-CNN-based computer-aided tools to simulate disaster scenarios and engage communities in preparedness efforts. The dynamic risk assessment algorithms presented in this paper, powered by GRU-CNN, enable continuous monitoring and analysis of diverse data sources, fostering a proactive approach to disaster preparedness. The integrated sensor network architecture enhances early warning capabilities, allowing for timely responses to emerging threats through the application of GRU-CNN methodologies. Furthermore, the human-centric resilience planning approach introduces virtual simulations using GRU-CNN to model disaster scenarios, facilitating the testing and refinement of strategies in a risk-free environment. This approach not only ensures the adaptability of plans but also engages and educates communities, fostering a culture of preparedness, with GRU-CNN playing a pivotal role in the process. Through these contributions, this research paper seeks to advance the discourse on disaster management in smart urban environments, emphasizing the integration and effectiveness of GRU-CNN in enhancing resilience and response strategies. By leveraging cutting-edge GRU-CNN technologies and prioritizing community involvement, the proposed techniques aim to provide a more robust and adaptive response to the challenges posed by natural and man-made disasters in urban settings.

Sales Navigator

Sales Navigator stands as a groundbreaking project at the forefront of data-driven financial analysis, leveraging advanced deep learning technology to revolutionize the prediction and understanding of sales prices in today's dynamic financial markets. This innovative system seamlessly integrates diverse data sources and employs sophisticated algorithms to decode intricate financial patterns over time. Users are equipped with real-time data updates, customizable dashboards, and timely alerts for significant market events, creating an enriched and dynamic user experience. At its core, Sales Navigator functions as an indispensable resource for financial professionals, offering comprehensive insights into the complexities of the financial markets. The system's deep learning capabilities empower investors, traders, and financial analysts by providing them with the tools to make well-informed investment decisions. Through the fusion of cutting-edge technology and financial expertise, Sales Navigator sets a new standard in predictive accuracy and responsiveness. In essence, Sales Navigator signifies a pioneering approach to financial analysis, where deep learning technology is harnessed to navigate the intricacies of the ever-evolving financial landscape. By offering a holistic understanding of market dynamics, Sales Navigator emerges as an asset, supporting financial professionals in their pursuit of success in today's data-driven and rapidly changing financial ecosystem. Index Terms: AI, ML, Sales Navigator, Data Analytics, Real time, Transformers.

Meta Insights: Analyzing Hero Performance in Mobile Legends with K-Nearest Neighbors

This research presents a thorough statistical analysis of hero performance in the latest Mobile Legends meta, employing the K-Nearest Neighbor (KNN) algorithm. Utilizing diverse data sources, the study explores factors influencing hero success, leveraging KNN's ability to identify intricate patterns in complex datasets. Through meticulous data collection, preprocessing, and application of the KNN algorithm, the research classifies and predicts hero performance based on similarities with neighboring heroes. Critical determinants such as win rates, popularity, and hero ban rates emerge, providing profound insights into gameplay strategies. The study emphasizes the importance of understanding meta dynamics, hero attributes, and player expertise for informed decision-making in hero selection within the dynamic landscape of Mobile Legends.

Enhancing Stock Market Forecasts with Double Deep Q-Network in Volatile Stock Market Environments

Stock market prediction is a subject of great interest within the finance industry and beyond. In this context, our research investigates the use of reinforcement learning through implementing the double deep Q-network (DDQN) alongside technical indicators and sentiment analysis, utilizing data from Yahoo Finance and StockTwits to forecast NVIDIA’s short-term stock movements over the dynamic and volatile period from 2 January 2020, to 21 September 2023. By incorporating financial data, the model’s effectiveness is assessed in three stages: initial reliance on closing prices, the introduction of technical indicators, and the integration of sentiment analysis. Early findings showed a dominant buy tendency (63.8%) in a basic model. Subsequent phases used technical indicators for balanced decisions and sentiment analysis to refine strategies and moderate rewards. Comparative analysis underscores a progressive increase in profitability, with average profits ranging from 57.41 to 119.98 with full data integration and greater outcome variability. These results reveal the significant impact of combining diverse data sources on the model’s predictive accuracy and profitability, suggesting that integrating sentiment analysis alongside traditional financial metrics can significantly enhance the sophistication and effectiveness of algorithmic trading strategies in fluctuating market environments.

Pathfinder- Carrer Guidance using Artificial Intelligence

This project introduces an innovative AI-driven Career Guidance System designed to assist individuals in making informed decisions about their professional paths. With the rapid evolution of industries and job markets, the need for personalized and adaptive career advice has become crucial. Our system leverages artificial intelligence techniques, including machine learning and natural language processing, to analyze user data and industry trends, providing tailored recommendations for career development. The AI model integrates diverse data sources, such as educational backgrounds, skills, and preferences, to create comprehensive user profiles. By employing advanced algorithms, the system generates insightful suggestions regarding suitable career paths, potential skill enhancements, and emerging job opportunities. Real-time updates ensure that users receive the latest information on market demands, enabling them to stay competitive and align their skills with industry trends

Crafting explainable artificial intelligence through active inference: A model for transparent introspection and decision-making

This paper explores the feasibility of constructing interpretable artificial intelligence (AI) systems rooted in active inference and the free energy principle. Initially, we offer a concise introduction to active inference, emphasizing its relevance to modeling decision-making, introspection, and the generation of both overt and covert actions. Subsequently, we delve into how active inference can serve as a foundation for designing explainable AI systems. Specifically, it enables us to capture essential aspects of "introspective" processes and generate intelligible models of decision-making mechanisms. We propose an architectural framework for explainable AI systems employing active inference. Central to this framework is an explicit hierarchical generative model that enables the AI system to monitor and elucidate the factors influencing its decisions. Importantly, this model's structure is designed to be understandable and verifiable by human users. We elucidate how this architecture can amalgamate diverse data sources to make informed decisions in a transparent manner, mirroring aspects of human consciousness and introspection. Finally, we examine the implications of our findings for future AI research and discuss potential ethical considerations associated with developing AI systems with (apparent) introspective capabilities.

Crafting explainable artificial intelligence through active inference: A model for transparent introspection and decision-making

This paper explores the feasibility of constructing interpretable artificial intelligence (AI) systems rooted in active inference and the free energy principle. Initially, we offer a concise introduction to active inference, emphasizing its relevance to modeling decision-making, introspection, and the generation of both overt and covert actions. Subsequently, we delve into how active inference can serve as a foundation for designing explainable AI systems. Specifically, it enables us to capture essential aspects of "introspective" processes and generate intelligible models of decision-making mechanisms. We propose an architectural framework for explainable AI systems employing active inference. Central to this framework is an explicit hierarchical generative model that enables the AI system to monitor and elucidate the factors influencing its decisions. Importantly, this model's structure is designed to be understandable and verifiable by human users. We elucidate how this architecture can amalgamate diverse data sources to make informed decisions in a transparent manner, mirroring aspects of human consciousness and introspection. Finally, we examine the implications of our findings for future AI research and discuss potential ethical considerations associated with developing AI systems with (apparent) introspective capabilities.

MMD-MII Model: A Multilayered Analysis and Multimodal Integration Interaction Approach Revolutionizing Music Emotion Classification

Music plays a vital role in human culture and society, serving as a universal form of expression. However, accurately classifying music emotions remains challenging due to the intricate nature of emotional expressions in music and the integration of diverse data sources. To address these challenges, we propose the Multilayered Music Decomposition and Multimodal Integration Interaction (MMD-MII) model. This model employs cross-processing to facilitate interaction between audio and lyrics, ensuring coherence in emotional representation. Additionally, we introduce a hierarchical framework based on the music theory, focusing on the main and chorus sections, with the chorus processed separately to extract precise emotional representations. Experimental results on the DEAM and FMA datasets demonstrate the effectiveness of the MMD-MII model, achieving accuracies of 49.68% and 49.54% respectively. Compared with the existing methods, our model outperforms in accuracy and F1 scores, offering promising implications for music recommendation systems, healthcare, psychology, and advertising, where accurate emotional analysis is essential.

The Alzheimer's Knowledge Base: A Knowledge Graph for Alzheimer Disease Research.

As global populations age and become susceptible to neurodegenerative illnesses, new therapies for Alzheimer disease (AD) are urgently needed. Existing data resources for drug discovery and repurposing fail to capture relationships central to the disease's etiology and response to drugs. We designed the Alzheimer's Knowledge Base (AlzKB) to alleviate this need by providing a comprehensive knowledge representation of AD etiology and candidate therapeutics. We designed the AlzKB as a large, heterogeneous graph knowledge base assembled using 22 diverse external data sources describing biological and pharmaceutical entities at different levels of organization (eg, chemicals, genes, anatomy, and diseases). AlzKB uses a Web Ontology Language 2 ontology to enforce semantic consistency and allow for ontological inference. We provide a public version of AlzKB and allow users to run and modify local versions of the knowledge base. AlzKB is freely available on the web and currently contains 118,902 entities with 1,309,527 relationships between those entities. To demonstrate its value, we used graph data science and machine learning to (1) propose new therapeutic targets based on similarities of AD to Parkinson disease and (2) repurpose existing drugs that may treat AD. For each use case, AlzKB recovers known therapeutic associations while proposing biologically plausible new ones. AlzKB is a new, publicly available knowledge resource that enables researchers to discover complex translational associations for AD drug discovery. Through 2 use cases, we show that it is a valuable tool for proposing novel therapeutic hypotheses based on public biomedical knowledge.

Explore How to Refine Social Media Marketing Strategies and Shape Consumer Travel Preferences: An In-depth Study on Airbnb

In a post-COVID-19 era of digital transformation and a gradually recovering tourism economy, the travel industry must continually seek innovative ways to enhance social media marketing strategies and better understand consumer behaviour. This paper provides insights into how social media marketing strategy optimisation can shape consumer travel preferences. Using Airbnb as a case study, this paper analyses the social media comments of Airbnb users to reveal consumers' overall positive attitudes towards Airbnb's services through sentiment analysis and explore how methods such as predictive analytics can be used to tailor social media marketing campaigns to provide a more personalised and effective service to potential travellers. The results show that consumers are very concerned about whether the service experience differs from what is advertised on social media, such as quality of accommodation, customer service and pricing strategies. Therefore, Airbnb should prioritise consumer feedback, improve social media content quality, and use predictive analytics to identify its target audience. Future research should incorporate diverse data sources and advanced analytics techniques to understand consumer needs better, establish stronger customer connections, and influence travel choices.

Apprenticeship Reforms in West Africa: An Outcome-Process Evaluation of a Pilot Dual Training Model-Based Apprenticeship Reform Scheme in Ghana

Context: Faced with deep challenges with access to formal education, many West African countries are increasingly taking steps to reform their informal apprenticeship systems to make them a quality skills development alternative for their teeming youth. A review of the literature shows that although different countries in the region are deploying different reform strategies, what is emerging as a dominant reform model is the "dual training model" (DTM), a collaborative arrangement in which the task of training apprentices is shared between informal trainers (master craftspersons, under their respective trade associations) and formal vocational training institutions (FVTIs). This paper presents an outcome-process evaluation of a DTM-based apprenticeship reform programme piloted in Ghana. Methods: Designed as a case study, the paper adopts an interpretivist approach, relying on diverse sources of data, both secondary and primary. Secondary data includes journal articles, attendance registers of participants, memorandums of understanding, relevant media reports, websites, and official reports by all relevant actors. The primary data originated from in-depth interviews with fourteen (14) key informants, as well as from overt and covert observations of respondents. Results: At the outcome level, the paper shows that the programme has largely failed in transferring new skills or in changing dominant poor practices among trainees, foundational objectives of the programme. At the process level, the paper revealed deep flaws in implementation; these are discussed in detail in an attempt to clarify the programme outcomes. Conclusion: The paper concludes that although the dual training model remains a potentially viable reform model in informal apprenticeships, its success ultimately depends on the quality of implementation, which in turn depends on the strength and quality of inter-stakeholder collaboration in programme design and implementation.

Combining the Emission Preprocessor HERMES with the Chemical Transport Model TM5-MP

Emission inventories (EIs) are vital for air quality modeling. Specific research goals often require modifying EIs from diverse data sources, demanding significant code development. In this study, we utilized and further developed the High Elective Resolution Modeling Emission System version three for Global and Regional domains (HERMESv3_gr). This user-friendly processing system was adapted for generating EIs compatible with the Chemistry Transport Model Tracel Model 5 Massive Parallel (TM5-MP). The results indicate that HERMESv3_gr is capable of generating EIs with negligible biases (10−7 relative differences) for TM5-MP, showcasing its effectiveness. We applied HERMESv3_gr to integrate the EI Regional Emission inventory in Asia (REAS) into the global EI Community Emission Data System (CEDS). Comparison of model results using CEDS alone and the integrated EI against measurement data for various pollutants globally revealed small improvements for carbon monoxide (1%) ethane (1–2%), and nitrogen oxide (2%) and larger for propane (5–7%). Ozone in the northern hemisphere improved by about 2% while in the southern hemisphere improvements of 5% could be observed. Our findings highlight the importance of carefully considering the effects of EI integration for accurate air quality modeling.

Predictive Models to Assess Risk of Persistent Opioid Use, Opioid Use Disorder, and Overdose.

This systematic review summarizes the development, accuracy, quality, and clinical utility of predictive models to assess the risk of opioid use disorder (OUD), persistent opioid use, and opioid overdose. In accordance with Preferred Reporting Items for a Systematic Review and Meta-analysis guidelines, 8 electronic databases were searched for studies on predictive models and OUD, overdose, or persistent use in adults until June 25, 2023. Study selection and data extraction were completed independently by 2 reviewers. Risk of bias of included studies was assessed independently by 2 reviewers using the Prediction model Risk of Bias ASsessment Tool (PROBAST). The literature search yielded 3130 reports; after removing 199 duplicates, excluding 2685 studies after abstract review, and excluding 204 studies after full-text review, the final sample consisted of 41 studies that developed more than 160 predictive models. Primary outcomes included opioid overdose (31.6% of studies), OUD (41.4%), and persistent opioid use (17%). The most common modeling approach was regression modeling, and the most common predictors included age, sex, mental health diagnosis history, and substance use disorder history. Most studies reported model performance via the c statistic, ranging from 0.507 to 0.959; gradient boosting tree models and neural network models performed well in the context of their own study. One study deployed a model in real time. Risk of bias was predominantly high; concerns regarding applicability were predominantly low. Models to predict opioid-related risks are developed using diverse data sources and predictors, with a wide and heterogenous range of accuracy metrics. There is a need for further research to improve their accuracy and implementation.

Integration and analysis of diverse healthcare data sources: A novel solution

Contemporary healthcare analytics requires informed decision-making through seamless integration, correlation, and curation of diverse data from sources like clinical trials, research publications, ubiquitous devices, and standard terminologies. Modern healthcare systems need to monitor temporal changes, manage key features, and deliver robust search capabilities, extending beyond electronic health records. However, existing systems lack readiness for comprehensive healthcare analytics tasks, necessitating a sophisticated solution. Our work introduces a groundbreaking comprehensive framework for managing, integrating, and processing continuously evolving healthcare data, with a focus on establishing an efficient architecture for data processing and ensuring interoperability and consistency. We incorporate a time dimension to capture critical changes for efficient data analysis and decision-making, extending from clinical trials to mapping clinical trial data to clinical research. Moreover, we curate disparate datasets, including trials, academic publications, standard medical terms, concepts, and ubiquitous device data. Employing highly efficient algorithms and methods, we optimize time and space complexity, validating the feasibility of our proposed solution. Our results demonstrate maximum linear change detection and update processing latency, showcasing efficiency compared to state-of-the-art methods. Additionally, our methods for profiling crucial entities in clinical trial data achieve consistent average accuracy, notably with the VSM model. This innovative approach significantly advances meeting dynamic requirements in contemporary healthcare analytics, particularly in clinical trials.