Pub Date : 2024-03-10DOI: 10.60087/jaigs.v2i1.p155
Jaydeep Thakker
In the realm of Industry 4.0, the utilization of artificial intelligence (AI) and machine learning for anomaly detection faces challenges due to significant computational demands and associated environmental consequences. This study aims to tackle the need for high-performance machine learning models while promoting environmental sustainability, contributing to the emerging concept of 'Green AI.' We meticulously assessed a wide range of machine learning algorithms, combined with various Multilayer Perceptron (MLP) configurations. Our evaluation encompassed a comprehensive set of performance metrics, including Accuracy, Area Under the Curve (AUC), Recall, Precision, F1 Score, Kappa Statistic, Matthews Correlation Coefficient (MCC), and F1 Macro. Concurrently, we evaluated the environmental footprint of these models by considering factors such as time duration, CO2 emissions, and energy consumption during training, cross-validation, and inference phases. While traditional machine learning algorithms like Decision Trees and Random Forests exhibited robust efficiency and performance, optimized MLP configurations yielded superior results, albeit with a proportional increase in resource consumption. To address the trade-offs between model performance and environmental impact, we employed a multi-objective optimization approach based on Pareto optimality principles. The insights gleaned emphasize the importance of striking a balance between model performance, complexity, and environmental considerations, offering valuable guidance for future endeavors in developing environmentally conscious machine learning models for industrial applications.
{"title":"Security Challenges of Vehicular Cloud Computing","authors":"Jaydeep Thakker","doi":"10.60087/jaigs.v2i1.p155","DOIUrl":"https://doi.org/10.60087/jaigs.v2i1.p155","url":null,"abstract":"In the realm of Industry 4.0, the utilization of artificial intelligence (AI) and machine learning for anomaly detection faces challenges due to significant computational demands and associated environmental consequences. This study aims to tackle the need for high-performance machine learning models while promoting environmental sustainability, contributing to the emerging concept of 'Green AI.' We meticulously assessed a wide range of machine learning algorithms, combined with various Multilayer Perceptron (MLP) configurations. Our evaluation encompassed a comprehensive set of performance metrics, including Accuracy, Area Under the Curve (AUC), Recall, Precision, F1 Score, Kappa Statistic, Matthews Correlation Coefficient (MCC), and F1 Macro. Concurrently, we evaluated the environmental footprint of these models by considering factors such as time duration, CO2 emissions, and energy consumption during training, cross-validation, and inference phases. \u0000 \u0000While traditional machine learning algorithms like Decision Trees and Random Forests exhibited robust efficiency and performance, optimized MLP configurations yielded superior results, albeit with a proportional increase in resource consumption. To address the trade-offs between model performance and environmental impact, we employed a multi-objective optimization approach based on Pareto optimality principles. The insights gleaned emphasize the importance of striking a balance between model performance, complexity, and environmental considerations, offering valuable guidance for future endeavors in developing environmentally conscious machine learning models for industrial applications.","PeriodicalId":517201,"journal":{"name":"Journal of Artificial Intelligence General science (JAIGS) ISSN:3006-4023","volume":"57 3","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-03-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140255640","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Meta-learning has emerged as a powerful paradigm in machine learning, enabling adaptive and fast learning systems capable of efficiently acquiring knowledge from various tasks and domains. This paper provides an overview of meta-learning techniques, focusing on their ability to leverage prior experience to facilitate the learning of new tasks. We explore the fundamental concepts, methodologies, and applications of meta-learning, emphasizing its role in enhancing the adaptability and speed of learning systems. By incorporating meta-learning strategies, algorithms can autonomously adapt to new tasks and data distributions, thereby improving performance and efficiency across diverse domains. This review sheds light on the current state-of-the-art in meta-learning research and highlights its potential implications for the future of artificial intelligence.
{"title":"Meta-Learning: Adaptive and Fast Learning Systems","authors":"Morshed Alom","doi":"10.60087/jaigs.v2i1.p97","DOIUrl":"https://doi.org/10.60087/jaigs.v2i1.p97","url":null,"abstract":"Meta-learning has emerged as a powerful paradigm in machine learning, enabling adaptive and fast learning systems capable of efficiently acquiring knowledge from various tasks and domains. This paper provides an overview of meta-learning techniques, focusing on their ability to leverage prior experience to facilitate the learning of new tasks. We explore the fundamental concepts, methodologies, and applications of meta-learning, emphasizing its role in enhancing the adaptability and speed of learning systems. By incorporating meta-learning strategies, algorithms can autonomously adapt to new tasks and data distributions, thereby improving performance and efficiency across diverse domains. This review sheds light on the current state-of-the-art in meta-learning research and highlights its potential implications for the future of artificial intelligence. \u0000 ","PeriodicalId":517201,"journal":{"name":"Journal of Artificial Intelligence General science (JAIGS) ISSN:3006-4023","volume":"13 8","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-03-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140259949","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
In the contemporary digital landscape, cybersecurity stands as a paramount concern due to the increasing sophistication and frequency of cyber threats. Artificial Intelligence (AI) has emerged as a potent tool in fortifying defenses against these evolving threats. This paper examines the multifaceted role of AI in cybersecurity, elucidating its applications in threat detection, vulnerability assessment, incident response, and predictive analysis. By leveraging machine learning algorithms, AI systems can swiftly analyze vast troves of data to identify anomalous patterns indicative of potential security breaches. Moreover, AI-driven technologies enable proactive defense mechanisms, empowering organizations to preemptively mitigate risks and safeguard sensitive information. However, the deployment of AI in cybersecurity also raises pertinent ethical and privacy considerations, necessitating a balanced approach towards its implementation. Through a comprehensive analysis, this paper underscores the imperative of integrating AI into cybersecurity frameworks to effectively mitigate threats in the digital age.
{"title":"The Role of AI in Cybersecurity: Addressing Threats in the Digital Age","authors":"Nicolas Guzman Camacho","doi":"10.60087/jaigs.v3i1.75","DOIUrl":"https://doi.org/10.60087/jaigs.v3i1.75","url":null,"abstract":"In the contemporary digital landscape, cybersecurity stands as a paramount concern due to the increasing sophistication and frequency of cyber threats. Artificial Intelligence (AI) has emerged as a potent tool in fortifying defenses against these evolving threats. This paper examines the multifaceted role of AI in cybersecurity, elucidating its applications in threat detection, vulnerability assessment, incident response, and predictive analysis. By leveraging machine learning algorithms, AI systems can swiftly analyze vast troves of data to identify anomalous patterns indicative of potential security breaches. Moreover, AI-driven technologies enable proactive defense mechanisms, empowering organizations to preemptively mitigate risks and safeguard sensitive information. However, the deployment of AI in cybersecurity also raises pertinent ethical and privacy considerations, necessitating a balanced approach towards its implementation. Through a comprehensive analysis, this paper underscores the imperative of integrating AI into cybersecurity frameworks to effectively mitigate threats in the digital age.","PeriodicalId":517201,"journal":{"name":"Journal of Artificial Intelligence General science (JAIGS) ISSN:3006-4023","volume":"17 6","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-03-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140262363","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The integration of Artificial Intelligence (AI) in the financial sector has ushered in disruptive technologies and unlocked a plethora of emerging opportunities. This paper provides an in-depth exploration of the transformative role of AI in finance, delineating its impact on various facets including investment strategies, risk assessment, fraud detection, customer service, and regulatory compliance. Leveraging machine learning algorithms, natural language processing, and predictive analytics, AI empowers financial institutions to process vast datasets, derive actionable insights, and automate decision-making processes with unprecedented precision and efficiency. Furthermore, AI-driven innovations facilitate personalized financial services, streamline operations, and catalyze the development of novel business models, thereby reshaping the competitive landscape of the finance industry. Nevertheless, the adoption of AI in finance necessitates careful consideration of ethical, privacy, and regulatory implications to ensure responsible and sustainable deployment. Through comprehensive analysis and case studies, this paper illuminates the disruptive potential and emerging opportunities afforded by AI in finance, paving the way for informed decision-making and strategic investment in this rapidly evolving domain.
{"title":"AI in Finance Disruptive Technologies and Emerging Opportunities","authors":"A.K.M. Kamruzzaman Khan","doi":"10.60087/jaigs.v3i1.76","DOIUrl":"https://doi.org/10.60087/jaigs.v3i1.76","url":null,"abstract":"The integration of Artificial Intelligence (AI) in the financial sector has ushered in disruptive technologies and unlocked a plethora of emerging opportunities. This paper provides an in-depth exploration of the transformative role of AI in finance, delineating its impact on various facets including investment strategies, risk assessment, fraud detection, customer service, and regulatory compliance. Leveraging machine learning algorithms, natural language processing, and predictive analytics, AI empowers financial institutions to process vast datasets, derive actionable insights, and automate decision-making processes with unprecedented precision and efficiency. Furthermore, AI-driven innovations facilitate personalized financial services, streamline operations, and catalyze the development of novel business models, thereby reshaping the competitive landscape of the finance industry. Nevertheless, the adoption of AI in finance necessitates careful consideration of ethical, privacy, and regulatory implications to ensure responsible and sustainable deployment. Through comprehensive analysis and case studies, this paper illuminates the disruptive potential and emerging opportunities afforded by AI in finance, paving the way for informed decision-making and strategic investment in this rapidly evolving domain.","PeriodicalId":517201,"journal":{"name":"Journal of Artificial Intelligence General science (JAIGS) ISSN:3006-4023","volume":"23 15","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-03-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140263470","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Traditional healthcare systems have long grappled with meeting the diverse needs of millions of patients, often resulting in inefficiencies and suboptimal outcomes. However, the emergence of machine learning (ML) has brought about a transformative shift towards value-based treatment, empowering healthcare providers to deliver personalized and highly effective care. Today's healthcare equipment and devices are equipped with internal applications that collect and store comprehensive patient data, serving as a rich resource for ML-driven predictive models. This research delves into the profound impact of ML on contemporary healthcare, highlighting its potential to significantly enhance patient care and optimize resource allocation. Our study presents a robust predictive model capable of accurately forecasting patient diseases based on input information and various parameters, leveraging extensive datasets encompassing diverse patient populations. We rigorously compared several ML algorithms, including Logistic Regression, K-Nearest Neighbors, XG Boost, and PyTorch, to identify the best-performing model. The achieved accuracies underscore the effectiveness of these ML techniques in disease prediction, highlighting the potential for improving patient outcomes. Beyond the technical aspects, we explore the broader implications of value-based treatment and ML integration for various healthcare stakeholders. By emphasizing the benefits of personalized and proactive medical care, our findings illustrate the substantial potential of ML-driven predictive healthcare models to revolutionize traditional healthcare systems. The adoption of ML lays the foundation for a more efficient, effective, and patient-centered medical ecosystem, supporting the sustainability and adaptability of healthcare systems in the face of expanding patient populations and complex medical needs.
长期以来，传统医疗保健系统一直在努力满足数百万患者的不同需求，结果往往是效率低下、疗效不佳。然而，机器学习（ML）的出现带来了向基于价值的治疗的转型，使医疗服务提供者有能力提供个性化和高效的医疗服务。如今的医疗保健设备和装置都配备了内部应用程序，可收集和存储全面的患者数据，为 ML 驱动的预测模型提供丰富的资源。本研究深入探讨了 ML 对当代医疗保健的深远影响，强调了它在显著增强患者护理和优化资源分配方面的潜力。我们的研究基于输入信息和各种参数，利用涵盖不同患者群体的广泛数据集，提出了一种能够准确预测患者疾病的强大预测模型。我们对 Logistic 回归、K-Nearest Neighbors、XG Boost 和 PyTorch 等多种 ML 算法进行了严格比较，以确定性能最佳的模型。所取得的准确率强调了这些 ML 技术在疾病预测中的有效性，突出了改善患者预后的潜力。除了技术方面，我们还探讨了基于价值的治疗和 ML 整合对各种医疗保健利益相关者的更广泛影响。通过强调个性化和前瞻性医疗保健的益处，我们的研究结果表明了以 ML 为驱动的预测性医疗保健模型在彻底改变传统医疗保健系统方面的巨大潜力。面对不断扩大的患者群体和复杂的医疗需求，采用 ML 可为建立一个更高效、有效和以患者为中心的医疗生态系统奠定基础，从而支持医疗保健系统的可持续性和适应性。
{"title":"Reinventing Wellness: How Machine Learning Transforms Healthcare","authors":"Mithun Sarker","doi":"10.60087/jaigs.v3i1.73","DOIUrl":"https://doi.org/10.60087/jaigs.v3i1.73","url":null,"abstract":"Traditional healthcare systems have long grappled with meeting the diverse needs of millions of patients, often resulting in inefficiencies and suboptimal outcomes. However, the emergence of machine learning (ML) has brought about a transformative shift towards value-based treatment, empowering healthcare providers to deliver personalized and highly effective care. Today's healthcare equipment and devices are equipped with internal applications that collect and store comprehensive patient data, serving as a rich resource for ML-driven predictive models. This research delves into the profound impact of ML on contemporary healthcare, highlighting its potential to significantly enhance patient care and optimize resource allocation. Our study presents a robust predictive model capable of accurately forecasting patient diseases based on input information and various parameters, leveraging extensive datasets encompassing diverse patient populations. We rigorously compared several ML algorithms, including Logistic Regression, K-Nearest Neighbors, XG Boost, and PyTorch, to identify the best-performing model. The achieved accuracies underscore the effectiveness of these ML techniques in disease prediction, highlighting the potential for improving patient outcomes. Beyond the technical aspects, we explore the broader implications of value-based treatment and ML integration for various healthcare stakeholders. By emphasizing the benefits of personalized and proactive medical care, our findings illustrate the substantial potential of ML-driven predictive healthcare models to revolutionize traditional healthcare systems. The adoption of ML lays the foundation for a more efficient, effective, and patient-centered medical ecosystem, supporting the sustainability and adaptability of healthcare systems in the face of expanding patient populations and complex medical needs.","PeriodicalId":517201,"journal":{"name":"Journal of Artificial Intelligence General science (JAIGS) ISSN:3006-4023","volume":"12 3","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-03-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140262375","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
This paper explores the intricate interaction between knowledge, innovation, and artificial intelligence (AI), underscoring the indispensable role of human involvement in this dynamic process. While AI progresses and permeates various aspects of society, it significantly influences knowledge generation, dissemination, and innovation. Nonetheless, the human factor remains pivotal in effectively harnessing the potential of AI. This study delves into the nuances of this symbiotic relationship, examining how humans contribute to AI advancement, shape its applications, and mitigate associated risks. Through a multidisciplinary perspective, it discusses strategies to cultivate synergy between AI capabilities and human expertise, ensuring that innovation is guided by ethical principles and human values. Ultimately, it underscores the imperative of comprehending and nurturing the human element amidst the evolving landscape of knowledge and AI-driven innovation.
{"title":"Intricate Dance of Knowledge, Innovation, and AI: Navigating the Human Element","authors":"Arabella Jo","doi":"10.60087/jaigs.v3i1.74","DOIUrl":"https://doi.org/10.60087/jaigs.v3i1.74","url":null,"abstract":"This paper explores the intricate interaction between knowledge, innovation, and artificial intelligence (AI), underscoring the indispensable role of human involvement in this dynamic process. While AI progresses and permeates various aspects of society, it significantly influences knowledge generation, dissemination, and innovation. Nonetheless, the human factor remains pivotal in effectively harnessing the potential of AI. This study delves into the nuances of this symbiotic relationship, examining how humans contribute to AI advancement, shape its applications, and mitigate associated risks. Through a multidisciplinary perspective, it discusses strategies to cultivate synergy between AI capabilities and human expertise, ensuring that innovation is guided by ethical principles and human values. Ultimately, it underscores the imperative of comprehending and nurturing the human element amidst the evolving landscape of knowledge and AI-driven innovation. \u0000 ","PeriodicalId":517201,"journal":{"name":"Journal of Artificial Intelligence General science (JAIGS) ISSN:3006-4023","volume":"36 20","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-03-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140262964","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
In the dynamic realm of technology, the fusion of Artificial Intelligence (AI) and Machine Learning (ML) with DevSecOps practices stands out as a pivotal catalyst for bolstering security, efficiency, and innovation in software development and deployment processes. This document explores effective strategies and optimal practices for maximizing the capabilities of AI/ML within the DevSecOps framework. Commencing with an overview of DevSecOps principles and the integral role of AI/ML, the document delves into specific tactics such as automated threat detection, predictive analytics for vulnerability management, and intelligent automation for continuous integration and deployment. Additionally, it addresses prominent challenges and considerations associated with the integration of AI/ML in DevSecOps, including data privacy, algorithm transparency, and ethical implications. Through illuminating case studies and real-world illustrations, the document showcases how organizations can leverage AI/ML technologies to streamline their DevSecOps pipelines, mitigate security risks, and cultivate a culture of ongoing enhancement. By embracing these strategies and adhering to best practices, organizations can harness the full potential of AI/ML to propel innovation, fortify resilience, and enhance agility in their DevSecOps endeavors.
{"title":"Unlocking the Potential of AI/ML in DevSecOps: Effective Strategies and Optimal Practices","authors":"Nicolas Guzman Camacho","doi":"10.60087/jaigs.v2i1.p89","DOIUrl":"https://doi.org/10.60087/jaigs.v2i1.p89","url":null,"abstract":"In the dynamic realm of technology, the fusion of Artificial Intelligence (AI) and Machine Learning (ML) with DevSecOps practices stands out as a pivotal catalyst for bolstering security, efficiency, and innovation in software development and deployment processes. This document explores effective strategies and optimal practices for maximizing the capabilities of AI/ML within the DevSecOps framework. Commencing with an overview of DevSecOps principles and the integral role of AI/ML, the document delves into specific tactics such as automated threat detection, predictive analytics for vulnerability management, and intelligent automation for continuous integration and deployment. Additionally, it addresses prominent challenges and considerations associated with the integration of AI/ML in DevSecOps, including data privacy, algorithm transparency, and ethical implications. Through illuminating case studies and real-world illustrations, the document showcases how organizations can leverage AI/ML technologies to streamline their DevSecOps pipelines, mitigate security risks, and cultivate a culture of ongoing enhancement. By embracing these strategies and adhering to best practices, organizations can harness the full potential of AI/ML to propel innovation, fortify resilience, and enhance agility in their DevSecOps endeavors.","PeriodicalId":517201,"journal":{"name":"Journal of Artificial Intelligence General science (JAIGS) ISSN:3006-4023","volume":"53 11","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-03-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140082564","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Artificial intelligence (AI) has become increasingly pervasive across various domains, including smartphones, social media platforms, search engines, and autonomous vehicles, among others. This study undertakes a scoping review of the current landscape of AI technologies, following the PRISMA framework, with the aim of identifying the most advanced technologies utilized in different domains of AI research. Three reputable journals within the artificial intelligence and machine learning domain, namely the Journal of Artificial Intelligence Research, the Journal of Machine Learning Research, and Machine Learning, were selected for this review. Articles published in 2022 were scrutinized against certain criteria: the technology must be tested against comparable solutions, employ commonly approved or well-justified datasets, and demonstrate improvements over comparable solutions. A crucial aspect of technology development identified in this review is the processing and exploitation of data collected from diverse sources. Given the highly unstructured nature of data, technological solutions should minimize the need for manual intervention by humans. The review indicates that creating labeled datasets is a labor-intensive process, leading to increased research focus on solutions leveraging unsupervised or semi-supervised learning technologies. Efficient updating of learning algorithms and the interpretability of predictions emerge as key considerations in the development of AI technologies. Moreover, in real-world applications, ensuring safety and providing explainable predictions are imperative before widespread adoption can be achieved. Thus, this review underscores the importance of addressing these factors to facilitate the responsible and effective integration of AI technologies into various domains.
{"title":"Exploring the Latest Trends in Artificial Intelligence Technology: A Comprehensive Review","authors":"Jeff Shuford, Md.mafiqul Islam","doi":"10.60087/jaigs.v2i1.p13","DOIUrl":"https://doi.org/10.60087/jaigs.v2i1.p13","url":null,"abstract":"Artificial intelligence (AI) has become increasingly pervasive across various domains, including smartphones, social media platforms, search engines, and autonomous vehicles, among others. This study undertakes a scoping review of the current landscape of AI technologies, following the PRISMA framework, with the aim of identifying the most advanced technologies utilized in different domains of AI research. Three reputable journals within the artificial intelligence and machine learning domain, namely the Journal of Artificial Intelligence Research, the Journal of Machine Learning Research, and Machine Learning, were selected for this review. Articles published in 2022 were scrutinized against certain criteria: the technology must be tested against comparable solutions, employ commonly approved or well-justified datasets, and demonstrate improvements over comparable solutions. A crucial aspect of technology development identified in this review is the processing and exploitation of data collected from diverse sources. Given the highly unstructured nature of data, technological solutions should minimize the need for manual intervention by humans. The review indicates that creating labeled datasets is a labor-intensive process, leading to increased research focus on solutions leveraging unsupervised or semi-supervised learning technologies. Efficient updating of learning algorithms and the interpretability of predictions emerge as key considerations in the development of AI technologies. Moreover, in real-world applications, ensuring safety and providing explainable predictions are imperative before widespread adoption can be achieved. Thus, this review underscores the importance of addressing these factors to facilitate the responsible and effective integration of AI technologies into various domains.","PeriodicalId":517201,"journal":{"name":"Journal of Artificial Intelligence General science (JAIGS) ISSN:3006-4023","volume":"5 2","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140425440","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Traditional healthcare systems have grappled with meeting the diverse needs of millions of patients, resulting in inefficiencies and suboptimal outcomes. However, the emergence of machine learning (ML) has ushered in a transformative paradigm shift towards value-based treatment, empowering healthcare providers to deliver personalized and highly effective care. Modern healthcare equipment and devices now integrate internal applications that collect and store comprehensive patient data, providing a rich resource for ML-driven predictive models. In this research article, we explore the profound impact of ML on contemporary healthcare, emphasizing its potential to significantly enhance patient care and optimize resource allocation. Our study presents a robust predictive model capable of accurately forecasting patient diseases based on input information and various parameters, leveraging extensive datasets encompassing diverse patient populations. We compared several ML algorithms, including Logistic Regression (accuracy: 0.796875), K-Nearest Neighbors (accuracy: 0.7864583333333334), XG Boost (accuracy: 0.78125), and PyTorch (accuracy: 0.7337662337662337), to identify the best-performing model. The achieved accuracies underscore the effectiveness of these ML techniques in disease prediction and underscore the potential for improving patient outcomes. Beyond the technical aspects, we explore the broader implications of value-based treatment and the integration of ML for various healthcare stakeholders. By emphasizing the numerous benefits of personalized and proactive medical care, our findings illustrate the substantial potential of ML-driven predictive healthcare models to revolutionize traditional healthcare systems. The adoption of ML in healthcare lays the foundation for a more efficient, effective, and patient-centered medical ecosystem, supporting the sustainability and adaptability of healthcare systems in the face of expanding patient populations and complex medical needs. This article significantly contributes to the field by providing comprehensive insights into the experimental stages, showcasing the achieved results, and highlighting the key conclusions derived from our study. By addressing the limitations of the previous abstract, we ensure a more informative and substantial overview of our research, offering valuable knowledge for researchers, practitioners, and decision-makers striving to leverage the power of ML in healthcare innovation.
传统的医疗保健系统一直在努力满足数百万患者的不同需求，导致效率低下、疗效不佳。然而，机器学习（ML）的出现带来了向基于价值的治疗模式的转变，使医疗服务提供者有能力提供个性化和高效的医疗服务。现代医疗保健设备和装置现在集成了内部应用程序，可收集和存储全面的患者数据，为 ML 驱动的预测模型提供了丰富的资源。在这篇研究文章中，我们探讨了 ML 对当代医疗保健的深远影响，强调了它在显著增强患者护理和优化资源分配方面的潜力。我们的研究基于输入信息和各种参数，利用涵盖不同患者群体的广泛数据集，提出了一种能够准确预测患者疾病的强大预测模型。我们比较了几种 ML 算法，包括逻辑回归（准确率：0.796875）、K-近邻（准确率：0.78645833333334）、XG Boost（准确率：0.78125）和 PyTorch（准确率：0.7337662337662337），以确定表现最佳的模型。所取得的准确率突出表明了这些 ML 技术在疾病预测方面的有效性，并彰显了改善患者预后的潜力。除了技术方面，我们还探讨了基于价值的治疗和整合 ML 对不同医疗保健利益相关者的更广泛影响。通过强调个性化和前瞻性医疗保健的诸多益处，我们的研究结果表明了人工智能驱动的预测性医疗保健模型在彻底改变传统医疗保健系统方面的巨大潜力。在医疗保健领域采用人工智能为建立一个更加高效、有效和以患者为中心的医疗生态系统奠定了基础，从而支持医疗保健系统在面对不断扩大的患者群体和复杂的医疗需求时的可持续性和适应性。本文全面介绍了实验阶段的情况，展示了取得的成果，并强调了从我们的研究中得出的重要结论，从而为该领域做出了重大贡献。通过解决上一篇摘要的局限性，我们确保对我们的研究进行更翔实、更实质性的概述，为努力在医疗创新中利用 ML 的力量的研究人员、从业人员和决策者提供有价值的知识。
{"title":"Revolutionizing Healthcare: The Role of Machine Learning in the Health Sector","authors":"Mithun Sarker","doi":"10.60087/jaigs.v2i1.p47","DOIUrl":"https://doi.org/10.60087/jaigs.v2i1.p47","url":null,"abstract":"Traditional healthcare systems have grappled with meeting the diverse needs of millions of patients, resulting in inefficiencies and suboptimal outcomes. However, the emergence of machine learning (ML) has ushered in a transformative paradigm shift towards value-based treatment, empowering healthcare providers to deliver personalized and highly effective care. Modern healthcare equipment and devices now integrate internal applications that collect and store comprehensive patient data, providing a rich resource for ML-driven predictive models. In this research article, we explore the profound impact of ML on contemporary healthcare, emphasizing its potential to significantly enhance patient care and optimize resource allocation. Our study presents a robust predictive model capable of accurately forecasting patient diseases based on input information and various parameters, leveraging extensive datasets encompassing diverse patient populations. We compared several ML algorithms, including Logistic Regression (accuracy: 0.796875), K-Nearest Neighbors (accuracy: 0.7864583333333334), XG Boost (accuracy: 0.78125), and PyTorch (accuracy: 0.7337662337662337), to identify the best-performing model. The achieved accuracies underscore the effectiveness of these ML techniques in disease prediction and underscore the potential for improving patient outcomes. Beyond the technical aspects, we explore the broader implications of value-based treatment and the integration of ML for various healthcare stakeholders. By emphasizing the numerous benefits of personalized and proactive medical care, our findings illustrate the substantial potential of ML-driven predictive healthcare models to revolutionize traditional healthcare systems. The adoption of ML in healthcare lays the foundation for a more efficient, effective, and patient-centered medical ecosystem, supporting the sustainability and adaptability of healthcare systems in the face of expanding patient populations and complex medical needs. This article significantly contributes to the field by providing comprehensive insights into the experimental stages, showcasing the achieved results, and highlighting the key conclusions derived from our study. By addressing the limitations of the previous abstract, we ensure a more informative and substantial overview of our research, offering valuable knowledge for researchers, practitioners, and decision-makers striving to leverage the power of ML in healthcare innovation.","PeriodicalId":517201,"journal":{"name":"Journal of Artificial Intelligence General science (JAIGS) ISSN:3006-4023","volume":"20 12","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140424814","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The significance of reference data in financial data analysis cannot be overstated, as it forms the bedrock for precise decision-making within the ever-evolving financial markets. This study delves into the inherent challenges and opportunities associated with harnessing reference data for comprehensive financial data analysis. Challenges encompass issues related to data quality, complexities in data integration, and regulatory compliance. Nevertheless, within these challenges lie opportunities for innovation, including advanced data analytics techniques, artificial intelligence, and blockchain technology, which have the potential to elevate the accuracy, efficiency, and transparency of financial data analysis. By effectively addressing these challenges and embracing these opportunities, financial institutions can unlock the full potential of reference data, enabling them to make informed decisions and attain a competitive advantage in the global arena.
{"title":"Navigating the Role of Reference Data in Financial Data Analysis: Addressing Challenges and Seizing Opportunities","authors":"Harish Padmanaban","doi":"10.60087/jaigs.v2i1.p78","DOIUrl":"https://doi.org/10.60087/jaigs.v2i1.p78","url":null,"abstract":"The significance of reference data in financial data analysis cannot be overstated, as it forms the bedrock for precise decision-making within the ever-evolving financial markets. This study delves into the inherent challenges and opportunities associated with harnessing reference data for comprehensive financial data analysis. Challenges encompass issues related to data quality, complexities in data integration, and regulatory compliance. Nevertheless, within these challenges lie opportunities for innovation, including advanced data analytics techniques, artificial intelligence, and blockchain technology, which have the potential to elevate the accuracy, efficiency, and transparency of financial data analysis. By effectively addressing these challenges and embracing these opportunities, financial institutions can unlock the full potential of reference data, enabling them to make informed decisions and attain a competitive advantage in the global arena.","PeriodicalId":517201,"journal":{"name":"Journal of Artificial Intelligence General science (JAIGS) ISSN:3006-4023","volume":"34 3","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140427468","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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