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Unlock growth with AI-powered personalization & predictive analytics. Learn how digital marketing services and agencies in Lucknow use these tools to drive results and boost engagement.

Engine failures on ships are expensive, and affect operational readiness critically due to long turn-around times for maintenance. Prior to the engine failures, there are signs of engine characteristic changes, for example, exhaust gas temperature (EGT), to indicate that the engine is acting abnormally. This is used as a precursor towards the modeling of failures. There is a threshold limit of 520 degree Celsius for the EGT prior to the need for human intervention. With this knowledge, the use of time series forecasting technique, to predict the crossing over of threshold, is appropriate to model the EGT as a function of its operating running hours and load. This allows maintenance to be scheduled just in time. When there is a departure of result from the predictive model, Cumulative Sum (CUSUM) Control charts can then be used to monitor the change early before an actual problem arises. This paper discusses and demonstrates the proof of principle for one engine and a particular operating profile of a commercial vessel with the use of predictive analytics. The realization with time series forecasting coupled with CUSUM control chart allows this approach to be extended to other attributes beyond EGT.

Only mathematics describes the infinite complexities of life within time. Our futures fixed points cannot be changed. The Simpson's knew Trump would concede to Lisa. Predictive analytics and the billions of choices and directions. When will the SCOTUS situation come to life? The terminal node and why it is fixed. Working now to influence future change. It does not matter what version they choose. Let's say it again, the CISA algorithm steals elections. Ramanjuan the genius. The 3x + 1 loop is back. Mendalbrot explains. Think patterns and numbers. All things are interconnected. Was Trump predicted long ago? 911 was orchestrated to delay. Portals, and a world within the world. Mars is the past, Venus is the future. Amazing Javier vids. VR future food. Plant coms. In fiction there is always a root of truth, so we must stick to the foundations that give us discernment. Learn more about your ad choices. Visit megaphone.fm/adchoices

Censorship is not about silencing you, it's about cutting off your information. Disallowing you is their goal. Stealing and obstruction are core tactics. Fake riots ginned up by fake politicians. The Committee On Public Information is back. Today's parallels with NAZI propaganda are frightening. Censorship is book burning. The only person who can tell you anything is POTUS and he is the ultimate insider. There are so many things going on. Hope is born within you, so look inside yourself for what resonates and follow your faith.

Engine failures on ships are expensive, and affect operational readiness critically due to long turn-around times for maintenance. Prior to the engine failures, there are signs of engine characteristic changes, for example, exhaust gas temperature (EGT), to indicate that the engine is acting abnormally. This is used as a precursor towards the modeling of failures. There is a threshold limit of 520 degree Celsius for the EGT prior to the need for human intervention. With this knowledge, the use of time series forecasting technique, to predict the crossing over of threshold, is appropriate to model the EGT as a function of its operating running hours and load. This allows maintenance to be scheduled just in time . When there is a departure of result from the predictive model, Cumulative Sum (CUSUM) Control charts can then be used to monitor the change early before an actual problem arises. This paper discusses and demonstrates the proof of principle for one engine and a particular operating profile of a commercial vessel with the use of predictive analytics. The realization with time series forecasting coupled with CUSUM control chart allows this approach to be extended to other attributes beyond EGT.

Perma.cc archive of https://med.nyu.edu/centers-programs/healthcare-innovation-delivery-science/predictive-analytics-unit created on 2022-03-19 22:15:30.040605+00:00.

With economic globalization and continuous development of e-commerce, customer relationship management (CRM) has become an important factor in growth of a company. CRM requires huge expenses. One way to profit from your CRM investment and drive better results, is through machine learning. Machine learning helps business to manage, understand and provide services to customers at individual level. Thus propensity modeling helps the business in increasing marketing performance. The objective is to propose a new approach for better customer targeting. We'll device a method to improve prediction capabilities of existing CRM systems by improving classification performance for propensity modeling. Supriya V. Pawar | Gireesh Kumar | Eashan Deshmukh"Predictive Analytics for High Business Performance through Effective Marketing" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-2 | Issue-2 , February 2018, URL: http://www.ijtsrd.com/papers/ijtsrd9502.pdf Article URL: http://www.ijtsrd.com/engineering/computer-engineering/9502/predictive-analytics-for-high-business-performance-through-effective-marketing/supriya-v-pawar

Colleges are increasingly using models to predict student behavior and intervene to change that behavior. Because of this, when projects involve partnering with a vendor, it is more important than ever to make the right choice about which vendor. In some ways, partnering with a vendor to use predictive analytics is similar to procuring any other technology product. But the complexity of the algorithms--and the predictions they produce--add another layer to the decision-making process. This guide gives administrators the tools to ask the right set of questions of predictive analytics vendors and provides a sense of what kind of answers they should expect. It focuses on ensuring that vendors use predictive analytics tools like early alert systems in an ethical manner.

In the ever-evolving landscape of data analytics, predictive analytics stands out as a powerful tool that enables organizations to forecast future trends and behaviors based on historical data. https://www.learnovita.com/data-analytics-certification-course-in-bangalore

Developers uniformly want to be "data-driven," but few truly are. This session will cover the building blocks of both basic and advanced analytics, leading the audience through the practical steps of instrumentation, uses in a complex organization and actionable insights. Topics will range from defining seemingly simple terminology to understanding tools like segmentation and cohort analysis, and parsing out the pros and cons of advanced machine-learning modeling techniques. The session will be led by Dmitri Williams, tenured professor at USC and current CEO, who has 15 years of experience in game behavior, big data and analysis. The techniques come out of projects with developers, and formulated from work done with the CIA, U.S. Army and National Science Foundation.

COVID-19 pandemic has received a serious attention from academia, industry and governments to stop the huge number of deaths and economic disruptions around the world. Many techniques have been used to control the spread of the pandemic by understanding its characteristics and behavior. However, because of the large amounts and complex characteristics of COVID-19 data, the querying and analysis of such data using conventional tools have become a challenging task. As a result, powerful and distributed tools are highly required for querying and analyzing this data effectively. In this paper, distributed system using Hive based on Hadoop cluster is used to query and analyze COVID-19 data to obtain meaningful information. Hadoop is employed as a scalable and reliable framework to accommodate such large amounts of data. Hive is used as a data warehouse that run on Hadoop cluster to perform querying and predictive analytics on huge COVID-19 datasets. Several experiments are performed to evaluate the performance of proposed system. Experiments show that the proposed system outperforms relational database management system (RDBMS) in terms of query processing time. Experiments also show that the proposed system has a better efficiency in terms of data load, I/O operation, reading and writing data. 

COVID-19 pandemic has received a serious attention from academia, industry and governments to stop the huge number of deaths and economic disruptions around the world. Many techniques have been used to control the spread of the pandemic by understanding its characteristics and behavior. However, because of the large amounts and complex characteristics of COVID-19 data, the querying and analysis of such data using conventional tools have become a challenging task. As a result, powerful and distributed tools are highly required for querying and analyzing this data effectively. In this paper, distributed system using Hive based on Hadoop cluster is used to query and analyze COVID-19 data to obtain meaningful information. Hadoop is employed as a scalable and reliable framework to accommodate such large amounts of data. Hive is used as a data warehouse that run on Hadoop cluster to perform querying and predictive analytics on huge COVID-19 datasets. Several experiments are performed to evaluate the performance of proposed system. Experiments show that the proposed system outperforms relational database management system (RDBMS) in terms of query processing time. Experiments also show that the proposed system has a better efficiency in terms of data load, I/O operation, reading and writing data. 

This research involves creating an efficient portfolio construction that aims to guide the retail investors about the significance of the data-driven decision-making using the analytical tool Python, especially for financial securities investments with a focus on mutual funds. A dataset comprising necessary information on nearly 625 mutual fund schemes from the dataset obtained from Kaggle has been utilized for analysis and study. The study focuses on applying the modern portfolio theory for portfolio construction proposed by Markowitz, which is a very popular financial theory, in real-world investment strategy in the case of constructing a mutual fund portfolio with an adjusted risk-return tradeoff. The methodology relies on the construction of a portfolio with modern portfolio theory concepts and predicting the possible outcomes of the portfolio with the Monte Carlo simulation technique by running the codes in Python and constructing two distinct portfolios: one with diversified and lower risk, comprising 15 mutual fund schemes for conservative investors, and another with minimum compromised risk, comprising 5 mutual fund schemes, which achieves a higher return than the previous one. The parameters taken for the choice of selecting the schemes from the data set are based on the renowned ones such as the Sharpe Ratio and Sortino Ratio. The findings reveal that the 15 schemes portfolio returns are in the range of 10% and 12% with risk levels between 1.5% and 2.5%, and the 5 schemes portfolio returns are in the range of 15% and 17% with risk levels between 4% and 4.5%. The optimum weights to be invested in each scheme to achieve maximum return at the lowest possible risk are also mentioned in proportion for both the portfolios. The findings can be interpreted in a way that the construction of a portfolio with rational decisions backed by data is more appropriate in the modern world with the availability of analytical tools such as Python for forecasting and predicting the potential return and constructing the portfolio based on that by minimizing risks with the traditional theories, which can be efficiently and easily used with technology.

This project aims to conduct a scoping review in accordance with the JBI methodology to map and evaluate the types, effectiveness, and implementation challenges of fall prevention and detection technologies in construction. The review will explore AI-powered systems, deep learning models, wearable sensors, and BIM-integrated hazard detection, with a focus on real-time safety applications. It will also examine key barriers, enablers, and performance outcomes such as model accuracy, site feasibility, and worker acceptance.

Perma.cc archive of https://weare4c.com/blog/2017-09-04-artificial-intelligence-and-predictive-analytics-in-sports-a-blessing-for-some-a-nightmare-for-others created on 2020-10-23 20:03:30+00:00.

Downloaded from Centers for Disease Control and Prevention (CDC) Youtube channel on 2025-02-01 20:26:24 https://youtube.com/watch?v=atElqLgJrLs -------- The September 2022 Preventive Medicine Grand Rounds (PMGR) features a presentation by Mr. Henry O’Connell, who presented how vocal biomarkers were used as a predictive analytics tool to assess both community health resource utilization and identification of at-risk populations. For continuing education (CE) credits, visit https://tceols.cdc.gov/; search for course WD4441-090722. CEs will be available after 10/10/2022 and expire 10/10/2024. Course access code: CDCPMRF

The integration of Artificial Intelligence (AI) in healthcare has ushered in a new era of predictive analytics for disease diagnosis and treatment. AI-driven healthcare predictive analytics leverages vast amounts of medical data, employing advanced machine learning and deep learning techniques to identify patterns and predict health outcomes. This approach enhances diagnostic accuracy, enables early detection of diseases, and personalizes treatment plans, thereby improving patient outcomes and optimizing healthcare resources. AI models can analyze diverse data sources, including electronic health records (EHRs), medical imaging, and genetic information, to provide comprehensive insights into patient health. Despite its potential, the implementation of AI in healthcare faces challenges such as data privacy concerns, the need for large, high-quality datasets, and the integration of AI systems into existing clinical workflows. This abstract reviews the current state of AI-driven healthcare predictive analytics, highlights key advancements, and discusses the challenges and future directions for the effective use of AI in disease diagnosis and treatment. By addressing these challenges, AI has the potential to revolutionize healthcare, making it more predictive, precise, and personalized. 

The integration of Artificial Intelligence (AI) in healthcare has ushered in a new era of predictive analytics for disease diagnosis and treatment. AI-driven healthcare predictive analytics leverages vast amounts of medical data, employing advanced machine learning and deep learning techniques to identify patterns and predict health outcomes. This approach enhances diagnostic accuracy, enables early detection of diseases, and personalizes treatment plans, thereby improving patient outcomes and optimizing healthcare resources. AI models can analyze diverse data sources, including electronic health records (EHRs), medical imaging, and genetic information, to provide comprehensive insights into patient health. Despite its potential, the implementation of AI in healthcare faces challenges such as data privacy concerns, the need for large, high-quality datasets, and the integration of AI systems into existing clinical workflows. This abstract reviews the current state of AI-driven healthcare predictive analytics, highlights key advancements, and discusses the challenges and future directions for the effective use of AI in disease diagnosis and treatment. By addressing these challenges, AI has the potential to revolutionize healthcare, making it more predictive, precise, and personalized. 

The integration of Artificial Intelligence (AI) in healthcare has ushered in a new era of predictive analytics for disease diagnosis and treatment. AI-driven healthcare predictive analytics leverages vast amounts of medical data, employing advanced machine learning and deep learning techniques to identify patterns and predict health outcomes. This approach enhances diagnostic accuracy, enables early detection of diseases, and personalizes treatment plans, thereby improving patient outcomes and optimizing healthcare resources. AI models can analyze diverse data sources, including electronic health records (EHRs), medical imaging, and genetic information, to provide comprehensive insights into patient health. Despite its potential, the implementation of AI in healthcare faces challenges such as data privacy concerns, the need for large, high-quality datasets, and the integration of AI systems into existing clinical workflows. This abstract reviews the current state of AI-driven healthcare predictive analytics, highlights key advancements, and discusses the challenges and future directions for the effective use of AI in disease diagnosis and treatment. By addressing these challenges, AI has the potential to revolutionize healthcare, making it more predictive, precise, and personalized. 

Das Ende des Zufalls •  Prognosen - Predictive Analytics •  Wissenschaftsdoku • ZDF 2015 •

This study examines how Hesitant Fuzzy Sets (HFS) and Machine Learning (ML) might improve healthcare predictive analytics. HFS, which accommodates uncertainty and hesitation in decision-making, is used to improve healthcare projections. Predictive analytics methods struggle with data ambiguity and imprecision, resulting in poor decision-making. Traditional ML algorithms may not be able to collect hesitant information, resulting in less accurate patient outcomes and treatment recommendations. The Integrating Hesitant Fuzzy Sets with ML (IHFS-ML) framework overcomes these issues by integrating HFS flexibility with advanced ML approaches. This connection allows the representation of ambiguous patient data for better healthcare analytics. Data pre-processing in the IHFS-ML framework improves healthcare analytics prediction. These methods transform uncertain fuzzy data into an ML-friendly format. Disease prediction, patient risk assessment, and therapeutic effectiveness analysis are recommended. The approach aims to improve healthcare decision-making and deliver new insights by merging hesitant and ambiguous information. IHFS-ML uses HFS to characterize imprecise and confusing patient data. These HFS are combined with powerful ML classifiers like Random Forest (RF) and Logistic Regression. The IHFS-ML system outperforms current prediction accuracy and reliability methods, suggesting it might transform healthcare analytics. HFS improves ML model interpretability, improving patient outcomes and healthcare decisions. Compared to other methods, the IHFS-ML model improves prediction analysis reliability by 99.7%, scalability by 97.6%, data pre-processing efficiency by 97.1%, interpretability by 98.9%, and accuracy by 97.8%.

This study examines how Hesitant Fuzzy Sets (HFS) and Machine Learning (ML) might improve healthcare predictive analytics. HFS, which accommodates uncertainty and hesitation in decision-making, is used to improve healthcare projections. Predictive analytics methods struggle with data ambiguity and imprecision, resulting in poor decision-making. Traditional ML algorithms may not be able to collect hesitant information, resulting in less accurate patient outcomes and treatment recommendations. The Integrating Hesitant Fuzzy Sets with ML (IHFS-ML) framework overcomes these issues by integrating HFS flexibility with advanced ML approaches. This connection allows the representation of ambiguous patient data for better healthcare analytics. Data pre-processing in the IHFS-ML framework improves healthcare analytics prediction. These methods transform uncertain fuzzy data into an ML-friendly format. Disease prediction, patient risk assessment, and therapeutic effectiveness analysis are recommended. The approach aims to improve healthcare decision-making and deliver new insights by merging hesitant and ambiguous information. IHFS-ML uses HFS to characterize imprecise and confusing patient data. These HFS are combined with powerful ML classifiers like Random Forest (RF) and Logistic Regression. The IHFS-ML system outperforms current prediction accuracy and reliability methods, suggesting it might transform healthcare analytics. HFS improves ML model interpretability, improving patient outcomes and healthcare decisions. Compared to other methods, the IHFS-ML model improves prediction analysis reliability by 99.7%, scalability by 97.6%, data pre-processing efficiency by 97.1%, interpretability by 98.9%, and accuracy by 97.8%.

This study examines how Hesitant Fuzzy Sets (HFS) and Machine Learning (ML) might improve healthcare predictive analytics. HFS, which accommodates uncertainty and hesitation in decision-making, is used to improve healthcare projections. Predictive analytics methods struggle with data ambiguity and imprecision, resulting in poor decision-making. Traditional ML algorithms may not be able to collect hesitant information, resulting in less accurate patient outcomes and treatment recommendations. The Integrating Hesitant Fuzzy Sets with ML (IHFS-ML) framework overcomes these issues by integrating HFS flexibility with advanced ML approaches. This connection allows the representation of ambiguous patient data for better healthcare analytics. Data pre-processing in the IHFS-ML framework improves healthcare analytics prediction. These methods transform uncertain fuzzy data into an ML-friendly format. Disease prediction, patient risk assessment, and therapeutic effectiveness analysis are recommended. The approach aims to improve healthcare decision-making and deliver new insights by merging hesitant and ambiguous information. IHFS-ML uses HFS to characterize imprecise and confusing patient data. These HFS are combined with powerful ML classifiers like Random Forest (RF) and Logistic Regression. The IHFS-ML system outperforms current prediction accuracy and reliability methods, suggesting it might transform healthcare analytics. HFS improves ML model interpretability, improving patient outcomes and healthcare decisions. Compared to other methods, the IHFS-ML model improves prediction analysis reliability by 99.7%, scalability by 97.6%, data pre-processing efficiency by 97.1%, interpretability by 98.9%, and accuracy by 97.8%.

The sheer scale of high-resolution raw data generated by simulation has motivated non-conventional approaches for data exploration referred as `immersive' and `in situ' query processing of the raw simulation data. Another step towards supporting scientific progress is to enable data-driven hypothesis management and predictive analytics out of simulation results. We present a synthesis method and tool for encoding and managing competing hypotheses as uncertain data in a probabilistic database that can be conditioned in the presence of observations.

Urban traffic congestion is a growing problem in cities, leading to notable delays, increased fuel consumption, and elevated air pollution levels. Effective traffic management is crucial for enhancing urban mobility and improving residents' quality of life. This paper presents a novel Internet of Things (IoT)-based predictive analytics framework that tackles challenges in traffic management. The method employs IoT sensors spread throughout the city, including real-time traffic cameras, vehicle counting equipment, and environmental monitors, to gather comprehensive data on traffic flow, speed, and density. We applied advanced machine learning techniques, particularly time series analysis and regression methods, to analyze the collected data and forecast future traffic conditions. Our model can pinpoint potential congestion hotspots by examining historical traffic trends in conjunction with real-time data and suggest optimal adjustments for traffic signals ahead of time. Testing our predictive analytics framework in a selected urban area showed an impressive 30% decrease in peak-hour congestion and a 20% enhancement in overall traffic flow. Furthermore, the analysis demonstrated a 15% reduction in average vehicle emissions throughout the trial period, underscoring the environmental advantages of the system. These results suggest that utilizing IoT technology alongside predictive analytics can enhance traffic management and support sustainable urban growth. By equipping city planners and traffic management agencies with practical insights, our research aids in the advancement of smarter cities capable of addressing the complexities of contemporary transportation issues. The findings of this study emphasize the possibility for wider implementation of IoT-driven solutions in urban planning, ultimately resulting in improved public safety, decreased environmental impact, and a better quality of life in urban areas.

Discover how predictive analytics is transforming emergency rooms by reducing wait times, optimizing resources, and improving patient outcomes.

Predictive Analytics Market

Artykuł w: Studia Iuridica Lublinensia Vol. 32, 2 (2023), s. 249-266 ; Tytuł równoległy: Profilowanie i analiza predykcyjna z wykorzystaniem zbiorów big data z perspektywy RODO ; Streszczenia w językach angielskim, polskim

Brian interviews Neil Seeman, Founder, and Chairman of RIWI Corp. Neil works on new product strategy, research and development, and special projects for RIWI. Neil invented RIWI's core intellectual property. He is the author or co-author of hundreds of articles in major media around the world, more than 25 peer-reviewed journal papers and several books and monographs. Prior to RIWI, he was Founder and Executive Director of the Innovation Cell. Neil's career began at a full-service Canadian law firm, later becoming a founding editorial board member of The National Post, and In-House Counsel on constitutional matters to the National Citizen's Coalition serving under former Prime Minister Stephen Harper.

In an era where data is often termed the "new oil," organizations are increasingly turning to predictive analytics to harness its potential. This powerful approach allows businesses to anticipate future trends and outcomes, enabling them to make informed decisions that can drive success. For more: https://www.learnovita.com/data-analytics-certification-course-in-bangalore

In order to stay competitive, retailers need to offer promotions and prices that are appealing to customers. 

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Gain unprecedented insights into how players interact within game-play in order to ultimately increase satisfaction, duration of game play and profits. With predictive analytics, game development and marketing team can analyze detailed in-game behavioral data and the personal attributes of customers to predict the lifetime value of each customer, segment customers accordingly, target them with focused marketing campaigns and quickly adjust the game experience to enhance loyalty. Gain a hands-on look at IBM SPSS Modeler, the easy-to-use, data mining and predictive modeling tool used to uncover hidden patterns and associations within the data to gain deep insight into player behavior... and predict what they'll do next.

Perma.cc archive of https://www.investopedia.com/terms/p/predictive-analytics.asp created on 2022-04-04 02:02:47.278349+00:00.

This study is a randomized controlled trial (RCT) designed to improve engagement in self-guided digital mental health interventions for displaced Ukrainian refugees. Using the EvolutionHealth.care platform, we will test the effectiveness of nudges, prompts, and gamification in increasing engagement. Participants will be randomly assigned to six experimental conditions, and engagement will be measured through click-through rates, session duration, and checklist completion. Findings will help develop AI-driven personalization models to optimize future digital mental health interventions. The study aims to create a scalable, culturally sensitive, and evidence-based digital health solution.

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Mark is joined by long-term industry veteran and friend Christian Berg to talk about surviving fifteen years as a contractor in analytics industry, changes he's seen in the market and in how project are approached, the value in getting involved in the community, and in a specially extended Christmas and New Year edition we look back at what was topical in 2017 and what are Christian's predictions for 2018 ... and appoint Christian as Head of our Best Practices Found on the Internet.

In an era of vast data availability, the ability to predict future trends accurately has become a critical advantage for businesses, policymakers, and researchers alike. This paper examines the predictive power inherent in data analytics and mining techniques and their applications in forecasting future trends across various domains. Through a comprehensive review of methodologies, case studies, and real-world applications, we explore how data analytics and mining enable the extraction of valuable insights from large datasets to anticipate trends in finance, healthcare, marketing, and beyond. We delve into the tools, and best practices employed in predictive modeling, emphasizing their role in enhancing decision-making processes and strategic planning.

The present systematic review aims to critically appraise studies that employed continuous, wearable-sensor monitoring—from admission through convalescence—to determine (i) which devices and algorithms have been tested, (ii) how sensor-derived activity or physiology relates to core clinical outcomes such as complications, length of stay and readmission, and (iii) what methodological gaps must be bridged before large-scale implementation and machine-learning-enabled early-warning systems become routine. By deliberately excluding the emerging but still limited body of wearables-centred RCTs and cohorts synthesised elsewhere, we aim to provide surgeons, nurses, physiotherapists and digital-health developers with an up-to-date roadmap for integrating objective mobility and vital-sign metrics into next-generation ERAS dash-boards, ultimately transforming postoperative care from reactive to proactive.

The present systematic review aims to critically appraise studies that employed continuous, wearable-sensor monitoring—from admission through convalescence—to determine (i) which devices and algorithms have been tested, (ii) how sensor-derived activity or physiology relates to core clinical outcomes such as complications, length of stay and readmission, and (iii) what methodological gaps must be bridged before large-scale implementation and machine-learning-enabled early-warning systems become routine. By deliberately excluding the emerging but still limited body of wearables-centred RCTs and cohorts synthesised elsewhere, we aim to provide surgeons, nurses, physiotherapists and digital-health developers with an up-to-date roadmap for integrating objective mobility and vital-sign metrics into next-generation ERAS dash-boards, ultimately transforming postoperative care from reactive to proactive.

In the era of digital transformation, supply chain management (SCM) is undergoing a profound shift driven by data science, predictive analytics, and big data. These technologies are not only enhancing operational efficiency but also redefining the strategic design and real-time control of complex supply networks. This paper explores their integration, applications, benefits, and future trends in supply chain management, emphasizing the transition from reactive to intelligent systems. As organizations strive for agility and resilience, data-driven insights enable faster decision-making, forecasting, optimization and proactive risk mitigation. The convergence of IoT, AI, and cloud computing further amplifies the potential for creating highly responsive and adaptive supply chains.

https://media.ccc.de/v/ds20-11319-from_data-mining_via_predictive_analytics_to_surveillance_captitalism Knowledge against your thoughts - a new to digital slavery? Der Kapitalismus, in dem wir leben, hält immer noch daran fest, unser Verlangen zu kontrollieren. Deshalb wird er untergehen, wenn er sich nicht ändert, sagt die amerikanische Ökonomin Shoshana Zuboff. Alles, was digitalisiert und in Information verwandelt werden kann, wird digitalisiert und in Information verwandelt. Zuboffs zweites Gesetz: Was automatisiert werden kann, wird automatisiert. Zuboffs drittes Gesetz: Jede Technologie, die zum Zwecke der Überwachung und Kontrolle kolonisiert werden kann, wird, was immer auch ihr ursprünglicher Zweck war, zum Zwecke der Überwachung und Kontrolle kolonisiert. Die Richtigkeit dieser 3 Gesetze zeigt sich in zunehmemdem Umfang. Unternehmen und Institutionen fischen in großem Maß Daten ab moderne KI errechnet Prognosen des zukünftigen Verhaltens anhand dieser Daten werden pausenlos überwacht und kontrolliert . 1984 war nur ein düsterer Zukunftsroman dies ist noch viel düstere Gegenwart. Die Überwachung ist subtil und verdeckt; sie ist eingebettet in Dinge, auf die wir tagein, tagaus angewiesen sind. Nur Experten, nur Informationswissenschaftler und Hacker begreifen noch, wie weit das alles fortgeschritten ist. Wir als Gesellschaft verstehen das nicht mehr. Die Infrastruktur für die Regelung der neuen Informationswege ist bis jetzt nur in kleinen Teilen vorhanden. Es gibt kein übergreifendes Konzept. Uns dämmert erst langsam, dass Einrichtungen, denen wir unser Vertrauen geschenkt und die wir als unsere Freunde angesehen haben, Facebook zum Beispiel oder Google, nicht nach einer neuen Logik handeln, sondern nach der altbekannten, die unseren Interessen zuwiderläuft. Welche Richtung die Informationstechnologie einschlägt, kommt darauf an, wie einige gesellschaftliche und ökonomische Kernfragen beantwortet werden. Zurzeit geschieht das ohne Regeln und Gesetze. Die Praxis trifft jetzt die Entscheidungen. Etwas geschieht, weil Facebook, weil Google, weil die Regierung der Vereinigten Staates es so wollen. Der rechtliche Rahmen fehlt. Ich bin weder Verschwörungstheoretiker noch Apokaplytiker, noch will ich Sie bekehren - doch lassen Sie mich Ihnen anhand von Fakten einige Denkanstösse geben. Wir haben ein institutionelles System aufgebaut, das perfekt auf die Erfordernisse der Massenproduktion und des Massenkonsums zugeschnitten ist und weit über die entsprechenden Firmen und Dienstleister hinausreicht. Die Logik der Massenproduktion wurde zur Grundlage unseres Erziehungssystems, unserer Krankenversorgung, aller Sphären unserer Gesellschaft. Seit dem letzten Jahrzehnt des 20. Jahrhunderts kommt es aber zu einer immer heftigeren Kollision zwischen dem neuen Bewusstsein, das ich psychologische Selbstbestimmung nenne, und einem Wirtschaftssystem, das auf große Handelsvolumen, geringe Produktkosten und Standardisierung angelegt ist, eigentlich nicht anders als zu Zeiten von Henry Ford. etzt aber sind wir auf dem Weg in eine Welt der dezentralisierten Wertschöpfung, des distributed capitalism. Das ist keine technologische Metapher. Die Dezentralisierung geht von den Individuen aus, die nunmehr die Quelle ökonomischer Werte sind. Individuen sind aber nicht innerhalb einer Organisation zu finden, sie treten nicht in konzentrierter Form auf, sie verteilen sich über ihre dezentralisierten Lebensräume. Folglich muss sich auch der Handel dezentralisieren, um in diesen Lebensräumen Wirkung zu zeigen. Heute haben wir erstmals eine technologische Infrastruktur, die ebenso dezentralisiert ist. Facebook schien einmal uns zu gehören. Es war unser Raum. Jetzt verstößt Facebook immer wieder gegen die ökonomische Logik des individuellen Raums, widersetzt sich unseren Interessen und zerstört unser Vertrauen. Google verhält sich nicht anders. Der Machtwille der Firma ist sichtbar geworden, auch ihre Manipulation von Algorithmen und ihre Bereitschaft zur Überwachung. Wir fühlen uns bloßgestellt, allein schon durch eine Google-Suche. Meine beiden Kinder haben Facebook innig geliebt. Heute rühren sie es nicht mehr an. Facebook, das ist für sie jetzt: die da. Und nicht mehr: wir. [email protected] https://datenspuren.de/2020/fahrplan/events/11319.html Source: https://www.youtube.com/watch?v=4qLzVUmWy4Y Uploader: media.ccc.de

https://media.ccc.de/v/ds20-11319-from_data-mining_via_predictive_analytics_to_surveillance_captitalism Knowledge against your thoughts - a new to digital slavery? Der Kapitalismus, in dem wir leben, hält immer noch daran fest, unser Verlangen zu kontrollieren. Deshalb wird er untergehen, wenn er sich nicht ändert, sagt die amerikanische Ökonomin Shoshana Zuboff. Alles, was digitalisiert und in Information verwandelt werden kann, wird digitalisiert und in Information verwandelt. Zuboffs zweites Gesetz: Was automatisiert werden kann, wird automatisiert. Zuboffs drittes Gesetz: Jede Technologie, die zum Zwecke der Überwachung und Kontrolle kolonisiert werden kann, wird, was immer auch ihr ursprünglicher Zweck war, zum Zwecke der Überwachung und Kontrolle kolonisiert. Die Richtigkeit dieser 3 Gesetze zeigt sich in zunehmemdem Umfang. Unternehmen und Institutionen fischen in großem Maß Daten ab moderne KI errechnet Prognosen des zukünftigen Verhaltens anhand dieser Daten werden pausenlos überwacht und kontrolliert . 1984 war nur ein düsterer Zukunftsroman dies ist noch viel düstere Gegenwart. Die Überwachung ist subtil und verdeckt; sie ist eingebettet in Dinge, auf die wir tagein, tagaus angewiesen sind. Nur Experten, nur Informationswissenschaftler und Hacker begreifen noch, wie weit das alles fortgeschritten ist. Wir als Gesellschaft verstehen das nicht mehr. Die Infrastruktur für die Regelung der neuen Informationswege ist bis jetzt nur in kleinen Teilen vorhanden. Es gibt kein übergreifendes Konzept. Uns dämmert erst langsam, dass Einrichtungen, denen wir unser Vertrauen geschenkt und die wir als unsere Freunde angesehen haben, Facebook zum Beispiel oder Google, nicht nach einer neuen Logik handeln, sondern nach der altbekannten, die unseren Interessen zuwiderläuft. Welche Richtung die Informationstechnologie einschlägt, kommt darauf an, wie einige gesellschaftliche und ökonomische Kernfragen beantwortet werden. Zurzeit geschieht das ohne Regeln und Gesetze. Die Praxis trifft jetzt die Entscheidungen. Etwas geschieht, weil Facebook, weil Google, weil die Regierung der Vereinigten Staates es so wollen. Der rechtliche Rahmen fehlt. Ich bin weder Verschwörungstheoretiker noch Apokaplytiker, noch will ich Sie bekehren - doch lassen Sie mich Ihnen anhand von Fakten einige Denkanstösse geben. Wir haben ein institutionelles System aufgebaut, das perfekt auf die Erfordernisse der Massenproduktion und des Massenkonsums zugeschnitten ist und weit über die entsprechenden Firmen und Dienstleister hinausreicht. Die Logik der Massenproduktion wurde zur Grundlage unseres Erziehungssystems, unserer Krankenversorgung, aller Sphären unserer Gesellschaft. Seit dem letzten Jahrzehnt des 20. Jahrhunderts kommt es aber zu einer immer heftigeren Kollision zwischen dem neuen Bewusstsein, das ich psychologische Selbstbestimmung nenne, und einem Wirtschaftssystem, das auf große Handelsvolumen, geringe Produktkosten und Standardisierung angelegt ist, eigentlich nicht anders als zu Zeiten von Henry Ford. etzt aber sind wir auf dem Weg in eine Welt der dezentralisierten Wertschöpfung, des distributed capitalism. Das ist keine technologische Metapher. Die Dezentralisierung geht von den Individuen aus, die nunmehr die Quelle ökonomischer Werte sind. Individuen sind aber nicht innerhalb einer Organisation zu finden, sie treten nicht in konzentrierter Form auf, sie verteilen sich über ihre dezentralisierten Lebensräume. Folglich muss sich auch der Handel dezentralisieren, um in diesen Lebensräumen Wirkung zu zeigen. Heute haben wir erstmals eine technologische Infrastruktur, die ebenso dezentralisiert ist. Facebook schien einmal uns zu gehören. Es war unser Raum. Jetzt verstößt Facebook immer wieder gegen die ökonomische Logik des individuellen Raums, widersetzt sich unseren Interessen und zerstört unser Vertrauen. Google verhält sich nicht anders. Der Machtwille der Firma ist sichtbar geworden, auch ihre Manipulation von Algorithmen und ihre Bereitschaft zur Überwachung. Wir fühlen uns bloßgestellt, allein schon durch eine Google-Suche. Meine beiden Kinder haben Facebook innig geliebt. Heute rühren sie es nicht mehr an. Facebook, das ist für sie jetzt: die da. Und nicht mehr: wir. [email protected] https://datenspuren.de/2020/fahrplan/events/11319.html Source: https://www.youtube.com/watch?v=4qLzVUmWy4Y Uploader: media.ccc.de

Personalized medicine, driven by advancements in computational methods and predictive analytics, has emerged as a revolutionary approach to healthcare. This review provides an in depth exploration of the foundations, key principles, and computational techniques that underpin personalized medicine. It highlights the significance of personalized medicine in tailoring treatments to individual patients, optimizing healthcare outcomes, and enhancing the quality of care. Additionally, this review discusses the challenges and future prospects of personalized medicine in the context of predictive analytics, offering insights into the evolving landscape of healthcare. Transitioning from the foundational understanding of personalized medicine, we delve into the pivotal role of predictive analytics within this paradigm. Predictive analytics, a branch of data science, is the driving force behind the precision and individualization inherent in personalized medicine. It harnesses the power of advanced computational methods and algorithms to process vast datasets and generate predictions about future events, in this case, patient outcomes and treatment responses. Snowza Chrysolite. D "Personalized Medicine and Predictive Analytics: A Review of Computational Methods" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-7 | Issue-5 , October 2023, URL: https://www.ijtsrd.com/papers/ijtsrd59982.pdf Paper Url: https://www.ijtsrd.com/medicine/other/59982/personalized-medicine-and-predictive-analytics-a-review-of-computational-methods/snowza-chrysolite-d

The Emergency Department (ED) simulation project was established to demonstrate how requirements-driven analysis and process simulation can help improve the quality of patient care for the Veterans Health Administration's (VHA) Veterans Affairs Medical Centers (VAMC). This project developed a web-based simulation prototype of patient flow in EDs, validated the performance of the simulation against operational data, and documented IT requirements for the ED simulation.

Learning Analytics (LA) captures the digital footprint of students' online learning activity. This study describes students' navigational behavior in an e-learning setting by processing the LA data obtained from Blackboard LMS. This is an attempt to understand the navigational behavior of students and the relationship with learning performance. The study was carried out with 88 learners from a Malaysian private university. The course sites' log data and students' performance were analyzed, and the results were as follows: 4 navigational behaviors played an important role in student's academic performance which are active days, total learning time, number of views, and days delayed in accessing the assessment. Active learning from Tuesdays to Thursdays had a significant positive effect on performance. It was found that the higher activities (total learning time, number of journals viewing) translate to better performance. Days delayed in attempting assessments had a significant but mixed effect on performance, depending on the type of assessment. However, the number of logins is insignificant. The findings of this study provide empirical evidence of the importance of self-discipline in online learning and provide instructors with a predictive measure as a call for early intervention to help online students. [For the full proceedings, see ED654100.]

In this paper, we combine ideas from machine learning (ML) and operations research and management science (OR/MS) in developing a framework, along with specific methods, for using data to prescribe decisions in OR/MS problems. In a departure from other work on data-driven optimization and reflecting our practical experience with the data available in applications of OR/MS, we consider data consisting, not only of observations of quantities with direct effect on costs/revenues, such as demand or returns, but predominantly of observations of associated auxiliary quantities. The main problem of interest is a conditional stochastic optimization problem, given imperfect observations, where the joint probability distributions that specify the problem are unknown. We demonstrate that our proposed solution methods are generally applicable to a wide range of decision problems. We prove that they are computationally tractable and asymptotically optimal under mild conditions even when data is not independent and identically distributed (iid) and even for censored observations. As an analogue to the coefficient of determination $R^2$, we develop a metric $P$ termed the coefficient of prescriptiveness to measure the prescriptive content of data and the efficacy of a policy from an operations perspective. To demonstrate the power of our approach in a real-world setting we study an inventory management problem faced by the distribution arm of an international media conglomerate, which ships an average of 1 billion units per year. We leverage both internal data and public online data harvested from IMDb, Rotten Tomatoes, and Google to prescribe operational decisions that outperform baseline measures. Specifically, the data we collect, leveraged by our methods, accounts for an 88% improvement as measured by our coefficient of prescriptiveness.

It is commonly believed that increasing the interpretability of a machine learning model may decrease its predictive power. However, inspecting input-output relationships of those models using visual analytics, while treating them as black-box, can help to understand the reasoning behind outcomes without sacrificing predictive quality. We identify a space of possible solutions and provide two examples of where such techniques have been successfully used in practice.

Downloaded from Centers for Disease Control and Prevention (CDC) Youtube channel on 2025-02-01 20:25:36 https://youtube.com/watch?v=xj2WVO4_caU -------- The September 2022 Preventive Medicine Grand Rounds (PMGR) features a presentation by Mr. Henry O’Connell, who presented how vocal biomarkers were used as a predictive analytics tool to assess both community health resource utilization and identification of at-risk populations. For continuing education (CE) credits, visit https://tceols.cdc.gov/; search for course WD4441-090722. CEs will be available after 10/10/2022 and expire 10/10/2024. Course access code: CDCPMRF Audio Description Video: https://www.youtube.com/watch?v=atElqLgJrLs This video can also be viewed at https://www.cdc.gov/prevmed/videos/pmgr-oconnell-09-07-2022-lowres.mp4

The open-source Global Database of Events, Language, and Tone (GDELT) is the most comprehensive and updated Big Data source of important terms extracted from international news articles . We focus only on GDELT's Singapore events to better understand the data quality of its news articles, accuracy of its term extraction, and potential for prediction. To test news completeness and validity, we visually compared GDELT (Singapore news articles' terms from 1979 to 2013) to Wikipedia's timeline of Singaporean history. To test term extraction accuracy, we visually compared GDELT (CAMEO codes and TABARI system of extraction from Singapore news articles' text from April to December 2013) to SAS Text Miner's term and topic extraction. To perform predictive analytics, we propose a novel feature engineering method to transform row-level GDELT from articles to a user-specified temporal resolution. For example, we apply a decision tree using daily counts of feature values from GDELT to predict Singapore stock market's Straits Times Index (^STI). Of practical interest from the above results is SAS Visual Analytics' ability to highlight the various impacts of June 2013 Southeast Asian haze and December 2013 Little India riot on Singapore. Although Singapore is unique as a sovereign city-state, a leading financial centre, has strong international influence, and consists of a highly multi-cultural population, the visual and predictive analytics reported here are highly applicable to another country's GDELT data.