International System for Total Early Disease Detection (InSTEDD) Platform
Taha A. Kass-Hout, M.D., M.S., Nicolas di Tada
InSTEDD, Palo Alto, California
Biosurveillance: Machine Learning And Disease Surveillance by Kass-Hout Di TadaTaha Kass-Hout, MD, MS
The majority of the designs, analyses and evaluations of early detection (or biosurveillance) systems have been geared towards specific data sources and detection algorithms. Much less effort has been focused on how these systems will "interact" with humans. For example, consider multiple domain experts working at different levels across different organizations in an environment where numerous biosurveillance algorithms may provide contradictory interpretations of ongoing events. We present a framework that consists of a collection of autonomous, machine learning-enabled analytic processes, services and tools that; for the first time, will seamlessly integrate surveillance and response systems with human experts.
The document discusses approaches for modern disease surveillance using collaboration and semantic web technologies. It describes how tools like InSTEDD Evolve use machine learning, social media, and geospatial data to improve early detection of disease outbreaks and facilitate effective coordination of public health responses. Key components of the proposed approach include automated analysis, user feedback loops, and representation of unstructured data to enable early detection and verification of health-related events.
Riff: A Social Network and Collaborative Platform for Public Health Disease S...Taha Kass-Hout, MD, MS
A hybrid (event-based and indicator-based) platform designed to streamline the collaboration between domain experts and machine learning algorithms for detection, prediction and response to health-related events (such as disease outbreaks or pandemics). The platform helps synthesize health-related event indicators from a wide variety of information sources (structured and unstructured) into a consolidated picture for analysis, maintenance of “community-wide coherence”, and collaboration processes. The platform offers features to detect anomalies, visualize clusters of potential events, predict the rate and spread of a disease outbreak and provide decision makers with tools, methodologies and processes to investigate the event.
Overview of Library & Systematic Review (LASYR) Infrastructure for Blockchain and Emerging Technologies project at IEEE Healthcare: Blockchain & AI event - 07 April 2021
This document discusses data management requirements for predictive modeling using large datasets from multiple clinical, specimen, and lab repositories. It notes the need to assemble complete and up-to-date datasets while maintaining quality assurance and transparency. Over time, data storage systems experience problems with exponential data growth, manual data curation difficulties, and challenges integrating heterogeneous databases across different research groups. The document examines a spectrum of potential data management approaches and highlights collaborative networks and use of open source platforms as ways to address these issues.
Why is the NIH investing $100M at the intersection of data science and health research? The NIH seeks to invest in ways to help researchers easily find, access, analyze, and curate research data. Researchers want visual analytics, and to build the database into a “social network” – being able to “friend” or “like” the data.
Biosurveillance: Machine Learning And Disease Surveillance by Kass-Hout Di TadaTaha Kass-Hout, MD, MS
The majority of the designs, analyses and evaluations of early detection (or biosurveillance) systems have been geared towards specific data sources and detection algorithms. Much less effort has been focused on how these systems will "interact" with humans. For example, consider multiple domain experts working at different levels across different organizations in an environment where numerous biosurveillance algorithms may provide contradictory interpretations of ongoing events. We present a framework that consists of a collection of autonomous, machine learning-enabled analytic processes, services and tools that; for the first time, will seamlessly integrate surveillance and response systems with human experts.
The document discusses approaches for modern disease surveillance using collaboration and semantic web technologies. It describes how tools like InSTEDD Evolve use machine learning, social media, and geospatial data to improve early detection of disease outbreaks and facilitate effective coordination of public health responses. Key components of the proposed approach include automated analysis, user feedback loops, and representation of unstructured data to enable early detection and verification of health-related events.
Riff: A Social Network and Collaborative Platform for Public Health Disease S...Taha Kass-Hout, MD, MS
A hybrid (event-based and indicator-based) platform designed to streamline the collaboration between domain experts and machine learning algorithms for detection, prediction and response to health-related events (such as disease outbreaks or pandemics). The platform helps synthesize health-related event indicators from a wide variety of information sources (structured and unstructured) into a consolidated picture for analysis, maintenance of “community-wide coherence”, and collaboration processes. The platform offers features to detect anomalies, visualize clusters of potential events, predict the rate and spread of a disease outbreak and provide decision makers with tools, methodologies and processes to investigate the event.
Overview of Library & Systematic Review (LASYR) Infrastructure for Blockchain and Emerging Technologies project at IEEE Healthcare: Blockchain & AI event - 07 April 2021
This document discusses data management requirements for predictive modeling using large datasets from multiple clinical, specimen, and lab repositories. It notes the need to assemble complete and up-to-date datasets while maintaining quality assurance and transparency. Over time, data storage systems experience problems with exponential data growth, manual data curation difficulties, and challenges integrating heterogeneous databases across different research groups. The document examines a spectrum of potential data management approaches and highlights collaborative networks and use of open source platforms as ways to address these issues.
Why is the NIH investing $100M at the intersection of data science and health research? The NIH seeks to invest in ways to help researchers easily find, access, analyze, and curate research data. Researchers want visual analytics, and to build the database into a “social network” – being able to “friend” or “like” the data.
InSTEDD: Integrated Global Early Warning and Response SystemInSTEDD
The document describes an integrated global early warning and response system developed by InSTEDD to streamline collaboration between domain experts and machine learning algorithms for detecting, predicting, and responding to health events. The system synthesizes health indicators from various structured and unstructured sources for analysis and visualization of potential outbreak clusters to aid decision making. It is currently being piloted in Southeast Asia to detect diseases, predict outbreak spread, and provide response tools.
The document describes a study that compared manual and computational thematic analyses of online comments about vaccine hesitancy conducted by teams of public health researchers. The researchers provided one team traditional tools for their analysis and the other team used the Computational Thematic Analysis Toolkit. Both teams independently analyzed the same large dataset of over 600,000 online comments. The researchers then compared the processes and results of the two analyses. They found that while the teams followed different processes, their analyses produced similar overlapping themes. The toolkit enabled researchers without programming skills to conduct computational analysis and facilitated working with large datasets, but also influenced their research process.
RIFF - A Social Network and Collaborative Platform For Public Health Disease ...InSTEDD
The document discusses public health disease surveillance and syndromic surveillance. It describes how public health surveillance involves ongoing collection and analysis of health data to support public health programs and prevention/control efforts. Syndromic surveillance monitors pre-diagnostic health data to identify potential cases/outbreaks requiring a public health response. The document advocates adopting a social and collaborative decision-making approach to facilitate early identification and assessment of potential health threats in order to recommend control measures.
Comparative study of decision tree algorithm and naive bayes classifier for s...eSAT Journals
Abstract The modeling and analysis of the epidemic disease outbreaks in huge realistic populations is a data intensive task that requires immense computational resources. Such effective computational support becomes useful to study disease outbreak and to facilitate decision making. Epidemiology is one of the traditional approaches which are being used for studying and analyzing the outbreaks of epidemic diseases. Although useful for obtaining numbers of sick, infected and recovered individuals in a population, this traditional approach does not capture the complexity of human interactions. The model is only limited to the person to person interaction in order to track the surveillance of disease and it also having performance issues with large realistic data. In this paper we propose, the combination of computational epidemiology and modern data mining techniques with their comparative analysis for the Swine Flu prediction. The clustering algorithm K mean is used to make a group or cluster of Swine Flu suspects in a particular area. The Decision tree algorithm and Naive Bayes classifier are applied on the same inputs to find out the actual count of suspects and predict the possible surveillance of a Swine Flu in a nearby area from suspected area. The performances of these techniques are compared, based on accuracy. In our case the Naive Bayes classifier performs better than decision tree algorithm while finding the accurate count of suspects. Keywords: Computational Epidemiology, Aerosol-borne Disease, Clustering, Predictive analysis.
Towards Decision Support and Goal AchievementIdentifying Ac.docxturveycharlyn
Towards Decision Support and Goal Achievement:
Identifying Action-Outcome Relationships From Social
Media
Emre Kıcıman
Microsoft Research
[email protected]
Matthew Richardson
Microsoft Research
[email protected]
ABSTRACT
Every day, people take actions, trying to achieve their per-
sonal, high-order goals. People decide what actions to take
based on their personal experience, knowledge and gut in-
stinct. While this leads to positive outcomes for some peo-
ple, many others do not have the necessary experience, knowl-
edge and instinct to make good decisions. What if, rather
than making decisions based solely on their own personal
experience, people could take advantage of the reported ex-
periences of hundreds of millions of other people?
In this paper, we investigate the feasibility of mining the
relationship between actions and their outcomes from the
aggregated timelines of individuals posting experiential mi-
croblog reports. Our contributions include an architecture
for extracting action-outcome relationships from social me-
dia data, techniques for identifying experiential social media
messages and converting them to event timelines, and an
analysis and evaluation of action-outcome extraction in case
studies.
1. INTRODUCTION
While current structured knowledge bases (e.g., Freebase)
contain a sizeable collection of information about entities,
from celebrities and locations to concepts and common ob-
jects, there is a class of knowledge that has minimal cov-
erage: actions. Simple information about common actions,
such as the effect of eating pasta before running a marathon,
or the consequences of adopting a puppy, are missing. While
some of this information may be found within the free text of
Wikipedia articles, the lack of a structured or semi-structured
representation make it largely unavailable for computational
usage. With computing devices continuing to become more
embedded in our everyday lives, and mediating an increasing
degree of our interactions with both the digital and physical
world, knowledge bases that can enable our computing de-
vices to represent and evaluate actions and their likely out-
comes can help individuals reason about actions and their
Permission to make digital or hard copies of all or part of this work for personal or
classroom use is granted without fee provided that copies are not made or distributed
for profit or commercial advantage and that copies bear this notice and the full citation
on the first page. Copyrights for components of this work owned by others than the
author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or
republish, to post on servers or to redistribute to lists, requires prior specific permission
and/or a fee. Request permissions from [email protected]
KDD’15, August 10-13, 2015, Sydney, NSW, Australia.
Copyright is held by the owner/author(s). Publication rights licensed to ACM.
ACM 978-1-4503-3664-2/15/08 ...$15.00.
DOI: http://dx.doi.org/10.1145 ...
A Systems Approach To Qualitative Data Management And AnalysisMichele Thomas
This article proposes a systematic approach to qualitative data management centered around a database with four main elements: (1) characteristics of data sources, (2) primary data collected from sources, (3) secondary data generated to assist interpretation, and (4) characteristics of coders. The approach tracks the analysis process from framing a research question to developing an empirical answer. It emphasizes distinguishing different types of data, tracking relationships between elements, and supporting reliability assessments to facilitate efficient and valid analysis.
Supervised Multi Attribute Gene Manipulation For Cancerpaperpublications3
Abstract: Data mining, the extraction of hidden predictive information from large databases, is a powerful new technology with great potential to help companies focus on the most important information in their data warehouses. Data mining tools predict future trends and behaviours, allowing businesses to make proactive, knowledge-driven decisions. The automated, prospective analyses offered by data mining move beyond the analyses of past events provided by retrospective tools typical of decision support systems.
They scour databases for hidden patterns, finding predictive information that experts may miss because it lies outside their expectations. Data mining techniques are the result of a long process of research and product development. This evolution began when business data was first stored on computers, continued with improvements in data access, and more recently, generated technologies that allow users to navigate through their data in real time. Data mining takes this evolutionary process beyond retrospective data access and navigation to prospective and proactive information delivery.
An Analysis of Outlier Detection through clustering methodIJAEMSJORNAL
This research paper deals with an outlier which is known as an unusual behavior of any substance present in the spot. This is a detection process that can be employed for both anomaly detection and abnormal observation. This can be obtained through other members who belong to that data set. The deviation present in the outlier process can be attained by measuring certain terms like range, size, activity, etc. By detecting outlier one can easily reject the negativity present in the field. For instance, in healthcare, the health condition of a person can be determined through his latest health report or his regular activity. When found the person being inactive there may be a chance for that person to be sick. Many approaches have been used in this research paper for detecting outliers. The approaches used in this research are 1) Centroid based approach based on K-Means and Hierarchical Clustering algorithm and 2) through Clustering based approach. This approach may help in detecting outlier by grouping all similar elements in the same group. For grouping, the elements clustering method paves a way for it. This research paper will be based on the above mentioned 2 approaches.
This document discusses challenges and potential solutions for improving data sharing in neuroscience. It notes that while there is a large amount of neuroscience data, it is unevenly distributed across repositories and databases. The document proposes creating a distributed "data sharing ecosystem" where data and related metadata are systematically tracked, linked and made available. Key elements would include unique IDs for all data objects, logging all activities, and developing accountability scores and influence measures to promote better data citizenship. However, concerns are raised about monitoring researchers and potential biases, which would need to be addressed for such a system to work.
Social networking sites are source of information for event detection, with specific reference of the road traffic activity
blockage and accidents or earth-quack sensing system. During this paper, we have a tendency to present a time period
observation system supposed for traffic occasion detection coming back from social media stream analysis. The system
fetches tweets coming back from social media/network as per a many search criteria; ways tweets/posts, by applying matter
content mining methods; last however not least works the classification of social networks posts. The goal is to assign
appropriate category packaging to each posts, as a result of connected with Associate in Nursing activity of traffic event or
maybe not. The traffic recognition system or framework was utilized for time period observation of varied areas of the road
network, taking into consideration detection of traffic occasions simply virtually in actual time, frequently before on-line
traffic news sites. All people utilized the support vector machine sort of a classification unit; what is more, we tend to
accomplish a good accuracy price of 95.76% by making an attempt a binary classification problem. All people were
conjointly capable to discriminate if traffic is triggered by Associate in nursing external celebration or not, by partitioning a
multi category classification issue and getting accuracy price of 88.89.
Infrastructures Supporting Inter-disciplinary Research - Exemplars from the UK NeISSProject
Infrastructures Supporting Inter-disciplinary Research - Exemplars from the UK . Talk given by Richard Sinnott at Urban Research Infrastructure Network Workshops, Melbourne, Brisbane, Sydney, September 2010.
Meliorating usable document density for online event detectionIJICTJOURNAL
Online event detection (OED) has seen a rise in the research community as it can provide quick identification of possible events happening at times in the world. Through these systems, potential events can be indicated well before they are reported by the news media, by grouping similar documents shared over social media by users. Most OED systems use textual similarities for this purpose. Similar documents, that may indicate a potential event, are further strengthened by the replies made by other users, thereby improving the potentiality of the group. However, these documents are at times unusable as independent documents, as they may replace previously appeared noun phrases with pronouns, leading OED systems to fail while grouping these replies to their suitable clusters. In this paper, a pronoun resolution system that tries to replace pronouns with relevant nouns over social media data is proposed. Results show significant improvement in performance using the proposed system.
The document discusses using WEKA and BioWeka to analyze DNA sequences and perform pattern matching. It summarizes how Eclat filtering and EM clustering are applied to a dataset containing DNA sequences from human and chimpanzee chromosomes. Eclat is used to extract codon frequencies as features, while EM clustering assigns sequences to clusters based on the mixture model with the highest posterior probability. The analysis aims to identify biologically relevant groups of genes and determine chromosomal similarities between humans and chimpanzees.
The document discusses collaboration in disease surveillance and response. It describes InSTEDD's hybrid approach to disease surveillance which combines various data sources to identify health risks. It also discusses tools developed by InSTEDD like GeoChat and Mesh4x that enable real-time information sharing and collaboration between organizations responding to disease outbreaks. The document emphasizes that collaboration is critical for effective outbreak containment and humanitarian response.
InSTEDD: Collaboration in Disease Surveillance & ResponseInSTEDD
The document discusses collaboration in disease surveillance and response. It describes InSTEDD's hybrid approach to disease surveillance which combines various data sources to identify health risks. It also discusses tools developed by InSTEDD like GeoChat and Mesh4x that enable real-time information sharing and collaboration between organizations responding to disease outbreaks. The document emphasizes that collaboration is critical for effective outbreak containment and humanitarian response.
Dynamic Rule Base Construction and Maintenance Scheme for Disease Predictionijsrd.com
Business and healthcare application are tuned to automatically detect and react events generated from local are remote sources. Event detection refers to an action taken to an activity. The association rule mining techniques are used to detect activities from data sets. Events are divided into 2 types' external event and internal event. External events are generated under the remote machines and deliver data across distributed systems. Internal events are delivered and derived by the system itself. The gap between the actual event and event notification should be minimized. Event derivation should also scale for a large number of complex rules. Attacks and its severity are identified from event derivation systems. Transactional databases and external data sources are used in the event detection process. The new event discovery process is designed to support uncertain data environment. Uncertain derivation of events is performed on uncertain data values. Relevance estimation is a more challenging task under uncertain event analysis. Selectability and sampling mechanism are used to improve the derivation accuracy. Selectability filters events that are irrelevant to derivation by some rules. Selectability algorithm is applied to extract new event derivation. A Bayesian network representation is used to derive new events given the arrival of an uncertain event and to compute its probability. A sampling algorithm is used for efficient approximation of new event derivation. Medical decision support system is designed with event detection model. The system adopts the new rule mapping mechanism for the disease analysis. The rule base construction and maintenance operations are handled by the system. Rule probability estimation is carried out using the Apriori algorithm. The rule derivation process is optimized for domain specific model.
This document discusses data mining algorithms for clustering healthcare data streams. It provides an overview of the K-means and D-stream algorithms, and proposes a framework for comparing them on healthcare datasets. The framework involves feature extraction from physiological signals, calculating risk components, and applying the K-means and D-stream algorithms to cluster the data. The results would show the effectiveness and limitations of each algorithm for clustering streaming healthcare data.
The new Pandemic Preparedness Citizen's Guide, edited by Sarah Booth, Kelsey Hills-Evans & Scott Teesdale to incorporate information around the recent COVID-19 pandemic.
Disease Reporting Hotline Launches to Stop Outbreaks in Cambodia InSTEDD
To improve disease reporting in Cambodia, the iLab Southeast Asia, in partnership with the Cambodian CDC and Skoll Global Threats Fund, launched a free to the public disease hotline built with InSTEDD's interactive voice response tool, Verboice.
Cambodia is in a 'hot zone region', susceptible to deadly disease spread. Timely reports from Health Centers across the country are critical to stopping outbreaks.
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Similar to International system for total early disease detection (in stedd) platform
InSTEDD: Integrated Global Early Warning and Response SystemInSTEDD
The document describes an integrated global early warning and response system developed by InSTEDD to streamline collaboration between domain experts and machine learning algorithms for detecting, predicting, and responding to health events. The system synthesizes health indicators from various structured and unstructured sources for analysis and visualization of potential outbreak clusters to aid decision making. It is currently being piloted in Southeast Asia to detect diseases, predict outbreak spread, and provide response tools.
The document describes a study that compared manual and computational thematic analyses of online comments about vaccine hesitancy conducted by teams of public health researchers. The researchers provided one team traditional tools for their analysis and the other team used the Computational Thematic Analysis Toolkit. Both teams independently analyzed the same large dataset of over 600,000 online comments. The researchers then compared the processes and results of the two analyses. They found that while the teams followed different processes, their analyses produced similar overlapping themes. The toolkit enabled researchers without programming skills to conduct computational analysis and facilitated working with large datasets, but also influenced their research process.
RIFF - A Social Network and Collaborative Platform For Public Health Disease ...InSTEDD
The document discusses public health disease surveillance and syndromic surveillance. It describes how public health surveillance involves ongoing collection and analysis of health data to support public health programs and prevention/control efforts. Syndromic surveillance monitors pre-diagnostic health data to identify potential cases/outbreaks requiring a public health response. The document advocates adopting a social and collaborative decision-making approach to facilitate early identification and assessment of potential health threats in order to recommend control measures.
Comparative study of decision tree algorithm and naive bayes classifier for s...eSAT Journals
Abstract The modeling and analysis of the epidemic disease outbreaks in huge realistic populations is a data intensive task that requires immense computational resources. Such effective computational support becomes useful to study disease outbreak and to facilitate decision making. Epidemiology is one of the traditional approaches which are being used for studying and analyzing the outbreaks of epidemic diseases. Although useful for obtaining numbers of sick, infected and recovered individuals in a population, this traditional approach does not capture the complexity of human interactions. The model is only limited to the person to person interaction in order to track the surveillance of disease and it also having performance issues with large realistic data. In this paper we propose, the combination of computational epidemiology and modern data mining techniques with their comparative analysis for the Swine Flu prediction. The clustering algorithm K mean is used to make a group or cluster of Swine Flu suspects in a particular area. The Decision tree algorithm and Naive Bayes classifier are applied on the same inputs to find out the actual count of suspects and predict the possible surveillance of a Swine Flu in a nearby area from suspected area. The performances of these techniques are compared, based on accuracy. In our case the Naive Bayes classifier performs better than decision tree algorithm while finding the accurate count of suspects. Keywords: Computational Epidemiology, Aerosol-borne Disease, Clustering, Predictive analysis.
Towards Decision Support and Goal AchievementIdentifying Ac.docxturveycharlyn
Towards Decision Support and Goal Achievement:
Identifying Action-Outcome Relationships From Social
Media
Emre Kıcıman
Microsoft Research
[email protected]
Matthew Richardson
Microsoft Research
[email protected]
ABSTRACT
Every day, people take actions, trying to achieve their per-
sonal, high-order goals. People decide what actions to take
based on their personal experience, knowledge and gut in-
stinct. While this leads to positive outcomes for some peo-
ple, many others do not have the necessary experience, knowl-
edge and instinct to make good decisions. What if, rather
than making decisions based solely on their own personal
experience, people could take advantage of the reported ex-
periences of hundreds of millions of other people?
In this paper, we investigate the feasibility of mining the
relationship between actions and their outcomes from the
aggregated timelines of individuals posting experiential mi-
croblog reports. Our contributions include an architecture
for extracting action-outcome relationships from social me-
dia data, techniques for identifying experiential social media
messages and converting them to event timelines, and an
analysis and evaluation of action-outcome extraction in case
studies.
1. INTRODUCTION
While current structured knowledge bases (e.g., Freebase)
contain a sizeable collection of information about entities,
from celebrities and locations to concepts and common ob-
jects, there is a class of knowledge that has minimal cov-
erage: actions. Simple information about common actions,
such as the effect of eating pasta before running a marathon,
or the consequences of adopting a puppy, are missing. While
some of this information may be found within the free text of
Wikipedia articles, the lack of a structured or semi-structured
representation make it largely unavailable for computational
usage. With computing devices continuing to become more
embedded in our everyday lives, and mediating an increasing
degree of our interactions with both the digital and physical
world, knowledge bases that can enable our computing de-
vices to represent and evaluate actions and their likely out-
comes can help individuals reason about actions and their
Permission to make digital or hard copies of all or part of this work for personal or
classroom use is granted without fee provided that copies are not made or distributed
for profit or commercial advantage and that copies bear this notice and the full citation
on the first page. Copyrights for components of this work owned by others than the
author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or
republish, to post on servers or to redistribute to lists, requires prior specific permission
and/or a fee. Request permissions from [email protected]
KDD’15, August 10-13, 2015, Sydney, NSW, Australia.
Copyright is held by the owner/author(s). Publication rights licensed to ACM.
ACM 978-1-4503-3664-2/15/08 ...$15.00.
DOI: http://dx.doi.org/10.1145 ...
A Systems Approach To Qualitative Data Management And AnalysisMichele Thomas
This article proposes a systematic approach to qualitative data management centered around a database with four main elements: (1) characteristics of data sources, (2) primary data collected from sources, (3) secondary data generated to assist interpretation, and (4) characteristics of coders. The approach tracks the analysis process from framing a research question to developing an empirical answer. It emphasizes distinguishing different types of data, tracking relationships between elements, and supporting reliability assessments to facilitate efficient and valid analysis.
Supervised Multi Attribute Gene Manipulation For Cancerpaperpublications3
Abstract: Data mining, the extraction of hidden predictive information from large databases, is a powerful new technology with great potential to help companies focus on the most important information in their data warehouses. Data mining tools predict future trends and behaviours, allowing businesses to make proactive, knowledge-driven decisions. The automated, prospective analyses offered by data mining move beyond the analyses of past events provided by retrospective tools typical of decision support systems.
They scour databases for hidden patterns, finding predictive information that experts may miss because it lies outside their expectations. Data mining techniques are the result of a long process of research and product development. This evolution began when business data was first stored on computers, continued with improvements in data access, and more recently, generated technologies that allow users to navigate through their data in real time. Data mining takes this evolutionary process beyond retrospective data access and navigation to prospective and proactive information delivery.
An Analysis of Outlier Detection through clustering methodIJAEMSJORNAL
This research paper deals with an outlier which is known as an unusual behavior of any substance present in the spot. This is a detection process that can be employed for both anomaly detection and abnormal observation. This can be obtained through other members who belong to that data set. The deviation present in the outlier process can be attained by measuring certain terms like range, size, activity, etc. By detecting outlier one can easily reject the negativity present in the field. For instance, in healthcare, the health condition of a person can be determined through his latest health report or his regular activity. When found the person being inactive there may be a chance for that person to be sick. Many approaches have been used in this research paper for detecting outliers. The approaches used in this research are 1) Centroid based approach based on K-Means and Hierarchical Clustering algorithm and 2) through Clustering based approach. This approach may help in detecting outlier by grouping all similar elements in the same group. For grouping, the elements clustering method paves a way for it. This research paper will be based on the above mentioned 2 approaches.
This document discusses challenges and potential solutions for improving data sharing in neuroscience. It notes that while there is a large amount of neuroscience data, it is unevenly distributed across repositories and databases. The document proposes creating a distributed "data sharing ecosystem" where data and related metadata are systematically tracked, linked and made available. Key elements would include unique IDs for all data objects, logging all activities, and developing accountability scores and influence measures to promote better data citizenship. However, concerns are raised about monitoring researchers and potential biases, which would need to be addressed for such a system to work.
Social networking sites are source of information for event detection, with specific reference of the road traffic activity
blockage and accidents or earth-quack sensing system. During this paper, we have a tendency to present a time period
observation system supposed for traffic occasion detection coming back from social media stream analysis. The system
fetches tweets coming back from social media/network as per a many search criteria; ways tweets/posts, by applying matter
content mining methods; last however not least works the classification of social networks posts. The goal is to assign
appropriate category packaging to each posts, as a result of connected with Associate in Nursing activity of traffic event or
maybe not. The traffic recognition system or framework was utilized for time period observation of varied areas of the road
network, taking into consideration detection of traffic occasions simply virtually in actual time, frequently before on-line
traffic news sites. All people utilized the support vector machine sort of a classification unit; what is more, we tend to
accomplish a good accuracy price of 95.76% by making an attempt a binary classification problem. All people were
conjointly capable to discriminate if traffic is triggered by Associate in nursing external celebration or not, by partitioning a
multi category classification issue and getting accuracy price of 88.89.
Infrastructures Supporting Inter-disciplinary Research - Exemplars from the UK NeISSProject
Infrastructures Supporting Inter-disciplinary Research - Exemplars from the UK . Talk given by Richard Sinnott at Urban Research Infrastructure Network Workshops, Melbourne, Brisbane, Sydney, September 2010.
Meliorating usable document density for online event detectionIJICTJOURNAL
Online event detection (OED) has seen a rise in the research community as it can provide quick identification of possible events happening at times in the world. Through these systems, potential events can be indicated well before they are reported by the news media, by grouping similar documents shared over social media by users. Most OED systems use textual similarities for this purpose. Similar documents, that may indicate a potential event, are further strengthened by the replies made by other users, thereby improving the potentiality of the group. However, these documents are at times unusable as independent documents, as they may replace previously appeared noun phrases with pronouns, leading OED systems to fail while grouping these replies to their suitable clusters. In this paper, a pronoun resolution system that tries to replace pronouns with relevant nouns over social media data is proposed. Results show significant improvement in performance using the proposed system.
The document discusses using WEKA and BioWeka to analyze DNA sequences and perform pattern matching. It summarizes how Eclat filtering and EM clustering are applied to a dataset containing DNA sequences from human and chimpanzee chromosomes. Eclat is used to extract codon frequencies as features, while EM clustering assigns sequences to clusters based on the mixture model with the highest posterior probability. The analysis aims to identify biologically relevant groups of genes and determine chromosomal similarities between humans and chimpanzees.
The document discusses collaboration in disease surveillance and response. It describes InSTEDD's hybrid approach to disease surveillance which combines various data sources to identify health risks. It also discusses tools developed by InSTEDD like GeoChat and Mesh4x that enable real-time information sharing and collaboration between organizations responding to disease outbreaks. The document emphasizes that collaboration is critical for effective outbreak containment and humanitarian response.
InSTEDD: Collaboration in Disease Surveillance & ResponseInSTEDD
The document discusses collaboration in disease surveillance and response. It describes InSTEDD's hybrid approach to disease surveillance which combines various data sources to identify health risks. It also discusses tools developed by InSTEDD like GeoChat and Mesh4x that enable real-time information sharing and collaboration between organizations responding to disease outbreaks. The document emphasizes that collaboration is critical for effective outbreak containment and humanitarian response.
Dynamic Rule Base Construction and Maintenance Scheme for Disease Predictionijsrd.com
Business and healthcare application are tuned to automatically detect and react events generated from local are remote sources. Event detection refers to an action taken to an activity. The association rule mining techniques are used to detect activities from data sets. Events are divided into 2 types' external event and internal event. External events are generated under the remote machines and deliver data across distributed systems. Internal events are delivered and derived by the system itself. The gap between the actual event and event notification should be minimized. Event derivation should also scale for a large number of complex rules. Attacks and its severity are identified from event derivation systems. Transactional databases and external data sources are used in the event detection process. The new event discovery process is designed to support uncertain data environment. Uncertain derivation of events is performed on uncertain data values. Relevance estimation is a more challenging task under uncertain event analysis. Selectability and sampling mechanism are used to improve the derivation accuracy. Selectability filters events that are irrelevant to derivation by some rules. Selectability algorithm is applied to extract new event derivation. A Bayesian network representation is used to derive new events given the arrival of an uncertain event and to compute its probability. A sampling algorithm is used for efficient approximation of new event derivation. Medical decision support system is designed with event detection model. The system adopts the new rule mapping mechanism for the disease analysis. The rule base construction and maintenance operations are handled by the system. Rule probability estimation is carried out using the Apriori algorithm. The rule derivation process is optimized for domain specific model.
This document discusses data mining algorithms for clustering healthcare data streams. It provides an overview of the K-means and D-stream algorithms, and proposes a framework for comparing them on healthcare datasets. The framework involves feature extraction from physiological signals, calculating risk components, and applying the K-means and D-stream algorithms to cluster the data. The results would show the effectiveness and limitations of each algorithm for clustering streaming healthcare data.
Similar to International system for total early disease detection (in stedd) platform (20)
The new Pandemic Preparedness Citizen's Guide, edited by Sarah Booth, Kelsey Hills-Evans & Scott Teesdale to incorporate information around the recent COVID-19 pandemic.
Disease Reporting Hotline Launches to Stop Outbreaks in Cambodia InSTEDD
To improve disease reporting in Cambodia, the iLab Southeast Asia, in partnership with the Cambodian CDC and Skoll Global Threats Fund, launched a free to the public disease hotline built with InSTEDD's interactive voice response tool, Verboice.
Cambodia is in a 'hot zone region', susceptible to deadly disease spread. Timely reports from Health Centers across the country are critical to stopping outbreaks.
At the Epihack Rio event, public health experts and technologists worked together to prototype new solutions to prevent disease spread. Over the course of the event, participants engaged in discussions to identify priority issues, formed cross-disciplinary teams, and worked intensely to develop mobile applications and data visualization tools to support health monitoring and reporting, especially around mass gatherings like the Olympics. The prototypes were presented at the end to seek feedback on their potential real-world applications.
This document discusses mHealth (mobile health) technologies and their implementation in Cambodia and other countries. It provides examples of mHealth projects that use SMS, voice calls, and smartphone apps to facilitate: (1) routine infectious disease reporting from health centers; (2) grassroots malaria case reporting and referral of patients; (3) inventory alerts of malaria drug stocks; (4) reproductive health services and education for families; and (5) health information and services for garment factory workers, new mothers, and diabetics. The document emphasizes using simple mobile technologies to enhance information sharing and improve health services for communities with limited Internet access or literacy.
This document proposes a new system to improve wildlife sickness reporting in three main ways:
1. It would provide rangers with an easier, faster mobile reporting method through a short online form or phone hotline to submit data like the species, number of sick/dead animals, location, and photos in real-time.
2. All reports would be collected in a unified, online database displayed on an interactive map for officials to quickly detect abnormal patterns or potential outbreaks and take immediate action.
3. The system would also include configurable SMS alerts to notify Ministry officials of unusual case counts in real-time for better monitoring of wildlife health trends connected to public health systems.
This document discusses the development of a participatory animal health surveillance system in Chiang Mai, Thailand. The system aims to improve surveillance by engaging more people, including farmers, villagers, and consumers. It plans to use smartphones and voice calls to collect reports of abnormal animal situations and product issues. The collected data will then be visualized on a map to help locate farms, markets, and slaughterhouses. The system also seeks to better register all animal farms and provide online education about animal health and food safety to the public. An initial demonstration of the solution's design was presented.
Mobile technologies landscape and opportunity for civil society organizations...InSTEDD
Channe talks about how mobile technologies can help Civil Society Organizations (CSOs) do more with less. Channe will tackle practical issues like how to get started and their process of design and implementation. Channe will walk you through several exciting projects, including mobile technologies in labor rights and health care and the use of mobile phone as a data collection tool.
When: 3:30 - 5:00pm. Friday 7th February 2014
Where: Himawari Hotel, Phnom Penh
Organized by: Development Innovations
https://www.eventbrite.com/e/mobile-technologies-landscape-and-opportunity-for-csos-in-cambodia-tickets-10444502789
Routine infectious disease reporting using SMS at Kean Svay operational distr...InSTEDD
This document discusses a project in Southeast Asia that developed technology tools to improve infectious disease reporting from health centers to operational districts. The tools aimed to enhance collaboration and information sharing. Previously, health centers reported diseases via radio, phone calls, or paper which caused delays. The new system allowed health centers to send weekly SMS reports on 12 diseases using standardized codes. This enabled earlier detection and response to outbreaks. The SMS system launched in 2010 and was later improved in 2011 with the addition of a reporting wheel to simplify coding. By 2012 an online application was created to aggregate reporting data.
Verboice - Voice based platform and impact to grassroots CambodiaInSTEDD
Verboice is a voice-based platform that uses open source technology to help partners improve information sharing and service delivery in their communities. It has been used successfully in projects in over 15 countries. Examples of projects using Verboice in Cambodia include a phone-based contraception support system for Marie Stopes clinics, a national election hotline providing basic election information, and an interactive phone quiz for garment factory workers on issues like salaries and health. The document discusses Verboice and its impact on empowering grassroots organizations in Cambodia through technology.
The iLab Southeast Asia presented at BarCamp Phnom Penh 2012 on how to use Google's Map Maker application. The iLab SEA team trained participants on how to add and edit locations, draw streets, rivers, and other important landmarks on the Google map.
"Technology with a Purpose" - Eduardo Jezierski speaks at Ignite Health Foo 2...InSTEDD
This document discusses various projects and initiatives by InSTEDD including developing tools for collecting birth complication data in Sierra Leone, running innovation labs in Cambodia and elsewhere, using mobile tools like GeoChat to help health workers in Thailand during floods, and detecting and containing a leptospirosis outbreak faster through discussion on such tools. It also references principles of collective action, data as an extractive industry, the use of mobile information systems in Haiti after the earthquake, and launching high-altitude balloons to inspire new perspectives on Earth. Overall the document touches on InSTEDD's work using technology to help address global health challenges and promote positive change.
Mobile health (mHealth) technologies show promise for improving HIV treatment and prevention by allowing healthcare providers to remotely monitor patients and disseminate medical information. The authors review several mHealth initiatives that have increased access to HIV testing and treatment through the use of text messages, video observations, and other mobile platforms. If designed and implemented properly, mHealth could help reduce costs and expand care for HIV-positive individuals around the world.
With a worldwide penetration rate of over 85%, the mobile phone has become one of the most transformative tools in human history. As mobile communication technologies become less expensive, faster, and more accessible, the ability of people, communities and institutions to share information and knowledge will continue to skyrocket. Specifically for Global Health, the use of mobile communication and network technologies for delivery of health care (mHealth) holds great promise for the future. In low resource settings, community health workers (CHWs) provide a backbone for the delivery of health care services. Often isolated and without significant formal education or training, CHWs can be seen as key connectors between their communities and the formal health care system. In the hands of CHWs, mHealth tools may facilitate effective task shifting; by expanding the pool of human resources, increasing the productivity of health systems, and lowering the cost of services. The reported experience with mHealth suggest a wide range of opportunities exist to improve ease, speed, completeness and accuracy of the work of CHWs. The outcomes associated with these sort of new capabilities can be expected to result in ongoing improvements in performance on key national health indicators. The presentation will examine the state of the art and science-- by describing a systematic review of the literature and citing examples in action -- and provide recommendations focused on the design and development of mHealth tools for use by CHWs to strengthen Global Health interventions.
Speaker Bio:
Dennis M. Israelski, M.D
www.instedd.org/team
InSTEDD focuses on four key areas: maternal/child health, infectious diseases, emergency management, and local innovation/leadership. It uses a social-technical approach and human-centered design process to develop technology tools and solutions for health challenges. Examples of tools include GeoChat for collaboration, Remindem for messaging, and Resource Map for tracking resources geographically.
Presentation by Channe Suy of the iLab Southeast Asia speaking at TEDxPhnom Penh. To see the video of this presentation, please go here: http://instedd.org/blog/from-the-ted-prize-to-tedxphnom-penh/
This document discusses InSTEDD, an organization that aims to improve global health, safety, and sustainable development through creating collaboration technologies, collaborating with end users, building local capacity, and ensuring usefulness and impact. It provides examples of projects in countries like Haiti, Argentina, and Kenya. InSTEDD supports humanitarian organizations through understanding contexts, creating appropriate technologies, and building local capabilities. Its technology tools are open source, customizable, work on basic phones without internet or literacy requirements, and are low-cost.
RIO 2.0 was a demo alley event focused on building technologies for social impact. Dennis M. Israelski, the President and CEO of InSTEDD and a Clinical Professor of Medicine at Stanford University School of Medicine, presented on February 2, 2011 about InSTEDD's work on real time malaria reporting.
InSTEDD is a non-profit founded in 2006 that designs open source technology tools to help communities collaborate and share information to improve health, safety, and development. It works with governments, organizations, and communities around the world. InSTEDD Innovation Labs (iLabs) act as hubs for technology transfer, collaboration, and entrepreneurial innovation serving the public good in different regions.
The InSTEDD Toolkit provides a collection of open source tools to help improve collaboration, innovation, and resiliency. The tools include messaging applications, opinion and status collection, information extraction, task management, disease monitoring, and more. All tools are available for anyone to use and build upon to increase social impact. InSTEDD is actively involved with users to evolve the tools and maximize positive outcomes.
This document describes mHealth tools developed by InSTEDD to help prevent maternal-to-child transmission of HIV, including Remindem for sending reminders via text, Verboice for interactive voice messages, Resource Map for tracking health resources, and Pollit for conducting mobile surveys. The tools are designed to help improve adherence to treatment, identify available prevention and treatment resources, fight stigma, and engage communities.
HCL Notes und Domino Lizenzkostenreduzierung in der Welt von DLAUpanagenda
Webinar Recording: https://www.panagenda.com/webinars/hcl-notes-und-domino-lizenzkostenreduzierung-in-der-welt-von-dlau/
DLAU und die Lizenzen nach dem CCB- und CCX-Modell sind für viele in der HCL-Community seit letztem Jahr ein heißes Thema. Als Notes- oder Domino-Kunde haben Sie vielleicht mit unerwartet hohen Benutzerzahlen und Lizenzgebühren zu kämpfen. Sie fragen sich vielleicht, wie diese neue Art der Lizenzierung funktioniert und welchen Nutzen sie Ihnen bringt. Vor allem wollen Sie sicherlich Ihr Budget einhalten und Kosten sparen, wo immer möglich. Das verstehen wir und wir möchten Ihnen dabei helfen!
Wir erklären Ihnen, wie Sie häufige Konfigurationsprobleme lösen können, die dazu führen können, dass mehr Benutzer gezählt werden als nötig, und wie Sie überflüssige oder ungenutzte Konten identifizieren und entfernen können, um Geld zu sparen. Es gibt auch einige Ansätze, die zu unnötigen Ausgaben führen können, z. B. wenn ein Personendokument anstelle eines Mail-Ins für geteilte Mailboxen verwendet wird. Wir zeigen Ihnen solche Fälle und deren Lösungen. Und natürlich erklären wir Ihnen das neue Lizenzmodell.
Nehmen Sie an diesem Webinar teil, bei dem HCL-Ambassador Marc Thomas und Gastredner Franz Walder Ihnen diese neue Welt näherbringen. Es vermittelt Ihnen die Tools und das Know-how, um den Überblick zu bewahren. Sie werden in der Lage sein, Ihre Kosten durch eine optimierte Domino-Konfiguration zu reduzieren und auch in Zukunft gering zu halten.
Diese Themen werden behandelt
- Reduzierung der Lizenzkosten durch Auffinden und Beheben von Fehlkonfigurationen und überflüssigen Konten
- Wie funktionieren CCB- und CCX-Lizenzen wirklich?
- Verstehen des DLAU-Tools und wie man es am besten nutzt
- Tipps für häufige Problembereiche, wie z. B. Team-Postfächer, Funktions-/Testbenutzer usw.
- Praxisbeispiele und Best Practices zum sofortigen Umsetzen
How to Interpret Trends in the Kalyan Rajdhani Mix Chart.pdfChart Kalyan
A Mix Chart displays historical data of numbers in a graphical or tabular form. The Kalyan Rajdhani Mix Chart specifically shows the results of a sequence of numbers over different periods.
Taking AI to the Next Level in Manufacturing.pdfssuserfac0301
Read Taking AI to the Next Level in Manufacturing to gain insights on AI adoption in the manufacturing industry, such as:
1. How quickly AI is being implemented in manufacturing.
2. Which barriers stand in the way of AI adoption.
3. How data quality and governance form the backbone of AI.
4. Organizational processes and structures that may inhibit effective AI adoption.
6. Ideas and approaches to help build your organization's AI strategy.
In the rapidly evolving landscape of technologies, XML continues to play a vital role in structuring, storing, and transporting data across diverse systems. The recent advancements in artificial intelligence (AI) present new methodologies for enhancing XML development workflows, introducing efficiency, automation, and intelligent capabilities. This presentation will outline the scope and perspective of utilizing AI in XML development. The potential benefits and the possible pitfalls will be highlighted, providing a balanced view of the subject.
We will explore the capabilities of AI in understanding XML markup languages and autonomously creating structured XML content. Additionally, we will examine the capacity of AI to enrich plain text with appropriate XML markup. Practical examples and methodological guidelines will be provided to elucidate how AI can be effectively prompted to interpret and generate accurate XML markup.
Further emphasis will be placed on the role of AI in developing XSLT, or schemas such as XSD and Schematron. We will address the techniques and strategies adopted to create prompts for generating code, explaining code, or refactoring the code, and the results achieved.
The discussion will extend to how AI can be used to transform XML content. In particular, the focus will be on the use of AI XPath extension functions in XSLT, Schematron, Schematron Quick Fixes, or for XML content refactoring.
The presentation aims to deliver a comprehensive overview of AI usage in XML development, providing attendees with the necessary knowledge to make informed decisions. Whether you’re at the early stages of adopting AI or considering integrating it in advanced XML development, this presentation will cover all levels of expertise.
By highlighting the potential advantages and challenges of integrating AI with XML development tools and languages, the presentation seeks to inspire thoughtful conversation around the future of XML development. We’ll not only delve into the technical aspects of AI-powered XML development but also discuss practical implications and possible future directions.
Building Production Ready Search Pipelines with Spark and MilvusZilliz
Spark is the widely used ETL tool for processing, indexing and ingesting data to serving stack for search. Milvus is the production-ready open-source vector database. In this talk we will show how to use Spark to process unstructured data to extract vector representations, and push the vectors to Milvus vector database for search serving.
Skybuffer SAM4U tool for SAP license adoptionTatiana Kojar
Manage and optimize your license adoption and consumption with SAM4U, an SAP free customer software asset management tool.
SAM4U, an SAP complimentary software asset management tool for customers, delivers a detailed and well-structured overview of license inventory and usage with a user-friendly interface. We offer a hosted, cost-effective, and performance-optimized SAM4U setup in the Skybuffer Cloud environment. You retain ownership of the system and data, while we manage the ABAP 7.58 infrastructure, ensuring fixed Total Cost of Ownership (TCO) and exceptional services through the SAP Fiori interface.
OpenID AuthZEN Interop Read Out - AuthorizationDavid Brossard
During Identiverse 2024 and EIC 2024, members of the OpenID AuthZEN WG got together and demoed their authorization endpoints conforming to the AuthZEN API
HCL Notes and Domino License Cost Reduction in the World of DLAUpanagenda
Webinar Recording: https://www.panagenda.com/webinars/hcl-notes-and-domino-license-cost-reduction-in-the-world-of-dlau/
The introduction of DLAU and the CCB & CCX licensing model caused quite a stir in the HCL community. As a Notes and Domino customer, you may have faced challenges with unexpected user counts and license costs. You probably have questions on how this new licensing approach works and how to benefit from it. Most importantly, you likely have budget constraints and want to save money where possible. Don’t worry, we can help with all of this!
We’ll show you how to fix common misconfigurations that cause higher-than-expected user counts, and how to identify accounts which you can deactivate to save money. There are also frequent patterns that can cause unnecessary cost, like using a person document instead of a mail-in for shared mailboxes. We’ll provide examples and solutions for those as well. And naturally we’ll explain the new licensing model.
Join HCL Ambassador Marc Thomas in this webinar with a special guest appearance from Franz Walder. It will give you the tools and know-how to stay on top of what is going on with Domino licensing. You will be able lower your cost through an optimized configuration and keep it low going forward.
These topics will be covered
- Reducing license cost by finding and fixing misconfigurations and superfluous accounts
- How do CCB and CCX licenses really work?
- Understanding the DLAU tool and how to best utilize it
- Tips for common problem areas, like team mailboxes, functional/test users, etc
- Practical examples and best practices to implement right away
AI 101: An Introduction to the Basics and Impact of Artificial IntelligenceIndexBug
Imagine a world where machines not only perform tasks but also learn, adapt, and make decisions. This is the promise of Artificial Intelligence (AI), a technology that's not just enhancing our lives but revolutionizing entire industries.
Ocean lotus Threat actors project by John Sitima 2024 (1).pptxSitimaJohn
Ocean Lotus cyber threat actors represent a sophisticated, persistent, and politically motivated group that poses a significant risk to organizations and individuals in the Southeast Asian region. Their continuous evolution and adaptability underscore the need for robust cybersecurity measures and international cooperation to identify and mitigate the threats posed by such advanced persistent threat groups.
Generating privacy-protected synthetic data using Secludy and MilvusZilliz
During this demo, the founders of Secludy will demonstrate how their system utilizes Milvus to store and manipulate embeddings for generating privacy-protected synthetic data. Their approach not only maintains the confidentiality of the original data but also enhances the utility and scalability of LLMs under privacy constraints. Attendees, including machine learning engineers, data scientists, and data managers, will witness first-hand how Secludy's integration with Milvus empowers organizations to harness the power of LLMs securely and efficiently.
Programming Foundation Models with DSPy - Meetup SlidesZilliz
Prompting language models is hard, while programming language models is easy. In this talk, I will discuss the state-of-the-art framework DSPy for programming foundation models with its powerful optimizers and runtime constraint system.
UiPath Test Automation using UiPath Test Suite series, part 6DianaGray10
Welcome to UiPath Test Automation using UiPath Test Suite series part 6. In this session, we will cover Test Automation with generative AI and Open AI.
UiPath Test Automation with generative AI and Open AI webinar offers an in-depth exploration of leveraging cutting-edge technologies for test automation within the UiPath platform. Attendees will delve into the integration of generative AI, a test automation solution, with Open AI advanced natural language processing capabilities.
Throughout the session, participants will discover how this synergy empowers testers to automate repetitive tasks, enhance testing accuracy, and expedite the software testing life cycle. Topics covered include the seamless integration process, practical use cases, and the benefits of harnessing AI-driven automation for UiPath testing initiatives. By attending this webinar, testers, and automation professionals can gain valuable insights into harnessing the power of AI to optimize their test automation workflows within the UiPath ecosystem, ultimately driving efficiency and quality in software development processes.
What will you get from this session?
1. Insights into integrating generative AI.
2. Understanding how this integration enhances test automation within the UiPath platform
3. Practical demonstrations
4. Exploration of real-world use cases illustrating the benefits of AI-driven test automation for UiPath
Topics covered:
What is generative AI
Test Automation with generative AI and Open AI.
UiPath integration with generative AI
Speaker:
Deepak Rai, Automation Practice Lead, Boundaryless Group and UiPath MVP
5th LF Energy Power Grid Model Meet-up SlidesDanBrown980551
5th Power Grid Model Meet-up
It is with great pleasure that we extend to you an invitation to the 5th Power Grid Model Meet-up, scheduled for 6th June 2024. This event will adopt a hybrid format, allowing participants to join us either through an online Mircosoft Teams session or in person at TU/e located at Den Dolech 2, Eindhoven, Netherlands. The meet-up will be hosted by Eindhoven University of Technology (TU/e), a research university specializing in engineering science & technology.
Power Grid Model
The global energy transition is placing new and unprecedented demands on Distribution System Operators (DSOs). Alongside upgrades to grid capacity, processes such as digitization, capacity optimization, and congestion management are becoming vital for delivering reliable services.
Power Grid Model is an open source project from Linux Foundation Energy and provides a calculation engine that is increasingly essential for DSOs. It offers a standards-based foundation enabling real-time power systems analysis, simulations of electrical power grids, and sophisticated what-if analysis. In addition, it enables in-depth studies and analysis of the electrical power grid’s behavior and performance. This comprehensive model incorporates essential factors such as power generation capacity, electrical losses, voltage levels, power flows, and system stability.
Power Grid Model is currently being applied in a wide variety of use cases, including grid planning, expansion, reliability, and congestion studies. It can also help in analyzing the impact of renewable energy integration, assessing the effects of disturbances or faults, and developing strategies for grid control and optimization.
What to expect
For the upcoming meetup we are organizing, we have an exciting lineup of activities planned:
-Insightful presentations covering two practical applications of the Power Grid Model.
-An update on the latest advancements in Power Grid -Model technology during the first and second quarters of 2024.
-An interactive brainstorming session to discuss and propose new feature requests.
-An opportunity to connect with fellow Power Grid Model enthusiasts and users.
Ivanti’s Patch Tuesday breakdown goes beyond patching your applications and brings you the intelligence and guidance needed to prioritize where to focus your attention first. Catch early analysis on our Ivanti blog, then join industry expert Chris Goettl for the Patch Tuesday Webinar Event. There we’ll do a deep dive into each of the bulletins and give guidance on the risks associated with the newly-identified vulnerabilities.
International system for total early disease detection (in stedd) platform
1. International System for Total Early Disease Detection (InSTEDD) Platform
Taha A. Kass-Hout, M.D., M.S., Nicolas di Tada
InSTEDD, Palo Alto, California
OBJECTIVE tected in location X, and with a certain spatio-
This paper describes a hybrid (event-based and indi- temporal pattern”). The human input and review
cator-based) surveillance platform designed to module is exposed as a set of functionalities that al-
streamline the collaboration between domain experts lows users to comment, tag, and rank the elements
and machine learning algorithms for detection, pre- (positive, neutral, or negative). Additionally, users
diction and response to health-related events (such as can generate and test multiple hypotheses in parallel,
disease outbreaks). further collect and rank sets of related items (evi-
dence), and model against baseline information (for
BACKGROUND
cyclical or known events). The platform maintains a
Over the last decade, the majority of the designs, list of ongoing possible threats allowing domain ex-
analyses and evaluations of early detection [1] (or perts to focus their field information and either con-
biosurveillance) systems have been geared towards firm or reject the hypotheses created. That feedback
specific data sources and detection algorithms. Much is then fed into the system to update (increase or de-
less effort has been focused on how these systems crease) the reliability of the sources and credibility of
will "interact" with humans [2]. For example, con- the users in light of their inferences or decisions.
sider multiple domain experts working at different
RESULTS
levels across different organizations in an environ-
ment where numerous biosurveillance algorithms The platform synthesizes health-related event indica-
may provide contradictory interpretations of ongoing tors from a wide variety of information sources
events. This paper discusses the anticipated contribu- (structured and unstructured) into a consolidated pic-
tion of social networking, machine learning and col- ture for analysis, maintenance of “community-wide
laboration techniques to address these emerging is- coherence” [3], and collaboration processes. This
sues by drawing upon methods, models and tech- helps detect anomalies, visualize clusters of potential
nologies that have been proven to work in other chal- events, predict the rate and spread of a disease out-
lenging domains. break and provide decision makers with tools, meth-
odologies and processes to investigate the event.
METHODS
Presently, the platform and associated modules are
The platform consists of several high-level modules, being piloted for the Mekong Basin region in SE
including: 1) Data gathering, 2) Automatic feature Asia.
extraction, data classification and tagging, 3) Human
CONCLUSIONS
input, hypotheses generation and review, 4) Predic-
tions and alerts output, and 5) Field confirmation and In this paper we describe a platform that enables de-
feedback. The data gathering module allows users tection, prediction and response to health-related
to collect information from several sources (SMS events through a collaborative approach that com-
messages, RSS feeds, email list (e.g., ProMed), bines data exploration, integration, search and infer-
documents, web pages, electronic medical records, encing – providing more complex analysis and
animal disease data, environmental feed, remote deeper insight. We believe that such a platform repre-
sensing, etc.). The automatic feature extraction, sents the next generation of early detection and re-
data classification and tagging module is an exten- sponse systems.
sible architecture that allows the introduction of ma- REFERENCES
chine learning algorithms (e.g., Bayesian). These [1] Ping Yan, Daniel Dajun Zeng, Hsinchun Chen: A Review of
components extract and augment the features (or Public Health Syndromic Surveillance Systems. ISI 2006; 249-
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(e.g., person-to-person, waterborne), etc. In addition, knowledge. Philosophy of Science 2007;74, 28-47.
these components help detect relationships between [3] Billman, D., Convertino, G. Shrager, J. Massar, JP. and Pirolli
these extracted features within a collaborative space P. The CACHE Study: Supporting Collaborative Intelligence.
or across different collaborative spaces. Furthermore, Paper presented at the HCIC 2006 Winter Workshop on Collabora-
tion, Cooperation, Coordination. Feb. 2006.
with human input, these components can suggest
possible events or event types (e.g., at the earliest Further Information:
stages of a disease outbreak: “there is an unknown Taha Kass-Hout, kasshout@instedd.org
respiratory event, transmitted person-to-person, de- http://www.instedd.org
Advances in Disease Surveillance 2008;5:108