PredPol: How Predictive Policing WorksPredPol, Inc
PredPol’s cloud-based predictive policing software enables law enforcement agencies to better prevent crime in their communities by generating predictions on the places and times that future crimes are most likely to occur.
PredPol’s technology has been helping law enforcement agencies to dramatically reduce crime in jurisdictions of all types and sizes, across the U.S. and overseas. Over the past year, Atlanta and Los Angeles have reduced specific crimes in targeted areas at rates ranging from nearly 20% to over 40%. Smaller jurisdictions, such as Norcross, Georgia, have seen nearly a 30% reduction in burglaries and robberies; in Alhambra, California, car burglaries have dropped 20% since the software technology was deployed.
Using advanced mathematics and computer learning, PredPol’s algorithms predict many types of crime, including property crimes, drug incidents, gang activity, and gun violence as well as traffic accidents.
Only three pieces of data are used to make predictions – type of crime, place of crime, and time of crime. No personal data is utilized in making these predictions.
Crime analysts and command staff using PredPol are 100% more effective than they are with traditional hotspot mapping at predicting where and when crimes are likely to occur. That means police have twice as many opportunities to deter and reduce crime.
Crime sensing with big data - Singapore perspectiveBenjamin Ang
This presentation examines the Potentials and Limitations of Using Big Data for Crime Estimation. Singapore laws discussed include Personal Data Protection Act (PDPA), Penal Code, Criminal Procedure Code (CPC). Topics covered include Crime Analysis, Crime Prediction, Algorithm Bias, and other risks. The video of this presentation can be found at https://youtu.be/kctB3lRLh2U
As per studies conducted by the University of California, it is observed that crime in any area follows the same pattern as that of earthquake aftershocks. It is difficult to predict an earthquake, but once it happens the aftershocks following it are quite predictable. Same is true for the crimes happening in a geographical area.
Using Data Mining Techniques to Analyze Crime PatternZakaria Zubi
Our proposed model will be able to extract crime patterns by using association rule mining and clustering to classify crime records on the basis of the values of crime attributes.
PredPol: How Predictive Policing WorksPredPol, Inc
PredPol’s cloud-based predictive policing software enables law enforcement agencies to better prevent crime in their communities by generating predictions on the places and times that future crimes are most likely to occur.
PredPol’s technology has been helping law enforcement agencies to dramatically reduce crime in jurisdictions of all types and sizes, across the U.S. and overseas. Over the past year, Atlanta and Los Angeles have reduced specific crimes in targeted areas at rates ranging from nearly 20% to over 40%. Smaller jurisdictions, such as Norcross, Georgia, have seen nearly a 30% reduction in burglaries and robberies; in Alhambra, California, car burglaries have dropped 20% since the software technology was deployed.
Using advanced mathematics and computer learning, PredPol’s algorithms predict many types of crime, including property crimes, drug incidents, gang activity, and gun violence as well as traffic accidents.
Only three pieces of data are used to make predictions – type of crime, place of crime, and time of crime. No personal data is utilized in making these predictions.
Crime analysts and command staff using PredPol are 100% more effective than they are with traditional hotspot mapping at predicting where and when crimes are likely to occur. That means police have twice as many opportunities to deter and reduce crime.
Crime sensing with big data - Singapore perspectiveBenjamin Ang
This presentation examines the Potentials and Limitations of Using Big Data for Crime Estimation. Singapore laws discussed include Personal Data Protection Act (PDPA), Penal Code, Criminal Procedure Code (CPC). Topics covered include Crime Analysis, Crime Prediction, Algorithm Bias, and other risks. The video of this presentation can be found at https://youtu.be/kctB3lRLh2U
As per studies conducted by the University of California, it is observed that crime in any area follows the same pattern as that of earthquake aftershocks. It is difficult to predict an earthquake, but once it happens the aftershocks following it are quite predictable. Same is true for the crimes happening in a geographical area.
Using Data Mining Techniques to Analyze Crime PatternZakaria Zubi
Our proposed model will be able to extract crime patterns by using association rule mining and clustering to classify crime records on the basis of the values of crime attributes.
Crime Analytics: Analysis of crimes through news paper articlesChamath Sajeewa
Crime analysis is one of the most important
activities of the majority of the intelligent and law enforcement
organizations all over the world. Generally they collect domestic
and foreign crime related data (intelligence) to prevent future
attacks and utilize a limited number of law enforcement
resources in an optimum manner. A major challenge faced by
most of the law enforcement and intelligence organizations is
efficiently and accurately analyzing the growing volumes of crime
related data. The vast geographical diversity and the complexity
of crime patterns have made the analyzing and recording of
crime data more difficult. Data mining is a powerful tool that can
be used effectively for analyzing large databases and deriving
important analytical results. This paper presents an intelligent
crime analysis system which is designed to overcome the above
mentioned problems. The proposed system is a web-based system
which comprises of crime analysis techniques such as hotspot
detection, crime comparison and crime pattern visualization. The
proposed system consists of a rich and simplified environment
that can be used effectively for processes of crime analysis.
Intelligence Led Policing for Police Decision MakersDeborah Osborne
Intelligence-Led Policing for Decision-Makers Webinar
Audio is at http://www.blogtalkradio.com/Deborah-Osborne/2009/09/23/Intelligence-Led-Policing-for-Decision-Makers-Webinar
This webinar, designed for law enforcement managers, covers the following topics:
* Intelligence: what it is, what it is not, and what it can be
* The role of the decision-maker in the intelligence cycle
* Defining Intelligence-Led Policing and the 3 i's cycle
* The 7 stages of Intelligence-Led Policing
* Resources for learning more about Intelligence-Led Policing
Crime Mapping & Analysis – Georgia Tech
Crime analysis is a law enforcement function that involves systematic analysis for identifying and analyzing patterns and trends in crime and disorder. Information on patterns can help law enforcement agencies deploy resources in a more effective manner, and assist detectives in identifying and apprehending suspects.
Anomaly detection is a topic with many different applications. From social media tracking, to cybersecurity, anomaly detection (or outlier detection) algorithms can have a huge impact in your organisation.
For the video please visit: https://www.youtube.com/watch?v=XEM2bYYxkTU
This slideshare has been produced by the Tesseract Academy (http://tesseract.academy), a company that educates decision makers in deep technical topics such as data science, analytics, machine learning and blockchain.
If you are interested in data science and related topics, make sure to also visit The Data Scientist: http://thedatascientist.com.
Cloud Technologies providing Complete Solution for all
AcademicProjects Final Year/Semester Student Projects
For More Details,
Contact:
Mobile:- +91 8121953811,
whatsapp:- +91 8522991105,
Office:- 040-66411811
Email ID: cloudtechnologiesprojects@gmail.com
Crime rate analysis using k nn in python
Forensic science is a scientific method of gathering and examining information about the past which is then used in the court of law. Digital Forensics is the use of scientifically derived and proven methods toward the preservation, collection, validation, identification, analysis, interpretation, documentation, and presentation of digital evidence derived from digital devices for the purpose of facilitation or furthering the reconstruction of events found to be criminal, or helping to anticipate unauthorized actions shown to be disruptive to planned operations.
Today's police departments utilize computers in almost every aspect of functioning. From extensive national databases to community service websites, computers have increased the efficiency of compiling, processing, researching and analyzing all forms of e data, which is equally applicable to the Police.
Intelligence led policing- pole sandbox (webinar 21012019) Neo4j
To help you explore how to prevent and solve crimes using the power of graphs we have developed the Crime Investigation Sandbox.
Data for the Crime Investigation Sandbox is organised based on the POLE data model, commonly used in policing and other security-related use cases. POLE stands for Persons, Objects, Locations, and Events.
The sandbox comes pre-loaded with sample data and a step-by-step guide with queries and explanations . In addition you might watch my video explaining the concept in detail. Everything you need to get going with your Crime Investigation!
Globally, public safety organizations are under severe pressure to carry out criminal inquiries with fast turnaround times, while keeping people and property safe.
Crime Analytics: Analysis of crimes through news paper articlesChamath Sajeewa
Crime analysis is one of the most important
activities of the majority of the intelligent and law enforcement
organizations all over the world. Generally they collect domestic
and foreign crime related data (intelligence) to prevent future
attacks and utilize a limited number of law enforcement
resources in an optimum manner. A major challenge faced by
most of the law enforcement and intelligence organizations is
efficiently and accurately analyzing the growing volumes of crime
related data. The vast geographical diversity and the complexity
of crime patterns have made the analyzing and recording of
crime data more difficult. Data mining is a powerful tool that can
be used effectively for analyzing large databases and deriving
important analytical results. This paper presents an intelligent
crime analysis system which is designed to overcome the above
mentioned problems. The proposed system is a web-based system
which comprises of crime analysis techniques such as hotspot
detection, crime comparison and crime pattern visualization. The
proposed system consists of a rich and simplified environment
that can be used effectively for processes of crime analysis.
Intelligence Led Policing for Police Decision MakersDeborah Osborne
Intelligence-Led Policing for Decision-Makers Webinar
Audio is at http://www.blogtalkradio.com/Deborah-Osborne/2009/09/23/Intelligence-Led-Policing-for-Decision-Makers-Webinar
This webinar, designed for law enforcement managers, covers the following topics:
* Intelligence: what it is, what it is not, and what it can be
* The role of the decision-maker in the intelligence cycle
* Defining Intelligence-Led Policing and the 3 i's cycle
* The 7 stages of Intelligence-Led Policing
* Resources for learning more about Intelligence-Led Policing
Crime Mapping & Analysis – Georgia Tech
Crime analysis is a law enforcement function that involves systematic analysis for identifying and analyzing patterns and trends in crime and disorder. Information on patterns can help law enforcement agencies deploy resources in a more effective manner, and assist detectives in identifying and apprehending suspects.
Anomaly detection is a topic with many different applications. From social media tracking, to cybersecurity, anomaly detection (or outlier detection) algorithms can have a huge impact in your organisation.
For the video please visit: https://www.youtube.com/watch?v=XEM2bYYxkTU
This slideshare has been produced by the Tesseract Academy (http://tesseract.academy), a company that educates decision makers in deep technical topics such as data science, analytics, machine learning and blockchain.
If you are interested in data science and related topics, make sure to also visit The Data Scientist: http://thedatascientist.com.
Cloud Technologies providing Complete Solution for all
AcademicProjects Final Year/Semester Student Projects
For More Details,
Contact:
Mobile:- +91 8121953811,
whatsapp:- +91 8522991105,
Office:- 040-66411811
Email ID: cloudtechnologiesprojects@gmail.com
Crime rate analysis using k nn in python
Forensic science is a scientific method of gathering and examining information about the past which is then used in the court of law. Digital Forensics is the use of scientifically derived and proven methods toward the preservation, collection, validation, identification, analysis, interpretation, documentation, and presentation of digital evidence derived from digital devices for the purpose of facilitation or furthering the reconstruction of events found to be criminal, or helping to anticipate unauthorized actions shown to be disruptive to planned operations.
Today's police departments utilize computers in almost every aspect of functioning. From extensive national databases to community service websites, computers have increased the efficiency of compiling, processing, researching and analyzing all forms of e data, which is equally applicable to the Police.
Intelligence led policing- pole sandbox (webinar 21012019) Neo4j
To help you explore how to prevent and solve crimes using the power of graphs we have developed the Crime Investigation Sandbox.
Data for the Crime Investigation Sandbox is organised based on the POLE data model, commonly used in policing and other security-related use cases. POLE stands for Persons, Objects, Locations, and Events.
The sandbox comes pre-loaded with sample data and a step-by-step guide with queries and explanations . In addition you might watch my video explaining the concept in detail. Everything you need to get going with your Crime Investigation!
Globally, public safety organizations are under severe pressure to carry out criminal inquiries with fast turnaround times, while keeping people and property safe.
The algorithms that are already changing your life By.Dr.Mahboob ali khan PhdHealthcare consultant
It is hoped that AI will relieve some of the pressure on busy hospitals by diagnosing disease and recommending treatment options quickly and efficiently.Medicine is primed to be a chief beneficiary of artificial intelligence. AI can diagnose diseases from telltale groups of symptoms, strange patterns in blood tests, and the subtle abnormalities that cells display as a disease begins takes hold. Time and again, AI systems are found to pick up signs of illness that are unknown to doctors, making the AIs more accurate as a result.
Crime analysis mapping, intrusion detection using data miningVenkat Projects
Crime analysis mapping, intrusion detection using data mining
Data Mining plays a key role in Crime Analysis. There are many different algorithms mentioned in previous research papers, among them are the virtual identifier, pruning strategy, support vector machines, and apriori algorithms. VID is to find relation between record and vid. The apriori algorithm helps the fuzzy association rules algorithm and it takes around six hundred seconds to detect a mail bomb attack. In this research paper, we identified Crime mapping analysis based on KNN (K – Nearest Neighbor) and ANN (Artificial Neural Network) algorithms to simplify this process. Crime Mapping is conducted and Funded by the Office of Community Oriented Policing Services (COPS). Evidence based research helps in analyzing the crimes. We calculate the crime rate based on the previous data using data mining techniques. Crime Analysis uses quantitative and qualitative data in combination with analytic techniques in resolving the cases. For public safety purposes, the crime mapping is an essential research area to concentrate on. We can identity the most frequently crime occurring zones with the help of data mining techniques. In Crime Analysis Mapping, we follow the following steps in order to reduce the crime rate: 1) Collect crime data 2) Group data 3) Clustering 4) Forecasting the data. Crime Analysis with crime mapping helps in understanding the concepts and practice of Crime Analysis in assisting police and helps in reduction and prevention of crimes and crime disorders.
Physical and Cyber Crime Detection using Digital Forensic Approach: A Complet...IJARIIT
Criminalization may be a general development that has significantly extended in previous few years. In
order, to create the activity of the work businesses easy, use of technology is important. Crime investigation analysis
is a section records in data mining plays a crucial role in terms of predicting and learning the criminals. In our
paper, we've got planned an incorporated version for physical crime as well as cybercrime analysis. Our approach
uses data mining techniques for crime detection and criminal identity for physical crimes and digitized forensic tools
(DFT) for evaluating cybercrimes. The presented tool named as Comparative Digital Forensic Process tool
(CDFPT) is entirely based on digital forensic model and its stages named as Comparative Digital Forensic Process
Model (CDFPM). The primary step includes accepting the case details, categorizing the crime case as physical crime
or cybercrime and sooner or later storing the data in particular databases. For physical crime analysis we've used kmeans
approach cluster set of rules to make crime clusters. The k-means method effects are a lot advantageous by the
utilization of GMAPI generation. This provides advanced and consumer-friendly visual-aid to k-means approach for
tracing the region of the crime. we have applied KNN for criminal identification with the
help of observing beyond crimes and finding similar ones that suit this crime, if no past document is discovered then
the new crime sample are introduced to the crime data-set. With the advancements of web, the network form has
become much more complicated and attacking methods are further more than that as well. For crime analysis
we're detecting the attacks executed on host system through an outsider the usage of
assorted digitized forensic tools to produce information security with the help of generating reports for an
event which could need any investigation. Our digitized technique aids the development of the society
by helping the investigation businesses to follow a custom-built investigative technique in crime analysis and criminal
identification as opposed to manually looking the database to analyze criminal activities, and as a
result facilitate them in combating crimes.
Running head: CRIME ANALYSIS 1
CRIME ANALYSIS TECHNOLOGY 2
Crime analysis is a function that usually involves the systemic analysis in identifying as well as analyzing the crime patterns and trends. Crime analysis is very important for law enforcement agencies as it helps law enforcers effectively deploy the available resources in a better and effective manner, which enables them to identify and apprehend suspects. Crime analysis is also very significant when it comes to arriving at solutions devised to come up with the right solution to solve the current crime problem and issues as well as coming up with the right prevention strategies. Since the year 2014, crime rates in the USA have increased steadily as per a study done by USAFacts, which is a non-partisan initiative (Osborne & Wernicke, 2013). With this increase in crime rates, which has majorly resulted in massive growth in technology, it is essential to come up with better means and ways of dealing with the increased crime rates. With the current advancement in technology, better law enforcement tools developed, which has enabled better crime deterrence in better and efficient ways. All this has been facilitated by the efforts of crime analysts who have come up with better tools and thus enabling the law enforcers to better deal with the crimes (Osborne & Wernicke, 2013). In this paper, I will consider the application of crime analysis technology and techniques in fighting crimes. Application of crime analysis technology and techniques used to make crime analysis more accurate and efficient.
Currently, the two technological tools that are used in predictive policing software have enabled security agencies to effectively use predictive policing ("Crime Analysis: Fighting Crime with Data," 2017). Application of this software has enabled better crime prevention as with data obtained in the previous crimes have been used to predict possible future severe crimes in a specific area.
Through the adoption and use of crime analysis, law enforcement agencies have been able to fight against crimes as when compared with the past effectively. The use of crime analysis comes at the right time, where there has been an increase in crime rates in the current digital error. In a survey done by Wynyard group in 2015, the study revealed that for every 10 law enforcement officials 9 of them believe that the use of current technology in crime analysis has had positive effects in helping the agencies in solving crimes as they can identify essential links and trends in crimes ("Crime Analysis: Fighting Crime with Data," 2017). In the same way, other sectors have benefited from data analysis with spreadsheets, databases, and mapping, law enforcers have been able to use data analysis to come up with a better decision. Crime analysis ha ...
Running head: CRIME ANALYSIS 1
CRIME ANALYSIS TECHNOLOGY 2
Crime analysis is a function that usually involves the systemic analysis in identifying as well as analyzing the crime patterns and trends. Crime analysis is very important for law enforcement agencies as it helps law enforcers effectively deploy the available resources in a better and effective manner, which enables them to identify and apprehend suspects. Crime analysis is also very significant when it comes to arriving at solutions devised to come up with the right solution to solve the current crime problem and issues as well as coming up with the right prevention strategies. Since the year 2014, crime rates in the USA have increased steadily as per a study done by USAFacts, which is a non-partisan initiative (Osborne & Wernicke, 2013). With this increase in crime rates, which has majorly resulted in massive growth in technology, it is essential to come up with better means and ways of dealing with the increased crime rates. With the current advancement in technology, better law enforcement tools developed, which has enabled better crime deterrence in better and efficient ways. All this has been facilitated by the efforts of crime analysts who have come up with better tools and thus enabling the law enforcers to better deal with the crimes (Osborne & Wernicke, 2013). In this paper, I will consider the application of crime analysis technology and techniques in fighting crimes. Application of crime analysis technology and techniques used to make crime analysis more accurate and efficient.
Currently, the two technological tools that are used in predictive policing software have enabled security agencies to effectively use predictive policing ("Crime Analysis: Fighting Crime with Data," 2017). Application of this software has enabled better crime prevention as with data obtained in the previous crimes have been used to predict possible future severe crimes in a specific area.
Through the adoption and use of crime analysis, law enforcement agencies have been able to fight against crimes as when compared with the past effectively. The use of crime analysis comes at the right time, where there has been an increase in crime rates in the current digital error. In a survey done by Wynyard group in 2015, the study revealed that for every 10 law enforcement officials 9 of them believe that the use of current technology in crime analysis has had positive effects in helping the agencies in solving crimes as they can identify essential links and trends in crimes ("Crime Analysis: Fighting Crime with Data," 2017). In the same way, other sectors have benefited from data analysis with spreadsheets, databases, and mapping, law enforcers have been able to use data analysis to come up with a better decision. Crime analysis ha.
10 Criminology in the FutureCriminology in the FutureKristop.docxhyacinthshackley2629
10 Criminology in the Future
Criminology in the Future
Kristopher Freitag, Javielle Watson, Michael Westphal, Starcia Zeigler
CJA/314
April 7, 2014
Judy Mazzucca
Technology is advancing in every aspect of the criminal justice system, from the investigation to the prosecution of the crimes. Crime fighting methodologies have the potential to greatly assist law enforcement in the war on crime. Some experts even think that some software and tools will be able to help prevent crime. (Yeung, n.d.). Methodologies, such as mandating DNA collection programs, biometrics, and implementing cybercrime spyware programs are on the list of the next big things of the future, when it comes to fighting crime. DNA testing helps law enforcement investigate and prosecute crimes, as well as clear the names of those who have been wrongfully convicted. There are currently about twenty states with laws requiring DNA collection at the time of the person’s arrest. The federal government also has this requirement. As, with any controversial subject, DNA testing has its critics. Some are saying that DNA testing is in violation of the Fourth Amendment, especially for those who have not been convicted of a crime. Others are concerned that DNA testing may open the doors for abuse of the genetic information being stored in the databases. (Berson, n.d.). Biometrics are automated methods of recognizing a person based on physiological or behavioral characteristics. Some of the features measured using biometrics are handwriting, voice, iris, hand geometry, vein, retinal, and fingerprints. Biometric based solutions provide personal data privacy, and confidential financial transactions, and are starting to become the foundation of an extensive array of highly secure identification and personal verification solutions. The need for highly secure identification and personal verification technologies is great, due to the increased number of transaction fraud and security breaches. This need is especially great in the areas of local, state, and federal governments. Infrastructures such as electronic banking, health and social services, law enforcement, and retail sales are already taking advantage of, and seeing the benefits of biometric technology. ("The Biometrics Consortium", n.d.).
As we become more and more dependent on technology, the increase of cybercrimes are skyrocketing, which has forced law enforcement to figure out ways of combatting cybercrimes. We have become extremely vulnerable to many cybercrimes, including social media fraud, which consists of cyber criminals using social media to steal the identities of unsuspecting people; and luring people to download malicious materials, or reveal their passwords; corporate security breaches, which consists of cyber criminals exploiting company employees via scams; and phishing, which involves cyber criminals targeting company employees by sending emails that appear to be from someone within the company. ("Homeland .
contributed articless e p t e m b e r 2 0 0 9 v o l.docxdonnajames55
contributed articles
s e p t e m b e r 2 0 0 9 | v o l . 5 2 | n o . 9 | c o m m u n i c at i o n s o f t h e a c m 133
d o i : 1 0 . 1 1 4 5 / 1 5 6 2 1 6 4 . 1 5 6 2 1 9 8
by RobeRt Willison and mikko siponen
I n f o r m a t I o n s e c u r I t y h a s b e c o m e I n c r e a s I n g ly
important for organizations, given their dependence
on ICT. Not surprisingly, therefore, the external
threats posed by hackers and viruses have received
extensive coverage in the mass media. Yet numerous
security surveys also point to the ‘insider’ threat of
employee computer crime. In 2006, for example, the
Global Security Survey by Deloitte reports that 28% of
respondent organizations encountered
considerable internal computer fraud.5
This figure may not appear high, but the
impact of crime perpetrated by insiders
can be profound. Donn Parker7 argues
that ‘cyber-criminals’ should be con-
sidered in terms of their criminal attri-
butes, which include skills, knowledge,
resources, access and motives (SKRAM).
It is as a consequence of such attributes,
acquired within the organization, that
employers can pose a major threat.
Hence, employees use skills gained
through their legitimate work duties for
illegitimate gain. A knowledge of securi-
ty vulnerabilities can be exploited, util-
ising resources and access are provided
by companies. It may even be the case
that the motive is created by the organi-
zation in the form of employee disgrun-
tlement. These criminal attributes aid
offenders in the pursuit of their crimi-
nal acts, which in the extreme can bring
down an organization.
In the main, companies have ad-
dressed the insider threat through a
workforce, which is made aware of its
information security responsibilities
and acts accordingly. Thus, security
policies and complementary educa-
tion and awareness programmes are
now commonplace for organizations.
That said, little progress has been
made in understanding the insider
threat from an offender’s perspective.
As organizations attempt to grapple
with the behavior of dishonest employ-
ees, criminology potentially offers a
body of knowledge for addressing this
problem. It is suggested that Situation-
al Crime Prevention (SCP),1 a relative
newcomer to criminology, can help en-
hance initiatives aimed at addressing
the insider threat.
In this article, we discuss how re-
cent criminological developments that
focus on the criminal act, represent a
departure from traditional criminolo-
gy, which examines the causes of crimi-
nality. As part of these recent develop-
ments we discuss SCP. After defining
this approach, we illustrate how it can
inform and enhance information secu-
rity practices.
overcoming
the insider:
reducing
employee
computer
crime through
situational
crime
prevention
134 c o m m u n i c at i o n s o f t h e a c m | s e p t e m b e r 2 0 0 9 | v o l . 5 2 | n o . 9
contributed artic.
5 role of data science in fraud detection1stepgrow
Data science plays a crucial role in fraud detection by utilizing predictive analytics, anomaly detection, machine learning algorithms, pattern recognition, and data visualization to effectively identify and prevent fraudulent activities.For more information Please visit the 1stepGrow website or AI and data science course
Strategic HR Leveraging AI for Smarter Human Resource ManagementSunil Jagani
The merger of Artificial Intelligence (AI) in Human Resources (HR) is changing how we do things in HR starting from the root. Think of it like this: AI is leading us from the old management of people and their work to a new intelligent way that makes everyone do better.
Major companies will be the drivers, aiming to implement AI into all kinds of HR processes by 2024. This is more than just about making jobs easier, it is about making better decisions and an environment where everyone feels valued and understood.
AI is indeed changing the game however there is a need to maintain the human touch in HR. Yes, AI can go through data and help us to find the best people for the job or to know how to help employees grow. However, HR is essentially, communicating, understanding each other, and working together to make the workplace better.
It is all about finding the equilibrium between using AI to make our jobs simpler and ensuring that we do not lose the individual relationships that make our workplaces human. This equilibrium is what will make the future of HR interesting and limitless in opportunities.
JWT Authentication and Role-Based AuthorizationSunil Jagani
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Their utilization in authentication lies in their ability to reliably transfer user identity and credentials between a client and a server, enabling user authentication without needing to repeatedly query the database or keep session information on the server. This, therefore, makes JWT a very effective, lightweight stateless authentication means.
On the contrary, role-based authorization is a system that limits access to resources by their assigned roles. This approach provides application-level access control, where users are given different access levels based on their roles.
AllianceTek Delivering Exceptional Value As Recognized by GoodFirm and ClutchSunil Jagani
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AI in Project Management An Overview - AllianceTekSunil Jagani
Artificial Intelligence (AI) has revolutionized project management, noticeable in developing innovations like HiveMind, a state-of-the-art, live AI assistant. This technological innovation is revolutionizing the standard project management processes in more than one way: improvement of team collaboration, simplification of project milestones tracking, and sophisticated analysis of project-related data.
AI is not only a trend in project management, but it is a paradigm shift that offers better-oiled systems and therefore better project results. Through automation of day-to-day activities and provision of relevant insights, AI tools make project teams work on making strategic decisions, and creative problem solving, thereby, increasing the general efficiency and effectiveness of project management practices.
What App Development Looks Like in - 2024Sunil Jagani
As the digital world evolves, it introduces fresh opportunities, challenges, and expectations from users. Today's novelties become tomorrow's necessities, emphasizing the importance for developers to stay updated. Identifying which trends can significantly enhance your application, making it smarter, more efficient, and captivating, is crucial.
Why is this significant? Apps that succeed are those that meet the immediate needs of users while foreseeing future requirements. They are developed by visionaries who recognize that leadership in app creation stems from proactive innovation. This mindset demands curiosity, adaptability, and a dedication to ongoing education.
For anyone embarking on their first or latest app project, the future of app development presents an open field ripe for exploration. It's where the minds that are wild with creativity and sensitive to thinking are leaving their mark. Getting involved in the most recent trends and technologies does not belong just to being up-to-date, it is also about taking participation in shaping the future.
The Future of App Development, All About the Revolutionary FlutterFlow AI GenSunil Jagani
The world of app development is continuously evolving, embracing new technologies and methodologies to create more user-centric and efficient applications. However, they came with higher costs and the need for specialized development teams for each platform.
A significant development in this field is the integration of AI in the process, a step that has proven to streamline mobile app development and enhance output quality. One of the champions of this evolution is FlutterFlow, which has now launched its AI assistant, the FlutterFlow AI Gen.
Should You Be Migrating from MVC to .NET CoreSunil Jagani
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7 Mobile App Analytics Tools in 2024 - AllianceTekSunil Jagani
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Best Practices for SharePoint Governance and Security in 2023Sunil Jagani
Explore the latest best practices for SharePoint Solutions governance and security in 2023. Read more to dive deep into essential features, user habits, and tools.
Top 10 New Features of SharePoint in 2023Sunil Jagani
What’s new in SharePoint 2023? In this blog, we talk about 10 features of SharePoint that can change business operations for the better. Read now to learn more.
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Unearth the vast capabilities of SharePoint Lists. Learn how to streamline data management, integrate seamlessly, and amplify business efficiency. Read now.
7 Upcoming Trends for Mobile App Development in 2023Sunil Jagani
The mobile app market has been growing exponentially in recent years, and it shows no signs of slowing down in 2023. In fact, the demand for mobile apps is only going to increase as more and more businesses recognize the importance of having a mobile presence. With the rise of new technologies and the changing preferences of consumers, it is essential for businesses to keep up with the latest trends in mobile app development.
The Role of Artificial Intelligence (AI) in Mobile App DevelopmentSunil Jagani
Wondering how artificial intelligence is shaping mobile development and finding innovative ways for developing mobile applications? Read the blog to know more.
Progressive Web Apps: The Future of Mobile DevelopmentSunil Jagani
Progressive Web Apps (PWAs) are in the rage and for all the right reasons. Learn more about this future-proof element of mobile development and how to use it.
Building Cross-Platform Mobile Apps A Comparison of FrameworksSunil Jagani
This blog provides insights into 4 cross-platform mobile application development frameworks. Read more to know which one you could use for your business.
Best Practices for Mobile App Security and Data PrivacySunil Jagani
Mobile app development is continually increasing and so are cybercrimes. Read on to get insights into how you can secure your app data from these cyber threats.
The Top 10 Benefits of Salesforce for Business GrowthSunil Jagani
How can Salesforce solutions help your business grow? Read on to find out how Salesforce CRM solutions improves customer relationships and increases sales productivity.
The Importance of Mobile Accessibility in Salesforce DesignSunil Jagani
Salesforce is a popular customer relationship management (CRM) platform that offers a wide range of tools to help businesses manage their customer data, sales processes, marketing campaigns, and customer service activities. With the rise of mobile technology, it is now more important than ever to ensure that Salesforce CRM is accessible and user-friendly on mobile devices. In this article, we will discuss the importance of mobile accessibility in Salesforce CRM and provide tips for designing a mobile-friendly Salesforce environment.
Explore our comprehensive data analysis project presentation on predicting product ad campaign performance. Learn how data-driven insights can optimize your marketing strategies and enhance campaign effectiveness. Perfect for professionals and students looking to understand the power of data analysis in advertising. for more details visit: https://bostoninstituteofanalytics.org/data-science-and-artificial-intelligence/
Opendatabay - Open Data Marketplace.pptxOpendatabay
Opendatabay.com unlocks the power of data for everyone. Open Data Marketplace fosters a collaborative hub for data enthusiasts to explore, share, and contribute to a vast collection of datasets.
First ever open hub for data enthusiasts to collaborate and innovate. A platform to explore, share, and contribute to a vast collection of datasets. Through robust quality control and innovative technologies like blockchain verification, opendatabay ensures the authenticity and reliability of datasets, empowering users to make data-driven decisions with confidence. Leverage cutting-edge AI technologies to enhance the data exploration, analysis, and discovery experience.
From intelligent search and recommendations to automated data productisation and quotation, Opendatabay AI-driven features streamline the data workflow. Finding the data you need shouldn't be a complex. Opendatabay simplifies the data acquisition process with an intuitive interface and robust search tools. Effortlessly explore, discover, and access the data you need, allowing you to focus on extracting valuable insights. Opendatabay breaks new ground with a dedicated, AI-generated, synthetic datasets.
Leverage these privacy-preserving datasets for training and testing AI models without compromising sensitive information. Opendatabay prioritizes transparency by providing detailed metadata, provenance information, and usage guidelines for each dataset, ensuring users have a comprehensive understanding of the data they're working with. By leveraging a powerful combination of distributed ledger technology and rigorous third-party audits Opendatabay ensures the authenticity and reliability of every dataset. Security is at the core of Opendatabay. Marketplace implements stringent security measures, including encryption, access controls, and regular vulnerability assessments, to safeguard your data and protect your privacy.
Levelwise PageRank with Loop-Based Dead End Handling Strategy : SHORT REPORT ...Subhajit Sahu
Abstract — Levelwise PageRank is an alternative method of PageRank computation which decomposes the input graph into a directed acyclic block-graph of strongly connected components, and processes them in topological order, one level at a time. This enables calculation for ranks in a distributed fashion without per-iteration communication, unlike the standard method where all vertices are processed in each iteration. It however comes with a precondition of the absence of dead ends in the input graph. Here, the native non-distributed performance of Levelwise PageRank was compared against Monolithic PageRank on a CPU as well as a GPU. To ensure a fair comparison, Monolithic PageRank was also performed on a graph where vertices were split by components. Results indicate that Levelwise PageRank is about as fast as Monolithic PageRank on the CPU, but quite a bit slower on the GPU. Slowdown on the GPU is likely caused by a large submission of small workloads, and expected to be non-issue when the computation is performed on massive graphs.
Techniques to optimize the pagerank algorithm usually fall in two categories. One is to try reducing the work per iteration, and the other is to try reducing the number of iterations. These goals are often at odds with one another. Skipping computation on vertices which have already converged has the potential to save iteration time. Skipping in-identical vertices, with the same in-links, helps reduce duplicate computations and thus could help reduce iteration time. Road networks often have chains which can be short-circuited before pagerank computation to improve performance. Final ranks of chain nodes can be easily calculated. This could reduce both the iteration time, and the number of iterations. If a graph has no dangling nodes, pagerank of each strongly connected component can be computed in topological order. This could help reduce the iteration time, no. of iterations, and also enable multi-iteration concurrency in pagerank computation. The combination of all of the above methods is the STICD algorithm. [sticd] For dynamic graphs, unchanged components whose ranks are unaffected can be skipped altogether.
Adjusting primitives for graph : SHORT REPORT / NOTESSubhajit Sahu
Graph algorithms, like PageRank Compressed Sparse Row (CSR) is an adjacency-list based graph representation that is
Multiply with different modes (map)
1. Performance of sequential execution based vs OpenMP based vector multiply.
2. Comparing various launch configs for CUDA based vector multiply.
Sum with different storage types (reduce)
1. Performance of vector element sum using float vs bfloat16 as the storage type.
Sum with different modes (reduce)
1. Performance of sequential execution based vs OpenMP based vector element sum.
2. Performance of memcpy vs in-place based CUDA based vector element sum.
3. Comparing various launch configs for CUDA based vector element sum (memcpy).
4. Comparing various launch configs for CUDA based vector element sum (in-place).
Sum with in-place strategies of CUDA mode (reduce)
1. Comparing various launch configs for CUDA based vector element sum (in-place).
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Empowering the Data Analytics Ecosystem: A Laser Focus on Value
The data analytics ecosystem thrives when every component functions at its peak, unlocking the true potential of data. Here's a laser focus on key areas for an empowered ecosystem:
1. Democratize Access, Not Data:
Granular Access Controls: Provide users with self-service tools tailored to their specific needs, preventing data overload and misuse.
Data Catalogs: Implement robust data catalogs for easy discovery and understanding of available data sources.
2. Foster Collaboration with Clear Roles:
Data Mesh Architecture: Break down data silos by creating a distributed data ownership model with clear ownership and responsibilities.
Collaborative Workspaces: Utilize interactive platforms where data scientists, analysts, and domain experts can work seamlessly together.
3. Leverage Advanced Analytics Strategically:
AI-powered Automation: Automate repetitive tasks like data cleaning and feature engineering, freeing up data talent for higher-level analysis.
Right-Tool Selection: Strategically choose the most effective advanced analytics techniques (e.g., AI, ML) based on specific business problems.
4. Prioritize Data Quality with Automation:
Automated Data Validation: Implement automated data quality checks to identify and rectify errors at the source, minimizing downstream issues.
Data Lineage Tracking: Track the flow of data throughout the ecosystem, ensuring transparency and facilitating root cause analysis for errors.
5. Cultivate a Data-Driven Mindset:
Metrics-Driven Performance Management: Align KPIs and performance metrics with data-driven insights to ensure actionable decision making.
Data Storytelling Workshops: Equip stakeholders with the skills to translate complex data findings into compelling narratives that drive action.
Benefits of a Precise Ecosystem:
Sharpened Focus: Precise access and clear roles ensure everyone works with the most relevant data, maximizing efficiency.
Actionable Insights: Strategic analytics and automated quality checks lead to more reliable and actionable data insights.
Continuous Improvement: Data-driven performance management fosters a culture of learning and continuous improvement.
Sustainable Growth: Empowered by data, organizations can make informed decisions to drive sustainable growth and innovation.
By focusing on these precise actions, organizations can create an empowered data analytics ecosystem that delivers real value by driving data-driven decisions and maximizing the return on their data investment.
2. SUMMARY
Controlling crimes and decreasing illegal activity is
getting a lot tougher for authorities. Even with all the
monitoring and scrutinization via patrols or undercover
missions, activity of crimes never seem to suppress.
Criminal acts are often works of masterminds and they
are uniquely crafted to pan out without making any
noise. So, by the time the police sort it out, the crimes
have already occured or is at its final stage where every
effort will be futile.
Therefore, there is only one way to stop crimes -
preventing it from happening at all. Emerging
technologies like BigData, Analytics, and Artificial
Intelligence has paved the way for predictive policing, a
term used to describe the latest way of preventing and
predicting crimes.
3. GLOBAL
INTERPRETATION
As per Beck and McCue 2015, "Predictive
policing is all about Proactive approach (action
taken a priory) of policing instead of the
traditional reactive one (action taken after
incidence)".
Charlie Beck, Chief of Detectives, Los Angeles
Police Department, and Colleen McCue,
President and CEO, MC2 Solutions, LLC, called
predictive policing, in general, “the next era in
policing.”
Predictive policing has been named as one of
the 50 best invention of 2011 by Times
Magazine
4. DEFINITION
In a broad way it is the application of
analytics and artificial intelligence for
prediction, identification and precaution of
any criminal activity, social threat and
public outrage.
5. HOW THE
IDEA WAS
ADOPTED?
The idea was influenced from the fact
- From weather forecasting to share
trading, we have been trying to
predict the future, in order to deal
better with the outcomes.
Hence, implementing the same logic
in preventing any sort of organized
crime made perfect sense.
6. AS GIVEN BY A REPORT OF RAND
CORPORATION PERRY ET.AL, PREDICTIVE
POLICING IS THE USE OF ANALYTICAL
TECHNIQUES TO IDENTIFY PROMISING
TARGETS FOR POLICE INTERVENTION
WITH THE GOAL OF PREVENTING CRIME,
SOLVING PAST CRIMES, AND IDENTIFYING
POTENTIAL OFFENDERS AND VICTIMS.
7. ECOMMERCE BUSINESS HAS A PROVEN
RECORD OF USING DATA ANALYTICS AND
PATTERN RECOLONIZATION TO ENDORSE
SPECIFIC PRODUCT TO NEEDFUL
CUSTOMERS.
11. UNDERSTANDING
THE
CRIME
This is about predicting where the
potential crime is going to happen
depending on the circumstances.
The above thing separates it from
“Minority report” where it is predicted
who will do the crime.
So, the strength of predictive policing is
to prevent organized crime instead of
capturing the criminal.
12. FIGURING OUT
THE
PATTERN
Like criminal, every crime have a signature.
The pattern is affected by geographic
location, time of crime, climate-weather.
In organized crime, most of the time criminal
share common byground.
This pattern helps to figuring out the crime.
Apart from these, the group behavior during
a public outrage is mostly influenced by
messages and videos spreading in social
networks during that course of time.
So, group behavior pattern can be used to
prevent such incidents.
13. IDENTIFYING
THE
TYPE
As gin by Perry et.al, Predictive policing
methods fall into four general categories:
1. Methods for predicting crimes
2. Methods for predicting offenders
3. Methods for predicting perpetrators'
identities
4. Methods for predicting victims of crime
17. DRAWBACKS
False positive reporting (falsely
predicting a crime to happen).
- This not only harasses the innocent
public but dilutes credibility of the system.
- After few false positive alarms, office
might ignore a true positive alarm
Officer might find it hard to follow
computer instructions than instincts.
Collection of data and making
database might hamper the privacy of
the public.
18. FUTURE
Clubbing with data analytics, Big Data,
and artificial intelligence, predictive
policing can lead to a healthy society
debarred of organized crime.
This can prevent the ever-increasing
cyber crime as well.
20. ABOUT
Sunil Jagani is the founder of AllianceTek, an IT consulting firm that
offers proactive services to thousands of clients across the world.
Besides managing his company that is headquarted in Pennsylvania
and has 9 offices located within and outside of US, he likes to write on
various topics.
He likes sharing his massive insight on emerging technologies like
Artificial Intelligence, BigData, Internet of Things, Mobile Technology,
etc., and how they can aid in improving the environment around us.
21. SUNIL JAGANI
Alliance Tek
100 Deerfield Lane, Suite 250
Malvern, PA 19355
Phone: 484-892-5713
Email: pa.info@alliancetek.com
https://twitter.com/suniljagani
https://www.facebook.com/alliancetek
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