IJERA (International journal of Engineering Research and Applications) is International online, ... peer reviewed journal. For more detail or submit your article, please visit www.ijera.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
Crime Analysis & Prediction System is a system to analyze & detect crime hotspots & predict crime.
It collects data from various data sources - crime data from OpenData sites, US census data, social media, traffic & weather data etc.
It leverages Microsoft's Azure Cloud and on premise technologies for back-end processing & desktop based visualization tools.
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.
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.
Our goal is to create a web application that would give insights to its user about the crime scenario and its various aspects in Chicago.
Our application will contain:
A search box/drop down list where user can select a district.
Geospatial analysis using ArcGIS maps and visualizations that are embedded into the web app which will be dynamically updated to show most interesting patterns or heat maps for that district.
Statistical analysis and visualizations on historical data to the user.
Prediction of the date when the next crime will happen and its probability.
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
Crime Analysis & Prediction System is a system to analyze & detect crime hotspots & predict crime.
It collects data from various data sources - crime data from OpenData sites, US census data, social media, traffic & weather data etc.
It leverages Microsoft's Azure Cloud and on premise technologies for back-end processing & desktop based visualization tools.
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.
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.
Our goal is to create a web application that would give insights to its user about the crime scenario and its various aspects in Chicago.
Our application will contain:
A search box/drop down list where user can select a district.
Geospatial analysis using ArcGIS maps and visualizations that are embedded into the web app which will be dynamically updated to show most interesting patterns or heat maps for that district.
Statistical analysis and visualizations on historical data to the user.
Prediction of the date when the next crime will happen and its probability.
A Comparative Study of Data Mining Methods to Analyzing Libyan National Crime...Zakaria 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.
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.
- Project Title: Chicago crime analysis
- Course name: Principles and Practice in Data Mining
- Semester: Autumn 2016
- Professor: Yuran SEO
- Sungkyunkwan University
- Department: philosophy
- Name: jangyoung seo
- Contact: laiha10@naver.com
An unsupervised learning
k-means clustering technique is used to identify and focus
on a set of crimes (prostitution, narcotics, burglary, battery
and interference with a public officer) recorded in the city
of Chicago. The crime data is supplemented with orthogonal
temperature and unemployment data. ANOVA and Kruskal-
Wallis statistical tests assess the temporal significance in
crimes clusters. The findings indicate various crime hot spots
which are temporal and location specific, and therefore may
act as input to the scheduling and allocation of policing resources.
IJERA (International journal of Engineering Research and Applications) is International online, ... peer reviewed journal. For more detail or submit your article, please visit www.ijera.com
Data mining, prediction, correlation, regression, correlation analysis, regre...IJERA Editor
The present work deals with the evaluation of some viscosity index improving additives. Three esters were
prepared by esterification of acrylic acid with alcohols having different alkyl chain length. The structures of the
prepared compounds were confirmed by Infra Red Spectroscopy. Three polymeric compounds were prepared by
free radical polymerization of the different acrylates with vinyl acetate. The molecular weights of the prepared
compounds were determined by Gel Permeation Chromatography. The prepared copolymers were evaluated as
viscosity index improvers for lube oil and the rheological properties of lube oil were studied. It was found that
the efficiency of the prepared additives as viscosity index improvers increases with increasing the molecular
weight and concentration of the prepared copolymers and it was found that the apparent viscosity decreases with
an increase in temperature.
A Comparative Study of Data Mining Methods to Analyzing Libyan National Crime...Zakaria 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.
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.
- Project Title: Chicago crime analysis
- Course name: Principles and Practice in Data Mining
- Semester: Autumn 2016
- Professor: Yuran SEO
- Sungkyunkwan University
- Department: philosophy
- Name: jangyoung seo
- Contact: laiha10@naver.com
An unsupervised learning
k-means clustering technique is used to identify and focus
on a set of crimes (prostitution, narcotics, burglary, battery
and interference with a public officer) recorded in the city
of Chicago. The crime data is supplemented with orthogonal
temperature and unemployment data. ANOVA and Kruskal-
Wallis statistical tests assess the temporal significance in
crimes clusters. The findings indicate various crime hot spots
which are temporal and location specific, and therefore may
act as input to the scheduling and allocation of policing resources.
IJERA (International journal of Engineering Research and Applications) is International online, ... peer reviewed journal. For more detail or submit your article, please visit www.ijera.com
Data mining, prediction, correlation, regression, correlation analysis, regre...IJERA Editor
The present work deals with the evaluation of some viscosity index improving additives. Three esters were
prepared by esterification of acrylic acid with alcohols having different alkyl chain length. The structures of the
prepared compounds were confirmed by Infra Red Spectroscopy. Three polymeric compounds were prepared by
free radical polymerization of the different acrylates with vinyl acetate. The molecular weights of the prepared
compounds were determined by Gel Permeation Chromatography. The prepared copolymers were evaluated as
viscosity index improvers for lube oil and the rheological properties of lube oil were studied. It was found that
the efficiency of the prepared additives as viscosity index improvers increases with increasing the molecular
weight and concentration of the prepared copolymers and it was found that the apparent viscosity decreases with
an increase in temperature.
International Journal of Engineering Research and Applications (IJERA) is a team of researchers not publication services or private publications running the journals for monetary benefits, we are association of scientists and academia who focus only on supporting authors who want to publish their work. The articles published in our journal can be accessed online, all the articles will be archived for real time access.
Our journal system primarily aims to bring out the research talent and the works done by sciaentists, academia, engineers, practitioners, scholars, post graduate students of engineering and science. This journal aims to cover the scientific research in a broader sense and not publishing a niche area of research facilitating researchers from various verticals to publish their papers. It is also aimed to provide a platform for the researchers to publish in a shorter of time, enabling them to continue further All articles published are freely available to scientific researchers in the Government agencies,educators and the general public. We are taking serious efforts to promote our journal across the globe in various ways, we are sure that our journal will act as a scientific platform for all researchers to publish their works online.
A Survey of String Matching AlgorithmsIJERA Editor
The concept of string matching algorithms are playing an important role of string algorithms in finding a place where one or several strings (patterns) are found in a large body of text (e.g., data streaming, a sentence, a paragraph, a book, etc.). Its application covers a wide range, including intrusion detection Systems (IDS) in computer networks, applications in bioinformatics, detecting plagiarism, information security, pattern recognition, document matching and text mining. In this paper we present a short survey for well-known and recent updated and hybrid string matching algorithms. These algorithms can be divided into two major categories, known as exact string matching and approximate string matching. The string matching classification criteria was selected to highlight important features of matching strategies, in order to identify challenges and vulnerabilities.
Fighting Accident Using Eye Detection forSmartphonesIJERA Editor
This paper is an attempt to investigate an important problem and approaches of human eye detection, blinking, and tracking. A new system was proposed and implemented using android technology for smartphones. System creatively reduces accidents due to drivers’ fatigue by focusing on treating the driver after fatigue has been detected to achieve decrease in accident likelihood.
Smartphone's have been the important tools in our society for the abundant functions including communication, entertainment and online office etc. as the pivotal devices of mobile computing. Smartphone development has also become more important than before. Android is one of the emerging leading operating systems for smartphones as an open source system platform. Many smartphones have adopted this platform and more smartphones will do so in the future. The proposed system is well-suited for real world driving conditions since it can be non-intrusive by using video cameras to detect changes. Driver operation and vehicle behavior can be implemented by equipping automobiles with the ability to monitoring the response of the driver. This involves periodically requesting the driver to send a response to the system to indicate alertness. The propose system based on eyes closer count & yawning count of the driver. By monitoring the eyes and face, it is believed that the symptoms of driver fatigue can be detected early enough to avoid a car accident and providing the driver with a warning if the driver takes his or her eye off the road.
The International Journal of Engineering and Science (IJES)theijes
The International Journal of Engineering & Science is aimed at providing a platform for researchers, engineers, scientists, or educators to publish their original research results, to exchange new ideas, to disseminate information in innovative designs, engineering experiences and technological skills. It is also the Journal's objective to promote engineering and technology education. All papers submitted to the Journal will be blind peer-reviewed. Only original articles will be published.
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.
A predictive model for mapping crime using big data analyticseSAT Journals
Abstract Crime reduction and prevention challenges in today’s world are becoming increasingly complex and are in need of a new technique that can handle the vast amount of information that is being generated. Traditional police capabilities mostly fall short in depicting the original division of criminal activities, thus contribute less in the suitable allocation of police services. In this paper methods are described for crime event forecasting, using Hadoop, by studying the geographical areas which are at greater risk and outside the traditional policing limits. The developed method makes the use of a geographical crime mapping algorithm to identify areas that have relatively high cases of crime. The term used for such places is hot spots. The identified hotspot clusters give valuable data that can be used to train the artificial neural network which further can model the trends of crime. The artificial neural network specification and estimation approach is enhanced by processing capability of Hadoop platform. Keywords— Crime forecasting; Cluster analysis; artificial neural networks; Patrolling; Big data; Hadoop; Gamma test.
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.
The paper emphasizes the human aspects of cyber incidents concerning protecting information and
technology assets by addressing behavioral analytics in cybersecurity for digital forensics applications.
The paper demonstrates the human vulnerabilities associated with information systems technologies and
components. This assessment is based on past literature assessments done in this area. This study also
includes analyses of various frameworks that have led to the adoption of behavioral analysis in digital
forensics. The study's findings indicate that behavioral evidence analysis should be included as part of the
digital forensics examination. The provision of standardized investigation methods and the inclusion of
human factors such as motives and behavioral tendencies are some of the factors attached to the use of
behavioral digital forensic frameworks. However, the study also appreciates the need for a more
generalizable digital forensic method.
The paper emphasizes the human aspects of cyber incidents concerning protecting information and
technology assets by addressing behavioral analytics in cybersecurity for digital forensics applications.
The paper demonstrates the human vulnerabilities associated with information systems technologies and
components. This assessment is based on past literature assessments done in this area. This study also
includes analyses of various frameworks that have led to the adoption of behavioral analysis in digital
forensics. The study's findings indicate that behavioral evidence analysis should be included as part of the
digital forensics examination. The provision of standardized investigation methods and the inclusion of
human factors such as motives and behavioral tendencies are some of the factors attached to the use of
behavioral digital forensic frameworks. However, the study also appreciates the need for a more
generalizable digital forensic method.
The paper emphasizes the human aspects of cyber incidents concerning protecting information and
technology assets by addressing behavioral analytics in cybersecurity for digital forensics applications.
The paper demonstrates the human vulnerabilities associated with information systems technologies and
components. This assessment is based on past literature assessments done in this area. This study also
includes analyses of various frameworks that have led to the adoption of behavioral analysis in digital
forensics. The study's findings indicate that behavioral evidence analysis should be included as part of the
digital forensics examination. The provision of standardized investigation methods and the inclusion of
human factors such as motives and behavioral tendencies are some of the factors attached to the use of
behavioral digital forensic frameworks. However, the study also appreciates the need for a more
generalizable digital forensic method.
This paper focuses on finding spatial and temporal criminal hotspots. It analyses two different real-world crimes datasets for Denver, CO and Los Angeles, CA and provides a comparison between the two datasets through a statistical analysis supported by several graphs. Then, it clarifies how we conducted Apriori algorithm to produce interesting frequent patterns for criminal hotspots. In addition, the paper shows how we used Decision Tree classifier and Naïve Bayesian classifier in order to predict potential crime types. To further analyse crimes’ datasets, the paper introduces an analysis study by combining our findings of Denver crimes’ dataset with its demographics information in order to capture the factors that might affect the safety of neighborhoods. The results of this solution could be used to raise people’s awareness regarding the dangerous locations and to help agencies to predict future crimes in a specific location within
a particular time.
SUPERVISED AND UNSUPERVISED MACHINE LEARNING METHODOLOGIES FOR CRIME PATTERN ...ijaia
Crime is a grave problem that affects all countries in the world. The level of crime in a country has a big
impact on its economic growth and quality of life of citizens. In this paper, we provide a survey of trends of
supervised and unsupervised machine learning methods used for crime pattern analysis. We use a spatiotemporal dataset of crimes in San Francisco, CA to demonstrate some of these strategies for crime
analysis. We use classification models, namely, Logistic Regression, Random Forest, Gradient Boosting
and Naive Bayes to predict crime types such as Larceny, Theft, etc. and propose model optimization
strategies. Further, we use a graph based unsupervised machine learning technique called core periphery
structures to analyze how crime behavior evolves over time. These methods can be generalized to use for
different counties and can be greatly helpful in planning police task forces for law enforcement and crime
prevention.
Supervised and Unsupervised Machine Learning Methodologies for Crime Pattern ...gerogepatton
Crime is a grave problem that affects all countries in the world. The level of crime in a country has a big
impact on its economic growth and quality of life of citizens. In this paper, we provide a survey of trends of
supervised and unsupervised machine learning methods used for crime pattern analysis. We use a spatiotemporal dataset of crimes in San Francisco, CA to demonstrate some of these strategies for crime
analysis. We use classification models, namely, Logistic Regression, Random Forest, Gradient Boosting
and Naive Bayes to predict crime types such as Larceny, Theft, etc. and propose model optimization
strategies. Further, we use a graph based unsupervised machine learning technique called core periphery
structures to analyze how crime behavior evolves over time. These methods can be generalized to use for
different counties and can be greatly helpful in planning police task forces for law enforcement and crime
prevention.
Kathryn E. ScarboroughEastern Kentucky UniversityMarc Ro.docxtawnyataylor528
Kathryn E. Scarborough
Eastern Kentucky University
Marc Rogers
Purdue University
Kelli Frakes
Eastern Kentucky University
Cristina San Martin
Purdue University
KKaatthhrryynn EE.. SSccaarrbboorroouugghh, PPhh..DD.., professor at the Department of Safety, Security, and Emergency
Management at Eastern Kentucky University, earned her Ph.D. in criminal justice from Sam Houston State
University. She also has an MA in applied sociology with a certificate in women’s studies from Old
Dominion and Norfolk State Universities, and a BS in criminal justice from the University of Southern
Mississippi. Prior to her teaching at Eastern Kentucky University, she was a police officer in Portsmouth,
Virginia, a United States Navy Hospital Corpsman/Emergency Medical Technician, and a chemical depen-
dency technician. In addition to her faculty role, Dr. Scarborough is Director for Research, Evaluation and
Testing for the Justice and Safety Center. Her current teaching and research interests include criminal
investigation, law enforcement technology, cyber crime and security, and police administration.
In her role as director for research, testing and evaluation, she has oversight of more than
70 projects funded by the Department of Homeland Security, the National Institute of Justice/Office of
Science and Technology, the State of Kentucky, and the Department of Defense. She also serves as project
director or codirector of the following projects: National Study on Criminal Investigation, the Digital
Evidence Assessment of Local and State Law Enforcement Organizations, the Rural Cyber Crime
Response and Prevention Team project, Cyber PAAL, and the ASIS International Security Trends project.
MMaarrcc RRooggeerrss,, PPhh..DD.., CISSP, CCCI, is the Chair of the Cyber Forensics Program in the Department of
Computer and Information Technology at Purdue University. He is an associate professor and also a
research faculty member at the Center for Education and Research in Information Assurance and
Security (CERIAS). Dr. Rogers was a senior instructor for (ISC)2, the international body that certifies
information system security professionals (CISSP), is a member of the quality assurance board for
(ISC)2’s SCCP designation, and is Chair of the Law, Compliance and Investigation Domain of interna-
tional Common Body of Knowledge (CBK) committee. He is a former police detective who worked in
the area of fraud and computer crime investigations. Dr. Rogers sits on the editorial board for several
professional journals and is a member of various national and international committees focusing on dig-
ital forensic science and digital evidence. He is the author of numerous book chapters, and journal pub-
lications in the field of digital forensics and applied psychological analysis. His research interests
include applied cyber forensics, psychological digital crime scene analysis, and cyber terrorism.
Chapter 24
Digital Evidence
477
M24_SCHM8860_01_SE_C24.QXD 2/4/08 ...
News document analysis by using a proficient algorithmIJERA Editor
News articles analyzing is one of the emerging research topic in the past few years. News paper discusses various types (political, education, employment, sports, agriculture, crime, medicine, business, etc) of news in different levels such as International, National, state and district level. In this news articles, crime discussion plays a major role because one crime leads to a many other crimes and also affect many other lives. In India, Madurai is one of the important places which have many historical monuments. Madurai is a sensitive place. This paper analyzes the crimes which occur in the year 2015 in and around Madurai. This analysis helps to police department to reduce the occurrence of crime in the future. This proposed system used Support Vector Machine (SVM) for effectively classify the document. News documents are preprocessed using pruning and stemming. From the stemmed words, the informative words are selected and weighted using feature selection methods such as Term-Frequency and Inverse Document Frequency (TF-IDF) and Chi-square. It returns the high dimensional vector space. It is reduced to low dimension using Latent Semantic Analysis (LSA) method. Compute the cosine similarity between the key document and news documents. Based on the value, the news documents are labeled as crime and non-crime. Some of the documents are used to train the SVM classifier. Some of the documents are used to test the performance of developed system. From the comparative study, it is identified that the performance of the proposed approach improves the classification accuracy.
Essentials of Automations: Optimizing FME Workflows with ParametersSafe Software
Are you looking to streamline your workflows and boost your projects’ efficiency? Do you find yourself searching for ways to add flexibility and control over your FME workflows? If so, you’re in the right place.
Join us for an insightful dive into the world of FME parameters, a critical element in optimizing workflow efficiency. This webinar marks the beginning of our three-part “Essentials of Automation” series. This first webinar is designed to equip you with the knowledge and skills to utilize parameters effectively: enhancing the flexibility, maintainability, and user control of your FME projects.
Here’s what you’ll gain:
- Essentials of FME Parameters: Understand the pivotal role of parameters, including Reader/Writer, Transformer, User, and FME Flow categories. Discover how they are the key to unlocking automation and optimization within your workflows.
- Practical Applications in FME Form: Delve into key user parameter types including choice, connections, and file URLs. Allow users to control how a workflow runs, making your workflows more reusable. Learn to import values and deliver the best user experience for your workflows while enhancing accuracy.
- Optimization Strategies in FME Flow: Explore the creation and strategic deployment of parameters in FME Flow, including the use of deployment and geometry parameters, to maximize workflow efficiency.
- Pro Tips for Success: Gain insights on parameterizing connections and leveraging new features like Conditional Visibility for clarity and simplicity.
We’ll wrap up with a glimpse into future webinars, followed by a Q&A session to address your specific questions surrounding this topic.
Don’t miss this opportunity to elevate your FME expertise and drive your projects to new heights of efficiency.
UiPath Test Automation using UiPath Test Suite series, part 4DianaGray10
Welcome to UiPath Test Automation using UiPath Test Suite series part 4. In this session, we will cover Test Manager overview along with SAP heatmap.
The UiPath Test Manager overview with SAP heatmap webinar offers a concise yet comprehensive exploration of the role of a Test Manager within SAP environments, coupled with the utilization of heatmaps for effective testing strategies.
Participants will gain insights into the responsibilities, challenges, and best practices associated with test management in SAP projects. Additionally, the webinar delves into the significance of heatmaps as a visual aid for identifying testing priorities, areas of risk, and resource allocation within SAP landscapes. Through this session, attendees can expect to enhance their understanding of test management principles while learning practical approaches to optimize testing processes in SAP environments using heatmap visualization techniques
What will you get from this session?
1. Insights into SAP testing best practices
2. Heatmap utilization for testing
3. Optimization of testing processes
4. Demo
Topics covered:
Execution from the test manager
Orchestrator execution result
Defect reporting
SAP heatmap example with demo
Speaker:
Deepak Rai, Automation Practice Lead, Boundaryless Group and UiPath MVP
GDG Cloud Southlake #33: Boule & Rebala: Effective AppSec in SDLC using Deplo...James Anderson
Effective Application Security in Software Delivery lifecycle using Deployment Firewall and DBOM
The modern software delivery process (or the CI/CD process) includes many tools, distributed teams, open-source code, and cloud platforms. Constant focus on speed to release software to market, along with the traditional slow and manual security checks has caused gaps in continuous security as an important piece in the software supply chain. Today organizations feel more susceptible to external and internal cyber threats due to the vast attack surface in their applications supply chain and the lack of end-to-end governance and risk management.
The software team must secure its software delivery process to avoid vulnerability and security breaches. This needs to be achieved with existing tool chains and without extensive rework of the delivery processes. This talk will present strategies and techniques for providing visibility into the true risk of the existing vulnerabilities, preventing the introduction of security issues in the software, resolving vulnerabilities in production environments quickly, and capturing the deployment bill of materials (DBOM).
Speakers:
Bob Boule
Robert Boule is a technology enthusiast with PASSION for technology and making things work along with a knack for helping others understand how things work. He comes with around 20 years of solution engineering experience in application security, software continuous delivery, and SaaS platforms. He is known for his dynamic presentations in CI/CD and application security integrated in software delivery lifecycle.
Gopinath Rebala
Gopinath Rebala is the CTO of OpsMx, where he has overall responsibility for the machine learning and data processing architectures for Secure Software Delivery. Gopi also has a strong connection with our customers, leading design and architecture for strategic implementations. Gopi is a frequent speaker and well-known leader in continuous delivery and integrating security into software delivery.
Smart TV Buyer Insights Survey 2024 by 91mobiles.pdf91mobiles
91mobiles recently conducted a Smart TV Buyer Insights Survey in which we asked over 3,000 respondents about the TV they own, aspects they look at on a new TV, and their TV buying preferences.
The Art of the Pitch: WordPress Relationships and SalesLaura Byrne
Clients don’t know what they don’t know. What web solutions are right for them? How does WordPress come into the picture? How do you make sure you understand scope and timeline? What do you do if sometime changes?
All these questions and more will be explored as we talk about matching clients’ needs with what your agency offers without pulling teeth or pulling your hair out. Practical tips, and strategies for successful relationship building that leads to closing the deal.
Dev Dives: Train smarter, not harder – active learning and UiPath LLMs for do...UiPathCommunity
💥 Speed, accuracy, and scaling – discover the superpowers of GenAI in action with UiPath Document Understanding and Communications Mining™:
See how to accelerate model training and optimize model performance with active learning
Learn about the latest enhancements to out-of-the-box document processing – with little to no training required
Get an exclusive demo of the new family of UiPath LLMs – GenAI models specialized for processing different types of documents and messages
This is a hands-on session specifically designed for automation developers and AI enthusiasts seeking to enhance their knowledge in leveraging the latest intelligent document processing capabilities offered by UiPath.
Speakers:
👨🏫 Andras Palfi, Senior Product Manager, UiPath
👩🏫 Lenka Dulovicova, Product Program Manager, UiPath
UiPath Test Automation using UiPath Test Suite series, part 3DianaGray10
Welcome to UiPath Test Automation using UiPath Test Suite series part 3. In this session, we will cover desktop automation along with UI automation.
Topics covered:
UI automation Introduction,
UI automation Sample
Desktop automation flow
Pradeep Chinnala, Senior Consultant Automation Developer @WonderBotz and UiPath MVP
Deepak Rai, Automation Practice Lead, Boundaryless Group and UiPath MVP
Kubernetes & AI - Beauty and the Beast !?! @KCD Istanbul 2024Tobias Schneck
As AI technology is pushing into IT I was wondering myself, as an “infrastructure container kubernetes guy”, how get this fancy AI technology get managed from an infrastructure operational view? Is it possible to apply our lovely cloud native principals as well? What benefit’s both technologies could bring to each other?
Let me take this questions and provide you a short journey through existing deployment models and use cases for AI software. On practical examples, we discuss what cloud/on-premise strategy we may need for applying it to our own infrastructure to get it to work from an enterprise perspective. I want to give an overview about infrastructure requirements and technologies, what could be beneficial or limiting your AI use cases in an enterprise environment. An interactive Demo will give you some insides, what approaches I got already working for real.
GraphRAG is All You need? LLM & Knowledge GraphGuy Korland
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https://arxiv.org/abs/2306.08302
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Link to video recording: https://bnctechforum.ca/sessions/selling-digital-books-in-2024-insights-from-industry-leaders/
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Watch this recorded webinar about real-time monitoring of application performance. See how to integrate Apache JMeter, the open-source leader in performance testing, with InfluxDB, the open-source time-series database, and Grafana, the open-source analytics and visualization application.
In this webinar, we will review the benefits of leveraging InfluxDB and Grafana when executing load tests and demonstrate how these tools are used to visualize performance metrics.
Length: 30 minutes
Session Overview
-------------------------------------------
During this webinar, we will cover the following topics while demonstrating the integrations of JMeter, InfluxDB and Grafana:
- What out-of-the-box solutions are available for real-time monitoring JMeter tests?
- What are the benefits of integrating InfluxDB and Grafana into the load testing stack?
- Which features are provided by Grafana?
- Demonstration of InfluxDB and Grafana using a practice web application
To view the webinar recording, go to:
https://www.rttsweb.com/jmeter-integration-webinar
JMeter webinar - integration with InfluxDB and Grafana
U24149153
1. Uttam Mande, Y.Srinivas, J.V.R.Murthy / International Journal of Engineering Research and
Applications (IJERA) ISSN: 2248-9622 www.ijera.com
Vol. 2, Issue 4, July-August 2012, pp.149-153
AN INTELLIGENT ANALYSIS OF CRIME DATA USING
DATA MINING & AUTO CORRELATION MODELS
Uttam Mande Y.Srinivas J.V.R.Murthy
Dept of CSE Dept of IT Dept of CSE
GITAM University GITAM University J.N.T.University
Visakhapatnam Visakhapatnam Kakinada
Abstract
The latest technological developments contributed
The usage of data mining concept help to explore the
significantly towards modernization, at the same
enormous data and making it possible in reaching the
time increased the concern about the security
ultimate goal of criminal analysis the usage of data
issues. These technologies have hindered the
mining techniques have several advantages it helps
effective analysis about the criminals. Application
to cluster the data based on criminal /crime and
of data mining concepts proved to yield better
thereby minimizing the search space. Based on the
results in this direction. In this paper, binary
clusters the classification algorithm can be applied to
clustering and classification techniques have been
classify the criminal in this paper we also used the
used to analyze the criminal data. The crime data
auto correlation model authenticate the criminal. The
considered in this paper is from Andhra Pradesh
rest of paper is organized as follows, section -2 of the
police department this paper aims to potentially
paper deals with deep insight into fundamentals’ of
identify a criminal based on the witness/clue at the
crime analysis, in section- 3 the concept of binary
crime spot an auto correlation model is further
clustering is presented, the auto correlation model is
used to ratify the criminal.
discussed in section -4, experimentation is
highlighted in section- 5, the section 6 of the paper
Key words: data mining, clustering, classification,
focus on the conclusion
autocorrelation, crime
2. INSIGHT INTO FUNDAMENTALS’ OF
1. INTRODUCTION
CRIME ANALYSIS
The present day, changes in social life style and
circumstances of living make the humans to come
Any crime investigation highlights primarily on two
across phenomena called crime. Various agencies
issues, 1) clue/crime links 2) criminal
such as POLICE Department, CBI are working
relating/identification. Crime clues play a vital role in
rigorously to combat the crime. But the challenges to
the proper identification of criminal. The clues help
analyze the crime and arrest the criminal activities is
the stepping stone towards the crime analysis, and
becoming more difficult as the crime rate is
criminal relating is the mapping of the criminal based
increasing [1][2],many models have been projected
on the clues with data available in the data base, by
by the researchers for effective analysis [3][4].the
the use of intelligent knowledge mapping.
main disadvantage is that the volume of data with
In this paper, we have considered the crime data base
respect to the crime activities and criminals increased
of criminals involved/accused in several types of
,and there is a great need for analyzing the data,
crimes the criminal activities considered in this paper
hence to have a better model the knowledge about the
are 1) robbery 2) murder 3) kidnapping 4) riots
crime & the criminal always is always advantageous.
This thought has driven towards the use of data
mining techniques [ 5] for analyzing this
voluminous data .
149 | P a g e
2. Uttam Mande, Y.Srinivas, J.V.R.Murthy / International Journal of Engineering Research and
Applications (IJERA) ISSN: 2248-9622 www.ijera.com
Vol. 2, Issue 4, July-August 2012, pp.149-153
2.1 crime links 3. BINARY CLUSTERING:
The various crime links that were considered include In order to simplify the analysis process the huge
dataset available is to be clustered. The clustering in
1) Crime location (place: restaurant, theater, road, this paper is based on the type of crime. A data set is
railway station, shop/gold shop, mall, house, generated from the database available from the
apartment ) Andhra Pradesh police department and a table is
2) Criminal attribute(hair, built, eyebrows ,nose created by considering the FIR report
,teeth, beard , age group,mustache,languages
known) The various fields considered including the criminal
3) Criminal psychological behavior can be identification numbers, criminal attributes, criminal
recognized by type of killing psychological behavior, crime location, time of crime
(day/night), witness /clue, the data set is generated by
We have considered the type of killing as using the binary data of 1’s & 0’s, 1’s indicating the
(smooth, removal of parts, harsh) which presence of attribute and 0’s indicating the absence of
attributes to the psychological behavior of the attribute then clustering of the binary data is done as
criminal proposed by Tao Hi (2005) using the binary
clustering
4) Modus operandi (object used for crime),
1)Pistol 2)Rope 3)Stick 4)Knife Crimes are categorized in many ways, here we have
given weights to each type of crime where weighing
These criminal links help to analyze the dataset
scheme is considered in the manner all the relative
there by making the crime investigators to plane
crimes will be given with near values , after applying
for identification of the criminal.
clustering algorithm on this type of crime feature we
have got four clusters of crime data they are robbery,
kidnap, murder and riot
2.2 Crime identification
In order to identify the criminals the
variables/links that are identified from section
2.1 are mapped to that of the data base and
previous knowledge there by solving the crime
incidents,
In order to identify a criminal together with
crime variables that are discussed in section 2.1
we have considered 1) witness available and 2)
clues available
The various witness considered for affective
identification are discussed in the above section
if the evidence/witness is not available we have
considered clues available from the forensics
such as finger prints ,in particular cases for the
identification we have considered the mapping of
both witness & clues to authenticate a criminal.
In this paper we have considered binary
clustering to cluster the data base based on type
of crime and the classification is carried out from Fig 1categories of crimes
the feature available.
150 | P a g e
3. Uttam Mande, Y.Srinivas, J.V.R.Murthy / International Journal of Engineering Research and
Applications (IJERA) ISSN: 2248-9622 www.ijera.com
Vol. 2, Issue 4, July-August 2012, pp.149-153
4. AUTO REGRESSION
In the absence of witness and clues by the forensic at
the criminal spot. In such situations, reverse
investigation has to be done
Suspect are listed out by
The type of crime , Type of killing / the way in
which the incident has happened.
Modus of operand – used , By the victim type
,Time of happening of the incident
If the incident is with respect to killing , then we
consider the reason for killing
Dacoit and murder ,Rape and murder ,Killing
took place at the time of riot Formulae for auto correlation and regression
Crime committed due to heatedness ,No clue
Modus of operand of crime 5. EXPERIMENTATION
Knife, Pistol, Bomb,rope The experimentation is carried out under mat lab
environment .the generated database considered for
Type of killing
experimentation is
Harsh,Sadistic,From near / from distance
Time of killing
Day time,Night time
Victim type
Age, Economical status, Gender, Strong ness of
victim
Using these feature like type of kill ,associated
crime mode of the crime, victim type the
suspects are generated from the criminal history
Among the suspects, to identify a criminal, the
correlation is to be established among the available
evidences and here we use the auto correlation model
to find the most likelihood criminal
The database of short listed criminals(suspects) will
be given to Autocorrelation model
Fig2 :the snap shot of data set
151 | P a g e
4. Uttam Mande, Y.Srinivas, J.V.R.Murthy / International Journal of Engineering Research and
Applications (IJERA) ISSN: 2248-9622 www.ijera.com
Vol. 2, Issue 4, July-August 2012, pp.149-153
1.Carlile of Berriew Q.C “Data mining: The new
weapon in the war on terrorism” retrived from the
If the witness is available, at the crime incident, or of Internet on 28-02-2011
the forensics reports are available, then in such cases,
identification of the criminal is a different case,
where the criminal is mapped with the data available
2.Cate H. Fred “Legal Standards for Data Mining”
at the crime spot with that of the database and if there retrieved from the internet on 12-03-2011
is a map, the criminal can be identified. If the http://www.hunton.com/files/tbl_s47Details/FileUplo
witness or forensics reports are not available, then we ad265/1250/Cate_Fourth_Amendment.pdf
will take the report on the way the crime has been
taken and we try to relate these features with the 3.Clifton Christopher (2011). “Encyclopedia
Autocorrelation model and try to investigate the Britannica: data mining”, Retrieved from the web on
20-01-2011
criminal. The criminal
4.Jeff and Harper, Jim “Effective Counterterrorism
and the Limited Role of Predictive Data Mining”
In the result highest positive value is considered as
retrieved from the web 12-02-2011
the most likelihood criminal
5. U.M. Fayyad and R. Uthurusamy, “Evolving Data
cid correlation values Mining into Solutions for Insights,” Comm. ACM,
Aug. 2002, pp. 28-31.
101 0.750249895876718
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154 0.713427738442316
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5. Uttam Mande, Y.Srinivas, J.V.R.Murthy / International Journal of Engineering Research and
Applications (IJERA) ISSN: 2248-9622 www.ijera.com
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