In this paper, we introduce two types of hybridization. The first contribution is the hybridization between the Viterbi algorithm and Baum Welch in order to predict crime locations. While the second contribution considers the optimization based on decision tree (DT) in combination with the Viterbi algorithm for criminal identification using Iraq and India crime dataset. This work is based on our previous work [1]. The main goal is to enhance the results of the model in both consuming times and to get a more accurate model. The obtained results proved the achievement of both goals in an efficient way.
A Survey on Data Mining Techniques for Crime Hotspots PredictionIJSRD
A crime is an act which is against the laws of a country or region. The technique which is used to find areas on a map which have high crime intensity is known as crime hotspot prediction. The technique uses the crime data which includes the area with crime rate and predict the future location with high crime intensity. The motivation of crime hotspot prediction is to raise people’s awareness regarding the dangerous location in certain time period. It can help for police resource allocation for creating a safe environment. The paper presents survey of different types of data mining techniques for crime hotspots prediction.
Real-time PMU Data Recovery Application Based on Singular Value DecompositionPower System Operation
Phasor measurement units (PMUs) allow for the enhancement of power system monitoring and control applications and they will prove even more crucial in the future, as the grid becomes more decentralized and subject to higher uncertainty. Tools that improve PMU data quality and facilitate data analytics workflows are thus needed. In this work, we leverage a previously described algorithm to develop a python application for PMU data recovery. Because of its intrinsic nature, PMU data can be dimensionally reduced using singular value decomposition (SVD). Moreover, the high spatio-temporal correlation can be leveraged to estimate the value of measurements that are missing due to drop-outs. These observations are at the base of the data recovery application described in this work. Extensive testing is performed to study the performance under different data drop-out scenarios, and the results show very high recovery accuracy. Additionally, the application is designed to take advantage of a high performance PMU data platform called PredictiveGrid™, developed by PingThings.
KEYWORDS
ISSUES RELATED TO SAMPLING TECHNIQUES FOR NETWORK TRAFFIC DATASETijmnct
The document discusses issues related to sampling techniques for network traffic datasets. It analyzes various sampling techniques for their ability to capture information while sampling imbalanced network traffic data. The key points are:
1) Network traffic data is huge, varying, and imbalanced with some classes distributed unequally. Sampling is needed to reduce training time for machine learning algorithms used to analyze the data, but sampling can lose important information.
2) The document evaluates random sampling, systematic sampling, stratified sampling, and re-sampling techniques using a dataset collected from Panjab University's network. It finds that random sampling can miss some protocol classes entirely, losing important information.
3) Careful sampling is needed to handle the
Here we describe federated learning based traffic flow prediction system. In federated learning we solve the problem of data security and also provide collaborative learning. model parameter are shared here ,not data
The main goal of cluster analysis is to classify elements into groupsbased on their similarity. Clustering has many applications such as astronomy, bioinformatics, bibliography, and pattern recognition. In this paper, a survey of clustering methods and techniques and identification of advantages and disadvantages of these methods are presented to give a solid background to choose the best method to extract strong association rules.
IRJET- Evidence Chain for Missing Data Imputation: SurveyIRJET Journal
This document summarizes research on techniques for imputing missing data. It begins with an abstract describing a new method called Missing value Imputation Algorithm based on an Evidence Chain (MIAEC) that provides accurate imputation for increasing rates of missing data using a chain of evidence. It then reviews several existing imputation techniques including mean, regression, likelihood-based methods, and nearest neighbor approaches. The document proposes extending MIAEC with MapReduce for large-scale data processing. Key advantages of MIAEC include utilizing all relevant evidence to estimate missing values and ability to process large datasets.
Prediction of nodes mobility in 3-D space IJECEIAES
Recently, mobility prediction researches attracted increasing interests, especially for mobile networks where nodes are free to move in the threedimensional space. Accurate mobility prediction leads to an efficient data delivery for real time applications and enables the network to plan for future tasks such as route planning and data transmission in an adequate time and a suitable space. In this paper, we proposed, tested and validated an algorithm that predicts the future mobility of mobile networks in three-dimensional space. The prediction technique uses polynomial regression to model the spatial relation of a set of points along the mobile node’s path and then provides a time-space mapping for each of the three components of the node’s location coordinates along the trajectory of the node. The proposed algorithm was tested and validated in MATLAB simulation platform using real and computer generated location data. The algorithm achieved an accurate mobility prediction with minimal error and provides promising results for many applications.
With the development of information security, the traditional image encryption methods have become
outdated. Because of amply using images in the transmission process, it is important to protect the confidential image
data from unauthorized access. This paper presents a new chaos based image encryption algorithm, which can improve
the security during transmission more effectively utilizes the chaotic systems properties, such as pseudo-random
appearance and sensitivity to initial conditions. Based on chaotic theory and decomposition and recombination of pixel
values, this new image scrambling algorithm is able to change the position of pixel, simultaneously scrambling both
position and pixel values. Experimental results show that the new algorithm improves the image security effectively to
avoid unscramble, and it also can restore the image as same as the original one, which reaches to the purposes of image
safe and reliable transmission.
A Survey on Data Mining Techniques for Crime Hotspots PredictionIJSRD
A crime is an act which is against the laws of a country or region. The technique which is used to find areas on a map which have high crime intensity is known as crime hotspot prediction. The technique uses the crime data which includes the area with crime rate and predict the future location with high crime intensity. The motivation of crime hotspot prediction is to raise people’s awareness regarding the dangerous location in certain time period. It can help for police resource allocation for creating a safe environment. The paper presents survey of different types of data mining techniques for crime hotspots prediction.
Real-time PMU Data Recovery Application Based on Singular Value DecompositionPower System Operation
Phasor measurement units (PMUs) allow for the enhancement of power system monitoring and control applications and they will prove even more crucial in the future, as the grid becomes more decentralized and subject to higher uncertainty. Tools that improve PMU data quality and facilitate data analytics workflows are thus needed. In this work, we leverage a previously described algorithm to develop a python application for PMU data recovery. Because of its intrinsic nature, PMU data can be dimensionally reduced using singular value decomposition (SVD). Moreover, the high spatio-temporal correlation can be leveraged to estimate the value of measurements that are missing due to drop-outs. These observations are at the base of the data recovery application described in this work. Extensive testing is performed to study the performance under different data drop-out scenarios, and the results show very high recovery accuracy. Additionally, the application is designed to take advantage of a high performance PMU data platform called PredictiveGrid™, developed by PingThings.
KEYWORDS
ISSUES RELATED TO SAMPLING TECHNIQUES FOR NETWORK TRAFFIC DATASETijmnct
The document discusses issues related to sampling techniques for network traffic datasets. It analyzes various sampling techniques for their ability to capture information while sampling imbalanced network traffic data. The key points are:
1) Network traffic data is huge, varying, and imbalanced with some classes distributed unequally. Sampling is needed to reduce training time for machine learning algorithms used to analyze the data, but sampling can lose important information.
2) The document evaluates random sampling, systematic sampling, stratified sampling, and re-sampling techniques using a dataset collected from Panjab University's network. It finds that random sampling can miss some protocol classes entirely, losing important information.
3) Careful sampling is needed to handle the
Here we describe federated learning based traffic flow prediction system. In federated learning we solve the problem of data security and also provide collaborative learning. model parameter are shared here ,not data
The main goal of cluster analysis is to classify elements into groupsbased on their similarity. Clustering has many applications such as astronomy, bioinformatics, bibliography, and pattern recognition. In this paper, a survey of clustering methods and techniques and identification of advantages and disadvantages of these methods are presented to give a solid background to choose the best method to extract strong association rules.
IRJET- Evidence Chain for Missing Data Imputation: SurveyIRJET Journal
This document summarizes research on techniques for imputing missing data. It begins with an abstract describing a new method called Missing value Imputation Algorithm based on an Evidence Chain (MIAEC) that provides accurate imputation for increasing rates of missing data using a chain of evidence. It then reviews several existing imputation techniques including mean, regression, likelihood-based methods, and nearest neighbor approaches. The document proposes extending MIAEC with MapReduce for large-scale data processing. Key advantages of MIAEC include utilizing all relevant evidence to estimate missing values and ability to process large datasets.
Prediction of nodes mobility in 3-D space IJECEIAES
Recently, mobility prediction researches attracted increasing interests, especially for mobile networks where nodes are free to move in the threedimensional space. Accurate mobility prediction leads to an efficient data delivery for real time applications and enables the network to plan for future tasks such as route planning and data transmission in an adequate time and a suitable space. In this paper, we proposed, tested and validated an algorithm that predicts the future mobility of mobile networks in three-dimensional space. The prediction technique uses polynomial regression to model the spatial relation of a set of points along the mobile node’s path and then provides a time-space mapping for each of the three components of the node’s location coordinates along the trajectory of the node. The proposed algorithm was tested and validated in MATLAB simulation platform using real and computer generated location data. The algorithm achieved an accurate mobility prediction with minimal error and provides promising results for many applications.
With the development of information security, the traditional image encryption methods have become
outdated. Because of amply using images in the transmission process, it is important to protect the confidential image
data from unauthorized access. This paper presents a new chaos based image encryption algorithm, which can improve
the security during transmission more effectively utilizes the chaotic systems properties, such as pseudo-random
appearance and sensitivity to initial conditions. Based on chaotic theory and decomposition and recombination of pixel
values, this new image scrambling algorithm is able to change the position of pixel, simultaneously scrambling both
position and pixel values. Experimental results show that the new algorithm improves the image security effectively to
avoid unscramble, and it also can restore the image as same as the original one, which reaches to the purposes of image
safe and reliable transmission.
This document summarizes a study on human activity recognition using mobile sensors. It analyzes the performance of a k-nearest neighbors classifier on a publicly available dataset containing accelerometer data from sensors on both arms during various activities. The study finds that some activities are recognized more accurately than others by the basic classifier. It also shows that combining data from both arms and selecting optimal features improves recognition performance compared to using each arm individually with all features.
This document discusses a novel method for intrusion awareness using Distributed Situational Awareness (D-SA). It proposes using D-SA and support vector machines (SVM) for network intrusion detection and classification. The method is evaluated using the KDD Cup 1999 intrusion detection dataset. Experimental results show the proposed D-SA method achieves higher detection rates compared to rule-based classification techniques.
Comparison Between Clustering Algorithms for Microarray Data AnalysisIOSR Journals
Currently, there are two techniques used for large-scale gene-expression profiling; microarray and
RNA-Sequence (RNA-Seq).This paper is intended to study and compare different clustering algorithms that used
in microarray data analysis. Microarray is a DNA molecules array which allows multiple hybridization
experiments to be carried out simultaneously and trace expression levels of thousands of genes. It is a highthroughput
technology for gene expression analysis and becomes an effective tool for biomedical research.
Microarray analysis aims to interpret the data produced from experiments on DNA, RNA, and protein
microarrays, which enable researchers to investigate the expression state of a large number of genes. Data
clustering represents the first and main process in microarray data analysis. The k-means, fuzzy c-mean, selforganizing
map, and hierarchical clustering algorithms are under investigation in this paper. These algorithms
are compared based on their clustering model.
The document discusses algorithms for mining frequent subgraphs from graph data. It begins by introducing graph mining and defining frequent subgraphs. The main algorithms are categorized into greedy search, inductive logic programming, and graph theory approaches. Graph theory approaches are further divided into Apriori-based and pattern growth algorithms. The algorithms are compared based on attributes like graph representation, search strategy, nature of input, and completeness of output. Key algorithms discussed include SUBDUE, AGM, and gSpan.
A FLOATING POINT DIVISION UNIT BASED ON TAYLOR-SERIES EXPANSION ALGORITHM AND...csandit
Floating point division, even though being an infrequent operation in the traditional sense, is
indis-pensable when it comes to a range of non-traditional applications such as K-Means
Clustering and QR Decomposition just to name a few. In such applications, hardware support
for floating point division would boost the performance of the entire system. In this paper, we
present a novel architecture for a floating point division unit based on the Taylor-series
expansion algorithm. We show that the Iterative Logarithmic Multiplier is very well suited to be
used as a part of this architecture. We propose an implementation of the powering unit that can
calculate an odd power and an even power of a number simultaneously, meanwhile having little
hardware overhead when compared to the Iterative Logarithmic Multiplier.
Network Flow Pattern Extraction by Clustering Eugine KangEugine Kang
This document discusses a study that uses clustering techniques to analyze network flow data and extract patterns of torrent usage at Korea University. The study transforms network flow data by time blocks. It then uses k-means clustering and principal component analysis to identify optimal cluster numbers and visualize cluster formations. The study finds that 7 clusters best captures distinct torrent usage patterns. It analyzes the stability of the clusters and identifies two clusters that show regular torrent usage during working hours and heavy overall usage. The goal is to help network administrators identify times of heavy bandwidth usage.
The document analyzes and compares the C4.5 and K-nearest neighbor (KNN) algorithms for clustering data and determining priority development areas in Papua Province, Indonesia. It first discusses Klassen typology for classifying areas based on economic growth and per capita income. It then provides an overview of the C4.5 and KNN algorithms, including pseudo code for analyzing their time complexities using the Big O notation. The study applies these algorithms to PDRB and economic growth data from 29 regencies in Papua from 2006-2012 to determine priority development areas, running the algorithms multiple times on different sized data sets to analyze running time.
IRJET- Smart Railway System using Trip Chaining MethodIRJET Journal
This document proposes a smart railway system using trip chaining and big data analysis of passenger information from smart cards. The system would collect data like passenger name, age, travel time, source and destination stations from smart cards. It would then use k-means clustering to group passengers by age and travel patterns. A naïve bayes classifier would predict passenger counts at each station. This analysis of passenger data could help the railway department improve infrastructure and services based on demand.
CORRELATION OF EIGENVECTOR CENTRALITY TO OTHER CENTRALITY MEASURES: RANDOM, S...csandit
In this paper, we thoroughly investigate correlations of eigenvector centrality to five centrality
measures, including degree centrality, betweenness centrality, clustering coefficient centrality,
closeness centrality, and farness centrality, of various types of network (random network, smallworld
network, and real-world network). For each network, we compute those six centrality
measures, from which the correlation coefficient is determined. Our analysis suggests that the
degree centrality and the eigenvector centrality are highly correlated, regardless of the type of
network. Furthermore, the eigenvector centrality also highly correlates to betweenness on
random and real-world networks. However, it is inconsistent on small-world network, probably
owing to its power-law distribution. Finally, it is also revealed that eigenvector centrality is
distinct from clustering coefficient centrality, closeness centrality and farness centrality in all
tested occasions. The findings in this paper could lead us to further correlation analysis on
multiple centrality measures in the near future
Enhancement of Error Correction in Quantum Cryptography BB84 ...butest
The document discusses several papers related to enhancing error correction in quantum cryptography protocols, measuring performance of blind source separation algorithms, detecting and counting faces in surveillance images, applying rough sets to web log mining, and using data mining techniques for financial prediction and analyzing RNA/interferon structures of hepatitis C virus.
Laura Florentina Stoica, Florian Mircea Boian, Florin Stoica, A Distributed CTL Model Checker, Proceeding of 10th International Conference on e-Business, ICE-B 2013, Reykjavik Iceland, paper 33, 29-31 July, pp. 379-386, ISBN: 978-989-8565-72-3, 2013
This document discusses using artificial neural networks for network intrusion detection. Specifically, it proposes a hybrid classification model that uses entropy-based feature selection to reduce the dataset, followed by four neural network techniques (RBFN, SOM, SMO, PART) for classification. It provides details on each neural network technique and the overall methodology, which uses 10-fold cross validation to evaluate performance based on standard criteria. The goal is to build an efficient intrusion detection system with low false alarms and high detection rates.
Comparison of search algorithms in Javanese-Indonesian dictionary applicationTELKOMNIKA JOURNAL
This study aims to compare the performance of Boyer-Moore, Knuth morris pratt, and Horspool algorithms in searching for the meaning of words in the Java-Indonesian dictionary search application in terms of accuracy and processing time. Performance Testing is used to test the performance of algorithm implementations in applications. The test results show that the Boyer Moore and Knuth Morris Pratt algorithms have an accuracy rate of 100%, and the Horspool algorithm 85.3%. While the processing time, Knuth Morris Pratt algorithm has the highest average speed level of 25ms, Horspool 39.9 ms, while the average speed of the Boyer Moore algorithm is 44.2 ms. While the complexity test results, the Boyer Moore algorithm has an overall number of n 26n2, Knuth Morris Pratt and Horspool 20n2 each.
Survey of Data Mining Techniques on Crime Data Analysisijdmtaiir
This document summarizes clustering techniques that can be applied to analyze crime data. It first introduces how data mining can help with crime prediction and analysis. It then discusses different clustering methods like K-means, AK-modes, and Expectation-Maximization that are used to group similar data objects. These clustering techniques aim to identify patterns in crime data to help law enforcement solve crimes faster. The document also reviews the role of data preprocessing before applying clustering algorithms to crime data.
Survey of Data Mining Techniques on Crime Data Analysisijdmtaiir
Data mining is a process of extracting knowledge
from huge amount of data stored in databases, data warehouses
and data repositories. Crime is an interesting application where
data mining plays an important role in terms of prediction and
analysis. Clustering is the process of combining data objects
into groups. The data objects within the group are very similar
and very dissimilar as well when compared to objects of other
groups. This paper presents detailed study on clustering
techniques and its role on crime applications. This study also
helps crime branch for better prediction and classification of
crimes
This document summarizes clustering techniques that can be used for crime data analysis. It first introduces how data mining can help with crime prediction and analysis. It then discusses different clustering methods like K-means, AK-modes, and Expectation-Maximization that are used to group similar crime data objects. These clustering algorithms aim to identify patterns in crime data to help law enforcement solve crimes. The document also reviews the role of data preprocessing before applying clustering techniques. In conclusion, it states that clustering and analyzing similarity measures can help crime analysts and law enforcement investigate cases and solve unsolved crimes faster.
ISSN 2395-650X
The "International Journal of Life Sciences Biotechnology and Pharma Sciences journal appears to be a valuable resource for those interested in staying updated on the latest developments and research in these important scientific fields of Life and science journal.
Predictive Modeling for Topographical Analysis of Crime RateIRJET Journal
1) The document discusses using machine learning techniques to predict crime patterns and types based on historical crime data.
2) It proposes using a random forest classification algorithm to analyze crime data and predict the type of crime that may occur in a particular area.
3) The random forest algorithm would be trained on a dataset containing information about past crimes like date, location, and crime type to make predictions about future crimes.
A data mining framework for fraud detection in telecom based on MapReduce (Pr...Mohammed Kharma
The outputs of this research is a design and implement a model using data mining to detect fraud cases targeting telecom environment where a huge volume of data should to be processed based on cloud computing infrastructure we will build using the most popular and powerful cloud computing framework MapReduce. We will use Data obtained from call details record (CDR) in billing repository and the result is subscriber subset that classified as fraudulent subscription in near online mode. This will help to reduce time in detecting fraud events and enhance revenue assurance team ability to identify fraudulent cases efficiently.
USE OF MARKOV CHAIN FOR EARLY DETECTING DDOS ATTACKSIJNSA Journal
DDoS has a variety of types of mixed attacks. Botnet attackers can chain different types of DDoS attacks to confuse cybersecurity defenders. In this article, the attack type can be represented as the state of the model. Considering the attack type, we use this model to calculate the final attack probability. The final attack probability is then converted into one prediction vector, and the incoming attacks can be detected early before IDS issues an alert. The experiment results have shown that the prediction model that can make multi-vector DDoS detection and analysis easier.
CRIME ANALYSIS AND PREDICTION USING MACHINE LEARNINGIRJET Journal
This document describes a study on analyzing crime data and predicting crimes using machine learning techniques. The study uses an Indian crime dataset to analyze past crimes and identify patterns. Regression, k-means clustering, and decision tree algorithms are implemented to predict the type of future crimes based on conditions. The algorithms can identify crime-prone areas and anticipate crimes. The proposed system aims to conduct criminal analysis, identify trends, disseminate knowledge to support crime prevention measures, and recognize recurring crime patterns to prevent future incidents.
Chaotic systems with pseudorandom number generate to protect the transmitted...nooriasukmaningtyas
Communication techniques have witnessed rapid development in recent years, especially the internet and the mobile network, which led to rapid data transmission. The latest developments, in turn, have come out with advanced decisions to secure information from eavesdropping. Myriad in-depth studies in cryptography. It was implemented with the intention of proposing a revolutionary solution to protect data by encryption techniques, tend maps, and logistics. This work had proposed a new design to the generator of pseudo-random numbers (GPRN) which had utilized multi chaotic systems. Synchronization of Multi-parameters chaotic arises in many applications, in natural or industrial systems. Many methods have been introduced for using a chaotic system in the encryption of data. Analysis of security of chaotic system had been executed on key sensitivity and key space.
This document summarizes a study on human activity recognition using mobile sensors. It analyzes the performance of a k-nearest neighbors classifier on a publicly available dataset containing accelerometer data from sensors on both arms during various activities. The study finds that some activities are recognized more accurately than others by the basic classifier. It also shows that combining data from both arms and selecting optimal features improves recognition performance compared to using each arm individually with all features.
This document discusses a novel method for intrusion awareness using Distributed Situational Awareness (D-SA). It proposes using D-SA and support vector machines (SVM) for network intrusion detection and classification. The method is evaluated using the KDD Cup 1999 intrusion detection dataset. Experimental results show the proposed D-SA method achieves higher detection rates compared to rule-based classification techniques.
Comparison Between Clustering Algorithms for Microarray Data AnalysisIOSR Journals
Currently, there are two techniques used for large-scale gene-expression profiling; microarray and
RNA-Sequence (RNA-Seq).This paper is intended to study and compare different clustering algorithms that used
in microarray data analysis. Microarray is a DNA molecules array which allows multiple hybridization
experiments to be carried out simultaneously and trace expression levels of thousands of genes. It is a highthroughput
technology for gene expression analysis and becomes an effective tool for biomedical research.
Microarray analysis aims to interpret the data produced from experiments on DNA, RNA, and protein
microarrays, which enable researchers to investigate the expression state of a large number of genes. Data
clustering represents the first and main process in microarray data analysis. The k-means, fuzzy c-mean, selforganizing
map, and hierarchical clustering algorithms are under investigation in this paper. These algorithms
are compared based on their clustering model.
The document discusses algorithms for mining frequent subgraphs from graph data. It begins by introducing graph mining and defining frequent subgraphs. The main algorithms are categorized into greedy search, inductive logic programming, and graph theory approaches. Graph theory approaches are further divided into Apriori-based and pattern growth algorithms. The algorithms are compared based on attributes like graph representation, search strategy, nature of input, and completeness of output. Key algorithms discussed include SUBDUE, AGM, and gSpan.
A FLOATING POINT DIVISION UNIT BASED ON TAYLOR-SERIES EXPANSION ALGORITHM AND...csandit
Floating point division, even though being an infrequent operation in the traditional sense, is
indis-pensable when it comes to a range of non-traditional applications such as K-Means
Clustering and QR Decomposition just to name a few. In such applications, hardware support
for floating point division would boost the performance of the entire system. In this paper, we
present a novel architecture for a floating point division unit based on the Taylor-series
expansion algorithm. We show that the Iterative Logarithmic Multiplier is very well suited to be
used as a part of this architecture. We propose an implementation of the powering unit that can
calculate an odd power and an even power of a number simultaneously, meanwhile having little
hardware overhead when compared to the Iterative Logarithmic Multiplier.
Network Flow Pattern Extraction by Clustering Eugine KangEugine Kang
This document discusses a study that uses clustering techniques to analyze network flow data and extract patterns of torrent usage at Korea University. The study transforms network flow data by time blocks. It then uses k-means clustering and principal component analysis to identify optimal cluster numbers and visualize cluster formations. The study finds that 7 clusters best captures distinct torrent usage patterns. It analyzes the stability of the clusters and identifies two clusters that show regular torrent usage during working hours and heavy overall usage. The goal is to help network administrators identify times of heavy bandwidth usage.
The document analyzes and compares the C4.5 and K-nearest neighbor (KNN) algorithms for clustering data and determining priority development areas in Papua Province, Indonesia. It first discusses Klassen typology for classifying areas based on economic growth and per capita income. It then provides an overview of the C4.5 and KNN algorithms, including pseudo code for analyzing their time complexities using the Big O notation. The study applies these algorithms to PDRB and economic growth data from 29 regencies in Papua from 2006-2012 to determine priority development areas, running the algorithms multiple times on different sized data sets to analyze running time.
IRJET- Smart Railway System using Trip Chaining MethodIRJET Journal
This document proposes a smart railway system using trip chaining and big data analysis of passenger information from smart cards. The system would collect data like passenger name, age, travel time, source and destination stations from smart cards. It would then use k-means clustering to group passengers by age and travel patterns. A naïve bayes classifier would predict passenger counts at each station. This analysis of passenger data could help the railway department improve infrastructure and services based on demand.
CORRELATION OF EIGENVECTOR CENTRALITY TO OTHER CENTRALITY MEASURES: RANDOM, S...csandit
In this paper, we thoroughly investigate correlations of eigenvector centrality to five centrality
measures, including degree centrality, betweenness centrality, clustering coefficient centrality,
closeness centrality, and farness centrality, of various types of network (random network, smallworld
network, and real-world network). For each network, we compute those six centrality
measures, from which the correlation coefficient is determined. Our analysis suggests that the
degree centrality and the eigenvector centrality are highly correlated, regardless of the type of
network. Furthermore, the eigenvector centrality also highly correlates to betweenness on
random and real-world networks. However, it is inconsistent on small-world network, probably
owing to its power-law distribution. Finally, it is also revealed that eigenvector centrality is
distinct from clustering coefficient centrality, closeness centrality and farness centrality in all
tested occasions. The findings in this paper could lead us to further correlation analysis on
multiple centrality measures in the near future
Enhancement of Error Correction in Quantum Cryptography BB84 ...butest
The document discusses several papers related to enhancing error correction in quantum cryptography protocols, measuring performance of blind source separation algorithms, detecting and counting faces in surveillance images, applying rough sets to web log mining, and using data mining techniques for financial prediction and analyzing RNA/interferon structures of hepatitis C virus.
Laura Florentina Stoica, Florian Mircea Boian, Florin Stoica, A Distributed CTL Model Checker, Proceeding of 10th International Conference on e-Business, ICE-B 2013, Reykjavik Iceland, paper 33, 29-31 July, pp. 379-386, ISBN: 978-989-8565-72-3, 2013
This document discusses using artificial neural networks for network intrusion detection. Specifically, it proposes a hybrid classification model that uses entropy-based feature selection to reduce the dataset, followed by four neural network techniques (RBFN, SOM, SMO, PART) for classification. It provides details on each neural network technique and the overall methodology, which uses 10-fold cross validation to evaluate performance based on standard criteria. The goal is to build an efficient intrusion detection system with low false alarms and high detection rates.
Comparison of search algorithms in Javanese-Indonesian dictionary applicationTELKOMNIKA JOURNAL
This study aims to compare the performance of Boyer-Moore, Knuth morris pratt, and Horspool algorithms in searching for the meaning of words in the Java-Indonesian dictionary search application in terms of accuracy and processing time. Performance Testing is used to test the performance of algorithm implementations in applications. The test results show that the Boyer Moore and Knuth Morris Pratt algorithms have an accuracy rate of 100%, and the Horspool algorithm 85.3%. While the processing time, Knuth Morris Pratt algorithm has the highest average speed level of 25ms, Horspool 39.9 ms, while the average speed of the Boyer Moore algorithm is 44.2 ms. While the complexity test results, the Boyer Moore algorithm has an overall number of n 26n2, Knuth Morris Pratt and Horspool 20n2 each.
Survey of Data Mining Techniques on Crime Data Analysisijdmtaiir
This document summarizes clustering techniques that can be applied to analyze crime data. It first introduces how data mining can help with crime prediction and analysis. It then discusses different clustering methods like K-means, AK-modes, and Expectation-Maximization that are used to group similar data objects. These clustering techniques aim to identify patterns in crime data to help law enforcement solve crimes faster. The document also reviews the role of data preprocessing before applying clustering algorithms to crime data.
Survey of Data Mining Techniques on Crime Data Analysisijdmtaiir
Data mining is a process of extracting knowledge
from huge amount of data stored in databases, data warehouses
and data repositories. Crime is an interesting application where
data mining plays an important role in terms of prediction and
analysis. Clustering is the process of combining data objects
into groups. The data objects within the group are very similar
and very dissimilar as well when compared to objects of other
groups. This paper presents detailed study on clustering
techniques and its role on crime applications. This study also
helps crime branch for better prediction and classification of
crimes
This document summarizes clustering techniques that can be used for crime data analysis. It first introduces how data mining can help with crime prediction and analysis. It then discusses different clustering methods like K-means, AK-modes, and Expectation-Maximization that are used to group similar crime data objects. These clustering algorithms aim to identify patterns in crime data to help law enforcement solve crimes. The document also reviews the role of data preprocessing before applying clustering techniques. In conclusion, it states that clustering and analyzing similarity measures can help crime analysts and law enforcement investigate cases and solve unsolved crimes faster.
ISSN 2395-650X
The "International Journal of Life Sciences Biotechnology and Pharma Sciences journal appears to be a valuable resource for those interested in staying updated on the latest developments and research in these important scientific fields of Life and science journal.
Predictive Modeling for Topographical Analysis of Crime RateIRJET Journal
1) The document discusses using machine learning techniques to predict crime patterns and types based on historical crime data.
2) It proposes using a random forest classification algorithm to analyze crime data and predict the type of crime that may occur in a particular area.
3) The random forest algorithm would be trained on a dataset containing information about past crimes like date, location, and crime type to make predictions about future crimes.
A data mining framework for fraud detection in telecom based on MapReduce (Pr...Mohammed Kharma
The outputs of this research is a design and implement a model using data mining to detect fraud cases targeting telecom environment where a huge volume of data should to be processed based on cloud computing infrastructure we will build using the most popular and powerful cloud computing framework MapReduce. We will use Data obtained from call details record (CDR) in billing repository and the result is subscriber subset that classified as fraudulent subscription in near online mode. This will help to reduce time in detecting fraud events and enhance revenue assurance team ability to identify fraudulent cases efficiently.
USE OF MARKOV CHAIN FOR EARLY DETECTING DDOS ATTACKSIJNSA Journal
DDoS has a variety of types of mixed attacks. Botnet attackers can chain different types of DDoS attacks to confuse cybersecurity defenders. In this article, the attack type can be represented as the state of the model. Considering the attack type, we use this model to calculate the final attack probability. The final attack probability is then converted into one prediction vector, and the incoming attacks can be detected early before IDS issues an alert. The experiment results have shown that the prediction model that can make multi-vector DDoS detection and analysis easier.
CRIME ANALYSIS AND PREDICTION USING MACHINE LEARNINGIRJET Journal
This document describes a study on analyzing crime data and predicting crimes using machine learning techniques. The study uses an Indian crime dataset to analyze past crimes and identify patterns. Regression, k-means clustering, and decision tree algorithms are implemented to predict the type of future crimes based on conditions. The algorithms can identify crime-prone areas and anticipate crimes. The proposed system aims to conduct criminal analysis, identify trends, disseminate knowledge to support crime prevention measures, and recognize recurring crime patterns to prevent future incidents.
Chaotic systems with pseudorandom number generate to protect the transmitted...nooriasukmaningtyas
Communication techniques have witnessed rapid development in recent years, especially the internet and the mobile network, which led to rapid data transmission. The latest developments, in turn, have come out with advanced decisions to secure information from eavesdropping. Myriad in-depth studies in cryptography. It was implemented with the intention of proposing a revolutionary solution to protect data by encryption techniques, tend maps, and logistics. This work had proposed a new design to the generator of pseudo-random numbers (GPRN) which had utilized multi chaotic systems. Synchronization of Multi-parameters chaotic arises in many applications, in natural or industrial systems. Many methods have been introduced for using a chaotic system in the encryption of data. Analysis of security of chaotic system had been executed on key sensitivity and key space.
Multi Object Tracking Methods Based on Particle Filter and HMMIJTET Journal
This document discusses multi-object tracking methods based on particle filtering and hidden Markov models. It provides an overview of particle filtering and how it can be used for object tracking by approximating the posterior distribution of objects in video frames. It also discusses how hidden Markov models can be used for prediction and tracking objects at the global layer. Specifically, it describes a multi-object tracking method that uses a coupled layer approach with a local layer tracked using particle filtering and a global layer tracked using a hidden Markov model. This improves tracking efficiency and reduces computational time compared to other recent multi-object trackers.
Hidden Markov Model Classification And Gabor Filter PreprocessingKukuh Setiawan
If you want to get the source code of the apps, just contact me at filomena.media@gmail.com or WA +6285641523180
See the video
https://youtu.be/OH2rjUXxuUk
Predictive Modeling for Topographical Analysis of Crime RateIRJET Journal
This document describes a proposed system to use machine learning methods to predict crime rates and types of crimes in specific areas based on historical crime data. The system would analyze crime data collected from websites including date, location, and crime type to identify patterns. Machine learning algorithms would be trained on the data to build predictive models. The goal is to help law enforcement agencies more quickly detect, resolve, and prevent crimes by predicting where and what types of crimes may occur based on the characteristics of past crimes.
This document presents an approach for generating valuable traffic density data to simulate route planning for patrol cars. It involves extracting location data from GPS and tracking devices of patrol cars over time. This data is used to calculate route frequencies, which are then encoded with color to represent density on a map. The route density data is then correlated with crime hotspot information to propose a new route planning simulation for law enforcement. This aims to more efficiently dispatch patrol cars by considering both traffic patterns and crime trends.
This document provides a survey of data mining techniques for crime prediction. It begins with an introduction to crime prediction and standard techniques like centrography, journey to crime, routine activity theory, and circle theory. It then discusses various data mining techniques used for crime prediction, including support vector machines, multivariate time series clustering, Bayesian networks, artificial neural networks, and fuzzy time series. For each technique, an example paper applying that technique is summarized. A table provides a comparative analysis of the techniques based on predictive accuracy, performance, and disadvantages. The document concludes that the discussed data mining prediction techniques can enhance accuracy and performance of crime prediction by identifying patterns in current and past crime data to predict future values.
Crime Data Analysis, Visualization and Prediction using Data MiningAnavadya Shibu
This paper presents a general idea about the
model of Data Mining techniques and diverse crimes. It also
provides an inclusive survey of competent and valuable
techniques on data mining for crime data analysis. The
objective of the data mining is to recognize patterns in
criminal manners in order to predict crime anticipate
criminal activity and prevent it. This project implements a
novel data mining techniques like KNN, Text Clustering, IR
tree for investigating the crime data sets and sorts out the
accessible problems. The collective knowledge of various
data mining algorithms tend certainly to afford an
enhanced, incorporated, and precise result over the crime
prediction in the banking sectors Our law enforcement
organizations require to be adequately outfitted to defeat
and prevent the crime. This project is developed using Java
as front-end and MySQL as back-end. Supporting
applications like Sunset, NetBeans are used to make the
portal more interactive.
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.
The document describes a study that uses an optimized k-means clustering algorithm to analyze crime data and predict crime patterns in India. The study collects crime data from 2001-2010, performs data preprocessing, and uses the elbow method to determine that the optimal number of clusters is 8. The k-means algorithm is then applied with k=8 to cluster the data. The results show that males commit more crimes in Madhya Pradesh and females in Maharashtra, and those under 18 commit more crimes in Chhattisgarh. UP has the highest total number of criminals. The optimized k-means approach improves accuracy and efficiency in crime analysis and prediction.
An Optimized Stacking Ensemble Model For Phishing Websites DetectionJoshua Gorinson
The document describes a study that proposes an optimized stacking ensemble model for detecting phishing websites. The model uses a genetic algorithm to optimize the parameters of several ensemble machine learning classifiers (random forests, AdaBoost, XGBoost, Bagging, GradientBoost, and LightGBM). The optimized classifiers are then ranked, and the top three are selected as base classifiers for a stacking ensemble model. The stacking ensemble model is tested on three datasets of phishing and legitimate websites, achieving detection accuracies of 97.16%, 98.58%, and 97.39% on the respective datasets.
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.
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.
Similar to Viterbi optimization for crime detection and identification (20)
Amazon products reviews classification based on machine learning, deep learni...TELKOMNIKA JOURNAL
In recent times, the trend of online shopping through e-commerce stores and websites has grown to a huge extent. Whenever a product is purchased on an e-commerce platform, people leave their reviews about the product. These reviews are very helpful for the store owners and the product’s manufacturers for the betterment of their work process as well as product quality. An automated system is proposed in this work that operates on two datasets D1 and D2 obtained from Amazon. After certain preprocessing steps, N-gram and word embedding-based features are extracted using term frequency-inverse document frequency (TF-IDF), bag of words (BoW) and global vectors (GloVe), and Word2vec, respectively. Four machine learning (ML) models support vector machines (SVM), logistic regression (RF), logistic regression (LR), multinomial Naïve Bayes (MNB), two deep learning (DL) models convolutional neural network (CNN), long-short term memory (LSTM), and standalone bidirectional encoder representations (BERT) are used to classify reviews as either positive or negative. The results obtained by the standard ML, DL models and BERT are evaluated using certain performance evaluation measures. BERT turns out to be the best-performing model in the case of D1 with an accuracy of 90% on features derived by word embedding models while the CNN provides the best accuracy of 97% upon word embedding features in the case of D2. The proposed model shows better overall performance on D2 as compared to D1.
Design, simulation, and analysis of microstrip patch antenna for wireless app...TELKOMNIKA JOURNAL
In this study, a microstrip patch antenna that works at 3.6 GHz was built and tested to see how well it works. In this work, Rogers RT/Duroid 5880 has been used as the substrate material, with a dielectric permittivity of 2.2 and a thickness of 0.3451 mm; it serves as the base for the examined antenna. The computer simulation technology (CST) studio suite is utilized to show the recommended antenna design. The goal of this study was to get a more extensive transmission capacity, a lower voltage standing wave ratio (VSWR), and a lower return loss, but the main goal was to get a higher gain, directivity, and efficiency. After simulation, the return loss, gain, directivity, bandwidth, and efficiency of the supplied antenna are found to be -17.626 dB, 9.671 dBi, 9.924 dBi, 0.2 GHz, and 97.45%, respectively. Besides, the recreation uncovered that the transfer speed side-lobe level at phi was much better than those of the earlier works, at -28.8 dB, respectively. Thus, it makes a solid contender for remote innovation and more robust communication.
Design and simulation an optimal enhanced PI controller for congestion avoida...TELKOMNIKA JOURNAL
This document describes using a snake optimization algorithm to tune the gains of an enhanced proportional-integral controller for congestion avoidance in a TCP/AQM system. The controller aims to maintain a stable and desired queue size without noise or transmission problems. A linearized model of the TCP/AQM system is presented. An enhanced PI controller combining nonlinear gain and original PI gains is proposed. The snake optimization algorithm is then used to tune the parameters of the enhanced PI controller to achieve optimal system performance and response. Simulation results are discussed showing the proposed controller provides a stable and robust behavior for congestion control.
Improving the detection of intrusion in vehicular ad-hoc networks with modifi...TELKOMNIKA JOURNAL
Vehicular ad-hoc networks (VANETs) are wireless-equipped vehicles that form networks along the road. The security of this network has been a major challenge. The identity-based cryptosystem (IBC) previously used to secure the networks suffers from membership authentication security features. This paper focuses on improving the detection of intruders in VANETs with a modified identity-based cryptosystem (MIBC). The MIBC is developed using a non-singular elliptic curve with Lagrange interpolation. The public key of vehicles and roadside units on the network are derived from number plates and location identification numbers, respectively. Pseudo-identities are used to mask the real identity of users to preserve their privacy. The membership authentication mechanism ensures that only valid and authenticated members of the network are allowed to join the network. The performance of the MIBC is evaluated using intrusion detection ratio (IDR) and computation time (CT) and then validated with the existing IBC. The result obtained shows that the MIBC recorded an IDR of 99.3% against 94.3% obtained for the existing identity-based cryptosystem (EIBC) for 140 unregistered vehicles attempting to intrude on the network. The MIBC shows lower CT values of 1.17 ms against 1.70 ms for EIBC. The MIBC can be used to improve the security of VANETs.
Conceptual model of internet banking adoption with perceived risk and trust f...TELKOMNIKA JOURNAL
Understanding the primary factors of internet banking (IB) acceptance is critical for both banks and users; nevertheless, our knowledge of the role of users’ perceived risk and trust in IB adoption is limited. As a result, we develop a conceptual model by incorporating perceived risk and trust into the technology acceptance model (TAM) theory toward the IB. The proper research emphasized that the most essential component in explaining IB adoption behavior is behavioral intention to use IB adoption. TAM is helpful for figuring out how elements that affect IB adoption are connected to one another. According to previous literature on IB and the use of such technology in Iraq, one has to choose a theoretical foundation that may justify the acceptance of IB from the customer’s perspective. The conceptual model was therefore constructed using the TAM as a foundation. Furthermore, perceived risk and trust were added to the TAM dimensions as external factors. The key objective of this work was to extend the TAM to construct a conceptual model for IB adoption and to get sufficient theoretical support from the existing literature for the essential elements and their relationships in order to unearth new insights about factors responsible for IB adoption.
Efficient combined fuzzy logic and LMS algorithm for smart antennaTELKOMNIKA JOURNAL
The smart antennas are broadly used in wireless communication. The least mean square (LMS) algorithm is a procedure that is concerned in controlling the smart antenna pattern to accommodate specified requirements such as steering the beam toward the desired signal, in addition to placing the deep nulls in the direction of unwanted signals. The conventional LMS (C-LMS) has some drawbacks like slow convergence speed besides high steady state fluctuation error. To overcome these shortcomings, the present paper adopts an adaptive fuzzy control step size least mean square (FC-LMS) algorithm to adjust its step size. Computer simulation outcomes illustrate that the given model has fast convergence rate as well as low mean square error steady state.
Design and implementation of a LoRa-based system for warning of forest fireTELKOMNIKA JOURNAL
This paper presents the design and implementation of a forest fire monitoring and warning system based on long range (LoRa) technology, a novel ultra-low power consumption and long-range wireless communication technology for remote sensing applications. The proposed system includes a wireless sensor network that records environmental parameters such as temperature, humidity, wind speed, and carbon dioxide (CO2) concentration in the air, as well as taking infrared photos.The data collected at each sensor node will be transmitted to the gateway via LoRa wireless transmission. Data will be collected, processed, and uploaded to a cloud database at the gateway. An Android smartphone application that allows anyone to easily view the recorded data has been developed. When a fire is detected, the system will sound a siren and send a warning message to the responsible personnel, instructing them to take appropriate action. Experiments in Tram Chim Park, Vietnam, have been conducted to verify and evaluate the operation of the system.
Wavelet-based sensing technique in cognitive radio networkTELKOMNIKA JOURNAL
Cognitive radio is a smart radio that can change its transmitter parameter based on interaction with the environment in which it operates. The demand for frequency spectrum is growing due to a big data issue as many Internet of Things (IoT) devices are in the network. Based on previous research, most frequency spectrum was used, but some spectrums were not used, called spectrum hole. Energy detection is one of the spectrum sensing methods that has been frequently used since it is easy to use and does not require license users to have any prior signal understanding. But this technique is incapable of detecting at low signal-to-noise ratio (SNR) levels. Therefore, the wavelet-based sensing is proposed to overcome this issue and detect spectrum holes. The main objective of this work is to evaluate the performance of wavelet-based sensing and compare it with the energy detection technique. The findings show that the percentage of detection in wavelet-based sensing is 83% higher than energy detection performance. This result indicates that the wavelet-based sensing has higher precision in detection and the interference towards primary user can be decreased.
A novel compact dual-band bandstop filter with enhanced rejection bandsTELKOMNIKA JOURNAL
In this paper, we present the design of a new wide dual-band bandstop filter (DBBSF) using nonuniform transmission lines. The method used to design this filter is to replace conventional uniform transmission lines with nonuniform lines governed by a truncated Fourier series. Based on how impedances are profiled in the proposed DBBSF structure, the fractional bandwidths of the two 10 dB-down rejection bands are widened to 39.72% and 52.63%, respectively, and the physical size has been reduced compared to that of the filter with the uniform transmission lines. The results of the electromagnetic (EM) simulation support the obtained analytical response and show an improved frequency behavior.
Deep learning approach to DDoS attack with imbalanced data at the application...TELKOMNIKA JOURNAL
A distributed denial of service (DDoS) attack is where one or more computers attack or target a server computer, by flooding internet traffic to the server. As a result, the server cannot be accessed by legitimate users. A result of this attack causes enormous losses for a company because it can reduce the level of user trust, and reduce the company’s reputation to lose customers due to downtime. One of the services at the application layer that can be accessed by users is a web-based lightweight directory access protocol (LDAP) service that can provide safe and easy services to access directory applications. We used a deep learning approach to detect DDoS attacks on the CICDDoS 2019 dataset on a complex computer network at the application layer to get fast and accurate results for dealing with unbalanced data. Based on the results obtained, it is observed that DDoS attack detection using a deep learning approach on imbalanced data performs better when implemented using synthetic minority oversampling technique (SMOTE) method for binary classes. On the other hand, the proposed deep learning approach performs better for detecting DDoS attacks in multiclass when implemented using the adaptive synthetic (ADASYN) method.
The appearance of uncertainties and disturbances often effects the characteristics of either linear or nonlinear systems. Plus, the stabilization process may be deteriorated thus incurring a catastrophic effect to the system performance. As such, this manuscript addresses the concept of matching condition for the systems that are suffering from miss-match uncertainties and exogeneous disturbances. The perturbation towards the system at hand is assumed to be known and unbounded. To reach this outcome, uncertainties and their classifications are reviewed thoroughly. The structural matching condition is proposed and tabulated in the proposition 1. Two types of mathematical expressions are presented to distinguish the system with matched uncertainty and the system with miss-matched uncertainty. Lastly, two-dimensional numerical expressions are provided to practice the proposed proposition. The outcome shows that matching condition has the ability to change the system to a design-friendly model for asymptotic stabilization.
Implementation of FinFET technology based low power 4×4 Wallace tree multipli...TELKOMNIKA JOURNAL
Many systems, including digital signal processors, finite impulse response (FIR) filters, application-specific integrated circuits, and microprocessors, use multipliers. The demand for low power multipliers is gradually rising day by day in the current technological trend. In this study, we describe a 4×4 Wallace multiplier based on a carry select adder (CSA) that uses less power and has a better power delay product than existing multipliers. HSPICE tool at 16 nm technology is used to simulate the results. In comparison to the traditional CSA-based multiplier, which has a power consumption of 1.7 µW and power delay product (PDP) of 57.3 fJ, the results demonstrate that the Wallace multiplier design employing CSA with first zero finding logic (FZF) logic has the lowest power consumption of 1.4 µW and PDP of 27.5 fJ.
Evaluation of the weighted-overlap add model with massive MIMO in a 5G systemTELKOMNIKA JOURNAL
The flaw in 5G orthogonal frequency division multiplexing (OFDM) becomes apparent in high-speed situations. Because the doppler effect causes frequency shifts, the orthogonality of OFDM subcarriers is broken, lowering both their bit error rate (BER) and throughput output. As part of this research, we use a novel design that combines massive multiple input multiple output (MIMO) and weighted overlap and add (WOLA) to improve the performance of 5G systems. To determine which design is superior, throughput and BER are calculated for both the proposed design and OFDM. The results of the improved system show a massive improvement in performance ver the conventional system and significant improvements with massive MIMO, including the best throughput and BER. When compared to conventional systems, the improved system has a throughput that is around 22% higher and the best performance in terms of BER, but it still has around 25% less error than OFDM.
Reflector antenna design in different frequencies using frequency selective s...TELKOMNIKA JOURNAL
In this study, it is aimed to obtain two different asymmetric radiation patterns obtained from antennas in the shape of the cross-section of a parabolic reflector (fan blade type antennas) and antennas with cosecant-square radiation characteristics at two different frequencies from a single antenna. For this purpose, firstly, a fan blade type antenna design will be made, and then the reflective surface of this antenna will be completed to the shape of the reflective surface of the antenna with the cosecant-square radiation characteristic with the frequency selective surface designed to provide the characteristics suitable for the purpose. The frequency selective surface designed and it provides the perfect transmission as possible at 4 GHz operating frequency, while it will act as a band-quenching filter for electromagnetic waves at 5 GHz operating frequency and will be a reflective surface. Thanks to this frequency selective surface to be used as a reflective surface in the antenna, a fan blade type radiation characteristic at 4 GHz operating frequency will be obtained, while a cosecant-square radiation characteristic at 5 GHz operating frequency will be obtained.
Reagentless iron detection in water based on unclad fiber optical sensorTELKOMNIKA JOURNAL
A simple and low-cost fiber based optical sensor for iron detection is demonstrated in this paper. The sensor head consist of an unclad optical fiber with the unclad length of 1 cm and it has a straight structure. Results obtained shows a linear relationship between the output light intensity and iron concentration, illustrating the functionality of this iron optical sensor. Based on the experimental results, the sensitivity and linearity are achieved at 0.0328/ppm and 0.9824 respectively at the wavelength of 690 nm. With the same wavelength, other performance parameters are also studied. Resolution and limit of detection (LOD) are found to be 0.3049 ppm and 0.0755 ppm correspondingly. This iron sensor is advantageous in that it does not require any reagent for detection, enabling it to be simpler and cost-effective in the implementation of the iron sensing.
Impact of CuS counter electrode calcination temperature on quantum dot sensit...TELKOMNIKA JOURNAL
In place of the commercial Pt electrode used in quantum sensitized solar cells, the low-cost CuS cathode is created using electrophoresis. High resolution scanning electron microscopy and X-ray diffraction were used to analyze the structure and morphology of structural cubic samples with diameters ranging from 40 nm to 200 nm. The conversion efficiency of solar cells is significantly impacted by the calcination temperatures of cathodes at 100 °C, 120 °C, 150 °C, and 180 °C under vacuum. The fluorine doped tin oxide (FTO)/CuS cathode electrode reached a maximum efficiency of 3.89% when it was calcined at 120 °C. Compared to other temperature combinations, CuS nanoparticles crystallize at 120 °C, which lowers resistance while increasing electron lifetime.
In place of the commercial Pt electrode used in quantum sensitized solar cells, the low-cost CuS cathode is created using electrophoresis. High resolution scanning electron microscopy and X-ray diffraction were used to analyze the structure and morphology of structural cubic samples with diameters ranging from 40 nm to 200 nm. The conversion efficiency of solar cells is significantly impacted by the calcination temperatures of cathodes at 100 °C, 120 °C, 150 °C, and 180 °C under vacuum. The fluorine doped tin oxide (FTO)/CuS cathode electrode reached a maximum efficiency of 3.89% when it was calcined at 120 °C. Compared to other temperature combinations, CuS nanoparticles crystallize at 120 °C, which lowers resistance while increasing electron lifetime.
A progressive learning for structural tolerance online sequential extreme lea...TELKOMNIKA JOURNAL
This article discusses the progressive learning for structural tolerance online sequential extreme learning machine (PSTOS-ELM). PSTOS-ELM can save robust accuracy while updating the new data and the new class data on the online training situation. The robustness accuracy arises from using the householder block exact QR decomposition recursive least squares (HBQRD-RLS) of the PSTOS-ELM. This method is suitable for applications that have data streaming and often have new class data. Our experiment compares the PSTOS-ELM accuracy and accuracy robustness while data is updating with the batch-extreme learning machine (ELM) and structural tolerance online sequential extreme learning machine (STOS-ELM) that both must retrain the data in a new class data case. The experimental results show that PSTOS-ELM has accuracy and robustness comparable to ELM and STOS-ELM while also can update new class data immediately.
Electroencephalography-based brain-computer interface using neural networksTELKOMNIKA JOURNAL
This study aimed to develop a brain-computer interface that can control an electric wheelchair using electroencephalography (EEG) signals. First, we used the Mind Wave Mobile 2 device to capture raw EEG signals from the surface of the scalp. The signals were transformed into the frequency domain using fast Fourier transform (FFT) and filtered to monitor changes in attention and relaxation. Next, we performed time and frequency domain analyses to identify features for five eye gestures: opened, closed, blink per second, double blink, and lookup. The base state was the opened-eyes gesture, and we compared the features of the remaining four action gestures to the base state to identify potential gestures. We then built a multilayer neural network to classify these features into five signals that control the wheelchair’s movement. Finally, we designed an experimental wheelchair system to test the effectiveness of the proposed approach. The results demonstrate that the EEG classification was highly accurate and computationally efficient. Moreover, the average performance of the brain-controlled wheelchair system was over 75% across different individuals, which suggests the feasibility of this approach.
Adaptive segmentation algorithm based on level set model in medical imagingTELKOMNIKA JOURNAL
For image segmentation, level set models are frequently employed. It offer best solution to overcome the main limitations of deformable parametric models. However, the challenge when applying those models in medical images stills deal with removing blurs in image edges which directly affects the edge indicator function, leads to not adaptively segmenting images and causes a wrong analysis of pathologies wich prevents to conclude a correct diagnosis. To overcome such issues, an effective process is suggested by simultaneously modelling and solving systems’ two-dimensional partial differential equations (PDE). The first PDE equation allows restoration using Euler’s equation similar to an anisotropic smoothing based on a regularized Perona and Malik filter that eliminates noise while preserving edge information in accordance with detected contours in the second equation that segments the image based on the first equation solutions. This approach allows developing a new algorithm which overcome the studied model drawbacks. Results of the proposed method give clear segments that can be applied to any application. Experiments on many medical images in particular blurry images with high information losses, demonstrate that the developed approach produces superior segmentation results in terms of quantity and quality compared to other models already presented in previeous works.
Automatic channel selection using shuffled frog leaping algorithm for EEG bas...TELKOMNIKA JOURNAL
Drug addiction is a complex neurobiological disorder that necessitates comprehensive treatment of both the body and mind. It is categorized as a brain disorder due to its impact on the brain. Various methods such as electroencephalography (EEG), functional magnetic resonance imaging (FMRI), and magnetoencephalography (MEG) can capture brain activities and structures. EEG signals provide valuable insights into neurological disorders, including drug addiction. Accurate classification of drug addiction from EEG signals relies on appropriate features and channel selection. Choosing the right EEG channels is essential to reduce computational costs and mitigate the risk of overfitting associated with using all available channels. To address the challenge of optimal channel selection in addiction detection from EEG signals, this work employs the shuffled frog leaping algorithm (SFLA). SFLA facilitates the selection of appropriate channels, leading to improved accuracy. Wavelet features extracted from the selected input channel signals are then analyzed using various machine learning classifiers to detect addiction. Experimental results indicate that after selecting features from the appropriate channels, classification accuracy significantly increased across all classifiers. Particularly, the multi-layer perceptron (MLP) classifier combined with SFLA demonstrated a remarkable accuracy improvement of 15.78% while reducing time complexity.
International Conference on NLP, Artificial Intelligence, Machine Learning an...gerogepatton
International Conference on NLP, Artificial Intelligence, Machine Learning and Applications (NLAIM 2024) offers a premier global platform for exchanging insights and findings in the theory, methodology, and applications of NLP, Artificial Intelligence, Machine Learning, and their applications. The conference seeks substantial contributions across all key domains of NLP, Artificial Intelligence, Machine Learning, and their practical applications, aiming to foster both theoretical advancements and real-world implementations. With a focus on facilitating collaboration between researchers and practitioners from academia and industry, the conference serves as a nexus for sharing the latest developments in the field.
TIME DIVISION MULTIPLEXING TECHNIQUE FOR COMMUNICATION SYSTEMHODECEDSIET
Time Division Multiplexing (TDM) is a method of transmitting multiple signals over a single communication channel by dividing the signal into many segments, each having a very short duration of time. These time slots are then allocated to different data streams, allowing multiple signals to share the same transmission medium efficiently. TDM is widely used in telecommunications and data communication systems.
### How TDM Works
1. **Time Slots Allocation**: The core principle of TDM is to assign distinct time slots to each signal. During each time slot, the respective signal is transmitted, and then the process repeats cyclically. For example, if there are four signals to be transmitted, the TDM cycle will divide time into four slots, each assigned to one signal.
2. **Synchronization**: Synchronization is crucial in TDM systems to ensure that the signals are correctly aligned with their respective time slots. Both the transmitter and receiver must be synchronized to avoid any overlap or loss of data. This synchronization is typically maintained by a clock signal that ensures time slots are accurately aligned.
3. **Frame Structure**: TDM data is organized into frames, where each frame consists of a set of time slots. Each frame is repeated at regular intervals, ensuring continuous transmission of data streams. The frame structure helps in managing the data streams and maintaining the synchronization between the transmitter and receiver.
4. **Multiplexer and Demultiplexer**: At the transmitting end, a multiplexer combines multiple input signals into a single composite signal by assigning each signal to a specific time slot. At the receiving end, a demultiplexer separates the composite signal back into individual signals based on their respective time slots.
### Types of TDM
1. **Synchronous TDM**: In synchronous TDM, time slots are pre-assigned to each signal, regardless of whether the signal has data to transmit or not. This can lead to inefficiencies if some time slots remain empty due to the absence of data.
2. **Asynchronous TDM (or Statistical TDM)**: Asynchronous TDM addresses the inefficiencies of synchronous TDM by allocating time slots dynamically based on the presence of data. Time slots are assigned only when there is data to transmit, which optimizes the use of the communication channel.
### Applications of TDM
- **Telecommunications**: TDM is extensively used in telecommunication systems, such as in T1 and E1 lines, where multiple telephone calls are transmitted over a single line by assigning each call to a specific time slot.
- **Digital Audio and Video Broadcasting**: TDM is used in broadcasting systems to transmit multiple audio or video streams over a single channel, ensuring efficient use of bandwidth.
- **Computer Networks**: TDM is used in network protocols and systems to manage the transmission of data from multiple sources over a single network medium.
### Advantages of TDM
- **Efficient Use of Bandwidth**: TDM all
Advanced control scheme of doubly fed induction generator for wind turbine us...IJECEIAES
This paper describes a speed control device for generating electrical energy on an electricity network based on the doubly fed induction generator (DFIG) used for wind power conversion systems. At first, a double-fed induction generator model was constructed. A control law is formulated to govern the flow of energy between the stator of a DFIG and the energy network using three types of controllers: proportional integral (PI), sliding mode controller (SMC) and second order sliding mode controller (SOSMC). Their different results in terms of power reference tracking, reaction to unexpected speed fluctuations, sensitivity to perturbations, and resilience against machine parameter alterations are compared. MATLAB/Simulink was used to conduct the simulations for the preceding study. Multiple simulations have shown very satisfying results, and the investigations demonstrate the efficacy and power-enhancing capabilities of the suggested control system.
Using recycled concrete aggregates (RCA) for pavements is crucial to achieving sustainability. Implementing RCA for new pavement can minimize carbon footprint, conserve natural resources, reduce harmful emissions, and lower life cycle costs. Compared to natural aggregate (NA), RCA pavement has fewer comprehensive studies and sustainability assessments.
Using recycled concrete aggregates (RCA) for pavements is crucial to achieving sustainability. Implementing RCA for new pavement can minimize carbon footprint, conserve natural resources, reduce harmful emissions, and lower life cycle costs. Compared to natural aggregate (NA), RCA pavement has fewer comprehensive studies and sustainability assessments.
A review on techniques and modelling methodologies used for checking electrom...nooriasukmaningtyas
The proper function of the integrated circuit (IC) in an inhibiting electromagnetic environment has always been a serious concern throughout the decades of revolution in the world of electronics, from disjunct devices to today’s integrated circuit technology, where billions of transistors are combined on a single chip. The automotive industry and smart vehicles in particular, are confronting design issues such as being prone to electromagnetic interference (EMI). Electronic control devices calculate incorrect outputs because of EMI and sensors give misleading values which can prove fatal in case of automotives. In this paper, the authors have non exhaustively tried to review research work concerned with the investigation of EMI in ICs and prediction of this EMI using various modelling methodologies and measurement setups.
Presentation of IEEE Slovenia CIS (Computational Intelligence Society) Chapte...University of Maribor
Slides from talk presenting:
Aleš Zamuda: Presentation of IEEE Slovenia CIS (Computational Intelligence Society) Chapter and Networking.
Presentation at IcETRAN 2024 session:
"Inter-Society Networking Panel GRSS/MTT-S/CIS
Panel Session: Promoting Connection and Cooperation"
IEEE Slovenia GRSS
IEEE Serbia and Montenegro MTT-S
IEEE Slovenia CIS
11TH INTERNATIONAL CONFERENCE ON ELECTRICAL, ELECTRONIC AND COMPUTING ENGINEERING
3-6 June 2024, Niš, Serbia
Manufacturing Process of molasses based distillery ppt.pptx
Viterbi optimization for crime detection and identification
1. TELKOMNIKA Telecommunication, Computing, Electronics and Control
Vol. 18, No. 5, October 2020, pp. 2378~2384
ISSN: 1693-6930, accredited First Grade by Kemenristekdikti, Decree No: 21/E/KPT/2018
DOI: 10.12928/TELKOMNIKA.v18i5.13398 2378
Journal homepage: http://journal.uad.ac.id/index.php/TELKOMNIKA
Viterbi optimization for crime detection and identification
Reem Razzaq Abdul Hussein1
, Salih Mahdi Al-Qaraawi2
, Muayad Sadik Croock3
1
College of Business Informatics, University of Information Technology and Communications, Iraq
2,3
Computer Engineering Department, University of Technology, Iraq
Article Info ABSTRACT
Article history:
Received Jun 24, 2019
Revised Mar 3, 2020
Accepted Mar 13, 2020
In this paper, we introduce two types of hybridization. The first contribution
is the hybridization between the Viterbi algorithm and Baum Welch in
order to predict crime locations. While the second contribution considers
the optimization based on decision tree (DT) in combination with the Viterbi
algorithm for criminal identification using Iraq and India crime dataset.
This work is based on our previous work [1]. The main goal is to enhance
the results of the model in both consuming times and to get a more accurate
model. The obtained results proved the achievement of both goals in
an efficient way.
Keywords:
Baum Welch algorithm
Decision tree
Hybridization
Viterbi algorithm
This is an open access article under the CC BY-SA license.
Corresponding Author:
Reem Razzaq Abdul Hussein,
College of Business Informatics,
University of Information Technology and Communications,
Baghdad, Iraq.
Email: reemrazzak@yahoo.com
1. INTRODUCTION
The data mining techniques are widely used in e-government, especially in the criminology field,
wherein they help police departments predicate criminals and detect information about crime locations.
The study of crime is expected not only to control present crime but also to analyze the criminal activities
so that future occurrences of the same incidents can be overcome. Government is trying to improve
the effectiveness of prevention of crimes that can be happened in clustering criminal features depending
on spatial and temporal criteria which are different in each country [1-11]. It also tries to discover
the correlation between different attributes, such as crime and demographics. Analyzing historical data is useful
to discover the crime pattern [12]. Past researchers in crime detection depended on data mining and machine
learning to develop systems that analyze patterns to have the highest probability of occurring and predict
the regions where such patterns are likely to happen. Some researchers utilized the algorithm of Baum-Welch
in past studies, which collected GPS data for predicting criminal movements. The work of [13] used attributes
according to their characteristics and trained an HMM for each cluster, obtaining an accuracy result of 13.85%.
The authors suggested data mining methods for crime detection and identification of criminal for Indian
country during the period of 2000 to 2012. They depended on clustering, used k-means, are based on crime
attributes, and made visualizations using Google Maps. They also used ANNs and forest decision trees (DTs)
for classification. The outcomes were computed by the Weka tool. The accuracy of the ANN was 90.02. They
applied 10-fold cross validation for the forest DTs, the accuracy in RMSE was 0.0751, and the time taken to
build the model was 4.24 s [14]. In [15], the proposed approach helped agencies to emphasize the security
2. TELKOMNIKA Telecommun Comput El Control
Viterbi optimization for crime detection and identification (Reem Razzaq Abdul Hussein)
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in Indian, their work needs to improve with more data mining method. Other researchers considered the use of
the Viterbi algorithm for identifying locations with a high probability of having a criminal depending
on the relationship between the location and types of crimes, such as murder, thief, and assault, they handle
idling time rather than accuracy problem. The Latent hidden Markov models are types of algorithms that have
been designed to detect crime activities by obtaining a sequence of observations from hidden values.
They developed an algorithm that fits regular vine copula to generate tree structures. These trees had been
employed to produce an emission matrix for hidden Markov model (HMM) algorithm, the fusion of coupled
parameters with two types of HMM algorithms, this work needs to improve accuracy and reduce execution
time. [1]. In this work, we attempt to overcome the problems in previous studies and develop the work of past
researcher [1, 3, 14], by proposing a hybridization between Baum Welch algorithm and Viterbi algorithms
in a side, and Viterbi algorithms for optimization of data set then applied DT, at the other side. The objective
of proposed work to handles an important topic of monitoring criminal activities and movements and indicates
the level of danger in locations, the preposd hybridization method reduces the consumed time and increases
accuracy, respectively.
2. HMM
The HMM is a robust model which can be used when states in a process are not observable, but
observed data depends on these hidden states. HMM depends on two main properties, which are:
− The observation at time t is produced by a process whose state Ht is hidden from the observer.
− The state of the hidden process represents the Markov chain [16-23].
At the other hand, the Viterbi is a dynamic programming algorithm that depends on transition and
emission matrices. In particular, the Viterbi algorithm can obtain path (state sequences) to generate output
sequences. It works by finding the maximum overall possible state sequence by considering a forwarding
algorithm. The Baum (1970) proposed the use of the Baum–Welch algorithm based on the computation of
the probabilistic methods of the Markov model. It also called the forward-backward method, wherein
the backward part represented the probability of partial observation sequences from the time (t+1) to end, can
be computed iteratively [24].
3. OVERVIEW OF ADOPTED DATASETS
In this work, two datasets have been used in the criminal field. The first one is the Iraqi dataset, while
the second dataset is India dataset [25]. The Iraq dataset consists of features such as as{age, gender, ID, crime
types, locations, gang, longitude, latitude} the type of features is categorization whereas the features of Indian
data set is included {states, murder, attempted to murder ,…, thief}, making a partitioning based on clustering
needs more investigation and analysis of the contexts in a discrete sequence dataset. Here, the needed to predict
the locations of the crimes, and the HMM to obtain the hidden pattern is required. As mentioned earlier,
the dataset is collected from a website and social media, where Baghdad city can be divided into different main
locations as shown in proposed Table 1.
While the representation of the second dataset is shown in propsed Table 2. The proposed system is
also applied to India dataset. As mentioned above, the HMM must generate ten locations: Loc1, Loc2, ….,
Loc32, where these locations represent the workflow of the proposed system in the clustering. Here, the needed
to predict the locations of the crimes, and the HMM to obtain the hidden pattern in this work as well as the two
hidden states with one observe the state.
Table 1. Location of Iraq representation Table 2. Location of Indian states representation
Seq Location Representation
1 Adamya Location 1(Loc_1)
2 Albayaa Location 2(Loc_2)
3 Hayalamel Location 3(Loc_3)
4 kadmiya Location 4(Loc_4)
5 Madaan Location 5(Loc_5)
6 Mansour Location 6(Loc_6)
7 Baghdadgedeeda Location 7(Loc_7)
8 Hasania Location 8(Loc_8)
9 Madeena Location 9(Loc_9)
10 Abogreeb Location 10(Loc_10)
state Representation
1 ANDHRA PRADESH Location 1(Loc_1)
2 ARUNACHAL PRADESH Location 2(Loc_2)
3 ASSAM Location 3(Loc_3)
4 BIHAR Location 4(Loc_4)
5 CHHATTISGARH Location 5(Loc_5)
.
.
. .
32 PUDUCHERRY
Location
32(Loc_32)
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4. PROPOSED METHOD
The Viterbi algorithm as an optimizer algorithm in two strategies is proposed. The first one is built
with Baum Welch, while the second one has been created in combination with the DT algorithm. Viterbi is
a supervised clustering algorithm that makes partitioning-based clustering for learning contexts in a discreet
sequence dataset. And it works as an outlier detector that removes low-probability data (abnormal data) that
explains in detail the following section.
4.1. Hybridization Viterbi algorithm with Baum Welch
We explain the elements of the proposed workflow that include the hybridization of the Viterbi
algorithm with Baum Welch to appear in three stages as shown in Figure 1. The Viterbi plays a major role
in enhancing the clustering model; in addition, the dataset formulates the structure of the dynamic model
using vine while the movement of criminals is determined by the generation of dynamic transition matrix
from dataset.
.
Figure 1. Hybridization Viterbi algorithm with Baum Welch
4.1.1. Emission matrix generation stage
The structure of matrix is generated using a simple vine couple to produce a simple tree structure
strategy for selecting a tree model based on vine algorithm and generating a compact model. Conditional
probability (Bayes theorem) is applied to the tree structure and used for assuming that the couple parameter
{month, location} is combined with crime types to produce three dimensions rather than two dimensions.
The Emission matrix can be embedded into HMM (Baum-Welch and Viterbi algorithms) and evaluated
using in (1),
Emi =[
𝑃(𝑙𝑜𝑐1/ 𝑚𝑢𝑟𝑑𝑒𝑟, 𝑆𝑒𝑝) ⋯ 𝑃(𝑙𝑜𝑐N/ 𝑚𝑢𝑟𝑑𝑒𝑟, 𝑆𝑒𝑝)
⋮ ⋱ ⋮
𝑃(𝑙𝑜𝑐1/ 𝑂𝑡ℎ𝑒𝑟𝑠, 𝑂𝑐𝑡) ⋯ 𝑃(𝑙𝑜𝑐N/ 𝑂𝑡ℎ𝑒𝑟𝑠, 𝑂𝑐𝑡)
] (1)
4.1.2. Transition matrix generation stage
The transition matrix is used to represent the Markov chain, and it is defined as a set of (location)
states (S = {s1_, s2..., sn}), where Locations are represented by criminal movements in a different location.
The transition is represented as the square array TN×N, where N= 10, for Iraq dataset, and N=32 for India dataset.
The transition matrix can be construction as the following algorithm, where the summation of the probability
of each row in the matrix must be an equal one.
4. TELKOMNIKA Telecommun Comput El Control
Viterbi optimization for crime detection and identification (Reem Razzaq Abdul Hussein)
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An algorithm of Transition Matrix
Function Transition (x), where x is location of crime
Returns Probability matrix of criminal’s movements
m = maximum value(x);
y = zeros(m,1) vertice
construct probability matrix p m×m
for k=1to n-1
y(x(k)) = y(x(k)) + 1;
p(x(k), x(k+1)) = p(x(k), x(k+1)) + 1; where k is the number of movements
end loop
if y ==0 then
p=0
else
p = (p div y);
end if
4.1.3. Sequence generation stage
The sequence is the third parameter of HMM, in this step is frist contribution which defines
the sequence of crime that happens, to obtain the most probable location of (crimes)states. In traditional and
proposal work, the switch has been made between states and sequences (crimes) in two important phases:
− Replacing the sequence value with a state value, (i.e. the sequence as coupled parameters {crime types}
is replaced with {locations}.
− Inserting states (locations) as a sequence into the Viterbi algorithm to generate the most probable sequence
of crimes as an output.
4.2. DT algorithms with Viterbi algorithms
Here, the second strategy of improving decision tree algorithm is explained. The adopted steps of
the proposed algorithm are illustrated in Figure 2 following the steps of:
Step 1: Start.
Step 2: Generate initialize the value of the EIMS matrix as mention in section 2 that computes the probability
between {crimes type, month} and {locations}.
Step 3: Generate initialize the value of the TRNS matrix, in the same way, that mention in section, to
determine criminal’s movements.
Step 4: A sequence of couple parameter initialization.
Figure 2. DT algorithms with Viterbi algorithms
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Step 5: apply the Viterbi algorithm which can be used as an optimization algorithm by the elimination of
the location with low probability as an outlier to maximize to increase the efficiency of time execution,
Step 6: Remove locations with low probability from the dataset.
Step 7: Generate new dataset, with improved quality of the dataset.
Step 8: Apply Decision Tree algorithm with hyperparameter, obtaining Nid as a label for first dataset, and
kidnapping as a label for the second dataset.
Step 9: End
5. CONTRIBUTIONS
The Vine Copula Baum-Welch algorithm can achieve good results with the hybrid Vine Copula
Viterbi algorithm for determining the sequential relation of past crime types, date, and locations. The speed
of execution and accuracy of the Baum-Welch algorithm are enhancedNid of Iraq can help to facilitate
the identification of criminals by the police and checkpoints. The contribution of this work, including a dataset,
is optimized using a Viterbi algorithm for outlier detection and generates a new dataset.which is used to
generate DT.
6. RESULT AND IMPLEMENTATION
The implementation of the proposed algorithm in both strategies can be explained in numerous stages:
Stage 1: Generate the Emission matrix, Each value of the probability matrix related to crime type in a specific
location. The main locations can then be determined from the column of crime type after transposition,
as mention Emission matrix initially It was built by the implementation of the theory of Bayes, then was trained
using latent Markov method ,the Implementation of generated matrix of the first dataset is shown in Table 3.
While the Implementation of generated Emission matrix of the second dataset is shown in Table 4. The two
features selected {thief, other IPC crimes}, the porobabilty crime occring appering in (32) states in indain
country, as mention Emission matrix initially It was built by Bayes thory, then was trained using latent
Markov method. Finally, The Emission matrix had been constructed with three parameters which had the main
role HMM algorithms.
Table 3. The implementation emission matrix of the frist dataset
Loc1 Loc2 Loc3 Loc10
Theft 0.1250 0 0 … 0
Murder 0.3333 0 0 … 0
Others 0.5000 0 0 … 0
Theft 0.4286 0.2857 0 … 0
Murder 0.0213 0.4894 0.1277 … 0.2340
Others 0 1.0000 0 … 0
Table 4. Emission matrix of the second dataset
loc1 loc2 loc3 ……. loc32
Thief 0.0538 0.0010 0.0137 ……….. 0.0076
Other IPC Crimes 0.0019 0 0.0259 …………. 0.0019
Stage 2: Generating a transition matrix for both datasets. The locations representation as a state diagram of
Baghdad city is shown in Figure 3. It is important to note that the transition stages express the movement of
criminals among location. The locations are represented in digraph of the Markov chain. Where the circle
shape represents the nodes (cities) in which the crimes occurred and the edges connecting the nodes between
them, to represent the possibility of transition between nodes.
Stage 3: Entering the sequence of crimes for both datasets.
Stage 4: Table 5 to Table 6 illustrates the comparison of the proposed algorithm with the traditional methods
in terms of accuracy, relative mean square error (RMSE) and consumed time. The RMSE is considered
for accuracy expression for HMM methods that includes Baum Welch and the proposed Baum Welch+Vetribi
as shown in Table 5. It is well shown that the proposed algorithm for both datasets decreases the RMSE,
clearly by (0.13).
At the other hand, Table 6 explains the comparison between the proposed (DT with Viterbi) and
traditional DT. In this table, the accuracy of a confusion matrix is considered as the main indicator. The DT
based on Matlab library for implementation.The accuracy of DT before the optimization is approximately
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(98%) for the first dataset, whereas it is nearly )93.8%( for the second dataset. The accuracy of DT after
optimization is improved by approximately )98.8% ( for the first dataset. For the second dataset, the accuracy
is also improved by around )95.9%( after optimization. The obtained results show that the proposed DT+Viterbi
increases the accuracy for both datasets.
Figure 3. State transition before optimization of Iraq dataset
Table 5. Comparison of results for RMSE
Baum Welch [2] Baum Welch +Viterbi (preposed work)
Dataset1 0.17 0.12
Dataset2 0.04 0.03
Table 6. Comparison of results for accuracy in the confusion matrix
Accuracy in the confusion matrix
DT [3] Prepoded DT+ viterbi (preposed work)
Dataset1 ~%98 ~%98.8
Dataset2 ~%93.8 ~%95.9
Table 7. Shows the time-consuming comparison of the proposed methods (Baum Welch + Viterbi)
with the traditional (Baum Welch) algorithms. Applied in two datasets, both improved to 0.05 and 0.08
respectively. It is proved that the proposed methods including Baun Welch outperform the traditional
approaches which had been improved by (~0.15 sec) while Deepika K.K and at [14], achieves 4.24 s with
0.0751 as RMSE.
Table 7. Comparison of results for the consumed time
Consumed Time in (second)
Baum Welch [1] Baum Welch +Viterbi (preposed work)
Dataset1 0.2 0.05
Dataset2 0.1 0.08
7. CONCLUSION
In this paper, a proposed HMM has produced to predicate the level of danger in a specific region.
The combination was done using Baum Welch with Viterbi from a side and DT with Viterbi at the other side
to get a more accurate model. The proposed methods can achieve promising results for considering the Viterbi
algorithm to determine the sequential relation of past crime types, with two latent parameters {month and
locations}. The optimization of using DT method helped the proposed algorithm in mining the optimal solution.
In addition, the consumed time is reduced in efficient performance.
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