A Survey on Crime Detection And Prediction Techniques

Crime is a preeminent issue where the main concern has been worried by individual, the local area and government. Wrongdoing forecast utilizes past information and in the wake of investigating information, anticipate the future wrongdoing with area and time. In present days sequential criminal cases quickly happen so it is a provoking assignment to anticipate future wrongdoing precisely with better execution. This paper examines about various wrongdoing expectation and location. A productive wrongdoing forecast framework speeds up the method involved with addressing violations.. Wrongdoing Prediction framework utilizes recorded information and examinations the information utilizing a few dissecting strategies and later can anticipate the examples and patterns of wrongdoing utilizing any of the underneath referenced methodologies. Keywords: Crime Analysis, Data Mining, Classifiaction , Clustering

A Multi Model Framework for Grading of Human Emotion Using CNN and Computer Vision

Emotion analysis is an area which is been widely used in the forensic crime detection domain, a mentoring device for depressed students, psychologically affected patient treatment. The current system helps only in identifying the emotions but not in identifying the level of emotions like whether the individual is truly happy/sad or pretending to be happy /sad. In this proposed work a novel methodology has been introduced. We have rebuilt the Traditional Local Binary Pattern (LBP) feature operator to image the expression and combine the abstract characteristics of facial expression learned from the neural network of deep convolution with the modified features of the texture of the LBP facial expression in the full connection layer. These extracted features have been subjected as input for CNN Alex Net to classify the level of emotions. The results obtained in this phase are used in the confusion matrix for analysis of grading of emotions like Grade-1, Grade-2, and Grade-3 obtained an accuracy of 87.58% in the comparative analysis.

Nanotechnology: A Powerful Tool in Forensic Science for Solving Criminal Cases

Nanotechnology has emerged as a phoenix in the field of forensic science and proved to be of great importance in solving criminal cases where other techniques failed to provide conclusive results. This field of science possess humongous potential in the field of forensic science and assist in crime detection. It holds huge amount of value in making a positive contribution in assisting forensic experts and scientists in nabbing the criminals and most importantly prevent any wrongful conviction. In the past decade, many researchers have reported the satisfactory application of Nano technique in Forensic Science for the analysis of latent fingerprints, drugs in alleged drug-facilitated crimes, warfare agent detection, DNA analysis, counter terrorism, GSR detection, post-blast residue analysis, security measures, etc. It has been proved to be a robust approach for the detection of crime with greater selectivity, sensitivity, reliability and results are produced in a timely appropriate manner. The constant development of nanotechnology and its application in the field of Forensic Science over the past decade has been highlighted in this review article.

Anti-Muslim Voting and Media Coverage of Immigrant Crimes

We study how news coverage of immigrant criminality impacts voting in one of the most controversial referendums in recent years – the 2009 Swiss minaret ban. We combine a comprehensive crime detection dataset with detailed information on newspaper coverage. We first document a large upward distortion in media reporting of immigrant crime during the prereferendum period. Exploiting quasi-random variations in crime incidence, we find a positive first-order effect of news coverage on support for the ban. Quantification shows that, in absence of the media bias, pro-ban vote would have decreased from 57.6% to 53.5% at the national level.

METHODOLOGY OF DETECTION OF COMPUTER CRIME OFFENSES

Computer crime is characterized by an exceptional phenomenological diversity that complicates its unique and precise conceptual definition, and the permanent intensive development of computer technologies contributes to the constant expansion of its forms and poses increasing challenges to the authorities responsible for combating it. After a brief review of the concept and importance of criminology as a science, and criminological methodology as a branch of criminology, and the conceptual definition and basic characteristics of crime in general, and computer crime, the paper analyzes the most important aspects of crime detection methodology in this area. The conclusion is that the basic characteristics of computer crime, difficult detection, complicated proving and ineffective prevention certainly encourage potential perpetrators to make a decision on taking criminal acts in this area, which again indicates in which direction to move in order to eliminate difficulties in the methodology of detection and collection of evidence in criminal offenses of computer crime.

Uncovering dynamic textual topics that explain crime

Crime analysis/mapping techniques have been developed and applied for crime detection and prevention to predict where and when crime occurs, leveraging historical crime records over a spatial area and covariates for the spatial domain. Some of these techniques may provide insights for understanding crime and disorder, especially, via interpreting the weights for the spatial covariates based on regression modelling. However, to date, the use of temporal covariates for the time domain has not played a significant role in the analysis. In this work, we collect time-stamped crime-related news articles, infer crime topics or themes based on the collection and associate the topics with the historical numeric crime counts. We provide a proof-of-concept study, where instead of adopting spatial covariates, we focus on temporal (or dynamic) covariates and assess their utility. We present a novel joint model tailored for the crime articles and counts such that the temporal covariates (latent variables, more generally) are inferred based on the data sources. We apply the model for violent crime in London.

An Optimized Machine Learning and Big Data Approach to Crime Detection

Crime detection is one of the most important research applications in machine learning. Identifying and reducing crime rates is crucial to developing a healthy society. Big Data techniques are applied to collect and analyse data: determine the required features and prime attributes that cause the emergence of crime hotspots. The traditional crime detection and machine learning-based algorithms lack the ability to generate key prime attributes from the crime dataset, hence most often fail to predict crime patterns successfully. This paper is aimed at extracting the prime attributes such as time zones, crime probability, and crime hotspots and performing vulnerability analysis to increase the accuracy of the subject machine learning algorithm. We implemented our proposed methodology using two standard datasets. Results show that the proposed feature generation method increased the performance of machine learning models. The highest accuracy of 97.5% was obtained when the proposed methodology was applied to the Naïve Bayes algorithm while analysing the San Francisco dataset.

Assessing Harm from Cyber Crime

This chapter discusses a harm concept that enables a more comprehensive assessment of the consequences of cyber crime. Even though harm resulting from cyber crime is not fundamentally different from harm that is caused by other forms of crime or crime in general, the reach, scope, and volume of crime facilitated by information and communications technology have transformed risks posed to individuals, organizations, and nations, and challenge conventional approaches of crime detection and prevention. Assessments of the impact of cyber crime have been focusing on estimating the cost in monetary value. However, most significant harm might not be experienced as a loss of money, but as a disruption or destabilization of systems that are built on trust. This article advocates for a human-centric approach to cyber security, which emphasizes harm mitigation strategies.

Survey of Human Gait Analysis and Recognition for Different Sensor Modalities

Gait is a behavioural biometric which sometimes changes due to diseases but it is still a strong identification metric that is widely used in forensic works, state biometric preserve sectors, and medical laboratories. Gait analysis sometimes helps to identify person’s present mental state which reflects on physiological therapy for improved biological system. There are various gait measurement forms which expand the research area from crime detection to medical enhancement. Many research works have been done so far for gait recognition. Many researchers focused on skeleton image of people to extract gait features and many worked on stride length. Various sensors have been used to detect gait in various light forms. This paper is a brief survey of works on gait recognition, collected from various sources of science and technology literature. We have discussed few efficient models that worked best as well as we have discussed about few data sets available.