Abstract Predicting crimes before they occur can save lives and losses of property. With the help of machine learning, many researchers have studied predicting crimes extensively. In this paper, we evaluate state-of-the-art crime prediction techniques that are available in the last decade, discuss possible challenges, and provide a discussion about the future work that could be conducted in the field of crime prediction. Although many works aim to predict crimes, the datasets they used and methods that are applied are numerous. Using a Systematic Literature Review (SLR) methodology, we aim to collect and synthesize the required knowledge regarding machine learning-based crime prediction and help both law enforcement authorities and scientists to mitigate and prevent future crime occurrences. We focus primarily on 68 selected machine learning papers that predict crime. We formulate eight research questions and observe that the majority of the papers used a supervised machine learning approach, assuming that there is prior labeled data, and however in some cases, there is no labeled data in real-world scenarios. We have also discussed the main challenges found while conducting some of the studies by the researchers. We consider that this research paves the way for further research to help governments and countries fight crime and decrease this for better safety and security. 

/pdf/machine-learning-in-crime-prediction-138ae5wb.pdf

Machine learning in crime prediction

Analysis of criminal spatial events in india using exploratory data analysis and regression

One potential approach for crime analysis that has shown promising results is data analytics, particularly descriptive and predictive techniques. Data analytics can explore former criminal incidents seeking hidden correlations and patterns, which potentially could be used in crime prevention and resource management. The purpose of this research is to build a crime analysis model using supervised techniques to predict the arrest status of serious crimes in Chicago. This is based on specific indicators, such as timeframe, location in terms of district, community, and beat, and crime type among others. We used time series and clustering techniques to help us identify influential features. Supervised machine learning algorithms then modelled the subset of features against incidents related to battery and assaults in specific timeframes and locations to predict the arrest status response variable. The models derived from Naïve Bayes, Decision Tree, and Support Vector Machine (SVM) algorithms reveal a high predictive accuracy rate at certain times in some communities within Chicago.

Crime Analyses Using Data Analytics

An attempt has been made to predict the subsurface lithology of the adjunct exploratory well drilled for hydrocarbon exploration. Both, decision trees and random forests algorithms of supervised machine learning are used for the comparison and prediction of the subsurface lithologies. It is generally observed that at each stage, the decision tree looks for the locally optimal alternative, whereas the random forest in contrast, makes use of many decision trees, which has a lower danger of overfitting. The random forest uses the predictions from all of the decision trees as opposed to just one and makes final predictions based on the majority of variables. Overall, the random forest is essentially a collection of decision trees, the output of which is combined to produce a single overall result. In the current research, the models are developed and trained to predict the subsurface lithology of the adjacent well with ~ 9000 well log data points, 70% of which are used for training and 30% for testing. Finally, the accuracy check using the F1-score is performed, showing 97 and 94% accuracy in subsurface lithology prediction for random forest and decision tree classifications, respectively. 

Subsurface Lithology Classification Using Well Log Data, an Application of Supervised Machine Learning

Artificial intelligence (AI) is one of the prominent technological developments in the 21st century. It has gained application in diverse fields, including healthcare, agriculture, and manufacturing. Like other global countries, Saudi Arabia has accelerated its efforts to develop AI. This study aims to investigate AI techniques’ influence on the Saudi judiciary system and determine possible future developments. The study was a literature review of 10 different international and Saudi Arabia publications on Artificial Intelligence in the Judiciary System using related titles, terms, and abstracts. The findings revealed that AI techniques are promising developments in the judiciary. Research evidence supports that augmenting human expertise with appropriate AI techniques like machine learning, text mining, NLP, and machine interpretation can improve efficiency, effectiveness, accuracy, transparency, and fairness in judicial processes. However, the studies focusing on AI applications in the Saudi judicial system are also limited. Despite the limited research, with inferences from the findings from studies in other contexts. AI is a promising development that can improve the Saudi judiciary system. Although these findings provide insight, they are inferential and not an accurate explanation of the influence of AI on the Saudi judicial system. Such gaps call for future studies to determine the influence of AI on the judicial system within the Saudi context. 

Artificial Intelligence in the Judiciary System of Saudi Arabia: A Literature Review

Machine Learning algorithms has proved its significant contribution in all major domains of technical and non-technical sectors. In present days, the Intelligence Bureau is also using Artificial Intelligence and Machine Learning based analytical approach to predict crime location using past crime data for a given geographical location. Availability of digital records of last few years crimes happened in a certain geographical location is helping crime control division to predict the possible zones for happening next crime and take some precautionary action to reduce the probability of occurring the unwanted event. The government of Boston city initiate to improve city by releasing Crime Incident Report dataset of Boston city to the public. The researcher or analyst can take their initiatives to develop some crime prediction models that can help Boston Police Department to identify the crime prone locations in Boston city and could take some well advance measure to reduce the crime. In this research work, we have analyzed the provided dataset and did an exploratory data analysis to identified the high and low crime prone locations, most severe and least severe crime, year-wise and month-wise crime and shooted and not-shooted crime cases in Boston city. The result presented in this study shows that random forest with Principle Component Analysis (PCA) improve the classification result by 9% in accuracy with comparison to simple decision tree, and PCA with decision tree gives 5% more accuracy than decision tree. Although the computation time is increased in PCA based algorithms in compare to simple decision tree. The proposed research work opens the doors for application of these supervised learning algorithms for prediction and classifying crimes in some other states.

Machine learning based analytical approach for geographical analysis and prediction of Boston City crime using geospatial dataset

In this fast-growing world of 21st century Machine Learning (ML) is one of the most powerful tools for solving a variety of real-life problems. Machine Learning helps both normal as well as differently-abled people. Facial Expression Recognition (FER) system can be widely used in different variety of research areas, such as diagnosis of mental disease and human physiological interaction detection. Machine Learning can automate processes by learning from different datasets. ML is commonly used for tasks like prediction, detection, regression and classification. The core idea of ML is to learn from previous and produce results accordingly. This result may be the output of new input or predictions based on past actions. With this, Deep Learning a subset of ML made possible to learn human facial expression from an image dataset and identify the facial expression performed in real life. The advancement in facial recognition systems helps in the purpose of diagnosis and also market feedback. But, due to various complexities and noise factors in facial expression images, there is still a need for improvement in this field. With different technological advancements in hardware and software, Facial Expression Recognition systems have been developed to help and support real-world applications.

CNN Based Facial Expression Recognition System Using Deep Learning Approach

In the twenty-first century, IoT and machine learning made possible and affordable to track the belonging objects on our mobile screen. Due to daily busy schedules, we generally miss our important belongings at many such places where frequent visit is not possible. Tracking of our goods and belongings is an open challenge in real-time situations. Technologies are matured enough to make this possible in real-time. In this research work, we have integrated IoT, AIML and GPS together in a Web-based and mobile-based app to solve this real-time issue. A low energy consumption device has been developed in this work which makes it more durable and environment friendly. The user can see his/her object on Google Map in real-time which minimizes the probability of miss or stolen his/her item or object. Advanced technologies with open-source tools have been integrated in this system to make it a high-performing system. An Android-based mobile application will also developed to track and monitor the bag/vehicle on mobile screen.

Real-Time Tracking System for Object Tracking Using Internet of Things (IoT)

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Machine learning based analytical approach for geographical analysis and prediction of Boston City crime using geospatial dataset

CNN Based Facial Expression Recognition System Using Deep Learning Approach

Real-Time Tracking System for Object Tracking Using Internet of Things (IoT)