Crime Prediction Using Machine Learning and Deep Learning: A Systematic Review and Future Directions

Mandalapu, Varun; Elluri, Lavanya; Vyas, Piyush; Roy, Nirmalya

doi:10.1109/access.2023.3286344

Cited by 27 publications

(8 citation statements)

References 80 publications

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“…The author [5] discusses in this work, India has enacted legislation and international conventions to prevent human trafficking, including the Constitution, Penal Code, Evidence Act, Criminal Procedure Code, and International Conventions. Human trafficking and forced labour are prohibited under the Constitution, and the Penal Code deals with trafficking and related offenses.…”

Section: Literature Surveymentioning

confidence: 99%

An Investigation on Machine Learning-Based Predictive Model for Human Trafficking in India

Saha,

DasGupta,

Saha

2024

Preprint

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The multi-faceted crime of human trafficking has long afflicted India, and particularly vulnerable to the plight of Indian citizens. The study is designed to resemble an operational expedition and aims at identifying the connection between human trafficking and noteworthy festivals in various Indian states. In order to decipher and analyze the events that take place during these festivals, we use several machine learning algorithms and focus on key leaves in each state to identify hidden links between cultural fests and vulnerable people's victimization. This proposal helps to identify latent issues and provides detailed explanations for their occurrence during festivals. We concentrate on the pivotal leaves in each state, aiming to find sheltered connections between our cultural fests and the exploitation of vulnerable people. Our methods assist in identifying data patterns, just as they would in unravelling a mystery like festive mortal trafficking. This study creates a model to assess the risk of human trafficking in specific regions or populations to help law enforcement and NGOs guide targeted interventions by identifying trends and signs in past data. By highlighting high-risk areas and promoting community engagement in fighting human trafficking, the prediction model raises awareness. A framework for model refining and adapting will be created in this study to ensure forecast accuracy when new data becomes available. The machine learning algorithms detected human trafficking trends on Indian data with high Mean Absolute Error, Mean Squared Error, Root Mean Squared Error, and R2 scores, while Random Forest Regressor performed best with 0.802.

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Section: Literature Surveymentioning

confidence: 99%

An Investigation on Machine Learning-Based Predictive Model for Human Trafficking in India

Saha,

DasGupta,

Saha

2024

Preprint

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“…In the field of AI, deep learning has changed the game by allowing machines to complete tasks previously thought impossible. Its ability to manage complicated and large datasets is one of its key strengths [187]. With data that are too numerous or complex for human processing, traditional ML algorithms frequently struggle.…”

Section: Applications Of Artificial Intelligence In Tissue Engineerin...mentioning

confidence: 99%

Resorbable GBR Scaffolds in Oral and Maxillofacial Tissue Engineering: Design, Fabrication, and Applications

Alavi,

Gholami,

Shahmabadi

et al. 2023

JCM

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Guided bone regeneration (GBR) is a promising technique in bone tissue engineering that aims to replace lost or injured bone using resorbable scaffolds. The promotion of osteoblast adhesion, migration, and proliferation is greatly aided by GBR materials, and surface changes are critical in imitating the natural bone structure to improve cellular responses. Moreover, the interactions between bioresponsive scaffolds, growth factors (GFs), immune cells, and stromal progenitor cells are essential in promoting bone regeneration. This literature review comprehensively discusses various aspects of resorbable scaffolds in bone tissue engineering, encompassing scaffold design, materials, fabrication techniques, and advanced manufacturing methods, including three-dimensional printing. In addition, this review explores surface modifications to replicate native bone structures and their impact on cellular responses. Moreover, the mechanisms of bone regeneration are described, providing information on how immune cells, GFs, and bioresponsive scaffolds orchestrate tissue healing. Practical applications in clinical settings are presented to underscore the importance of these principles in promoting tissue integration, healing, and regeneration. Furthermore, this literature review delves into emerging areas of metamaterials and artificial intelligence applications in tissue engineering and regenerative medicine. These interdisciplinary approaches hold immense promise for furthering bone tissue engineering and improving therapeutic outcomes, leading to enhanced patient well-being. The potential of combining material science, advanced manufacturing, and cellular biology is showcased as a pathway to advance bone tissue engineering, addressing a variety of clinical needs and challenges. By providing this comprehensive narrative, a detailed, up-to-date account of resorbable scaffolds’ role in bone tissue engineering and their transformative potential is offered.

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“…DL Models are an advanced version of ML which have been greatly enhanced the prediction achievements in various domains such as health, business, agriculture, law enforcement and so on [17]. DL algorithms hold the potential to significantly enhance the ability to analyze and recognize crimes in real-time, providing a crucial tool in the ongoing fight against criminal activity.…”

Section: Introductionmentioning

confidence: 99%

A Systematic Review on the use of Machine Learning and Deep Learning Techniques for Crime Prediction from Social Media

2024

ijarcs

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The goal of crime prediction is to help law enforcement authorities to prevent crimes before they happen by identifying and reducing potential future crimes. In present days, crime cases are rapidly occur so it is a challenging task to accurately predict and classify the future crimes, as criminal patterns are adaptable and constantly evolve, making it a challenging task. Sentiment analysis is essential for deciphering the feelings conveyed in text since social media has grown into a popular venue for individuals to communicate their thoughts, feelings, views, and comments. This analysis is particularly valuable for making informed decisions in business, politics and government agencies especially to identify crimes. However, it faces the difficulties like lexical diversity and dataset imbalance. In order to make better decisions and maybe lower the risk of predicting the suspects in a crime, early crime prediction has lately made use of Artificial Intelligence (AI) models like Deep Learning (DL) and Machine Learning (ML). When applied to crime data, ML and DL models may assist pinpoint possible crime hotspots and foretell when crimes may occur. Crime prediction and categorization using social media posts is covered in depth in this paper, which offers a comprehensive overview of several ML and DL frameworks. Initially, different ML and DL based crime prediction models designed by many researchers are examined in brief. The next step is to do a comparison research to learn about the shortcomings of those frameworks and provide an alternative method for effectively predicting crimes based on social media postings.

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Crime Prediction Using Machine Learning and Deep Learning: A Systematic Review and Future Directions

Cited by 27 publications

References 80 publications

An Investigation on Machine Learning-Based Predictive Model for Human Trafficking in India

An Investigation on Machine Learning-Based Predictive Model for Human Trafficking in India

Resorbable GBR Scaffolds in Oral and Maxillofacial Tissue Engineering: Design, Fabrication, and Applications

A Systematic Review on the use of Machine Learning and Deep Learning Techniques for Crime Prediction from Social Media

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