International Research Journal of Engineering and Technology (IRJET)
e-ISSN: 2395-0056
Volume: 12 Issue: 05 | May 2025
p-ISSN: 2395-0072
www.irjet.net
Criminal Investigation Tracker With Suspect Prediction using Machine Learning 1Vishal Rajage , 2Yash shingarer, 3Aditya deshmukh , 4Pandurang kengar, 5Pravin Hajare , 6S.A.Hajare 1,2,3,4,5UG Students, Department of Computer Science and Engineering, SVERI’s College of Engineering Pandharpur,
Maharashtra India 6Assistant Professor, Department of Computer Science and Engineering, SVERI’s College of Engineering
Pandharpur, Maharashtra India -----------------------------------------------------------------------***----------------------------------------------------------------------patterns, and assist in narrowing down suspect profiles, ABSTRACT
thereby enhancing the speed and accuracy of investigations [3][4]. Suspect prediction systems employ machine learning (ML) algorithms trained on historical crime data, behavioral patterns, and contextual information to suggest potential suspects for ongoing investigations [2][5]. By recognizing recurring elements—such as modus operandi, location-based activity, or criminal records—these systems provide law enforcement agencies with actionable insights that may not be immediately apparent through traditional methods [4][6]. This capability is particularly valuable in high-priority cases where time-sensitive decisions are critical. The proposed system, Criminal Investigation Tracker Using Suspect Prediction, is designed to support officers by maintaining digital records of cases, generating automated leads, and predicting likely suspects based on similarity with past cases [6][7]. The platform also enables centralized access to case files, evidence, and investigation progress, improving interdepartmental collaboration and information transparency [1][8]. Despite the potential benefits, deploying AI in criminal investigations introduces technical and ethical challenges. These include ensuring data privacy, minimizing algorithmic bias, and handling incomplete or inconsistent data [5][9]. However, with appropriate design choices—such as regular dataset updates, interpretability of prediction models, and human-in-the-loop verification—these issues can be mitigated effectively [3][9]. As digital transformation continues across sectors, the role of smart crimetracking systems is expected to grow, representing a significant shift in how modern investigations are conducted [2][8].
The growing complexity of criminal activities demands smarter and more efficient investigation techniques. This project, Criminal Investigation Tracker Using Suspect Prediction, aims to assist law enforcement agencies by providing a digital platform to manage investigations and predict potential suspects using data-driven analysis. The system collects and organizes case-related information such as incident details, evidence, and witness reports into a centralized database. Leveraging machine learning algorithms, it analyzes historical data patterns to suggest possible suspects based on similarities with past crimes, criminal profiles, and behavioural indicators. By automating data tracking and suspect prediction, the system enhances the accuracy and speed of investigations, reduces human error, and improves decision-making processes. This project not only modernizes traditional investigation methods but also contributes to the field of smart policing, offering a scalable and efficient tool for future criminal justice initiatives.
KEYWORDS Deepfake Detection, Machine Learning, Database Management, Convolutional Neural Networks, Temporal Analysis
1. INTRODUCTION Criminal investigations have traditionally relied on manual processes, which can be time-consuming, prone to human error, and inefficient in handling large volumes of data. With the increasing complexity and frequency of crimes, especially in urban regions, there is a pressing need to modernize investigative workflows through technology-enabled solutions [1][2].
2. LITERATURE SURVEY Literature Survey
One of the most promising approaches in this context is the integration of artificial intelligence (AI) and data analytics into criminal tracking systems. These intelligent systems can analyze vast datasets, identify
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With the growing importance of intelligent systems in law enforcement, numerous studies have been conducted to integrate machine learning, data mining, and digital case tracking in criminal investigations.
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