A Fingerprint plays an important role in identifying an individual in forensic and criminal investigations. Fingerprint ridge density is considered one of the most important features for sex classification. The present study intends to classify sex using fingerprint ridge density through a machine learning model, i.e., Random Forest. A total of 2040 fingerprints of 204 participants (102 males and 102 females) were collected from the north Indian population using a standard methodology. Ridge density in the three topological areas of fingerprints,i.e., radial, ulnar, and proximal areas, was assessed. Taking all the areas into consideration, the data of fingerprint ridge density was used to train the Random forest algorithm. The training and testing of the model data were taken in a ratio of 70:30, respectively (training dataset = 1428; testing dataset = 612). Random forest provided an accuracy of 81.53% in sex classification using fingerprint ridge density. The paper discusses the evaluation report of the accuracy of the parameters of the Random forest in detail. The study concludes that the machine learning models, such as Random forest can be utilized for sex classification from fingerprint ridge density. The study proposes its direct application in forensic examinations, especially when there is no clue about the perpetrator, and the sex of the perpetrator can be predicted from fingerprints recovered from the crime scene using the present customized model.
Fingermarks are frequently left on metal surfaces such as kitchen utensils, door handles, or elevator buttons in crime scenes. They are crucial forensic evidence to identify individuals and link them to crimes. Fingermark development on metal surfaces targets either the fingermark residues or the substrate. This study aimed to develop a rapid fingermark development method based on displacement reactions between copper (II) sulphate and various types of metal substrates, such as brass, galvanized iron, and low-carbon steel. Immersion of the metal substrate was more effective in fingermark visualization than applying the solution using a dropper. The optimized concentrations of copper (II) sulphate solution for fingermark visualization were found to be 0.7 M for brass, 0.5 M for galvanized iron, and 0.2 M for low-carbon steel. Sebaceous-rich fingermarks were visualized after the 5th depletion on brass and galvanized iron, and even after the 7th depletion on low-carbon steel. Further improvement is required before incorporating the application of copper (II) sulphate onto metal substrates to visualize fingermarks in real crime cases, due to the destructive nature of substrate submersion.
Unidentified Aerial Phenomena (UAP) refer to aerial anomalies that cannot be identified as known objects or natural occurrences. Despite historical reports, research into the medical impacts of UAP encounters remains in its early stages, lacking a systematic framework and substantial clinical data. This review provides an overview of the medical evidence regarding UAP-related injuries, including clinical case reports, injury mechanisms, epidemiological data, and the application of neuroimaging and forensic medicine. By analyzing declassified U.S. Defense Intelligence Agency documents, medical case reports, and scientific studies, we highlight the multisystem health issues associated with UAP contact, particularly neurological damage and non-ionizing electromagnetic radiation effects. We also explore the significant rise in UAP incident reports near sensitive military and nuclear facilities, suggesting a growing concern for human health. Future research must focus on prospective studies, interdisciplinary collaboration, and advanced forensic technologies to better understand the long-term pathophysiological mechanisms underlying UAP-induced injuries.
Ground-penetrating radar (GPR) has emerged as one of the most valuable non-invasive technologies in forensic science, enabling subsurface imaging in investigations involving clandestine graves, missing persons recovery, concealed evidence, and mass fatality incidents. The technique transmits short electromagnetic pulses into the ground and records the reflected energy generated by contrasts in dielectric properties between subsurface materials. These reflections allow forensic practitioners to delineate buried anomalies with centimetre-scale accuracy while preserving the integrity of the crime scene. This review documents the evolution of GPR from its earliest forensic applications through to current state-of-the-art systems, focusing on core methodologies, data acquisition and processing protocols, and integrated approaches combining electrical resistivity tomography (ERT), LiDAR, and artificial intelligence. Case studies drawn from diverse settings, including volcanic caves, urban environments, ice-covered water bodies, and tropical forests, demonstrate both the operational versatility of GPR and the contextual limitations that practitioners must recognise. Signal attenuation in high-moisture soils, interpretive ambiguity in heterogeneous environments, and inconsistent operator training remain the principal constraints on GPR performance. These challenges highlight the need for standardised protocols, certified training, and evidence-based deployment criteria. Emerging technologies, including drone-mounted GPR arrays, convolutional neural network-based radargram interpretation, and three-dimensional (3D) subsurface reconstruction, are expected to improve detection precision, reduce field time, and extend operational capability in challenging forensic scenarios. By critically evaluating the published literature and identifying research priorities, this review demonstrates that GPR is not merely a useful adjunct but an increasingly indispensable tool in modern forensic investigations, with the potential to support ethical, time-efficient, and scientifically defensible recovery operations.
