artificial intelligence

This paper shows the developments and directions in feature processing. We begin by revisiting conventional feature processing methods, then focus on deep feature extraction techniques and the application of feature processing. The article also analyzes the current research challenges and outlines future development directions, providing valuable insights in related fields.

Background: Human trafficking is a significant global issue that affects millions of individuals, where victim identification remains a major challenge. Traditional methods such as DNA or fingerprint analysis are not always viable, necessitating alternative forensic approaches.Methods: This article reviews the role of dental identification  in human trafficking cases through an extensive analysis of existing literature. The study incorporates forensic odontology techniques, including dental charting, radiographic analysis, bite mark analysis, age estimation, and emerging technologies like Artificial Intelligence (AI).Results: Findings indicate that dental identification methods are essential for victim identification, especially when conventional methods prove ineffective. AI integration enhances the accuracy and efficiency of dental forensic investigations, addressing challenges such as record access and cross-border complexities.Conclusion: Dental identification, augmented by AI advancements, is an indispensable tool in forensic investigations related to human trafficking. The study underscores the necessity of international collaboration and technological innovation to enhance forensic practices.

The integration of artificial intelligence (AI) technology into various fields, particularly healthcare, has demonstrated considerable potential in improving efficiency and accuracy. However, the potential risks associated with unprofessional or inappropriate use of AI cannot be overlooked. The current landscape of healthcare demonstrates a growing reliance on AI tools, which is expected to expand in the future. The existing literature highlights the effectiveness  of various AI applications, including chatbots, in specific medical domains. This study aims to review relevant literature in the pharmaceutical and galenic fields while evaluating a prominent AI chatbot provider. Based on the findings, this article presents critical considerations for researchers and practitioners. A thorough assessment of the benefits and risks associated with AI technologies is essential as these tools become increasingly prevalent in pharmaceutical practices.

Biotechnology has always played an important role in tackling global concerns, particularly in the Global South, where socioeconomic gaps sometimes stymie scientific progress. Recent advances in synthetic biology and computational technologies have the potential to revolutionize biotechnology in these locations. Synthetic biology allows for the creation and manipulation of biological systems, with promise applications in healthcare, agriculture, and environmental control. Computational methods such as machine learning and artificial intelligence help to optimize synthetic biology processes, enabling innovations that are suited to local requirements. The combination of these cutting-edge technologies with traditional biotechnological techniques has the potential to dramatically improve the Global South's ability to solve issues such as disease outbreaks, food security, and sustainable development. This abstract outline the critical intersections of synthetic biology and computational advancements and their potential to empower the Global South, highlighting the need for supportive policies and capacity-building initiatives to maximize their impact.

Advanced forensic approaches are necessary to handle digital crimes, as they must provide transparent methods that foster trust and enable interpretable evidence in judicial investigations. The current black-box machine learning models deployed in traditional digital forensics tools accomplish their tasks effectively yet fail to meet legal standards for admission in court because they lack proper explainability.This study creates an Explainable Artificial Intelligence (XAI) system for digital forensics to improve flagging events as legal evidence by establishing high levels of trust and transparency. A digital evidence system employs interpretable machine learning models together with investigative analysis techniques for the detection and classification of computer-based irregularities, which generate clear explanations of the observed anomalies.The system employs three techniques, including SHAP (Shapley Additive Explanations) alongside LIME (Local Interpretable Model-agnostic Explanations) and counterfactual reasoning to deliver understandable explanations about forensic findings, thus enhancing investigation clarity for law enforcement agents and attorneys as well as stakeholder professionals.The system performs successfully on actual digital forensic datasets, thus boosting investigation speed while minimizing false alerts and improving forensic decision explanations. The system must demonstrate GDPR and digital evidence admission framework compliance to maintain legal and ethical correctness for usage in court procedures.Forensic digital investigations need explainable Artificial Intelligence as an essential integration for creating reliable and legally sound practices.