Compliance Challenges in Forensic Image Analysis Under the Artificial Intelligence Act

Current need to fight evolving threats in the context of fast evolving threat landscape makes the field of digital forensics extremely challenging especially due to the rapidly growing cybercrime. This paper looks at how artificial intelligence (AI) and machine learning (ML) enhance the methods of artificial intelligence (AI) and machine learning (ML) to help digital forensic in cybercrime detection and evidence analysis, in furtherance, improving the overall years of a cyber-investigator. More and more sophisticated techniques like ransomware, phishing, and deepfake technology are being used by cybercriminals and no longer constantly working methods. From the law enforcement and cybersecurity professionals to forensic suits, AI driven tools offer an easy way to analyze large data sets efficiently, finding patterns and anomalies that would otherwise go unnoticed. Machine learning algorithms helps in automating the task of a classification of digital evidence and anomaly detection using supervised and unsupervised algorithms. Additionally, the study also assesses the effects of engineering innovations of RFM refinements, AI based automation and predictive analytics in cyber vigilance. The paper discusses the ethical considerations in this context, such as data privacy, algorithmic bias and transparency while talking about forensic AI applications. The paper also discusses how successful AI has been in law enforcement and private sector cybersecurity, two areas of life where AI is truly transforming. However, lack of regulatory standards, low levels of interdisciplinary teamwork, and insufficient training of relevant workforces are the main challenges that face the development of AI is potential while minimizing risk. Two future heads emphasize blockchain integration for evidence securely, quantum computing for rapid encryption, as well as cross-sectoral partnerships to pioneer in the footprint. Engineering principles and the application of AI driven automation can be leveraged to advance the discipline of digital forensics to a more proactive, adaptive state that furthers in the development of robust responses to cyber threats in a more and more interconnected world.

Artificial intelligence has been applied in wildfire science and management since the 1990s, with early applications including neural networks and expert systems. Since then, the field has rapidly progressed congruently with the wide adoption of machine learning (ML) methods in the environmental sciences. Here, we present a scoping review of ML applications in wildfire science and management. Our overall objective is to improve awareness of ML methods among wildfire researchers and managers, as well as illustrate the diverse and challenging range of problems in wildfire science available to ML data scientists. To that end, we first present an overview of popular ML approaches used in wildfire science to date and then review the use of ML in wildfire science as broadly categorized into six problem domains, including (i) fuels characterization, fire detection, and mapping; (ii) fire weather and climate change; (iii) fire occurrence, susceptibility, and risk; (iv) fire behavior prediction; (v) fire effects; and (vi) fire management. Furthermore, we discuss the advantages and limitations of various ML approaches relating to data size, computational requirements, generalizability, and interpretability, as well as identify opportunities for future advances in the science and management of wildfires within a data science context. In total, to the end of 2019, we identified 300 relevant publications in which the most frequently used ML methods across problem domains included random forests, MaxEnt, artificial neural networks, decision trees, support vector machines, and genetic algorithms. As such, there exists opportunities to apply more current ML methods — including deep learning and agent-based learning — in the wildfire sciences, especially in instances involving very large multivariate datasets. We must recognize, however, that despite the ability of ML models to learn on their own, expertise in wildfire science is necessary to ensure realistic modelling of fire processes across multiple scales, while the complexity of some ML methods such as deep learning requires a dedicated and sophisticated knowledge of their application. Finally, we stress that the wildfire research and management communities play an active role in providing relevant, high-quality, and freely available wildfire data for use by practitioners of ML methods.

This study investigates the legitimacy of relying on Artificial Intelligence (AI) in deriving Islamic legal rulings (fatwas), and outlines the necessary legal, technical, and ethical safeguards. The research poses a central question: Can AI serve as an independent agent of ijtihad (Islamic legal reasoning)? Using descriptive, analytical, and comparative methodologies, the paper examines practical cases such as fatwa chatbots, automated contract systems, and forensic image analysis in Sharia contexts. The findings confirm that AI lacks the qualifications required for legal and moral accountability—such as consciousness, intention, and jurisprudential reasoning—and therefore cannot independently issue fatwas. However, AI can be employed as an auxiliary tool under strict scholarly supervision. The paper concludes with a set of comprehensive guidelines ensuring the integration of AI into Islamic jurisprudence remains subservient to established doctrinal authority.

Purpose Law enforcement agencies in the United States are relying on state fusion centers for intelligence to develop actionable, data-driven reports that increase efficiency and improve investigations in crime prevention and homeland security. This study assesses the extent to which artificial intelligence and machine learning (AI/ML) are increasingly shaping intelligence operations in law enforcement and the functions of state fusion centers in supporting intelligence-led policing (ILP). The reliance and integration of AI/ML is improving analytic accuracy, situational awareness and information and data sharing and collaboration among law enforcement and homeland security agencies. This study examines the state of contemporary academic literature, assesses AI/ML applications used in law enforcement and builds a conceptual and theoretical framework centered on ILP policing. It also relies on empirical data, case study applications, and DHS assessments to explore the degree to which AI-driven processes and analytics enhance criminal intelligence, investigative efficiencies, situational awareness and predictive policing. The analysis, while focusing on the opportunities and challenges of using AI/ML tools in law enforcement, also highlights the need for ethical governance, transparency and accountability when relying on advanced technologies for crime prevention and policing. Design/methodology/approach This study utilizes qualitative methods, including a thematic content analysis of government and think tank/practitioner reports as well as academic literature on the benefits, costs and ethical factors regarding variations in the implementation of AI/ML tools for law enforcement intelligence products and resource allocation. For cross-validation of operational outcomes, it examines publicly available information in the DHS Fusion Center Annual Assessment, Bureau of Justice Statistics, LEMAS and RAND Corporation assessments of intelligence-led policing. Findings Qualitative Results Federal and state sources report fusion centers and law enforcement agencies integrating advanced analytic and ML-enabled tools into each step in the criminal intelligence lifecycle process. However, ethical and structural challenges limit and constrain technology-driven narratives in fusion centers. Given these challenges, there is a consistent qualitative and thematic pattern: state fusion centers now function as criminal intelligence analytic hubs or resources that leverage the most contemporary analytic and data-driven tools for criminal intelligence and law enforcement investigations. Interrelated themes describe AI/ML technologies in terms of shaping, constraining and complicating the intelligence lifecycle in fusion centers and law enforcement operations. Seven specific themes emerged from latent coding are illustrated in the chart. Research limitations/implications There are limitations on the collection of quantitative data since DHS, leading think tanks and NGOs do not disclose specific figures on the proportion of AI/ML tools. The DHS Fusion Center Annual Assessment process monitors technology adoption and the growth of analytic capabilities throughout the national network of fusion centers; however, the specific quantitative statistics are not disclosed in public summaries (DHS, 2024). Second, publicly available data and information constitute the bulk of empirical sources. Consequently, this study relies primarily on qualitative narrative reporting, not quantitative performance metrics. Third, publication bias is likely present in industry and government sources as these reports may provide overly optimistic observations and conclusions while overlooking ethical dilemmas, failures and challenges. Moreover, qualitative thematic analysis could reflect broader structural narratives as opposed to empirical outcomes. Finally, since AI/ML adoption varies across fusion centers and according to technology levels, qualitative themes identified in this study must be read as representative patterns and not as universally generalizable. Originality/value Fusion center utilization of AI/ML technologies is as much an operational tool as it is a policy, governance and ethical challenge. Successful and professional use in support of law enforcement is about placing technological innovations firmly within institutional accountability and constitutional guardrails. On the one hand, AI/ML tools are enhancing analytical intelligence production by accelerating analytic workflows, predictive modeling and expanding data/information integration capabilities. AI/ML are extending ILP concepts by offering improvements in situational awareness and threat identification and operational efficiencies. On the other, substantial constraints hinder responsible use of these technologies. In the absence of standardized oversight frameworks, data-quality issues, algorithmic bias and the lack of professional development, workforce capacity and critical skills on the part of fusion center analysts will cancel the benefits of these tools.

Sub-seasonal forecasting, which involves predicting weather conditions two to six weeks in advance, presents a major challenge for operational centres due to limited predictability from initial or slowly varying boundary conditions. In light of recent advances in artificial intelligence (AI) and machine learning (ML) methods for short-range forecasts (<14 d), the European Centre for Medium-Range Weather Forecasts, with support from the World Meteorological Organisation, has launched the AI Weather Quest: an international, open and scientifically rigorous research competition designed to evaluate AI/ ML methods for sub-seasonal forecasting. Targeting participants from a diverse range of organisations including academia, operational forecasting centres, and technology companies, the AI Weather Quest is a pioneering large-scale, multidisciplinary initiative aimed at advancing reliable sub-seasonal predictions through AI and ML. Competing teams submit weekly probabilistic forecasts of two-metre temperature, accumulated precipitation, and mean sea level pressure. Submissions are made in real time, allowing models to be evaluated under operational-like conditions. To foster a competitive environment, an evaluation system has been established that calculates global ranked probability skill scores and displays team performance on live leaderboards. Leading teams will be spotlighted quarterly and yearly, recognising both top-performing teams and innovative methodological contributions. Detailed information regarding registration, forecast submission, and the dedicated tools provided are available on the AI Weather Quest website.

The city's efforts to improve traffic make a big step forward when license plates can be read. It explains how an intelligent transportation system should work and what steps should be taken for it to be successful. Because the number of cars is growing so quickly, ANPR is a must-have for managing traffic control. Automatic number plate recognition (ANPR) is mostly used to keep an eye on traffic and keep people safe. Number plate recognition uses image processing and the most recent advances in technology to automatically read the characters on a vehicle's license plate. In the last few years, there have been a number of technological advances in the field of studying how to read license plates. Image processing techniques like OCR make it possible for traffic surveillance to solve a wide range of problems that come up during criminal investigations, the collection of tolls, the monitoring of traffic, the regulation of speed, and the management of parking, among other things. If you want to control traffic and keep an eye on a large number of people in a transportation system, you need an ANPR system. Image processing techniques and the collection of photos of vehicles for use in the dataset have made it possible to keep an eye on traffic on a large scale. Automatic License Plate Reader (ANPR) is a way to take the steps needed to run a good intelligent transportation network. Traffic control has become an absolute must because the number of cars has grown so quickly. The main goal of ANPR is to keep an eye on and record traffic for defense purposes. To read the text on license plates, number plate recognition uses image processing, optical character recognition (OCR), and edge detection technology. The model is made up of three different parts called, respectively, the module for car detection, the module for license plate segmentation, and the module for recognition. Image processing made us more determined to stop a wide range of illegal activities, such as armed robberies of cars, breaking traffic rules, and the way law enforcement is handled. This review study looked at all of the different designs for reading license plates that have been put into use so far.

Breast cancer was the fourth leading cause of cancer-related death worldwide, and early mammography screening could decrease the breast cancer mortality. Artificial intelligence (AI)-assisted diagnose system based on machine learning (ML) methods can help improve the screening accuracy and efficacy. This study aimed to systematically review and make a meta-analysis on the diagnostic accuracy of mammography diagnosis of breast cancer through various ML methods. Springer Link, Science Direct (Elsevier), IEEE Xplore, PubMed and Web of Science were searched for relevant studies published from January 2000 to September 2021. The study was registered with the PROSPERO International Prospective Register of Systematic Reviews (protocol no. CRD42021284227). A Quality Assessment of Diagnostic Accuracy Studies-2 (QUADAS-2) was used to assess the included studies, and reporting was evaluated using the Preferred Reporting Items for Systematic reviews and Meta-Analyses (PRISMA). The pooled summary estimates for sensitivity, specificity, the area under the receiver operating characteristic curve (AUC) for three ML methods (convolutional neural network [CNN], artificialneuralnetwork [ANN], supportvectormachine [SVM]) were calculated. A total of 32 studies with 23,804 images were included in the meta-analysis. The overall pooled estimate for sensitivity, specificity and AUC was 0.914 [95% CI 0.868-0.945], 0.916 [95% CI 0.873-0.945] and 0.945 for mammography diagnosis of breast cancer through three ML methods. The pooled sensitivity, specificity and AUC of CNN were 0.961 [95% CI 0.886-0.988], 0.950 [95% CI 0.924-0.967] and 0.974. The pooled sensitivity, specificity and AUC of ANN were 0.837 [95% CI 0.772-0.886], 0.894 [95% CI 0.764-0.957] and 0.881. The pooled sensitivity, specificity and AUC of SVM were 0.889 [95% CI 0.807-0.939], 0.843 [95% CI 0.724-0.916] and 0.913. Machine learning methods (especially CNN) show excellent performance in mammography diagnosis of breast cancer screening based on retrospective studies. More rigorous prospective studies are needed to evaluate the longitudinal performance of AI.

Background Recently, the need for artificial intelligence (AI) and machine learning (ML) methods in drug development and research is gaining high concern and more grounds. Moreover, providing pharmaceutical and related schools with non-commercial, free-to-use programming languages, software and tools is becoming an unavoidable need. The R programming language can be easily used, through the correct and simplified codes and packages, in conducting unsupervised ML methods, such as principal component analysis (PCA) and hierarchical clustering analysis (HCA), after calculating relevant descriptors of drugs and molecules. Objective The objective of this study was to assess the enhancement of non-computer sciences-based students’ perception of the use of machine learning methods such as PCA and HCA using R-programming in drug formulation. Results Undergraduate students were taught to use R program to derive PCA distinguishable plots such as score, loading and scree, in addition to HCA dendrograms, in the context of developing new pharmaceutical formulations. Surveys conducted pre- and post-teaching the course proved that implementation of such ML methods can help in better understanding and exploring the data, in order to derive meaningful conclusions, and make informed decisions that help develop pharmaceutical formulations of premium quality, with minimal resources consumption. Conclusion We hereby report the easy use of R-programming in applications and activities that introduce undergraduate Pharmaceutical Engineering and Biotechnology students to ML methods. Student surveys showed better student satisfaction and understanding of AI applications in solving pharmaceutical problems. We claim that these students and early_career researchers, who are non-specialists in computer science, can utilize R-programming to perform important pharmaceutical applications through the step-by-step guide and codes provided in this article. Graphical Abstract

Mental disorders are a significant medical and social issue globally. Currently, approximately 970 million individuals suffer from mental disorders, with over 300 million diagnosed with depression or bipolar disorder. Recently, there has been significant advancement in digital technologies, particularly in artificial intelligence, encompassing machine learning and deep learning. Given the growing interest in their use in psychiatry and the need to develop new approaches to psychiatric care. This review explores the current and promising directions for the application of artificial intelligence technologies in clinical practice, focusing on patients with depression and bipolar disorder. A literature search was conducted from January to February 2024 in the databases PubMed, Google Scholar, and eLibrary using the following keywords: «психиатрия» ("psychiatry"), «психическое здоровье» ("mental health"), «психическое расстройство» ("psychiatric disorder"), «депрессия» ("depression"), «депрессивный эпизод» ("depressive episode"), «рекуррентное депрессивное расстройство» ("recurrent brief depression"), «биполярное расстройство» ("bipolar disorder"), «машинное обучение» ("machine learning"), «глубокое обучение» ("deep learning"), «искусственный интеллект» ("artificial intelligence"); "psychiatry", "mental health", "psychiatric disorder", "depression", "depressive episode", "major depressive disorder", "bipolar disorder", "machine learning", "deep learning", "artificial intelligence". Studies on the use of artificial intelligence technologies in patients with depression and bipolar disorders and review articles discussing the difficulties of their application in psychiatry were excluded. Publications in Russian and English in the past 10 years were selected. The most commonly used machine learning models for diagnosing patients with affective disorders utilize neuroimaging data (primarily magnetic resonance imaging and electroencephalography), text, audio, and video data and data from electronic devices, molecular-genetic markers, and clinical indicators. The models were trained using mono- or multimodal datasets. Notably, many of the reviewed studies have significant limitations, making the implementation of artificial intelligence technologies in clinical practice challenging. These include small sample sizes, low representativeness and standardization, inclusion of “noise” and correlated variables, and absence of validation using independent datasets. Studies on machine learning methods have demonstrated promising results in the early diagnosis of affective episodes and in predicting treatment responses. However, their clinical application is limited, owing to insufficient validation. Well-designed prospective cohort studies and the creation of extensive, high-quality datasets and models capable of uncovering new relationships between variables are required to address this limitation.

In this paper, a comprehensive review on Alzheimer's disease (AD) is carried out, and an exploration of the two machine learning (ML) methods that help to identify the disease in its initial stages. Alzheimer's disease is a neurocognitive disorder occurring in people in their early onset. This disease causes the person to suffer from memory loss, unusual behavior, and language problems. Early detection is essential for developing more advanced treatments for AD. Machine learning (ML), a subfield of Artificial Intelligence (AI), uses various probabilistic and optimization techniques to help computers learn from huge and complicated data sets. To diagnose AD in its early stages, researchers generally use machine learning. The survey provides a broad overview of current research in this field and analyses the classification methods used by researchers working with ADNI data sets. It discusses essential research topics such as the data sets used, the evaluation measures employed, and the machine learning methods used. Our presentation suggests a model that helps better understand current work and highlights the challenges and opportunities for innovative and useful research. The study shows which machine learning method holds best for the ADNI data set. Therefore, the focus is given to two methods: the 18-layer convolutional network and the 3D convolutional network. Hence, CNNs with multi-layered fetch more accurate results as compared to 3D CNN. The work also contributes to the use of the ADNI data set, where the classification of training and testing samples is divided with such a number that brings the highest accuracy achieved with 18-layer CNN. The work concentrates on the early prediction of Alzheimer's disease with machine learning methods. Thus, the accuracy achieved is 98% for 18-layer CNN.

Background. &#x0D; Machine learning is a promising field for organization in the age of development of high-tech methods of management and organization of the company. As a rule, this term is used in relation to artificial intelligence, namely, machines that could learn independently. Thus, the main goal of this work is to assess the prospects for using these methods for solving various problems in a corporation.&#x0D; Methods. &#x0D; The article introduces the main methods of machine learning, their analysis, linear and non-linear learning methods are given, their use in practice is indicated, and the key advantages of using a trained artificial intelligence in a company are identified.&#x0D; Result. &#x0D; As a result, the author proposes ways of using machine learning methods in a firm, analyzes their advantages and disadvantages, identifies the problems of implementing artificial intelligence learning opportunities in practice.

The global healthcare ecosystem is being changed thanks to substantial advancements in the fields of machine learning (ML) and artificial intelligence (AI). The potential of ML and AI to improve patient care, increase diagnostic accuracy, optimize treatment regimens, and reduce administrative procedures is covered in this chapter as we investigate the many methods and uses of ML and AI within the healthcare industry. ML and AI have the potential to change healthcare delivery, resource allocation, and disease prevention through the use of large-scale data analysis, predictive modeling, and intelligent decision-making systems. This chapter presents a thorough review of the existing ML and AI methods used in healthcare, emphasizing their advantages, difficulties, and potential future applications. The global healthcare ecosystem might be changed by the introduction of ML and AI, resulting in improved patient outcomes. ML and AI can help expand access to healthcare by enabling remote diagnosis and telemedicine, especially in underserved areas. This aligns with the goal of ensuring healthy lives and promoting well-being for all ages.

This paper presents a systematic review of studies related to Artificial Intelligence (AI) and Machine Learning (ML) applications in Supply Chain Management (SCM). Our systematic search studied 109 journal articles published between 2007 and 2025. It was presented definitions for AI, ML, and SCM and a bibliometric analysis was carried out to examine trends and patterns in the existing literature. The 109 articles were grouped into five clusters based on keywords and abstracts, identifying promising research areas and proposes directions for future investigations. The current literature on AI and ML in SCM is expanding, with an increasing number of publications and growing diversity in applications, although significant gaps remain warranting attention. Various AI and ML techniques, including supervised learning algorithms, neural networks, and optimization techniques, have been studied in scenarios such as demand forecasting, inventory management, and risk analysis. Additionally, AI and ML methods are expected to gain broader acceptance in SCM scenarios, such as, application of Generative AI (GenAI) and Large Multimodal Models (LMMs); Quantum ML techniques to handle with large SCM data; to implement SHAP (SHapley Additive exPlanations) for demand forecasting and fraud detection; and to use Generative AI models (like, GPT-4) to generate synthetic supply chain scenarios.

Personal identification of an individual has always been a major concern in forensic science. Reconstruction of the facial profile is considered as one of the final stages in the process of identification. Nevertheless, recent advancements in artificial intelligence (AI) and machine learning (ML) have demonstrated remarkable potential in predictive modelling and forensic applications. The current study uses customised machine learning models to predict facial dimensions based on dental and jaw parameters. A sample of 422 participants (201 males and 221 females) from a North Indian population was collected and analysed. Dental casts, anthropometric facial measurements and photographs of the participants were collected with informed consent. ML models such as Support Vector Regression (SVR), Random Forest Regression (RFR), Decision Tree Regression (DTR), and Linear Regression (LR) were trained using dental and jaw measurements as input features for the models. The results show that the ML models predicted the facial dimensions with an accuracy of 90–94% and a very low prediction error of 0.1–0.9 across all facial measurements. Among the models, SVR and LR models perform well, followed by RFR, whereas DFR yielded comparatively lower accuracy. The findings demonstrate that machine learning models (SVR, RFR, DTR, and LR) can be used as novel approach to predict facial dimensions from jaw and teeth parameters. These techniques can be combined with other facial reconstruction techniques to produce more precise and accurate outcomes. The reliability and accuracy in predicting the facial dimensions indicate that the results can be applied in the practical and real situations such as personal identification, forensic investigations, disaster victim identification cases, and archaeological remains where only jaw and teeth are available for examination. Integrating ML-based predictions with traditional facial reconstruction techniques could enhance the accuracy and reliability of forensic identification methodologies.

This paper sheds light on how Artificial Intelligence (AI) is reshaping the landscape of crime detection and prevention, bringing about a significant change in traditional law enforcement methods. With society increasingly embracing AI-driven solutions, it's crucial to understand how they contribute to public safety. We'll dive into various aspects of AI, such as predictive policing, video surveillance analysis, forensic science, and criminal profiling, to see how they enhance law enforcement capabilities and streamline crime resolution processes.Starting with predictive policing, AI algorithms sift through massive datasets to identify patterns and predict future criminal activity, empowering law enforcement to take proactive measures. Similarly, AI-powered video surveillance systems enhance real-time monitoring and anomaly detection, allowing for quick identification of suspicious behavior in urban environments.Moving on, we'll explore AI's impact on forensic analysis, where it expedites processes like DNA sequencing, fingerprint recognition, and ballistics analysis, speeding up the resolution of cold cases. Additionally, we'll delve into AI's role in criminal profiling, highlighting its ability to decipher behavioral patterns and predict criminal motivations, though ethical considerations remain paramount.We'll also address ethical concerns surrounding AI deployment in law enforcement, including privacy issues and algorithmic biases. Emphasizing the need for transparency and accountability, we advocate for responsible use of AI-powered crime detection systems to uphold ethical standards and protect individual rights.