Network Forensics and Traffic Analysis With Machine Learning DOI

Yara Shamoo

Advances in information security, privacy, and ethics book series, Journal Year: 2024, Volume and Issue: unknown, P. 79 - 116

Published: Dec. 6, 2024

Network forensics plays a crucial role in identifying, monitoring, and analyzing network traffic to uncover malicious activities provide evidence cyber incidents. The integration of machine learning techniques into significantly enhances the ability detect anomalies, identify patterns, respond threats real-time. This chapter explores application algorithms analysis, detailing various methodologies their effectiveness distinguishing between legitimate traffic. We examine case studies that demonstrate advantages these over traditional methods, highlighting potential for improving cybersecurity practices. Additionally, challenges future directions field analysis using are discussed, emphasizing need continued innovation adaptation emerging threats.

Language: Английский

A review on artificial intelligence for the diagnosis of fractures in facial trauma imaging DOI Creative Commons
Tuan D. Pham, Simon Holmes, Paul Coulthard

et al.

Frontiers in Artificial Intelligence, Journal Year: 2024, Volume and Issue: 6

Published: Jan. 5, 2024

Patients with facial trauma may suffer from injuries such as broken bones, bleeding, swelling, bruising, lacerations, burns, and deformity in the face. Common causes of facial-bone fractures are results road accidents, violence, sports injuries. Surgery is needed if patient would be deprived normal functioning or subject to based on findings radiology. Although image reading by radiologists useful for evaluating suspected fractures, there certain challenges human-based diagnostics. Artificial intelligence (AI) making a quantum leap radiology, producing significant improvements reports workflows. Here, an updated literature review presented impact AI special reference fracture detection The purpose gain insights into current development demand future research trauma. This also discusses limitations overcome important issues investigation order make applications more effective realistic practical settings. publications selected were their clinical significance, journal metrics, indexing.

Language: Английский

Citations

9
Diagnostic Applications of AI in Sports: A Comprehensive Review of Injury Risk Prediction Methods DOI Creative Commons
Carmina Liana Mușat,

Claudiu Mereuţă,

Aurel Nechita

et al.

Diagnostics, Journal Year: 2024, Volume and Issue: 14(22), P. 2516 - 2516

Published: Nov. 10, 2024

This review provides a comprehensive analysis of the transformative role artificial intelligence (AI) in predicting and preventing sports injuries across various disciplines. By exploring application machine learning (ML) deep (DL) techniques, such as random forests (RFs), convolutional neural networks (CNNs), (ANNs), this highlights AI's ability to analyze complex datasets, detect patterns, generate predictive insights that enhance injury prevention strategies. AI models improve accuracy reliability risk assessments by tailoring strategies individual athlete profiles processing real-time data. A literature was conducted through searches PubMed, Google Scholar, Science Direct, Web Science, focusing on studies from 2014 2024 using keywords 'artificial intelligence', 'machine learning', 'sports injury', 'risk prediction'. While power supports both team sports, its effectiveness varies based unique data requirements risks each, with presenting additional complexity integration tracking multiple players. also addresses critical issues quality, ethical concerns, privacy, need for transparency applications. shifting focus reactive proactive management, technologies contribute enhanced safety, optimized performance, reduced human error medical decisions. As continues evolve, potential revolutionize prediction promises further advancements health performance while addressing current challenges.

Language: Английский

Citations

6
Predicting Heart Failure: A Comparative Approach between Artificial Neural Networks and Support Vector Machines DOI Open Access
José Luis Herrera Salazar, Magdalena Cecilia Talla Linderman, Orlando Iparraguirre-Villanueva

et al.

International Journal of Online and Biomedical Engineering (iJOE), Journal Year: 2025, Volume and Issue: 21(01), P. 41 - 55

Published: Jan. 16, 2025

In recent years, cardiovascular diseases have become increasingly important as a leading cause of death globally. heart failure (HF), chronic disease affecting some 26 million people worldwide, has growing pandemic. Its prevention is national and global emergency. India, between 1.3 4.6 adults suffer from HF, despite advances in therapy prevention, mortality morbidity remain high, with significant costs to the healthcare system. The purpose this study conduct comparative evaluation ML models for predicting HF. support vector machine (SVM) artificial neural network (ANN) were analyzed determine which model offers superior accuracy. A dataset Kaggle platform x records features was used train models. results indicated that SVM best predictor HF an accuracy 79%, far exceeds ANNs 77%. It concluded learning (ML) method known shows outstanding effective performance task failure.

Language: Английский

Citations

0
A Narrative Review in Application of Artificial Intelligence in Forensic Science: Enhancing Accuracy in Crime Scene Analysis and Evidence Interpretation DOI

Abirami Arthanari,

Sushmitha Raj,

Vignesh Ravindran

et al.

Journal of international oral health, Journal Year: 2025, Volume and Issue: 17(1), P. 15 - 22

Published: Jan. 1, 2025

Abstract Aim: This review examines the transformative potential of artificial intelligence (AI) in forensic science, emphasizing its applications crime scene analysis, evidence interpretation, digital forensics, and odontology. It highlights AI’s ability to enhance accuracy, efficiency, reliability while addressing ethical practical challenges. Materials Methods: A systematic search was conducted across PubMed, Web Science, Scopus, Google Scholar, complemented by manual reviews key journals grey literature. The included studies on AI odontology other domains published past decade. Predefined inclusion exclusion criteria were applied, duplicates removed. Full-text ensure relevance, with disagreements resolved through consensus a third reviewer rigor. Results: has significantly enhanced practices automating analysis improving accuracy. streamlines reconstruction, accelerates processes analyzing large datasets, advances dental forensics rapid victim identification bite mark analysis. AI-powered biometric systems suspect facial recognition pattern-matching technologies. However, limitations such as algorithmic bias, data privacy issues, resource disparities pose challenges widespread adoption. Conclusion: is revolutionizing science providing precision, investigations. Addressing concerns transparency, fairness, accountability crucial for responsible implementation. Future advancements should prioritize development explainable unbiased algorithms, privacy-preserving techniques, frameworks. Interdisciplinary collaborations global policy guidelines are essential equitable integration ultimately advancing justice equity criminal system.

Language: Английский

Citations

0
Exploring Artificial Intelligence (AI) in Forensic Pathology and Autopsy Analysis DOI
Rishabha Malviya, Ashima Jain, Sahil Lal

et al.

IGI Global eBooks, Journal Year: 2025, Volume and Issue: unknown, P. 125 - 146

Published: Feb. 28, 2025

Forensic medicine has long relied on conventional autopsy techniques both to establish a cause of death and criminal investigation. Nevertheless, the arrival artificial intelligence (AI) brought new era, transforming workflow. The integration AI in setting exemplifies paradigm shift with novel technologies providing for investigative approaches. Among these, VIRTOPSY as an advanced imaging technique is gaining prominence, complementing traditional autopsies further refining forensic examinations. Based review recent advancements, practical uses, future prospects, this provides comprehensive picture implication contemporary medicine. It highlights potential enhance precision, increase reliability evidence, aid efforts at social good.

Language: Английский

Citations

0
Enhancing Crime Scene Analysis DOI

Saquib Ahmed,

M. Farhanullah Khan,

Bhupinder Singh

et al.

IGI Global eBooks, Journal Year: 2025, Volume and Issue: unknown, P. 63 - 84

Published: Feb. 28, 2025

Conventional approaches often find it challenging to adapt the growing complexity and data volume in crime scene analysis. The advent of artificial intelligence technologies, such as machine learning, computer vision, natural language processing, is transforming processing evidence by improving efficiency, precision, scalability. AI algorithms can swiftly analyse extensive datasets, uncovering patterns relationships that may be overlooked human investigators. For example, AI-driven tools enable rapid examination digital DNA samples, significantly alleviating backlogs forensic laboratories. This chapter also explores application reconstructing scenes through sophisticated 3D modelling techniques, which offer investigators a detailed perspective events enhance courtroom presentations. Additionally, addresses ethical issues related use science, including privacy concerns, algorithmic bias, importance oversight.

Language: Английский

Citations

0
Ethics, Algorithms, and the Rules of Evidence DOI
Abhishek Benedict Kumar, Karun Sanjaya

IGI Global eBooks, Journal Year: 2025, Volume and Issue: unknown, P. 103 - 124

Published: Feb. 28, 2025

Forensic intelligence, combined with the power of deep learning, has made significant leaps in revolutionizing crime investigation by allowing law enforcement agencies to process complex data, identify patterns, and predict criminal behaviors efficiency. Traditional forensic methods can be improved through machine learning techniques implementation natural language processing, which alter digital investigations. A few key ways that these two approaches benefit computer investigations include automating analysis evidence, enhancing accuracy biometrics, detecting related hacking activities traditional methods. It supports data-driven policing improves speed case settlements. Yet, concerns including algorithmic bias, data privacy, legal admissibility AI-generated evidence underscore ethical social implications technologies. This chapter will discuss transformative intelligence its applications, ethics, future.

Language: Английский

Citations

0
„KI“ in der Rechtsmedizin – von der Forschung in die Praxis: Welche Herausforderungen ergeben sich? DOI Creative Commons
Markus Rüther, Simon B. Eickhoff,

L. M. König

et al.

Rechtsmedizin, Journal Year: 2025, Volume and Issue: unknown

Published: March 7, 2025

Citations

0
The paradigm of digital health: AI applications and transformative trends DOI
Zubia Rashid, Hania Ahmed, Neha Nadeem

et al.

Neural Computing and Applications, Journal Year: 2025, Volume and Issue: unknown

Published: March 15, 2025

Language: Английский

Citations

0
Future Horizons: The Potential Role of Artificial Intelligence in Cardiology DOI Open Access
Octavian Stefan Patrascanu, Dana Tutunaru, Carmina Liana Mușat

et al.

Journal of Personalized Medicine, Journal Year: 2024, Volume and Issue: 14(6), P. 656 - 656

Published: June 19, 2024

Cardiovascular diseases (CVDs) are the leading cause of premature death and disability globally, to significant increases in healthcare costs economic strains. Artificial intelligence (AI) is emerging as a crucial technology this context, promising have impact on management CVDs. A wide range methods can be used develop effective models for medical applications, encompassing everything from predicting diagnosing determining most suitable treatment individual patients. This literature review synthesizes findings multiple studies that apply AI technologies such machine learning algorithms neural networks electrocardiograms, echocardiography, coronary angiography, computed tomography, cardiac magnetic resonance imaging. narrative 127 articles identified 31 papers were directly relevant research, broad spectrum applications cardiology. These included ECG, MRI aimed at various cardiovascular artery disease, hypertrophic cardiomyopathy, arrhythmias, pulmonary embolism, valvulopathies. The also explored new risk assessment, automated measurements, optimizing strategies, demonstrating benefits In conclusion, integration artificial cardiology promises substantial advancements treating diseases.

Language: Английский

Citations

3