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New Approach of Artificial Intelligence in Digital Forensic Investigation: A Literature Review 1

International Conference on Advances in Communication technology and Computer Engineering, ICACTCE 2023

作者： Verma, Raghav Garg, Sonia Kumar, Karan Gupta, Gaurav Salehi, Waleed Pareek, Piyush Kumar Kniežova, Jaroslava Yogananda School of Artificial Intelligence Computers and Data Science Himachal Pradesh Solan173229 India Mullana Ambala133207 India Department of Artificial Intelligence and Machine Learning Nitte Meenakshi Institute of Technology Bengaluru India Faculty of Management Comenius University in Bratislava 25 Odbojárov 10 Bratislava82005 Slovakia

ISBN: (数字)9783031371646

ISBN: (纸本)9783031371639

A Digital Forensic is a subfield of forensic science known as "digital forensic science" that focuses on the recovery and examination of data from digital devices that are connected to cybercrime. Computer forensics was the original meaning of the word "digital forensics." it has broadened to include any device that could store digital data. National policies on digital forensics didn't start to arise until the beginning of the twenty-first century. The process of locating, protecting, analyzing, and documenting digital evidence is known as digital forensics. When necessary, this is done so that evidence can be presented in court. AI in Digital forensics is one of the technologies that is developing the fastest, and it has had a significant impact on the methods and tools used to examine, monitor, and visualize crime scenes as well as develop effective strategies for dealing with impending threats and attacks on the internet and cyberspace. Modern technology has replaced human labor with machine-oriented designed work that has the capability and ability to minimize error and increase quality, reducing human effort and achieving maximum results with the fewest possible errors. It can influence the outcome and analyze the evidence in the field of digital forensics in a more effective manner to track the outcomes. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Artificial intelligence

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A Comprehensive Review of Mojo: A High-Performance Programming Language

A Comprehensive Review of Mojo: A High-Performance Programmi...

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Mobile Computing and Sustainable Informatics (ICMCSI), International Conference on

作者： Parth Dhananjay Akre Utkarsha Pacharaney Department of Artificial Intelligence and Data Science Faculty of Engineering and Technology Datta Meghe Institute of Higher Education and Research Wardha Maharashtra India Department of Artificial Intelligence and Machine Learning Faculty of Engineering and Technology Datta Meghe Institute of Higher Education and Research Wardha Maharashtra India

ISBN: (数字)9798331522667

ISBN: (纸本)9798331522674

As artificial intelligence continues to advance at an unprecedented pace, the selection of programming languages significantly affects development processes, workflows, and outcomes. Mojo, a novel programming language, has gained attention through its integration of Python-like syntax, Rust-inspired safety features, and superior performance characteristics. This paper evaluates Mojo across four critical dimensions: distinctive features, performance metrics, ecosystem maturity, and practical applications, while examining potential adoption barriers. Positioned at the intersection of high-level programming simplicity and low-level performance optimization, Mojo demonstrates particular utility for artificial intelligence developers requiring both flexibility and computational efficiency. Through analysis of prominent libraries, including Endia, Lightbug HTTP, Basalt, and ***, this study examines Mojo's capabilities and limitations. By presenting a comparative analysis of Mojo's features, emerging ecosystem, and applicability to artificial intelligence development, this research addresses the current literature gap and provides substantive insights into this emerging programming language.

关键词： Reviews Ecosystems Syntactics Programming Libraries Real-time systems Safety Artificial intelligence Optimization Python

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Enhanced Vision Transformer with Lion Optimizer for Accurate Classification of Monkeypox Skin Images 4

Enhanced Vision Transformer with Lion Optimizer for Accurate...

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4th International Conference on Technological Advancements in Computational Sciences, ICTACS 2024

作者： Bhatnagar, Roheet Hassan, Esraa Saber, Abeer Shams, Mahmoud Y. Elbedwehy, Samar Manipal University Jaipur Department of Computer Science and Engineering Rajasthan India Kafrelsheikh University Faculty of Artificial Intelligence Dept. of Machine Learning Kafrelsheikh33516 Egypt Damietta University Faculty of Computers and Artificial Intelligence Information Technology Dept. Damietta34517 Egypt Kafrelsheikh University Faculty of Artificial Intelligence Dept. of Data Science Kafrelsheikh33511 Egypt

ISBN: (纸本)9798350387490

Monkeypox is a contagious viral infection caused by the monkeypox virus (Mpoxvirus). Characterized by skin lesions, fever, respiratory difficulties, swollen lymph nodes, and neurological complications, MPX can be fatal. Recent outbreaks have spread the disease across Europe, Australia, the United States, and Africa. Traditionally diagnosed through PCR testing of skin lesions, this process poses significant risks to medical personnel due to potential exposure to the highly contagious virus. However, the usage artificial intelligence (AI) is revolutionizing diagnostics, providing a safer and more efficient approach to MPX detection. In this paper, a new AI technique based on vision transformer and lion optimizer for classifying Monkeypox Skin Images. The presented model used for classifying the dataset and results reached accuracy of 99.6%. © 2024 IEEE.

关键词： Classification Deep learning Diagnosis Monkeypox

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Enhancing Interpretability: The Role of Explainable AI in Healthcare Diagnostics

Enhancing Interpretability: The Role of Explainable AI in He...

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Electronics and Renewable Systems (ICEARS), International Conference on

作者： Nikita Zade Meher Langote Prateek Verma Department of Artificial Intelligence & Data Science Faculty of Engineering & Technology Datta Meghe Institute of Higher Education (DU) Sawangi Maharashtra India Department of Artificial Intelligence & Machine Learning Faculty of Engineering & Technology Datta Meghe Institute of Higher Education (DU) Sawangi Maharashtra India

ISBN: (数字)9798331509675

ISBN: (纸本)9798331509682

XAI is now transforming the use of AI in diagnosing diseases by overcoming some of the problems inherent in most black-box approaches. In time-sensitive speciality areas like computer-aided diagnosis, image analysis, and predictive modelling, SHAP, LIME, and attention-based XAI techniques explain how AI decides to help doctors and other healthcare professionals trust AI. Therefore, this paper aims to clarify why XAI is needed in healthcare, introduce techniques, and show their examples in cancer diagnosis and disease prognosis. XAI improves diagnostic performance and satisfies regulatory and ethical requirements for increased usage in clinical practice. Because of XAI, clinicians can use the best of artificial intelligence to complement their knowledge in enhancing the treatment of patients. That is why the outlook of XAI in healthcare includes the next steps in achieving higher scalability, managing bias, and working with diverse regulatory frameworks, which will define the future of AI solutions in healthcare.

关键词： Renewable energy sources Explainable AI Scalability Closed box Medical services Predictive models Regulation Artificial intelligence Prognostics and health management Cancer

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Multilingual Facial Emotion Decoding and Posture Recognition using Random Forest Classifier

Multilingual Facial Emotion Decoding and Posture Recognition...

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Intelligent Technologies (ACOIT), Asian Conference on

作者： K Sangeetha Vs Balaji M Manju S Renuka Devi P Velayutham Department of Artificial Intelligence and Machine Learning Rajalakshmi Engineering College Chennai India Department of Artificial Intelligence and Data Science Rajalakshmi Engineering College Chennai India Department of Computer Science and Engineering Aarupadai Veedu Institute of Technology Chennai India

ISBN: (数字)9798350374957

ISBN: (纸本)9798350374964

Effective communication is crucial for success in various interactions, including personal and online interviews. The work proposed is to refine the communication effectiveness and extend the understanding of interactions by incorporating new system functions of real-time emotion and body language capture. Facial and body landmarks are analyzed using Google’s Mediapipe to ensure accurate identification of the frequently performed emotions and frequently posed postures. The proposed system uses three classifiers, namely Random Forest, Gradient Boosting, and Ridge Classifier, all integrated in one pipeline; this makes our work distinct. In the testing phase the classifiers are checked to avoid misidentification of emotions or incorrect posture in different situations. One of the major novelties of this work is its ability to address users of different languages, thus it is relevant anywhere in the world. This dynamic and robust system provides accurate emotion analysis achieving the accuracy of 92%, enhancing understanding in various communication contexts and setting a new standard for interview and interaction analysis.

关键词： Emotion recognition Accuracy Face recognition Pipelines Real-time systems Multilingual Interviews Random forests Standards Testing

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A Machine learning Approach to Exploring Ecological Determinacy of Socially Significant Diseases in Moscow

A Machine Learning Approach to Exploring Ecological Determin...

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International Conference on Management of Large-Scale System Development (MLSD)

作者： Anna Marunko Tatiana Zolotova Faculty of Information Technology and Big Data Analysis Financial University Under the Government of the Russian Federation Moscow Russia Department of Data Analysis and Machine Learning Financial University Under the Government of the Russian Federation Moscow Russia

A study of the relationship between some selected environmental indicators and the presence of socially significant diseases in the population of Moscow was carried out. A statistically significant correlation was confirmed. Machine learning models were built during the regression analysis. A web interface was developed to automate the processes of predicting the population’s morbidity.

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Octopus: Experiences with a Hybrid Event-Driven Architecture for Distributed Scientific Computing

Octopus: Experiences with a Hybrid Event-Driven Architecture...

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High Performance Computing, Networking, Storage and Analysis, SC-W: Workshops of the International Conference for

作者： Haochen Pan Ryan Chard Sicheng Zhou Alok Kamatar Rafael Vescovi Valérie Hayot-Sasson André Bauer Maxime Gonthier Kyle Chard Ian Foster Department of Computer Science University of Chicago Chicago IL USA Data Science and Learning Division Argonne National Laboratory Lemont IL USA Department of Computer Science Southern University of Science and Technology China Department of Computer Science Illinois Institute of Technology Chicago IL USA

ISBN: (数字)9798350355543

ISBN: (纸本)9798350355550

Scientific research increasingly relies on distributed computational resources, storage systems, networks, and instruments, ranging from HPC and cloud systems to edge devices. Event-driven architecture (EDA) benefits applications targeting distributed research infrastructures by enabling the organization, communication, processing, reliability, and security of events generated from many sources. To support the development of scientific EDA, we introduce Octopus, a hybrid, cloud-to-edge event fabric designed to link many local event producers and consumers with cloud-hosted brokers, and to provide a fabric for developing resilient applications. Octopus can be scaled to meet demand, permits the deployment of highly available Triggers for automatic event processing, and enforces fine-grained access control. We identify requirements in self-driving laboratories, scientific data automation, online task scheduling, epidemic modeling, and dynamic workflow management use cases, and present results demonstrating Octopus’ ability to meet those requirements. Octopus supports producing and consuming events at a rate of over 4.2 M and 9.6 M events per second, respectively, from distributed clients.

关键词： Automation Scientific computing Instruments Laboratories Process control Computer architecture Organizations Dynamic scheduling Fabrics Reliability

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Identifying General Mechanism Shifts in Linear Causal Representations

arXiv

引用

arXiv 2024年

作者： Chen, Tianyu Bello, Kevin Locatello, Francesco Aragam, Bryon Ravikumar, Pradeep Department of Statistics and Data Sciences University of Texas Austin United States Booth School of Business University of Chicago United States Machine Learning Department Carnegie Mellon University United States Institute of Science and Technology Austria

We consider the linear causal representation learning setting where we observe a linear mixing of d unknown latent factors, which follow a linear structural causal model. Recent work has shown that it is possible to recover the latent factors as well as the underlying structural causal model over them, up to permutation and scaling, provided that we have at least d environments, each of which corresponds to perfect interventions on a single latent node (factor). After this powerful result, a key open problem faced by the community has been to relax these conditions: allow for coarser than perfect single-node interventions, and allow for fewer than d of them, since the number of latent factors d could be very large. In this work, we consider precisely such a setting, where we allow a smaller than d number of environments, and also allow for very coarse interventions that can very coarsely change the entire causal graph over the latent factors. On the flip side, we relax what we wish to extract to simply the list of nodes that have shifted between one or more environments. We provide a surprising identifiability result that it is indeed possible, under some very mild standard assumptions, to identify the set of shifted nodes. Our identifiability proof moreover is a constructive one: we explicitly provide necessary and sufficient conditions for a node to be a shifted node, and show that we can check these conditions given observed data. Our algorithm lends itself very naturally to the sample setting where instead of just interventional distributions, we are provided datasets of samples from each of these distributions. We corroborate our results on both synthetic experiments as well as an interesting psychometric dataset. The code can be found at https://***/TianyuCodings/iLCS. © 2024, CC BY.

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Automatic Human Life Rescue Analysis Model with Incorporation of Deep learning Algorithm

Automatic Human Life Rescue Analysis Model with Incorporatio...

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Sustainable Communication Networks and Application (ICSCNA), International Conference on

作者： V.K.G. Kalaiselvi Prithikhaa S Divya Darshini J S Bharath Reddy Anandkumar B Shanmugasundaram Hariharan Department of Information Technology Sri Sairam Engineering College Tamilnadu India Department of CSE KG Reddy College of Engineering and Technology Telangana India Department of Artificial Intelligence and Machine Learning Vardhaman College of Engineering India Department of Artificial Intelligence and Data Science Vardhaman College of Engineering Hyderabad India

ISBN: (数字)9798331530013

ISBN: (纸本)9798331530020

In today's fast-paced world, every passing moment can mean the difference between life and death. To address this critical issue, we have developed an innovative application designed to provide rapid emergency medical response. Ambulance services play a pivotal role in emergency healthcare by offering vital transportation and care. However, limited access to ambulance contact information often hampers patients' ability to secure timely assistance during emergencies. With the widespread adoption of smartphones and mobile applications, the proposed solution integrates React Native with a Convolutional Neural Network (CNN) to create an advanced mobile app for law enforcement, capable of real-time car accident detection. Utilizing datasets from Kaggle, images are resized to optimize CNN training. Through transfer learning, the CNN architecture effectively extracts spatial features, enabling accurate accident prediction, validated by robust evaluation metrics such as precision and recall. The React Native app facilitates swift notifications to law enforcement, utilizing cutting-edge deep learning technology to enhance road safety and improve emergency response efficiency. This project aims to streamline emergency services, minimizing response and transport times, thereby saving lives and fostering safer communities.

关键词： Deep learning Training Law enforcement Medical services Emergency services Road safety Mobile applications Convolutional neural networks Time factors Accidents

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CROSS-technology GENERALIZATION IN SYNTHESIZED SPEECH DETECTION: EVALUATING AST MODELS WITH MODERN VOICE GENERATORS

arXiv

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arXiv 2025年

作者： Ustinov, Andrew Yordanov, Matey Kuchma, Andrei Bychkov, Mikhail Department of Machine Learning and Data Analytics ITMO University Russia AI Innovation Hub ANTEI Limited Hong Kong Department of Integrative Systems and Design Hong Kong University of Science and Technology AI Innovation Hub ANTEI Limited Hong Kong

This paper evaluates the Audio Spectrogram Transformer (AST) architecture for synthesized speech detection, with focus on generalization across modern voice generation technologies. Using differentiated augmentation strategies, the model achieves 0.91% EER overall when tested against ElevenLabs, NotebookLM, and Minimax AI voice generators. Notably, after training with only 102 samples from a single technology, the model demonstrates strong cross-technology generalization, achieving 3.3% EER on completely unseen voice generators. This work establishes benchmarks for rapid adaptation to emerging synthesis technologies and provides evidence that transformer-based architectures can identify common artifacts across different neural voice synthesis methods, contributing to more robust speech verification systems. © 2025, CC BY-NC-ND.

关键词： Spectrographs

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