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Decoding Voice Authenticity: An Exploration of Deep Learning and Audio Feature Techniques 3

Decoding Voice Authenticity: An Exploration of Deep Learning...

引用

3rd international conference on intelligent data Communication Technologies and Internet of Things, IDCIoT 2025

作者： Yalla, Swathi Tejah Raju, Morampudi Gopi Prashanth Nagaraju, D. Ali, Mohammad Farhaan Department of AI & Data Science CBIT Hyderabad India

ISBN: (纸本)9798331527549

The increasing sophistication of spoofing, mimicry, and deepfake technologies exposes critical vulnerabilities in voice authentication systems, including the inability to generalize across diverse attack types, reliance on narrow feature sets, and a lack of interpretability, which seriously limits their applicability in the real world. This review will analyze the state-of-the-art techniques for Logical Access, Presentation Attacks, and Deepfake manipulations. It points out the limitations of the traditional models and progress based on modern frameworks. It explores using advanced audio features Mel-Spectrogram and MFCC along with LightGBM classifiers and incorporates SHAP (Shapley Additive Explanations) for model transparency and cosine similarity matrices for mimicry detection. The study identifies a dual-layered framework that attains high accuracy and robustness across datasets while ensuring adaptability to emerging attack strategies. It addresses the critical need for scalable, interpretable, and adaptive voice authentication solutions and provides a foundation for effectively mitigating evolving audio spoofing threats. © 2025 ieee.

关键词： Deep learning

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A SIEM Architecture for Multidimensional Anomaly Detection 11

A SIEM Architecture for Multidimensional Anomaly Detection

引用

11th ieee international conference on intelligent data acquisition and advanced computing systems - Technology and Applications (IDAACS)

作者： Laue, Tim Kleiner, Carsten Detken, Kai-Oliver Klecker, Timo Univ Appl Sci & Arts Hanover Ricklinger Stadtweg 120 Hanover NH 30459 USA DECOIT GmbH Fahrenheitstr 9 D-28359 Bremen Germany

ISBN: (纸本)9781665426053

In recent years businesses and organizations have experienced an increase in the occurrence of IT-security related threats, causing the compromise of sensitive information, disruption of everyday operations, and ultimately financial damage. Meanwhile, these attacks have become more varied and sophisticated, making them increasingly hard to detect. In order to address these issues we initiated the GLACIER(1)-project [1]. As a part of the project we created an architecture, which can be realized as an in-house operated SIEM system for SMEs. In addition to SIEM-specific tasks like network data collection, normalization, enrichment and storage, the systems main purpose is to supply data to advanced multidimensional analysis algorithms. These provide a novel way to reliably detect security-related anomalies. Found anomalies are displayed in a GUI, which allows giving feedback for tuning the anomaly detection algorithm, while also providing access to network actors for quick incidence responses. The architecture can be implemented using exclusively free, open-source components and is suitable for both information technology (IT) and operational technology (OT) environments.

关键词： SIEM intrusion detection security architecture multi-dimensional data anaomaly detection open source security

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Three-dimensional Reconstruction of Wire-structured Optical Welds and Point Cloud Filtering Methods

Three-dimensional Reconstruction of Wire-structured Optical ...

引用

2024 ieee international conference on Cognitive computing and Complex data

作者： Shen, Yili Zeng, Chuixi Wuzhou Univ Sch Mech & Resource Engn Wuzhou Peoples R China Guangxi Aiibbot Intelligent Technol Co Tech Dept Nanning Peoples R China

ISBN: (纸本)9798350352900;9798350352894

Three-dimensional reconstruction of weld surfaces is essential for evaluating weld quality. However, the fixed positions of the camera and laser light source prevent direct reconstruction within the camera's coordinate system using line structured light scanning. To address this, we mounted the camera and laser light source on a six-axis robot and calibrated the transformation matrix between the base and camera coordinate systems. This setup allows us to generate a point cloud representing the weld surface within the base coordinate system. Considering the specific characteristics of 3D reconstruction and the topographic features of weld surfaces, we developed a reduced-dimensional point cloud filtering method. Comparative analysis with traditional filtering algorithms shows that this method effectively eliminates isolated and cluster noise, ensuring accurate topographic reconstruction of weld surfaces.

关键词： line structured light weld seam 3D reconstruction point cloud filtering

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A Meticulous Analysis of Diverse Human Traits in Avid Readers and Non-Readers Through advanced data Analytics and Machine Learning Approach

A Meticulous Analysis of Diverse Human Traits in Avid Reader...

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2024 ieee international conference on Information Technology, Electronics and intelligent Communication systems, ICITEICS 2024

作者： Aziz, Farook Abdul Sowmya, V.J. Hindustan Institute of Technology and Science Department of Computer Applications Chennai India

ISBN: (纸本)9798350382693

In the contemporary digital landscape, the enduring practice of reading books encounters formidable challenges amidst the pervasive allure of instant digital gratification. This paper embarks on a nuanced exploration, dissecting the intricate interplay between digital culture and the enduring significance of reading. While past research has predominantly scrutinized the dwindling popularity of reading and its repercussions on empathy and academic performance, our study takes a deeper dive. We focus on scrutinizing the ramifications of reading, or its absence, on pivotal aspects including stress levels, sleep quality, memory, and cognitive functions. Through rigorous comparative analysis between avid readers and non-readers, our aim is to elucidate the comprehensive impact of reading on human well-being. Furthermore, as part of our research endeavor, we intend to employ sophisticated machine learning models to predict an individual's psychological profile, particularly focusing on their position within the psychology spectrum. Leveraging cutting-edge sophisticated machine learning techniques and data visualization techniques, this research endeavors to unravel the subtle connections between reading habits and diverse dimensions of well-being, underscoring the paramount importance of nurturing a steadfast reading habit. © 2024 ieee.

关键词： Emotional intelligence

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Optimization of UAV-Femtocell systems Positioning via Game Theory to Geolocate Mobile Terminals in a Post-Earthquake Scenario 11

Optimization of UAV-Femtocell Systems Positioning via Game T...

引用

11th ieee international conference on intelligent data acquisition and advanced computing systems - Technology and Applications (IDAACS)

作者： Avanzato, Roberta Beritelli, Francesco Raciti, Fabio Spataro, Enrica Univ Catania DIEEI Dept Elect Elect & Comp Sci Engn Viale Andrea Doria 6 I-95125 Catania Italy Univ Catania DMI Dept Dept Math & Comp Sci Viale Andrea Doria 6 I-95125 Catania Italy

ISBN: (纸本)9781665426053

The use of UAV-Femtocell systems for the geolocation of mobile terminals has recently been introduced in the literature as an alternative and effective method in civil protection scenarios and applications. In this context, one of the important aspects concerns the optimization of geolocation times, bearing in mind the limited duration of the flight time of drones. This paper proposes a game theory-based approach to geolocation using UAV-Femtocell systems. A game structure is proposed in which the rules of drone movement are defined according to criteria and utility functions that are linked to the power measurements of the mobile terminals and obtained by the UAV-Femtocell systems in order to optimize the geolocation process through a series of successive rounds in which the Nash equilibria are reached.

关键词： Localization Algorithm UAVs Game Theory Flight Strategy and Energy Efficiency Optimization

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Anomaly Detection in Meteorological data Using Machine Learning Techniques

Anomaly Detection in Meteorological Data Using Machine Learn...

引用

2025 ieee international Students' conference on Electrical, Electronics and Computer Science, SCEECS 2025

作者： Rawal, Uddhav Patel, Subhas Vit Bhopal University School of Computing Science Engineering and Artificial Intelligence Madhya Pradesh Sehore466114 India

ISBN: (纸本)9798331529833

Anomaly detection is a very important component in weather forecasting, disaster preparedness and climate modeling in meteorology. We use Density Based Spatial Clustering of Applications with Noise, Isolation Forest, Local Outlier Factor, Elliptic Envelope and One Class Support Vector Machine to detect anomalies in Tunisian meteorological data in this study. Key meteorological parameters, such as wind speed, rainfall and temperature, was analyzed as the important indicators of atmospheric conditions. DBSCAN detected local density changes very well and is therefore a good choice for anomaly detection in areas with complex spatial patterns from the models used. Isolation Forest algorithm was able to find global anomalies with robust performance, giving very high true positive rates with minimum false positives. The use of these machine learning techniques is complementary and this dual approach shows how they can be used to detect different kinds of anomalies. The results show how machine learning can be used to make environmental monitoring and weather prediction much more accurate and reliable, with the ability to detect anomalies precisely and reliably. Having such capabilities is critical for taking proactive actions in response to extreme weather events, and to be able to intervene at the right time to minimize the impacts of the events. Additionally, these models could be integrated into real time monitoring systems to provide additional utility in optimizing their use and provide actionable insight for disaster management and climate resilience. Further research could involve the fusion of these algorithms with deep learning methods, larger datasets, and real time deployment to build intelligent systems that can learn and adapt to changing environmental conditions. This would be a large advance in applying machine learning in meteorology. © 2025 ieee.

关键词： intelligent systems

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Some Thoughts on the Classification Standard of intelligent Autonomous Level of Measurement and Control System 9

Some Thoughts on the Classification Standard of Intelligent ...

引用

9th international conference on Big data and Information Analytics, BigDIA 2023

作者： Yuan, Guangfu Yang, Liu Han, Haoran Li, Yong Shao, Cong Zhan, Siyu Gansu Photoelectric Measurement Technology Research Laboratory Gansu735018 China University of Electronic Science and Technology of China Laboratory of Intelligent Collaborative Computing Chengdu China Trusted Cloud Computing and Big Data Key Laboratory of Sichuan Province Chengdu611731 China

ISBN: (纸本)9798350330076

Intelligence is an important trend in the development of measurement and control systems. However, there is still a lack of unified understanding of the intelligent autonomous classification of the measurement and control system. The establishment of intelligent evaluation standards in this field has become an urgent research topic. This paper first gives an overview of the aviation measurement and control system, and then takes the classification standards of autonomy in the fields of automobile driving automation in China and the United states, the autonomy and autonomy level of typical ships in China, and the automation of civil unmanned aircraft system in China as a reference. A 5-level intelligent grading standard based on task adaptability, environmental complexity and human-computer interaction is proposed. The classification standard is practical and easy to understand. It can provide reference for the establishment of intelligent standards for measurement and control systems, improve the application speed of intelligent autonomous technology in the field of measurement and control, and broaden the application prospect of intelligent measurement and control systems. © 2023 ieee.

关键词： Unmanned aerial vehicles (UAV)

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A Comparative Analysis of Deep Learning-Based Object Detectors for Embedded systems

A Comparative Analysis of Deep Learning-Based Object Detecto...

引用

ieee international Workshop on intelligent data acquisition and advanced computing systems: Technology and Applications

作者： Yehor Boltov Inna Skarga-Bandurova Maryna Derkach G.E. Pukhov Institute for Modelling in Energy Engineering Kyiv Ukraine Oxford Brookes University Oxford UK Ternopil Ivan Puluj National Technical University Ternopil Ukraine

The paper presents a comparative analysis of popular deep learning-based object detectors, focusing on evaluating their real-time performance on embedded platforms. The objective was to identify the optimal algorithm that can efficiently operate within limited computational resources and memory constraints of embedded systems while minimizing energy consumption. We tested a set of popular detectors, such as Faster R-CNN, YOLO, and SSD MobileNet and evaluated their performance on the testing platform with CUDA cores. A faster variant of bounding box suppression, blazing fast Non-Maximum Suppression (NMS), was employed to enhance efficiency. Additionally, the generalized intersection over union (GIoU) metric was adopted as the optimal testing method, allowing for more accurate object localization and detection. Results demonstrate that the SSD MobileNet-V2 and Tiny YOLO detectors exhibit promising real-time performance on embedded platforms, although their accuracy is diminished compared to larger models.

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5G based Edge computing for Internet of Medical Things in Fast Healthcare data Processing and Management 2

5G based Edge Computing for Internet of Medical Things in Fa...

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2nd ieee international conference on advanced Technologies in intelligent Control, Environment, computing and Communication Engineering, ICATIECE 2022

作者： Rao, Sunita Kumari, Neeraj Jaipur National University Department of Engineering and Technology Jaipur India Teerthanker Mahaveer University Department of Computing Science and IT Moradabad India

ISBN: (纸本)9781665493963

Internet of Medical Things is one of the fastest growing fields in technology, and it is predicted to bring about the largest technological delivery ever. Medical systems have been pushed to improve online services across the board by the COVID-19 pandemic, as the edge-enabled Online learning system in the healthcare system offers incredible opportunities. With the introduction of cutting-edge digital health technologies, patients will be able to avoid unneeded, preventative hospitalizations. Furthermore, the utilization of adaptable and readily available media by both patients and medical professionals would allow telehealth-based medical platforms to make healthcare more efficient and economical. © 2022 ieee.

关键词： Online systems

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Levy Flight Osprey Optimization Algorithm for Task Scheduling in Cloud computing

Levy Flight Osprey Optimization Algorithm for Task Schedulin...

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2024 international conference on intelligent Algorithms for Computational Intelligence systems, IACIS 2024

作者： Chintale, Pradeep Malviya, Rajesh Kumar Merla, Narendra Babu Chinna, Pavana Prasanthi Goli Desaboyina, Gopi Sure, Tharun Anand Reddy Sei Investment Company Enterprise Cloud Platform Collegeville United States Ntt Data Cloud Data & Ai Practice PlanoTX United States S&p Global Ratings Ratings New York United States Individual Contributors C&o United States Sei Investments Sei Public Cloud Engineering PA United States ServiceNow Mobile Applications CA United States

ISBN: (纸本)9798350360660

Task scheduling in a Cloud computing (CC) environment offers flexible and scalable computing resources that encounter critical challenges that significantly affect the system performance. The existing methods often suffer from minimal convergence rates due to ineffective task scheduling, leading to a degradation in the quality of task scheduling in the CC environment. This paper proposes the Levy Flight Osprey Optimization Algorithm (LFOOA) for task scheduling, which provides high-quality scheduling solutions due to its effective search mechanisms. The novel approach aims to improve the convergence rate and reduce the time required for evaluation. The significance of task scheduling in CC lies in its responsibility for assigning tasks to each virtual machine, optimising execution time, and minimizing energy consumption. The existing methods namely, Particle Swarm Optimization (PSO), Random Double Adaptive Whale Optimization Algorithm (RDWOA) and Whale Optimization Algorithm (WOA) and PS-Grey Wolf Optimization (PSGWO) are evaluated in the proposed method. The LDOOA approach achieves better performance with a makespan of 97, resource utilization of 99%, and energy consumption of 1.1. © 2024 ieee.

关键词： Optimization algorithms

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