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Image-based DeepFake Detection Using Artificial Intelligence

Image-based DeepFake Detection Using Artificial Intelligence

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international conference on communication systems and Network Technologies (CSNT)

作者： Shailendra Kumar Singh Gautam Kumar Aditya Kumar Dubey Ritik Yadav Vishal Rai Anil Sharma Department of Computer Science and Engineering Amity University Noida India

ISBN: (数字)9798331531935

ISBN: (纸本)9798331531942

the increasing sophistication of deepfake technology poses significant risks to privacy, security, and public trust in digital media. As a result, deepfake detection has become a critical research area, with artificial intelligence -driven approaches playing a central role. this paper presents a comprehensive analysis of convolutional neural network -based deepfake detection methods, highlighting key challenges such as dataset limitations, social media compression effects, real-time detection constraints, and the rapid evolution of manipulation techniques. While existing detection models demonstrate promising accuracy, challenges remain in terms of generalizability, computational efficiency, and robustness against complex manipulations, including audio and full-body deepfakes. this study explores strategies to enhance detection performance through data augmentation and optimized architectural design. Furthermore, it discusses potential future directions, such as leveraging multimodal analysis, federated learning, and explainable AI, to improve the reliability and adaptability of deepfake detection systems.

关键词： Deepfakes Privacy Social networking (online) Federated learning Explainable AI Media Real-time systems Robustness Convolutional neural networks Security

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Active and passive beamforming based on RWMMSE and Gradient Projection for IRS-aided MU-MISO systems 14

Active and passive beamforming based on RWMMSE and Gradient ...

引用

14th international conference on Information and communication Technology Convergence, ICTC 2023

作者： Kimaryo, Seraphin Bahingayi, Eduard E. Lee, Kyungchun Seoul National University of Science and Technology Department of Electrical and Information Engineering Seoul Korea Republic of Muhimbili University of Health and Allied Sciences Biomedical Engineering Unit Dar es Salaam Tanzania United Republic of Seoul National University of Science and Technology Department of Electrical and Information Engineering The Research Center for Electrical and Information Technology Seoul Korea Republic of

ISBN: (纸本)9798350313277

the intelligent reflecting surface (IRS), a planar array consisting of many low-cost configurable reflecting elements, offers many new possibilities, such as creating a virtual line of sight, interference cancellation, wireless power transfer, etc. To efficiently achieve these goals, the real-time reconfiguration of its elements, which is still challenging, needs to be as simple as possible. Prior works for downlink IRS-aided multi-user multiple-input single-output (MISO) systems, which are noted to have high complexity, are extended in this work by incorporating them with novel techniques that lower their complexity. In doing so, we create a weighted sum rate problem and adopt a reduced weighted minimum mean square error (RWMMSE) algorithm, which is an advanced low-complexity version of the weighted minimum mean square error (WMMSE) scheme to optimize the active precoders, whereas a gradient projection (GP) algorithm, whose procedure of choosing the step size is redesigned to avoid the high-complexity iterative processes, is adopted for optimization of passive beamformers. Finally, the presented numerical results demonstrate the effectiveness of our proposed scheme in attaining comparable performance to the benchmark schemes with a significant complexity reduction. © 2023 IEEE.

关键词： active beamforming Intelligent reflecting surface (IRS) multiple-input single-output (MISO) passive beamforming reduced WMMSE (RWMMSE)

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Person Detection using Proposed YOLOv8 algorithm

Person Detection using Proposed YOLOv8 algorithm

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international conference on communication systems and Network Technologies (CSNT)

作者： Justin Jayaraj K Padmacharan V Balasubramaniam Vadivel Gokulraj G Ramachandramoorthy K B Kirthika K Department of Computer Science and Engineering Sri Ramakrishna Institute of Technology Coimbatore India Department of Computer Science and Engineering KPR Institute of Engineering and Technology Coimbatore India

ISBN: (数字)9798331531935

ISBN: (纸本)9798331531942

Surveillance systems are vital for detecting and preventing crime, especially in urban environments where real-time monitoring is crucial. this research proposes an advanced surveillance system that combines proposed YOLOv8 object detection with DeepSORT tracking for tracking a person in crowded place, enhanced with a DenseNet backbone, achieves 97.1% detection accuracy and an mAP of 0.971 at an IoU of 0.5, outperforming YOLOv3 and YOLOv5. the model makes use of Spatial Pyramid Pooling (SPP) and Feature Pyramid networks (FPN) for multi-scale detection and an anchor-free approach for improved accuracy. Integrated with DeepSORT, the system tracks individuals reliably across frames, even in dynamic environments. Experimental results demonstrate that this approach significantly improves detection and tracking efficiency, making it ideal for automated surveillance and crime prevention in urban areas.

关键词： YOLO Accuracy Surveillance Prevention and mitigation Urban areas Transfer learning Feature extraction Real-time systems Public security Reliability

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Detection of Deep Vein thrombosis Using Machine Learning for CT Images 15th

Detection of Deep Vein Thrombosis Using Machine Learning for...

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15th international conference on Soft Computing and Pattern Recognition, SoCPaR 2023 and 14th World Congress on Nature and Biologically Inspired Computing, NaBIC 2023

作者： Srinivasmurthy, Ch. Raju, Pandu Ranga Venkatakrishnamoorthy, T. Padmaja, K. Devaraju, T. Madhavi, K. Reddy Electronics and Communication GITAM University A.P. Visakhapatnam India Annamacharya Institute of Technology and Sciences A.P. Rajampet India Sasi Institute of Technology and Engineering A.P. Tadepalligudem India SV College of Engineering A.P. Tirupati India Mohan Babu University A.P. Tirupati India

ISBN: (纸本)9783031810794

this paper’s primary goal is to provide an overview of Deep Vein thrombosis (DVT) and its contributing factors. there is currently no clinical diagnostic for the DVT, despite it being the largest cause of disease and mortality in the globe. DVT refers to the development of blood clots in deep veins. Afflicted deep leg veins are frequently the femoral vein, popliteal vein, calf veins, or pelvic deep veins. If left untreated, it’s a potentially dangerous illness that could result in morbidity and even death. D dimer, a common laboratory screening assay, has high sensitivities but low specificities, making clinical identification of DVT difficult. More precise and noninvasive diagnostic methods are needed in order to effectively localize, monitor, and diagnose DVT in patients during and after therapy. Imaging methods are often utilized in conventional clinical therapy because they may quickly discover and identify the DVT. Due to its non-invasive nature, impedance plethysmography technology could be utilized to produce a portable DVT diagnosis equipment. On the other hand, the IP signal is susceptible to motion artifacts. therefore, it is crucial to create MA reduction methods that are tailored to the IP signal. It can also get rid of motion with a large amplitude. © the Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Noninvasive medical procedures

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Reachability Analysis for Cyber-Physical systems: Are We there Yet? 1

引用

14th international NASA Formal Methods Symposium (NFM)

作者： Chen, Xin Sankaranarayanan, Sriram Univ Dayton Dayton OH 45469 USA Univ Colorado Boulder CO 80309 USA

ISBN: (数字)9783031067730

ISBN: (纸本)9783031067730;9783031067723

Reachability analysis is a fundamental problem in verification that checks for a given model and set of initial states if the system will reach a given set of unsafe states. Its importance lies in the ability to exhaustively explore the behaviors of a model over a finite or infinite time horizon. the problem of reachability analysis for Cyber-Physical systems (CPS) is especially challenging because it involves reasoning about the continuous states of the system as well as its switching behavior. Each of these two aspects can by itself cause the reachability analysis problem to be undecidable. In this paper, we survey recent progress in this field beginning with the success of hybrid systems with affine dynamics. We then examine the current state-of-the-art for CPS with nonlinear dynamics and those driven by "learning-enabled" components such as neural networks. We conclude with an examination of some promising directions and open challenges.

关键词： Embedded systems

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Social Fragmentation Transitions in Large-Scale Parameter Sweep Simulations of Adaptive Social networks 1

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14th international conference on Parallel Processing and Applied Mathematics, PPAM 2022

作者： Sayama, Hiroki Center for Collective Dynamics of Complex Systems Binghamton University State University of New York BinghamtonNY13902-6000 United States Waseda Innovation Lab Waseda University Tokyo Shinjuku169-8050 Japan

ISBN: (数字)9783031304453

ISBN: (纸本)9783031304446

Social fragmentation transition is a transition of social states between many disconnected communities with distinct opinions and a well-connected single network with homogeneous opinions. this is a timely research topic with high relevance to various current societal issues. We had previously studied this problem using numerical simulations of adaptive social network models and found that two individual behavioral traits, homophily and attention to novelty, had the most statistically significant impact on the outcomes of social network evolution. However, our previous study was limited in terms of the range of parameter values examined, and possible interactions between multiple behavioral traits were largely ignored. In this study, we conducted a substantially larger-scale parameter sweep numerical experiment of the same model with expanded parameter ranges by an order of magnitude in each parameter dimension, resulting in a total of 116,640 simulation runs. To capture nontrivial interactions among behavioral parameters, we modeled and visualized the dependence of outcome measures on the model parameters using artificial neural networks. Results show that, while the competition between homophily and attention to novelty is still the primary determinant of social fragmentation, another transition plane emerges when individuals have strong social conformity behavior, which was not previously known. this implies that social fragmentation transition can also occur in the homophily-social conformity trade-off, the two behavioral traits that have very similar microscopic individual-level effects but produce very different macroscopic collective-level outcomes, illustrating the nontrivial macroscopic dynamics of complex collective systems. © 2023, the Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Neural networks

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Comparison of the Use of the DEMUCS Neural Network On Different Platforms for the Separation of Sources Of Musical Origin

Comparison of the Use of the DEMUCS Neural Network On Differ...

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international conference on Computational Intelligence and communication networks (CICN)

作者： Raul Pérez Alarcón Luis Marcelo Pacheco Alvaro Ciro Rodriguez Favio Guevara Puente Ivan Petrlik Yuri Pomachagua Software Engineering Department Universidad Nacional Mayor de San MarcosUNMSM Lima Peru Department of Electronic Engineering and Informatics Universidad Nacional Federico Villarreal UNFV Lima Peru Systems Engineering Department Universidad Nacional Federico Villarreal UNFV Lima Peru

ISBN: (纸本)9781665487726

this paper makes a comparison between 3 systems deployed on different platforms (Web, Desktop, Mobile) which implement the DEMUCS neural network, responsible for separating sources of musical origin. the objective of this work is to determine on which platform the neural network can be executed more quickly for the use of the average user and from this to propose an optimal architecture for standard development. For this purpose, we selected 12 songs to be separated in the systems of the 3 platforms mentioned and we measured the time it takes for each system to execute the required separation and thus choose the best platform as a starting point. the results and conclusions of the work support the reason for choosing the platform, from which the development architecture was proposed.

关键词： Neural networks Music Computer architecture Time measurement Hardware Service-oriented architecture Delays

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A Novel Transformer-Based Model for Dialog State Tracking 14th

A Novel Transformer-Based Model for Dialog State Tracking

引用

14th international conference on Cross-Cultural Design (CCD)

作者： Miao, Yu Liu, Kuilong Yang, Wenbo Yang, Changyuan Alibaba Grp Hangzhou 311121 Peoples R China

ISBN: (纸本)9783031060496;9783031060502

Dialog state tracking (DST) is a core component in the task-oriented dialog systems. Many previous approaches regard DST as a classification task for a set of predefined slot-value pairs, but these approaches can not handle the situation of dynamic ontology. Other methods consider slot as span-based, while these methods are insufficient when the target slot-value cannot be found as a word segment in the dialog context. To mitigate these problems, we propose a Transformer-based model for DST. the proposed method can achieve the DST task by extracting the slot-value from the dialog context or classifying for the slot with limited values. Experimental evidence shows that our proposed model can achieve competitive performance on the WOZ 2.0 dataset while being 20x faster than the state-of-the-art model. To demonstrate the effectiveness of our proposed model on multi-domain, we experiment with the recently released MultiWOZ-2.0 dataset. the experiment shows that our model can achieve promising result.

关键词： Dialog State Tracking (DST) Multi-domain dialogue systems Pretrained-model

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Automated Pneumonia Detection from Chest X-Ray Images Using Deep Convolutional Neural networks

Automated Pneumonia Detection from Chest X-Ray Images Using ...

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international conference on Information, Intelligence, systems and Applications (IISA)

作者： Orestis Papadimitriou Athanasios Kanavos Manolis Maragoudakis Department of Information and Communication Systems Engineering University of the Aegean Samos Greece Department of Informatics Ionian University Corfu Greece

Recent advancements in deep learning have shown promising results in various clinical image analysis tasks. Among the most commonly performed radiological examinations, chest radiographs play a crucial role and have been extensively investigated for various applications. the availability of large, publicly accessible chest X-ray datasets in recent years has sparked research interest. Pneumonia diagnosis typically relies on the analysis of X-ray radiographs by highly trained experts, a process that can be time-consuming and prone to disagreements among radiologists. In this study, we utilized a dataset of 5,856 high-resolution frontal-view chest X-ray images for training, validation, and testing of our model. the proposed model achieved an impressive accuracy of 96.6%, sensitivity of 98.1%, specificity of 92.4% and precision of 97.2%. these results demonstrate the excellent performance of the model in accurately identifying pneumonia cases and normal cases.

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OPEES: Online Proctored Entrance Examination System with Degree Program Recommender for Colleges and Universities 14

OPEES: Online Proctored Entrance Examination System with Deg...

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14th IEEE international conference on Humanoid, Nanotechnology, Information Technology, communication and Control, Environment, and Management, HNICEM 2022

作者： Bulauitan, Joriz Caezar B. De Jesus, Ashley L. Lopez, Katherine Mega F. Villarama, Darren Joshua A. Goh, Marie Luvett I. Raguro, Ma. Corazon F. Lagman, Ace C. FEU Institute of Technology P. Paredes Manila Sampaloc Philippines

ISBN: (纸本)9781665464932

the college entrance examination is vital for program admission. Typically, entrance examinations are conducted onsite using paper and pens. When the COVID-19 pandemic hit, the entrance examination was lifted and physical gatherings were prohibited. Since many schools cannot offer an online admissions exam, they rely on grades and interviews to admit and qualify students for degree programs. However, academic standards differ between schools, and grades may not be enough to assess students' capacity. thus, this study aims to develop an Online Proctored Entrance Examination System (OPEES) with Degree Program Recommender for colleges and universities to help institutions administer onsite or online entrance tests and generate course suggestions using a rulebased algorithm. the study employed the scrum methodology in software development. OPEES allows applicants to submit applications online, and institutions can manage user accounts, tailor exams and degree programs' criteria, manage exam dates, and assign proctors. Online proctoring using Jitsi, an opensource multiplatform voice, video, and instant messaging tool with end-to-end encryption, ensures exam integrity. the system's features were evaluated by 102 respondents, comprised of end-users (students and school personnel) and IT professionals, using the FURPS (Functionality, Usability, Reliability, Performance, and Supportability) software quality model. In the software evaluation, the overall system proved to be functional as perceived by the respondents, as manifested by the mean rating of 4.61. In conclusion, the system's architecture was deemed feasible and offers a better way to streamline admission examinations and determine a student's applicable degree program by enabling institutions to customize their exams and degree program requirements. It will be beneficial to look into recommendation system algorithms and historical enrollment data to improve the system's use case. © 2022 IEEE.

关键词： Recommender systems

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