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Extracting Facial Features to Detect Deepfake Videos Using Machine Learning

International Journal of Advanced computer science and Applications 2025年第4期16卷 834-842页

作者： Aslam, Ayesha Mir, Jamaluddin Zaman, Gohar Rahman, Atta Salam, Asiya Abdus Ali, Farhan Alhiyafi, Jamal Bakry, Aghiad Gul, Mustafa Jamal Gollapalli, Mohammed Mahmud, Maqsood Department of Computer Science Abbottabad University of Science and Technology Havelian Pakistan Department of Computer Science College of Computer Science and Information Technology Imam Abdulrahman Bin Faisal University P.O. Box 1982 Dammam31441 Saudi Arabia Department of Computer Information Systems College of Computer Science and Information Technology Imam Abdulrahman Bin Faisal University P.O. Box 1982 Dammam31441 Saudi Arabia College of Electronics and Information Engineering Shenzhen University Shenzhen China Department of Computer Science Kettering University FlintMI United States Department of Business Administration University of York York YO10 5DD Heslington United Kingdom Department of Information Technology & Engineering Sydney Met SydneyNSW2000 Australia School of Computing Ulster University Northern Ireland Belfast United Kingdom

Generative adversarial networks (GANs) have gained popularity for their ability to synthesize images from random inputs in deep learning models. One of the notable applications of this technology is the creation of realistic videos known as deepfakes, which have been misused on social media platforms. The difficulty lies in distinguishing these fake videos from real ones with the naked eye, leading to significant concerns. This study proposes a supervised machine learning approach to effectively differentiate between real and counterfeit videos by detecting visual artifacts. To achieve this, two facial features are extracted: eye blinking and nose position, utilizing landmark detection techniques. Both features were trained on supervised machine learning classifiers and evaluated using the publicly available UADFV and Celeb-DF deepfake datasets. The experiments successfully demonstrate that the proposed method achieves a promising and superior performance, with an area under the curve (AUC) of 97% for deepfake detection in contrast to state-of-the-art methods investigating the same datasets. © (2025), (science and Information Organization). All rights reserved.

关键词： Support vector machines

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Automatic text summarization using deep reinforced model coupling contextualized word representation and attention mechanism

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Multimedia Tools and Applications 2024年第1期83卷 733-762页

作者： Aliakbarpour, Hassan Manzuri, Mohammad Taghi Rahmani, Amir Masoud Department of Computer Engineering Science and Research Branch Islamic Azad University Tehran Iran Computer Engineering Department Sharif University of Technology Tehran Iran Future Technology Research Center National Yunlin University of Science and Technology 123 University Road Section 3 Yunlin Douliou64002 Taiwan

With the rapid and unprecedented growth of textual data in recent years, there is a remarkable need for automatic text summarization models to retrieve useful information from these large numbers of textual documents without human intervention within a reasonable time. Text summarization is commonly performed based on extractive and abstractive paradigms. Although different machine learning and deep learning based methods have been proposed for the task of text summarization during the last decades, they are still in their early steps of development and their potential has yet to be fully explored. Accordingly, a new summarization model is proposed in this paper which takes advantage of both extractive and abstractive text summarization models as a single unified model based on the strategy gradient of reinforcement learning. The proposed model also employs the combination of convolutional neural network and gated recurrent unit in both extraction and abstraction modules besides attention mechanism. Moreover, language models, namely Word2Vec and BERT, are used as the backbone of the proposed model to better express sentence semantics as a word vector. We conducted our experiments on widely-studied text summarization datasets (CNN\Daily Mail and DUC-2004) and according to the empirical results, not only the proposed model achieved higher accuracy compared to both extractive and abstractive summarization models in terms of ROUGE metric but also its generated summaries presented higher saliency and readability based on human evaluation. © 2023, The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature.

关键词： Reinforcement learning

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Automated Scoring Model for Semi-Open-Ended Question in Scientific Explanation

Automated Scoring Model for Semi-Open-Ended Question in Scie...

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2024 IEEE International Conference on Cybernetics and Innovations, ICCI 2024

作者： Lertchaturaporn, Teevit Songmuang, Pokpong Thammasat University Faculty Of Science And Technology Department Of Computer Science Pathumthani Thailand

ISBN: (纸本)9798350370096

Scientific explanation is a crucial skill for analyzing data and drawing reasonable conclusions, especially in the context of semi-open-ended and open-ended questions. However, evaluating such questions requires significant effort from teachers, leading to a labor-intensive grading process influenced by class size and teacher expertise, potentially compromising the question's reliability. Consequently, students may lack valuable feedback, hindering the effective development of their skills. This paper presents our research focus on designing a system to automatically score students' responses. We leverage natural language processing techniques and machine learning algorithms to construct an automatic assessment model for scientific explanations in semi-open-ended questions. The responses were obtained from eighth-grade students in the fundamental science subject of the Thai curriculum, totaling 100 responses. Initially, Thai natural language processing is employed to preprocess the responses. Subsequently, N-Gram and word embedding techniques are utilized to represent the responses in a trainable format. Finally, the performance of various machine learning models trained with different word representation techniques is reported using F1-score. The experimental results demonstrate that a common method of word representation, combined with a simple machine learning model, can achieve high performance. © 2024 IEEE.

关键词： Students

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Cyber-attack detection based on a deep chaotic invasive weed kernel optimized machine learning classifier in cloud computing

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Soft Computing 2025年第4期29卷 2015-2030页

作者： Indrasena Reddy, M. Siva Kumar, A.P. Subba Reddy, K. Department of Computer Science and Engineering BVRIT Hyderabad College of Engineering for Women Hyderabad500090 India Department of Computer Science and Engineering Jawaharlal Nehru Technological University Ananthapuramu Andhra Pradesh Ananthapuramu India Department of Computer Science and Engineering Rajeev Gandhi Memorial College of Engineering and Technology Andhra Pradesh Nandyala India

Nowadays, Cloud Computing has attracted a lot of interest from both individual users and organization. However, cloud computing applications face certain security issues, such as data integrity, user privacy, and service availability. The only better solution to such issues is to identify and prevent cyber threats before they can cause seriously harm the cloud computing system. Several methods have been suggested so far to detect the attack in cloud computing (CC), but no one method attains satisfactory results. To overwhelm these drawbacks, a Cyber Security Attack Detection using Deep Kernel Machine Learning optimized with Chaotic Invasive Weed Optimization algorithm is proposed in this paper for Cloud Computing. Here, the data is amassed from CSE-CIC-IDS2018 and Bot-IoT datasets. Subsequently, a pre-processing step involving redundancy reduction and missing value replacement for uncertainty removal is achieved through the Developed Random Forest with Local Least Squares (DRFLLS) method. By utilizing Entropy-Kurtosis based feature selection approach, the pre-processing data is given to the feature selection to identify the optimal features. The selected features are supplied to the Deep Kernel Machine Learning classifier (DK-ML), which is optimized using the Chaotic Invasive Weed Optimization algorithm (Chaotic-IWOA) for classification. This classification process classifies the data as normal or anomalous categories with anomalies encompassing, like DoS, DDoS, Theft attacks, and normal attacks. The proposed technique is activated in MATLAB. The proposed technique achieves 24.88%, 17.98%, 45.65%, 35.95% better accuracy for CSE-CIC-IDS2018 dataset, and 23.93%, 13.94%, 32.94%, and 29.04% better accuracy for Bot-IoT dataset. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2025.

关键词： Cyber attacks

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The Number of Random 2-SAT Solutions Is Asymptotically Log-Normal 27

The Number of Random 2-SAT Solutions Is Asymptotically Log-N...

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27th International Conference on Approximation Algorithms for Combinatorial Optimization Problems, APPROX 2024 and the 28th International Conference on Randomization and Computation, RANDOM 2024

作者： Chatterjee, Arnab Coja-Oghlan, Amin Müller, Noela Riddlesden, Connor Rolvien, Maurice Zakharov, Pavel Zhu, Haodong Department of Computer Science TU Dortmund Germany Department of Mathematics and Computer Science Eindhoven University of Technology Netherlands

ISBN: (纸本)9783959773485

We prove that throughout the satisfiable phase, the logarithm of the number of satisfying assignments of a random 2-SAT formula satisfies a central limit theorem. This implies that the log of the number of satisfying assignments exhibits fluctuations of order √n, with n the number of variables. The formula for the variance can be evaluated effectively. By contrast, for numerous other random constraint satisfaction problems the typical fluctuations of the logarithm of the number of solutions are bounded throughout all or most of the satisfiable regime. © Arnab Chatterjee, Amin Coja-Oghlan, Noela Müller, Connor Riddlesden, Maurice Rolvien, Pavel Zakharov, and Haodong Zhu.

关键词： Constraint satisfaction problems

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Internal organizational factors influencing ICT adoption for sustainable growth

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Discover Global Society 2024年第1期2卷 1-14页

作者： Oyetade, Kayode Harmse, Anneke Zuva, Tranos Department of Computer Science Vaal University of Technology Vaal South Africa

Organizations today depend increasingly on the effective adoption of Information and Communication technology (ICT) to ensure sustainable growth in a competitive global landscape. However, research on the internal factors influencing successful ICT adoption, especially through case studies, remains limited. This study seeks to address this gap by exploring the internal factors such as organizational culture, leadership styles, employee attitudes, resource availability, user perceptions, and peer influence that drive ICT adoption and align with organizational strategies. Drawing on a literature review and qualitative case study analysis of peer-reviewed articles, books, and reports published from 2003 to 2024, the study examines how these factors impact ICT integration and contribute to long-term success. Case studies from leading organizations illustrate how ICT strategies are implemented effectively in diverse contexts. The study is guided by theoretical frameworks, including Innovation Diffusion Theory (IDT) and the technology Acceptance Model (TAM), extending their application to internal organizational dynamics. The findings extend existing theoretical frameworks such as Innovation Diffusion Theory (IDT) and technology Acceptance Model (TAM) by applying them to internal organizational contexts, illustrating the role of leadership, culture, and resources in ICT adoption. Practically, this study provides recommendations for developing an ICT-driven culture, aligning technology initiatives with strategic goals, and investing in infrastructure to overcome adoption barriers and create sustainable technological progress in a globalized society. © The Author(s) 2024.

关键词： Case studies Global competitiveness ICT adoption Internal factors Organisational strategy Sustainable growth

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A Comparative Analysis of Noise Reduction Methods in Sentiment Analysis on Noisy Bangla Texts 9

A Comparative Analysis of Noise Reduction Methods in Sentime...

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9th Workshop on Noisy and User-Generated Text, W-NUT 2024

作者： Elahi, Kazi Toufique Rahman, Tasnuva Binte Shahriar, Shakil Sarker, Samir Shawon, Md. Tanvir Rouf Shahariar, G.M. Department of Computer Science and Engineering Ahsanullah University of Science and Technology Dhaka Bangladesh

ISBN: (纸本)9798891760875

While Bangla is considered a language with limited resources, sentiment analysis has been a subject of extensive research in the literature. Nevertheless, there is a scarcity of exploration into sentiment analysis specifically in the realm of noisy Bangla texts. In this paper, we introduce a dataset (NC-SentNoB) that we annotated manually to identify ten different types of noise found in a pre-existing sentiment analysis dataset comprising of around 15K noisy Bangla texts. At first, given an input noisy text, we identify the noise type, addressing this as a multi-label classification task. Then, we introduce baseline noise reduction methods to alleviate noise prior to conducting sentiment analysis. Finally, we assess the performance of fine-tuned sentiment analysis models with both noisy and noise-reduced texts to make comparisons. The experimental findings indicate that the noise reduction methods utilized are not satisfactory, highlighting the need for more suitable noise reduction methods in future research endeavors. We have made the implementation and dataset presented in this paper publicly available1 © 2024 Association for Computational Linguistics.

关键词： Sentiment analysis

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A Deep Neural Network Approach with Pioneering Local Dataset to Recognize Doctor's Handwritten Prescription in Bangladesh

A Deep Neural Network Approach with Pioneering Local Dataset...

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2024 International Conference on Advances in Computing, Communication, Electrical, and Smart Systems, iCACCESS 2024

作者： Mia, Abdur Rahim Chowdhury, Mohammad Abdullah-Al-Sajid Mamun, Abdullah Al Ruddra, Aurunave Mollik Tanny, Nawshin Tabassum Ahsanullah University of Science and Technology Department of Computer Science and Engineering Dhaka Bangladesh

ISBN: (纸本)9798350350289

This thesis introduces an innovative machine learning solution to a prevalent healthcare challenge in Bangladesh: the legibility of handwritten prescriptions. Where 97.1% of Bangladeshi doctors relying on handwritten prescriptions, this study addresses a critical gap in patient safety and prescription accuracy. The motivation for this research stems from the urgent need to improve legibility in prescriptions, a factor that directly impacts patient care and medication errors. At the core of our study is the development of a specialized machine learning system, tailored to the nuances of Bangladeshi prescriptions. Our research is the first in Bangladesh to apply machine learning, specifically the VGG16 model, to interpret and digitize handwritten prescriptions. This innovative study is underpinned by a unique dataset, the first of its kind, comprising diverse hand-writing styles and medical terminologies collected from various Bangladeshi hospitals. A pivotal achievement of this research is the effective adaptation and application of the VGG16 model. This model demonstrated remarkable accuracy in recognizing and interpreting medical handwriting, thereby ensuring precise medication instructions and dosages. The success of this system represents a major advancement in medication safety and is a critical step toward mitigating the risks associated with misin-terpreted prescriptions. By enhancing the clarity and accuracy of prescription interpretation, this thesis contributes significantly to the improvement of healthcare delivery in Bangladesh. It not only exemplifies the transformative power of machine learning in refining healthcare practices but also establishes a foundational model for creating safer, more efficient healthcare systems in developing nations. © 2024 IEEE.

关键词： Deep neural networks

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AI-Driven Pattern Recognition in Medicinal Plants: A Comprehensive Review and Comparative Analysis

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computers, Materials & Continua 2024年第11期81卷 2077-2131页

作者： Mohd Asif Hajam Tasleem Arif Akib Mohi Ud Din Khanday Mudasir Ahmad Wani Muhammad Asim Department of Information Technology Baba Ghulam Shah Badshah UniversityRajouriJammu and Kashmir185234India Department of Computer Science Samarkand International University of TechnologySamarkand141500Uzbekistan EIAS Data Science Lab College of Computer and Information SciencesPrince Sultan UniversityRiyadh11586Saudi Arabia

The pharmaceutical industry increasingly values medicinal plants due to their perceived safety and costeffectiveness compared to modern *** the extensive history of medicinal plant usage,various plant parts,including flowers,leaves,and roots,have been acknowledged for their healing properties and employed in plant *** images,however,stand out as the preferred and easily accessible source of *** plant identification by plant taxonomists is intricate,time-consuming,and prone to errors,relying heavily on human *** intelligence(AI)techniques offer a solution by automating plant recognition *** study thoroughly examines cutting-edge AI approaches for leaf image-based plant identification,drawing insights from literature across renowned *** paper critically summarizes relevant literature based on AI algorithms,extracted features,and results ***,it analyzes extensively used datasets in automated plant classification *** also offers deep insights into implemented techniques and methods employed for medicinal plant ***,this rigorous review study discusses opportunities and challenges in employing these AI-based ***,in-depth statistical findings and lessons learned from this survey are highlighted with novel research areas with the aim of offering insights to the readers and motivating new research *** review is expected to serve as a foundational resource for future researchers in the field of AI-based identification of medicinal plants.

关键词： Pattern recognition artificial intelligence machine learning deep learning image processing plant leaf identification

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Realtime Devices Manager: Arduino based automatic system 15

Realtime Devices Manager: Arduino based automatic system

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15th International Conference on Communications, COMM 2024

作者： Alioanei, Cosmin National University of Science and Technology Politehnica Computer Science Department Bucharest Romania

ISBN: (纸本)9798350355956

Nowadays, the resources considered unlimited in the past are no longer interminable at all, due to the difficult social and political contexts. Therefore, a significant waste reduction is needed, for economic reasons and so on. In the absence of a national efficiency strategy, each entity must carry out all actions with sustainability in mind. An important parameter through which such actions can be carried out is the consumption of electricity. More than that, as in the public domain the area of digitization of documents and data is at a low level, Realtime Devices Manager wants to combine both perspectives and offer an intelligent solution for the area of servers or laboratories within public institutions. Through a cost-effective implementation and by solving significant issues, efficiency parameters have been reached, Realtime Devices Manager being a suitable solution for the existing problems. © 2024 IEEE.

关键词： Arduino digitization energy consumption

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