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检索条件"任意字段=Conference on Applications of Artificial Neural Networks in Image Processing II"

共 2623 条记录，以下是61-70 订阅

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Deepfake image and Video Detection Using Deep Learning Algorithms

Deepfake Image and Video Detection Using Deep Learning Algor...

引用

Multi-Agent Systems for Collaborative Intelligence (ICMSCI), International conference on

作者： Srivanth Srinivasan Nischitha P Akshita Chavan Mohana Artificial Intelligence & Machine Learning Engineering RV College of Engineering® Bengaluru India Computer Science & Engineering (Cyber Security) RV College of Engineering® Bengaluru India

ISBN: (数字)9798331509828

ISBN: (纸本)9798331509835

Deep learning (DL) algorithms are swiftly finding applications in computer vision and natural language processing. Nonetheless, they can also be employed for creating convincing deepfakes, which are challenging to distinguish from reality. The advancements in image and video technology and tools, especially on social media platforms, potentially lead to misuse for malicious purposes like blackmail or defamation. To tackle this issue, several group of researchers tried upon spreading or creating awareness on real or fake data. The proposed approach involves combining Deepfake generation using GANs and Autoencoders with a Deepfake detection method. The aim of this initiative is exclusively to combat disinformation and online fraud for the welfare of the general population. Deepfakes, products of AI, have become increasingly realistic, rendering it nearly difficult to distinguish the content. Auto-encoders with sufficient time can achieve about 92 % accuracy. As the generator improves, the discriminator performance worsens as it struggles to differentiate real or fake data. A perfect generator results in 50% accuracy. With advancements in computational capacity and data availability, the proposed DDM (Deepfake Detection Model) has achieved greater accuracy rate of up to 92.3%.

关键词： Deep learning Deepfakes Accuracy Social networking (online) Streaming media Feature extraction Generators Convolutional neural networks artificial intelligence Research and development

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Intelligent Human Behaviour Detection Leveraging ConvLSTM and LRCN Deep Learning Approaches

Intelligent Human Behaviour Detection Leveraging ConvLSTM an...

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International conference on Signal processing and Communication (ICSC)

作者： S Chakradhar Amingad Harika Janmanchi Mohammed Sultan K Shalini S Hariharan Bhanu Prakash Maddala Artificial Intelligence and Data Science Vardhaman College of Engineering Hyderabad Telangana India Artificial Intelligence and Data Science Vardhaman college of engineering Hyderabad India

ISBN: (数字)9798331511890

ISBN: (纸本)9798331511906

This research delves into intelligent human behavior detection from video sequences by employing advanced deep learning methodologies. The study harnesses the capabilities of Convolutional Long Short-Term Memory (ConvLSTM) and Long-term Recurrent Convolutional networks (LRCN) architectures, which are adept at capturing both temporal and spatial dynamics in video data. Using the extensive Kinetics dataset, which includes a wide range of human activities and contexts, the video data was converted into sequential image frames for model training. The ConvLSTM and LRCN models were implemented and trained with the TensorFlow/Keras framework on GPU-accelerated systems. During training, both models exhibited effective learning, with improved accuracy and reduced loss. However, validation metrics revealed fluctuations, indicating possible overfitting and challenges in generalizing to new data. To address these issues, regularization techniques such as dropout and early stopping were applied, alongside comprehensive hyperparameter tuning. Additionally, the study investigated the use of transfer learning to enhance model performance. This research lays the groundwork for sophisticated video-based human behavior detection systems, with applications in surveillance, healthcare, and human-computer interaction. Future work will aim to bolster model robustness and broaden the scope of action recognition.

关键词： Training Deep learning Surveillance Video sequences Transfer learning Robustness Behavioral sciences Convolutional neural networks Long short term memory Tuning

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Improvement of image Classification Technique Using Transfer Learning

Improvement of Image Classification Technique Using Transfer...

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Advances in Electrical, Computing, Communication and Sustainable Technologies (ICAECT), International conference on

作者： Devanand Bhonsle Ruhi Uzma Priyanka Prasad Rushikesh Bankar Manjushree Nayak Sampada Vishwas Massey Tanu Rizvi Eiectrical Engineering Shri Shanakaracharya Technical Campus Bhilai India Electrical Engineering Anjuman College of Engineering & Technology Nagpur India Artificial Intelligence and Machine Learning Shri Shanakaracharya Institute of Professional Management & Tech Bhilai India Electronics & Telecommunication Engineering G. H. Raisoni Institute of Engineering and Technology Nagpur India Computer Science Engineering NIST University Berhampur Odisha India Computer Science Engineering Shri Shanakaracharya Technical Campus Bhilai India

ISBN: (数字)9798350356236

ISBN: (纸本)9798350356243

image classification is one of the fundamental task in the digital image processing and computer vision. It is very essential to get high accuracy in it which requires substantial computational resources and a large dataset for the training using deep neural networks. Transfer learning technique is one of the solution which mitigate various challenges by leveraging pre-trained models on large datasets. In this paper; the effectiveness of the transfer technique is exhibited for the purpose of enhancing the image classification. A comprehensive survey has been done of the recent advancement for the purpose to investigate the strategies of transfer learning technique including feature extraction, fine tuning and domain adaption. Experimental results exhibits the efficacy of the transfer learning in the improvement of classification accuracy with less computational complexity. This paper also discusses practical consideration, challenges and future research scope for the utilization of transfer learning for image classification applications.

关键词： Training Surveys Adaptation models Accuracy Computational modeling Transfer learning Lighting License plate recognition Tuning image classification

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GRU Based Multiview Summarisation to obtain fast effective Key-frame

GRU Based Multiview Summarisation to obtain fast effective K...

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International conference on Computer, Electrical & Communication Engineering (ICCECE)

作者： Vijay Anand Kandaswamy S Joshua Kumaresan Department of Electronics and Communication Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences. Saveetha University Chennai India

ISBN: (数字)9798331513894

ISBN: (纸本)9798331513900

This paper proposes a novel approach to video summarisation by introducing a feature variance model that enhances video frame extraction. In typical video record aggregation systems, a variety of image processing tasks, such as acquisition, preprocessing, segmentation, and feature extraction, should be carried out with high precision to guarantee seamless system performance. Among these, feature extraction is essential since it breaks down large amounts of visual data into more manageable, informative vectors, and the success of the system is directly impacted by how effectively this process functions. Widely Recurrent neural networks (RNNs) are used in supervised video summarisation processes to obtain a model for the temporal dependencies between video frames. RNN’s ability to learn long-range memory in the video is limited, and CNN often focusses on local information while losing long-term knowledge when the input sequence is lengthy. Therefore, a Gated Recurrent Unit based neural Network (GRU-NN) for video summarisation is proposed, to classify them into keyframes and non-keyframes. Keyframes are selected for summarisation, while non keyframes are discarded or reduced. We explore various keyframe extraction techniques, analyse the performance of the proposed summarisation model, and discuss the results. Finally, we offer insights into the model’s potential improvements and its broader applications in video summarisation tasks.

关键词： Deep learning image segmentation Computer vision Analytical models Computational modeling Visual analytics System performance Feature extraction Vectors artificial intelligence

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A Literature Review of Machine Learning Techniques for Dance Recognition and Robotic Vision

A Literature Review of Machine Learning Techniques for Dance...

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Robotics and Technologies for Industrial Automation (ROBOTHIA), IEEE International conference on

作者： Lee Wei San Samuel-Soma M. Ajibade Muhammed Basheer Jasser Adefemi Ayodele Babatunde Adedotun Ajayi Mbiatke Anthony Bassey Department of Data Science and Artificial Intelligence Faculty of Engineering and Technology Sunway University Selangor Darul Ehsan Malaysia Research Centre for Nanomaterials and Energy Technology (RCNMET) Faculty of Engineering and Technology Sunway University Selangor Malaysia Research Centre for Human-Machine Collaboration (HUMAC) Faculty of Engineering and Technology Sunway University Petaling Jaya Selangor Malaysia University of East London London UK Department of Cyber security Faculty of Computing & Informatics Ladoke Akintola University of Technology Ogbomoso Nigeria Department of Business Administration Universiti Tun Hussein Onn Malaysia Batu Pahat Malaysia

ISBN: (数字)9798350356755

ISBN: (纸本)9798350356762

image recognition, powered by machine learning (ML), has significantly advanced applications in both dance movement recognition and robotic vision. This review examines key ML techniques, including Convolutional neural networks (CNNs), Deep neural networks (DNNs), Self-Organizing Maps (SOMs), and Long Short-Term Memory (LSTM) networks, alongside pose estimation methods like OpenPose and Part Affinity Fields (PAFs). These techniques enhance dance classification, real-time feedback, and motion analysis, with OpenPose + LSTMs and PAFs + LSTMs demonstrating the highest accuracy. Notwithstanding progress, obstacles such as high computational costs, data dependency, and real-time implementation challenges persist. Beyond dance, these methods are critical in robotic vision, intelligent automation, and industrial image processing, enabling autonomous robotic navigation, defect detection in manufacturing, and AI-driven motion tracking. By leveraging human movement analysis for robotics, ML improves human-robot interaction, robotic-assisted rehabilitation, and industrial automation. Despite progress, challenges such as high computational demands, data dependency, and real-time constraints remain. This review explores future directions, including multimodal data fusion, hybrid AI models, and real-time optimization, bridging the gap between AI-driven motion systems and intelligent automation to enhance adaptability and efficiency across domains.

关键词： Intelligent automation Humanities Solid modeling image recognition Accuracy Service robots Robot sensing systems Real-time systems Robots Long short term memory

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Intelligent Resource Allocation for Edge-IoT: Enhancing QoE with Deep Reinforcement Learning

Intelligent Resource Allocation for Edge-IoT: Enhancing QoE ...

引用

Multi-Agent Systems for Collaborative Intelligence (ICMSCI), International conference on

作者： B Sreedhar C Sasikala V T Ram Pavan Kumar M Ruhisulthana Shaik Syed Muqthadar Ali Yadaiah Balagoni Department of CSE (AI&ML) G Pulla Reddy Engineering College Kurnool India Department of CSE Srinivasa Ramanujan Institute of Technology Ananthapuramu India Department of Computer Science Kakaraparti Bhavanarayana College Vijayawada India Department of ECE Sir C Ramalinga Reddy Polytechnic College Eluru India Department of CSE CVR College Of Engineering Hyderabad India Department of Emerging Technologies Mahatma Gandhi Institute of Technology Gandipet India

ISBN: (数字)9798331509828

ISBN: (纸本)9798331509835

A resource allocation framework zeroed in on Quality of Experience (QoE) that is explicitly intended for the heterogeneous edge Internet of things (IoT) environment's dynamic and fluctuated situation. applications for the Internet of Things (IoT) are confronting edge figures. Be that as it may, because of the heterogeneity of usages, the edge cloud finds it trying to relegate complex compelled resources (handling, memory, limit, information transmission, etc) with limitations in light of the Quality of Service (QoS) necessities of clients. In this exploration, we tackle the issue of resource allocation in Edge-IoT structures by supporting a canny framework called Deep Edge that assigns resources to the different IoT applications with the sole motivation behind working on the Quality of Experience (QoE) of the clients. To accomplish this objective, we create a complex QoE model that considers adjusting the different prerequisites of Internet of Things applications to the accessible edge resources. The arrangement is accomplished by choosing a QoS necessity arrive at that the accessible resources can meet. Moreover, we propose a brilliant two-stage deep reinforcement learning (DRL) move toward that successfully allots edge resources to help IoT applications and further develop QoE for people. Not at all like the famous DRL, our arrangement utilizes deep neural networks (DNN) to design the Edge-IoT state to joint resource allocation movement, which incorporates resource allocation and QoS class. Along these lines, DNN can be used to deal with exercises' exploration. The cooperative exertion not just builds the quality of experience (QoE) of clients and fulfills the prerequisites of assorted applications, yet it likewise adjusts the QoS necessities to the accessible resources.

关键词： image edge detection Quality of service artificial neural networks Information processing Deep reinforcement learning Dynamic scheduling Quality of experience Internet of Things Resource management Edge computing

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Advancing Peer Review Integrity: Automated Reviewer Assignment Techniques with a Focus on Deep Learning applications 1st

Advancing Peer Review Integrity: Automated Reviewer Assignme...

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1st International conference on Computation of artificial Intelligence and Machine Learning, ICCAIML 2024

作者： Bhaisare, Bhumika Bharati, Rajesh Department of Computer Engineering Dr. D. Y. Patil Institute of Technology Pune Pimpri India

ISBN: (纸本)9783031714801

The reviewer assignment process in academic research papers is crucial for ensuring peer review integrity and fairness, and it is often referred to as the reviewer assignment problem (RAP). This initial step lays the foundation for the entire review process. To address the limitations of manual reviewer assignment, there is a growing trend among journal and conference editors to explore automated solutions. For quite some time, machine learning techniques have found application in this domain, recent attention has shifted toward the application of Deep Learning in the education sector. Deep Learning, rooted in neural networks with diverse processing components, is gaining prominence in fields like natural language processing (NLP) and image recognition. In this comprehensive review, we aim to evaluate the key research achievements in the realm of reviewer assignment algorithms. It commences by presenting background information and discussing the necessity for automated reviewer assignment. The paper then systematically examines the existing research in this domain, with a particular focus on recent advancements in Deep Learning techniques. The review provides an unbiased assessment of the current algorithms’ strengths and weaknesses. Overall, this analysis emphasizes the growing significance of RAP research and the demand for further innovation and framework development in this field. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Adversarial machine learning

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Advancing Techniques for Deepfake Detection and Evaluation: Challenges and Innovations

Advancing Techniques for Deepfake Detection and Evaluation: ...

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Computational Intelligence, Communication Technology and Networking (CICTN), International conference on

作者： Pradum Kumar Singh Sourav Sharma Ritu Sachdeva School of Computer Applications Manav Rachna International Institute of Research & Studies Faridabad India

ISBN: (数字)9798331530389

ISBN: (纸本)9798331530396

The proliferation of deepfakes-also termed artificial intelligence-generated synthetic media poses unprecedented challenges to the digital authenticity of media, for media integrity, and to societies’ trust in its credibility. Despite such significant technological advancements, current methodologies for detecting deepfakes remain somewhat fragmented, reactive, and often unable to keep pace with rapidly evolving technologies in generative AI. This article considers a comprehensive multi-modal approach toward deepfake detection by integrating an advanced machine learning algorithm, novel statistical correlation techniques, and forensic image analysis that would fill in the critical gaps in the existing frameworks. By using an extensive dataset of 10,000 synthetic and authentic media samples from diverse domains, we developed a hybrid neural network architecture that attained 94.3% accuracy on identifying AI-generated content. Our methodology utilized ensemble learning by combining spatial-temporal inconsistency detection, biological signal analysis, and advanced feature extraction algorithms. Key findings demonstrate significant vulnerabilities in current deepfake generation models, with our proposed technique successfully identifying subtle artifacts and inconsistencies across multiple generative AI platforms. This research conclusively demonstrates that proactive, adaptive detection strategies are critical to mitigating the potential risks associated with synthetic media, providing a robust framework for future technological interventions in digital forensics and media authentication.

关键词： Deepfakes Machine learning algorithms Generative AI neural networks Digital forensics Signal processing algorithms Authentication Media Feature extraction Information integrity

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A Novel Approach to Brain Tumor Classification Using RNN and CNN Algorithm

A Novel Approach to Brain Tumor Classification Using RNN and...

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Automation and Computation (AUTOCOM), International conference on

作者： Hannah Jessie Rani R Trapty Agarwal J Jesupriya Tarang Bhatnagar Swagat Kumar Jena Mithhil Arora School of Engineering and Technology Electrical and Electronics Engineering JAIN (Deemed to be University) Bangalore Karnataka Maharishi School of Engineering & Technology Maharishi University of Information Technology Uttar Pradesh India Department of Computer Applications (DCA) Presidency College Bengaluru India Centre of Research Impact and Outcome Chitkara University Rajpura Punjab India Department of Computer Science and Engineering Siksha ‘O’ Anusandhan (Deemed to be University) Bhubaneswar Odisha India Chitkara Centre for Research and Development Chitkara University Baddi Himachal Pradesh India

ISBN: (数字)9798331542375

ISBN: (纸本)9798331542382

They are aiming to establish a solution for brain tumors that are prevailing neurological diseases and subsequently leading to cancer-related deaths. Correct classification of brain tumors is important for proper diagnosis and planning treatment. Existing methods for brain tumor classification are performed manually and tend to be subjective, not to mention time-consuming. Hence, we require an automated and effective mechanism for brain tumor classification. The present study introduces a novel ensemble procedure utilizing a combination of the Recurrent neural Network (RNN) and Convolutional neural Network (CNN) for brain cancer categories. The LSTM RNN is used to analyze the time-varying features of the magnetic resonance imaging (MRI) scans, whereas 3D CNNs are exploited for spatial appearance descriptions. These two network types collaborate in the brain tumor classification process to determine each class according to its properties. This method enhances the accuracy and swiftness of brain tumor classification. They trained their RNN-CNN model using thousands of MRI scans and found that they could be used to distinguish between these different types of bratypesumors accurately. In addition, our method also decreased processing time compared with standard methods used for manual analysis. Finally, the overall results suggest that our proposed method could be an efficient automated solution for brain tumor classification with the potential to improve early diagnosis and treatment planning of patients suffering from such malignant disease. It is also readily integrated into current health systems, providing it a worthwhile position for neurologists.

关键词： Recurrent neural networks image analysis Accuracy Magnetic resonance imaging Brain tumors Brain modeling Planning Convolutional neural networks artificial intelligence Medical diagnostic imaging

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Improving Student Stress Analysis: Novel Methods for Collecting, Preprocessing, and Fusing Features 3rd

Improving Student Stress Analysis: Novel Methods for Collect...

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3rd International conference on Advancements in Smart Computing and Information Security, ASCIS 2024

作者： Bhatt, Zankhana Dobariya, Ashwin Faculty of Computer Application Marwadi University Gujarat Rajkot India

ISBN: (纸本)9783031862984

Due to the adverse effects of stress on students’ health and academic success, it is to create an effective method for stress detection. This study is used to predict student stress by combining biosignal data with image processing techniques. Employs various image pre processing of facial images in this research like Gaussian Blur, Median Blur, Bilateral Filters, and Canny edge detection to enhance the extraction of features. The Multi-task Cascaded Convolutional neural Network (MTCNN) detects and aligns facia landmarks for precise data collection. Wearable device data on respiration, oxygen, temperature, and heart rate is combined with previously processed images. The robust dataset from physiological and visual characteristics may benefit future stress prediction machine learning applications. This comprehensive real-time stress detection method allows the development of customized interventions to improve student health and academic performance. The author is developing new artificial minority oversampling methods and improving machine learning models. The list includes KNN, Decision Trees, Random Forests, and XG Boost. The author has achieved accuracy rates and F1-macro score over 90% with KNN and SVM and expects even better results with other supervised machine learning algorithms. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Convolutional neural networks

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