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Adaptive residual convolutional neural network for moiré image restoration

International Journal of Information Technology (Singapore) 2025年第2期17卷 783-791页

作者： Malagi, Vindhya P. Naresh, E. Mithra, C. Suresh, B. V. N. V. Krishna Department of AI & amp ML Dayananda Sagar College of Engineering Bengaluru India Department of Information Technology Manipal Institute of Technology Bengaluru Manipal Academy of Higher Education Manipal India Department of Computer Science and Engineering School of Computing Vel Tech Rangarajan Dr. Sagunthala R & amp D Institute of Science and Technology Chennai 600062 India Computer Science & amp Engineering KLEF KL University Andhra Pradesh Vaddeswaram India

Restoring Moiré images presents significant challenges due to the complex interference patterns that obscure image details. These patterns often degrade the quality of images, making accurate restoration crucial for various applications. Effective Moiré image restoration requires advanced techniques to overcome the difficulties posed by these intricate artifacts. This study introduces an Adaptive Residual Convolutional Neural Network (RCNN) for Moiré image restoration, augmented with the Zebra Optimization Algorithm (ZOA) to enhance both feature extraction and restoration accuracy. The hybrid model leverages ZOA's capability to balance exploration and exploitation, optimizing network parameters to improve learning efficiency. Tested on the DIV2K dataset, this approach demonstrates significant improvements in handling complex Moiré patterns, as evidenced by enhanced Peak Signal-to-Noise Ratio (PSNR) and Structural Similarity Index Measure (SSIM) values. © The Author(s) 2024.

关键词： Adaptive residual convolutional neural network Moiré images Peak signal-to-noise ratio Structural similarity index measure Zebra optimization algorithm

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Transfer Learning in Endoscopic Imaging: A Machine Vision Approach to GIT Disease Identification

Transfer Learning in Endoscopic Imaging: A Machine Vision Ap...

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Communications and computer science (InCCCS), International Conference on

作者： Jayavrinda Vrindavanam Pradeep Kumar Gaurav Kamath Abhilash S Bharadwaj Department of Computer Science and Engineering(AI & ML) Dayananda Sagar University Bengaluru India

ISBN: (数字)9798350358858

ISBN: (纸本)9798350358865

In the context of rapidly rising Gastro-Intestinal tract related diseases among the population, the study explores the use of ai and ml that can enable early detection of the GI tract related abnormalities. The study after a detailed review of the existing studies, proposes on transfer learning to fine tune model weights trained on large data sets for GI-tract applications. The study has implemented models such as EfficientNet, MobileNet V3 Large, ResNet-50, DenseNet-201 and CNN (Convolutional Neural Network) on the endoscopic images. The top performing model, EfifcientNet2S, showcased superior proficiency identifying the eight distinct GIT diseases. The paper highlights the scope of applying transfer learning neural network architectures in enhancing disease diagnosis as well as selecting optimal architectures for accurate and efficient medical image classification in gastrointestinal pathology.

关键词： Pathology Transfer learning Neural networks computer architecture Gastrointestinal tract Medical diagnosis Convolutional neural networks

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Revolutionizing Cancer Diagnosis with Deep Learning: A Case Study Using U-Net 7

Revolutionizing Cancer Diagnosis with Deep Learning: A Case ...

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7th International Conference on Contemporary Computing and Informatics, IC3I 2024

作者： Goyal, Himanshu Rai Al-Farouni, Mohammed Deepak, A. Reddy, R. Akhilesh Yadav, Kanchan Revathi, V. Graphic Era Deemed to be University Department of Computer Science & Engineering Dehradun India The Islamic university College of technical engineering Department of computers Techniques engineering Najaf Iraq Saveetha Institute of Medical and Technical Sciences Saveetha University Saveetha School of Engineering Tamilnadu Chennai India Mlr Institute of Technology Department of CSE-AI&ML Telangana Hyderabad India Gla University Department of Electrical Engineering Mathura India New Horizon College of Engineering Department of Applied Sciences Bangalore India

ISBN: (纸本)9798350350067

The U-Net architecture is a convolutional neural network model that is well-known for its skills in biomedical image segmentation. This study investigates the revolutionary potential that the U-Net architecture presents in the context of cancer diagnosis. We guarantee that the input is of high quality by using sophisticated preprocessing methods, which we do by constructing a robust U-Net-based system that is specifically designed for the segmentation and classification of tumour regions in medical imaging data. An exhaustive examination that makes use of criteria such as the Dice similarity coefficient, precision, recall, and F1 score indicates that our model performs substantially better than conventional techniques when it comes to identifying and distinguishing malignant tissues. Case studies that cover a wide range of cancer types provide further validation of the flexibility and practical application of the system. These studies also highlight the significant influence that deep learning has on increasing diagnosis accuracy, lowering the risk of human error, and overall patient outcomes in the field of cancer treatment. © 2024 IEEE.

关键词： Convolutional neural networks

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A Novel Image Caption Generation Based on CNN and RNN

A Novel Image Caption Generation Based on CNN and RNN

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Advances in Computing Research on science engineering and Technology (ACROSET), International Conference on

作者： Hemlata Parmar Manish Rai Utsav Krishan Murari Department of Computer Science and Engineering(AI&ML) Manipal University Jaipur Jaipur India Department of Journalism and Mass Communication Sharda University Greater Noida Greater Noida India

ISBN: (数字)9798350388800

ISBN: (纸本)9798350388817

The field of image comprehension has experienced notable advancements as a result of the emergence of deep learning techniques and advancements in computer vision technology. The effort of generating coherent and detailed textual descriptions for images, known as image labeling, is a significant challenge that requires the integration of natural language processing and image processing techniques. This abstract provides an in-depth analysis of approaches employed for generating photo labels, with a particular focus on current advancements within the sector. The generation of picture labels involves the use of two main components in the pipeline, namely syntax interpreter and a photographic converter. The picture encoder uses (CNNs) to process images. For extracting visual The field of image comprehension has experienced notable advancements as a result of the emergence of deep learning techniques and advancements in computer vision technology. The effort of generating coherent and detailed textual descriptions for images, known as image labeling, is a significant challenge that requires the integration of natural language processing and image processing techniques. For extracting visual details from input pictures. To generate labels on a individual basis, a language decoder employs (RNNs) like(LSTM) or (GRUs) to analyse the context representation of the image. The results obtained from this study underscore the advantages of employing (GRU) in comparison to (LSTM) for the purpose of generating labels and constructing sentences. In addition to this, recent advancements in transformer-based design, such as the transformer model, have shown promising statistics concerning labelling of pictures. The models have demonstrated the ability to effectively imitate the connections between visual and textual modalities, and they achieve this by leveraging the self-attention mechanism found in transformers, which allows for capturing long-range interdependencies. The production

关键词： Deep learning Visualization computer vision Reinforcement learning Syntactics Transformers Feature extraction Natural language processing Labeling Proposals

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An Analysis of Privacy Attacks in Supervised Machine Learning Algorithms

An Analysis of Privacy Attacks in Supervised Machine Learnin...

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Computing, sciences and Communications (ICCSC), International Conference on

作者： Kavya Gupta Anurag Gupta Abha Kiran Raipoot Department ofCSE(AI&ML) KIET Group of Institutions Ghaziabad India Department of CSE(AI) KIET Group of Institutions Ghaziabad India School of Computer Science & Engineering Galgotias University Greater Noida India

ISBN: (数字)9798350353648

ISBN: (纸本)9798350353655

With the widespread integration of machine learning (ml) into diverse applications, it has become paramount to scrutinize its implications for security and privacy. Despite the burgeoning body of work in privacy, there is a noticeable gap in research attention between the security and privacy aspects of ml, specifically supervised machine learning (S-ml). This paper bridges this gap by analyzing research papers focusing on privacy attacks against S-ml over the past years. This study offers a comprehensive overview of prevalent defense mechanisms and initiates a thoughtful discussion on unresolved challenges and prospective research directions. As ml continues to permeate various domains, the scientific community's interest has expanded beyond performance metrics to encompass broader concerns such as security, privacy, fairness, and explainability. This survey investigates state-of-the-art privacy-related attacks, elucidating common design patterns and distinctive characteristics. Through this exploration, we identify several open problems that demand further investigation, emphasizing the critical need to fortify privacy frameworks alongside the escalating ubiquity of ml technologies.

关键词： Surveys Measurement Privacy Data privacy Machine learning algorithms Quantum computing Organizations Machine learning Robustness Security

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Multilingual Transformer for Dynamic Cricket Commentary Generation

Multilingual Transformer for Dynamic Cricket Commentary Gene...

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Computing and Data science (ICCDS), International Conference on

作者： Gaurav Kamath Udayabhaskara N Department of Computer Science and Engineering (AI and ML) Dayananda Sagar University Bangalore India

ISBN: (数字)9798350365337

ISBN: (纸本)9798350365344

The sport of Cricket is growing in popularity which necessitates the use of technology to adhere to popular game standards. Since this is a data rich and most elaborate game, understanding this for someone who's new to the game had been challenging. We are aiming to solve this unavailability of the game through native commentary to audience of all major languages. To outline our entire research work we ideate a Cricket Game ai through computer vision, use it to produce text commentary of game-play and perform Machine Translation(MT) to produce native commentary for a targeted language, As afirst part of the outlined process in this work we focus on the implementation of the machine translation component of this pipeline, evaluating various methods including Seq2seq and transformer based architectures. Our method of cross feeding Seq2Seq with Transformer model results in Bilingual Evaluation Understudy (BEL U) score on Validation set of 2.08.

关键词： Computational modeling Pipelines Games Data science Transformers Real-time systems Machine translation

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Intrusion Detection System in Wireless Sensor Networks using Modified Recurrent Neural Network with Long Short-Term Memory 2

Intrusion Detection System in Wireless Sensor Networks using...

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2nd IEEE International Conference on Integrated Circuits and Communication Systems, ICICACS 2024

作者： Ramkumar, K. Alzubaidi, Laith H. Malathy, V. Venkatesh, T. Kruthika, C.G. Srm Institute of Science and Technology Department of Computer Science and Engineering Chennai India The Islamic University Najaf Iraq Sr University School of Engineering Department of Ece Telangana India Kpr Institute of Engineering and Technology Department of Ece Coimbatore India Nitte Meenakshi Institute of Technology Department of Ai and Ml Bengaluru India

ISBN: (纸本)9798350317558

Leveraging deep learning, Intrusion Detection Systems (IDS) for Wireless Sensor Networks (WSN) enhance security through advanced analytics, detecting and mitigating network intrusions in real-time for robust protection. Evaluations and scalability considerations are critical for improving the applicability and robustness of intrusion detection systems based on deep learning techniques. This paper introduces a novel approach: a modified Recurrent Neural Network with Long Short-Term Memory (RNN-LSTM) for IDS in WSN classification, utilizing the NSL-KDD dataset. Rigorous performance validation demonstrates outstanding metrics, including an accuracy of 99.95%, precision at 99.93%, and a robust recall of 95.69%, with the F1-score reaching 99.80%, surpassing benchmarks set by existing methods. Notably, current approaches like Deep Neural Network (DNN), Convolutional Neural Network-LSTM (CNN-LSTM), Conditional Generative Adversarial Network (CGAN), and Stacked Autoencoders-DNN (SAE-DNN) face limitations in achieving optimal accuracy and robustness. The proposed RNN-LSTM method exhibits significant advancements in addressing these challenges for effective intrusion detection in WSN © 2024 IEEE.

关键词： Convolutional Neural Network Deep Neural Network Intrusion Detection System Long Short-Term Memory Wireless Sensor Network

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Pandemic state’s impact on agriculture

Pandemic state’s impact on agriculture

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International Conference on Artificial Intelligence, Blockchain, Computing and Security, ICABCS 2023

作者： Sheeja Kumari, V. Wilfred Blessing, N.R. Rohith Bhat, C. Sudha, I. Michael, G. Manikandan, T. Department of Computational Intelligence – Institute of AI & ML SIMATS School of Engineering SIMATS University Chennai India IT Department College of Computing and Information Sciences University of Technology and Applied Sciences-Ibri Oman Department of Computer Science and Engineering Saveetha School of Engineering SIMATS Chennai India

ISBN: (纸本)9781032669663

The COVID-19 outbreak has significantly disrupted the agricultural system in India. In spite of this, the most recent quarterly GDP forecasts post-COVID scenario demonstrate the tenacity and durability of the Indian agricultural sector, which is anticipated to be the only sector to record a positive growth rate of 4% for fiscal years (FY) 2020 and 2021. Similarly, growth for the most recent quarter is projected to have been 5.89%, representing a reduction of 2.4% point. In this context, our goal is to compile a summary of the early evidence on the impact of COVID-19 on the agricultural sector in India, including production, marketing, and consumption, and then to design a set of pragmatic strategies to recover and thrive after the pandemic. According to the survey’s findings, the pandemic has disrupted production and marketing through labour and logistical restrictions, while the negative income shock has restricted access to markets and increased food commodity prices, altering consumption patterns. The pandemic caused great emotional, social, economic, and bodily harm to everyone involved in India’s agriculture industry. The state has responded to the crisis by seizing the opportunity it presents and launching a variety of programmes and necessary changes. We suggest a ten-point plan that includes social safety nets, family farming, the trading of buffer stock, staggered procurement, and secondary agriculture in order to revitalise and prosper after the epidemic. © 2024, CRC Press/Balkema. All rights reserved.

关键词： COVID-19

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Malware Detection Using the Machine Learning Based Modified Partial Swarm Optimization Approach 1

Malware Detection Using the Machine Learning Based Modified ...

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1st IEEE International Conference on Applied Intelligence and Sustainable Computing, ICaiSC 2023

作者： Sivakumar, S. Saminathan, S. Ranjana, R. Mohan, M. Pareek, Piyush Kumar Department of Computer Science Perambalur India Srinivasan College of Arts and Science Department of Computer Science and IT Perambalur India Panimalar Engineering College ECE Department India AP/CSE KPR Institute of Engineering and Technology India Nitte Meenakshi Institute of Technology Department of AI and ML Bengaluru India

ISBN: (纸本)9798350323795

Malware analysis includes a crucial step after malware detection called malware categorization, which classifies dangerous files. There have been many reported static and dynamic methods for classifying malware up to this point. The ml-MD strategy presented in this study uses static methods to categorise various malware families and is based on machine learning. In order to detect malware, we create a new machine learning-based framework. The characteristics from the dataset are extracted in this case using principal component analysis (PCA). In order to offer the best malware detection solutions, introduce a machine learning-based Modified Particle Swarm Optimization (MPSO) algorithm. Improved Accuracy and detection rate using ml-based MPSO technique. The effectiveness of the suggested technique in detecting malware is demonstrated by the experimental results on several benchmark data sets, which greatly outperform alternative approaches. © 2023 IEEE.

关键词： Machine learning

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Robust Face Recognition Using Histogram of Oriented Gradients and Auto-Associative Memory Network

Robust Face Recognition Using Histogram of Oriented Gradient...

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International Conference on Communication and Electronics Systems (ICCES)

作者： Jay Kant Pratap Singh Yadav Zainual Abdin Jaffery Laxman Singh Electrical Engineering Department Jamia Millia Islamia (Central University) New Delhi India Computer Science & Engg.(AI&ML) Department KIET Group of Institutions Ghaziabad India

ISBN: (数字)9798350377972

ISBN: (纸本)9798350377989

Facial recognition technology is based on a person's facial features. It is often the core of applications such as biometrics, access control, and forensics. However, due to environmental factors such as dust accumulation on the camera lens or equipment failure, the accuracy of the system is often questioned; especially when it comes to noise and partial occlusions in the test images. This paper proposes a new approach for automatic face recognition using histogram oriented gradient features of facial images with auto-associative memory in the form of a “Multi-Connection Hopfield Neural Network”. The method described here efficiently stores and retrieves facial patterns in noisy or partially occluded environments. In testing, this proposed approach achieved a recognition rate of 98.5% on the ORL dataset, outperforming these other state-of-the-art methods. Furthermore, this method maintains 95.0% accuracy even with 50% random noise and exhibits superior performance and robustness under partial occlusion and variable lighting conditions.

关键词： Histograms Accuracy Face recognition Noise Lighting Hopfield neural networks Robustness Computational efficiency Noise measurement Testing

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