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EAMultiRes-DSPP: an efficient attention-based multi-residual network with dilated spatial pyramid pooling for identifying plant disease

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Neural Computing and Applications 2024年第26期36卷 16141-16161页

作者： Al-Gaashani, Mehdhar S. A. M. Muthanna, Ammar Chelloug, Samia Allaoua Kumar, Neeraj School of Resources and Environment University of Electronic Science and Technology of China 4 1st Ring Rd East 2 Section Sichuan Chengdu610056 China 6 Miklukho-Maklaya St Moscow117198 Russia Department of Telecommunication Networks and Data Transmission The Bonch-Bruevich Saint-Petersburg State University of Telecommunications 6 Miklukho-Maklaya St Saint Petersburg Saint Petersburg193232 Russia Department of Information Technology College of Computer and Information Sciences Princess Nourah bint Abdulrahman University P.O. Box 84428 Riyadh610101 Saudi Arabia Department of Computer Science Thapar Institute of Engineering and Technology Patiala147004 India

Convolutional neural networks (CNNs) have made substantial contributions to the domain of plant disease diagnosis, attaining noteworthy levels of accuracy. The primary objective of this study is to enhance the capabilities of CNNs within this particular field. In this article, we suggest a unique strategy utilizing MultiRes blocks and attention mechanisms to enhance the classification performance of CNNs for plant diseases. Our solution involves stacking four MultiRes blocks, each of which gradually increases the number of filters to prevent excessive propagation of memory requirements to deeper network nodes. In order to collect more spatial information, we also implement a residual link and a 1 × 1 convolutional layer. After each MultiRes block, we employ a Convolutional Block Attention Module to emphasize vital information and decrease redundant noise by inferring attention mappings along the channel and spatial dimensions. Finally, our method includes the use of a Dilated Spatial Pyramid Pooling module which is designed to extract information from the input image at various scales. Our evaluation leveraged a comprehensive dataset compiled from multiple sources, including the New Plant Diseases dataset, Corn dataset, and Coffee dataset, encompassing a wide range of plant leaves both healthy and diseased, across various classes. This diverse collection of images, augmented by several techniques to enhance data variance and model robustness, provided a solid foundation for assessing the performance of our proposed model. Experiments demonstrate that this method outperforms conventional machine learning techniques and delivers high classification accuracy for plant diseases, with a precision of 99.34%, recall of 99.25%, f1-score of 99.29%, and accuracy of 99.35%. Our proposed model has the potential to considerably enhance the effectiveness and efficiency of diagnosing plant diseases in the agricultural industry. © The Author(s), under exclusive licence to Springer-

关键词： Image classification

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Diabetic Foot Ulcer Detection and Classification using ConvMixer Model

Diabetic Foot Ulcer Detection and Classification using ConvM...

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Smart Electronics and Communication (ICOSEC), International Conference on

作者： N Anusha R Vidhya L Jani Anbarasi D Dhanya S. Pushparani Geetha Mohan Department of IoT School of Computer Science and Engineering Vellore Institute of Technology Vellore Campus Vellore India Department of Information Technology Agni College of Technology Chennai India School of Computer Science and Engineering Vellore Institute of Technology Chennai India Department of Artificial Intelligence and Data Science Mar Ephraem College of Engineering and Technology Elavuvilai India Department of CSE Meenakshi College of Engineering Chennai India Computer Science Institute of Applied Technology Abu Dhabi

ISBN: (数字)9798331504403

ISBN: (纸本)9798331504410

Diabetic foot ulcer (DFUs) are a common and significant consequence of diabetes, frequently resulting in severe illness and amputations if not promptly identified and treated. The growing occurrence of DFUs is a substantial obstacle to healthcare globally. Precise and prompt categorization of DFUs is essential for efficient treatment and avoidance of consequences. This research analyzed ConvMixer model for comprehensive classification designed to accurately evaluate DFU. The DFU images are preprocessed using the watershed algorithm that helps in separating different regions of an image, such as the ulcer and surrounding tissues, to improve the neural network’s ability to extract high-quality information. The framework utilizes depthwise and pointwise convolutions to mix spatial and channel-wise information in an efficient manner to effectively extract pertinent information from various clinical DFU dataset. The proposed work features a multi-layered depthwise and pointwise convolutional architecture, created especially to manage the complex patterns and variances found in various diabetic foot ulcer stages and variants (DFUs). By integrating these convolutions and skip associations, the network facilitates hierarchical extraction of features and robust representation of features. Experimental results on a substantial and varied DFU dataset demonstrate that the ConvMixer model achieves an impressive accuracy rate of $\mathbf{9 7. 1 6 \%}$, with a precision, F1-score and a sensitivity of $\mathbf{9 7 \%}$, $\mathbf{9 7 \%}$ and 99.08% respectively.

关键词： Analytical models Sensitivity Accuracy Watersheds Medical services Feature extraction Transformers Diabetes Classification algorithms data mining

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Enhanced Synthetic Images Classification with Vision Transformers and Effective data Augmentation Techniques

Enhanced Synthetic Images Classification with Vision Transfo...

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IEEE International Conference on Multi Topic

作者： Khadija Haider Muhammad Sohail Abbas Asif Muhammad Muhid Qaiser Department of Artificial Intelligence and Data Science National University of Computer and Emerging Sciences Islamabad Pakistan Department of Software Engineering National University of Computer and Emerging Sciences Islamabad Pakistan

ISBN: (数字)9798331507213

ISBN: (纸本)9798331507220

Synthetic images have made significant advancements, resulting in highly realistic and high-quality visuals generated at an unprecedented speed. However, the proliferation of synthetic images raises growing concerns regarding their potential misuse, ethical implications, and associated risks in various sectors. This study focuses on leveraging data augmentation techniques to enhance the accuracy and reliability of synthetic image detection. In our study, we fine-tuned a Vision Transformer (ViT) on the CIFAKE dataset, comprising real and synthetic images, augmented using various techniques such as transformations, photometric adjustments, geometric changes, and noise addition. The best results were obtained with ViT fine-tuned by distortions and noise augmentation, achieving an accuracy rate of 98.66%, demonstrating that sophisticated augmentation techniques improve performance. data augmentation plays a crucial role in handling the increasing realism and sophistication of synthetic images, contributing to more effective content moderation, digital forensics efforts, and the mitigation of potential risks associated with synthetic media.

关键词： Performance evaluation computer vision Visualization Accuracy Computational modeling Transformers Distortion data augmentation Reliability Image classification

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Owl: Differential-Based Side-Channel Leakage Detection for CUDA Applications

Owl: Differential-Based Side-Channel Leakage Detection for C...

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International Conference on Dependable Systems and Networks (DSN)

作者： Yu Zhao Wenjie Xue Weijie Chen Weizhong Qiang Deqing Zou Hai Jin National Engineering Research Center for Big Data Technology and System Hubei Key Laboratory of Distributed System Security Hubei Engineering Research Center on Big Data Security School of Cyber Science and Engineering Huazhong University of Science and Technology Wuhan China Jinyinhu Laboratory Wuhan China National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab Cluster and Grid Computing Lab School of Computer Science and Technology Huazhong University of Science and Technology Wuhan China

ISBN: (数字)9798350341058

ISBN: (纸本)9798350341065

Over the past decade, various methods for detecting side-channel leakage have been proposed and proven to be effective against CPU side-channel attacks. These methods are valuable in assisting developers to identify and patch side-channel vulnerabilities. Nevertheless, recent research has revealed the feasibility of exploiting side-channel vulnerabilities to steal sensitive information from GPU applications, which are beyond the reach of previous side-channel detection methods. Therefore, in this paper, we conduct an in-depth examination of various GPU features and present Owl, a novel side-channel detection tool targeting CUDA applications on NVIDIA GPUs. Owl is designed to detect and locate side-channel leakage in various types of CUDA applications. When tracking the execution of CUDA applications, we design a hierarchical tracing scheme and extend the A-DCFG (Attributed Dynamic Control Flow Graph) to address the massively parallel execution in CUDA, ensuring Owl's detection scalability. After completing the initial assessment and filtering, we conduct statistical tests on the differences in program traces to determine whether they are indeed caused by input variations, subsequently facilitating the positioning of side-channel leaks. We evaluate Owl's capability to detect side-channel leaks by testing it on Libgpucrypto, PyTorch, and nvJPEG. Meanwhile, we verify that our solution effectively handles a large number of threads. Owl has successfully identified hundreds of leaks within these applications. To the best of our knowledge, we are the first to implement side-channel leakage detection for general CUDA applications.

关键词： Target tracking Statistical analysis Filtering Scalability Graphics processing units Side-channel attacks Feature extraction

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HaWANet: Road Scene Understanding with Multi-modal Sensor data Using Height-Width-Driven Attention Network 23rd

HaWANet: Road Scene Understanding with Multi-modal Sensor D...

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23rd International Conference of the Italian Association for Artificial Intelligence, AIxIA 2024

作者： Chatterjee, Soumick Xu, Jiahua Kuzhipathalil, Adarsh Nürnberger, Andreas Faculty of Computer Science Otto von Guericke University Magdeburg Magdeburg Germany Data and Knowledge Engineering Group Otto von Guericke University Magdeburg Magdeburg Germany Genomics Research Centre Human Technopole Milan Italy Hertie Institute for Clinical Brain Research Tuebingen Germany XenomatiX NV Leuven Belgium

ISBN: (纸本)9783031806063

In recent years, the field of autonomous vehicles and driverless technology has seen remarkable advancements, driven by contributions from mainstream automotive manufacturers and open-source projects. This research aims to develop a pipeline for road scene understanding through semantic segmentation. The proposed pipeline utilises a multi-modal segmentation model, incorporating greyscale images and point cloud data from Xenolidar, specifically designed to capture the structural priors of highway road scenes. The fusion of input modalities and the design of an encoder-decoder architecture with a novel attention scheme called HaWANet is introduced, which focuses on the height and width contextual information to improve the accuracy of road segmentation, are the primary aspects explored for the proposed model. The output of the encoder is a two-dimensional point cloud, which effectively represents the road’s planar nature, and is crucial for improving the accuracy of road segmentation, particularly in edge cases, addressing current challenges in autonomous driving research. This research, aimed at addressing the segmentation problem for multimodal sensor data, has presented significant performance improvement over single-modal approaches. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Semantic Segmentation

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Image based and Point Cloud based Methods for 3D View Reconstruction in Real-time Environment

Image based and Point Cloud based Methods for 3D View Recons...

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IEEE Annual Computing and Communication Workshop and Conference (CCWC)

作者： Arya Agrawal Teena Sharma Nishchal K. Verma Shantaram Vasikarla School of Computer Science and Engineering Vellore Institute of Technology Vellore India Mehta Family School of Data Science and Artificial Intelligence Indian Institute of Technology Guwahati India Dept. of Electrical Engineering Indian Institute of Technology Kanpur India Dept. of Computer Science California State University Northridge CA

3D view reconstruction in a real-time unknown environment poses several challenges to vision-based applications. Herein, this paper outlines real-time experimental results and analysis of 3D view reconstruction methods. Since real-time experimentation requires inputs from different sensors, this paper outlines the results of methods from the following categories: image based methods and point cloud based methods. This paper explains the detailed steps involved in 3D view reconstruction followed by an explanation of the methods. It provides detailed discussions, advantages, limitations, and empirical results, along with presenting differences between algorithms. For analysis of experimental results, two benchmarked datasets i.e., KITTI 3D benchmark suite and ShapeNet are considered. Furthermore, the paper explores potential future scopes, including the utilization of generative models, unsupervised learning, advanced sensors, and many more.

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INFOCON: CONCEPT DISCOVERY WITH GENERATIVE AND DISCRIMINATIVE INFORMATIVENESS

arXiv

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arXiv 2024年

作者： Liu, Ruizhe Luo, Qian Yang, Yanchao HKU Musketeers Foundation Institute of Data Science The University of Hong Kong Hong Kong School of Electronics Engineering and Computer Science Peking University China Department of Electrical and Electronic Engineering The University of Hong Kong Hong Kong

We focus on the self-supervised discovery of manipulation concepts that can be adapted and reassembled to address various robotic tasks. We propose that the decision to conceptualize a physical procedure should not depend on how we name it (semantics) but rather on the significance of the informativeness in its representation regarding the low-level physical state and state changes. We model manipulation concepts - discrete symbols - as generative and discriminative goals and derive metrics that can autonomously link them to meaningful sub-trajectories from noisy, unlabeled demonstrations. Specifically, we employ a trainable codebook containing encodings (concepts) capable of synthesizing the end-state of a sub-trajectory given the current state - generative informativeness. Moreover, the encoding corresponding to a particular sub-trajectory should differentiate the state within and outside it and confidently predict the subsequent action based on the gradient of its discriminative score - discriminative informativeness. These metrics, which do not rely on human annotation, can be seamlessly integrated into a VQ-VAE framework, enabling the partitioning of demonstrations into semantically consistent sub-trajectories, fulfilling the purpose of discovering manipulation concepts and the corresponding sub-goal (key) states. We evaluate the effectiveness of the learned concepts by training policies that utilize them as guidance, demonstrating superior performance compared to other baselines. Additionally, our discovered manipulation concepts compare favorably to human-annotated ones while saving much manual effort. Our code is available at: https://***/InfoCon/. © 2024, CC BY.

关键词： Trajectories

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Diabetes Prediction Using Machine Learning Techniques: A Case Study from Jordan

Diabetes Prediction Using Machine Learning Techniques: A Cas...

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Information Technology (ACIT)

作者： Raed Alazaidah Radwan Batyha Ghassan Samara Esraa Al Henawi Maram Y. Al-Safarini Ahmad A. Samhan Ali Elrashidi Mo’Ath Alluwaici Deana Al-Omari Department of Data Science and AI Faculty IT Zarqa University Zarqa Jordan Department of Computer Science Faculty of IT Applied Science Private University Amman Jordan Department of Computer Science Faculty of IT Zarqa University Zarqa Jordan Department of Software Engineering Faculty of IT Zarqa University Zarqa Jordan Department of Electrical Engineering University of Business and Technology Jeddah Saudi Arabia

ISBN: (数字)9798331540012

ISBN: (纸本)9798331540029

Diabetes is one of the most common diseases in Jordan. It is the main reason of death among Jordanian adult citizens. Worldwide, 48% of all deaths are due to Diabetes occurred before the age of 70 years. Hence, this research is interested in the early prediction of this disease among Jordanian people. To achieve this main objective, Machine Learning (ML) is utilized through a large number of classification models and considering five well-known evaluation metrics. These classification models have been trained on a primary dataset that has been collected for the purpose of the research. The results revealed that BayesNet and NaiveBayes are the best classification models to handle the task of the early prediction of Diabetes in Jordan

关键词： Measurement Machine learning Predictive models Diabetes Bayes methods Information technology Diseases

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Exploring Equilibrium Strategies in Network Games with Generative AI

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IEEE Network 2024年

作者： Yang, Yaoqi Du, Hongyang Sun, Geng Xiong, Zehui Niyato, Dusit Han, Zhu Army Engineering University of PLA College of Communications Engineering Nanjing210000 China University of Hong Kong Department of Electrical and Electronic Engineering Pok Fu Lam Hong Kong Jilin University College of Computer Science and Technology Changchun130012 China Nanyang Technological University College of Computing and Data Science 639798 Singapore Singapore University of Technology and Design Information Systems Technology and Design Pillar Singapore College of Computing and Data Science NTU Singapore University of Houston Department of Electrical and Computer Engineering HoustonTX77004 United States

Game theory offers a powerful framework for analyzing strategic interactions among decision-makers, providing tools to model, analyze, and predict their behavior. However, implementing game theory can be challenging due to difficulties in deriving solutions, understanding interactions, and ensuring optimal performance. Traditional non-AI and discriminative AI approaches have made valuable contributions but struggle with limitations in handling large-scale games and dynamic scenarios. In this context, generative AI emerges as a promising solution because of its superior data analysis and generation capabilities. This paper comprehensively summarizes the challenges, solutions, and outlooks of combining generative AI with game theory. We start with reviewing the limitations of traditional non-AI and discriminative AI approaches in employing game theory, and then highlight the necessity and advantages of integrating generative AI. Next, we explore the applications of generative AI in various stages of the game theory lifecycle, including model formulation, solution derivation, and strategy improvement. Additionally, from game theory viewpoint, we propose a generative AI-enabled framework for optimizing machine learning model performance against false data injection attacks, supported by a case study to demonstrate its effectiveness. Finally, we outline future research directions for generative AI-enabled game theory, paving the way for its further advancements and development. © 1986-2012 IEEE.

关键词： Generative adversarial networks

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Fun Soundify: Music Generation powered by AI

Fun Soundify: Music Generation powered by AI

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International Conference on Computing Communication Control and Automation (ICCUBEA)

作者： Vanita G. Kshirsagar Shubhangi Suryawanshi Akshad Pawar Yadvendra Wagh Nikhil Kolhe Dakshata Pawar Manisha Mane Prashant G. Ahire Rahul A. Patil Artificial Intelligence and Data Secience Dr. D. Y. Patil Institute of Technology Pune Artificial Intelligence and Data Science Dr. D. Y. Patil Institute of Technology Pune India Computer Science and Engineering Symbiosis Institute of Technology Pune India Computer Engineering Pimpri Chinchwad College of Engineering Pune India

ISBN: (数字)9798350391770

ISBN: (纸本)9798350391787

Fun Soundify embodies an exciting exploration at the intersection of artificial intelligence and artistic ingenuity, marking a bold step forward in the realm of music composition. Inspired by pioneering research in neural network-based music generation, our endeavor is to push the boundaries of creativity within the musical domain. Exploring advanced deep learning models, including Recurrent Neural Networks (RNNs), Long Short-Term Memory networks (LSTMs), and attention mechanisms, our objective is to revolutionize music creation and appreciation. Drawing from insights gleaned from the original study, we have adapted and expanded upon these findings to construct an innovative AI-driven platform. Our mission is centered around unraveling the complexities of musical language and structure, with a particular emphasis on generating melodies infused with human-like creativity and emotion. Leveraging a diverse dataset and state-of-the-art machine learning techniques, our aspiration is to compose melodies that not only captivate but also inspire. Fun Soundify's AI-Powered Melodies Generation project serves as a testament to the limitless possibilities that machine learning offers in the realm of artistic innovation. Beyond merely crafting captivating melodies, our vision extends to fostering collaborative creation between humans and machines.

关键词： Training Deep learning Technological innovation Adaptation models Attention mechanisms Recurrent neural networks Music Long short term memory Creativity Recommender systems

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