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Efficient Non-Local Similarity-based Image Dehazing: A Pixel-Level Approach for Enhanced Performance and Robustness

Efficient Non-Local Similarity-based Image Dehazing: A Pixel...

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International conference of Electronics, Communication and Aerospace Technology (ICECA)

作者： A. Swetha Rani K Venkata Lakshmi Keerthi M. V. Nageswara Rao G.V. Pradeep Kumar V. V. Satyanarayana Tallapragada Kuruma Purnima Department of ECE G Pullaiah College of Engineering and Technology Kurnool India Department of ECE Sri Venkateswara College of Engineering Tirupati India Associate Dean-Academics GMR Institute of Technology Rajam India Department of ECE Chaitanya Bharathi Institute of Technology Hyderabad India Department of ECE Mohan Babu University Tirupati India Department of CSE Siddharth Institute of Engineering & Technology Puttur India

ISBN: (数字)9798350367904

ISBN: (纸本)9798350367911

This research study proposes an efficient approach for image dehazing using non-local similarity-based methods. Dehazing has roots in many applications, including image enhancement of underwater imagery, satellite aerial images, remote sensing imagery, and many others. The proposed method considers the challenges of single-image dehazing. The method involves the identification and recognition of haze lines. After identifying haze lines, a regularization process is introduced to consider the variance of the estimated haze lines. This regularization ensures that only the pixels adhering to the model's assumptions contribute to the reconstruction process. The proposed algorithm works at the pixel level rather than at the patch or regions of patch-level techniques. This results in improved speed, robustness, and reduced sensitivity to parameters like patch size and content. Performance analysis was done using several focus measures. Experimental results demonstrate the effectiveness of the proposed method. The simulation results prove that the proposed method outperforms the state-of-the-art methods.

关键词： visualization Sensitivity Satellites Simulation Robustness Performance analysis Image restoration Remote sensing optimization Image reconstruction

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Cutting-Edge Image Recognition Leveraging Deep Learning and Machine Learning for Enhanced Accuracy

Cutting-Edge Image Recognition Leveraging Deep Learning and ...

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Artificial Intelligence and Quantum Computation-Based Sensor Application (ICAIQSA), International conference on

作者： Abhishek Shrivastava Vinesh Kumar Jay Prakash Maurya School of Mechanical Engineering VIT Bhopal University Sehore India School of Computing Science Engineering and Artificial Intelligence VIT Bhopal University Sehore India School of Computing Science & Engineering VIT Bhopal University Sehore India

ISBN: (数字)9798331517953

ISBN: (纸本)9798331517960

This paper investigates advanced techniques in image recognition and classification by integrating deep learning and machine learning approaches to achieve higher accuracy. Through the implementation of sophisticated training algorithms, the study demonstrates enhanced performance in recognizing and categorizing images across various data models. A major turning point in the development of image identification technology came in 2012 when deep neural networks were introduced. These networks surpassed earlier cutting-edge algorithms and completely changed the computer vision industry. This progress has brought us closer to achieving human-level accuracy in tasks such as identity verification. The role of large datasets like ImageNet is crucial, as they provide the foundation for the success of deep learning. With continuous research pushing the limits of picture identification and producing major advances in human knowledge, deep learning has a huge influence on business, society, and technology. Additional research in this area might lead to creative uses that revolutionize our relationship with our surroundings. Key topics discussed include data pre-processing, post-processing, model optimization, and accuracy enhancement. The findings highlight the potential of cutting-edge technologies to advance image classification and recognition in various sectors, such as medical imaging and visual analysis. The approach emphasizes scalability and adaptability, ensuring that models can be effectively applied to real-world scenarios. Future research will focus on refining these models to handle even more complex image datasets, further enhancing their practical utility and reliability.

关键词： Deep learning Training Adaptation models visualization Image recognition Accuracy Computational modeling Visual systems Data models Reliability

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LLMs as Debate Partners: Utilizing Genetic Algorithms and Adversarial Search for Adaptive Arguments

LLMs as Debate Partners: Utilizing Genetic Algorithms and Ad...

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Engineering Informatics, IEEE

作者： Prakash Aryan Department of Computer Science Birla Institute of Technology and Science Pilani - Dubai Campus Dubai UAE

ISBN: (数字)9798331505776

ISBN: (纸本)9798331505783

This paper introduces DebateBrawl, an innovative AI-powered debate platform that integrates Large Language Models (LLMs), Genetic Algorithms (GA), and Adversarial Search (AS) to create an adaptive and engaging debating experience. DebateBrawl addresses the limitations of traditional LLMs in strategic planning by incorporating evolutionary optimization and game-theoretic techniques. The system demonstrates remarkable performance in generating coherent, contextually relevant arguments while adapting its strategy in real-time. Experimental results involving 23 debates show balanced outcomes between AI and human participants, with the AI system achieving an average score of ${2. 7 2}$ compared to the human average of ${2. 6 7}$ out of 10. User feedback indicates significant improvements in debating skills and a highly satisfactory learning experience, with 85% of users reporting improved debating abilities and ${7 8 \%}$ finding the AI opponent appropriately challenging. The system’s ability to maintain high factual accuracy (92% compared to ${7 8 \%}$ in human-only debates) while generating diverse arguments addresses critical concerns in AI-assisted discourse. DebateBrawl not only serves as an effective educational tool but also contributes to the broader goal of improving public discourse through AI-assisted argumentation. The paper discusses the ethical implications of AI in persuasive contexts and outlines the measures implemented to ensure responsible development and deployment of the system, including robust fact-checking mechanisms and transparency in decision-making processes.

关键词： Training Ethics visualization Large language models Decision making Strategic planning Problem-solving Artificial intelligence Research and development Genetic algorithms

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Enhancing Immersive Experiences through 3D Point Cloud Analysis: A Novel Framework for Applying 2D Visual Saliency Models to 3D Point Clouds

Enhancing Immersive Experiences through 3D Point Cloud Analy...

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International Workshop on Quality of Multimedia Experience, QoMEx

作者： Marouane Tliba Xuemei Zhou Irene Viola Pablo Cesar Aladine Chetouani Giuseppe Valenzise Frederic Dufaux PRISME University of Orleans Centrum Wiskunde & Informatica Centrum Wiskunde & Informatica TU Delft Centrum Wiskunde & Informatica PRISME University of Orleans CNRS CentraleSupélec L2S

ISBN: (数字)9798350361582

ISBN: (纸本)9798350361599

In the new area of immersive multimedia environments, understanding and manipulating visual attention are crucial for enhancing user experience. This study introduces an innovative framework that extends traditional 2D saliency maps to the analysis of 3D point clouds, a step forward in adapting saliency prediction to more complex and immersive environments. Our framework centers on the orthographic projection of 3D point clouds onto 2D planes, enabling the application of established 2D saliency models to this novel context. We further delve into the evaluation of these models on a 3D point cloud eye-tracking dataset, exploring various projection settings and thresholding techniques to maintain the integrity of saliency information in the transition from 2D to 3D. This research not only bridges a gap in applying visual attention models to 3D data but also offers insights into the optimization of quality of experience in immersive multimedia systems.

关键词： Point cloud compression Solid modeling visualization Analytical models Adaptation models Three-dimensional displays Predictive models

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Road Image Deblurring with Nonlinear Activation Free Network

Road Image Deblurring with Nonlinear Activation Free Network

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IEEE International conference on Control System, Computing and Engineering (ICCSCE)

作者： Mohd Ikmal Fitri Maruzuki Muhammad Khusairi Osman Aqmal Shafiq Shafie Samsul Setumin Anas Ibrahim Hariyanti Mohd Saleh Mohd Shukri Mohd Tahir Azmir Hasnur Rabiain Cawangan Pulau Pinang Electrical Engineering Studies College of Engineering Universiti Teknologi MARA (UiTM) Pulau Pinang Malaysia Cawangan Pulau Pinang Civil Engineering Studies College of Engineering Universiti Teknologi MARA (UiTM) Pulau Pinang Malaysia Faculty of Electronic Engineering Technology Universiti Malaysia Perlis Arau Perlis THB Maintenance Sdn. Bhd. Pekan Simpang Kuala Kedah Malaysia THB Maintenance Sdn. Bhd. Alor Setar Kedah Malaysia

ISBN: (数字)9798350364507

ISBN: (纸本)9798350364514

Current deblurring methods struggle with real-world scenarios where images are often blurred or noisy, posing significant challenges to existing pavement crack detection techniques. Thus, the aim of this research is to develop and evaluate a novel approach utilizing a nonlinear activation-free network (NAFNet) to address image blurring as a preprocessing step, with the primary goal of improving the reliability and accuracy of pavement crack detection in standard datasets and real-world pavement images under various challenging conditions. The scope of this study is to enhance pavement crack detection by developing a robust and accurate NAFNet designed specifically for road image deblurring, evaluated using standard pavement crack datasets. We adopt NAFNet, which innovatively replaces batch normalization with pixel-level layer normalization and utilizes a U-Net structure with skip connections and optimized the network with SGD (NAFNet-SGD). From the experimental results, quantitatively, the NAFNet-SGD model outperformed the others, achieving the highest PSNR of $\mathbf{3 2. 8 6 4 2}$ and an SSIM of 0.9605, while qualitatively, images processed with NAFNetSGD exhibited the highest quality with superior visual clarity and sharpness. Thus, in conclusion, NAFNet-SGD outperforms other optimizers like Adam and AdamW in terms of both quantitative metrics and visual quality.

关键词： visualization Accuracy Roads Manuals Robustness Image restoration Safety Noise measurement Standards optimization

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Enhancing Potato Plant Disease Detection using CNNs with Transfer Learning: A Robust AI-Driven Approach

Enhancing Potato Plant Disease Detection using CNNs with Tra...

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International conference of Electronics, Communication and Aerospace Technology (ICECA)

作者： K. Little Flower P. Siva Padmini Sk. Rabbani Krishna Kishore Datti P. Kannan V. Vivekanandhan Department of CSE St. Peters Engineering College Secunderabad Telangana India Department of CSE (DS) Marri Laxman Reddy Institute of Technology and Management Hyderabad Telangana India Department of CSE Malla Reddy Engineering College Secunderabad Telangana India Department of AIML St Martin's Engineering College Hyderabad Telangana India Department of ECE Francis Xavier Engineering College Tirunelveli Tamil Nadu India

ISBN: (数字)9798350367904

ISBN: (纸本)9798350367911

A crop of global importance, potatoes are particularly vulnerable to illnesses that can result in large losses for farmers. Conventional detection techniques, which depend on manual inspection, are frequently time-consuming and prone to human mistake. In order to overcome this, Transfer Learning was combined with Convolutional NeuralNetworks (CNNs) to create an improved disease detection method, utilizing pre-trained models like ResNet50 and VGG16. A collection of more than $\mathbf{3 0, 0 0 0}$ high-resolution images of potato leaves, categorized into healthy and diseased classes, was used to evaluate the effectiveness of this technique. Through model optimization and data augmentation, the methodology yielded an accuracy of about $\mathbf{9 9 \%}$, much surpassing that of conventional models such as Random Forest and Support Vector Machines, which yielded approximately 88% and 85% accuracy, respectively. Furthermore, the model’s effectiveness and scalability were demonstrated by the application of Transfer Learning, which cut training time by almost 40%. This invention provided a reliable approach for early and accurate disease management in potato cultivation, improving accuracy over traditional methods by 11%. ResNet50: Residual Network 50 layers, VGG16: Visual Geometry Group 16 layers

关键词： Training Support vector machines Plant diseases visualization Technological innovation Accuracy Transfer learning Crops Data models Residual neural networks

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GLCM-Based Feature Extraction and CNN Approach for Fruit Freshness Detection

GLCM-Based Feature Extraction and CNN Approach for Fruit Fre...

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International conference on Artificial Intelligence and Data Sciences (AiDAS)

作者： Mas Azza Abu Bakar Zalikha Zulkifli Masurah Mohamad Samsiah Ahmad Lumut Maritime Terminal Sdn Bhd Perak Malaysia College of Computing Informatics & Mathematics Universiti Teknologi MARA Perak Malaysia

ISBN: (数字)9798331528553

ISBN: (纸本)9798331528560

The industry responsible for providing fresh fruits to the market plays a crucial role in ensuring the quality and safety of the produce. Classifying and grading the freshness of fruits is essential to maintain high standards. The presence of harmful bacteria in fruits poses a significant threat to the global agriculture sector. Recently, there has been an increased awareness of the vulnerability of fruits to various diseases, adding economic pressure on the agriculture sector worldwide. To address this issue, the proposed system employs a combination of image processing algorithms and deep learning models to analyze the visual characteristics of fruits and accurately determine their freshness. Addressing the challenges associated with subjective visual judgment and time-consuming manual inspection, the research introduces a methodology termed Machine Learning Life Cycle (MLLC), comprising six distinct phases from data gathering to the test model. Utilizing a carefully curated dataset sourced from Kaggle, featuring fresh and rotten apples, oranges, and bananas, the research involves training and classification process diagrams and algorithm implementation covering image pre-processing and Convolutional Neural Network (CNN) based model training. Evaluation metrics, including classification reports and confusion matrices, reveal assessed performance across fruits, with bananas demonstrating the highest accuracy which is 85%. The research aims to develop a web-based deep learning system for fruit freshness detection but has a limitation in optimal feature segmentation using the Gray-Level Co-occurrence Matrix (GLCM) for fruit freshness detection. Challenges include selecting relevant features across diverse fruit images, potential overfitting with deep convolutional layers, and balancing model complexity with data availability. Future works should explore advanced feature extraction techniques, mitigate overfitting risks, and enhance user interface inclusivity through

关键词： Deep learning Training visualization Accuracy Machine learning algorithms Biological system modeling Feature extraction Data models Convolutional neural networks optimization

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Vessel Visual Surveillance Based on YOLOv8 Architecture : Daytime and Night Comparison

Vessel Visual Surveillance Based on YOLOv8 Architecture : Da...

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Intelligent Cybernetics Technology & Applications (ICICyTA), International conference on

作者： Khusnul Muchlisin Reza Fuad Rachmadi Supeno Mardi Susiki Nugroho Department of Electrical Engineering Institut Teknologi Sepuluh Nopember Surabaya Indonesia

ISBN: (数字)9798331506490

ISBN: (纸本)9798331506506

This paper is conducted by researchers focusing on the field of vessel visual surveillance using deep learning techniques. They aim to develop an effective surveillance system to enhance safety and security in the maritime environment. This study investigates the performance of the YOLOv8 architecture in detecting vessels during daytime and night. YOLOv8 is a powerful and efficient object detection algorithm that has shown promising results in various applications. The researchers compare the performance of the model trained on a daytime dataset when applied to night images, to assess its generalization ability under different lighting conditions. This paper focuses on vessel surveillance in maritime environments, including ports, shipping lanes, and other water areas. The ability to accurately detect and track vessels under varying lighting conditions is crucial for efficient traffic management and incident prevention. This study is conducted to address the challenges of round-the-clock vessel surveillance, both during the day and at night. Varying lighting conditions can significantly affect the performance of object detection algorithms, making it essential to evaluate and optimize models for 24/7 operation. Reliable visual vessel surveillance is critical for various applications, including maritime traffic management, collision prevention, illegal activity detection, and emergency response. By developing robust surveillance systems based on deep learning technologies like YOLOv8, researchers aim to enhance the safety, security, and efficiency of maritime operations. The YOLOv8 model is trained using an extensive dataset of daytime vessel images. The trained model is then evaluated on night images to assess its detection performance under different lighting conditions. The results show that while the model achieves high accuracy on daytime data, its performance deteriorates on night images, with some cases of false positives and false negatives. To optimize the m

关键词： Deep learning visualization Surveillance Prevention and mitigation Lighting Object detection Data models Security Reliability optimization

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Implementing Genetic Algorithms for optimization in Neuro-Cognitive Rehabilitation Robotics

Implementing Genetic Algorithms for Optimization in Neuro-Co...

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Cognitive Robotics and Intelligent Systems (ICC - ROBINS), International conference on

作者： L. B. Abhang Ravindra Changala Anudeb Ghosh Prabhakar S Manage Vuda Sreenivasa Rao B Kiran Bala Automation and Robotics Dept Pravara Rural Engineering College Loni Under. SPPU University Pune Department of Information Technology Guru Nanak Institutions Technical Campus Hyderabad India Department of Environmental Studies Siksha - Bhavana Visva-Bharati Santiniketan Department of Electronics and Communication KLE Technological University's MSSCET Belagavi Campus Belagavi Karnataka Department of Computer Science and Engineering Koneru Lakshmaiah Education Foundation Vaddeswaram A.P the Department Department of Artificial Intelligence and Data Science K.Ramakrishnan College of Engineering Trichy

ISBN: (数字)9798350372748

ISBN: (纸本)9798350372755

The study explores the intersection of robotics and neuro-cognitive rehabilitation to offer personalized assistance for individuals with neurological disorders, emphasizing autism spectrum disorder (ASD). The proposed methodology integrates Genetic Algorithms (GAs) to optimize the performance of robotic interventions. The data collection phase involves compiling a comprehensive image dataset capturing facial expressions, gestures, and relevant visual cues associated with ASD. Leveraging robotic platforms designed for therapeutic interventions, feature extraction techniques identify intricate patterns within the data. Advanced algorithms, including GAs, classify the dataset into positive (ASD) and negative (non-ASD) categories. The framework introduces a Diagnosis Matrix for enhanced diagnostic precision, correlating observed robotic interactions with clinical assessments. An Ontology Knowledge base adapts responses based on evolving patient needs. The proposed method surpasses all others, achieving an accuracy of 95.08% and demonstrating superior precision, recall, and F1-score metrics. This indicates the efficacy of the proposed approach in achieving a well-balanced performance with high accuracy and robustness in correctly identifying positive instances. The results underscore the potential of the proposed method for classification tasks, showcasing its superiority in comparison to traditional SVM, CNN, and even a well-established deep learning architecture like VGG-16. The Receiver Operating Characteristic curve validates the model's discriminatory power.

关键词： Support vector machines Neurological diseases visualization Knowledge based systems Receivers Ontologies Robustness

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A Multi-Bit Near-RRAM based Computing Macro with Highly Computing Parallelism for CNN Application

A Multi-Bit Near-RRAM based Computing Macro with Highly Comp...

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Design, Automation and Test in Europe conference and Exhibition

作者： Kuan-Chih Lin Hao Zuo Hsiang-Yu Wang Yuan-Ping Huang Ci-Hao Wu Yan-Cheng Guo Shyh-Jye Jou Tuo-Hung Hou Tian-Sheuan Chang Institute of Electronics National Yang Ming Chiao Tung University Hsinchu Taiwan

ISBN: (数字)9783981926385

ISBN: (纸本)9798350348606

Resistive random-access memory (RRAM) based compute-in-memory (CIM) is an emerging approach to address the demand for practical implementation of artificial intelligence (AI) on resource constrained edge devices by reducing the power-hungry data transfer between memory and processing unit. However, the state-of-the-art RRAM CIM designs fail to strike a balance between precision, energy efficiency, throughput, and latency. This work merges the techniques of CIM and compute-near-memory (CNM) to deliver high precision, high energy efficiency, high throughput, and low latency. In this paper, a 256Kb RRAM based CNM macro fabricated in TSMC 40 nm process is presented featuring: 1) opposite weight mapping with variation-robust SA to mitigate the impact of RRAM device variations on MAC (Multiply-Accumulate) results; 2) switched-capacitor-based analog multiplication circuit to achieve highly parallel computing of 128 4-bit by 4-bit MAC result with low power consumption and high operation speed; and 3) joint optimization of hardware and software to compensate for the accuracy loss after considering the non-idealities of circuits. The macro achieves a low latency of 17ns and high energy efficiency of 71 TOPS/W for MAC operations with 4-bit input, 4-bit weight and 4-bit output precision. It is used to accelerate the convolution process in the Light-CSPDenseN et AI model, resulting in a high accuracy of 86.33% on Visual Wake Words dataset.

关键词： visualization Parallel processing In-memory computing Throughput Energy efficiency Common Information Model (computing) Software

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