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10th International conference on Communication and Signal processing, ICCSP 2024

作者： Singh, Aniket Kothari, Rushil Arulalan, V. Srm Institute of Science and Technology Department of Computing Technologies Chengalpattu District Tamil Nadu Kattankulathur603203 India

ISBN: (纸本)9798350353068

The need to accurately identify dog breeds is important due to their popularity and role as companions. With an increase in the variety of dog breeds worldwide, there is a pressing demand for better classification methods. Advances in artificial intelligence have resulted in the creation of a new model that combines YOLOv7 with the Whale Optimization Algorithm (WOA) in a Convolutional Neural Network (CNN) setup, designed specifically for detecting dog breeds. This model uses the extensive Stanford Dogs dataset, which includes 120 unique breeds, and has been thoroughly tested. It has shown to be more accurate and precise than current methods. The effectiveness of this model in identifying dog breeds marks a significant improvement in the field and suggests its usefulness in various areas such as veterinary services, dog behavior studies, and improved pet management systems. Additionally, this method showcases the benefits of merging advanced machine learning techniques with nature-inspired algorithms to address complex identification challenges, making a significant contribution to the development of AI-based solutions in animal identification and related areas. © 2024 IEEE.

关键词： image recognition

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Finding Concrete Cracks in a Building using Improved Deep Learning algorithms 2

Finding Concrete Cracks in a Building using Improved Deep Le...

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2nd International conference on Augmented Intelligence and Sustainable systems, ICAISS 2023

作者： Devisurya, V. Paulson, J. Seshadhri, M. Varshini, P. Nagaraj, P. Muneeswaran, V. Kongu Engineering College Department of Information Technology Erode India Kalasalingam Academy of Research and Education Department of Computer Science and Engineering Virudhunagar India Kalasalingam Academy of Research and Education Department of Electronics and Communication Engineering Virudhunagar India

ISBN: (纸本)9798350325799

To ensure functionality and durability, engineering constructions must have fractures identified and repaired. Since cracks are typically discovered through visual inspection, the examiner's personal judgment plays a vital part in both the detection of concrete cracks and the estimation of their characteristics. In the past, this has helped with both the evaluation of the crack phenomenon and the repair of various structures. The number of engineering structures has expanded as a result of industrial development, but relative to that, the level of skill in the crack-detecting field has not improved. Therefore, a quicker yet more precise method is required. The image processing method detects the cracks and their characteristics simultaneously. The formulation of the algorithm and the implementation is simple. However, other flaws, such as mistaking noises for fractures and a lack of accuracy in spotting micro-cracks, have turned into major obstacles for this technology. Finding transverse cracks in concrete structures is another critical problem. Therefore, a thorough analysis of the most recent articles is required to establish an accurate technique. In order to comprehend the potential of this technology, a critical study of the image-processing phenomenon and a thorough literature review has been done in this paper. A universal DenseNet121 classification-based algorithm with camera pictures for crack identification could be an effective strategy with greater accuracy. © 2023 IEEE.

关键词： image enhancement

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Enhancing Sign Language Recognition Using Deep Convolutional Neural Networks 14

Enhancing Sign Language Recognition Using Deep Convolutional...

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14th International conference on Information, Intelligence, systems and Applications, IISA 2023

作者： Kanavos, Athanasios Papadimitriou, Orestis Mylonas, Phivos Maragoudakis, Manolis University of the Aegean Department of Information and Communication Systems Engineering Samos Greece University of West Attica Department of Informatics and Computer Engineering Athens Greece Ionian University Department of Informatics Corfu Greece

ISBN: (纸本)9798350318067

The advancements in sensing technologies and AI algorithms have opened up a wide range of possibilities for developing applications to meet the needs of individuals who are deaf or hard of hearing. Sign language plays a vital role in the lives of people with hearing and speaking disabilities. This research aims to explore digital image processing and machine learning methods for efficiently building a sign language dataset and creating a sign language interface system. The proposed system utilizes a Convolutional Neural Network (CNN) to analyze and interpret hand gestures and poses, converting them into natural language. The developed CNN model specifically focuses on improving the accuracy of predicting the American Sign Language alphabet. Despite variations in dataset conditions and size, the model achieved an exceptional accuracy rate of 98.73%. Additionally, it demonstrated a low loss value of 0.0539, indicating its robust performance. © 2023 IEEE.

关键词： image classification

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Classification and recognition of soybean leaf diseases in Madhya Pradesh and Chhattisgarh using Deep learning methods 2

Classification and recognition of soybean leaf diseases in M...

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2nd International conference on Paradigm Shifts in Communications Embedded systems, Machine Learning and Signal processing, PCEMS 2023

作者： Dixit, Shriniket Kumar, Anant Haripriya, Akash Bohre, Khitij Srinivasan, Kathiravan SCOPE Vellore India School of Bioscience and Technology Vellore India Oriental College of Science and Technology SCOPE Bhopal India Acropolis Institute of Technology SCOPE Indore India

ISBN: (纸本)9798350310719

Soybean is a major economic crop worldwide. So proper disease control measures must be implemented to reduce losses. These diseases can significantly affect the yield and quality of soybeans. Machine vision and pattern recognition technologies can help accurately diagnose crop diseases and minimize financial losses for soybean farmers. Many research papers discuss the use of deep learning algorithms for imagebased disease detection, including for soybean crops based on CNN, SVM, KNN, etc. However, lacking a well-curated dataset for soybean diseases is a challenge. Additionally, many existing research papers focus more on demonstrating the approach's feasibility rather than providing solutions to the specific problems faced in a particular region. The proposed deep learning-based classification system for soybean leaf diseases can help identify Angular Leaf spots, Bacterial blight, Soybean Rust, and Downy mildew. An image dataset was created, and image-enhancing techniques were applied during pre-processing. The proposed classifier system achieved an efficiency of 83.9%, 93.01%, and 71.98% in classifying diseases using CNN, Resnet-V2, and KNN classifiers, respectively. © 2023 IEEE.

关键词： image enhancement

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An Enhanced Approach to Content-based image Retrieval using Harris Hawks Optimization and Automated image Captioning 8

An Enhanced Approach to Content-based Image Retrieval using ...

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8th International conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud), I-SMAC 2024

作者： Padate, Roshni Gupta, Ashutosh Chakrabarti, Prasun Sharma, Arvind Fr. Conceicao Rodrigues College of Engineering Computer Engineering Mumbai India Sir Padampat Singhania University Computer Science and Engineering Udaipur India Sir Padampat Singhania University Department of Mathematics Udaipur India

ISBN: (纸本)9798350376425

This paper introduces an innovative approach to Content-Based image Retrieval (CBIR) that leverages Harris Hawks Optimization (HHO) to improve feature selection and retrieval accuracy. CBIR systems are increasingly important in fields such as medical imaging and remote sensing, where efficient and accurate image retrieval is essential. Inspired by the cooperative hunting strategies of Harris hawks, the HHO algorithm efficiently balances exploration and exploitation, optimizing the feature extraction process to enhance retrieval outcomes. The proposed system is evaluated on various datasets and compared to other optimization techniques, such as Particle Swarm Optimization (PSO) and Genetic algorithms (GA). The findings show that the HHO-optimized system outperforms these methods in terms of precision, recall, and F1-scores, while also reducing retrieval ***, the incorporation of image captioning into the CBIR framework further improves the user experience. By integrating Computer Vision (CV) and Natural Language processing (NLP), the system generates descriptive captions along- side retrieved images, offering valuable textual context. This capability allows users to quickly evaluate the relevance of retrieved images, particularly in complex fields like medical imaging. Experimental results demonstrate that the combination of HHO-based optimization and image captioning significantly enhances both retrieval accuracy and user satisfaction, making it a powerful solution for large-scale image retrieval tasks. © 2024 IEEE.

关键词： Convolutional neural networks

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New Type-2 Fuzzy Logic Algorithm Based image Segmentation for Medical Imaging Applications 9

New Type-2 Fuzzy Logic Algorithm Based Image Segmentation fo...

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9th IEEE International conference on Smart Structures and systems, ICSSS 2023

作者： Sangeetha, S. Latha, B. Anna University Veltech Multitech Dr. Rangarajan Dr. Sakunthala Engineering College Department of Information Technology Chennai India Sri Sairam Engineering College Department of Computer Science and Engineering Chennai India

ISBN: (纸本)9798350384208

Medical image segmentation is a plan that has a lot of potential. The achievement of automated picture segmentation makes it simple to gather biomedical and anatomical information. In terms of the subject, more study and work have increased efficacy. In the field of image processing, this work provides a type-2 fuzzy system-based image application. Computer aided diagnosis and medical image analysis both heavily rely on segmentation. This study presents a fuzzy type 2 algorithm based automated threshold selection method for the division of therapeutic pictures. It has been established that fuzzy set theory may be an effective tool for recitation surroundings when the facts are unclear or inaccurate. The algorithms have been put into practice and evaluated using brain MRI (magnetic resonance imaging) images. The outcomes have been evaluated and documented. The comparison of the controller's efficiency of CNN has 92%. © 2023 IEEE.

关键词： image segmentation

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Research on image Quality Assessment Methods for Weld Negatives 3

Research on Image Quality Assessment Methods for Weld Negati...

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3rd International conference on Digital Society and Intelligent systems, DSInS 2023

作者： Liu, Jiayue Wang, Lei Li, Mingfei Deng, Cong Luo, Weijian Li, Xufeng Lu, Shengzi Zhang, Ruida Chen, Junyang Dong, Shaohua Guangdong Institute of Special Equipment Inspection and Research Guangdong Province Guangzhou510000 China OIL&GAS Pipeline Research Center Beijing102200 China

ISBN: (纸本)9798350331387

para>Defects in pipeline welds are fatal for pipelines, considering that weld negatives need to be electronically preserved due to high preservation costs, easy damage, etc., and that most of the weld defects are judged with the help of intelligent computer recognition algorithms, the assessment of the quality of weld negatives after digitization is critical. At present, domestic and foreign research on the method of weld negative image quality assessment is still immature, especially after the processing of the image details is not clear, and the low quality of the image will directly affect the accuracy of the weld negative defect identification and determination. Therefore, this paper proposes a reference-free image quality assessment algorithm based on analytic sparse representation for this problem, which objectively evaluates the image quality of weld negatives, helps identify and judge weld defects, and has a vital practical role in guaranteeing the regular operation of pipelines. © 2023 IEEE.

关键词： Welds

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2024 2nd International conference on Computer, Communication and Control, IC4 2024

2024 2nd International Conference on Computer, Communication...

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2nd International conference on Computer, Communication and Control, IC4 2024

ISBN: (纸本)9798350387933

The proceedings contain 134 papers. The topics discussed include: an adaptive storage switching algorithm for fault-tolerant network attached storage systems;Covid-19 prediction using machine learning algorithms;energy management of hybrid electric vehicles using cascaded fuzzy logic controller;dynamic lane management with IoT for real-time lane configuration and traffic flow;a closer look at sclera: emerging trends in biometric security;cognitive vision companion: an ai-enhanced support system for the visually impaired;advances in medical image processing for liver tumor recognition: a comprehensive survey;a gradient boosting algorithm to predict energy consumption for home applications;and review on text classification using improved deep learning models.

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Now you see me, now you don't: an exploration of religious exnomination in DALL-E

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ETHICS AND INFORMATION TECHNOLOGY 2024年第2期26卷 27-27页

作者： Alfano, Mark Abedin, Ehsan Reimann, Ritsaart Ferreira, Marinus Cheong, Marc Macquarie Univ Dept Philosophy Sydney NSW Australia Univ Melbourne Sch Comp & Informat Syst Melbourne Vic Australia Flinders Univ S Australia Coll Business Govt & Law Adelaide SA Australia

Artificial intelligence (AI) systems are increasingly being used not only to classify and analyze but also to generate images and text. As recent work on the content produced by text and image Generative AIs has shown (e.g., Cheong et al., 2024, Acerbi & Stubbersfield, 2023), there is a risk that harms of representation and bias, already documented in prior AI and natural language processing (NLP) algorithms may also be present in generative models. These harms relate to protected categories such as gender, race, age, and religion. There are several kinds of harms of representation to consider in this context, including stereotyping, lack of recognition, denigration, under-representation, and many others (Crawford in Soundings 41:45-55, 2009;in: Barocas et al., SIGCIS conference, 2017). Whereas the bulk of researchers' attention thus far has been given to stereotyping and denigration, in this study we examine 'exnomination', as conceived by Roland Barthes (1972), of religious groups. Our case study is DALL-E, a tool that generates images from natural language prompts. Using DALL-E mini, we generate images from generic prompts such as "religious person." We then examine whether the generated images are recognizably members of a nominated group. Thus, we assess whether the generated images normalize some religions while neglecting others. We hypothesize that Christianity will be recognizably represented more frequently than other religious groups. Our results partially support this hypothesis but introduce further complexities, which we then explore.

关键词： Artificial intelligence Natural language processing Natural language generation DALL-E Exnomination

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Automated Bone Segmentation Using a Hybrid Model of DRLSE and Faster R-CNN 25

Automated Bone Segmentation Using a Hybrid Model of DRLSE an...

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25th IEEE conference on Signal processing - algorithms, Architectures, Arrangements, and Applications (IEEE SPA)

作者： Naderian, Amirkhashayar Sanei, Saeid Nottingham Trent Univ Dept Comp Sci Nottingham England

ISBN: (数字)9788362065424

ISBN: (纸本)9788362065424

The computer aided diagnosis systems from radiological images has been of interest to researchers mostly for detection of bone fracture or dislocation. The accuracy highly depends on bone segmentation. Any improvement of such systems, particularly for noisy X-ray images, is very valuable. Classical image segmentation depending on image homogeneity are time consuming and require pixel-wise labelling. On the other hand, saliency map based approaches fail to detect the region around the fracture or segment the dislocated bones. In our research we have used transfer learning to train the faster regional convolutional neural network (FCNN) alongside distance regularized level set evolution (DRLSE) to have accurate bone segmentation without any pixel-wise labelling enabling segmentation of the region around the fracture and dislocated bones. We applied the proposed method to a number of hand X-ray images and achieved accuracy values of 95% and average precision-recall of 0.96.

关键词： Bone segmentation distance regularized level set evolution Faster R-CNN image processing medical imaging transfer learning X-ray

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