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PetNet: Detection of Diseases in Dog Skin 4th

PetNet: Detection of Diseases in Dog Skin

引用

4th International Conference on Advanced Research in Technologies information Innovation and Sustainability-ARTIIS-Annual

作者： Gomez-Lozano, Aldo Montalvo, Peter Univ Peruana Ciencias Aplicadas Lima Peru

ISBN: (纸本)9783031840777;9783031840784

The present study is focused on the detection of certain skin diseases in dogs by using the ResNet-50 neural network architecture. The main objective of the research is to improve the diagnosis of skin diseases in dogs. The problem addressed lies in the need for efficient and reliable detection of dermatological diseases in dogs, which represents a challenge for veterinary professionals, due to the similarity of various skin diseases. The methodology used is based on the convolutional neural network model ResNet50, pre-trained with a large number of images of different categories. The model is specifically adapted to identify and classify canine skin conditions, these are dermatitis, pyoderma and scabies. A transfer learning technique is used for this purpose, taking advantage of the knowledge previously acquired by the model. The PetNet model based on Resnet50 has achieved 85% accuracy in classifying skin diseases in dogs. Similarly, individually PetNet has achieved 81%, 84% and 92% for detecting dermatitis, pyoderma and scabies, respectively. These results support the usefulness of the proposed model as an effective tool for the diagnosis of skin diseases in dogs. In summary, this research presents an innovative approach for the detection of skin diseases in dogs by implementing the PetNet model based on Resnet50. The results obtained reflect a high accuracy in the classification of canine skin conditions, which can significantly contribute to improve veterinary care and improve the quality of life of dogs.

关键词： ResNet50 computer vision dogs skin diseases detection

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Conceptualizing a Generative AI-Based Solution for Emotional Intelligence Interventions: Application to Education and Training 5th

Conceptualizing a Generative AI-Based Solution for Emotional...

引用

5th International Symposium on Intelligent Computing Systems

作者： Bouslama, Faouzi Dubai Acad City Campus CIS Dept Higher Coll Technol Dubai U Arab Emirates

ISBN: (纸本)9783031829307;9783031829314

In the past years, generative artificial intelligence (AI) models have shown significant potential in augmenting human capabilities across various domains such as business and finance, creative arts and media, and education and training. This paper explores the development of a generative AI-based solution designed specifically to generate text for emotional intelligence (EI) interventions. Emotional intelligence, the ability to recognize, understand, and manage emotions, plays a critical role in personal and professional success. Despite the growing importance of EI, interventions aimed at enhancing these skills often face challenges due to the subjective nature of emotional experiences, and the complexity of human emotions and some of EI assessment tools. This paper proposes an innovative conceptual approach that proposes to leverage generative AI techniques to create personalized and contextually relevant text for EI training and development. The domain of interest is the education and training with a focus on students' EI skills, academic success, and future career development. The AI model can be trained on a diverse corpus of emotionally rich content, allowing it to generate nuanced and empathetic responses tailored to individual students' needs. Two approaches are considered. Fine-tuning existing models and prompt engineering using GPT-4 are considered. Application to generating prompts for the EI interventions is demonstrated. By integrating generative AI technologies, the proposed conceptual solution aims to enhance the effectiveness of EI interventions, making them more accessible, engaging, and impactful.

关键词： Artificial intelligence (AI) Emotional intelligence (EI) Generative AI EI interventions Conceptualization Prompt engineering

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Sign Language Recognition Using Convolutional Neural Network 4th

Sign Language Recognition Using Convolutional Neural Network

引用

4th International Conference on Advanced Network Technologies and Intelligent Computing-ANTIC-Annual

作者： Potluri, Tejaswi Vallurupalli Nageswara Rao Vignana Jyothi Inst En Vignana Jyothi NagarPragathi Nagar Hyderabad 500090 Telangana India

ISBN: (纸本)9783031837920;9783031837937

Sign language detection is essential for improving accessibility, communication, and inclusion for individuals who are deaf or hard of hearing. This technology enables smooth communication across various environments, including schools, workplaces, healthcare settings, and everyday interactions. By accurately interpreting sign language gestures, it bridges communication barriers, providing equal access to information and services. In education, sign language detection supports language learning and academic success for deaf students, while in healthcare, it ensures that medical information is conveyed accurately, enhancing care quality. In the workplace, it fosters inclusivity by enabling communication between deaf employees and their colleagues, promoting equal opportunities and professional growth. Additionally, during emergencies, it can save lives by enabling swift communication with emergency responders. This study presents a robust sign language detection system based on convolutional neural networks (CNNs), trained on a dataset of 2,515 images representing 36 distinct ASL gestures. The system employs a comprehensive preprocessing pipeline, with images standardized, resized, and split for training and validation, resulting in an accuracy rate of 85.87% across ASL letters and numerals. Real-time testing, facilitated by OpenCV's video capture, demonstrates the model's effectiveness in live gesture recognition with minimal delay. Despite minor errors in misclassification and occlusion, the system shows strong potential to support inclusive communication across settings and to further advance sign language recognition technology.

关键词： Convolutional neural networks Image processing Feature extraction

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Analyzing Artificial Intelligence in the Tourism Sector: A Bibliometric Study of the Past Decade

Analyzing Artificial Intelligence in the Tourism Sector: A B...

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4th International Conference on Advanced Research in Technologies information Innovation and Sustainability-ARTIIS-Annual

作者： Moreira, Andre Monteiro, Redney Cunha, Carlos R. Inst Politecn Braganca Res Ctr Digitalizat & Intelligent Robot CeDRI Lab Associado Sustentabilidade & Tecnol Regioes M P-5300253 Braganca Portugal

ISBN: (纸本)9783031834349;9783031834356

Artificial Intelligence is among the main technologies favored by researchers across different sectors, being as the research topic in focus or as supporting tool, at the same time is also a topic of interest and source of curiosity both for corporations and organizations as well to the common user, over the last years, this general interest has only increased with the technological evolution and investment. The Tourism sector, including hospitality, museums, or travel companies, among others, see this technology primarily as a means to solve current problems that surpass the capacity of humans, which are more prompt to error, such as forecasting. This paper is the result of a bibliometric analysis over 1045 journal articles selected across the databases Web of science and Scopus, by presenting an overview of the R&D in the last ten years surrounding the research and use of artificial intelligence in the tourism sector, it contributes to future research, as well interested actors, through the detection of patterns, principal sources, and contributors for this topic of research.

关键词： Artificial Intelligence Machine Learning Deep Learning Tourism Bibliometric Analysis

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Leveraging Deep Learning for Accurate Classification of Leptograpsus Crabs Based on Morphological Measurements 5th

Leveraging Deep Learning for Accurate Classification of Lept...

引用

5th International Symposium on Intelligent Computing Systems

作者： Malik, Usman Malik, Mustafa Malik, Asif Christ Church Univ Canterbury England Univ Kent Canterbury England Higher Coll Technol Sharjah U Arab Emirates

ISBN: (纸本)9783031829307;9783031829314

This research explores the application of deep learning techniques to classify species and sex of Leptograpsus crabs. The dataset comprised of various morphological measurements, that included the carapace width and length. A convolutional neural network (CNNs) for feature extraction and classification was used. The study achieved 97.5% accuracy for species classification and 95% accuracy for sex classification, with high ROC AUC values (1.0 for species and 0.99 for sex). To ensure model robustness overfitting prevention strategies, such as early stopping and dropout layers, were employed. Additionally, SHAP analysis identified carapace width and carapace length as the most influential features. This research demonstrates the efficacy of advanced AI methodologies in biological classification, contributing to both artificial intelligence and ecological research, with potential applications in biodiversity monitoring and conservation efforts.

关键词： Deep Learning Convolutional Neural Networks (CNNs) Ecological Research Crab Species Classification Morphological Measurements

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How Can Heuristics Be Communicated? 1

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8th International Conference on computer-Human Interaction Research and Applications-CHIRA-Annual

作者： Evans, Isabel Porter, Chris Micallef, Mark Univ Malta Msida Malta

ISBN: (数字)9783031826337

ISBN: (纸本)9783031826320;9783031826337

This position paper proposes a model for choosing how to communicate heuristics in a meaningful, usable and accessible manner. The model is a matrix of nine shapes, where we define 'shape' as a combination of the heuristic's format and its directiveness. Examining heuristics used in UX and software testing practices, we found a variety of formats and levels of directiveness. We discuss these shapes with example heuristics from UX, software testing, and everyday life. We present the outcome from a pilot of the model in one specific context. The shape of a heuristic may contribute to its understandability and usefulness in specific contexts, while different contexts may necessitate different ways of communicating heuristics. This includes considering the effect that the shape of a heuristic has on its accessibility and inclusion, suggesting that shape may be one important aspect of heuristics' design and evaluation.

关键词： Heuristics Human computer interaction Design methodology User-centred design Accessibility Inclusion

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VisionLingua: Empowering Multimodal Understanding with Cutting-Edge Language Models 4th

VisionLingua: Empowering Multimodal Understanding with Cutti...

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4th International Conference on Advanced Network Technologies and Intelligent Computing-ANTIC-Annual

作者： Kamboj, Deepika Gola, Kamal Kumar Arya, Shikha UPES Dehra Dun Uttarakhand India COER Univ Roorkee Uttarakhand India Indian Inst Technol Roorkee Uttarakhand India

ISBN: (纸本)9783031837951;9783031837968

Recent developments in large language models (LLMs) have demonstrated significant progress in both language understanding and generation. In particular, GPT-4, a model capable of processing both text and images, presents challenges in understanding its internal operations. To investigate this, we propose VisionLingua, a model that integrates a fixed visual encoder with the Vicuna language model through a projection layer. Our study shows that aligning a visual module with a state-of-the-art language model unlocks a broad range of capabilities, including detailed image description, conversion of hand-drawn sketches into digital formats, interpretation of complex visual phenomena, and detection and resolution of issues in images. In early experiments, VisionLingua's performance was hindered by the limited size of the training dataset, resulting in inconsistent text outputs. To improve performance, we implemented a second training phase using an expanded dataset with detailed image descriptions, which significantly enhanced the model's ability to produce coherent and accurate text outputs.

关键词： Visual languages

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Lung Region Segmentation from Chest-Radiographs Using EfficientNetB7 4th

Lung Region Segmentation from Chest-Radiographs Using Effici...

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4th International Conference on Advanced Network Technologies and Intelligent Computing-ANTIC-Annual

作者： Kumar, Tarun Garg, Vipul Soni, Pramod Kumar Uttam, Arun Kumar Radha Govind Grp Inst Meerut Uttar Pradesh India Swami Vivekanand Subharti Univ Dept CSE Meerut Uttar Pradesh India Manipal Univ Jaipur Jaipur Rajasthan India PSIT Coll Higher Educ Dept Comp Applicat Kanpur Uttar Pradesh India

ISBN: (纸本)9783031837890;9783031837906

In the modern world, lung diseases are most common due to various factors like environmental issues, pandemics etc. and these diseases can be mild (influenza) or severe (lung cancer) etc. for the identification of lung disorders it is necessary to use imaging modalities like chest radiographs (CXRs), CT and MRI so that the infected area can be easily identified. In this paper, a computationally efficient TL-based method is used to segment lung regions from CXRs. The proposed method utilizes U-Net-based encoder-decoder architecture along with the TL mechanism having EfficientNetB7 as encoder which is pre-trained on ImageNet. The method's effectiveness is assessed using several quantitative metrics and has obtained the highest accuracy and F1-Score of 98 and IoU of 96 on the JSRT dataset. The performance is further matched with state-of-art CNN architecture and obtained better results in Accuracy and IoU parameters.

关键词： Deep learning Transfer learning Lung segmentation medical images

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Efficiency Meets Resilience: Accelerating Object Detection in Embedded Environments Through Compressive Sensing 4th

Efficiency Meets Resilience: Accelerating Object Detection i...

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4th International Conference on Intelligent Systems and Pattern Recognition-ISPR

作者： Bouderbal, Imene Amamra, Abdenour Ecole Natl Preparatoire Etud Ingn Badji Mokhtar Rouiba BP 05 Algiers Algeria Ecole Mil Polytech Bordj El Bahri BP 17 Algiers Algeria

ISBN: (纸本)9783031821554;9783031821561

Optimizing convolutional neural networks (CNNs) for real-time performance in resource-constrained settings remains a challenge. This study introduces a novel approach to CNN design, integrating compressive sensing (CS) techniques to enhance both efficiency and robustness. Our proposed CS-based CNN architecture facilitates real-time object detection in embedded environments by significantly reducing computational complexity and improving transmission efficiency. Specifically, the CS paradigm is employed as a sampling-reconstruction network within the detection CNN, resulting in streamlined processing and reduced model complexity. Previous research demonstrated a remarkable 57% reduction in floating-point operations per second (FLOPs) and a 30% increase in speed for the redesigned SSD model compared to its original counterpart [4]. In this study, we extend our investigation to assess the resiliency of the CS-based CNN architecture against adversarial perturbations. Utilizing different augmented test sets, we evaluate the performance of the CS-based model alongside its traditional baseline. Our findings reveal that the CS-based CNN exhibits superior resilience, with a smaller decline in mean average precision (mAP) under adversarial conditions: a drop of 29.1% for the CS-based model compared to a 50.8% drop for the original architecture, for the worst-case scenario. These results underscore the potential of CS-based CNNs to enable efficient and robust real-time computer vision applications in embedded environments.

关键词： Convolutional Neural Networks Compressive Sensing Real-time Performance Robustness Adversarial attacks Embedded Systems Object Detection

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Causal Discovery with Interactive Human Inputs 8th

Causal Discovery with Interactive Human Inputs

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2024 World Congress in computer science, computer Engineering and Applied Computing-CSCE

作者： Cao, Jin Liu, Renxiong Nokia Bell Labs 600 Mt Ave Murray Hill NJ 07974 USA Meta Platforms 1 Hacker Way Menlo Pk CA 94025 USA

ISBN: (纸本)9783031856273;9783031856280

Integrating expert knowledge into causal structure learning from observational data has proven beneficial. Unlike prior approaches where human knowledge operates independently of structure discovery, we propose a framework for causal discovery with interactive human input. Within our framework, human experts offer edge relations within a local context defined by a machine learning algorithm, iteratively refining the causal graph estimate. Results on simulated and real networks demonstrate superior accuracy with human input. We also analyze the effects of various human inputs and node selection strategies on algorithm performance.

关键词： Causal discovery Structure learning Markov Blanket Greedy Equivalent Search Human AI teaming Contextual knowledge

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