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Classification of Skin Cancer With Using Color-ILQP and MEETG 6

Classification of Skin Cancer With Using Color-ILQP and MEET...

引用

6th international conference on pattern Recognition and Image analysis (IPRIA)

作者： Armi, Laleh Ebrahimpour-Komleh, Hossein Univ Kashan Dept Comp & Elect Engn Kashan Iran

ISBN: (纸本)9798350314557

Skin cancer is one of the most common forms of cancer in the world that has grown dramatically over the past decades. Malignant melanoma is the deadliest type of skin cancer. Melanocytic nevi are benign whereas melanoma is malignant. Most skin cancers are treatable in the early stages. So, rapid diagnosis and the importance of early stage can be very important to cure it and increasing day by day. Today, artificial intelligence can represent an important role in medical image diagnosis. The aim of this paper is to an auto-diagnosis system can be deployed to help dermatologists in identifying melanoma that may facilitate early detection of melanoma, and hence substantially reduce the mortality chance of this dangerous malignancy. We used image processing tools to diagnose melanoma skin cancer. In this paper, the advantage of improved local quinary pattern (ILQP) is used as texture feature extraction method and used mixture of ELM-based experts with a trainable gating network (MEETG) for skin cancer classification. Our proposed method achieved the classification accuracy on f and d datasets, 97.05% and 86.61% respectively.

关键词： Improve local quinary pattern Mixture of ELM-based experts with a trainable gating network Melanoma Skin cancer Skin disease

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Artificial intelligence-Based analysis of Biological Signals and Attention Assessment Methods 24

Artificial Intelligence-Based Analysis of Biological Signals...

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3rd international Symposium on robotics, Artificial intelligence and Information Engineering, RAIIE 2024

作者： Li, Zhijing Li, Hong Jiang, Yiping Guangdong University of Science and Technology Dongguan China

ISBN: (纸本)9798400718311

Biological information processing plays a pivotal role in modern life, with applications ranging from health monitoring to personalized medicine. This study endeavors to delve into a method employing artificial neural networks for the analysis of biological signals, specifically focusing on assessing individual attention levels. By harnessing physiological data such as heart rate variability and skin conductance, coupled with the utilization of artificial neural networks for data analysis and modeling, a more comprehensive understanding of individual attention states can be attained. In the experimental phase, a diverse pool of individuals was tested, and the experimental results were validated using recorded videos. The findings underscore the efficacy of the artificial neural network-based method in assessing individual attention levels, achieving an accuracy rate of 76.9%. This research not only introduces novel methodologies but also opens avenues for leveraging artificial intelligence technology in biological signal analysis and attention assessment. Future research endeavors could delve deeper into exploring additional features of biological signals and enhancing model performance to bolster assessment accuracy and practical applicability. © 2024 Copyright held by the owner/author(s). Publication rights licensed to ACM.

关键词： Artificial neural networks Attention assessment Biological signal analysis Wearable technology

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intelligence in Human-Robot Collaboration - Overview, Challenges and Directions

Intelligence in Human-Robot Collaboration - Overview, Challe...

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2023 international conference Automatics and Informatics, ICAI 2023

作者： Madzharova-Atanasova, Katya Shakev, Nikola Faculty of Electroniks and Automation TU-Sofia Plovdiv Branch Plovdiv Bulgaria

ISBN: (纸本)9798350312911

This article presents an overview of the trends in the development of modern industrial robotics. Special attention is given to human-robot interaction and the potential that collaborative robots reveal. In the article, robotic systems are classified according to different criteria. An analysis of the intelligent technologies used in the robotic systems in the shared workspace was performed. The opportunities provided by the development of artificial intelligence are also discussed. © 2023 IEEE.

关键词： Intelligent robots

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analysis of Effect Self-Healing on Corrosion Concrete using Ultrasonic Method

Analysis of Effect Self-Healing on Corrosion Concrete using ...

引用

2023 international conference on Artificial intelligence robotics, Signal and Image Processing, AIRoSIP 2023

作者： Afanda, Rivky Zaki, Ahmad Universitas Muhammadiyah Yogyakarta Magister of Civil Engineering Yogyakarta Indonesia

ISBN: (纸本)9798350327472

Corrosion is one of the main causes of structural failure and involves high costs for repairs. From this problem, self-healing technology can have a positive impact. This paper presents an assessment of corroded concrete and self-healing using the NDT Ultrasonic Pulse Velocity method. The results of the UPV testing showed that during corrosion, the highest average velocity value obtained was 4263. After self-healing, the highest average velocity value obtained was 4701. These results indicate that the increase in velocity is due to the healing agent covering the internal damage in the concrete. © 2023 IEEE.

关键词： Nondestructive examination

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Wireless Sensor Network-Driven IoT Solution for Advanced Borewell Rescue robotics 4

Wireless Sensor Network-Driven IoT Solution for Advanced Bor...

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4th IEEE international conference on Emerging Techniques in Computational intelligence, ICETCI 2024

作者： Meera, V. Mercia, Sharon Nesa PadmaPriya, J. Murugavalli, S. Panimalar Engineering College Artificial Intelligence And Data Science Chennai City Campus Chennai India Panimalar Engineering College Computer Science Engineering Chennai City Campus Chennai India Panimalar Engineering College Department of Computer Science Engineering Chennai India Panimalar Engineering College Department of Computer Science Engineering Chennai City Campus Chennai India

ISBN: (纸本)9798350387063

The fact that deaths from bore wells persist in India is highly alarming, particularly when young people are involved. Since 2009, there have been more than 40 documented child deaths, and the National Disaster Response Force (NDRF) reported a worrisome 70% failure rate for rescue efforts. The solution must be found quickly and effectively. In response to this humanitarian problem, this paper has proposed a novel design for a robotics device. This proposed system is a creative illustration of how human interaction and technology may be used in tandem to swiftly evaluate the victims and guarantee their safe release. The main element of this solution is a skillfully designed robotic arm that smoothly integrates Internet of Things (IoT) devices (Rasp Pi or Arduino) and is powered by high-precision servo motors. Among the many features it offers are an advanced APR (Auto Playback Recorder) system, an infrared-based CCTV camera, a strong LED lighting system, advanced sensors (Passive Infrared, Ultrasonic Distance Measurement, Heartbeat, Temperature, and Gas), and a dual-functional mask. Artificial intelligence (AI) is another tactic that will be used in the rescue operation. This paper discusses the pressing need for safe, efficient bore well rescues and offers a comprehensive analysis of the methodology. This project uses cutting edge technology and human collaboration to stop more catastrophes and increase the speed and effectiveness of life-saving procedures in emergency situations. In an effort to contribute to the creation of a more secure and safe future, this research is presented as a critical first step in lowering the death toll related to Indian bore wells. © 2024 IEEE.

关键词： Wireless sensor networks

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Enhanced Activity Recognition Through Joint Utilization of Decimal Descriptors and Temporal Binary Motions 16th

Enhanced Activity Recognition Through Joint Utilization of D...

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16th international conference on Computational Collective intelligence (ICCCI)

作者： Gnouma, Mariem Yahia, Samah Ejbali, Ridha Zaied, Mourad Res Team Intelligent Machines Gabes Tunisia Modelisat Anal & Control Syst Gabes Tunisia Univ Gabes Natl Engn Sch Gabes St Omar Ibn El Khattab Gabes 6029 Zrig Tunisia

ISBN: (纸本)9783031708183;9783031708190

Human Action Recognition (HAR) has been a prominent area of research within machine learning over the last few decades. Its applications span domains such as visual surveillance, robotics, and pedestrian detection. Despite the numerous techniques introduced by computer vision researchers to address HAR, persistent challenges include dealing with redundant features and computational cost. This paper specifically addresses the challenge of silhouette-based human activity recognition. While previous research on silhouette-based HAR has predominantly focused on recognition from a singular perspective, the aspect of view invariance has often been overlooked. This paper presents a novel framework that aims to achieve view-invariant Human Action Recognition. The proposed approach integrates a pre-processing stage based on the extraction of multiple 2D Differential History Binary Motions (DHBMs) from spatio-temporal frames capturing human motion. These multi-batch DHBMs are then used to capture and analyse human behaviour using the Decimal Descriptor pattern (DDP) approach. This strategy enhances the extraction of intricate details from image data, contributing to a more robust HAR methodology. The selected features are processed by the Sparse Stacked Auto-encoder (SSAE), a representative of deep learning methods, to provide effective detection of human activity. The subsequent classification is performed using Softmax. The experiments are conducted on publicly available datasets, namely IXMAS and KTH. The results of the study demonstrate the superior performance of our methodology compared to previous approaches, achieving higher levels of accuracy.

关键词： Human activities recognition Differential History of Binary Motion Decimal Descriptor pattern Deep Learning

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Stacked Multi-Head Cross Attention for Image Recognition 24

Stacked Multi-Head Cross Attention for Image Recognition

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1st international conference on Image Processing Machine Learning and pattern Recognition

作者： Liu, Shuaichen Du, Xiao Wu, Tieru Jilin Univ Sch Artificial Intelligence Changchun Jilin Peoples R China Jilin Univ Sch Math Changchun Jilin Peoples R China

ISBN: (纸本)9798400707032

Long-range dependency plays a critical role in extracting intricate image features particularly in tasks involving image recognition. In previous study, the significance of long-range positional dependencies has been proved in both image classification and image segmentation. Based on this, we introduce a Multi-Head Cross Attention module, namely MHCA, along with four different operators, which are designed to capture and integrate contextual information at every pixel position within feature maps, spanning both horizontal and vertical directions, with parallel fashion, thus can transfer information and share weights across multiple heads of features. Moreover, by stacking our module twice, forming MHCA(2) layer, the whole context of each pixel in feature can be captured, with more lighter computation burden than general full connection or Non-local networks, and it is designed to be seamlessly plugged into existing network architectures. By replacing specific convolution layer in convolutional networks with a MHCA(2) layer, we construct MHCA network. Through extensive experiments upon various datasets, we demonstrate the validity of our approach. Furthermore, comparative analysis with similar methodologies highlight the superior performance of our method.

关键词： Cross Attention Image Classification Image Segmentation Long-range Dependency Lightweight Architecture

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Artificial intelligence-Based on Automatic Detection of Diabetic Eye Diseases : A Systematic Review 4th

Artificial Intelligence-Based on Automatic Detection of Diab...

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4th international conference on Intelligent Systems and pattern Recognition-ISPR

作者： Ba, Mandicou Mbaye, Amadou Diop, Idy Bah, Alassane Cheikh Anta Diop Univ UCAD Med Imaging & Bioinformat Lab LIMBI Polytech Higher Sch ESP Dakar Senegal

ISBN: (纸本)9783031821554;9783031821561

The diagnosis of eye diseases, especially those related to diabetes, has long posed enormous challenges for ophthalmologists in developing countries. In Africa, the main difficulty stems from the limited number of technologies and/or equipment available. Nowadays, with the advancement of technology and the proliferation of artificial intelligence models, the detection and analysis of eye diseases are becoming increasingly easier. It is clear that existing prediction systems can diagnose eye disorders such as glaucoma, cataracts, diabetic retinopathy, etc., but sometimes with very low accuracy. Manual diagnosis of fundus images by ophthalmologists also constitutes a slow, expensive, tedious task and may even be prone to errors. However, despite this, it is worth noting that some doctors still continue to practice this method. This paper highlights the crucial role of artificial intelligence systems, particularly those based on machine learning or deep learning, in the early detection of diabetes-related eye disorders in Africa. In a continent where the prevalence of diabetes is increasing, but resources are limited, these technologies offer significant potential to improve access to ocular healthcare and reduce the workload of healthcare professionals. This also underscores the importance of promoting research in the field of artificial intelligence in ophthalmology, especially in the African context.

关键词： Artificial intelligence Machine Learning Deep Learning Diabetic Retinopathy Glaucoma Cataracts

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A Bi-LSTM and AutoEncoder Based Framework for Multi-step Flight Trajectory Prediction 8

A Bi-LSTM and AutoEncoder Based Framework for Multi-step Fli...

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8th international conference on Control and robotics Engineering (ICCRE)

作者： Wu, Han Liang, Yan Zhou, Bin Sun, Hao Northwestern Polytech Univ Sch Automat Xian Peoples R China

ISBN: (纸本)9798350345650

Trajectory prediction (TP) is a key component in the route planning for civil aircraft. Most existing methods obtain multi-step TP via iterating the one-step TP model, which generally generates large cumulative error due to deviate from the original evolutionary pattern. To improve the situation, this paper proposes a multi-step TP framework with three modules: the Bi-directional Long Short-Term Memory Network (Bi-LSTM) based multi-step TP module, AutoEncoder based multi-step TP module, and voting fusion module. In the Bi-LSTM based multistep TP method, to avoid the forgetting of evolutionary characteristics, the Bi-LSTM is designed to directly extract the mapping relationship between input of historical trajectory fragments and output of multi-step labels via data- driven method. In the AutoEncoder based multi-step TP module, the Bi-LSTM is deigned to learn mapping relationship between the input and core evolutionary features from output labels extracted via the encoder, and then the decoder is adopted to reconstruct predictions by outputs from Bi-LSTM. Third, the voting method was used to fuse the per-dimension predictions from the above two modules and further to refine multi-step predictions. The proposed multi-step TP framework is applied to real flight trajectory prediction of civil aircraft and outperforms multiple deep learning methods in the terms of RMSE and MAE.

关键词： multi-step trajectory prediction AutoEncoder BiLSTM time series analysis

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Tumor Segmentation Algorithm for Liver CT Images Based on Generative Adversarial Networks 6

Tumor Segmentation Algorithm for Liver CT Images Based on Ge...

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6th IEEE international conference on pattern Recognition and Artificial intelligence, PRAI 2023

作者： Zhen, Hu Jinzhi, Zhou Lili, Guo Li, Gong Southwest University of Science and Technology Sichuan Provincial Key Laboratory of Robotics for Special Environment Mianyang China

ISBN: (纸本)9798350325485

An automatic segmentation algorithm based on a generative adversarial network was proposed to solve the problems of insufficient segmentation, fuzzy segmentation, boundary ambiguity, and low segmentation accuracy in the existing liver tumor CT image segmentation methods. The algorithm first performs liver pre-segmentation on CT images to reduce the influence of irrelevant information. Secondly, the generation network of GAN uses Dual Attention Unet (DAUnet), which introduces a dual attention mechanism in the jump connection to enhance the characteristics of liver tumors. Finally, the prediction accuracy of tumor images is improved by GAN-generated adversarial training, and a mixed loss function is introduced in the training process. Finally, experimental results on LiTS data show that the proposed algorithm's Dice similarity coefficient (DSC) value reaches 76.15%, 3.63% higher than that of the Unet algorithm. DAUnet can effectively improve the performance of tumor segmentation in liver images by generating adversarial training. © 2023 IEEE.

关键词： Generative adversarial networks

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