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Early autism detection using ML on behavioural pattern 4th

Early autism detection using ML on behavioural pattern

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4th Annual international conference on data Science, machine learning and Blockchain Technology, AICDMB 2023

作者： Patwari, Renuka Havare, Suyog Bhosure, Vibodh Jagdale, Yash Surve, Divya Vidyalankar Institute of Technology Mumbai India

ISBN: (纸本)9781032426853

Autistic Spectrum Disorder (ASD) is one of the main neurological disorders. the use of verbal, expressive, intellectual, and social skills is restricted by ASD, a mental condition. Unfortunately, there are substantial wait times for an autistic spectrum disorder diagnosis because the current diagnostic processes are time-consuming and inefficient. We can now anticipate autism early on because of advances in artificial intelligence (AI) and machine learning (ML). this research attempts to investigate the potential of Decision Tree, K-Nearest Neighbour, and Logistic Regression for the analysis and prediction of ASD issues in toddlers. To create a web application that can accurately identify an individual’s autistic features, the study proposes an autism prediction model leveraging ML approaches. On a publicly accessible, non-clinical dataset, the proposed strategies are evaluated. the dataset is related to ASD screening in toddlers which comprises 1054 instances and 18 attributes (including class variable). Results from the application of various ML techniques point to the better performance of a logistic regression-based model on the dataset, with 100% accuracy under both scenarios including and excluding the QChat-10-Score attribute. © 2024 the Author(s).

关键词： Decision trees

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Ctransnet: A Modern Deep learning Model to Estimate Tropical Cyclone Intensity 4

Ctransnet: A Modern Deep Learning Model to Estimate Tropical...

引用

4th IEEE international conference on pattern recognition and machine learning, PRML 2023

作者： Shan, Xingchen Li, Jiagen Ren, Kaijun University of National Defense Technology College of Computer Science ChangSha China University of National Defense Technology College of Meteorology and Oceanography ChangSha China

ISBN: (纸本)9798350324303

Tropical cyclone (TC) intensity estimation is a crucial factor in disaster protection. Fast and accurate intensity estimation can provide data support for TC forecasts. With the popularization and development of deep learning networks, massive meteorological and oceanic data, such as remote sensing satellite images and multidimensional reanalysis data, have been fully utilized, providing new methods and ideas for estimating TC intensity. However, challenges still exist in the current state of deep learning in TC estimation: the research area is mainly in the Northwest Pacific Ocean, and there is a lack of universal deep learning models for the global region. In this study, we combine the CBAM module's adaptive attention mechanism with the Swin Transformer's excellent global modeling ability in computer vision tasks and propose a new deep learning network named CtransNet. this network consists of two main branches: feature extraction net, fine-grained intensity classification and numerical estimation net. the first branch is used to extract high-order semantic vectors from remote sensing images, the second is to output categorical intensity probability distribution and intensity estimation. Massive experiments were carried out on the TCIR dataset, which includes multi-channel remote sensing satellite images of TC in the Northwest Pacific Ocean, the Eastern Pacific Ocean (including the Central Pacific Ocean), the Indian Ocean, the Atlantic Ocean and the Southern Hemisphere. the root mean squared error (RMSE) in the intensity estimation task reached 11.85kt. Compared with the traditional method, the performance is improved by about 31.5%. © 2023 IEEE.

关键词： Probability distributions

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machine learning for Real-Time Language Translation: A Server-Side Approach to Overcome Cross-Cultural Communication Challenges 4

Machine Learning for Real-Time Language Translation: A Serve...

引用

4th international conference on Soft Computing for Security Applications, ICSCSA 2024

作者： Elango, S. Mohanaprakash, T.A. Siva, D. Gowtham, A. Sasikumar, A.N. Sunitha, T. Department of Computer Science and Engineering SOET CMR University Bengaluru India Department of Computer Science and Applications SRM Institute of Science and Technology Chennai India Madanapalle Institute of Technology & Science AP India Department of Computer Science and Engineering Panimalar Engineering College Chennai India Department of Artificial Intelligence & Data Science Saveetha Engineering College Chennai India

ISBN: (纸本)9798331515720

the evolution of human civilization has been intrinsically linked to advancements in technology, leading to the development of multiple languages as mediums of communication. However, this linguistic diversity poses significant challenges in cross-cultural communication. Despite numerous efforts and achieving near state-of-the-Art results in language translation, a universal solution remains elusive. this research study proposes an advanced approach to machine translation by utilizing server-side machine learning to enable seamless communication across various languages. By sending GET or POST requests through JQuery, users can efficiently translate text. the process is structured into three key steps: speech recognition (conversion of spoken words to text), translation using machine learning algorithms, and speech synthesis (conversion of translated text to audio). this approach aims to enhance real-Time translation capabilities, offering a more effective and user-friendly solution for overcoming language barriers. © 2024 IEEE.

关键词： Adversarial machine learning

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Pedestrian Behavior recognition Algorithm Based on Multi-Source Domain Transfer learning 24

Pedestrian Behavior Recognition Algorithm Based on Multi-Sou...

引用

4th international conference on Control and Intelligent Robotics, ICCIR 2024

作者： Yu, Haiyan Huo, Aiqing Nan, Siyuan School of Electronic Engineering Xi'An Shiyou University Xi'an China

ISBN: (纸本)9798400709937

To solve the problem of data imbalance between source domain and target domain of pedestrian behavior in transfer learning process, a multi-source domain transfer learning pedestrian behavior recognition algorithm based on EMD (Earth Mover's Distance) measurement is proposed. Firstly, the feature information of pedestrian behavior image data is extracted under the condition of limited sample resources and mapped to a unified feature space. secondly, the distribution difference between different data domains (source domain and target domain) is quantified by EMD. Finally, transfer learning is carried out by making full use of similar source domain data, so as to effectively reduce the distribution distance between different data domains, make up for the problem of insufficient target domain data, and reduce the impact of uneven data distribution on recognition accuracy. Compared with other models, the proposed algorithm achieves accuracy of 76.7%, which improves the recognition effect of target domain and pedestrian behavior. the research results can effectively solve the problems of insufficient data and uneven data distribution in pedestrian behavior target domain. © 2024 held by the owner/author(s).

关键词： Adversarial machine learning

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Mixed Attention Interleaved Execution Cascade Architecture for Fittings Instance Segmentation in Overhead Transmission Line Images 4

Mixed Attention Interleaved Execution Cascade Architecture f...

引用

4th IEEE international conference on pattern recognition and machine learning, PRML 2023

作者： Yang, Yilong Zhong, Shangping Chen, Kaizhi Fuzhou University College of Computer and Data Science Fuzhou China

ISBN: (纸本)9798350324303

recognition and localization of the fittings in overhead transmission line (OTL) images are the basis of fittings status detection and fault diagnosis. Most of the current recognition and localization of the fittings are based on object detection methods, which cannot accurately locate the fittings, and existing instance segmentation methods have limited accuracy for instance segmentation of fittings in complex OTL scenarios. To solve these problems, in the interleaved execution part, we inherit the idea of Hybrid Task Cascade (HTC) and add a direct information path to the same-stage box and mask branches to reinforce the coupled relationship between detection and segmentation;in the mask branch part, we sequentially apply the efficient channel attention (ECA) module and the dilated spatial attention (DSA) module and then insert them into the mask branch to improve the cross-stage information communication in the cascade architecture and mask prediction. Combining them results in Mixed Attention Interleaved Execution Cascade (MAIEC), a new cascade architecture for instance segmentation. Extensive experiments on the OTL fittings dataset reveal the effectiveness of the proposed method. the proposed MAIEC improves the AP of the box and mask predictions by respectively 2.0% and 1.5% compared to the strong HTC baseline. © 2023 IEEE.

关键词： Object detection

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RoboCup SPL instance segmentation based on improved U-Net 4

RoboCup SPL instance segmentation based on improved U-Net

引用

4th international conference on Computer Vision and pattern Analysis, ICCPA 2024

作者： Zhang, Kaijing Hefei University of Technology No. 485 Danxia road Shushan district Anhui province Hefei China Hefei University of Technology Feicuihu Campus No. 485 Danxia Road Anhui Province Hefei City China

ISBN: (纸本)9781510682528

Semantic segmentation is often used in robots' environment perception and object recognition, which can help robots better understand the surrounding environment and perform correct tasks. In the semantic segmentation of football robots, the semantic segmentation model based on the U-Net model works better in the context of football robot competitions, but there is still room for optimization. In response to the special situational needs of football robots, this paper carries out optimization measures such as data enhancement, adding an attention mechanism, and changing the physical mark extraction model based on the U-Net model, and the overall performance has been improved. On the official data set provided by RoboCup SPL, the accuracy rate finally reached 96.25%. © 2024 SPIE.

关键词： data modeling Image segmentation machine learning Semantics Performance modeling Education and training Robots Feature extraction Mathematical optimization

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A Framework for Video Based Sign Language Interpretation Using machine learning and Statistical Methods 4

A Framework for Video Based Sign Language Interpretation Usi...

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4th international conference on Sentiment Analysis and Deep learning, ICSADL 2025

作者： Swathi, D. Susmetha, K. Sreeji, S. Mangairkarasi, S. B.E Computer Science and Engineering with Data Science Sathyabama Institute of Science and Technology Semmancheri TamilNadu Chennai600119 India Sathyabama Institute of Science and Technology Semmancheri Department of Computer Science and Engineering TamilNadu Chennai6001109 India

ISBN: (纸本)9798331523923

the Sign Language recognition System has been designed to capture video input, process it to detect hand gestures, and translate these gestures into readable text. the project consists of several key components and steps: Video Processing: Using OpenCV, the system captures frames from the video input. MediaPipe processes these frames to detect and track hand landmarks in real time. OpenCV capabilities allow for efficient frame extraction and basic image processing tasks such as resizing and normalization. Hand Detection and Tracking: MediaPipe pre-trained models identify and track hand movements within the video frames. the accurate detection and tracking of the hand movements are critical for the subsequent recognition of the sign language gestures. Sign Language recognition: the core system is the deep learning model, trained using the TensorFlow and Keras on a dataset of sign language gestures. the model learns to classify the detected hand movements into corresponding sign language characters or words. Convolutional Neural Networks (CNNs) are typically used for task due to their effectiveness in image recognition tasks. Text Display: Once the system recognizes the signs, it converts them into text and displays the output. this can be done through a console output or a graphical user interface (GUI) built with Tkinter. the GUI provides a user friendly experience, allowing users to see the translated text in real-time. © 2025 IEEE.

关键词： Convolutional neural networks

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AI-based Malware threat Prediction through CNN-SVM Ensemble 4

AI-based Malware Threat Prediction through CNN-SVM Ensemble

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4th international conference on Next Generation Computing Applications, NextComp 2024

作者： Shoniwa, Mufaro Veerabudren, Karel Sharma, Mrinal Middlesex University Mauritius School of Science and Technology Uniciti Flic en Flac Mauritius

ISBN: (纸本)9798331517403

the dynamic terrain of malware attacks presents noteworthy obstacles to cyber security, necessitating for proactive and resilient detection techniques. Conventional signature-based methods struggle to keep pace with the new malware strains and obfuscation strategies. A systematic literature review was conducted to investigate existing methods for malware threat prediction and detection using machine learning and deep learning techniques. the review identified several promising approaches: convolutional neural networks, graph neural networks, and visual malware characterization achieving 95-99% accuracy on malware classification and detection tasks. However, major gaps were identified in the models' generalizability across diverse malware types, robustness against evasion attempts, lack of interpretability due to the black-box nature of deep learning models, and limited evaluation on real-world emerging threats as opposed to controlled datasets. this project aimed to develop an AI-based threat predictive algorithm that leverages the power of deep learning and machine learning for effective malware detection and prediction. the suggested method utilises an ensemble approach that combines a convolutional neural network (CNN) for pattern recognition in malware code structures with a support vector machine (SVM) for robust decision boundaries in the feature space, thereby enhancing generalization, interpretability and adversarial resilience. By evaluating the model on the MalImg dataset, the system achieved 92.37% accuracy. Although the developed system exhibits optimal outcomes, several areas could use more improvement. this project contributes to the ongoing efforts in combating malware threats and highlights the potential of combining deep learning and traditional machine learning techniques for effective threat prediction and detection. © 2024 IEEE.

关键词： Adversarial machine learning

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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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Detecting Irregular Contexts Using Image recognition and data Analysis for Elderly Monitoring 4

Detecting Irregular Contexts Using Image Recognition and Dat...

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4th IEEE international conference on Electronic Communications, Internet of things and Big data, ICEIB 2024

作者： Chen, Sinan Nakamura, Masahide Yasuda, Kiyoshi Kobe University Kobe Japan Riken Center for Advanced Intelligence Project Tokyo Japan

ISBN: (纸本)9798350360721

In developed countries including Japan, the population is rapidly aging, and dealing with the increasing number of people requiring care is an urgent issue. Assistive technology (AT) can solve problems in medical care, welfare, and home care issues with engineering technology. Previous studies proposed sensors and physical devices for the elderly, but individualized detection and analysis have not yet been realized. thus, we developed proposed a method for detecting and analyzing situations in the daily life of the elderly. the time-series data was analyzed using an image recognition pre-trained model with lightweight machine learning. the model helps to improve quality of life (QoL) and promote a stable daily life for the elderly at home. © 2024 IEEE.

关键词： data handling

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