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Early Detection and Effective Treatment for ADHD using Machine Learning for Sri Lankan Children 5

Early Detection and Effective Treatment for ADHD using Machi...

引用

5th International Conference on Advancements in Computing, ICAC 2023

作者： Kumarasingha, R.M.I.S. Dissanayake, D.M.C.L.B. Pathirathne, P.S.R. Ranathunga, K.M.M.Y.S. Rajapakshe, Samantha Kalapuge, Vindhya Sri Lanka Institute of Information Technology Department of Software Engineering Malabe Sri Lanka

ISBN: (纸本)9798350358131

Attention Deficit Hyperactivity Disorder (ADHD) is a prevalent neurodevelopmental disorder that significantly impacts children's behavior, attention, and ability to learn. In Sri Lanka, there is limited awareness, resources, and accessibility to therapy services for children with ADHD. Early identification of ADHD is crucial for effective treatment, but traditional approaches in Sri Lanka have limitations in accurately identifying the disorder and providing adequate interventions. To address these challenges, this research emphasizes the development of a comprehensive mobile application utilizing game-based features, machine learning, image processing, motion detection, and speech recognition technology to enhance the early detection and treatment of ADHD in Sri Lankan children. The primary objective of this research is to accurately identify ADHD symptoms in children and offer treatments through a mobile application. The application offers interactive activities designed to address specific ADHD symptoms, supporting children's cognitive development, improving attention, concentration, memorizing, the ability to follow directions, and the ability to respond to a command. This research adopts a multidisciplinary approach, integrating principles from psychology, computer science, and machine learning. The findings inform the development of more accessible and effective interventions for children with ADHD in Sri Lanka and similar settings, ultimately improving the quality of life for affected children. © 2023 IEEE.

关键词： Speech recognition

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Research on Innovative Artificial Intelligence Education Assisted by Big Data 3

Research on Innovative Artificial Intelligence Education Ass...

引用

3rd International Conference on engineering Education and information technology, EEIT 2023

作者： Hong, Lei Zhang, Dongyang University of Electronic Science and Technology of China School of Information and Software Engineering Chengdu China

ISBN: (纸本)9798350326840

Artificial intelligence (AI) is an important driving force leading the new round of technological and industrial revolution, and the construction of AI major has also entered a period of rapid development since the attention of colleges. However, limited by the lack of development time, regarding to the cultivation of innovative talents in the field of AI, the problems of incomplete curriculum construction, insufficient innovation spirit and poor engineering ability are more prominent, along with the increased demand for superior AI talents from employer. Thus, this paper deeply analyzes the defects of current AI teaching, aiming at exploring talent cultivating path assisted by education big data from students. Furthermore, a novel five-stage model for AI major education is proposed to guide students to reinforce the elementary knowledge, foster the spirit of innovation and address the practical issues. The comprehensive analysis indicates that the proposed teaching model is conducive to stimulating learning enthusiasm of students, and significantly bringing the talent cultivation system to a higher level. © 2023 IEEE.

关键词： Big data

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Control and Management of Network Service Quality Based on SRV6 20

Control and Management of Network Service Quality Based on S...

引用

20th International Computer Conference on Wavelet Active Media technology and information Processing, ICCWAMTIP 2023

作者： Yang, Xiao Taoran, Wang Pinle, Liu School of Information and SoftWare Engineering University of Electronic Science and Technology of China Chengdu China

ISBN: (纸本)9798350318982

With the development of Internet and communication technology, Quality of Service has become an important indicator to measure and manage network performance, in order to meet the needs of diversified network applications, solve the problem of network congestion and meet the needs of users. At the same time, SRv6, as an extended protocol based on IPv6, provides the possibility of innovation and development for the network architecture through flexible routing and service customization. P4, as a data plane programming language that has emerged in recent years, has excellent programmability and adaptability for designing high-performance network systems. In depth study of QoS and SRv6 will provide new ideas and solutions for network optimization, user experience improvement and network sustainable development. © 2023 IEEE.

关键词： Quality of service

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A Bilateral Stable Matching Dispatching Method in Crowdsource Food Delivery Service 29

A Bilateral Stable Matching Dispatching Method in Crowdsourc...

引用

29th IEEE International Conference on Parallel and Distributed Systems, ICPADS 2023

作者： Huang, Feihong Jiang, Wei University of Electronic Science and Technology of China School of Information and Software Engineering Chengdu China

ISBN: (纸本)9798350330717

On-demand food delivery is currently the fastest- growing and most time-sensitive form of logistics in all logistics industry. As the demographic dividend gradually diminishes, an increasing number of platforms are adopting crowdsourced delivery. However, individual crowdsourced riders consider opportunity costs when accepting orders, which determines their decision to make deliveries. Frequent order rejections lead to orders not being finished within the appointed time, and the imbalance in workload among riders also contributes to a high rider attrition rate. In this paper, we propose a stable dispatching algorithm based on bilateral stable matching between orders and riders. This algorithm considers multiple objectives such as system efficiency, service quality, rider experience, and workload balance to address the instability in the dispatching process. Additionally, we design a method to calculate the target dispatching order quantity by fitting the order density function using dispatching order quantity from different time periods. Finally, we demonstrate the effectiveness of the proposed dispatching method through a comparative analysis of experimental results. © 2023 IEEE.

关键词： balanced dispatch bilateral match crowdsource dispatch food delivery dispatch

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BORT-RD: An Improved Model for software engineering Design Feature Generation 9

BORT-RD: An Improved Model for Software Engineering Design F...

引用

9th Annual International Conference on Network and information Systems for Computers, ICNISC 2023

作者： Li, Zhenhao Lei, Hang University of Electronic Science and Technology of China School of Information and Software Engineering Chengdu China

ISBN: (纸本)9798350306682

By studying the connections and dependencies between requirement features and design features in software development documentation, and incorporating the advantages of various Transformer-like models, a BORT-RD model applicable to generating design features is proposed in this text. This model is based on the syntactic decomposition of requirement documents and design documents to train feature phrases. The model has been improved in terms of input embedding, attention calculation and fine-turning, targeting the characteristics of software engineering documents. Through experiments, the model's precision, accuracy and recall have been increased to 81.18%, 94.4%, and 83.1%, respectively, showing significant improvements compared to traditional classical Transformer models. © 2023 IEEE.

关键词： software design

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A Comparison of Deep Learning and Machine Learning Methods for Water Quality Prediction 3

A Comparison of Deep Learning and Machine Learning Methods f...

引用

3rd International Conference on Intelligent Data Communication Technologies and Internet of Things, IDCIoT 2025

作者： Srinivasa Gupta, N. Valarmathi, B. Hiwanj, Ishaan Department of Manufacturing Engineering School of Mechanical Engineering Vellore Institute of Technology Tamil Nadu Vellore India Department of Software and Systems Engineering School of Computer Science Engineering and Information Systems Vellore Institute of Technology Tamil Nadu Vellore India Department of Information Technology School of Computer Science Engineering and Information Systems Vellore Institute of Technology Tamil Nadu Vellore India

ISBN: (纸本)9798331527549

Predicting water quality is essential to preserving human health and environmental sustainability. Traditional water quality assessment methods often face scalability and real-time monitoring limitations. With accuracies of 62%, 72%, 83%, 69%, 63%, 66%, 71%, 63%, and 64%, respectively, the current techniques utilized were Logistic Regression, Decision Trees, Random Forest Regressor, Extreme Gradient Boosting, Naive Bayes, K-nearest neighbors, Support Vector Machine, AdaBoost, and Bagging [9]. This study addresses these challenges by leveraging Adaptive Synthetic Sampling (ADASYN) to balance the dataset and evaluating model performance on datasets of 5,000 and 10,000 entries per class. A robust dataset obtained from Kaggle was used, with five models – Long Short-Term Memory (LSTM), Feed Forward Neural Network (FFNN), Categorical Boosting (CatBoost), Extreme Gradient Boosting (XGBoost), and Random Forest – evaluated and compared. The proposed methods demonstrate significant improvements in accuracy, with XGBoost achieving the highest accuracy of 95.53%, followed by Random Forest at 93.98%. This work underscores the importance of advanced machine learning techniques in addressing the limitations of traditional methods, enhancing accuracy, scalability, and adaptability in water quality prediction. These findings contribute to advancing environmental monitoring and management practices with reliable, data-driven insights. © 2025 IEEE.

关键词： Support vector machines

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Impulsive Stabilization of Unconstrained Multilayer Recurrent Neural Networks with Node-Based Time-varying Delays 8

Impulsive Stabilization of Unconstrained Multilayer Recurren...

引用

8th IEEE International Conference for Convergence in technology, I2CT 2023

作者： Wang, Xiangxiang Yu, Yongbin Feng, Xiao Han, Xinyi Wang, Jingya Cai, Jingye University of Electronic Science and Technology of China School of Information and Software Engineering Chengdu China

ISBN: (纸本)9798350334012

This article discusses the exponential stabilization of node-dependent delayed multilayer neural networks (NDDMNNs) under impulsive control. To address different modeling requirements in complicated applications, node-based interlayer and intralayer parameters are presented to design the neural network model, indicating that The nodes constituting the network can have different structures. Meanwhile, the novel model considers the node-dependent time-varying delays, and this article develops the sparse matrix approach to translate the node-dependent delayed NDDMNNs model into an multiple delayed model, ensuring that the vector form of NDDMNNs can be constructed and studied by using existing technical approaches. Then, an analytical framework with super-Laplacian matrix and time-dependent Lyapunov function methods is proposed to derive exponential stabilization results. Finally, a numerical simulation example is given to verify the obtained results. © 2023 IEEE.

关键词： Stabilization

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Adapting Pedagogical Frameworks to Online Learning: Simulating the Tutor Role with LLMs and the QUILT Model 6

Adapting Pedagogical Frameworks to Online Learning: Simulati...

引用

6th International Conference on Advancements in Computing, ICAC 2024

作者： Madhubhashana, Sandaru Ranhinda, Vibhashana Jayakody, Dineth Shavinda, Lakmuthu Nawarathne, Malithi Haddela, Prasanna Sri Lanka Institute of Information Technology Dept. of Software Engineering Malabe Sri Lanka Sri Lanka Institute of Information Technology Faculty of Computing Malabe Sri Lanka

ISBN: (纸本)9798331517878

This paper presents SoloScholar, a novel asynchronous online learning platform designed to deliver personalized education to large undergraduate student groups by integrating established pedagogical frameworks with Large Language Models (LLMs). Leveraging Bloom's Revised Taxonomy, learners are categorized into beginner, intermediate, and advanced levels to personalize the educational content. The platform employs the Questioning and Understanding to Improve Learning and Thinking (QUILT) model to structure tutorial sessions and simulate the teaching role of a tutor at a Higher Education Institute (HEI). To generate contextually relevant and cognitively appropriate questions, carefully crafted prompts are fed into LLMs, synthesizing information from reliable knowledge sources. The efficacy of SoloScholar was evaluated through a dual assessment approach: a technical evaluation using the Retrieval Augmented Generation Assessment (RAGAS) framework and a human evaluation involving academic experts. The technical evaluation demonstrated high scores in answer relevancy and context utilization across all learning levels. Similarly, quantitative responses from academic experts indicated strong satisfaction with the quality and appropriateness of the generated content. The findings underscore the potential of integrating LLMs with pedagogical models to enhance personalization in online education. SoloScholar effectively addresses challenges associated with large-group teaching by providing adaptive learning experiences that cater to diverse cognitive needs. © 2024 IEEE.

关键词： Contrastive Learning

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A Comprehensive Data Augmentation Method for Modern Cigarette Packaging Defect Detection 7

A Comprehensive Data Augmentation Method for Modern Cigarett...

引用

7th International Conference on Pattern Recognition and Artificial Intelligence, PRAI 2024

作者： Ma, Yanjin Rao, Yunbo Xue, Junmin He, Qixue School of Information and Software Engineering University of Electronic Science and Technology of China Chengdu China Chengdu Information Technology of Chinese Academy of Sciences Co. Ltd Chengdu China

ISBN: (纸本)9798350350890

This study addresses the issue of insufficient quantity and incomplete types of defect detection datasets for cigarette packages in modern industrial production line scenarios. We propose a cigarette packaging defect data augmentation method. The method consists of three components: preprocessing for obtaining cigarette package defect data through segmentation and cropping based on Segment Anything Model(SAM), generation of cigarette package defects using Cigarette Box Defect-Improved Denoising Diffusion Probabilistic Model (CBD-IDDPM), and We expanded the original dataset by utilizing 600 generated images and trained different defect detection models using the augmented dataset. The experimental results validate the effectiveness of our dataset augmentation method for cigarette package defect detection. The mAP50-95 of YOLOv8n has increased from 73% to 75.2%. © 2024 IEEE.

关键词： CBD-IDDPM data augmentation defect detection image generation

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An Automatic Classification Framework using Multi-Modal Deep Learning for Lung Diseases 23

An Automatic Classification Framework using Multi-Modal Deep...

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2023 8th International Conference on Biomedical Signal and Image Processing, ICBIP 2023

作者： Farhan, Abobaker M. Q. Yang, Shangming Al-Malahi, Abdulrahman School of Information and Software Engineering University of Electronic Science and Technology of China Chengdu China

ISBN: (纸本)9798400707698

Disease identification across multiple modalities remains a challenging task. Recent research has explored early, automatic disease prediction using single modality inputs and computer vision with deep learning techniques. In this study, we introduce the Robust Multi-modal Deep Learning-based (RMDL) framework for automatic lung disease classification using both computed tomography (CT) and X-ray scans. The RMDL framework comprises segmentation, feature extraction, and classification. Input lung images undergo pre-processing, segmentation, and region of interest (ROI) extraction. The core contribution of RMDL lies in its deep learning-based automatic feature extraction and classification. We modify the Convolutional Neural Network (CNN) model to address overfitting and exploding gradients, enabling efficient feature learning with minimal computing cost. The modified CNN model employs L1 normalization and novel convolutional layers for automatic low-dimensional feature extraction. We further utilize the Long Short-Term Memory (LSTM) classifier to mitigate the exploding gradient problem, enabling accurate and early lung disease prediction. RMDL's performance was assessed using CXRTD and CCSC datasets. CCS CONCEPTS ĝ€¢ Keywords: Classification, CNN, automatic disease prediction, deep learning, multi-modal. © 2023 ACM.

关键词： Extraction

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