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2024 International Conference on Engineering and Computing, ICECT 2024

作者： Akhlaq, Filza Ali, Subhan Imran, Ali Shariq Daudpota, Sher Muhammad Kastrati, Zenun Dept. of Computer Science Gjovik2815 Norway Sukkur IBA University Dept. of Computer Science Sukkur Sindh Pakistan Linnaeus University Dept. of Informatics Växjö351 95 Sweden

ISBN: (纸本)9798350349719

Medical image analysis has undergone significant advancements with the integration of machine learning techniques, particularly in the realm of bone anomaly detection. The availability of recent datasets and the lack of benchmarking and explainability components provide numerous opportunities in this domain. This study proposes a benchmarking approach to a recently published FracAtlas dataset utilizing state-of-The-Art deep-learning models coupled with explainable artificial intelligence (XAI) having two distinct modules. The first module involves the binary classification of fractures in different body parts and explains the decision-making process of the best-performing model using an XAI technique known as EigenCAM. EigenCAM generates heatmaps on every layer of the YOLOv8m model to explain how the model reached a conclusion and localizes the fracture using a heatmap. To verify the heatmap, we also detected fractures using the YOLOv8m detection model, which achieved a mAP@O.5 of 59.5%, outperforming the baseline results on this dataset. The second module involves a multi-class classification task to categorize images into one of the five anatomical regions. The best-performing model for binary classification is the YOLOv8m model, with an accuracy of 83.1%, whereas the best-performing model for multi-class classification is the YOLOv8s, achieving an accuracy of 96.2%. © 2024 IEEE.

关键词： Medical imaging

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Towards Early Diagnosis and Intervention: An Ensemble Voting Model for Precise Vital Sign Prediction in Respiratory Disease

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IEEE Journal of Biomedical and Health Informatics 2023年 PP卷 1-13页

作者： Ahmed, Usman Lin, Jerry Chun-Wei Srivastava, Gautam Department of Computer Science Electrical Engineering and Mathematical Sciences Western Norway University of Applied Sciences Bergen Norway Dept. of Math and Computer Science Brandon University Canada

Worldwide, cardiovascular and chronic respiratory diseases account for approximately 19 million deaths annually. Evidence indicates that the ongoing COVID-19 pandemic directly contributes to increased blood pressure, cholesterol, as well as blood glucose levels. Timely screening of critical physiological vital signs benefits both healthcare providers and individuals by detecting potential health issues. This study aims to implement a machine learning-based prediction and classification system to forecast vital signs associated with cardiovascular and chronic respiratory diseases. The system predicts patients' health status and notifies caregivers and medical professionals when necessary. Utilizing real-world data, a linear regression model inspired by the Facebook Prophet model was developed to predict vital signs for the upcoming 180 seconds. With 180 seconds of lead time, caregivers can potentially save patients' lives through early diagnosis of their health conditions. For this purpose, a Naïve Bayes classification model, a Support Vector Machine model, a Random Forest model, and genetic programming-based hyper tunning were employed. The proposed model outdoes previous attempts at vital sign prediction. Compared with alternative methods, the Facebook Prophet model has the best mean square in predicting vital signs. A hyperparameter-tuning is utilized to refine the model, yielding improved short- and long-term outcomes for each and every vital sign. Furthermore, the F-measure for the proposed classification model is 0.98 with an increase of 0.21. The incorporation of additional elements, such as momentum indicators, could increase the model's flexibility with calibration. The findings of this study demonstrate that the proposed model is more accurate in predicting vital signs and trends. IEEE

关键词： Decision trees

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An Ensemble-based Deep Learning Model for Multi-class Flower Recognition

An Ensemble-based Deep Learning Model for Multi-class Flower...

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2023 International Conference on Next-Generation Computing, IoT and Machine Learning, NCIM 2023

作者： Rabbi, Md Fozle Dola, Sraboni Das Taha, Noorjahan Binte Kader Al Mahmud, Tamim Islam, Muhammad Nazrul Razzaque, Md. Abdur Al Faisal, Faiz Green University of Bangladesh Dept. of Computer Science and Engineering Dhaka Bangladesh Military Institute of Science and Technology Dept. of Computer Science and Engineering Dhaka Bangladesh University of Dhaka Dept. of Computer Science and Engineering Dhaka1000 Bangladesh

ISBN: (纸本)9798350316001

Many species around the globe heavily depend on flowers, and even the use of flowers for medical purposes is enormous, which has been in practice since ancient times. In modern botany, agronomy, and species research, the identification and classification of flowers mainly depend on human intervention, which requires a lot of human resources, and the recognition accuracy rate is low. On the other hand, due to the vast number of flowers with different types, it is quite a challenge and increases the complexity of the accurate detection and classification process. Moreover, the traditional CNN model and other pre-trained models like- VGG16, VGG19, Xception, and MobileNet-V2 failed to ensure high validation accuracy. In this research, we have designed an ensemble-based deep learning model consisting of two pre-trained models DenseNet-201 and EfficientNet-B7, which we renamed 'FlowerConvNet'. This paper considers classifying five categories of flowers consisting of 4242 images, collected from the Kaggle flower recognition dataset. In this research study, the proposed deep learning model FlowerConvNet using transfer learning can obtain a competitive result with 95% validation accuracy. © 2023 IEEE.

关键词： Learning systems

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Lightweight Attention Based Deep CNN Framework for Human Facial Emotion Detection from Video Sequences

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SN computer science 2025年第1期6卷 1-12页

作者： Kant, Krishna Shah, Dipti B. Computer Science Dept. P.G Department of Computer Science and Technology Sardar Patel University V.V. Nagar Gujrat Anand 388120 India

Emotions with their intensities are associated with the action of humans which decides the behaviour of an *** recent research has gained enormous attention in the domain of emotion detection due to automatic facial emotion detection. The prime goal of facial emotion recognition (FER) is intended to map different facial expressions from video sequences to specific emotional states. Human facial emotion recognition (HFER) has captivated the attention of researchers for its excellent real time application which serves the society. This research work has proposed a novel lightweight attention based deep convolution neural framework for human facial emotion detection from video sequences on CK + dataset. A dense convolutional network (DenseNet-201) has been applied to strengthen feature propagation to find a solution to the vanishing-gradient problem, and significantly lower the parameter *** provide the model for better visual perceptibility, an attention block is applied between the convolution and dense layer. The proposed model achieves good accuracy of 98.8% while using less memory and processing in comparison to state-of-the-art approaches to reach good performance on CK + data set. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2024.

关键词： Attention network Deep convolution neural network DensNet-201 Emotion detection Human face detection Intensity Video surveillance

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Benefits of Machine Learning Explanations: Improved Learning in an AI-assisted Sequence Prediction Task 25

Benefits of Machine Learning Explanations: Improved Learning...

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30th International Conference on Intelligent User Interfaces, IUI 2025

作者： Liang, Yu Collaris, Dennis Willemsen, Martijn C. Van Wijk, Jarke J. Human-Technology Interaction Eindhoven University of Technology Eindhoven Netherlands Dept. Mathematics and Computer Science Eindhoven University of Technology Eindhoven Netherlands Jheronimus Academy of Data Science Den Bosch Netherlands

ISBN: (纸本)9798400713064

Research in Explainable AI (XAI) has shown that explanations can improve users' understanding of AI models, improve user performance and potentially reduce overreliance on AI predictions. However, this is mostly evaluated by static rather than dynamic measures, and the role of XAI on learning over trials is rarely studied. In this study, we use a context-free sequence prediction task, in which 458 participants predict the next symbol in a fixed sequence (with some noise) over 80 trials. We compare performance with AI and XAI advice against no AI support, and subsequently we test for learning by taking away the AI support after 40 trials (i.e., a reversal study design). Our results show that users learn faster with XAI than with AI without explanations or no AI and are better able to recover in performance from the removal of AI. However, the benefits of XAI on learning are much smaller for more difficult tasks. This work demonstrates the benefits of repeated measures user studies and multilevel modeling to better understand learning processes in XAI. It also shows the potential of AI explanations to help users to learn and poses XAI design suggestions to support learning in human-AI collaboration. © 2025 Copyright held by the owner/author(s).

关键词： Adversarial machine learning

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ABC-DE-WOA: A New Hybrid Algorithm for Optimization Problems 4

ABC-DE-WOA: A New Hybrid Algorithm for Optimization Problems

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4th International Conference on Circuits, Control, Communication and Computing, I4C 2022

作者： Pradeep Kumar, D. Sowmya, B.J. Kanavalli, Anita Vincent, Annvin Dept. of Computer Science and Engineering Bangalore India Dept. of Artificial Intelligence and Data Science Bangalore India

ISBN: (纸本)9798350397475

One of the most effective swarm intelligence-based algorithms for many global optimization issues is the Artificial Bee Colony (ABC) strategy. Despite the fact that there are many Artificial Bee Colony (ABC) variants, the algorithm normally has a low convergence rate. Therefore, it is still essential to moderate an algorithm's intensity and diversity. In this instance, the standard Artificial Bee Colony (ABC) algorithm has been combined with the Whale Optimization Algorithm (WOA) and Differential Evolution Algorithm (DE) to generate a novel Hybrid Artificial Bee Colony algorithm (ABC), Artificial Bee Colony-Differential Evolution-Whale Optimization Algorithm (ABC-DE-WOA). For simple benchmark problems with up to 100 dimensions, 50 dimensions, 30 dimensions, and 10 dimensions, the proposed hybrid technique is compared with Artificial Bee Colony (ABC) variants like Artificial Bee Colony-Whale Optimization Algorithm (ABC-WOA), Artificial Bee Colony-Differential Evolution Algorithm (ABC-DE), and original Artificial Bee Colony Algorithm (ABC). The results show that the proposed technique performs better than its competitors in terms of convergence speed. © 2022 IEEE.

关键词： Global optimization

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Personalized Video Prefetching Mechanism Based on User Viewing History in Edge Computing Environment

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Transactions of the Korean Institute of Electrical Engineers 2023年第5期72卷 620-627页

作者： Moon, YangChan Lim, Mingyu Dept. of Smart ICT Convergence Konkuk University Korea Republic of Dept. of Computer Science and Engineering Konkuk University Korea Republic of

The personalized video service collects and analyzes user information to provide differentiated services for each user. Additionally, it is impossible to avoid delays when sending a video from a server to a client. However, if prefetching is performed at the edge node, transmission delay can be reduced, and personalized service can be provided by utilizing user information at each edge node. In this paper, we propose a personalized video service based on edge nodes. The proposed method operates so that the edge node performs personalized prefetching in which the number of prefetching is adjusted by calculating the weights of five selection criteria based on the user's viewing record. We found that the proposed method has the highest prefetching accuracy through implementation and experiments using the simulation of user behavior of selecting and viewing videos. © 2023 Korean Institute of Electrical Engineers. All rights reserved.

关键词： Edge computing

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Does a Dense Point Cloud for Training data Generation Improve Segmentation Accuracy?

Does a Dense Point Cloud for Training Data Generation Improv...

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2023 IEEE International Conference on Big data, Bigdata 2023

作者： Wada, Marin Sudo, Hiroaki Adachi, Miho Miyamoto, Ryusuke Meiji University Graduate School of Science and Technology Dept. of Computer Science Kawasaki Japan Meiji University School of Science and Technology Dept. of Computer Science Kawasaki Japan

ISBN: (纸本)9798350324457

Semantic segmentation can provide significant information for a robot to actualize autonomous moving. To increase the classification accuracy of segmentation, appropriate datasets should be prepared, which requires huge human labors. The authors attempt to realize a semi-automatic method that generates two dimensional images having pixel-wise class labels from 3D point clouds obtained by a 3D scanner. In this study, dense point clouds are generated to improve the quality of training samples. Experimental results using data obtained around the course of the Tsukuba Challenge, which is the famous competition for autonomous moving robots in Japan, showed that the dense point clouds effective but appropriate variety of textures should be included in the training samples. © 2023 IEEE.

关键词： 3D scanned data dataset generation density of point clouds semantic segmentation

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Review on TIRADS Scoring Based Thyroid Nodule Malignancy Detection 9

Review on TIRADS Scoring Based Thyroid Nodule Malignancy Det...

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9th International Conference on Smart Computing and Communications, ICSCC 2023

作者： James, Jisha Anand, H.S. Dept. of Computer Science and Engineering Kochi India Muthoot Institute of Technology and Science Kochi India Cvv Institute of Science and Technology Dept. of Computer Science Ernakulam India

ISBN: (纸本)9798350314090

Thyroid nodules are a common clinical finding, and their accurate risk stratification is essential for determining appropriate management strategies. The Thyroid Imaging Reporting and data System (TIRADS) has emerged as a valuable tool for evaluating thyroid nodules by integrating various ultrasound features to predict malignancy. This research article aims to assess the efficacy of TIRADS scoring in detecting thyroid nodule malignancy and its potential as a standardized diagnostic tool. A comprehensive literature review was conducted to identify studies that evaluated the diagnostic performance of TIRADS scoring systems in different patient populations. The included studies demonstrated that TIRADS scoring systems exhibited substantial accuracy, sensitivity, and specificity in predicting thyroid nodule malignancy. The incorporation of ultrasound features, such as nodule size, composition, echogenicity, margins, and presence of calcifications, allowed for a standardized and reproducible risk stratification. TIRADS scoring systems also demonstrated the ability to optimize the decision-making process for further diagnostic workup, reducing unnecessary fine-needle aspiration biopsies and surgical interventions. The article also discusses the limitations and challenges associated with TIRADS scoring. © 2023 IEEE.

关键词： Machine learning

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Performance Analysis of a Scalable Federated Learning-Based Agricultural Network 4

Performance Analysis of a Scalable Federated Learning-Based ...

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4th IEEE International Conference on Emerging Techniques in Computational Intelligence, ICETCI 2024

作者： Chakraborty, Shatanik Sarkar, Maharnab Gayen, Avijit Jana, Angshuman Chakraborty, Nilotpal Meghnad Saha Institute of Technology Dept. of Computer Science & Engg. Kolkata India Govt. College Of Engineering & Textile Technology Dept. of Computer Science & Engg. Berhampore India Indian Institute of Information Technology Dept. of Computer Science & Engg. Guwahati India Techno India University Dept. of Computer Science & Engg. West Bengal India

ISBN: (纸本)9798350387063

Although agriculture is a substantial part of the Indian economy, it encounters several challenges. Precision agri-culture is a farming management strategy that aims to overcome these challenges. It optimizes several components of the farming process using data and technology, resulting in increased crop yields while reducing waste and raising total resource use effi-ciency. Thus, in its context, we present a study on the development of a Federated Learning-based Agricultural Network consisting of a popular federated learning algorithm FedAvg to perform image classification on an open dataset, Plant Village. In this work, we specifically analyze the performance of the proposed Federated Learning-based Agricultural Network in terms of its scalable deployment. The proposed network yields an average classification accuracy of 97.52 % on test data (C ≈ 70), and provides a host of other benefits it derives from its federated nature, such as upholding the privacy of local client data as only the model updates are shared between the clients and the aggregation server. The prime objective of our work has been to study and explore the possibility of applying federated learning in the agriculture sector so that more efficient and privacy- preserving smart farming networks could be developed. © 2024 IEEE.

关键词： Agribusiness

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