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Ulcerative Colitis Image Classification Using Federated Deep learning 4th

Ulcerative Colitis Image Classification Using Federated Deep...

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

作者： Al-Refai, Mohammed Alkhaza'leh, Shahed Alzu'bi, Ahmad Jordan Univ Sci & Technol Fac Comp & Informat Technol Dept Comp Sci Irbid 22110 Jordan

ISBN: (纸本)9783031821523;9783031821530

Ulcerative Colitis is a chronic inflammatory bowel disease characterized by varying degrees of disease severity, often measured using the Mayo endoscopic score. the handling of medical data, encompassing sensitive personal health records and clinical information, necessitates stringent privacy protections. this study introduces a federated deep learning framework that leverages fine-tuned deep learning models trained via transfer learning on a dataset of endoscopic images. Employing DenseNet-121 as the foundational architecture, this approach enables the extraction and encoding of generic descriptors from ulcerative colitis images across multiple clients. We evaluated the training efficiency of our federated learning model in comparison to traditional centralized model, where the federated learning model outperformed the centralized approach by achieving an F1-score of 78% and a Quadratic Weighted Kappa score of 71%, and enhancing convergence speeds in terms of the reduction in the number of training rounds required. the results affirm the model's capability in accurately diagnosing ulcerative colitis while ensuring the confidentiality of patient data, underscoring the viability of federated learning in sensitive healthcare applications.

关键词： Federated learning Ulcerative Colitis Image Classification Neural Networks

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Proceedings - 4th IEEE 2023 international conference on Computing, Communication, and Intelligent Systems, ICCCIS 2023

Proceedings - 4th IEEE 2023 International Conference on Comp...

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4th IEEE international conference on Computing, Communication, and Intelligent Systems, ICCCIS 2023

ISBN: (纸本)9798350306118

the proceedings contain 194 papers. the topics discussed include: comparative study of DDoS detection and mitigation techniques;a machine learning-based blockchain model for the storage of maternal health records and safety prediction;enhancing digital investigation: leveraging ChatGPT for evidence identification and analysis in digital forensics;managing metadata in data warehouse for data quality and data stewardship in telecom industry – a compact survey;a review on detection and prevention of the DDoS attacks in the blockchain;analysis of face recognition technique: plastic surgery altered face;image classification using federated averaging algorithm;navigating the gray area: a three-label framework for uncovering uncertainty in fake news;and improvement in validation score with loss function for breast cancer detection using deep learning.

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Enhanced Feline Facial recognition: Advancing Cat Face Detection with YOLOv8 and TensorRT 4

Enhanced Feline Facial Recognition: Advancing Cat Face Detec...

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

作者： Wang, Teng University College Durham University DurhamDH1 3LE United Kingdom

ISBN: (纸本)9781510682528

In the field of computer vision, the ability to accurately detect and recognise animal features in various environments is an area of growing interest and application. this study presents an advanced cat face detection system utilising the You Only Look Once Version 8 (YOLOv8) architecture, enhanced with TensorRT optimisation for real-time processing. the approach involves a comprehensive data augmentation process to improve detection accuracy across diverse cat breeds and environmental conditions. Performance evaluation is based on quantifiable metrics;the optimised model achieves a notable reduction in inference time from 50.1ms to 0.9ms and a decrease in GPU power usage from 77 watts to 63 watts, without compromising accuracy. the accelerated processing speed and reduced power requirements make the system highly suitable for real-time applications, such as pet monitoring systems or behaviour analysis tools, where rapid and accurate detection is paramount. the research highlights the potential of deep learning algorithms in precise animal feature recognition and contributes to the field of computer vision by addressing challenges in small, diverse object detection. © 2024 SPIE.

关键词： Object detection data modeling Performance modeling Education and training Facial recognition systems Animals Computer vision technology Deep learning

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Forecasting Degree Completion: A Naïve Bayes Predictive Model for Students' Success 4

Forecasting Degree Completion: A Naïve Bayes Predictive Mod...

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4th international conference on Advances in Electrical, Computing, Communication and Sustainable Technologies, ICAECT 2024

作者： Campanilla, Bell S. Cebu Technological University College of Computer Information and Communications Technology Cebu City Philippines

ISBN: (纸本)9798350343670

the foundation for personal growth, social development, and economic progress is deniably rooted in education, particularly tertiary education, within the context of the Philippines. While colleges and universities play a pivotal role in shaping the country's human capital, a concerning issues emerges in the form of tertiary dropouts. A substantial number of students fail to complete their tertiary education, presenting a critical challenge to tha nation's development. this study aimed to utilized the Naïve Bayes algorithm to predict the students' degree completion and to provide results for the university administration to develop appropriate early interventions for students who are at risk. the study utilized the Knowledge Discovery in database (KDD) to extract accurate knowledge from data. It involves data preparation, data processing, data transformation and knowledge presentation. Rapid miner was used for data exploration, predective modelling, and data analytics. the study revealed that students having a higher GPA has the probability to complete or finish a college degree. the researcher recommend to re-designed polices to strengthen the university programs in addressing the students dropouts. For future researchers may add more attributes in determining the students degree completion. © 2024 IEEE.

关键词： college dropouts data mining degree completion machine learning naïve bayes

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Predictive Analytics in Sports: Using machine learning to Forecast Outcomes and Medal Tally Trends at the 2024 Summer Olympics 4

Predictive Analytics in Sports: Using Machine Learning to Fo...

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4th international conference on Technological Advancements in Computational Sciences, ICTACS 2024

作者： Moolchandani, Jhankar Chole, Vikrant Sahu, Santosh Kumar, Rahul Shukla, Anshita Kumar, Ashok Amity School of Engineering and Technology Amity University Department of Computer Science and Engineering Madhya Pradesh Gwalior India

ISBN: (纸本)9798350387490

this paper focuses on using machine learning approaches in predicting the medal projections and analyzing the medal distribution pattern in the 2024 Summer Olympics. Due to the availability of a large number of variables both at the individual athlete level and the country level as well as event specific measures, various statistical models are trained to predict the number of medals and unearthing traits that are associated with success in Olympics. the evaluation of the predictions of the models is done using linear regression and random forest along with support vector classifier, as well as artificial neural network classifiers. Using the research findings, it becomes evident that predictive analytics has the capacity of offering a better understanding of the performance of sport teams and formulation of strategies for future competition events. As is observed from the above findings the random forest model sampled the highest accuracy of 99. 034 percent in the general set out of the four models;thus, it is recommended ideal for complicated set data investigations Based on the results of the experiments, it is clearly observed that the random forest has the highest accuracy of 99. 034. © 2024 IEEE.

关键词： Predictive analytics

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2023 4th international conference on Artificial Intelligence and data Sciences: Discovering Technological Advancement in Artificial Intelligence and data Science, AiDAS 2023 - Proceedings

2023 4th International Conference on Artificial Intelligence...

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4th international conference on Artificial Intelligence and data Sciences, AiDAS 2023

ISBN: (纸本)9798350318432

the proceedings contain 65 papers. the topics discussed include: final year project repository with automated classification using TF-IDF;identification of road surface defects using multiclass support vector machine;adaptive ids concept with PRBS multi inputs multi outputs (MIMO) and matched filtering algorithm;3D visualization of femoral shaft fractures;harnessing natural language processing for mental health detection in Malay text: a review;a survey on using spatio-temporal networks for rainfall prediction;authorship attribution in Bahasa Indonesia using twitter dataset on political topic;implementing vision transformer to model emotions recognition from facial expressions;BERT-BiLSTM architecture to modelling depression recognition for Indonesian text from English social media;and Xception based transfer learning combined with residual block for facial emotion recognition.

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Algorithmic study of multi-sensor fusion-based vehicle navigation: comparative analysis of Kalman filter and machine learning models 4

Algorithmic study of multi-sensor fusion-based vehicle navig...

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

作者： Zhang, Jiayang Fuzhou University Fuzhou China

ISBN: (纸本)9781510682528

the purpose of this study is to investigate the development and evaluation of a high integrity navigation system for vehicular applications, focusing on the fusion of Global Positioning System (GPS) and Inertial Measurement Unit (IMU) data. the study compares the similarities and differences in performance between the Kalman filter (a traditional GPS/IMU integration method) and machine learning models. the experiments are based on KITTI GPS/IMU sequences, and the impact of these methods on the performance of the navigation system is evaluated by introducing different noise levels. First, a Kalman filter is used to fuse the GPS/IMU data and the estimation error of the trajectories is investigated by adjusting the noise level. Second, a machine learning model is introduced to compare its performance under different parameter configurations using random forest regression as an example. In addition, the effects of different parameters on the performance of the two methods are analyzed, which provides an important reference for choosing a suitable navigation system. the results show that the Kalman filter model basically outperforms the machine learning model in terms of mean square error (MSE) and mean absolute error (MAE) of trajectory estimation. However, the random forest regression model performs best after tuning. the paper concludes with a comparison between the Kalman filter and the random forest regression model, emphasizing the robustness and adaptability of the random forest regression model in GPS/IMU fusion. the research results provide insights for the selection and design of navigation systems. © 2024 SPIE.

关键词： Kalman filters

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Balancing Class Distribution in Microarray Analysis Using GenAI 4th

Balancing Class Distribution in Microarray Analysis Using Ge...

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

作者： Anurekha, G. Amutha, S. Nivethika, K. Vel Tech Rangarajan Dr Sagunthala R&D Inst Sci & Dept Comp Sci & Engn Chennai Tamil Nadu India

ISBN: (纸本)9783031821554;9783031821561

Microarrays are sophisticated datasets that have a substantial number of features and a limited number of samples. this can lead to a class imbalance problem. Balancing class distribution is an important research domain. In this paper, a novel variational auto encoder model is proposed for class imbalance mitigation. the proposed model employs an adversarial loss function to generate simulated data. the proposed model is evaluated using the autism gene expression dataset. the obtained experimental results prove that the data generated from the proposed model have obtained noteworthy results compared with the original dataset by employing three different classifiers.

关键词： Class imbalance Deep learning Microarray Simulated data Variational autoencoder

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Research on Face recognition System Based on Intelligent machine learning Algorithm 4

Research on Face Recognition System Based on Intelligent Mac...

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4th IEEE international conference on Power, Electronics and Computer Applications, ICPECA 2024

作者： Zou, Xiaosong School of Design and Art Jingdezhen Ceramic University Jingdezhen China

ISBN: (纸本)9798350359589

this paper introduces a real-time face detection technology based on TMS320C6201. through the confidential communication between each subsystem, the synchronization of each subsystem is completed, and the real-time face recognition, feature code extraction, and the closest face matching are carried out. Firstly, Grabcut foreground extraction method is used for pre-background segmentation of recognized images to reduce external interference, and then face detection and identification are carried out according to the segmentation effect. A parallel MPI program is developed by transforming the traditional serialization-based face information updating method into a parallel method. this paper applies existing MPI-based methods and existing web-based facial information acquisition methods to improve the efficiency of existing face recognition technologies. It realizes the distributed processing of the update algorithm in the original face recognition system and enhances the ability of the system to process a large amount of data to achieve the purpose of improving the system performance. the experimental results show that the system combining grab cut and Adaboost algorithm can improve the recognition rate and detection rate, and the recognition speed is faster. © 2024 IEEE.

关键词： Facial recognition grid card algorithm identification simulation test

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Towards Exploring Adversarial learning for Anomaly Detection in Complex Driving Scenes 1

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4th international conference on Deep learning theory and Applications (DeLTA)

作者： Habib, Nour Cho, Yunsu Buragohain, Abhishek Rausch, Andreas Tech Univ Clausthal Inst Software & Syst Engn Arnold Sommerfeld Str 1 D-38678 Clausthal Zellerfeld Germany

ISBN: (数字)9783031390593

ISBN: (纸本)9783031390586;9783031390593

One of the many Autonomous Systems (ASs), such as autonomous driving cars, performs various safety-critical functions. Many of these autonomous systems take advantage of Artificial Intelligence (AI) techniques to perceive their environment. But these perceiving components could not be formally verified, since, the accuracy of such AI-based components has a high dependency on the quality of training data. So machine learning (ML) based anomaly detection, a technique to identify data that does not belong to the training data could be used as a safety measuring indicator during the development and operational time of such AI-based components. Adversarial learning, a sub-field of machine learning has proven its ability to detect anomalies in images and videos with impressive results on simple data sets. therefore, in this work, we investigate and provide insight into the performance of such techniques on a highly complex driving scenes dataset called Berkeley DeepDrive.

关键词： Adversarial learning Artificial intelligence Anomaly detection Berkeley DeepDrive (BDD)

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