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Preserving Accuracy in Federated learning via Equitable Model and Efficient Aggregation 6th

Preserving Accuracy in Federated Learning via Equitable Mo...

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6th international conference on Recent Trends in image processing and pattern recognition, RTIP2R 2023

作者： Mehdi, Muntazir Makkar, Aaisha Conway, Myra Sama, Lakshit University of Derby DerbyDE22 1GB United Kingdom Capgemini Engineering Adelaide Australia

ISBN: (纸本)9783031530814

machine learning has revolutionized research by extracting complicated patterns from complex data, particularly in healthcare and medical imaging, where accurate diagnosis is critical. The concept of federated learning has gained popularity in the field of machine learning as a viable technique for addressing privacy issues in distributed settings. This research explores federated learning in healthcare, demonstrating its capability to achieve results comparable to centralized data while enhancing the accuracy of deep learning models for clinical data interpretation. To ensure reliable model performance during federated learning rounds, this study introduces a proactive mechanism for coordinating server updates with equitable client modifications. The equitable model, designed to reduce accuracy fluctuations, consistently improves accuracy across multiple training rounds on a non-IID dataset. We achieved smooth accuracy improvement by implementing the novel Equitable model, resulting in robust model development. As healthcare AI continues to advance, federated learning emerges as a critical tool for developing precise prediction models while preserving patient data privacy and aligning with increasingly strict data standards worldwide, such as GDPR regulations. This strategic approach not only promotes ethical, efficient, and secure progress in medical research and practice, but it also emphasizes the importance of protecting patient data privacy while utilizing machine learning’s potential. © 2024, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Clinical research

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Iris Grid image Classification using Naive Bayes for Human Biometric System 1

Iris Grid Image Classification using Naive Bayes for Human B...

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1st international conference on Information System and Information Technology, ICISIT 2022

作者： Wibawa, Adhi Dharma Pamungkas, Yuri Perdana, Muhammad Ilham Rachmatika, Ratih Institut Teknologi Sepuluh Nopember Department of Electrical Engineering Surabaya Indonesia Institut Teknologi Sepuluh Nopember Department of Computer Engineering Surabaya Indonesia

ISBN: (数字)9781665402002

ISBN: (纸本)9781665402002

Biometrics is a measurement of a person's physical and behavioral characteristics. Iris image is one of many biometrics data such as fingerprint, voice, face, and gait that can be used as an identifier. Iris is the colored part of the eye that helps the pupil see clearly and regulates light entry. Iris recognition is one of the important topics in biometric systems because of its unique pattern. Several related studies have been carried out to automatically obtain the most efficient method to understand and recognize the iris for human verification. This study proposes an analysis of iris images for biometrics systems with effective image processing techniques for system recognition. CVBL Iris image dataset was used in this study with 4320 iris images. After converting the iris image into a rectangle form, the Grid iris image experiment was implemented to find the highest accuracy. Several iris image grid-size were simulated to find the best accuracy. Multinomial Naive Bayes is used as a classifier. The Naive Bayes method is a machine learning method that uses probability calculations (rules-based). This algorithm uses probability and statistical methods, which predict future probabilities based on the previous data. The study results indicate that the proposed method can recognize the iris by identifying its fibers and encoding the fibers data using a grid image approach, with a classification accuracy of 92.37%, using an iris grid size of 70x50 pixels. This research can be useful for developing human biometric systems based on iris with a simple preprocessing approach. © 2022 IEEE.

关键词： Biometrics

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An Effective Automatic recognition Approach of Chinese Herbal Medicine by Micro image processing 21

An Effective Automatic Recognition Approach of Chinese Herba...

引用

21st IEEE international conference on Ubiquitous Computing and Communications / 5th IEEE international conference on Data Science and Computational Intelligence / 21st IEEE international conference on Computer and Information Technology

作者： Pang, Guangyao Yu, Zhenming Pan, Honglin Xie, Chengwen Gong, Ping Zhu, Xiaoying Peng, Zizhen Wuzhou Univ Guangxi Key Lab Machine Vis & Intelligent Control Wuzhou 543002 Peoples R China Wuzhou Univ Guangxi Coll & Univ Key Lab Ind Software Technol Wuzhou 543002 Peoples R China

ISBN: (纸本)9781665477260

The identification of traditional Chinese medicine is the key to control the quality of traditional Chinese medicine and ensure the safety and effectiveness of clinical medication. Compared with the physical and chemical identification methods with expensive equipment and complex operation, microscopic image identification of traditional Chinese medicine is an effective method with low cost. However, this method still has a high learning cost and identification errors due to staff fatigue. Therefore, this paper designs an effective automatic recognition approach of Chinese herbal medicine by micro image processing. The core of this method is the introduction of transfer learning and data enhancement methods, which effectively alleviates the problem of insufficient number of microscopic image data samples in the microscopic recognition of traditional Chinese medicine, and realizes the automatic recognition of traditional Chinese medicine. We construct a library of microscopic recognition features of Chinese herbal medicine, and designe evaluation experiments on this basis. The results show that the recognition performance of our method is better than that of SSD method, especially the F1 value is increased by 7.25 %.

关键词： image recognition identification of traditional Chinese medicine transfer learning data enhancement microscopic image

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Fake News Investigation Using Ensemble machine learning Techniques 6th

Fake News Investigation Using Ensemble Machine Learning Tech...

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6th international conference on Recent Trends in image processing and pattern recognition, RTIP2R 2023

作者： Jain, Jai Dubey, Vansh Sama, Lakshit Kumar, Vimal Singh, Simarpreet Budhiraja, Ishan Arora, Ruchika Bennett University Uttar Pradesh Greater Noida India Capgemini Engineering Melbourne Australia SR University Ananthasagar Telangana Hasanparthy Hanumakonda India

ISBN: (纸本)9783031530814

The classification of any information as true or false has piqued the curiosity of researchers all around the world. Different types of studies are done to document the impact of misleading and fake news on the general public, as well as people’s reactions to such news. Falsified news or fabricated posts are any textual or visual content that is fake/false that is created in order for readers to believe in anything that isn’t true. For instance, a news item headlined "Beasts in White Aprons" was recently circulated on the microblogging platform-Facebook, by an acknowledged reporter from Srinagar, J &K, and many began to believe it, despite the fact that it was completely false. Therefore, the main goal of this research is to apply various machine learning models to distinguish between real and fraudulent news. By using several machine learning models to discriminate between authentic and false news, we add to the expanding body of research on identifying fake news in this work. Our model performs better in scenarios in which there is limited data. © 2024, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Decision trees

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NeuroAI: Emerging Artificial Intelligence and image processing Techniques in Neuroscience for Enhanced Medical Diagnosis of Brain Tumor 1

NeuroAI: Emerging Artificial Intelligence and Image Processi...

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1st international conference on Pioneering Developments in Computer Science and Digital Technologies, IC2SDT 2024

作者： Bhatt, stuti Sharma, Sachin Bhadula, Shuchi Graphic Era Deemed to be University Department of Computer Science and Engineering Dehradun India Graphic Era Deemed to be University Graphic Era Hill University Department of Computer Science and Engineering Dehradun India

ISBN: (纸本)9798350365016

The combination of Artificial Intelligence (AI) and Neuroscience speaks to an advantageous relationship with significant suggestions. AI, a multidisciplinary field in CS which points to imitate insights in machines, whereas Neuroscience explores the brain's structure and cognitive capacities. The union of these disciplines has driven to shared headways. Neuroscience has altogether affected AI, motivating profound neural systems based on natural neural systems. AI exceeds expectations in assignments such as NLP, object detection, and speech recognition. AI, drawing from neuroscience, utilizes support learning standards watched in people and creatures to create autonomous algorithms. AI's capacity to analyze complex information and reveal unapparent design is important for interpreting neuroscience information. AI interfaces can extricate neural signals from the human brain and interpret them into commands for gadgets like automated arms, supporting those with versatility impediments. AI moreover contributes to restorative areas, analyzing neuroimaging information and supporting early discovery of neurological disarranges. This paper conducts a comprehensive audit and usage of the AI and neuroscience cooperative energy, particularly brain tumor, emphasizing their merging for untimely revelation and forecast of neurological clutters. This paper primarily centers on early identifying and localizing the brain tumor locale utilizing division of patient's MRI pictures. © 2024 IEEE.

关键词： image enhancement

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A Minimalistic Model for Converting Basic Cars into Semi-Autonomous Vehicles Using AI and image processing 1

A Minimalistic Model for Converting Basic Cars into Semi-Aut...

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1st IEEE international conference on Applied Intelligence and Sustainable Computing, ICAISC 2023

作者： Kashyap, Amith Ray, Aniket Kalyan, B. Bhushan, Pushpesh NMIT Dept of ISE Bengaluru India

ISBN: (纸本)9798350323795

Autonomous vehicles are vehicles that can operate without human intervention, and can go anywhere a traditional vehicle can go and perform any task that a skilled human driver can. These vehicles can address environmental and safety concerns by reducing crashes and decreasing reliance on fossil fuels. Human error accounts for 94% of road accidents, which can be significantly reduced by self-driving cars. In our project, we aim to convert any car with basic cruise control features into a semi-autonomous vehicle using image detection and processing, machine learning, and artificial intelligence. Our system will be a minimalistic model of the real-time product, where we aim to achieve functions such as lane switching, sign detection, object detection, and braking. By using this technology, we can create a safer and more sustainable driving experience for everyone. © 2023 IEEE.

关键词： Autonomous vehicles

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An Efficient Real-Time Word-Level recognition of Indian Sign Language 4th

An Efficient Real-Time Word-Level Recognition of Indian Sign...

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4th international conference on Innovations in Computational Intelligence and Computer Vision, ICICV 2024

作者： Chaitanya Madhav, R. Shet, Prasad Mohan Murali, K.S. Likhith, R. Mamatha, H.R. Department of Computer Science PES University Karnataka Bengaluru India

ISBN: (纸本)9789819769919

Advancements in technology have catalyzed innovations in machine learning and artificial intelligence, particularly in image recognition. While existing research has predominantly focused on static sign recognition for Sign Language, this study proposes a real-time dynamic sign recognition of Indian Sign Language. Given the fundamental importance of communication, especially for the hearing and speech-impaired, this research addresses the critical need for instant translation of sign language into text. To resolve this communication gap, this research introduces a comprehensive dataset using references from the "Indian Sign Language Research and Training Centre New Delhi" an official government-backed YouTube Channel dedicated to Indian Sign Language, which standardizes Indian Sign Language across the country. The dataset comprises 60 signs encompassing verbs, animals, vegetables, legal terms, fruits, emotions and more. The authors utilized Mediapipe as a feature extractor to train a total of nine models, including seven deep learning models namely LstM, CNN, GRU, Bidirectional LstM, stacked LstM, Hybrid CNN-GRU, and Convolutional LstM, along with two machine learning models, namely Random Forest and XGBoost. Notably, the Convolutional LstM model achieved the highest accuracy of 99.88%. Reliable evaluation metrics like Mean Average Precision, Confusion Matrix, Accuracy, F1-Score were employed, which shows the correctness of the signs predicted and ensures the precision of dynamic sign recognition. The culmination of this research results in a real-time system capable of instantaneously identifying dynamic signs. This contribution spans diverse domains such as pattern recognition, Computer Vision, Video recognition, etc., addressing key challenges like the need for gloves or additional sensors, the requirement for many videos to train a single sign, utilization of simple signs, high processing power demand, signer dependency, occlusions, pushing the boundaries of sig

关键词： Deep learning

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Small-Scale CNN-N model for Covid-19 Anomaly Detection and Localization From Chest X-Rays 1

Small-Scale CNN-N model for Covid-19 Anomaly Detection and L...

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1st international conference on Artificial Intelligence Trends and pattern recognition, ICAITPR 2022

作者： Marusani, Jagadeesh Sudha, B.G. Darapaneni, Narayana Pes University Data Science and Machine Learning Bengaluru India Data Science and Machine Learning Great Learning Bengaluru India Artificial Intelligence and Machine Learning Great Learning Bengaluru India

ISBN: (数字)9781665425216

ISBN: (纸本)9781665425216

Covid-19 has been posing a serious challenge to scientists and health organizations around the world in terms of detection and its treatment. Common methods are CT-Scans and X-rays to analyze the images of lungs for COVID-19. These days diagnosing covid-19 by manually looking at the reports has become difficult and challenging in the pandemic. Pneumonia and pulmonary infections along with covid-19 cause inflammation and fluids in the lungs. Covid-19 X-rays are very similar to viral and bacterial Pneumonia X-rays. So it becomes very difficult to differentiate between covid-19 and Pneumonia. In this paper we propose a computer vision model to detect the presence of covid19 infection along with the localization of the infection in the lungs. 6337 images consisting of Negative for pneumonia, Typical Appearance, Intermediate Appearance and Atypical Appearance is considered. Although there are pre-trained CNN models which perform well on the data, this paper aims at reducing the size of the model and validate its performance on other datasets. Different image sizes are also considered. A small scale CNN model is built from scratch to detect and localize covid-19 abnormalities on chest radiographs using object detection algorithms like Yolov5 with different weights. There is a significant reduction in model size and parameters compared to many state of the art pre-trained models thereby ensuring efficient detection of covid-19 anomalies and show the region of infection to ensure timely treatment before it causes severe infection. © 2022 IEEE.

关键词： Object detection

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Search-Time Efficient Device Constraints-Aware Neural Architecture Search 10th

Search-Time Efficient Device Constraints-Aware Neural Archit...

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10th Biennial international conference on pattern recognition and machine Intelligence (PReMI)

作者： Dutta, Oshin Kanvar, Tanu Agarwal, Sumeet Indian Inst Technol Delhi India

ISBN: (纸本)9783031451690;9783031451706

Edge computing aims to enable edge devices, such as IoT devices, to process data locally instead of relying on the cloud. However, deep learning techniques like computer vision and natural language processing can be computationally expensive and memory-intensive. Creating manual architectures specialized for each device is infeasible due to their varying memory and computational constraints. To address these concerns, we automate the construction of task-specific deep learning architectures optimized for device constraints through Neural Architecture Search (NAS). We present DCA-NAS, a principled method of fast neural network architecture search that incorporates edge-device constraints such as model size and floating-point operations. It incorporates weight sharing and channel bottleneck techniques to speed up the search time. Based on our experiments, we see that DCA-NAS outperforms manual architectures for similar sized models and is comparable to popular mobile architectures on various image classification datasets like CIFAR-10, CIFAR-100, and imagenet-1k. Experiments with search spaces-DARTS and NAS-Bench-201 show the generalization capabilities of DCA-NAS. On further evaluating our approach on HardwareNAS-Bench, device-specific architectures with low inference latency and state-of-the-art performance were discovered.

关键词： Neural Architecture Search DARTS Meta-learning Edge Inference Constrained Optimization

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Sugarcane Bud Detection Using YOLOv5 6th

Sugarcane Bud Detection Using YOLOv5

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6th international conference on Recent Trends in image processing and pattern recognition, RTIP2R 2023

作者： Sindhe, P. Swathi Hegadi, Ravindra S. Department of Computer Science Central University of Karnataka Kadaganchi Karnataka Kalaburagi India

ISBN: (纸本)9783031530814

This paper addresses the labor-intensive and wasteful nature of the traditional sugarcane bud cutting method. To overcome these challenges, the proposed approach leverages YOLOv5 technology for sugarcane bud identification. The machine-learning model is trained with diverse data samples, enabling it to accurately distinguish between sugarcane buds and other elements in the images. The implementation yields the best of 79% accuracy in bud detection. By automating the process, the proposed method significantly reduces labor and time requirements while minimizing sugarcane wastage. This innovation presents promising implications for the sugarcane industry, as it streamlines bud identification and optimizes resource utilization. Furthermore, the adoption of YOLOv5 technology can potentially extend to other agricultural domains, offering opportunities for enhanced crop management and sustainable farming practices. © 2024, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： machine learning

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