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15th International Conference on Advances in computing, Control, and Telecommunication Technologies, ACT 2024

作者： Anuvarshini, G.B. Varshaa, D. Sujithra, M. Devi, D. Sudha Department of Computing- Data Science Coimbatore Institute of Technology Coimbatore India

ISBN: (纸本)9798331300579

Federated learning has emerged as a promising technique in machine learning, enabling collaborative training across distributed datasets. Particularly in fields like healthcare, where data privacy is paramount, federated learning offers a means to improve privacy and security. This paper addresses the critical need for safeguarding sensitive medical data by proposing a privacy-preserving federated learning algorithm. Leveraging homomorphic encryption, the proposed algorithm ensures that the learning process protects both data and model integrity. By employing a secure multi-party computation protocol, the algorithm defends against potential adversaries seeking to compromise the deep learning model. Evaluation on real-world medical datasets demonstrates the effectiveness of the approach in preserving privacy and security amidst collaborative learning environments. © Grenze Scientific Society, 2024.

关键词： Adversarial machine learning

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Plant Disease Identification under Imbalanced dataset using Hybrid Deep Learning Method

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Journal of The Institution of Engineers (India): Series A 2025年第1期106卷 19-29页

作者： Zhou, Changjian Zhang, Xin Department of Data and Computing Northeast Agricultural University Harbin150030 China School of Electrical and Information Northeast Agricultural University Harbin150030 China

Recent studies suggest that plant disease identification via computational approaches is vital for agricultural production. However, there is still a large gap that needs to be bridged while the training data is imbalanced when the number of samples in different categories of the dataset varies greatly. To solve this limitation, a hybrid deep learning method combining deep residual network, dense network, and deep convolution generative adversarial network (DCGAN) is proposed for plant disease identification in this work, which takes advantage of these three models. Including 34,501 original images with 33 categories are collected, where one category contains 4442 samples and another contains 74, causing the phenomenon of data imbalance. Importantly, the imbalanced dataset has a negative impact on training performance. To address this issue, the DCGAN is introduced for data augmentation to make up for the limit of training data in this research. In addition, the residual and dense network are combined as a novel deep learning method to improve prediction ability. Together, the original and generated images are integrated as a mixed dataset for training, and only the original images were utilized for testing. Experimental results indicated that the presented approach achieved 0.977 F1-score and 0.987 test accuracy, outperformed the existing state-of-the-art models. These findings indicate that the hybrid deep learning approach, through the ingenious integration of the strengths of three sub-networks, significantly enhances the generalization capability of the model. This methodology not only optimizes overall performance but also underscores the profound potential in tackling these complex problems. Furthermore, a smartphone-based point-to-point identification system was designed to provide convenience for users in practical application. © The Institution of Engineers (India) 2024.

关键词： Generative adversarial networks

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Attention boosted Individualized Regression 38

Attention boosted Individualized Regression

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38th Conference on Neural Information Processing Systems, NeurIPS 2024

作者： Yang, Guang Cao, Yuan Feng, Long Department of Data Science College of Computing City University of Hong Kong Hong Kong Department of Statistics and Actuarial Science School of Computing and Data Science The University of Hong Kong Hong Kong

Different from classical one-model-fits-all strategy, individualized models allow parameters to vary across samples and are gaining popularity in various fields, particularly in personalized medicine. Motivated by medical imaging analysis, this paper introduces a novel individualized modeling framework for matrix-valued data that does not require additional information on sample similarity for the individualized coefficients. Under our framework, the model individualization stems from an optimal internal relation map within the samples themselves. We refer to the proposed method as Attention boosted Individualized Regression, due to its close connections with the self-attention mechanism. Therefore, our approach provides a new interpretation for attention from the perspective of individualized modeling. Comprehensive numerical experiments and real brain MRI analysis using an ADNI dataset demonstrated the superior performance of our model. © 2024 Neural information processing systems foundation. All rights reserved.

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Patterns Recognition of Sentimental Patterns from the X-dataset using Deep Learning Algorithm 4

Patterns Recognition of Sentimental Patterns from the X-Data...

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4th International Conference on Soft computing for Security Applications, ICSCSA 2024

作者： Konduri, Praveen S R Department of Data Science School of Computing Mohan Babu University Tirupati India

ISBN: (纸本)9798331515720

This research investigates the application of deep learning techniques to sentiment analysis, a field focused on mining online platforms for subjective assessments. By combining deep neural networks (DNNs) with particle swarm optimization (PSO), we propose a hybrid method to improve feature selection and enhance the accuracy of sentiment classification. The study employs a dataset collected from Twitter using the Ruby Twitter API and evaluates the performance of the proposed method using k-fold cross-validation. The results are compared with the previously used firefly search (FS) method to demonstrate the effectiveness of the hybrid approach. This research contributes to the advancement of sentiment analysis techniques and their application in various domains, such as social media monitoring and market research. © 2024 IEEE.

关键词： Particle swarm optimization (PSO)

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Real-time updating of dynamic social networks for COVID-19 vaccination strategies

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Journal of Ambient Intelligence and Humanized computing 2024年第3期15卷 1981-1994页

作者： Cheng, Sibo Pain, Christopher C. Guo, Yi-Ke Arcucci, Rossella Data Science Instituite Department of Computing Imperial College London London United Kingdom Department of Earth Science and Engineering Imperial College London London United Kingdom

Vaccination strategy is crucial in fighting the COVID-19 pandemic. Since the supply is still limited in many countries, contact network-based interventions can be most powerful to set an efficient strategy by identifying high-risk individuals or communities. However, due to the high dimension, only partial and noisy network information can be available in practice, especially for dynamic systems where contact networks are highly time-variant. Furthermore, the numerous mutations of SARS-CoV-2 have a significant impact on the infectious probability, requiring real-time network updating algorithms. In this study, we propose a sequential network updating approach based on data assimilation techniques to combine different sources of temporal information. We then prioritise the individuals with high-degree or high-centrality, obtained from assimilated networks, for vaccination. The assimilation-based approach is compared with the standard method (based on partially observed networks) and a random selection strategy in terms of vaccination effectiveness in a SIR model. The numerical comparison is first carried out using real-world face-to-face dynamic networks collected in a high school, followed by sequential multi-layer networks generated relying on the Barabasi-Albert model emulating large-scale social networks with several communities. © The Author(s) 2023.

关键词： COVID-19

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Predictive Stress Management for Working Women in Educational Institutions using Federated Learning and Wearable Technology 8

Predictive Stress Management for Working Women in Educationa...

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8th International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud), I-SMAC 2024

作者： Devi, D. Sudha Sujithra, M. Velvadivu, P. Kavya, V.V. Jhunandhini, S. Data Science Coimbatore Institute of Technology Department of Computing Tamil Nadu Coimbatore India

ISBN: (纸本)9798350376425

The proposed methodology uses Artificial Neural Networks (ANN) to predict stress levels of women working in educational institutions by analyzing physiological and demographic data collected through wearable technology. TensorFlow Federated framework is used here to ensure the data privacy by enabling secure and decentralized training of the proposed model. The approach integrates various steps, including data preprocessing, feature selection, and model training with federated learning. Based on predicted stress levels, the platform provides personalized recommendations to manage stress and improve mental well-being, while keeping user data private. The process involves collecting data from surveys and wearable devices, optimizing the model through techniques like hyperparameter tuning and regularization, and deploying the system via a web interface for real-time stress management. © 2024 IEEE.

关键词： Adversarial machine learning

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Analysis of Factors influencing Early Detection of Breast Cancer 3

Analysis of Factors influencing Early Detection of Breast Ca...

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3rd International Conference on Emerging Trends in Electrical, Control, and Telecommunication Engineering, ETECTE 2024

作者： Malik, Hassal Sonia Sial, Umaira Ahmad Bukhari, Faisal Department of Data Science Faculty of Computing and Information Technology University of the Punjab Lahore Pakistan

ISBN: (纸本)9798331504861

Carcinoma, particularly breast cancer, poses a formidable challenge, especially in Pakistan where it stands as the most prevalent cancer among women. This research investigates the complex landscape of breast cancer in Pakistan, utilizing a dataset from local hospitals. Through comprehensive statistical analyses and advanced machine learning techniques, including XGBoost and RandomForest, the study uncovers significant associations between socio-economic and psychological factors and the timing of breast cancer diagnosis among Pakistani women. Factors such as embarrassment, fear of treatment, and lack of awareness emerge as crucial determinants. The findings provide valuable insights for tailored interventions and contribute to the global fight against breast cancer. ©2024 IEEE.

关键词： Lung cancer

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Polycystic Ovary Syndrome Prediction through CNN based Image Analysis: A Deep Learning Based Approach 8

Polycystic Ovary Syndrome Prediction through CNN based Image...

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8th International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud), I-SMAC 2024

作者： Karthik, Yazhini Sruthi, R. Sujithra, M. Coimbatore Institute of Technology Department of Computing - Data Science Tamil Nadu Coimbatore India

ISBN: (纸本)9798350376425

Polycystic Ovary Syndrome (PCOS) is a prevalent hormonal disorder affecting women during their reproductive years. It is characterized by irregular menstrual cycles, excessive hair growth, and the presence of cysts in the ovaries. The diagnosis of PCOS typically involves a combination of clinical evaluations and laboratory tests. The paper proposes a novel approach using Convolutional Neural Networks (CNNs) for predicting PCOS through the analysis of ultrasound images. The dataset comprises images obtained from ovarian ultrasound scans of both PCOS-positive and PCOS-negative individuals. These images undergo preprocessing and augmentation techniques such as rescaling, shearing, zooming, flipping, and rotation to enhance the diversity of the training data. Multiple CNN-based models with varying architectures are constructed, altering parameters such as the number of convolutional and pooling layers, activation functions, and other hyperparameters. The models are trained and validated using a split of the dataset into training and validation sets, with metrics like loss and accuracy monitored during training to assess model performance. Upon completion of training, the models are tested using unseen data to evaluate their predictive capabilities. The results demonstrate that deep learning models trained on ultrasound images can accurately predict the presence or absence of PCOS. Such models hold promise in aiding healthcare professionals with early diagnosis and management of PCOS, ultimately improving patient care and outcomes. © 2024 IEEE.

关键词： Ultrasonic imaging

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Peer Tutoring and its Impact on Academic Achievements

Peer Tutoring and its Impact on Academic Achievements

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2024 International Conference on IT and Industrial Technologies, ICIT 2024

作者： Aqeel, Muhammad Hassan, Manahil Ghani, Affaf Zaheer, Manahil Anjum, Bilal Bukhari, Faisal Department of Data Science Faculty of Computing and Information Technologys University of the Punjab Lahore Pakistan

ISBN: (纸本)9798331504465

Peer tutoring is gaining attention rapidly nowadays, providing a learning environment where students have the opportunity to learn and grow through collaboration and effective communication. This study focuses on the impact of peer tutoring by collecting data from different students as well as surveys and then analyzing the pre and post-tutoring assessments to determine the impact of peer tutoring. It employs statistical techniques to study the impact of peer tutoring on both tutee and tutor, their self-confidence, and the overall academic achievement gained through tutoring. © 2024 IEEE.

关键词： Federated learning

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Classification of Fetal Health on Cardiotocograph data using Machine Learning Techniques 15

Classification of Fetal Health on Cardiotocograph Data using...

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15th International Conference on Advances in computing, Control, and Telecommunication Technologies, ACT 2024

作者： Deepa, M.S. Sujithra, M. Department of Computing-Data Science Coimbatore Institute of Technology Tamil Nadu Coimbatore India

ISBN: (纸本)9798331300579

Pregnancy and fetus development is an incredibly intricate biological process that can go wrong when the health of fetus or the mother gets ***, India has the highest number of stillbirths, with an estimated 5,92,100 deaths per year, and a WHO estimated rate of 22 per 1000 total *** is one procedure used to check whether the fetus is growing as *** diagnostic procedure measures the mother's uterine contractions and the fetus' heartbeat, usually in the third trimester of pregnancy when the fetus heart is fully *** paper aims to classify the results into one of three states (physiological, suspicious or abnormal) based on cardiotocographic data using machine learning algorithms and compare the accuracies of different ML classification models such as logistic regression, k-nearest neighbor, decision tree, random forest classifier and support vector machine. © Grenze Scientific Society, 2024.

关键词： Support vector machines

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