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8th International Conference on Inventive Systems and Control, ICISC 202

作者： Shareef, D.K. Jyothsna, V. School of Computing Department of Computer Science and Engineering Andhra Pradesh Tirupati India School of Computing Department of Data Science Andhra Pradesh Tirupati India

ISBN: (纸本)9798350386578

This research study presents a comprehensive survey of deep learning methods applied in order to improve the security along with accuracy of the mobile sink position prediction in Vehicular Pattern Wireless Sensor Networks (V-WSNs). V-WSNs are pivotal in modern wireless communication, especially in dynamic environments like urban traffic systems. The survey begins with an overview of V-WSNs, highlighting their unique characteristics, applications, and challenges, with a particular focus on mobile sink position prediction. It then delves into the nuances of routing protocols in V-WSNs, discussing the advantages and limitations of existing methods, and underscores the necessity for deep learning-based solutions. The paper further explores the integration of deep learning in WSNs, examining its applications, benefits, and inherent challenges. The significance of accurate and secure routing is analyzed, followed by an in-depth discussion on the integration of deep learning for enhancing these aspects in V-WSN routing. The paper also addresses the current challenges and open issues in mobile sink prediction and secure, accurate routing, paving the way for future research directions. A comparative analysis of various deep learning approaches and their practical applications in real-world scenarios is presented, offering insights into the effectiveness of these techniques. The survey concludes by summarizing the key findings and reaffirming the importance of integrating deep learning for secure and accurate mobile sink position prediction in V-WSNs. © 2024 IEEE.

关键词： Wireless sensor networks

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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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GAM-GCN: Decomposing Graph Convolutional Networks via Generalized Additive Models 15

GAM-GCN: Decomposing Graph Convolutional Networks via Genera...

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15th IEEE International Conference on Knowledge Graph, ICKG 2024

作者： Liu, Chen Yu, Lequan Han, Kai Yin, Guosheng The University of Hong Kong School of Computing and Data Science Hong Kong Hong Kong

ISBN: (纸本)9798331508821

The vast adoption of graph neural networks (GNNs) in broad applications calls for versatile interpretability tools so that a better understanding of the GNNs' intrinsic structures can be gained. We propose an interpretable GNN framework to decompose the prediction into the additive combination of node features' main effects and the contributions of edges. The key component of our framework is the generalized additive model with the graph convolutional network (GAM-GCN) that allows for global node feature interpretations. The inherent interpretability of GAM and the expressive power of GCN are preserved and naturally connected. Further, the effects of neighboring edges are measured by edge perturbation and surrogate linear modeling, and the most important subgraph can be selected. We evaluate the proposed approach using extensive experiments and show that it is a useful tool for interpreting GNNs in the attempt to unlock the black box. © 2024 IEEE.

关键词： Graph neural networks

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XGCN:a library for large-scale graph neural network recommendations

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Frontiers of Computer science 2024年第3期18卷 247-249页

作者： Xiran SONG Hong HUANG Jianxun LIAN Hai JIN National Engineering Research Center for Big Data Technology and System Services Computing Technology and System LabCluster and Grid Computing LabSchool of Computer Science and TechnologyHuazhong University of Science and TechnologyWuhan 430074China Microsoft Research Asia Beijing 100080China

1 Introduction Graph Neural Networks(GNNs)have gained widespread adoption in recommendation systems,and nowadays there is a pressing need to effectively manage large-scale graph data[1].When it comes to large graphs,GNNs may encounter the scalability issue stemming from their multi-layer messagepassing ***,scaling GNNs has emerged as a crucial research area in recent years,with numerous scaling strategies being proposed.

关键词： scaling gained operations

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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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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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dataset of Quotation Attribution in German News Articles 30

Dataset of Quotation Attribution in German News Articles

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Joint 30th International Conference on Computational Linguistics and 14th International Conference on Language Resources and Evaluation, LREC-COLING 2024

作者： Petersen-Frey, Fynn Biemann, Chris House of Computing and Data Science & Language Technology Group Universität Hamburg Germany

ISBN: (纸本)9782493814104

Extracting who says what to whom is a crucial part in analyzing human communication in today's abundance of data such as online news articles. Yet, the lack of annotated data for this task in German news articles severely limits the quality and usability of possible systems. To remedy this, we present a new, freely available, creative-commons-licensed dataset for quotation attribution in German news articles based on WIKINEWS. The dataset provides curated, high-quality annotations across 1000 documents (250,000 tokens) in a fine-grained annotation schema enabling various downstream uses for the dataset. The annotations not only specify who said what but also how, in which context, to whom and define the type of quotation. We specify our annotation schema, describe the creation of the dataset and provide a quantitative analysis. Further, we describe suitable evaluation metrics, apply two existing systems for quotation attribution, discuss their results to evaluate the utility of our dataset and outline use cases of our dataset in downstream tasks. © 2024 ELRA Language Resource Association: CC BY-NC 4.0.

关键词： annotation attribution dataset German news quotation quote

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Traffic Sign Detection Using YOLOv8 2

Traffic Sign Detection Using YOLOv8

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2nd International Conference on Intelligent data Communication Technologies and Internet of Things, IDCIoT 2024

作者： Kumar, Rahul Gupta, Aniket Rajeswari, D. School of Computing College of Engineering and Technology Department of Data Science and Business Systems India

ISBN: (纸本)9798350327533

Traffic signals and other signs like parking., stop signs., etc. have become very crucial in autonomous and s elf-driving cars as it helps the smart system to comply with the basic traffic rules along with that it helps navigate routes based on the signs thus enabling a more secure driving experience for the drivers. There have been a lot of new algorithms that have emerged in the past recent years regarding this. In this paper., this research has used the new YOLOv8 object detection system to help us detect traffic signs as it is much fas ter and more precis e than its previous iterations. To improve the algorithm., this paper has used a dataset comprising photos of traffic signs taken at different angles and different light intensities. This system can predict the traffic signs with 93% accuracy. © 2024 IEEE.

关键词： Autonomous vehicle Deep Learning Traffic sign detection yolov8

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