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Prediction of Fetal Brain Anomalies Using Deep Learning Models 5

Prediction of Fetal Brain Anomalies Using Deep Learning Mode...

引用

5th International Conference on IoT Based Control Networks and Intelligent Systems, ICICNIS 2024

作者： Subburaj, Venkateshwari Pandiyarajan, Pandiselvam School of Computing Department of Computer Application Kalasalingam Academy of Research and Education TamilNadu Krishnankoil India School of Computing Department of Computer Science and Engineering Kalasalingam Academy of Research and Education TamilNadu Krishnankoil India

ISBN: (纸本)9798331518097

Fetal brain anomaly prediction is important for fetal medicine, as well as for prenatal health care. Fetal anomalies are classified into two types, anomalies in the fetus' body parts including heart, lung, and kidney anomalies in facial features. Thus, to predict the anomalies in the fetal brain, various deep-learning models are used in this paper. The LeNet, AlexNet, ResNet, VGGNet, GoogLeNet and ZFNet were used. From the results obtained, the ResNet method achieved 99% accuracy with the given 3D ultrasound images of the fetal brain compared with other models and benchmark algorithms like SVM, Logistic regression, and KNN. The results of this paper are useful to identify fetal anomalies. Hence, this approach will be useful for improving outcomes for individuals and society as much as possible. © 2024 IEEE.

关键词： Prediction models

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Intelligent Framework for Task Placement and Resource Allocation for IoT in the Fog-Cloud Scenario

Intelligent Framework for Task Placement and Resource Alloca...

引用

2022 IEEE International Conference on Current Development in engineering and Technology, CCET 2022

作者： Manihar, Shifa Patel, Ravindra Agrawal, Sanjay Computer Science Engineering University Institute of Technology Rgpv Bhopal India Computer Engineering and Application Nitttr Bhopal India

ISBN: (纸本)9781665454155

In order to identify the latency-critical applications, this research develops an intelligent task placement and resource allocation framework (ITPRAF) that makes use of supervised feed forward neural networks. This algorithm runs through two stages. To decide whether to schedule a task for execution in the fog or send it to the cloud in the first part, this machine learning-based system uses the task's attributes as input. To ensure the best resource usage, jobs that are put to fog are reshuffled keeping into account the priority at the fog queue in the second phase. Four mechanisms for resource allocation in fog are presented in this research. Through the simulation results from various iterations, the reaction time, resource consumption, and delay satisfaction rate of each scenario were compared. © 2022 IEEE.

关键词： Internet of things

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Construction of PCOS Prediction Model based on BP Neural Network 6

Construction of PCOS Prediction Model based on BP Neural Net...

引用

6th International Conference on Mobile Computing and Sustainable Informatics, ICMCSI 2025

作者： Hu, Xianqing Yadav, Amit Khan, Asif Sah, Abhishek Pratap Azam, Sami School of Computer and Software Chengdu Neusoft University Chengdu China Faculty of Science and Technology Charles Darwin University NT Australia Department of Computer Application Integral University Lucknow India School of Management Science and Engineering University of Electronic Science and Technology of China Chengdu China

ISBN: (纸本)9798331522667

Polycystic ovary syndrome (PCOS), a common endocrine-metabolic disorder affecting about 10-13% of women during reproductive age worldwide, often leads to irregular menstruation, infertility, obesity, and long-term health risks such as diabetes and cardiovascular disease. In this paper, clinical data from PCOS patients and healthy control group were collected from ten hospitals containing 37 key indicators such as age, weight, BMI, menstrual cycle, etc. BP (Back Propagation) Neural Network prediction models were constructed and compared using the collected data and the cross-validation method was used for parameter tuning. It was found that BP Neural Network performed particularly well on the test set, and both demonstrated high prediction accuracy and generalization ability which provided strong evidence for the early identification and early valuable intervention opportunities for PCOS patients. © 2025 IEEE.

关键词： Prediction models

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Mask Wearing Detection System for Epidemic Control Based on STM32 6th

Mask Wearing Detection System for Epidemic Control Based on ...

引用

6th International Conference on Innovative Computing and Communication, ICICC 2023

作者： Luoli Yadav, Amit Khan, Asif Varish, Naushad Singh, Priyanka Thakkar, Hiren Kumar Department of Computer and Software Chengdu Neusoft University Chengdu China College of Engineering IT and Environment Charles Darwin University DarwinNT Australia Department of Computer Application Integral University Lucknow India Department of Computer Science and Engineering GITAM University Hyderabad502329 India Department of Computer Science and Engineering SRM University Andhra Pradesh Amravati522502 India Department of Computer Science and Engineering Pandit Deendayal Energy Univrsity Gujarat Gandhinagar382007 India

ISBN: (纸本)9789819940707

This paper designs an epidemic prevention and control mask wearing detection system based on STM32, which is used to monitor the situation of people wearing masks. Tiny-YOLO detection algorithm is adopted in the system, combined with image recognition technology, and two kinds of image data with and without masks are used for network training. Then, the trained model can be used to carry out real-time automatic supervision on the wearing of masks in the surveillance video. When the wrong wearing or not wearing masks are detected, the buzzer will send an alarm, so as to effectively monitor the wearing of masks and remind relevant personnel to wear masks correctly. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd 2024.

关键词： Image recognition

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Healthcare management in Bigdata with Blockchain and IOT integrated System 1

Healthcare management in Bigdata with Blockchain and IOT int...

引用

1st IEEE International Conference on Advances in Computing, Communication and Networking, ICAC2N 2024

作者： Kaliyahan, Shalini Bhatnagar, Prachi Gupta, Shilpi Jain, Archana Malik, Suraj Gochhait, Saikat Sharma, Aditi Pandey, Mahima Shankar IIMT University Department of Computer Science and Application Uttar Pradesh Meerut India Meerut Institute of Technology Department of Computer Science and Engineering Uttar Pradesh Meerut India IIMT University Department of Computer Science and Engineering Uttar Pradesh Meerut India Constituent of Symbiosis International Deemed University Symbiosis Institute of Digital and Telecom Management Pune India Galgotia College of Engineering and Technology Department of Computer Science and Engineering Uttar Pradesh Greater Noida India

ISBN: (纸本)9798350356816

In 21st decade, healthcare is one of the biggest challenges, and the current scenario very flexible to adapt the effective execution technology solutions like internet of things (IoT). Through this a patient may access the treatment remotely and rapidly. Most suitable and effective applications need complex data of personal care, and reliability among the entire participated component or parties. Blockchain technology has been captured the industry domain with its computation and trusted model among participated parties. This paper presents a data storage decision framework for managing blockchain storage using on-chain and off-chain methods. There is a need for making balanced decisions between the very expensive on-chain storage methods and the less secure off-chain storage, towards optimizing blockchain storage costs. © 2024 IEEE.

关键词： Cloud computing architecture

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PMT-IQA: Progressive Multi-task Learning for Blind Image Quality Assessment 20th

PMT-IQA: Progressive Multi-task Learning for Blind Image Qu...

引用

20th Pacific Rim International Conference on Artificial Intelligence, PRICAI 2023

作者： Pan, Qingyi Guo, Ning Qingge, Letu Zhang, Jingyi Yang, Pei Department of Computer Technology and Application Qinghai University Xining China Center for Statistical Science and Department of Industrial Engineering Tsinghua University Beijing China Department of Computer Science North Carolina A and T State University Greensboro United States

ISBN: (纸本)9789819970247

Blind image quality assessment (BIQA) remains challenging due to the diverse types of distortion and variable image content, which complicates the distortion patterns crossing different scales and aggravates the difficulty of the regression problem for BIQA. However, existing BIQA methods often fail to consider multi-scale distortion patterns and image content, and there has limited research on improving the performance of quality regression models through specific learning strategies. In this paper, we propose a simple yet effective Progressive Multi-Task Image Quality Assessment (PMT-IQA) model, which contains a multi-scale feature extraction module (MS) and a progressive multi-task learning module (PMT), to help the model learn complex distortion patterns and better optimize the regression issue to align with the law of human learning process from easy to hard. To verify the effectiveness of the proposed PMT-IQA model, we conduct experiments on four widely used public datasets, and the experimental results indicate that the performance of PMT-IQA is superior to the comparison approaches, and both MS and PMT modules improve the model’s performance. The source code for this study is available at https://***/pqy000/PMT-IQA. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd 2024.

关键词： Image enhancement

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Enhancing Cloud Infrastructure Security with GraphSAGE-Based Intrusion Detection Systems 1

Enhancing Cloud Infrastructure Security with GraphSAGE-Based...

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1st International Conference on Intelligent Systems and Computational Networks, ICISCN 2025

作者： Kumar, G. Nanda Kishor Syed, Shakir Mogal, Amit Karbhari Abid, Tahera Perada, Anvesh Suneetha, Sampathirao University of South Florida United States Malla Reddy University Department of Computer Science and Engineering Hyderabad India Bargersville IN United States Mvp Samaj's Cmcs College Department of Computer Science and Application Nashik India Nawab Shah Alam Khan College of Engineering and Technology Department of Information Technology Hyderabad India Drexel University Department of Electrical and Computer Engineering Philadelphia United States Koneru Lakshmaiah Education Foundation Department of Computer Science and Engineering Vaddeswaram India

ISBN: (纸本)9798331529246

Cloud computing provides scalable and adaptable infrastructure however is significantly susceptible to cyber-attacks. This work presents a hybrid methodology that integrates the GOA and GA for feature selection, alongside the Residual GraphSAGE model for intrusion detection. The hybrid GOA-GA method enhances feature selection by optimizing the balance between exploration and exploitation, mitigating overfitting, and augmenting classification performance. The Residual GraphSAGE model alleviates over smoothing while effectively capturing node and edge interactions for precise intrusion classification. Assessment on a benchmark dataset reveals exceptional performance, attaining an accuracy of 91.74%, surpassing established models such as GCN and ResNet. This study emphasizes the need of hybrid optimization and sophisticated graph-based learning in improving cloud infrastructure security. © 2025 IEEE.

关键词： Network intrusion

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Research on Improved Quick Sort Algorithm with Duplicate Value Handling 8

Research on Improved Quick Sort Algorithm with Duplicate Val...

引用

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

作者： Hemin Yadav, Amit Khan, Asif Sah, Abhishek Pratap Chengdu Neusoft University College of Computer and Software Chengdu China Charles Darwin University College of Engineering It and Environment NT Australia Integral University Department of Computer Application Lucknow India University of Electronic Science and Technology of China School of Management Science and Engineering Chengdu China

ISBN: (纸本)9798350376425

An improved algorithm based on Quick Sort algorithm research method is proposed to deal with prevailing duplicate values in the sorting of data. The duplicate values are specially processed, which effectively reduces the number of division and greatly reduces the number of recursions. The experimental results show that the time efficiency is improved by more than 50% when dealing with large data than the traditional Quick Sort algorithm without reducing the space efficiency. © 2024 IEEE.

关键词： Duplicate Value Quick Sort Recursion

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Multi-Modal Emotion and Gesture Recognition System for Humans and Animals Using computer Vision and Deep Learning 5

Multi-Modal Emotion and Gesture Recognition System for Human...

引用

5th International Conference on IoT Based Control Networks and Intelligent Systems, ICICNIS 2024

作者： Nair, Rekha R Babu, Tina Kishore, S. Shivaswamy, Smitha Aruna, S. Bhargavi, Manchikalapudi Alliance School of Advanced Computing Alliance University Department of Computer Science and Engineering Bengaluru India Department of Computer Application Karnataka Mangaluru India Acharya Institute of Technology Department of Computer Applications Bengaluru India School of Science and Computer Studies CMR University Bangalore India Hyderabad India

ISBN: (纸本)9798331518097

Emotion recognition through facial expressions and gesture analysis has emerged as a key area of research in computer vision and human-computer interaction. The ability to automatically detect and classify human emotions has wide-ranging applications, from enhancing user experiences to improving mental health monitoring and surveillance systems. This project builds on this background to develop a comprehensive system for recognizing emotions and gestures in both humans and animals. The system integrates facial emotion recognition, hand gesture interpretation, and animal emotion detection into a single platform. Using advanced deep learning models and computer vision libraries, the system classifies facial expressions into categories such as happiness, sadness, anger, fear, surprise, and neutral. The gesture recognition component interprets hand signals to convey messages like 'need help' or 'need water.' An innovative aspect of this work is the extension of emotion recognition to animals, particularly focusing on species like dogs and monkeys. Implemented primarily in Python, the system leverages libraries such as OpenCV and DeepFace. By combining these different modalities of nonverbal communication analysis, the project presents a versatile solution with potential applications in human-computer interaction, mental health monitoring, security, and animal welfare. © 2024 IEEE.

关键词： Gesture recognition

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The Open Source Threat Intelligence Relational Dataset and Its Optimal Implementation

The Open Source Threat Intelligence Relational Dataset and I...

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2024 Asia Pacific Conference on Innovation in Technology, APCIT 2024

作者： Yang, Yaru Li, Junyu Zhang, Ronghua Sah, Abhishek Pratap Yadav, Amit Khan, Asif Chengdu Neusoft University Department of Computer and Software Chengdu China University of Electronic Science and Technology of China School of Management Science and Engineering Chengdu China Charles Darwin University College of Engineering IT and Environment NT Australia Integral University Department of Computer Application Lucknow India

ISBN: (纸本)9798350361537

Threat intelligence provides a platform for cybersecurity engineers for attack traceability, which provides substantial knowledge database logs to defend against future security threats. Threat intelligence relationship extraction based on deep learning solves the challenge of threat knowledge construction to a certain extent but still faces problems such as lack of open-source datasets and the inability of the model to accurately correlate threat entities with potential relationships. Therefore, for cybersecurity research work, this paper designs a threat ontology, constructs the threat relationship dataset TreatRE by remote supervision, and opens this dataset. The dataset contains 12000 utterances and 12 threat relations from 500 CTIs, and it performs well in multiple relation models trained on deep learning methods. Meanwhile, we propose a multisensory attention-based threat intelligence relationship extraction method MAtt, which combines location perception, self-attention perception, and neuronal memory perception to further improve the threat relationship extraction effect. Experimental results show that the trained model based on TreatRE can more accurately extract the knowledge objects and their relationships described in threat intelligence. An accuracy score of 95.4% can be obtained using the MAtt method, which is 3.48% more than the best baseline compared with the same type of relationship extraction model. © 2024 IEEE.

关键词： Cybersecurity

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