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3rd International Conference on Electrical, Electronics, Information and Communication Technologies, ICEEICT 2024

作者： Ramesh, S. Mohankumar, N. Kovarasan, Rajesh Kambattan Selvamanikandan, R. Yuvaraj, S. Sujatha, S. Srm Institute of Science and Technology School of Computing College of Engineering and Technology Department of Computing Technologies Chennai Tamil Nadu Kattankulathur India Maharashtra Nagpur India Vel Tech Rangarajan Dr.Sagunthala R&d Institute of Science and Technology Department of Computer Science and Engineering Tamil Nadu Chennai India Care College of Engineering Department of Electrical and Electronics Engineering Tamil Nadu Trichy India Faculty of Engineering and Technology Department of Electronics and Communication Engineering Tamil Nadu India Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Department of Biomedical Engineering Tamil Nadu Chennai India

ISBN: (纸本)9798350369083

This research introduces cloud-powered virtual doctor robots to improve elderly care and chronic illness management. This system uses recurrent neural networks (RNNs) to give personalized medical care to elderly chronic disease patients. These virtual doctor robots may integrate with cloud computing infrastructure to access huge medical databases for real-time patient data analysis and individualized treatment and care suggestions. Healthcare technology has advanced by using RNNs to learn from patient data, adapt to changing health situations, and predict prospective health issues. RNNs can interpret sequential data, thus virtual doctor robots may identify trends in patient health parameters, forecast illness development, and suggest risk mitigation strategies. Cloud-powered virtual doctor robots provide a scalable solution to conventional healthcare systems rising issues. These robots can work effectively in resource-constrained areas by offloading computational chores to distant servers, providing continuous medical expertise and lowering healthcare staff reliance. This technique might improve aged care and reduce load on overworked healthcare systems. Using proactive monitoring, timely intervention, and personalized guidance, virtual doctor robots with RNNs can improve health outcomes, empower patients to manage chronic conditions, and create a more sustainable healthcare model. © 2024 IEEE.

关键词： Medical robotics

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Comparison of Novel Machine Learning Algorithms for Predicting Chronic Renal Disease

Comparison of Novel Machine Learning Algorithms for Predicti...

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2024 International Conference on Computational Intelligence for Green and Sustainable Technologies, ICCIGST 2024

作者： Hima Bindu, G.B. Valluru, Dinesh Vachhani, Harshita Sunil Kumar, M. Ganesh, D. Neelima, P. The Apollo University School of Technology Department of of CSE Andhra Pradesh Chittoor India MLRITM Engineering College Department of IT Telangana Hyderabad India Pratibha College of Commerce and Computer Studies Chinchwad Dept.of Computer Science Maharshtra Pune India School of Computing Andhra Pradesh Tirupati India SPMVV School of Engineering & Technology Department of CSE Tiruapti India

ISBN: (纸本)9798350381887

Chronic Renal Disease (CRD) is a serious sickness that affects the entire world and is the primary cause of mortality for many other illnesses. Due to the lack of visible symptoms at the beginning of CRD, people usually neglect the illness. Early CRD diagnosis enables individuals to receive prompt therapy to slow the course of their illness. Because it quickly and accurately identifies potentials, model-based machine learning can help doctors accomplish this aim. In this paper, an ML strategy for CRD forecasting. The UCI machine learning repository provided the CRD dataset, which has a high number of uncertain data values. To address unknown values or unknown data, KNN estimation was employed, which picks multiple instances that contain the most comparable measures to manage the undetermined data value for every insufficient sample. Missing data is prevalent in real therapy situations, therefore individuals may skip particular measurements for a variety of reasons. The technique is generated using five machine learning algorithms (SVM, Logistic Regression, k-nearest neighbor, Random Forest, and Neural Network). Random forest outscored the other models of machine learning regarding accuracy. © 2024 IEEE.

关键词： Adversarial machine learning

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A New Model for Predicting Pandemic Impact on Worldwide Academic Rankings

A New Model for Predicting Pandemic Impact on Worldwide Acad...

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2023 International Conference on New Frontiers in Communication, Automation, Management and Security, ICCAMS 2023

作者： Sharada, K.A. Swathi, R. Basi Reddy, A. Selvan, R.Senthamil Sivaranjani, Lingala Hkbk College of Engineering Department of Computer Science and Engineering Bengaluru India Sri Venkateswara College of Engineering Department of Computer Science and Engineering Tirupati India Mohan Babu University School of Computing Department of Computer Science and Engineering Tirupati India Annamacharya Institute of Technology and Sciences Department of Electronics and Communication Engineering Tirupati India Mohan Babu University School of Computing Tirupati India

ISBN: (纸本)9798350317060

People must stay home during pandemics because physical life stops. Pandemic dread closes schools nationwide, hurting students academically. If so, educating kids properly to avoid academic issues is difficult. During the COVID-19 epidemic, schools provided pupils with online instruction. Career and academic tenure were unaffected. This runs counter to what happened during various pandemics throughout history when school years were lost due to widespread illness. Human-computer interface (HCI) technology advancements have made this all feasible. Interference. Many schools made substantial efforts to adapt to the modern teaching paradigm, yet they still ranked poorly internationally in at least one crucial area. As a result, they're worldwide standing took a dramatic nosedive, which will take a lot of time and work to recover from. This study highlights why certain top universities fell in the worldwide rankings during global disruption, such as the recent COVID-19 epidemic, and what may be done to reverse this trend. Potential future pandemics. The unique methodology provided in this paper aids academics in anticipating the influence of epidemics on their overall worldwide rankings, allowing them to make timely recovery choices and strategies. The task is to build a model using sophisticated Machine Learning techniques, with a massive ranking dataset as input. © 2023 IEEE.

关键词： COVID-19

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Prediction of AIDS/HIV Infection using Custom Artificial Neural Networks Model 1

Prediction of AIDS/HIV Infection using Custom Artificial Neu...

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

作者： Sneha, B.J. Shriyashreshta Kodipalli, Ashwini Martis, Roshan Joy Ushasree, A. Devamane, Shridhar B. Global Academy of Technology Department of Ai & Data Science Bangalore India Manipal Institute of Technology Department of Electronics & Communication Engineering Bangalore India Gokaraju Lailavathi Engineering College Department of Computer Science & Engineering Hyderabad India Amrita Vishwavidyapeetam School of Computing Department of Cs Mysuru India

ISBN: (纸本)9798331529246

HIV is a serious disease that impairs immunity. Without treatment, the infection may progress through three stages, which might drastically shorten a person's life. Artificial neural networks (ANNs) are utilized to detect the HIV infection and even to find treatments for it. The AIDS viral infection is predicted in this review. The aim of ANN is to precisely classify patients according to whether or not they have HIV/AIDS. We have formed a custom ANN model and applied it to the Kaggle dataset that is accessible to the public, compared which optimizers offer the best accuracy and whether employing dropouts yields improved accuracy. With Gradient Descent, we achieved the maximum accuracy of 86.92% it is also one of the most commonly used optimizers due to its simplicity and effectiveness, followed by SGD with 86.45% it is a variation of gradient descent in which a random subset of data as opposed to the complete dataset is used to update the parameter and RMSprop with 86.21% uses the average of recent gradient magnitudes to modify the learning rate for each parameter. This aids with training stabilization, particularly when dealing with sparse or noisy data. The accuracy increased to 87.62% after we introduced dropouts to the hidden layer with optimizer as gradient descent, which produced the best results. Loss vs. Accuracy graphs and ROC curve have been plotted. With dropouts, the custom ANN model performed even better on the Gradient Descent optimizer. © 2025 IEEE.

关键词： ANN dropouts HIV/AIDS optimizers

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QoS-aware Dynamic Service Caching and Updating in Cost-efficient Multi-Access Edge computing 22

QoS-aware Dynamic Service Caching and Updating in Cost-effic...

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22nd IEEE International Symposium on Parallel and Distributed Processing with Applications, ISPA 2024

作者： Lu, Shuaibing Jin, Xin Wu, Jie Zhou, Shuyang Wu, Shen Yang, Jackson Yan, Ran Beijing University of Technology College of Computer Science Beijing China Temple University Center for Networked Computing United States Beijing Jiaotong University School of Software Engineering Beijing China

ISBN: (纸本)9798331509712

In the context of mobile edge computing, achieving dynamic service caching and updating to guarantee the QoS of users and reduce system costs is a challenging problem. However, existing research still has certain deficiencies in considering the dynamic behavior of users and the limited storage resources of edge servers. To address this problem, this paper proposes three novel strategies for the different stages of service caching and updating to jointly optimize the delay and cost. At the initial service caching stage, we propose a caching strategy based on dynamic programming, taking into account the constraint of limited memory resources. Given the dynamic behavior of users, we formulate the joint optimization problem as a Markov Decision Process (MDP) and design a service extension strategy based on Q-learning at the service updating decision-making stage and a replacement strategy taking both the distribution of service replications and service access frequency into account at the service updating replacement stage to guarantee the QoS of users. We effectively tackle the challenges arising from the dynamic behavior of users and limited storage resources. Through extensive comparative experiments, our approach outperforms traditional strategies by significantly reducing user latency and system cost. © 2024 IEEE.

关键词： Markov processes

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Lithological Facies Classification Using Attention-Based Gated Recurrent Unit

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Tsinghua Science and technology 2024年第4期29卷 1206-1218页

作者： Yuwen Liu Yulan Zhang Xingyuan Mao Xucheng Zhou Jingwen Chang Wenwei Wang Pan Wang Lianyong Qi College of Computer Science and Technology China University of Petroleum(East China)Qingdao 266580China Shandong Provincial University Laboratory for Protected Horticulture Weifang University of Science and TechnologyShouguang 262700China School of Mechanical Electrical and Information EngineeringShandong UniversityWeihai 264209China Shandong Provincial Key Laboratory of Network Based Intelligent Computing University of JinanJinan 250022China School of Modern Posts Nanjing University of Posts and TelecommunicationsNanjing 210023China

Lithological facies classification is a pivotal task in petroleum geology, underpinning reservoir characterization and influencing decision-making in exploration and production operations. Traditional classification methods, such as support vector machines and Gaussian process classifiers, often struggle with the complexity and nonlinearity of geological data, leading to suboptimal performance. Moreover, numerous prevalent approaches fail to adequately consider the inherent dependencies in the sequence of measurements from adjacent depths in a well. A novel approach leveraging an attention-based gated recurrent unit (AGRU) model is introduced in this paper to address these challenges. The AGRU model excels by exploiting the sequential nature of well-log data and capturing long-range dependencies through an attention mechanism. This model enables a flexible and context-dependent weighting of different parts of the sequence, enhancing the discernment of key features for classification. The proposed method was validated on two publicly available datasets. Results demonstrate a considerably improvement over traditional methods. Specifically, the AGRU model achieved superior performance metrics considering precision, recall, and F1-score.

关键词： attention mechanism facies classification GRU MLP(multilayer perceptron)

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Hair Style Recommendations using CNN and AR 9

Hair Style Recommendations using CNN and AR

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9th International Conference on Communication and Electronics Systems, ICCES 2024

作者： Chinnasamy, P. Deepalakshmi, P. Achireddy, Vummadi Sathiya, T. Ravi, Rongala Priya, D. Divya School of Computing Kalasalingam Academy of Research and Education Department of Computer Science and Engineering Tamilnadu India Anurag Engineering College Department of Cse Ananthagiri Telangana Kodad India Sona College of Technology Department of Cse Salem India Hyderabad India Mlr Institute of Technology Department of Computer Science and Engineering Hyderabad India

ISBN: (纸本)9798350377972

This research begins with the collecting of a varied dataset of annotated facial photos to ensure accurate representation across populations. The photographs will be pre-processed to standardize their size, illumination, and orientation. On this dataset, we will fine-tune a pre-trained CNN, such as ResNet50, to identify various face forms before evaluating accuracy against a separate test set. Furthermore, we will create a dataset of facial photos combined with various haircuts classified by face shape, and use the CNN to extract relevant features and embeddings. A matching algorithm will match user facial features to hairdo embeddings, ranking recommendations according to similarity metrics. The AR component will leverage ARKit or ARCore for real-time face tracking, allowing users to realistically test on different haircuts overlayed on their live camera feeds. The final solution will be available on mobile and online platforms, with a user-friendly design and options for saving preferred haircuts and providing feedback. This project tackles the demand for a more engaging and individualized hairstyle picking experience, utilizing advanced AI and AR technologies to improve customer happiness and interactivity. © 2024 IEEE.

关键词： Convolutional neural networks

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Learned multiphysics inversion with differentiable programming and machine learning

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Leading Edge 2023年第7期42卷 474-486页

作者： Louboutin, Mathias Yin, Ziyi Orozco, Rafael Grady, Thomas J. Siahkoohi, Ali Rizzuti, Gabrio Witte, Philipp A. Møyner, Olav Gorman, Gerard J. Herrmann, Felix J. Georgia Institute of Technology School of Earth and Atmospheric Sciences AtlantaGA United States Georgia Insitute of Technology School of Computational Science and Engineering AtlantaGA United States Georgia Institute of Technology College of Computing GA Atlanta United States Utrecht University Utrecht Netherlands Microsoft Corp. RedmondWA United States SINTEF Trondheim Norway Imperial College London Department of Earth Science and Engineering London United Kingdom

We present the Seismic Laboratory for Imaging and Modeling/Monitoring open-source software framework for computational geophysics and, more generally, inverse problems involving the wave equation (e.g., seismic and medical ultrasound), regularization with learned priors, and learned neural surrogates for multiphase flow simulations. By integrating multiple layers of abstraction, the software is designed to be both readable and scalable, allowing researchers to easily formulate problems in an abstract fashion while exploiting the latest developments in high-performance computing. The design principles and their benefits are illustrated and demonstrated by means of building a scalable prototype for permeability inversion from time-lapse crosswell seismic data, which, aside from coupling of wave physics and multiphase flow, involves machine learning. © 2023 The Authors. Published by the Society of Exploration Geophysicists. All article content, except where otherwise noted (including republished material), is licensed under a Creative Commons Attribution 4.0 Unported License (CC BY). See.

关键词： Multiphysics

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SMS-Shield: A Lightweight Approach for Smishing Detection using Machine Learning 1

SMS-Shield: A Lightweight Approach for Smishing Detection us...

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1st International Conference on Innovative engineering Sciences and Technological Research, ICIESTR 2024

作者： Samad, Saleem Raja Abdul Ganesan, Pradeepa Thangam, S. Balasubramaniyan, Sundaravadivazhagan Rajiakodi, Saranya Al Kaabi, Amna Salim Rashid University of Technology and Applied Sciences-Shinas College of Computing and Information Sciences Information Technology Department Shinas Oman Amrita School of Computing Amrita Vishwa Vidyapeetham Department of Computer Science and Engineering Karnataka Bengaluru India University of Technology and Applied Sciences-Al-Musannah College of Computing and Information Sciences Information Technology Department Al-Musannah Oman Central University of Tamil Nadu Department of Computer Science Tamil Nadu Thiruvarur India

ISBN: (纸本)9798350348637

SMS continues to be an indispensable component of contemporary digital communication. Because it is so widely available, cost-effective, reliable, and delivers information instantly. Its versatility enables its utilization for real-time chats, alerts, and verification purposes. Nevertheless, the ubiquitous nature of SMS renders it an easy prey for smishing, a deceitful form of cyberattack. Cybercriminals exploit the trust associated with SMS messages to deceive recipients. Smishing, an increasingly prevalent deceptive SMS fraud, poses a threat to both businesses and individuals. These messages include websites or phone numbers and purport to be from banks, governmental bodies, or well-known businesses. These misleading text messages lure their users into disclosing private information or clicking on links that could be harmful in many ways. Early detection of such malicious messages prevents users from falling victim to them. Various techniques were presented by researchers to accurately detect smishing attacks. Machine learning models exhibit great potential as a strategy to counter the increasingly dangerous SMS phishing attacks. Machine learning models can discern legitimate from fraudulent SMS messages by analyzing datasets. This paper introduces a novel method (SMS-SHIELD), which combines Machine Learning and Natural Language Processing techniques to enhance smishing detection. This approach yields outstanding results, with random forest and extreme gradient boosting algorithms achieving remarkable accuracy rates of 99.05% and 98.03%, coupled with impressive F1-scores of 98.95% and 97.86%, respectively. © 2024 IEEE.

关键词： Phishing

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MLPFormer: MLP-integrated transformer for colorectal histopathology whole slide image segmentation

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Neural computing and Applications 2025年第6期37卷 4651-4661页

作者： Wang, Yuxuan Li, Dan Li, Xuechen Guo, Yan Zuo, Yanfei Shen, Linlin Department of Computer Science Wenzhou-Kean University Wenzhou China School of Electronics and Information Engineering Wuyi University Jiangmen China Department of Histo Pathology Shanghai Histo Pathology Diagnostic Center Shanghai China College of Computer Sicence and Software Engineering Shenzhen University Shenzhen China National Engineering Laboratory for Big Data System Computing Technology Shenzhen University Shenzhen China

Colorectal intraepithelial neoplasia is a precancerous lesion of colorectal cancer, which is mainly diagnosed using pathological images. According to the characteristics of lesions, precancerous lesions can be classified into four different grades, i.e., background, normal, low and high level. However, due to the continuity of lesion development, tissue in different stages has high similarity. Recently, visual transformers have achieved impressive results in different visual tasks, due to their capacity of capturing long-range dependencies. However, due to computational cost, transformer cannot well extract detailed feature, which is important for lesion grading. To accurately segment pathological tissues with high similarity, in this work, we embed the multi-head MLP mixer module in the transformer network to extract cell and edge information. Furthermore, we propose a hierarchical MLP decoder to facilitate multi-scale feature fusion. The proposed transformer, namely MLPFormer, achieves remarkable results against competitive baselines on the Histo-CRC Biopsy dataset. The experimental results demonstrate that MLPFormer significantly outperforms the competitive baselines, i.e., a 3% dice improvement is achieved over the SegFormer series. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2024.

关键词： Image segmentation

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