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Innovative Computing, Intelligent Communication and Smart Electrical Systems (ICSES), International Conference on

作者： L. Velmurugan R. K Maheswari K. Janani K. Agalya M. Poornima K.C. Gayathri School of Computing Science and Engineering (SCOPE VIT Bhopal University Sehore Madhya Pradesh India Artificial Intelligence and Data Science M.A.M College of Enginnering and Technology Trichy Tamilnadu India Department of Data science and computer applications Manipal institute of Technology Manipal Academy of Higher Education Manipal India Department of Computer Science Engineering Sri Eshwar College of Engineering Coimbatore India Department of Information Technology OASYS Institute of Technology Trichy Tamil Nadu India Chettinad School of physiotherapy (CSP Chettinad Hospital and Research Institute (CHRI) Chettinad Academy of Research and Education (CARE) Chennai India

ISBN: (数字)9798331543624

ISBN: (纸本)9798331543631

Early detection of brain tumors is crucial for effective treatment, yet it poses a significant challenge due to the rapid nature of the disease's progression. This study explores the application of ResNet, aimed at improving the accuracy of brain tumor diagnosis in magnetic resonance imaging (MRI) scans. Our model was trained on a dataset comprising 253 MRI images, which included 155 confirmed cases of tumors. The residual learning framework of ResNet effectively captures intricate features and mitigates issues such as vanishing gradients. The results indicate that the ResNet model achieved an impressive accuracy rate of 97%, surpassing traditional detection methods. We evaluated the model's performance using metrics like accuracy and F1-score, demonstrating its robustness. To improve the interpretability of our model's predictions, we utilized Grad-CAM (Gradient-weighted Class Activation Mapping), which highlighted critical areas in the MRI scans that influenced the model's decisions. Our findings confirm ResNet’ s effectiveness in medical imaging and stress the importance of model interpretability in healthcare, allowing radiologists to make informed diagnostic choices.

关键词： Deep learning Accuracy Magnetic resonance imaging Brain tumors Medical services Predictive models Brain modeling Robustness Stress Biomedical imaging

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Uncovering Android Zero-Day Threats: The Zero-Vuln Approach with Deep and Zero-Shot Learning

Uncovering Android Zero-Day Threats: The Zero-Vuln Approach ...

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Computing and data Analytics (ICCDA), International Conference on

作者： Suresh Palarimath Pyingkodi Maran Wilfred Blessing N. R S. J Kavitha Cibi. A Subitha G. Sutherlin College of Computing and Information Sciences University of Technology and Applied Sciences Salalah Sultanate of Oman Department of Computer Applications Kongu Engineering College Erode India College of Computing and Information Sciences University of Technology and Applied Sciences Ibri Sultanate of Oman Department of Computer Science and Engineering Vels Institute of Science Technology and Advanced Studies (VISTAS) Pallavaram Chennai India Data & Analytics / Visualization - Delivery Virtusa Consulting Services Private Limited Chennai India Department of Computer Science and Engineering Stella Mary's College of Engineering Kanyakumari India

ISBN: (数字)9798331542573

ISBN: (纸本)9798331542580

The growing occurrence of cybersecurity hazards, such as Android zero-day vulnerabilities, poses substantial difficulties because they are unattended and imperceptible. With the increasing popularity of Android smartphones, hackers are taking advantage of these weaknesses to quickly spread advanced malware. Conventional detection approaches frequently prove inadequate due to the absence of shown criteria for emergent threats. In order to address this crucial problem, this article presents the Zero-Vuln method, an advanced technique specifically developed to identify and categorize previously unidentified malware on Android platforms. Zero-Vuln combines sophisticated supervised deep learning methods with zero-shot learning algorithms to accurately detect zero-day threats. Through the utilization of comprehensive datasets, the method attains an average classification metric score of 84.22%, showcasing exceptional accuracy, precision, and recall. This advanced solution significantly improves the ability to identify and handle Android zero-day threats, representing a major progress in cybersecurity research and implementation. The Zero-Vuln strategy provides a strong and adaptable solution for the constantly changing mobile security risks, representing a notable advancement in the sector.

关键词： Measurement Instruments Zero shot learning Malware Mobile security Hazards Reliability Protection Smart phones Research and development

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Monkeypox Image Recognition: Leveraging ResNet-MobileNet Fusion for Enhanced Performance

Monkeypox Image Recognition: Leveraging ResNet-MobileNet Fus...

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Information science, computer Technology and Transportation (ISCTT), International Conference on

作者： Yi-Zhi Liu Tsung-Ming Lo Wan-Ying Zhang Hong-Tao Zhang Zi-Jing Lin Xin-Yan Liu Yu Chen Nanfang College-Guangzhou School of Electrical and Computer Engineering Guangzhou China Dongguan University of Technology Guangdong-Taiwan College of Industrial Science & Technology Dongguan China Department of Artificial Intelligence and Data Science Guangzhou Xinhua University Dongguan China

ISBN: (数字)9798350388435

ISBN: (纸本)9798350388442

In recent years, the monkeypox virus has become a global health concern. Monkeypox is highly transmissible and usually results in severe skin damage and systemic complications. Therefore, accurate and rapid detection of monkeypox skin symptoms is crucial for timely diagnosis and treatment, as well as for informing outbreak control strategies and vaccine development efforts. In this paper, we propose an efficient method for monkeypox image recognition using deep learning techniques that integrate ResNet and MobileNet models. By integrating the residual learning architecture of ResN et and the deep detached convolutional design of MobileNet, our method achieves an ultra-high classification accuracy of 99.3%, along with a mean absolute error (MAE) of 0.0119 and a root mean square error (RMSE) of 0.1558.

关键词： Image recognition Accuracy Corporate acquisitions Telemedicine Transportation computer architecture Feature extraction Skin Vaccines Viruses (medical)

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Recent advances in mobile edge computing and content caching

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Digital Communications and Networks 2020年第2期6卷 189-194页

作者： Sunitha Safavat Naveen Naik Sapavath Danda B.Rawat Data Science and Cybersecurity Center(DSC2) Department of Electrical Engineering and Computer ScienceHoward UniversityWashingtonDC20059USA

The demand for digital media services is increasing as the number of wireless subscriptions is growing *** order to meet this growing need,mobile wireless networks have been advanced at a tremendous pace over recent ***,the centralized architecture of existing mobile networks,with limited capacity and range of bandwidth of the radio access network and low bandwidth back-haul network,can not handle the exponentially increasing mobile ***,we have seen the growth of new mechanisms of data caching and delivery methods through intermediate caching *** this paper,we present a survey on recent advances in mobile edge computing and content caching,including caching insertion and expulsion policies,the behavior of the caching system,and caching optimization based on wireless *** of the important open challenges in mobile edge computing with content caching are identified and *** have also compared edge,fog and cloud computing in terms of *** of this paper will get a thorough understanding of recent advances in mobile edge computing and content caching in mobile wireless networks.

关键词： Mobile edge computing Content caching MEC

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Optimization techniques for preserving privacy in data mining

Optimization techniques for preserving privacy in data minin...

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International Conference on Electronics, Communication and Computing Technologies (ICECCT)

作者： K.Renuka Devi K. Balasamy M. Prathyusha R. Jeevitha P. Balasubramanie Malathi Eswaran Department of Information Technology Dr. Mahalingam college of Engineering and Technology Pollachi Department of Artificial Intelligence and Data Science Bannari amman Institute of Technology Sathyamangalam Department of IT MVSR engineering College Hyderabad Department of CSE KPR Institute of Engineering and Technology Coimbatore Department of Computer science and engineering Kongu Engineering College Perundurai Erode

data mining is one of the significant area where it plays a predominant role in extracting important factors and trends from large volume of data. This covers various areas such as healthcare, education, entertainment, finance, e-commerce applications etc., The data mining domain has used a variety of algorithms, including supervised, unsupervised, semi-supervised, and reinforcement learning techniques. Under healthcare arena, it deals with huge amount of sensitive data such as patients' data such as their name, age, health records. Those sensitive data have been utilized by the intruders for extracting the original data and also became a prey for the authorized access. Hence, the privacy is one of the serious concern that should be addressed. Various privacy preserving in data mining (PPDM) techniques such as anonymization, perturbation, condensation and cryptographic methods are available to protect those data. In this paper, the optimization techniques such as Genetic algorithm(GA) under evolutionary method and Particle swarm optimization(PSO) under meta heuristic method have been discussed and how it plays an important part in providing more optimal results by securing those sensitive and important information from the unauthorized access.

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Simultaneous Super-Resolution and Denoising on MRI via Conditional Stochastic Normalizing Flow

Simultaneous Super-Resolution and Denoising on MRI via Condi...

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2023 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2023

作者： Liu, Zhenhong Wang, Xingce Wu, Zhongke Zhu, Yi-Cheng Frangi, Alejandro F. Beijing Normal University School of Artificial Intelligence Beijing China Peking Union Medical College Hospital Department of Neurology Beijing China Christabel Pankhurst Institute Division of Informatics Imaging and Data Sciences School of Health Sciences The Department of Computer Science School of Engineering The University of Manchester United Kingdom Department of Cardiovascular Sciences The Department of Electrical Engineering Ku Leuven Leuven Belgium Lan Turing Institute London United Kingdom

ISBN: (纸本)9798350337488

Magnetic resonance imaging (MRI) scans often suffer from noise and low-resolution (LR), which affect the diagnosis and treatment results obtained for patients. LR images and noise come together with MRI, and the existing methods solve image super-resolution (SR) reconstruction and denoising tasks in a step-by-step manner, which influences the overall real distribution of the MRI data. In this paper, we present a simultaneous SR and denoising algorithm based on a stochastic normalizing flow (SNF), named the MR image SR and denoising model based on an SNF (SRDSNF). SRDSNF adds the encoded information of the input image as the conditional information to each reverse step of the stochastic normalizing flow, which realizes a consistent description of the spatial distribution between the reconstruction result and the input image. We introduce rangenull space decomposition and subsequence sampling strategies to enhance the consistency of the input and output data and increase the generation speed of the model. Simultaneous SR and denoising tasks experiment is carried out using the BrainWeb and NFBS datasets. The experimental results show that good SR and denoising results are obtained with fewer sampling steps, these results are consistent with the ground truths, and the structural similarity and peak signal-to-noise ratio of the results are also higher than those of the comparison methods. The proposed method demonstrates potential clinical promise. © 2023 IEEE.

关键词： Denoising Diffusion model MR image Stochastic normalizing flow Super-resolution

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End-to-End Congestion Avoidance Scheme for Underwater High Speed Communication Networks

End-to-End Congestion Avoidance Scheme for Underwater High S...

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Smart Generation Computing, Communication and Networking (SMART GENCON), International Conference on

作者： Shivank Romil Jain Reena R Prema S. Kadam Sunanda Das Swati Singh Department of Computer Science & Engineering Vivekananda Global University Jaipur India Centre of Interdisciplinary Research in Business and Technology Chitkara University Institute of Engineering and technology Chitkara University Punjab India Department of Computer Science and Engineering Prince Shri Venkateshwara Padmavathy Engineering College Chennai Department of Artificial Intelligence & Data Science Vishwakarma Institute of Information Technology Pune INDIA Computer Science and Engineering Faculty of Engineering and Technology Bangalore Karnataka India Maharishi School of Engineering and Technology Maharishi University of Information Technology Uttar Pradesh India

The cease-to-give up Congestion Avoidance Scheme for Underwater high pace verbal exchange Networks, commonly known as E2E-UCAN, is a critical technique for lowering plausible throughput by using efficiently warding off congestion inside a community. E2E-UCAN is a disbursed congestion control mechanism which uses statistics obtained from cease-to-end feedbacks to regulate sending prices on the give up systems. By means of monitoring the differences among expected network overall performance and actual performance, E2E-UCAN is able to pick out congestion stages inside the device, and is therefore able to regulate to prevent congestion before it happens. This enables to improve the stableness of the community and to increase the throughput of a network. Moreover, E2E-UCAN is likewise capable of efficaciously coping with queuing, avoiding packet loss in non-uniform networks, and in preferred, permitting applications to maximize performance.

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Efficient or Powerful? Trade-offs Between Machine Learning and Deep Learning for Mental Illness Detection on Social Media

arXiv

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arXiv 2025年

作者： Ding, Zhanyi Wang, Zhongyan Zhang, Yeyubei Cao, Yuchen Liu, Yunchong Shen, Xiaorui Tian, Yexin Dai, Jianglai University of Pennsylvania School of Engineering and Applied Science United States New York University Center for Data Science United States Georgia Institute of Technology College of Computing United States University of California Berkeley Department of EECS United States Northeastern University Khoury College of Computer Science United States

Social media platforms provide valuable insights into mental health trends by capturing user-generated discussions on conditions such as depression, anxiety, and suicidal ideation. Machine learning (ML) and deep learning (DL) models have been increasingly applied to classify mental health conditions from textual data, but selecting the most effective model involves trade-offs in accuracy, interpretability, and computational efficiency. This study evaluates multiple ML models, including logistic regression, random forest, and LightGBM, alongside deep learning architectures such as ALBERT and Gated Recurrent Units (GRUs), for both binary and multi-class classification of mental health conditions. Our findings indicate that ML and DL models achieve comparable classification performance on medium-sized datasets, with ML models offering greater interpretability through variable importance scores, while DL models are more robust to complex linguistic patterns. Additionally, ML models require explicit feature engineering, whereas DL models learn hierarchical representations directly from text. Logistic regression provides the advantage of capturing both positive and negative associations between features and mental health conditions, whereas tree-based models prioritize decision-making power through split-based feature selection. This study offers empirical insights into the advantages and limitations of different modeling approaches and provides recommendations for selecting appropriate methods based on dataset size, interpretability needs, and computational constraints. © 2025, CC BY.

关键词： Contrastive Learning

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Tutorial on Using Machine Learning and Deep Learning Models for Mental Illness Detection

arXiv

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arXiv 2025年

作者： Zhang, Yeyubei Wang, Zhongyan Ding, Zhanyi Tian, Yexin Dai, Jianglai Shen, Xiaorui Liu, Yunchong Cao, Yuchen University of Pennsylvania School of Engineering and Applied Science United States New York University Center for Data Science United States Georgia Institute of Technology College of Computing United States University of California Department of EECS Berkeley United States Northeastern University Khoury College of Computer Science United States

Social media has become an important source for understanding mental health, providing researchers a way to detect conditions like depression from user-generated posts. This tutorial provides practical guidance to address common challenges in applying machine learning and deep learning methods for mental health detection on these platforms. It focuses on strategies for working with diverse datasets, improving text preprocessing, and addressing issues such as imbalanced data and model evaluation. Real-world examples and step-by-step instructions demonstrate how to apply these techniques effectively, with an emphasis on transparency, reproducibility, and ethical considerations. By sharing these approaches, this tutorial aims to help researchers build more reliable and widely applicable models for mental health research, contributing to better tools for early detection and intervention. © 2025, CC BY.

关键词： Adversarial machine learning

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CXR Scan:X-Ray Image Scanning Application for Lung Cancer and Tuberculosis 13

CXR Scan:X-Ray Image Scanning Application for Lung Cancer an...

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13th IEEE Annual Information Technology, Electronics and Mobile Communication Conference, IEMCON 2022

作者： Jayasooriya, A.M.U.J. Wickramasekara, T.M.A.M. Jayasinghe, I.C. Gunaratne, U.A. Weerasinghe, Lokesha Dassanayake, Gaya Thamali Department of Data Science Sri Lanka Institute of Information Technology New Kandy Road Malabe Sri Lanka Department of Computer Science and Software Engineering Sri Lanka Insittite of Information Technology New Kandy Road Malabe Sri Lanka Department of Information Technology Sri Lanka Institue of Information Technology Kandy Road Malabe Sri Lanka

ISBN: (数字)9781665463164

ISBN: (纸本)9781665463164

The initial criterion for identifying lung disorders is chest radiographs. The three major lung illnesses that pose the greatest threat to public health are tuberculosis, pneumonia, and lung cancer. Chest X-ray diagnosis of pulmonary illnesses is a challenging undertaking that requires high experience. In rural places, it can be difficult to locate skilled radiologists. Due to the high frequency of TB and lung cancer radiological similarities, many individuals with lung cancer are initially misdiagnosed as having TB and treated incorrectly. According to a recent WHO survey, millions of people die each year as a result of delayed or incorrect diagnoses of lung diseases. This death rate can be reduced, by early detection of certain disorders. This paper proposes a system with 4 main components;Image processing of chest X-rays to identify the disease using Convolutional Neural networks;Predicting the probability of having LC or TB using multivariate data classification techniques;Recommending medicine and related information to support the decision-making process using gaussian naïve bayes, logistic regression model and decision tree classification methods;Visualizing the X-ray image using Augmented Reality. © 2022 IEEE.

关键词： Convolutional neural networks

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