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research on Biomedical engineering 2025年第2期41卷 1-19页

作者： A, Padmashree Sankar, P. Alkhayyat, Ahmad Muniyandy, Elangovan Department of Computer Science and Business Systems Bannari Amman Institute of Technology Tamil Nadu Sathyamangalam India Department of Computer Science and Engineering Saveetha Engineering College Tamil Nadu Chennai India Department of Computers Techniques Engineering College of Technical Engineering The Islamic University Najaf Iraq Department of Computers Techniques Engineering College of Technical Engineering The Islamic University of Al Diwaniyah Al Diwaniyah Iraq Department of Biosciences Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Tamil Nadu Chennai India Applied Science Research Center Applied Science Private University Amman Jordan

Purpose: The primary objective of this research is to develop a comprehensive framework for the analysis of brain tumor images, addressing the complexities of detection, segmentation, and classification. Given the intricate nature of brain anatomy and tumor variability, the study aims to enhance the accuracy and efficiency of brain tumor diagnosis using advanced imaging and processing techniques. Methods: The framework utilizes Brain Tumor Segmentation (BraTS) 2019 and 2020 datasets, applying an AMW-PM filter to enhance image quality. Tumor segmentation employs a slimmable transformer with hybrid axial attention for precision. Classification uses similarity navigated graph neural networks (SNGNNs), optimized with the crayfish algorithm (CFO) to minimize loss and enhance performance. Results: The framework’s effectiveness was validated using key performance metrics, showing significant improvements in tumor detection and classification. The combination of advanced segmentation techniques and the SNGNN classifier, optimized by the CFO, led to a fivefold increase in diagnostic accuracy. These improvements demonstrated a substantial enhancement in the model's ability to correctly identify tumor types and provide reliable outputs for clinical use. Conclusion: The proposed framework delivers a robust solution for brain tumor analysis, integrating cutting-edge techniques for image processing, segmentation, and classification. The results not only highlight improvements in diagnostic accuracy but also contribute to better clinical decision-making in managing brain tumors. This framework sets the foundation for future advancements in brain tumor diagnostics. © The Author(s), under exclusive licence to The Brazilian Society of Biomedical engineering 2025.

关键词： Graph neural networks

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A new hybrid MobileNet-MLP model for secured data communication using signature verification and deep learning

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Iran Journal of computer science 2024年第1期8卷 233-252页

作者： Pandiyaraju, V. Ganapathy, Sannasi Thangaramya, K. Nahar, R. Raunak Kannan, A. School of Computer Science and Engineering Vellore Institute of Technology Chennai India Department Computer Science and Engineering Education National Institute of Technical Teachers Training and Research (NITTTR) Bhopal India School of Computer Science and Engineering Vellore Institute of Technology Vellore India

Signature verification plays a critical role in various industries, including finance and document authentication. However, traditional verification techniques have limitations, such as a lack of robustness and an inability to effectively capture intricate patterns and relationships within the signature images. This paper introduces an innovative approach that addresses the drawbacks by integrating conventional machine learning methods with deep learning techniques. For this purpose, we design a new hybrid MobileNet model using deep learning to extract features from the signature images. Here, the Multilayer Perceptron (MLP) classifier is also used to carry out the binary classification over the extracted features. The experimental results of the proposed model demonstrate that the amalgamated technique surpasses the conventional methods and underscores the possibilities of merging traditional machine learning and deep learning in signature verification. Moreover, the proposed model overcomes the limitations of the traditional signature verification techniques by leveraging the strengths of both deep learning and traditional machine learning. Finally, it obtains a more robust and efficient solution and proves it by providing high security to the data in terms of accuracy. © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2024.

关键词： Convolutional neural network Feature extraction MobileNet Multilayer perceptron Signature verification

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Enhanced Emotion Recognition Using a Hybrid Autoencoder-LSTM Model Optimized with a Hybrid ACO-WOA Algorithm for Hyperparameter Tuning

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International Journal of Advanced computer science and Applications 2025年第4期16卷 861-876页

作者： Waiker, Vinod Ramesh, Janjhyam Venkata Naga Bala, Ms Kiran Krishnaiah, V.V. Jaya Rama Jackulin, T. Muniyandy, Elangovan Shahin, Osama R. Datta Meghe Institute of Management Studies Maharashtra Nagpur India Department of CSE Graphic Era Hill University Dehradun248002 India Department of CSE Graphic Era Deemed To Be University Uttarakhand Dehradun248002 India Department of Computer Science College of Engineering and Computer Science Jazan University Jazan Saudi Arabia Department of Computer Science and Engineering Koneru Lakshmaiah Education Foundation Andhra Pradesh Vaddeswaram India Department of CSE Panimalar Engineering College Tamil Nadu Chennai India Department of Biosciences-Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Chennai602 105 India Applied Science Research Center Applied Science Private University Amman Jordan Department of Computer Science College of Computer and Information Sciences Jouf University Saudi Arabia Physics and Mathematics Department Faculty of Engineering Helwan University Helwan Egypt

Emotion recognition is vital in the human computer interaction because it improves interaction. Therefore, this paper proposes an improved method for emotion recognition regarding the Hybrid Autoencoder-Long Short-Term Memory (LSTM) model and the newly developed hybrid approach of the Ant Colony Optimization (ACO) and Whale Optimization Algorithm (WOA) for hyperparameters tuning. In this case, Autoencoder can reduce input data dimensionality for input data and find the features relevant for the model’s work. In addition, LSTM is able to work with temporal structures of sequential inputs like speech and videos. The contribution of this research lies in the novel combination method of ACO-WOA which aims at tweaking hyperparameters of Autoencoder-LSTM model. Global aspect of ACO and WOA thereby improve the search efficiency and the accuracy of the proposed emotion recognition system and its generalization capacity. In context with the benchmark dataset for the experimentations of emotion recognition, it has established the efficiency of the proposed model in terms of the conventional methods. Recall rates in recognitive intended various emotions and different modalities were also higher in the hybrid Autoencoder-LSTM model. The optimization algorithms like the ACO-WOA also supported in reducing the computational cost which arose due to hyperparameters tuning. The implementation of this paper is done through Python Software. This implementation shows a high accuracy of 94.12% and 95.94% for audio datasets and image datasets respectively when compared with other deep learning models of Conv LSTM and VGG16. Therefore, the research shows that the presented hybrid approach can be a useful solution for successfully employing emotion recognition for enhancing the creation of the empathetic AI systems and for improving user interactions within various fields including healthcare, entertainment, and customer support. © (2025), (science and Information Organization). All rights

关键词： Ant colony optimization

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Soft-GNN:towards robust graph neural networks via self-adaptive data utilization

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Frontiers of computer science 2025年第4期19卷 1-12页

作者： Yao WU Hong HUANG Yu SONG Hai JIN National Engineering Research Center for Big Data Technology and System Service Computing Technology and System LabCluster and Grid Computing LabSchool of Computer Science and TechnologyHuazhong University of Science and TechnologyWuhan 430074China College of Information and Communication National University of Defense TechnologyWuhan 430019China Department of Computer Science and Operations Research Universitéde MontréalMontreal H3C 3J7Canada

Graph neural networks(GNNs)have gained traction and have been applied to various graph-based data analysis tasks due to their high ***,a major concern is their robustness,particularly when faced with graph data that has been deliberately or accidentally polluted with *** presents a challenge in learning robust GNNs under noisy *** address this issue,we propose a novel framework called Soft-GNN,which mitigates the influence of label noise by adapting the data utilized in *** approach employs a dynamic data utilization strategy that estimates adaptive weights based on prediction deviation,local deviation,and global *** better utilizing significant training samples and reducing the impact of label noise through dynamic data selection,GNNs are trained to be more *** evaluate the performance,robustness,generality,and complexity of our model on five real-world datasets,and our experimental results demonstrate the superiority of our approach over existing methods.

关键词： graph neural networks node classification label noise robustness

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Customer Segmentation Analysis Using Clustering Algorithms 3rd

Customer Segmentation Analysis Using Clustering Algorithms

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3rd International Conference on Machine Learning, Internet of Things and Big Data, ICMIB 2023

作者： Reddy, Biyyapu Sri Vardhan Rishikeshan, C.A. Dagumati, VishnuVardhan Prasad, Ashwani Singh, Bhavya School of Computer Science and Engineering Vellore Institute of Technology Chennai India Department of Computer Science and Engineering School of Computing Amrita Vishwa Vidyapeetham Chennai India

ISBN: (纸本)9789819939312

Customer segmentation has been deployed as a prudent marketing strategy by companies to ensure that their investments are less risky and more judicious. Segmenting customers helps the companies to divide the customers into groups that reflect similarity and maximize the value of each customer to the business. The main goal of this research is to use a machine learning clustering approach called K-means clustering to accomplish consumer segmentation. Besides, the research work is also focused on performing exploratory data analysis on the given dataset. To group the customers into clusters, a K-means clustering algorithm is performed on the customer dataset. To achieve optimization and validation of the clusters, popular heuristic, interpretation, and approximation methods have been included in this paper. Further, for analyzing and visualizing the important facets of the customer dataset and the operation of the K-means algorithm, the paper presents some colorful and informative representations. The implementation of this research work has been done in the R programming language. The outcome of this work includes visualizing the segments of the mall customers in the form of clusters based on their spending scores and annual incomes. Furthermore, a better customer segmentation could be achieved by taking product reviews and customer feedback into consideration. Nevertheless, customer segmentation remains a prospective topic for many researchers and companies due to dynamic customer behavior. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： K-means clustering

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Enhancing Email Spam Detection Using Advanced AI Techniques

Enhancing Email Spam Detection Using Advanced AI Techniques

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2024 International Conference on Decision Aid sciences and Applications, DASA 2024

作者： Al-Dmour, Nidal A. Kousar, Shagufta Khan, Areeba Ihsan, Anaum Abbas, Tahir Saeed, Ali Q College of Engineering Department of Computer Engineering Mutah University Jordan Applied Science Research Center Applied Science Private University Amman Jordan The TIMES Institute Department of Computer Science Multan60000 Pakistan Lahore Pakistan Northern Technical University Computer Center Ninevah Iraq

ISBN: (纸本)9798350369106

This review paper investigates the advances of artificial intelligence (AI) in the field of email spam detection. The study addresses AI-based techniques used for email spam filtering by classifying threats such as keywords, content-based, sender IDs, and phrases. Email spam remains one of the most pressing issues today, with spammers sending malicious links in junk folders to compromise confidential information. As spam filtering becomes increasingly complex, AI and machine learning techniques like Naïve Bayes, Support Vector Machine (SVM), and Random Forest are commonly used to mitigate these threats. This study proposes a novel framework that combines traditional machine learning models with deep neural networks to develop a robust and flexible spam detection system. The framework is designed to enhance the detection capabilities of the classifier by applying a series of key sequential steps, each intended to improve the system's ability to distinguish between legitimate emails and spam. The paper details the methodology, providing a step-by-step explanation of each phase to demonstrate how the framework advances email spam detection. © 2024 IEEE.

关键词： Deep learning Email spam detection machine learning naive-Bayes SVC

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NetProbe: Deep Learning-Driven DDoS Detection with a Two-Tiered Mitigation Strategy 25

NetProbe: Deep Learning-Driven DDoS Detection with a Two-Tie...

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26th International Conference on Distributed computing and Networking, ICDCN 2025

作者： Jha, Prianshu Singh, Gurpreet Kumar, Amit Agrawal, Dewesh Patel, Yashwant Singh Forough, Javad Department of Computer Science and Engineering Thapar Institute of Engineering and Technology Punjab Patiala India Department of Computing Science Umea University Umeå Sweden

ISBN: (纸本)9798400710629

Web servers are the backbone of modern Internet infrastructure, serving as the primary medium for online information distribution. Despite their critical role, web servers are susceptible to cyber-attacks. While current firewall mechanisms provide some level of protection against cyber threats, the evolving nature of these attacks and emerging vulnerabilities continue to pose significant risks. One of the most prevalent yet lethal attacks known today is DDoS (Distributed Denial of Service) attacks. These growing risks emphasize the urgent need for dynamic and robust threat detection and mitigation systems. This paper presents a comparative analysis of ensemble learning models (e.g., Random Forest, XGBoost, and LightGBM) and neural network-based models (e.g., Graph Neural Networks (GNN), Long Short-Term Memory networks (LSTM) with attention layers, and Gated Recurrent Units (GRU)) for DDoS attack detection and classification. Based on this analysis, we propose a real-time DDoS attack detection system integrated with a mitigation mechanism. The proposed system utilizes a two-tiered mitigation strategy assisted by UFW (Uncomplicated Firewall) and Apache server configuration files to block the incoming and outgoing traffic associated with suspicious IP addresses. The system's overall complexity, integrating both detection and response processes, ensures its efficiency in real-time environments while handling large volumes of traffic. Furthermore, the proposed approach achieves 15% improvement in detection accuracy and 20% reduction in false positives compared to traditional techniques, making it an effective and scalable solution for modern web server security. © 2025 Copyright held by the owner/author(s).

关键词： Decision trees

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Enhanced Detection of Eye Coloboma through Global Attention U-Net (GA-UNet) for Accurate Ophthalmic Image Segmentation 4

Enhanced Detection of Eye Coloboma through Global Attention ...

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4th International Conference on Power, Energy, Control and Transmission Systems, ICPECTS 2024

作者： Dhayanithi, A. Bala, K. Bharath Institute of Higher Education and Research School of Computing Department of Computer Science and Engineering Tamil Nadu Chennai600073 India

ISBN: (纸本)9798331508845

Eye coloboma is classified as a congenital eye disease that results in developmental anomalies of the eye that may result in eye diseases that essentially alter visions. Fractional and timely diagnosis still matter significantly in its effective managing and treating. This work presents a solution with a Transformer-Enhanced U-Net model that was developed with Transfer Learning to help augment the market's ability to identify and delineate eye coloboma from medical images. This work further extend the gains made in U-Net, that has achieved fairly well especially in the biomedical image segmentation problem, with the inclusion of transformer modules for the incorporation of the long range dependencies and contextual information. This enhancement help the model to attend more on such small features as coloboma and overall diagnostic accuracy in addition. By using Transfer Learning with a pretrained backbone such as Efficient Net, feature representations from large pre-existing datasets are fed to the system as a solution to the reduction of training time and while the performance of the system drops no further effect is *** approach involves the use of fine-tuning with Transformer-Enhanced U-Net model utilizing a subset of the eye images, which are labelled according to the existence of coloboma. Next, by adopting the self-attention mechanism, the model becomes able to pay attention to the relevant areas in the image, and hence is sensitive to any form of coloboma. In initial experiments, this scheme outperforms the standard convolutional networks for segmenting complex regions with minimal false negative rates. Moreover, by comparing the features extracted at deep layers with ground truth, the effectiveness of the model to realistic clinical usage is established given that the model is robust to changes in imaging conditions. The purpose of this research therefore is to develop an Auto-Generated diagnostic tool that will be more reliable as it assists the

关键词： Image segmentation

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Fake Social Media Profile Identification and Report Using Machine Learning

Fake Social Media Profile Identification and Report Using Ma...

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2024 International Conference on Signal Processing, Computation, Electronics, Power and Telecommunication, IConSCEPT 2024

作者： Chinnasamy, P. Ayyasamy, Ramesh Kumar Subramaniam, Suthashini Sangodiah, Anbuselvan Iftikhar, Aqsa Kiran, Ajmeera Kalsalingam Academy of Research and Education Department of Computer Science and Engineering School of Computing TamilNadu Srivilliputtur India Kampar Malaysia School of Computing Faculty of Computing and Engineering Quest International University Perak Ipoh30250 Malaysia MLR Institute of Technology Department of Computer Science and Engineering Hyderabad India

ISBN: (纸本)9798331540685

In the digital age, Online Social Networking platforms are integral to daily life, but they also attract malicious bots that mimic human behaviour and pose cybersecurity threats. This literature review focuses on recent Machine Learning-based techniques for detecting and classifying bots on major social media platforms. It covers supervised, semi-supervised, and unsupervised methods, dataset details, and feature categories. While the technology is advancing, an ongoing challenge lies in the creation of ever- evolving bots to evade detection. The study offers insights into the evolving landscape of cybersecurity and the need for continual research in this field, but potential drawbacks may include data privacy concerns, The pervasive influence of the internet and social media as platforms for real-time information dissemination and their potential to shape perceptions and lives. It highlights the concern that unverified and malicious content can significantly impact users' perceptions. The paper offers a taxonomy for classifying malicious information at various stages and presents technologies to combat the issue from content generation to containment. The results illustrate the efficacy of machine learning in improving the dependability and safety of social media platforms, offering a scalable approach to address the increasing problem of online deception. © 2024 IEEE.

关键词： Bot (Internet)

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Intelligent Personality Assessment and Verification from Handwriting using Machine Learning

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International Journal of Information Technology (Singapore) 2024年第7期16卷 4541-4546页

作者： Mishra, Pradeep Kumar Mishra, Gouri Sankar Abidi, Ali Imam Kumar, Rajiv Sahoo, Ashok Kumar Maini, Tarun Department of Computer Science and Application SSET Sharda University Department of Computer Science and Engineering SSET Sharda University NVIDIA AI Learning & amp Research Center Department of Applied Computational Science and Engineering G L Bajaj Institute of Technology and Management Greater Noida Graphic Era Hill University School of Computer Science and Engineering Galgotias University

It is possible to tell a lot about a person just by looking at their handwriting. The way someone writes might tell you a lot about who, they are as a person. There has been a lot of study on the possibility of deducing aspects of a person’s character from their handwriting. In this piece, we’ll look at the different personality qualities and how they might be utilized to make an evaluation. The report includes a dataset showing a correlation between several traits of personality. The Big Five Personality Traits are mapped using handwritten personality data. It was suggested that a comparison table should be used to spot distinctive character quirks. The article includes a review of previous studies and an assessment of the current status of the topic. Handwriting embodies have both creativity and expressiveness. © Bharati Vidyapeeth's institute of computer Applications and Management 2024.

关键词： Agreeableness Conscientiousness Extraversion Neuroticism Openness

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