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Adversarial-Learning-Based Taguchi Convolutional Fuzzy Neural Classifier for Images of Lung Cancer

IEEE Access 2024年 12卷 72766-72776页

作者： Lin, Cheng-Jian Lin, Xue-Qian Jhang, Jyun-Yu National Chin-Yi University of Technology Department of Computer Science and Information Engineering Taichung41170 Taiwan National Taichung University of Science and Technology Department of Computer Science and Information Engineering Taichung40401 Taiwan

Deep learning technology has extensive application in the classification and recognition of medical images. However, several challenges persist in such application, such as the need for acquiring large-scale labeled data, configuring network parameters, and handling excessive network parameters. To address these challenges, in this study, we developed an adversarial-learning-based Taguchi convolutional fuzzy neural classifier (AL-TCFNC) for classifying malignant and benign lung tumors displayed in computed tomography images. In the framework of the developed AL-TCFNC, a fuzzy neural classifier replaces a conventional fully connected network, thereby reducing the number of network parameters and the training duration. To reduce experimental cost and training time, the Taguchi method was used. This method helps to identify the optimal combination of model parameters through a small number of experiments. The transfer learning of models across databases often results in subpar performance because of the paucity of labeled samples. To resolve this problem, we used a combination of maximum mean discrepancy and cross-entropy for adversarial learning with the proposed model. Two data sets, namely the SPIE-AAPM Lung CT Challenge data set and LIDC-IDRI Lung Imaging Research data set, were used to validate the AL-TCFNC model. When the AL-TCFNC model was used for transfer learning, it exhibited an accuracy rate of 89.55% and outperformed other deep learning models in terms of classification performance. © 2013 IEEE.

关键词： Fuzzy neural networks

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Machine learning electron-phonon interactions in two-dimensional semiconducting materials: The case of zero-point renormalization

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Physical Review Materials 2024年第10期8卷 L101001页

作者： Anubhab Haldar Quentin Clark Marios Zacharias Feliciano Giustino Sahar Sharifzadeh Department of Electrical and Computer Engineering Department of Philosophy Oden Institute for Computational Engineering and Sciences Department of Physics Division of Materials Science and Engineering

We utilize first-principles theory to investigate the role of electron-phonon interactions within a dataset of monolayer materials. Using density functional theory to describe excited-state transitions and the special displacement method to describe the role of phonons, we analyze the relationship between simple physical observables and electron-phonon coupling strength. For over 100 materials, we compute the band gap renormalization due to zero-point vibrational (ZPR) motion as a measure of electron-phonon interactions and train a machine learning model based on physical parameters. We demonstrate that the strength of electron-phonon interactions is highly dependent on the band gap, dielectric constant, and degree of ionicity, all of which can be physically justified. We then apply this model to 1302 2D materials, predicting the ZPR, which for five randomly selected materials tested agree well with the first-principles predictions. This work provides an approach for quantitatively predicting the ZPR as a measure of electron-phonon interactions in 2D materials.

关键词： Electron-phonon coupling Electronic structure 2-dimensional systems Machine learning

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Genetic Algorithm-optimized k-nearest Neighbors and Support Vector Machines for Breast Cancer Detection in Resource-constrained Environments

IAENG International Journal of Computer Science

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IAENG International Journal of computer science 2025年第3期52卷 848-861页

作者： Alemu, Abebe Girma, Anteneh Abebe, Mesfin Srinivasagan, Ramasamy Adama Science and Technology University Adama Ethiopia Computer Science/Cyber-Security department University of the District of Columbia WashingtonDC United States Computer Science and Engineering department Adama Science and Technology University Adama Ethiopia Computer Engineering department CCSIT King Faisal University Al Hufuf Saudi Arabia

Breast cancer poses a significant global threat, highlighting the urgent need for early detection to reduce mortality rates. Researchers are working to minimize the occurrence of false positives and false negatives, thereby improving the efficiency of breast cancer detection models. To achieve this, they employ advanced techniques such as artificial intelligence, machine learning, deep learning, and computational intelligence. Support vector machines (SVM) and k-nearest neighbors (KNN) are two popular lightweight machine-learning techniques.;however, their effectiveness depends on proper feature selection and parameter tuning. Genetic algorithm optimization provides a solution by intelligently selecting relevant features and fine-tuning parameters, which enhances classification accuracy for early diagnosis. This study demonstrates the effectiveness of a hybrid computational intelligence model that utilizes genetic algorithms for feature selection. The proposed GAKNN-SVM model shows superior performance in detecting breast tumors, utilizing the Wisconsin Breast Cancer Diagnostic dataset. The results indicate significant improvements, with accuracy, sensitivity, and specificity rates reaching 98.25%, 98.15%, and 98.41%, respectively, based on 171 test samples. Overall, genetic algorithms and machine learning approaches hold great promise for improving breast cancer detection accuracy, ultimately leading to better diagnostic outcomes and reduced mortality rates, especially in resource-constrained environments. © (2025), (International Association of Engineers). All rights reserved.

关键词： Support vector machines

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Intelligent E-Commerce Chatbot for Personalized Product Recommendations Using Text Mining and Machine Learning 2

Intelligent E-Commerce Chatbot for Personalized Product Reco...

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2nd International Conference on Advances in Computation, Communication and Information Technology, ICAICCIT 2024

作者： Minu, M.S. Adithya Kavya SreeRanjini Department of Computer Science and Engineering SRM Institute of Science and Technology Ramapuram Chennai India Department of Computer Science and Engineering with Specialization in Big Data Analytics SRM Institute of Science and Technology Ramapuram Chennai India

ISBN: (纸本)9798331541217

Innovative technology solutions have been developed in response to the growing need for effective and customized client contact on e-commerce platforms. This work introduces an intelligent chatbot system that uses machine learning and text mining techniques to improve user satisfaction and answer customer concerns. Advanced Natural Language Processing (NLP) models have been trained on large datasets, allowing for precise user intent interpretation and pertinent product recommendations. A strong recommendation engine is included into the system architecture to aid in decision-making, ultimately enhancing the customer experience by providing individualized, real-time help. The study also highlights the significance of data privacy by using privacy-preserving techniques to safeguard user data. The system's scalability and performance benefits are highlighted through thorough comparisons with current models. By showcasing the useful uses of machine learning and text mining in enhancing customer contact and service delivery, this study makes a substantial contribution to the development of AI-driven solutions in e-commerce. © 2024 IEEE.

关键词： Big data Chatbot Cost data Mining e-Commerce Hadoop Machine Learning Product Purchasing Recommendation

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HCBiL-DMN: an effective food infestation detection from stored food grains using deep learning model

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Multimedia Tools and Applications 2025年第14期84卷 14047-14070页

作者： Vedavathi, N. Suhas Bharadwaj, R. Department of Computer Science and Engineering Vidyavardhaka College of Engineering Karnataka Mysuru570002 India Department of Information Science and Engineering The National Institute of Engineering Karnataka Mysuru570008 India

Food Infestation Detection is more important for food safety and health concerns. It is a challenging task to separate the grains into infested or non-infested. It is found that in the existing system, there is no effective approach for the early detection of pest populations and for classifying crops as infested or non-infested. Therefore, the identification and categorization of early-stage grain infestations is in high demand. To address the above problem, this research presents a better technique for detecting and classifying infested and non-infested grains. Initially, the maize, wheat and olive image datasets are collected, which are then pre-processed using data normalization and adaptive wiener filtering (AdwF) approach to improve the excellence of the input images. Image quality helps in achieving better performance and also helps in analyzing the quality of food grains effectively. Feature extraction is done using the Dense Residual Pyramidal Dilated Convolution Network (DR-PDCNet) technique, and feature selection is done using the Linear Discriminant Coati Optimization (LD-CoA) algorithm. The Hybrid Convolutional Bi-LSTM based Deep Maxout Network (HCBiL-DMN) model is used to detect the infected food grains in the preserved food grains with high accuracy. This research explores the potential and future possibilities for using technology to ensure the quality, assessment, and monitoring of nutritional grains during storage. The performance results are achieved with 98.3% accuracy, 99.12% recall, 98.65% precision, 98.02% MCC, and 99.32% F1 score. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Convolution

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A systematic literature review of video forgery detection techniques

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Multimedia Tools and Applications 2025年 1-51页

作者： Aulakh, Manpreet Kaur Kanwal, Navdeep Bansal, Manish Department of Computer Science and Engineering Punjabi University Patiala Punjab Patiala India Department of Computer Science Baba Farid Group of Institutions Punjab Bathinda India

In the last decade, due to the widespread and inexpensive availability of digital video cameras, digital videos (DV) are employed for security purposes daily, and they are generally regarded as a more credible form of evidence than still photographs. Due to the tremendous growth of video editing tools, anyone with access to advanced editing software and a modern Smartphone can easily do digital video manipulations and fake it. As a result, to utilize video content as proof in court, it is necessary to evaluate and determine whether it is original or modified. To check the integrity and validity of video recordings, digital forgery detection techniques are required. The objective of the study is to present a systematic review of techniques for detecting forgery in digital videos. We conducted a systematic literature review (SLR) in this study to present a detailed review of the initial and recent research efforts in Digital video forgery detection, summarizing 260 relevant papers from 2000 to 2023 that have presented a variety of techniques. For analysis, we have presented our references in three different ways: according to the type of forgery detected, according to the type of model or technique used and according to the feature used for forgery detection. We look through the several datasets that are cited in articles and determine their applicable domain. Then, we looked at the numerous measuring metrics employed by different research papers and compared the effectiveness of deep and non-deep models in each category of forgery that was found. Finally, research gaps concerning passive video forgery detection are classified and highlighted. A comparison between our survey and other existing survey articles has been presented in the paper. Researchers who wish to work on video forgery detection will get assistance to determine what kind of efforts in forgery detection work is still required. This survey will also help to select techniques and features based on their

关键词： Video recording

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An efficient security testing for android application based on behavior and activities using RFE-MLP and ensemble classifier

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Multimedia Tools and Applications 2025年第15期84卷 15357-15386页

作者： Kumar, Pawan Singh, Sukhdip Department of Computer Science and Engineering Deenbandhu Chhotu Ram University of Science and Technology Murthal Sonipat131039 India

An enormous amount of applications that are available for download permits users to enhance the functionality of the devices with brand-new features, which is a significant factor in the growing popularity of smartphones and tablets. In reality, third-party applications for business and entertainment are abundantly available in official stores, with the majority of them being offered for free. However, vulnerable applications may expose private information (such as emails, global GPS location, financial information, and phone contacts). To overcome these issues, the log files are collected for normalizing and selecting the features related to the behaviour and activities using the Min–Max and Recursive Feature Elimination (RFE) based on the MLP. The selected features are learned by using the ensemble learning algorithm, which is a stacking of Bi-directional Long Short Term Memory (Bi-LSTM), Deep Neural Network (DBN) and Radial Basis Functional Network (RBFN) giving the collected learned features from each algorithm to the Meta classifier for predicting the malware. The ensemble learning algorithm then feeds the gathered learned features from each method to the Meta classifier for malware prediction. The suggested strategy protects users' privacy by alerting them when it detects malicious apps and removing them. Accuracy, Precision, Specificity, and Kappa performance measures are assessed and contrasted with the proposed and current models. The proposed model achieved performance metrics values are 99.78%, 99.56%, 99.85% and 98.89%. Thus, the security testing for Android applications based on behaviour and activities using RFE-MLP and Ensemble classifier performs better than the existing model. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Long short-term memory

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Explainable AI model for PDFMal detection based on gradient boosting model

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Neural Computing and Applications 2024年第34期36卷 21607-21622页

作者： Elattar, Mona Younes, Ahmed Gad, Ibrahim Elkabani, Islam Department of Computer Science Faculty of Science Tanta University Tanta31527 Egypt Department of Mathematics and Computer Science Faculty of Science Alexandria University Alexandria21511 Egypt Faculty of Computer Science and Engineering Al Alamein International University Alamein51718 Egypt

Portable document formats (PDFs) are widely used for document exchange due to their widespread usage and versatility. However, PDFs are highly vulnerable to malware attacks, which pose significant security risks. Existing defense mechanisms often struggle to effectively detect and mitigate these threats, highlighting the need for more robust solutions. This paper introduces a robust framework that uses advanced tree-based ensemble models to detect malicious PDFs using the Evasive-PDFMal2022 dataset. The proposed model achieves a recall rate of 100%, an accuracy rate of 99.95%, and a fast inference time of 0.1723 s. Furthermore, the framework exhibits minimal false positive and false negative rates, ensuring a high level of precision in distinguishing between malicious and benign PDFs. Shapley additive explanations are used to improve the interpretability and reliability of the model’s predictions. The results highlight the effectiveness of the proposed model in improving PDF document security and addressing the challenges posed by malware attacks. © The Author(s) 2024.

关键词： Explainable artificial intelligence (XAI) Malicious PDF Malware detection Tree-based ensemble models

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Empowering Biomedical Named Entity Recognition Through Multi-Tagger Collaboration 27

Empowering Biomedical Named Entity Recognition Through Multi...

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27th European Conference on Artificial Intelligence, ECAI 2024

作者： Zhao, Jin Xie, Jian Zhu, Tinghui Guo, Qian Li, Zhixu Xiao, Yanghua Shanghai Key Laboratory of Data Science School of Computer Science Fudan University China Department of Computer Science and Engineering East China University of Science and Technology China

ISBN: (纸本)9781643685489

Biomedical Named Entity Recognition (BioNER) plays a crucial role in automatically identifying specific categories of entities from biomedical texts. Currently, region-based methods have shown promising performance in BioNER. However, existing paradigms in the region-based methods suffer from inherent limitations, including the generation of negative samples, and the ignorance of token dependencies. To overcome these limitations, we propose a new paradigm, implemented as Token Cascade Tagger (TCT), which combines span identification and category classification. The TCT utilizes category information to enhance the correlation between the heads and tails of entities, effectively reducing the generation of negative samples. Additionally, we introduce a Token Dependency Tagger (TDT) that captures token dependencies within entity spans by identifying the longest span in a sentence. The TDT filters out incorrect spans and further improves the accuracy of span detection obtained from the TCT. Furthermore, we employ a multi-task learning framework to optimize both the TCT and TDT, leading to superior performance in BioNER. Extensive experiments on publicly available biomedical datasets demonstrate our method outperforms the previous state-of-the-art methods, achieving 92.44%, 92.54%, and 81.26% on NCBI-Disease, BC5CDR, and GENIA, respectively, in terms of F1 score. © 2024 The Authors.

关键词： Multi-task learning

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Customized Movie Recommendation for Enhanced Entertainment

Customized Movie Recommendation for Enhanced Entertainment

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2024 International Conference on Cybernation and Computation, CYBERCOM 2024

作者： Minu, M.S. Talapalli, Srikar Chevuri, Saahithi Medhasri, T.S. SRM Institute of Science and Technology Department of Computer Science Engineering Ramapuram Chennai India SRM Institute of Science and Technology Department of Computer Science Engineering with Specialization in Big Data Analytics Ramapuram Chennai India

ISBN: (纸本)9798331542344

The surging popularity of online movie databases has created a challenge for viewers: choosing a film from a massive library can be overwhelming. In this paper, it proposes to design a new hybrid movie recommendation powered by machine learning techniques as a solution to improve user experience. Among those techniques, two are combined into this paper: content-based filtering, using the real features of a movie such as genre and a director, and collaborative filtering, which uses the rating provided by users. This hybrid approach thus improves the intrinsic weaknesses that reside in both these methods individually. On the other hand, content-based filtering tends to reduce exploration to movies of the exact type one is doing well at, whereas, inversely, collaborative filtering settings often mean that a user will be exposed only to films within his or her already-recognized taste profile. It is expected that the proposed hybrid system will solve both problems by balancing this line between exploring familiar cinematic territory and making new recommendations based on both user preferences and the entire collective intelligence of the user base. The approach would aim to enrich user satisfaction by facilitating the selection of movies and providing a richer and more diversified movie-viewing experience. © 2024 IEEE.

关键词： Collaborative filtering

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