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AMachine Learning Approach to User Profiling for Data Annotation of Online Behavior

computers, Materials & Continua 2024年第2期78卷 2419-2440页

作者： Moona Kanwal Najeed AKhan Aftab A.Khan Computer Science and Information Technology Department NED University of Engineering and TechnologyKarachiPakistan Computer Engineering Department Sir Syed University of Engineering and TechnologyKarachiPakistan College of Education Health and Human ServicesLongwood UniversityFarmvilleVAUSA

The user’s intent to seek online information has been an active area of research in user *** profiling considers user characteristics,behaviors,activities,and preferences to sketch user intentions,interests,and *** user characteristics can help capture implicit and explicit preferences and intentions for effective user-centric and customized content *** user’s complete online experience in seeking information is a blend of activities such as searching,verifying,and sharing it on social ***,a combination of multiple behaviors in profiling users has yet to be *** research takes a novel approach and explores user intent types based on multidimensional online behavior in information *** research explores information search,verification,and dissemination behavior and identifies diverse types of users based on their online engagement using machine *** research proposes a generic user profile template that explains the user characteristics based on the internet experience and uses it as ground truth for data *** feedback is based on online behavior and practices collected by using a survey *** participants include both males and females from different occupation sectors and different *** data collected is subject to feature engineering,and the significant features are presented to unsupervised machine learning methods to identify user intent classes or profiles and their *** techniques are evaluated,and the K-Mean clustering method successfully generates five user groups observing different user characteristics with an average silhouette of 0.36 and a distortion score of *** average is computed to identify user intent type *** user intent classes are then further generalized to create a user intent template with an Inter-Rater Reliability of 75%.This research successfully extracts different user types based on th

关键词： User intent cluster user profile online search information sharing user behavior search reasons

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One-Shot Sequential Federated Learning for Non-IID Data by Enhancing Local Model Diversity 24

One-Shot Sequential Federated Learning for Non-IID Data by E...

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32nd ACM International Conference on Multimedia, MM 2024

作者： Wang, Naibo Deng, Yuchen Feng, Wenjie Fan, Shichen Yin, Jianwei Ng, See-Kiong Institute of Data Science National University of Singapore Singapore Singapore School of Mathematics and Statistics Changchun University of Technology Changchun China School of Computer Science and Technology Xidian University Xi'an China College of Computer Science and Technology Zhejiang University Hangzhou China

ISBN: (纸本)9798400706868

Traditional federated learning mainly focuses on parallel settings (PFL), which can suffer significant communication and computation costs. In contrast, one-shot and sequential federated learning (SFL) have emerged as innovative paradigms to alleviate these costs. However, the issue of non-IID (Independent and Identically Distributed) data persists as a significant challenge in one-shot and SFL settings, exacerbated by the restricted communication between clients. In this paper, we improve the one-shot sequential federated learning for non-IID data by proposing a local model diversity-enhancing strategy. Specifically, to leverage the potential of local model diversity for improving model performance, we introduce a local model pool for each client that comprises diverse models generated during local training, and propose two distance measurements to further enhance the model diversity and mitigate the effect of non-IID data. Consequently, our proposed framework can improve the global model performance while maintaining low communication costs. Extensive experiments demonstrate that our method exhibits superior performance to existing one-shot PFL methods and achieves better accuracy compared with state-of-the-art one-shot SFL methods on both label-skew and domain-shift tasks (e.g., 6%+ accuracy improvement on the CIFAR-10 dataset). Our code and supplementary are available online: https://***/NaiboWang/FedELMY. © 2024 ACM.

关键词： Federated learning

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Breast cancer images Classification using a new transfer learning technique

Iraqi Journal for Computer Science and Mathematics

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Iraqi Journal for computer science and Mathematics 2023年第1期4卷 167-180页

作者： Mukhlif, Abdulrahman Abbas Al-Khateeb, Belal Mohammed, Mazin Abed Computer Science Department College of Computer Science and Information Technology University of Anbar Anbar Ramadi31001 Iraq

Breast cancer is one of the most common types of cancer among women, which requires building smart systems to help doctors and early detection of cancer. Deep learning applications have emerged in many fields, especially in health care, but there are still some limitations in this technique, such as the small number of classified medical images needed to train deep learning models, to solve this problem, transfer learning technology appeared based on transferring knowledge from pre-trained models on ImageNet and setting them to the current task, but there is still a problem that the features extracted from ImageNet are not medical, so this paper aims to find a solution to this problem by using new techniques for transfer learning by taking advantage of the presence of unclassified medical images of the same disease to reduce the impact of ImageNet. The proposed approach was applied to the modified Xception model to classify the histological images of breast cancer in the ICIAR 2018 dataset into four classes: invasive carcinoma, in situ carcinoma, benign, and normal. The proposed approach has obtained 99%, 99.003%, 98.995%, 99%, 98.55%, and 99.14% for accuracy, precision, recall, F1-score, sensitivity, and specificity respectively. Our work has been compared with previous works. © 2023 Iraqi Journal for computer science and Mathematics. All rights reserved.

关键词： Image classification

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Tackling cyberbullying: a multilingual approach to cyberbullying detection in India

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International Journal of Intelligent Systems Technologies and Applications 2025年第1-2期23卷 116-132页

作者： Nawshad, Shahwar Farooq, Umar Singh, Parvinder Khurana, Surinder Singh Bansal, Anam Department of Computer Science and Technology Central University of Punjab VPO-Ghudda Punjab Bathinda151401 India

In India's evolving digital world, women are particularly vulnerable to cyberbullying due to differences in education, limited digital literacy, and pervasive cybersecurity risks. This research focuses on creating a system to detect cyberbullying targeting Indian women. Acknowledging the country's linguistic diversity, we adopt a multilingual approach, constructing a dataset incorporating English, Hindi-English swear words, common Indian slang, and offensive lexicons. We apply various machine learning algorithms to classify cyberbullying incidents. Upon evaluating the results, the relevance vector machine (RVM) algorithm emerged as the most effective, achieving 82.61% and 84.82% accuracy scores in detecting cyberbullying over English and Hinglish datasets, respectively. These findings provide crucial insights for crafting strategies to safeguard Indian women in the digital space. However, more sophisticated and hybrid models are planned for the future to address image, video, and audio-based cyberbullying against women. Copyright © 2025 Inderscience Enterprises Ltd.

关键词： Adversarial machine learning

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Scheduling Bi-Objective Lot-Streaming Hybrid Flow Shops with Consistent Sublots via an Enhanced Artificial Bee Colony Algorithm

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Complex System Modeling and Simulation 2025年第1期5卷 46-67页

作者： Benxue Lu Kaizhou Gao Peiyong Duan Adam Slowik School of Computer Science Liaocheng UniversityLiaocheng 252000China Macao Institute of Systems Engineering Macao University of Science and TechnologyMacao 999078China School of Information and Automation Engineering Qilu University of TechnologyJinan 250353China Department of Electronics and Computer Science Koszalin University of TechnologyKoszalin 75-453Poland

This work addresses bi-objective hybrid flow shop scheduling problems considering consistent sublots(Bi-HFSP_CS).The objectives are to minimize the makespan and total energy ***,the Bi-HFSP_CS is formalized,followed by the establishment of a mathematical ***,enhanced version of the artificial bee colony(ABC)algorithms is proposed for tackling the Bi-HFSP_***,fourteen local search operators are employed to search for better *** different Q-learning tactics are developed to embed into the ABC algorithm to guide the selection of operators throughout the iteration ***,the proposed tactics are assessed for their efficacy through a comparison of the ABC algorithm,its three variants,and three effective algorithms in resolving 95 instances of 35 different *** experimental results and analysis showcase that the enhanced ABC algorithm combined with Q-learning(QABC1)demonstrates as the top performer for solving concerned *** study introduces a novel approach to solve the Bi-HFSP_CS and illustrates its efficacy and superior competitive strength,offering beneficial perspectives for exploration and research in relevant domains.

关键词： hybrid flow shop scheduling Q-learning lot streaming local search

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Enhancing text understanding of decoder-based model by leveraging parameter-efficient fine-tuning method

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Neural Computing and Applications 2025年第9期37卷 6899-6913页

作者： Feroze, Wasif Cheng, Shaohuan Jimale, Elias Lemuye Jakhro, Abdul Naveed Qu, Hong School of Computer Science and Engineering University of Electronic Science and Technology of China Chengdu China Department of Information Technology Shaheed Benazir Bhutto University Naushahro Feroze Campus Naushahro Feroze Pakistan

Machine reading comprehension (MRC) is a fundamental natural language understanding task in natural language processing, which aims to comprehend the text of a given passage and answer questions based on it. Understanding implicit information, deducing the logical structure of information, and connecting context from different pieces of information make the MRC task difficult. Most current state-of-the-art approaches for MRC are using encoder-based models. However, no earlier research proposed a decoder-only model for MRC question-answering datasets, although language models based on this category achieved unprecedented performance in different generative tasks. In this paper, we propose a parameter-efficient fine-tuning framework that effectively increases MRC capabilities on decoder-only large language models. This framework designs the process for MRC and introduces the low-rank adaptation (LoRA) method to effectively fine-tune the large model with many parameters, even with lower hardware resource requirements than the previous methods. In addition, we also integrate a quantized model inference strategy for the fine-tuned model to improve practicability further. We conducted experiments on four types of MRC datasets. After extensive experiments, our results show that our model achieved a significant performance boost over baselines and outperformed other strong models for MRC. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2025.

关键词： Decoding

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VoteDroid: a new ensemble voting classifier for malware detection based on fine-tuned deep learning models

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Multimedia Tools and Applications 2025年第12期84卷 10923-10944页

作者： Bakır, Halit Faculty of Engineering and Natural Sciences Department of Computer Engineering Sivas University of Science and Technology Sivas Turkey

In this work, VoteDroid a novel fine-tuned deep learning models-based ensemble voting classifier has been proposed for detecting malicious behavior in Android applications. To this end, we proposed adopting the random search optimization algorithm for deciding the structure of the models used as voter classifiers in the ensemble classifier. We specified the potential components that can be used in each model and left the random search algorithm taking a decision about the structure of the model including the number of each component that should be used and its location in the structure. This optimization method has been used to build three different deep learning models namely CNN-ANN, pure CNN, and pure ANN. After selecting the best structure for each DL model, the selected three models have been trained and tested using the constructed image dataset. Afterward, we suggested hybridizing the fine-tuned three deep-learning models to form one ensemble voting classifier with two different working modes namely MMR (Malware Minority Rule) and LMR (Label Majority Rule). To our knowledge, this is the first time that an ensemble classifier has been fine-tuned and hybridized in this way for malware detection. The results showed that the proposed models were promising, where the classification accuracy exceeded 97% in all experiments. © The Author(s) 2024.

关键词： Android malware

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Differential Evolution Strategy with Chebyshev Chaos Based Mutation

Journal of Network Intelligence

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Journal of Network Intelligence 2024年第1期9卷 613-628页

作者： Sitenda, Amos Song, Pei-Cheng Chu, Shu-Chuan Chen, Shi-Huang College of Computer Science and Engineering Shandong University of Science and Technology Qingdao266590 China Department of Computer Science and Information Engineering Shu-Te UniversityKaohsiung Kaohsiung824 Taiwan

Differential Evolution (DE) is a potent stochastic evolutionary optimization algorithm garnering increasing research attention. Over the years, it has been found applicable in solving diverse real-world problems. DE employs mutation, crossover, and selection operators to guide populations toward optimal or nearly optimal solutions. However, the standard DE mutation strategies have found limitations in balancing exploration and exploitation effectively, thus prompting research into possible improvements. This study introduces a novel mutation strategy named Chebyshev Infused Chaos Mutation Strategy (CICMS). where a chaotic sequence partially guides the process of donor vector generation. Rigorous evaluations were conducted, comparing our modified DE against the standard DE and seven other metaheuristic algorithms, including Genetic Algorithm, Particle Swarm Optimization and Gravitational Search Algorithm. Experiments were performed using the challenging CEC 2014 benchmark functions, consisting of 30 objective functions. Results indicate substantial improvements in convergence speed and solution quality, highlighting the potential of our novel mutation strategy to enhance DE’s practicality in addressing complex optimization problems. This research contributes valuable insights to the dynamic field of optimization algorithms with implications for a wide range of applications. © 2024, Taiwan Ubiquitous Information CO LTD. All rights reserved.

关键词： Chaos theory

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Optimizing Neural Network Training: A Markov Chain Approach for Resource Conservation

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2024年 1-14页

作者： Wang, Ke Huang, Xianting Tan, Cong Yiu, Siu-Ming Chen, Zicong Lei, Xiaolin College of Information Science and Technology Jinan University Guangzhou China Department of Computer Science the University of Hong Kong Hong Kong China

The additional resource consumption generated during the repeated training processes of neural networks, including time and computational power costs, is a problem of significant concern. We, therefore, propose a method that utilizes Markov Chain to predict the training outcomes of neural networks with the same structure. This method’s training is based on prior experience to optimize the parameter adjustment process, thereby reducing the number of times training must be started from scratch and lowering time costs. By predicting training outcomes and reducing forward and backward propagation computations, among other factors, computational resource consumption significantly decreases. Simultaneously, since Markov Chain represents a clear mathematical model, the properties of probability transition offer greater interpretability compared to traditional methods. In an era where explainable artificial intelligence is equally crucial, a more transparent training method could have greater application potential in many important scenarios. The dual benefits they provide exemplify the advantage of our approach. Regarding the critical part, we have theoretically and experimentally demonstrated that, under certain conditions, the neural network training process possesses Markov property and becomes a Markov process after clustering. IEEE

关键词： Forecasting

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A comparative study of machine learning in diabetes classification: Decision trees, logistic regression, and SVM 2

A comparative study of machine learning in diabetes classifi...

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2nd International Conference on Big Data, Computational Intelligence, and Applications, BDCIA 2024

作者： Zhu, Xinglong Liao, Huitong Zhang, Tingting Zhang, Lujuan Li, Xueri Department of Computer Science Guangdong University of Science and Technology Dongguan China

ISBN: (纸本)9781510689053

As the application of computer science in healthcare continues to expand, machine learning techniques have become an important tool for disease diagnosis. In this study, we trained and predicted diabetes datasets by preprocessing, standardising and normalising the data, and then using three machine learning models, aiming to improve the accuracy of diabetes prediction and the value of clinical application. The experimental results show that these machine learning models exhibit a high degree of accuracy and reliability in the field of diabetes diagnosis, among which the SVM performs the best on normalised data, with the highest values of accuracy, precision, recall, F1-score, and AUC evaluation metrics, showing the good performance of the support vector machine in predicting diabetes. The decision tree model performed consistently on raw and standardised data, while the logistic regression model performed best on AUC values. This study not only verifies the effectiveness of machine learning models in diabetes prediction, but also provides clinicians with accurate diagnostic assistance, which is of significant academic and practical significance. © 2025 SPIE.

关键词： Logistic regression

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