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Correction to: Software effort estimation using convolutional neural network and fuzzy clustering (Neural Computing and Applications, (2024), 36, 23, (14449-14464), 10.1007/s00521-024-09855-z)

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Neural Computing and Applications 2024年第10期37卷 7445-7445页

作者： Azzeh, Mohammad Alkhateeb, Abedalrhman Nassif, Ali Bou Department of Data Science Princess Sumaya University for Technology Amman Jordan Department of Computer Science Lakehead University Thunder Bay Canada Department of Computer Engineering University of Sharjah Sharjah United Arab Emirates

In this article the affiliation details for Author Mohammad Azzeh, Abedalrhman Alkhateeb and Ali Bou Nassif were incorrectly given as 'Department of data science, Princess Sumaya University for Technology, Amman, Jordan, Department of computer science, Lakehead University, Thunder Bay, Canada and Department of computer engineering, University of Sharjah, Sharjah, UAE' but the affiliation details for Author Mohammad Azzeh should have been 'Department of data science, Princess Sumaya University for Technology, Amman, Jordan', Author Abedalrhman Alkhateeb should have been 'Department of computer science, Lakehead University, Thunder Bay, Canada' and Author Ali Bou Nassif should have been 'Department of computer engineering, University of Sharjah, Sharjah, UAE'. The original article has been corrected. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2024.

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An Intelligent LLM-Powered Personalized Assistant for Digital Banking Using LangGraph and Chain of Thoughts

An Intelligent LLM-Powered Personalized Assistant for Digita...

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International Symposium on Intelligent Systems and Informatics (SISY)

作者： Arafat Md Easin Saha Sourav Orosz Tamás Data Science and Engineering Dept. Eötvös Loránd University (ELTE) Budapest Hungary Dept. of Computer Science and Engineering United International University (UIU) Dhaka Bangladesh

ISBN: (数字)9798350385601

ISBN: (纸本)9798350385618

Text-based applications and chatbots are increasingly popular for delivering banking services and educational tools, offering convenient and efficient solutions for users. Whereas, personalized assistants have transformed user engagement in the digital banking space by utilizing Large Language Models (LLMs) in conjunction with autonomous agents. This study proposes the development of an intelligent personalized assistant for digital banking, utilizing a multi-agent framework based on the LangGraph and Chain of Thoughts (COT) prompting. While COT guarantees context-aware replies, the LangGraph design maps characteristics to nodes to improve user interactions. The objectives of this system are to enhance task efficiency and elevate the capabilities of digital banking assistants. We present a customizable digital banking system powered by LLM-based models, designed to deliver an interactive and personalized banking experience. The system supports a range of services, including adding money, transferring funds, paying bills, accessing telco services like mobile recharge, managing savings interest rates, DPS schemes, fixed deposits, and answering FAQs related to banking information. Therefore, integrating COT for logical reasoning enhances the effectiveness of multi-agent systems, as each single agent benefits from the structured reasoning process. In addition, LangGraph is employed for structured data management, enabling the assistant to support and accelerate various digital banking processes efficiently. The code implementation of this work is available for public access at: https://***/srv-sh/digital_agent.

关键词： Protocols Large language models Refining Banking Cognition Systems support Security Resilience Periodic structures Multi-agent systems

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Optimized data Processing for Hard Drive Failure Prediction 5

Optimized Data Processing for Hard Drive Failure Prediction

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5th International Conference on Emerging Systems and Intelligent Computing, ESIC 2025

作者： Sivakumar, M. Ragavendra, R. Satapathy, Sandeep Kumar Mishra, Shruti Mallick, Pradeep Kumar Vellore Institute of Technology School of Computer Science & Engineering Tamilnadu Chennai India Vellore Institute of Technology Centre for Advanced Data Science Tamil Nadu Chennai India Kiit University School of Computer Engineeing Odisha India

ISBN: (纸本)9798331522100

With the rise of big data, efficient data handling techniques have become increasingly crucial. Hard drive failure prediction is a critical domain where accurate and timely data handling is paramount. This research paper introduces a data analysis and pre-processing approach for hard drive failure prediction based on machine learning techniques. A large dataset encompassing hard drive operational data, metadata, and error rates was collected. Extensive pre-processing was performed, including data cleaning and transformation. Multiple Machine Learning algorithms, including random forests, were employed to train predictive models for hard drive failure. The results demonstrate the effectiveness of our approach in achieving high accuracy for hard drive failure prediction, highlighting the significance of our pre-processing methods. Additionally, this paper compares existing machine learning algorithms and explores the role of big data in enhancing hard drive failure prediction. The utilization of big data and advanced data handling techniques has led to improved accuracy and predictive capabilities, making it an indispensable factor in this field. © 2025 IEEE.

关键词： data curation

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An Overview of Facial Micro-Expression Analysis: data, Methodology and Challenge

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IEEE Transactions on Affective Computing 2023年第3期14卷 1857-1875页

作者： Xie, Hong-Xia Lo, Ling Shuai, Hong-Han Cheng, Wen-Huang National Yang Ming Chiao Tung Univresity Institute of Electronics Hsinchu300093 Taiwan National Yang Ming Chiao Tung Univresity Department of Electrical and Computer Engineering Hsinchu300093 Taiwan National Chung Hsing University Artificial Intelligence and Data Science Program Taichung40227 Taiwan

Facial micro-expressions indicate brief and subtle facial movements that appear during emotional communication. In comparison to macro-expressions, micro-expressions are more challenging to be analyzed due to the short span of time and the fine-grained changes. In recent years, micro-expression recognition (MER) has drawn much attention because it can benefit a wide range of applications, e.g., police interrogation, clinical diagnosis, depression analysis, and business negotiation. In this survey, we offer a fresh overview to discuss new research directions and challenges these days for MER tasks. For example, we review MER approaches from three novel aspects: macro-to-micro adaptation, recognition based on key apex frames, and recognition based on facial action units. Moreover, to mitigate the problem of limited and biased ME data, synthetic data generation is surveyed for the diversity enrichment of micro-expression data. Since micro-expression spotting can boost micro-expression analysis, the state-of-the-art spotting works are also introduced in this paper. At last, we discuss the challenges in MER research and provide potential solutions as well as possible directions for further investigation. © 2010-2012 IEEE.

关键词： Surveys

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Anomaly Detection in Social Networks using Improved Local Outlier Factor

Anomaly Detection in Social Networks using Improved Local Ou...

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Intelligent Systems and Computational Networks (ICISCN), International Conference on

作者： Rana Veer Samara Sihman Bharattej R Muntader Mhsnhasan Sowmya M Prabhakaran M V K. Ghamya Department of Computer Science Saint Leo University FL USA Department of Computers Techniques Engineering College of Technical Engineering The Islamic University of Al Diwaniyah Al Diwaniyah Iraq Department of Artificial Intelligence and Data Science Nitte Meenakshi Institute of Technology Bengaluru India Department of Computer Science and Engineering (Cyber Security) Dhanalakshmi Srinivasan College of Engineering and Technology Mamallapuram India Department of Computer Science and Engineering (AI&ML) Mohan Babu University Tirupati India

ISBN: (数字)9798331529246

ISBN: (纸本)9798331529253

In the present era, Anomaly Detection (AD) aims to identify malicious activities in social networks. Even though previous researchers suggest effective methods for AD still there are limitations such as a lack of labeled data and struggled to handle high dimensional data. In this research, Improved Local Outlier Factor (ILOF) is proposed to detect anomalies and effectively handles the high dimensional data by using distance metric to calculate local density. Primarily, the data is collected from CERT insider threat and then preprocessed with truncation to remove extreme values for effective feature extraction. After that, the Principal Component Analysis (PCA) is incorporated to extract most informative features such as principal components to identify anomalies as data points. Finally, the extracted features are fed into proposed ILOF uses density-based approach to calculate the local density effectively for detecting anomalies in social networks. From the results, proposed ILOF model attained better outcomes compared to existing Bidirectional Long Short-Term Memory (BiLSTM) in terms of accuracy, precision, recall and F1-score as 98.50%, 97.08%, 98.25% and 97.66% respectively.

关键词： Measurement Accuracy Social networking (online) High dimensional data Transfer learning Feature extraction Real-time systems Intelligent systems Anomaly detection Principal component analysis

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LLM4Decompile: Decompiling Binary Code with Large Language Models

arXiv

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arXiv 2024年

作者： Tan, Hanzhuo Luo, Qi Li, Jing Zhang, Yuqun Department of Computer Science and Engineering Southern University of Science and Technology Shenzhen China Department of Computing The Hong Kong Polytechnic University Hong Kong Research Centre for Data Science & Artificial Intelligence Hong Kong

Decompilation aims to convert binary code to high-level source code, but traditional tools like Ghidra often produce results that are difficult to read and execute. Motivated by the advancements in Large Language Models (LLMs), we propose LLM4Decompile, the first and largest open-source LLM series (1.3B to 33B) trained to decompile binary code. We optimize the LLM training process and introduce the LLM4Decompile-End models to decompile binary directly. The resulting models significantly outperform GPT-4o and Ghidra on the HumanEval and ExeBench benchmarks over 100% in terms of re-executability rate. Additionally, we improve the standard refinement approach to fine-tune the LLM4DecompileRef models, enabling them to effectively refine the decompiled code from Ghidra and achieve a further 16.2% improvement over the LLM4Decompile-End. LLM4Decompile demonstrates the potential of LLMs to revolutionize binary code decompilation, delivering remarkable improvements in readability and ex-ecutability while complementing conventional tools for optimal results. © 2024, CC BY.

关键词： Binary codes

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A Multimodal Content Moderation System using Adversarial Machine Learning

A Multimodal Content Moderation System using Adversarial Mac...

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Applied Artificial Intelligence and Computing (ICAAIC), International Conference on

作者： A. Barveen M.K. Mohamed Faizal S. Geetha Department of Artificial Intelligence and Data Science M.I.E.T. Engineering College Trichy Tamilnadu Department of Artificial Intelligence and Data Science K.Ramakrishnan College of Engineering Trichy Tamilnadu Department of Computer Applications University College of Engineering Trichy Tamilnadu

ISBN: (数字)9798350375190

ISBN: (纸本)9798350375206

Memes are a visual representation of images with text embedded in them that conveys the thoughts and feelings of a peculiar audience and is mainly intended to elicit humor. It is widely spread across social media platforms in the guise of sardonic images, humorous jokes, and several other viral sensations. After the profound success of the late fusion technique that combines multimodal features, researchers have used stacked LSTM to extract textual features, whereas VGG16 has been used to extract visual features. Apart from this, BiLSTM and CNN have been used to extract contextual features. This study suggests a unique deep learning-based method for classifying objectionable memes within a multimodal dataset to address this problem. The proposed system uses machine learning to train the classifier on labeled data, which allows it to categorize the information accurately. The proposed system uses both text and image data. To improve user experience and safety, offensive images are blurred, and offensive words are shuffled using adversarial training.

关键词： Training Learning systems Deep learning Visualization Technological innovation Social networking (online) Feature extraction

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Enhanced Heart Disease Classification: An Ensemble-Based Deep Learning Approach

Enhanced Heart Disease Classification: An Ensemble-Based Dee...

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IEEE International Conference on Signal Processing, Informatics, Communication and Energy Systems (SPICES)

作者： Nivaashini M Sathish Kumar G Uma Maheshwari G Soundariya R S Arun Kumar B Vijaya G Department of Artificial Intelligence and Data Science Sri Eshwar College of Engineering Coimbatore Tamil Nadu India Department of Computer Science and Engineering Dr. Mahalingam College of Engineering and Technology Coimbatore Tamil Nadu India Department of Computer Technology Bannari Amman Institute of Technology Sathyamangalam Tamil Nadu India Department of Artificial Intelligence and Data Science Faculty of Engineering Karpagam Academy of Higher Education Coimbatore Tamil Nadu India Department of Computer Science and Engineering Sri Krishna College of Engineering and Technology Coimbatore Tamil Nadu India

ISBN: (数字)9798350376135

ISBN: (纸本)9798350376142

The WHO's assessment indicates a rising prevalence of heart disease, exacerbated by challenges in detection and diagnosis in India due to population growth. Machine Learning (ML) is increasingly used for precise judgments in healthcare, aiming to accurately diagnose heart-related illnesses. This study proposes a Deep Learning (DL) ensemble method, combining ML classifiers to enhance accuracy. The ensemble model outperforms AdaBoost, XGBoost, Random Forest, Decision Tree, SVM, and Logistic Regression with 98.3% accuracy on a UCI dataset. Correlation-based Feature Selection (CFS) for relevant characteristic selection improves the model's accuracy, recall, precision, and f1-score. This research contributes to improved disease prediction and categorization in cardiovascular health, vital for effective healthcare interventions and outcomes.

关键词： Heart Deep learning Support vector machines Accuracy Medical services Signal processing SPICE Regression tree analysis Random forests Diseases

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An Analysis of Human Factors in Malware Attacks

An Analysis of Human Factors in Malware Attacks

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International Conference on computer and Applications (ICCA)

作者： Jawza Alotaibi Jihad M. Al Ja'am Mohammed Hoda Dept. of Computing and Data Science Oryx Universal College in Partnership with Liverpool John Moores University Doha Qatar Dept. of computer Science and Engineering Kuwait College of Science and Technology Doha Kuwait

ISBN: (数字)9798350367560

ISBN: (纸本)9798350367577

This research study investigates human responses to malware attacks, focusing on attacker motivations, individual reactions, and key factors influencing user vulnerability and resilience. Using a structured survey questionnaire, the study gathered data from 104 participants with diverse demographics across various occupations. The data was analyzed using SPSS statistical software and Python-based machine learning algorithms. The survey included demographic information and responses to cybersecurity-related questions about human behavior and experience. Machine learning techniques identified ten critical factors as significant predictors of vulnerability to malware attacks. The prediction model indicated that 55 out of 104 participants were vulnerable, with higher vulnerability observed among female participants and those in non-IT professions. This study contributes valuable insights into the human factors influencing cybersecurity resilience, providing a foundation for developing effective strategies to mitigate malware attack vulnerability.

关键词： Surveys Training Machine learning algorithms Human factors Organizations Machine learning Predictive models Particle measurements Malware Resilience

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On the Influence of data Resampling for Deep Learning-Based Log Anomaly Detection: Insights and Recommendations

arXiv

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arXiv 2024年

作者： Ma, Xiaoxue Zou, Huiqi He, Pinjia Keung, Jacky Li, Yishu Yu, Xiao Sarro, Federica The Department of Electronic Engineering and Computer Science Hong Kong Metropolitan University Hong Kong The Department of Computer Science Johns Hopkins University Baltimore United States The School of Data Science Chinese University of Hong Kong Shenzhen China The Department of Computer Science City University of Hong Kong Hong Kong The State Key Laboratory of Blockchain and Data Security Zhejiang University Hangzhou China The Department of Computer Science University College London London United Kingdom

Numerous Deep Learning (DL)-based approaches have gained attention in software Log Anomaly Detection (LAD), yet class imbalance in training data remains a challenge, with anomalies often comprising less than 1% of datasets like Thunderbird. Existing DLLAD methods may underperform in severely imbalanced datasets. Although data resampling has proven effective in other software engineering tasks, it has not been explored in LAD. This study aims to fill this gap by providing an in-depth analysis of the impact of diverse data resampling methods on existing DLLAD approaches from two distinct perspectives. Firstly, we assess the performance of these DLLAD approaches across four datasets with different levels of class imbalance, and we explore the impact of resampling ratios of normal to abnormal data on DLLAD approaches. Secondly, we evaluate the effectiveness of the data resampling methods when utilizing optimal resampling ratios of normal to abnormal data. Our findings indicate that oversampling methods generally outperform undersampling and hybrid sampling methods. data resampling on raw data yields superior results compared to data resampling in the feature space. These improvements are attributed to the increased attention given to important tokens. By exploring the resampling ratio of normal to abnormal data, we suggest generating more data for minority classes through oversampling while removing less data from majority classes through undersampling. In conclusion, our study provides valuable insights into the intricate relationship between data resampling methods and DLLAD. By addressing the challenge of class imbalance, researchers and practitioners can enhance DLLAD performance. © 2024, CC BY-NC-ND.

关键词： Anomaly detection

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