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IEEE Transactions on Artificial Intelligence 2024年第10期5卷 5325-5339页

作者： Yu, Hang Liu, Weixu Zhu, Nengjun Li, Pengbo Luo, Xiangfeng Shanghai University School of Computer Engineering and Science Shanghai200444 China

With the development of the e-commerce platform, more and more reviews of its various formats continue to appear. Reviews help people buy the right item faster, and instead, spam reviews reduce the user experience. To be able to detect spam reviews, statistical machine learning-based methods were commonly used in the past, but these approaches ignored the correlation between reviews. With the development of the graph fraud detection model, people have started to graph model the review data. However, typical graph fraud detection models still have problems with interpretability. Therefore, we propose here an interpretable graph fraud detection model for spam reviews, which is also named IN-GFD. As for the interpretability issue, we leveraged the relationship against the predicted score and whether a review is spam or not to build a loss function on top of the feature-embedding matrix, and introduced a scoring difference threshold mechanism, which can allow our model to have antehoc interpretability. In addition, to address class imbalance issues, IN-GFD utilizes the oversampling of the spam nodes to balance them with normal nodes and introduces an edge-loss function to learn new edge relationships. After extensive experiments, our method proves to be better than other state-of-the-arts (SOTA) models in terms of fraud detection and offers the benefit of interpretability. Finally, our study combines detection models with antehoc interpretability, offering a promising direction in review detection. Our approach has wide applicability, detecting spam reviews in datasets with user reviews and providing reasonable interpretations. © 2020 IEEE.

关键词： Feature extraction

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LoRaDIP: Low-Rank Adaptation With Deep Image Prior for Generative Low-Light Image Enhancement

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2025年第4期6卷 909-920页

作者： Zhao, Zunjin Shi, Daming Shenzhen University College of Computer Science and Software Engineering Shenzhen518060 China

This article presents LoRaDIP, a novel low-light image enhancement (LLIE) model based on deep image priors (DIPs). While DIP-based enhancement models are known for their zero-shot learning, their expensive computational cost remains a challenge. In addressing this issue, our proposed LoRaDIP introduces a low-rank adaptation technique, significantly reducing computational expenses without compromising performance. The contributions of this work are threefold. First, we eliminate the need for estimating initial illumination and reflectance, opting instead to directly estimate the illumination map from the observed image in a generative fashion. The illumination is parameterized by a DIP network. Second, considering the overparameterization of DIP networks, we introduce a low-rank adaptation technique to decrease the number of trainable parameters, thereby reducing computational demands. Third, differing from the existing DIP-based models that rely on a preset fixed number of iterations to halt the optimization process of Retinex decomposition, we propose an automatic stopping criterion based on stable rank, preventing unnecessary iterations. LoRaDIP not only inherits the advantage of requiring only the single input image but also exhibits reduced computational costs while maintaining or even surpassing the performance of state-of-the-art models. © 2020 IEEE.

关键词： Zero-shot learning

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Intrumer:A Multi Module Distributed Explainable IDS/IPS for Securing Cloud Environment

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computers, Materials & Continua 2025年第1期82卷 579-607页

作者： Nazreen Banu A S.K.B.Sangeetha Department of Computer Science and Engineering SRM Institute of Science and TechnologyVadapalani CampusChennai600026Tamil NaduIndia

The increasing use of cloud-based devices has reached the critical point of cybersecurity and unwanted network *** environments pose significant challenges in maintaining privacy and *** approaches,such as IDS,have been developed to tackle these ***,most conventional Intrusion Detection System(IDS)models struggle with unseen cyberattacks and complex high-dimensional *** fact,this paper introduces the idea of a novel distributed explainable and heterogeneous transformer-based intrusion detection system,named INTRUMER,which offers balanced accuracy,reliability,and security in cloud settings bymultiplemodulesworking together within *** traffic captured from cloud devices is first passed to the TC&TM module in which the Falcon Optimization Algorithm optimizes the feature selection process,and Naie Bayes algorithm performs the classification of *** selected features are classified further and are forwarded to the Heterogeneous Attention Transformer(HAT)*** this module,the contextual interactions of the network traffic are taken into account to classify them as normal or malicious *** classified results are further analyzed by the Explainable Prevention Module(XPM)to ensure trustworthiness by providing interpretable *** the explanations fromthe classifier,emergency alarms are transmitted to nearby IDSmodules,servers,and underlying cloud devices for the enhancement of preventive *** experiments on benchmark IDS datasets CICIDS 2017,Honeypots,and NSL-KDD were conducted to demonstrate the efficiency of the INTRUMER model in detecting network trafficwith high accuracy for different *** outperforms state-of-the-art approaches,obtaining better performance metrics:98.7%accuracy,97.5%precision,96.3%recall,and 97.8%*** results validate the robustness and effectiveness of INTRUMER in securing diverse cloud environments against sophisticated cyber threats.

关键词： Cloud computing intrusion detection system transformers and explainable artificial intelligence(XAI)

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Hybrid optimization assisted deep ensemble classification framework for skin cancer detection

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Multimedia Tools and Applications 2025年第1期84卷 63-94页

作者： Pukhta, Irfan Rashid Rout, Ranjeet Kumar Department of Computer Science and Engineering National Institute of Technology Srinagar190006 India

In 2018, there were 1 million occurrences of non-melanoma cancer and 288,000 occurrences of malignant skin cancer (MM) recorded worldwide. Given the aging of the population and limited resources for medical care, a complete strategy to prevent skin cancer must include both practical detection and accurate diagnosis. Dermatologists and family physicians may find it challenging to identify skin cancer, particularly MM, at an early phase. The purpose of this paper is to suggest a paradigm for skin cancer detection, where pre-processing is the initial step for which median filter (MF) is employed. The pre-processed image undergoes segmentation, which is done by an Improved DBSCAN approach. Once the segmentation is over, features like "GLCM, Local Binary pattern (LBP), Local Vector Pattern (LVP), and Improved Center Symmetric Local Ternary Pattern (CSLTP)" are extracted. This is the final phase, where an ensemble classifier (EC) that combines the models like "Convolutional Neural network (CNN), Long Short Term Memory (LSTM), and Bidirectional Gated Recurrent Unit (Bi-GRU)" is deployed. Further, weights of CNN, Bi-GRU, and LSTM are tuned by Hawks Updated Blue Monkey Algorithm with Gaussian Map Randomness (HUBM-GMR) based hybrid optimization. The study will ultimately validate the HUBM-GMR 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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Slender loris optimization algorithm for solving optimization problems

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Journal of Intelligent and Fuzzy Systems 2024年第4期46卷 8799-8810页

作者： Lakshmi, A. Vijaya Vaitheki, K. Joseph, K. Suresh Department of Computer Science School of Engineering and Technology Pondicherry University India

Over the years, numerous optimization problems have been addressed utilizing meta-heuristic algorithms. Continuing initiatives have always been to create and develop new, practical algorithms. This work proposes a novel meta-heuristic approach employing the slender Loris optimization algorithm (SLOA), miming slender Loris behavior. The behavior includes foraging, hunting, migration and communication with each other. The ultimate goal of the devised algorithm is to replicate the food-foraging behaviour of Slender Loris (SL) and the quick movement of SL when threatened (i.e.) their escape from predators and also mathematically modelled the special communication techniques of SL using their urine scent smell. SLOA modelled SL’s slow food foraging behaviour as the exploitation phase, and moving between the tree and escaping from a predator is modelled as the exploration phase. The Eyesight of slender Loris plays a vital role in food foraging during nighttime in dim light. The operator’s Eyesight is modelled based on the angle of inclination of SL. The urine scent intensity is used here to be instrumental in preventing already exploited territory activities, which improves algorithm performance. The suggested algorithm is assessed and tested against nineteen benchmark test operations and evaluated for effectiveness with standard widely recognized meta-heuristics algorithms. The result shows SLOA performing better and achieving near-optimal solutions and dominance in exploration–exploitation balance in most cases than the existing state-of-the-art algorithms. © 2024 – IOS Press. All rights reserved.

关键词： Benchmarking

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Enhancing the efficiency of lung cancer screening: predictive models utilizing deep learning from CT scans

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Neural Computing and Applications 2025年 1-19页

作者： Tawfeek, Medhat A. Alrashdi, Ibrahim Alruwaili, Madallah Shaban, Warda M. Talaat, Fatma M. Department of Computer Science Faculty of Computers and Information Menoufia University Shebin Elkom32511 Egypt Department of Computer Science College of Computer and Information Sciences Jouf University Sakakah Saudi Arabia Department of Computer Engineering and Networks College of Computer and Information Sciences Jouf University Sakakah Saudi Arabia Department of Communication and Electronics Engineering Nile Higher Institute for Engineering and Technology Mansoura Egypt Faculty of Artificial Intelligence Kafrelsheikh University Kafrelsheikh Egypt Faculty of Computer Science and Engineering New Mansoura University Gamasa35712 Egypt

Lung cancer is the most lethal form of cancer. This paper introduces a novel framework to discern and classify pulmonary disorders such as pneumonia, tuberculosis, and lung cancer by analyzing conventional X-ray and CT scan images called lung cancer risk prediction (LCRP) model. LCRP has four modules, namely data collection and preprocessing, data augmentation module, image segmentation module, and prediction module. Actually, LCRP employs three deep learning models;sequential model, functional model, and transfer model on publicly available training datasets. Convolutional neural networks (CNNs) have emerged as a highly effective field in machine learning, particularly for image datasets in the field of biomedical applications. The primary goal is to validate these models by comparing their performance with other models in order to determine their effectiveness in addressing challenging datasets. Our research has revealed a noteworthy enhancement in the efficiency of binary and multi-class classification using mask R-CNN image segmentation. During the model training process, a combination of Adam and stochastic gradient descent dual optimizers has been used to improve performance. LCRP have outperformed current pre-trained models by minimizing training parameters, computational costs, and overhead. It introduces 98.5% accuracy, 88.7% specificity, 89% sensitivity, 89.2% precision, and 89.09% F-measure. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2025.

关键词： Lung cancer

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SuspAct: novel suspicious activity prediction based on deep learning in the real-time environment

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Neural Computing and Applications 2024年第34期36卷 21307-21320页

作者： Kansal, Sachin Jain, Akshat Kumar Biswas, Moyukh Bansal, Shaurya Mahindru, Namay Kansal, Priya Computer Science Engineering Department Thapar Institute of Engineering and Technology Punjab Patiala India

In today’s evolving landscape of video surveillance, our study introduces SuspAct, an innovative ensemble model designed to detect suspicious activities in real time swiftly. Leveraging advanced Long-term Recurrent Convolutional Networks (LRCN), SuspAct represents a significant advancement in intelligent surveillance technology. By combining insights from various LRCN models through the Majority Voting ensemble technique, SuspAct enhances its overall robustness, outperforming traditional surveillance methods. Through rigorous experimentation on large-scale datasets, we demonstrate SuspAct’s superiority in proactive crime prevention, showcasing its potential to revolutionize security protocols and contribute substantially to public safety. Our work addresses the challenges posed by the escalating volume of video data and lays a strong foundation for future advancements in intelligent video surveillance technology. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2024.

关键词： Deep learning

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Statistical Process Monitoring Based on Ensemble Structure Analysis

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IEEE/CAA Journal of Automatica Sinica 2024年第8期11卷 1889-1891页

作者： Likang Shi Chudong Tong Ting Lan Xuhua Shi Faculty of Electrical Engineering and Computer Science Ningbo UniversityNingbo 315211China

Dear Editor,This letter presents a novel process monitoring model based on ensemble structure analysis(ESA).The ESA model takes advantage of principal component analysis(PCA),locality preserving projections(LPP),and multi-manifold projections(MMP)models,and then combines the multiple solutions within an ensemble result through Bayesian *** the developed ESA model,different structure features of the given dataset are taken into account simultaneously,the suitability and reliability of the ESA-based monitoring model are then illustrated through ***:The requirement for ensuring safe operation and improving process efficiency has led to increased research activity in the field of process monitoring.

关键词： ensemble preserving letter

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LBPNET: Detection of Fake Face Currency through LBP-Based Convolutional Neural Networks 5

LBPNET: Detection of Fake Face Currency through LBP-Based Co...

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

作者： Reddy, Y.V. Bhaskar Kumar, C. Satya Ashok, Rayalla Mandlem, Sai Nithin Reddy, P. Ashok Reddy, M.Babu Department of Computer Science and Engineering Mylavaram India Vardhaman College of Engineering Department of Computer Science and Engineering India Krishna University Department of Computer Science Machilipatnam India

ISBN: (纸本)9798331522100

The escalating sophistication of counterfeit face currency demands innovative solutions for robust detection. In this study, we propose LBPNET, a novel approach employing Local Binary Pattern (LBP) feature extraction and Convolutional Neural Networks (CNNs) to address the challenges posed by realistic fake currencies. LBP descriptors are extracted from currency images, capturing subtle patterns invisible to the human eye. These descriptors serve as inputs for a CNN, named LBPNET, trained to discern genuine from counterfeit face currencies. The model undergoes a comprehensive training phase, incorporating diverse currency images, to establish a generalized and adaptive detection system. In testing, new currency images are evaluated using the trained LBPNET to identify the presence of fake or non-fake currency, showcasing the model's adaptability to emerging counterfeit variations. This approach integrates machine learning and image processing to enhance detection capabilities, offering a promising solution for the evolving landscape of counterfeit face currency across forensic and social media domains. © 2025 IEEE.

关键词： Feature Selection

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Energy-efficient resource allocation in blockchain-based Cybertwin-driven 6G

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Journal of Ambient Intelligence and Humanized Computing 2024年第1期15卷 103-114页

作者： Salim, Mikail Mohammed Pan, Yi Park, Jong Hyuk Department of Computer Science and Engineering Seoul National University of Science and Technology Seoul Korea Republic of Department of Computer Science Georgia State University Atlanta United States

Inefficient task scheduling schemes compromise network performance and increase latency for delay intolerant tasks. Cybertwin based 6G services support data logging of operational queries for appropriate resource allocation in Multi-Access Edge and Cloud Computing. In this paper, a Cybertwin assisted task scheduling scheme is proposed for energy-efficient job allocation to Edge-Cloud based on delay tolerance. Cybertwins using the Logger Function and the Digital Asset Function exchange Digital Assets with cloud operators using Smart Contracts to ensure maximum computational resources. The novelty of this study resides in using Communication Assistant Cybertwins to build profiles for all network entities and assign tasks based on their delay tolerance and the available resources of each Edge Datacenter and Cloud service provider. Compared with the Full Offloading scheme, experimental results demonstrate the higher energy efficiency of the task scheduling scheme. Offloading tasks to multiple data centers and smart contract-based selection of cloud operators result in reduced computation time. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2022.

关键词： Smart contract

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