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2nd International Conference on Business Analytics for technology and Security, ICBATS 2023

作者： Zeshan Shabbir, Muhammad Naseer, Iftikhar Akhter, Shamim Abubakar, Muhammad Issa, Ghassan F. Hassaan Mehmood, Muhammad Comsats University Faculty of Information and Technology Islamabad Pakistan The Superior University Faculty of Computer Science & Information Technology Lahore Pakistan Minhaj University School of Library & Information Sciences Lahore Pakistan Fast School of Computing National University of Computer and Emerging Sciences Lahore Pakistan Skyline University College School of Information Technology University City Sharjah Sharjah1797 United Arab Emirates NCBA&E School of Computer Science Lahore Pakistan Applied Science Private Universit Applied Science Research Center Amman11937 Jordan

ISBN: (纸本)9798350335644

Fake user accounts on social media are a serious threat. Artificial intelligence can play an important role in getting rid of fake user accounts. Twitter has a problem with fake accounts that are run by automated bots. These bots are used for bad things like spamming, sabotaging trends, and getting more followers. In this research, publicly available information about Twitter users, such as their activity patterns, profile information, and tweets, to figure out how real each one is. Twitter's API and other ways of getting information will be used to get user data. The proposed model consists of a machine learning technique, and it detects whether the user accounts are real or fake. Various algorithms such as logistic regression, long short-term memory, K-mean, and random forest are applied to evaluate the proposed model. The experimental results show that the random forest algorithm has obtained the highest accuracy 0.7557, a precision of 0.7277, and F1 of 0.7943 among all other algorithms. © 2023 IEEE.

关键词： Logistic regression

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DeepWare: Imaging Performance Counters With Deep Learning to Detect Ransomware

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IEEE Transactions on Computers 2023年第3期72卷 600-613页

作者： Ganfure, Gaddisa Olani Wu, Chun-Feng Chang, Yuan-Hao Shih, Wei-Kuan Dire Dawa University Institute of Technology School of Computing Department of Computer Science Dire Dawa1487 Ethiopia Harvard University Department of Computer Science Cambridge United States Academia Sinica Institute of Information Science Taipei115 Taiwan National Tsing Hua University Department of Computer Science Hsinchu City300 Taiwan

In the year passed, rarely a month passes without a ransomware incident being published in a newspaper or social media. In addition to the rise in the frequency of ransomware attacks, emerging attacks are very effective as they utilize sophisticated techniques to bypass existing organizational security perimeter. To tackle this issue, this paper presents 'DeepWare,' which is a ransomware detection model inspired by deep learning and hardware performance counter (HPC). Different from previous works aiming to check all HPC results returned from a single timing for every running process, DeepWare carries out a simple yet effective concept of 'imaging hardware performance counters with deep learning to detect ransomware,' so as to identify ransomware efficiently and effectively. To be more specific, DeepWare monitors the system-wide change in the distribution of HPC data. By imaging the HPC values and restructuring the conventional CNN model, DeepWare can address HPC's nondeterminism issue by extracting the event-specific and event-wise behavioral features, which allows it to distinguish the ransomware activity from the benign one effectively. The experiment results across ransomware families show that the proposed DeepWare is effective at detecting different classes of ransomware with the 98.6% recall score, which is 84.41%, 60.93%, and 21% improvement over RATAFIA, OC-SVM, and EGB models respectively. DeepWare achieves an average MCC score of 96.8% and nearly zero false-positive rates by using just a 100 ms snapshot of HPC data. This timeliness of DeepWare is critical on the ground that organizations and individuals have the opportunity to take countermeasures in the first stage of the attack. Besides, the experiment conducted on unseen ransomware families such as CoronaVirus, Ryuk, and Dharma demonstrates that DeepWare has excellent potential to be a useful tool for zero-day attack detection. © 1968-2012 IEEE.

关键词： Feature extraction

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Empowering Diagnosis: Cutting-Edge Segmentation and Classification in Lung Cancer Analysis

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Computers, Materials & Continua 2024年第6期79卷 4963-4977页

作者： Iftikhar Naseer Tehreem Masood Sheeraz Akram Zulfiqar Ali Awais Ahmad Shafiq Ur Rehman Arfan Jaffar Faculty of Computer Sciences&Information Technology The Superior UniversityLahore54000Pakistan Intelligent Data Visual Computing Research(IDVCR) Lahore54000Pakistan Information System Department College of Computer and Information SciencesImam Mohammad Ibn Saud Islamic University(IMSIU)Riyadh11432Saudi Arabia School of Computer Science and Electronic Engineering(CSEE) University of EssexWivenhoe ParkColchesterCO43SQUK

Lung cancer is a leading cause of global mortality *** detection of pulmonary tumors can significantly enhance the survival rate of ***,various Computer-Aided Diagnostic(CAD)methods have been developed to enhance the detection of pulmonary nodules with high ***,the existing method-ologies cannot obtain a high level of specificity and *** present study introduces a novel model for Lung Cancer Segmentation and Classification(LCSC),which incorporates two improved architectures,namely the improved U-Net architecture and the improved AlexNet *** LCSC model comprises two distinct *** first stage involves the utilization of an improved U-Net architecture to segment candidate nodules extracted from the lung ***,an improved AlexNet architecture is employed to classify lung *** the first stage,the proposed model demonstrates a dice accuracy of 0.855,a precision of 0.933,and a recall of 0.789 for the segmentation of candidate *** suggested improved AlexNet architecture attains 97.06%accuracy,a true positive rate of 96.36%,a true negative rate of 97.77%,a positive predictive value of 97.74%,and a negative predictive value of 96.41%for classifying pulmonary cancer as either benign or *** proposed LCSC model is tested and evaluated employing the publically available dataset furnished by the Lung Image Database Consortium and Image Database Resource Initiative(LIDC-IDRI).This proposed technique exhibits remarkable performance compared to the existing methods by using various evaluation parameters.

关键词： Lung cancer segmentation AlexNet U-Net classification

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Local and Global Feature-Aware Dual-Branch Networks for Plant Disease Recognition

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植物表型组学（英文） 2024年第4期6卷 828-840页

作者： Jianwu Lin Xin Zhang Yongbin Qin Shengxian Yang Xingtian Wen Tomislav Cernava Quirico Migheli Xiaoyulong Chen Text Computing & Cognitive Intelligence Engineering Research Center of National Education Ministry College of Computer Science and TechnologyGuizhou UniversityGuiyang 550025China State Key Laboratory of Public Big Data College of Computer Science and TechnologyGuizhou UniversityGuiyang 550025China College of Big Data and Information Engineering Guizhou UniversityGuiyang 550025China Guizhou-Europe Environmental Biotechnology and Agricultural Informatics Oversea Innovation Center in Guizhou University Guizhou Provincial Science and Technology DepartmentGuiyang 550025China Text Computing & Cognitive Intelligence Engineering Research Center of National Education Ministry College of Computer Science and TechnologyGuizhou UniversityGuiyang 550025China State Key Laboratory of Public Big Data College of Computer Science and TechnologyGuizhou UniversityGuiyang 550025China College of Big Data and Information Engineering Guizhou UniversityGuiyang 550025China Guizhou-Europe Environmental Biotechnology and Agricultural Informatics Oversea Innovation Center in Guizhou University Guizhou Provincial Science and Technology DepartmentGuiyang 550025China College of Big Data and Information Engineering Guizhou UniversityGuiyang 550025China School of Biological Sciences Faculty of Environmental and Life SciencesUniversity of SouthamptonSouthampton S017 1BJUK Dipartimento di Agraria and NRD-Nucleo di Ricerca sulla Desertificazione Università degli Studi di SassariSassariItaly Guizhou-Europe Environmental Biotechnology and Agricultural Informatics Oversea Innovation Center in Guizhou University Guizhou Provincial Science and Technology DepartmentGuiyang 550025China College of Life Sciences Guizhou UniversityGuiyang 550025China

Accurate identification of plant diseases is important for ensuring the safety of agricultural *** neural networks(CNNs)and visual transformers(VTs)can extract effective representations of images and have been widely used for the intelligent recognition of plant disease ***,CNNs have excellent local perception with poor global perception,and VTs have excellent global perception with poor local *** makes it difficult to further improve the performance of both CNNs and VTs on plant disease recognition *** this paper,we propose a local and global feature-aware dual-branch network,named LGNet,for the identification of plant *** specifically,we first design a dual-branch structure based on CNNs and VTs to extract the local and global ***,an adaptive feature fusion(AFF)module is designed to fuse the local and global features,thus driving the model to dynamically perceive the weights of different ***,we design a hierarchical mixed-scale unit-guided feature fusion(HMUFF)module to mine the key information in the features at different levels and fuse the differentiated information among them,thereby enhancing the model's multiscale perception ***,extensive experiments were conducted on the Al Challenger 2018 dataset and the self-collected corn disease(SCD)*** experimental results demonstrate that our proposed LGNet achieves state-of-the-art recognition performance on both the Al Challenger 2018 dataset and the SCD dataset,with accuracies of 88.74％and 99.08％,respectively.

关键词： global plant disease dual-branch feature-aware local networks recognition

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Predictive Maintenance for Industrial Drones in the Industrial Internet of Things Using Federated Learning and Explainable AI 51

Predictive Maintenance for Industrial Drones in the Industri...

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51st International Conference on Computers and Industrial Engineering, CIE 2024

作者： Hasan, Syed Irtija Nimmy, Sonia Farhana Kamal, Md Sarwar School of Computing and Information Technology University of Wollongong Wollongong 2522 NSW Australia School of Business UNSW Canberra Campbell 2612 ACT Australia School of Computer Science University of Technology Sydney Ultimo 2007 NSW Australia

Predictive maintenance is crucial for optimizing industrial operations by reducing downtime and maintenance costs. We propose a novel approach integrating Federated Learning (FL) and Explainable AI (XAI) to enhance predictive maintenance in Industrial Internet of Things (IIoT) environments. Our methodology leverages decentralized sensor data from various IoT devices, including drones, across industrial plants to enable collaborative model training while ensuring data privacy. We utilize a Stochastic Gradient Descent (SGD) federated algorithm for FL, which allows for the training of a global predictive maintenance model with local updates from each device, thereby improving model accuracy and adhering to strict data privacy regulations. Recognizing the opaque nature of many AI approaches, and the critical need for transparency in maintenance decision-making, we employ XAI techniques. Specifically, we utilize Counterfactual Explanations and Feature Importance Analysis with SHapley Additive exPlanations (SHAP), which provide interpretable explanations for maintenance predictions. These explanations empower industrial engineers and managers to understand how factors such as temperature, vibration, rotor speed, and environmental conditions like fog, dust, and smoke influence machinery performance. Our approach has been validated on operational datasets from manufacturing and process industries, demonstrating significant improvements in predicting equipment failures and optimizing maintenance actions. This contributes to advanced predictive maintenance strategies and efficient asset management in IIoT, showcasing a scalable framework that combines privacy, accuracy, and explainability. We have made all resources available on GitHub at https://***/Sarwar-UTS/-Industrial-Drones-in-the-Industrial-Internet-of-Things-Using-Federated-Learning-and-Explainable-AI to support and encourage future studies and advancements based on our findings. © 2024 Computers and Industrial

关键词： Explainable AI Federated Learning Industrial IoT IoT Data Analytics Predictive Maintenance

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Online Dynamic Scaling of Microservices with Fair Probabilistic Routing 22

Online Dynamic Scaling of Microservices with Fair Probabilis...

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22nd IEEE International Symposium on Parallel and Distributed Processing with Applications, ISPA 2024

作者： Chen, Yang Lin, Shisheng Hu, Yi Wang, Liangyuan Hu, Menglan Lai, Pan Xie, Yuanai College of Computer Science South-Central Minzu University China Key Laboratory of Cyber-Physical Fusion Intelligent Computing State Ethnic Affairs Commission China School of Electronic Information and Communications Huazhong University of Science and Technology China Hubei Key Laboratory of Smart Internet Technology China

ISBN: (纸本)9798331509712

Microservice architecture, as an emerging network architecture, has gained widespread adoption in latency-sensitive applications within the realm of mobile edge computing (MEC). In MEC networks, these latency-sensitive applications necessitate the concurrent processing of numerous service requests, which are composed of microservices. The complex dependencies between microservices and frequent data communication between servers contribute to the intricacy of deploying and routing microservice instances within the network. Moreover, the dynamic and unpredictable nature of service request traffic significantly complicates the timeliness and efficiency of service deployment and request routing strategies. However, existing research predominantly focuses on static network environments and neglects the time-varying characteristics of service request traffic in realistic scenarios. Consequently, we address the joint optimization problem of service deployment and request routing in the presence of dynamic service request traffic. To model the inherent data dependencies and analyze service request response latency, we employ the open Jackson queuing network. We propose a fine-grained microservice dynamic scaling (FMDS) algorithm to capture the dynamic fluctuations in service request traffic within the network. This algorithm scales microservice instances based on the principle of equal proportional change, obtaining a service deployment scheme that minimizes costs while satisfying latency constraints. Furthermore, we introduce a recursive path search algorithm that explores the service deployment scheme to determine the node forwarding probability for the entire network, adhering to the principles of fair routing. Simulation results show that the proposed method effectively improves network latency stability by 75% and enhances the timeliness of the service deployment strategy. © 2024 IEEE.

关键词： microservice request routing service deployment services computing

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Feature Extraction to Evaluate the Quality of Data Using Machine Learning Technique

Feature Extraction to Evaluate the Quality of Data Using Mac...

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Distributed computing and Electrical Circuits and Electronics (ICDCECE), IEEE International Conference on

作者： R Pratheesh V. Divya School of Computing Sciences Vels Institute of Science Technology & Advanced Studies Chennai India Department of Information Technology School of Computing Sciences Vels Institute of Science Technology & Advanced Studies Chennai India

ISBN: (数字)9798350318609

ISBN: (纸本)9798350318616

Data quality analysis is a crucial step in ensuring the reliability and accuracy of data used in machine learning models. The proposed approach utilized with machine learning algorithms to identify outliers in the data. Outliers can indicate errors are anomalies that might affect the quality of the dataset. Data Profiling is used to identify patterns and distributions. Missing Data Imputation predict the missing values. Data Validation and Cleansing, Data Duplication Detection, Inconsistency Detection are considered as the primary analysis to be done. The feature extraction process read the sample test dataset, pre-process the dataset by cleaning the data for data duplication, inconsistency through dynamic algorithms principal component analysis (PCA), Linear Discriminant Analysis (LDA) and independent component analysis (ICA) is considered here. The proposed feature extraction data is further opted for pattern validation through isolated forest algorithm. The performance of the system is evaluated using accuracy, precision, and Recall. The LDA model outperforms with accuracy of 99% for the given testset.

关键词： Machine learning algorithms Heuristic algorithms System performance Machine learning Independent component analysis Feature extraction Cleaning

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SHIELD: an evaluation benchmark for face spoofing and forgery detection with multimodal large language models

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Visual Intelligence 2025年第1期3卷 1-25页

作者： Shi, Yichen Gao, Yuhao Lai, Yingxin Wang, Hongyang Feng, Jun He, Lei Wan, Jun Chen, Changsheng Yu, Zitong Cao, Xiaochun School of Electronic Information and Electrical Engineering Shanghai Jiao Tong University Shanghai 200030 China School of Information Science and Technology Shijiazhuang Tiedao University Shijiazhuang 050043 China Department of Artificial Intelligence Xiamen University Fujian Xiamen 361005 China Electrical Engineering Department UCLA Los Angeles 90095 CA United States New Laboratory of Pattern Recognition(NLPR) Institute of Automation Chinese Academy of Sciences Beijing 100190 China College of Electronics and Information Engineering Shenzhen University Shenzhen 518060 China School of Computing and Information Technology Great Bay University Guangdong Dongguan 523000 China School of Cyber Science and Technology Shenzhen Campus of Sun Yat-sen University Shenzhen 518107 China

Multimodal large language models (MLLMs) have demonstrated strong capabilities in vision-related tasks, capitalizing on their visual semantic comprehension and reasoning capabilities. However, their ability to detect subtle visual spoofing and forgery clues in face attack detection tasks remains underexplored. In this paper, we introduce a benchmark, SHIELD, to evaluate MLLMs for face spoofing and forgery detection. Specifically, we design true/false and multiple-choice questions to assess MLLM performance on multimodal face data across two tasks. For the face anti-spoofing task, we evaluate three modalities (i.e., RGB, infrared, and depth) under six attack types. For the face forgery detection task, we evaluate GAN-based and diffusion-based data, incorporating visual and acoustic modalities. We conduct zero-shot and few-shot evaluations in standard and chain of thought (COT) settings. Additionally, we propose a novel multi-attribute chain of thought (MA-COT) paradigm for describing and judging various task-specific and task-irrelevant attributes of face images. The findings of this study demonstrate that MLLMs exhibit strong potential for addressing the challenges associated with the security of facial recognition technology applications. © The Author(s) 2025.

关键词： Face anti-spoofing (FAS) Face forgery detection Multi-attribute chain of thought (MA-COT) Multimodal large language models (MLLMs)

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Front end Component Adaptation Based on X-Means Algorithm 10

Front end Component Adaptation Based on X-Means Algorithm

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10th International Conference on Computer and Communications, ICCC 2024

作者： Yu, Hongsheng Niu, Jiacun Zhang, Peng Xie, Chen Shenyang Institution of Computing Technology Chinese Academy of Science Shenyang China University of Chinese Academy of Science Shenyang China School of Information Science and Engineering Linyi University Linyi China School of Information Science and Engineering University of Jinan Jinan China

Current responsive layouts and the K-means algorithm have certain shortcomings when handling different screen sizes, such as the need for manual page adjustments, insufficient adaptability, and issues with component overlap. To address these problems, this paper introduces the X-means algorithm, which dynamically adjusts the clustering structure through the Bayesian information Criterion, eliminating the tediousness of manually setting the value of k and making screen size grouping more precise. In this way, the X-means algorithm significantly improves adaptability, ensuring that each device receives the optimal component layout within its resolution range, thereby enhancing user experience. © 2024 IEEE.

关键词： Adaptive front-end design Multi-device adaptation X-means clustering

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Prediction analysis for Parkinson disease using multiple feature selection & classification methods

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Multimedia Tools and Applications 2023年第27期82卷 42995-43012页

作者： Hema, M.S. Maheshprabhu, R. Reddy, K. Sudheer Guptha, M. Nageswara Pandimurugan, V. Computer Science and engineering Sri Venkateshwara college of engineering Bengaluru India Mechanical Engineering Aurora Scientific and Technological Institute Hyderabad India Information Technology Anurag University Hyderabad India School of Computing Networking and Communications SRMIST Kattankulathur Chennai India

Diagnosis of Parkinson's disease (PD) is a difficult undertaking that requires a variety of testing and clinical trials. Despite all these testing and trials, there is a considerable risk of misdiagnosis of Parkinson's disease, which causes a delay in making decisions about the best treatment for the patients. Computer aided diagnosis can improve the experiences and helps the doctors to predict PD patients. Four sorts of classification algorithms with various types of feature selection approaches are proposed in this paper for successful PD diagnosis. Support Vector Machine-SVM, Nave Bayes-NB, K-Nearest Neighbor-KNN, and Random Forest-RF are the categorization techniques employed. All the dataset's features are not necessary for classification. To choose the relevant characteristics, four feature selection approaches are used. Least Absolute Shrinkage and Selection Operator (LASSO), Backward-forward, rough set, and tree-based feature selection techniques are used. Experimentation is carried out using the Parkinson's Progressive Markers Initiative (PPMI) dataset. Four classification algorithms are constructed, together with and without feature selection procedures, and comparative research is conducted. To measure the performance of the classifiers, various evaluation methodologies are used. Overall Random Forest classifier with four feature selection methods’ gives best results with average accuracy is 96% and rough set feature selection for all three classifiers’ average accuracy is 97%. © 2023, The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature.

关键词： Classification (of information)

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