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

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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In-depth review of AI-enabled unmanned aerial vehicles: trends, vision, and challenges

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Discover Artificial Intelligence 2024年第1期4卷 1-24页

作者： Pal, Osim Kumar Shovon, MD Sakib Hossain Mridha, M.F. Shin, Jungpil Department of Electrical & Computer Engineering University of Patras Rion-Patras Patras26504 Greece Department of Computer Science American International University-Bangladesh Dhaka Bangladesh Department of Computer Science and Engineering The University of Aizu Aizu Aizuwakamatsu965-8580 Japan

In recent times, AI and UAV have progressed significantly in several applications. This article analyzes applications of UAV with modern green computing in various sectors. It addresses cutting-edge technologies such as green computing, generative AI, future scope, and related concerns in UAV. The research investigates the role of green computing and generative AI in combination with UAVs for navigation, object recognition and tracking, wildlife monitoring, precision agriculture, rescue operations, surveillance, and UAV communication. This study examines how modern computing technologies and UAVs are being applied in agriculture, surveillance, disaster management, and other areas. The ethics of UAV and AI applications, including safety, legal frameworks, and other issues, are thoroughly investigated. This research examines AI-based UAV applications across different disciplines, using open-source data and current advancements for future growth in this domain. This investigation will aid future researchers in their exploration of UAVs using cutting-edge computing technologies. © The Author(s) 2024.

关键词： Unmanned aerial vehicles (UAV)

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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年第17期37卷 11459-11477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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Identification of Software Bugs by Analyzing Natural Language-Based Requirements Using Optimized Deep Learning Features

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computers, Materials & Continua 2024年第3期78卷 4379-4397页

作者： Qazi Mazhar ul Haq Fahim Arif Khursheed Aurangzeb Noor ul Ain Javed Ali Khan Saddaf Rubab Muhammad Shahid Anwar Department of Computer Science and Engineering and International Bachelor’s Program in Informatics Yuan Ze UniversityZhongliTaoyuan320315Taiwan Department of Computer Science National University of PakistanRawalpindiPunjab46000Pakistan Department of Computer Software Engineering MCSNational University of Sciences and TechnologyIslamabad44000Pakistan Department of Computer Engineering College of Computer and Information SciencesKing Saud UniversityP.O.Box 51178Riyadh11543Saudi Arabia Department of Computer Science School of PhysicsEngineering and Computer ScienceUniversity of HertfordshireHatfieldAL109ABUK Department of Computer Engineering College of Computing and InformaticsUniversity of SharjahSharjah27272United Arab Emirates Department of AI and Software Gachon UniversitySeongnam-SI13120South Korea

Software project outcomes heavily depend on natural language requirements,often causing diverse interpretations and issues like ambiguities and incomplete or faulty *** are exploring machine learning to predict software bugs,but a more precise and general approach is *** bug prediction is crucial for software evolution and user training,prompting an investigation into deep and ensemble learning ***,these studies are not generalized and efficient when extended to other ***,this paper proposed a hybrid approach combining multiple techniques to explore their effectiveness on bug identification *** methods involved feature selection,which is used to reduce the dimensionality and redundancy of features and select only the relevant ones;transfer learning is used to train and test the model on different datasets to analyze how much of the learning is passed to other datasets,and ensemble method is utilized to explore the increase in performance upon combining multiple classifiers in a *** National Aeronautics and Space Administration(NASA)and four Promise datasets are used in the study,showing an increase in the model’s performance by providing better Area Under the Receiver Operating Characteristic Curve(AUC-ROC)values when different classifiers were *** reveals that using an amalgam of techniques such as those used in this study,feature selection,transfer learning,and ensemble methods prove helpful in optimizing the software bug prediction models and providing high-performing,useful end mode.

关键词： Natural language processing software bug prediction transfer learning ensemble learning feature selection

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Enhancing link prediction in graph data augmentation through graphon mixup

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Neural Computing and Applications 2025年第8期37卷 6267-6282页

作者： Sultana, Tangina Hossain, Md. Delowar Morshed, Md. Golam Lee, Young-Koo Department of Electronics and Communication Engineering Hajee Mohammad Danesh Science and Technology University Dinajpur Bangladesh Department of Computer Science and Engineering Kyung Hee University Yongin Korea Republic of Department of Computer Science and Engineering Hajee Mohammad Danesh Science and Technology University Dinajpur Bangladesh

Link prediction in complex networks is a fundamental problem with applications in diverse domains, from social networks to biological systems. Traditional approaches often struggle to capture intricate relationships in graphs, leading to suboptimal predictions. To address this, we introduce a novel method called graphon mixup (GM), which leverages the power of graphons to enhance link prediction. The augmentation strategy involves generating a synthetic graph by combining the original graph with a graphon-based synthetic graph. This process, expressed as a weighted combination of adjacency matrices, strategically blends real and synthetic information, enriching the training dataset. GM formulates link prediction as a joint optimization problem, aligning the characteristics of the synthetic graph with the true underlying structure. The objective is to minimize cross-entropy loss between predicted and true edge probabilities. A detailed computational complexity analysis evaluates the time and space requirements, aiding in understanding the efficiency and scalability of GM across different datasets and network sizes. Empirical validation on benchmark datasets demonstrates GM’s effectiveness in consistently improving average precision across diverse network types. The proposed method enhances the generalization capabilities of link prediction models, providing a more robust framework capable of accurate predictions even in the presence of noise or unseen patterns. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2024.

关键词： Prediction models

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Conditional-GAN-Based Face Inpainting Approaches With Symmetry and View-Degree Utilization

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IEEE Access 2024年 12卷 87467-87478页

作者： Hong, Tzung-Pei Wu, Jin-Hang Su, Ja-Hwung Yin, Tang-Kai National University of Kaohsiung Department of Computer Science and Information Engineering Kaohsiung811726 Taiwan National Sun Yat-sen University Department of Computer Science and Engineering Kaohsiung804201 Taiwan

Recently, image inpainting has been proposed as a solution for restoring the polluted image in the field of computer vision. Further, face inpainting is a subfield of image inpainting, which refers to a set of image editing algorithms re-conducting the missing regions in face smoothly. Actually, face inpainting is more challenging than general image inpainting because it needs more face structure information. Although a number of past studies were proposed for face inpainting by using face segmentation, face edge and face topology, there is some important information ignored, such as geometric and symmetric properties. Based on such concepts, in this paper, we propose a two-stage face inpainting method called CGAN (Conditional Generative Adversarial Network) which integrates face landmarks and Generative Adversarial Network (called GAN). In the first stage, the face landmark is predicted as the condition, providing GAN with important information of geometry and symmetry. The main idea in this stage is to dynamically adjust the loss by the proposed view degree. Accordingly, the masked face image and the corresponding face landmark are used as conditions input to the GAN in the second stage. Finally, the missing-regions are inpainted by the proposed CGAN. To reveal the effectiveness of proposed method, a number of evaluations were conducted on real datasets. The experimental results show that, the proposed method predicts a better face landmark by information of geometric structures and symmetric outlooks, and thereupon the proposed CGAN reconstructs the missing regions superior to the compared methods. © 2013 IEEE.

关键词： Image reconstruction

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An efficient sentiment analysis technique based on fine-tuned EdBERT for virtual learning environments

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Multimedia Tools and Applications 2025年第18期84卷 20161-20175页

作者： Srivastav, Gaurav Kant, Shri Srivastava, Durgesh Sharma, Neha Hu, Yu-Chen Department of Computer Science and Engineering Sharda University Gautam Budh Nagar India Department of Computer Science and Engineering Chitkara University Institute of Engineering & Technology Chitkara University Punjab Rajpura India Department of Computer Science Tunghai University No. 1727 Sec. 4 Boulevard Xitun District Taichung City407224 Taiwan

With artificial intelligence propelling rapid technological advances, many tools and frameworks have surfaced to assist virtual learning settings. They all make unique claims about how best to facilitate distance education, particularly in light of the pandemic. This paper presents a new model called "EdBERT," which is created explicitly for sentiment analysis of evaluations written by virtual learners with unmatched categorization accuracy. With Google BERT, EdBERT improves sentiment analysis specifically for educational feedback datasets. Using a comparison analysis and the "fine-tuned Google BERT," taught at several levels to understand language, context, and emotions, the methodology accurately classifies learners' feedback. The discussion includes thorough testing and analysis of the accuracy of the model, as well as information on its effectiveness and operation. The model and its operation are described in the discussion together with reliable evidence from analyses and accuracy tests. EdBERT is a cutting-edge model in AIEd sentiment analysis, boasting the best evaluation matrix to date with accuracy rates of 87.89%, F1-score of 88%, precision of 89%, and recall of 88%. These evaluation matrix values outperform all other current models covered in the article. In an effort to increase the productivity of students, teachers, and the learning environment, academic academics and scientists are delving into the relatively young field of AIEd. Trustworthy sentiment analysis may be produced using three fundamental sentiment classes: deep learning and natural language processing models. Plutchik's wheel of emotion, which will aid in more accurately recording feelings using artificial intelligence and deep learning, can be used to further this study. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Sentiment analysis

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Latency Minimization Using an Adaptive Load Balancing Technique in Microservices Applications

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computer Systems science & engineering 2023年第7期46卷 1215-1231页

作者： G.Selvakumar L.S.Jayashree S.Arumugam Department of Computer Science and Engineering KPR Institute of Engineering and TechnologyCoimbatore641407India Department of Computer Science and Engineering PSG College of TechnologyCoimbatore641004India Department of Computer Science and Engineering Nandha College of EngineeringErode638052India

Advancements in cloud computing and virtualization technologies have revolutionized Enterprise Application Development with innovative ways to design and develop complex *** Architecture is one of the recent techniques in which Enterprise Systems can be developed as fine-grained smaller components and deployed *** methodology brings numerous benefits like scalability,resilience,flexibility in development,faster time to market,*** the advantages;Microservices bring some challenges *** microservices need to be invoked one by one as a *** most applications,more than one chain of microservices runs in parallel to complete a particular requirement To complete a user’s *** results in competition for resources and the need for more inter-service communication among the services,which increases the overall latency of the application.A new approach has been proposed in this paper to handle a complex chain of microservices and reduce the latency of user requests.A machine learning technique is followed to predict the weighting time of different types of *** communication time among services distributed among different physical machines are estimated based on that and obtained insights are applied to an algorithm to calculate their priorities dynamically and select suitable service instances to minimize the latency based on the shortest queue waiting *** were done for both interactive as well as non interactive workloads to test the effectiveness of the *** approach has been proved to be very effective in reducing latency in the case of long service chains.

关键词： Microservices load balancing cloud computing latency optimization netflix

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Deep Learning Modeling for Potato Breed Recognition

IEEE Transactions on AgriFood Electronics

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IEEE Transactions on AgriFood Electronics 2024年第2期2卷 419-427页

作者： Rahman, Md. Ataur Khan, Abbas Ali Hasan, Md. Mehedi Rahman, Md. Sadekur Habib, Md. Tarek Daffodil International University Department of Computer Science and Engineering Dhaka1207 Bangladesh Independent University Department of Computer Science and Engineering Dhaka1207 Bangladesh

Potatoes are one of the world's most popular and economically important crops. For many uses in agriculture, breeding, and trading, accurate recognition of potato breeds is important. In recent years, deep learning algorithms have become effective tools for breed recognition tasks using pictures, which inspires researchers to explore their potential for recognizing potato breeds. The paper presents extensive research on the application of deep learning for potato breed recognition. The recognition of potatoes has been effectively performed using the five state-of-the-art deep learning models VGG16, ResNet50, Mobile-Net, Inception-v3, and a customized CNN. These models have been modeled to differentiate between several potato breeds based on their unique visual characteristics, such as size, shape, color, texture, and skin pattern, by being trained on images of various potato breeds. The performance of each of the deep learning models is evaluated through thorough evaluation and testing. Among the models, the customized CNN model gives the best accuracy. The customized CNN model's accuracy is 94.84%. We do not just evaluate the accuracy but rather some other indicative metrics, such as F1-score, recall, and precision, too. © 2024 IEEE.

关键词： Neural networks

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GPU Usage Time-Based Ordering Management Technique for Tasks Execution to Prevent Running Failures of GPU Tasks in Container Environments

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computers, Materials & Continua 2025年第2期82卷 2199-2213页

作者： Joon-Min Gil Hyunsu Jeong Jihun Kang Department of Computer Engineering Jeju National UniversityJeju-do63243Republic of Korea Department of Computer Science Korea National Open UniversitySeoul03087Republic of Korea

In a cloud environment,graphics processing units(GPUs)are the primary devices used for high-performance *** exploit flexible resource utilization,a key advantage of cloud *** users share GPUs,which serve as coprocessors of central processing units(CPUs)and are activated only if tasks demand GPU *** a container environment,where resources can be shared among multiple users,GPU utilization can be increased by minimizing idle time because the tasks of many users run on a single ***,unlike CPUs and memory,GPUs cannot logically multiplex their ***,GPU memory does not support over-utilization:when it runs out,tasks will ***,it is necessary to regulate the order of execution of concurrently running GPU tasks to avoid such task failures and to ensure equitable GPU sharing among *** this paper,we propose a GPU task execution order management technique that controls GPU usage via time-based *** technique seeks to ensure equal GPU time among users in a container environment to prevent task *** the meantime,we use a deferred processing method to prevent GPU memory shortages when GPU tasks are executed simultaneously and to determine the execution order based on the GPU usage *** the order of GPU tasks cannot be externally adjusted arbitrarily once the task commences,the GPU task is indirectly paused by pausing the *** addition,as container pause/unpause status is based on the information about the available GPU memory capacity,overuse of GPU memory can be prevented at the *** a result,the strategy can prevent task failure and the GPU tasks can be experimentally processed in appropriate order.

关键词： Cloud computing container GPGPU resource management

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