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Intrusion Detection in 5G Cellular Network Using Machine Learning

computer systems science & Engineering 2023年第11期47卷 2439-2453页

作者： Ishtiaque Mahmood Tahir Alyas Sagheer Abbas Tariq Shahzad Qaiser Abbas Khmaies Ouahada Knowledge Unit of Systems and Technology UMT Sialkot CampusSialkot51040Pakistan Department of Computer Science Lahore Garrison UniversityLahore54000Pakistan Faculty of Computer Science National College of Business Administration and EconomicsLahore54660Pakistan Department of Electrical and Computer Engineering COMSATS University IslamabadSahiwal CampusSahiwal57000Pakistan Faculty of Computer and Information Systems Islamic University of Madinah Madinah42351Saudi Arabia Department of Computer Science&IT University of SargodhaSargodha40100Pakistan Department of Electrical and Electronic Engineering Science University of JohannesburgP.O Box 524Auckland ParkJohannesburg2006South Africa

Attacks on fully integrated servers,apps,and communication networks via the Internet of Things(IoT)are growing *** devices’effectiveness harms end users,increases cyber threats and identity theft,raises costs,and negatively impacts income as problems brought on by the Internet of Things network go unnoticed for extended *** on Internet of Things interfaces must be closely monitored in real time for effective safety and *** the 1,2,3,and 4G cellular networks,the 5th generation wireless 5G network is indeed the great invasion of mankind and is known as the global advancement of cellular *** to this day,experts are working on the evolution’s sixth generation(6G).It offers amazing capabilities for connecting everything,including gadgets and machines,with wavelengths ranging from 1 to 10 mm and frequencies ranging from 300 MHz to 3 *** gives you the most recent *** countries have already established this technology within their *** is the most crucial aspect of using a 5G *** of the absence of study and network deployment,new technology first introduces new gaps for attackers and *** Protocol(IP)attacks and intrusion will become more prevalent in this *** efficient approach to detect intrusion in the 5G network using a Machine Learning algorithm will be provided in this *** research will highlight the high accuracy rate by validating it for unidentified and suspicious circumstances in the 5G network,such as intruder hackers/*** applying different machine learning algorithms,obtained the best result on Linear Regression Algorithm’s implementation on the dataset results in 92.12%on test data and 92.13%on train data with 92%precision.

关键词： Intrusion detection system machine learning confidentiality integrity availability

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Deep learning-based multi-task prediction of response to neoadjuvant chemotherapy using multiscale whole slide images in breast cancer:A multicenter study

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Chinese Journal of Cancer Research 2025年第1期37卷 28-47页

作者： Qin Wang Feng Zhao Haicheng Zhang Tongpeng Chu Qi Wang Xipeng Pan Yuqian Chen Heng Zhou Tiantian Zheng Ziyin Li Fan Lin Haizhu Xie Heng Ma Lan Liu Lina Zhang Qin Li Weiwei Wang Yi Dai Ruijun Tang Jigang Wang Ping Yang Ning Mao School of Information and Electronic Engineering Shandong Technology and Business UniversityYantai 264005China Big Data and Artificial Intelligence Laboratory Yantai Yuhuangding Hospital of Qingdao UniversityYantai 264000China Shandong Provincial Key Medical and Health Laboratory of Intelligent Diagnosis and Treatment for Women’s Diseases Yantai Yuhuangding HospitalYantai 264000China Department of Radiology Yantai Yuhuangding Hospital of Qingdao UniversityYantai 264000China School of Computer Science and Technology Shandong Technology and Business UniversityYantai 264005China School of Computer Science and Information Security Guilin University of Electronic TechnologyGuilin 541004China School of Medical Imaging Binzhou Medical UniversityYantai 264003China Department of Radiology Jiangxi Cancer Hospitalthe Second Affiliated Hospital of Nanchang Medical CollegeNanchang 330006China Department of Radiology the First Affiliated Hospital of China Medical UniversityShenyang 400042China Department of Radiology Weifang Hospital of Traditional Chinese MedicineWeifang 262600China Department of Medical Imaging Affiliated Hospital of Jining Medical UniversityJining 272029China Department of Radiology the Peking University Shenzhen HospitalShenzhen 518036China Department of Pathology Guilin Traditional Chinese Medicine HospitalGuilin 541002China Department of Pathology the Affiliated Hospital of Qingdao UniversityQingdao 266555China Department of Pathology Yantai Yuhuangding Hospital of Qingdao UniversityYantai 264000China

Objective:Early predicting response before neoadjuvant chemotherapy(NAC)is crucial for personalized treatment plans for locally advanced breast cancer *** aim to develop a multi-task model using multiscale whole slide images(WSIs)features to predict the response to breast cancer NAC more ***:This work collected 1,670 whole slide images for training and validation sets,internal testing sets,external testing sets,and prospective testing sets of the weakly-supervised deep learning-based multi-task model(DLMM)in predicting treatment response and pCR to *** approach models two-by-two feature interactions across scales by employing concatenate fusion of single-scale feature representations,and controls the expressiveness of each representation via a gating-based attention ***:In the retrospective analysis,DLMM exhibited excellent predictive performance for the prediction of treatment response,with area under the receiver operating characteristic curves(AUCs)of 0.869[95%confidence interval(95%CI):0.806−0.933]in the internal testing set and 0.841(95%CI:0.814−0.867)in the external testing *** the pCR prediction task,DLMM reached AUCs of 0.865(95%CI:0.763−0.964)in the internal testing and 0.821(95%CI:0.763−0.878)in the pooled external testing *** the prospective testing study,DLMM also demonstrated favorable predictive performance,with AUCs of 0.829(95%CI:0.754−0.903)and 0.821(95%CI:0.692−0.949)in treatment response and pCR prediction,*** significantly outperformed the baseline models in all testing sets(P<0.05).Heatmaps were employed to interpret the decision-making basis of the ***,it was discovered that high DLMM scores were associated with immune-related pathways and cells in the microenvironment during biological basis ***:The DLMM represents a valuable tool that aids clinicians in selecting personalized treatment strategies for breast cancer patients.

关键词： Artificial intelligence breast cancer digital pathology whole slide images

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Systematic Literature Review of Topic Labeling

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IEEE Access 2025年 13卷 93124-93147页

作者： Mekaoui, Salma Chaker, Ilham Zarghili, Arsalane Nikolov, Nikola S. University of Limerick Department of Computer Science and Information Systems LimerickV94 T9PX Ireland University Sidi Mohamed Ben Abdellah Laboratory of Intelligent Systems and Applications Faculty of Sciences and Technology Route Immouzer Fez Morocco

The rapid growth of textual data on the web has led researchers to develop methods in Natural Language Processing (NLP) to process, understand, and identify topics... Among these methods, Topic Modeling helps extract relevant topics, represented as clusters of words. However, interpreting these clusters into meaningful topics remains a challenge. This limitation has led to further research into topic labelingan approach for assigning comprehensive and semantically meaningful labels to topic modeling results, ensuring that they are interpretable and understandable from a human perspective. In this paper, we present a Systematic Literature Review (SLR) on topic labeling. This review explores its definition, geographical and time distribution, methodologies, datasets, evaluation methods, successes, and challenges. This paper presents an SLR on topic labeling, synthesizing insights from 41 high-quality studies. It serves as a rich source of information for researchers interested in investigating different approaches for discovering topics within textual data. It addresses the various aspects of topic labeling and includes discussions that highlight the challenges of this approach, encouraging further research in this field. © 2013 IEEE.

关键词： Nameplates

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Root Pose Decomposition Towards Generic Non-rigid 3D Reconstruction with Monocular Videos

Root Pose Decomposition Towards Generic Non-rigid 3D Reconst...

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International Conference on computer Vision (ICCV)

作者： Yikai Wang Yinpeng Dong Fuchun Sun Xiao Yang Department of Computer Science and Technology Beijing National Research Center for Information Science and Technology (BNRist) State Key Lab on Intelligent Technology and Systems Tsinghua University RealAI

This work focuses on the 3D reconstruction of non-rigid objects based on monocular RGB video sequences. Concretely, we aim at building high-fidelity models for generic object categories and casually captured scenes. To this end, we do not assume known root poses of objects, and do not utilize category-specific templates or dense pose priors. The key idea of our method, Root Pose Decomposition (RPD), is to maintain a per-frame root pose transformation, meanwhile building a dense field with local transformations to rectify the root pose. The optimization of local transformations is performed by point registration to the canonical space. We also adapt RPD to multi-object scenarios with object occlusions and individual differences. As a result, RPD allows non-rigid 3D reconstruction for complicated scenarios containing objects with large deformations, complex motion patterns, occlusions, and scale diversities of different individuals. Such a pipeline potentially scales to diverse sets of objects in the wild. We experimentally show that RPD surpasses state-of-the-art methods on the challenging DAVIS, OVIS, and AMA datasets. We provide video results in https://***.

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MedDietAgent: An AI-based Mobile App for Harmonizing Individuals' Dietary Choices with the Mediterranean Diet Pattern 46

MedDietAgent: An AI-based Mobile App for Harmonizing Individ...

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46th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2024

作者： Konstantakopoulos, Fotios S. Sfakianos, Michail Georga, Eleni I. Mavrokotas, Konstantinos I. Katsarou, Daphne N. Chalatsis, Konstantinos Zapadiotis, Charalambos Panousi, Anastasia Plimakis, Sifis Eleftheriou, Sofia Kanellou, Anastasia Fotiadis, Dimitrios I. University of Ioannina Unit of Medical Technology and Intelligent Information Systems Materials Science and Engineering Department IoanninaGR 45110 Greece Biomedical Research Institute Forth IoanninaGR 45110 Greece AppArt a Software Solutions Company Based in Athens GR 15343 Greece Dbc Diadikasia a Leading Business Consulting Firm in Athens 296 Kifissias Avenue & 40 Navarinou str HalandriGR 15232 Greece University of West Attica Department of Food Science and Technology Egaleo AthensGR 12243 Greece

ISBN: (纸本)9798350371499

Recently, there has been an increasing interest in applying technological advances to offer specific dietary recommendations in the field of nutrition and health. Dietary recommendation systems are advanced tools designed to assist individuals in making well-informed and health-conscious decisions on their food choices, taking into account their personal needs, preferences, and health targets or habits. In this study, we present an AI-based mobile app for harmonizing individuals' dietary choices with the pattern of the Mediterranean diet. A combination of computer vision, natural language processing, machine learning, and reinforcement techniques are used to record the nutritional information via images or speech and to generate dynamic recommendations tailored to the user's performance across key nutritional areas, encompassing calories, combined fats, proteins, carbohydrates, sugars, dietary fibers, sodium intake, fruits, vegetables, and dairy products. The image-based dietary assessment subsystem achieves a mean absolute percentage error of 3.73%, while the reinforcement learning subsystem achieves a 96% average reward. Then, a well-designed approach was taken to develop the MedDietAgent mobile app, using cutting-edge technologies and applying a simplistic approach. One of the key aspects of MedDietAgent is its ability to offer dynamic recommendations by monitoring the user's environment. © 2024 IEEE.

关键词： Electronic health record

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Ensemble Deep Learning with Chimp Optimization Based medical Data Classification

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intelligent Automation & Soft Computing 2023年第2期35卷 1643-1655页

作者： Ashit Kumar Dutta Yasser Albagory Majed Alsanea Hamdan I.Almohammed Abdul Rahaman Wahab Sait Department of Computer Science and Information Systems College of Applied SciencesAlMaarefa UniversityAd DiriyahRiyadh13713Kingdom of Saudi Arabia Department of Computer Engineering College of Computers and Information TechnologyTaif UniversityTaif21944Kingdom of Saudi Arabia Department of Computing Arabeast CollegesRiyadh11583Kingdom of Saudi Arabia Department of Basic Medical Sciences College of Medicine.Almaarefa UniversityAlMaarefa UniversityAd DiriyahRiyadh13713Kingdom of Saudi Arabia Department of Archives and Communication King Faisal UniversityAl AhsaHofuf31982Kingdom of Saudi Arabia

Eye state classiﬁcation acts as a vital part of the biomedical sector,for instance,smart home device control,drowsy driving recognition,and so *** modiﬁcations in the cognitive levels can be reﬂected via transforming the electro-encephalogram(EEG)*** deep learning(DL)models automated extract the features and often showcased improved outcomes over the conventional clas-siﬁcation model in the recognition *** paper presents an Ensemble Deep Learning with Chimp Optimization Algorithm for EEG Eye State Classiﬁ-cation(EDLCOA-ESC).The proposed EDLCOA-ESC technique involves min-max normalization approach as a pre-processing ***,wavelet packet decomposition(WPD)technique is employed for the extraction of useful features from the EEG *** addition,an ensemble of deep sparse autoencoder(DSAE)and kernel ridge regression(KRR)models are employed for EEG Eye State classiﬁ***,hyperparameters tuning of the DSAE model takes place using COA and thereby boost the classiﬁcation results to a maximum *** extensive range of simulation analysis on the benchmark dataset is car-ried out and the results reported the promising performance of the EDLCOA-ESC technique over the recent approaches with maximum accuracy of 98.50%.

关键词： EEG eye state data classiﬁcation deep learning medical data analysis chimp optimization algorithm

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The Diagnosis of Cardiovascular Disease Using Simple Blood Biomarkers Through AI and Big Data

The Diagnosis of Cardiovascular Disease Using Simple Blood B...

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Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)

作者： Vasileios Pezoulas Georg Ehret Kevin Dobretz Dimitrios I. Fotiadis Antonis I. Sakellarios Dept. of Materials Science and Engineering Unit of Medical Technology and Intelligent Information Systems Biomedical Resarch Institute FORTH-IMBB Ioannina Greece Department of Internal Medicine Hôpitaux Universitaires de Genève Geneva Switzerland Dept. of Mechanical and Aeronautics Engineering University of Patras Patras Greece Dept. of Materials Science and Engineering Unit of Medical Technology and Intelligent Information Systems University of Ioannina Ioannina Greece

ISBN: (数字)9798350371499

ISBN: (纸本)9798350371505

Cardiovascular disease (CVD) is the leading cause of global mortality, diagnosed primarily through costly imaging modalities which are often overused in asymptomatic patients. Our project aims to develop an AI-based solution for CVD risk stratification using routine blood biomarkers, serving as a pre-imaging test. We used anonymized data from over 500,000 UK Biobank (UKB) patients with CVD assessments. Initially, 701 features including demographics, blood tests, medical conditions, and clinical assessments were selected. The UKB dataset was refined using an automated data curation pipeline to deal with outliers, duplicated fields, and missing values. Then, a hybrid XGBoost classifier was employed, with a scalable loss function, to address overfitting effects during the training process, yielding 0.83 accuracy, 0.82 sensitivity, and 0.84 specificity in diagnosing CVD comorbidities. Key biomarkers identified included blood pressure, BMI, and age. To our knowledge, this is the first case study which utilizes the UKB data towards the identification of cost-effective CVD (non-imaging) risk factors, thus reducing the reliance on imaging modalities.

关键词： Training Sensitivity Pipelines Imaging Biomarkers Big Data Blood pressure Cardiovascular diseases Engineering in medicine and biology Blood

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Wearable and interactive multicolored photochromic fiber display

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Light(science & Applications) 2024年第3期13卷 454-465页

作者： Pan Li Yuwei Wang Xiaoxian He Yuyang Cui Jingyu Ouyang Ju Ouyang Zicheng He Jiayu Hu Xiaojuan Liu Hang Wei Yu Wang Xiaoling Lu Qian Ji Xinyuan Cai Li Liu Chong Hou Ning Zhou Shaowu Pan Xiangru Wang Huamin Zhou Cheng-Wei Qiu Yan-Qing Lu Guangming Tao State Key Laboratory of Material Processing and Die&Mould Technology School of Materials Science and Engineering and Wuhan National Laboratory for OptoelectronicsHuazhong University of Science and TechnologyWuhan 430074China Key Laboratory of Vascular Aging(HUST) Ministry of EducationWuhan 430030China School of Optoelectronic Science and Engineering University of Electronic Science and Technology of ChinaChengdu 611731China Department of Electrical and Computer Engineering National University of SingaporeSingapore 117583Singapore National Laboratory of Solid State Microstructures Key Laboratory of Intelligent Optical Sensing and ManipulationCollege of Engineering and Applied Sciencesand Collaborative Innovation Center of Advanced MicrostructuresNanjing UniversityNanjing 210023China School of Performing Arts Wuhan Conservatory of MusicWuhan 430060China School of Mechanical Science and Engineering Huazhong University of Science and TechnologyWuhan 430074China School of Architecture and Urban Planning Huazhong University of Science and TechnologyWuhan 430074China School of Fashion Beijing Institute of Fashion TechnologyBeijing 100029China School of Optical and Electronic Information Huazhong University of Science and TechnologyWuhan 430074China Tongji Medical College Huazhong University of Science and TechnologyWuhan 430074China State Key Laboratory for Modification of Chemical Fibers and Polymer Materials College of Materials Science and EngineeringDonghua UniversityShanghai 201620China

Endowing flexible and adaptable fiber devices with light-emitting capabilities has the potential to revolutionize the current design philosophy of intelligent,wearable interactive ***,significant challenges remain in developing fiber devices when it comes to achieving uniform and customizable light effects while utilizing lightweight ***,we introduce a mass-produced,wearable,and interactive photochromic fiber that provides uniform multicolored light *** designed independent waveguides inside the fiber to maintain total internal reflection of light as it traverses the *** impact of excessive light leakage on the overall illuminance can be reduced by utilizing the saturable absorption effect of fluorescent materials to ensure light emission uniformity along the transmission *** addition,we coupled various fluorescent composite materials inside the fiber to achieve artificially controllable spectral radiation of multiple color systems in a single *** prepared fibers on mass-produced kilometer-long using the thermal drawing *** fibers can be directly integrated into daily wearable devices or clothing in various patterns and combined with other signal input components to control and display patterns as *** work provides a new perspective and inspiration to the existing field of fiber display interaction,paving the way for future human–machine integration.

关键词： fiber interactive colored

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Three-stage data generation algorithm for multiclass network intrusion detection with highly imbalanced dataset

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International Journal of intelligent Networks 2023年第1期4卷 202-210页

作者： Chui, Kwok Tai Gupta, Brij B. Chaurasia, Priyanka Arya, Varsha Almomani, Ammar Alhalabi, Wadee Department of Electronic Engineering and Computer Science School of Science and Technology Hong Kong Metropolitan University Hong Kong Taichung 413 Taiwan Department of Computer Science and Information Engineering Asia University Taichung 413 Taiwan School of Computing Engineering and Intelligent Systems Ulster University United Kingdom Department of Business Administration Asia University Taiwan School of Information Technology Skyline University College SharjahP.O. Box 1797 United Arab Emirates Al- Balqa Applied University Jordan Immersive Virtual Reality Research Group King Abdulaziz University Jeddah21589 Saudi Arabia Symbiosis International University Pune India Lebanese American University Beirut1102 Lebanon Birkbeck University of London United Kingdom Chandigarh University Chandigarh India

The Internet plays a crucial role in our daily routines. Ensuring cybersecurity to Internet users will provide a safe online environment. Automatic network intrusion detection (NID) using machine learning algorithms has recently received increased attention recently. The NID model is prone to bias towards the classes with more training samples due to highly imbalanced datasets across different types of attacks. The challenge in generating additional training data for minority classes is the generation of insufficient data. The study's purpose is to address this challenge, which extends the data generation ability by proposing a three-stage data generation algorithm using the synthetic minority over-sampling technique, a generative adversarial network (GAN), and a variational autoencoder. A convolutional neural network is employed to extract the representative features from the data, which were fed into a support vector machine with a customised kernel function. An ablation study evaluated the effectiveness of the three-stage data generation, feature extraction, and customised kernel. This was followed by a performance comparison between our study and existing studies. The findings revealed that the proposed NID model achieved an accuracy of 91.9%–96.2% in the four benchmark datasets. In addition, it outperformed existing methods such as GAN-based deep neural networks, conditional Wasserstein GAN-based stacked autoencoder, synthesised minority oversampling technique-based random forest, and variational autoencoder-based deep neural network, by 1.51%–28.4%. © 2023 The Authors

关键词： Support vector machines

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Unsupervised Multimodal Clustering for Semantics Discovery in Multimodal Utterances

arXiv

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arXiv 2024年

作者： Zhang, Hanlei Xu, Hua Long, Fei Wang, Xin Gao, Kai State Key Laboratory of Intelligent Technology and Systems Department of Computer Science and Technology Tsinghua University China School of Information Science and Engineering Hebei University of Science and Technology China Technology Development Co. Ltd Nanchang330036 China

Discovering the semantics of multimodal utterances is essential for understanding human language and enhancing human-machine interactions. Existing methods manifest limitations in leveraging nonverbal information for discerning complex semantics in unsupervised scenarios. This paper introduces a novel unsupervised multimodal clustering method (UMC), making a pioneering contribution to this field. UMC introduces a unique approach to constructing augmentation views for multimodal data, which are then used to perform pre-training to establish well-initialized representations for subsequent clustering. An innovative strategy is proposed to dynamically select high-quality samples as guidance for representation learning, gauged by the density of each sample’s nearest neighbors. Besides, it is equipped to automatically determine the optimal value for the top-K parameter in each cluster to refine sample selection. Finally, both high- and low-quality samples are used to learn representations conducive to effective clustering. We build baselines on benchmark multimodal intent and dialogue act datasets. UMC shows remarkable improvements of 2-6% scores in clustering metrics over state-of-the-art methods, marking the first successful endeavor in this domain. The complete code and data are available at https://***/thuiar/UMC. Copyright © 2024, The Authors. All rights reserved.

关键词： Semantics

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