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Advanced Optimized Anomaly Detection System for IoT Cyberattacks Using Artificial Intelligence

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Computers, Materials & Continua 2024年第2期78卷 1525-1545页

作者： Ali hamid Farea Omar h.Alhazmi Kerem Kucuk Department of Computer Science Taibah UniversityMedina42315Saudi Arabia Department of Software Engineering Kocaeli UniversityKocaeli41001Turkey

while emerging technologies such as the Internet of Things(IoT)have many benefits,they also pose considerable security challenges that require innovative solutions,including those based on artificial intelligence(AI),given that these techniques are increasingly being used by malicious actors to compromise IoT *** an ample body of research focusing on conventional AI methods exists,there is a paucity of studies related to advanced statistical and optimization approaches aimed at enhancing security *** contribute to this nascent research stream,a novel AI-driven security system denoted as“AI2AI”is presented in this ***2AI employs AI techniques to enhance the performance and optimize security mechanisms within the IoT *** also introduce the Genetic Algorithm Anomaly Detection and Prevention Deep Neural Networks(GAADPSDNN)sys-tem that can be implemented to effectively identify,detect,and prevent cyberattacks targeting IoT ***,this system demonstrates adaptability to both federated and centralized learning environments,accommodating a wide array of IoT *** evaluation of the GAADPSDNN system using the recently complied wUSTL-IIoT and Edge-IIoT datasets underscores its *** an impressive overall accuracy of 98.18%on the Edge-IIoT dataset,the GAADPSDNN outperforms the standard deep neural network(DNN)classifier with 94.11%***,with the proposed enhancements,the accuracy of the unoptimized random forest classifier(80.89%)is improved to 93.51%,while the overall accuracy(98.18%)surpasses the results(93.91%,94.67%,94.94%,and 94.96%)achieved when alternative systems based on diverse optimization techniques and the same dataset are *** proposed optimization techniques increase the effectiveness of the anomaly detection system by efficiently achieving high accuracy and reducing the computational load on IoT devices through the adaptive selection of active features.

关键词： Internet of Things security anomaly detection and prevention system artificial intelligence optimization techniques

Pre-activated macrophage membrane-encased aggregationinduced emission featuring nanoparticles:a novel possibility for tuberculosis treatment

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Signal Transduction and Targeted Therapy 2024年第7期9卷 2848-2850页

作者： Qiang Cai Yihao Tian Quazi T.h.Shubhra Department of Neurosurgery Renmin Hospital of Wuhan UniversityWuhanChina Department of human Anatomy and histology and Embryology School of Basic Medical SciencesWuhan UniversityWuhanChina Institute of Chemistry University of Silesia in KatowiceSzkolna 9KatowicePoland

In a recent study published in Nature Nanotechnology,Li et *** the synthesis of a novel photosensitizer with unique aggregation-induced emission(AIE)characteristics.1 They microencapsulated this photosensitizer inside engineered nanoparticles(NPs)coated with pre-activated macrophage cell membranes(CMs),an innovative approach marking a significant leap in nanomedicine by offering a pathway to potent tuberculosis(TB)treatments that bypass antibiotic resistance with unparalleled precision.

关键词： tuberculosis macrophage aggregation

Research on fault time prediction method for high speed rail BTM unit based on multi method interactive validation

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high-speed Railway 2024年第3期2卷 164-171页

作者： Fu L. Gou J. Sun C. Li h. Liu w. School of Information Science and Technology Southwest Jiaotong University Chengdu 611756 China China Railway Signal and Communication Research and Design Institute Group Co Ltd Beijing 100089 China

The Balise Transmission Module (BTM) unit of the on-board train control system is a crucial component. Due to its unique installation position and complex environment, this unit has a higher fault rate within the on-board train control system. To conduct fault prediction for the BTM unit based on actual fault data, this study proposes a prediction method combining reliability statistics and machine learning, and achieves the fusion of prediction results from different dimensions through multi-method interactive validation. Firstly, a method for predicting equipment fault time targeting batch equipment is introduced. This method utilizes reliability statistics to construct a model of the remaining faultless operating time distribution considering uncertainty, thereby predicting the remaining faultless operating probability of the BTM unit. Secondly, considering the complexity of the BTM unit's fault mechanism, the small sample size of fault cases, and the potential presence of multiple fault features in fault text records, an individual-oriented fault prediction method based on Bayesian-optimized Gradient Boosting Regression Tree (Bayes-GBRT) is proposed. This method achieves better prediction results compared to linear regression algorithms and random forest regression algorithms, with an average absolute error of only 0.224 years for predicting the fault time of this type of equipment. Finally, a multi-method interactive validation approach is proposed, enabling the fusion and validation of multi-dimensional results. The results indicate that the predicted fault time and the actual fault time conform to a log-normal distribution, and the parameter estimation results are basically consistent, verifying the accuracy and effectiveness of the prediction results. The above research findings can provide technical support for the maintenance and modification of BTM units, effectively reducing maintenance costs and ensuring the safe operation of high-speed railway, thus ha

关键词： high speed rail BTM unit Machine learning Multi method interactive verification Remaining faultless operating time

On an isotropic porous solid cylinder:the analytical solution and sensitivity analysis of the pressure

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Applied Mathematics and Mechanics(English Edition) 2024年第9期45卷 1499-1522页

作者： h.ASGhARI L.MILLER R.PENTA J.MERODIO Departamento de Matematica Aplicada a las TIC ETS de Ingenieria de Sistemas InformaticosUniversidad Politecnica de MadridMadrid 28031Spain School of Mathematics and Statistics University of GlasgowGlasgow G128QQU.K.

within this work,we perform a sensitivity analysis to determine the influence of the material input parameters on the pressure in an isotropic porous solid *** provide a step-by-step guide to obtain the analytical solution for a porous isotropic elastic cylinder in terms of the pressure,stresses,and elastic *** obtain the solution by performing a Laplace transform on the governing equations,which are those of Biot's poroelasticity in cylindrical polar *** enforce radial boundary conditions and obtain the solution in the Laplace transformed domain before reverting back to the time *** sensitivity analysis is then carried out,considering only the derived pressure *** analysis finds that the time t,Biot's modulus M,and Poisson's ratio ν have the highest influence on the pressure whereas the initial value of pressure P_(0) plays a very little role.

关键词： sensitivity analysis Laplace transform cylindrical polar coordinate Biot's modulus cylinder

Long-term impact of artificial intelligence on colorectal adenoma detection in high-risk colonoscopy

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world Journal of Gastrointestinal Endoscopy 2024年第6期16卷 335-342页

作者： Kenneth w Chow Matthew T Bell Nicholas Cumpian Maryanne Amour Ryan h hsu Viktor E Eysselein Neetika Srivastava Michael w Fleischman Sofiya Reicher Department of Medicine Harbor-UCLA Medical CenterTorranceCA 90502United States Department of Bioengineering Jacobs School of EngineeringUniversity of CaliforniaSan DiegoLa JollaCA 92093United States Department of Gastroenterology Harbor-UCLA Medical CenterTorranceCA 90502United States

BACKGROUND Improved adenoma detection rate(ADR)has been demonstrated with artificial intelligence(AI)-assisted ***,data on the real-world appli-cation of AI and its effect on colorectal cancer(CRC)screening outcomes is *** To analyze the long-term impact of AI on a diverse at-risk patient population undergoing diagnostic colonoscopy for positive CRC screening tests or *** AI software(GI Genius,Medtronic)was implemented into the standard proced-ure protocol in November *** was collected on patient demographics,procedure indication,polyp size,location,and *** screening outcomes were evaluated before and at different intervals after AI introduction with one year of *** we evaluated 1008 colonoscopies(278 pre-AI,255 early post-AI,285 established post-AI,and 190 late post-AI).The ADR was 38.1%pre-AI,42.0%early post-AI(P=0.77),40.0%established post-AI(P=0.44),and 39.5%late post-AI(P=0.77).There were no significant differences in polyp detection rate(PDR,baseline 59.7%),advanced ADR(baseline 16.2%),and non-neoplastic PDR(baseline 30.0%)before and after AI *** In patients with an increased pre-test probability of having an abnormal colonoscopy,the current generation of AI did not yield enhanced CRC screening metrics over high-quality *** the potential of AI in colonoscopy is undisputed,current AI technology may not universally elevate screening metrics across all situations and patient *** studies that analyze different AI systems across various patient populations are needed to determine the most effective role of AI in optimizing CRC screening in clinical practice.

关键词： Artificial intelligence Colonoscopy Adenoma detection rate Screening Colorectal adenoma

Estimating Critical Clearing Time of Grid Faults Using DA of State-Reduction Model of Power Systems

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CSEE Journal of Power and Energy Systems 2024年第2期10卷 807-820页

作者： Yang Liu Zhongyang Chen huanjin Yao Lin Yi Q.h.wu School of Electric Power Engineering South China University of TechnologyGuangzhou 510640China China Southern Power Grid Research Institute Guangzhou 510663China

This paper proposes a critical clearing time (CCT) estimation method by the domain of attraction (DA) of a state-reduction model of power systems using sum of squares (SOS) programming. By exploiting the property of the Jacobian matrix and the structure of the boundary of the DA, it is found the DA of the state-reduction model and that of the full model of a power system are topological isomorphism. There are one-to-one correspondence relationships between the number of equilibrium points, the type of equilibrium points, and solutions of the two system models. Based on these findings, an expanding interior algorithm is proposed with SOS programming to estimate the DA of the state-reduction model. State trajectories of the full model can be transformed to those of the state-reduction model by orthogonal or equiradius projection. In this way, CCT of a grid fault is estimated with the DA of the state-reduction model. The calculational burden of SOS programming in the DA estimation using the state-reduction model is rather small compared with using the full model. Simulation results show the proposed expanding interior algorithm is able to provide a tight estimation of DA of power systems with higher accuracy and lower time costs.

关键词： Critical clearing time domain of attraction expanding interior algorithm state-reduction model SOS programming

MPDP: A Probabilistic Architecture for Microservice Performance Diagnosis and Prediction

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Computer Systems Science & Engineering 2024年第5期48卷 1273-1299页

作者： Talal h.Noor Department of Computer Science College of Computer Science and EngineeringTaibah UniversityMadinah42353Saudi Arabia

In recent years,container-based cloud virtualization solutions have emerged to mitigate the performance gap between non-virtualized and virtualized physical ***,there is a noticeable absence of techniques for predicting microservice performance in current research,which impacts cloud service users’ability to determine when to provision or de-provision *** microservice performance poses challenges due to overheads associated with actions such as variations in processing time caused by resource contention,which potentially leads to user *** this paper,we propose,develop,and validate a probabilistic architecture named Microservice Performance Diagnosis and Prediction(MPDP).MPDP considers various factors such as response time,throughput,CPU usage,and othermetrics to dynamicallymodel interactions betweenmicroservice performance indicators for diagnosis and *** experimental data fromourmonitoring tool,stakeholders can build various networks for probabilistic analysis ofmicroservice performance diagnosis and prediction and estimate the best microservice resource combination for a given Quality of Service(QoS)*** generated a dataset of microservices with 2726 records across four benchmarks including CPU,memory,response time,and throughput to demonstrate the efficacy of the proposed MPDP *** validate MPDP and demonstrate its capability to predict microservice *** compared various Bayesian networks such as the Noisy-OR Network(NOR),Naive Bayes Network(NBN),and Complex Bayesian Network(CBN),achieving an overall accuracy rate of 89.98%when using CBN.

关键词： Cloud computing microservices monitoring performance QoS diagnosis prediction Bayesian network

Inhibiting the oligomerization of mycobacterial DNA-directed RNA polymerase(RNAP)using natural compound via in-silico techniques

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Medicine in Novel Technology and Devices 2024年第1期21卷 97-109页

作者： Ehssan h.Moglad Department of Pharmaceutics College of PharmacyPrince Sattam Bin Abdulaziz UniversityP.O.Box 173 Alkharj 11942Saudi Arabia Department of Microbiology and Parasitology Institute of Medicinal and Aromatic Plants ResearchThe National Center for ResearchP.O.Box 2404KhartoumSudan

Mycobacterium tuberculosis(Mtb)is responsible for the spread of tuberculosis(TB).The current study employed virtual screening of 2569 natural compounds against the DNA-directed RNA polymerase(RNAP)of Mtb to identify the possible binders that can inhibit its *** in-silico methodology included molecular docking to the compounds,further,the stability and flexibility of the best complexes were studied using molecular dynamics simulation,the MM/GBSA binding free energy technique with energy decomposition,PCA,FEL,steered MD simulation,and umbrella *** virtual screenings were conducted for the five RNAP subunits(chains A,B,C,D,and E)to identify a compound capable of inhibiting RNAP oligomerization.A promising compound,isoestradiol 3-benzoate,exhibited a low binding score(−7.28kcal/mol to−8.21kcal/mol)and showed binding ability with all subunits of the ***,the five complexes with isoestradiol 3-benzoate were selected for molecular dynamics simulation ***,RMSD showed that isoestradiol 3-benzoate bound with chain E showed the lowest RMSD of 0.49nm,while with chains A and B it had the most stable and consistent conformations with RMSD of 1.75nm and 1.2nm,*** h-bond between isoestradiol 3-benzoate and chains C and E showed the highest occupancy(58.27%,45.33%,and 50.80%,42.25%,11.75%).Moreover,the MMPBSA technique showed that isoestradiol 3-benzoate had a strong association with chains B and C(ΔGbind=−126.25±2.03 and−129.27±2.25).Additionally,free energy decomposition,PCA,FEL-steered MD simulation,and umbrella sampling were also performed to validate the association of the ligand with the *** 3-benzoate binds strongly to chains B and E;therefore,it should be considered as viable candidate for inhibiting the formation of RNAP protein complex,concluded in this study.

关键词： Mycobacterium tuberculosis DNA-Directed RNA polymerase Docking Steered MD simulation Natural compounds Binding free energy Umbrella sampling

Flexible neural probes:a review of the current advantages,drawbacks,and future demands

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Journal of Zhejiang University-Science B(Biomedicine & Biotechnology) 2024年第2期25卷 153-167页

作者： Sara PIMENTA Joao R.FREITAS Joséh.CORREIA CMEMS-UMinho University of MinhoGuimarães4800-058Portugal LABBELS-Associate Laboratory Braga/GuimarãesPortugal

Brain diseases affect millions of people and have a huge social and economic *** use of neural probes for studies in animals has been the main approach to increasing knowledge about neural network ***,neuroscientists are trying to develop new and more effective therapeutic approaches to treating neurological *** implementation of neural probes with multifunctionalities(electrical,optical,and fluidic interactions)has been increasing in the last few years,leading to the creation of devices with high temporal and spatial *** the applicability of,and elements integrated into,neural probes has also led to the necessity to create flexible interfaces,reducing neural tissue damage during probe implantation and increasing the quality of neural acquisition *** this paper,we review the fabrication,characterization,and validation of several types of flexible neural probes,exploring the main advantages and drawbacks of these ***,future developments and applications are ***,this review aims to present the currently available flexible devices and future appropriate avenues for development as possible guidance for future engineered devices.

关键词： Brain knowledge Flexible interface Multifunctional probe