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Network Security Enhanced with Deep Neural Network-Based Intrusion Detection System

Computers, Materials & Continua 2024年第7期80卷 1457-1490页

作者： Fatma S.Alrayes Mohammed Zakariah Syed Umar Amin Zafar Iqbal Khan Jehad Saad Alqurni Information Systems department College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityRiyadh11671Saudi Arabia College of Computer and Information sciences King Saud UniversityRiyadh11362Saudi Arabia College of Computer and Information sciences Prince Sultan UniversityRiyadh11586Saudi Arabia department of Educational Technologies College of EducationImam Abdulrahman Bin Faisal UniversityP.O.Box 1982Dammam31441Saudi Arabia

This study describes improving network security by implementing and assessing an intrusion detection system(IDS)based on deep neural networks(DNNs).The paper investigates contemporary technical ways for enhancing intrusion detection performance,given the vital relevance of safeguarding computer networks against harmful *** DNN-based IDS is trained and validated by the model using the NSL-KDD dataset,a popular benchmark for IDS *** model performs well in both the training and validation stages,with 91.30%training accuracy and 94.38%validation ***,the model shows good learning and generalization capabilities with minor losses of 0.22 in training and 0.1553 in ***,for both macro and micro averages across class 0(normal)and class 1(anomalous)data,the study evaluates the model using a variety of assessment measures,such as accuracy scores,precision,recall,and F1 *** macro-average recall is 0.9422,the macro-average precision is 0.9482,and the accuracy scores are ***,macro-averaged F1 scores of 0.9245 for class 1 and 0.9434 for class 0 demonstrate the model’s ability to precisely identify anomalies *** research also highlights how real-time threat monitoring and enhanced resistance against new online attacks may be achieved byDNN-based intrusion detection systems,which can significantly improve network *** study underscores the critical function ofDNN-based IDS in contemporary cybersecurity procedures by setting the foundation for further developments in this *** research aims to enhance intrusion detection systems by examining cooperative learning techniques and integrating up-to-date threat knowledge.

关键词： Machine-learning Deep-learning intrusion detection system security privacy deep neural network NSL-KDD Dataset

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Temporally consistent video colorization with deep feature propagation and self-regularization learning

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Computational Visual Media 2024年第2期10卷 375-395页

作者： Yihao Liu Hengyuan Zhao Kelvin CKChan Xintao Wang Chen Change Loy Yu Qiao Chao Dong Shenzhen Institute of Advanced Technology Chinese Academy of SciencesShenzhen 518055China University of Chinese Academy of sciences Beijing 100049China department of Electrical&Computer Engineering Nanyang Technological University50 Nanyang AvenueSingapore 639798 Applied Research Center Tencent PCGShenzhenChina

Video colorization is a challenging and highly ill-posed *** recent years have witnessed remarkable progress in single image colorization,there is relatively less research effort on video colorization,and existing methods always suffer from severe flickering artifacts(temporal inconsistency)or unsatisfactory *** address this problem from a new perspective,by jointly considering colorization and temporal consistency in a unified ***,we propose a novel temporally consistent video colorization(TCVC)*** effectively propagates frame-level deep features in a bidirectional way to enhance the temporal consistency of ***,TCVC introduces a self-regularization learning(SRL)scheme to minimize the differences in predictions obtained using different time *** does not require any ground-truth color videos for training and can further improve temporal *** demonstrate that our method can not only provide visually pleasing colorized video,but also with clearly better temporal consistency than state-of-the-art methods.A video demo is provided at https://***/watch?v=c7dczMs-olE,while code is available at https://***/lyh-18/TCVC-Tem porally-Consistent-Video-Colorization.

关键词： video colorization temporal consistency feature propagation self-regularization

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Predicting the Risk of Arterial Stiffness in Coal Miners Based on Different Machine learning Models

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Biomedical and Environmental sciences 2024年第1期37卷 108-111页

作者： CHEN Qian Wei HUANG Xue Zan DING Yu ZHU Feng Ren WANG Jia ZOU Yuan Jie DU Yuan Zhen ZHANG Ya Jun HUI Zi Wen ZHU Feng Lin MU Min The Project was supported by Open Research Grant of Joint National-Local Engineering Research Centre for Safe and Precise Coal Mining Anhui University of Science and TechnologyHefei 232001AnhuiChina School of Public health Anhui University of Science and TechnologyHefei 232001AnhuiChina Key Laboratory of Industrial Dust Prevention and Control Occupational Safety and HealthMinistry of EducationAnhui University of Science and TechnologyHefei 232001AnhuiChina Anhui Institute of Occupational Safety and health Anhui University of Science and TechnologyHefei 232001AnhuiChina Joint Research Center of Occupational Medicine and health Institute of Grand HealthHefei Comprehensive National Science CenterAnhui University of Science and TechnologyHefei 232001AnhuiChina department of Occupational&Environmental health School of Public HealthTongji Medical CollegeHuazhong University of Science and TechnologyWuhan 430030HubeiChina Key Laboratory of Environment and health Ministry of Education&Ministry of Environmental Protectionand State Key Laboratory of Environmental Health(Incubating)School of Public HealthTongji Medical CollegeHuazhong University of Science and TechnologyWuhan 430030HubeiChina

Coal is one of the world’s main energy resources,accounting for approximately 68%of China’s current total power ***,several studies have demonstrated that dust,exhaust fumes,and other harmful factors in coal mines increase the risk of cardiovascular disease(CVD)among miners^([1]).Arterial stiffness(AS)is an independent risk factor of CVD,and epidemiological studies have shown that AS plays a vital role in assessing the risk of CVD^([2]).

关键词： Arterial cardiovascular assessing

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Model Agnostic Meta-learning(MAML)-Based Ensemble Model for Accurate Detection of Wheat Diseases Using Vision Transformer and Graph Neural Networks

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Computers, Materials & Continua 2024年第5期79卷 2795-2811页

作者： Yasir Maqsood Syed Muhammad Usman Musaed Alhussein Khursheed Aurangzeb Shehzad Khalid Muhammad Zubair department of Creative Technologies Faculty of Computing and Artificial IntelligenceAir UniversityIslamabad44000Pakistan department of Computer Engineering College of Computer and Information SciencesKing Saud UniversityP.O.Box 51178Riyadh11543Saudi Arabia department of Computer Engineering Bahria School of Engineering and Applied SciencesBahria UniversityIslamabad44000Pakistan Laboratory for Neuro&Psychophysiology KU LeuvenLeuven3000Belgium

Wheat is a critical crop,extensively consumed worldwide,and its production enhancement is essential to meet escalating *** presence of diseases like stem rust,leaf rust,yellow rust,and tan spot significantly diminishes wheat yield,making the early and precise identification of these diseases vital for effective disease *** advancements in deep learning algorithms,researchers have proposed many methods for the automated detection of disease pathogens;however,accurately detectingmultiple disease pathogens simultaneously remains a *** challenge arises due to the scarcity of RGB images for multiple diseases,class imbalance in existing public datasets,and the difficulty in extracting features that discriminate between multiple classes of disease *** this research,a novel method is proposed based on Transfer Generative Adversarial Networks for augmenting existing data,thereby overcoming the problems of class imbalance and data *** study proposes a customized architecture of Vision Transformers(ViT),where the feature vector is obtained by concatenating features extracted from the custom ViT and Graph Neural *** paper also proposes a Model AgnosticMeta learning(MAML)based ensemble classifier for accurate *** proposedmodel,validated on public datasets for wheat disease pathogen classification,achieved a test accuracy of 99.20%and an F1-score of 97.95%.Compared with existing state-of-the-art methods,this proposed model outperforms in terms of accuracy,F1-score,and the number of disease pathogens *** future,more diseases can be included for detection along with some other modalities like pests and weed.

关键词： Wheat disease detection deep learning vision transformer graph neural network model agnostic meta learning

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Design of a Lightweight Compressed Video Stream-Based Patient Activity Monitoring System

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Computers, Materials & Continua 2024年第1期78卷 1253-1274页

作者： Sangeeta Yadav Preeti Gulia Nasib Singh Gill Piyush Kumar Shukla Arfat Ahmad Khan Sultan Alharby Ahmed Alhussen Mohd Anul Haq department of Computer Science&Applications Maharshi Dayanand UniversityRohtak124001India University Institute of Technology Rajiv Gandhi Proudyogiki VishwavidyalayaBhopal462033India department of Computer Science College of ComputingKhon Kaen UniversityKhon Kaen40002Thailand department of Computer Science College of Computer and Information SciencesMajmaah UniversityAl Majmaah11952Kingdom of Saudi Arabia department of Computer Engineering College of Computer and Information SciencesMajmaah UniversityAl Majmaah11952Kingdom of Saudi Arabia

Inpatient falls from beds in hospitals are a common *** falls may result in severe *** problem can be addressed by continuous monitoring of patients using *** advancements in deep learning-based video analytics have made this task of fall detection more effective and *** with fall detection,monitoring of different activities of the patients is also of significant concern to assess the improvement in their *** computation-intensive models are required to monitor every action of the patient *** requirement limits the applicability of such ***,to keep the model lightweight,the already designed fall detection networks can be extended to monitor the general activities of the patients along with the fall *** by the same notion,we propose a novel,lightweight,and efficient patient activity monitoring system that broadly classifies the patients’activities into fall,activity,and rest classes based on their *** whole network comprises three sub-networks,namely a Convolutional Neural Networks(CNN)based video compression network,a Lightweight Pose Network(LPN)and a Residual Network(ResNet)Mixer block-based activity recognition *** compression network compresses the video streams using deep learning networks for efficient storage and retrieval;after that,LPN estimates human ***,the activity recognition network classifies the patients’activities based on their *** proposed system shows an overall accuracy of approx.99.7% over a standard dataset with 99.63% fall detection accuracy and efficiently monitors different events,which may help monitor the falls and improve the inpatients’health.

关键词： Fall detection activity recognition human pose estimation accuracy

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Sorl1 knockout inhibits expression of brain-derived neurotrophic factor:involvement in the development of late-onset Alzheimer's disease

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Neural Regeneration Research 2024年第7期19卷 1602-1607页

作者： Mingri Zhao Xun Chen Jiangfeng Liu Yanjin Feng Chen Wang Ting Xu Wanxi Liu Xionghao Liu Mujun Liu Deren Hou department of Neurology The Third Xiangya Hospital of Central South UniversityChangshaHunan ProvinceChina Center for Medical Genetics&Hunan Key Laboratory of Medical Genetics School of Life SciencesCentral South UniversityChangshaHunan ProvinceChina department of Cell Biology School of Life SciencesCentral South UniversityChangshaHunan ProvinceChina

Sortilin-related receptor 1(SORL1)is a critical gene associated with late-onset Alzheimer’s ***1 contributes to the development and progression of this neurodegenerative condition by affecting the transport and metabolism of intracellularβ-amyloid precursor *** better understand the underlying mechanisms of SORL1 in the pathogenesis of late-onset Alzheimer s disease,in this study,we established a mouse model of SorI1 gene knockout using cluste red regularly inters paced short palindro mic repeats-associated protein 9 *** found that Sorl1-knocko ut mice displayed deficits in learning and ***,the expression of brain-derived neurotrophic factor was significantly downregulated in the hippocampus and co rtex,and amyloidβ-protein deposits were observed in the brains of 5orl1-knockout *** vitro,hippocampal neuronal cell synapses from homozygous Sorl1-knockout mice were *** expression of synaptic proteins,including Drebrin and NR2B,was significantly reduced,and also their ***,by knocking out the Sorl1 gene in N2a cells,we found that expression of the N-methyl-D-aspartate receptor,NR2B,and cyclic adenosine monophosphate-response element binding protein was also *** findings suggest that SORL1 participates in the pathogenesis of late-onset Alzheimer s disease by regulating the N-methyl-D-aspartate receptor NR2B/cyclic adenosine monophosphate-response element binding protein signaling axis.

关键词： brain-derived neurotrophic factor late-onset Alzheimer’s disease N-methyl-D-aspartate receptor sortilin-related receptor 1 synapse

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Artificial Intelligence for Maximizing Agricultural Input Use Efficiency: Exploring Nutrient, Water and Weed Management Strategies

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Phyton-International Journal of Experimental Botany 2024年第7期93卷 1569-1598页

作者： Sumit Sow Shivani Ranjan Mahmoud F.Seleiman Hiba M.Alkharabsheh Mukesh Kumar Navnit Kumar Smruti Ranjan Padhan Dhirendra Kumar Roy Dibyajyoti Nath Harun Gitari Daniel O.Wasonga department of Agronomy Dr.Rajendra Prasad Central Agricultural UniversityPusaSamastipurBihar848125India department of Plant Production College of Food and Agriculture SciencesKing Saud UniversityRiyadh11451Saudi Arabia department of Crop sciences Faculty of AgricultureMenoufia UniversityShibin El-Kom32514Egypt department of Water Resources and Environmental Management Faculty of Agricultural TechnologyAl Balqa Applied UniversitySalt19117Jordan Division of Agronomy ICAR-Indian Agricultural Research InstitutePusa CampusNew Delhi110012India department of Soil Science Dr.Rajendra Prasad Central Agricultural UniversityPusaSamastipurBihar848125India department of Agricultural Science and Technology School of Agriculture and Environmental SciencesKenyatta UniversityNairobi43844Kenya department of Crop sciences University of Illinois Urbana-ChampaignUrbanaIL 61801USA

Agriculture plays a crucial role in the economy,and there is an increasing global emphasis on automating agri-cultural *** the tremendous increase in population,the demand for food and employment has also increased signiﬁ*** methods traditionally used to meet these requirements are no longer ade-quate,requiring solutions to issues such as excessive herbicide use and the use of chemical *** of technologies such as the Internet of Things,wireless communication,machine learning,artiﬁcial intelligence(AI),and deep learning shows promise in addressing these ***,there is a lack of comprehensive documentation on the application and potential of AI in improving agricultural input efﬁ*** address this gap,a desk research approach was used by utilizing peer-reviewed electronic databases like PubMed,Scopus,Goo-gle Scholar,Web of Science,and Science Direct for relevant *** of 327 initially identiﬁed articles,180 were deemed pertinent,focusing primarily on AI’s potential in enhancing yield through better management of nutrients,water,and *** into account researchﬁndings worldwide,we found that AI technologies could assist farmers by providing recommendations on the optimal nutrients to enhance soil quality and deter-mine the best time for irrigation or herbicide *** present status of AI-driven automation in agricul-ture holds signiﬁcant promise for optimizing agricultural input utilization and reducing resource waste,particularly in the context of three pillars of crop management,i.e.,nutrient,irrigation,and weed management.

关键词： Agriculture artiﬁcial intelligence crop management nutrient irrigation weed management resource use efﬁciency

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Free-ranging dogs match a human’s preference in a foraging task

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Current Zoology 2024年第3期70卷 343-349页

作者： Giulia Cimarelli Magdelena Juskaite Friederike Range Sarah Marshall-Pescini Domestication Lab Konrad Lorenz Institute of EthologyDepartment of Interdisciplinary Life SciencesUniversity of Veterinary Medicine ViennaVeterinärplatz 11210ViennaAustria department of Biology Education Stockholm UniversityStockholmSweden

Social learning is a mechanism used by many species to effciently gain information about their *** many animals live in an environment where members of other species are present,little is known about interspecifc social *** and urbanized species provide the opportunity to investigate whether nonhuman animals can learn from heterospecifcs such as humans,who are integral parts of their social *** domestic dogs Canis familiaris have been intensively researched for their ability to learn from humans,most studies have focused on dogs living as ***,free-ranging dogs represent the majority of the world’s dog population,they live alongside humans,scavenge on human refuse,and are subject to natural and sexual ***,free-ranging dogs with extensive exposure to humans and their artifacts provide the opportunity to investigate interspecifc social learning in a naturalistic setting,where learning from humans might be a beneft for *** we tested individual free-ranging dogs in a between-subject design:Dogs in the control group could spontaneously choose between two novel and differently patterned food-delivering *** the experimental group,instead,dogs could frst observe an unfamiliar human approaching and eating from 1 of the 2 *** provide the frst evidence that free-ranging dogs match the choice of an unfamiliar *** results show that at least simple forms of interspecifc social learning might be involved in dogs’success in living alongside humans in a complex urbanized environment.

关键词： Canis familiaris dogs domestication foraging free-ranging dogs social learning urbanization.

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Implication of machine learning techniques to forecast the electricity price and carbon emission:Evidence from a hot region

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Geoscience Frontiers 2024年第3期15卷 259-271页

作者： Suleman Sarwar Ghazala Aziz Aviral Kumar Tiwari department of Finance and Economics College of BusinessUniversity of JeddahJeddahSaudi Arabia department of Business Administration College of Administrative and Financial SciencesSaudi Electronic UniversityJeddahSaudi Arabia Indian Institute of Management BodhgayaIndia

The current study examines the significant determinants of electricity consumption and identifies an appropriate model to forecast the electricity price *** main contribution is focused on eastern region of Saudi Arabia,a relatively hottest geographical area full of energy resources but with different electricity consumption *** relative irrelevance of temperature as predicting factor of electricity consumption is quite surprising and contradicts the previous *** the eastern region,electricity price has negative association with electricity *** comparing traditional and machine learning,it is found that machine learning techniques offer better *** the machine learning techniques,the support vector machine has the lowest errors in forecasting the electricity ***,the support vector machine approach is used to forecast the trend of carbon emissions caused by electricity *** findings have policy implications and offer valuable suggestions to policymakers while addressing the determinants of electricity consumption and forecasting electricity prices.

关键词： Electricity consumption Carbon emission Artificial neural network Support vector machine Saudi Arabia

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Dynamic Treatment Strategy of Chinese Medicine for Metastatic Colorectal Cancer Based on Machine learning Algorithm

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Chinese Journal of Integrative Medicine 2024年第11期30卷 993-1000页

作者： XU Yu-ying LI Qiu-yan YI Dan-hui CHEN Yue ZHAI Jia-wei ZHANG Tong SUN Ling-yun YANG Yu-fei department of Oncology Xiyuan Hospital China Academy of Chinese Medical Sciences School of Statistics Renmin University of China Graduate School Beijing University of Chinese Medicine

Objective: To establish the dynamic treatment strategy of Chinese medicine(CM) for metastatic colorectal cancer(m CRC) by machine learning algorithm, in order to provide a reference for the selection of CM treatment strategies for m CRC. Methods: From the outpatient cases of m CRC in the department of Oncology at Xiyuan Hospital, China Academy of Chinese Medical sciences, 197 cases that met the inclusion criteria were screened. According to different CM intervention strategies, the patients were divided into 3 groups: CM treatment alone, equal emphasis on Chinese and Western medicine treatment(CM combined with local treatment of tumors, oral chemotherapy, or targeted drugs), and CM assisted Western medicine treatment(CM combined with intravenous regimen of Western medicine). The survival time of patients undergoing CM intervention was taken as the final evaluation index. Factors affecting the choice of CM intervention scheme were screened as decision variables. The dynamic CM intervention and treatment strategy for m CRC was explored based on the cost-sensitive classification learning algorithm for survival(CSCLSurv). Patients' survival was estimated using the Kaplan-Meier method, and the survival time of patients who received the model-recommended treatment plan were compared with those who received actual treatment plan. Results: Using the survival time of patients undergoing CM intervention as the evaluation index, a dynamic CM intervention therapy strategy for m CRC was established based on *** CM intervention strategies for m CRC can be selected according to dynamic decision variables, such as gender, age, Eastern Cooperative Oncology Group score, tumor site, metastatic site, genotyping, and the stage of Western medicine treatment at the patient's first visit. The median survival time of patients who received the modelrecommended treatment plan was 35 months, while those who receive the actual treatment plan was 26.0 months(P=0.06). Conclusions:

关键词： Chinese medicine cost sensitive classification learning for survival dynamic treatment strategy metastatic colorectal cancer

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