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Hybrid Grey Wolf and Dipper Throated Optimization in Network Intrusion Detection systems

computers, Materials & Continua 2023年第2期74卷 2695-2709页

作者： Reem Alkanhel Doaa Sami Khafaga El-Sayed M.El-kenawy Abdelaziz A.Abdelhamid Abdelhameed Ibrahim Rashid Amin Mostafa Abotaleb B.M.El-den Department of Information Technology College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Computer Sciences College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Communications and Electronics Delta Higher Institute of Engineering and TechnologyMansoura35111Egypt Department of Computer Science Faculty of Computer and Information SciencesAin Shams UniversityCairo11566Egypt Department of Computer Science College of Computing and Information TechnologyShaqra University11961Saudi Arabia Computer Engineering and Control Systems Department Faculty of EngineeringMansoura UniversityMansoura35516Egypt Department of Computer Science University of Engineering and TechnologyTaxilaPakistan Department of System Programming South Ural State UniversityChelyabinsk454080Russia

The Internet of Things(IoT)is a modern approach that enables connection with a wide variety of devices *** to the resource constraints and open nature of IoT nodes,the routing protocol for low power and lossy(RPL)networks may be vulnerable to several routing ***’s why a network intrusion detection system(NIDS)is needed to guard against routing assaults on RPL-based IoT *** imbalance between the false and valid attacks in the training set degrades the performance of machine learning employed to detect network ***,we propose in this paper a novel approach to balance the dataset classes based on metaheuristic optimization applied to locality-sensitive hashing and synthetic minority oversampling technique(LSH-SMOTE).The proposed optimization approach is based on a new hybrid between the grey wolf and dipper throated optimization *** prove the effectiveness of the proposed approach,a set of experiments were conducted to evaluate the performance of NIDS for three cases,namely,detection without dataset balancing,detection with SMOTE balancing,and detection with the proposed optimized LSHSOMTE *** results showed that the proposed approach outperforms the other approaches and could boost the detection *** addition,a statistical analysis is performed to study the significance and stability of the proposed *** conducted experiments include seven different types of attack cases in the RPL-NIDS17 *** on the 2696 CMC,2023,vol.74,no.2 proposed approach,the achieved accuracy is(98.1%),sensitivity is(97.8%),and specificity is(98.8%).

关键词： Metaheuristics grey wolf optimization dipper throated optimization dataset balancing locality sensitive hashing SMOTE

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Leveraging Smart Video-Based Learning to Enhance Student Engagement and Collaboration 6

Leveraging Smart Video-Based Learning to Enhance Student Eng...

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6th International Conference on Advancements in Computing, ICAC 2024

作者： Serasinghe, Chanukya Perera, Nashali Samarasinghe, Devindu Muthusinghe, Madusha Wijesundara, Malitha Sri Lanka Institute of Information Technology Dept. of Computer Science and Software Engineering Malabe Sri Lanka Sri Lanka Institute of Information Technology Faculty of Humanities and Sciences Malabe Sri Lanka

ISBN: (纸本)9798331517878

The COVID-19 pandemic has expedited the shift in education to online learning, which has exposed shortcomings in virtual learning environments' personalization and engagement. This research addresses these issues by developing a web application designed to enhance student engagement and learning efficiency. The system has gamified collaboration features, video chapter segmentation, and personalized video recommendation system. The methodology comprises models for generating chapter titles and for precise video transcription, and machine learning algorithms for tailored suggestions depending on user input. Interactive Questions and Answers modules and real-time synchronized video playback are included. The results demonstrate how the system's personalized content, interactive tests, and teamwork tools increase user engagement and comprehension. The efficacy of personalized material and real-time synchronization in enhancing learning results is discussed. This innovative approach offers a comprehensive solution to the challenges of online education, creating a more immersive and interactive learning experience. © 2024 IEEE.

关键词： Federated learning

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Normative multiagent systems: A dynamic generalization 25

Normative multiagent systems: A dynamic generalization

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25th International Joint Conference on Artificial Intelligence, IJCAI 2016

作者： Huang, Xiaowei Ruan, Ji Chen, Qingliang Su, Kaile Department of Computer Science Jinan University China Department of Computer Science University of Oxford United Kingdom School of Engineering Computer and Mathematical Sciences Auckland University of Technology New Zealand Institute for Integrated and Intelligent Systems Griffith University Brisbane Australia

Social norms are powerful formalism in coordinating autonomous agents' behaviour to achieve certain objectives. In this paper, we propose a dynamic normative system to enable the reasoning of the changes of norms under different circumstances, which cannot be done in the existing static normative systems. We study two important problems (norm synthesis and norm recognition) related to the autonomy of the entire system and the agents, and characterise the computational complexities of solving these problems.

关键词： Autonomous agents

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Optimizing Depression Prediction in Older Adults: A Comparative Study of Feature Extraction and Machine Learning Models

Optimizing Depression Prediction in Older Adults: A Comparat...

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2024 International Conference on Control, Automation and Diagnosis, ICCAD 2024

作者： Javeed, Ashir Anderberg, Peter Ghazi, Ahmad Nauman Javeed, Asim Dallora, Ana Luiza Berglund, Johan Sanmartin Blekinge Institute of Technology Department of Health Blekinge Karlskrona37179 Sweden Blekinge Institute of Technology Department of Software Engineering Blekinge Karlskrona37179 Sweden Times Institute Department of Computer Science Multan60650 Pakistan

ISBN: (纸本)9798350361025

Depression emerged as a major public health concern in older adults, and timely prediction of depression has become a difficult problem in medical informatics. The latest studies have attentiveed on feature transformation and selection for better depression prediction. In this study, we assess the performance of various feature extraction algorithms, including principal component analysis (PCA), independent component analysis (ICA), locally linear Embedding (LLE), and t-distributed stochastic neighbor embedding (TSNE). These algorithms are combined with machine learning (ML) classifier algorithms such as Gaussian Naive Bayes (GNB), Logistic Regression (LR), K-nearest-neighbor (KNN), and Decision Tree (DT) to enhance depression prediction. In total, sixteen automated integrated systems are constructed based on the above-mentioned feature extraction methods and ML classifiers. The performance of all of these integrated models is assessed using data from the Swedish National Study on Aging and Care (SNAC). According to the experimental results, the PCA algorithm combined with the Logistic Regression (LR) model provides 89.04% depression classification accuracy. As a result, it is demonstrated that the PCA is a more suitable feature extraction method for depression data than ICA, LLE, and TSNE. © 2024 IEEE.

关键词： Feature extraction

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Intelligent multi-service platform for building management 2

Intelligent multi-service platform for building management

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2nd IEEE International Conference on Advanced Trends in Information Theory, ATIT 2020

作者： Prokhorov, Oleksandr Pronchakov, Yurii Fedorovich, Oleg National Aerospace University 'Kharkiv Aviation Institute' Department of Computer Science and Information Technology Kharkiv Ukraine National Aerospace University 'Kharkiv Aviation Institute' Department of Software Engineering and Business Kharkiv Ukraine

ISBN: (纸本)9781728197999

This paper proposes a cloud platform that provides support for all stages of the development of intelligent building management systems, their adaptation for solving applied problems for applications of the Internet of Things and management in building automation systems, storing knowledge and data bases in cloud data centers, providing access to intelligent systems as services remotely via the web interface. The model and structure of an intelligent multi-service platform for building management is presented, which includes the following components: monitoring and management of engineering subsystems;access control;accounting and control of working hours;renting;resident/tenant portal;video content analysis based on neural networks and deep learning;resource analytics. The functions of the platform components are considered in detail, screens of the system operation in one of the business centers are shown. © 2020 IEEE.

关键词： Internet of things

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Spatial-Temporal Graph Attention Network for Video-Based Gait Recognition 5th

Spatial-Temporal Graph Attention Network for Video-Based Gai...

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5th Asian Conference on Pattern Recognition, ACPR 2019

作者： Wu, Xinhui An, Weizhi Yu, Shiqi Guo, Weiyu García, Edel B. College of Computer Science and Software Engineering Shenzhen University Shenzhen China Department of Computer Science and Engineering Southern University of Science and Technology Shenzhen China Shenzhen Institute of Artificial Intelligence and Robotics for Society Shenzhen China Watrix Technology Limited Co. Ltd. Beijing China Havana Cuba

ISBN: (纸本)9783030412982

Gait is a kind of attractive feature for human identification at a distance. It can be regarded as a kind of temporal signal. At the same time the human body shape can be regarded as the signal in the spatial domain. In the proposed method, we try to extract discriminative feature from video sequences in the spatial and temporal domains by only one network, Spatial-Temporal Graph Attention Network (STGAN). In spatial domain, we designed one branch to select some distinguished regions and enhance their contribution. It can make the network focus on these distinguished regions. We also constructed another branch, a Spatial-Temporal Graph (STG), to discover the relationship between frames and the variation of a region in temporal domain. The proposed method can extract gait feature in the two domains, and the two branches in the model can be trained end to end. The experimental results on two popular datasets, CASIA-B and OU-ISIR Treadmill-B, show the proposed method can improve gait recognition obviously. © 2020, Springer Nature Switzerland AG.

关键词： Gait analysis

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Vibration control of a flexible timoshenko beam under unknown external disturbances

Vibration control of a flexible timoshenko beam under unknow...

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作者： Ge, Shuzhi Sam Zhang, Shuang He, Wei Institute of Intelligent Systems and Information Technology School of Computer Science and Engineering University of Electronic Science and Technology of China Chengdu 610054 China Department of Electrical and Computer Engineering National University of Singapore Singapore 117576 Singapore

ISBN: (纸本)9789881725592

In this paper, we present the vibration control of a Timoshenko beam under the external unknown spatiotemporally varying distributed disturbance and time-varying boundary disturbances. The model of the Timoshenko beam extracted from a class of flexible systems is represented by a hybrid model including both nonhomogeneous partial differential equations (PDEs) and ordinary differential equations (ODEs). To suppress the vibration of the system and reduce the effects of the external disturbances, adaptive boundary control is proposed based on Lyapunov's direct method. With the proposed boundary control strategy, the states of the system are proven to be uniformly ultimately bounded and converge to a small neighborhood of zero by choosing the design parameters. Several simulations are shown to illustrate the effectiveness of the proposed adaptive boundary control. © 2011 Chinese Assoc of Automati.

关键词： Vibration control

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Identifying significant genetic regulatory networks in the prostate cancer from microarray data based on transcription factor analysis and conditional independency

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BMC Medical Genomics 2009年第1期2卷 1-19页

作者： Yeh, Hsiang-Yuan Cheng, Shih-Wu Lin, Yu-Chun Yeh, Cheng-Yu Lin, Shih-Fang Soo, Von-Wun Department of Computer Science National Tsing Hua University HsinChu 300 Taiwan Institute of Information Systems and Applications National Tsing Hua University HsinChu 300 Taiwan Department of Computer Science and Information Engineering National University of Kaohsiung Kaohsiung 811 Taiwan

Background. Prostate cancer is a world wide leading cancer and it is characterized by its aggressive metastasis. According to the clinical heterogeneity, prostate cancer displays different stages and grades related to the aggressive metastasis disease. Although numerous studies used microarray analysis and traditional clustering method to identify the individual genes during the disease processes, the important gene regulations remain unclear. We present a computational method for inferring genetic regulatory networks from micorarray data automatically with transcription factor analysis and conditional independence testing to explore the potential significant gene regulatory networks that are correlated with cancer, tumor grade and stage in the prostate cancer. Results. To deal with missing values in microarray data, we used a K-nearest-neighbors (KNN) algorithm to determine the precise expression values. We applied web services technology to wrap the bioinformatics toolkits and databases to automatically extract the promoter regions of DNA sequences and predicted the transcription factors that regulate the gene expressions. We adopt the microarray datasets consists of 62 primary tumors, 41 normal prostate tissues from Stanford Microarray Database (SMD) as a target dataset to evaluate our method. The predicted results showed that the possible biomarker genes related to cancer and denoted the androgen functions and processes may be in the development of the prostate cancer and promote the cell death in cell cycle. Our predicted results showed that sub-networks of genes SREBF1, STAT6 and PBX1 are strongly related to a high extent while ETS transcription factors ELK1, JUN and EGR2 are related to a low extent. Gene SLC22A3 may explain clinically the differentiation associated with the high grade cancer compared with low grade cancer. Enhancer of Zeste Homolg 2 (EZH2) regulated by RUNX1 and STAT3 is correlated to the pathological stage. Conclusions. We provide a computat

关键词： Microarray Data Cluster Coefficient Microarray Dataset Dependent Gene Network Motif

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Machine learning methods in electrocardiography classification 8

Machine learning methods in electrocardiography classificati...

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8th International Conference Advanced computer Information Technologies, ACIT 2018

作者： Mykoliuk, Iryna Jancarczyk, Daniel Karpinski, Mikolaj Kifer, Viktor Department of Computer Science and Automatics University of Bielsko-Biala 2 Willowa St Bielsko-Biala Poland Faculty of Computer Information Systems and Software Engineering Ternopil Ivan Puluj National Technical University 56 Ruska St Ternopil Ukraine

This document reports results that tend to confirm the applicability of the machine learning combined with signal processing for automatic atrial fibrillation detection from a short single lead electrocardiography rec... 详细信息

ISBN: (纸本)9789666544899

关键词： Electrocardiograms

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COVID 19 Navigator Taxi Application for Urban Mobility during Pandemic Period 2

COVID 19 Navigator Taxi Application for Urban Mobility durin...

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2nd International Conference on Advanced Research in Computing, ICARC 2022

作者： Peiris, Rojith Kanchana, Bimsara Perera, Damitha Wickramarathne, Jagath Sri Lanka Institute of Information Technology Faculty of Graduate Studies and Research Malabe Sri Lanka Sri Lanka Institute of Information Technology Faculty of Computer Science and Software Engineering Malabe Sri Lanka

ISBN: (纸本)9781665407410

This research paper focuses on increasing the awareness between taxi users and can help to protect themselves in COVID-19 and take precautions. Since 2019 the global pandemic of Covid-19 is spreading at an astonishing rate and causes a negative impact for economic, social, and cultural factors. Government agencies are warning people to reduce the transportation and to maintain social distance. To stop the spread of COVID-19 required to identify persons who are susceptible to infection and need to trace the COVID-19 positive first contacts. Most people are reduced to using public transportations and taxi services due to unidentifiable health conditions in earlier users. The proposed approach can be used to track the taxi drivers and their passengers previous COVID-19 status as well as navigate the safest route by showing the COVID-19 contamination areas. By using this approach users can be aware of earlier users of the taxi service and COVID-19 status of the taxi driver or passenger before taking the trip as well as if they get touch with any COVID-19 patients, can take immediate precautions. This application helps to increase the usage of taxis by making users trust and confidence against COVID-19 infection. By testing with real users our system was able to trace down 45 passengers and 16 drivers within 3 months. © 2022 IEEE.

关键词： COVID-19

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