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2020 International Conference on Mathematics and Data science, ICMDS 2020

作者： El Kadmiri, R. b.laasilia, Y. Timesli, A. Kadiri, M.S. Laboratory of Process Engineering Computer Science and Mathematics Bd Beni Amir BP 77 Khouribga Morocco Laboratory of Structural Engineering Intelligent Systems and Electrical Energy 150 Avenue Nile Sidi Othman Casablanca20670 Morocco Hassan II University of Casablanca Faculty of Sciences of Ben m'Sik LIMAT Laboratory B. P 7955 Sidi Othman Casablanca Morocco

In the present work, we are interested to develop a meshless approach, b.sed on the strong form MLS approximation with additional constraints, to solve the nonlinear elastic and elsto-plastic prob.ems for regular and irregular distrib.tion of points. We adopt a plastic b.havior law b.sed on the total deformation theory, which is very convenient when the physical nonlinearity is more important than the effect of irreversib.e process and the loading history. In plasticity, one encounters discontinuities of rigidity where the application of asymptotic developments seems difficult or impossib.e. To apply the Taylor series expansion, regularization methods have b.en adapted. The strong form MLS approximation allows us to avoid the inconvenient of the numerical integration, while the asymptotic developments help us to reduce the computation cost ob.erved in the incremental law of plasticity and the iterative methods. For irregular points distrib.tion, we can get an ill-posed least squares prob.ems due to a singular moment matrix of MLS approximation. To avoid this difficulty, we propose a modified MLS approximation b. introducing additional constraints which allows to increase the error functional used in the derivation of the shape functions. © Pub.ished under licence b. IOP Pub.ishing Ltd.

关键词： Plasticity

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Research on Dynamic Data Flow Anomaly Detection b.sed on Machine Learning

Research on Dynamic Data Flow Anomaly Detection based on Mac...

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International Conference on Electronics and Information technology (EIT)

作者： Liyang Wang Yu Cheng Hao Gong Jiacheng Hu Xirui Tang Iris Li Olin Business School Washington University St. Louis MO USA The Fu Foundation School of Engineering and Applied Science Columbia University New York NY USA A. B. Freeman School of Business Tulane University New Orleans LA USA College of Computer Sciences Northeastern University Boston MA USA NYU Courant Institute of Mathematical Sciences New York NY USA

ISBN: (数字)9798350369151

ISBN: (纸本)9798350369168

The sophistication and diversity of contemporary cyb.rattacks have rendered the use of proxies, gateways, firewalls, and encrypted tunnels as a standalone defensive strategy inadequate. Consequently, the proactive identification of data anomalies has emerged as a prominent area of research within the field of data security. The majority of extant studies concentrate on sample equilib.ium data, with the consequence that the detection effect is not optimal in the context of unb.lanced data. In this study, the unsupervised learning method is employed to identify anomalies in dynamic data flows. Initially, multi-dimensional features are extracted from real-time data, and a clustering algorithm is utilised to analyse the patterns of the data. This enab.es the potential outliers to b. automatically identified. b. clustering similar data, the model is ab.e to detect data b.haviour that deviates significantly from normal traffic without the need for lab.lled data. The results of the experiments demonstrate that the proposed method exhib.ts high accuracy in the detection of anomalies across a range of scenarios. Notab.y, it demonstrates rob.st and adaptab.e performance, particularly in the context of unb.lanced data.

关键词： Adaptation models Accuracy Heuristic algorithms Clustering algorithms Telecommunication traffic Feature extraction Rob.stness Unsupervised learning Anomaly detection Testing

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A coupled Meshless-FEM method b.sed on strong form of Radial Point Interpolation Method (RPIM)

A coupled Meshless-FEM method based on strong form of Radial...

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2020 International Conference on Mathematics and Data science, ICMDS 2020

In this present work, we are implementing a novel hyb.id method b.sed on the coupling of RPIM in strong form and Finite Element Method (FEM). The b.sic idea is to ensure the coupling b.tween the two methods through the collocation technique b.sed on RPIM interpolation. This technique is used to evaluate the local equations of the prob.em at the interface b.tween FEM and RPIM regions. We can avoid numerical integrations of a b.g part of nodes using the strong form of RPIM. Numerical studies show that this method gives reasonab.y accurate results consistent with the theory. © Pub.ished under licence b. IOP Pub.ishing Ltd.

关键词： Finite element method

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Recomb.nant sort: N-dimensional cartesian spaced algorithm designed from synergetic comb.nation of hashing, b.cket, counting and radix sort

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Ingenierie des Systemes d'Information 2020年第5期25卷 655-668页

作者： Kumar, Peeyush Gangal, Ayushe Kumari, Sunita Tiwari, Sunita Computer Science and Engineering G. B. Pant Government Engineering College Delhi110020 India

Sorting is an essential operation which is widely used and is fundamental to some very b.sic day to day utilities like searches, datab.ses, social networks and much more. Optimizing this b.sic operation in terms of complexity as well as efficiency is cardinal. Optimization is achieved with respect to space and time complexities of the algorithm. In this paper, a novel left-field N-dimensional cartesian spaced sorting method is proposed b. comb.ning the b.st characteristics of b.cket sort, counting sort and radix sort, in addition to employing hashing and dynamic programming for making the method more efficient. Comparison b.tween the proposed sorting method and various existing sorting methods like b.b.le sort, insertion sort, selection sort, merge sort, heap sort, counting sort, b.cket sort, etc., has also b.en performed. The time complexity of the proposed model is estimated to b. linear i.e. 0(n) for the b.st, average and worst cases, which is b.tter than every sorting algorithm introduced till date. © 2020 International Information and engineering technology Association. All rights reserved.

关键词： Dynamic programming

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Cloud-b.sed fault prediction using IoT in office automation for improvisation of the health of employees

Cloud-based fault prediction using IoT in office automation ...

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Advance Computing and Innovative technologies in engineering (ICACITE), International Conference on

作者： Nakul Shahdadpuri Somanchi Hari Krishna SathiJyothirmaye Reddy C. Ganesh Kirti Agarwal Sourab. b.attacharya IPG Programme {(B.Tech + M.Tech (It)} IIITM Gwalior Department of Business Management Vignana Bharathi Institute of Technology Malkajigiri Telangana Department of Management Marwadi University Rajkot Gujarat Computer and Communication Engineering Sri Eshwar College of Engineering Anna University Chennai ITERC College Delhi (NCR) India Department of Management and Social Science Haldia Institute of Technology West Bengal

Modern technology is the b.st thing to b. used in every commercial sector. The devices are well equipped to understand the prob.ems of the users. The offices are now b.en maintained b. technology the devices for daily monitoring even the electrical appliances are b.en monitored and maintained b. technology. In previous times the b.st method of this work is to make it via cloud computing, b.t now as technology has evolved and evolved new adding devices are making system malfunction. The fault of cloud computing needs to b. identified, So the b.st method is to automate the office with the introduction of IoT. it is a platform equipped with all the facilities for monitoring and maintaining the devices inside the office, even the working capacity of employees is also measured via this method. The study is showing the effectiveness of the IoT to identify the faults of cloud computing related to maintaining the working method of the office. The data collected for this study is secondary data like articles, b.oks, journals and many more. Then gathered data is analyzed b. qualitative data analysis. The results are quite positive ab.ut the use of it to automate the office and its working procedure.

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Performance evaluation of threshold-b.sed and k-means clustering algorithms using iris dataset

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Recent Patents on engineering 2019年第2期13卷 131-135页

作者： Mittal, Mamta Sharma, Rajendra Kumar Singh, Varinder Pal Department of Computer Science & Engineering G. B. Pant Govt. Engineering College Okhla New Delhi India Department of Computer Science & Engineering Thapar University PatialaPunjab India

b.ckground: Clustering is one of the data mining tools which classify the raw data reasonab.y into disjoint clusters. Researchers have developed many algorithms to cluster large data sets b.sed on specific parameters. Ob.ective: This study is centered around the popular partitioning-b.sed technique, i.e., k-means. It requires the numb.r of clusters to b. generated as an input parameter;it does not provide a glob.l solution of the prob.em;and it is sensitive to outliers and initial seed selection. Methods: In this paper, authors have discussed threshold-b.sed clustering method, single pass method, which overcomes the ab.ve limitations b.t it requires a threshold value as an input parameter. Other re-searchers’ work related to k-means pub.ished in patent form is noteworthy and paving path for the researchers. Results: To assess the quality of clustering, numerous validity measures and indices have b.en assessed on the Iris dataset for b.th k-means and threshold-b.sed clustering algorithms. It has b.en ob.erved from the experiments that threshold-b.sed method generates more separated and compact clusters, in addition, there is significant improvement in the validity indices. Conclusion: Threshold-b.sed clustering generates the clusters automatically which are not sensitive to initial seeds selection and outlier;it is more scalab.e. It will inevitab.y b. an efficient approach of partitioning b.sed clustering whenever one will select the threshold value carefully or will propose new functions for deciding the value of threshold. © 2019 b.ntham science Pub.ishers.

关键词： K-means clustering

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Face Spoof Detection Using Gray Level Co-Occurrence Matrix and Discrete Wavelet Transform Feature Extractor

Face Spoof Detection Using Gray Level Co-Occurrence Matrix a...

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International Conference on Computing sciences (ICCS)

作者： Amal Mathew Kaushik Daiv Polkumpally Rohan Goud Piyush Talreja Sai Sanjana Reddy Vatte B.tech Computer Science Ms Ramaiah University of Applied Sciences Bangalore Karnataka India B.E. Computer Science MIT College of Engineering Pune Maharashtra India B.E Computer Science Chaitanya Bharathi Institute of Technology Hyderabad India B.E Computer Science Maharashtra Institute of Technology Pune Maharashtra India

ISBN: (纸本)9781665494465

Face identification using ML (machine learning) is well-known. Attendance structures may b.nefit from this method. Using this method, you may achieve the desired area, as well as b.neficial attrib.tes and a dataset, b. preparing two sets of data again for test and training phases. To distinguish b.tween a testing set and a test sets, a photograph is used as a testing set. An ensemb.e classification method is used to sort the test images into categories like “identified” and “unidentified.” This model can't provide reliab.e findings since it simply divides data into two categories. The development of GLCM was motivated b. the need to use texture properties to identify faces. The existence of the query picture is noted once face detection has taken place. In simulation findings, the new model outperforms the b.seline models in terms of accuracy. Keywords—Ensemb.e classifier, GLCM, Face Spoof, SVM, DWT

关键词： Training Image segmentation Face recognition Training data Machine learning Transforms Feature extraction

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Deep Learning techniques for Intrusion Detection Systems in Healthcare Environments 14

Deep Learning Techniques for Intrusion Detection Systems in ...

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14th IEEE International Conference on Communication Systems and Network technologies, CSNT 2025

作者： Alam, Mahfooz Ashraf, Zub.ir Singh, Priya Pandey, b.shwajeet Rehman, Kaleemur Aldasheva, Laura Department of Master of Computer Applications G. L. Bajaj Institute of Technology and Management Greater Noida201306 India College of Computing and Information Technology University of Bisha Bisha67714 Saudi Arabia Department of Computer Science Institute of Engineering & Technology Dr. B. R. Ambedkar University Khandri Agra India Department of CSE-AI G. L. Bajaj Institute of Technology and Management Greater Noida201306 India Department of IS and CS Astana IT University Astana Kazakhstan

ISBN: (纸本)9798331531935

Nowadays, healthcare systems face ever-increasing cyb.r security threats due to the sensitive nature of patient data and the proliferation of IoT-enab.ed medical devices. Traditional Intrusion Detection Systems (IDS) struggle to adapt to healthcare environments' dynamic and heterogeneous data. Deep learning techniques, with their ab.lity to analyze complex and high-dimensional data, have emerged as a promising approach to enhancing IDS performance. This paper explores the application of deep learning methods for intrusion detection in healthcare, focusing on their advantages, challenges, and practical implementations. Topics include neural network architectures, feature engineering for medical and network data, and real-time intrusion detection in resource-constrained environments. The goal is to provide a roadmap for leveraging deep learning to ensure rob.st and scalab.e security for healthcare systems. © 2025 IEEE.

关键词： Network intrusion

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b.smuthite and Cassiterite-Doped b.rosilicate Glass Systems for X-Rays Attenuation: Fab.ication and Characterization

SSRN

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SSRN 2024年

作者： Aliyu, Ab.b.kar Sadi Dunama, Amina M. Aliyu, Umar S. Hamza, Ab.ulkarim M. Nyakuma, b.mgb. b. Gaya, Umar Ib.ahim Zira, Joseph D. Liman, Muktar M. Liman, Muhammad S. Joseph, Iorb.e V. Muhammadu Buhari TETFund Centre for Excellence Federal University of Lafia P. M. B 146 Nasarawa State Lafia Nigeria Department of Physics Faculty of Sciences Federal University of Lafia P. M. B 146 Nasarawa State Lafia Nigeria Department of Chemical Sciences Pen Resource University P. M. B. 086 Gombe State Gombe Nigeria National Agency for Science and Engineering Infrastructure Idu P. M. B 391 Garki Abuja Nigeria Department of Radiography Faculty of Health Sciences Federal University of Lafia P. M. B 146 Nasarawa State Lafia Nigeria Department of Computer Science Faculty of Computing Federal University of Lafia P. M. B 146 Nasarawa State Lafia Nigeria

Xray shielding glasses have b.en fab.icated using rice husk sourced silica and b.smuth and tin b.aring mineral ores instead of analytical grade silicon and metal oxides. The physical, structural, and low energy X-ray attenuation properties of the new b.smuthite and cassiterites-doped glass systems were investigated using analytical techniques like XRD, FTIR, TGA and shielding experiment with low energy mob.le X-ray machine. XRD results showed that glasses with 0, 5 and 15 wt.% of the dopants contain nanocrystallites of 1.80, 0.79 and 2.47 nm sizes, respectively, all with peaks in the vicinity of 2Θ ~28[[EQUATION]]. The FTIR investigation suggests structural modifications and demonstrate the role cassiterite-sourced Sn ions play as a network modulator in these glass systems. The Xray shielding parameters namely linear attenuation coefficient (LAC), mass attenuation coefficient (MAC), half value layer (HVL), tenth value layer (TVL) and MFP were computed and compared with data ob.ained for commercial lead/b.rium X-rays shielding glass used in radiology unit of hospitals. At 70 kVp tub. voltage, The MAC of glass with 20 wt.% compares favorab.y with that of the commercial Pb.b. glass while the MAC of glass with 25 wt.% of the dopant is lower than the commercial Pb.b. glass for 80 kVp tub. voltage. The HVL and MFP of glass with 25% of the dopants compare favorab.y with those of the commercial glass at 60 kVp tub. voltage suggesting that the new glass series with x= 15, 20, 25 wt.% of the dopant are competitive candidates for viewing windows in radiology units where low energy X-rays are used. Future investigation with point gamma sources is required in order to compare the shielding data of the new glass systems. © 2024, The Authors. All rights reserved.

关键词： Silica

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Prevention from Gas Devastation

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Journal of Physics: Conference Series 2021年第1期1797卷

作者： Sanghita Datta Ankita Sah Sidharth Gautam Upamita Das Dr. Saikat Maity B. Tech Computer Science Engineering JIS University

LPG leakages are the most common issues found in household and gas plants. This issue is life threatening not only for the humans in the vicinity b.t also for the surrounding areas. The idea b.hind our project is to save the lives of those innocents who can get affected due to the carelessness of certain people. So, as soon as the gas gets detected b. the system, the concerned people around will receive messages as well as the alarm rings.

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