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IoT Malware Detection Tool with CNN Classification for Small Devices

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IoT Malware Detection Tool with CNN Classification for Small...

International Conference on Contemporary Computing and Informatics (IC3I)

作者： Gunjan Bhatnagar Yogendra Kumar Rajoria Md. Sakeel M. Vigenesh G. Premananthan Deepika Dongre School of Engineering and Computer Dev Bhoomi Uttarakhand University Uttarakhand India Department of Mathematics School of Basic & Applied Sciences K. R. Mangalam University Gurugram India Department of Computer Applications Invertis University Bareilly India Department of Computer Science Engineering Karpagam Academy of Higher Education Coimbatore India Department of Electronics and Communication Engineering Karpagam College of Engineering Coimbatore India AI & Ds Vishwakarma Institute of Information Technology Pune India

Since the number of linked devices continues to grow quickly, the Web of Everything (IoT) is being employed change today in more industries. More issues in regards to safety, stability, and supportability are brought on by an increase in smart devices, particularly as it relates to 5G technology. We are concentrating on the safety component of IoT systems in just this article because it is a relatively new field. For varied purposes, such as cost savings or a desire to forego the use of generation system, many Internet device manufacturers need not take security into account when designing their products. The opponent may use those potentially dangerous technologies to carry out several destructive assaults. As a result, we created a device capable of recognizing malicious activity from a particular IoT networks node. They are capable to offer detection methods for IoT utilizing a primary key that may be put inside the system using a multilayer brain networks for identification and surveillance. The accomplishment dispels any misconceptions about methods for deep training while demonstrating how that systems can be simply adapted and deployed to every system.

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Norm inequalities for the generalised commutator in Banach algebras

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SUT Journal of mathematics 2021年第2期57卷 185-199页

作者： Dragomir, Silvestru Sever College of Engineering and Science Victoria University PO Box 14428 Melbourne City MC 8001 Australia DST-NRF Centre of Excellence in the Mathematical and Statistical Sciences School of Computer Science and Applied Mathematics University of the Witwatersrand Private Bag 3 Johannesburg 2050 South Africa

In this paper, by utilising the Riesz functional calculus in a Banach algebra B, we provide some norm inequalities for the generalized commutator f(y)z- zf(x) where x;y;z ϵ B and f is an analytic function for which the elements f(y) and f(x) exist. Some examples for the resolvent and exponential functions are also given. © 2021, Tokyo University of Science. All rights reserved.

关键词： Analytic functions Axponential and logarithmic function on Banach algebra Banach algebras Lipschitz type inequalities

Learning for Interval Prediction of Electricity Demand: A Cluster-based Bootstrapping Approach

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arXiv 2023年

作者： Dube, Rohit Gautam, Natarajan Banerjee, Amarnath Nagarajan, Harsha Industrial and Systems Engineering Texas A & M University College StationTX77843 United States Electrical Engineering and Computer Science Syracuse University SyracuseNY13210 United States Applied Mathematics and Plasma Physics Los Alamos National Laboratory Los AlamosNM87545 United States

Accurate predictions of electricity demands are necessary for managing operations in a small aggregation load setting like a Microgrid. Due to low aggregation, the electricity demands can be highly stochastic and point estimates would lead to inflated errors. Interval estimation in this scenario, would provide a range of values within which the future values might lie and helps quantify the errors around the point estimates. This paper introduces a residual bootstrap algorithm to generate interval estimates of day-ahead electricity demand. A machine learning algorithm is used to obtain the point estimates of electricity demand and respective residuals on the training set. The obtained residuals are stored in memory and the memory is further partitioned. Days with similar demand patterns are grouped in clusters using an unsupervised learning algorithm and these clusters are used to partition the memory. The point estimates for test day are used to find the closest cluster of similar days and the residuals are bootstrapped from the chosen cluster. This algorithm is evaluated on the real electricity demand data from EULR (End Use Load Research) and is compared to other bootstrapping methods for varying confidence intervals. © 2023, CC BY.

关键词： Machine learning

A BERNOULLI-BARYCENTRIC RATIONAL MATRIX COLLOCATION METHOD WITH PRECONDITIONING FOR A CLASS OF EVOLUTIONARY PDES∗

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arXiv 2024年

作者： Luo, Wei-Hua Gu, Xian-Ming Carpentieri, Bruno Guo, Jun School of Mathematics and Physics Hunan University of Arts and Science Hunan Changde415000 China School of Mathematics Southwestern University of Finance and Economics Sichuan Chengdu611130 China Bernoulli Institute for Mathematics Computer Science and Artificial Intelligence University of Groningen Nijenborgh 9 P.O. Box 407 Groningen9700 AK Netherlands Faculty of Engineering Free University of Bozen-Bolzano Bolzano39100 Italy College of Applied Mathematics Chengdu University of Information Technology Sichuan Chengdu610225 China

We propose a Bernoulli-barycentric rational matrix collocation method for two-dimensional evolutionary partial differential equations (PDEs) with variable coefficients that combines Bernoulli polynomials with barycentric rational interpolations in time and space, respectively. The theoretical accuracy O { (2π)−N + hdxx−1 + hydy−1} of our numerical scheme is proven, where N is the number of basis functions in time, hx and hy are the grid sizes in the x, y-directions, respectively, and 0 ≤ dx ≤ bh−xa , 0 ≤ dy ≤ dh−yc . For the efficient solution of the relevant linear system arising from the discretizations, we introduce a class of dimension expanded preconditioners that take the advantage of structural properties of the coefficient matrices, and we present a theoretical analysis of eigenvalue distributions of the preconditioned matrices. The effectiveness of our proposed method and preconditioners are studied for solving some real-world examples represented by the heat conduction equation, the advection-diffusion equation, the wave equation and telegraph *** Codes 65M70, 65Y05, 65D25 Copyright © 2024, The Authors. All rights reserved.

关键词： Linear systems

Radiative heat transfer in MHD copper-based polymer nanofluid over a sphere using larger radius and inter particle spacing of nanoparticles

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Results in Engineering

Results in engineering 2025年 26卷

作者： Darvesh, Adil Boukhris, Imed Collantes Santisteban, Luis Jaime Al-Buriahi, M.S. Elqahtani, Zainab Mufarreh Akgül, Ali García, Lucerito Katherine Ortiz Hassani, Murad Khan Department of Mathematics and Statistics Hazara University Mansehra21300 Pakistan Department of Physics Faculty of Science King Khalid University P.O. Box 960 Abha Saudi Arabia Department of Mathematics Universidad Nacional Pedro Ruiz Gallo Lambayeque Peru Department of Physics Sakarya University Sakarya Turkey Department of Physics College of science Princess Nourah bint Abdulrahman University P.O. Box 84428 Riyadh11671 Saudi Arabia Department of Electronics and Communication Engineering Saveetha School of Engineering SIMATS Tamilnadu Chennai India Siirt University Art and Science Faculty Department of Mathematics Siirt56100 Turkey Department of Computer Engineering Biruni University Topkapı Istanbul34010 Turkey Near East University Mathematics Research Center Department of Mathematics Near East Boulevard PC: 99138 Nicosia Mersin 10 Turkey Applied Science Research Center. Applied Science Private University Amman Jordan Universidad Nacional Pedro Ruiz Gallo Lambayeque Peru Ghazni University Department of Mathematics Afghanistan

The impact of nanoparticle size and interparticles spacing play a crucial role in fluid theology due to its significance on enhance heat transfer performance, which is crucial in many engineering and industrial processes particularly in thermal management systems. Due to this attention, the proposed study aims to explore the influence of nanoparticle radius and inter-particle spacing on thermal transport in a Copper (Cu) Polymer nanofluid over a sphere with radiative and magnetohydrodynamic (MHD) effects. Physical model incorporates Carreau fluid viscosity model. In addition, with incorporation of exponential heat generation and thermal radiation, the analysis reveals how tuning nanoparticle geometry significantly enhances heat transfer performance, which is critical for advanced thermal management systems. The governing nonlinear partial differential equations (PDEs) are transformed into ordinary differential equations (ODEs) using similarity transformations and solved using the well-known bvp4c computational method. Results of proposed study indicate that larger nanoparticles and wider inter-particle spacing improve thermophysical and rheological behavior of fluid specifically thermal conductivity and fluid viscosity, which enhanced heat transport and intensified the temperature profile. Conversely, velocity profile decreases for smaller particle sizes and spacing but improves with increased nanoparticle dimensions. © 2025 The Author(s)

关键词： Radiant heating

A Probabilistic Framework for Pruning Transformers Via a Finite Admixture of Keys

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A Probabilistic Framework for Pruning Transformers Via a Fin...

International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Tan M. Nguyen Tam Nguyen Long Bui Hai Do Duy Khuong Nguyen Dung D. Le Hung Tran-The Nhat Ho Stan J. Osher Richard G. Baraniuk Department of Mathematics University of California Los Angeles USA Department of Electrical and Computer Engineering Rice University Houston USA FPT Software AI Center Ha Noi Vietnam College of Engineering and Computer Science Vin University Vietnam Applied Artificial Intelligence Institute Deakin University Geelong Australia Department of Statistics and Data Sciences The University of Texas Austin USA

Pairwise dot product-based self-attention is key to the success of transformers which achieve state-of-the-art performance across a variety of applications in language and vision, but are costly to compute. It has been shown that most attention scores and keys in transformers are redundant and can be removed without loss of accuracy. In this paper, we develop a novel probabilistic framework for pruning attention scores and keys in transformers. We first formulate an admixture model of attention keys whose input data to be clustered are attention queries. We show that attention scores in self-attention correspond to the posterior distribution of this model when attention keys admit a uniform prior distribution. We then relax this uniform prior constraint and let the model learn these priors from data, resulting in a new Finite Admixture of Keys (FiAK). The learned priors are used for pruning away redundant attention scores and keys in the baseline transformers, improving the diversity of attention patterns that the models capture. We corroborate the efficiency of transformers pruned with FiAK on the ImageNet object classification and WikiText-103 language modeling tasks. Our experiments demonstrate that transformers pruned with FiAK yield similar or better accuracy than the baseline dense transformers while being much more efficient in terms of memory and computational cost.

关键词： Computational modeling Memory management Signal processing Transformers Probabilistic logic Data models Acoustics

Convergence of uncertainty estimates in Ensemble and Bayesian sparse model discovery

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arXiv 2023年

作者： Gao, L. Mars Fasel, Urban Brunton, Steven L. Kutz, J. Nathan Paul G. Allen School of Computer Science & Engineering University of Washington United States Department of Aeronautics Imperial College LondonSW7 2AZ United Kingdom Department of Mechanical Engineering University of Washington SeattleWA98195 United States Department of Applied Mathematics and Electrical and Computer Engineering University of Washington SeattleWA98195 United States

Sparse model identification enables nonlinear dynamical system discovery from data. However, the control of false discoveries for sparse model identification is challenging, especially in the low-data and high-noise limit. In this paper, we perform a theoretical study on ensemble sparse model discovery, which shows empirical success in terms of accuracy and robustness to noise. In particular, we analyse the bootstrapping-based sequential thresholding least-squares estimator. We show that this bootstrapping-based ensembling technique can perform a provably correct variable selection procedure with an exponential convergence rate of the error rate. In addition, we show that the ensemble sparse model discovery method can perform computationally efficient uncertainty estimation, compared to expensive Bayesian uncertainty quantification methods via MCMC. We demonstrate the convergence properties and connection to uncertainty quantification in various numerical studies on synthetic sparse linear regression and sparse model discovery. The experiments on sparse linear regression support that the bootstrapping-based sequential thresholding least-squares method has better performance for sparse variable selection compared to LASSO, thresholding least-squares, and bootstrapping-based LASSO. In the sparse model discovery experiment, we show that the bootstrapping-based sequential thresholding least-squares method can provide valid uncertainty quantification, converging to a delta measure centered around the true value with increased sample sizes. Finally, we highlight the improved robustness to hyperparameter selection under shifting noise and sparsity levels of the bootstrapping-based sequential thresholding least-squares method compared to other sparse regression methods. Copyright © 2023, The Authors. All rights reserved.

关键词： Nonlinear dynamical systems

On the nature of optical thermodynamic pressure exerted in highly multimoded nonlinear systems

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arXiv 2024年

作者： Ren, Huizhong Pyrialakos, Georgios G. Wu, Fan O. Jung, Pawel S. Efremidis, Nikolaos K. Khajavikhan, Mercedeh Christodoulides, Demetrios Ming Hsieh Department of Electrical and Computer Engineering University of Southern California Los AngelesCA90089 United States CREOL The College of Optics and Photonics University of Central Florida OrlandoFL32816 United States Faculty of Physics Warsaw University of Technology Koszykowa 75 Warsaw00-662 Poland Department of Mathematics and Applied Mathematics University of Crete Heraklion Crete70013 Greece

The theory of optical thermodynamics provides a comprehensive framework that enables a self-consistent description of the intricate dynamics of nonlinear multimoded photonic systems. This theory, among others, predicts a pressure-like intensive quantity (pˆ) that is conjugate to the system's total number of modes (M) - its corresponding extensive variable. Yet at this point, the nature of this intensive quantity is still nebulous. In this paper, we elucidate the physical origin of the optical thermodynamic pressure and demonstrate its dual essence. In this context, we rigorously derive an expression that splits pˆ into two distinct components, a term that is explicitly tied to the electrodynamic radiation pressure and a second entropic part that is responsible for the entropy change. We utilize this result to establish a formalism that simplifies the quantification of radiation pressure under nonlinear equilibrium conditions, thus eliminating the need for a tedious evaluation of the Maxwell stress tensor. Our theoretical analysis is corroborated by numerical simulations carried out in highly multimoded nonlinear optical structures. These results may provide a novel way in predicting radiation pressure processes in various nonlinear electromagnetic settings. Copyright © 2024, The Authors. All rights reserved.

关键词： Stress tensor

A channel-wise attention-based representation learning method for epileptic seizure detection and type classification

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Journal of Ambient Intelligence and Humanized Computing 2023年第7期14卷 9403-9418页

作者： Baghdadi, Asma Fourati, Rahma Aribi, Yassine Daoud, Sawsen Dammak, Mariem Mhiri, Chokri Chabchoub, Habib Siarry, Patrick Alimi, Adel Esprit School of Business Computer Science and Applied Mathematics Department Tunis Tunisia University of Sfax BP 1173 Sfax3038 Tunisia Laboratoire Images Signaux et Systèmes Intelligents Université Paris Est Créteil 120 Rue Paul Armangot Vitry sur Seine94400 France University of Jendouba Jendouba Tunisia Sciences and Technology Department University College of Ranyah Taif University Taif Saudi Arabia Department of Neurology Habib Bourguiba University Hospital Sfax Tunisia Neuroscience Laboratory Sfax university LR-12-SP-19 Sfax Tunisia College of Business Al Ain University Abu Dhabi United Arab Emirates LiSSi Université de Paris 12 Créteil94010 France Department of Electrical and Electronic Engineering Science Faculty of Engineering and the Built Environment University of Johannesburg Johannesburg South Africa

Epilepsy affect almost 1% of the worldwide population. An early diagnosis of seizure types is a crucial patient-dependent step for the treatment selection process. The selection of the proper treatment relies on the correct identification of the seizure type. As such, identifying the seizure type has the biggest immediate influence on therapy than the seizure detection, reducing the neurologist’s efforts when reading and detecting seizures in EEG recordings. Most of the existing seizure detection and classification methods are conceptualized following the patient-dependent schema thus fail to perform well with unknown cases. Our work focuses on patient-independent schema for seizure type classification and pays more attention to the explainability of the underlying attention mechanism of our method. Using a channel-wise attention mechanism, a quantification of the EEG channels contribution is enabled. Therefore, results become more interpretable and a visualization of brain lobes contribution by seizure types is allowed. We evaluate our model for seizure detection and type classification on CHB-MIT and the recently released TUH EEG Seizure, respectively. Our model is able to classify 8 seizure types with an accuracy of 98.41%, directly from raw EEG data without any preprocessing. A case study showed a high correlation between the neurological baselines and the interpretable results of our model. © 2023, The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.

关键词： Patient treatment