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20th International Joint Conference on computer science and Software Engineering, JCSSE 2023

作者： Somwong, Bhattarapong Kumphet, Kritsana Massagram, Wansuree Naresuan University Faculty of Science Department of Computer Science and Information Technology Phitsanulok65000 Thailand

ISBN: (纸本)9798350300505

This paper presents an audio classification system specifically created for Portenta H7, an Arduino-based microcontroller. The proposed model utilizes Edge Impulse AI platform, which allows the creation of accurate and efficient classification models optimized for embedded systems. To evaluate the system performance, a set of experiments was conducted on a dataset of audio samples from four classes: chainsaw, handsaw, gunshot, and laugh - each depicted sounds involving illegal logging and poaching threat in the forests. The results demonstrate that the proposed approach achieved high accuracy for gunshot, satisfying accuracy for chainsaw and laugh, and unacceptable accuracy for handsaw from our satellite-enabled system. The proposed system also has potential applications in forest protection as well as various domains, such as smart homes, security systems, and healthcare, where accurate audio classification can enable intelligent decision-making. © 2023 IEEE.

关键词： Edge computing

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Neuroadaptive Containment Control for Nonlinear Multiagent systems With Input Saturation: An Event-Triggered Communication Approach

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IEEE Transactions on systems, Man, and Cybernetics: systems 2025年第5期55卷 3163-3173页

作者： Wang, Xin Zhang, Sen Li, Huaqing Zhang, Wei Li, Hongyi Huang, Tingwen Southwest University College of Electronic and Information Engineering Chongqing400715 China Shenzhen University of Advanced Technology Faculty of Computer Science and Control Engineering Shenzhen518055 China

In this article, a neuroadaptive event-triggered containment control strategy combined with the dynamic surface control (DSC) approach is proposed for nonlinear multiagent systems (MASs) with input saturation. Based on the event-triggered communication mechanisms, the updates of neural network weight and controllers are implemented solely under triggering conditions of violation, which markedly reduces unnecessary communication resources and minimizes inefficient control costs compared with the traditional control method. Radial basis function neural networks (RBF NNs) are employed to handle the nonlinear uncertainties of MASs. Simultaneously, an adaptive compensatory mechanism is incorporated within the backstepping process to address the nonlinear effect posed by input saturation. Additionally, we demonstrate the system stability by extending the Lyapunov theorem to jump and continuous scenarios while excluding Zeno behavior, realizing that all followers can enter the convex hull constructed by leaders. Finally, the effectiveness of the proposed methodology is verified through application simulations. © 2013 IEEE.

关键词： Radial basis function networks

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Automating Object Identification from Hyper Spectral Data with Markov Random Fields

Automating Object Identification from Hyper Spectral Data wi...

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2024 International Conference on Optimization Computing and Wireless Communication, ICOCWC 2024

作者： Chaudhary, Chetan Devrani, Rekha Aadil, Mir Maharishi School of Engineering & Technology Maharishi University of Information Technology Uttar Pradesh India Vivekananda Global University Department of Computer Science & Engineering Jaipur India Department of Computer Science and Information Technology Bangalore India

ISBN: (纸本)9798350383348

Hyper Spectral statistics (HSD) is a type of fact acquired from optical sensors, which include satellites, scanners, and microscopes that captures and measures various wavelengths of energy. Object identification from HSD is problematic because of the high spectral variability in pixels of the image and the great variety of product classes usually present. Markov Random Fields (MRF) is a graphical model that may be used to version spatial dependencies among neighboring pixels which can assist in decorating the object identification process. This summary explores the feasibility of automating item identity from HSD through MRF. We introduce the idea of a priori information, which includes labels assigned to distinct spectral signatures within the image and a classification system that uses MRF as a priori information to enhance object identity accuracy. We describe and demonstrate the exact rules for this challenge via simulation. The consequences of the take look advocate that utilizing the MRF-based category gadget to improve item identification accuracy from HSD is possible. © 2024 IEEE.

关键词： classification consequences Hyper Spectral microscopes neighboring

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A Dynamic Pricing Method with LoRa Technique in Smart Parking systems Along with the Edge-Cloud Based Computing 1

A Dynamic Pricing Method with LoRa Technique in Smart Parkin...

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1st International Conference on Innovative Sustainable Technologies for Energy, Mechatronics and Smart systems, ISTEMS 2024

作者： Singh, Baldev Kumar, Ritesh Neetha, S.S. Vivekananda Global University Department of Computer Science and Engineering Jaipur India Maharishi School of Engineeing and Technology Maharishi University of Information Technology Uttar Pradesh India Department of Computer Science and Information Technology Bangalore India

ISBN: (纸本)9798350384246

The increasing quantity of automobiles on the road these days has resulted in chronic traffic jams and difficulties with urban traffic control. Increased traffic bottlenecks are mostly caused by unlicensed and unregulated parking. The management of present parking spots is ineffective, and traffic-related problems are made worse by a lack adequate real-time advice and general ignorance. The constrained growth of highways and other transportation and related infrastructure highlights the urgent need for a clever, flexible, and effective parking solution. We suggest a complex multi-layer design for intelligent parking facilities to overcome these issues. This system uses cutting-edge extended, low-power wireless communications, powerful edge and cloud computing processing capacity, and sensor nodes with different parameters for parking spots. By dynamically managing large areas and providing drivers with real-time information on parking spaces and related services via continuous vehicle tracking, our proposed system seeks to transform parking management. We also provide a dynamic pricing strategy that aims to maximise parking authority income while guaranteeing drivers have the best possible availability of parking spaces. By addressing the problems caused by careless parking and improving general traffic control, this all-encompassing strategy aims to improve the effectiveness and smooth operation of the city's transport network. © 2024 IEEE.

关键词： Traffic congestion

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A Collaborative Approach for Secured Routing in Mobile Ad-Hoc Network

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Intelligent Automation & Soft Computing 2023年第2期35卷 1337-1351页

作者： W.Gracy Theresa A.Gayathri P.Rama Department of Computer Science and Engineering Panimalar Institute of TechnologyChennai600123Tamil NaduIndia Department of Information Technology Vellore Institute of TechnologyVellore632014Tamil NaduIndia Department of Computer Science and Engineering Loyola Institute of TechnologyChennai600123Tamil NaduIndia

Mobile computing is the most powerful application for network com-munication and connectivity,given recent breakthroughs in theﬁeld of wireless networks or Mobile Ad-hoc networks(MANETs).There are several obstacles that effective networks confront and the networks must be able to transport data from one system to another with adequate *** most applications,a frame-work must ensure that the retrieved data reﬂects the transmitted *** driv-ing to other nodes,if the frame between the two nodes is deformed in the data-link layer,it must be *** link-layer protocols immediately disregard the frame and enable the high-layer protocols to transmit it *** other words,because of asset information must be secured from threats,information is a valu-able *** MANETs,some applications necessitate the use of a network method for detecting and blocking these *** a secure intrusion detection system in the network,which provides security to the nodes and route paths in the network,is a major difﬁculty in *** on the network can jeopardize security issues discovered by the intrusion detection system engine,which are then blocked by the network’s intrusion prevention *** bringing the Secure Intrusion Detection system(S-IDS)into the network,a new technique for implementing security goals and preventing attacks will be *** Secure Energy Routing(SER)protocol for MANETs is introduced in this *** protocol addresses the issue of network security by detecting and preventing attacks in the *** data transmission in the MANET is forwarded using Elliptical Curve Cryptography(ECC)with an objective to improve the level of *** Simulator–2 is used to simulate the network and experiments are compared with existing methods.

关键词： Mobile ad-hoc network(MANET) intrusion detection system secure energy routing(SER) elliptical curve cryptography(ECC) security

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Spatiotemporal crowds features extraction of infrared images using neural network

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Journal of Ambient Intelligence and Humanized Computing 2024年第4期15卷 2543-2556页

作者： Al-Oraiqat, Anas M. Drieiev, Oleksandr Drieieva, Hanna Meleshko, Yelyzaveta AlRawashdeh, Hazim Al-Oraiqat, Karim A. Hasan, Yassin M. Y. Maricar, Noor Khan, Sheroz Department of Computer Science College of Engineering and Information Technology Onaizah Colleges Qassim Saudi Arabia Department of Cybersecurity and Software Central Ukrainian National Technical University Kropyvnytskyi Ukraine Department of Artificial Intelligence Faculty of Information Technology Zarqa University Zarqa Jordan Department of Electrical Engineering College of Engineering and Information Technology Onaizah Colleges Qassim Saudi Arabia

Crowds can lead up to severe disasterous consequences resulting in fatalities. Videos obtained through public cameras or captured by drones flying overhead can be processed with artificial intelligence-based crowd analysis systems. Being a hot area of research over the past few years, the goal is not only to identify the presence of crowds but also to predict the probability of crowd-formation in order to issue timely warnings and preventive measures. Such systems will significantly reduce the probablity of the potential disasters. Developing effective systems is a challenging task, especially due to factors such as naturally occuring diverse conditions, variations in people or background pixel areas, noise, behaviors of individuals, relative amounts/distributions/directions of crowd movements, and crowd building reasons. This paper proposes an infrared video processing system based on U-Net convolutional neural network for crowd monitoring in infrared video frames to help estimate the people crowd with normal or abnormal trends. The proposed U-Net architecture aims to efficiently extract crowd features, achieve sufficient people marking-up accuracy, competitively with optimal network configurations in terms of the depth and number of filters to consequently minimise the number of coefficients. For further faster processing, hardware resources/implementation area savings, and lower power, the optimized network coefficients measured are represented in Canonic-Signed Digit with minimal number of nonzero (± 1) digits, minimizing the number of underlying shift-add/subtract operations of all multipliers. The achieved significantly reduced computational cost makes the proposed U-Net effectively suitable for resource-constrained and low power applications. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2024.

关键词： Convolutional neural networks

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Environmental Impact of Freight Transport – Freight Footprint as a New Freight Transport Indicator

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Periodica Polytechnica Transportation Engineering 2024年第1期52卷 18-23页

作者： Boldizsár, Adrienn Department of Information Technology GAMF Faculty of Engineering and Computer Science John von Neumann University Izsáki u. 10. KecskemétH-6000 Hungary

One of the most pressing questions today is how to prevent or slow down climate change. As a service sector, transport significantly contributes to this and increases greenhouse gas emissions. Furthermore, creating long-term sustainable social, economic and environmental processes by eliminating current systemic failures can also be a significant challenge for individual nations. One thing is sure: the status quo is not sustainable. Taking all these aspects into account, this paper seeks to answer the question of the extent to which the performance of freight transport in each EU Member State contributes to the change in the ecological footprint and how national characteristics influence this. The research introduces a newly established indicator, the freight transport footprint, which is based on a family of footprint formulas and specifically seeks to answer the extent to which freight transport burdens our environment. © 2024 Budapest University of technology and Economics. All rights reserved.

关键词： Carbon footprint

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Plagiarism Detection and Similarity Checking Program using Machine Learning and String Matching Algorithm 9

Plagiarism Detection and Similarity Checking Program using M...

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9th International Conference on Communication and Electronics systems, ICCES 2024

作者： Bhavana, Munugapati Rao, Koppula Srinivas Koduru, Gagan Kumar Vatsal, K.Vijay Krupa Parvathi, S. Sravani, Mogiligidda Mlr Institute of Technology Affiliated to Jntu Dept. of Computer Science & Information Technology Hyderabad India Mlr Institute of Technology Affiliated to Jntu Department of Computer Science & Engineering Hyderabad India Mlr Institute of Technology Hyderabad India Malla Reddy University Department of Computer Science & Engineering Hyderabad India Mlr Institute of Technology Dept.of Computer Science & Information Technology Hyderabad India Mvsr Engineering College Department of Information Technology India

ISBN: (纸本)9798350377972

This research proposes a system for detecting plagiarism in academic submissions. The system utilizes advanced text comparison algorithms to identify instances of plagiarism, ensuring academic integrity and promoting a culture of originality among students. By providing educators with a tool to efficiently detect plagiarism, this system aims to streamline the evaluation process and enhance the overall learning experience for students. This system has the potential to significantly reduce plagiarism incidents and foster a more ethical and productive academic environment. © 2024 IEEE.

关键词： Contrastive Learning

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Analyzing the Role of Time Series Analysis in Early Detection of Merkel Cell Carcinoma

Analyzing the Role of Time Series Analysis in Early Detectio...

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2024 International Conference on Optimization Computing and Wireless Communication, ICOCWC 2024

作者： Singh, Akhilendra Pratap Seth, Jyoti Nachappa, M.N. Maharishi School of Engineering & Technology Maharishi University of Information Technology Uttar Pradesh India Vivekananda Global University Department of Computer Science & Engineering Jaipur India Department of Computer Science and Information Technology Bangalore India

ISBN: (纸本)9798350383348

this paper investigates the potential of using time collection evaluation to detect Merkel cellular Carcinoma (MCC) early. MCC is a competitive shape of pores and skin. Early detection is critical to the growing survival rate. Time collection evaluation is an adequate records mining technique used to degree diffused styles in facts. This look aims to discover the usage of time series analysis to stumble on early signs and symptoms of MCC and evaluate the results to present strategies that include Random forest and decision Tree classifiers. First, fixed healthcare information collected from patients with MCC became amassed, and the related time series was created. Next, different versions of time collection models consisting of ARIMA (car-Regressive included transferring common) and SARIMA (Seasonal ARIMA) become used to measure the extent of temporal trends inside the records. Subsequently, the models were compared in opposition to the present system learning classifiers and assessed in phrases of accuracy, precision, take into account, and F1-score. © 2024 IEEE.

关键词： adjustments discovering Regressive sickness stumble

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The Smart Predictive Analysis of Stock Market Performance using Restricted Boltzmann Machines

The Smart Predictive Analysis of Stock Market Performance us...

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2024 International Conference on Optimization Computing and Wireless Communication, ICOCWC 2024

作者： Devrani, Rekha Mahajan, Prerna Kumar, Dinesh Vivekananda Global University Department of Computer Science & Engineering Jaipur India Department of Computer Science and Information Technology Bangalore India Maharishi School of Engineering & Technology Maharishi University of Information Technology Uttar Pradesh India

ISBN: (纸本)9798350383348

Restricted Boltzmann Machines (RBMs) are a system getting-to-know algorithm that may be used to predict the overall performance of inventory markets. This painting aims to analyze the overall performance of inventory markets via growing an RBM version. The RBM version will benefit from the temporal correlation of stock marketplace information and put it to use to make more correct predictions. The version is educated using historical facts, and the model parameters are adjusted to optimize the prediction accuracy. This painting demonstrates the use of an RBM model for predicting the returns of stocks and analyzing the stock marketplace's overall performance. The outcomes from these paintings show that the proposed RBM version effectively predicts the overall performance of inventory markets and provides an alternative version for forecasting stock markets. Confined Boltzmann Machines (RBMs) are a form of synthetic neural network which can be used for unsupervised studying tasks consisting of dimensionality discount, function mastering, and collaborative filtering. RBMs have been correctly used in numerous applications, including sentiment evaluation, natural language processing, and computer imagination and prescient. In current years, there was a surge of hobby in using RBMs to expect inventory market overall performance. On the way to expand a predictive model, RBMs extract separable functions from the uncooked market data, which can be pattern inputs. The extracted capabilities are used because of the input to a supervised mastering version to predict stock marketplace overall performance based on previous statistics. A vital advantage of using RBMs for feature extraction is that they can identify latent components of the facts, which would, in any other case, remain hidden from the consumer. By combining RBMs with supervised learning, it is viable to develop models which can be more powerful than those using either technique for my part. Because of this reality,

关键词： algorithms correlations historical inventory stock

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