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47th ICT and Electronics Convention, MIPRO 2024

作者： Lang, V. Melanšek, M. Šorgo, A. I. Osnovna Šola Celje Celje Slovenia University Of Maribor Faculty Of Natural Sciences And Mathematics Maribor Slovenia University Of Maribor Faculty Of Electrical Engineering And Computer Science Slovenia

ISBN: (纸本)9798350382495

Due to the many possibilities offered by smartphones, the question arises as to how they can be used as a didactic tool in the classroom if they clearly support learning. Such use can provide new opportunities for students to demonstrate their understanding of subject matter by using smart cell phones that enable m-learning (learning anywhere, anytime). As with the use of any information and communication technology (ICT), the use of m-ICT is an important prerequisite for use by teachers. There is no national regulation for the use of cell phones in schools that would dictate to schools or students how to use these devices. Therefore, schools must rely on themselves and their own internal rules when deciding on the use of cell phones. We would like to determine the frequency of ICT use in class, the propensity of students to use smartphones in class, the actual use of smartphones and the use of mobile applications in class. We obtained data showing that 83% of respondents use smartphones for teaching purposes. Respondents who have already used phones are very interested in using this type of technology in the classroom, but we could not find any significant statistical differences between the two genders. The remaining 17% stated that they have never used smartphones in their teaching but believe that they could be useful and should be used in practice. We concluded that students are inclined to use smartphones for educational purposes and that the majority are active on the World Wide Web. Even though smart phones are not yet widely used in schools today or are not used daily, we can conclude that this type of use is perceived as beneficial and will increase over time. © 2024 IEEE.

关键词： Smartphones

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Experiment on Multi-hop LoRa for Tracking Application

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International Journal of Sensors, Wireless Communications and Control 2023年第1期13卷 29-39页

作者： Sagala, Albert Lubis, Ramot Silalahi, Gomgom Ginting, Estika V. Simorangkir, Andreas J. D. Faculty of Informatics and Electrical Engineering Institut Teknologi Del Toba Samosir Indonesia Department of Computer Science Binus University West Jakarta Indonesia

Background: The Internet of Things (IoT) is an embryonic model that converts real-world things (objects) into smarter devices. IoT is applicable to a variety of application domains, including healthcare, smart grid, and agriculture. Low Power Wide Area Network (LPWAN) technology can transmit data over long distances with a low data rate without using the internet. There-fore, this technology is suitable for sending sensor measurement data in Internet of Things (IoT) applications. LPWANs, such as LoRa, use a star topology to reach their coverage to the maximum power that can be transmitted, considering the environmental conditions in the test area and the LoRa parameters used. Whereas for application development in rural areas, there is no internet connection, and often longer coverage is required from the sensor node to the gateway. Objective: This study aims to increase the transmission range from the end node to the gateway by adding an intermediate node. It is used as a relay from the end node to the gateway. Method: Static routing was applied so that any broadcast data from the end node would be for-warded first to the intermediate node and then to the gateway. Results: The LoRa transmission range expanded by changing the topology from peer-to-peer to multi-hop. An intermediate node between the transmitter (end node) and the gateway is available. In the experiment performed, a multi-hop with static routes successfully expanded the distance from 150 meters (single hop) to 250 meters (multi-hop), with a packet delivery ratio of 97%. In this experiment, GPS Ublox Neo was used for the sensor position and was successfully tested on the end node with an accuracy rate of 2.4% for latitude and 0.2% for longitude. Conclusion: The transmission range from the sensor node to the gateway can be extended using static routing by installing an intermediate node between the gateway and the sensor node. The data from the end node is successfully received by the gateway. © 2023 Be

关键词： energy efficiency GPS ublox neo internet of things LoRa tracking LPWAN multi-hop wireless sensor networks

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Optimizing Bearing Fault Detection:CNN-LSTM with Attentive TabNet for Electric Motor Systems

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computer Modeling in engineering & sciences 2024年第12期141卷 2399-2420页

作者： Alaa U.Khawaja Ahmad Shaf Faisal Al Thobiani Tariq Ali Muhammad Irfan Aqib Rehman Pirzada Unza Shahkeel Nautical Science Department Faculty of MaritimeKing Abdulaziz UniversityJeddah22230Saudi Arabia Department of Computer Science COMSATS University IslamabadSahiwal CampusSahiwal57000Pakistan Marine Engineering Department Faculty of MaritimeKing Abdulaziz UniversityJeddah22230Saudi Arabia Artificial Intelligence and Sensing Technologies(AIST)Research Center University of TabukTabuk71491Saudi Arabia Electrical Engineering Department College of EngineeringNajran UniversityNajran61441Saudi Arabia

Electric motor-driven systems are core components across industries,yet they’re susceptible to bearing *** fault diagnosis poses safety risks and economic instability,necessitating an automated *** study proposes FTCNNLSTM(Fine-Tuned TabNet Convolutional Neural Network Long Short-Term Memory),an algorithm combining Convolutional Neural Networks,Long Short-Term Memory Networks,and Attentive Interpretable Tabular *** model preprocesses the CWRU(Case Western Reserve University)bearing dataset using segmentation,normalization,feature scaling,and label *** architecture comprises multiple 1D Convolutional layers,batch normalization,max-pooling,and LSTM blocks with dropout,followed by batch normalization,dense layers,and appropriate activation and loss ***-tuning techniques prevent *** were conducted on 10 fault classes from the CWRU *** was benchmarked against four approaches:CNN,LSTM,CNN-LSTM with random forest,and CNN-LSTM with gradient boosting,all using 460 *** FTCNNLSTM model,augmented with TabNet,achieved 96%accuracy,outperforming other *** establishes it as a reliable and effective approach for automating bearing fault detection in electric motor-driven systems.

关键词： Electric motor-driven systems bearing faults automation fine tunned convolutional neural network long short-term memory fault detection

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Numerical analysis of a Kretschmann surface plasmon resonance sensor with silver/TiO_(2)/BaTiO_(3)/silver/graphene for refractive index sensing

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Optoelectronics Letters 2023年第10期19卷 583-586页

作者： Huda A.Zain Malathy Batumalay Hazli R.A.Rahim Sulaiman Wadi Harun Department of Electrical Engineering University of MalayaKuala Lumpur50603Malaysia Faculty of Data Science&IT INTI International UniversityNilaiNegeri Sembilan71800Malaysia Faculty of Electronic and Computer Engineering Universiti Teknikal Malaysia MelakaMelakaMalaysia

The sensitivity of a Kretschmann surface plasmon resonance(SPR)sensor was *** Kretschmann setup had multiple layers,a BK7 prism,silver,barium titanate(BaTiO_(3)),titanium dioxide(TiO_(2)),and *** BaTiO_(3)and TiO_(2)coatings were sandwiched between two silver *** sensitivity of 260°/RIU has been *** graphene layers are added to the configuration to improve sensitivity and as a bio-compatibility *** configuration can be used for biochemical sensors.

关键词： silver surface resonance

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EfficientViT: An Efficient Vision Transformer for Fire and Smoke Image Classification 1

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4th International Conference on Smart Systems: Innovations in Computing, SSIC 2023

作者： Gourisaria, Mahendra Kumar Patel, Ayush V. Chatterjee, Rajdeep Singh, Vijander School of Computer Engineering KIIT Deemed to Be University Odisha Bhubaneswar751024 India Department of Computer Science and Engineering Manipal University Jaipur Rajasthan Jaipur302034 India Department of ICT and Natural Science Faculty of Information Technology and Electrical Engineering NTNU—Norwegian University of Science and Technology Aalesund Norway

ISBN: (数字)9789819736904

ISBN: (纸本)9789819736898

Given the variety of fire and smoke, which are distinguished by variances in texture and color, it is extremely difficult to detect fire and smoke from visual imagery. A significant amount of economic and environmental harm has resulted from fire and smoke incidents around the world. In order to prevent property damage and save lives, fires must be detected as soon as possible, for example, forest fires, which frequently occur in the summer yet are easily avoidable. Using cutting-edge deep learning models including Convolutional Neural Network (CNN), VGG16, Vision Transformer (ViT), and MobileViT, we offer a thorough method for classifying fire and smoke in this study. We also provide an innovative model dubbed EfficientViT, which combines the advantages of the MobileViT and Inverted Residual layers. The correct classification of visual images into the categories of fire and smoke is our main goal. We show that EfficientViT beats the other developed models, obtaining an amazing accuracy of 0.92, through a thorough experimentation and evaluation. This demonstrates how well our suggested model works at correctly differentiating between incidents of fire and smoke. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Convolutional neural networks

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Gabor Wavelet Based Fused Texture Features for Identification of Mungbean Leaf Diseases 1st

Gabor Wavelet Based Fused Texture Features for Identificati...

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1st International Conference on Machine Intelligence and Emerging Technologies, MIET 2022

作者： Majumder, Sarna Mazumder, Badhan Islam, S. M. Taohidul Faculty of Computer Science and Engineering Patuakhali Science and Technology University Patuakhali Bangladesh Department of Computer and Communication Engineering Patuakhali Science and Technology University Patuakhali Bangladesh Department of Computer Science and Engineering Dhaka International University Dhaka Bangladesh Department of Electrical and Electronics Engineering Patuakhali Science and Technology University Patuakhali Bangladesh

ISBN: (纸本)9783031346187

Early diagnosis of crop plant disease is crucial since many of these diseases pose a considerable threat not only to global food security but also towards agricultural productivity. Aiming that, for detection of mungbean leaf diseases at the beginning stage, we introduce a novel approach based on Gabor Wavelet Transform (GWT) and Cubic SVM in this paper. We perform GWT to decompose the given images into eighteen directional sub-bands and then extract fusion of Gabor Wavelet (GW) based texture features from each detailed GWT coefficient sub-band. Finally, Cubic SVM was deployed to classify three different disease classes by using these GW based features, conducting cross validation at 10 fold. Outcomes of our experimental evaluation using our self-prepared dataset of mungbean leaf diseases exhibit that our proposed method yields overall a sensitivity of 91.11%, a specificity of 95.56%, a precision of 91.39% and an accuracy of 91.11%. Moreover, outcomes obtained from our comparative analysis confirm the supremacy of our proposed framework over 3 currently existing approaches. © 2023, ICST Institute for computer sciences, Social Informatics and Telecommunications engineering.

关键词： Crops

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Unsupervised Vessel Segmentation Technique Using LTP and PCA 4

Unsupervised Vessel Segmentation Technique Using LTP and PCA

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4th International Conference on Communication Technologies, ComTech 2023

作者： Faridoon, Fatima Khan, Khurram Umair, Mir Yasir Mahmood, Zahid COMSATS University Department of Electrical and Computer Engineering Abbottabad Islamabad Pakistan GIK Institute of Engineering Sciences and Technology Faculty of Computer Science and Engineering KPK Topi Pakistan National University of Sciences and Technology Department of Electrical Engineering Islamabad Pakistan

ISBN: (纸本)9798350337211

This paper presents a retinal vessels segmentation method than can be used by the ophthalmologists to diagnose different retinal diseases, such as thickening, thinning, or splitting. The proposed method consists of various interconnected steps. Initially, the G-channel is isolated from the input colored RGB image. Later, the Principal Component Analysis (PCA) is applied on the Green channel. Next, the Local Ternary Pattern (LTP) is used that feeds the processed image to the Morphological Filter Bank (MFB). The MFB uses various filters, such as spur, majority, and opening operations to segment the vessels image. Simulations are performed on the DRIVE and the STARE datasets. The proposed method yields mean accuracy of 94.07% and 93.45% on DRIVE and STARE datasets, respectively. Moreover, the proposed method is computationally less complex as compared to the few published methods. © 2023 IEEE.

关键词： Principal component analysis

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A Novel Cluster Analysis-Based Crop Dataset Recommendation Method in Precision Farming

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computer Systems science & engineering 2023年第9期46卷 3239-3260页

作者： K.R.Naveen Kumar Husam Lahza B.R.Sreenivasa Tawfeeq Shawly Ahmed A.Alsheikhy H.Arunkumar C.R.Nirmala Department of Computer Science&Engineering Bapuji Institute of Engineering&TechnologyDavangereKarnatakaIndia Department of Information Technology Faculty of Computing and Information TechnologyKing Abdulaziz UniversityJeddahSaudi Arabia Department of Information Science&Engineering Bapuji Institute of Engineering&TechnologyDavangereKarnatakaIndia Department of Electrical Engineering Faculty of Engineering at RabighKing Abdulaziz UniversityJeddahSaudi Arabia Department of Electrical Engineering College of EngineeringNorthern Border UniversityArarSaudi Arabia

Data mining and analytics involve inspecting and modeling large pre-existing datasets to discover decision-making *** agriculture uses datamining to advance agricultural *** farmers aren’t getting the most out of their land because they don’t use precision *** harvest crops without a well-planned recommendation *** crop production is calculated by combining environmental conditions and management behavior,yielding numerical and categorical *** existing research still needs to address data preprocessing and crop categorization/***,statistical analysis receives less attention,despite producing more accurate and valid *** study was conducted on a dataset about Karnataka state,India,with crops of eight parameters taken into account,namely the minimum amount of fertilizers required,such as nitrogen,phosphorus,potassium,and pH *** research considers rainfall,season,soil type,and temperature parameters to provide precise cultivation recommendations for high *** presented algorithm converts discrete numerals to factors first,then reduces ***,the algorithm generates six datasets,two fromCase-1(dataset withmany numeric variables),two from Case-2(dataset with many categorical variables),and one from Case-3(dataset with reduced factor variables).Finally,the algorithm outputs a class membership allocation based on an extended version of the K-means partitioning method with lambda *** presented work produces mixed-type datasets with precisely categorized crops by organizing data based on environmental conditions,soil nutrients,and ***,the prepared dataset solves the classification problem,leading to a model evaluation that selects the best dataset for precise crop prediction.

关键词： Data mining crop prediction k-prototypes k-means cluster machine learning

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Smart Meter Application Analysis using PLS-SEM Deep Neural Network: A Case Study

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Journal of engineering science and Technology Review 2023年第3期16卷 16-27页

作者： Thao, Nguyen Thi Phuong Duc, Minh Ly Bilik, Petr Faculty of Electrical and Electronics Engineering Ton Duc Thang University Ho Chi Minh City Viet Nam Faculty of Commerce Van Lang University Ho Chi Minh City Viet Nam Department of Cybernetics and Biomedical Engineering Faculty of Electrical Engineering and Computer Science VSB–Technical University of Ostrava 17. Listopadu2172/ 15 Ostrava708 00 Czech Republic

Smart meters measure, control, analyze, and predict the amount of electricity, water, and gas used. In developing countries, where there is no consensus to accept the use of smart meters, there are many possible risks when using smart meters. However, there are also many benefits of smart meters. This study conducts an overall assessment of the demand and impact of the smart meter in the southern region of Vietnam through a survey of 500 samples. This article examines information technology system (IS) related factors and engineering model-related factors according to technical readiness such as optimism, innovation, insecurity, and discomfort. Accompanying that is the expectation of smart meters, for Vietnamese people's intention to constantly use smart meters. Most of the previous studies on smart meter systems have focused on analyzing the impact of factors affecting the application using single-step structural equation modeling (SEM). In this study, it is proposed to use a 2-layer model between the research model of the multi-analysis method by combining the Partial Least Squares - Structural Equation Modeling (PLS-SEM) and Artificial Neural Networks (ANN) model was performed for additional analysis for the results of PLS-SEM, and ANN has higher predictive accuracy than PLS-SEM because ANN has the ability to perform well for both linear relational model and linear relationship model and non-linear with high prediction. First, the PLS-SEM model evaluates the factors affecting the intention to use the smart meter system. Second, the ANN ranks the impact factors of the critical predictors from the PLS-SEM model, and the Critical Performance Map Analysis (IPMA) analyzes the exact results for the critical performance of the variables elements. The results of this study show that the quality of information and quality of system factors of the IS model have a negative impact on users' intention to use smart meters. At the same time, factors of Optimization and innovati

关键词： Deep neural networks

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Finite-time synchronization control for a class of delayed neural networks:an improved two-step control method

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science China(Information sciences) 2025年

作者： Yue CHEN Song ZHU Mouquan SHEN Xiaoyang LIU Shiping WEN School of Mathematics China University of Mining and Technology College of Electrical Engineering and Control Science Nanjing Technology University School of Computer Science and Technology Jiangsu Normal University Faculty of Engineering and Information Technology University of Technology Sydney

In this paper,a two-step control method is proposed,leveraging the generalized Halanay inequality and existing finite-time stability theorems,to achieve finite-time synchronization for a class of neural networks with bounded time-varying *** the first step,the system state is attenuated from V （t0） to γV （t0） using the generalized Halanay inequality,where0<γ 1 is a free *** the second step,by applying existing finite-time stability theorems,the system state further decays from γV （t0） to *** on the above ideas,two novel finite-time stability lemmas for the error system are presented,and the convergence rate as well as the settling time is ***,the value of γ that results in the shortest settling time for the error system is also *** the help of the derived lemmas,several sufficient algebraic criteria are established to achieve finite-time synchronization between the considered delayed neural *** results of this paper not only improve the existing two-step control method but also overcome the limitations of certain one-step finite-time control ***,the validity and practical applicability of the obtained theoretical results are demonstrated through two numerical examples and an image protection experiment.

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