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18th International Conference on Open Source Systems and Technologies, ICOSST 2024

作者： Abbasi, Humza Fazal Abbasi, Merium Fazal Hamayat, Faizan Bahria University Department of Computer Science Islamabad Pakistan CESAT Department of Electronics Engineering Islamabad Pakistan University of Engineering and Technology Taxila Taxila Electrical Engineering Department Pakistan

ISBN: (纸本)9798331508692

The generation of sketches from portraits has been an active area of research for decades due to its applications in various sectors such as entertainment and education. However, due to numerous qualitative factors involved in sketching, programmatically generating sketches from images has been a challenging task. Recent advances in generative artificial intelligence (GAI) seem very promising in solving these challenges. In specific, generative adversarial networks (GANs) like conventional Pix2Pix, were specifically designed for accurate and efficient image-to-image translation tasks. In this research, inspired by GAI and original Pix2Pix, we proposed a GANs-based Pix2Pix++ technique for translating portrait images into sketches. We replaced U-Net with the UNet++ for generator in Pix2Pix++ GAN and trained the proposed model on a self-collected dataset for sketch generation. Furthermore, we evaluated the proposed model's performance both qualitatively and quantitively and performed a comparative analysis with existing techniques. Proposed Pix2Pix++ GAN got MS-SSIM median scores of 84%, PSNR of 21.17, and MSE median score of 495.55. Experimental results showed our proposed technique outperforms the existing techniques in terms of capturing minute details, tonal variations, realistic appearance, and generalization. © 2024 IEEE.

关键词： Generative adversarial networks

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Optimizing SVM Hyperparameters for Breast Cancer Data Classification using Hybrid Particle Swarm Optimization 1

Optimizing SVM Hyperparameters for Breast Cancer Data Classi...

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1st International Conference on Innovative engineering Sciences and Technological Research, ICIESTR 2024

作者： Sulaiman, A.T. Bello-Salau, H. Onumanyi, A.J. Salawudeen, A.T. Adebiyi, R.F. Mu'azu, M.B. Ahmadu Bello University Zaria Department of Computer Engineering Nigeria Pretoria South Africa University of Jos Department of Electrical and Electronics Engineering Nigeria

ISBN: (纸本)9798350348637

The classification efficacy of Support Vector Machines (SVMs) heavily relies on hyperparameter selection, and optimizing parameters such as kernel type and regularization is particularly challenging due to the non-convex nature of the SVM objective function. In response to this challenge, we introduce a novel approach, the trailPSO technique, which combines Particle Swarm Optimization (PSO) with Smell Agent Optimization (SAO) to achieve a balance between exploration and exploitation. The trailPSO algorithm is applied to optimize SVM hyperparameters, resulting in the trailPSOSVM model. We assess the performance of trailPSOSVM by employing it to classify breast cancer datasets, revealing superior outcomes compared to conventional methods. Notably, the proposed approach attains 100% accuracy with zero errors, showcasing its ability to identify optimal hyperparameter settings for enhanced classification accuracy and robustness. This study is part of an ongoing efforts towards extending the trailPSO algorithm for sentiment analysis and addressing imbalanced datasets in the realm of natural language processing. © 2024 IEEE.

关键词： Particle swarm optimization (PSO)

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Generic Deep-Learning-Based Time Series Models for Aviation Accident Analysis and Forecasting

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SN computer Science 2024年第1期5卷 32页

作者： Monika Verma, Seema Kumar, Pardeep Department of Computer Science Banasthali Vidyapith Rajasthan Jaipur India Department of Electrical and Electronics Engineering Education NITTTR Madhya Pradesh Bhopal India Department of Electronics and Communication Engineering SGT University Haryana Gurugram India

The global civil aviation is growing day by which is resulting into more congestion into the air transportation system. Over the past few years, many efforts have been put in the direction to improve the aviation safety and avoid fatal air accidents. Learning from the previous accidents could be crucial to enhance the safety standards and minimise the possibility of such aviation safety events. Hence, this manuscript provides an analysis of time-series-based machine learning models to investigate the impact of aviation accident injuries and fatalities. Time series models are highly capable of predicting aviation accidents due to their statistical association. This study has examined ASRS database containing accident and incident data from 1988 to 2020 by employing time series models such as autoregressive integrated moving average (ARIMA), seasonal ARIMA with an exogenous factor (SARIMAX), deep-learning-based long short-term memory (LSTM), bidirectional LSTM (Bi-LSTM), and gated recurrent unit (GRU). Here, MSE, RMSE, and MAE are used as performance indicators. The results proved that the Bi-LSTM prediction was superior compared to the other models. Deep-learning-based analysis helps to bring forward the various hidden patterns along with essential information for accident prevention and reducing the accident probability for improving the aviation safety. Ultimately, overall aviation safety level will enhance by minimising the likelihood of occurrence of unsafe events and aviation management optimise their actions towards safety © 2023, The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd.

关键词： Accidents ARIMA Aviation Bi-directional LSTM GRU LSTM Prediction SARIMAX Time series

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Research on Surface Electropotential Signal and Vibration Measurements of Plants for Long-Term Remote Monitoring 6

Research on Surface Electropotential Signal and Vibration Me...

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6th International Congress on Human-computer Interaction, Optimization and Robotic Applications, HORA 2024

作者： Akyüz, Yusuf Tarık Kaya, İsmail Ulutaş, Mustafa Yeşim, E.R. Durğut, Zübeyr Department of Electrical&Electronics Engineering Karadeniz Technical University Trabzon Turkey Department of Computer Engineering Karadeniz Technical University Trabzon Turkey

ISBN: (纸本)9798350394634

In the realm of plants electropotential signals and remote plant's signal monitoring, this study focuses on the variations of surface electropotential and vibration of plant leaves. Given that signals obtained from plants are not as strong as the electromyogram signals of the human body, measuring these signals without introducing amplifier noise or filter time dispersion presents a significant challenge. To address this, we employ direct analog-to-digital conversion using high-resolution ADCs and a peak detection-based signal measurement technique with a high-frequency sampling rate to capture variations as accurately as possible. We developed a high-impedance electrode for long-term viability that neither physically affects the plant body nor interferes with plant signals due to its high impedance. Some signals are challenging to distinguish, whether they are the plant's own, from other branches, or received from other plants. In isolated environments where external interferences are minimized, we are able to distinguish between plant electropotentials and vibrations with clarity. Plants has a electrophysical response that it also has a reflection on physical vibrations because of its soft tissue piezo effect. In this study two plant vibration measurement methods are designed and comparatively tested. This methods has shown that there are significant difference between invasive and non invasive measurements. © 2024 IEEE.

关键词： Electrodes

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IoT Based Smart Refrigerator Using Machine Learning 2

IoT Based Smart Refrigerator Using Machine Learning

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2nd International Conference on Artificial Intelligence and Machine Learning Applications, AIMLA 2024

作者： Devanath, B. Dharunish, A.P. Mohana Bharathi, S. Suganth, R. Banupriya, V. Ramesh, M. M Kumarasamy College of Engineering Department of Electrical and Electronics Engineering Karur India M Kumarasamy College of Engineering Department of Computer Science and Business Systems Karur India

ISBN: (数字)9798350349221

ISBN: (纸本)9798350349221

The fridge is the most constantly employed menage/ kitchen electrical device encyclopedically for conserving food. The leading sectors of smart widgets include refrigerators in the kitchen. The Web of Effects (IoT) denotes the tools and systems employed to link the factors deduced from the detectors and selectors. Primarily this contrivance is employed for different purposes such as storing flora, fruits, etc. This design transforms any pre-existing fridge into an intelligent, budget-friendly outfit with exercise sensors. The proposed design is educated and worked grounded on the medium of learning machines, and it contrasts the condition of the aliment, for case, Mass, quantum, etc. The significance of this undertaking will involve barring food decay, dwindling affections, and promoting a more restorative way of life for contemporary mortal beings. The arrangement of dish recommendations grounded on the veggies being in the caddy is carried out through an abecedarian algorithm of machine literacy, which categorizes the vegetables grounded on the tinges, latterly suggesting a specific form. It'll be canny enough to warn the stoner IoT about the present condition of food papers through an Android operation on our cellular device. © 2024 IEEE.

关键词： Refrigerators

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Combustion-assisted low-temperature ZrO_(2)/SnO_(2) films for high-performance flexible thin film transistors

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npj Flexible electronics 2024年第1期8卷 132-142页

作者： Bongho Jang Junil Kim Jieun Lee Geuntae Park Gyuwon Yang Jaewon Jang Hyuk-Jun Kwon Department of Electrical Engineering and Computer Science DGISTDaeguSouth Korea School of Electronic and Electrical Engineering Kyungpook National UniversityDaeguSouth Korea School of Electronics Engineering Kyungpook National UniversityDaeguSouth Korea Convergence Research Advanced Centre for Olfaction DGISTDaeguSouth Korea

We developed high-performance flexible oxide thin-film transistors(TFTs)using SnO_(2) semiconductor and high-k ZrO_(2) dielectric,both formed through combustion-assisted sol-gel *** method involves the exothermic reaction of fuels and oxidizers to produce high-quality oxide films without extensive external *** combustion ZrO_(2) films were revealed to have an amorphous structure with a higher proportion of oxygen corresponding to the oxide network,which contributes to the low leakage current and frequency-independent dielectric *** ZrO_(2)/SnO_(2) TFTs fabricated on flexible substrates using combustion synthesis exhibited excellent electrical characteristics,including a field-effectmobility of 26.16 cm^(2)/Vs,a subthreshold swing of 0.125 V/dec,and an on/off current ratio of 1.13×10^(6) at a low operating voltage of 3 ***,we demonstrated flexible ZrO_(2)/SnO_(2) TFTs with robust mechanical stability,capable of withstanding 5000 cycles of bending tests at a bending radius of 2.5 mm,achieved by scaling down the device dimensions.

关键词： film high dielectric

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TESL-Net: A Transformer-Enhanced CNN for Accurate Skin Lesion Segmentation 25

TESL-Net: A Transformer-Enhanced CNN for Accurate Skin Lesio...

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25th International Conference on Digital Image Computing: Techniques and Applications, DICTA 2024

作者： Iqbal, Shahzaib Zeeshan, Muhammad Mehmood, Mehwish Khan, Tariq Razzak, Imran Department of Electrical Engineering Islamabad Pakistan School of Electronics Electrical Engineering and Computer Science Queen's University Belfast United Kingdom School of Computer Science and Engineering University of New South Wales Sydney Australia

ISBN: (纸本)9798350379037

Early detection of skin cancer relies on precise segmentation of dermoscopic images of skin lesions. However, this task is challenging due to the irregular shape of the lesion, the lack of sharp borders, and the presence of artefacts such as marker colours and hair follicles. Recent methods for melanoma segmentation are U-Nets and fully connected networks (FCNs). As the depth of these neural network models increases, they can face issues like the vanishing gradient problem and parameter redundancy, potentially leading to a decrease in the Jaccard index of the segmentation model. In this study, we introduced a novel network named TESL-Net for the segmentation of skin lesions. The proposed TESL-Net involves a hybrid network that combines the local features of a CNN encoder-decoder architecture with long-range and temporal dependencies using bi-convolutionallong-short-term memory (Bi-ConvLSTM) networks and a Swin transformer. This enables the model to account for the uncertainty of segmentation over time and capture contextual channel relationships in the data. We evaluated the efficacy of TESL-Net in three commonly used datasets (ISIC 2016, ISIC 2017, and ISIC 2018) for the segmentation of skin lesions. The proposed TESL-Net achieves state-of-the-art performance, as evidenced by a significantly elevated Jaccard index demonstrated by empirical results. © 2024 IEEE.

关键词： Convolutional neural networks

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Wearable Saudi Sign Language Recognition Device Based on Neural Network 21

Wearable Saudi Sign Language Recognition Device Based on Neu...

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21st International Multi-Conference on Systems, Signals and Devices, SSD 2024

作者： Algahtani, Shouq Zuhairy, Rana Almarghalani, Olla Aljohani, Mawadda Elmanfaloty, Rania A. King Abdulaziz University Department of Electrical and Computer Engineering Jeddah Saudi Arabia Alexandria Higher Institute of Engineering and Technology Department of Electronics and Communication Engineering Alexandria Egypt

ISBN: (纸本)9798350374131

In Saudi Arabia, over 750,000 individuals are deaf or mute, often facing communication barriers due to limited sign language familiarity among the general population. This research introduces a wearable device leveraging neural networks to recog-nize Saudi Sign Language, aiming to bridge this communication gap. The device simultaneously processes data from both hands, integrating inputs from the non-dominant hand with those from the dominant. Three primary phases define the system: data acquisition, machine learning model development, and real-time sign recognition. Data were gathered using electromyography (EMG) and inertial measurement unit (IMU) sensors, facilitated by the Arduino IDE, with two Arduino Nano 33 BLE micro-controllers managing collection and transmission. The dataset, comprising 25 signs from four subjects with each sign repeated 20 times, informed the machine learning process. Utilizing Python, a model incorporating dual Convolutional Neural Networks for feature extraction from sensor readings and dense neural networks for training was built. Subsequent sign recognition was realized by deploying the model on Arduino via TensorFlow Lite. Impressively, this innovative system boasts a 92 % accuracy rate, marking a significant stride toward inclusive communication solutions. © 2024 IEEE.

关键词： Data acquisition

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Deep Learning PNN Based Fault Monitoring System for Three Phase Industrial Drive System 7

Deep Learning PNN Based Fault Monitoring System for Three Ph...

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7th International Conference on Circuit Power and Computing Technologies, ICCPCT 2024

作者： Chempavathy, B. Raju, K. David Mani, P.K. Vijayalakshmi, S. Janaki, N. Karthikeyan, D. New Horizon College of Engineering Department of Computer Science and Engineering Bangalore India Malla Reddy Engineering College for Women Department of Electrical and Electronics Engineering Hyderabad India Vel Tech Multi Tech Dr. Rangarajan Dr. Sakunthala Engineering College Department of Electrical and Electronics Engineering Chennai India K. Ramakrishnan College of Technology Department of Electrical and Electronics Engineering Tiruchirappalli Tamil Nadu Samayapuram India Technology and Advanced Studies Vels Institute of Science Department of Electrical and Electronics Engineering Tamilnadu Chennai India SRM Institute of Science and Technology Department of Electrical and Electronics Engineering Chennai India

ISBN: (纸本)9798350372816

Asynchronous motors are widely used in a variety of sectors. Even though induction motors are strong and dependable, they are susceptible to a variety of problems. Faults in induction motors can result in horrible events such as operator injuries, manufacturing disruption, and raw material loss. As a result, defect identification has become more crucial in induction motor management. Among the different problems that can occur in the motor, bearing failure is a severe issue that can cause catastrophic damage to the machine if not detected at an early stage of the fault. As consequently, the functioning of bushings in induction machinery must be checked on a regular basis. This research proposes a novel method for analyzing speed using discrete wavelet transform (DWT) and identifying bearing difficulties using Probabilistic Neural Network (PNN) optimized by improved salp swarm algorithm. The stator electricity is examined and classified when an induction motor is operating under varied loading conditions with good and troublesome bearings. Scale-Invariant Feature Transform (SIFT) is a feature extraction method that condenses visual content into a set of points that may be used to find similar patterns in other images. The proposed PNN classifier can classify different types of bearing faults, and the empirical findings validate the created approach. PNN-based motor bearing recognizing defects and diagnosis outperforms SVM and ANN classifiers in this regard. The Shortest path accuracy of the PNN Classifier is 98.5% respectively. © 2024 IEEE.

关键词： Discrete wavelet transforms

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TSDSI Standards Driven Implementation of Smart Radio Environment 17

TSDSI Standards Driven Implementation of Smart Radio Environ...

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17th IEEE International Conference on Advanced Networks and Telecommunications Systems, ANTS 2023

作者： Shrivastava, Amit K. Kumar, Satish Sahu, Khushboo Sonwani, Bhavesh Sharma, Dixit Srivastava, Dipti S. Garg, Navneet Kherani, Arzad Alam Bhatia, Vimal Mukherjee, Soumava Mukhopadhyay, Samrat Das, Priyanka Mahapatra, Rajarshi Das, Debanjan Lall, Brejesh Iit Department of Electrical Engineering and Computer Science Bhilai India Iiit Department of Electronics and Communication Engineering Bangalore India Iiit Department of Electronics and Communication Engineering Naya Raipur India Department of Electronics Engineering Dhanbad India Iit Department of Electrical Engineering Indore India Iit Department of Electrical Engineering Jodhpur India Iit Department of Electrical Engineering Delhi India

ISBN: (纸本)9798350307672

This paper presents a prototype implementation of a smart radio environment (SRE) based communication system. A reflecting plate, reconfigurable intelligent surface (RIS) is placed between the transmitter and receiver to build the SRE. The RIS can rotate to reflect the incident wave to a particular direction. The experimental model consists of a channel estimator (CE), a generic RIS controller (GRC), and a RIS-specific controller (RSC). The channel estimator is equipped with two universal software radio peripheral's (USRP's) that act as transmitter and receiver. Further, based on the desired throughput requirements, the CE sends commands to the GRC, which in turn sends commands to the RSC that controls the RIS. Since a RIS can be designed and fabricated in many ways, a Telecommunications Standards Development Society, India (TSDSI) standard is in place to list the interface/messages between the wireless system and the RIS. These standardized commands are used to exchange messages between the GRC and RSC. With this setup, the received power improvement of around 20 dBm has been achieved if RIS is deployed. Also, an algorithm at GRC finds the beamforming angle where the received power is maximized. © 2023 IEEE.

关键词： Controllers

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