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2024 International Conference on Communication, computer sciences and engineering, IC3SE 2024

作者： Srividhya, Ch. Sapthami, I. Nagpal, Amandeep Pachauri, Praveen Chandrashekar, Rakesh Jabbar, Ahmed Sabah Abed AL-Zahra Institute Of Aeronautical Engineering Department Of Computer Science And Engineering Telangana Hyderabad India Mlr Institute Of Technology Department Of Computer Science And Engineering Telangana Hyderabad India Lovely Professional University Phagwara India Government Polytechnic Siwan India New Horizon College Of Engineering Bangalore India College Of Medical Technology The Islamic University Medical Laboratory Technology Department Najaf Iraq

ISBN: (纸本)9798350366846

In the ever-growing field of radio communications, better ways of modeling and boosting channels are needed, especially for channels that change location in the near field. Here, we present a new way to use deep learning to improve the performance of radio communication channels. Our plan, called the 'Proposed Method,' uses complex neural network structures to model and improve channel features. We tested the proposed method against six well-known ones, including Orthogonal Matching Pursuit, Maximum Likelihood Estimation, Least Squares, Compressive Sensing, Singular Value Decomposition, and Beamforming, to see how well it works. To rate how well something works, we use things like Mean Absolute Error (MAE), Signal-to-Noise Ratio (SNR), Bit Error Rate (BER), Computational Complexity, and Root Mean Squared Error (RMSE). The results show that the Proposed Method is better because it models and estimates channels more accurately and has lower RMSE, MAE, BER, and MSE. The Proposed Method also has a higher signal-to-noise ratio (SNR), which means the quality of the data is better. In addition, because it is simple to compute, it is a good answer for real-world problems. © 2024 IEEE.

关键词： Beamforming

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Performance Analysis of Various on Demand Multipath Routing Protocols 4

Performance Analysis of Various on Demand Multipath Routing ...

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4th International Conference on Information Management and Machine Intelligence, ICIMMI 2022

作者： Goyal, Dinesh Yadav, Kalu Ram Dept. of Computer Science and Engineering Poornima Institute of Engineering and Technology Jaipur India Dept. of Computer Science and Engineering Government Polytechnic College Kota India

ISBN: (纸本)9781450399937

MANET is a network of self-organized mobile nodes whose topology changes dynamically. There is no pre existing infrastructure is available for network setup. Nodes in MANET are mobile in nature which acts as a router as well as host. For transmitting the data and forwarding it to the desire destination MANET use multi-hop communication to transmitting the data if nodes are not in direct communication range. MANET is self configuring in nature and nodes are also work in cooperative manner to meet the performance requirements. Many on-demand multipath routing protocols have been developed in recent years, with the designers claiming that their protocol has the greatest performance. We have been done on comparison and evaluation of existing three multipath routing (SMR, ROAM and MP-DSR) algorithms using NS2 (Network Simulator 2). This paper presents performance evaluations of SMR,ROAM and MP-DSR multipath routing protocols using *** is analyzed on the average delay and packet delivery ratio metrics . For performance comparison we consider the network of 25, 50 and 100 nodes that are dispersed at random around the region of 800mX800m. Other than MP-DSR and SMR, simulation findings indicate that the ROAM protocol performs better in terms of average end-to-end delay. Result analysis also demonstrates that ROAM is also performing better in terms of PDR. © 2022 ACM.

关键词： Routing protocols

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Chinese Speech Emotion Recognition Based on Pre-Trained Model 5

Chinese Speech Emotion Recognition Based on Pre-Trained Mode...

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5th International Conference on Big Data and Artificial Intelligence and Software engineering, ICBASE 2024

作者： Xu, Sukui Jiang, Nan Lian, Shun Pan, Jingmin Anhui Institute of Information Technology College of Computer and Software Engineering Wuhu China Fujian Polytechnic of Information Technology School of General Education Fuzhou China Anhui University School of Computer Science and Technology Hefei China

ISBN: (纸本)9798331506612

This paper proposes a method based on large-scale speech pre-trained models for the task of Chinese speech emotion recognition. Similar to the transfer learning approach in image classification task, speech pre-trained model is used as a fixed feature extractor to extract features from speech data, followed by a fully connected network for classification. Experimental results show that, on the seven-class speech emotion recognition task of the Chinese M3ED dataset, the proposed method achieves classification accuracies of 0.506 and 0.560 using base and large sized pre-trained models respectively, surpassing the baseline accuracy of 0.454 for the ECAPA-TDNN model. © 2024 IEEE.

关键词： Transfer learning

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3D Gaze Estimation via Binocular Disparity and Feature Refinement 10

3D Gaze Estimation via Binocular Disparity and Feature Refin...

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10th International Conference on Big Data Computing and Communications, BIGCOM 2024

作者： Wang, Chengxin Chen, Qiuxia Tian, Ying Yang, Jinfeng School of Computer Science and Software Engineering University of Science and Technology Liaoning Anshan China Institute of Applied Artificial Intelligence of the Guangdong-Hong Kong-Macao Greater Bay Area Shenzhen Polytechnic Shenzhen China

ISBN: (纸本)9798331509538

The eyes are the windows to the soul and are crucial for studying human behavior. Therefore, gaze estimation has attracted much attention in the field of computer vision. In recent years, convolutional neural networks have driven significant progress in gaze estimation research. Despite the remarkable achievements of gaze technology based on full-face images, the inability to obtain full-face images in specific scenarios makes research based on eye images particularly important. This paper proposes a three-dimensional gaze estimation model that takes binocular images as input, using VGG16 as the backbone network for feature extraction. To enhance the feature extraction capability, we have embedded channel attention and spatial attention mechanisms. Meanwhile, to improve the stability of gaze estimation, we classify real gaze angles. First, we perform a preliminary screening of gaze direction through a classification task, and then perform linear regression with the true angle. Finally, we optimize gaze features using binocular vision differences to finetune the gaze estimator. Experiments have shown that our method performs well on three public datasets. © 2024 IEEE.

关键词： Convolutional neural networks

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Packaging of 15-kV Silicon Carbide Half-Bridge Module Enabled by a Nonlinear Resistive Polymer Nanocomposite Field-Grading Coating 10

Packaging of 15-kV Silicon Carbide Half-Bridge Module Enable...

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10th IEEE Workshop on Wide Bandgap Power Devices and Applications, WiPDA 2023

作者： Zhang, Zichen Lu, Shengchang Nicholas, Carl Lynch, Justin Morgan, Adam Sung, Woongje Ngo, Khai D.T. Lu, Guo-Quan Virginia Tech Blacksburg CPES The Bradley Department of Electrical and Computer Engineering United States College of Nanoscale Science and Engineering SUNY Polytechnic Institute Albany United States

ISBN: (纸本)9798350337136

Emerging medium-voltage silicon carbide devices offer the potential to achieve more efficient and compact power electronics for grid-Tied applications. However, the lack of an effective insulation solution for packaging the devices has slowed their widespread adoption. In this work, a 15-kV silicon carbide MOSFET half-bridge module was designed and fabricated. The module was built by silver-sintering the device chips on an aluminum nitride direct-bond-copper substrate with 1.0-mm thick ceramic. The insulation of the module was enhanced by coating a nonlinear resistive polymer nanocomposite along the electrode edges on the substrate prior to silicone-gel encapsulation. The field-grading effect of the coating was tested on aluminum nitride direct-bond-copper substrates. The coated substrates showed an average partial discharge inception voltage of 20.2 kVpeak, which is 85% higher than the uncoated ones and 33% higher than the devices' rated voltage. Finally, prototypes of the modules with functional 15-kV dice were fabricated and tested. The blocking capability of the devices were unchanged after packaging. The fabricated modules in this work will serve as testbed for evaluating the coating's effectiveness under fast pulse-width modulation excitations during switching tests. © 2023 IEEE.

关键词： Substrates

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Application of TQWT based filter-bank for sleep apnea screening using ECG signals

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Journal of Ambient Intelligence and Humanized Computing 2024年第1期15卷 893-904页

作者： Nishad, Anurag Pachori, Ram Bilas Acharya, U. Rajendra Discipline of Electrical Engineering Indian Institute of Technology Indore Indore453552 India Department of Electronics and Computer Engineering Ngee Ann Polytechnic Singapore599489 Singapore Department of Biomedical Engineering School of Science and Technology SIM University Singapore599491 Singapore Department of Biomedical Engineering Faculty of Engineering University of Malaya Kuala Lumpur50603 Malaysia

The sleep apnea is a disease in which there is the absence of airflow during respiration for at least 10 s. It may occur several times during the night sleep. This disease can lead to many types of cardiovascular diseases. To detect this disease, signals obtained from many channels of polysomnography are to be observed visually by physicians for the long duration. This procedure is expensive, time-consuming, and subjective. Hence, it is required to build an automated system to detect the sleep apnea with few channels. This paper uses single-lead electrocardiogram (ECG) signal to detect apneic and non-apneic events. The proposed method uses tunable-Q wavelet transform (TQWT) based filter-bank instead of TQWT to decompose the segment of ECG signal into several constant bandwidth sub-band signals. Then centered correntropies are computed from the various sub-band signals. The obtained features are then fed to the various classifiers to select the optimum performing classifier. In this work, we have obtained the highest classification accuracy, specificity, and sensitivity of 92.78%, 93.91%, and 90.95% respectively using random forest classifier. Hence, our developed prototype is ready for validation with the huge database and clinical usage. © Springer-Verlag GmbH Germany, part of Springer Nature 2018.

关键词： Filter banks

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Enhancing Cyber Bullying Detection Using Convolutional Neural Network 4

Enhancing Cyber Bullying Detection Using Convolutional Neura...

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4th International Conference on Smart Electronics and Communication, ICOSEC 2023

作者： Dedeepya, P. Karishma, D. Manuri, Siva Ganesh Raghuvaran, Tata Shariff, Vahiduddin Sindhura, S. Pvp Siddhartha Institute of Technology Computer Science Department Vijayawada India Rgm College of Engineering and Technology Computer Science Department Nandyal India Sir C R Reddy College of Engineering Computer Science Department Eluru India Sir C R Reddy Polytechnic College Ece Department Eluru India Nri Institute of Technology Computer Science Department Agiripalli India

ISBN: (纸本)9798350300888

The abstract focuses on the prevalence of cyber bullying among young individuals on social media platforms. As the popularity of these platforms continues to grow, the prevalence of cyber bullying incidents is also increasing. The aim is to leverage Deep learning methods and create a DL model capable of automatically identifying social media bullying through the analysis of word similarities within tweets. To find word similarities in the tweets that were transcribed by bullies, use deep learning, to create a deep learning model that can mechanically detect bullying acts on social media. The purpose of the work is to demonstrate the creation of software which can recognize harassed tweets, posts, and other forms of online communication. It has been recommended for practice a model constructed on deep learning to find bullying on Twitter. CNN is preferred to understand the significance of text and to group it appropriately, whilst NLP is developed for the purpose of processing the records. Moreover, the Twitter API is utilized in order to get tweets, and then those tweets are fed into a model which concludes whether or not they establish bullying. © 2023 IEEE.

关键词： Convolutional neural networks

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Paddy disease classification using machine learning technique

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Materials Today: Proceedings 2022年 64卷 883-887页

作者： Sobiyaa, P. Jayareka, K.S. Maheshkumar, K. Naveena, S. Rao, Koppula Srinivas Department of Information Technology Bannari Amman Institute of Technology Sathyamangalam Tamil Nadu Erode India Department of Computer Science & Engineering Sona College of Technology Erode Erode India Department of Computer Engineering Sri Ranganathar Institute of Polytechnic College Athipalayam Tamil Nadu Coimbatore India Department of Computer Science & Engineering MLR Institute of Technology Hyderabad Hyderabad India

Early disease detection plays a vital role in protection of paddy crops. In earlier days the detection of disease was done through seeing or by examining in a laboratory. The observation made visually needs experts and it might vary for each individual which leads to error and laboratory testing requires more time and might not be able to deliver the outcome within a time. To get the better of this issue, image processing-based Machine learning approach used to detect the diseases and classify the diseases. We mainly focused on rice (Oryza sativa) diseases. The images contain the leaves and stems which are affected by disease collected from the paddy fields. The dataset contains five different classes of diseases (1) Rice Blast (2) Bacterial Leaf Blight (3) Sheath Blight (4) Healthy leaves. The early detection of diseases will help farmers to increase their yield. © 2022

关键词： Machine learning

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An Empirical Study of Domain Adaptation: Are We Really Learning Transferable Representations?

An Empirical Study of Domain Adaptation: Are We Really Learn...

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2022 IEEE International Conference on Big Data, Big Data 2022

作者： Josselyn, Nicholas Yin, Biao Zhang, Ziming Rundensteiner, Elke Department of Data Science Worcester Polytechnic Institute WorcesterMA United States Department of Computer Science Worcester Polytechnic Institute WorcesterMA United States Department of Electrical and Computer Engineering Worcester Polytechnic Institute WorcesterMA United States

ISBN: (纸本)9781665480451

Deep learning often relies on the availability of a large amount of high-quality labeled data, which can be very limited in novel domains. To address such data scarcity, domain adaptation is one promising approach that allows for deep networks to leverage large amounts of available data from a source domain to enhance the model's efficacy on the target domain of interest. However, while there is a plethora of alternate models for domain adaptation proposed over many years in the literature, there is a dearth of studies that objectively compare the relative effectiveness of these models in a rigorous, empirical study. To fill this gap, we provide a thorough, unbiased, empirical study of five state-of-the-art (SOTA) deep domain adaptation models proposed over the past 6 years whose codes are publicly available. Models are evaluated on the complex and diverse domain adaptation tasks featured in the DomainNet benchmark dataset as well as the popular Office-31 dataset. Our results suggest that (1) all 5 models perform similarly, on average, and do not even significantly beat the oldest model, and (2) counter to their intended purpose, the transfer loss functions in the literature do not contribute significantly to learning transferable representations. Our observations suggest that domain adaptation research needs to more thoroughly compare newly proposed models against existing works, along with assessing their loss functions' utility thoroughly. Our code and data splits are made public for reproducibility of results by the community. © 2022 IEEE.

关键词： Deep learning

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Studying the Different Platforms for Betterment of Mobile Healthcare Applications 1

Studying the Different Platforms for Betterment of Mobile He...

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1st International Conference on Healthcare Innovations, Software and engineering Technologies, HISET 2024

作者： Memon, Shabana Patil, Seema Chaturvedi, Aarti Narasimha Prasad, L.V. Patil, Supriya Dalavi, Dhanaji Department of Management Studies Pune India Jaywant College of Engineering and Polytechnic Department of Computer Science Engineering Karad India Jaywant College of Management Karad India Institute of Aeronautical Engineering Department of Computer Science and Engineering Hyderabad India Krishna Vishwa Vidyapeeth [Deemed to Be University Department of Obstetrics and Gynecology Karad India Mahavidyalaya Rethare Karad India

ISBN: (纸本)9798350360707

It is mostly known health is wealth, as if health is good, we can obtain more wealth but if health is not good, we cannot get wealth. The use of mobile devices by health care professionals (HCPs) has altered several aspects of clinical practice. The development of medical software programs, or applications, for these platforms has accelerated due to the increasing use of handheld gadgets in healthcare settings. A plethora of apps are available these days to assist medical professionals alongside a wide range of important tasks, such as time and information management, accessing and maintaining health records, patient communication, obtaining references and details, patient management and monitoring, clinical decision-making, and medical education and training. The advent of mobile computing devices, such as tablet computers and smartphones after the initial release of personal digital assistants (PDAs), has had a significant impact on numerous industries, including medical. Thus, with time there will be change in the platform with increasing mobile health for improving healthcare to reduce the lives of losses for small reasons. © 2024 IEEE.

关键词： Electronic health record

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