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Proceedings of the 2021 4th international conference on Computing and Communications Technologies, ICCCT 2021

Proceedings of the 2021 4th International Conference on Comp...

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4th international conference on Computing and Communications Technologies, ICCCT 2021

ISBN: (纸本)9781665414470

the proceedings contain 117 papers. the topics discussed include: character recognition Tamil language in printed images using convolutional neural network (CNN) analysis;face recognition and mask detection technology for surveillance systems;share market prediction using long short term memory and artificial neural network;convolutional recommended neural network system based on user reviews for movies;automated data driven preprocessing and training of classification models;reward based smart bin with waste segregation for encouraging people in waste management system;installation of automated sericulture functional unit in rural area;prediction of parkinson's disease using hybrid feature selection based techniques;and enhanced ransomware detection techniques using machine learning algorithms.

关键词： learning algorithms

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Improving Gravitational Wave Detection with 2D Convolutional Neural Networks 25

Improving Gravitational Wave Detection with 2D Convolutional...

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25th international conference on pattern recognition (ICPR)

作者： Fan, Siyu Wang, Yisen Luo, Yuan Schmitt, Alexander Yu, Shenghua Shanghai Jiao Tong Univ Dept Comp Sci & Engn Shanghai Peoples R China Peking Univ Key Lab Machine Percept MoE Sch EECS Beijing Peoples R China Katholieke Univ Leuven Fac Econ & Business FEB Brussels Belgium Chinese Acad Sci Joint Lab Radio Astron Technol Natl Astron Observ Beijing Peoples R China

ISBN: (纸本)9781728188089

Sensitive gravitational wave (GW) detectors such as that of Laser Interferometer Gravitational-wave Observatory (LIGO) realize the direct observation of GW signals that confirm Einstein's general theory of relativity. However, it remains challenges to quickly detect faint GW signals from a large number of time series with background noise under unknown probability distributions. Traditional methods such as matched-filtering in general assume Additive White Gaussian Noise (AWGN) and are far from being real-time due to its high computational complexity. To avoid these weaknesses, one-dimensional (1D) Convolutional Neural Networks (CNNs) are introduced to achieve fast online detection in milliseconds but do not have enough consideration on the trade-off between the frequency and time features, which will be revisited in this paper through data pre-processing and subsequent two-dimensional (2D) CNNs during offline training to improve the online detection sensitivity. In this work, the input data is pre-processed to form a 2D spectrum by Short-time Fourier transform (STFT), where frequency features are extracted without learning. then, carrying out two 1D convolutions across time and frequency axes respectively, and concatenating the time-amplitude and frequency-amplitude feature maps with equal proportion subsequently, the frequency and time features are treated equally as the input of our following two-dimensional CNNs. the simulation of our above ideas works on a generated data set with uniformly varying SNR (2-17), which combines the GW signal generated by PYCBC and the background noise sampled directly from LIGO. Satisfying the real-time online detection requirement without noise distribution assumption, the experiments of this paper demonstrate better performance on average compared to that of 1D CNNs, especially in the cases of lower SNR (4-9).

关键词： Signal analysis Deep learning Neural networks

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Subject-Transfer Decoding using the Convolutional Neural Network for Motor Imagery-based Brain-Computer Interface 44

Subject-Transfer Decoding using the Convolutional Neural Net...

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44th Annual international conference of the IEEE-Engineering-in-Medicine-and-Biology-Society (EMBC)

作者： Jeong, Ji-Hyeok Kim, Keun-Tae Kim, Dong-Joo Lee, Song Joo Kim, Hyungmin Korea Univ Dept Brain & Cognit Engn Seoul South Korea Korea Inst Sci & Technol Bion Res Ctr Seoul South Korea Korea Univ Dept Neurol Coll Med Seoul South Korea Korea Univ Dept Artificial Intelligence Seoul South Korea

ISBN: (数字)9781728127828

ISBN: (纸本)9781728127828

Various pattern-recognition or machine learning-based methods have recently been developed to improve the accuracy of the motor imagery (MI)-based brain-computer interface (BCI). However, more research is needed to reduce the training time to apply it to the real-world environment. In this study, we propose a subject-transfer decoding method based on a convolutional neural network (CNN) which is robust even with a small number of training trials. the proposed CNN was pre-trained with other subjects' MI data and then fine-tuned to the target subject's training MI data. We evaluated the proposed method using the BCI competition IV data2a, which had the 4-class MIs. Consequently, on the same test dataset, with changing the number of training trials, the proposed method showed better accuracy than the self-training method, which used only the target subject's data for training, as averaged 86.54 +/- 7.78% (288 trials), 85.76 +/- 8.00% (240 trials), 84.65 +/- 8.11% (192 trials), and 83.29 +/- 8.25% (144 trials), respectively, which was 4.94% (288 trials), 6.10% (240 trials), 9.03% (192 trials), and 12.31% (144 trials)-point higher than the self-training method. Consequently, the proposed method was shown to be effective in maintaining classification accuracy even with the reduced training trials.

关键词： Training learning systems Biological system modeling Brain-computer interfaces Decoding Convolutional neural networks

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Spectrum Management for Wireless Resources in 5G Networks using Gated Graph Convolutional Network and Whale Optimization Algorithm

Spectrum Management for Wireless Resources in 5G Networks us...

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Distributed Computing and Electrical Circuits and Electronics (ICDCECE), IEEE international conference on

作者： Kuzhaloli S Hyba AbdulJalee Lanka Padma Yogesh Ramaswamy K. Radha Department of Electrical and Electronics Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Chennai India Department of Computers Techniques Engineering College of Technical Engineering The Islamic University Najaf Iraq Department of Computer Science and Engineering SRKR Engineering College Bhimavaram India Department of AI & DS Vivekananda College of Engineering for Women Namakkal India

ISBN: (数字)9798331533663

ISBN: (纸本)9798331533670

Recently, Artificial Intelligence (AI)-driven spectrum management in 5G networks leverages learning techniques to reduce allocation of resources and identify the patterns in traffic that leads to effective communication of wireless devices. However, it faced challengers include complex network and providing security to data. To overcome these challenges, various machine learning (ML) techniques are used to recognize patterns from complex data and make accurate predictions and Deep learning (DL) techniques used to handle high dimensional data. Initially, the Multiple Input Multiple Output (MIMO) signal transmission is processed through Proximal Policy Optimization (PPO) for dynamic beamforming. Further, channel estimation is performed using Gated Graph Convolutional Network (GGCN) to enhance multi-user spectrum efficiency. Finally, Improved Whale Optimization Algorithm (IWOA) is utilized for spectrum optimization. the proposed GGCN-IWOA model acquired better results with 5.3 Mean Square Error (MSE), 4.2 Mean Absolute Error (MAE) and 2.8 Root Mean Square Error (RMSE) when compared to Convolutional Neural Network with Gated Recurrent Units (CNN-GRU) model respectively.

关键词： Graph convolutional networks Array signal processing 5G mobile communication Complex networks Logic gates Whale optimization algorithms pattern recognition Communication system security Optimization Radio spectrum management

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data Augment Method for Power System Transient Stability Assessment Based on DeepSMOTE

Data Augment Method for Power System Transient Stability Ass...

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Power Engineering (ICPE), international conference on

作者： Binyu Ma Jun Yang Jinpeng Shen Yangzhou Pei Xiaotao Peng Kezheng Jiang Dan Liu Kan Cao Hubei Engineering and Technology Research Center for AC/DC Intelligent Distribution Network School of Electrical Engineering and Automation School of Electrical Engineering and Automation Wuhan University Wuhan University Wuhan China State Grid Hubei Electric Power Research Institute Wuhan China

data-driven transient stability assessment (TSA) method has become the focus of power system stability and security research in recent years. However, in actual power system, transient unstable data only accounts for a small portion of all data. Unbalanced data is not conducive to the machine learning model's judgment of instability situations. A data augment method based on DeepSMOTE is proposed to generate high-quality unstable datasets. then, an extreme learning machine (ELM) classifier is used to select the generated dataset with the highest G-mean value, which is used to fuse with the original dataset to construct a balanced dataset with a similar number of transient stability and instability samples. Finally, TSA is implemented based on the balanced dataset. It has been verified in the modified IEEE 39-bus system simulation example containing new energy that DeepSMOTE can effectively generate data that follows the distribution characteristics of the original data of power system, thereby improving the recognition rate of unstable samples in TSA. Moreover, it has strong robustness under wide area noise and effectively balances power system instability data.

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AI-powered Lung Cancer Diagnosis: the Potential of Artificial Neural Networks

AI-powered Lung Cancer Diagnosis: The Potential of Artificia...

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Innovation in Technology (ASIANCON), Asian conference on

作者： K Akshitha Roopashree R Ashwini Kodipalli Trupthi Rao Dept of AI and DS Global Academy of Technology Banglore Karnataka India

ISBN: (数字)9798350354218

ISBN: (纸本)9798350354225

the persistence of lung cancer on the global health scene necessitates the need for early and accurate techniques that may improve patient survival. Recent breakthroughs in Artificial Neural Networks (ANNs) have shown potential in medical image analysis mainly used in detecting and classifying lung cancer from imaging data including clinical datasets. the current study investigates the utilization of ANN models in detecting lung cancers using datasets classified into malignant and benign. the approaches involve elaborate data preprocessing, optimizing ANN architecture with different activation functions, and incorporating dropout mechanism to reduce overfitting. Furthermore, findings indicate that optimized ANN models give high accuracy rates of up to 95% comparable to traditional machine learning techniques thus establishing their efficiency in noninvasive lung cancer detection.

关键词： Technological innovation Accuracy Lung cancer Artificial neural networks Transforms Predictive models pattern recognition Reliability Predictive analytics Random forests

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Enhancing Few-Shot learning with Integrated data and GAN Model Approaches

Enhancing Few-Shot Learning with Integrated Data and GAN Mod...

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Digital Society and Intelligent Systems (DSInS), international conference on

作者： Yinqiu Feng Aoran Shen Jiacheng Hu Yingbin Liang Shiru Wang Junliang Du Columbia University New York USA University of Michigan Ann Arbor USA Tulane University New Orleans USA Northeastern University Seattle USA Dartmouth College Hanover USA Shanghai Jiao Tong University Shanghai China

ISBN: (数字)9798331528829

ISBN: (纸本)9798331528836

this paper presents an innovative approach to enhancing few-shot learning by integrating data augmentation with model fine-tuning in a framework designed to tackle the challenges posed by small-sample data. Recognizing the critical limitations of traditional machine learning models that require large datasets-especially in fields such as drug discovery, target recognition, and malicious traffic detection-this study proposes a novel strategy that leverages Generative Adversarial Networks (GANs) and advanced optimization techniques to improve model performance with limited data. Specifically, the paper addresses the noise and bias issues introduced by data augmentation methods, contrasting them with model-based approaches, such as fine-tuning and metric learning, which rely heavily on related datasets. By combining Markov Chain Monte Carlo (MCMC) sampling and discriminative model ensemble strategies within a GAN framework, the proposed model adjusts generative and discriminative distributions to simulate a broader range of relevant data. Furthermore, it employs MHLoss and a reparameterized GAN ensemble to enhance stability and accelerate convergence, ultimately leading to improved classification performance on small-sample images and structured datasets. Results confirm that the MhERGAN algorithm developed in this research is highly effective for few-shot learning, offering a practical solution that bridges data scarcity with high-performing model adaptability and generalization.

关键词： Training Adaptation models Target recognition Generative adversarial networks data augmentation data models Drug discovery Artificial intelligence Few shot learning Convergence

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machine learning-Based Scene Classification Using thepade’s SBTC, LBP, and GLCM 4th

Machine Learning-Based Scene Classification Using Thepade’s...

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4th international conference on Futuristic Trends in Networks and Computing Technologies, FTNCT 2021

作者： thepade, Sudeep D. Idhate, Mrunal E. Department of Computer Engineering Pimpri Chinchwad College of Engineering Pune411044 India

ISBN: (纸本)9789811950360

Photographs have become a necessary part of human life. Images are being captured all across the world. One of the significant issues in scene classification is that the photographs are taken in a variety of locations. there are many diverse scenes in these locations, such as an airport, a restaurant, a cafeteria, and a forest. Indoor scene classification and outdoor scene classification are the most common types of scene categorization. Scene classification is challenging, which poses issues in research and image processing. Image classification becomes challenging because of the numerous distinct items present in various sceneries, causing a machine to become confused about the scene’s categories. the current challenge with scene classification results in low-classification accuracy. the object comes in various shapes and sizes and can be found in various locations, which is one of the reasons for the scene classification’s poor accuracy. We will perform feature extraction for pattern recognition and multiple machine learning models for image categorization using the intel image classification dataset. thepade’s SBTC n-ary feature extraction approach for RGB and LUV color plane models and fusion LBP and GLCM is presented in this study. When the three individual feature extraction methods are compared with the fusion of thepade’s SBTC and GLCM with RGB color plane, the fusion of thepade’s SBTC and GLCM with RGB color plane yielded greater accuracy. © 2022, the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Feature extraction

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Spatiotemporal Visual Tracker Based on Deformable Attention

Spatiotemporal Visual Tracker Based on Deformable Attention

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pattern recognition and machine learning (PRML), IEEE international conference on

作者： Zhuo Wu Askar Hamdulla Yucheng Huang School of Computer Science and Technology(School of Cyberspace Security) Xinjiang University Urumqi China School of Intelligence Science and Technology(School of Future Technology) Xinjiang University Urumqi China

ISBN: (数字)9798350355925

ISBN: (纸本)9798350355932

At present, the visual tracking method based on Transformer has made significant progress, and has shown excellent performance on various data sets. However, the traditional Transformer tracker pays too much attention to global features, and its intensive attention calculation will increase the space-time complexity of the tracking algorithm. And the features of the relevant region will be affected by the features of the non-relevant region, resulting in a decrease in tracking accuracy. In addition, the Transformer tracker lacks the ability to utilize the spatiotemporal information between frames, which further hinders the improvement of its tracking performance. To solve these problems, this paper proposes a new tracker SVT-DA based on deformable attention. the tracker captures local and global important features through CNN and deformable attention. In addition, it uses a cross-attention module nested with self-attention to achieve deep semantic information interaction between template features and search features. In order to verify the effectiveness of the proposed method, we conduct experiments on LaSOT, GOT-10K, OTB100 and VAU123 datasets. the result indicators are 4%~5% higher than the baseline method. the experimental results show that our method improves the tracking performance while reducing the number of model parameters, and is highly competitive with other state-of-the-art methods.

关键词： Visualization Target tracking Semantics Transformers Feature extraction Spatiotemporal phenomena pattern recognition Convolutional neural networks Information exchange Resilience

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Classifying Eye-Tracking data Using Saliency Maps 25

Classifying Eye-Tracking Data Using Saliency Maps

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25th international conference on pattern recognition (ICPR)

作者： Rahman, Shafin Rahman, Sejuti Shahid, Omar Abdullah, Md Tahmeed Sourov, Jubair Ahmed North South Univ Elect & Comp Engn Dhaka Bangladesh Univ Dhaka Robot & Mechatron Engn Dhaka Bangladesh

ISBN: (纸本)9781728188089

A plethora of research in the literature shows how human eye fixation pattern varies depending on different factors, including genetics, age, social functioning, cognitive functioning, and so on. Analysis of these variations in visual attention has already elicited two potential research avenues: 1) determining the physiological or psychological state of the subject and 2) predicting the tasks associated with the act of viewing from the recorded eye-Iixation data. To this end, this paper proposes a visual saliency based novel feature extraction method for automatic and quantitative classification of eye-tracking data, which is applicable to both of the research directions. Instead of directly extracting features from the fixation data, this method employs several well-known computational models of visual attention to predict eye fixation locations as saliency maps. Comparing the saliency amplitudes, similarity and dissimilarity of saliency maps with the corresponding eye fixations maps gives an extra dimension of information which is effectively utilized to generate discriminative features to classify the eye-tracking data. Extensive experimentation using Saliency4ASD [1], Age Prediction [2], and Visual Perceptual Task [3] dataset show that our saliency-based feature can achieve superior performance, outperforming the previous state-of-the-art methods [2], [4], 5] by a considerable margin. Moreover, unlike the existing application-specific solutions, our method demonstrates performance improvement across three distinct problems from the real-life domain: Autism Spectrum Disorder screening, toddler age prediction, and human visual perceptual task classification, providing a general paradigm that utilizes the extra-information inherent in saliency maps for a more accurate classification.

关键词： Eye-tracking Visual Saliency Autism Spectrum Disorder Toddler Age Prediction Visual Perceptual Task

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