2023 IEEE 5th International Conference on Artificial Intelligence Circuits and Systems (AICAS)最新文献_第8页

Efficient Parameter Learning of Bayesian Network with Latent Variables from High-Dimensional Data 高维数据隐变量贝叶斯网络的有效参数学习

2023 IEEE 5th International Conference on Artificial Intelligence Circuits and Systems (AICAS) Pub Date : 2023-06-11 DOI: 10.1109/AICAS57966.2023.10168662

Xinran Wu, Xiang Chen, Kun Yue

引用次数: 1

Computer-Aided-Prediction of Body Constitution with Efficient Cock-Tail Learning 基于高效鸡尾学习的体质计算机辅助预测

2023 IEEE 5th International Conference on Artificial Intelligence Circuits and Systems (AICAS) Pub Date : 2023-06-11 DOI: 10.1109/AICAS57966.2023.10168613

Guang Shi, Yirong Kan, Renyuan Zhang

{"title":"Computer-Aided-Prediction of Body Constitution with Efficient Cock-Tail Learning","authors":"Guang Shi, Yirong Kan, Renyuan Zhang","doi":"10.1109/AICAS57966.2023.10168613","DOIUrl":"https://doi.org/10.1109/AICAS57966.2023.10168613","url":null,"abstract":"In this paper, the clinical data thru the questionnaire of body constitution (BC) is analyzed by multiple efficient machine learning algorithms for wide use in traditional Chinese medicine (TCM). This research aims at precisely categorizing the BCs from the life-style; offering the health guidance on the life-styles for recovering the so-called \"biased\" BCs to the healthy status known as the \"Gentle BC\". The key features of life-style are identified by machine learning (ML). However, the conventional sole ML algorithm for such application, known as random forest (RF), partial least squares (PLS), or least absolute shrinkage and selection operator (LASSO) hardly offers a small set of significant life-style features. In this work, a special scheme of LASSO learning technology is developed for identifying the reasonably few medical features and improve the diagnosis accuracy simultaneously. By pairing each \"biased\" BC against the gentle BC, the categorization task is conducted with reduced features. Similarly to the federated learning process, the common features among multiple algorithms are refined. From the real clinical data validation, the BC categorization accuracy is 94.6% which is 24.7% higher than the state-of-the-art (SOTA) works; the average key features are reduced to 17 where the best effort of SOTA is 31. Finally, the common key features are summarized among multiple algorithms.","PeriodicalId":296649,"journal":{"name":"2023 IEEE 5th International Conference on Artificial Intelligence Circuits and Systems (AICAS)","volume":"25 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116118763","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A 40nm area-efficient Effective-bit-combination-based DNN accelerator with the reconfigurable multiplier 具有可重构乘法器的40nm区域高效有效位组合DNN加速器

2023 IEEE 5th International Conference on Artificial Intelligence Circuits and Systems (AICAS) Pub Date : 2023-06-11 DOI: 10.1109/AICAS57966.2023.10168550

Yanghan Zheng, Zhaofang Li, Kaihang Sun, Kuang Lee, K. Tang

引用次数: 0

PCB Identification Based on Machine Learning Utilizing Power Consumption Variability 基于功耗可变性的机器学习的PCB识别

2023 IEEE 5th International Conference on Artificial Intelligence Circuits and Systems (AICAS) Pub Date : 2023-06-11 DOI: 10.1109/AICAS57966.2023.10168655

Anupam Golder, A. Raychowdhury

引用次数: 0

Live Demonstration: Supervised-learning-based Visual Quantification for Image Enhancement 现场演示:基于监督学习的图像增强视觉量化

2023 IEEE 5th International Conference on Artificial Intelligence Circuits and Systems (AICAS) Pub Date : 2023-06-11 DOI: 10.1109/AICAS57966.2023.10168650

W. Zhang, Junfeng Chang, Zizhao Peng, Lei Chen, F. An

引用次数: 0

Multi-Head Attention based Bi-LSTM for Anomaly Detection in Multivariate Time-Series of WSN 基于多头关注的双lstm的WSN多元时间序列异常检测

2023 IEEE 5th International Conference on Artificial Intelligence Circuits and Systems (AICAS) Pub Date : 2023-06-11 DOI: 10.1109/AICAS57966.2023.10168670

Mustafa Matar, Tian Xia, Kimberly Huguenard, D. Huston, S. Wshah

{"title":"Multi-Head Attention based Bi-LSTM for Anomaly Detection in Multivariate Time-Series of WSN","authors":"Mustafa Matar, Tian Xia, Kimberly Huguenard, D. Huston, S. Wshah","doi":"10.1109/AICAS57966.2023.10168670","DOIUrl":"https://doi.org/10.1109/AICAS57966.2023.10168670","url":null,"abstract":"Anomaly detection is a widely utilized technique in the field of wireless sensor networks (WSNs) data stream analysis, aimed at identifying unusual events or anomalies in an early stage. However, the constraints of WSN applications pose a significant challenge in achieving effective and efficient anomaly detection. In this work, we proposes a new multi-head attention-based Bi-LSTM approach for anomaly detection in multivariate time-series. Rather than modeling the time series of individual sensor independently, the proposed approach models the time series of multiple sensors concurrently, taking into account potential latent interactions among them, thereby enhancing the accuracy of anomaly detection. The proposed approach does not require labeled data and can be directly applied in real-world scenarios where labeling a large stream of data from heterogeneous sensors is both difficult and time-consuming. Finally, empirical evaluations using a real-world WSN demonstrate effectiveness and robustness of the proposed approach, outperforming traditional deep learning approaches.","PeriodicalId":296649,"journal":{"name":"2023 IEEE 5th International Conference on Artificial Intelligence Circuits and Systems (AICAS)","volume":"50 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131063422","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1

LungHeart-AtMe: Adventitious Cardiopulmonary Sounds Classification Using MMoE with STFT and MFCCs Spectrograms LungHeart-AtMe:使用MMoE与STFT和MFCCs频谱进行非定式心肺音分类

2023 IEEE 5th International Conference on Artificial Intelligence Circuits and Systems (AICAS) Pub Date : 2023-06-11 DOI: 10.1109/AICAS57966.2023.10168624

Changyan Chen†, Qing Zhang, Shirui Sheng, Huajie Huang, Yuhang Zhang, Yongfu Li

{"title":"LungHeart-AtMe: Adventitious Cardiopulmonary Sounds Classification Using MMoE with STFT and MFCCs Spectrograms","authors":"Changyan Chen†, Qing Zhang, Shirui Sheng, Huajie Huang, Yuhang Zhang, Yongfu Li","doi":"10.1109/AICAS57966.2023.10168624","DOIUrl":"https://doi.org/10.1109/AICAS57966.2023.10168624","url":null,"abstract":"Adventitious cardiopulmonary (lung and heart) sound detection and classification through a digital stethoscope plays a vital role in early diagnosis and telehealth services. However, automatically detecting the adventitious sounds is challenging since they are easily susceptible to each other’s influence and noises. In this paper, for the first time, we simultaneously classify adventitious lung and heart sounds using our proposed LungHeart-AtMe model based on a mixed dataset of the ICBHI 2017 lung sounds dataset and the PhysioNet 2016 heart sounds dataset. Based on characteristics of lung and heart sounds, Wavelet Decomposition is applied first to perform noise reduction, then two time-frequency feature extraction techniques, which are Short Time Fourier Transform (STFT) and Mel Frequency Cepstral Coefficients (MFCCs), are chosen to extract preliminary features of sounds and transform sounds data to spectrograms that are easy to analyze. Our LungHeart-AtMe model is improved by introducing MMoE structure and by using the attention mechanism-based CNN model to extend its global feature extraction capability. From our experimental result, LungHeart-AtMe has achieved a Sensitivity of 71.55% and a Specificity of 28.06% for cardiopulmonary sounds classification.","PeriodicalId":296649,"journal":{"name":"2023 IEEE 5th International Conference on Artificial Intelligence Circuits and Systems (AICAS)","volume":"7 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129622406","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1

Live Demonstration: Face Recognition at The Edge Using Fast On-Chip Deep Learning Neuromorphic Chip 现场演示:使用快速片上深度学习神经形态芯片的边缘人脸识别

2023 IEEE 5th International Conference on Artificial Intelligence Circuits and Systems (AICAS) Pub Date : 2023-06-11 DOI: 10.1109/AICAS57966.2023.10168667

Zhengqing Zhong, Tengxiao Wang, Haibing Wang, Zhihua Zhou, Junxian He, Fang Tang, Xichuan Zhou, Shuangming Yu, Liyuan Liu, N. Wu, Min Tian, Cong Shi

{"title":"Live Demonstration: Face Recognition at The Edge Using Fast On-Chip Deep Learning Neuromorphic Chip","authors":"Zhengqing Zhong, Tengxiao Wang, Haibing Wang, Zhihua Zhou, Junxian He, Fang Tang, Xichuan Zhou, Shuangming Yu, Liyuan Liu, N. Wu, Min Tian, Cong Shi","doi":"10.1109/AICAS57966.2023.10168667","DOIUrl":"https://doi.org/10.1109/AICAS57966.2023.10168667","url":null,"abstract":"Spiking neural networks (SNNs) and neuromorphic systems have attracted ever increasing interests recently, due to their high computational and energy efficiencies originated from closely imitating the functional mechanism of cerebral cortex, which adopts sparse spikes for information processing. In this work, we present a low-cost real-time face recognition system for potential edge-side intelligent applications. This system is mainly built upon our prior reported MorphBungee neuromorphic chip, which is capable of fast on-chip deep learning for fully-connected (FC) SNN of up to 4 layers, 1K spiking neurons and 256K synapses, under a low power consumption of about 100 mW. Our face recognition system achieves 20-fps and 30-fps image frame rates for real-life human face learning and inference, respectively, and obtains a high face recognition accuracy of 100% among 6 persons. It demonstrates that our face recognition system with the neuromorphic chip is suitable for resource-limited real-time intelligent edge applications.","PeriodicalId":296649,"journal":{"name":"2023 IEEE 5th International Conference on Artificial Intelligence Circuits and Systems (AICAS)","volume":"29 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124562046","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Convergent Waveform Relaxation Schemes for the Transient Analysis of Associative ReLU Arrays 关联ReLU阵列暂态分析的收敛波形松弛方案

2023 IEEE 5th International Conference on Artificial Intelligence Circuits and Systems (AICAS) Pub Date : 2023-06-11 DOI: 10.1109/AICAS57966.2023.10168567

I. Elfadel

引用次数: 0

An Interpretable Pixel Intensity Reconstruction Model for Asynchronous Event Camera 异步事件相机的可解释像素强度重建模型

2023 IEEE 5th International Conference on Artificial Intelligence Circuits and Systems (AICAS) Pub Date : 2023-06-11 DOI: 10.1109/AICAS57966.2023.10168635

Hongwei Shan, Lichen Feng, Yueqi Zhang, Zhangming Zhu

引用次数: 0