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Connected Systems & Intelligence (CSI), International Conference on

作者： Disha S Deekshitha B Anwitha A Kavyashree U M Shrikanth Rao S.K Roshan Joy Martis Dept. of Electronics and Communication Engineering Vivekananda College of Engineering & Technology Puttur D.K India Dept. of Computer Science and Engineering Global Academy of Technology Bengaluru India

ISBN: (数字)9781665458153

ISBN: (纸本)9781665458160

Atrial Fibrillation (AF) is a life-threatening heart rhythm disorder. AF diagnosis is very essential and important aspect for healthcare professionals. Early detection of AF using Electrocardiogram (ECG) plays an important role in the clinical practice. Manual interpretation of ECG signals to detect AF is time-consuming and needs higher expertise, and it is subject to variability among experts. Detecting AF in a timely and effective manner still remains a difficult challenge. In this paper, we propose a Deep Learning (DL) based AF detection method using Physionet challenge 2017 dataset. VGG16 architecture is used for the classification purpose. With the help of Discrete Wavelet Transform (DWT) the ECG signals are denoised. The RR intervals are computed and are subjected to VGG16 for classification. The class specific accuracies of normal, AF, and other rhythms are calculated. The proposed method achieves overall accuracy of 97.60%. The proposed method can be used as an assisted tool by the physician in their clinical practice.

关键词： Deep learning Industries Heart Heart beat Atrial fibrillation Medical services Transforms

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Adaptive Wavelet Filters as Practical Texture Feature Amplifiers for Parkinson’s Disease Screening in OCT

arXiv

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arXiv 2025年

作者： Zhang, Xiaoqing Shi, Hanfeng Li, Xiangyu Ye, Haili Xu, Tao Li, Na Hu, Yan Lv, Fan Chen, Jiangfan Liu, Jiang Center for High Performance Computing and Shenzhen Key Laboratory of Intelligent Bioinformatics Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Shenzhen China Research Institute of Trustworthy Autonomous Systems Department of Computer Science and Engineering Southern University of Science and Technology Shenzhen518055 China State Key Laboratory of Ophthalmology Optometry and Vision Science Wenzhou Medical University Wenzhou China Centre for Computational Science and Mathematical Modelling Coventry University Coventry United Kingdom School of Computer Science University of Nottingham Ningbo China Ningbo315100 China School of Ophthalmology and Optometry Wenzhou Medical University Wenzhou325035 China Department of Electronic and Information Engineering Changchun University Changchun130022 China

Parkinson’s disease (PD) is a prevalent neurodegenerative disorder globally. The eye’s retina is an extension of the brain and has great potential in PD screening. Recent studies have suggested that texture features extracted from retinal layers can be adopted as biomarkers for PD diagnosis under optical coherence tomography (OCT) images. Frequency domain learning techniques can enhance the feature representations of deep neural networks (DNNs) by decomposing frequency components involving rich texture features. Additionally, previous works have not exploited texture features for automated PD screening in OCT. Motivated by the above analysis, we propose a novel Adaptive Wavelet Filter (AWF) that serves as the Practical Texture Feature Amplifier to fully leverage the merits of texture features to boost the PD screening performance of DNNs with the aid of frequency domain learning. Specifically, AWF first enhances texture feature representation diversities via channel mixer, then emphasizes informative texture feature representations with the well-designed adaptive wavelet filtering token mixer. By combining the AWFs with the DNN stem, AWFNet is constructed for automated PD screening. Additionally, we introduce a novel Balanced Confidence (BC) Loss by mining the potential of sample-wise predicted probabilities of all classes and class frequency prior, to further boost the PD screening performance and trustworthiness of AWFNet. The extensive experiments manifest the superiority of our AWFNet and BC over state-of-the-art methods in terms of PD screening performance and trustworthiness. © 2025, CC BY-NC-SA.

关键词： Neurodegenerative diseases

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Human Age and Gender Prediction using Machine Learning Algorithm

Human Age and Gender Prediction using Machine Learning Algor...

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Mobile Networks and Wireless Communications (ICMNWC), IEEE International Conference on

作者： Jayaprada S. Hiremath Shantakumar B. Patil Premjyoti S. Patil Computer Science and Engineering VTU Belgaum India Sai Vidya Institute of Technology Bengaluru India Nagarjuna College of Engineering and Technology Bengaluru India

ISBN: (纸本)9781665446075

From the past few years, much exertion has been carried out for age assessment & recognition of gender through face images. The images of Human’s faces uncover different data like gender, age, and nationality. computer vision and pattern recognition in the discipline of computer vision represent an ideal role in human beings through automatic detection of face, recognition of gesture and face, and finding age and gender. Here through the paper describes a survey on predicting age and gender using face images. Various research has been developed on age and gender prediction using various features like face features, body pose, MRI brain images, giants, and pam. The face features can give more features for age and gender identification.

关键词： Wireless communication computer vision Machine learning algorithms Image recognition Face recognition Magnetic resonance imaging Video surveillance

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Green Extraction and Optimization of Bioactive Compounds from Solanum Torvum Swartz. Using Ultrasound-Aided Solvent Extraction Method Through Rsm, Anfis and Machine Learning Algorithm

SSRN

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SSRN 2023年

作者： Petchimuthu, Priya Sumanth, Gorla Buchayyagari Kunjiappan, Selvaraj Kannan, Suthendran Pandian, Sureshbabu Ramkumar Sundar, Krishnan Department of Biotechnology Kalasalingam Academy of Research and Education Tamilnadu Krishnankoil626126 India Department of Computer Science and Engineering Kalasalingam Academy of Research and Education Tamilnadu Krishnankoil626126 India Department of Information Technology Kalasalingam Academy of Research and Education Tamilnadu Krishnankoil626126 India

Biologically important phytocompounds based on the total polyphenolic, flavonoid contents and their free radical scavenging potential were completely cropped from Solanum torvum Sw. through ultrasound-aided solvent extraction (UASE) method. The face-centered central composite design (FCCD) in response surface methodology (RSM) was applied to optimize the effective extraction parameters, and adaptive neuro-fuzzy inference system (ANFIS), as well as machine learning (ML) algorithm models were used to validate the optimized extraction parameters for the highest yield of bioactive phytocompounds. The effect of five extraction parameters, namely methanol concentration (X1: 55-65% in v/v with water), ultrasound-intensity (X2: 50-60 W cm-2), temperature (X3: 30-45 °C), time (X4: 15-25 minutes) and particle size (X5: 0.5-1.5 mm) at five levels (very low (-2.37), low (-1), medium (0), high (+1) and very high (+2.37)) were studied to highest yields of total polyphenolic (TP;(y1)), total flavonoid contents (TF;(y2)) and their free radical scavenging abilities (%DPPH*sc (y3), %ABTS*sc(y4), and %H2O2*sc (y5)). The optimal condition was attained at X1=65%, X2= 60 Wcm-2, X3=45 °C, X4=15 minutes, and X5=0.5 mm, under this condition, the highest yields of y1= 830.677 mg gallic acid equivalents (GA)/g, y2=535.38 mg rutin equivalents (RU)/g, y3=75.22%, y4=84.12%, and y5=85.02%. Most of the experimental values well matched the predicted responses of the RSM model. Further, ANFIS and random forest-machine learning algorithm were utilized to compare and validate the optimized extraction parameters. Also, GC-MS spectrometry was carried out to investigate the bioactive phytocompound’s presence in the optimized extract. © 2023, The Authors. All rights reserved.

关键词： Ultrasonics

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Cross-Fundus Transformer for Multi-modal Diabetic Retinopathy Grading with Cataract

arXiv

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arXiv 2024年

作者： Xiao, Fan Hou, Junlin Zhao, Ruiwei Feng, Rui Zou, Haidong Lu, Lina Xu, Yi Zhang, Juzhao Academy for Engineering and Technology Fudan University Shanghai China School of Computer Science Fudan University Shanghai China Shanghai Eye Diseases Prevention & Treatment Center Shanghai China Shanghai Jiao Tong University School of Medicine Shanghai China

Diabetic retinopathy (DR) is a leading cause of blindness worldwide and a common complication of diabetes. As two different imaging tools for DR grading, color fundus photography (CFP) and infrared fundus photography (IFP) are highly-correlated and complementary in clinical applications. To the best of our knowledge, this is the first study that explores a novel multi-modal deep learning framework to fuse the information from CFP and IFP towards more accurate DR grading. Specifically, we construct a dual-stream architecture Cross-Fundus Transformer (CFT) to fuse the ViT-based features of two fundus image modalities. In particular, a meticulously engineered Cross-Fundus Attention (CFA) module is introduced to capture the correspondence between CFP and IFP images. Moreover, we adopt both the single-modality and multi-modality supervisions to maximize the overall performance for DR grading. Extensive experiments on a clinical dataset consisting of 1,713 pairs of multi-modal fundus images demonstrate the superiority of our proposed method. Our code will be released for public access. © 2024, CC BY-NC-ND.

关键词： Color photography

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Ultra-Low Energy 5G IoT Devices with Updatable AI Models: A Dynamic AI-IoT Perspectives

Ultra-Low Energy 5G IoT Devices with Updatable AI Models: A ...

引用

Communications and Information Technologies (GCCIT), Global Conference on

作者： Dharmesh Dhabliya K. Yuvaraj Rahul Chauhan Dikshit Sharma Kala Priyadarshini G Shankar Prasad S Department of Information Technology Vishwakarma Institute of Technology Pune India Department of Computer Science Karpagam Academy of Higher Education Coimbatore Computer Science and Engineering Graphic Era Hill University Dehradun Adjunct Professor Graphic Era Deemed to be University Dehradun Uttarakhand India Centre of Research Impact and Outcome Chitkara University Rajpura Punjab India Department of Electrical and Electronics Engineering Prince Shri Venkateshwara Padmavathy Engineering College Chennai School of Management - UG Management JAIN (Deemed to be University) Bangalore Karnataka

ISBN: (数字)9798350388916

ISBN: (纸本)9798350388923

Ultra-Low-Power (ULP) Internet of Things (IoT) devices are becoming more important in the fast developing 5G and possible 6G networks, and this study tackles the problem of incorporating dynamic AI capabilities into these devices. We present the Dynamic AI-IoT design, a new framework that aims to do away with the tedium of firmware upgrades. This design makes use of Narrowband IoT (NB-IoT) to make cloud interactions run smoothly and includes specialized firmware modifications to make dynamic interactions with TinyML models possible. Many sectors rely on deep learning that is based on massive amounts of high-quality data. The information accumulation of the framework relies on real-time acquisitions, which makes it difficult to directly implement deep learning in a real-time system. But such systems' analytical activities can't be done by collecting data for a lengthy period since they need to be executed in real time. This research presents a novel progressive deep-learning architecture and performs experiments on picture identification to address the challenges of high-quality data collection, high-timeliness data processing, and the difficulties of directly integrating deep-learning algorithms in real-time systems. The experimental findings demonstrate the efficacy, performance, and ability to get a conclusion comparable to the deep-learning architecture using large-scale data. An advanced method for managing memory is presented for the effective handling of AI tasks; it is based on memory alignment and the dynamic resolution of AI activities. Through the use of ULP IoT gadgets on a 5G testbed, empirical investigations show that the implementation of a Dynamic AI-IoT network is feasible. With an average inference duration of around 46 ms and model updates averaging less than one second, the findings are impressive.

关键词： Deep learning 5G mobile communication Heuristic algorithms Tiny machine learning Data collection Real-time systems Internet of Things Artificial intelligence Microprogramming Narrowband

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Finger in Camera Speaks Everything: Unconstrained Air-Writing for Real-World

arXiv

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arXiv 2024年

作者： Wu, Meiqi Huang, Kaiqi Cai, Yuanqiang Hu, Shiyu Zhao, Yuzhong Wang, Weiqiang School of Computer Science and Technology University of Chinese Academy of Sciences Beijing100049 China Beijing University of Posts and Telecommunications Beijing100876 China School of Artificial Intelligence University of Chinese Academy of Sciences Beijing100049 China Center for Research on Intelligent System and Engineering Institute of Automation Chinese Academy of Sciences Beijing100190 China Center for Research on Intelligent System and Engineering National Laboratory of Pattern Recognition Institute of Automation Chinese Academy of Sciences Beijing100190 China University of Chinese Academy of Sciences Beijing100049 China CAS Center for Excellence in Brain Science and Intelligence Technology Shanghai200031 China

Air-writing is a challenging task that combines the fields of computer vision and natural language processing, offering an intuitive and natural approach for human-computer interaction. However, current air-writing solutions face two primary challenges: (1) their dependency on complex sensors (e.g., Radar, EEGs and others) for capturing precise handwritten trajectories, and (2) the absence of a video-based air-writing dataset that covers a comprehensive vocabulary range. These limitations impede their practicality in various real-world scenarios, including the use on devices like iPhones and laptops. To tackle these challenges, we present the groundbreaking air-writing Chinese character video dataset (AWCV-100K-UCAS2024), serving as a pioneering benchmark for video-based air-writing. This dataset captures handwritten trajectories in various real-world scenarios using commonly accessible RGB cameras, eliminating the need for complex sensors. AWCV-100K-UCAS2024 includes 8.8 million video frames, encompassing the complete set of 3,755 characters from the GB2312-80 level-1 set (GB1). Furthermore, we introduce our baseline approach, the video-based character recognizer (VCRec). VCRec adeptly extracts fingertip features from sparse visual cues and employs a spatio-temporal sequence module for analysis. Experimental results showcase the superior performance of VCRec compared to existing models in recognizing air-written characters, both quantitatively and qualitatively. This breakthrough paves the way for enhanced human-computer interaction in real-world contexts. Moreover, our approach leverages affordable RGB cameras, enabling its applicability in a diverse range of scenarios. The code and data examples will be made public at https://***/wmeiqi/AWCV. Copyright © 2024, The Authors. All rights reserved.

关键词： Natural language processing systems

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Unprecedented Optimum Combination of 2dof+Tid and Fopd Controllers for Frequency Stability of Electric Vehicle-Integrated Hybrid Power Grids

SSRN

引用

SSRN 2024年

作者： Ghadi, Yazeed Yasin Alqahtani, Mohammed H. Gowtham, N. Department of computer science and software engineering Al Ain university Abu Dhabi15322 United Arab Emirates Al Ain University United Arab Emirates Department of Electrical and Electronics Engineering Manipal Institute of Technology Bengaluru Manipal Academy of Higher Education Manipal India

The primary objective of this study is to address the load frequency management problem in a complex power grid comprising several regions and diverse power generation sources. The strategy suggested entails integrating the Two Degree of Freedom Tilted Integral Derivative (2DOF+TID) with the Fractional Order Proportional Derivative (FOPD) controller in a single structure. Furthermore, the settings of the (2DOF+TID)-FOPD controller have been adjusted using a unique optimization method known as the Transit Search (TS) Optimization Methodology. On a dual-area renewable-based power grid comprised of electric vehicles, solar and wind farms, and conventional generation power plants such as thermal, gas, and hydraulic power plants, we evaluated our proposed TS-based (2DOF+TID)-FOPD controller. The TS's enhanced optimization capabilities have been demonstrated in comparison to previously published optimization methods, including Light Spectrum Optimization (LSO) and Geometric Octal Zones Distance Estimation (GOZDE) optimizer. Furthermore, the results indicate that the controller being shown exhibits greater performance in comparison to conventional control techniques in terms of the dynamic response characteristics of the system. This remains valid under various circumstances, encompassing diverse load disruptions, uncertainties in the system, physical constraints, and a substantial incorporation of renewable energy. The controller surpasses other controllers in terms of maximum overshoot, undershoot values, and settling durations. The suggested implementation of the TS-based (2DOF+TID)-FOPD regulator has led to a significant improvement in the system's performance. Specifically, it has achieved a 77% decrease in overshoot, a 35% decrease in undershoot, and a 27% decrease in settling time compared to other control methods. © 2024, The Authors. All rights reserved.

关键词： Controllers

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A Method for the Identification of Multiple Labels in Social Media Text using CNN Approach

A Method for the Identification of Multiple Labels in Social...

引用

Communications and Information Technologies (GCCIT), Global Conference on

作者： Dharmesh Dhabliya Vikrant Sharma N. Thangarasu Prakriti Kapoor V. Mahalakshmi Sampangi Rama Reddy B.R Department of Information Technology Vishwakarma Institute of Technology Pune India Computer Science and Engineering Graphic Era Hill University Dehradun Adjunct Professor Graphic Era Deemed to be University Dehradun Uttarakhand India Department of Computer Science Karpagam Academy of Higher Education Coimbatore Centre of Research Impact and Outcome Chitkara University Rajpura Punjab India Department of Electrical and Electronics Engineering Prince Shri Venkateshwara Padmavathy Engineering College Chennai School of Sciences Physics & Electronics JAIN (Deemed to be University) Bangalore Karnataka

ISBN: (数字)9798350388916

ISBN: (纸本)9798350388923

People are able to communicate their thoughts and perspectives with one another through various online settings, including social networks, news websites, and blogs. Because so many individuals instantaneously post their thoughts on the internet, the number of submissions that have been submitted has increased dramatically. It is critical for establishments and organizations to do an effective and speedy analysis of the massive amounts of data at their disposal in order to provide summaries that discuss the perspectives or sentiments of individuals. In this article, we offer a scalable framework that makes sentiment classification by assessing the compound probability scores of the most generally used methods in sentiment analysis using a fuzzy inference mechanism in a CNN-style way. This evaluation is carried out in order to make the framework applicable to a broad range of situations. The valance of an individual's attitude may be estimated by the designed fuzzy inference system by calculating the compound scores of the valance aware dictionary, word embedding, and count vectorization procedures. The suggested technique differs from the convolutional neural networks (CNNs) in that it permits weighting of base learners and combines the strengths of each algorithm using fuzzy rules. Another distinction is that the proposed method is more general. The process of estimating sentiment based on textual data can be handled as either a two-class (positive and negative) or a three-class (positive, neutral, and negative) problem. The former has less challenges than the latter. We carried out the experimental study on four different tagged social network data sets, and discovered that the suggested strategy delivers gains in accuracy for both 2-class and 3-class classifications.

关键词： Fuzzy logic Training Sentiment analysis Social networking (online) Standards organizations Organizations Media Information and communication technology Convolutional neural networks Compounds

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DG-CNN:Introducing Margin Information into Convolutional Neural Networks for Breast Cancer Diagnosis in Ultrasound Images

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Journal of computer science & technology 2022年第2期37卷 277-294页

作者： Xiao-Zheng Xie Jian-Wei Niu Senior Member Xue-Feng Liu Qing-Feng Li Yong Wang Jie Han Shaojie Tang State Key Laboratory of Virtual Reality Technology and Systems School of Computer Science and Engineering Beihang UniversityBeijing 100191China Beijing Advanced Innovation Center for Big Data and Brain Computing Beihang UniversityHangzhou 310051China Department of Diagnostic Ultrasound National Cancer CenterChinese Academy of Medical SciencesPeking Union Medical CollegeBeijing 100021China Naveen Jindal School of Management The University of Texas at DallasRichardsonTX 75080-3021U.S.A.

Although using convolutional neural networks(CNNs)for computer-aided diagnosis(CAD)has made tremendous progress in the last few years,the small medical datasets remain to be the major bottleneck in this *** address this problem,researchers start looking for information out of the medical *** efforts mainly leverage information from natural images via transfer *** recent research work focuses on integrating knowledge from medical practitioners,either letting networks resemble how practitioners are trained,how they view images,or using extra *** this paper,we propose a scheme named Domain Guided-CNN(DG-CNN)to incorporate the margin information,a feature described in the consensus for radiologists to diagnose cancer in breast ultrasound(BUS)*** DG-CNN,attention maps that highlight margin areas of tumors are first generated,and then incorporated via different approaches into the *** have tested the performance of DG-CNN on our own dataset(including 1485 ultrasound images)and on a public *** results show that DG-CNN can be applied to different network structures like VGG and ResNet to improve their *** example,experimental results on our dataset show that with a certain integrating mode,the improvement of using DG-CNN over a baseline network structure ResNet 18 is 2.17%in accuracy,1.69%in sensitivity,2.64%in specificity and 2.57%in AUC(Area Under Curve).To the best of our knowledge,this is the first time that the margin information is utilized to improve the performance of deep neural networks in diagnosing breast cancer in BUS images.

关键词： medical consensus domain knowledge breast cancer diagnosis margin map deep neural network

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