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AI-Enabled Analysis of 3-D CT Scans for Diagnosis of COVID-19 & its Severity

AI-Enabled Analysis of 3-D CT Scans for Diagnosis of COVID-1...

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Acoustics, Speech, and signal processing Workshops (ICASSPW), IEEE international conference on

作者： Dimitrios Kollias Anastasios Arsenos Stefanos Kollias Queen Mary University of London UK National Technical University of Athens Greece

This paper describes the 3rd COVID-19 Competition, taking place in the AI-enabled medical image analysis (AIMIA) Workshop of the 2023 IEEE international conference on Acoustics, Speech and signal processing (ICASSP 2023). The 3rd COVID-19 Competition is a continuation of the Competitions held at ECCV 2022 and ICCV 2021 conferences, and aims to tackle the challenges of whole slide image and CT/MRI/X-ray analysis/processing and to identify research opportunities in the context of Digital Pathology and Radiology/COVID19. The 3rd COVID-19 Competition consists of two Challenges targeting COVID19 detection and COVID19 severity detection. Both Challenges are based on an extended version of the database used in the 1st and 2nd COV19D Competitions, the COV19-CT-DB database, which includes chest CT scan series. A large part of the COV19-CT-DB database is annotated for COVID-19 detection and consists of 8,0003-D CT scans. About 1,0003-D CT scans of the database are also annotated with respect to four COVID-19 severity conditions. Both parts have been split in training, validation and test datasets. These are used for training and validation of machine learning models, as well as for evaluation. The paper further describes the baseline methods for the 3rd COVID-19 Competition, which are deep learning approaches, based on CNN-RNN networks. Their performance on detecting the existence and the severity of COVID-19 is reported.

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Optimized SVM based on improved whale algorithm for EEG signal pattern classification

Optimized SVM based on improved whale algorithm for EEG sign...

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Power Electronics, Computer Applications (ICPECA), IEEE international conference on

作者： Junqiang Peng Lijun Liu Tiangong University Tianjin China

In order to improve the classification accuracy of moving image electroencephalogram ( EEG ) signals, aiming at the problem that the traditional support vector machine (SVM ) parameter value is not accurate, which leads to poor classification model, this study introduces Sobol sequence, reverse learning strategy and piecewise nonlinear time-varying factor. In order to improve the random layout strategy of boundary processing, Levy flight strategy improves the traditional whale algorithm, combines the improved whale algorithm with SVM, and compares the combined SVM classifier with other classifiers for EEG signal classification. The results show that the accuracy of the optimization algorithm is 2 ~ 4 percentage points.

关键词： Support vector machines machine learning algorithms Sociology Pattern classification Whales Brain modeling Electroencephalography

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Comparing the Predictive Accuracy of machine learning Algorithms for Neonatal Mortality Risk Classification 3rd

Comparing the Predictive Accuracy of Machine Learning Algori...

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3rd international conference on machine Intelligence and signal processing, MISP 2021

作者： Sivarajan, A. Bala Aditya, A. Sivasankar, E. Department of CSE National Institute of Technology Tiruchirappalli Tiruchirappalli India

ISBN: (纸本)9789811908392

Neonatal mortality is a major concern in developing countries and is a crude indicator of the prevalence of poverty in a society. Neonatal risk analysis is an important decision-making tool for gauging progress and penetration of medical services in a specific region, especially in developing countries. This research compares the accuracy of the predictions of various machine learning algorithms and the impact of different sampling techniques, when attempting to classify neonatal mortality. We have conducted different rounds of experiments with six sampling techniques and seven machine learning algorithms on a neonatal death dataset. Further, groups of three base classifiers chosen among the seven machine learning algorithms were made into six different ensemble combinations and trained to improve the classification accuracy. The proposed diagnostic support system with an ensemble of random forest classifier, Gaussian NB, and AdaBoost with pipeline sampling achieved an accuracy and ROC AUC scores above 98% and 96%, respectively. This decision support tool will facilitate medical practitioner to identify infants at risk of dying before 28 days of life. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Developing countries

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ConvoWaste: An Automatic Waste Segregation machine Using Deep learning

ConvoWaste: An Automatic Waste Segregation Machine Using Dee...

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international conference on Robotics,Electrical and signal processing Techniques (ICREST)

作者： Md. Shahariar Nafiz Shuvra Smaran Das Md. Kishor Morol Abdullah Al Juabir Dip Nandi Department of Electrical and Electronic Engineering American International University-Bangladesh (AIUB) Department of Computer Science American International University-Bangladesh (AIUB)

Nowadays, proper urban waste management is one the biggest concerns for maintaining a green and clean environment. An automatic waste segregation system can be a viable solution to improve the sustainability of the country and to boost up the circular economy. This paper proposes a machine to segregate the waste into the different parts with the help of smart object detection algorithm using ConvoWaste in the field of Deep Convolutional Neural Network (DCNN), and image processing technique. In this paper, the deep learning and image processing techniques are applied to classify the waste precisely and the detected waste is placed inside the corresponding bins with the help of a servo motor-based system. This machine has the provision to notify the responsible authority regarding the waste level of the bins and the time to trash out the bins filled with garbage by using the ultrasonic sensors placed in each bin and the dual-band GSM-based communication technology. The entire system is controlled remotely through an android app in order to dump the separated waste in a desired place by its automation properties. The use of this system can aid the process of recycling resources that were initially destined to become waste, utilizing natural resources and turning these resources back into the usable products. Thus, the system helps to fulfill the criteria of circular economy through the resource optimization and extraction. Finally, the system is made to provide the services at a low cost with higher accuracy level in terms of the technological advancement in the field of Artificial Intelligence (AI). We have got 98% accuracy for our ConvoWaste deep learning model.

关键词： Deep learning Waste management Training Image processing signal processing algorithms signal processing Turning

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Optimized Fault Detection and Classification for 3-Phase Electric Drives-An AI/ML Approach 3

Optimized Fault Detection and Classification for 3-Phase Ele...

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3rd international conference on Innovations in Science and Technology for Sustainable Development, ICISTSD 2022

作者： Giri, Antarip Mehendale, Niranjan Waghode, Nitesh Cognizant Technology Solutions India India

ISBN: (纸本)9781665499361

This paper illustrates the creation of robust data inference pipeline for fault classification in a 3-phase induction motor which enables to analyze, evaluate, and compare various machine learning (ML) classification algorithms for detecting inter-Turn short circuit faults in induction motor. The data is analyzed in the time and frequency domain using signal processing techniques such as FFT/PSD and further processed with filtering, and peak detection to extract the important feature dataset for classification algorithm. A Recursive Feature Elimination (RFE) with six different estimator settings are compared to select optimum feature set, which ensured enhanced performance for classification model. Further, five classification algorithms viz, random forest, nearest neighbors, and gradient boosting classifier, linear support vector machines and Naïve Bayes, are devised, tuned, and compared for train-Test accuracy, time profile etc. The study concludes that RFE with decision-Tree based estimator followed by random forest classifier gives the results with highest accuracy (~98%). Further, adding cross-validation technique to RFE (RFECV), ~90% decrease in training time is observed. This work can be extended for analysis of multiple frequencies for VFD based control and varying operating loads. © 2022 IEEE.

关键词： Induction motors

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Customer Adaptive Automated Trading System with Capital Risk Analysis using machine learning 3

Customer Adaptive Automated Trading System with Capital Risk...

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3rd international conference on signal processing and Communication (ICPSC)

作者： Agarwal, Harsh Kandoi, Aditi Ahir, Bhavya Dhage, Sudhir Sardar Patel Inst Technol Dept Comp Engn Mumbai 400058 Maharashtra India

ISBN: (纸本)9781665428644

Stock market plays a huge role in the economy of our country. Several attempts have been made to analyse and predict the stock market. While the existing systems try to exploit the patterns of stock prices using historical data, they do not take into the account the poor performance of the system. Moreover, there is no system which provides user specific trading strategies. The proposed solution explores filtration and different trading strategies using RoCE and Fuzzy Logic to solve the problem and predict the portfolio values. It also takes into consideration the sentiment aspect of trading using NLP and combines the two to efficiently to perform trading for even those users who have smattering knowledge about stock market thereby making it suitable for everyone.

关键词： Automated System Algorithmic Trading Price Prediction Data Analysis

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Perspective of signal processing-based on brain-computer interfaces using machine learning methods

Perspective of signal processing-based on brain-computer int...

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3rd international conference on Biomedicine and Bioinformatics Engineering, ICBBE 2023

作者： He, Ruyue Department of Electronics and Electrical Engineering University of Electronic Science and Technology of China Chengdu China

ISBN: (纸本)9781643684321

The application of artificial intelligence (AI) algorithms is an indispensable portion of developing brain-computer interfaces (BCI). With the continuous development of AI concepts and related technologies. AI algorithms such as neural networks play an increasingly powerful and extensive role in brain-computer interfaces. However, brain-computer interfaces are still facing many technical challenges. Due to the limitations of AI algorithms, brain-computer interfaces not only work with limited accuracy, but also can only be applied to certain simple scenarios. In order to explore the future directions and improvements of AI algorithms in the area of brain-computer interfaces, this paper will review and analyse the advanced applications of AI algorithms in the field of brain-computer interfaces in recent years and give possible future enhancements and development directions for the controversial parts of them. This review first presents the effects of different AI algorithms in BCI applications. A multi-objective classification method is compared with evolutionary algorithms in feature extraction of data. Then, a kind of supervised learning algorithm based on Event Related Potential (ERP) tags is presented to achieve a high accuracy in the process of pattern recognition. Finally, as an important experimental paradigm for BCI, a combined TFD-PSR-CSP feature extraction method, is explained for the problem of motor imagery. The 'Discussion' part comprehensively analyses the advantages and disadvantages of the above algorithms and proposes a deep learning-based artificial intelligence algorithm in order to solve the problems arising from the above algorithms. © 2023 IOS Press. All rights reserved.

关键词： Brain computer interface

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Apnea Event Detection Using machine learning Technique for the Clinical Diagnosis of Sleep Apnea Syndrome 3

Apnea Event Detection Using Machine Learning Technique for t...

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3rd international conference on signal processing and Communication (ICPSC)

作者： Srinivasulu, Avvaru Mohan, Saranga Harika, T. Srujana, P. Revathi, Y. GITAM Dept Elect Elect & Commun Engn Bengaluru Campus Bangalore Karnataka India

ISBN: (纸本)9781665428644

A sort of rest problem that is portrayed by breaks in breathing or examples of shallow or phenomenal breathing during rest is known as sleep apnea. Side effects of sleep apnea may happen when a patient is not having any admittance to a doctor. Absence of access is the main source of information misfortune which is fundamental and basic in conclusion and treatment. Since the relationship between ECG sign and sleep apnea issue has been demonstrated and as per the way that other bio-signal cannot be recorded in the method of recording Holter ECG. In this examination, sleep apnea ages were distinguished by means of managed ECG information handling. The information is gathered from Physio net ATM. The detection method can be acquired by using time, frequency, and statistical features of Electrocardiogram signals. Apnea event detection using the extracted features is evaluated through different classifiers. Ensemble-Bagged Tree classifier has shown a higher accuracy of 89.6% with MIT-BIH Apnea datasets than that of decision tree, support vector machine, and K-nearest neighborhood classifiers.

关键词： Sleep Apnea Electrocardiogram (ECG) Ensemble-Bagged Tree decision tree support vector machine K-nearest neighborhood

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Surface Electromyogram Based Techniques for Upper and Lower Extremity Rehabilitation Therapy - A Comprehensive Review 3

Surface Electromyogram Based Techniques for Upper and Lower ...

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3rd international conference on signal processing and Communication (ICPSC)

作者： Mathew, Anila Rajalakshmy, P. Karunya Inst Technol & Sci Dept Robot Engn Coimbatore Tamil Nadu India

ISBN: (纸本)9781665428644

In the last few decades, prosthetics has gained a noteworthy advancement within the therapeutic and biomechanics field. Prosthesis is originally a part of the biomechanics field, the science of combining electro-mechanical devices with human muscles, skeleton, nervous systems, etc. The prosthetic limbs are fantastically valuable to amputees in delivering their daily work, thereby providing a great support in the patient rehabilitation. This article focuses on the background of upper and lower limb prosthetics, the signal processing techniques, classification algorithms used and its applications for patient rehabilitation. Surface EMG (sEMG) based prostheses has been picking up in the rehabilitation field as technology progresses. The limb with EMG sensors measures and decode the electrical impulses in the muscles of the residual limb and then translate it into movement. Thus, the user is able to activate the bionic limb on his own thinking. Such developing novel technologies has revolutionized the field of prosthetics. It could be a milestone in bridging the gap between disability and ability, between human limitation and human potential.

关键词： Prosthetics Surface Electromyogram(sEMG) Rehabilitation machine learning

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Natural Language processing Enabled Cognitive Disease Prediction Model for Varied Medical Records Implemented over ML Techniques 3

Natural Language Processing Enabled Cognitive Disease Predic...

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3rd international conference on signal processing and Communication (ICPSC)

作者： Kamra, Vikas Kumar, Praveen Mohammadian, Masoud Amity Univ Uttar Pradesh Noida India Amity Univ Uttar Pradesh CSE Dept Noida India Univ Canberra Fac Sci & Technol Canberra ACT 2601 Australia

ISBN: (纸本)9781665428644

Artificial Intelligence technologies are progressing into fields that were formerly believed to be still the domain of domain analysts. Thanks to recent advances in digital form of information processing, artificial learning, and computational resources, which helped personnel involved in healthcare services in various domains. In certain cases, AI and NLP can handle healthcare functions even well than individuals. One of the area in which machine learning along with natural language processing can work is psychological diseases. Anomalous thinking, emotions, and actions are characteristics of mental illnesses. Aberrant inner interactions and actions that defy social expectations could indicate the existence of a cognitive disease. This research paper proposes a cognitive disease prediction model for psychological disorders like, bipolar disorder, obsessive compulsive disorder, and schizophrenia. In the mainstream medical industry, providing cognitive health treatment entails new ways to treat individuals with mental illnesses. Proposed e-healthcare systems provide an extra layer which helps healthcare professional and patients by saving their time in early diagnosis.

关键词： Healthcare Informatics Schizophrenia Artificial Intelligence Natural Language processing Mental Illnesses

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