this study is one of many that investigate the relationship between determiningthe nutritional ingredients in food and calculating the calories using data analysis utilizing machinelearning techniques. Due to the av...
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the proceedings contain 60 papers presendted at a virtual meeting. the special focus in this conference is on Emerging Technologies in Computer Engineering: Cognitive Computing and Intelligent. the topics include: Mac...
ISBN:
(纸本)9783031070112
the proceedings contain 60 papers presendted at a virtual meeting. the special focus in this conference is on Emerging Technologies in Computer Engineering: Cognitive Computing and Intelligent. the topics include: machinelearning Based Rumor Detection on Twitter data;performance Analysis of machinelearning Algorithms in Intrusion Detection and Classification;fruit Classification Using Deep Convolutional Neural Network and Transfer learning;assessment of Network Intrusion Detection System Based on Shallow and Deep learning Approaches;an Efficient Classifier Model for Opinion mining to Analyze Drugs Satisfaction Among Patients;a Case Study on machinelearning Techniques for Plant Disease Identification;detection of Network Intrusion Using machinelearning Technique;scrutinization of Urdu Handwritten Text recognition withmachinelearning Approach;a machinelearning Approach for Multiclass Sentiment Analysis of Twitter data: A Review;efficacy of Online Event Detection with Contextual and Structural Biases;analysis of Text Classification Using machinelearning and Deep learning;evaluation of Fairness in Recommender Systems: A Review;detection of Liver Disease Using machinelearning Techniques: A Systematic Survey;Association Rule Chains (ARC): A Novel datamining Technique for Profiling and Analysis of Terrorist Attacks;classification of Homogeneous and Non Homogeneous Single Image Dehazing Techniques;comparative Study to Analyse the Effect of Speaker’s Voice on the Compressive Sensing Based Speech Compression;Employing an Improved Loss Sensitivity Factor Approach for Optimal DG Allocation at Different Penetration Level Using ETAP;a Method for data Compression and Personal Information Suppressing in Columnar databases;the Impact and Challenges of Covid-19 Pandemic on E-learning;identification of Dysgraphia: A Comparative Review;blockchain Framework for Agro Financing of Farmers in South India.
Recently, advanced technologies have unlimited potential in solving various problems with a large amount of data. However, these technologies have yet to show competitive performance in brain-computer interfaces (BCIs...
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ISBN:
(数字)9781665490627
ISBN:
(纸本)9781665490627
Recently, advanced technologies have unlimited potential in solving various problems with a large amount of data. However, these technologies have yet to show competitive performance in brain-computer interfaces (BCIs) which deal with brain signals. Basically, brain signals are difficult to collect in large quantities, in particular, the amount of information would be sparse in spontaneous BCIs. In addition, we conjecture that high spatial and temporal similarities between tasks increase the prediction difficulty. We define this problem as sparse condition. To solve this, a factorization approach is introduced to allow the model to obtain distinct representations from latent space. To this end, we propose two feature extractors: A class-common module is trained through adversarial learning acting as a generator;Class-specific module utilizes loss function generated from classification so that features are extracted with traditional methods. To minimize the latent space shared by the class-common and class-specific features, the model is trained under orthogonal constraint. As a result, EEG signals are factorized into two separate latent spaces. Evaluations were conducted on a single-arm motor imagery dataset. From the results, we demonstrated that factorizing the EEG signal allows the model to extract rich and decisive features under sparse condition.
Deep neural networks (DNNs) have achieved great success in various applications across many disciplines. However, their superior performance is susceptible to training set bias and noise label corruption. In order to ...
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Naive Bayes has been widely used in many applications because of its simplicity and ability in handling both numerical data and categorical data. However, lack of modeling of correlations between features limits its p...
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ISBN:
(数字)9781665490627
ISBN:
(纸本)9781665490627
Naive Bayes has been widely used in many applications because of its simplicity and ability in handling both numerical data and categorical data. However, lack of modeling of correlations between features limits its performance. In addition, noise and outliers in the real-world dataset also greatly degrade the classification performance. In this paper, we propose a feature augmentation method employing a stack auto-encoder to reduce the noise in the data and boost the discriminant power of naive Bayes. the proposed stack auto-encoder consists of two auto-encoders for different purposes. the first encoder shrinks the initial features to derive a compact feature representation in order to remove the noise and redundant information. the second encoder boosts the discriminant power of the features by expanding them into a higher-dimensional space so that different classes of samples could be better separated in the higher-dimensional space. By integrating the proposed feature augmentation method withthe regularized naive Bayes, the discrimination power of the model is greatly enhanced. the proposed method is evaluated on a set of machine-learning benchmark datasets. the experimental results show that the proposed method significantly and consistently outperforms the state-of-the-art naive Bayes classifiers.
the bone age estimation is clinically important as it can be used by physicians to read and report medial images such as bone scintigraphy considering age-related bone changes. Some recent research has applied deep le...
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ISBN:
(数字)9781665490627
ISBN:
(纸本)9781665490627
the bone age estimation is clinically important as it can be used by physicians to read and report medial images such as bone scintigraphy considering age-related bone changes. Some recent research has applied deep learning techniques to assess bone age and have achieved positive results. Whole-body bone scintigraphy usually contains millions of pixels, which is computationally infeasible. this problem can be solved effectively with multiple instance learning by treating a whole image as a bag of instances. However, many approaches usually assume that there are no dependencies or ordering among instances. In the present study, we propose a multiple instance learning based method for bone age prediction using whole-body bone scan images. Our network architecture combines attention-based multiple instances learning with a multi-cross attention module to handle large input images and dependencies among instances. the experiments conducted on the Chonnam National University Hwasun Hospital data set have shown the potential performance of our model. the proposed framework can be used as a robust support tool for clinicians to analyze and prognosticate bone aging and age-related diseases.
Withthe rapid growth of the power grid scale and the increasing complexity of grid structures, the scale of partial discharge data in electrical equipment has gradually increased from the TB level to the PB level. th...
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the proceedings contain 60 papers. the special focus in this conference is on Soft Computing and Signal Processing. the topics include: A Hybrid Approach on Lexical Indian Sign Language recognition;defining the C...
ISBN:
(纸本)9789811986680
the proceedings contain 60 papers. the special focus in this conference is on Soft Computing and Signal Processing. the topics include: A Hybrid Approach on Lexical Indian Sign Language recognition;defining the Convergence of Artificial Intelligence and Cyber Security with a Case Study of Email Spam Detection;determiningthe Attribute Priority Among the Maintenance and Support Phases in Software Development Phases Using Intuitionistic Fuzzy Analytical Hierarchy Process;Impact of COVID-19 on Energy Generation in India;decentralized Payment Architecture for E-Commerce and Utility Transactions with Government Verified Identities;a machinelearning-Based Approach for Enhancing Student learning Experiences;book Genre Classification System Using machinelearning Approach: A Survey;a New Method for Imbalanced data Reduction Using data Based Under Sampling;Resource-Based Prediction in Cloud Computing Using LSTM with Autoencoders;Arduino UNO-Based COVID Smart Parking System;low-Cost Smart Plant Irrigation Control System Using Temperature and Distance Sensors;a Novel Approach to Universal Egg Incubator Using Proteus Design Tool and Application of IoT;dynamic Resource Allocation Framework in Cloud Computing;Region-Wise COVID-19 Vaccination Distribution Modelling in Tamil Nadu Using machinelearning;detection of Phishing Websites Using machinelearning;detection of Phishing Website Using Intelligent machinelearning Classifiers;computational learning Model for Prediction of Parkinson’s Disease Using machinelearning;eliminating Environmental Context for Fall Detection Based on Movement Traces;performance Analysis of Regression Models in Stock Price Prediction;review on the Image Encryption with Hyper-Chaotic Systems;Object Detection Using Mask R-CNN on a Custom dataset of Tumbling Satellite;decentralized Blockchain-Based Infrastructure for Numerous IoT Setup;Sentiment Analysis Toward COVID-19 Vaccination Based on Twitter Posts.
Combining deep learning algorithms, mental health assessments, and sarcasm recognition creates an intriguing synergy for deciphering emotional states in textual data. Utilizing cutting-edge technologies to predict men...
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