The proceedings contain 324 papers. The topics discussed include: sentiment analysis of self-driving car dataset: a comparative study of deep learning approaches;unleashing the potential of boosting techniques to opti...
The proceedings contain 324 papers. The topics discussed include: sentiment analysis of self-driving car dataset: a comparative study of deep learning approaches;unleashing the potential of boosting techniques to optimize station-pairs passenger flow forecasting;a cross-platform movie filtering and recommendation system using big data analytics;a comparative analysis of advanced machinelearning algorithms to diagnose Parkinson’s disease;exploring twitter sentiments for predicting match outcomes in the game of cricket;economic order quantity models with exponential demand rate and single level trade credit;identification of brain diseases using image classification: a deep learning approach;and a short review for handwritten math expression recognition techniques.
The proceedings contain 28 papers. The topics discussed include: identification of the onset of dementia of older adults in the age of internet of things;applying Internet of things and machine-learning for personaliz...
ISBN:
(纸本)9781728104041
The proceedings contain 28 papers. The topics discussed include: identification of the onset of dementia of older adults in the age of internet of things;applying Internet of things and machine-learning for personalized healthcare: issues and challenges;identification of illegal forum activities inside the dark net;kernel logistic regression: a robust weighting for imbalanced classes with noisy labels;video-based measurement of physiological parameters using peak-to-valley method for minimization of initial dead zone;domain knowledge driven FRBR and cataloguing for the future libraries;a review of strengths and weaknesses of spatiotemporal data analysis techniques;and using electronic health records and machinelearning to make medical-related predictions from non-medical data.
The proceedings contain 276 papers. The topics discussed include: transfer learning based neural machine translation of English-Khasi on low-resource settings;integration of renewable energy sources with power managem...
The proceedings contain 276 papers. The topics discussed include: transfer learning based neural machine translation of English-Khasi on low-resource settings;integration of renewable energy sources with power management strategy for effective bidirectional vehicle to grid power transfer;classification of breast thermal images into healthy/cancer group using pre-trained deep learning schemes;offline HWR accuracy enhancement with image enhancement and deep learning techniques;detection of network attacks using machinelearning and deep learning models;a hybrid classifier-based ontology driven image tag recommendation framework for social image tagging;sarcasm detection using bidirectional encoder representations from transformers and graph convolutional networks;a factor based multiple imputation approach to handle class imbalance;smart facial emotion recognition with gender and age factor estimation;and a hybrid data-driven framework for spam detection in online social network.
The proceedings contain 273 papers. The topics discussed include: transfer learning based neural machine translation of English-Khasi on low-resource settings;integration of renewable energy sources with power managem...
The proceedings contain 273 papers. The topics discussed include: transfer learning based neural machine translation of English-Khasi on low-resource settings;integration of renewable energy sources with power management strategy for effective bidirectional vehicle to grid power transfer;classification of breast thermal images into healthy/cancer group using pre-trained deep learning schemes;detection of network attacks using machinelearning and deep learning models;a hybrid classifier-based ontology driven image tag recommendation framework for social image tagging;sarcasm detection using bidirectional encoder representations from transformers and graph convolutional networks;a factor based multiple imputation approach to handle class imbalance;smart facial emotion recognition with gender and age factor estimation;and a hybrid data-driven framework for spam detection in online social network.
Lack of planning and regulations around the landfills has resulted and continues to result in severe environmental damage to the immediate environment around the landfills. Our study systematically reviews the literat...
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Class imbalance a common challenge in machinelearning, often results in skewed predictions and misrepresentative model assessments, highlighting the need for effective countermeasures. Our detailed survey dives into ...
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Annotation tools serve a critical role in the generation of datasets that fuel machinelearning applications. With the advent of Foundation Models, particularly those based on Transformer architectures and expansive l...
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Annotation tools serve a critical role in the generation of datasets that fuel machinelearning applications. With the advent of Foundation Models, particularly those based on Transformer architectures and expansive language models, the capacity for training on comprehensive, multimodal datasets has been substantially enhanced. This not only facilitates robust generalization across diverse data categories and knowledge domains but also necessitates a novel form of annotation-prompt engineering-for qualitative model fine-tuning. This advancement creates new avenues for machine intelligence to more precisely identify, forecast, and replicate human behavior, addressing historical limitations that contribute to algorithmic inequities. Nevertheless, the voluminous and intricate nature of the data essential for training multimodal models poses significant engineering challenges, particularly with regard to bias. No consensus has yet emerged on optimal procedures for conducting this annotation work in a manner that is ethically responsible, secure, and efficient. This historical literature review traces advancements in these technologies from 2018 onward, underscores significant contributions, and identifies existing knowledge gaps and avenues for future research pertinent to the development of Transformer-based multimodal Foundation Models. An initial survey of over 724 articles yielded 156 studies that met the criteria for historical analysis;these were further narrowed down to 46 key papers spanning the years 2018-2022. The review offers valuable perspectives on the evolution of best practices, pinpoints current knowledge deficiencies, and suggests potential directions for future research. The paper includes six figures and delves into the transformation of research landscapes in the realm of machine-assisted behavioral annotation, focusing on critical issues such as bias.
Natural Language processing (NLP) includes the task of classifying texts. The proposed paper uses the ability and power of machinelearning and deep learning techniques for sentiment analysis on restaurant reviews. Wi...
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Electric and autonomous vehicles (EV and AV) are spreading quickly on the road and producing extensive amounts of data that must be stored and retrieved securely. The ultimate goal of collecting these data is to feed ...
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Oral cancer (OC)is frequently occurring cancer that affects various areas within the oral cavity. Despite the advancement of sophisticated diagnostic and therapeutic techniques, the morbidity and mortality rates assoc...
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