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A narrative review on the characterisation of automated human emotion detection systems using biomedical sensors and machine intelligence

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International Journal of Reasoning-based Intelligent Systems 2023年第3-4期15卷 266-276页

作者： Dutta, Stobak Mishra, Brojo Kishore Mitra, Anirban Chakraborty, Amartya Department of Computer Science and Engineering GIET University Odisha India Department of Computer Science and Engineering Amity University Kolkata India Department of Computer Science and Engineering University of Engineering and Management Kolkata India

In our day-To-day life, emotion plays an essential role in decision-making and human interaction. For many years, psychologists have been trying to develop many emotional models to explain the human emotional or affective states. Automated emotion recognition is a popular research problem increasingly utilised in marketing, education, health sector, and human-robot interaction. There are different ways of emotion recognition, namely uni-modal and multi-modal solutions. However, it depends upon the purpose for which it is to be used. The primary focus of this paper is to provide a detailed review of the existing literature in this domain with the help of three verticals. Initially, the standard databases used for elicitation of human emotions are discussed in brief. Next, the different sensing approaches used to gather physiological signals are discussed. Finally, a thorough review of the state-of-The-Art is given, with reference to the emotional states from the circumplex model of valence-Arousal plane. © 2023 Inderscience Publishers. All rights reserved.

关键词： Electrocardiograms

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An Enhanced Aspect-Based Sentiment Analysis Model Based on RoBERTa For Text Sentiment Analysis

Informatica (Slovenia)

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Informatica (Slovenia) 2025年第14期49卷 193-202页

作者： Chauhan, Amit Sharma, Aman Mohana, Rajni Jaypee University of Information Technology HP Solan173234 India Department of Computer Science Amity School of Engineering and Technology Amity University Punjab Punjab Mohali140306 India

Using an aspect-based sentiment analysis task, sentiment polarity towards specific aspect phrases within the same sentence or document is to be identified. The process of mechanically determining the underlying attitude or opinion indicated in the text is known as sentiment analysis. One of the most important aspects of natural language processing is sentiment analysis. The RoBERTa transformer model was pretrained in a self-supervised manner using a substantial corpus of English data. This means it was pretrained solely with raw texts and an algorithmic process to generate inputs and labels from those texts. No human labelling was involved, allowing it to utilise a vast amount of publicly available data. The authors of this work provide a thorough investigation of aspect-based sentiment analysis with RoBERTa. The RoBERTa model and its salient characteristics are outlined in this work, followed by an analysis of the model's optimisation by the authors for aspect-based sentiment analysis. The authors compare the RoBERTa model with other state-of-the-art models and evaluate its performance on multiple benchmark datasets. Our experimental results show that the RoBERTa model is effective for this important natural language processing task, outperforming competing models on sentiment analysis tasks. Based on the SemEval-2014 variant benchmarking datasets, the restaurant and laptop domains have the highest accuracy, scoring 92.35 % and 82.33 %, respectively. © 2025 Slovene Society Informatika. All rights reserved.

关键词： Natural language processing systems

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Sign Language Recognition using Convolutional Neural Network 14

Sign Language Recognition using Convolutional Neural Network

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14th International Conference on Cloud Computing, Data science and engineering, Confluence 2024

作者： Gulati, Nancy Rajput, Abhishek Singh, Ankur Computer Science and Engineering Amity University Noida India

ISBN: (纸本)9798350344837

The Sign Language Recognition (SLR) sector has seen significant progress in recent years, driven by the growing need for technology to bridge the communication gap for the deaf and hard-of-hearing community. This research paper synthesizes the methods, results, and limitations of some prominent influential articles in the field of SLR, highlighting the state of the art and remaining challenges. By consolidating knowledge from diverse research efforts, this research provides a comprehensive understanding of the current landscape and is a valuable resource for researchers, practitioners, and stakeholders committed to promoting accessibility and inclusion for deaf people. This research also highlights work done on the Sign Language model using Convolutional Neural Network(CNN) along with an average accuracy of 99.98 percent. © 2024 IEEE.

关键词： Convolutional neural networks

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Predictive Data Analysis to Support Decision-Making Based on Long-Term Impacts of Disasters 1

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5th International Conference on computer and Communication Technologies, IC3T 2023

作者： Mishra, Shailendra Kumar Rahamatkar, Surendra Amity School of Engineering and Technology Department of Computer Science and Engineering Amity University Chhattisgarh Raipur India

ISBN: (数字)9789819997077

ISBN: (纸本)9789819997060

Research into disaster relief is a field with a lot of potential, and predictions made about its future are based on historical data. It is a difficult task to analyze the long-term consequences of a disaster. Governments, NGOs, and other organizations struggle to make sense of disaster data. It is hard to assess the impact of different factors, such as education, employment, agriculture, and the social sector, on people living in affected areas. This research paper represents a framework to evaluate the long-term effects of disasters, by means of machine learning algorithms. The algorithms mentioned in this paper are Random Forest, Decision Tree, Gradient-boosted Decision Tree (GBDT), and XGBoost. It seeks to analyze historical disaster data, identify impacts, and predict the effects of similar disasters in the future. The framework also suggests preemptive measures and the impact on those involved. Finally, the proposed framework will be able to make long-term analyses and recommendations, using data from past events. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Disasters

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HViTML: Hybrid vision transformer with machine learning-based classification model for glaucomatous eye

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Multimedia Tools and Applications 2025年 1-24页

作者： Singh, Piyush Bhushan Singh, Pawan Dev, Harsh Batra, Devanshu Chaurasia, Brijesh Kumar Department of Information Technology Pranveer Singh Institute of Technology Uttar Pradesh Kanpur India Department of Computer Science and Engineering Amity School of Engineering and Technology Amity University Uttar Pradesh Lucknow India Department of Computer Science and Engineering Babu Banarasi Das University Uttar Pradesh Lucknow India Department of Computer Science and Engineering Pranveer Singh Institute of Technology Uttar Pradesh Kanpur India

Glaucoma is currently one of the most significant causes of permanent blindness. Fundus imaging is the most popular glaucoma screening method because of the compromises it has to make in terms of portability, size, and cost. In recent years, convolution neural networks (CNNs) have revolutionized computer vision. Convolution is a "local" CNN technique that is only applicable to a small region surrounding an image. Vision Transformers (ViT) use self-attention, which is a "global" activity since it collects information from the entire image. As a result, the ViT can successfully gather distant semantic relevance from an image. This study examined several optimizers, including Adamax, SGD, RMSprop, Adadelta, Adafactor, Nadam, and Adagrad. With 1750 Healthy and Glaucoma images in the IEEE fundus image dataset and 4800 healthy and glaucoma images in the LAG fundus image dataset, we trained and tested the ViT model on these datasets. Additionally, the datasets underwent image scaling, auto-rotation, and auto-contrast adjustment via adaptive equalization during preprocessing. The results demonstrated that preparing the provided dataset with various optimizers improved accuracy and other performance metrics. Additionally, according to the results, the Nadam Optimizer improved accuracy in the adaptive equalized preprocessing of the IEEE dataset by up to 97.8% and in the adaptive equalized preprocessing of the LAG dataset by up to 92%, both of which were followed by auto rotation and image resizing processes. In addition to integrating our vision transformer model with the shift tokenization model, we also combined ViT with a hybrid model that consisted of six different models, including SVM, Gaussian NB, Bernoulli NB, Decision Tree, KNN, and Random Forest, based on which optimizer was the most successful for each dataset. Empirical results show that the SVM Model worked well and improved accuracy by up to 93% with precision of up to 94% in the adaptive equalization preprocess

关键词： Decision trees

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Enhanced Cyclone Intensity Estimation Through CNN Analysis of INSAT-3D IR Imagery 12th

Enhanced Cyclone Intensity Estimation Through CNN Analysis o...

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12th International Conference on Recent Trends in Computing, ICRTC 2024

作者： Kumar, Divvela Vishnu Sai Arora, Deepak Tiwari, Shivam Department of Computer Science and Engineering Amity School of Engineering and Technology Amity University Lucknow Campus Lucknow India Department of Computer Science and Engineering Sharda University Greater Noida India

ISBN: (纸本)9789819788354

Cyclone forecasting using satellite pictures involves anticipating the cyclone’s intensity in advance of its arrival. The results of this study can inform people’s preparations for the cyclone. In order to save lives and reduce damage, cyclone strength forecast is crucial. As a result of the many dangers they pose, these cyclones are among the most expensive natural disasters that have ever occurred. Five separate CNN (Convolutional Neural Network) models are presented by the authors to assess the cyclone’s severity using INSAT-3D infrared pictures. While many methods exist for making such an assessment, the authors’ top model yields an RMSE of 10.02 kts for intensity. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Convolutional neural networks

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Machine Learning Classification of Shoulder Pain Pathologies: A Scoping Review 15

Machine Learning Classification of Shoulder Pain Pathologies...

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15th International Conference on Computing Communication and Networking Technologies, ICCCNT 2024

作者： Shukla, Garima Bhagat, Khushi Chaudhari, Vedant Navaneeth Krishnan, E. Bhadauria, Mansi Chauhan Chouksey, Yash Amity University Mumbai Amity School of Engineering and Technology Department of Computer Science and Engineering Mumbai India

ISBN: (纸本)9798350370249

Shoulder problem is a widely known musculoskeletal disorder that impacts heavily on the life of patients. Effective treatment for this must be preceded by accurate and timely diagnosis but traditional diagnostic techniques have always been subjective and less reliable. In recent years, machine learning (ML) techniques have shown promising results in improving the accuracy of diagnosing shoulder pain, addressing the limitations of traditional diagnostic methods. Nonetheless, there is a failure to translate these ML models into practical clinical tools that can easily be employed by healthcare professionals. In addition, this paper will present an overview of current understanding and methodologies employed for diagnosing and managing shoulder pain along with reviewing the use of ML techniques in medical image diagnostics. It also suggests development of a user-friendly and accessible diagnosis tool for shoulder pathologies aimed at linking modern research and daily practice in the medical field. © 2024 IEEE.

关键词： Patient treatment

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Decoding the Script: A Bi-LSTM Based Journey into Doctor's Handwriting for Precise Prescription Understanding

Decoding the Script: A Bi-LSTM Based Journey into Doctor's H...

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2024 International Conference on Smart Devices, ICSD 2024

作者： Khan, Mohd Ayan Arif, Hamza Singh, Vishal Sharma, Dolly Amity University Department of Computer Science and Engineering Noida India 3rd Amity University Department of Computer Science and Engineering Noida India

ISBN: (纸本)9798350394412

Deciphering a doctor's handwritten prescription has been a complex task for human beings. Research has been done to automate the understanding of handwritten text, but transcribing doctor's handwritten prescriptions still requires the advancement to build a method that can be applied in real life. This paper presents a new hybrid model built on a fusion of CNN and Bi-LSTM architectures trained with CTC loss for the difficult task of decoding non-printed handwritten physicians' prescriptions. The unification of signals such as spatial feature extraction (CNN) as well as sequential dependency decrease (Bi-LSTM) with the aim of misinterpretation mitigation retrieves a very high recognition accuracy over a single, segmented test collection. With iterative training methodology, which focuses on computational limitations, the approach results in continuous learning, thus eliminating the problem of overfitting. The given model is very good at understanding the heterogeneity of handwriting and contextual relationships in prescription texts. This scientific addition leads to improvements in patient care, streamlined workflows, and healthcare information management improvements as the ultimate performance of the model is the astonishing 98.40% accuracy level it offers. © 2024 IEEE.

关键词： Deep learning

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Prevention of Attacks in Vehicular Adhoc Networks

Prevention of Attacks in Vehicular Adhoc Networks

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2024 IEEE International Students' Conference on Electrical, Electronics and computer science, SCEECS 2024

作者： Anjali, Thuvva Goyal, Rajeev G.n, Balaji Amity University Computer Science and Engineering Amity School of Engineering and Technology Gwalior India Vellore Institute of Technology Computer Science and Engineering School of Cse Vellore India

ISBN: (纸本)9798350348460

Over the past years, numerous methods have been developed to identify anomalies in traditional VANETs networks. A survey of VANET anomaly detection and mitigation methods is presented in this analysis. This survey looks into a variety of VANET research projects, attack types, anomaly detection methods, and mitigation strategies. VANETs provide wireless communication among vehicles as well as between vehicles and infrastructure. Its major objective is to improve road safety, comfort, and convenience. VANET varies from ad hoc networks due to its specific features. Several methods for identifying anomalies in conventional VANET networks have been developed over time. This work examines VANET anomaly detection and prevention technologies. Various VANET research studies, different types of attacks, anomaly detection, and VANET protection measures. The current investigation seeks to address various sorts of vehicle network assaults as well as existing security methods for coping with these attacks. This work will help academics understand the taxonomy of automotive network security vulnerabilities, which can subsequently be researched further to develop unique solutions. © 2024 IEEE.

关键词： Identity Based Signature Intelligent Transport System Intrusion Detection System Vehicle to Vehicle Vehicular Adhoc Network

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Analysis of Blockchain Security Applications in Electronic Health Records Standardization

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Recent Advances in computer science and Communications 2023年第5期16卷 19-30页

作者： Mishra, Princy Khandelwal, Brijesh Dewangan, Bhupesh Kumar Amity School of Engineering Amity University Raipur India Department of Computer Science and Engineering School of Engineering O.P. Jindal University Raigarh India

Healthcare data is most unorganized and decentralized in many countries, including India. EHR (Electronic Health Record) has increased its acceptability and importance as it assists in medical research and helps backtrack the origin of disease. EHR may be generated at multiple sources like hospitals, clinics, path labs, or pharmacies. This paper is mainly targeted towards understanding the feasibility and different processes that could be adopted for medical data security;for this purpose, we have performed a systematic literature review. This research explores existing EHR frameworks, their benefits and challenges, as well as a brief comparison and applicability of various blockchain *** part of the review, we have discussed prior works, the background of blockchain in the healthcare domain, and bibliometric analysis on ample research papers from 2011 to 2021. © 2023 Bentham science Publishers.

关键词： Smart contract

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