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advances in Early Detection and Monitoring of Retinopathy in Preterm Infants Using CNN and MLP Models

Advances in Early Detection and Monitoring of Retinopathy in...

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international conference on Circuit, Power and Computing Technologies (ICCPCT)

作者： G. Hubert S. Silvia Priscila Department of Computer Applications Bharath Institute of Higher Education and Research Chennai Tamil Nadu India Department of Computer Science Bharath Institute of Higher Education and Research Chennai Tamil Nadu

ISBN: (数字)9798350372816

ISBN: (纸本)9798350372823

this research showcases significant advancements in the timely identification and monitoring of retinopathy among preterm infants through the integration of Multilayer Perceptron (MLP) and Convolutional Neural Network (CNN) models. We eradicate the necessity for subjective assessments by ensuring a precise and automated diagnosis through the implementation of these contemporary models. the CNN is capable of extracting robust features from retinal images, and the MLP model enhances the predictive precision and monitoring functionalities even further. By integrating these models, our approach enhances the precision and velocity of retinopathy diagnosis, thereby resulting in improved therapeutic outcomes. the proposed framework significantly improves neonatal care by incorporating cutting-edge technology that enables continuous and advanced detection of retinopathy in premature infants.

关键词： Pediatrics Accuracy Retinopathy Predictive models Multilayer perceptrons Feature extraction Retina

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Multilayer Gaussian Feature Extraction Algorithm for Sky Image Classification 4th

Multilayer Gaussian Feature Extraction Algorithm for Sky Ima...

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4th international conference on advances in Computational science and Engineering, ICACSE 2023

作者： Petwan, Montha Ku-Mahamud, Ku Ruhana Saengpetch, Piyawat Zamin, Norshuhani Faculty of Science and Technology Suratthani Rajabhat University Khun Taleay Suratthani Muang Thailand School of Computing Universiti Utara Malaysia Kedah Sintok Malaysia Department of Information and Communications Technology College of Engineering Shibaura Institute of Technology Tokyo Japan College of Computer Studies De La Salle University Manila Philippines

ISBN: (纸本)9789819729760

the classification of sky images holds significant importance in the field of meteorology as it enables the accurate prediction of weather patterns and climatic conditions. the multi-view features of super pixels have been employed in many algorithms for detection and recognition of various cloud types. However, these algorithms neglect the local structure of region and the mean information of pixel features. these aspects are crucial in capturing the discriminative information necessary for complex data distributions in the sky images. this paper proposes the Multilayer Gaussian (MLG) algorithm to extract the features of sky images using the Gaussian smoothness standard deviation techniques. this method involves capturing the average information within each local patch by calculating the mean of the pixel values. Hence, the informative feature that facilitates the distinction of cloud formations in sky image primarily resides in the average information included within each localized region. Moreover, it provides a comprehensive representation of the local patch and exhibits resistance to noise. the Kiel and SWIMCAT datasets were used to evaluate the proposed MLG algorithm based on the metrics number of extracted features, similarity values, precision, recall, and f-measure using the decision tree and kernel support vector machine. the experimental results show that the performance of MLG surpassed all benchmark feature extraction algorithms. © the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Support vector machines

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Nonlinear Model Predictive Control for Optimal Dose Administration in Radiotherapy 15th

Nonlinear Model Predictive Control for Optimal Dose Administ...

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15th Advanced Doctoral conference on Computing, Electrical and Industrial Systems, DoCEIS 2024

作者： Araújo, João C. G. Guerreiro, Bruno J. Oliveira, Luis B. da Silva, Filipe Ferreira CTS-UNINOVA and LASI Department of Electrical and Computer Engineering NOVA School of Science and Technology Caparica2829-516 Portugal Institute for Systems and Robotics LARSyS Lisbon1049-001 Portugal CEFITEC Department of Physics NOVA School of Science and Technology Caparica2829-516 Portugal

ISBN: (纸本)9783031638503

this work discusses the nonlinear modeling of healthy and cancer cell population growth under the influence of radiotherapy, and proposes predictive control techniques to determine the optimal dose for tumor control, while sparing surrounding healthy tissue. Radiation effects are modelled by a linear-quadratic formalism, coupled with a novel population-specific delayed dose response. Numerical simulations of the open- and closed-loop system are conducted, and robustness against model uncertainty is studied. Results illustrate the controller’s performance when dealing with imperfect information and unexpected system behavior, and how it can be tuned to produce the desired treatment outcome. However, optimal operation is conditioned by a limited prediction horizon, as well as strict adherence to treatment schedules and dose restrictions. © IFIP international Federation for Information Processing 2024.

关键词： Nonlinear systems

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Ensemble Approach to Classify Spam SMS from Bengali Text 7th

Ensemble Approach to Classify Spam SMS from Bengali Text

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Proceedings of the 7th international conference on advances in Computing and Data sciences, ICACDS 2023

作者： Al Maruf, Abdullah Al Numan, Abdullah Haque, Md. Mahmudul Jidney, Tasmia Tahmida Aung, Zeyar Dhaka Bangladesh Dhaka Bangladesh Ahsanullah University of Science and Technology Dhaka Bangladesh Khalifa University Abu Dhabi United Arab Emirates

ISBN: (纸本)9783031379390

the Short Message Service (SMS) is a popular communication tool, but it has some security weaknesses, such as the influx of spam messages from cyber criminals. While several studies have been conducted on filtering and categorizing spam messages in various languages, including English, limited research has been done on detecting spam in Bengali (endonym Bangla) text. this study aims to fill this gap by classifying Bengali SMS messages as either spam or ham (legitimate messages). To accomplish this, the study used machine learning algorithms, including support vector machine (SVM) with a linear kernel and decision tree (DT), logistic regression (LR), and random forest (RF) with various parameters, as baseline models. Ensemble approaches, such as bagging, boosting, and stacking, were then used to enhance the performance of the models. the results show that the ensemble approach successfully identified spam messages in Bengali text, with XGBoost producing the most favorable outcome. the contribution of this study lies in its focus on Bengali text and the demonstration of the ensemble method’s performance on a small dataset. the tool developed in this study can provide a secure and efficient SMS service to customers by reducing the burden of spam messages and improving the overall user experience. Additionally, the tool can be marketed as a value-added service for customers who are concerned about the security of their personal and financial information. Overall, this study highlights the importance of machine learning algorithms, specifically ensemble methods, in detecting spam messages in Bengali text and provides a valuable contribution to the field of SMS security. © 2023, the Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Classification (of information)

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ARTIFICIAL INTELLIGENCE AND GENDER EQUALITY: A SYSTEMATIC MAPPING STUDY 15

ARTIFICIAL INTELLIGENCE AND GENDER EQUALITY: A SYSTEMATIC MA...

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15th international conference on ICT, Society and Human Beings, ICT 2022, 19th international conference on Web Based Communities and Social Media, WBC 2022 and 14th international conference on e-Health, EH 2022 - Held at the 16th Multi conference on computer science and Information Systems, MCCSIS 2022

作者： Patón-Romero, J. David Vinuesa, Ricardo Jaccheri, Letizia Baldassarre, Maria Teresa Sem Sælands Vei 7 Trondheim7034 Norway FLOW Engineering Mechanics KTH Royal Institute of Technology StockholmSE-100 44 Sweden Via Edoardo Orabona 4 Bari70126 Italy

ISBN: (纸本)9789898704405

Sustainability is not only understood as a manner to safeguard the environment, but also to fight against injustices and inequalities that exist on social and economic level. One of the biggest challenges that exists in social sustainability is to achieve gender equality, as defended by the Sustainable Development Goal (SDG) 5 of the 2030 Agenda. But this is a complex challenge and must be addressed from different spheres and fields of knowledge. Artificial Intelligence (AI) has proven to be an essential asset in the development of new and innovative technologies. Its development, adoption, and constant use by a growing part of the world’s population demonstrates the social impact it entails and the importance of also becoming an asset for social sustainability and, in this case especially, for gender equality. that is why this study aims to collect the current knowledge about the fields of AI and gender equality, through the development of a Systematic Mapping Study (SMS) that identifies the most significant advances in this regard, as well as the main gaps that must be covered. the results and findings obtained in this work show the novelty of joint analysis of both areas, as well as increasing attention they have received in recent years. Likewise, they also demonstrate the need to address specific and urgent issues within gender equality, both in the field of AI and caused by its development. © 2022 15th international conference on ICT, Society and Human Beings, ICT 2022, 19th international conference on Web Based Communities and Social Media, WBC 2022 and 14th international conference on e-Health, EH 2022 - Held at the 16th Multi conference on computer science and Information Systems, MCCSIS 2022. All rights reserved.

关键词： Artificial intelligence

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Mood-Based Movie Recommendation System Using Sentiment Analysis 4th

Mood-Based Movie Recommendation System Using Sentiment Analy...

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4th international conference on Machine Learning, advances in Computing, Renewable Energy and Communication, MARC 2023

作者： Bhushan, Shashi Singh, Anuj Kumar Kumar, Sandeep Sharmila Bhuyan, Bikram Pratim Universiti Teknologi PETRONAS Seri Iskandar Malaysia School of Computing Science and Engineering Galgotias University Greater Noida India Department of Computer Science and Engineering Tulas Institute Dehradun India Department of Electronics and Communication Engineering Raj Kumar Goel Institute of Technology Ghaziabad India LISV Laboratory University Paris-Saclay 10-12 Av.de l’Europe Paris France

ISBN: (纸本)9789819752300

this research project aims to develop an Android application that uses voice command sentiment analysis to recommend movies based on a user’s current mood. Movies are an essential part of people’s lives, offering a necessary disconnect from the outside world and reducing stress and anxiety. While online streaming platforms like Netflix and Amazon Prime Video provide access to movies, they do not have access to a user’s current mood, limiting the ability to make personalised recommendations. By analysing voice attributes, including pitch, tone, and gender, our application aims to provide personalised genre suggestions to reduce stress and anxiety based on a user’s current sentiment. the project will utilise Android Studio and IntelliJ Idea as IDEs and incorporate ML algorithms. the end product will be an Android application that recommends movies based on a user’s mood. © the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Video streaming

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Highest Accuracy Based Automated Depression Prediction Using Natural Language Processing 1

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9th international conference on Mining Intelligence and Knowledge Exploration, MIKE 2023

作者： tharun, S.V. Saranya, G. Tamilvizhi, T. Surendran, R. Department of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Chennai India Department of Computer Engineering Panimalar Engineering College Chennai India Department of Computer Science and Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Chennai India

ISBN: (数字)9783031440847

ISBN: (纸本)9783031440830

In today's fast-paced society, psychological health issues such as anxiety, depression, and stress have become prevalent among the general population. Researchers have explored the use of machine learning algorithms to predict the likelihood of depression in individuals. As datasets related to depression become more abundant and machine learning technology advances, there is an opportunity to develop intelligent systems capable of identifying symptoms of depression in written material. By applying natural language processing and machine learning algorithms to analyze written text, such as social media posts, emails, and chat messages, researchers can potentially identify patterns and linguistic cues associated with depression. these patterns may include changes in word usage, tone, and sentiment. the dataset consists of text-based questions on this information channel. At present, machine learning techniques are highly effective for analyzing data and identifying problems. Researchers have conducted comparisons of the accuracy achieved by different machine learning algorithms using the complete set of attributes as well as a subset of selected attributes. In summary, while the potential for AI to aid in mental health diagnosis and treatment is exciting, it's important to proceed with care and consideration for the complexities of the field and the needs of patients. © 2023, the Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Decision trees

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A New Grey Correlational Compromise Ranking Approach for Portfolio Selection for Investment in ESG Stocks 7th

A New Grey Correlational Compromise Ranking Approach for Por...

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Proceedings of the 7th international conference on advances in Computing and Data sciences, ICACDS 2023

作者： Biswas, Sanjib Gupta, Sayan Upadhyay, Arun Bandyopadhyay, Gautam Shaw, Rahul Calcutta Business School South 24 Parganas West Bengal Bishnupur743503 India Dr. B. C. Roy Engineering College Academy of Professional Courses Fuljhore Paschim Barddhaman West Bengal Durgapur713206 India Department of Business Management NSHM Business School Durgapur Arrah Shibtala Via Muchipara West Bengal Durgapur713212 India Department of Management Studies National Institute of Technology Mahatma Gandhi Road A-Zone West Bengal Durgapur713209 India

ISBN: (纸本)9783031379390

In recent years environmental, social, and governance (ESG) aspects have gained a notable emphasis. the present paper primarily aims to select a portfolio of the stocks listed to ESG index at BSE, India based on their market performance. To this end the ongoing research proposes a new extension of compromise ranking of alternatives from distance to ideal solution (CRADIS) model by using grey correlational approach to foster a flexible decision-making under uncertainty. through comparison with other multi-criteria decision making (MCDM) models it is revealed that the grey correlational CRADIS (GC-CRADIS) provides a reasonably reliable result. the sensitivity analysis (with flexible changes in the given conditions) shows the stability in the result. A possible portfolio of ESG stocks is resulted using GC-CRADIS. Further, the current paper attempts to delve into the association of the stock performance with ESG score. However, no significant association of stock performance with others is noticed. the findings of the paper help in formulating a portfolio of ESG stocks for investment. © 2023, the Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Investments

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Aspect-Adaptive Knowledge-based Opinion Summarization 20th

Aspect-Adaptive Knowledge-based Opinion Summarization

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20th Principle and Practice of Data and Knowledge Acquisition Workshop, PKAW 2024, held in conjunction with the 21st Pacific Rim international conference on Artificial Intelligence, PRICAI 2024

作者： Wang, Guan Li, Weihua Lai, Edmund M.-K. Bai, Quan Auckland University of Technology Auckland1010 New Zealand University of Tasmania HobartTAS7005 Australia

ISBN: (纸本)9789819600250

the increase in online information has overwhelmed users with opinions and comments on various products and services, making decision-making a daunting task. Text summarization can help by distilling long or multiple documents into concise, relevant content. Recent advances in Large Language Models (LLM) have shown great potential in this area. the existing text summarization approaches often lack the "adaptive" nature required to capture diverse aspects in opinion summarization, which is particularly detrimental to users with specific preferences. In this paper, we introduce an Aspect-adaptive Knowledge-based Opinion Summarization model for product reviews. this model generates summaries that highlight specific aspects of reviews, providing users with targeted, relevant information quickly. Our extensive experiments with real-world datasets explicitly demonstrate that our model surpasses current state-of-the-art methods. It effectively adapts to user needs, producing efficient, aspect-focused summaries that help users make informed decisions based on their unique preferences. © the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Self-supervised learning

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Dynamic Talking-Head Generation with Speech Emotion Recognition and Intensity Detection 13

Dynamic Talking-Head Generation with Speech Emotion Recognit...

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13th international conference on Electronics, Communications and Networks, CECNet 2023

作者： Fu, Chen Yu, Yang Zhang, Zhiqiang Weng, Wei Zhou, Jinjia Graduate School of Science and Engineering Hosei University Japan School of Computer Science and Technology Southwest University of Science and Technology China Institute of Liberal Arts and Science Kanazawa University Japan

ISBN: (纸本)9781643684802

In recent years, speech emotion recognition (SER) and emotive talking head generation have drawn significant attention. However, existing methods either focus predominantly on emotion recognition without considering its intensity or produce emotionally consistent talking heads, neglecting the rich expressiveness of human interactions. Addressing these limitations, our study aims to bridge these two areas, proposing an innovative talking-head generation approach that further enhances the realism of the generated video. Within the SER domain, we develop a dual-level model that not only improves the accuracy of identifying emotions in speech data but also assesses their intensity. this dual-capacity model significantly enhances the depth and subtlety of emotional recognition, achieving an industry-leading accuracy rate of 73.5%. On the talking head front, this work generates emotionally expressive talking heads that reflect both emotional state and intensity. Beyond this, the model also simulates human facial dynamics like blinking, thus offering a more comprehensive and realistic representation of human speech. these advances effectively address the shortcomings of existing methods and provide a new avenue for the accurate portrayal of emotional intensity in synthesized talking heads. Extensive experimental results demonstrate the effectiveness and superiority of our approach over existing methodologies in both SER and talking head generation. © 2024 the authors and IOS Press.

关键词： Emotion Recognition

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