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ESG Investing: A Statistically Valid Approach to Data-Driven Decision Making and the Impact of ESG Factors on Stock Returns and Risk

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IEEE Access 2024年 12卷 69434-69444页

作者： Teja, Kamurthi Ravi Liu, Chuan-Ming National Taipei University of Technology International Program of Electrical Engineering and Computer Science Taipei10608 Taiwan National Taipei University of Technology Department of Computer Science and Information Engineering Taipei10608 Taiwan

This study examines the impact of environmental, social, and governance (ESG) factors on economic investment from a statistical perspective, aiming to develop a tested investment strategy that capitalizes on the connection between ESG factors and financial performance. ESG investing: A statistically valid approach to data-driven decision-making (ESGI-SVADDM) investment strategy is based on a rigorous, statistically valid approach that utilizes data, math, statistics, and data science libraries to drive investment decisions, eliminating the need for personal opinions and subjectivity. The process includes establishing an investment thesis, formulating testable hypotheses (HPS), retrieving and refining relevant data, calculating relevant measures, and testing and validating all hypotheses. The study uses S&P 500 stock data and ESG data from Sustainalytics to test the hypotheses. The empirical tests conducted revealed a negative correlation between ESG risk and expected returns, as well as a positive trend in the relationship between ESG risk and the overall risk of stocks. Moreover, the study found that higher ESG risk scores are associated with lower returns for investors, and that adopting a strategy of investing in stocks with low ESG risk and shorting stocks with high ESG risk yields superior returns compared to the market portfolio. © 2013 IEEE.

关键词： Investments

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Automated recognition of Myanmar sign language using deep learning module

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International Journal of Information technology (Singapore) 2024年第2期16卷 633-640页

作者： Kyaw, Nwe Ni Mitra, Pabitra Sinha, G.R. Department of Computer Science and Engineering Indian Institute of Technology Kharagpur West Bengal Kharagpur India Computer Science and Engineering GSFC University Vadodara Gujarat India

The recognition of sign language in real-time poses a significant challenge due to the dynamic nature of hand gestures, and determination of the start and end points of each gesture becomes difficult in a video stream required in the recognition system. For diverse range of applications, researchers have undertaken vision-based gesture recognition studies. In this current research, deep learning approach is employed to develop a novel model specifically designed for the classification of sign language. Our study focuses on generating datasets comprising videos of Myanmar sign language (MSL) at the word level, incorporating various lighting conditions and complex backgrounds. The implementation of a Convolutional Neural Network (CNN) model allows us to present a unique approach to hand gesture recognition, using a comprehensive process involving data collection, parameter analysis, CNN model creation, as well as training and testing phases, aimed at facilitating precise recognition tasks. Notably, the utilization of the Stochastic Gradient Descent with Momentum (SGDM) optimizer resulted in a remarkable 98% success rate, while the Adam optimizer demonstrated an even higher success rate of 99%. Therefore, our proposed system is well-suited for the development of wearable real-time systems capable of automatically recognizing MSL. © The Author(s), under exclusive licence to Bharati Vidyapeeth's institute of computer Applications and Management 2024. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s);author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

关键词： Classification Convolutional neural network Deep learning Hand gestures Myanmar sign language (MSL) recognition

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A Post-Stroke Rehabilitation System With Compensatory Movement Detection Using Virtual Reality and Electroencephalogram Technologies

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IEEE Access 2024年 12卷 61418-61432页

作者： Shih, Chi-Huang Lin, Pei-Jung Chen, Yen-Lin Chen, Shu-Ling National Chin-Yi University of Technology Department of Computer Science and Information Engineering Taichung411030 Taiwan Feng Chia University Department of Information Engineering and Computer Science Taichung40724 Taiwan National Taipei University of Technology Department of Computer Science and Information Engineering Taipei106344 Taiwan Hungkuang University Department of Nursing Taichung43302 Taiwan

Stroke is a leading cause of global population mortality and disability, imposing burdens on patients and caregivers, and significantly affecting the quality of life of patients. Therefore, in this study, we aimed to explore the application of virtual reality technology in physical therapy by using immersive interactive training and designing rehabilitation modes for individual and group settings. We also aimed to provide patients with stroke with a comprehensive home-based rehabilitation and treatment plan, ultimately enhancing training effectiveness. Patients can engage in home-based rehabilitation through this system and undergo functional, mirror, and constraint-induced therapies tailored to different task contents. Simultaneously, using brain-computer interface technology, an emotion analysis mechanism was designed to map the patients' brainwave signal data onto a two-dimensional space of positive-negative valence arousal;this approach can enable remote physical therapists to discern the patients' emotional states during the rehabilitation process through virtual spaces, facilitating timely adjustments to rehabilitation tasks. Moreover, to prevent compromised effectiveness owing to improper training postures leading to compensation, the system offers real-time identification and recording, promptly issuing alerts when compensation occurs. The system provides a multiuser virtual rehabilitation space, enabling timely corrections and data observations, offering patients with stroke a home-based rehabilitation program, thereby realizing a localized aging care model. © 2013 IEEE.

关键词： Virtual reality

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A novel brain tumor segmentation and classification model using deep neural network over MRI-flair images

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Multimedia Tools and Applications 2025年第17期84卷 17645-17676页

作者： Rajendirane, Rajmohan Ananth Kumar, T. Sandhya, S.G. Hu, Yu-Chen Department of Computing Technologies SRM Institute of Science and Technology Kattankulathur Campus Tamil Nadu Kattankulathur India Department of Computer Science and Engineering IFET College of Engineering Tamil Nadu Villupuram India Department of Computer Science and Engineering Annamalai University Chidambaram India Department of Computer Science Tunghai University No. 1727 Sec. 4 Boulevard Xitun District Taichung City407224 Taiwan

Brain tumors pose a significant health concern globally, with their detection and diagnosis being crucial for timely intervention and treatment planning. These abnormal growths can develop within the brain or originate from other parts of the body, spreading to the brain. They can be benign or malignant, and their impact on cognitive and physical function can vary widely depending on their location, size, and type. Detecting brain tumors involves a combination of imaging techniques and clinical assessment. Magnetic Resonance Imaging (MRI) is one of the primary imaging modalities used for this purpose due to its ability to provide detailed images of the brain's anatomy and pathology. Advanced image processing techniques and machine learning algorithms play an increasingly important role in enhancing the accuracy and efficiency of brain tumor detection. This study presents a comprehensive approach for enhancing brain tumor detection in MRI images using a combination of advanced techniques. Firstly, an improved K-means clustering algorithm is introduced to segment tumor regions effectively. Following this, Median Filtering is applied to refine image quality and reduce noise interference. Subsequently, a deep neural network architecture is employed for precise tumor classification utilizing the BRATS dataset. The proposed methodology aims to improve the accuracy and efficiency of brain tumor detection, offering potential advancements in medical image analysis and diagnosis. Experimental results demonstrate promising outcomes, indicating the effectiveness of the proposed approach in accurately identifying tumor regions in MRI scans. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024.

关键词： Magnetic resonance imaging

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Proposed AES-based Obfuscation and Encryption Model for Cloud Data Security 1

Proposed AES-based Obfuscation and Encryption Model for Clou...

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1st International Conference on Intelligent Systems and Computational Networks, ICISCN 2025

作者： Singh, Prabhdeep Singh, Pawan Agarwal, Abhay Kumar Kumar, Anil Amity School of Engineering and Technology Department of Computer Science and Engineering Lucknow India Kamla Nehru Institute of Technology Department of Computer Science and Engineering Sultanpur India Amity University Amity School of Engineering and Technology Lucknow India

ISBN: (纸本)9798331529246

Cloud Computing (CC) offers a diverse range of services along with huge data storage across a network. CC has collaborated with varied emerging technologies like IoT because of its numerous advantages. Despite CC's benefits, cloud-based servers are not always trustworthy in real-world scenarios. Users' privacy will be compromised if they send their data straight to cloud platforms since crucial information will be exposed to cloud servers. Thereby, this work develops a data security model in the cloud with two stages, proposed secured data storage and proxy re-encryption. Initially, the data is stored securely in the cloud based on improved AES-based encryption as well as obfuscation-based encryption. Based on the request from the data user, proxy re-encryption is done using Kronecker product-based encryption. The enhanced AES achieved the lowest value of 0.025 for the CCA attack with a key size of 128 bits. Thus, the final encoded key is obtained. © 2025 IEEE.

关键词： Cloud platforms

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Modeling of Optimal Deep Learning Based Flood Forecasting Model Using Twitter Data

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Intelligent Automation & Soft Computing 2023年第2期35卷 1455-1470页

作者： G.Indra N.Duraipandian Department of Computer Science and Engineering RMK College of Engineering and TechnologyChennai601206India Department of Computer Science and Engineering Saveetha Engineering CollegeChennai602106India

Aﬂood is a signiﬁcant damaging natural calamity that causes loss of life and *** work on the construction ofﬂood prediction models intended to reduce risks,suggest policies,reduce mortality,and limit property damage caused byﬂ*** massive amount of data generated by social media platforms such as Twitter opens the door toﬂood *** of the real-time nature of Twitter data,some government agencies and authorities have used it to track natural catastrophe events in order to build a more rapid rescue ***,due to the shorter duration of Tweets,it is difﬁcult to construct a perfect prediction model for determiningﬂ*** learning(ML)and deep learning(DL)approaches can be used to statistically developﬂood prediction *** the same time,the vast amount of Tweets necessitates the use of a big data analytics(BDA)tool forﬂood *** this regard,this work provides an optimal deep learning-basedﬂood forecasting model with big data analytics(ODLFF-BDA)based on Twitter *** suggested ODLFF-BDA technique intends to anticipate the existence ofﬂoods using tweets in a big data *** ODLFF-BDA technique comprises data pre-processing to convert the input tweets into a usable *** addition,a Bidirectional Encoder Representations from Transformers(BERT)model is used to generate emotive contextual embed-ding from ***,a gated recurrent unit(GRU)with a Multilayer Convolutional Neural Network(MLCNN)is used to extract local data and predict theﬂ***,an Equilibrium Optimizer(EO)is used toﬁne-tune the hyper-parameters of the GRU and MLCNN models in order to increase prediction *** memory usage is pull down lesser than 3.5 MB,if its compared with the other algorithm *** ODLFF-BDA technique’s performance was validated using a benchmark Kaggle dataset,and theﬁndings showed that it outperformed other recent approaches signiﬁcantly.

关键词： Big data analytics predictive models deep learning ﬂood prediction twitter data hyperparameter tuning

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Reinforcement Learning with Quasi-Hyperbolic Discounting (Extended Abstract) 10

Reinforcement Learning with Quasi-Hyperbolic Discounting (Ex...

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10th Indian Control Conference, ICC 2024

作者： Eshwar, S.R. Motwani, Mayank Roy, Nibedita Thoppe, Gugan Department of Computer Science and Automation Indian Institute of Science Bengaluru India Department of Computer Science and Engineering Indian Institute of Technology Bombay India

ISBN: (纸本)9798331517212

Human decision-making is better modeled via discounting schemes such as quasi-hyperbolic and hyperbolic than classical ones such as exponential or average reward. In [3], we initiated the study of reinforcement learning with Quasi-Hyperbolic (QH) discounting and proposed the first model-free algorithm for finding a type of policy called the Markov perfect equilibrium. This paper provides a summary of that algorithm and our other findings on its convergence. © 2024 IEEE.

关键词： Contrastive Learning

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Vector Quantized Convolutional Autoencoder Network for LDCT Image Reconstruction with Hybrid Loss

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SN computer Science 2024年第1期5卷 2页

作者： Ramanathan, Shalini Ramasundaram, Mohan Department of Computer Science and Engineering National Institute of Technology Tiruchirappalli Tamil Nadu Trichy India

Medical image reconstruction is the process of creating high-quality and accurate images. During acquisition, these devices capture raw measurements or signals that represent the internal structures of the human body. However, these raw measurements are often noisy or incomplete. Low-dose CT is a medical imaging technique that uses a reduced amount of radiation to obtain detailed cross-sectional images of the body. Deep learning for low-dose CT is an emerging field that utilizes advanced artificial intelligence techniques to enhance the image quality and diagnostic accuracy of CT scans acquired with reduced radiation doses. A convolutional autoencoder for low-dose CT is a specific type of deep-learning architecture designed to address the challenges of reducing radiation doses while maintaining image quality. They are effectively trained on large datasets of both low-dose and standard-dose CT images to learn patterns and features that can compensate for the noise and artifacts present in low-dose scans. This research offers a new vector quantization-based convolutional autoencoder network strategy for CT image reconstruction. In vector quantization, discrete data elements are mapped to a set of representative vectors known as codebook entries. Each data element is associated with the nearest codebook entry based on a defined distance metric. This mapping allows for the efficient representation of discrete data by replacing each element with its corresponding codebook entry. Discrete data representation is essential for the efficient storage and transmission of information during the image reconstruction task. The results’ quality is assessed based on the perceptual and bias-reducing loss functions. On the LoDoPaB-CT benchmark dataset, experimental evaluations are done. Its findings demonstrated that, in terms of quantitative and visual evaluation, respectively, the proposed network obtained better performance metric values and better noise reduction results. © 2023,

关键词： Autoencoders Convolutional neural network medical image reconstruction Deep learning Low-dose computed tomography Quantization

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Implementation of YOLOv9 in Agricultural AI for Enhanced Weed Detection

Implementation of YOLOv9 in Agricultural AI for Enhanced Wee...

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2024 International Conference on Intelligent Computing and Sustainable Innovations in technology, IC-SIT 2024

作者： Duraisamy, Premkumar Deepika, A. Niranjani, V. Jeevitha, R. Sibishree, M. Oviya, A. KPR Institute of Engineering and Technology Department of Computer Science and Engineering Coimbatore India Sri Eshwar College of Engineering Department of Computer Science and Engineering Coimbatore India KPR Institute of Engineering and Technology Department of Electronics and Communication Engineering Coimbatore India

ISBN: (纸本)9798350369175

Weeds significantly hinder agricultural productivity by reducing crop yields and increasing production costs. Leveraging artificial intelligence (AI) is critical for equipping farmers with early detection capabilities to implement effective weed management strategies. Among AI technologies, deep learning (DL) techniques are especially effective for analyzing agricultural field images to identify weed species. This paper reviews state-of-the-art DL methodologies, with a focus on the YOLOv9 model for weed detection. Our findings highlight YOLOv9's superior effectiveness, achieving an accuracy of $\mathbf{9 0 \%}$, due to its robust architecture and advanced feature extraction. The model's proficiency in accurately identifying weeds is evident from our thematic analysis. We also explore various image acquisition devices, including robots, drones, and mobile phones. Comparative evaluations show that YOLOv9 consistently outperforms other DL techniques, achieving remarkable accuracy and speed. This study includes a comparative analysis of several algorithms like SSD, Mask R-CNN, and Fast R-CNN, underscoring YOLOv9's superior performance. This paper serves as a valuable resource for researchers and practitioners, guiding future efforts in weed management and precision agriculture by emphasizing YOLOv9's transformative potential. © 2024 IEEE.

关键词： Agricultural productivity Deep learning Image acquisition object detection Weed detection YOLOv9 algorithm

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Mental Health Chatbot

Mental Health Chatbot

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2024 IEEE Students Conference on engineering and Systems, SCES 2024

作者： Harini, V.K. Bhavadharini, R.M. Upreti, Charvi Alex, Libin Schoo of Computer Science and Engineering Vellore Institute of Technology Chennai India Schoo of Computer Science and Engineering Vellore Institute of technology India

ISBN: (纸本)9798350374711

As per a 2023 publication, addressing mental health problems in India holds immense significance due to the scale of human value impact involved, particularly given the country's population size. It is crucial to recognize that mental health issues affect a significant portion of the population and can lead to severe consequences if left unaddressed. With this paper, we aim to contribute to the advancement of mental healthcare provision by developing a task-oriented, closed-domain chatbot. This chatbot will serve as a valuable tool for individuals seeking support and information regarding mental health issues. The paper compares existing models which use various embeddings and transformers and focuses on the development of a task-oriented, closed-domain chatbot using a retrieval-based approach with text data. The maximum accuracy of 91.48% was achieved in the LSTM model. © 2024 IEEE.

关键词： bag-of-words chatbot closed domain mental health RoBERTa task oriented model word2vec

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