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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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Comparison of Geographically Weighted Regression (GWR) and Mixed Geographically Weighted Regression (MGWR) Models on the Poverty Levels in Central Java in 2023

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IAENG International Journal of Applied Mathematics 2024年第12期54卷 2746-2757页

作者： Alya, Najma Attaqiya Almaulidiyah, Qothrotunnidha Farouk, Bailey Reshad Rantini, Dwi Ramadan, Arip Othman, Fazidah Engineering Department Data Science Technology Study Program Universitas Airlangga Indonesia Engineering Department Data Science Technology Study Program Faculty of Advanced Technology and Multi-discipline Universitas Airlangga Indonesia Information System Study Program Department of Industrial and System Engineering Telkom University Surabaya Campus Indonesia Department of Computer System and Technology Faculty of Computer Science and Information Technology University of Malaya Malaysia

Poverty is considered a serious global issue that must be immediately eradicated by Sustainable Development Goals (SDGs) 1, namely ending poverty anywhere and in any form. As a developing country, poverty is a complex issue faced by Indonesia. Tackling poverty in Indonesia has become a key focus of the government’s strategic priorities. This issue of poverty is experienced by all provinces in Indonesia, including Central Java. The poverty rate in Central Java stands at 10.77% or 3.79 million people. The causes of poverty are explained by several factors. Using these various factors, a mapping model can be conducted to determine the number of poor populations in Central Java. This research employs Geographically Weighted Regression (GWR) and Mixed Geographically Weighted Regression (MGWR) approaches to compare the effectiveness of these models in analyzing the number of poor populations in Central Java in 2023. The kernel function weighting used in this study is Fixed Gaussian. The results showed that the MGWR model outperformed both the OLS regression and GWR models, achieving an Akaike Information Criterion (AIC) of 62.766, an R2 of 82.3%, and a Mean Squared Error (MSE) of 0.177. Consequently, it can be inferred that the MGWR model was more suitable for explaining the poverty levels in Central Java. © (2024), (International Association of Engineers). All Rights Reserved.

关键词： Logistic regression

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Design of a highly efficient crop damage detection ensemble learning model using deep convolutional networks

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Journal of Ambient Intelligence and Humanized Computing 2023年第8期14卷 10811-10821页

作者： Dhande, Akshay Malik, Rahul Department of Electronics and Telecommunication Engineering Lovely Professional University Punjab Phagwara India Department of Computer Science and Engineering Lovely Professional University Punjab Phagwara India

Damages to crops happen due to natural calamities, irregular fertilization, improper treatment, etc. Estimation of this damage is important in order to plan and execute corrective action strategies. To perform this estimation with high accuracy, both satellite and near-field images are needed. Satellite images assists in evaluation of damages due to natural calamities, while near-field images assist in evaluation of damage due to plant diseases. Separate models are designed for processing these images, which limits their correlative analysis;and thereby reduces overall accuracy of damage detection. To remove this drawback, this text proposes a deep convolutional network (DCN) design that integrates both near-field and far-field images in order to perform effective correlation. Moreover, design of an integrated model would assist researchers to identify and resolve dataset-specific performance gaps. The proposed model analyses different datasets, and estimates performance of context-sensitive classification methods. These are integrated to improve efficiency for multimodal augmented image classification deployments via correlative analysis. This analysis allows the system to predict crop-damages with higher efficiency than individual models. The model is trained for detection of areas which are infected by natural calamities, thereby assisting farm experts to undertake corrective measures based on specific area. Results of proposed model are compared with some of the recently developed state-of-the-art methods, and it is observed that the former model achieves 10% better accuracy, 8% better precision and 5% better recall performance. This evaluation is done on a large number of datasets, thereby assisting in model validation and estimating its usage for multiple type of crops. This text also recommends future research directions that can be undertaken for performance improvement of the underlying model. © 2022, The Author(s), under exclusive licence to Springer-Verla

关键词： Convolution

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Markov Model Based Risk Prediction of Patients with Kidney Diseases

Markov Model Based Risk Prediction of Patients with Kidney D...

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2024 OPJU International Technology Conference on Smart Computing for Innovation and Advancement in Industry 4.0, OTCON 2024

作者： Chaki, Shambhab Rakshit, Pratyusha Chowdhury, Archana Jadavpur University Department of Electronics and Telecommunication Engineering India Christian College of Engineering and Technology Department of Computer Science and Engineering Bhilai India

ISBN: (纸本)9798350373783

The paper proposes a novel machine learning approach for early prediction of risk of a patient suffering from severe kidney-related diseases (KD). The training phase consists of two steps. First, the records of the already existing patients with KD are divided into groups based on the similarity of the sequences of their clinical activities, including diagnoses, lab tests and medications. Next, a Markov chain model is employed to mathematically represent the clinical activity-sequences of patients in each group. The prediction phase also consists of two steps. First, the support of each group to the clinical-activities of the unknown patient P is evaluated. P is then assigned with the group with the highest support. Next, the Markov chain model of the winning group is used to predict the possible finite sequence of the clinical activities of P in future. Comparative study shows better performance of the proposed algorithm over other state-of-the-art algorithms. © 2024 IEEE.

关键词： Electronic health record

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Ultrasound-based AI for COVID-19 detection: a comprehensive review of public and private lung ultrasound datasets and studies

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Multimedia Tools and Applications 2025年 1-55页

作者： Morshed, Abrar Al Shihab, Abdulla Jahin, Md Abrar Al Nahian, Md Jaber Hossain Sarker, Md Murad Ibne Wadud, Md Sharjis Uddin, Mohammad Istiaq Imtiaz Siraji, Muntequa Anjum, Nafisa Shristy, Sumiya Rajjab Rahman, Tanvin Khatun, Mahmuda Rubel Dewan, Md Hossain, Mosaddeq Sultana, Razia Chakma, Ripel Emon, Sonet Barua Islam, Towhidul Hussain, Mohammad Arafat Department of Computer Science and Engineering Ahsanullah University of Science and Technology Dhaka1208 Bangladesh Department of Electrical and Telecommunication Engineering Daffodil International University Dhaka1216 Bangladesh Khulna9203 Bangladesh Okinawa904-0412 Japan Department of Information and Communication Technology Bangladesh University of Professionals Dhaka1216 Bangladesh Department of Information and Communication Technology Comilla University Cumilla3506 Bangladesh Department of Biomedical Physics and Technology University of Dhaka Dhaka1000 Bangladesh Department of Computer Science and Engineering International Islamic University Chittagong Chittagong4318 Bangladesh Department of Electrical and Electronic Engineering Islamic University of Technology Gazipur1704 Bangladesh Department of Electronics and Telecommunication Engineering Chittagong University of Engineering and Technology Chittagong4349 Bangladesh Department of Computer Science and Engineering Notre Dame University Bangladesh Dhaka1000 Bangladesh Department of Electrical and Electronic Engineering Khulna University of Engineering & Technology Khulna9203 Bangladesh Department of Computer Science and Engineering Institute of Science and Technology Dhaka1209 Bangladesh Department of Electronics and Communication Engineering Institute of Science and Technology Dhaka1209 Bangladesh Department of Mathematics Jahangirnagar University Dhaka1342 Bangladesh Department of Electrical and Electronic Engineering Daffodil International University Dhaka1216 Bangladesh Department of Electrical and Electronic Engineering Dhaka University of Engineering and Technology Gazipur1707 Bangladesh Department of Computer Science and Telecommunication Engineering Noakhali Science and Technology University Noakhali3814 Bangladesh Department of Computer Science and Engineering Northern University Bangladesh Dhaka1230 Bangladesh Boston Children’s Hospital Harvard Medical School BostonMA02

The COVID-19 pandemic has affected millions of people globally, with respiratory organs being strongly affected in individuals with comorbidities. Medical imaging-based diagnosis and prognosis have become increasingly popular in clinical settings for detecting COVID-19 lung infections. Among various medical imaging modalities, ultrasound stands out as a low-cost, mobile, and radiation-safe imaging technology. In this comprehensive review, we focus on AI-driven studies utilizing lung ultrasound (LUS) for COVID-19 detection and analysis. We provide a detailed overview of both publicly available and private LUS datasets and categorize the AI studies according to the dataset they used. Additionally, we systematically analyzed and tabulated the studies across various dimensions, including data preprocessing methods, AI models, cross-validation techniques, and evaluation metrics. In total, we reviewed 60 articles, 41 of which utilized public datasets, while the remaining employed private data. Our findings suggest that ultrasound-based AI studies for COVID-19 detection have great potential for clinical use, especially for children and pregnant women. Our review also provides a useful summary for future researchers and clinicians who may be interested in the field. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2025.

关键词： COVID-19

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Coevolution with Bee Colony: Paradigm Shift in Many Objective Optimization

Coevolution with Bee Colony: Paradigm Shift in Many Objectiv...

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2024 OPJU International Technology Conference on Smart Computing for Innovation and Advancement in Industry 4.0, OTCON 2024

作者： Khoirom, Selina Pramanik, Shreya Rakshit, Pratyusha Chowdhury, Archana Jadavpur University Department of Electronics and Telecommunication Engineering India Christian College of Engineering and Technology Department of Computer Science and Engineering Bhilai India

ISBN: (纸本)9798350373783

The research offers a unique coevolution-based many-objective optimization (MaOO) approach to benefit from the underlying parallelism of the evolutionary process. The proposed MaOO handles individual objectives in parallel using artificial bee colony (ABC) algorithm. After achieving convergence, a group of good-quality solutions is carefully chosen from each ABC population, dealing with a specific objective. Next, the groups are combined to form a union set. Finally, a fuzzy membership-induced rank measure is developed to discover the top-ranked equally good members of the union set, showing the approximate Pareto optimal solutions to the given MaOO problem. The proposed MaOO algorithm, referred to as fuzzy-bee colony (FBC) is compared with three state-of-the-art techniques. Experiments undertaken reveal that FBC outperforms its contenders with respect to the performance metrics. © 2024 IEEE.

关键词： Optimization algorithms

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An RNN Based Approach to Predict Next Word in Bangla Language 1st

An RNN Based Approach to Predict Next Word in Bangla Langu...

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1st International Conference on Machine Intelligence and Emerging Technologies, MIET 2022

作者： Mahmud, Asif Rony, Md. Nazmul Hasan Bhowmik, Deba Dip Kuri, Ratnadip Rana, A. R. M. Mahmudul Hasan Department of Computer Science and Telecommunication Engineering Noakhali Science and Technology University Noakhali Bangladesh

ISBN: (纸本)9783031346187

The communication paradigm has changed with time, and text messaging has incredibly increased among people of all aspects. In addition, people use email and social media sites for interactive communication. As a result, people spend a significant amount of time typing the entire content, which is repetitious and time-killing. Therefore, word prediction technologies have been introduced to make message exchange faster and easier for predicting the next most probable word, allowing participants to choose it from a list of recommended words rather than entering it. This paper suggests a technique that predicts the next most relevant and acceptable word in Bangla, the eighth most speaking language in the world. We applied two Recurrent Neural Network models named Bi-directional LSTM (Long Short-Term Memory) and Bi-directional GRU (Gated Recurrent Unit) as language development models to our data set, which is compiled from different Bangla newspapers, storybooks, and Bangla Wikipedia. We also applied different n-grams with different LSTMs, and our approach found a maximum accuracy of 99.43% for a 5-gram dataset using the Bi-LSTM model. © 2023, ICST Institute for computer sciences, Social Informatics and telecommunications engineering.

关键词： Long short-term memory

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Twitter Based Emotion Recognition Using Bi-LSTM 2

Twitter Based Emotion Recognition Using Bi-LSTM

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2nd IEEE International Conference on Trends in Quantum Computing and Emerging Business Technologies, TQCEBT 2024

作者： Anbazhagan, K. Kurlekar, Supriya Brindha, T.V. Sudhish Reddy, D. Saveetha School of Engineering Department of Computer Science and Engineering Chennai India Department of Electronics and Telecommunication Engineering JSPM Narhe Technical Campus Pune India Sri Venkateshwara College of Engineering and Technology Department of Computer Science and Engineering Tiruvallur India Rmd Engineering College Department of Computer Science and Engineering Tiruvallur India

ISBN: (纸本)9798350384277

Twitter and other social media platforms have evolved into crucial sources of information and communication for billions of people across the world. The massive amount of data collected on these platforms enables insights into people's emotions and feelings. Emotion identification, in particular, has gotten a lot of attention because of its potential uses in industries like marketing, healthcare, and social study. "Emotional states of individuals, also known as moods, are central to expressing thoughts, ideas, and opinions, which influence attitudes and behavior."In this study, we present a method for identifying the emotion or mood of a tweet and classifying it into the appropriate emotional category. The work mentioned in this paper aims to identify and analyze the mood and emotion displayed by people in their text tweets to offer ideas. We gathered tweets and replies on a particular set of themes to create a dataset with text, user, emotion, sentiment, and other data. We used the dataset to compute user impact ratings based on various user and tweet-related factors to analyze feelings and emotions in tweets and their replies. Our model used a brand-new bidirectional long short-term memory (BiLSTM) and previously learned word embedding vectors. Finally, using the latest data, we made generalized and specific consumer suggestions based on Twitter's conduct. According to the experimental findings, the suggested model has 95.56% and 99.16% prediction accuracy. Modern performance improvement was achieved for the model. © 2024 IEEE.

关键词： Tweets

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Artificial visual perception neural system using a solution-processable MoS_(2)-based in-memory light sensor

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Light(science & Applications) 2023年第8期12卷 1583-1594页

作者： Dayanand Kumar Lana Joharji Hanrui Li Ayman Rezk Ammar Nayfeh Nazek El-Atab Smart Advanced Memory Devices and Applications(SAMA)LaboratoryElectrical and Computer Engineering ProgramComputer Electrical Mathematical Science and Engineering DivisionKing Abdullah University of Science and Technology(KAUST)Thuwal 23955Kingdom of Saudi Arabia Department of Electrical Engineering and Computer Science Khalifa UniversityAbu Dhabi 127788United Arab Emirates

Optoelectronic devices are advantageous in in-memory light sensing for visual information processing,recognition,and storage in an energy-efficient ***,in-memory light sensors have been proposed to improve the energy,area,and time efficiencies of neuromorphic computing *** study is primarily focused on the development of a single sensing-storage-processing node based on a two-terminal solution-processable MoS2 metal-oxide-semiconductor(MOS)charge-trapping memory structure—the basic structure for charge-coupled devices(CCD)—and showing its suitability for in-memory light sensing and artificial visual *** memory window of the device increased from 2.8 V to more than 6V when the device was irradiated with optical lights of different wavelengths during the program ***,the charge retention capability of the device at a high temperature(100 ℃)was enhanced from 36 to 64%when exposed to a light wavelength of 400 *** larger shift in the threshold voltage with an increasing operating voltage confirmed that more charges were trapped at the Al_(2)O_(3)/MoS_(2) interface and in the MoS_(2) layer.A small convolutional neural network was proposed to measure the optical sensing and electrical programming abilities of the *** array simulation received optical images transmitted using a blue light wavelength and performed inference computation to process and recognize the images with 91%*** study is a significant step toward the development of optoelectronic MOS memory devices for neuromorphic visual perception,adaptive parallel processing networks for in-memory light sensing,and smart CCD cameras with artificial visual perception capabilities.

关键词： visual neural process

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Cultural Heritage Monitoring and Predictive Maintenance using Internet of Things: Assessment and Future Aspects

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Journal of Mobile Multimedia 2024年第6期20卷 1211-1250页

作者： Um-E-habiba Shaikh, Faisal Karim Chowdhry, Bhawani Shankar Department of Computer Science Shaheed Zulfikar Ali Bhutto Institute of Science and Technology Hyderabad Pakistan Telecommunication Engineering Department Mehran University of Engineering and Technology Jamshoro Pakistan Faculty of Electrical Electronics and Computer Engineering Mehran University of Engineering and Technology Jamshoro Pakistan

This study presents a detailed survey on the use of the Internet of Things (IoT) for predictive maintenance and monitoring of cultural heritage, focusing on museums and exhibitions. The integration of IoT in these fields offers significant potential for optimizing maintenance and supervision of heritage resources while also enhancing maintenance strategies. This review examines the application of IoT devices, data analysis, and predictive algorithms in monitoring the state of heritage resources to improve tourist experiences and conserve cultural resources. Key enabling technologies can enable adaptive interactions and engagement with enhanced navigation. Visualization technologies are also common, offering hands-on displays. The study also emphasizes the value of accessibility and interactivity, providing personalization and content sharing features. Several initiatives prioritize the delivery of digital content through mobile applications, internet browsers, and downloadable *** study examines how IoT-enabled predictive maintenance systems can recognize potential hazards, like resource decay or environmental dangers, enabling immediate action. This survey also highlights trends, challenges, and future research in IoT and cultural heritage monitoring. © 2025 River Publishers.

关键词： heritage monitoring IoTs predictive maintenance trends

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