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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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Falcon Optimization Algorithm-Based Energy Efficient Communication Protocol for Cluster-Based Vehicular Networks

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computers, Materials & Continua 2024年第3期78卷 4243-4262页

作者： Youseef Alotaibi B.Rajasekar R.Jayalakshmi Surendran Rajendran Department of Software Engineering College of ComputingUmm Al-Qura UniversityMakkah21955Saudi Arabia Department of Electronics and Communication Engineering Sathyabama Institute of Science and TechnologyChennai600119India Department of Computer Science and Engineering Panimalar Engineering CollegeChennai600123India Department of Computer Science and Engineering Saveetha School of EngineeringSaveetha Institute of Medical and Technical ScienceChennai602105India

Rapid development in Information technology(IT)has allowed several novel application regions like large outdoor vehicular networks for Vehicle-to-Vehicle(V2V)*** networks give a safe and more effective driving experience by presenting time-sensitive and location-aware *** communication occurs directly between V2V and Base Station(BS)units such as the Road Side Unit(RSU),named as a Vehicle to Infrastructure(V2I).However,the frequent topology alterations in VANETs generate several problems with data transmission as the vehicle velocity differs with ***,the scheme of an effectual routing protocol for reliable and stable communications is *** research demonstrates that clustering is an intelligent method for effectual routing in a mobile ***,this article presents a Falcon Optimization Algorithm-based Energy Efficient Communication Protocol for Cluster-based Routing(FOA-EECPCR)technique in *** FOA-EECPCR technique intends to group the vehicles and determine the shortest route in the *** accomplish this,the FOA-EECPCR technique initially clusters the vehicles using FOA with fitness functions comprising energy,distance,and trust *** the routing process,the Sparrow Search Algorithm(SSA)is derived with a fitness function that encompasses two variables,namely,energy and distance.A series of experiments have been conducted to exhibit the enhanced performance of the FOA-EECPCR *** experimental outcomes demonstrate the enhanced performance of the FOA-EECPCR approach over other current methods.

关键词： Vehicular networks communication protocol clustering falcon optimization algorithm routing

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Vision-based Robotic Arm Control for Screwdriver Bit Placement Tasks

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Sensors and Materials 2024年第3期36卷 1003-1018页

作者： Lin, Cheng-Jian Lin, Pei-Jung Shih, Chi-Huang Department of Computer Science and Information Engineering National Chin-Yi University of Technology Taichung411 Taiwan Department of Information Engineering and Computer Science Feng Chia University Taichung407 Taiwan

Robotic arms are widely used in the automation industry to package and deliver classified objects. When the products are small objects with very similar shapes, such as screwdriver bits with slightly different threads, pointed tips, and thicknesses, object selection and assembly often lead to misjudgment. We have developed a practical robotic arm control system based on vision detection techniques for screwdriver bits' placement. In addition to effectiveness, easy deployment and high flexibility in the field are also taken into account. The vision-based system consists of four processing stages in the following order: world coordinate conversion from image pixel coordinates, object detection, edge detection, and object orientation. In the first stage, a manual two-point marking method is proposed to easily configure the coordinate conversion for robot operating system (ROS)-based manipulators. For the following stages, we focus on the fine integration of state-of-the-art methods for the technical feasibility of the screwdriver bit placement. Such integration includes the selection between object detection methods and the data flow control among system stages. The experimental results show that (1) in detecting screwdriver bits, You Only Look Once (YOLO) v4 outperforms YOLOv7 and Single Shot MultiBox Detector at an accuracy rate of 99.51%;(2) in the edge detection, the object detection output can better illustrate the object contour than a whole image, achieving a mean absolute error of 0.86% in estimating the object angle;and (3) a successful real-time replacement rate of 96% is achieved for 12 screwdriver bits randomly scattered on a conveyor belt. © 2024 M Y U Scientific Publishing Division. All rights reserved.

关键词： Robotic arms

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Intelligent surveillance support system

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Discover Internet of Things 2023年第1期3卷 9页

作者： Saketh, Meduri Nandal, Neha Tanwar, Rohit Reddy, B. Pradyun Department of Computer Science Engineering Gokaraju Rangaraju Institute of Engineering and Technology Hyderabad India Geethanjali College of Engineering and Technology Hyderabad India School of Computer Science University of Petroleum and Energy Studies Dehradun India Department of Computer Science Engineering Sreenidhi Institute of Engineering and Technology Hyderabad India

The Intelligent Surveillance Support System(ISSS) is an innovative software solution that enables real-time monitoring and analysis of security footage to detect and identify potential threats. This system incorporates advanced features such as face recognition, alarm on theft detection, visitors in/out detection and motion detection, to provide a comprehensive and reliable security solution. The implementation of this software aims to improve the efficiency of surveillance systems, thereby enhancing the safety and security of public and private spaces. The focus of this study is on performing the aforementioned tasks in real time while utilizing enhanced algorithms from the OpenCV Library, such as LBPH and Haar Cascading, which enhance the use of machine perception and help us produce outcomes with an accuracy of about 95% after multiple runs. With the rapid advancements in technology and the increasing need for surveillance in today’s world, the Intelligent Surveillance Support System holds immense potential in the field of security and surveillance. © The Author(s) 2023.

关键词： Face recognition

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Integrating Transformer and Bidirectional Long Short-Term Memory for Intelligent Breast Cancer Detection from Histopathology Biopsy Images

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computer Modeling in engineering & sciences 2024年第10期141卷 443-458页

作者： Prasanalakshmi Balaji Omar Alqahtani Sangita Babu Mousmi Ajay Chaurasia Shanmugapriya Prakasam Department of Computer Science College of Computer ScienceKing Khalid UniversityAbha61451Saudi Arabia College of Science and Arts in Rijal Alma King Khalid UniversityAbha61451Saudi Arabia Department of Information Technology Muffakham Jah College of Engineering and TechnologyHyderabad500034India Department of Computer Science and Business Systems Rajalakshmi Engineering CollegeThandalam602105India

Breast cancer is a significant threat to the global population,affecting not only women but also a threat to the entire *** recent advancements in digital pathology,Eosin and hematoxylin images provide enhanced clarity in examiningmicroscopic features of breast tissues based on their staining *** cancer detection facilitates the quickening of the therapeutic process,thereby increasing survival *** analysis made by medical professionals,especially pathologists,is time-consuming and challenging,and there arises a need for automated breast cancer detection *** upcoming artificial intelligence platforms,especially deep learning models,play an important role in image diagnosis and ***,the histopathology biopsy images are taken from standard data ***,the gathered images are given as input to the Multi-Scale Dilated Vision Transformer,where the essential features are ***,the features are subjected to the Bidirectional Long Short-Term Memory(Bi-LSTM)for classifying the breast cancer *** efficacy of the model is evaluated using divergent *** compared with other methods,the proposed work reveals that it offers impressive results for detection.

关键词： Bidirectional long short-term memory breast cancer detection feature extraction histopathology biopsy images multi-scale dilated vision transformer

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A Voice Assistive Mobile Application Tool to Detect Cardiovascular Disease Using Machine Learning Approach

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Biomedical Materials and Devices 2024年第2期2卷 1246-1257页

作者： Uddin, Khandaker Mohammad Mohi Dey, Samrat Kumar Babu, Hafiz Md. Hasan Department of Computer Science and Engineering Southeast University School of Science and Technology Bangladesh Open University Department of Computer Science and Engineering University of Dhaka

Many people all around the world suffer from heart disease, which is regarded as a severe illness. In healthcare, especially cardiology, it is crucial to accurately and quickly diagnose cardiac problems. In this research, we proposed an accurate and efficient mobile application-based system for diagnosing cardiac disease based on machine learning approaches. The developed mobile application is voice assistive, which makes the proposed application more user-friendly. Numerous machine learning methods have been examined in this study to predict and diagnose cardiovascular disease (CVD). A detailed comparative study was also drawn using eighteen (18) classification algorithms such as Support Vector Machines, Logistic Regression, Linear SVC, K-Nearest Neighbors, Naive Bayes, Stochastic Gradient Descent, Gradient Boosting, Ridge, Bagging, Random Forest, Decision Tree, XGB, LGBM, Extra Trees Perceptron, and Voting Classifier (hard or soft voting). Sixty-eight thousand nine hundred seventy-five (68,975) samples from various patients were used to test the performance of each approach. According to the study, Random Forest and Decision Trees have the best accuracy levels at 99.9%. However, the Support Vector Machine classifier had the lowest performance, barely achieving 60% accuracy. © The Author(s), under exclusive licence to Springer science+Business Media, LLC 2024.

关键词： Cardiovascular heart disease Detection Healthcare informatics Machine learning Mobile application

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An Efficient Scalp Inspection and Diagnosis System Using Multiple Deep Learning-Based Modules un système efficace d'inspection et de diagnostic du cuir chevelure utilisant plusieurs modules basés sur l'apprentissage profond

IEEE Canadian Journal of Electrical and Computer Engineering

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IEEE Canadian Journal of Electrical and computer engineering 2024年第1期47卷 22-35页

作者： Chen, Liang-Bi Chang, Wan-Jung Chiu, Yi-Chan Huang, Xiang-Rui National Penghu University of Science and Technology Department of Computer Science and Information Engineering Penghu880011 Taiwan National Kaohsiung University of Science and Technology Department of Electronic Engineering Kaohsiung807618 Taiwan Southern Taiwan University of Science and Technology Department of Electronic Engineering Tainan710 Taiwan

The conventional approach to scalp inspection in the hairdressing industry relies on manually interpreting scalp symptom images. Hairdressers provide treatments based on visual assessment, leading to potential inaccuracies and misjudgments. To address these shortcomings, this article proposes a novel multimodal deep learning-based scalp inspection and diagnosis system. The proposed system employs various artificial intelligence (AI) object recognition modules, such as single-shot multibox detector (SSD)-MobileNetV2, SSD-InceptionV2, Faster region-based convolutional neural network (R-CNN)-InceptionV2, and Faster R-CNN-Inception-ResNetV2-Atrous (2). These modules form a diverse scalp symptom recognition module integrated into an AI recognition server. This study included nine scalp symptoms, encompassing four primary conditions (dandruff, hair loss, gray hair, and oily hair), as well as five special conditions (folliculitis, chemical residue, mold, fungi, fungus, and psoriasis). The efficiency of the proposed system is evaluated through experiments, and adjustments are made to the neural network architecture to achieve optimal performance across diverse symptoms. The experimental results showed that Faster-R-CNN-Inception-ResNetV2-Atrous (2) excels in recognizing chemical residue and oily hair symptoms (accuracies of 89.33% and 87.75%, respectively);Faster-R-CNN-Inception-ResNetV2-Atrous (4) outperforms in recognizing dandruff, folliculitis, fungal, and psoriasis symptoms (accuracies ranging from 88.77% to 99.72%);and Faster-R-CNN-Inception-ResNetV2-Atrous (4) is the best-performing method overall. © 2021 IEEE.

关键词： Inspection

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Rumor detection from online social media through feature selection and machine learning

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Multimedia Tools and Applications 2025年 1-25页

作者： Gurrapu, Neelima Baydeti, Nagaraju Department of Computer Science and Engineering National Institute of Technology Nagaland Nagaland Chumukedima797 103 India

Recently, Rumor Spreading over Online Social Media is found as one of the serious issue, which causes severe damage to society, organization and individuals. To control the rumor spread, rumor detection is found as one of the probable solution and gained huge interest in the research community. However, most of the past methods characterize the rumormonger behavior based on their own profiles and completely neglected the reputation features. A hybrid method for rumor identification by combining the user reputation feature with other features like user features, meta-content based features and linguistic features is proposed. The major novelty lies at the representation of users through their interaction with his/her followers in different communities. The own profile based features can be evaded easily but the reputation of follows cannot be evaded easily. Totally 44 features are used to characterize each tweet and trained through different classifiers. Four classifiers namely K-Nearest Neighbour (KNN), Support Vector Machine (SVM), Linear Discriminant Analysis (LDA) and Principal Component Analysis (PCA) are used to train the system and the performance is analyzed through two standard datasets namely Zubiaga and Kwon. The obtained results show that the proposed method can recognize rumor with higher F1-score. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Feature Selection

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A Two-Stage Deep Learning Approach for Optimizing Fashion Product Recommendations

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SN computer science 2025年第4期6卷 1-13页

作者： Buradagunta, Suvarna Balakrishna, Sivadi Department of Computer Science and Engineering Vignan’s Foundation for Science Technology and Research Guntur India Department of Advanced Computer Science and Engineering Vignan’s Foundation for Science Technology and Research Guntur India

Online shopping platforms are experiencing rapid growth, necessitating effective product recommendation systems to enhance customer satisfaction by recommending visually similar products. Traditional statistical techniques often result in less accurate recommendations. This study encompasses two primary tasks: Query Product Classification and Product Recommendation Retrieval. The initial job employs a Deep Neural Network, which takes high-level feature representations derived from the Xception and VGG16 for the query image. The features are further analyzed to forecast the category of the product. The second step is retrieving analogous products by calculating cosine similarity, facilitating the identification of visually comparable items within the product database. By leveraging diverse feature representations, the proposed approach improves the precision and relevance of recommendations. Experimental evaluations on fashion product images and Shoe datasets demonstrate significant performance improvements over existing models, achieving accuracies of 91.76%, and 82.23% respectively. These results underscore the capability of the system to provide superior recommendations in online fashion shopping scenarios, emphasizing its effectiveness in improving customer experience. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2025.

关键词： Content-based recommendation Image based recommendation system Pretrained models Recommendation systems VGG-16 Visual product recommendation Xception

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IRS-enabled NOMA communication systems:A network architecture primer with future trends and challenges

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Digital Communications and Networks 2024年第5期10卷 1503-1528页

作者： Haleema Sadia Ahmad Kamal Hassan Ziaul Haq Abbas Ghulam Abbas Muhammad Waqas Zhu Han Faculty of Electrical Engineering and Telecommunications and Networking(TeleCoN)Research Center Ghulam Ishaq Khan Institute of Engineering and Technology(GIKI)Topi 23640Pakistan Faculty of Computer Science and Engineering GIKITopi 23640Pakistan Department of Computer Engineering College of Information TechnologyUniversity of Bahrain32028Bahrain School of Engineering Edith Cowan UniversityPerth WA6027Australia Department of Electrical and Computer Engineering University of HoustonHoustonTX 77004USA Department of Computer Science and Engineering Kyung Hee UniversitySeoul 446-701South Korea

Non-Orthogonal Multiple Access(NOMA)has already proven to be an effective multiple access scheme for5th Generation(5G)wireless *** provides improved performance in terms of system throughput,spectral efficiency,fairness,and energy efficiency(EE).However,in conventional NOMA networks,performance degradation still exists because of the stochastic behavior of wireless *** combat this challenge,the concept of Intelligent Reflecting Surface(IRS)has risen to prominence as a low-cost intelligent solution for Beyond 5G(B5G)*** this paper,a modeling primer based on the integration of these two cutting-edge technologies,i.e.,IRS and NOMA,for B5G wireless networks is *** in-depth comparative analysis of IRS-assisted Power Domain(PD)-NOMA networks is provided through 3-fold ***,a primer is presented on the system architecture of IRS-enabled multiple-configuration PD-NOMA systems,and parallels are drawn with conventional network configurations,i.e.,conventional NOMA,Orthogonal Multiple Access(OMA),and IRS-assisted OMA *** by this,a comparative analysis of these network configurations is showcased in terms of significant performance metrics,namely,individual users'achievable rate,sum rate,ergodic rate,EE,and outage ***,for multi-antenna IRS-enabled NOMA networks,we exploit the active Beamforming(BF)technique by employing a greedy algorithm using a state-of-the-art branch-reduceand-bound(BRB)*** optimality of the BRB algorithm is presented by comparing it with benchmark BF techniques,i.e.,minimum-mean-square-error,zero-forcing-BF,and ***,we present an outlook on future envisioned NOMA networks,aided by IRSs,i.e.,with a variety of potential applications for 6G wireless *** work presents a generic performance assessment toolkit for wireless networks,focusing on IRS-assisted NOMA *** comparative analysis provides a solid foundation for the dev

关键词： Intelligent reflecting surface Non-orthogonal multiple access 6G Beamforming Sum rate Energy efficiency

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