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Enhancing Stock Price Prediction Through Integration of Astrological and Astronomical Data using XGBoost

Procedia computer science 2025年 258卷 3970-3979页

作者： Rekha R Nair Tina Babu Kishore S Vishal S Sheila Mahapatra Department of Computer Science and Engineering Alliance School of Advanced Computing Alliance University Bengaluru India Department of Computer Applications Nitte Deemed to be university Nitte Institute of Professional Education Mangalore Karnataka India Department of Computer Applications School of Engineering Dayananda Sagar University Bengaluru India Department of Electrical and Electronics Engineering Alliance University Bangalore India

Predicting stock prices continues to be a significant challenge in the financial industry, prompting researchers to investigate various methods to improve accuracy. Hence proposed a novel approach by incorporating astrological and astronomical data into XGBoost models for stock price classification and prediction, utilizing deep neural networks (DNN). The dataset is collected from NASA JPL Horizons and Jaganath Hora software. The raw data is preprocessed with feature engineering, data cleaning, []and normalization to obtain a better forecasting stock prices and classification results. The proposed data executed with machine learning models, such as logistic regression, decision trees, random forest, SVM, XGB Classifier, LSTM, RNN, and CNN other than XGB Regressor for the forcasting. The proposed research is evaluated for classification with accuracy, precision, recall, F1 score, and forecasting with RMSE, MAE, R 2 , AIC, and BIC. The XGBoost classifier achieved 99% accuracy in predicting stock market movements, surpassing other models in determining whether the next day’s stock price would be higher or lower than the previous day’s price, while the DNN performed the best among all algorithms for overall stock price prediction. This approach could influence investment strategies, potentially affecting wealth distribution and financial market stability.

关键词： Independent Component Analysis Principal Component Analysis Probable Neural Network Expectation Maximization algorithm Natural language processing

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LLaVA-Endo:a large language-and-vision assistant for gastrointestinal endoscopy

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Frontiers of computer science 2025年第4期19卷 121-123页

作者： Jieru YAO Xueran LI Qiang XIE Longfei HAN Yiwen JIA Nian LIU Dingwen ZHANG Junwei HAN School of Automation Northwestern Polytechnical UniversityXi’an 710072China Institute of Artificial Intelligence Hefei Comprehensive National Science CenterHefei 230088China AHU-IAI AI Joint Laboratory Anhui UniversityHefei 230039China Institute of Advanced Technology University of Science and Technology of ChinaHefei 230026China School of Computer and Artificial Intelligence Beijing Technology and Business UniversityBeijing 100048China Department of Gastroenterology The Third Affiliated Hospital of Anhui Medical University(Hefei First People’s Hospital)Hefei 230061China The Computer Vision Department Mohamed Bin Zayed University of Artificial IntelligenceMasdarAbu Dhabi 200120United Arab Emirates Xijing Hospital The Fourth Military Medical UniversityXi’an 710032China

1 Introduction Endoscopy plays a crucial role in the diagnoses and treatment of gastrointestinal(GI)diseases[1],as it helps to identify abnormalities,classify lesion,and determine treatment *** GI endoscopic examinations,physicians may encounter practical hindrances,i.e.,fatigue,stress,or limited experience,which can lead to erroneous *** intelligence(AI)-assisted GI endoscopy technology has emerged to address these limitations[2].

关键词： stress giendoscopy fatigue identify abnormalitiesclassify lesionidentification artificialintelligence

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Mapping the Landscape of Generative AI in Network Monitoring and Management

arXiv

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arXiv 2025年

作者： Bovenzi, Giampaolo Cerasuolo, Francesco Ciuonzo, Domenico Di Monda, Davide Guarino, Idio Montieri, Antonio Persico, Valerio Pescapé, Antonio University of Naples Federico II Italy DIETI IMT School for Advanced Studies Lucca Italy Department of Computer Science University of Verona Italy

Generative Artificial Intelligence (GenAI) models such as LLMs, GPTs, and Diffusion Models have recently gained widespread attention from both the research and the industrial communities. This survey explores their application in network monitoring and management, focusing on prominent use cases, as well as challenges and opportunities. We discuss how network traffic generation and classification, network intrusion detection, networked system log analysis, and network digital assistance can benefit from the use of GenAI models. Additionally, we provide an overview of the available GenAI models, datasets for large-scale training phases, and platforms for the development of such models. Finally, we discuss research directions that potentially mitigate the roadblocks to the adoption of GenAI for network monitoring and management. Our investigation aims to map the current landscape and pave the way for future research in leveraging GenAI for network monitoring and management. © 2025, CC BY-NC-ND.

关键词： Generative adversarial networks

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‘Slightly disappointing’ vs. ‘worst sh** ever’: tackling cultural differences in negative sentiment expressions in AI-based sentiment analysis

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Journal of Computational Social science 2025年第3期8卷 1-31页

作者： Hafner, Franziska Sofia Hafner, Lena Corizzo, Roberto Department of Computer Science American University Washington USA Oxford Internet Institute University of Oxford Oxford UK Department of Politics and International Studies University of Cambridge Cambridge UK

Advertisers, politicians, and social scientists alike have an interest in the current zeitgeist. With people expressing their sentiments in online comments, the Internet has become a great source for ‘reading’ the zeitgeist via artificial intelligence-based sentiment analysis (SA). At this, negative sentiments are of special concern. They can serve as early indicators for events that require action, such as dropping customer satisfaction, discontent amongst potential voters, or a threat of social unrest. However, due to cultural differences in how negative sentiments are expressed, conventional SA methods typically classify such texts with higher accuracy for some ethnicities compared to others. In this paper, we demonstrate this using a large real-world corpus of Google Maps reviews. Across eight ethnic groups, linguistic patterns vary more starkly in negative (1 star) reviews than in neutral (2–4 star) and positive (5 star) reviews. Consequently, ethnicity-blind SA methods ‘struggle’ to classify negative reviews correctly. To mitigate this problem, we propose a novel SA method, based on balanced training and subsequent ethnicity-conscious fine-tuning. Our approach is simultaneously able to mitigate bias and enhance overall model performance. Thus, we hope to contribute to a more equal appreciation of negative sentiments of ethnically diverse customer-, voter-, or research populations, and, consequently, a more nuanced approximation of the overall zeitgeist.

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AI-Enhanced Child Handwriting Analysis: A Framework for the Early Screening of Dyslexia and Dysgraphia

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SN computer science 2025年第5期6卷 1-26页

作者： Rangasrinivasan, Sahana Sumi Suresh, M. S. Olszewski, Abbie Setlur, Srirangaraj Jayaraman, Bharat Govindaraju, Venu Department of Computer Science and Engineering University at Buffalo Buffalo USA Department of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Amritapuri Kollam India College of Education and Human Development University of Nevada-Reno Reno USA Amrita Institute of Advanced Research Elburn USA

Dyslexia and dysgraphia are two specific learning disabilities (SLDs) that are prevalent among children. To minimize the negative impact these SLDs have on a child’s academic and social-emotional development, it is crucial to identify dyslexia and dysgraphia at an early age, enabling timely and effective intervention. The first step in this process is screening, which helps determine if a child requires further instruction or a more in-depth assessment. Current screening tools are expensive, require additional administration time beyond regular classroom activities, and are designed to screen exclusively for one condition, not for both dyslexia and dysgraphia, which often share some common behavioral characteristics. Most dyslexia screeners focus on speech and oral tasks and exclude writing activities. However, analyzing children’s writing samples for behavioral signs of dyslexia and dysgraphia can offer valuable insights into the screening process, which can be time-consuming. As a solution, we propose a co-designed framework for building artificial intelligence (AI) tools that could boost the efficiency of screening and aid practitioners such as speech-language pathologists (SLPs), occupational therapists, general educators, and special educators by simplifying their tasks. This paper reviews current screening methods employed by practitioners, the use of AI-based systems in identifying dyslexia and dysgraphia, and the handwriting datasets available to train such systems. The paper also outlines a framework for developing an AI-integrated screening tool that can identify writing-based behavioral indicators of dyslexia and dysgraphia in children’s handwriting. This framework can be used in conjunction with current screening tools like the Dysgraphia and Dyslexia Behavioral Indicator Checklist (DDBIC). The paper also proposes a methodology for collecting children’s offline and online handwriting samples to build a valuable dataset for developing AI solutions. The pr

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Hybridization of One- and Two-Point Bandits Convex Optimization in Non-stationary Environments 24th

Hybridization of One- and Two-Point Bandits Convex Optimiz...

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24th International Conference on Algorithms and Architectures for Parallel Processing, ICA3PP 2024

作者： Zeng, Gailun Guo, Jianxiong Advanced Institute of Natural Sciences Beijing Normal University Zhuhai China Hong Kong Baptist University Hong Kong Guangdong Key Lab of AI and Multi-modal Data Processing Department of Computer Science BNU-HKBU United International College Zhuhai China

ISBN: (纸本)9789819615278

Bandit Convex Optimization (BCO) is an imperative analysis framework when dealing with sequential decision-making problems. Considering to balance the computational cost and bounds of regrets, in this paper, we propose a hybridized algorithm of one- and two-point bandit convex models in non-stationary environments and use a more general performance measure dynamic regret, which records the cumulative difference between function loss and a feasible comparator sequence during the time horizon T. The path length of a comparator sequence PT reveals the non-stationarity of environments. Our proposed algorithm builds an upper bound of dynamic regret O((1+PT)1/2[β(λT)1/2+((1-λ)T)3/4]), where the parameter λ can dynamically adjust the bound guarantee to balance the computational cost in real applications. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Comparators (optical)

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Deep Learning Based FOD Detection For Runway Health Monitoring

Deep Learning Based FOD Detection For Runway Health Monitori...

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AIML-Applications for Engineering & Technology (ICAET), International Conference on

作者： Amarjeet Kumar Aanya Mittal Abhishek Dixit Pooja Agrawal School of Robotics Defence Institute of Advanced Technology Pune India Department of Computer Science Manipal University Jaipur Rajasthan India Department of Aerospace Defence Institute of Advanced Technology Pune India

ISBN: (数字)9798350355611

ISBN: (纸本)9798350355628

This paper focuses on the safety of airport runways, which is crucial for aviation industries. Foreign Object Debris (FOD) i.e. objects like nails or bolts can cause significant harm to aircraft and potentially lead to catastrophic incidents and fatalities. This study delves into detecting objects on runways by using versions of the YOLO (You Only Look Once) model. Further, it also presents the differences in detecting objects and comparing the accuracy across a range of objects, paying attention to their ability to identify even the smallest things that have a potential danger. In order to identify the model variation and enable real-time runway surveillance, the proposed methodology considers the trade-off between detection speed and precision. The results illustrate the effectiveness of the latest YOLO model in object recognition over existing models, this can support the improvement of runway safety and the development of automated systems that monitor runway conditions.

关键词： YOLO Deep learning Accuracy Atmospheric modeling Surveillance Robustness Real-time systems Safety Object recognition Aircraft

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A Comprehensive Survey on Federated Learning Applications in Computational Mental Healthcare

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computer Modeling in Engineering & sciences 2025年第1期142卷 49-90页

作者： Vajratiya Vajrobol Geetika Jain Saxena Amit Pundir Sanjeev Singh Akshat Gaurav Savi Bansal Razaz Waheeb Attar Mosiur Rahman Brij B.Gupta Institute of Informatics and Communication University of DelhiDelhi110021India Maharaja Agrasen College University of DelhiDelhi110096India Computer Engineering Ronin InstituteMontclairNJ 07043USA Department of Research and Innovation Insights2TechinfoJaipur302001India University Centre for Research and Development(UCRD) Chandigarh UniversityChandigarh140413India Management Department College of Business AdministrationPrincess Nourah bint Abdulrahman UniversityRiyadh11671Saudi Arabia CCRI&Department of Computer Science and Information Engineering Asia UniversityTaichung413Taiwan Symbiosis Centre for Information Technology(SCIT) Symbiosis International UniversityPune411057India Center for Interdisciplinary Research University of Petroleum and Energy Studies(UPES)Dehradun248007India

Mental health is a significant issue worldwide,and the utilization of technology to assist mental health has seen a growing *** aims to alleviate the workload on healthcare professionals and aid *** applications have been developed to support the challenges in intelligent healthcare ***,because mental health data is sensitive,privacy concerns have *** learning has gotten some *** research reviews the studies on federated learning and mental health related to solving the issue of intelligent healthcare *** explores various dimensions of federated learning in mental health,such as datasets(their types and sources),applications categorized based on mental health symptoms,federated mental health frameworks,federated machine learning,federated deep learning,and the benefits of federated learning in mental health *** research conducts surveys to evaluate the current state of mental health applications,mainly focusing on the role of Federated Learning(FL)and related privacy and data security *** survey provides valuable insights into how these applications are emerging and evolving,specifically emphasizing FL’s impact.

关键词： Depression emotional recognition intelligent healthcare systems mental health federated learning stress detection sleep behaviour

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Exponentially Better Bounds for Quantum Optimization via Dynamical Simulation

arXiv

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arXiv 2025年

作者： Catli, Ahmet Burak Simon, Sophia Wiebe, Nathan Department of Physics University of Toronto Canada Department of Computer Science University of Toronto Canada Pacific Northwest National Laboratory RichlandWA United States Canadian Institute for Advanced Studies Toronto Canada

We provide several quantum algorithms for continuous optimization that do not require any gradient estimation. Instead, we encode the optimization problem into the dynamics of a physical system and coherently simulate the time evolution. This allows us, in certain cases, to obtain exponentially better query upper bounds relative to the best known upper bounds for gradient-based optimization schemes which utilize quantum computers only for the evaluation of gradients. Our first two algorithms can find local optima of a differentiable function f : RN → R by simulating either classical or quantum dynamics with friction via a time-dependent Hamiltonian. We show that these methods require O(Nκ2/h2xϵ) queries to a phase oracle to find an ϵ-approximate local optimum of a locally quadratic objective function, where κ is the condition number of the Hessian matrix and hx is the discretization spacing. In contrast, we show that gradient-based methods require O(N(1/ϵ)κ log(3)/4) queries. Our third algorithm can find the global optimum of f by preparing a classical low-temperature thermal state via simulation of the classical Liouvillian operator associated with the Nosé Hamiltonian. We use results from the quantum thermodynamics literature to bound the thermalization time for the discrete system. Additionally, we analyze barren plateau effects that commonly plague quantum optimization algorithms and observe that our approach is vastly less sensitive to this problem than standard gradient-based optimization. Our results suggests that these dynamical optimization approaches may be far more scalable for future quantum machine learning, optimization and variational experiments than was widely believed. Copyright © 2025, The Authors. All rights reserved.

关键词： Hamiltonians

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Artificial Intelligence in Health Care Computing and Assessments in Oman: an Effectual Study

Artificial Intelligence in Health Care Computing and Assessm...

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Machine Learning and Autonomous Systems (ICMLAS), International Conference on

作者： Wilfred Blessing N. R Hemalatha Gunasekaran Hariharan B. Jaber Saleh Salim Al Jabri Kavitha S. J Sutherlin Subitha G. College of Computing and Information Sciences University of Technology and Applied Sciences Ibri Sultanate of Oman Department of Computational Intelligence SRM Institute of Science and Technology Kattankulathur Tamil Nadu India Department of Computer Science and Engineering Vels Institute of Science Technology and Advanced Studies Chennai India Department of Computer Science and Engineering Stella Mary's College of Engineering Aruthenganvilai Tamil Nadu India

ISBN: (数字)9798331505745

ISBN: (纸本)9798331505752

Health care plays a vital responsibility in a human life span. World countries targets to provide better care for their citizens. Digital transformations and innovations in Artificial Intelligence (AI) technologies helps in deep for the novel solutions to speed-up the decision making process in health management and patient consulting. AI branches including Machine Learning (ML), Deep Learning (DL), Neural Networks (NN), Natural Language Processing (NLP) and so forth can support innovations in heal care domain. This paper presents the comprehensive and noble review on recent existing tools and techniques and also recommends advanced techniques for health care industry that are to be used in Sultanate of Oman. Furthermore, overcoming AI challenges are resulted and presented through the study. This work mainly aimed to produce the multiple dimensions and challenges that helps to develop AI powered real-life applications in health care support system.

关键词： Deep learning Technological innovation Ethics Reviews Decision making Surgery Medical services Natural language processing Medical diagnostic imaging Diseases

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