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20th IEEE India Council International Conference, INDICON 2023

作者： Patel, Manali Jariwala, Krupa Chattopadhyay, Chiranjoy Svnit Department of Cse Surat India Flame University School of Computing and Data Science Pune India

ISBN: (纸本)9798350305593

Investors and financial analysts require accurate predictions of stock prices to make informed decisions. We present Momentum Spillover Network (MSNet), a deep learning model that predicts stock prices on the National Stock Exchange of India by considering interdependencies existing between stocks in the same sector. Traditional approaches have focused solely on capturing hidden temporal dependencies, ignoring the impact of interconnectedness between stocks. To evaluate MSNet, we experimented with data from companies with the largest market capitalization from various sectors that covers over 70% of the NIFTY-50 index. MSNet effectively identifies the most impactful peer companies and predicts the future price movement of a target company with higher accuracy than existing deep learning approaches, highlighting the importance of incorporating relational dependency between stocks along with temporal dependency. © 2023 IEEE.

关键词： Deep Learning Momentum Spillover Effect Relational Modeling Stock Market Forecasting Temporal Modeling

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Developing a Visual Dashboard to Improve Research Activity Assessment 11

Developing a Visual Dashboard to Improve Research Activity A...

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11th International Conference on Computer, Control, Informatics and its Applications, IC3INA 2024

作者： Nur Rahman, Muhammad Zaki Prakasa, Esa Bijaksana, Moch Arif Bachelor of Data Science Faculty of Informatics Telkom University Bandung Indonesia Bandung Indonesia School of Computing Telkom University Bandung Indonesia

ISBN: (纸本)9798331542313

In research institutions, the ability to monitor and analyze publication output is crucial for assessing productivity and achieving strategic goals. This paper presents a dashboard designed to visualize and analyze publication data from a research center of the Indonesian public institution. The dashboard addresses several key questions: (1) whether the current number of publications is on track to meet the institution’s targets, (2) the comparison between journal and proceeding publications, (3) individual researcher productivity, (4) research group productivity, and (5) the collaborative efforts among researchers and groups. By leveraging this tool, stakeholders can gain insights into publication trends and identify areas for improvement. The findings reveal significant trends in publication types and collaboration patterns, offering a comprehensive overview of the institution’s research output. This study contributes to the understanding of research productivity metrics and their implications for strategic planning in academic research. © 2024 IEEE.

关键词： Collaboration Metrics Dashboard Visualization Publication data Research Productivity

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Shrinkage Estimation for Location and Scale Parameters of Logistic Distribution Under Record Values

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Annals of data science 2024年第4期11卷 1209-1224页

作者： Gupta, Shubham Vishwakarma, Gajendra K. Elsawah, A.M. Department of Mathematics & amp Computing Indian Institute of Technology Dhanbad826004 India Department of Statistics and Data Science Faculty of Science and Technology Beijing Normal University-Hong Kong Baptist University United International College Zhuhai519087 China Guangdong Provincial Key Laboratory of Interdisciplinary Research and Application for Data Science BNU-HKBU United International College Zhuhai519087 China Department of Mathematics Faculty of Science Zagazig University Zagazig44519 Egypt

Logistic distribution (LogDis) is frequently used in many different applications, such as logistic regression, logit models, classification, neural networks, physical sciences, sports modeling, finance and health and disease studies. For instance, the distribution function of the LogDis has the same functional form as the derivative of the Fermi function that can be used to set the relative weight of various electron energies in their contributions to electron transport. The LogDis has wider tails than a normal distribution (NorDis), so it is more consistent with the underlying data and provides better insight into the likelihood of extreme events. For this reason the United States Chess Federation has switched its formula for calculating chess ratings from the NorDis to the LogDis. The outcomes of many real-life experiments are sequences of record-breaking data sets, where only observations that exceed (or only those that fall below) the current extreme value are recorded. The practice demonstrated that the widely used estimators of the scale and location parameters of logistic record values, such as the best linear unbiased estimators (BLUEs), have some defects. This paper investigates the shrinkage estimators of the location and scale parameters for logistic record values using prior information about their BLUEs. Theoretical and computational justifications for the accuracy and precision of the proposed shrinkage estimators are investigated via their bias and mean square error (MSE), which provide sufficient conditions for improving the proposed shrinkage estimators to get unbiased estimators with minimum MSE. The performance of the proposed shrinkage estimators is compared with the performances of the BLUEs. The results demonstrate that the resulting shrinkage estimators are shown to be remarkably efficient. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2023.

关键词： Mean square error

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Blood Test Report-Based Prediction and Classification of Diseases Using Artificial Neural Networks 1

Blood Test Report-Based Prediction and Classification of Dis...

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1st International Conference for Women in computing, InCoWoCo 2024

作者： Kodipalli, Ashwini Prajwal, N. Vignesh, Vamshi B. Rao, Trupthi Gargi, N. Devamane, Shridhar B. Global Academy of Technology Dept. Artificial Intelligence & Data Science Karnataka Bangalore India School of Computing Amrita Vishwavidyapeetam Department of Computer Science Mysuru India

ISBN: (纸本)9798331518943

Through early intervention and individualized treatment plans, timely disease detection and personalized healthcare can advance patient results and reduce healthcare costs. With the aim to categorize medical conditions, we present a dataset in this study that includes blood sample parameters like haemoglobin, cholesterol, and glucose. To analyse these parameters, we created a three-layered artificial neural network (ANN). Achieving an accuracy of 93.42% on the test set, the ANN was trained and validated on the dataset. With the aim of reducing overfitting and enhance model generalization, dropout layers were added. Furthermore, a comparative analysis with various optimizers was carried out. The results highlight the ANN's potential as a means for timly diagnosis, personalized healthcare, improving patient outcomes and lowering long-term healthcare costs. They also demonstrate the ANN's ability to accurately classify diseases. © 2024 IEEE.

关键词： Anaemia ANN Artificial Neural Networks Blood Test Classification Deep learning

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Development of an Enhanced Quality Score Calculation Method for Accurate Assessment of Hotel Quality 5

Development of an Enhanced Quality Score Calculation Method ...

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5th International Conference on Advancements in computing, ICAC 2023

作者： Athurugiriya, A. A. A. Gaweshini Sumathipala, Prasanna Hemachandra, K.M. Tharindu Anupa Sri Lanka Institute of Information Technology Faculty of Computing Department of Data Science Malabe Sri Lanka

ISBN: (纸本)9798350358131

In this research paper, an enhanced hotel quality scoring method (EHQSM), is presented as a revolutionary way to analyze hotel quality. The EHQSM combines sentiment analysis, HOLSERV Plus dimensions classification, and amenity scores to deliver a more thorough review. The study generates an overall sentiment score by doing sentiment analysis on reviews categorized into HOLSERV Plus dimensions then thoughtfully averaging it using dimensionspecific weights collected from a survey. The availability of necessary amenities is considered when calculating the amenity score. The novel feature of this approach is how the amenity score, and sentiment score are carefully combined and efficiently weighted using values from the survey. The result is an improved quality score that accurately and thoroughly describes the quality of hotel services. With the goal of facilitating informed decisions for both hotel owners and attentive tourists, this technique establishes an updated standard for the evaluation of hospitality quality. © 2023 IEEE.

关键词： Sentiment analysis

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The Generalized Fixed Point Iteration Method for AVE

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Journal of Mathematical Research with Applications 2024年第6期44卷 837-849页

作者： Changfeng MA Jing KANG School of Data and Computer Science Xiamen Institute of Technology College of Mathematics and Informatics Fujian Normal University

In this paper, based on the previous published work by Ke et al.(2019) and Li et al.(2022), by using the matrix splitting technique, generalized fixed point iteration method(GFPI) is established to solve the absolute value equation(AVE). The proposed method not only includes SOR-like method, FPI method, MFPI method and so on, but also generates some special versions. Some convergence conditions of the proposed method with different iteration error norms are presented. Furthermore, methods corresponding to other splitting methods are studied in detail. The effectiveness and feasibility of the proposed method are confirmed by some numerical experiments.

关键词： absolute value equations fixed point iteration convergence

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Adaptive strategy automation with large language models in paradoxical games

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Physical Review Research 2025年第2期7卷 L022012-L022012页

作者： Kang Hao Cheong Jie Zhao Tao Wen Division of Mathematical Sciences School of Physical and Mathematical Sciences Nanyang Technological University Singapore S637371 Singapore College of Computing and Data Science (CCDS) Nanyang Technological University Singapore S639798 Singapore

Finding an ideal sequence in games is crucial for maximizing gains in various scenarios. This process requires extensive investigation, which is both time consuming and demands significant domain expertise. The emergence of large language models (LLMs) represents a significant turning point in addressing this issue, attributable to their potent analytical capabilities. In this context, LLMs can serve to substantially alleviate the human labor needed to manage these complexities. Through comprehensive simulations of coin-tossing games, we have demonstrated that the adaptive switching strategies formulated by LLMs surpass predefined sequences in profitability when applied to certain paradoxical games. Furthermore, our experimental findings indicate that the proposed method not only automates the identification of effective strategies but also provides adaptability to various forms of these paradoxical games.

关键词： Computer simulation languages

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CausalCFF: Causal Analysis between User Stress Level and Contextually Filtered Features Extracted from Mobile Sensor data

CausalCFF: Causal Analysis between User Stress Level and Con...

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2025 CHI Conference on Human Factors in computing Systems, CHI EA 2025

作者： Zhang, Panyu Jung, Gyuwon Ahmed, Uzair Lee, Uichin Graduate School of Data Science KAIST Daejeon South Korea School of Computing KAIST Daejeon South Korea

ISBN: (纸本)9798400713958

Nowadays, it’s possible to deliver interventions through mobile technologies to improve users’ mental and physical health. Causal analysis may help researchers identify the potential causes of the health issues and design proper interventions. However, in previous studies, causal analysis is mainly conducted between single sensor data features such as walking activity duration and perceived stress. There is a lack of research into causal analysis between more complicated behavior features which could be derived from multiple sensor features and target well-being labels. To address this gap, we propose CausalCFF, a framework that investigates causal relationships between contextually filtered behavioral features (e.g., walking duration at workplace locations) and well-being outcomes (e.g., stress). Our analysis identifies frequent workplace visits during periods of reduced home time as the most salient cause for elevated stress levels, highlighting the framework’s ability to target context-specific behavioral biomarkers for human well-being. The code is also made available¹ © 2025 Copyright held by the owner/author(s).

关键词： Causal Analysis Contextually Filtered Features Human Behavior Mobile Sensor data Stress

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Detecting APT-Exploited Processes through Semantic Fusion and Interaction Prediction

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Computers, Materials & Continua 2024年第2期78卷 1731-1754页

作者： Bin Luo Liangguo Chen Shuhua Ruan Yonggang Luo School of Cyber Science and Engineering Sichuan UniversityChengdu610065China Key Laboratory of Data Protection and Intelligent Management(Sichuan University) Ministry of EducationChengdu610065China Cyber Science Research Institute Sichuan UniversityChengdu610065China

Considering the stealthiness and persistence of Advanced Persistent Threats(APTs),system audit logs are leveraged in recent studies to construct system entity interaction provenance graphs to unveil threats in a ***-based provenance graph APT detection approaches require elaborate rules and cannot detect unknown attacks,and existing learning-based approaches are limited by the lack of available APT attack samples or generally only perform graph-level anomaly detection,which requires lots of manual efforts to locate attack *** paper proposes an APT-exploited process detection approach called ThreatSniffer,which constructs the benign provenance graph from attack-free audit logs,fits normal system entity interactions and then detects APT-exploited processes by predicting the rationality of entity ***,ThreatSniffer understands system entities in terms of their file paths,interaction sequences,and the number distribution of interaction types and uses the multi-head self-attention mechanism to fuse these ***,based on the insight that APT-exploited processes interact with system entities they should not invoke,ThreatSniffer performs negative sampling on the benign provenance graph to generate non-existent edges,thus characterizing irrational entity interactions without requiring APT attack *** last,it employs a heterogeneous graph neural network as the interaction prediction model to aggregate the contextual information of entity interactions,and locate processes exploited by attackers,thereby achieving fine-grained APT *** results demonstrate that anomaly-based detection enables ThreatSniffer to identify all attack *** to the node-level APT detection method APT-KGL,ThreatSniffer achieves a 6.1%precision improvement because of its comprehensive understanding of entity semantics.

关键词： Advanced persistent threat provenance graph multi-head self-attention graph neural network

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Enhancing Hotel Customer Satisfaction Assessment with Semantic Search-based Scoring 5

Enhancing Hotel Customer Satisfaction Assessment with Semant...

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5th International Conference on Advancements in computing, ICAC 2023

作者： Hemachandra, K.M Tharindu Anupa Sumathipala, Prasanna Athurugiriya, A.A.A Gaweshini Sri Lanka Institute of Information Technology Department of Data Science Faculty of Computing Malabe Sri Lanka

ISBN: (纸本)9798350358131

Within the hospitality sector, ensuring customer satisfaction takes precedence, particularly in economies such as Sri Lanka, where tourism serves as a vital growth catalyst. Interestingly, disparities often arise between customer ratings and their written evaluations. This study presents an innovative strategy that utilizes semantic search techniques to extract comprehensive insights from customer feedback, leading to the development of a robust hotel scoring system. By exploring intricate emotions via advanced linguistic analysis, this method reveals underlying cues that conventional metrics often overlook. The method's effectiveness is validated through a comparative study with established hotel ratings. This advancement not only refines precise customer satisfaction assessment for hotels but also bears strategic significance for countries aiming to fortify their tourism sectors. In this era centered around data, this research provides a pivotal standpoint for optimizing hospitality evaluation, thereby enriching guest experiences, and guiding well-informed operational choices. © 2023 IEEE.

关键词： Sentiment analysis

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