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Technological Advancement of Big Data Analytics in Enhancing Financial Decision-Making and Trade Performance in Emerging Markets 1

Technological Advancement of Big Data Analytics in Enhancing...

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1st international conference on advances in computer science, Electrical, Electronics, and Communication Technologies, CE2CT 2025

作者： Sandeep, N.K. Bolla, Ramya Lakshmi Kakde, Hrishikesh Satyakumar Ivan, Casanova-Villalba Cesar Meera, K.L. Inumula, Krishna Murthy Vidya Vikas Institute of Engineering and Technology Karnataka Mysore India ERP Analysts OH United States Institute of Management and Research MGM University Maharashtra Chhatrapati Sambhajinagar India Universidad Técnica Luis Vargas Torres de Esmeraldas Sede Santo Domingo Santo Domingo Ecuador Dayananda Sagar Business Academy Karnataka India Maharashtra Pune India

ISBN: (纸本)9798331518578

The revolutionary potential of big data analytics (BDA) is causing a dramatic transformation in the field of financial risk management (FRM). Financial companies are now leveraging massive databases as more than just historical information to change risk management practices. The financial industry has seen a paradigm shift as a result of machine learning's (ML) convergence, which has enabled businesses to gain deeper insights and make well-informed decisions. This abstract examines the various connections between these developments and demonstrates their effects on risk management, financial operations, predictive analytics, and customer-focused services. Proactive risk assessment, accurate forecast models, and customised financial plans are made possible by the integration of large databases and complex technologies. It has problems with interpretability, data privacy, and ethical use even though it is revolutionising the sector. The transformational potential and related ramifications of ML in the financial arena are examined in this paper. © 2025 IEEE.

关键词： Decentralized finance

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Dual-Pyramid Attention Collaborative Network for Oracle Bone Inscription Classification

Dual-Pyramid Attention Collaborative Network for Oracle Bone...

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2025 IEEE international conference on Acoustics, Speech, and Signal Processing, ICASSP 2025

作者： Gao, Jiaying Giunchiglia, Fausto Zhao, Tongyu Li, Chuntao Xu, Hao School of Artificial Intelligence Jilin University Changchun130012 China Department of Information Engineering and Computer Science University of Trento Trento14-38122 Italy College of Computer Science and Technology Jilin University Changchun130012 China School of Engineering University of Minho Guimarães4804-533 Portugal School of Archaeology Jilin University Changchun130012 China

ISBN: (纸本)9798350368741

Recent advances in oracle bone inscriptions (OBI) classification have explored various strategies such as zero-shot learning, augmentation, and complex convolution architectures. These strategies ultimately represent samples as global feature vectors in various forms but often fail to effectively capture the inherent multi-scale and region-specific features of OBI. The reliance on global feature vectors ignores the subtle differences between different scales and the uneven importance of different regions in the inscriptions. To address this issue, we propose a dual-pyramid attention collaborative network that enables classification models to learn OBI multi-scale attention. The dual-pyramid structure covers the convolutional and skeletonized features of pyramids. The spatial collaborative attention between level pairs corrects the bias produced by the pre-trained convolutional feature extractor. Experiments show that our model reduces training parameters by an average of 38% and improves accuracy by an average of 3.89% compared to state-of-the-art models. © 2025 IEEE.

关键词： Collaborative Attention Computational Archaeology Multi-Scale Feature OBI Classification

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Hyperspectral Images for Crop Prediction using Machine and Deep Learning Models 3

Hyperspectral Images for Crop Prediction using Machine and D...

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3rd international conference on Electronics and Renewable Systems, ICEARS 2025

作者： Mahalakshmi, R. Kumutha, K. Sakthivanitha, M. Lakshmi, R. Bagavathi Sirajudeen, Mohamed Padmanabhan, Sankar Vels Institute of Science Technology & Advanced Studies Department of Computer Applications-PG Tamil Nadu Chennai India Vels Institute of Science Technology Advanced Studies Department of Computer Applications-PG Tamil Nadu Chennai India Vels Institute of Science Technology & Advanced Studies Department of Computer Application Tamil Nadu Chennai India Vels Institute of Science Technology & Advanced Studies Department of Information Technology Tamil Nadu Chennai India Nilgiri College of Arts and Science Pg and Research Department of Computer Science Tami Nadu The Nilgiris India Hindustan Institute of Technology and Science Department of Electronics and Communication Engineering Tamil Nadu Chennai India

ISBN: (纸本)9798331509675

Crop classification is essential for local and national governments to make informed agricultural decisions. Remote sensing technology has made it possible to employ high-resolution hyperspectral images (HSIs) for land cover classification for decades. Machine Learning (ML) and Deep Learning (DL) are becoming more popular for HSI categorization due to advances in processing capacity and technology. HSIs can accurately identify crop types due to their narrow and continuous spectral band reflection. This study compares traditional machine learning (ML) models like Support Vector Machines (SVM) and Random Forest (RF) to deep learning (DL) models like Convolutional Neural Networks (CNN) and Recurrent Neural Networks (RNN) for hyperspectral image classification (HSI). The HSI categorization was evaluated using performance criteria such as accuracy, precision, recall, and the F1-Score. CNN achieved the highest accuracy of 96%, demonstrating that it is the most reliable solution for complex HSI classification tasks because it successfully captures spatial and spectral data. © 2025 IEEE.

关键词： Classification Deep Learning(DL) Hyperspectral Images(HSI) Machine Learning(ML)

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Evaluating the Efficacy of Deep Learning Models in Identifying and Mitigating Cybersecurity Threats and Attacks 1

Evaluating the Efficacy of Deep Learning Models in Identifyi...

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1st international conference on advances in computer science, Electrical, Electronics, and Communication Technologies, CE2CT 2025

作者： Sheela, R. Rajagopal, Smitha Nagarajan, Harikumar Premsai, Ranga Aravindan, M. Chakraborty, Abhijay Presidency College Bangalore India Alliance University Bangalore India NJ United States Technical fellow of Cybersecurity and Identity management MD United States Agni College of Technology Department of Electrical and Electronics Engineering Tamilnadu Chennai India Amity Law School Amity University Rajasthan India

ISBN: (纸本)9798331518578

Due to rapid advancement in digital infrastructure and Internet cyber security threats increased in sophisticated way that necessitate advanced strategies for identification and mitigation. The present study uses deep learning models that act as powerful tool for identifying and mitigating cyber security threats and attacks that leverages the capability for detection of complex patterns using large datasets. The Efficacy of the deep learning model can be evaluated using the study for identification of different types of cyber security threats such as DoS, DDoS, ransomware, password hacking and information threat. The proposed model identifies and mitigates the threat in less time and it was tested with two different data sets BoT and ToN. The proposed model is measured and evaluated using various performance metrics using cyber security data sets. The study explores the model interpretability and robustness for understanding the potentiality and implications based on cyber security solutions. Study findings shows that the proposed model shows highest accuracy of 99.9% and it is comparatively higher than other existing models in identification and mitigation of threats and attacks. © 2025 IEEE.

关键词： Cyber attacks

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20th international conference on Data science, ICDATA 2024, held as part of the World Congress in computer science, computer engineering and Applied Computing, CSCE 2024

20th International Conference on Data Science, ICDATA 2024, ...

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20th international conference on Data science, ICDATA 2024, held as part of the World Congress in computer science, computer engineering and Applied Computing, CSCE 2024

ISBN: (纸本)9783031858550

The proceedings contain 37 papers. The special focus in this conference is on Data science. The topics include: Trust as a Classification Tool: Analyzing Collaboration in Senate Floor Speeches on Gun Legislation Post-Uvalde and Sandy Hook;an Application of Convolutional Neural Networks to Chaotic Systems;artificial Intelligence Data Reduction Algorithm for Streaming Readout in High Energy Physics Experiment;user Behavior Based Implicit Personality Detection in Recommendation Systems;proactive Management of Delays in the French Railway Network: A Seasonal Machine Learning Based Approach;CNN to BiLSTM: Enhancing Setswana Named Entity Recognition;Assessing the Impact of Homelessness on COVID-19 Hospitalization Rates in Patients with Underlying Medical Conditions Through Explainable AI;leveraging Ensemble Learning Paradigms for B2B E-Commerce Fraud Detection;exploring Equity: Integrating Knowledge Graphs in Fairness Testing Methodologies;explainable Machine Learning Approach for Intelligent Edits of Medicaid Home Healthcare Services Claims;authAttLyzer-V2: Unveiling Code Authorship Attribution Using Enhanced Ensemble Learning Models and Generating Benchmark Dataset;Real-Time AI Voice Clone Detection: A Deep Learning Approach to Safeguard Authenticity;Enhancing Security with Automated Boom Gate Access Through License Plate Recognition Utilising YOLOv8 Model;generating Culturally Appropriate Avatars;NLP-Guided Synthesis: Transitioning from Sequential Programs to Distributed Programs;Generative AI Techniques for the Simulation of Groundwater Well Data at Hanford Site;Enhancing Financial Analysis with Generative AI: Utilizing Large Language Models for Efficient Data Extraction;Leveraging NLP and Large Language Models for Clinical Documentation Improvement: A Medical AI Chatbot Approach;Harnessing LLMs to Build an Autonomous Marketing Agent;Generative AI Use in One Health, One System as Healthcare is Global;dataDock: An Open Source Data Hub for Research;big Data Techniques i

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AI-Driven Clinical Decision Support Systems: Revolutionizing Healthcare with Predictive Models

AI-Driven Clinical Decision Support Systems: Revolutionizing...

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2025 IEEE international conference on Computational, Communication and Information Technology, ICCCIT 2025

作者： Kukreti, Sanjeev Shrivastava, Anurag Chandrashekar, Rakesh Rani, K. Pushpa Badhoutiya, Arti Lakhanpal, Sorabh Graphic Era Deemed to Be University Department of Computer Science & Engineering Dehradun India Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Tamil Nadu Chennai India New Horizon College of Engineering Department of Mechanical Engineering Bangalore India MLR Institute of Technology Department of Computer Science and Engineering Telangana Hyderabad India GLA University Department of Electrical Engineering Mathura India Lovely Professional University Punjab Phagwara India

ISBN: (纸本)9798331512965

The integration of artificial intelligence (AI) into Clinical Decision Support Systems (CDSS) is changing the face of healthcare by allowing AI powered predictive, data driven insights, enabling medical practitioners to prolong their diagnosis and treatment planning. To make better and faster clinical judgements, CDSS based on AI using machine learning techniques such as ensemble methods and deep learning is discussed in this paper. He said most of the focus is on models that can predict future events, diagnose plagues and deliver tailored treatment programs sifting through mountains of health care data, such as genetics, medical imaging and EHRs. Some of the main problems we work with as we implement AI driven CDSS is its data privacy, model interpretability and integration to current healthcare processes. In addition this study summarizes recent advances in artificial intelligence (AI), enabling real time analysis and making real time decisions to improve patient outcomes and reduce workloads of healthcare personnel. Case examples that employ AI-CDSS to identify diseases, optimize treatments and predict risk are provided to show how they can change the face of clinical practice. Finally, we explore where AI healthcare may be going from here with a focus on the importance of deploying these systems with patient well being in mind and with full compliance to the relevant regulations. © 2025 IEEE.

关键词： AI-Driven CDSS Clinical Decision Support Healthcare Machine Learning Predictive Models

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Research on the Next POI Recommendation System Based on Graph Neural Networks and Attention Mechanism 1

Research on the Next POI Recommendation System Based on Grap...

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1st international conference on Intelligent Manufacturing and Cloud Computing, ICIMCC 2024

作者： Li, Weilong School of Computer Science Guangdong University of Petrochemical Technology Guangdong Maoming525000 China

ISBN: (纸本)9781643685779

In order to enhance the accuracy of the POI recommendation system, a model that dynamically captures user behavior patterns was established by combining Graph Neural Networks (GNNs) and the Attention Mechanism. The experimental results show that the model improved the Top-1 accuracy by 15%, and the Top-5 and Top-10 accuracy by 13% and 15%, respectively. The conclusion indicates that the proposed method significantly improves recommendation performance and provides effective support for personalized recommendations. © 2025 The Authors.

关键词： Neural network models

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SMS Spam Detection using Advance Naive-Bayes Approach

SMS Spam Detection using Advance Naive-Bayes Approach

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2025 international conference on Pervasive Computational Technologies, ICPCT 2025

作者： Arora, Yojna Gupta, Neha Rathore, Yogesh Singh Bansal, Nidhi Mehta, Aina Chadha, Shikha Kumar, Ashwani Sharda University Department of Computer Science and Engineering Greater Noida India Symbiosis University of Applied Sciences School of Computer Science & Information Technology Madhya Pradesh Indore India Sharda University School of Engineering and Technology Department of Computer Science and Applications Uttar Pradesh Greater Noida India School of Engineering and Technology Department of Computer Science & Engineering Haryana Faridabad India Bennett University School of Computer Science Engineering and Technology India

ISBN: (纸本)9798331508685

Recently, with increased use of mobile phones, it has transformed into a multibillion-dollar Short Message Service or SMS. However, the drop in the cost of messaging services has led to an increased number of unsolicited commercial messages, or spam, being delivered to mobile phones. Several reasons exist for which traditional algorithms used in email filtering may not efficiently classify the spam in SMS such as the absence of strong SMS spam databases, with the short length and features of the message, the informal language used, etc. This paper uses a real SMS spam database After preprocessing the data and extraction of relevant features, Naïve Bayes machine learning techniques is applied to get the results. Then it is analyzed using confusion matrix, accuracy, precision and recall values. A learning curve with clearly defined relationship between the training and testing data set and overall error at the end. © 2025 IEEE.

关键词： Adversarial machine learning

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Enhancing NLP for Indic Languages with Limited Resources: A Study of Transformer Models for Translation and Summarization

Enhancing NLP for Indic Languages with Limited Resources: A ...

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2025 IEEE international conference on Computational, Communication and Information Technology, ICCCIT 2025

作者： Bhati, Abhivyakt Gupta, Ved Kumar Shah, Hare Ram Nagar, Rajesh Nimawat, Sunil Computer Science & Engineering-AIML IPS Academy Institute of Engineering & Science Indore India Computer Science & Engineering IPS Academy Institute of Engineering & Science SAGE University Indore India Computer Science & Engineering Indore India IQAC Cell Indore India Computer Science & Engineering IPS Academy Institute of Engineering & Science Indore India

ISBN: (纸本)9798331512965

Significant advancements have been made in natural language processing (NLP), especially in numerous languages. The vast linguistic diversity of India presents unique challenges for automated language processing tasks as Indian languages other than Hindi are marginalized in both cases;datasets which incur a vast toll on summarization models that require huge datasets to be trained on. This paper presents a comparative and comprehensive analysis of multiple transformer-based models which have been trained and/or fine-tuned for lesser-used Indic languages. Purely focusing on Natural Language Translation (NLT) and evaluating its efficiency in generating contextually accurate summaries for languages such as Bengali and Marathi. Models such as mT5, IndicBART, mBART and other multilingual NLP models will be benchmarked and assessed using the ROUGE score. This study highlights the model's strengths and weaknesses in handling the limitations summarization of low-resource Indic languages. The findings will contribute heavily to the development of NLP tools aimed at improving summarization inclusivity across India's diverse languages. © 2025 IEEE.

关键词： Translation (languages)

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advances in Deep Learning for Tuberculosis Detection in Chest X-Rays: A Review of Diagnostic Accuracy 3

Advances in Deep Learning for Tuberculosis Detection in Ches...

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3rd international conference on Intelligent Data Communication Technologies and Internet of Things, IDCIoT 2025

作者： Jayakumar, K. Muthupandi, G. Dhayanidhi, R. Pavithra, S. Dept. of Computer Science and Engineering Chennai Institute of Technology Tamilnadu Chennai600069 India

ISBN: (纸本)9798331527549

Tuberculosis (TB) is one of the leading causes of deaths globally, mainly in low- and middle-income countries. Early and accurate detection is crucial for effective treatment and disease control. In this paper, models like CNNs-VGG16, ResNet, and DenseNet, to name a few, are taken into an exhaustive study. Optimization techniques, including transfer learning and ensemble methods, which can increase accuracy in the challenging diverse clinical settings, will be presented. Equitable healthcare outcome is achieved in terms of future research directions which include representative datasets and interpretability of the model. © 2025 IEEE.

关键词： Disease control

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