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Predicting Customer Purchase Intent Using Logistic Regression: A data-Driven Approach 2

Predicting Customer Purchase Intent Using Logistic Regressio...

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2nd international conference on Machine Learning and Autonomous systems, ICMLAS 2025

作者： Rajarajachozhan, C. Karthika, G. Rajakrishnammal, A. Mano, M. Kumar, P. Nithish Jayahari, E. Saveetha Engineering College Dept. of Electronics and Communication Engineering Tamil Nadu Chennai India Saveetha Engineering College Dept. of Artificial Intelligence and Data Science Tamil Nadu Chennai India Saveetha Engineering College Dept. of Computer Science and Engineering Tamil Nadu Chennai India

ISBN: (纸本)9798331505745

Predicting customer purchase intent is crucial for modern businesses in order to improve engagement, refine marketing strategies, and increase revenue. Conventional rule-based systems encounter difficulties with dynamic and extensive customer data, limiting their flexibility and precision. This project employs a machine learning methodology utilizing logistic regression to forecast customer intent based on demographic, transactional, and behavioral data. The proposed model analyses input features including age, browsing history, and previous purchases, employing sophisticated data preprocessing methods such as One-Hot Encoding and feature scaling to guarantee data integrity. Through hyperparameter tuning and the assessment of critical metrics including accuracy, precision, recall, and Fl-score, the system provides dependable and actionable insights. Real-time predictive capabilities enable businesses to customize customer interactions, facilitating targeted marketing and enhancing customer experience. This project underscores the capacity of predictive analytics to tackle real-world issues, facilitating data-driven decision-making and sustainable business development. © 2025 ieee.

关键词： Logistic regression

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Characterizing Vulnerabilities in Microservices: Source, Age and Severity 22

Characterizing Vulnerabilities in Microservices: Source, Age...

引用

22nd ieee international conference on Software Architecture, ICSA 2025

作者： Beahan, Samuel Ullah, Faheem Chalmers, Lachlan Fatima, Uswa Shahin, Mojtaba The University of Adelaide Adelaide Australia Zayed University United Arab Emirates The University of Adelaide Australia National University of Science and Technology Islamabad Pakistan Rmit University Melbourne Australia

ISBN: (纸本)9798331520908

Microservices architecture has become a popular choice for developing scalable, cloud-native applications because of its modularity. However, this architecture introduces unique security risks due to its distributed nature and dependency management requirements. While existing literature has investigated some security challenges and proposed mitigation strategies, there is a lack of comprehensive research on the security vulnerabilities present within systems using this architecture. To bridge this gap, we used three vulnerability detection tools to analyze security vulnerabilities across 30 open-source microservices projects from GitHub, identifying three sources of vulnerabilities: 'application code', 'dependencies', and 'container configurations'. Vulnerabilities related to request and data handling were most common, stemming from container misconfigurations and outdated dependencies. Dependency-related vulnerabilities are new but most fall into pre-established CWE and OWASP top 10 categories. Most of the detected vulnerabilities fall under a severity of medium to high. While the emergence of microservices has not introduced new vulnerabilities, the severity of existing vulnerabilities urges developers to implement secure data and request handling, address container misconfigurations, and update dependencies timely. © 2025 ieee.

关键词： Network security

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Role of Machine Learning in Climate Change Prediction and Mitigation 2

Role of Machine Learning in Climate Change Prediction and Mi...

引用

2nd international conference on Machine Learning and Autonomous systems, ICMLAS 2025

作者： Jikar, Nayan Tale, Yash Tale, Abhay Barhate, Aditya Verma, Prateek Jikar, Aman Datta Meghe Institute of Higher Education and Research Faculty of Engineering and Technology Department of Artificial Intelligence and Machine Learning Maharashtra Wardha442001 India Datta Meghe Institute of Higher Education and Research Faculty of Engineering and Technology Department of Artificial Intelligence and Data Science Maharashtra Wardha442001 India Datta Meghe Institute of Higher Education and Research Faculty of Engineering and Technology Department of Computer Science and Engineering Maharashtra Wardha442001 India

ISBN: (纸本)9798331505745

Climate change is one of the biggest challenges of the 21st century, with profound environmental, economic, and social impacts. Machine learning (ML) has emerged as a transformative tool to address climate challenges through data-driven solutions, offering predictive capabilities for extreme weather events and enhancing the efficiency of clean-energy systems. This technology benefits the optimization of renewable energy generation, intelligent grid management, and resource utilization towards reducing greenhouse gas emissions. Moreover, ML promotes climate justice through targeted region-specific intervention and inclusive solution-making for the most vulnerable. Several technical and ethical challenges associated with data quality, scalability, and model generalization must still be overcome for ML to realize its potential. This paper reviews the role of ML in climate change prediction and mitigation discusses ongoing real-world applications and outlines the future research agenda necessary to address existing challenges. Integrating ML with emerging technologies like the Internet of Things (IoT) and explainable AI, alongside global collaboration and a focus on climate justice, will be essential in advancing effective climate action. © 2025 ieee.

关键词： Global warming

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Integrating Explainable Machine Learning (XAI) in Stroke Medicine: Opportunities and Challenges for Early Diagnosis and Prevention 2

Integrating Explainable Machine Learning (XAI) in Stroke Med...

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2nd international conference on Machine Learning and Autonomous systems, ICMLAS 2025

作者： Shobana, V. Maheshwari, S. Savithri, M. Ramasamy, Siva Shankar Kumar, N. Dr. N.G.P. Arts and Science College Department of Computer Science with Cyber Security Coimbatore India Dr. N.G.P. Arts and Science College Department of Computer Science Coimbatore India Christ University Department of Data Science Bengaluru India International College of Digital Innovation Chiang Mai University Thailand

ISBN: (纸本)9798331505745

Stroke is a leading cause of mortality and disability worldwide, emphasizing the critical need for early diagnosis and prevention. Machine learning (ML) has demonstrated significant potential in improving stroke prediction and management by analysing complex datasets for risk stratification, diagnosis, and treatment planning. However, the adoption of ML in stroke medicine is limited by the opacity of these models, which can hinder clinical trust and decision-making. Explainable Artificial Intelligence (XAI) addresses this challenge by making ML models more interpretable and transparent, enabling healthcare professionals to understand, validate, and trust their outputs. This research work explores the integration of XAI in stroke medicine, highlighting its potential to enhance early diagnosis, personalized prevention strategies, and treatment planning. We discuss the opportunities XAI provides in identifying high-risk patients, uncovering critical predictors, and enabling informed clinical decisions. Furthermore, we examine challenges such as ensuring model reliability, addressing biases in stroke datasets, and navigating ethical considerations related to patient data privacy and algorithmic accountability. © 2025 ieee.

关键词： Diagnosis

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Digital Touchpoints: Generating Synthetic data for Elderly Smartphone Interactions 11

Digital Touchpoints: Generating Synthetic Data for Elderly S...

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11th international conference on Information and Communication Technologies for Ageing Well and e-Health, ICT4AWE 2025

作者： Maqbool, Bilal Herold, Sebastian Department of Mathematics and Computer Science Faculty of Health Science and Technology Karlstad University Karlstad Sweden

ISBN: (纸本)9789897587436

Context: Ensuring smartphone interfaces are usable and accessible is essential for elderly users, particularly those with motor impairments, who face challenges with touchscreen interactions. Problem: Hand tremors and limited motor control can hinder touchscreen accuracy and efficiency. Meanwhile, recruiting elderly participants for usability studies can be challenging, often resulting in limited interaction data. Objectives: This study aimed to investigate elderly users’ smartphone interaction patterns, identify key challenges, and generate synthetic data to address data scarcity for usability research. Method: A custom-designed mobile app collected interaction data from 51 elderly participants performing tapping, dragging, and tracing tasks. Hand steadiness was assessed using accelerometer data. Gaussian Process Regression (GPR) and Long Short-Term Memory (LSTM) models were used to generate synthetic datasets replicating user interaction patterns. Results: Users with shaky hands struggled with precision tasks, especially involving smaller GUI elements, while larger elements improved performance. Continuous control was also found to be challenging in tracing tasks. Synthetic datasets successfully replicated spatial, temporal, and distributional metrics, demonstrating potential utility in future usability evaluation research. Conclusions: Inclusive GUI designs and adaptive features can improve accessibility for the elderly with limited motor control. Synthetic data can offer a potential solution for further usability evaluation research in building AI-driven design evaluation tools, reducing reliance on resource-intensive participant recruitment in earlier prototypes. Future work should examine diverse tasks and scenarios and involve people with severe motor impairments. Copyright © 2025 by SCITEPRESS - science and Technology Publications, Lda.

关键词： Usability engineering

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Time Series Forecasting with Hybrid SARIMA 2

Time Series Forecasting with Hybrid SARIMA

引用

2nd international conference on Machine Learning and Autonomous systems, ICMLAS 2025

作者： Pramothini, S. Aakash, P. Vm, Vanmathi Somasundaram, K. Thangavel, Senthil Kumar Amrita School of Physical Sciences Coimbatore Department of Mathematics Tamil Nadu India Amrita School of Computing Department of Computer science and Engineering Amrita Vishwa Vidyapeetham Tamil Nadu Coimbatore India

ISBN: (纸本)9798331505745

Forecasting is an important task in any organization so that they can predict the targets and modify their strategies in order to improve their productivity of sales for the upcoming future. This paper primarily deals about the prediction of products for sales. The focus has been mainly on forecasting techniques like SARIMA and it is optimized using a gradient boosting algorithm to improve the prediction rate. The dataset that has been used for current predictions is a source from Kaggle. The data comprises of both linear and non-linear data to simulate the real time sales. © 2025 ieee.

关键词： data Analysis gradient boosting prediction sales analysis SARIMA

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data-Driven Framework for Optimizing Internship Efficiency and Addressing Skills Mismatch in Malaysian Higher Education 22

Data-Driven Framework for Optimizing Internship Efficiency a...

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22nd international Learning and Technology conference, L and T 2025

作者： Abas, Ashardi Ibrahim, Mohd Mokhzani Bin Azmi, N.H. University Pendidikan Sultan Idris Faculty of Computing Meta Technology Perak Malaysia University Pendidikan Sultan Idris Faculty of Science and Mathematics Perak Malaysia

ISBN: (纸本)9798331532215

The persistent skills mismatch between higher education outcomes and labour market needs presents a significant challenge for graduate employability, particularly in fast-evolving sectors such as artificial intelligence (AI) and data science. This paper introduces a data-driven internship framework designed to bridge this gap by optimising student placements, providing real-time feedback, and ensuring academic curricula remain aligned with labour market demands. The framework integrates three systems: the Student Industry Placement System (SIPS), which uses machine learning to match students with relevant internships;the Intern Learning Management System (ILMS), which tracks skill development through continuous feedback loops;and the Industry Profiling System (IPS), which leverages predictive analytics to provide real-time labour market insights. The framework was evaluated through a mixed-methods approach, incorporating pilot program data from five universities and 15 industry partners and simulated data based on historical internship trends. The SIPS algorithm achieved a placement accuracy of 89%, significantly improving over traditional methods (60-65%). 78% of students received continuous feedback through the ILMS, resulting in measurable improvements in technical skills (30% increase in coding proficiency) and problem-solving abilities (25% improvement). The IPS enabled universities to update 15% of their curricula in response to real-time labour market data, particularly in high-growth sectors such as AI and digital transformation. 68% of the students secured full-time employment within six months of graduation, with 65% in roles directly aligned with their internships. These findings demonstrate the framework's scalability and potential for reducing skills mismatch, making it a viable model for other emerging economies. © 2025 ieee.

关键词： Commerce

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Role of Quantum Cryptography in Securing Communication Channels in Industry 4.0 1

Role of Quantum Cryptography in Securing Communication Chann...

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1st international conference on Advances in Computer science, Electrical, Electronics, and Communication Technologies, CE2CT 2025

作者： Patil, Sonal Pramod Sankaran, Vikram Nattamai Lamsoge, Kalpit Alagarsundaram, Poovendran Howard, Eric Guduru, Prasanna G H Raisoni College of Engineering and Management Pune India Innovation Consultant Maharashtra Nagpur India Humetis Technologies Inc KingstonNJ United States Macquarie University Department of Physics and Astronomy Australia National Sanskrit University Department of Computer Science Andhra Pradesh Tirupati India Atlanta GA United States

ISBN: (纸本)9798331518578

In the context of Industry 4.0, which is characterised by networked systems, Internet of Things devices, and cyber-physical networks, quantum cryptography is essential to strengthening communication channels. In contrast to traditional cryptography techniques, quantum cryptography uses quantum physics concepts, notably quantum key distribution (QKD), to provide unmatched security. By taking advantage of the quantum property of superposition and measurement-induced disruption, QKD offers tamper-evident key exchanges, protecting data from efforts at eavesdropping. This guarantees that any communication interception causes system disruption and warns users of possible dangers. Quantum cryptography reduces the risks associated with traditional encryption techniques in Industry 4.0, as sensitive data moves across automated systems, smart factories, and decentralised supply chains. This is especially true as quantum computing advances. Industries can attain strong data integrity, confidentiality, and resistance against new cyberthreats by incorporating quantum-secured channels, opening the door for a dependable and safe digital transformation. This presentation highlights how quantum cryptography is revolutionising the security paradigms of Industry 4.0. © 2025 ieee.

关键词： Supply chains

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Unified Graph and Hypergraph Neural Network for Next-item Recommendation

Unified Graph and Hypergraph Neural Network for Next-item Re...

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

作者： Zhang, Xinyu Jiang, Qize Zhang, Hanyuan Sun, Weiwei School of Computer Science Shanghai Key Laboratory of Data Science Fudan University Shanghai China Shanghai Institute of Intelligent Electronics & Systems Shanghai China

ISBN: (纸本)9798350368741

The task of next-item recommendation is a crucial component in recommendation systems. The challenge of this task lies in extracting complex interaction information from users' historical interactions with items. While prior research has transformed users' interaction histories into graphs and hypergraphs to mine high-order interactions, the integration of both remains uncharted. Existing methods treat graphs and hypergraphs separately in representation learning, missing out on their shared attributes. Addressing this gap, we introduce a novel unified framework that integrates graphs and hypergraphs into one unified message passing paradigm. Our method employs graph neural networks and hypergraph neural networks to continuously conduct representation learning, achieving end-to-end next item recommendation. Specifically, our framework consists of the following components: 1) a unique method for graph and hypergraph construction from interaction histories, with items as nodes and users as hyperedges;2) a novel message passing framework compatible with both graph and hypergraph neural networks;and 3) an innovative hypergraph neural network aggregation module enhanced with time and position encoders. The test results on multiple public benchmarks verify that our method outperforms current best practices in performance. © 2025 ieee.

关键词： Graph Neural Network Hypergraph Neural Network Next-item Recommendation User Modeling

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LOFT: A Lock-free and Adaptive Learned Index with High Scalability for Dynamic Workloads 25

LOFT: A Lock-free and Adaptive Learned Index with High Scala...

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20th European conference on Computer systems, EuroSys 2025, co-located 30th ACM international conference on Architectural Support for Programming Languages and Operating systems, ASPLOS 2025

作者： Mo, Yuxuan Hua, Yu Wuhan National Laboratory for Optoelectronics School of Computer Huazhong University of Science and Technology China

ISBN: (纸本)9798400711961

In order to mitigate memory overheads and reduce data movements in the critical path, a computational and space-efficient learned index is promising to deliver high performance and complement traditional index structures, which unfortunately works well only in static workloads. In dynamic workloads, the learned indexes incur performance degradation with poor scalability due to inefficient data placement for newly inserted items and intensive lock contention. Furthermore, the model retraining is time-consuming and blocking, which hampers the performance of index operations. In order to meet the needs of dynamic workloads and efficient retraining, we propose LOFT, a highly scalable and adaptive learned index with lock-free design and self-tuning retraining technique to provide high throughput and low latency. LOFT enables all index operations to be concurrently executed in a lock-free manner by using Compare- and-Swap (CAS) primitive and an expanded learned bucket to handle the overflowed data. To minimize the impact of model retraining, LOFT leverages a shadow node to serve the clients’ requests and accelerates the retraining process with the aid of index operations. To accommodate dynamic workloads, LOFT determines when and how to perform retraining based on inferred access patterns. Our extensive evaluation on YCSB and real-world workloads demonstrates that LOFT effectively improves the performance by up to 14× than state-of-the-art designs. © 2025 Copyright held by the owner/author(s).

关键词： Concurrency control

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