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2025 International Conference on Pervasive Computational Technologies, ICPCT 2025

作者： Shravan, M. Kakad, Shital Mulani, Umar M Sharma, Geeti Tiwari, Mohit Mary Shiba, C. Sr University School of Business Telangana Warangal India Vishwakarma Institute of Technology Artificial Intelligence and Data Science Maharashtra Pune India Kj College of Engineering and Management Research Pune India India Bharati Vidyapeeth's College of Engineering Department of Computer Science and Engineering Delhi Delhi India R.M.K Engineering College Department of Computer Science and Business Systems Thiruvallur India

ISBN: (纸本)9798331508685

Blockchain, initially developed as the underlying technology for Bitcoin, has garnered significant attention for its applications beyond cryptocurrency, particularly in complex non-monetary domains. Utilizing cryptographic methods like hashing and asymmetric encryption, combined with a distributed consensus model, Blockchain-based ledgers achieve high levels of security and immutability, effectively removing the need for third-party intermediaries. Meanwhile, the rapid growth in Internet of Things (IoT) devices being connected to networks raises substantial concerns around security and privacy. Addressing these security challenges is essential for the expanding IoT landscape. This paper explores the blockchain solution by integrating cryptocurrency with IoT for enhanced security, reviewing recent studies and applications to evaluate Blockchain's role in IoT security, pinpoint challenges, and propose Blockchain-driven solutions to improve security in the IoT ecosystem. © 2025 IEEE.

关键词： Blockchain Cryptocurrency Internet of Things privacy Security

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Beyond Fixed Variables: Expanding-variate Time Series Forecasting via Flat Scheme and Spatio-temporal Focal Learning

arXiv

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arXiv 2025年

作者： Ma, Minbo Tang, Kai Li, Huan Teng, Fei Zhang, Dalin Li, Tianrui School of Computing and Artificial Intelligence Southwest Jiaotong University Chengdu China School of Data Science and Engineering Zhejiang University Hangzhou China Hangzhou Dianzi University Hangzhou China

Multivariate Time Series Forecasting (MTSF) has long been a key research focus. Traditionally, these studies assume a fixed number of variables, but in real-world applications, Cyber-Physical Systems often expand as new sensors are deployed, increasing variables in MTSF. In light of this, we introduce a novel task, Expanding-variate Time Series Forecasting (EVTSF). This task presents unique challenges, specifically (1) handling inconsistent data shapes caused by adding new variables, and (2) addressing imbalanced spatiotemporal learning, where expanding variables have limited observed data due to the necessity for timely operation. To address these challenges, we propose STEV, a flexible spatio-temporal forecasting framework. STEV includes a new Flat Scheme to tackle the inconsistent data shape issue, which extends the graph-based spatio-temporal modeling architecture into 1D space by flattening the 2D samples along the variable dimension, making the model variable-scale-agnostic while still preserving dynamic spatial correlations through a holistic graph. Additionally, we introduce a novel Spatio-temporal Focal Learning strategy that incorporates a negative filter to resolve potential conflicts between contrastive learning and graph representation, and a focal contrastive loss as its core to guide the framework to focus on optimizing the expanding variables. To evaluate the effectiveness of STEV, we benchmark EVTSF performance on three real-world datasets from various domains and compare it against three potential solutions employing state-of-the-art (SOTA) MTSF models tailored for EVSTF. Experimental results show that STEV significantly outperforms its competitors, especially in handling expanding variables. Notably, STEV, with only 5% of observations during the expanding period, is on par with SOTA MTSF models trained with complete data. Further exploration of various expanding scenarios underscores the generalizability of STEV in real-world applications. © 2025,

关键词： Spatio-temporal data

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Spatio-Temporal Machine Learning Approaches for Bus Travel Time Prediction

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Procedia Computer Science 2025年 257卷 553-560页

作者： Gokull Subramanian Aditya Verma Lelitha Vanajakshi Wadhwani School of Data Science & AI Indian Institute of Technology Madras India School of Civil Engineering Vellore Institute of Technology India Professor Department of Civil Engineering Indian Institute of Technology Madras India Robert Bosch Centre for Data Science and Artificial Intelligence (RBCDSAI) Indian Institute of Technology Madras India

Predicting bus travel times is critical for enhancing passenger satisfaction, optimizing route planning, and improving overall system efficiency. This study explores spatio-temporal modeling techniques to predict travel times for a bus route in Chennai, India, leveraging spatial dependencies across route sections and temporal variations in travel conditions. Machine learning algorithms, including Weighted K-Nearest Neighbors (KNN) and clustering-based approaches, were compared with deep learning methods like Recurrent Neural Networks (RNN). The models incorporated handcrafted spatio-temporal features and real-time data to improve prediction accuracy. The results reveal that machine learning models with spatio-temporal feature engineering performed competitively with deep learning models utilizing real-time data. The study presents the strengths and limitations of each approach, offering insights into effective spatio-temporal modeling for bus travel time prediction.

关键词： travel time prediction KNN clustering RNN spatio-temporal modeling

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Magnetic Levitation Control Using Deep Reinforcement Learning and Prioritized Experience Replay with Action Noise Variations 4

Magnetic Levitation Control Using Deep Reinforcement Learnin...

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4th International Conference on Power, Control and Computing Technologies, ICPC2T 2025

作者： Pare, Gopala Shil, Manas Pillai, G.N. Dey, Arnab Department of Electrical Engineering Indian Institute of Technology Roorkee India Mehta Family School of Data Science and Artificial Intelligence Indian Institute of Technology Roorkee India

ISBN: (纸本)9798331530402

In recent years, Reinforcement Learning (RL) has emerged as a promising approach for addressing control problems in complex, nonlinear environments. The ability of RL algorithms to autonomously learn optimal control policies without requiring an explicit system model makes them particularly suited for real-time applications in fields like robotics and industrial automation. This paper investigates the application of RL algorithms for controlling a magnetic levitation (maglev) system, which presents significant challenges due to its nonlinear dynamics. The Twin Delayed Deep Deterministic Policy Gradient (TD3) algorithm and the integration of Prioritized Experience Replay (PER) are employed to enhance learning efficiency. The impact of varying action noise (random perturbations added to actions) for exploration and system performance is explored;results demonstrate that TD3 with PER achieves superior stability, faster convergence, and reduced rise and settling times without overshoot. These findings emphasize the importance of tailored exploration strategies in optimizing control policies for complex environments, highlighting the potential of advanced RL techniques for real-time control applications. © 2025 IEEE.

关键词： Reinforcement learning

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Leveraging Machine Learning to Assess Factors Influencing Undernourishment in Nigeria 1st

Leveraging Machine Learning to Assess Factors Influencing Un...

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1st International Conference on Intelligent Systems, Blockchain, and Communication Technologies, ISBCom 2024

作者： Mobark, Nada Elfiky, Noha Ahmedin, Ahmed Ibrahim, Ameera Abdel-Aziz, M. Hamad, Safwat Faculty of Computers and Information South Valley University Qena83523 Egypt Business Analytics and Data Science Department School of Economics and Business Administration Saint Mary’s College of California MoragaCA94575 United States Department of Artificial Intelligence Engineering Faculty of Computer Science and Engineering King Salman International University South Sinai46511 Egypt

ISBN: (纸本)9783031823763

Notwithstanding economic progress, Nigeria continues to countenance significant food security issues. This study investigates the root causes of this problem and attempts to develop predictive models to address hunger in Nigeria. The research covering data from 2000 to 2022, sourced primarily from the World Bank and other institutions. The trend analysis, along with the linear model, indicates that investment in farming is crucial for reducing undernourishment. Additionally, there is a strong relationship between agricultural expenditure, exports, access to electricity, and unemployment in relation to undernourishment. In this paper, a linear model, correlation analysis, ARIMA, and trend analysis are utilized to demonstrate how undernourishment, representing hunger and food insecurity, is influenced by other factors. As a result, it is recommended to maintain the unemployment rate between 3.5% and 3.7%, while ensuring that exceeds 5% of government spending is recommended. In other words, to combat hunger in Nigeria, it is important to focus on investing in agriculture, creating jobs, and improving infrastructure. This aligns with our findings and with another research around the world. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： ARIMA Nigeria predictive model Undernourishment

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Phenotype and Genotype Based Sample Aware Surrogate-Assisted Genetic Programming in Dynamic Flexible Job Shop Scheduling

IEEE Transactions on Artificial Intelligence

引用

IEEE Transactions on artificial intelligence 2025年

作者： Zhu, Luyao Zhang, Fangfang Zhu, Xiaodong Chen, Ke Zhang, Mengjie Zhengzhou University School of Electrical and Information Engineering Zhengzhou450001 China Victoria University of Wellington Centre for Data Science and Artificial Intelligence School of Engineering and Computer Science Wellington6140 New Zealand Longmen Laboratory Luoyang471000 China

Genetic programming (GP) has been widely applied to evolve scheduling heuristics for dynamic flexible job shop scheduling (DFJSS). However, the evaluation of GP individuals is computationally expensive, especially in large scale DFJSS scenarios. A k-nearest neighbor (KNN) based surrogate has been successfully used to reduce individual evaluation time for GP by predicting the fitness of an individual with the most similar sample in KNN. Particularly, the phenotypes of GP individuals have been utilised to generate samples for KNN-based surrogates with a precondition that the fitness of individuals with the same phenotype is the same or similar. However, their real fitness may differ greatly due to different input decision situations for fitness calculations in DFJSS. Thus, only considering phenotypes of GP individuals to extract samples could decrease the accuracy of KNN surrogates. This paper proposes a KNN-based surrogate assisted GP algorithm by considering both the phenotype and genotype of GP individuals to generate samples. Specifically, a genotypic characterisation based on terminal frequency is designed to measure the similarity of individual genotypes. The results show that with the same training time, the proposed algorithm can converge fast and achieve better scheduling heuristics than the state-of-the-art algorithms in most examined scenarios. With the same number of generations, the proposed algorithm can obtain comparable performance but only needs about one third training time of baseline GP. The effectiveness of the proposed algorithm is also verified from different aspects, e.g., relation between genotype correlation and fitness difference of individuals, and population diversity. © 2025 IEEE.

关键词： Genetic programming

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A Survey on Evolutionary Feature Selection in Multi-Label Classification

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IEEE Transactions on Evolutionary Computation 2025年

作者： Hancer, Emrah Xue, Bing Zhang, Mengjie Victoria University of Wellington Center for Data Science and Artificial Intelligence School of Engineering and Computer Science Wellington6140 New Zealand Burdur Mehmet Akif Ersoy University Department of Software Engineering Burdur15039 Turkey

Multi-label classification (MLC) involves assigning multiple labels to each instance from a predefined set of labels. With the increasing prevalence of multi-label datasets in real-world problems, MLC has become a popular area of research. These datasets frequently contain irrelevant, redundant, or noisy features, highlighting the importance of feature selection in MLC tasks. As a result, numerous multi-label feature selection (MLFS) approaches have been introduced in the literature. Given their effective search capabilities, evolutionary computation (EC) techniques have been adopted to develop MLFS approaches. However, there is a notable absence of a comprehensive survey dedicated to EC-based MLFS approaches to MLC tasks. Although few attempts have been made to address this gap, they do not provide detailed discussions on EC-based approaches. They also tend to overlook the strengths and weaknesses of existing approaches, especially regarding search, optimization, and evaluation processes. This paper aims to fill this gap by presenting a comprehensive survey of EC-based MLFS approaches, focusing on the latest advancements, current challenges, and future directions. To be specific, we categorize EC-based MLFS approaches based on different criteria and provide detailed descriptions for each category. © 1997-2012 IEEE.

关键词： Evolutionary Computation Multi-Label Classification Multi-Label Feature Selection Multi-Objective Optimization

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Advancing Multi-Party Dialogue Systems with Speaker-ware Contrastive Learning

arXiv

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arXiv 2025年

作者： Hu, Zhongtian Cui, Yiwen Li, Ronghan Zhao, Meng Wang, Lifang School of Computer Science and Engineering Northwestern Polytechnical University China School of Computer Science and Technology Xidian University China School of Artificial Intelligence and Big Data Henan University of Technology China

Dialogue response generation has made significant progress, but most research has focused on dyadic dialogue. In contrast, multi-party dialogues involve more participants, each potentially discussing different topics, making the task more complex. Current methods often rely on graph neural networks to model dialogue context, which helps capture the structural dynamics of multi-party conversations. However, these methods are heavily dependent on intricate graph structures and dataset annotations, and they often overlook the distinct speaking styles of participants. To address these challenges, we propose CMR, a Contrastive learning-based Multi-party dialogue Response generation model. CMR uses self-supervised contrastive learning to better distinguish "who says what." Additionally, by comparing speakers within the same conversation, the model captures differences in speaking styles and thematic transitions. To the best of our knowledge, this is the first approach to apply contrastive learning in multi-party dialogue generation. Experimental results show that CMR significantly outperforms state-of-the-art models in multiparty dialogue response tasks. © 2025, CC BY.

关键词： Self-supervised learning

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Can xLLMs Understand the Structure of Dialog? Exploring Multilingual Response Generation in Complex Scenarios

arXiv

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arXiv 2025年

Multilingual research has garnered increasing attention, especially in the domain of dialogue systems. The rapid advancements in large language models (LLMs) have fueled the demand for high-performing multilingual models. However, two major challenges persist: the scarcity of high-quality multilingual datasets and the limited complexity of existing datasets in capturing realistic dialogue scenarios. To address these gaps, we introduce XMP, a high-quality parallel Multilingual(X) dataset sourced from Multi-party Podcast dialogues1. Each sample in the dataset features at least three participants discussing a wide range of topics, including society, culture, politics, and entertainment. Through extensive experiments, we uncover significant limitations in previously recognized multilingual capabilities of LLMs when applied to such complex dialogue scenarios. For instance, the widely accepted multilingual complementary ability of LLMs is notably impacted. By conducting further experiments, we explore the mechanisms of LLMs in multilingual environments from multiple perspectives, shedding new light on their performance in real-world, diverse conversational contexts. © 2025, CC BY.

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Gold Price Forecasting Using Machine Learning Techniques 1st

Gold Price Forecasting Using Machine Learning Techniques

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1st International Research Conference on Computing Technologies for Sustainable Development, IRCCTSD 2024

作者： John, Binu Nidhina, K. Artificial Intelligence and Data Science SCMS School of Engineering and Technology Vidya Nagar Kerala Cochin683572 India Computer Science and Engineering SCMS School of Engineering and Technology Vidya Nagar Kerala Cochin683572 India

ISBN: (纸本)9783031823824

This project aims to create an advanced machine learning system for accurate gold price prediction in response to the growing importance of gold prices in reflecting sentiment in the global economy and helping informed decision-making in international trade. By utilizing state-of-the-art machine learning techniques such as random forest regression, decision trees, and support vector machines in addition to large historical datasets of market indices and related commodities, the project seeks to identify complex patterns and trends influencing the dynamics of gold prices. From a methodological standpoint, the research focuses on optimizing both predictive accuracy and generalizability through extensive data preprocessing, exploratory analysis, and model training. Essential metrics like the root mean square error (RMSE) and R2 score are used in performance evaluation. The results show how well the random forest regression model outperforms the other two techniques with an R2 score of 0.990 and a low RMSE, demonstrating remarkable accuracy and reliability. The project’s goal is to provide organizations with practical insights for proactive risk management and well-informed decision-making in the dynamic world of international commerce and finance by combining state-of-the-art technology with empirical research. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Support vector regression

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