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Machine learning and data augmentation in the proxy means test for poverty targeting

Statistical Journal of the IAOS 2023年第4期39卷 961-977页

作者： Wobcke, Wayne Mariyah, Siti School of Computer Science and Engineering UNSW Data Science Hub (uDASH) University of New South Wales Sydney NSW Australia

Recent years have seen increased interest in the use of alternative data sources in the definition and production of official statistics and indicators for the UN Sustainable Development Goals. In this paper, we consider the application of data science to the production of official statistics, illustrating our perspective through the use of poverty targeting as an application. We show that machine learning can play a central role in the generation of official statistics, combining a variety of types of data (survey, administrative and alternative). We focus on the problem of poverty targeting using the Proxy Means Test in Indonesia, comparing a number of existing statistical and machine learning methods, then introducing new approaches in the spirit of small area estimation that utilize area-level features and data augmentation at the subdistrict level to develop more refined models at the district level, evaluating the methods on three districts in Indonesia on the problem of estimating 2020 per capita household expenditure using data from 2016-2019. The best performing method, XGBoost, is able to reduce inclusion/exclusion errors on the problem of identifying the poorest 40% of the population in comparison to the commonly used Ridge Regression method by between 4.5% and 13.9% in the districts studied. © 2023 - IOS Press. All rights reserved.

关键词： data augmentation machine learning Poverty targeting

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A Comprehensive Exploration of Real-Time 3-D View Reconstruction Methods

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2024年第12期5卷 5915-5927页

作者： Agrawal, Arya Sharma, Teena Verma, Nishchal K. Vellore Institute of Technology School of Computer Science and Engineering Vellore632014 India Indian Institute of Technology Mehta Family School of Data Science and Artificial Intelligence Guwahati781039 India Indian Institute of Technology Kanpur Department of Electrical Engineering 208016 India

Real-time 3-D view reconstruction in an unfamiliar environment poses complexity for various applications due to varying conditions such as occlusion, latency, precision, etc. This article thoroughly examines and tests contemporary methodologies addressing challenges in 3-D view reconstruction. The methods being explored in this article are categorized into volumetric and mesh, generative adversarial network based, and open source library based methods. The exploration of these methods undergoes detailed discussions, encompassing methods, advantages, limitations, and empirical results. The real-time testing of each method is done on benchmarked datasets, including ShapeNet, Pascal 3D+, Pix3D, etc. The narrative highlights the crucial role of 3-D view reconstruction in domains such as robotics, virtual and augmented reality, medical imaging, cultural heritage preservation, etc. The article also anticipates future scopes by exploring generative models, unsupervised learning, and advanced sensor fusion to increase the robustness of the algorithms. © 2020 IEEE.

关键词： Generative adversarial networks

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VaeDiff-DocRE: End-to-end data Augmentation Framework for Document-level Relation Extraction 31

VaeDiff-DocRE: End-to-end Data Augmentation Framework for Do...

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31st International Conference on Computational Linguistics, COLING 2025

作者： Tran, Khai Phan Hua, Wen Li, Xue School of Electrical Engineering and Computer Science The University of Queensland Australia Department of Data Science and Artificial Intelligence The Hong Kong Polytechnic University Hong Kong

ISBN: (纸本)9798891761964

Document-level Relation Extraction (DocRE) aims to identify relationships between entity pairs within a document. However, most existing methods assume a uniform label distribution, resulting in suboptimal performance on real-world, imbalanced datasets. To tackle this challenge, we propose a novel data augmentation approach using generative models to enhance data from the embedding space. Our method leverages the Variational Autoencoder (VAE) architecture to capture all relation-wise distributions formed by entity pair representations and augment data for underrepresented relations. To better capture the multi-label nature of DocRE, we parameterize the VAE's latent space with a Diffusion Model. Additionally, we introduce a hierarchical training framework to integrate the proposed VAE-based augmentation module into DocRE systems. Experiments on two benchmark datasets demonstrate that our method outperforms state-of-the-art models, effectively addressing the long-tail distribution problem in DocRE. Our code is released at: https://***/khaitran22/VaeDiff-DocRE. © 2025 Association for Computational Linguistics.

关键词： Computational linguistics

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AI Powered Chatbot For Mental Health Treatment 1

AI Powered Chatbot For Mental Health Treatment

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1st International Conference on Technological Innovations and Advance Computing, TIACOMP 2024

作者： Jayabhaduri, R. Vijayaraghavan, Aadesh Ajay Karthik, R. Ceralaathan, G. Sai Sailesh, S. Alliance University Computer Science and Engineering Bengaluru562106 India Sri Venkateswara College of Engineering Artificial Intelligence and Data Science Taluk Sriperumbudur602117 India

ISBN: (纸本)9798350392111

In the recent days, most people of all ages have their mental health affected by various factors like stress, anxiety, depression, fear, phobia and trauma. Hence it is mandatory for people to take care of their mental health. People may hesitate to approach therapists in real life due to societal stigmas. As many people are unable to access or afford mental health services, our research work aims at developing a SAAC Mental health chatbot to mimic a therapist to provide personalized support and guidance, learn about an individual's unique needs and preferences, and tailor their responses accordingly with 24/7 support by handling speech and text queries. SAAC chatbot also maintains chat history and notifies users by sending alerts to ensure follow-ups. © 2024 IEEE.

关键词： Anxiety Chatbot Depression Fear Mental Health Phobia Speech Therapist Trauma

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Millibots: Integrating Deep Learning Algorithms for Autonomous Control in Small-Scale Robotics 15

Millibots: Integrating Deep Learning Algorithms for Autonomo...

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15th International Conference on Computing Communication and Networking Technologies, ICCCNT 2024

作者： Sneha, A. Angel Diwan, B. Stella Maris College Of Engineering Artificial Intelligence and Data Science Department Nagercoil India Joesphs College Of Engineering Department of Computer Science St Chennai India

ISBN: (纸本)9798350370249

Millibots, miniature robotic platforms, have emerged as pivotal tools in various domains, ranging from medical interventions to environmental monitoring. However, their diminutive size presents formidable challenges in achieving autonomous navigation and control. In this paper, we delve into the realm of millibots and propose a groundbreaking approach to tackle these challenges by integrating deep learning algorithms for autonomous control. Leveraging the power of deep learning, we address the intricate interplay between perception, decision-making, and locomotion in small-scale robotics. Our methodology not only overcomes the limitations inherent to millibots but also unlocks new frontiers in their application. Through a comprehensive review of related work, meticulous experimentation, and insightful analysis, we demonstrate the unparalleled efficacy of our approach. This paper not only accelerates the advancement of small-scale robotics but also envisions a new era of autonomous exploration and innovation. © 2024 IEEE.

关键词： Motion planning

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Unveiling Consumer Segmentation: Harnessing K-means Clustering Using Elbow and Silhouette for Precise Targeting 3rd

Unveiling Consumer Segmentation: Harnessing K-means Clusteri...

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3rd International Conference on Computing and Communication Networks, ICCCN 2023

作者： Saraswat, Shweta Agrohi, Vaibhav Kumar, Mahesh Lamba, Monica Kaur, Raminder Department of Artificial Intelligence and Data Science Department Arya College of Engineering Rajasthan Jaipur India Department of Computer Science and Engineering Department Arya College of Engineering Rajasthan Jaipur India Department of Computer Science and Engineering Department Graphics Era Deemed to Be University Dehradun India Department of Computer Science and Engineering Department JECRC University Jaipur India

ISBN: (纸本)9789819708918

Consumer segmentation is essential for accurate targeting and successful marketing efforts in today’s competitive business environment. Modern marketing groups individuals by interests and attributes. Segmentation drives targeting, personalization, and ROI. This article segments customers using K-means clustering. The Elbow approach and Silhouette score determine the appropriate number of clusters and increase segmentation accuracy. They also examine the possibility of precision targeting and customized marketing techniques across sectors. Businesses may optimize marketing, improve customer happiness, and increase profits by using K-means clustering. To compete in today’s market, this research helps marketers enhance targeting. Elbow and Silhouette K-means clustering may enhance client segmentation, engagement, loyalty, and economic success. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： K-means clustering

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A Hybrid Relational Approach Toward Stock Price Prediction and Profitability

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2024年第11期5卷 5844-5854页

作者： Patel, Manali Jariwala, Krupa Chattopadhyay, Chiranjoy Sardar Vallabhbhai National Institute of Technology Department of Computer Science and Engineering Surat395007 India FLAME University School of Computing and Data Sciences Pune412115 India

An accurate estimation of future stock prices can help investors maximize their profits. The current advancements in the area of artificial intelligence (AI) have proven prevalent in the financial sector. Besides, stock market prediction is difficult owing to the considerable volatility and unpredictability induced by numerous factors. Recent approaches have considered fundamental, technical, or macroeconomic variables to find hidden complex patterns in financial data. At the macro level, there exists a spillover effect between stock pairs that can explain the variance present in the data and boost the prediction performance. To address this interconnectedness defined by intrasector stocks, we propose a hybrid relational approach to predict the future price of stocks in the American, Indian, and Korean economies. We collected market data of large-, mid-, and small-capitalization peer companies in the same industry as the target firm, considering them as relational features. To ensure efficient feature selection, we have utilized a data-driven approach, i.e., random forest feature permutation (RF2P), to remove noise and instability. A hybrid prediction module consisting of temporal convolution and linear model (TCLM) is proposed that considers irregularities and linear trend components of the financial data. We found that RF2P-TCLM gave the superior performance. To demonstrate the real-world applicability of our approach in terms of profitability, we created a trading method based on the predicted results. This technique generates a higher profit than the existing approaches. © 2024 IEEE.

关键词： Profitability

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Secure-JobChain: Revolutionizing Job Recruitment with Blockchain Technology

Secure-JobChain: Revolutionizing Job Recruitment with Blockc...

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2024 IEEE International Conference on Intelligent Systems and Advanced Applications, ICISAA 2024

作者： Pavithra, S. Geethanjali, S. Vidya, B. Suria, S. Rahul, B.S. Kishore, R. Computer Science and Engineering Rajiv Gandhi College of Engineering and Technology Pondicherry India SRM Institute of Science and Technology Data Science and Business Systems Kattankulathur India SRM Institute of Science and Technology Electronics and Communication Engineering Kattankulathur India

ISBN: (纸本)9798331539948

The traditional job recruitment process is time consuming and also undergoes certain difficulties such as data privacy, lack of transparency, and inefficient credential verification. These difficulties lead to mistrust and also delay the process of hiring. There is a need for improvement in this process that could benefit both the companies and the job applicants. In this paper, a decentralized recruitment platform based on blockchain technology is proposed. This proposed technique will overcome the above-mentioned difficulties by ensuring secure, transparent, and efficient connections between the recruitment companies and the prospective candidates. This solution uses the merits of blockchain i.e., immutability and automation to record job postings, applications, and hiring decisions on an immutable ledger, protecting personal data with cryptographic techniques and automating qualification verification with smart contracts. By implementing the decentralization concepts in Job recruitment, it is proven that the transparency in the interview process is improved by 9 0 and fraud risks are reduced considerably. Moreover, the efficiency of the entire job recruitment process is improved largely. Thus, the decentralized solution proposed in this paper demonstrates its potential in revolutionizing the job recruitment process by providing a secure, transparent and efficient hiring ecosystem. © 2024 IEEE.

关键词： Smart contract

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ENERGY EFFICIENT SECURITY PROTOCOL FOR HEALTHCARE INTERNET OF THINGS

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Telecommunications and Radio engineering (English translation of Elektrosvyaz and Radiotekhnika) 2024年第12期83卷 65-78页

作者： Babu, Ravi Mahesh Malluvalasa, S.N. Lakshmi Devi, M. Kalpana Karthik, Sagirala Satya Rakesh, M. Prasad, Satamraju Krishna 522508 India Department of Artificial Intelligence and Data Science Koneru Lakshmaiah Education Foundation Green Fields Andhra Pradesh Vaddeswaram522302 India Department of Electronics and Communication Engineering Department of Computer Science and Engineering Newton’s Institute of Engineering Guntur Andhra Pradesh Macherla India

The Energy Efficient Security for Internet of Things (EESP-IoT) protocol aims to achieve a harmonious equilibrium between energy preservation, security, and network optimization within the ever-changing domain of the internet of things (IoT). EESP-IoT coordinates the actions of IoT devices where each device independently optimizes its energy usage throughout the network to ensure stability and equity. Adaptive techniques such as power optimization, alternating between sleep and wake modes, and energy harvesting reduce energy inefficiency, while assuring security by using lightweight specifically designed cryptographic techniques. By including energy and security considerations, the protocol achieves both efficiency and resilience against a range of security threats. EESP-IoT exhibited exceptional energy conservation skills, while maintaining comparable levels of throughput and latency. The protocol underwent testing in a simulated environment to assess its ability to handle a large number of devices and its effectiveness in various network circumstances. The findings confirmed that EESP-IoT is capable of maintaining a high degree of network efficiency and strong security procedures, while considerably prolonging the lifespan of IoT devices. © 2024 by Begell House, Inc.

关键词： Energy utilization

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A Bioinspired Method for Optimal Task Scheduling in Fog-Cloud Environment

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computer Modeling in engineering & sciences 2025年第3期142卷 2691-2724页

作者： Ferzat Anka Ghanshyam G.Tejani Sunil Kumar Sharma Mohammed Baljon Data Science Application and Research Center(VEBIM) Fatih Sultan Mehmet Vakif UniversityIstanbul34445Turkiye Department of Industrial Engineering and Management Yuan Ze UniversityTaoyuan320315Taiwan Applied Science Research Center Applied Science Private UniversityAmman11937Jordan Department of Information System College of Computer and Information SciencesMajmaah UniversityMajmaah11952Saudi Arabia Department of Computer Engineering College of Computer and Information SciencesMajmaah UniversityMajmaah11952Saudi Arabia

Due to the intense data flow in expanding Internet of Things(IoT)applications,a heavy processing cost and workload on the fog-cloud side become *** of the most critical challenges is optimal task *** this is an NP-hard problem type,a metaheuristic approach can be a good *** study introduces a novel enhancement to the Artificial Rabbits Optimization(ARO)algorithm by integrating Chaotic maps and Levy flight strategies(CLARO).This dual approach addresses the limitations of standard ARO in terms of population diversity and convergence *** is designed for task scheduling in fog-cloud environments,optimizing energy consumption,makespan,and execution time simultaneously three critical parameters often treated individually in prior *** conventional single-objective methods,the proposed approach incorporates a multi-objective fitness function that dynamically adjusts the weight of each parameter,resulting in better resource allocation and load *** analysis,a real-world dataset,the Open-source Google Cloud Jobs dataset(GoCJ_dataset),is used for performance measurement,and analyses are performed on three considered *** are applied with well-known algorithms:GWO,SCSO,PSO,WOA,and ARO to indicate the reliability of the proposed *** this regard,performance evaluation is performed by assigning these tasks to Virtual Machines(VMs)in the resource *** are performed on 90 base cases and 30 scenarios for each evaluation *** results indicated that the proposed algorithm achieved the best makespan performance in 80% of cases,ranked first in execution time in 61%of cases,and performed best in the final parameter in 69% of *** addition,according to the obtained results based on the defined fitness function,the proposed method(CLARO)is 2.52%better than ARO,3.95%better than SCSO,5.06%better than GWO,8.15%better than PSO,and 9.41%better than WOA.

关键词： Improved ARO fog computing task scheduling GoCJ_dataset chaotic map levy flight

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