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BMWP: the first Bengali math word problems dataset for operation prediction and solving

Discover Artificial Intelligence 2025年第1期5卷 1-15页

作者： Mondal, Sanchita Khatua, Debnarayan Mandal, Sourav Prasad, Dilip K. Sekh, Arif Ahmed School of Medical Science and Technology IIT Kharagpur West Bengal Kharagpur721302 India Department of Mathematics and Statistics Vignan’s Foundation for Science Technology and Research Andhra Pradesh Guntur512213 India School of Computer Science and Engineering XIM University Odisha Puri752050 India Department of Computer Science Uit the Arctic University of Norway Tromsø9017 Norway

Solving math word problems of varying complexities is one of the most challenging and exciting research questions in artificial intelligence (AI), particularly in natural language processing (NLP) and machine learning (ML). Foundational language models such as GPT must be evaluated for intelligence, and solving word problems is a key method for this assessment. These problems become especially difficult when presented in low-resource regional languages such as Bengali. Word problem solving integrates the cognitive domains of language processing, comprehension, and transformation into real-world solutions. During the past decade, advances in AI and machine learning have significantly progressed in addressing this complex issue. Although researchers worldwide have primarily utilized datasets in English and some in Chinese, there has been a lack of standard datasets for low-resource languages such as Bengali. In this pioneering study, we introduce the first Bengali Math Word Problem Benchmark Data Set (BMWP), comprising 8653 word problems. We detail the creation of this dataset and the benchmarking methods employed. Furthermore, we investigate operation prediction from Bengali word problems using state-of-the-art deep learning (DL) techniques. We implemented and compared various standard DL-based neural network architectures, achieving an accuracy of 92±2%. The data set and the code will be available at https://***/SanchitaMondal/BMWP. © The Author(s) 2025.

关键词： Recurrent neural networks

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An Empirical Study on Automated Test Generation Tools for Java:Effectiveness and Challenges

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Journal of computer science & technology 2024年第3期39卷 715-736页

作者：刘相君余萍马晓星 State Key Laboratory for Novel Software Technology Nanjing UniversityNanjing 210023China Department of Computer Science and Technology Nanjing UniversityNanjing 210023China CCF ACM IEEE

Automated test generation tools enable test automation and further alleviate the low efficiency caused by writing hand-crafted test ***,existing automated tools are not mature enough to be widely used by software testing *** paper conducts an empirical study on the state-of-the-art automated tools for Java,i.e.,EvoSuite,Randoop,JDoop,JTeXpert,T3,and *** design a test workflow to facilitate the process,which can automatically run tools for test generation,collect data,and evaluate various ***,we conduct empirical analysis on these six tools and their related techniques from different aspects,i.e.,code coverage,mutation score,test suite size,readability,and real fault detection *** discuss about the benefits and drawbacks of hybrid techniques based on experimental ***,we introduce our experience in setting up and executing these tools,and summarize their usability and ***,we give some insights into automated tools in terms of test suite readability improvement,meaningful assertion generation,test suite reduction for random testing tools,and symbolic execution integration.

关键词： automated test generation search-based software testing random testing symbolic execution

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Estimating the State of Health for Lithium-ion Batteries:A Particle Swarm Optimization-Assisted Deep Domain Adaptation Approach

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IEEE/CAA Journal of Automatica Sinica 2023年第7期10卷 1530-1543页

作者： Guijun Ma Zidong Wang Weibo Liu Jingzhong Fang Yong Zhang Han Ding Ye Yuan The School of Mechanical Science and Engineering Huazhong University of Science and TechnologyWuhan 430074China the College of Electrical Engineering and Automation Shandong University of Science and TechnologyQingdao 266590China the Department of Computer Science Brunel University LondonUxbridgeMiddlesexUB83PHUnited Kingdom the School of Information Science and Engineering Wuhan University of Science and TechnologyWuhan 430081China the School of Artificial Intelligence and Automation Huazhong University of Science and TechnologyWuhan 430074China

The state of health(SOH)is a critical factor in evaluating the performance of the lithium-ion batteries(LIBs).Due to various end-user behaviors,the LIBs exhibit different degradation modes,which makes it challenging to estimate the SOHs in a personalized *** this article,we present a novel particle swarm optimization-assisted deep domain adaptation(PSO-DDA)method to estimate the SOH of LIBs in a personalized manner,where a new domain adaptation strategy is put forward to reduce cross-domain distribution *** standard PSO algorithm is exploited to automatically adjust the chosen hyperparameters of developed DDA-based *** proposed PSODDA method is validated by extensive experiments on two LIB datasets with different battery chemistry materials,ambient temperatures and charge-discharge *** results indicate that the proposed PSO-DDA method surpasses the convolutional neural network-based method and the standard DDA-based *** Py Torch implementation of the proposed PSO-DDA method is available at https://***/mxt0607/PSO-DDA.

关键词： Deep transfer learning domain adaptation hyperparameter selection lithium-ion batteries(LIBs) particle swarm optimization state of health estimation(SOH)

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A Spectral Convolutional Neural Network Model Based on Adaptive Fick’s Law for Hyperspectral Image Classification

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computers, Materials & Continua 2024年第4期79卷 19-46页

作者： Tsu-Yang Wu Haonan Li Saru Kumari Chien-Ming Chen School of Artificial Intelligence(School of Future Technology) Nanjing University of Information Science&TechnologyNanjing210044China College of Computer Science and Engineering Shandong University of Science and TechnologyQingdao266590China Department of Mathematics Chaudhary Charan Singh UniversityMeerutUttar Pradesh250004India

Hyperspectral image classification stands as a pivotal task within the field of remote sensing,yet achieving highprecision classification remains a significant *** response to this challenge,a Spectral Convolutional Neural Network model based on Adaptive Fick’s Law Algorithm(AFLA-SCNN)is *** Adaptive Fick’s Law Algorithm(AFLA)constitutes a novel metaheuristic algorithm introduced herein,encompassing three new strategies:Adaptive weight factor,Gaussian mutation,and probability update *** adaptive weight factor,the algorithmcan adjust theweights according to the change in the number of iterations to improve the performance of the *** helps the algorithm avoid falling into local optimal solutions and improves the searchability of the *** probability update strategy helps to improve the exploitability and adaptability of the *** the AFLA-SCNN model,AFLA is employed to optimize two hyperparameters in the SCNN model,namely,“numEpochs”and“miniBatchSize”,to attain their optimal ***’s performance is initially validated across 28 functions in 10D,30D,and 50D for CEC2013 and 29 functions in 10D,30D,and 50D for *** results indicate AFLA’s marked performance superiority over nine other prominent optimization ***,the AFLA-SCNN model was compared with the Spectral Convolutional Neural Network model based on Fick’s Law Algorithm(FLA-SCNN),Spectral Convolutional Neural Network model based on Harris Hawks Optimization(HHO-SCNN),Spectral Convolutional Neural Network model based onDifferential Evolution(DE-SCNN),SpectralConvolutionalNeuralNetwork(SCNN)model,and SupportVector Machines(SVM)model using the Indian Pines dataset and Paviauniversity *** experimental results show that the AFLA-SCNN model outperforms other models in terms of Accuracy,Precision,Recall,and F1-score on Indian Pines and Pavia *** them,the Accuracy of the AFLA-SCNN model on India

关键词： Adaptive Fick’s law algorithm spectral convolutional neural network metaheuristic algorithm intelligent optimization algorithm hyperspectral image classification

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A novel method for indian vehicle registration number plate detection and recognition using CNN

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Journal of Intelligent and Fuzzy Systems 2024年第4期46卷 8563-8585页

作者： Pandey, Vibha Choubey, Siddhartha Patra, Jyotiprakash Mall, Shachi Choubey, Abha Department of Computer Science Engineering Shri Shankaracharya Technical Campus Chhattisgarh Bhilai India Department of Computer Science Engineering Shri Shankaracharya Institute of Professional Management and Technology Chhattisgarh Raipur India School of Computer Science and Engineering Galgotias University Greater Noida India

Automated reading of license plate and its detection is a crucial component of the competent transportation system. Toll payment and parking management e-payment systems may benefit from this software’s features. License plate detection and identification algorithms abound, and each has its own set of strengths and weaknesses. computer vision has advanced rapidly in terms of new breakthroughs and techniques thanks to the emergence and proliferation of deep learning principles across several branches of AI. The practice of automating the monitoring process in traffic management, parking management, and police surveillance has become much more effective thanks to the development of Automatic License Plate Recognition (ALPR). Even though license plate recognition (LPR) is a technology that is extensively utilized and has been developed, there is still a significant amount of work to be done before it can achieve its full potential. In the last several years, there have been substantial advancements in both the scientific community’s methodology and its level of efficiency. In this era of deep learning, there have been numerous developments and techniques established for LPR, and the purpose of this research is to review and examine those developments and approaches. In light of this, the authors of this study suggest a four-stage technique to automated license plate detection and identification (ALPDR), which includes, image pre-processing, license plate extraction, character segmentation, and character recognition. And the first three phases are known as "extraction," "preprocessing," and "segmentation," and each of these processes has been shown to benefit from its own unique technique. In light of the fact that character recognition is an essential component of license plate identification and detection, the Convolution Neural Network (CNN), MobileNet, Inception V3, and ResNet 50 have all been put through their paces in this regard. © 2024 – IOS Press. All rights re

关键词： Image classification

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Predicting Academic Procrastination Among university Students Using Autoencoder Model 27

Predicting Academic Procrastination Among University Student...

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27th International Conference on Advanced Communications technology, ICACT 2025

作者： Sackey, Anthony Kingsley Appiah, Benjamin Adu, Adolph Sedem Yaw Baah, Emmanuel Kwesi Boamah, Eric Nana Agyekum Department of Computer Science Ho Technical University Ho Ghana Department of Computer Science Lancaster University Ghana Department of Computer Science Kwame Nkrumah University o fScience & Technology Kumasi Ghana

ISBN: (纸本)9791188428137

university students engaged in online learning often exhibit high levels of academic procrastination. Thus, various studies have investigated this phenomenon to provide solutions to mitigate its impact on academic work. However, these studies largely treated procrastination as a predictor of various outcomes, while other studies that treated procrastination as an outcome annotated the dataset manually. Investigating academic procrastination as an outcome gives a detailed insight to enable effective intervention for students, while manual annotation can be time-consuming and costly with large datasets. Other studies have also used machine learning algorithms to learn on clickstream, demographic, and assessment data to predict procrastination. The results of these studies show that they can be improved using other machine learning algorithms. This study, therefore, uses autoencoder, an artificial neural network, to uncover procrastination patterns ("no procrastinate", "likelihood", "procrastinate") among students’ based on their clickstream, demographics, assessment, and admission data. In doing so, we leveraged the ability of autoencoders to extract meaningful data features and process data that can only be monitored in an online setting. Subsequently, the study uses a logistic regression algorithm to verify the result’s accuracy, precision, F1 score, and recall. The model performed optimally with a minimal precision of 99%. The findings also suggest that older students and students with high academic scores do not often procrastinate. In contrast, students who have taken a course severally often procrastinate. Copyright © 2024 Global IT Research InstitutNovembere (GiRI)

关键词： Logistic regression

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Data to Diagnosis: Harnessing STRING Insights to Unravel the Biomarkers of Alzheimer's 15

Data to Diagnosis: Harnessing STRING Insights to Unravel the...

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

作者： Rajeev, Anagha Jose, Bijoy A. Johnson, Sinoy Koyyappurath, Sayuj Cochin University of Science and Technology Department of Computer Science Kochi India Cochin University of Science and Technology Department of Biotechnology Kochi India

ISBN: (纸本)9798350370249

Alzheimer's disease, shrouded in complexity, poses a challenge to predictive modeling due to extensive missing data in gene and protein interaction analyses. Identifying pivotal 'hub genes' crucial for cellular networks becomes elusive, hampering effective disease understanding. Machine learning models, essential for accurate predictions, exacerbated by diverse disease manifestations. The challenge intensifies with fragmented, inconsistent data sources laden with gaps and format disparities. Our research introduces the 'Hub Gene Convergence Schema,' a novel approach that systematically identifies pivotal hub genes, significantly enhancing predictive accuracy for Alzheimer's disease, enabling early diagnosis and personalized treatment. The intended 'Hub Gene Convergence Schema,' a diagram integral to our project, further enhances our approach. The ultimate goal is adaptive models facilitating early diagnosis, personalized treatment, and a profound comprehension of Alzheimer's, marking a crucial step in our relentless pursuit of combating this formidable adversary. © 2024 IEEE.

关键词： Alzheimer's disease Hub genes interactome Predictive modeling

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Automatic Positioning of Intelligent Mobile Robot Based on Improved Front-End Scanning Matching

Journal of Network Intelligence

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Journal of Network Intelligence 2025年第1期10卷 559-572页

作者： Wang, Hong Wang, Yi-Gui Zhang, Jie Department of Computer and Software Engineering Shandong College of Electronic Technology Jinan250200 China School of Computer Science and Technology Shandong Jianzhu University Jinan250101 China College of Agricultural Science and Technology Yana Royal Polytechnic University Chiang Mai50000 Thailand

Traditional autonomous navigation methods for mobile robots mainly rely on geometric feature-based LiDAR scan-matching algorithms, but in complex environments, this method is often affected due to the presence of moving objects, occlusions, and other interfering factors, resulting in a decrease in positioning accuracy. With the growing demand for robotics applications in logistics, security, exploration and other fields, the need for robust autonomous navigation and high-precision mapping in highly dynamic and complex environments is becoming more and more urgent. To solve this problem, an improved front-end scan matching algorithm based on Frequency Modulated Continuous Wave (FMCW) LiDAR is proposed in this paper. Firstly, by using the target Doppler velocity information provided by the FMCW LiDAR, we design a novel point cloud segmentation algorithm based on velocity clustering, which is able to effectively distinguish between stationary and moving objects, and avoid the dynamic interference affecting the position estimation. Secondly, we introduce the Gaussian Mixture Model Sampling Consistency (GMMSC) algorithm, which is more robust to reject the mis-matched pairs in the scanning matching process and improve the alignment accuracy. Finally, based on the residual high-quality matched pairs, we combine the classical ICP algorithm with the robust kernel function to further enhance the stability of the position estimation in the case of occlusion and local mismatch. The experimental results show that the proposed improved algorithm significantly improves the mapping capability of mobile robots in complex environments compared with the existing techniques. The average relative error of the improved front-end scanning matching algorithm is reduced by 40.6 % compared with the pre-improved one. © 2025, Taiwan Ubiquitous Information CO LTD. All rights reserved.

关键词： Mobile robots

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EarLock: Personal Authentication System for Hearables Using Sound Leakage Signals

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IEEE Transactions on Mobile Computing 2025年第7期24卷 6183-6196页

作者： Amesaka, Takashi Watanabe, Hiroki Sugiura, Yuta Sugimoto, Masanori Shizuki, Buntarou Keio University Department of Information and Computer Science Yokohama108-0073 Japan Future University Hakodate Department of Media Architecture Hokkaido 041-0803 Japan Hokkaido University Graduate School of Information Science and Technology Sapporo060-0808 Japan University of Tsukuba Department of Computer Science Tsukuba305-8577 Japan

Earphone-type wearable devices, also known as "hearables," will have many functions in the future. Some of those functions will require authentication of the wearer for access to the user's privacy information or settlement of payments. In this study, we propose a new personal authentication system for hearables called EarLock. EarLock authenticates the wearer by acquiring and analyzing ear canal and auricle shape information using sound leakage from the device. The system can be implemented using a speaker and external microphone that are highly compatible with hearables. We implemented three prototype devices and investigated EarLock’s authentication performance under various practical scenarios, including walking conditions, noisy environments, and situations with object interference. Experimental results showed that the in-ear, open-ear, and bone-conduction devices achieved balanced accuracy (BAC) scores of 87.2–93.7%, 83.4– 94.7%, and 85.9–90.0%. © 2002-2012 IEEE.

关键词： Information leakage

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Semantic Segmentation of Lumbar Vertebrae Using Meijering U-Net(MU-Net)on Spine Magnetic Resonance Images

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computer Modeling in Engineering & sciences 2025年第1期142卷 733-757页

作者： Lakshmi S V V Shiloah Elizabeth Darmanayagam Sunil Retmin Raj Cyril Department of Computer Science and Engineering College of Engineering GuindyAnna UniversityChennai600025India Department of Information Technology Madras Institute of TechnologyAnna UniversityChrompetChennai600044India

Lower back pain is one of the most common medical problems in the world and it is experienced by a huge percentage of people *** to its ability to produce a detailed view of the soft tissues,including the spinal cord,nerves,intervertebral discs,and vertebrae,Magnetic Resonance Imaging is thought to be the most effective method for imaging the *** semantic segmentation of vertebrae plays a major role in the diagnostic process of lumbar *** is difficult to semantically partition the vertebrae in Magnetic Resonance Images from the surrounding variety of tissues,including muscles,ligaments,and intervertebral discs.U-Net is a powerful deep-learning architecture to handle the challenges of medical image analysis tasks and achieves high segmentation *** work proposes a modified U-Net architecture namely MU-Net,consisting of the Meijering convolutional layer that incorporates the Meijering filter to perform the semantic segmentation of lumbar vertebrae L1 to L5 and sacral vertebra ***-colour mask images were generated and used as ground truth for training the *** work has been carried out on 1312 images expanded from T1-weighted mid-sagittal MRI images of 515 patients in the Lumbar Spine MRI Dataset publicly available from Mendeley *** proposed MU-Net model for the semantic segmentation of the lumbar vertebrae gives better performance with 98.79%of pixel accuracy(PA),98.66%of dice similarity coefficient(DSC),97.36%of Jaccard coefficient,and 92.55%mean Intersection over Union(mean IoU)metrics using the mentioned dataset.

关键词： computer aided diagnosis(CAD) magnetic resonance imaging(MRI) semantic segmentation lumbar vertebrae deep learning U-Net model

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