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Feature engineering in learning-to-rank for community question answering task

International Journal of computers and applications 2024年第8期46卷 555-566页

作者： Sajid, Nafis Hasan, Md. Rashidul Ibrahim, Muhammad Department of Computer Science and Engineering University of Dhaka Dhaka Bangladesh

Community question answering (CQA) forums are Internet-based platforms where users ask questions about a topic and other expert users try to provide solutions. Many CQA forums such as Quora, Stackoverflow, Yahoo!Answer, StackExchange exist with a lot of user-generated data. These data are leveraged in automated CQA ranking systems where similar questions (and answers) are presented in response to the user's query. In this work, we empirically investigate a few aspects of this domain. First, in addition to traditional features like Term Frequency (TF), Inverse Document Frequency (IDF), Best Match 25 (BM25), etc., we introduce a Bidirectional Encoder Representations from Transformers (BERT)-based feature that captures the semantic similarity between the question and answer. Second, most of the existing research works have focus on features extracted only from the question part;features extracted from answers have not been explored extensively. We combine both types of features in a linear fashion. Third, using our proposed concepts, we conduct an empirical investigation with different rank-learning algorithms, some of which have not been used so far in CQA domain. On three standard CQA datasets, our proposed framework achieves state-of-the-art performance (0.56, 0.58 and 0.60 NDCG@10 values). We also analyze the importance of the features we use in our investigation. This work is expected to guide the practitioners to devise a better set of features for the CQA retrieval task. © 2024 Informa UK Limited, trading as Taylor & Francis Group.

关键词： Semantics

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Multi-feature Fusion Deep Network for Skin Disease Diagnosis

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Multimedia Tools and applications 2025年第1期84卷 419-444页

作者： Gairola, Ajay Krishan Kumar, Vidit Sahoo, Ashok Kumar Diwakar, Manoj Singh, Prabhishek Garg, Deepak Department of Computer Science and Engineering Graphic Era Hill University Dehradun India Department of Computer Science and Engineering Graphic Era deemed to be university Dehradun India School of Computer Science Engineering and Technology Bennett University Greater Noida India School of Computer Science and Artificial Intelligence SR University Warangal Telangana Warangal506371 India

The skin acts as an important barrier between the body and the external environment, playing a vital role as an organ. The application of deep learning in the medical field to solve various health problems has generated increasing interest. The objective of this research is to create a new model using a deep network with multi-feature fusion that enables accurate identification of various skin diseases. This paper proposes a fully fused network (FFN), which includes an improved single block (ISB) and an improved fusion block (IFB) to achieve optimal performance. To implement this, a convolutional neural network-based model for multi-class recognition of skin images has been developed, in which ISB is used to segment diseases in skin images. The IFB module is designed to enhance the effectiveness of the fused network. The model is built and evaluated using our proprietary dataset (Skin_disease_v1) along with publicly available datasets, namely ISIC2016, ISIC2017, and HAM10000. The highest accuracy achieved in experiments was 86% for ISB, 90% for IFB, and 92% for FFN using HAM10000 (for ResNet101V2), HAM10000 (for Resnet50 + ResNet101V2), and HAM10000 (for Resnet50 + ResNet101V2). This shows an accuracy improvement of 9.2%, 13.2%, and 15.2% compared to the state-of-the-art ISB, IFB, and FFN approaches, respectively. Our network outperforms existing networks in terms of performance. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Deep learning

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SMILES2DTA: a CNN-based approach for identifying drug candidates and predicting drug-target binding affinity

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Neural Computing and applications 2024年第4期37卷 2891-2910页

作者： Mukit, Hasanul Hossain, Sayeed Farabi, Mirza Milan Chowdhury, Mehrab Zaman Pritom, Ahmed Iqbal Rana, Humayan Kabir Department of Computer Science and Engineering Green University of Bangladesh Narayanganj-1461 Dhaka Bangladesh Department of Computer Science The University of Alabama at Birmingham BirminghamAL United States Department of Computer Science and Engineering Islamic University of Technology Gazipur Bangladesh

Computational approaches can speed up the drug discovery process by predicting drug-target affinity, otherwise it is time-consuming. In this study, we developed a convolutional neural network (CNN)-based model named SMILES2DTA (Simplified Molecular Input Line Entry System to Drug-Target Affinity) for predicting the binding affinity between a drug and a target protein. The model utilizes CNNs to process sequences of both drug SMILES and target proteins. SMILES2DTA generates multiple sequences from a single drug SMILES sequence, validates them based on Lipinski’s rule of five, and assesses their binding affinity against a target protein sequence. We evaluated our model using publicly available datasets and compared its performance to state-of-the-art methods. The results showed that SMILES2DTA outperformed the existing methods and demonstrated improved accuracy, mean squared error, and area under the precision-recall curve. SMILES2DTA has the potential to speed up the drug discovery process by reducing the time and cost complexity of identifying effective drugs. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2024.

关键词： Drug discovery

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3D brain image based tumor classification using ensemble of reinforcement transfer-based belief neural networks

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Multimedia Tools and applications 2025年第14期84卷 14001-14028页

作者： Arora, Shraddha Lamba, Monika Department of Computer Science and Engineering The NorthCap University Haryana Gurugram India

The procedure of segmenting a brain tumour is an essential step in the field of medical image processing. In order to improve the efficacy of disease treatment choices and the likelihood of patient survival, the timely detection of a brain tumour is not only highly essential but a vital factor. For the purpose of cancer diagnosis, manually segmenting brain tumours from the large number of MRI images that are generated in clinical practice is a hard and time-consuming process. Automatic brain tumour image segmentation is a technique that absolutely has to exist. Using deep learning and machine learning architectures for segmentation and feature extraction, this research proposes a novel approach to the detection of brain tumours. In this case, the input consists of a collection of 3-D brain slice pictures that have been smoothed and with noise eliminated. After that, the picture that has been processed is segmented by using a convolutional radial function architecture that centres on contours. The segmented image features retrieval has been accomplished by the use of an ensemble of reinforcement transfer-based belief neural networks. The results of the experimental study have been completed with regard to precision, recall, accuracy, F-1 score, area under the curve, dice coefficient, and root mean square error. The strategy that was recommended was successful in achieving 97.57% accuracy, 97.15% precision, 89.37% recall, 83.56% F-1 score, 69.59% area under the curve, 63.83% dice coefficient, and 55.56% root mean square error. The sagittal, coronal, and axial planes of the pictures were shown in order to acquire the specific position of the tumour as well as its characteristics, which included its height, breadth, and depth. When it comes to the prediction of tumours, the findings of the assessment of semantic segmentation based on deep learning networks are highly positive. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Spri

关键词： Feature extraction

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Recent Trends in Sign Language Detection System Using Machine Learning Algorithms 2

Recent Trends in Sign Language Detection System Using Machin...

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2nd IEEE International Conference on IoT, Communication and Automation Technology, ICICAT 2024

作者： Sharma, Keshav Jain, Naman Upadhyay, Adarsh Mishra, Annu Chaturvedi, Ravi Prakash Pruthi, Jyoti Sharda School of Engineering & Technology Department of Computer Science & Engineering Greater Noida India Sharda School of Engineering & Technology Department of Computer Science & Applications Greater Noida India

ISBN: (纸本)9798350368109

This study summarises current advances in sign language recognition systems, emphasising trends, problems, and prospects. Twenty key research publications are analysed, spanning a wide range of sign language recognition topics, from low-power bio-inspired armband solutions to sophisticated neural network structures. These technologies' incorporation into human-robot interactions, as well as their potential to improve communication accessibility, are highlighted. The communication gap limits social integration, access to services, information, and education for individuals who rely on sign language. To address this issue, our research aims to develop an innovative method for real-time translation of sign language into spoken and written languages. We propose a comprehensive system called "SignText"that utilizes Large Language Models (LLMs) and Python programming to interpret and translate sign language into subtitles. Through this approach, we aim to bridge the communication gap and enhance the lives of individuals who use sign language. Initial results demonstrate promising accuracy, with an average recognition rate of over 90% for basic signs and gestures, and up to 85% and 70% for more complex and similar signs, respectively. These results show significant progress toward fostering greater inclusion and accessibility in social interactions and daily life. © 2024 IEEE.

关键词： Human robot interaction

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Drawing a parallel between the trend of confirmed COVID-19 deaths in the winters of 2022/2023 and 2023/2024 in Italy, with a prediction

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Mathematical Biosciences and engineering 2024年第3期21卷 3742-3754页

作者： Roccetti, Marco Department of Computer Science and Engineering University of Bologna Bologna Italy

We studied the weekly number and the growth/decline rates of COVID-19 deaths of the period from October 31, 2022, to February 9, 2023, in Italy. We found that the COVID-19 winter wave reached its peak during the three holiday weeks from December 16, 2022, to January 5, 2023, and it was definitely trending downward, returning to the same number of deaths as the end of October 2022, in the first week February 2023. During this period of 15 weeks, that wave caused a number of deaths as large as 8,526. Its average growth rate was +7.89% deaths per week (10 weeks), while the average weekly decline rate was -15.85% (5 weeks). At the time of writing of this paper, Italy has been experiencing a new COVID-19 wave, with the latest 7 weekly bulletins (October 26, 2023 – December 13, 2023) showing that deaths have climbed from 148 to 322. The weekly growth rate had risen by +14.08% deaths, on average. Hypothesizing that this 2023/2024 wave will have a total duration similar to that of 2022/2023, with comparable extensions of both the growth period and the decline period and similar growth/decline rates, we predict that the number of COVID-19 deaths of the period from the end of October 2023 to the beginning of February 2024 should be less than 4100. A preliminary assessment of this forecast, based on 11 of the 15 weeks of the period, has already confirmed the accuracy of this approach. ©2024 the Author(s), licensee AIMS Press.

关键词： COVID-19

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Enhanced prediction of agricultural CO2 emission using ensemble machine learning-based imputation approach

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Neural Computing and applications 2024年第36期36卷 22867-22887页

作者： Thendral, M. Sathya Abinaya, S. Devi, M. K. Kavitha Department of Computer Science and Engineering Thiagarajar College of Engineering Madurai625015 India School of Computer Science and Engineering Vellore Institute of Technology Chennai600127 India

The agricultural sector contributes significantly to greenhouse gas emissions, which cause global warming and climate change. Numerous mathematical models have been developed to predict the greenhouse gas emissions from agriculture. However, the database utilized for prediction has thousands of missing values and imbalanced data records due to various factors, such as environmental disasters, sensor failure, maintenance issues, and budgetary constraints. Many researchers have either completely discarded records with missing values or have only partially addressed the issue, leading to less precise predictions. This study proposes a machine learning-based ensemble approach that uses multiple imputation by chained equations (MICE), K-nearest neighbors (KNN), and MissForest techniques to impute the missing values by reducing uncertainties and improves robustness in predictions. Furthermore, Synthetic Minority Oversampling Technique (SMOTE R) addresses data imbalance in CO2 emissions prediction by generating synthetic data to balance the target variable's distribution in the regression problem. Thereby the proposed SMOTE R and ensemble imputation approach aims to improve prediction accuracy and reliability by tackling data completeness and distribution issues simultaneously. The experiments are conducted on the GRACEnet database shows that the proposed approach outperforms the existing work in measure of R2, MAE, and RMSE metrics. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2024.

关键词： Greenhouse gas emissions

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A systematic mapping to investigate the application of machine learning techniques in requirement engineering activities

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CAAI Transactions on Intelligence Technology 2024年第6期9卷 1412-1434页

作者： Shoaib Hassan Qianmu Li Khursheed Aurangzeb Affan Yasin Javed Ali Khan Muhammad Shahid Anwar School of Computer Science and Engineering Nanjing University of Science and TechnologyNanjingJiangsuChina Department of Computer Engineering College of Computer and Information SciencesKing Saud UniversityRiyadhSaudi Arabia School of Software Northwestern Polytechnical UniversityXianShaanxiChina Department of Computer Science School of PhysicsEngineering&Computer ScienceUniversity of HertfordshireHatfieldUK Department of AI and Software Gachon University Seongnam-siSeongnamSouth Korea

Over the past few years,the application and usage of Machine Learning(ML)techniques have increased exponentially due to continuously increasing the size of data and computing *** the popularity of ML techniques,only a few research studies have focused on the application of ML especially supervised learning techniques in Requirement engineering(RE)activities to solve the problems that occur in RE *** authors focus on the systematic mapping of past work to investigate those studies that focused on the application of supervised learning techniques in RE activities between the period of 2002–*** authors aim to investigate the research trends,main RE activities,ML algorithms,and data sources that were studied during this ***-five research studies were selected based on our exclusion and inclusion *** results show that the scientific community used 57 *** those algorithms,researchers mostly used the five following ML algorithms in RE activities:Decision Tree,Support Vector Machine,Naïve Bayes,K-nearest neighbour Classifier,and Random *** results show that researchers used these algorithms in eight major RE *** activities are requirements analysis,failure prediction,effort estimation,quality,traceability,business rules identification,content classification,and detection of problems in requirements written in natural *** selected research studies used 32 private and 41 public data *** most popular data sources that were detected in selected studies are the Metric Data Programme from NASA,Predictor Models in Software engineering,and iTrust Electronic Health Care System.

关键词： data sources machine learning requirement engineering supervised learning algorithms

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Road Extraction From Remote Sensing Images Using U-Net

Road Extraction From Remote Sensing Images Using U-Net

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2024 International Conference on Advances in Modern Age Technologies for Health and engineering science, AMATHE 2024

作者： Sai Kumar, D. Lokesh Jyothi, V. Esther Vardhani, Chennu Meghana, Bayarla Priya, Bobbili Hema Sharika, Alladi Computer Science And Engineering Pvp Siddhartha Engineering College Vijayawada India Vr Siddhartha Engineering College Department Of Computer Applications Vijayawada India

ISBN: (纸本)9798350371567

The enhancement of the transportation database through updated road networks is a critical aspect with a wide range of applications. Traditional methods rely on onsite measurements or human interpretation of remote sensing images for mapping purposes. However, these methods have significant drawbacks, including high expenses, lengthy processing times, and a substantial workforce requirement. To overcome this challenge, we propose a method leveraging the U-Net architecture for road extraction from multispectral satellite images. The satellite imagery used is sampled from the DigitalGlobe Images dataset, covering images captured over Thailand, Indonesia, and India, with a ground resolution of 50 cm/pixel. The U-Net model is trained on both original and augmented data, employing convolutional layers, max-pooling, and up-sampling to extract and reconstruct road masks. Performance evaluation is conducted using custom validation sets, measuring metrics such as IOU and loss. Results demonstrate the effectiveness of the proposed system, achieving high accuracy and validation scores. The extracted road networks contribute to enhancing transportation databases, offering valuable insights for urban planning and management. © 2024 IEEE.

关键词： Roads and streets

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Scalable data management in global health crises: Leveraging blockchain technology

IET Blockchain

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IET Blockchain 2024年第S1期4卷 596-615页

作者： Al Jobaid, Sakib Kabir, Upama Jahan, Mosarrat Department of Computer Science and Engineering University of Dhaka Dhaka Bangladesh

Effective data management is crucial in navigating any health crisis. With proper data management protocols in place, stakeholders can swiftly adapt to evolving circumstances during challenging times. A recent event like the COVID-19 pandemic has unequivocally revealed its significance. It is essential to conduct disease surveillance, practice preventive measures, and devise policies to contain the situation. As the process involves massive data growth, it demands an acute level of oversight and control. Monitoring this vast sensitive data faces multifaceted limitations, namely data tampering, breach of privacy, and centralized data stewardship. In response to these challenges, we propose an innovative blockchain-enabled scalable data management scheme in light of the COVID-19 scenario. However, blockchain cannot scale in a large ecosystem due to storing all contents in every participating node. This work addresses this shortcoming by proposing a lightweight solution that groups nodes into clusters, resulting in less memory and processing overhead. Moreover, it adopts an off-chaining technique to reduce the memory load of every node and, thereby, the entire network. The experimental results demonstrate that it attains approximately 85% and 94% storage reduction per node and the whole network, respectively, and an 87% reduction in transaction processing time. © 2024 The Author(s). IET Blockchain published by John Wiley & Sons Ltd on behalf of The Institution of engineering and Technology.

关键词： COVID-19

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