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ICRM: An intelligent citation recommendation mechanism based on BERT and weighted BoW models

Journal of Intelligent and Fuzzy Systems 2024年第4期46卷 10135-10150页

作者： Chang, Chih-Yung Yang, Yu-Ting Zhang, Qiaoyun Lin, Yi-Ti Roy, Diptendu Sinha Department of Computer Science and Information Engineering Tamkang University New Taipei Taiwan Department of English Tamkang University New Taipei Taiwan Department of Computer Science and Engineering National Institute of Technology Shillong India

With the field of technology has witnessed rapid advancements, attracting an ever-growing community of researchers dedicated to developing theories and techniques. This paper proposes an innovative ICRM (Intelligent Citation Recommendation Mechanism), designed to automate the process of suggesting the appropriate number of citations for individual brackets within a document. The proposed ICRM comprises three phases: Coarse-grained Weighted Bag of Word (WCBW), Fine-grained SciBERT (FSB) and Citation Adjustment phases. Firstly, the WCBW phase employs TF-IDF to extract keywords from both target and candidate documents, forming vectors that capture word significance along with metadata like authorship, keywords, and titles. It aims to identify relevant papers from a database, serving as initial candidates for each bracket. Secondly, the FSB phase employs the SciBERT model to assess the similarity between candidate documents and the local context around brackets, enhancing the precision of recommendations. It refines this selection by analyzing candidate-document relationships within the proximity of the brackets. Lastly, the Citation Adjustment phase tackles overlapping citations and ensures that recommended citation numbers align with user-defined criteria, resolving issues of imbalance. The simulation results demonstrate that the proposed ICRM outperforms existing models significantly in terms of precision, recall and F1-score. © 2024 - IOS Press. All rights reserved.

关键词： Artificial intelligence

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A lightweight filter based feature selection approach for multi-label text classification

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Journal of Ambient Intelligence and Humanized Computing 2023年第9期14卷 12345-12357页

作者： Dhal, Pradip Azad, Chandrashekhar Department of Computer Science and Engineering National Institute of Technology Jamshedpur India

Multi-label Text Classification (MTC) is a challenging task in Natural Language Processing (NLP). The goal of the MTC task is to label a document with a set of labels. By incorporating various term weighting schemes in MTC, high dimensional feature space has been generated;due to that, multi-label learning algorithms face substantial problems in performing MTC tasks. To deal with these issues, Feature Selection (FS) approaches are effective solutions. This paper proposes a Lightweight Term-weighting FS (LwTwFS) approach based on a modified Chi-square (CHI) filter-based FS method to deal with this issue. The modified CHI approach works for Inter-Class Concentration (ICC) and Intra-Class Dispersion (ICD), and its strength has been increased by adding positive and negative correlations. A novel modified equation has been introduced to distribute the features among the categories (i.e., here, multi-label) in the corpus. The proposed modified CHI-based FS approach works on the term weighting-based Feature Extraction (FE) approach. Multi-Layer Perceptron (MLP) has been used in the classification phase due to the adaptive learning property, which refers to learning how to do tasks based on data provided during training or prior experience. We have used two publicly available multi-label corpora for experimental verification: the Arxiv Academic Paper Dataset (AAPD) and the Reuters Corpus Volume I (RCVI-V2). According to the results, in terms of performance, the LwTwFS methodology combined with the MLP classifier surpasses other combinations in terms of Jaccard Score (JS), Hamming Loss (HL), Ranking Loss (RL), Precision (Pr), Recall (Re), and F-micro and F-macro. For the AAPD corpus, the LwTwFS method achieves the best JS, HL, RL, Pr, F-micro, and F-macro values, which are 0.9636, 0.0121, 0.0303, 0.9636, 0.9882, and 0.9894. For the RCVI-V2 corpus, the LwTwFS method achieves the best JS, Pr, Re, F-micro, and F-macro values of 1.0000, and HL, RL values of 0.0000. Empirical res

关键词： Feature Selection

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Automation of franchise-based data storage, management and analysis using Amazon Web Services

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International Journal of Cloud Computing 2023年第6期12卷 575-585页

作者： Oswal, Shreya Gundawar, Shubham Modani, Varun Shingare, Ashwini Department of Computer Engineering Vishwakarma Institute of Technology Pune411037 India

The wave of computer automation in business has revolutionised the way companies and employees interact with their customers and each other. Robotic process automation (RPA) not only mimics human actions involving complex, high volume and routine tasks but has also extended the creative problem-solving capabilities and productivity of human beings and deliver superior business results (Leshob et al., 2018;Ghosh, 2018). Amazon Web Services (AWS) has created a dramatic cultural shift in infrastructure provisioning from a fairly manual process of physical machines and software configuration. This paper proposes to use Amazon AWS to automate the task of scheduled uploading of data from different franchises, managing the database, analysing the data and storing the data on the supervisor’s machine. This reduces the redundant tasks of daily uploading data to company servers, analysing the data and downloading the data. The analysed data can be used by the company to improve the basic functioning and acknowledge various issues and problems. Thus, it aims to offer infrastructure as a service (IaaS) by providing virtualised computing resources over the internet. Copyright © 2023 Inderscience Enterprises Ltd.

关键词： Robotics

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KidneyMultiNet: A Web-Based Automatic System for Kidney Disease Detection Using Hybrid Machine Learning Model From CT Scan Images

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Biomedical Materials and Devices 2024年 1-12页

作者： Rana, Sahab Uddin Nur-A-Alam, Md. Akter, Sadeka Likhon, Md. Nur Hosain Department of Computer Science and Engineering Dhaka University of Engineering & amp Technology Gazipur Dhaka Bangladesh Department of Computer Science and Engineering Sunamgonj Science and Technology University Sylhet Bangladesh Department of Computer Science and Engineering Dhaka International University Dhaka Bangladesh

Chronic renal disease is the term used to describe kidney function that gradually declines. The kidneys’ final byproduct of eliminating waste and surplus fluid from the bloodstream is urine. Abnormal accumulations of fluid, electrolytes, and waste products can occur in the body when chronic renal disease progresses to a critical degree. Nonetheless, kidney disease has been predicted using various artificial machine learning algorithm approaches. In this study, authors present the “KidneyMultiNet” framework, which combines two models of convolutional neural networks Densenet201 and Xception, which are based on the idea of transfer learning and can predict kidney disease based on kidney CT scan datasets. The “KidneyMultiNet” design that has been suggested makes a thorough and reasonable diagnosis of kidney disease in the kidney conceivable. The authors used a publicly available kidney CT scan image dataset to train and test the proposed model. To determine the efficacy of the output from suggested advanced machine learning CNN methods, the author fed them picture data using a train generator. Then, the author used a rate of learning to decrease the datasets within the proposed model. After completing various learning procedures with many kidney pictures, the proposed model could accurately predict kidney disease, reaching a maximum performance of 99.92%. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024.

关键词： Convolutional neural network Image processing Kidney disease Machine learning

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An Integrated Approach for Analysing Sentiments on Social Media

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Informatica (Slovenia) 2023年第2期47卷 213-220页

作者： Tandon, Vrinda Mehra, Ritika Department of Computer Science and Engineering Dev Bhoomi Institute of Technology India

Sentiment analysis is an analytical subfield of Natural Language Processing (NLP) to determine opinion or emotion associated with the body of the text. The requirement for social media sentiment analysis has exceptionally increased with the growing extent of online activities in form of user generated content like posts or comments on social networking platforms. People often share their thoughts, opinions and reviews openly which can further be leveraged to analyze what they feel about a particular topic or their reviews/ feedback about a certain service. This study covers different approaches to conduct social media sentiment analysis on Twitter dataset both balanced and imbalanced obtained from Kaggle. For text analysis, we have implemented various classification techniques such as: Naive Bayes Classification and Support Vector Classification (SVC). It was concluded that SVC on twitter dataset surpassed other classification techniques in terms of performance. © 2023 Slovene Society Informatika. All rights reserved.

关键词： Sentiment analysis

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Security to text (S2T): multi-layered based security approaches for secret text content

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Multimedia Tools and Applications 2025年第15期84卷 15151-15171页

作者： Kashid, Shamal Awasthi, Lalit K. Berwal, Krishan Department of Computer Science and Engineering National Institute of Technology Uttarakhand IN Srinagar India Faculty of Communication Engineering Military College of Telecommunication Engineering IN Mhow India

In the digital world, text data is produced in an unstructured manner across various communication channels. Extracting valuable information from such data with security is crucial and requires the development of techniques in text mining, information retrieval, and natural language processing (NLP). To solve this issue, we introduce two novel approaches: keyword extraction (KE) and a multi-layered secret sharing scheme (MLSS) to provide security to extracted keywords rather than overall text documents. The KE approach encompasses a sequence of text pre-processing procedures, including tokenization, stopword removal, stemming, and bag of words representation, followed by indexing. This methodology aims to efficiently extract keywords from text datasets. For this research work, we have proposed three datasets, including text messages, whatsapp messages, and electronic mail. MLSS enhances the security of extracted textual data by leveraging text pre-processing steps. This scheme ensures better confidentiality and the non-revealment of sensitive information. Additionally, we evaluate our KE model on our dataset as well as on standard datasets. Experimental results demonstrate the effectiveness of our proposed security to text (S2T) model, which outperform existing state-of-the-art approaches. The model obtains a 100% correlation between the reconstructed text and the original text. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024.

关键词： Extraction

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Understanding Brain Network Stimulation for Emotion Analyzing Connectivity Feature Map from Electroencephalography

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International Journal of Advanced computer Science and Applications 2025年第4期16卷 542-551页

作者： Maria, Mahfuza Akter Akhand, M.A.H. Kamal, Abdus Samad Department of Computer Science and Engineering Khulna University of Engineering & Technology Khulna9203 Bangladesh Graduate School of Science and Technology Gunma University Kiryu376-8515 Japan

In understanding brain functioning by Electroencephalography (EEG), it is essential to be able to not only identify more active brain areas but also understand connectivity among different areas. The functional and efficient connectivity networks of the brain have been examined in this study by constructing a connectivity feature map (CFM) with four widely used connectivity methods from the Database for Emotion Analysis Using Physiological Signals (DEAP) emotional EEG data to research how this connectivity’s patterns are influenced by emotion. According to the investigation results, emotions are mainly related to the parietal, central, and frontal regions. The parietal region is more responsible for emotion alteration among these three regions. Positive emotions are associated with more direct correlations and dependencies than negative ones. When experiencing negative emotions, the regions of the brain function more synchronously as well as there are less flow of information. Whether direct or inverse, there is less correlation between brain regions in the higher frequency band than in the lower frequency band. Higher frequencies are associated with increased dependence and directed information transfer between brain regions. Generally, the electrodes in the same lobe show stronger connectivity than those in different lobes. At a glance, the present study is a comprehensive analysis to understand brain network stimulation for emotion from EEG, and it significantly differs from the existing emotion recognition studies typically focused on recognition proficiency. © (2025), (Science and Information Organization). All rights reserved.

关键词： Emotion Recognition

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THC-DFECC-based privacy preserved smart contract creation for cloud data security

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International Journal of Information technology (Singapore) 2024年第7期16卷 4191-4207页

作者： Tiwari, Chandra Sekhar Jha, Vijay Kumar Department of Computer Science and Engineering Birla Institute of Technology Mesra

Currently, cloud services have become a novel business model for information services owing to the rapid development of Cloud Computing (CC) technology. Hence, an efficient Smart Contract (SC) creation for cloud data security is proposed in this work. Primarily, the registered Data Owner (DO) selects the file to be uploaded. Then, the data is securitized utilizing Twisted Hessian Curve-based Digit Folding Elliptic Curve Cryptography (THC-DFECC). Afterward, the data is securely uploaded into the Interplanetary File System (IPFS). Thereafter, the DO creates the SC. By utilizing Gini Canberra-k-anonymity (GC-k-anonymity), the SC data is preserved, and it is hashed using the Exponential Entropy-based SWIFFT hashing algorithm (EE-SWIFFT). After that, it is securely uploaded into the BlockChain (BC). Conversely, the Data User (DU) sends the data access request to IPFS. Next, IPFS generates the token, which is verified with the data access request. If it matches, the hashed data is sent to DU. Lastly, the verification for accessing the data is performed in which the SC is created by DU and verified with the BC. If it matches, the verification at Cloud Server Authority (CSA) is performed. According to the experimental results, the proposed technique withstands a higher security level as compared to existing methodologies. © Bharati Vidyapeeth's Institute of computer Applications and Management 2024.

关键词： Exponential entropy based SWIFFT hashing algorithm (EE-SWIFFT) Gini canberra-k-anonymity (GC-k-Anonymity) Public key based secure socket shell (SSH) hexadecimal Smart contract Twisted hessian curve based digit folding elliptic curve cryptography (THC-DFECC)

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Modelling of COVID-19 spread time and mortality rate using machine learning techniques

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International Journal of Intelligent Information and Database Systems 2023年第2期16卷 143-166页

作者： Arrabi, Ahmad Al-Mousa, Amjed Computer Engineering Department Princess Sumaya University for Technology Amman Jordan

One of the main issues in dealing with the COVID-19 global pandemic is that governments cannot predict the time it spreads or the mortality rate. If known, these two factors would have helped governments take appropriate measures without being excessively cautious and negatively impacting populations’ mental health and economic outcomes. This paper presents a machine learning (ML)-based model that helps assess the rate at which the virus spreads in a country as well as the mortality based on multiple health, social, economic, and political factors. The method predicts how long a country’s cases take to reach 5%, 10%, 15%, and 20% of its population. The prediction was conducted by regularised linear regression models and support vector machine regression (SVR). The SVR model achieved the highest median accuracy of 97%. Meanwhile, the ridge regression model achieved the best median accuracy of 84% for predicting the mortality rate. Copyright © 2023 Inderscience Enterprises Ltd.

关键词： COVID-19

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Unlocking Eye Gestures with Earable Inertial Sensing for Accessible HCI 17

Unlocking Eye Gestures with Earable Inertial Sensing for Acc...

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17th International Conference on COMmunication Systems and NETworkS, COMSNETS 2025

作者： Chugh, Garvit Chakraborty, Suchetana Chakraborty, Sandip Indian Institute of Technology Department of Computer Science and Engineering Jodhpur India Indian Institute of Technology Department of Computer Science and Engineering Kharagpur India

ISBN: (纸本)9798331531195

Eye gestures are widely used in many applications, including device control, biometrics, visual analytics, and health-care, like Alzheimer's, accessibility, etc. The conventional method for eye gesture detection needs visual information processing devices or smart glasses to capture the movement patterns of the eyeballs. In this paper, we present a novel framework that utilizes inertial measurement unit (IMU) sensors embedded in earable devices for real-time eye gesture detection, which can work as a complementary to visual information processing devices. The framework translates specific eye movements into actionable commands, enabling hands-free interaction for users with limited mobility. Through extensive experimentation with 14 participants across diverse activities such as sitting, walking, running, and driving, the framework demonstrated high accuracy in eye gesture recognition, achieving a macro F1 score of 0.85 on our self-collected dataset. © 2025 IEEE.

关键词： Eye movements

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