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

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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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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"Survey and analysis of blockchain technologies with respect to: properties, algorithms, architecture, models, evolution and framework"

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Multimedia Tools and Applications 2025年第14期84卷 13571-13615页

作者： Tiwari, Nikita Ranjan, Prakash Biswal, Pradeep Kumar Barde, Chetan Sinha, Neha Department of Computer Science and Engineering Indian Institute of Information Technology Bhagalpur Bhagalpur India Department of Electronics and Communication Engineering Indian Institute of Information Technology Bhagalpur Bhagalpur India

The growing realm of blockchain technology has captivated researchers and practitioners alike with its promise of decentralized, secure, and transparent transactions. This paper presents a comprehensive survey and analysis of various blockchain technologies, focusing on their fundamental properties, architectural designs, operational models, evolutionary trends, and overarching frameworks. Through an extensive review of existing literature and empirical studies, this research synthesizes the multifaceted landscape of blockchain, elucidating key concepts such as consensus mechanisms, smart contracts, scalability, interoperability, and privacy protocols. By examining the evolution of blockchain from its inception to its current state and beyond, this paper delineates the trajectory of technological advancements and identifies emerging paradigms. Furthermore, it critically evaluates the strengths and limitations of diverse blockchain architectures and models, offering insights into their applicability across different domains and use cases. Finally, this study culminates in the proposal of a comprehensive framework that encapsulates the essential elements necessary for understanding, implementing, and harnessing the transformative potential of blockchain technologies. Through this holistic analysis, this research contributes to a deeper understanding of blockchain ecosystems and provides valuable guidance for future research directions and practical implementations. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Blockchain

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Lewis base multisite ligand engineering in efficient and stable perovskite solar cells

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Journal of Energy Chemistry 2024年第12期99卷 277-291页

作者： Danqing Ma Dongmei He Qing Zhu Xinxing Liu Yue Yu Xuxia Shai Zhengfu Zhang Sam Zhang Jing Feng Jianhong Yi Jiangzhao Chen Key Laboratory of Optoelectronic Technology&Systems(Ministry of Education) College of Optoelectronic EngineeringChongqing UniversityChongqing 400044China Faculty of Materials Science and Engineering Kunming University of Science and TechnologyKunming 650093YunnanChina Institute of Physical and Engineering Science/Faculty of Science Kunming University of Science and TechnologyKunming 650500YunnanChina School of Aeronautics Harbin Institute of TechnologyHarbin 150001HeilongjiangChina HIT Zhengzhou Research Institute Zhengzhou 450000HenanChina

Perovskite solar cells(PSCs) have stood out from many photovoltaic technologies due to their flexibility,cost-effectiveness and high-power conversion efficiency(PCE). Nevertheless, the further development of PSCs is greatly hindered by the trap-induced non-radiative recombination losses and poor long-term work stability. In the past decade, the huge advancements have been obtained on suppressing nonradiative recombination and enhancing device durability. Among them, the multisite ligands(MSLs) engineering plays a crucial role in precise control and directional modification of functional layers and interfaces,which contributes to markedly increased PCE and lifetimes of PSCs. In view of this, this review summarizes the advances of MSLs in PSCs. From the perspective of functional groups and chemical interaction,the modulation mechanisms of properties of different functional layers and interfaces and device performance via various MSLs are deeply investigated and revealed. Finally, the prospects for the application and development direction of MSLs in PSCs are legitimately proposed.

关键词： Perovskite solar cells Multisite ligands Defect passivation Ion migration suppression Stability

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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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An Integrated Approach for Analysing Sentiments on Social Media

Informatica (Slovenia)

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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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A Study on the Impact of Sentiment Analysis on Stock Market Prediction

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Recent Advances in computer science and Communications 2023年第1期16卷 73-93页

作者： Dhanasekaren, Kiran Aluri, Sri Teja Karthikeyan, Neeraj Baskaran, Saravanan Hari Selvanambi, Ramani School of Computer Science and Engineering Vellore Institute of Technology Vellore India

Background: Investors estimate how a company's stock or financial instrument will perform in the future, which is known as the stock market prediction. Stock markets are one of the many industries that have benefited substantially from the incredible breakthroughs in machine learning. To effectively estimate these markets, many researchers and companies are continually researching and developing various state-of-the-art approaches and algorithms. Objective: The objective is to predict stock prices based on public sentiments. With a big collection of data from microblogging sites like Twitter, it is possible to analyse the thoughts or feelings of users on a wide scale. These sentiments play a major part in the way the stock market works. We review multiple papers and provide the advantages and disadvantages of various methods. Methods: An in-depth examination of the most recent methodologies for predicting stock market values using sentiment analysis is offered, as well as the multiple consequences for stock markets when epidemics or major events occur. Results: According to the findings, impact sentiment analysis has a significant part in predicting stock market price movement, allowing for greater profit. Conclusion: With modern machine learning and deep learning processes, we can forecast stock costs with a few degrees of precision. This research examines how stock expectations have changed over time, as well as the most recent and effective technique for forecasting, supplying, and minimizing speculators' losses. © 2023 Bentham science Publishers.

关键词： Sentiment analysis

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Retrieval of Interactive Requirements of Data Intensive Applications using Random Forest Classifier

Informatica (Slovenia)

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Informatica (Slovenia) 2023年第9期47卷 35-50页

作者： Raymond, Renita Anouncia, S. Margret School of Computer Science and Engineering Vellore Institute of Technology Vellore India

Classifying requirements in data-intensive systems based on their interactions can assist the requirements engineering process in becoming more systematic and transparent, resulting in higher requirement compliance and software project completion. However, understanding the requirements centred on interactions with the system is particularly tough due to the increased complexity of big data. In most cases, awareness of interaction-based requirements is critical in moving forward with prediction and decision-making. As a result, the classification of interactive requirements plays a critical role in removing the difficulties from unclear requirements. Various approaches to effective requirement classification are being devised. However, due to inadequate requirement management reflecting the fast-changing organizational change, classification accuracy does not achieve its maximum potential. The best approach for reducing misclassification rate and retrieving interactive requirements for data-intensive systems would be to use Word Embedding and Random Forest Classifier retrieval mechanism, as none of the studies to date have emphasized it. It also assessed the impact by comparing the results to metrics derived from the Random Forest classifier's training on word count characteristics. The data set used to experiment with the classification, particularly for interaction-based needs, is unique to our work and has not been covered by any other studies to date. The researchers will benefit from this study as they will better understand the requirement classification process. With an F1 score of 0.91, precision of 0.89, and recall of 0.93, statistical analysis showed that Word Embedding followed by Random Forest Classifier produced a relatively high classification result to differentiate interactive requirements for data-intensive systems. © 2023 Slovene Society Informatika. All rights reserved.

关键词： Classification (of information)

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E-voting system using cloud-based hybrid blockchain technology

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Journal of Safety science and Resilience 2024年第1期5卷 102-109页

作者： Beulah Jayakumari S Lilly Sheeba Maya Eapen Jani Anbarasi Vinayakumar Ravi A.Suganya Malathy Jawahar Department of Information Technology Tagore Engineering College Chennai India School of Computer Science and Engineering SRM Institute of Science and Technology Ramapuram ChennaiIndia Department of Information Technology Jerusalem College of Engineering ChennaiIndia School of Computer Science and Engineering Vellore Institute of Technology Chennai India Center for Artificial Intelligence Prince Mohammad Bin Fahd UniversityKhobarSaudi Arabia Leather Process Technology Division CSIR-Central Leather Research Institute ChennaiIndia

With the invention of Internet-enabled devices,cloud and blockchain-based technologies,an online voting system can smoothly carry out election *** pandemic situations,citizens tend to develop panic about mass gatherings,which may influence the decrease in the number of *** urges a reliable,flexible,transparent,secure,and cost-effective voting *** proposed online voting system using cloud-based hybrid blockchain technology eradicates the flaws that persist in the existing voting system,and it is carried out in three phases:the registration phase,vote casting phase and vote counting phase.A timestamp-based authentication protocol with digital signature validates voters and candidates during the registration and vote casting *** smart contracts,third-party interventions are eliminated,and the transactions are secured in the blockchain ***,to provide accurate voting results,the practical Byzantine fault tolerance(PBFT)consensus mechanism is adopted to ensure that the vote has not been modified or ***,the overall performance of the proposed system is significantly better than that of the existing *** performance was analyzed based on authentication delay,vote alteration,response time,and latency.

关键词： E-voting Cloud Blockchain Smart contract Consensus mechanism

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Flu-Net: two-stream deep heterogeneous network to detect flu like symptoms from videos using grey wolf optimization algorithm

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Journal of Ambient Intelligence and Humanized Computing 2023年第6期14卷 7733-7745页

作者： Gupta, Himanshu Imran, Javed Sharma, Chandani Department of Computer Science and Engineering Quantum University Roorkee India Computer Science and Engineering Department Thapar Institute of Engineering and Technology Patiala India

The outbreak of COVID-19 (also known as Coronavirus) has put the entire world at risk. The disease first appears in Wuhan, China, and later spread to other countries, taking a form of a pandemic. In this paper, we try to build an artificial intelligence (AI) powered framework called Flu-Net to identify flu-like symptoms (which is also an important symptom of Covid-19) in people, and limit the spread of infection. Our approach is based on the application of human action recognition in surveillance systems, where videos captured by closed-circuit television (CCTV) cameras are processed through state-of-the-art deep learning techniques to recognize different activities like coughing, sneezing, etc. The proposed framework has three major steps. First, to suppress irrelevant background details in an input video, a frame difference operation is performed to extract foreground motion information. Second, a two-stream heterogeneous network based on 2D and 3D Convolutional Neural Networks (ConvNets) is trained using the RGB frame differences. And third, the features extracted from both the streams are combined using Grey Wolf Optimization (GWO) based feature selection technique. The experiments conducted on BII Sneeze-Cough (BIISC) video dataset show that our framework can 70% accuracy, outperforming the baseline results by more than 8%. © 2023, The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.

关键词： COVID-19

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