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arXiv 2025年

作者： Sharma, Kiran Khurana, Parul School of Engineering & Technology BML Munjal University Haryana Gurugram122413 India Center for Advanced Data and Computational Science BML Munjal University Haryana Gurugram122413 India School of Computer Applications Lovely Professional University Punjab Phagwara144411 India

Retracted citations remain a significant concern in academia as they perpetuate misinformation and compromise the integrity of scientific literature despite their invalidation. To analyze the impact of retracted citations, we focused on two retraction categories: plagiarism and fake peer review. The data set was sourced from Scopus and the reasons for the retraction were mapped using the Retraction Watch database. The retraction trend shows a steady average growth in plagiarism cases of 1.2 times, while the fake peer review exhibits a fluctuating pattern with an average growth of 5.5 times. Although fewer papers are retracted in the plagiarism category compared to fake peer reviews, plagiarism-related papers receive 2.5 times more citations. Furthermore, the total number of retracted citations for plagiarized papers is 1.8 times higher than that for fake peer review papers. Within the plagiarism category, 46% of the retracted citations are due to plagiarism, while 53.6% of the retracted citations in the fake peer review category are attributed to the fake peer review. The results also suggest that fake peer review cases are identified and retracted more rapidly than plagiarism cases. Finally, self-citations constitute a small percentage of citations to retracted papers but are notably higher among citations that are later retracted in both the categories. © 2025, CC BY.

关键词： Inflation

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A Power-System Economic Dispatching Based on Enhanced Group Search Optimizer 1

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18th International Conference on Intelligent Information Hiding and Multimedia Signal Processing, IIH-MSP 2022

作者： Pan, Jeng-Shyang Nguyen, Trong-The Nguyen, Trinh-Dong Nguyen, Vinh-Tiep Dao, Thi-Kien College of Computer Science and Engineering Shandong University of Science and Technology Qing-Dao266590 China Department of Information Management Chaoyang University of Technology Taichung41349 Taiwan University of Information Technology Ho Chi Minh City700000 Viet Nam Vietnam National University Ho Chi Minh City700000 Viet Nam Fujian Provincial Key Laboratory of Big Data Mining and Applications Fujian University of Technology Fuzhou350118 China

ISBN: (数字)9789819901050

ISBN: (纸本)9789819901043

Economic dispatch (ED) is one of the critical tasks in a power system’s optimization issues, but its characteristics with non-convex, high-dimensional, non-linear, and non-differentiable, so that caused solutions are more challenging in reality. This paper suggests an enhanced group search optimizer (EGSO) for solving the ED model with the valve-point effect and the different fuels. The EGSO adopts the original group search optimizer (GSO) by changing polar to Cartesian coordinate’s conversion and adding cyclic translation operators to the search behavior of the finder and walker to avoid falling into local optimal and slow convergence of the GSO. In the experiment section, the testing power systems of the ED model are used to validate the EGSO performance. The experimental results reveal that EGSO outperformed the other algorithms regarding convergence speed, the time required to complete calculations, and impacts with lower fuel cost. Additionally, with excellent outcomes in optimization issues involving multiple local optimal values show the EGSO is practical, quick, and efficient in addressing the challenging ED model. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Genetic algorithms

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An Improved Honey Badger Algorithm for Electric Vehicle Charge Orderly Planning

Journal of Network Intelligence

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Journal of Network Intelligence 2022年第2期7卷 332-346页

作者： Chen, Ran-Fan Luo, Hao Huang, Kuan-Chun Nguyen, Trong-The Pan, Jeng-Shyang College of Computer Science and Mathematics Fujian University of Technology Fuzhou350118 China School of Aeronautics and Astronautics Zhejiang University HangZhou310000 China Department of Industrial and Systems Engineering Chung Yuan University Taoyuan320-33 Taiwan Multimedia Communications Lab VNUHCM-University of Information Technology Ho Chi Minh City700000 Viet Nam Vietnam National University Ho Chi Minh City700000 Viet Nam Fujian Provincial Key Laboratory of Big Data Mining and Applications Fujian University of Technology Fuzhou350118 China College of Computer Science and Engineering Shandong University of Science and Technology Qingdao266590 China

Multiple electric vehicles simultaneously connected to a distribution load network will significantly impact the stability of the power grid. This paper proposes an improved Honey-badger algorithm (IHBA) through elite reverse learning, spiral update, and wild dog survival strategies to optimize the orderly charging of electric vehicles (EV). Objective functions of EV charge orderly planning are taken by using load fluctuation satisfaction, user cost satisfaction, and user convenience satisfaction to achieve the high efficiency of orderly charging. The benchmark test function and the electric vehicle se-quential charging problem were employed to evaluate the proposed IHBA approach. The obtained results are compared with the other algorithms in the literature, which indicate that the IHBA algorithm does not only provides more accurate outcome but better con-vergence speed than the other competitors. © 2022;Taiwan Ubiquitous Information CO LTD. All rights reserved.

关键词： Food products

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EDSep: An Effective Diffusion-Based Method for Speech Source Separation

EDSep: An Effective Diffusion-Based Method for Speech Source...

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International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Jinwei Dong Xinsheng Wang Qirong Mao School of Computer Science and Communication Engineering Jiangsu University Audio Speech and Language Processing Group School of Computer Science Northwestern Polytechnical University Xi’an China Provincial Key Laboratory of Computational Intelligence and New Technologies in Low-Altitude Digital Agriculture Jiangsu Engineering Research Center of Big Data Ubiquitous Perception and Intelligent Agriculture Applications Zhenjiang China

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Generative models have attracted considerable attention for speech separation tasks, and among these, diffusion-based methods are being explored. Despite the notable success of diffusion techniques in generation tasks, their adaptation to speech separation has encountered challenges, notably slow convergence and suboptimal separation outcomes. To address these issues and enhance the efficacy of diffusion-based speech separation, we introduce EDSep, a novel single-channel method grounded in score matching via stochastic differential equation (SDE). This method enhances generative modeling for speech source separation by optimizing training and sampling efficiency. Specifically, a novel denoiser function is proposed to approximate data distributions, which obtains ideal denoiser outputs. Additionally, a stochastic sampler is carefully designed to resolve the reverse SDE during the sampling process, gradually separating speech from mixtures. Extensive experiments on databases such as WSJ0-2mix, LRS2-2mix, and VoxCeleb2-2mix demonstrate our proposed method’s superior performance over existing diffusion and discriminative models, validating its efficacy.

关键词： Training Adaptation models Technological innovation Source separation databases Stochastic processes Differential equations Speech enhancement Diffusion models Mathematical models

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Effectiveness of Teletherapy in Managing Voice Disorders: A Systematic Review

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Indian Journal of Otolaryngology and Head & Neck Surgery 2025年 1-17页

作者： Varma, Maya Nayak, Srikanth Rao, Rohini R. Devadas, Usha Department of Speech and Hearing Manipal College of Health Professions Manipal Academy of Higher Education Manipal India Department of Audiology and Speech-Language Pathology Yenepoya Medical College Yenepoya University (Deemed to be University) Mangalore India Department of Data Science and Computer Applications Manipal Institute of Technology Manipal Academy of Higher Education Manipal India

Telerehabilitation has developed as one of the important aspects of health care delivery in the current era. Hence the present study aimed to systematically review the literature and understand the effectiveness of teletherapy in the management of voice disorders. A comprehensive search was performed in the PUBMED, CINAHL, and Scopus databases using Boolean operators, adhering to the PRISMA. The methodological quality of the selected studies was assessed using the ROBINS-I tool. The review included 12 articles meeting the inclusion criteria, featuring various study designs. All studies reported positive outcomes from televoice therapy intervention across multiple voice outcome measures, irrespective of whether participants received direct or indirect or a combination of voice therapy techniques. Studies comparing teletherapy with face-to-face therapy indicated that teletherapy is as effective as in-person therapy. However, the overall risk of bias assessment for the included studies, using the ROBINS-I tool, ranged from moderate to serious. This systematic review revealed that teletherapy is a feasible alternative to in-person therapy for voice disorders. However, there is a need to understand the effectiveness of teletherapy in demonstrating different voice therapy techniques. Further studies are needed to understand the application of various/particular voice therapy techniques via telepractice.

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EmT: A Novel Transformer for Generalized Cross-subject EEG Emotion Recognition

arXiv

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arXiv 2024年

作者： Ding, Yi Tong, Chengxuan Zhang, Shuailei Jiang, Muyun Li, Yong Liang, Kevin Lim Jun Guan, Cuntai College of Computing and Data Science Nanyang Technological University 50 Nanyang Avenue Singapore639798 Singapore Wilmar International Singapore School of Computer Science and Engineering Key Laboratory of New Generation Artificial Intelligence Technology and Its Interdisciplinary Applications Southeast University Nanjing210096 China

Integrating prior knowledge of neurophysiology into neural network architecture enhances the performance of emotion decoding. While numerous techniques emphasize learning spatial and short-term temporal patterns, there has been limited emphasis on capturing the vital long-term contextual information associated with emotional cognitive processes. In order to address this discrepancy, we introduce a novel transformer model called emotion transformer (EmT). EmT is designed to excel in both generalized cross-subject EEG emotion classification and regression tasks. In EmT, EEG signals are transformed into a temporal graph format, creating a sequence of EEG feature graphs using a temporal graph construction module (TGC). A novel residual multi-view pyramid GCN module (RMPG) is then proposed to learn dynamic graph representations for each EEG feature graph within the series, and the learned representations of each graph are fused into one token. Furthermore, we design a temporal contextual transformer module (TCT) with two types of token mixers to learn the temporal contextual information. Finally, the task-specific output module (TSO) generates the desired outputs. Experiments on four publicly available datasets show that EmT achieves higher results than the baseline methods for both EEG emotion classification and regression tasks. The code is available at https://***/yi-ding-cs/EmT. Copyright © 2024, The Authors. All rights reserved.

关键词： Emotion Recognition

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Self-Citations in Academic Excellence: Analysis of the Top 1% Highly Cited India-Affiliated Research Papers

arXiv

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arXiv 2025年

作者： Sharma, Kiran Khurana, Parul School of Engineering & Technology BML Munjal University Gurugram Haryana-122413 India Center for Advanced Data and Computational Science BML Munjal University Gurugram Haryana-122413 India School of Computer Applications Lovely Professional University Phagwara Punjab-144411 India

Citations demonstrate the credibility, impact, and connection of a paper with the academic community. Self-citations support research continuity, but, if excessive, may inflate metrics and raise bias concerns. The aim of the study is to examine the role of self-citations towards the research impact of India. To study this, 3.58 million papers affiliated with India from 1947 to 2024 in the Scopus database were downloaded, and 2.96 million were filtered according to document type and publication year up to 2023. Further filtering based on high citation counts identified the top 1% of highly cited papers, totaling 29,556. The results indicate that the impact of Indian research, measured by highly cited papers, has grown exponentially since 2000, reaching a peak during the 2011–2020 decade. Among the citations received by these 29,556 papers, 6% are self-citations. Papers with a high proportion of self-citations (>90%) are predominantly from recent decades and are associated with smaller team sizes. The findings also reveal that smaller teams are primarily domestic, whereas larger teams are more likely to involve international collaborations. Domestic collaborations dominate smaller team sizes in terms of both self-citations and publications, whereas international collaborations gain prominence as team sizes increase. The results indicate that while domestic collaborations produce a higher number of highly cited papers, international collaborations are more likely to generate self-citations. The top international collaborators in highly cited papers are the USA, followed by UK, and Germany. © 2025, CC BY.

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Generative Adversarial Networks (GANs) in the Wild: applications, Challenges, and Future Prospects

Generative Adversarial Networks (GANs) in the Wild: Applicat...

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Disruptive Technologies (ICDT), International Conference on

作者： Rakesh Kumar Pooja Chaudhary Shilpi Gupta Manoj Singhal Department of Computer Engineering & Applications GLA University Mathura UP India Department of CSE (Data Science) Raj Kumar Goel Institute of Technology Ghaziabad UP India Department of Computer Science & Engineering Meerut Institute of Technology Meerut UP India Department of Information Technology GL Bajaj Institute of Technology & Management Greater Noida UP India

Generative adverse Networks (GANs) have become increasingly famous for a variety of tasks in picture and audio processing, especially unsupervised getting to know. GANs encompass components, a “generator” and a “discriminator,” that are skilled adversarial. The generator is tasked with producing a pattern of statistics, and the discriminator is tasked with figuring out whether the information is “actual” or “fake.” The generator “learns” through trial and mistakes as it attempts to create samples that appear practical to the discriminator. Through the antagonistic schooling manner, the generator can create more accurate “fake” records while the discriminator can emerge as better at distinguishing between “actual” and “faux” records. GANs have been applied to various duties, which include picture era, textual content to photosynthesis, photo-to-image translation, and automatic music composition. Recent tries have been made to apply GANs to extra complicated obligations in the wild, including self-reliant use and drug discovery. GANs have inspired an ever-developing quantity of industries because of their potential to generate sensible facts with minimal human input.

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Classification and Discovery of Brain Tumor Using Ensemble Deep Learning

Classification and Discovery of Brain Tumor Using Ensemble D...

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Network, Multimedia and Information Technology (NMITCON) International Conference on

作者： Kasanagottu Srilatha Chekka Sravani Mikkilineni Vasudevarao Ajmeera Kiran D Chitra Pundru Chandra Shaker Reddy Department of Data Science Geethanjali College of Engineering and Technology Hyderabad India Computer Science and Engineering Marri Laxman Reddy Institute of Technology and Management Hyderabad Telangana India Department Of Computer Applications Dayananda Sagar Business Academy Karnataka India Computer Science and Engineering MLR Institute of Technology Dundigal Hyderabad Telangana India Master of Business Administration Panimalar Engineering College Chennai Tamil Nadu India School of Computer Science and Artificial Intelligence SR University Warangal Telangana India

ISBN: (数字)9798350372892

ISBN: (纸本)9798350372908

As healthcare IT develops at a dizzying rate, the age of big data is drawing near. Tumor disease prediction, monitoring, diagnosis, and treatment are all profoundly affected by data mining and analysis. Brain-tumors(BT) are considered the most terrible and deadly disease due to their aggressive nature, short survival rate, and diverse spectrum of symptoms. Inadequate medical therapy for patients with misdiagnosed brain tumors lowers their survival prospects. The ability to differentiate amid abnormal &normal tissues makes BT detection extremely difficult. An accurate diagnosis allows the patient to undergo effective therapy and has a better chance of long-term survival. Because of the abnormal pattern of lesion distribution, there are still convinced difficulties in diagnosing BTs, even though there has been a lot of research on the subject. Because tiny areas may seem healthy, it might be challenging to locate a zone with few lesions. Classification accuracy is negatively impacted, and selecting and extracting useful characteristics becomes more difficult. An important part is the automated categorization of precancerous BTs utilizing deep &machine learning strategies. This study presents a Convolutional-Neural-Network-Long-Short-Term-Memory (CNN-LSTM) model, a hybrid DL-technique, for the purpose of MRI-based brain tumor classification and prediction. An MRI brain imaging collection is utilized for our experiments. An effective preprocessing of the data is the first step in using a CNN to extract major characteristics from images. With a 99.3% significant classification accuracy, 99.2% precision, 99.1% recall, and 99.1% F1-measure, the suggested model successfully predicts the brain tumor.

关键词： Accuracy Computational modeling Medical treatment Predictive models Brain modeling Feature extraction Convolutional neural networks Lesions data mining Diseases

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Federated Learning in the Era of Privacy Preservation: A Comprehensive Review and Future Directions

Federated Learning in the Era of Privacy Preservation: A Com...

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Disruptive Technologies (ICDT), International Conference on

作者： Kushagra Kulshreshtha Bhawna Goel Hoshiyar Singh Kanyal Vikas Singhal Department of Computer Engineering & Applications GLA University Mathura UP India Department of CSE (Data Science) Raj Kumar Goel Institute of Technology Ghaziabad UP India Department of Computer Science & Engineering ABES Institute of Technology Ghaziabad UP India Department of Information Technology Greater Noida Institute of Technology Greater Noida UP India

Federated studying is a disbursed system learning method that lets a set of dispensed users, or clients, collaboratively learn a shared predictive model without sharing any statistics. Through taking part in the model education method, every purchaser contributes to the development of a worldwide, predictive model without sacrificing its very own information privacy. This privateness renovation may be achieved through various techniques, which include differential privateness, cozy multi-celebration computation, and information obfuscation. In this way, model schooling isn't always a task exclusively for centralized facts creditors, but as an alternative, a large-scale collaboration amongst multiple entities with disparate hobbies and facts silos. In the technology of privacy protection, federated studying represents a powerful tool that enables the construction of predictive fashions that might be knowledgeable by means of various information resources at the same time as retaining the integrity and privateness of the individual statistics units.

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