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Effi-CNN: real-time vision-based system for interpretation of sign language using CNN and transfer learning

Multimedia Tools and Applications 2025年第6期84卷 3137-3159页

作者： Pranav Katarya, Rahul Big Data Analytics and Web Intelligence Laboratory Computer Science & Engineering Department Delhi Technological University New Delhi India

The deaf and mute population has difficulty conveying their thoughts and ideas to others. Sign language is their most expressive mode of communication, but the general public is callow of sign language;therefore, the mute and deaf have difficulty communicating with others. A system that can correctly translate sign language motions to speech and vice versa in real time is required to overcome this communication barrier. Effi-CNN, a vision-based Sign Language Recognition (SLR) system employing transfer learning with EfficientNetB2, is proposed in this paper. We have also developed a system covering sign gestures to text in real time. Our approach was evaluated on eight publicly available datasets, including the Massey University gesture dataset, the ArSL2018 dataset, the MNIST-ASL dataset, and others. When comparing our results to state-of-the-art algorithms, the experimental findings showed that our proposed work is more successful than existing models. Our proposed model delivered an accuracy of 99.90 on MNIST dataset. The results show that our Effi-CNN surpasses most currently existing solutions and can categorize many gestures with a low error rate. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2025.

关键词： Convolutional neural networks

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Navigating the Complexities of Municipal Waste Management: Enhancing Cost Prediction for Sustainable Urban Solutions 1st

Navigating the Complexities of Municipal Waste Management: E...

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1st International Research Conference on Computing Technologies for Sustainable Development, IRCCTSD 2024

作者： Minu, M.S. Samal, Tushar Bisani, Khushi Tewari, Aishwarya Department of Computer Science and Engineering with Specialisation in Big Data Analytics SRM Institute of Science and Technology Ramapuram Campus Chennai India

ISBN: (纸本)9783031823824

The escalating rates of municipal waste generation in urban areas worldwide present a critical challenge for effective waste management. This paper examines the complexities surrounding municipal waste management, emphasizing the significance of accurate cost prediction for sustainable decision-making. Urbanization, driven by population growth and rising standards of living, intensifies waste generation, while shifts in consumption patterns further complicate waste management efforts. Environmental concerns and regulatory pressures mandate innovative waste reduction strategies. Accurate cost prediction is paramount for budgetary planning and resource allocation, enabling proactive measures to optimize operational efficiency and mitigate financial risks. Emerging methodologies, including data analytics and predictive modeling, offer promising avenues for enhancing cost prediction accuracy by capturing the multifaceted drivers of cost variability. By leveraging these methodologies, decision-makers can forecast municipal waste management costs with precision, informing strategic initiatives to foster sustainable waste management practices in urban areas. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Decentralized finance

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Harnessing Medical big data: Integrating Computational Insights for Enhanced Patient Outcomes 24

Harnessing Medical Big Data: Integrating Computational Insig...

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7th International Conference on Digital Medicine and Image Processing, DMIP 2024

作者： Syed Masood, Jafar Ali Ibrahim Vinayagam, Jai Kumar Onteru, Ranjit Kumar Kopparapu, Umamaheswararao Parabathini, Bulah Pushpa Rani Department of IoT School of Computer Science and Engineering Vellore Institute of Technology Tamil Nadu Vellore632014 India Centre for Big Data Analytics QIS College of Engineering and Technology Andhra Pradesh Ongole India

ISBN: (纸本)9798400709586

The rapid proliferation of medical big data has opened unprecedented opportunities for enhancing patient outcomes through advanced computational analysis. This paper explores the integration of big data analytics with clinical practices to deliver personalized medicine, optimize treatment protocols, and improve patient care. Our findings demonstrate that a synergistic approach between computational insights and clinical decision-making can significantly advance healthcare outcomes. This study provides a comprehensive framework for harnessing medical big data, addressing key challenges, and proposing solutions to integrate these insights into everyday clinical practice. Copyright © 2024 held by the owner/author(s). Publication rights licensed to ACM.

关键词： Personalized medicine

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T³Planner: Multi-Phase Planning Across Structure-Constrained Optical, IP, and Routing Topologies

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IEEE Journal on Selected Areas in Communications 2025年第5期43卷 1823-1839页

作者： Yijun Hao Shusen Yang Fang Li Yifan Zhang Cong Zhao Xuebin Ren Peng Zhao Chenren Xu Shibo Wang National Engineering Laboratory for Big Data Analytics Xi’an Jiaotong University Xi’an China National Engineering Laboratory for Big Data Analytics and the Ministry of Education Key Laboratory for Intelligent Networks and Network Security Xi’an Jiaotong University Xi’an China School of Computer Science Peking University Beijing China Department of Computer Science and Engineering The Chinese University of Hong Kong Hong Kong China National Engineering Laboratory for Big Data Analytics XiÃan Jiaotong University XiÃan China

Network topology planning is an essential multi-phase process to build and jointly optimize the multi-layer network topologies in wide-area networks (WANs). Most existing practices target single-phase/layer planning, and are incapable of satisfying all rigorous topological structure constraints (e.g., dual-homing rings) defined by network standards and operators, especially in large-scale networks. These significantly limit their usability and performance in production networks. We consider a general topology planning problem with typical structure constraints over three essential phases (greenfield, reconfiguration, and site expansion) and topological layers (optical, IP, and routing topologies). We present, T3Planner, a novel practical solver to this problem in production. Specifically, we develop a structure-driven encoder based on graph neural network (GNN) for concise structure encoding, and design a new learning framework with optical-centric layer compression/reconstruction and rule-aided reinforcement learning (RL) for fast convergence and high performance. Extensive experiments on nine real topologies demonstrate that T3Planner scales to large optical networks with hundreds of sites, saves 46.6% cost, and supports $3.12\times $ more demand when compared to related existing approaches.

关键词： Topology Network topology Planning IP networks Optical fiber networks Routing Optical fibers Optical feedback Production Graph neural networks

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Real-Time Energy Efficient Communication for Large-Scale Industrial Internet of Things 2nd

Real-Time Energy Efficient Communication for Large-Scale Ind...

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2nd International Conference on Sustainability: Developments and Innovations, ICSDI 2024

作者： Khan, Musfira Khan, Muazzam A. Farman, Haleem Malik, Asad W. Ramzan, Muhammad Sher Islamabad Pakistan Department of Computer Science Quaid-I-Azam University Islamabad Pakistan ICESCO Chair for Big Data Analytics and Edge Computing Quaid-I-Azam University Islamabad Pakistan Smart Systems Engineering Lab College of Engineering Prince Sultan University Riyadh Saudi Arabia King Abdulaziz University Jeddah Saudi Arabia

ISBN: (纸本)9789819783441

Industrial Internet of Things (IIoTs) comprises large-scale sensors deployed to monitor and control the system. Deploying IoT devices presents challenges due to frequent disconnections and sudden power drainage in intermediate devices responsible for routing data. Consequently, the deployed network becomes sporadic, hindering connectivity with the industrial controller system, especially critical during emergencies. Furthermore, IoT node failures lead to a short network lifetime, which is intolerable in real-time industrial applications. This paper introduces a multi-hop clustering framework designed for reliable communication in industrial environments. The framework dynamically forms clusters, employing cluster heads and backup nodes to stabilize communication in the absence of a cluster head. Each cluster selects a backup node during the initial cluster head selection and later through data piggybacking. The proposed framework provides efficient communication across the IIoT network with the help of a backup and multi-hop clustering mechanism. The proposed framework is compared with two existing protocols i.e. cluster-based coordination routing (CRC) and energy-efficient quality of service routing (EEQSRP) protocols. The results show a considerable decrease in sensor energy consumption and packet delay. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Energy utilization

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Empirical Analysis of Nature-Inspired Algorithms for Autism Spectrum Disorder Detection Using 3D Video dataset

arXiv

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arXiv 2025年

作者： Panchal, Aneesh Khan, Kainat Katarya, Rahul Department of Computational and Data Sciences Indian Institute of Science Karnataka Bangalore560012 India Big Data Analytics and Web Intelligence Laboratory Department of Computer Science & Engineering Delhi Technological University Delhi110042 India

Autism Spectrum Disorder (ASD) is a chronic neurodevelopmental disorder symptoms of which includes repetitive behaviour and lack of social and communication skills. Even though these symptoms can be seen very clearly in social but a large number of individuals with ASD remains undiagnosed. In this paper, we worked on a methodology for detection of ASD from 3-dimensional walking video dataset, utilizing supervised machine learning (ML) classification algorithms and using nature-inspired optimization algorithms for feature extraction from dataset. The proposed methodology involve the classification of ASD using supervised ML classification algorithm and extracting important and relevant features from the dataset using nature-inspired optimization algorithms. We also included the ranking coefficients for finding the initial leading particle. This selection of particle significantly reduces the computation time and hence, improving the total efficiency and accuracy for the ASD detection. To evaluate the efficiency of the proposed methodology, we deployed various combination of classification algorithm and nature-inspired algorithms resulting in an outstanding classification accuracy of 100% using random forest classification algorithm and gravitational search algorithm for feature selection. The application of the proposed methodology with different datasets would enhance the robustness and generalizability of the proposed methodology. Due to high accuracy and less total computation time, the proposed methodology will offer a significant contribution to the medical and academia fields, providing a foundation for future research and advancements in ASD diagnosis. Copyright © 2025, The Authors. All rights reserved.

关键词： Diseases

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A Hybrid Neural Network with Smart Skip Connections for High-Precision, Low-Latency EMG-Based Hand Gesture Recognition

arXiv

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arXiv 2025年

作者： Wazir, Hafsa Ahmad, Jawad Khan, Muazzam A. Jan, Sana Ullah Khan, Fadia Ali Khan, Muhammad Shahbaz ICESCO Chair for Big Data Analytics and Edge Computing Quaid-i-Azam University Islamabad Pakistan Cybersecurity Center Prince Mohammad Bin Fahd University Alkhobar Saudi Arabia School of Computing Engineering and the Built Environment Edinburgh Napier University Edinburgh United Kingdom Department of Computer Science HITEC University Taxila Pakistan

lectromyography (EMG) is extensively used in key biomedical areas, such as prosthetics, and assistive and interactive technologies. EMG signals measure the electrical activity of muscles during different motions. EMG signals play a key role in gesture recognition studies, such as hand gesture recognition. This paper presents a new hybrid neural network named ConSGruNet for precise and efficient hand gesture recognition. The proposed model comprises convolutional neural networks with smart skip connections in conjunction with a Gated Recurrent Unit (GRU). The proposed model is trained on the complete Ninapro DBl dataset. The proposed model boasts an accuracy of 99.7% in classifying 53 classes in just 25 milliseconds. In addition to being fast, the proposed model is lightweight with just 3,946 KB in size. The fast inference time and lightweight architecture of the proposed model makes it suitable for resource constrained loT devices. Moreover, the proposed model has also been evaluated for the reliability parameters, i.e., Cohen's kappa coefficient, Matthew's correlation coefficient, and confidence intervals. The close to ideal results of these parameters validate the models performance on unseen data. © 2025, CC BY-NC-ND.

关键词： Convolutional neural networks

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Method and steps for diagnosing the possibility of corporate bankruptcy using massive news articles

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IEIE Transactions on Smart Processing and Computing 2020年第1期9卷 13-21页

作者： Jin, Hoon Joo, Dong-Won Department of Computer Science and Engineering Kyonggi University Suwon Korea Republic of Big Data Analytics Team Xinapse Co. Ltd. Seoul Korea Republic of

Abstract: In the aftermath of the fourth industrial revolution, artificial intelligence and big data technology have been used in various fields in South Korea, and the techniques are being applied to, and are complemented by, various service fields in which they had not been introduced before. Especially in order to secure credit stability for companies borrowing from financial institutions, and to preemptively respond to risks, attempts to forecast the possibility of bankruptcy and adopt the forecasts into the actual business are actively conducted by major domestic banks by using online news articles and social networking site data. In this study, we suggest a specified step added to the established machine language analysis processes for opinion mining using massive amounts of text, and we provide an essential tool for it. Also, we designed an algorithm for diagnosing corporate bankruptcy, which has never been proposed in the related research area. Through deep exploratory data analysis and domain-specific data analysis, we implement a rule-based automatic tagger for labeling massive amounts of unlabeled news articles and have devised a novel prediction algorithm. As a result, we achieved 92% accuracy, with an area under the curve of 0.96 with respect to performance, and a hit ratio of 50% among the 26 candidates on a list predicted by financial data analysis. Thus, we recognize that the result of our study is sufficiently complementary to financial data analysis in performance and values, but several limitations remain, such as data coverage, reliability, and incompleteness due to linguistic characteristics. Copyrights © 2020.

关键词： Machine learning

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Identification of new disease genes from protein–protein interaction network

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Journal of Ambient Intelligence and Humanized Computing 2018年 1-9页

作者： Masood, M. Mohamed Divan Manjula, D. Sugumaran, Vijayan Department of Computer Science and Engineering Anna University Chennai India Center for Data Science and Big Data Analytics Department of Decision and Information Sciences School of Business Administration Oakland University RochesterMI48309 United States

Human beings are susceptible to diseases caused by a variety of reasons. The causal factors for diseases include microorganisms (viruses and bacteria), environmental factors and largely, genetics. As most diseases are caused by the genes of a particular living organism, it is necessary to analyze the genetic characteristics to treat the disease effectively. The genetics analysis is done in this research using protein–protein interaction network (PPI), which contains information on a gene’s interaction with other genes of a particular living organism. Using the PPI network, new genes for a particular disease can be discovered by applying the shortest path algorithm on the PPI network, given that genes reside next to one another in the network. Consequently, the shortest path algorithm that is executed on the PPI network will retrieve the most similar genes that are related to a particular disease. From the retrieved candidate genes, irrelevant genes are filtered out and the remaining are taken as novel genes. © 2018 Springer-Verlag GmbH Germany, part of Springer Nature

关键词： Genes

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A two-stage deep learning-based hybrid model for daily wind speed forecasting

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Heliyon 2025年第1期11卷 e41026页

作者： Band, Shahab S. Ameri, Rasoul Qasem, Sultan Noman Mehdizadeh, Saeid Gupta, Brij B. Pai, Hao-Ting Shahmirzadi, Danyal Salwana, Ely Mosavi, Amir Future Technology Research Center National Yunlin University of Science and Technology Douliu Taiwan Department of Information Management International Graduate School of Artificial Intelligence National Yunlin University of Science and Technology Douliu Taiwan Computer Science Department College of Computer and Information Sciences Imam Mohammad Ibn Saud Islamic University (IMSIU) Riyadh 11432 Saudi Arabia Computer Science Department Faculty of Applied Science Taiz University Taiz 6803 Yemen Water Engineering Department Urmia University Urmia Iran Department of Computer Science and Information Engineering Asia University Taichung 413 Taiwan Symbiosis Centre for Information Technology (SCIT) Symbiosis International University Pune India Center for Interdisciplinary Research University of Petroleum and Energy Studies (UPES) Dehradun India Department of Big Data Business Analytics National Pingtung University Pingtung Taiwan of Engineering Science and Technology National Yunlin University of Science and Technology 123 University Road Yunlin Douliou 64002 Taiwan Institute of Visual Informatics Universiti Kebangsaan Malaysia Selangor Bangi 43600 Malaysia John von Neumann Faculty of Informatics Obuda University Budapest Hungary Ludovika University of Public Service Budapest Hungary

Global adoption of wind energy continues to increase, while improving the efficiency of turbine settings requires reliable wind speed (WS) models. The latest models rely on artificial intelligence (AI) optimizations which constructs tests on a range of novel hybrid models to examine the reliability. Gradient Boosting (GB), Random Forest (RF), and Long Short-Term Memory (LSTM) are used in new combinations for data pre-processing. A Time Varying Filter-based Empirical Mode Decomposition (TVFEMD) model is coupled with the GB and LSTM standalone models, to create TVFEMD-GB and TVFEMD-LSTM hybrids, which are run in competition with each other. Eventually, a preferred hybrid form is established, simultaneous hybridization of TVFEMD with GB and LSTM. This study is the first to hybridize these fundamental systems, and create a TVFEMD-GB-LSTM model that can forecast WS. This study finds that the novel hybrid models exhibit superior performance to standalone GB and LSTM models, opening the pathway to alternative WS prediction techniques. © 2024 The Authors

关键词： Artificial intelligence big data data science Forecasting Gradient boosting Long short-term memory Machine learning Wind speed

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