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A transfer learning approach for continuous speech recognition system in Indian language sadri

International Journal of Information technology (Singapore) 2025年 1-10页

作者： Paul, Shubhojeet Bhattacharjee, Vandana Saha, Sujan Kumar Department of Computer Science and Engineering Birla Institute of Technology Mesra Jharkhand Ranchi 835215 India Department of Computer Science and Engineering National Institute of Technology West Bengal Durgapur 713209 India

Sadri is the most widely used language of the Chotanagpur Plateau region of India. This is primarily a spoken language and developing an automatic speech recognition (ASR) system in Sadri is extremely important. When we searched the literature, we found no open ASR system or relevant resources in Sadri. So, we worked on developing an ASR system in Sadri. We created a Sadri speech corpus of around 20 h, employing 53 native speakers. Then, we implemented the baseline model using Convolutional Neural Networks (CNN) and Recurrent Neural Networks (RNN). As the size of the training data was not sufficient, we investigated a transfer learning technique to improve performance. We found that ASR data are openly available in other related languages such as Hindi and Bengali. We used Bengali and Hindi data of 100 h each in a transfer learning framework along with the Sadri data. In our experiments, we found that the transfer learning-based model outperforms the baseline model. © Bharati Vidyapeeth's institute of computer Applications and Management 2025.

关键词： ASR Deep learning Indian language Sadri Speech recognition

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A Comparative Study on the Properties of Carbon Fiber-Reinforced Polymer Composites Developed by Hand Layup and Vacuum Bagging Molding Techniques

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Journal of The Institution of Engineers (India): Series D 2024年第1期106卷 263-275页

作者： Amar, Thakur Abhinav Kumar, Amit Yadav, D.K. Department of Production and Industrial Engineering Birla Institute of Technology Jharkhand Mesra Ranchi835215 India Department of Mechanical Engineering National Institute of Technology Bihar Patna800005 India Department of Computer Science and Engineering National Institute of Technology Jharkhand Jamshedpur831014 India

The present study addressed the fabrication of carbon fiber-reinforced polymer (CFRP) composites using two different techniques: manual layup and vacuum bagging molding. The purpose was to compare their unique properties. CFRP composites employ woven roving mat carbon fiber as a reinforcing material, combined with an epoxy matrix consisting of Araldite and hardener. The resin to fiber ratio has been maintained at a 1:1 ratio, while the resin to hardener ratio has been maintained at a 10:1 ratio by weight. Various stacking sequences of carbon/epoxy composites (0/90) were made using both approaches. Optimization of several process parameters was carried out to enhance the mechanical properties of the polymer composites. Every laminate was composed of four to eight layers. Various carbon/epoxy composite orientations were subjected to testing until failure using a universal testing machine, in accordance with the relevant applicable standards (ASTM-D3039), in order to determine the ultimate tensile strength, % elongation (or ductility), and elastic constant. Furthermore, the adhesion intensity between the fiber and resin was assessed by conducting microstructural studies using a scanning electron microscope. The results demonstrated that the samples fabricated using the vacuum bagging mold technique exhibited superior tensile strength compared to the samples fabricated using the hand layup approach. © 2024, The Institution of Engineers (India).

关键词： Resins

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A Case Study on CALVEASE: AI Driven IoT Framework for Dystocia Prediction

A Case Study on CALVEASE: AI Driven IoT Framework for Dystoc...

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2023 IEEE Global Conference on Information Technologies and Communications, GCITC 2023

作者： Sharma, Anmol Hooda, Nishtha Hu, Yu Hen Singh Rana, Prashant University of Wisconsin-Madison Electrical and Computer Engineering Department Madison United States Indian Institute of Information Technology Department of Computer Science Una India Thapar Institute of Engineering and Technology Department of Computer Science and Engineering Patiala India

ISBN: (纸本)9798350308167

The ability to predict cow calving easiness cost-effectively, especially in the dairy industry where cattle suffer from a variety of unpredictable deadly illnesses and high breeding expenses assist farmers in improving reproductive performance and, thereby, farmers profitability. In this research work, machine learning and Internet-of-Things (IOT) based CALVEASE framework for monitoring dairy herd and predicting calving easiness is studied as a multi-classification problem. The CALVEASE framework collects activity information through sensors deployed in cow's collar belt in the first stage. The collected information stored at the cloud platform is utilized to train machine learning models for predicting the cow calving easiness in the second stage. On empirically testing the various models during field testing, random forest outperforms the other models showing an accuracy of 99.58% hence, the tested framework was found to be efficacious for boosting the dairy herd productivity. © 2023 IEEE.

关键词： Calving ease score Data analytics Machine learning Multi-class Classification

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Artificial Intelligence Enabled Future Wireless Electric Vehicles with Multi-Model Learning and Decision Making Models

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Tsinghua science and technology 2024年第6期29卷 1776-1784页

作者： Gajula Ramesh Anil Kumar Budati Shayla Islam Louai A.Maghrabi Abdullah Al-Atwai Department of Computer Science and Engineering Gokaraju Rangaraju Institute of Engineering&TechnologyHyderabad 500090India Institute of Computer Science and Digital Innovation(ICSDI) UCSI UniversityKuala Lumpur 56000Malaysia also with Department of ECE KoneruLakshmaiah Education FoundationHyderabad 500090India ICSDI UCSI UniversityKuala Lumpur 56000Malaysia Department of Software Engineering College of EngineeringUniversity of Business and TechnologyJeddah 21448Kingdom of Saudi Arabia Department of Computer Science Applied CollegeUniversity of TabukTabuk 47512Kingdom of Saudi Arabia

In the contemporary era,driverless vehicles are a reality due to the proliferation of distributed technologies,sensing technologies,and Machine to Machine(M2M)***,the emergence of deep learning techniques provides more scope in controlling and making such vehicles energy *** existing methods,it is understood that there have been many approaches found to automate safe driving in autonomous and electric vehicles and also their energy ***,the models focus on different aspects *** is need for a comprehensive framework that exploits multiple deep learning models in order to have better control using Artificial Intelligence(AI)on autonomous driving and energy *** this end,we propose an AI-based framework for autonomous electric vehicles with multi-model learning and decision *** focuses on both safe driving in highway scenarios and energy *** deep learning based framework is realized with many models used for localization,path planning at high level,path planning at low level,reinforcement learning,transfer learning,power control,and speed *** reinforcement learning,state-action-feedback play important role in decision *** simulation implementation reveals that the efficiency of the AI-based approach towards safe driving of autonomous electric vehicle gives better performance than that of the normal electric vehicles.

关键词： wireless vehicles deep learning multi-model learning reinforcement learning Artificial Intelligence(Al)

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Experimental Investigation and Weibull Statistical Analysis on Tensile Behaviour of Carbon/Epoxy Composites

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Journal of The Institution of Engineers (India): Series D 2024年第3期105卷 1373-1383页

作者： Amar, Thakur Abhinav Kumar, Amit Yadav, D.K. Department of Production and Industrial Engineering Birla Institute of Technology Ranchi JH Mesra835215 India Department of Mechanical Engineering National Institute of Technology BH Patna800005 India Department of Computer Science and Engineering National Institute of Technology JH Jamshedpur831014 India

This paper is concerned with the experimental and theoretical evaluation of the tensile strength of carbon fabric-reinforced epoxy composites developed using hand lay-up and vacuum bagging techniques. The developed CFRP composite laminates were subjected to tensile tests as per ASTM D3039 at different strain rates of 1 s−1, 10 s−1, and 100 s−1. The statistical analysis of the experimental results was conducted using a two-parameter Weibull distribution function. The results showed an increase in tensile strength with an increase in strain rate for composite laminates produced using both fabrication techniques. The tensile modulus also showed an increasing trend with respect to strain rate, but the increase was marginal, while elongation at break showed a decreasing trend. For all cases of strain rate, the vacuum bag-fabricated composite laminates showed higher tensile strength and modulus values except for elongation at break when compared with the laminates fabricated with hand lay-up. Cumulative probability density data, along with Weibull parameters, were utilised to determine theoretical tensile strength. The projected tensile strength values closely matched the experimental results, with a minimal difference of approximately 6.2% observed in the context of composite laminates fabricated under vacuum bag conditions. Fracture analysis identified distinct failure mechanisms: adhesive failure at the fabric/matrix interface for the hand lay-up method and brittle fracture as the predominant failure mode for laminates produced using vacuum bagging techniques. © The Institution of Engineers (India) 2023.

关键词： Tensile strength

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A Secure Framework for WSN-IoT Using Deep Learning for Enhanced Intrusion Detection

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computers, Materials & Continua 2024年第10期81卷 471-501页

作者： Chandraumakantham Om Kumar Sudhakaran Gajendran Suguna Marappan Mohammed Zakariah Abdulaziz S.Almazyad School of Computer Science&Engineering Vellore Institute of TechnologyChennai CampusChennai600127India School of Electronics&Engineering Vellore Institute of TechnologyChennai CampusChennai600127India Department of Computer Sciences and Engineering College of Applied ScienceKing Saud UniversityRiyadh11543Saudi Arabia Department of Computer Engineering College of Computer and Information SciencesKing Saud UniversityRiyadh11543Saudi Arabia

The security of the wireless sensor network-Internet of Things(WSN-IoT)network is more challenging due to its randomness and self-organized *** detection is one of the key methodologies utilized to ensure the security of the *** intrusion detection mechanisms have issues such as higher misclassification rates,increased model complexity,insignificant feature extraction,increased training time,increased run time complexity,computation overhead,failure to identify new attacks,increased energy consumption,and a variety of other factors that limit the performance of the intrusion system *** this research a security framework for WSN-IoT,through a deep learning technique is introduced using Modified Fuzzy-Adaptive DenseNet(MF_AdaDenseNet)and is benchmarked with datasets like NSL-KDD,UNSWNB15,CIDDS-001,Edge IIoT,Bot *** this,the optimal feature selection using Capturing Dingo Optimization(CDO)is devised to acquire relevant features by removing redundant *** proposed MF_AdaDenseNet intrusion detection model offers significant benefits by utilizing optimal feature selection with the CDO *** results in enhanced Detection Capacity with minimal computation complexity,as well as a reduction in False Alarm Rate(FAR)due to the consideration of classification error in the fitness *** a result,the combined CDO-based feature selection and MF_AdaDenseNet intrusion detection mechanism outperform other state-of-the-art techniques,achieving maximal Detection Capacity,precision,recall,and F-Measure of 99.46%,99.54%,99.91%,and 99.68%,respectively,along with minimal FAR and Mean Absolute Error(MAE)of 0.9%and 0.11.

关键词： Deep learning intrusion detection fuzzy rules feature selection false alarm rate accuracy wireless sensor networks

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A new technology perspective of the Metaverse:Its essence,framework and challenges

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Digital Communications and Networks 2024年第6期10卷 1653-1665页

作者： Feifei Shi Huansheng Ning Xiaohong Zhang Rongyang Li Qiaohui Tian Shiming Zhang Yuanyuan Zheng Yudong Guo Mahmoud Daneshmand School of Computer and Communication Engineering University of Science and Technology BeijingBeijing100083China Jinzhong University Shanxi030619China Department of Business Intelligence and Analytics and the Department of Computer Science Stevens Institute of TechnologyHobokenNJ07030USA

The Metaverse depicts a parallel digitalized world where virtuality and reality are *** has economic and social systems like those in the real world and provides intelligent services and *** this paper,we introduce the Metaverse from a new technology perspective,including its essence,corresponding technical framework,and potential technical ***,we analyze the essence of the Metaverse from its etymology and point out breakthroughs promising to be made in time,space,and contents of the Metaverse by citing Maslow's Hierarchy of ***,we conclude four pillars of the Metaverse,named ubiquitous connections,space convergence,virtuality and reality interaction,and human-centered communication,and establish a corresponding technical ***,we envision open issues and challenges of the Metaverse in the technical *** work proposes a new technology perspective of the Metaverse and will provide further guidance for its technology development in the future.

关键词： Metaverse Technical framework Ubiquitous connections Space convergence Virtuality and reality interaction Human-centered communication

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Behavior Based Group Recommendation from Social Media Dataset by Using Deep Learning and Topic Modeling

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SN computer science 2024年第6期5卷 712页

作者： Mukta, Md. Saddam Hossain Ahmed, Jubaer Raiaan, Mohaimenul Azam Khan Fahad, Nur Mohammad Islam, Muhammad Nazrul Imtiaz, Nafiz Islam, Md. Adnanul Ali, Mohammed Eunus Azam, Sami LUT School of Engineering Sciences Lappeenranta-Lahti University of Technology Department of Computer Science and Engineering United International University Department of Computer Science and Engineering Military Institute of Science and Technology Department of HCC Action Lab Monash University Department of Computer Science and Engineering Bangladesh University of Engineering and Technology Faculty of Science and Technology Charles Darwin University

In this digital era, users frequently share their thoughts, preferences, and ideas through social media, which reflect their Basic Human Values. Basic Human Values (aka values) are the fundamental aspects of human behavior, which define what we consider important, and worth having and pursue them. Existing studies identify the values of individuals from different social network usages such as Facebook and Reddit. However, discovering the similarity (or diversity) of value priorities among the members in a group is important since we can reveal many interesting insights such as finding a set of target customers, identifying the chain of misdeed groups, searching for similar acquaintances in workplaces, etc. In this paper, a graph dataset is compiled using the strongest correlation among the features and then we apply a graph clustering technique to identify a suitable hedonist group (i.e., one dimension of values) for users’ recommendations. Then, we also propose a behavior based (i.e., value) group recommendation technique by analyzing users’ contextual and psychological attributes. Finally, we validate those group members in real life by introducing two hypotheses. In particular, we analyze the tweets of a total of 1140 users collected from Twitter. We obtain a substantial intra-cluster correlation coefficient (ICC) and silhouette clustering coefficient (SCC) scores of 65% and 76%, respectively, among the members in our discovered group. © The Author(s) 2024.

关键词： Graph neural network (GNN) Group recommendation Psychological attributes Social media Topic modeling Twitter Values

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Statistical methods for feature selection: unlocking the key to improved accuracy

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International Journal of computers and Applications 2023年第6期45卷 433-443页

作者： Thiyam, Bidyapati Dey, Shouvik Department of Computer Science and Engineering National Institute of Technology Nagaland Dimapur India

The ever-growing amount of data generated by modern networks poses significant challenges for intrusion detection systems (IDS) in effectively analyzing and classifying security risks. Therefore, it is crucial to identify the most biased characteristics for building efficient and effective IDS algorithms. However, not all features are equally informative or relevant for intrusion detection. In response to these problems, this study proposes a Hybrid approach that uses traditional and advanced statistical techniques. The proposed method effectively validates the features generated from the hybrid model and set-operation theorem to provide the best optimal subset of features for IDS. Various machine learning methods are used to test the proposed model on three popular IDS datasets: NSL-KDD, UNSW NB15, and CIC-DDoS2019. The experimental findings show that the suggested hybrid technique improves IDS performance effectively and efficiently, providing a viable answer to the issues that intrusion detection systems confront. © 2023 Informa UK Limited, trading as Taylor & Francis Group.

关键词： Intrusion detection

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Multilevel Augmentation for Identifying Thin Vessels in Diabetic Retinopathy Using UNET Model

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Intelligent Automation & Soft Computing 2023年第2期35卷 2273-2288页

作者： A.Deepak Kumar T.Sasipraba Faculty of Computer Science and Engineering Sathyabama Institute of Science and TechnologyChennai600119India Department of Computer Science and Engineering St.Joseph’s Institute of TechnologyChennai600119India

Diabetic Retinopathy is a disease,which happens due to abnormal growth of blood vessels that causes spots on the vision and vision *** techniques are applied to identify the disease in the early stage with different methods and *** Learning(ML)techniques are used for analyz-ing the images andﬁnding out the location of the *** restriction of the ML is a dataset size,which is used for model *** problem has been overcome by using an augmentation method by generating larger datasets with multidimensional *** models are using only one augmentation tech-nique,which produces limited features of dataset and also lacks in the association of those data during DR detection,so multilevel augmentation is proposed for *** proposed method performs in two phases namely integrated aug-mentation model and dataset correlation(***).It eliminates overﬁt-ting problem by considering relevant *** method is used for solving the Diabetic Retinopathy problem with a thin vessel identiﬁcation using the UNET *** based image segmentation achieves 98.3%accuracy when com-pared to RV-GAN and different UNET models with high detection rate.

关键词： Image segmentation diabetic retinopathy image augmentation semantic segmentation CNN

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