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7th International Conference on Microelectronics and Telecommunication engineering, ICMETE 2023

作者： Rana, Anup Sarkar, Bikramjit Parida, Raj Kumar Adhikari, Saurabh Anandha Lakshmi, R. Akila, D. Pal, Souvik Liverpool Business School Liverpool John Moores University Liverpool United Kingdom Department of Computer Science and Engineering JIS College of Engineering Kalyani India School of Computing Science and Engineering Galgotias University Greater Noida India School of Engineering Swami Vivekananda University Kolkata India Department of Computer Application Anna Adarsh College for Women Chennai India Department of Computer Applications Saveetha College of Liberal Arts and Sciences SIMATS Deemed University Chennai India Department of Management Information Systems Saveetha College of Liberal Arts and Sciences Saveetha Institute of Medical and Technical Sciences Chennai India Kolkata India

ISBN: (纸本)9789819995615

The Indian pharmaceutical industry has witnessed exponential growth over the past few decades to become the third largest globally in volume terms and a leading supplier of affordable generics. This paper provides a comprehensive assessment of the industry’s evolution, structure, performance metrics, priorities, challenges and future outlook. Secondary data from government reports, industry associations, company documents and academic literature is synthesized to develop a holistic perspective. The analysis indicates that innovation, research productivity, clinical trials, ethical practices and sustainability shape the industry’s development. Strategic priorities include strengthening R&D capabilities, expanding exports, leveraging digital technologies across operations, ensuring robust supply chains and adopting global quality and ESG standards. Investments in innovation have risen but remain below international benchmarks. While generics account for majority revenues, emerging segments like biosimilars and complex drugs offer growth opportunities. Digital adoption is increasing with data analytics and AI gaining prominence. However, issues related to regulations, pricing control, IP protection, skill shortages and environmental impact need resolution through appropriate reforms and public–private collaboration. India’s vision to become a leading pharmaceutical innovation and manufacturing hub would require reinforcing policy frameworks around IP protection, pricing, regulatory efficiency, skill building and technology adoption. Overall, the industry holds immense potential for beneficial growth but realizing it would necessitate concentrated efforts from stakeholders to build organizational capabilities, foster collaboration and imbibe global best practices. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Supply chains

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A haptic device-based reproduction system of active finger movement and its evaluation using sensory evoked potentials 15

A haptic device-based reproduction system of active finger m...

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15th Biomedical engineering International Conference, BMEiCON 2023

作者： Ozawa, Yusuke Yoshimura, Natsue Uehara, Kazumasa Seki, Kazuhiko Tokyo Institute of Technology School of Engineering Yokohama Japan Tokyo Institute of Technology School of Computing Yokohama Japan Toyohashi University of Technology Department of Computer Science and Engineering Toyohashi Japan National Center of Neurology and Psychiatry Department of Neurophysiology Kodaira Japan

ISBN: (纸本)9798350345247

To develop an experimental system that could be used for physiological experiments on voluntary finger movements in humans, we developed an experimental system that can realize passive finger movements by reproducing recorded users' own active movement trajectories using a haptic device called SPIDAR-GCC. The efficacy of the system was verified through high agreement of the finger trajectories between active and passive movements and no muscle activations during the passive movements. We further confirmed that high signal-to-noise ratio sensory evoked potentials (SEPs) derived from median nerve stimulation of the wrist was observed by electrical stimulation exceeding motor threshold without muscle fatigue throughout the experiment. As N20/P25 component of SEPs from 20 participants showed amplitude changes according to the movement conditions, these results suggested that the proposed experimental system has the capability to be utilized as a physiological experimental system for elucidating the neural control mechanisms of voluntary movements. © 2023 IEEE.

关键词： Cell proliferation

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Enhancing Facility Safety for Autonomous Gas Inspection Drones Leveraging Convolutional Neural Networks and loT Technology 11

Enhancing Facility Safety for Autonomous Gas Inspection Dron...

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11th International Conference on Reliability, Infocom Technologies and Optimization, ICRITO 2024

作者： Latha, S. Asha, P. Srinivasan, V. Prasanna Elangovan, K. Thamizhamuthu, R. Sujatha, S. Department of Biotechnology Tamil Nadu Salem India Sathyabama Institute of Science and Technology Department of Computer Science and Engineering Tamil Nadu Chennai India R.M.D. Engineering College Department of Information Technology Tamil Nadu Chennai India Dhanalakshmi Srinivasan Engineering College Department of Mechanical Engineering Tamil Nadu Chennai India Srm Institute of Science and Technology Department of Computing Technologies Tamil Nadu Chennai India Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Department of Biomedical Engineering Tamil Nadu Chennai India

ISBN: (纸本)9798350350357

To increase facility safety by integrating autonomous gas inspection drones with Convolutional Neural Networks (CNNs) and Internet of Things (loT) technologies. Due to their ability to reach difficult situations, unmanned aerial vehicles are increasingly used for gas inspection in industry. However, drone safety and gas detection accuracy remain major issues. It offers a comprehensive method that uses CNN s for real-time image analysis and classification to help the drone locate gas leaks with high accuracy. CNN is trained on a broad dataset of gas leak situations and environmental conditions. loT technology also allows the drone and centralized monitoring system to communicate in real-time for data sharing and decision-making. Our method can adjust to changing lighting and weather and discriminate between innocuous abnormalities and gas leaks. Advanced navigation algorithms let the drone traverse complicated industrial sites while avoiding obstacles and maximizing coverage. Experimental findings show the suggested technology improves facility safety by lowering gas leak reaction times and operator risk. CNNs with loT technologies increase gas leak detection and allow proactive maintenance, improving industrial facility safety and efficiency. It advances autonomous systems for industrial applications by tackling gas inspection safety problems. The suggested framework lays the groundwork for integrating innovative technology to improve autonomous gas inspection drone capabilities and safety. © 2024 IEEE.

关键词： Gases

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Aspect category and sentiment polarity detection using a permutation language-based transformer model

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Procedia computer science 2025年 258卷 4179-4189页

作者： Ashok Kumar Jayaraman Tina Ester Trueman Gayathri Ananthakrishnan Abirami Murugappan Erik Cambria Manikandan G Satanik Mitra HCLTech Engineering and R&D Services Chennai Tamil Nadu India Department of Computer Science University of the People California USA School of Computer Science Engineering and Information Systems Vellore Institute of Technology Vellore India Department of Information Science and Technology Anna University Chennai India College of Computing and Data Science Nanyang Technological University Nanyang Singapore School of Computer Science and Engineering Vellore Institute of Technology Vellore India Department of Management BITS Pilani India

Aspect-based sentiment analysis (ABSA) performs fine-grained analysis on text to determine a specific aspect category and a sentiment polarity. Recently, machine learning models have played a key role in ABSA tasks. In particular, transformer-based pre-trained models have achieved promising results in natural language processing tasks. Therefore, we propose a permutation based XLNet fine-tuning model for aspect category detection and sentiment polarity detection. Our model learns bidirectional contexts via positional encoding and factorization order. We evaluate the proposed permutation language model on three ABSA datasets, namely, SentiHood, SemEval 2015, and SemEval 2016. Specifically, we studied the ABSA tasks in a constrained system with a multi-class environment. Our result indicates that the proposed permutation language model achieves a better result.

关键词： Aspect-based sentiment analysis permutation language model deep learning transformers multiclass classification

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Artificial Intelligent based Models for Event Extraction using Customer Support Applications 2

Artificial Intelligent based Models for Event Extraction usi...

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2nd International Conference on Augmented Intelligence and Sustainable Systems, ICAISS 2023

作者： Mayuranathan, M. Akilandasowmya, G. Jayaram, B. Velrani, K. Sundara Kumar, M Jogendra Vidhya, R.G. Srm Valliammai Engineering College Department of Computer Science Engineering India Sri Sairam Institute of Technology Department of Computer Communication and Engineering India Rmk Engineering College Department: Computer Science and Business Systems Tamilnadu Gummidipoondi Taluk Tiruvallur District Kavaraipettai India School of Computing Sathyabama Institute of Technology Information Technology India Koneru Lakshmaiah Education Foundation Department of Cse AP Vaddeswaram India Hkbk College of Engineering Department of Ece Bangalore India

ISBN: (纸本)9798350325799

Recently, AI and NLP have been used to improve customer care. Customer support relies on event extraction and relevant data. In customer support applications, AI-based algorithms extract events. Event extraction identifies and extracts specific events or activities from textual data like customer service chats, emails, and social media interactions. Customer support teams may discover issues, trends, and improve the customer experience by automatically extracting events. Customer support event extraction can be done using AI-based models. Event extraction relies on named entity recognition (NER), sentiment analysis, and text classification. NER classifies customer names, product names, and problem keywords. Text classification classifies client issues into preset categories like billing, technical support, or product feedback, while sentiment analysis assesses the customer's emotional tone. Event extraction AI models use machine learning algorithms. Support vector machines (SVM) and random forests can be trained on annotated customer support data to reliably classify events. Recurrent neural networks (RNNs) and transformers can capture complicated patterns in textual data, making them promising event extraction methods. Customer support applications benefit from AI-based event extraction algorithms. It frees up support teams to focus on client issues by automating evaluation and categorization. It also helps firms identify emerging trends and enhance their products and services to answer client complaints. AI-based customer support event extraction models face hurdles. Labeled training data is costly and time-consuming to obtain. Ensuring model generalizability across multiple domains or languages is ongoing research. In conclusion, AI-based event extraction in customer support systems can improve customer experiences and support team efficiency. © 2023 IEEE.

关键词： Support vector machines

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Unveiling Underwater Structures: MobileNet vs. EfficientNet in Sonar Image Detection

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Procedia computer science 2024年 233卷 518-527页

作者： P A Arjun S Suryanarayan R S Viswamanav S Abhishek T Anjali Department of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Amritapuri India Rolls-Royce Inc Bangalore India

Deep learning techniques have led to an increased use of Convolutional Neural Networks (CNN) in recognizing images for marine surveys and classifying underwater objects. Applying CNN for the automatic identification of targets in side-scan sonar (SSS) images can boost accuracy and efficiency. Using transfer learning and deep learning as lenses, this work explores the field of underwater item detection. Comparing performance of MobileNet and EfficientNet in performing the task of underwater item classification from sonar images is the central research objective of this study. These two well-known convolutional neural networks were the subject of a thorough investigation to help explain this. In order to fine-tune pre-trained models on a dataset of sonar images, the study approach used the transfer learning technique. We conducted tests on MobileNet and EfficientNet and thoroughly assessed each of their accuracy levels. This study has two main conclusions. First, showing promise as a strong model for underwater object identification, EfficientNet performed exceptionally well in the classification challenge.

关键词： Side-Scan Sonar Convolutional Neural Networks Transfer Learning MobileNetV3 EfficientNetV2

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Automation of Text Summarization Using Hugging Face NLP

Automation of Text Summarization Using Hugging Face NLP

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Emerging Technology (INCET), International Conference for

作者： Asmitha M Aashritha Danda Hemanth Bysani Rimjhim Padam Singh Sneha Kanchan Department of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Bengaluru India Department of Internet Engineering and Computer science Universiti Tunku Abdul Rahman Malaysia

ISBN: (数字)9798350361155

ISBN: (纸本)9798350361162

Within the expansive domain of “Natural Language Processing” (NLP), the task of “text summarization” emerges as a foundational element, playing a pivotal role in distilling relevant information from extensive textual corpora. In the digital age, the importance of efficient summarization becomes increasingly critical, given the overwhelming volume of textual information. This comprehensive study delves into the intricacies of both extractive and abstractive summarization techniques, placing a specific focus on transformer-based models like BERT and GPT. These models, celebrated for their remarkable capabilities in context comprehension and coherent summarization, are rigorously evaluated alongside established methods like TF-IDF, TextRank, Sumy, Fine Tuning Transformers, Model-T5, LSTM, greedy, and beam search. The practical implications of text summarization extend across diverse fields, encompassing news stories, academic papers, and social media content, underscoring its broad utility in various domains. This study not only incorporates cutting-edge models but also explores a gamut of evaluation methods to discern the quality of summarization. By intertwining theory and application, this research positions itself at the forefront of evolving summarization approaches, shedding light on the transformative impact on information consumption patterns. The dynamic landscape of summarization methods underscores the need for continuous research and innovation, as technological advancements continue to reshape how individuals access and comprehend information.

关键词： Technological innovation Accuracy Social networking (online) Text summarization Transformers Natural language processing Task analysis

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Next-Gen Parking Facilities: Deep Learning for Face and License Plate based Access

Next-Gen Parking Facilities: Deep Learning for Face and Lice...

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International Conference on computing for Sustainable Global Development (INDIACom)

作者： K Balasubramanian N Kannan S Sivakami S Senthil P Immaculate Rexi Jenifer N Murali Department of Computer Science and Engineering E. G. S. Pillay Engineering College Nagapattinam India Department of Computer Science and Engineering K. Ramakrishnan College of Technology Trichy India Department of Computer Science and Engineering School of Computing SASTRA

Parking congestion has become a major problem in today’s metropolitan environments, resulting in lost time, higher emissions, and irritated drivers. There is a severe shortage of parking places in metropolitan areas because of the ever-growing number of the vehicles and the ever-growing urban population. This shortage has exacerbated traffic congestion and environmental pollution in addition to making it difficult and time-consuming to find a parking space. Conventional parking management systems are finding it harder and harder to handle these issues; they frequently depend on physical barriers or manual ticketing. Innovative approaches that incorporate automation and technology are becoming more prevalent to address this problem. Conventional parking management systems frequently depend on human intervention and physical infrastructure, which leads to inefficiencies and constrained scalability. In contrast, new opportunities for a more intelligent, effective, and user-friendly experience are made possible by the incorporation of deep learning methods and cutting-edge computer vision technology into parking management. This system makes use of two essential elements: automatically recognizing license plate numbers for vehicle identification and facial recognition technology for person identification. Through the smooth integration of these technologies, the system not only makes parking easier, but it also improves security and maximizes parking space usage. We will examine the essential elements, advantages, and possible effects of the Face and Number Plate-Based Smart Parking System in this survey. According to experimental findings, employing a face- and number-plate verification method can increase the efficiency of smart parking systems.

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Cloud Cover Removal from Remote Sensing Data using GANs Based on Attention Mechanism

Cloud Cover Removal from Remote Sensing Data using GANs Base...

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International Conference on Image Information Processing

作者： B Siva Jyothi Natha Reddy D Sasikala Computer Science and Engineering Amrita Vishwa Vidyapeetham School of Computing Chennai

ISBN: (数字)9798350371406

ISBN: (纸本)9798350371413

Cloud interference is a significant challenge in remote sensing applications, impacting the quality and reliability of data used for environmental analysis, disaster monitoring, and urban planning. In this study, we present an innovative approach to remove clouds from remote sensing data by employing Generative Adversarial Networks (GANs) with an integrated attention mechanism. Our method is designed to effectively restore regions obscured by clouds while preserving the integrity of the remaining image. We introduce a specialized architecture called U-net based Attention GAN (UAGAN) to address cloud cover issues. In this architecture, the generator network utilizes attention mechanisms to identify and rectify cloud-affected areas. The attention mechanism dynamically emphasizes relevant spatial regions during cloud removal, enhancing the precision of the restoration process. To evaluate our approach, we conducted experiments using the RICE dataset and trained our UAGAN model. Our method’s effectiveness was assessed using quantitative metrics, including the Structural Similarity Index (SSIM), Root Mean Squared Error (RMSE), and Peak Signal-to-Noise Ratio (PSNR). Comparative analysis of three different models, namely vanilla GAN, U-net GAN, and UAGAN, demonstrates that UAGAN outperforms the others with impressive results, achieving a PSNR of 29.901 dB, RMSE of 7.891, and SSIM of *** research contributes to the advancement of cloud removal techniques in remote sensing, offering a promising solution for improving data quality and enhancing the accuracy of downstream applications. The integration of GANs and attention mechanisms showcases the potential for innovative approaches to address complex challenges in remote sensing data analysis.

关键词： Measurement PSNR Clouds Disasters Urban planning Weather forecasting Generative adversarial networks

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A Novel Approach for Photo-Realistic High Quality Sign Language Video Synthesis

A Novel Approach for Photo-Realistic High Quality Sign Langu...

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IEEE International Conference on Intelligent Techniques in Control, Optimization and Signal Processing (INCOS)

作者： Pravine Mukesh R Natarajan B Elakkiya R G.K. Vaidhya Murali P Geetha K Department of Computer Science and Engineering (AIE) Amrita School of Computing Amrita Vishwa Vidyapeetham Chennai India School of Computer Science and Engineering VIT University Chennai Tamilnadu India Department of Computer Science Birla Institute of Technology and Science Dubai United Arab Emirates Department of Computer Science and Engineering SRM Institute of Science and Technology Vadapalani Chennai India Department of CSE Saveetha school of Engineering Saveetha Institute of Medical and Technical Sciences Chennai Tamilnadu India Department of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Chennai India

ISBN: (数字)9798350361186

ISBN: (纸本)9798350361193

This research offers a brand-new framework for producing high-quality, photo-realistic sign language videos using sign language corpora. The framework consists of three main components such as Neural Machine Translation (NMT) using transformer, an open pose estimation approach, and a Generative Adversarial Network (GAN) with VGG-19 classifier. The NMT transformer translates the sign language corpus into a sequence of sign glosses, pose estimation model performs mapping of equivalent signer images and further assists to generate sign images. The GAN with VGG-19 classifier then generates high- quality sign language videos from the sign language images. The proposed method performs at the cutting edge in terms of evaluation on three benchmark dataset using metrics such as PSNR, SSIM, FID, and Inception Score. In addition, the framework offers better translation outcomes with less processing overhead and produces high-quality, photo-realistic sign language videos without the need for animation or avatar techniques. The proposed framework has the potential to offer the uninterrupted communication medium between common people with deaf-mute society. The evaluation scores highlights the improved perfor- mance of the proposed model compared with earlier approaches in terms of visual quality.

关键词： Sign language Adaptation models Visualization Pose estimation Assistive technologies Signal processing Generative adversarial networks

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