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9th IEEE International Conference for Convergence in Technology, I2CT 2024

作者： Yerramreddy, Dhanvanth Reddy Marasani, Jayasurya Gowtham, Ponnuru Sathwik Venkata Harshit, Guduru Anjali Amrita Vishwa Vidyapeetham Amrita School of Computing Dept. of Computer Science And Engineering Amritapuri India Amrita Vishwa Vidyapeetham School of Engineering Dept. of Electronics & Communication Engineering Amritapuri India

ISBN: (纸本)9798350394474

Speech recognition plays a pivotal role in the realm of natural language processing that deals in converting the language into the written text, providing human-computer interaction and enables us to use it widely for applications starting with voice assistants and delving upto the transcription services. Due to the complexity present in performing the task of speech recognition has led to the development of various models to enhance accuracy and efficiency. Our project mainly delves into three prominent speech recognition models that are Whisper, Wav2Vec2 and Speechbrain each of them representing distinct approaches of transcribing spoken language. The significance of the models used lies in their potential to perform better for realtime applications by improving the accuracy and reliability of speech recognition. To evaluate the best model that performs effectively, an array of metrics are used including levenshtein distance and it's similarity percentage, jaccard similarity along with semantic similarity that provides an additional layer of evaluation describing the model's understanding of contextual meaning in spoken language. Out of the three models, Speech Brain model outperformed all the other models through the calculation of Word Error Rate (WER), Character Error rate (CER) and BLEU score. The results have shown the model's efficiency in converting spoken language(audio files) into precise and contextually relevant text. The results shows that these models contribute to the field of speech recognition highlighting the strengths of each approach and among them considering speechbrain as an ideal solution for accurate and meaningful transcription. © 2024 IEEE.

关键词： Speech recognition

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Revolutionizing Scene Text Recognition: Unleashing the Power of Dual Step Attention Mechanism in the Encoder 6th

Revolutionizing Scene Text Recognition: Unleashing the Power...

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6th International Conference on Recent Innovations in computing, ICRIC 2023

作者： Selvam, Prabu Sumathi, M. Maniappan, Varun Padmavathi, A. Natarajan, B. Syed Husain, S. Department of Computer Science and Engineering SRM Institute of Science & Technology Tiruchirapalli Campus Tiruchirapalli India School of Computing SASTRA Deemed University Thanjavur India Department of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Chennai601103 India Department of Electronics and Communication Engineering K. Ramakrishnan College of Engineering Tiruchirappalli India

ISBN: (纸本)9789819734412

Text recognition in natural scenes presents a significant challenge in computer vision despite its widespread applications in real-life scenarios. Deep learning advancements have notably enhanced the precision of scene text recognition. The prevailing framework for scene text recognition currently revolves around the encoder–decoder architecture, with Connectionist Temporal Classification (CTC) and attention mechanisms serving as popular text recognition techniques. However, existing methods rooted in the encoder–decoder architecture primarily integrate the attention mechanism into the decoder, limiting their capacity to identify characters accurately. Conversely, CTC-based methods exhibit faster processing times and impose fewer computational burdens, yet they are comparatively less effective than their attention-based counterparts. We introduce a new text recognition network based on a transformer-based network to address these limitations. Our proposed network introduces the Dual Step Attention Mechanism (DSAM), wherein the encoder's attention mechanism captures the overall text location within the image. In contrast, the decoder's attention mechanism precisely identifies the location of each character in the word image. In addition, we have employed a bidirectional encoding to capture the capture character sequence in both directions. To validate the efficacy of our approach, we conducted a comprehensive performance evaluation comparing our proposed network with state-of-the-art methods across six diverse text datasets, namely IIIT5K, SVT, IC13, IC15, SVTP, and CUTE. The results indicate that our model outperforms existing approaches across various metrics. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Deep learning

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A lightweight identity based generalized signcryption scheme for secure communication in standard model

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International Journal of Information Technology (Singapore) 2024年第2期16卷 1221-1229页

作者： Singh, Tej Kumar, Mukesh Gelmecha, Demissie Jabir Thakur, Ganesh Kumar Tyagi, Vikas Sori, Worku Jifara Department of Applied Sciences ABES Engineering College UP Ghaziabad India Institute of Applied Sciences & Humanities GLA University UP Mathura India Department of Electronics and Communication Engineering School of Electrical Engineering and Computing Adama Science and Technology University Adama Ethiopia Department of CSE IMS Engineering College UP Ghaziabad India Department of Computer Science School of Electrical Engineering and Computing Adama Science and Technology University Adama Ethiopia

The implementation of secure communication in the Industrial Internet of Things (IIoT) has made and it is a significant security concern. Identity-based generalized signcryption scheme is one among the cryptographic tools available that offers a secure way out to meet the security needs of IIoT. Our proposal is a lightweight scheme, an efficient identity-based generalized signcryption that works as an encryption signature, or signcryption scheme using only one algorithm in a standard model. This scheme is appropriate for storage-limited environments like wireless sensor networks and smart cards due to its lightweight nature. This means that it can be implemented in a range of IIoT applications where resources are limited, without compromising on security or efficiency. © The Author(s), under exclusive licence to Bharati Vidyapeeth's Institute of Computer Applications and Management 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s);author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

关键词： Bilinear Pairing Generalized signcryption Identity-based cryptography Signcryption

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Multi-attribute wearable pressure sensor based on multilayered modulation with high constant sensitivity over a wide range

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Nano Research 2023年第5期16卷 7583-7592页

作者： Ning Li Song Gao Yang Li Jianwen Liu Wenhao Song Guozhen Shen School of Information Science and Engineering University of JinanJinan 250022China Shandong Provincial Key Laboratory of Network Based Intelligent Computing University of JinanJinan 250022China School of Integrated Circuits and Electronics Beijing Institute of TechnologyBeijing 100081China School of Aeronautical and Astronautical Engineering Purdue University West LafayetteIN 47907USA

Flexible pressure sensors capable of monitoring diverse physiological signals and body movements have garnered tremendous attention in wearable electronic ***,high constant sensitivity over a wide pressure range combined with breathability,biocompatibility,biodegradability is pivotal for manufacturing of reliable pressure sensors in practical sensing *** this work,inspired by the multilayered structure of skin epidermis,we propose and demonstrate a multi-attribute wearable piezoresistive pressure sensor consisting of multilayered gradient conductive poly(ε-caprolactone)nanofiber membranes *** response to externally applied pressure,a layer-by-layer current path is activated inside the multilayered membranes composites,leading to the most salient sensing performance of high constant sensitivity of 33.955 kPa^(−1) within the pressure range of 0–80 *** proposed pressure sensor also exhibits a fast response–relaxation time,a low detection limit,excellent stability,which can be successfully used to measure human physiological ***,an integrated sensor array system that can locate objects’positions is constructed and applied to simulate sitting posture *** results indicate that the proposed pressure sensor holds great potential in health monitoring and wearable electronic devices.

关键词： wearable pressure sensors multilayer structure high constant sensitivity multi-attribute physiological signals monitoring

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Detection of Bullying Text: A Multi-faceted Approach using Machine Learning and Natural Language Processing 5

Detection of Bullying Text: A Multi-faceted Approach using M...

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5th International Conference on Smart electronics and Communication, ICOSEC 2024

作者： Kumar, Kotyada Mohan Kiran Ullas, Karthik Reddy, Vivek Srikar Snehitha, Munaga Sai Malkhed, Mahadevi E. Kumar, K. Dinesh Amrita School of Computing Amrita Vishwa Vidyapeetham Dept. of Computer Science and Engineering Bengaluru India Amrita School of Engineering Amrita Vishwa Vidyapeetham Dept. of Electronics and Computer Engineering Bengaluru India

ISBN: (纸本)9798331504403

With the dawn of the new age of communication, i.e., social media, the world has come into the palms of every individual at every corner of the world. However, easier access to messaging implies easier forms of abuse. As the usage of social media increases, the other side of communication, bullying, also increases. Cyberbullying refers to the concept of harassing individuals or groups through means of the internet with the help of messages or multimedia. Cyberbullying has a huge impact on mental health and research is ongoing in this area to accurately detect bullying text. Existing models are embedded into social media platforms to either ban or apply sanctions on the users involved in the passage of such materials. The proposed study aims to develop a model with a similar approach to detect cyberbullying in comment text. Also, sentiment analysis is performed on the comment text to get an idea of the message's context, thus improving the chances of bullying detection. The work is divided into two stages. In the first stage, the text and the sentiment score are directly passed to the machine learning models. Later, during the second stage word embedding using Word2Vec, a natural language processing technique, is performed on the comment text to obtain vectorized features which are then given to the machine learning models along with sentiment features. It was observed that the latter model shows high levels of success and that it can be deployed in real-world applications. © 2024 IEEE.

关键词： Adversarial machine learning

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Bioinspired Polarized Skylight Orientation Determination Artificial Neural Network

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Journal of Bionic engineering 2023年第3期20卷 1141-1152页

作者： Huaju Liang Hongyang Bai Ke Hu Xinbo Lv School of Energy and Power Engineering Nanjing University of Science and TechnologyNanjing210094China Neuromorphic Computing Laboratory China Nanhu Academy of Electronics and Information TechnologyJiaxing314001China Guidance Radar General Department Xi’an Electronic Engineering Research InstituteXi’an710100China School of Automation Nanjing University of Science and TechnologyNanjing210094China

This paper proposes an artificial neural network to determine orientation using polarized skylight. This neural network has specific dilated convolution, which can extract light intensity information of different polarization directions. Then, the degree of polarization (DOP) and angle of polarization (AOP) are directly extracted in the network. In addition, the exponential function encoding of orientation is designed as the network output, which can better reflect the insect’s encoding of polarization information and improve the accuracy of orientation determination. Finally, training and testing were conducted on a public polarized skylight navigation dataset, and the experimental results proved the stability and effectiveness of the network.

关键词： Polarized light navigation Orientation determination Artificial neural network

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Detecting, Demodulating & Decoding LoRa 15

Detecting, Demodulating & Decoding LoRa

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15th International Conference on computing Communication and Networking Technologies, ICCCNT 2024

作者： Reddy, Pagilla Manohar Kak, Archit Nisha, K.S. Abhijith, C.B. Kattamuri, Sahithya Parasuram, Sadanala Manoj Amrita Vishwa Vidyapeetham Amrita School of Computing Department of Computer Science and Engineering Amritapuri India Amrita Vishwa Vidyapeetham Department of Electronics and Communication Engineering Amritapuri India

ISBN: (纸本)9798350370249

This paper examines the LoRaWAN low-power, wide-area network technology, analyzing its physical layer modulation and network architecture while identifying potential security vulnerabilities like jamming, replay attacks, eavesdropping, and device compromises. To address these issues, the paper develops deep learning models, utilizing spectrograms and convolutional neural networks (CNNs) such as ResNet, InceptionV3, EfficientNet, and custom architectures, for accurate LoRa signal detection and classification. Additionally, this paper presents a reverse-engineering process to uncover LoRa's encoding and decoding algorithms, facilitating the creation of an open-source LoRa physical layer implementation (gr-lora) within the GNU Radio framework. This implementation will empower ongoing research into LoRaWAN security and the development of innovative IoT applications. © 2024 IEEE.

关键词： Reverse engineering

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A Deep Learning-based Smart System for Ornaments and Devices Detection during Check-In of Entrance Exams 5

A Deep Learning-based Smart System for Ornaments and Devices...

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5th International Conference on Innovative Data Communication Technologies and Application, ICIDCA 2024

作者： Krishnapriya, P.S. Nikitha, Gundam Kiran, Kalla Vishwas Uday Aparna, Kuruba Priya, Singamsetty Chandu Department of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Amritapuri India Department of Electronics and Communication Engineering Amrita Vishwa Vidyapeetham Amritapuri India

Nowadays, individuals are experiencing an era characterized by a pandemic situation. Given the challenging circumstances face these days, it is imperative to develop a real-time model that automatically detects whether candidates are wearing ornaments or carrying any devices before appearing for an entrance examination, as it serves as a vital precautionary measure for our health and safety. The proposed system will function in two stages: firstly, it will employ the YOLOv7 algorithm, which operates on Convolutional Neural Networks (CNN), to detect any gadgets or ornaments worn by the candidate. Additionally, we incorporated Mobile Net and HairNet into the code, utilizing TensorFlow's Keras API. Notably, all the datasets used in this system have been created from scratch. Upon successfully detecting gadgets such as wristwatches, Bluetooth devices, earrings, and other materials, the system will promptly display a warning message, denying entry to the respective individuals. Moreover, the proposed system includes real-time monitoring of the examination hall through CCTV cameras. As a cost-effective, time-saving, and accurate solution, it can be implemented across various examinations to ensure candidates' and invigilators' safety and adhere to examination norms. © 2024 The Authors. Published by Elsevier B.V.

关键词： Object detection

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Automatic Brain Tumor Detection Classification Applying Multiple a Deep Learning Method

Automatic Brain Tumor Detection Classification Applying Mult...

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2024 International Conference on System, Computation, Automation and Networking, ICSCAN 2024

作者： Vijiyakumar, K. Muthunagai, R. Bhanusree, A. Nikhilsowrabh, K. School of Computing Srm Institute of Science and Technology Department of Computing Technology Chenngalpet district Kattankulathur India Manakula Vinayagar Institute of Technology Department of Electrical & Electronics Engineering Puducherry India Srm Institute of Science and Technology Department of Computing Technologies Chennai India

ISBN: (纸本)9798331510022

Automated detection of defects in medical imaging has emerged as a crucial domain within various diagnostic applications, especially with MRI regarding tumor detection. 'While accuracy in the detection of tumors is important in providing vital information regarding tissues which have deviated from their normal state, it's even more important for effective treatment planning'. Currently, the diagnosis of tumors from brain MRI scans relies on human assessment, which is infeasible for large image data and error-prone based on judgments of humans. Thus, there is a pressing need for robust and efficient automated classification systems that can overcome these bottlenecks. The core idea of our project is the development of the most advanced tumor detection methods, which saves precious time for radiologists but at a very high accuracy and, thus, reduces mortality levels in patients. In our approach, we have used several CNNs - VGG19, ResNet50, and MobileNetV2, which we have applied through transfer learning for detecting tumors from brain MRIs. We also incorporate into our model an ensemble of these varied CNN architectures that leverages their strengths since the nature and diversity of tumors are intricate. In fact, the ensemble method is really intended to boost the wide-ranging accuracy and strength of the procedure in the detection of tumors. The proposed system utilizes deep learning techniques in combination with image classification to accurately and efficiently identify cancerous cells in brain MRI scans. After that, the web application, developed using Flask, will provide a user-friendly interface for uploading MRI images and then instantaneously return predictions to a doctor on the potential presence of a tumor. © 2024 IEEE.

关键词： Convolutional Neural Networks (CNNs) Deep Learning EnsembleMethod Flask Web Application MRI Imaging TransferLearning Tumor Detection

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AOBL-IPACO: A novel and optimized algorithm to mitigate losses in electrical grid systems

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International Journal of Information Technology (Singapore) 2025年第3期17卷 1919-1927页

作者： Gupta, Rahul Khanna, Ashish Virdee, Bal School of Electrical and Electronics Engineering Maharaja Agrasen Institute of Technology Himachal Pradesh India School of Computer Science Engineering & amp Technology Maharaja Agrasen Institute of Technology Delhi India School of Computing & amp Digital London Metropolitian University London United Kingdom

This paper shows the framework for solving the economic optimization problem in electrical grid system using Advanced Oppositional Based Learning (AOBL) technique with Invasive Plant Ant Colony Optimization (IPACO) algorithm. The work focuses on application of newly developed algorithm called Advance Oppositional Invasive Plant Ant Colony Optimization (AOIPACO) to solve the various constraints of single objective problem. The artificial algorithm leads to direction of exploration for new space of ant colonies with process of threats elimination from native candidates in energy management. During simulation process, the proposed algorithm reduces the power loss along with improvement of cost factor and computational time. The proposed AOIPACO approach is implemented on standard IEEE-5, 13 and 40 bus systems and generates optimal results as compared to other conventional techniques while solving the complex problem of optimization. Further, this meta heuristic technique help to improve the quality of power market system with minimum computation error at fast convergence rate. © Bharati Vidyapeeth's Institute of Computer Applications and Management 2024.

关键词： Cost function Economic and Emission Dispatch (EED) Invasive Ant Colony Optimization Invasive Plant Optimization Oppositional Base Learning (OBL)

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