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

作者： Pallithodiyil, Akshita Prasanth Srikrishna, Atyam Preetham Sai, Gutha Venkat Paripoorna, D. Sathiya, R.R. Amrita Vishwa Vidyapeetham Amrita School of Computing Department of Computer Science and Engineering Coimbatore India

ISBN: (纸本)9798350370249

This paper addresses the significant global impact of lung cancer, the primary cause of cancer-related deaths worldwide and the second leading cause of mortality following cardiovascular disease. The challenge lies in detecting lung cancer, often diagnosed at an advanced stage, complicating treatment efforts. The proposed model integrates CT (Computed Tomography) and PET (Positron-Emission Tomography) scans to enhance lung cancer prediction. CT scans offer detailed anatomical insights, while PET scans excel in identifying abnormal metabolic activity and precise tumour localization. This combined approach provides a comprehensive assessment, empowering healthcare professionals with improved diagnostic accuracy and better-informed treatment planning for lung cancer patients. © 2024 IEEE.

关键词： classification CNN (Convolutional Neural Network) CT Early Fusion NIfTI (Neuroimaging Informatics Technology Initiative) Non-small Cell Lung Cancer (NSCLC) PET ResNet Structural Similarity Index Measure (SSIM) SVM

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On Differentiation Among Pilot Signals of Multiple Mobile Targets in Retro-Reflective Beamforming for Wireless Power Transmission

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engineering 2023年第11期30卷 55-62页

作者： Xin Wang Long Li Tie Jun Cui Mingyu Lu School of Electrical Engineering Chongqing UniversityChongqing 400044China School of Electronic Engineering Xidian UniversityXi’an 710071China State Key Laboratory of Millimeter Waves School of Information Science and EngineeringSoutheast UniversityNanjing 210096China Department of Electrical and Computer Engineering West Virginia University Institute of TechnologyBeckleyWV 25801USA

An experimental study is conducted on several retro-reflective beamforming schemes for wireless power transmission to multiple wireless power receivers(referred to herein as“targets”).The experimental results demonstrate that,when multiple targets broadcast continuous-wave pilot signals at respective frequencies,a retro-reflective wireless power transmitter is capable of generating multiple wireless power beams aiming at the respective targets as long as the multiple pilot signals are explicitly separated from one another by the wireless power ***,various practical complications are identified when the pilot signals of multiple targets are not appropriately differentiated from each other by the wireless power ***,when multiple pilot signals are considered to be carried by the same frequency,the wireless power transmission performance becomes heavily dependent on the interaction among the pilot signals,which is highly undesirable in *** conclusion,it is essential for a retro-reflective wireless power transmitter to explicitly discriminate multiple targets’pilot signals among each other.

关键词： Wireless power transmission Microwave antenna array Retro-reflective beamforming Pilot signal Multiple access

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Vehicle Detection in Unmanned Aerial Imagery Through Advance You Only Look Once Architectures 15

Vehicle Detection in Unmanned Aerial Imagery Through Advance...

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

作者： Nishitha, U. Lokesh, V. Kaushik, Tata Singh, Rimjhim Padam Amrita Vishwa Vidyapeetham Amrita School of Computing Department of Computer Science & Engineering Bengaluru India

ISBN: (纸本)9798350370249

The detection of vehicles by unmanned aerial vehicles (UAVs) is gaining substantial attention in the field of traffic control. In aerial images, vehicles have a unique perspective and take up less pixels than other objects in the broader data set.. This study uses the YOLOv8 (You Only Look Once version 8) deep learning architecture to propose an enhanced method of vehicle recognition in aerial photos. This work goes beyond the conventional YOLOv8 implementation by introducing new methods for data augmentation to enhance the efficiency and the model's resiliency. To build a diverse and enriched dataset, the suggested methodology makes use of cutting-edge techniques such image cropping, contrast correction, and brightness improvement. Training the YOLOv8 model on this enhanced dataset produces encouraging results;on a different test set with ground truth annotations, a noteworthy achievement of 7 0 % accuracy in vehicle detection was made. This accomplishment highlights the effectiveness of our strategy and shows a notable increase in accuracy over earlier approaches. The study demonstrates the potential of YOLOv8 in tackling the difficulties of aerial vehicle detection and highlights the crucial role that data augmentation plays in improving model performance and resilience. © 2024 IEEE.

关键词： Aerial photography

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Detection and Taxonomy of Aircraft using Synthetic Aperture Radar Imaging 2

Detection and Taxonomy of Aircraft using Synthetic Aperture ...

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2nd IEEE International Conference on Integrated Circuits and Communication Systems, ICICACS 2024

作者： Sairam, D.V. Tamilselvi, M. Devi, S Gayathri Bhuvaneswari, R. Amrita Vishwa Vidyapeetham Department of Computer Science and Engineering Amrita School of Computing Chennai India Roever Engineering College Department of Computer Science and Engineering Tamilnadu Perambalur India Sri Ramakrishna Institute of Technology Department of Computer Science and Engineering Coimbatore India

ISBN: (纸本)9798350317558

Improvements in aircraft detection are necessary to improve surveillance. This work investigates the exact detection and classification of airplanes using YOLOv8 in conjunction with Synthetic Aperture Radar (SAR) photos. SAR imaging performs well in dimly lit and unfavorable environments. The objective is to use YOLOv8's object detection to identify aircraft kinds and categories in SAR pictures. A variety of SAR datasets are gathered, preprocessed, and trained on the model to allow for a thorough assessment of YOLOv8's performance in differentiating between different types of aircraft. Measures that assess model performance across aircraft classes include mean average precision (mAP), recall, and loss. The results demonstrate the effectiveness of YOLOv8 and show promising results in terms of speed and precision. This work drives progress in aerial surveillance by highlighting the robustness of SAR when combined with cutting edge deep learning for aircraft taxonomy. The achieved 98.5% mean average precision (mAP) attests to exceptional performance in aircraft detection and categorization from SAR images, reinforcing the utility of this integrated approach in bolstering security systems. © 2024 IEEE.

关键词： Aerospace Surveillance Aircraft Detection Aircraft Taxonomy Classification Deep Learning Imaging Techniques Machine Learning Synthetic Aperture Radar (SAR)

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Enhancing Abstractive Text Summarization with Proximal Policy Optimization 4

Enhancing Abstractive Text Summarization with Proximal Polic...

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4th International Conference on Advances in electrical, computing, Communication and Sustainable Technologies, ICAECT 2024

作者： Reddy, K. Lokeshwar Phani Shanmukh, M. Charan Kumar, M. Kumar M, Tharun Arjun Kumar, C. Prasanna Kumar, R. Venkatraman, K. Amrita Vishwa Vidyapeetham Amrita School of Computing Department of Computer Science and Engineering Chennai India

ISBN: (纸本)9798350343670

In the realm of Natural Language Processing (NLP), Abstract Text Summarization (ATS) holds a crucial position, involving the transformation of lengthy textual content into concise summaries while retaining essential information. This research paper delves into the utilization of advanced algorithms such as Seq-Seq Transfer Learning, Pegasus, and Proximal Policy Optimization for the abstraction text summarization process. Specifically, this study incorporates cutting-edge techniques like deep neurotemporal models and Reinforcement Learning (RL), with a focus on Preorder Language Models (PTLM). The integration of knowledge into the development process further enhances the efficacy of ATS applications. The paper meticulously examines the recent advancements in abstract concept extraction, addressing both historical and existing challenges while proposing innovative solutions. Emphasizing the significance of data and metrics in abstract ATS, this research incorporates the widely used ROUGE metric for evaluation. By comparing influential models, the study provides a comprehensive overview of the field's evolution. In addition, it explores the integration of transformative graph-based Transformer architecture and PTLM, which have revolutionized NLP. The research paper concludes by offering valuable insights into the future of abstract text summarization, paving the way for further exploration and innovation in the domain. © 2024 IEEE.

关键词： Abstract Text Summarization DQN Algorithm Natural Language Processing Pegasus Algorithm Seq-Seq Algorithm Transfer Learning

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Border Detection for Camera-Captured Document Images Using Transformers 5

Border Detection for Camera-Captured Document Images Using T...

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5th IEEE Global Conference for Advancement in Technology, GCAT 2024

作者： Kandala, Mahita Kaushik, M. Vignesh, G.S. Pati, Peeta Basa Bengaluru Amrita Vishwa Vidyapeetham Amrita School of Computing Department of Computer Science and Engineering India

ISBN: (纸本)9798350376685

In the era of digitization, accurately delineating the boundaries of camera-captured documents stands as a crucial yet challenging task, impacting various processes such as Optical Character Recognition (OCR) and document analysis. This paper presents a novel approach to document boundary detection using SegFormer, an innovative model leveraging Transformers' capabilities for contextual understanding. The research aims to address the limitations of traditional methods by harnessing Segformer's ability to capture intricate spatial relationships within images. In addition, a comparative analysis with two state-of-the-art models, UNet and DeepLabV3+, is conducted to evaluate the effectiveness of SegFormer in document boundary detection tasks. The experimentation involves training and testing the models on a dataset of camera-captured document images, meticulously annotated and segmented to ensure robust performance across diverse layouts. Through this research, valuable insights into the application of Transformers in document boundary detection are contributed, highlighting the importance of context-aware models in segmentation tasks. © 2024 IEEE.

关键词： Image segmentation

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Towards Effective Detection of Inappropriate Language in Social Media Context Using BERT 5

Towards Effective Detection of Inappropriate Language in Soc...

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5th International Conference on IoT Based Control Networks and Intelligent Systems, ICICNIS 2024

作者： Varshitha, Sankranthi Suthiksha, P.G. Natarajan, S. Amrita School of Computing Department of Computer Science and Engineering Amrita Vishwa Vidyapeetham Chennai India

ISBN: (纸本)9798331518097

In the fast-growing phase of social media, detecting the offensive language has become more crucial, since people are using different languages and cultural expressions. This paper represents a new way to handle the problems faced to detecting offensive language in different languages and understanding in which context it has been used. We have used BERT that is Bidirectional Encoding Representation from Transformers, a powerful learning tool where it will handle multiple datasets such as Tamil-English, Malayalam-English. This approach is more accurate and efficient when compared with the traditional models such as Random Forest and Decision Tree. This tool is designed in such a way that it enables easier understanding for both the native and non-native speakers in order to understand the foul or offensive content which is in context. Our model addresses the challenge of detecting offensive language in multilingual data and focuses more on understanding the sarcasm, humor and slang which machines predict inaccurately. © 2024 IEEE.

关键词： Social networking (online)

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Deciphering Poxvirus Strains: Feature-Driven classification of Monkeypox 4

Deciphering Poxvirus Strains: Feature-Driven classification ...

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4th International Conference on Advances in electrical, computing, Communication and Sustainable Technologies, ICAECT 2024

作者： Mehul, Daripelli Sasikala, D. Amrita Vishwa Vidyapeetham Amrita School of Computing Department of Computer Science and Engineering Chennai India

ISBN: (纸本)9798350343670

This research outlines a method for monkeypox virus classification from images, emphasizing the effective management of augmented datasets to ensure balanced class distribution and provide a robust foundation for model training. The study harnesses the capabilities of advanced pre-trained deep learning models, specifically ResNet50, EfficientNetB3, and DenseNet169, utilizing transfer learning techniques to adapt them to the task at hand. Techniques such as batch normalization and dropout are used to reduce overfitting and improve the generalization capacity of the models. Then, using the optimizer and categorical cross-entropy loss, these models are fine-tuned for monkeypox virus classification. One noteworthy addition is the inclusion of interactive learning, which gives users dynamic control over the training process by enabling them to intervene at predetermined intervals. Performance metrics such as accuracy, confusion matrix are used in the evaluation of the model. In order to guarantee a trustworthy evaluation of the models' performance, an independent test dataset is utilized. The most impressive accomplishment is the ensemble learning method, which combines the predictions from all three models,ResNet50,EfficientNetB3 and DenseNet169 with a simple averaging procedure. The classification report shows that this all-encompassing methodology consistently produces excellent results, with precision, recall, and F1-score consistently exceeding 0.97. In a time when healthcare challenges are constantly changing, this method shows promise as a useful tool for image-based diagnosis. © 2024 IEEE.

关键词： DenseNet169 EfficientNetB3 Monkeypox ResNet50 Virus

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Real-Time Speech-to-Text Holographic Communication for the Deaf Children and Elderly 4

Real-Time Speech-to-Text Holographic Communication for the D...

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4th International Conference on Multimedia Processing, Communication and Information Technology, MPCIT 2024

作者： Charan, Sreevathsa Sree Srihitha, Vemula Palaniswamy, Suja Chethana, Savarala Amrita School of Computing Amrita Vishwa Vidyapeetham Department of Computer Science & Engineering Bengaluru India

ISBN: (纸本)9798350375466

In the field of assistive technologies, innovative solutions that cater to the needs of individual with disabilities are paramount for fostering inclusive communication environments. The proposed work introduces an innovative real-time speech-to-text conversion system using holographic display technology to aid communication for the deaf and hard-of-hearing (DHH). The system incorporates two complementary speech recognition approaches as each contribute uniquely to improve the system's overall performance and accuracy in different ways and also reduce the latency. First approach is a conventional speech recognition module and another is an enhanced version of the Wav2vec2 model, which has been retrained to improve its efficacy with diverse speech patterns. Hosted on a Raspberry Pi and interfaced with an USB microphone, the system captures spoken language effectively. Upon recognition, the text is instantly displayed on a holographic screen made up of LED matrices and set against an acrylic glass surface, creating a visually engaging representation of floating text. The integration of these two recognition methods enhances the system's accuracy and reliability in processing real-time speech. By providing a visual output of spoken words, this system not only bridges communication gaps but also enriches the interaction experience within various social and professional settings. The application of holographic technology in this context is pioneering, offering substantial promise for developing accessible communication tools that are both functional and inclusive. This work underscores the potential of advanced display and speech processing technologies to transform interactions for individuals with hearing impairments, fostering greater inclusivity and engagement in everyday communications. © 2024 IEEE.

关键词： Speech recognition

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

作者： Akhilesh, P. Amal Krishna, K. Karthick Bharadwaj, S. Venugopalan, Manju Amrita Vishwa Vidyapeetham Amrita School of Computing Department of Computer Science And Engineering Bengaluru India

ISBN: (纸本)9798350394474

Technology is expanding and applicants are making their resumes look better by developing their skill set for the current environment. Each resume now looks very promising and manually screening resumes and finding out the potential candidate in time consuming and is not an efficient method. In this paper, we propose a method using Natural Language Processing (NLP) and Spark to filter relevant resumes. The work proposes a cosine similarity-based approach to measure text-similarity and hence find worthy candidates based on key information provided by the employer. The implementation of the proposed approach on a Spark platform helps in handling large-scale datasets, enabling efficient semantic similarity analysis in the context of job descriptions. The experiments conclude that the proposed method can efficiently speed up the process in short - listing worthy candidates and can effectively replace the process of hiring managers going through each resume. Our model demonstrates superior performance compared to a non-Spark-based resume filtering system, achieving an average reduction in processing time by 80%, translating to an average runtime decrease from 5 seconds to 1. This efficiency gain is scalable, presenting enhanced performance in candidate shortlisting, especially with larger datasets. © 2024 IEEE.

关键词： Large datasets

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