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12th International Conference on Recent Trends in Computing, ICRTC 2024

作者： Bhuvana, S. Anantha Prabha, P. Deva Priya, M. Christy Jeba Malar, A. Karthick, S. School of Computer Science Engineering and Information Systems Vellore Institute of Technology Vellore India Department of Computer Science and Engineering PSG Institute of Technology and Applied Research Tamilnadu Coimbatore India Department of Computer Science and Engineering Karpagam College of Engineering Tamilnadu Coimbatore India Department of Information Technology Sri Krishna College of Technology Tamilnadu Coimbatore India Department of Computer Science and Engineering Delhi - NCR Campus SRM Institute of Science and Technology Uttar Pradesh Ghaziabad India

ISBN: (纸本)9789819789450

Stress is a state of mental or emotional strain due to adversative or challenging situations. A human may undergo bad life experiences or events, and it is a significant issue to be dealt in today's society. It comes as a result of family situations, health concerns, work environments and people's behavior among other things. It affects people of various ages, including children, teenagers, middle-aged people and senior citizens. It has serious consequences affecting human body and life. These problems can be addressed by detecting mental stress at an early stage. The main goal of the propounded system is to identify human stress using a pre-trained Mini-Xception model. Additional measures such as eye moments, mouth activity and head motion are employed for determining the level of human stress. FER2013 dataset is used and the system is tested for all kinds of emotions. Human stress level is experimented by capturing video using webcam. From the experimental outcomes, it is evident that the proposed system offers better performance in terms of Precision, Accuracy, Specificity and Sensitivity in contrast to traditional methods. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Eyebrow displacement FER2013 dataset Mini-Xception model Stress detection

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Calligraphy Alphabet Perception Using Artificial Intelligence 9

Calligraphy Alphabet Perception Using Artificial Intelligenc...

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9th International Conference on science, Technology, engineering and Mathematics, ICONSTEM 2024

作者： Venkatesh, S. Jeevitha, D. Gnanaselvi, J. Anitha Srm Institute of Science And Technology Department of Data Science And Business Systems School of Computing Chennai Kattankulathur India Jeppiaar Engineering College Department of Computer Science And Engineering Chennai India

ISBN: (纸本)9798350365092

The human brain has a simple time analyzing and processing images. The brain is able to rapidly deconstruct and distinguish an image's various components when the eye perceives it. With the Convolutional Neural Network (CNN) as its foundation, this research suggests deep learning conceptual models. When the algorithms are compared, it becomes clear that CNN-based classification of handwritten alphabets performs better than other algorithms in terms of accuracy. The Manual Net, Alex Net, and LeNet Architectures are among the CNN algorithms employed in this research. The convulational layer, max pooling, flattening, feature assortment, rectifier lined unit, and completely linked softmaxx layers are each components of the aforementioned designs. The proposed network is tested using an image dataset comprising 530 training photos and 2756 testing images. The top precision and cost-efficient model will be used in the Django context to build a handler line for supplying the appeal to be recognized and obtaining the productivity outcome of recognized appeal. © 2024 IEEE.

关键词： Convolutional neural networks

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Deep Learning Approaches for Pneumonia Classification in Healthcare 3

Deep Learning Approaches for Pneumonia Classification in Hea...

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3rd International Conference on Innovative Practices in Technology and Management, ICIPTM 2023

作者： Sriram, G.K. Karunasagar, A. Sudharsan, R. Malini, A. Thiagarajar College of Engineering Department of Computer Science and Business Systems Madurai India

ISBN: (纸本)9798350336238

In the past two decades, there has been a sharp rise in the use of deep learning for medical image processing and analysis. Recent challenges, for instance, the most well-known ImageNet computer Vision competition, have almost entirely incorporated deep learning approaches for providing the best result. The concept of Image classification was later extended to Image Segmentation and Object Detection which proved to perform extremely well using state-of-the-art classification algorithms as their backbone architecture. The accuracy of the algorithm and approach has a significant impact on the medical field as there is a constant need for accurate and computationally efficient models. The existing object detection and segmentation approaches need large data for providing accurate results, unlike classification algorithms in which accuracy can be achieved with a relatively smaller amount of data. Hence, for the overall increase of model accuracy, there is a need for image augmentation to be incorporated. In this paper, several deep learning methodologies such as classification, object detection, ensemble, and segmentation for pneumonia classification and detection have been reviewed and an ensemble-based approach for the classification of Pneumonia using chest X-rays has been proposed. © 2023 IEEE.

关键词： Object detection

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Integrated preparation and manipulation of high‑dimensional flying structured photons

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eLight 2024年第1期4卷 185-192页

作者： Haoqi Zhao Yichi Zhang Zihe Gao Jieun Yim Shuang Wu Natalia MLitchinitser Li Ge Liang Feng Department of Electrical and Systems Engineering University of PennsylvaniaPhiladelphiaPA 19104USA Department of Materials Science and Engineering University of PennsylvaniaPhiladelphiaPA 19104USA Department of Electrical and Computer Engineering and Department of Physics Duke UniversityDurhamNC 27708USA Department of Engineering Science and Physics College of Staten IslandCUNYStaten IslandNY 10314USA The Graduate Center CUNYNew YorkNY 10016USA

The hope for a futuristic global quantum internet that provides robust and high-capacity quantum information transfer lies largely on qudits,the fundamental quantum information carriers prepared in high-dimensional superposition ***,preparing and manipulating N-dimensional flying qudits as well as subsequently establishing their entanglement are still challenging tasks,which require precise and simultaneous maneuver of 2(N-1)parameters across multiple degrees of ***,using an integrated approach,we explore the synergy from two degrees of freedom of light,spatial mode and polarization,to generate,encode,and manipulate flying structured photons and their formed qudits in a four-dimensional Hilbert space with high quantum fidelity,intrinsically enabling enhanced noise resilience and higher quantum data *** four eigen spin–orbit modes of our qudits possess identical spatial–temporal characteristics in terms of intensity distribution and group velocity,thereby preserving long-haul coherence within the entirety of the quantum data transmission *** leveraging the bi-photon entanglement,which is well preserved in the integrated manipulation process,we present versatile spin–orbit cluster states in an extensive dimensional Hilbert *** cluster states hold the promise for quantum error correction which can further bolster the channel robustness in long-range quantum communication.

关键词： cluster thereby preparing

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Semantic2Graph: graph-based multi-modal feature fusion for action segmentation in videos

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Applied Intelligence 2024年第2期54卷 2084-2099页

作者： Zhang, Junbin Tsai, Pei-Hsuan Tsai, Meng-Hsun Department of Computer Science and Information Engineering National Cheng Kung University Tainan701 Taiwan Institute of Manufacturing Information and Systems National Cheng Kung University Tainan701 Taiwan Department of Computer Science National Yang Ming Chiao Tung University Hsinchu300 Taiwan

Video action segmentation have been widely applied in many fields. Most previous studies employed video-based vision models for this purpose. However, they often rely on a large receptive field, LSTM or Transformer methods to capture long-term dependencies within videos, leading to significant computational resource requirements. To address this challenge, graph-based model was proposed. However, previous graph-based models are less accurate. Hence, this study introduces a graph-structured approach named Semantic2Graph, to model long-term dependencies in videos, thereby reducing computational costs and raise the accuracy. We construct a graph structure of video at the frame-level. Temporal edges are utilized to model the temporal relations and action order within videos. Additionally, we have designed positive and negative semantic edges, accompanied by corresponding edge weights, to capture both long-term and short-term semantic relationships in video actions. Node attributes encompass a rich set of multi-modal features extracted from video content, graph structures, and label text, encompassing visual, structural, and semantic cues. To synthesize this multi-modal information effectively, we employ a graph neural network (GNN) model to fuse multi-modal features for node action label classification. Experimental results demonstrate that Semantic2Graph outperforms state-of-the-art methods in terms of performance, particularly on benchmark datasets such as GTEA and 50Salads. Multiple ablation experiments further validate the effectiveness of semantic features in enhancing model performance. Notably, the inclusion of semantic edges in Semantic2Graph allows for the cost-effective capture of long-term dependencies, affirming its utility in addressing the challenges posed by computational resource constraints in video-based vision models. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Semantics

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Leveraging Deep Learning Models for Machine-Generated Text Detection Using Transformer-Based Models

Leveraging Deep Learning Models for Machine-Generated Text D...

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2024 International Conference on Innovative Computing, Intelligent Communication and Smart Electrical systems, ICSES 2024

作者： Suthanthiradevi, P. Revathy, G. Rejini, K. Muthu Lakshmi, V. School of Computing Srm institute of Science and Technology Department of Data Science and Business Systems Kattankulathur Chennai India Vels Institute of science Technology and Advanced studies Department of Computer science and engineering Chennai117 India Amrita college of Engineering and Technology Department of Computer Science Engineering Nagercoil India St. Joseph's College of Engineering Department of Computer Science and Engineering OMR Chennai India

ISBN: (纸本)9798331543617

GPT is a large language model (LLM) derived from natural language processing that can generate a human-like text using machine learning. However, these models raise questions about authenticity and reliability of material, particularly in fields such as journalism, social media, and academia, despite their usefulness for automating text-based tasks. Detecting machine-generated text is thus an important difficulty in ensuring content integrity. This study investigates the use of huge language models as a technique for recognizing machine-generated material. The author proposes a comprehensive detection model by evaluating the language patterns, syntactic structures, and stylistic traits that separate AI-generated literature from human writing. In addition, this research investigate the possibilities of fine-tuning models designed expressly for text identification tasks and evaluate their performance using LLM - Detect AI Generated Text datasets. In digital ecosystems, LLMs are effective at detecting AI-generated text, providing a novel approach for content moderation, academic integrity checks, and synthetic media detection. An increasingly AI-powered future will require a model that can discriminate between human and machine-generated writing in real-time. According to experimental findings, the CNN architecture's design combined with the use of DistilBERT embeddings allows for the effective and efficient classification of AI generated text data, achieving an exceptional 98% accuracy rate. © 2024 IEEE.

关键词： Adversarial machine learning

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Fabrication of SiC particle dispersed AZ91D magnesium alloy composite by injection molding method and their properties

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Keikinzoku/Journal of Japan Institute of Light Metals 2024年第3期74卷 141-146页

作者： Hideshima, Yasutoshi Maeda, Fumiya Fukuta, Tadao Ozaki, Koichi Graduate School of Computer Science and Systems Engineering Okayama Prefectural University 111 Kuboki Okayama Soja-shi719-1197 Japan Process Technology Development Department Technology Development Division Seiko Epson Co. Suwa-shi Nagano Japan Faculty of Computer Science and Systems Engineering Okayama Prefectural University Okayama Soja-shi Japan

AZ91D magnesium alloy chips were coated with SiC powder using a binder, and injection molding was attempted using SiC powder coated magnesium chips as raw material. The metallurgical structure of the injection molded products showed uniform dispersion of SiC particles in the matrix, and there were no significant defects such as voids at the SiC particle-matrix interface. The hardness and 0.2% proof stress of the injection molded products tended to improve with increasing SiC content, while the elongation decreased with increasing SiC content. The Young's modulus of the injection molded products improved with increasing SiC content. It was confirmed that the specific bending rigidity of the injection molded products also improves due to the significant increase in the Young's modulus, although the addition of SiC leads to an increase in density. © 2024 The Japan Institute of Light Metals.

关键词： Silicon carbide

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Improved Beluga Whale Optimizer-Based Cluster Head Selection in FANET 3

Improved Beluga Whale Optimizer-Based Cluster Head Selection...

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3rd IEEE International Conference on Mobile Networks and Wireless Communications, ICMNWC 2023

作者： Venkatasubramanian, S. Saranathan College of Engineering Department of Computer Science and Business Systems Trichy India

ISBN: (纸本)9798350317022

Important issues have arisen for the reliability of communications in FANETs as of the high-speed mobility of unmanned aerial vehicles (UAVs). One of the more recent Meta-heuristic (MH) methods, makes use of the Beluga Whale optimizer (BWO) to optimize network parameters in FANET. However, BWO may get stuck in local optima and converge too quickly because of its lack of diversity. This paper provides two steps to improve the original BWO procedure. Opposition-based learning (OBL), also known as OBWO's first stage, helps to speed up the search procedure and improve the learning procedure so that a higher quality generation of candidate keys for the basic BWO can be selected. In addition, a powerful cluster maintenance algorithm for continually updating UAV clusters is proposed. The simulation findings show that the projected OBWO scheme outperforms state-of-the-art clustering techniques by a wide margin in terms of packet delivery ratio, network lifetime to cluster head altering ratio, and energy usage. © 2023 IEEE.

关键词： Beluga Whale Optimization Cluster Head Selection Energy Ratio Flying ad hoc network Opposition-Based Learning Packet delivery ratio

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Multi-layer composite autoencoders for semi-supervised change detection in heterogeneous remote sensing images

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science China(Information sciences) 2023年第4期66卷 124-125页

作者： Jiao SHI Tiancheng WU Hanwen YU A.K.QIN Gwanggil JEON Yu LEI School of Electronics and Information Northwestern Polytechnical University School of Resources and Environment University of Electronic Science and Technology of China Department of Computer Science and Software Engineering Swinburne University of Technology Department of Embedded Systems Engineering Incheon National University

With the increasing complexity of application scenarios, the fusion of different remote sensing data types has gradually become a trend, which can greatly improve the utilization of massive remote sensing *** the problem of change detection for heterogeneous remote images can be much more complicated than the traditional change detection for homologous remote sensing images,

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Skin cancer detection with MobileNet-based transfer learning and MixNets for enhanced diagnosis

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Neural Computing and Applications 2024年第34期36卷 21383-21413页

作者： Zakariah, Mohammed Al-Razgan, Muna Alfakih, Taha Department of Computer Sciences and Engineering College of Applied Science King Saud University Riyadh11543 Saudi Arabia Department of Software Engineering College of Computer and Information Sciences King Saud University Riyadh11345 Saudi Arabia Department of Information Systems College of Computer and Information Sciences King Saud University Riyadh11543 Saudi Arabia

Skin cancer poses a significant health hazard, necessitating the utilization of advanced diagnostic methodologies to facilitate timely detection, owing to its escalating prevalence in recent years. This paper proposes a novel approach to tackle the issue by introducing a method for detecting skin cancer that uses MixNets to enhance diagnosis and leverages mobile network-based transfer learning. Skin cancer has diverse forms, each distinguishable by its structural attributes, morphological characteristics, texture, and coloration. The pressing demand for accurate and efficient diagnostic instruments has spurred the investigation of novel techniques. The present study utilizes the ISIC dataset, comprising a validation set of 660 images and a training set of 2637 images. Moreover, the research employs a combination of MixNets and mobile network-based transfer learning as its chosen approach. Transfer learning is a technique that leverages preexisting models to enhance the diagnostic capabilities of the proposed system. Integrating MobileNet and MixNets allows for utilizing their respective functionalities, resulting in a dual-model methodology that enhances the comprehensiveness of skin cancer diagnosis. The results demonstrate impressive performance metrics, with MobileNet and MixNets models, and the proposed approach achieves an outstanding accuracy rate of 99.58%. The above findings underscore the efficacy of the dual-model method in effectively discerning between benign and malignant skin lesions. Moreover, the present study aims to examine the potential integration of emerging technologies to enhance the accuracy and practicality of diagnostics within real-world healthcare settings. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2024.

关键词： Diseases

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