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2024 International Conference on science, technology, engineering and Management, ICSTEM 2024

作者： Divya Priya, D. Subramaniam, Suthashini Kumar, P. Vinoth Theam, Lim Jit Ayyasamy, Ramesh Kumar Krishnan, Kesavan Department of Computer Science Engineering MLR Institue of Technology Hyderabad India Perak Kampar Malaysia Department of Artificial Intelligence and Data Science St Joseph's Institute of Technology OMR CHENNAI India Faculty of Computer Science and Information Technology Universiti Malaya Kuala Lumpur Malaysia

ISBN: (纸本)9798350376913

In this study, we implemented a multi-level queue scheduling algorithm for a hospital with three wards: General, Pandemic, and Arogya Sree. The Pandemic ward uses priority scheduling, while the others use FCFS. It also calculates distances to nearby airports based on geolocation to identify oxygen airlift points. This work allows us to eliminate human interference in resource allocation by automating the process based on priority derived from the doctor’s diagnosis. This ensures that maximum efficiency is reached. The common people faced many problems during the pandemic, mainly related to costs incurred and resource availability. We want to solve this by removing the human factor from the equation. © 2024 IEEE.

关键词： Scheduling algorithms

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Ensemble Deep Learning Framework for Classification of Skin Lesions

Ensemble Deep Learning Framework for Classification of Skin ...

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2023 International Conference on Circuit Power and Computing Technologies, ICCPCT 2023

作者： Patil, Shantakumar B Ramana Rao, H.V. Chatrapathy, K. Kiran, Ajmeera Kumar, A Shiva Shaker Reddy, Pundru Chandra Sai Vidya Institute of Technology Computer Science and Engineering Bengaluru Karnataka Rajanukunte India Hyderabad India School of Computing &information Technology REVA University Karnataka Bengaluru India MLR Institute of Technology Computer Science and Engineering Hyderabad Telangana Dundigal India CMR College of Engineering & Technology Computer Science and Engineering Telangana Hyderabad India School of Computer Science and Engineering Lovely Professional University Punjab Phagwara India

ISBN: (纸本)9798350333244

Skin cancer is one of the majority prevalent malignancies that can be diagnosed visually and then further investigated with dermoscopic analysis and other procedures. Several skin-lesion (SL) classification methods using deep learning (DL) based on convolution-neural-network (CNN) and annotated skin photos illustrate modified results because visual observation provides the opportunity to use artificial-intelligence (AI) to intercept the various skin images at an early stage. In this regard, the research proposes a trustworthy method for identifying skin cancer using dermoscopy images, with the goal of bettering the visual perception and diagnostic abilities of medical experts. SL region of interest (RoI) segmentation from dermoscopy pictures was performed using swarm intelligence (SI) algorithms, and the RoI marked as the best segmentation oucome was subjected to feature extraction using the Grasshopper Optimization Algorithm (GOA). Using CNN and the ISIC-2017, ISIC-2018, and PH-2 data sets, the SLs are divided into two categories. Classification accuracy-sensitivity,-specificity-F -measure, precision, MCC, dice coefficient, and Jaccard index are used to evaluate the results of the proposed segmentation and classification strategies, with an average of 98.42%, 977.33%, and 0.97040%, respectively. Our proposed model outperforms the state-of the-art in every metric we considered © 2023 IEEE.

关键词： Learning algorithms

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Improved YOLOv8n Models for Object Detection in Remote Sensing Images

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IEEE Access 2025年 13卷 97889-97900页

作者： Chen, Young-Long Hung, Kai-Chun Zhang, Jia-Yun Lin, Ling-Wei National Taichung University of Science and Technology Department of Computer Science and Information Engineering China National Taichung University of Science and Technology College of Information and Distribution Science China

In recent years, numerous efficient object detectors have emerged in computer vision. However, applying these models to remote sensing images remains challenging due to complex backgrounds, high object scale variation, and the difficulty of detecting small objects. Many existing approaches prioritize accuracy but overlook the trade-off between precision and efficiency, limiting their usability in resource-constrained environments. To address these challenges, we propose a lightweight model based on the original YOLOv8n, culminating in the Transformer with Bi-directional and Coordinate Attention-YOLOv8n (TBC-YOLO8n) model. Our approach introduces a weighted bidirectional feature pyramid network that adaptively assigns importance to different feature maps, enhancing multi-scale object detection. Additionally, we integrate transformer blocks to improve global feature representation and capture long-range dependencies, alongside a coordinate attention block before each detection head to enhance localization by incorporating spatial information. Unlike previous YOLO variants, our method optimizes feature fusion strategies and integrates advanced attention mechanisms specifically tailored for remote sensing images, effectively addressing small-object detection, large-scale variations, and complex backgrounds. Compared to the original YOLOv8n, our proposed TBC-YOLO8n model improves mAP@50 from 90.2% to 94.5%, with a slight increase in inference time from 3.0 ms to 4.0 ms. Experimental results confirm the robustness and effectiveness of our model, making it a practical choice for real-time remote sensing applications with limited computational resources. © 2013 IEEE.

关键词： Remote sensing

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Heart Disease Prediction Using Machine Learning Algorithms

Heart Disease Prediction Using Machine Learning Algorithms

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2023 Innovations in Power and Advanced Computing Technologies, i-PACT 2023

作者： Priya, M. Anitha, A. Nallakaruppan, M.K. Raju, J. Raghavan, R. Srivastava, Divyansh School of Computer Science Engineering and Information Systems Vellore Institute of Technology Vellore India School of Electrical Engineering Vellore Institute of Technology Vellore India

ISBN: (纸本)9798350325188

Heart disease is one of the destructive infections that an enormous populace of individuals all over the planet grieves. The prediction of heart disease is very much urged because of increasing death rates. The conventional method of prediction isn't adequate for such a disease. Evolving a clinical determination framework based on machine learning concepts gives more exact conclusions than the conventional way for the prediction of such illness. In this paper, we have used different machine learning algorithms to accurately foresee whether an individual has a heart illness or not. The concepts of machine learning like linear prediction KNN, SVM, and, ANN to predict the probability of heart disease in an individual are used. The medical and general details of the person like BMI, alcohol and tobacco consumption, etc. are used to make the prediction. © 2023 IEEE.

关键词： ANN Heart Disease KNN Machine Learning Neural Network SVM

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Chaotic Analysis of Pedestrian Flow Series for Vehicular Networking in Complex Interaction Environment 9

Chaotic Analysis of Pedestrian Flow Series for Vehicular Net...

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9th International Conference on computer and Communications, ICCC 2023

作者： Qi, Siqi Wang, Lusheng Kai, Caihong Zhu, Kongjin Hefei University of Technology School of Computer Science and Information Engineering Hefei China Hefei University of Technology School of Automotive and Transportation Engineering Hefei China

ISBN: (纸本)9798350317251

Interaction between self-driving cars and pedestrians is a key issue for the design of vehicular networking and auto-drive algorithms. Pedestrian movement usually exhibits irregular and complex behaviors, dramatically increasing the difficulty of such design. By choosing a suitable method to analyze the pedestrian flow and obtain its major features, self-driving cars and service robots could intelligently avoid collision with pedestrians. Therefore, this paper analyzes the chaotic characteristics of the pedestrian flow time series of different locations in real campus scenes qualitatively and quantitatively. The optimal delay and optimal embedding dimension of pedestrian flow time series are obtained by the mutual information method and Cao's method, respectively. The largest Lyapunov exponent, correlation dimension, and Kolmogorov entropy are used to quantitatively analyze its chaos, and recurrence plot is used for qualitative demonstrations. According to this study, pedestrian flow time series are definitely chaotic, which provides essential theoretical support for pedestrian flow prediction and auto-drive algorithm design in future vehicular networking. © 2023 IEEE.

关键词： Lyapunov methods

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Secure Rotation Invariant Face Detection System for Authentication

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computers, Materials & Continua 2022年第1期70卷 1955-1974页

作者： Amit Verma Mohammed Baljon Shailendra Mishra Iqbaldeep Kaur Ritika Saini Sharad Saxena Sanjay Kumar Sharma Department of Computer Science&Engineering Chandigarh Group of CollegesMohali140317India Department of Computer Engineering College of Computer&Information ScienceMajmaah University11952Saudi Arabia Department of Computer Science and Engineering Thapar Institute of Engineering&TechnologyPatiala147004India Department of Computer Centre&Campus Network Facility Science Complex PO Central UniversityGachibowliHyderabad500046TelanganaIndia

Biometric applications widely use the face as a component for recognition and automatic *** rotation is a variable component and makes face detection a complex and challenging task with varied angles and *** problem has been investigated,and a novice algorithm,namely RIFDS(Rotation Invariant Face Detection System),has been *** objective of the paper is to implement a robust method for face detection taken at various *** to achieve better results than known algorithms for face *** RIFDS Polar Harmonic Transforms(PHT)technique is combined with Multi-Block Local Binary Pattern(MBLBP)in a hybrid *** MBLBP is used to extract texture patterns from the digital image,and the PHT is used to manage invariant rotation *** this manner,RIFDS can detect human faces at different rotations and with different facial *** RIFDS performance is validated on different face databases like LFW,ORL,CMU,MIT-CBCL,JAFFF Face Databases,and Lena *** results show that the RIFDS algorithm can detect faces at varying angles and at different image resolutions and with an accuracy of 99.9%.The RIFDS algorithm outperforms previous methods like Viola-Jones,Multi-blockLocal Binary Pattern(MBLBP),and Polar HarmonicTransforms(PHTs).The RIFDS approach has a further scope with a genetic algorithm to detect faces(approximation)even from shadows.

关键词： Pose variations face detection frontal faces facial expressions emotions

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Authorizable Tripartite Entanglement Routing via 3-GHZ State in Quantum Networks

Authorizable Tripartite Entanglement Routing via 3-GHZ State...

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2024 IEEE Global Communications Conference, GLOBECOM 2024

作者： Huang, Shao-Min Wei, Ching-Ting Zhan, Kai-Xu Le Touze, Juliette Chou Kuo, Jian-Jhih Wang, Chih-Yu National Chung Cheng University Dept. of Computer Science and Information Engineering Chiayi Taiwan Academia Sinica Research Center for Information Technology Innovation Taipei City Taiwan

ISBN: (纸本)9798350351255

Traditional end-to-end entanglement typically operates between two parties. However, such a setup may fall short when three parties are involved. GHZ states, a multi-qubit entangled state, provide an approach to such a problem. A fusion node is first chosen, and then the paths from all end nodes to the fusion node are fused to create an entangled state between all parties. The selection of the fusion node becomes critical since it highly affects the success probability of the whole process. Thus, in this paper, we explore the promising scenario for multiple requests of authorizable tripartite teleportation and introduce a novel optimization problem to maximize the (expected) total profit for 3-GHZ requests. Via extensive simulation results, we show that our algorithm can outperform existing approaches significantly. © 2024 IEEE.

关键词： Quantum entanglement

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DBNLP: detecting bias in natural language processing system for India-centric languages

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International Journal of information technology (Singapore) 2025年 1-16页

作者： T. G, Keerthan Kumar Mendke, Saish Parihar, Rohit Mayya, Samarth Venkatesh, Spoorthy Koolagudi, Shashidhar G. Department of Information Science and Engineering Siddaganga Institute of Technology Karnataka Tumkur 572102 India Department of Computer Science and Engineering National Institute of Technology Karnataka Surathkal Karnataka Mangalore 575025 India Department of Artificial Intelligence and Machine Learning CSPIT CHARUSAT Gujarat Anand 388421 India

Natural language processing (NLP) is gaining widespread interest and seeing advancements rapidly due to its attractive and exhilarating applications. NLP models are being developed in search engines for real-world scenarios such as language translation, sentiment analysis, chat-bots such as ChatGPT, and auto-completion. These models are trained on a vast corpus of online data, exposing them to harmful biases and stereotypes towards various communities. The models learn these biases, making harmful and undesirable predictions about particular genders, religions, races, and professions. Biases in NLP systems can perpetuate societal biases and discrimination, leading to unfair and unequal treatment of individuals or groups. It is crucial to identify these biases, which will help mitigate them. Most of the literary works in this area have been primarily Western-centric, focusing on the English language, making it tough to use them for Indian models and languages. In this work, we propose a model called Detecting Bias in Natural Language Processing System for India-Centric Languages (DBNLP), which aims to identify the biases relevant to the Indian context present in the text-based language models, particularly for the English and Hindi languages. The DBNLP presents three techniques for bias identification based on (1) a Context Association Test (CAT), (2) a template-based perturbation technique for various co-domain associations, and (3) a co-occurrence count-based corpus analysis technique. Further, this work showcases how India-centric models such as IndicBERT, MuRIL, and datasets such as IndicCorp are biased toward various demographic categories. Detecting bias in natural language processing systems for India-centric languages is essential to creating fair, diverse, and inclusive models that benefit society. © Bharati Vidyapeeth's Institute of computer Applications and Management 2025.

关键词： Diverse and Inclusive models Harmful biases and stereotypes India-centric models Indian context Natural language processing

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Deep Learning: A Detailed Analysis of Various Image Augmentation Techniques 1

Deep Learning: A Detailed Analysis of Various Image Augmenta...

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1st IEEE International Conference on Networking and Communications, ICNWC 2023

作者： Swathi, S. Rajalakshmi, M. Senniappan, Vijayalakshmi Sri Sairam Institute of Technology Department of Computer Science and Engineering Chennai India Coimbatore Institute of Technology Department of Information Technology Coimbatore India Park College of Engineering Tech Department of Information Technology Coimbatore India

ISBN: (纸本)9798350336009

Deep learning has been performing reasonably well in computer vision tasks that call for a high volume of photos, although gathering images is often expensive and challenging. Different picture augmentation techniques have been put forth as practical and efficient solutions to this problem Understanding current algorithms is critical when developing new processes or determining the best approaches for a certain task. With deep learning, some of the data pre-processing that is typically required for machine learning is avoided. Unstructured text and visual data can be handled by these algorithms, which can also automate feature extraction and lessen the need for human experts. With a brand-new taxonomy of usable data, we undertake a complete survey of picture augmentation for deep learning in this work. We discuss the difficulties in computer vision tasks and vicinity distribution to give you a fundamental understanding of why we want picture augmentation. Based on the study, we think that our survey provides a clearer knowledge that may be used to select the best techniques or create original algorithms for real-world uses. © 2023 IEEE.

关键词： computer vision

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Fusion enhancement : UAV target detection based on multi-modal GAN 7

Fusion enhancement : UAV target detection based on multi-mod...

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7th IEEE information technology and Mechatronics engineering Conference, ITOEC 2023

作者： Li, Rui Peng, Yi Yang, Qingqing Kunming University of Science and Technology College of Information Engineering and Automation Kunming China Kunming University of Science and Technology Yunnan Key Laboratory of Computer Science Kunming China

ISBN: (纸本)9798350334197

UAV target detection has important application value in the fields of modern military, security and emergency rescue. In order to improve the accuracy and robustness of UAV target detection, this paper uses image fusion technology to enhance the information of input images, and combines YOLOv5 target detection model to introduce attention mechanism for improvement. In the experiment, the generative adversarial network (GAN) is used as the image fusion technology, and the fusion image is generated by changing the structure of the generator and the discriminator. At the same time, the attention mechanism is introduced into the YOLOv5 model to enhance the focus of target detection. The experimental results show that the proposed improved algorithm has achieved significant performance improvement in the UAV target detection task. © 2023 IEEE.

关键词： Unmanned aerial vehicles (UAV)

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