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A Comprehensive Study of Available and Prospective Object Detection Methodologies 1

A Comprehensive Study of Available and Prospective Object De...

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1st international conference on computing, Sciences and Communications, ICCSC 2024

作者： Manisha Kumar, Avadhesh Yadav, Dileep Kumar School of Computer Science Engineering Galgotias University Greater Noida India

ISBN: (纸本)9798350353648

Visual recognition and recognition of objects have created a new field of relevance in modern times, posing severe issues for computer vision. Object detection technology is increasingly being used in a variety of applications, the most noteworthy of which include surveillance systems, self-driving vehicles, and pedestrian identification. One of the most significant and difficult issues is visual identification and obj ect detection, which has enticed an area of significance in modern years. The most well-known applications of object detection include surveillance, self-driving automobiles, pedestrian detection, and other areas of daily life. The primary objective of visual object detection is to recognized objects that fit into a certain targeted object with excellent navigation in a real scene or input image, as we know to assign a specific class label to each object instance due to the rapid growth of deep learning models, the performance of object detectors has significantly boosted, deep learning-based detection algorithms have been extensively investigated once again. This paper provides a comprehensive overview of the most recent advancements in visual object identification using deep learning and also describes a huge corpus of recent research and used it to analyses conventional and modern obj ect detectors. Here this study discussion the backbone architecture for obj ect recognition, followed by a methodical deconstruction of existing learning algorithms. Some important datasets and metrics for objects detection are also explored. Finally, this paper describes object detection applications and suggests various future directions. © 2024 ieee.

关键词： Intelligent systems

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Frontend Subsystem Development In Traditional Market Digitalization E-Commerce applications 18

Frontend Subsystem Development In Traditional Market Digital...

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18th international conference on Telecommunication systems, Services, and applications, TSSA 2024

作者： Zakaria, Usman Juhana, Tutun School of Electrical Engineering and Informatics Bandung Institute of Technology Bandung Indonesia

ISBN: (纸本)9798331527686

Traditional market e-commerce applications aim to expand the market reach and potential buyers of traders in traditional markets, develop low-cost and sustainable solutions, and facilitate the dissemination of real-time data information about products and buyers' desires that are easy to see, and able to communicate directly. between traders and buyers. The Virtual Market application is designed to fulfill general activities that occur in traditional markets, such as buying and selling activities, conversations and advertising. The main characteristic of shopping in traditional markets is that there is bargaining between traders and buyers, so this application presents a bargaining feature that will facilitate this activity. This application will go through a testing and verification stage to determine that the application created is in accordance with the specified final assignment objectives. In this paper, the frontend system will be designed and designed as a whole, starting from designing the application by considering the user interface and user experience to implementing the design on mobile devices using the chosen programming language. © 2024 ieee.

关键词： Marketplaces

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Enhanced Decision Support System for Financial Risk Assessment Using Hybrid Fuzzy Logic and Machine Learning

Enhanced Decision Support System for Financial Risk Assessme...

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2024 international conference on Computational Intelligence and computing applications, ICCICA 2024

作者： Kaur, Taranjot Rani, Kavita Thakur, Payal Talwandi, Navjot Singh Ghg Khalsa College Of Education Dept Of Computer Applications Punjab India Piet Dept Of Cse Punjab India Chandigarh University Dept Of Cse Punjab India

ISBN: (纸本)9798350306446

This paper proposes an advanced framework for financial risk assessment leveraging a synergistic blend of fuzzy logic and machine learning techniques. In today's volatile financial markets, accurate risk evaluation is imperative for effective decision-making. Traditional methods often fall short in handling uncertainty and complexity inherent in financial data. Hence, this study introduces a novel approach integrating fuzzy logic for handling imprecise information and machine learning algorithms for predictive modeling. The proposed system employs a hybrid model that amalgamates fuzzy inference systems with ensemble learning, harnessing the strengths of both paradigms. Fuzzy logic aids in capturing vague and qualitative aspects of financial risk assessment, while machine learning algorithms like random forests or gradient boosting enhance predictive accuracy by learning from historical patterns and trends. Furthermore, the study presents a comprehensive evaluation using real-world financial datasets to demonstrate the efficacy and superiority of the proposed hybrid approach compared to conventional methods. Performance metrics such as accuracy, precision, and recall are utilized to validate the system's effectiveness in risk assessment across different market scenarios. This research contributes to the burgeoning field of financial risk management by providing a robust decision support system capable of handling uncertainties inherent in financial markets, thereby assisting stakeholders in making informed and proactive risk mitigation strategies © 2024 ieee.

关键词： Decision support systems

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Sign Language Recognition systems: A Comprehensive Review 1

Sign Language Recognition Systems: A Comprehensive Review

引用

1st international conference on Pioneering Developments in Computer Science and Digital Technologies, IC2SDT 2024

作者： Aggarwal, Rahul Meena, Anjali Kaur, Navleen Singh, Rajveer Patel, Suryamani National Institute of Technology Jalandhar Department of Computer Science and Engineering Jalandhar India

ISBN: (纸本)9798350365016

Recently, emphasis has been placed on the applications of ISL since they assist people with HIs in their day- to-day lives in diversely populated India. This review paper aims to present years of development in real-time ISL recognition systems, the progress they brought, the issues they face, and the other ways it is being utilized in this ever-evolving area. We critically review the approaches for real-time ISL recognition from two aspects - the method and the assessment procedure based on the literature survey and recent developments and tendencies in the ISL research field. The current review accrues previous literature research to identify critical deficits and future suggestions to enrich the literature on assistive technologies for the deaf and hard-of-hearing people in India. © 2024 ieee.

关键词： Adversarial machine learning

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4th international conference on Recent Trends in Machine Learning, IoT, Smart Cities, and applications, ICMISC 2023

4th International Conference on Recent Trends in Machine Lea...

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4th international conference on Recent Trends in Machine Learning, IoT, Smart Cities, and applications, ICMISC 2023

ISBN: (纸本)9789819994410

The proceedings contain 69 papers. The special focus in this conference is on Recent Trends in Machine Learning. The topics include: Implementation of Dual-Band Dielectric Resonator Antenna for 5G applications;defect Detection in Metal Surfaces Using Computer Vision;liver Cirrhosis Prediction Using Machine Learning Classification Techniques;a Recent Survey on Risk Factors Affecting the Blood Pressure in India;real-time Monitoring System for Breakdown Analysis and OEE in the Wire Drawing Industry;tooth Sensitivity Device—Detection and Diagnosing of Sensitivity in the Dental Pulp;recognition of Skin Cancer;ioT-Based Smart Street Lighting Surveillance System;rock Segmentation of real Martian Scenes Using Dual Attention Mechanism-Based U-Net;IAAS: IoT-Based Automatic Attendance System with Photo Face Recognition in Smart Campus;hardware Implementation of Moving Object Detection Using Background Subtraction Algorithm;crime Pattern Identification and Prediction Using Machine Learning;IMICE: An Improved Missing Data Imputation Using Machine Learning;analyzing Students’ Opinion on E-Learning—Indian Students’ Perspective;rash Driving Detection and Alerting System;Logistic-Based OVA-CNN Model for Alzheimer’s Disease Detection and Prediction Using MR Images;Comparative Study of CNNs for Camouflaged Object Detection;3D Avatar Reconstruction Using Multi-level Pixel-Aligned Implicit Function;Helmet Detection Using YOLO-v5 and Paddle OCR for embedded systems;defogNet: A Residual Network for Removal of Fog Using Weighted Combination Loss;Text-to-Image Generation Model with DNN Architecture and Computer Vision for embedded Devices Using Quantization Technique;one-Shot Learning for Archaeological Site Data Using Deep Neural Network on embedded systems;enhanceNet: A Deep Neural Network for Low-Light Image Enhancement with Image Restoration;intelligent Prediction of Cardiac Abnormality.

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Optimizing Multimodal Transformers for Medical Image Captioning: Enhancing Automated Descriptions via AI systems 6

Optimizing Multimodal Transformers for Medical Image Caption...

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6th ieee international conference on Image Processing, applications and systems, IPAS 2025

作者： Arman, Mithila Jahan, Md. Khurshid Dhrubo, Ahmed Faizul Haque Rhaman, Md. Mahfuzur Choya, Sumaya Binte Zilani Dohan, Din Mohammad Ul Islam Sajid, Md. Ashiq Rabiul Alam, Md. Golam BRAC University Dept. of CSE Dhaka Bangladesh North South University Dept. of ECE Dhaka Bangladesh George Mason University Dept. of Computational Data Science FairfaxVA United States American International University-Bangladesh Dept. of CSE Dhaka Bangladesh

ISBN: (纸本)9798331506520

In contemporary diagnostic workflows, medical image captioning has emerged as a pivotal advancement, combining deep learning methodologies and transformer architectures to enhance accuracy and efficiency in medical interpretations. This paper proposes the optimization of multimodal transformers for automated medical image captioning, focusing on integrating Vision Transformers (ViT) and Bidirectional Auto-Regressive Transformers (BART) with novel variations such as Swin Transformers and GPT-2. We use a robust multimodal AI framework to explore how these architectures synergize to generate coherent and diagnostically relevant captions for radiological images. We assess the performance of multiple transformer models by employing the ROCO dataset, containing paired X-ray images and expert-generated reports. Our findings demonstrate that the ViT + BART combination yields the most stable and accurate captions, minimizing training and validation loss. In contrast, DEiT + MBART displayed instability, highlighting the need for further hyperparameter tuning. Through this comparative analysis, we underscore the critical role of transformer-based models in reducing the cognitive load on medical professionals, enhancing diagnostic accuracy, and promoting real-time, automated medical image interpretation. © 2025 ieee.

关键词： Medical imaging

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Metamaterial/Metasurface-Based Antenna with Improved Gain for Wireless Power Transfer systems 4

Metamaterial/Metasurface-Based Antenna with Improved Gain fo...

引用

4th ieee China international Youth conference on Electrical Engineering, CIYCEE 2023

作者： Islam, Tanvir Basherlou, Haleh Jahanbakhsh Ojaroudi Parchin, Naser University of Houston Department of Electrical and Computer Engineering HoustonTX77204 United States Edinburgh Napier University School of Computing Engineering and the Built Environment Edinburgh United Kingdom

ISBN: (纸本)9798350305562

This research presents a novel ultra-wideband (UWB) antenna designed for future wireless power transfer systems. The antenna features improved radiation capabilities and incorporates a metamaterial-based structure. Its design includes a modified triangular patch as the main radiation element, complemented by three embedded split-ring metamaterial resonators, inspired by a compact 4×4 metasurface unit cells layout, all integrated into an FR4 substrate. The study demonstrates the antenna's effective operation within the frequency range of 2.8-12 GHz, covering the UWB spectra of 3.1-10.6 GHz. The findings confirm that the proposed design successfully achieves the desired frequency response, radiation characteristics, and gain levels, rendering it suitable for applications in ultra-wideband and wireless power-transferring systems. © 2023 ieee.

关键词： Resonators

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embedded Soft computing applications Using Low Power VLSI Techniques

Embedded Soft Computing Applications Using Low Power VLSI Te...

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Communication, computing and Signal Processing (IICCCS), ieee international conference on

作者： Jami Venkata Suman G. M. Anitha Priyadarshini P. Krishna Rao Niddana Siri Chandana Department of ECE GMR Institute of Technology Rajam Andhra Pradesh India Department of ECE Anurag University Hyderabad India Department of ECE Aditya Institute of Technology and Management Tekkali Andhra Pradesh India

ISBN: (数字)9798350390759

ISBN: (纸本)9798350390766

Energy deficit VLSI addresses are important for several purposes, especially in IC and microchip development. Energy-efficient, compact, and durable electronics require low-power VLSI addressing. Modern integrated circuit design must address power limits, temperature regulation, and environmental concerns. Soft computing is used in embedded systems. Soft computing is an AI discipline that develops intelligent systems that can handle uncertainty, imprecision, and imperfect truth. Soft computing uses approximation reasoning and probabilistic tactics to solve complex real-world problems instead of mathematical models. The work emphasizes low-power VLSI addressing for cost-effective and durable soft computing applications embedding. These low-power VLSI techniques in integrated soft computing applications can enhance battery life, energy utilization, and performance while maintaining precision and predictability. Low-power VLSI techniques in embedded soft computing systems enhance energy efficiency, battery life, cost, and dependability. These tactics encourage the development of cutting-edge, environmentally friendly embedded systems that can perform complex soft computing tasks within power constraints.

关键词： Performance evaluation Technological innovation embedded systems Power demand Uncertainty Very large scale integration Energy efficiency Batteries Telecommunication computing Wearable devices

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Learning of embedded System for Incorporating: Organization 3rd

Learning of Embedded System for Incorporating: Organization

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3rd Biennial international conference on Future Learning Aspects of Mechanical Engineering, FLAME 2022

作者： Agrawal, Reeya Badhoutiya, Arti GLA University UP Mathura India

ISBN: (纸本)9789819913275

In a number of disciplines and applications embedded systems now play an essential role, such as network embedded systems, real-time embedded systems that provide for vital mission areas with time limits, stand-alone systems with network routers, etc. A superb processor implementation built to suit consumers’ demanding requirements. In addition, there is a broad deployment of sensor networks to give enhanced capabilities, but a specific operating system must be given in order to control such types of embedded devices. This paper introduces several software infrastructures capable of supporting such embedded systems. embedded systems are also used in the medical, transportation, and wireless sensor network fields, specifically in medical imaging, vital signs, car electric vehicles, and Wi-Fi modules. it should be evolved and able to function on a LAN or a wide-ranging shared network. It must also be ensured that it offers the same user environment facilities on the basis of their demands. Precision and precision are vital as the environment changes. Incorporated operating systems for a certain purpose are designed. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： embedded systems

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applications of Machine Learning Techniques for Renewable Resource Management 2

Applications of Machine Learning Techniques for Renewable Re...

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2nd international conference on Recent Trends in Microelectronics, Automation, computing, and Communications systems, ICMACC 2024

作者： Khot, Suhas Takawale, Amruta Kulkarni, Nikita Kulkarni, Rohan Nalawade, Himanshu S. Barfe, Rahul KJCOEMR EnTC Engineering Department Pune India KJCOEMR Electrical Engineering Department Pune India KJCOEMR Computer Engineering Departemnt Pune India

ISBN: (纸本)9798350366570

Renewable resource management plays a pivotal role in advancing sustainable development, particularly concerning energy production and utilization. The integration of Artificial Intelligence (AI) and Machine Learning (ML) techniques has significantly bolstered the efficiency and effectiveness of managing renewable resources. This survey paper offers a comprehensive overview of recent advancements in AI and ML applications within the realm of renewable resource management. It delves into areas such as renewable energy prediction, resource allocation, optimization strategies, and decision-making processes. Through this exploration, we aim to dissect various AI and ML algorithms, their associated benefits, challenges, and outline potential future research directions within the renewable resource domain. Our study centers on conducting an extensive survey of AI and ML techniques as applied to renewable resource management, practical analyses using Linear and Polynomial Regression models are performed on a real-world dataset to showcase the applicability and benefits of AI and ML in renewable resource management. The overarching goal is to unravel how AI and ML methodologies can elevate decision-making processes, finetune resource utilization strategies, and foster sustainable energy practices within the renewable resource sector. This paper offers a detailed examination of the transformative impact of AI and ML techniques on the renewable energy landscape. It delves into the pivotal role played by predictive modeling, optimization algorithms, and data analytics in surmounting the challenges inherent in renewable resource management. © 2024 ieee.

关键词： Adversarial machine learning

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