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Signal Processing and Advance Research in Computing (SPARC), International Conference on

作者： Govind Murari Upadhyay Prashant Vats Surabhi Shanker Saloni Bhushan Sonu Mittal Manju Mandot Dept. of Computer Application Manipal University Jaipur Jaipur Rajasthan India Dept. of CSE School of CSE Manipal University Jaipur Jaipur Rajasthan India Dept. of CSE School of Engineering & Technology K.R. Mangalam University Sohna Road Gurugram Haryana India Dept. of Computer Science/IT Chhatrapati Shivaji Maharaj University Panvel Maharashtra India Dept. of CSE Amity Institute of Information Technology Amity University Noida Uttar Pradesh India Dept. of Computer Science/IT J. R. N. Rajasthan Vidyapeeth University Udaipur Rajasthan India

ISBN: (数字)9798350385205

ISBN: (纸本)9798350385212

Unmanned Aerial Vehicles (UAVs) have substantially advanced the sector by providing improved real-time monitoring, data collecting, and situational awareness capabilities when integrated into disaster management techniques. This study examines the creative uses of unmanned aerial vehicles (UAVs) in disaster relief, emphasising how they may speed up reaction times and increase operational effectiveness in emergency situations. With an emphasis on their usefulness in human detection, damage assessment, and coordination efforts, we propose a thorough framework that describes the design, deployment, and evaluation of UAV systems customised for catastrophe situations. The study shows how UAVs may be used to enhance processes of decision-making and enable more efficient recovery and catastrophe response through instances and quantitative evaluation. The results demonstrate how UAV technology may revolutionise conventional disaster management procedures and provide insightful information for future studies and real-world applications in the field.

关键词： Technological innovation Signal processing algorithms Disaster management Transforms Medical services Signal processing Autonomous aerial vehicles Regulation Real-time systems Relays

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Exploring the Capabilities and Limitations of Generative AI applications, Challenges, and Future Directions

Exploring the Capabilities and Limitations of Generative AI ...

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Pervasive Computational Technologies (ICPCT), International Conference on

作者： Gulbir Singh Meenakshi Poonam Sharma Anjul Bhardwaj Department of Computer Science and Engineering Graphic Era Hill University Haldwani Campus Nanital Uttarakhand India MMICT&BM Maharishi Markandeshwar (Deemed to be University) Mullana Ambala Haryana India Dept. of Computer Application Divine Institute of Management & Studies Ziradei Siwan Bihar India

ISBN: (数字)9798331508685

ISBN: (纸本)9798331519476

Unprecedented capabilities for content generation, predictive analytics, and automation are made available by the introduction of Generative Artificial Intelligence (AI) technologies, which uses in a new age of industrial innovation. The purpose of this study is to investigate the transformational potential of generative AI from the perspective of exploring its capabilities and limitations and highlighting its role in the development of future technological developments. With the help of this study, we want to highlight important unsolved issues in contemporary generative AI paradigms. These challenges need to be addressed in order to further improve the capabilities, variety, and dependability of these concepts. Our goal is to give researchers with significant insights that will allow them to explore successful research areas, which will ultimately lead to the creation of generative AI systems that are more robust and accessible.

关键词： Industries Economics Technological innovation Generative AI Production Regulation Predictive analytics Monitoring Business Testing

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RJB-Net: Residual Deep Learning with Joint Bilateral Denoising Network for Remote Sensing Image Fusion

RJB-Net: Residual Deep Learning with Joint Bilateral Denoisi...

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Innovation in Technology (ASIANCON), Asian Conference on

作者： Krupa Patel Indranil Misra Narendra Patel S. Manthira Moorthi Debajyoti Dhar Dept. of Computer Science & Engineering Birla Vishvakarma Mahavidyalaya Engineering College Anand India Signal and Image Processing Group Space Application Centre(SAC) Indian Space Research Organisation (ISRO) Ahmedabad India

Remote sensing image fusion amalgamate information from panchromatic and multispectral remote sensing images to generate an optimum representative image. In this paper, we have proposed a novel method for image fusion known as Residual Deep Learning with Joint Bilateral Denoising Network (RJB-Net). The RJB-Net procedure is initiated with panchromatic image denoising using a joint bilateral filter to suppress noise and retain edge information. RJB-Net model is trained at image patches covering all major spectral classes. The denoised panchromatic image along with multispectral data is input to the residual deep learning network for inference and generates a fused image. The remote sensing datasets used for RJB-Net training and evaluation are Indian Cartosat-1 panchromatic and Resourcesat-2 multispectral imagery. The experiment section compares the RJB-Net denoising output with prominent spatial domain image filtering methods both qualitatively and quantitatively. It is found that RJB-Net fused images inject the spatial information from panchromatic images in a controlled manner while retaining the spectra characteristics of multispectral data. The merging performance of RJB-Net is compared with state-of-the-art remote sensing image fusion techniques that range from classical to recent deep learning model. The proposed method RJB-Net generates superior results among all fusion methods at different feature targets and compared the performance both visually as well as with image quality metrics.

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Revolutionizing Healthcare Record Management: Secure Documentation Storage and Access through Advanced Blockchain Solutions

arXiv

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arXiv 2025年

作者： Brijwani, Geeta N. Ajmire, Prafulla E. Junaid, Mohammad Atique Mohammad Charasia, Suhashini Awadhesh Bhende, Deepali O. Computer Science Dept. Sant Gadge Baba Amravati University Amravati India Department of Computer Science Faculty of Engineering & Technology Sant Gadge Baba Amravati University India Department of Computer Science Rashtrasant Tukadoji Maharaj Nagpur University Nagpur India Department of Master of Computer Application Rashtrasant Tukadoji Maharaj Nagpur University Nagpur India

Integrating blockchain technology into healthcare systems presents a transformative approach to documenting, storing, and accessing electronic health records (EHRs). This research introduces a novel blockchain-based EHR system designed to significantly enhance security, scalability, and accessibility compared to existing solutions. Current systems primarily utilize SHA-256 for security and either IPFS or centralized storage, which, while effective, have limitations in providing comprehensive data integrity and security. The proposed system leverages a hybrid security algorithm combining Argon2 and AES and integrates a hybrid storage and consensus mechanism utilizing IPFS and PBFT. This multifaceted approach ensures robust encryption, efficient consensus, and high fault tolerance. Furthermore, the system incorporates Multi-Factor Authentication (MFA) to safeguard against unauthorized access. It utilizes advanced blockchain tools like MetaMask, Ganache, and Truffle to facilitate seamless interaction with the decentralized network. Simulation results demonstrate that this system offers superior protection against data breaches and enhances operational efficiency. Specifically, the proposed hybrid model substantially improves data integrity, consensus efficiency, fault tolerance, data availability, latency, bandwidth utilization, throughput, memory usage, and CPU usage across various healthcare applications. To validate the performance and security of the proposed system, comprehensive analyses were conducted using real-world healthcare scenarios. The findings highlight the significant advantages of the blockchain-based EHR system, emphasizing its potential to revolutionize healthcare data management by ensuring secure, reliable, and efficient handling of sensitive medical information. © 2025, CC0.

关键词： Electronic health record

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Evaluation of Machine-Learned Price Prediction Models For Block-Chain Technology

Evaluation of Machine-Learned Price Prediction Models For Bl...

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Innovations in High Speed Communication and Signal Processing (IHCSP), International Conference on

作者： Vikas Goel Ashutosh Sharma Raju Ranjan Amit Kumar Sharma Dept. of IT KIET Group of Institutions Ghaziabad India Dept. of Computer Application IMSUC-Ghaziabad Ghaziabad India School of Computing Science & Engineering Galgotia University Greater Noida India Dept. of CS&E Ch. Charan Singh University Campus Meerut India

As a new form of technology, blockchain has much to offer. The technology has a wide range of potential uses and advancements, depending on the field of application. However, further study is required to realise the potential of advanced technologies like Blockchain and smart contracts. There is a lot of potential for investigation because the technology is so new. This research looked at the origins of the Blockchain and analysed several different blockchain solutions. Ethereum was discovered to be the most often used platform for constructing distributed apps after being compared to other platforms. Therefore, a distributed smart energy microgrid application will be built on the Ethereum platform. There is a coin, or token, exclusive to each blockchain platform. Bitcoin's relationship to other cryptocurrencies was measured using the Karl Pearson correlation coefficient. The research concludes that ether's price is most sensitive to changes in the price of bitcoin. On a preferred Ethereum network, a distributed application for managing smart microgrids has been built. The smart contract constitutes the heart of this programme. As a result, we also take care of the smart contract's layout, coding, and optimization. In comparison to the original, less GAS is consumed by this smart contract. The entire distributed app has been built and thoroughly tested. Smart Energy Microgrid Distributed application framework results demonstrate that the application functioned as expected.

关键词： Surveys Analytical models Technological innovation Smart contracts Time series analysis Bitcoin Microgrids

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Quasi-Oppositional Satin Bowerbird with Deep Learning based Content based Image Retrieval 6

Quasi-Oppositional Satin Bowerbird with Deep Learning based ...

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6th International Conference on Computing Methodologies and Communication, ICCMC 2022

作者： Singh, Devesh Pratap Joseph, Susheel George Selvi, V. Tamil Karunakaran, S. Appasami, G. Jegajothi, B. Graphic Era Deemed to be University Department of Computer Science Engineering Dehradun248002 India Kristu Jyoti College of Management and Technology Dept. of Computer Application Kottayam India R.M.D. Engineering College Department of ECE Tamil Nadu 601206 India Vardhaman College of Engineering Department of ECE Hyderabad501218 India NIT Department of Computer Applications Tamilnadu Tiruchirappalli India Chennai Institute of Technology Centre for Energy Research Tamil Nadu Chennai India

ISBN: (纸本)9781665410281

Content-based image retrieval (CBIR) is commonly employed to retrieve images from a massive set of unlabeled images. The design of CBIR model faces several limitations, as it is mainly based on the extraction of image features to calculate the similarity amongst the query image (QI) and database images. The recent advances of deep learning (DL) models help to attain remarkable retrieval outcomes. In this view, this paper presents a novel quasi-oppositional satin bowerbird optimizer with Densely Connected Networks (QOSBO-DCN) for CBIR. The proposed QOSBO-DCN technique aims to properly retrieve the images related to the QI in an effective and automated manner. The proposed QOSBO-DCN technique derives a DenseNet-77 model as a feature extractor to derive feature vectors from the QI and database images. Besides, the QOSBO algorithm is utilized to adjust the hyperparameter values of the DenseNet-77 model in such a way that the retrieval performance can be improved. Additionally, Euclidean distance is used as a similarity measurement approach to determine the highly resembling images and retrieve them. The simulation analysis of the QOSBO-DCN technique is performed using Corel10K dataset and the results reported the betterment of the QOSBO-DCN technique over the existing techniques. © 2022 IEEE.

关键词： Deep learning

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Narrow Down Forecast Range: Using Knowledge of Past Operations and Attribute-Dependent Thresholding in Good Fishing Ground Prediction

Narrow Down Forecast Range: Using Knowledge of Past Operatio...

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OCEANS

作者： Haruki Konii Teppei Nakano Yasumasa Miyazawa Tetsuji Ogawa Dept. of Communications and Computer Engineering Waseda University Tokyo Japan Intelligent Framework Laboratory Tokyo Japan Application Laboratory Japan Agency for Marine-Earth Science and Technology Yokohama Japan

In this study, an attempt has been made to highly narrow down the results of predicting good fishing grounds. In the automatic prediction of good fishing grounds using meteorological and oceanographic information, there is a trade-off between reducing the number of missed detections of good fishing grounds and narrowing down the forecast range. For example, the forecast range will inevitably widen if good fishing grounds are tried to be detected perfectly, which has a negative impact on fishers’ operational decision-making. This study, therefore, attempts to introduce techniques to narrow down the forecast range, such as the use of past operational information and stricter thresholding for judgment, to the convolutional autoencoder-based good fishing ground prediction. Experimental comparisons conducted using actual catch information in bullet tuna trolling demonstrated the effectiveness of the proposed method; it succeeded in narrowing down the forecast range of good fishing grounds without increasing the number of good fishing grounds missed.

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Image-Based Attendance System using Facial Recognition

Image-Based Attendance System using Facial Recognition

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International Conference on System Modeling & Advancement in Research Trends (SMART)

作者： Vineet Kumar Chauhan Trilok Singh Abhishek Dixit Raushan Kumar Singh Pradeep Kumar Singh Juginder Pal Singh Dept. of Computer Science & Engg TMU University Moradabad India Dept. of Computer Science & Engg. Invertis University Bareilly India Dept. of Information Technology Vardhaman College of Engineering Hyderabad India Dept. of Computer Engg. and Application GLA University Mathura India

ISBN: (纸本)9781665487351

In today's digital age, attendance systems utilizing facial recognition are essential in schools, universities, companies, etc. One feature of the human body that might help identify a person is the face. Using a camera, we initially built a database of various people's faces for this project, and the recognizer algorithm would use this information. The system will be prepared to take attendance on its own after the database has been established. A cross-check will be performed between the recently acquired face image and the database. The attendance will be recorded and the data immediately saved into the excel sheet if the same face's image is found in the database. This attendance-based system using Face Recognition will be developed using PCA (Principal Component Analysis) and CNN (Convolutional Neural Network).

关键词： Databases Face recognition Companies Wavelet analysis Market research Information age Linear discriminant analysis

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Particle swarm optimizer based epithelial layer segmentation in CIElab color space

Particle swarm optimizer based epithelial layer segmentation...

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Recent Advances and Innovations in engineering (ICRAIE)

作者： Swarnajit Ray Krishna Gopal Dhal Prabir Kumar Naskar Dept. of Computer Science and Engineering Maulana Abul Kalam Azad University of Technology Nadia West Bengal India Dept. of Computer Science and Application Midnapore College (Autonomous) Paschim Medinipur West Bengal India Dept. of Computer Science and Engineering Government College of Engineering & Textile Technology Serampore West Bengal India

The study of epithelial tissue is critical for the pathologist to determine its grade and the presence of various diseases. Proper segmentation of the epithelial layer plays a significant role in different disease detection like Oral Sub-mucous Fibrosis (OSF), multi-class grading of Oral Squamous Cell Carcinoma (OSCC), and Epithelial Biopsy Image Classifier, etc. In this study, our main aim is automatic segmentation of epithelial layer from pathology images with is very helpful for computer-Aided Diagnosis (CAD) based system. Here author used particle swarm optimization (PSO) and k-means clustering (KM) methods in CIElab color space for oral epithelial layer segmentation and also used different color spaces and its component for comparative study. Experimental results showed that the CIElab color space gave the best result among all other color spaces. The segmented images are compared with ground truth images and PSO with CIElab color space outperformed all other methods with an accuracy of 98.43%.

关键词： Image segmentation Visualization Technological innovation Pathology Image color analysis Numerical analysis Particle swarm optimization

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Virtual Simulation based Visual Object Tracking via Deep Reinforcement Learning

Virtual Simulation based Visual Object Tracking via Deep Rei...

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International Conference on Information Science and Communications Technologies (ICISCT)

作者： Khurshedjon Farkhodov Jin-Hyeok Park Suk-Hwan Lee Ki-Ryong Kwon Dept. of Artificial Intelligence Convergence Pukyong National Univercity Busan South Korea Dept. of IT Convergence and Application Engineering Pukyong National University Busan South Korea Dept. of Computer Engineering Donga University Busan South Korea

The current research field of object tracking has become noticeably popular among researchers where AI techniques take place with high-level accuracy. An algorithm with multifunctional abilities had proposed in different proposals in recent years. We proposed a tracking technique integrated with a virtual reality simulator – the AirSim (Areal Informatics and Robotics Simulation) City Environ model using one of the DRL models to control with a drone agent to examine a realistic environment. Additionally, the suggested method had tested via the two public: VisDrone2019 and OTB-100 datasets to compare with conventional strategies to show better performance among recent works.

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