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A novel image descriptor based on dot-diffused block truncation coding with bit probability

Journal of Ambient Intelligence and Humanized Computing 2023年第11期14卷 14841-14858页

作者： Riyono, Dwi Sun, Chi-Chia Guo, Jing-Ming Wong, KokSheik Department of Electrical Engineering National Taiwan University of Science and Technology Taipei Taiwan Department of Electrical Engineering National Formosa University Yunlin Huiwei Taiwan Faculty of Computer Science and Information Technology Monash University-Malaysia Campus Kuala Lumpur Malaysia

With the advent of mobile smart devices and ubiquitous network connections, digital images can now be conveniently captured, edited, and shared online worldwide. The ever-increasing number of pictures poses a technically challenging, yet unavoidable problem of efficiently and accurately finding a desired image. In this work, we develop a new image retrieval system based on an image compression technique, namely dot-diffused block truncation coding (DDBTC) with bit probability. Specifically, the color feature derived from color distribution and the bitmap feature (BF) derived from both edges and textures jointly describe the image. The degree of similarity between two images is then measured by their respective color and BFs by using the modified Canberra distance metric. Experimental results show that the proposed feature descriptor achieves superior image retrieval performance as compared to the former DDBTC image retrieval feature and conventional non-DDBTC based features. Additional experiments on image classification verify that the proposed feature descriptor outperforms conventional image classification methods. © 2018, Springer-Verlag GmbH Germany, part of Springer Nature.

关键词： Color

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Implementing Robust Security Protocols for Virtual Images in Cloud Computing Platform

Implementing Robust Security Protocols for Virtual Images in...

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2024 International Conference on Artificial Intelligence and Quantum Computation-Based Sensor Applications, ICAIQSA 2024

作者： Tiwari, Shiv Kumar Gupta, Manish Neogi, Subhrendu G. Amity School of Engineering and Technology Amity University Department of Computer Science Engineering Gwalior India The Neotia University Department of Computer Science Engineering West Bengal India

ISBN: (纸本)9798331517953

The new technology, with the aid of newly emerging knowledge known as cloud computing, can provide resources remotely and on demand. With the use of cloud computing, users can operate in settings where they are not dependent on physical infrastructure. Numerous protocols facilitate the utilization of cloud computing resources. Cloud computing is Internet-based technology that is made available to consumers of its resources and services and is billed depending on demand and usage. All cloud servers cannot see the precise place where the user is to be configured. User-generated secure keys are employed in the proposed work for both virtual machine (VM) switching and authentication. Every time a valid user is authenticated, a secure pass key is utilized to guarantee security and authentication between the user and the system. © 2024 IEEE.

关键词： Cloud platforms

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Semantic Segmentation of Lumbar Vertebrae Using Meijering U-Net(MU-Net)on Spine Magnetic Resonance Images

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computer Modeling in engineering & sciences 2025年第1期142卷 733-757页

作者： Lakshmi S V V Shiloah Elizabeth Darmanayagam Sunil Retmin Raj Cyril Department of Computer Science and Engineering College of Engineering GuindyAnna UniversityChennai600025India Department of Information Technology Madras Institute of TechnologyAnna UniversityChrompetChennai600044India

Lower back pain is one of the most common medical problems in the world and it is experienced by a huge percentage of people *** to its ability to produce a detailed view of the soft tissues,including the spinal cord,nerves,intervertebral discs,and vertebrae,Magnetic Resonance Imaging is thought to be the most effective method for imaging the *** semantic segmentation of vertebrae plays a major role in the diagnostic process of lumbar *** is difficult to semantically partition the vertebrae in Magnetic Resonance Images from the surrounding variety of tissues,including muscles,ligaments,and intervertebral discs.U-Net is a powerful deep-learning architecture to handle the challenges of medical image analysis tasks and achieves high segmentation *** work proposes a modified U-Net architecture namely MU-Net,consisting of the Meijering convolutional layer that incorporates the Meijering filter to perform the semantic segmentation of lumbar vertebrae L1 to L5 and sacral vertebra ***-colour mask images were generated and used as ground truth for training the *** work has been carried out on 1312 images expanded from T1-weighted mid-sagittal MRI images of 515 patients in the Lumbar Spine MRI Dataset publicly available from Mendeley *** proposed MU-Net model for the semantic segmentation of the lumbar vertebrae gives better performance with 98.79%of pixel accuracy(PA),98.66%of dice similarity coefficient(DSC),97.36%of Jaccard coefficient,and 92.55%mean Intersection over Union(mean IoU)metrics using the mentioned dataset.

关键词： computer aided diagnosis(CAD) magnetic resonance imaging(MRI) semantic segmentation lumbar vertebrae deep learning U-Net model

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Transformer based Hybrid System for Speech Transcription and Disorder Detection 4

Transformer based Hybrid System for Speech Transcription and...

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4th International Conference on Ubiquitous Computing and Intelligent Information Systems, ICUIS 2024

作者： Bhargavi, N.M. Bharadwaj, M. Shashank Koushik, M. Alva, Khushi U. Vedavathi, N. Vidyavardhaka College of Engineering Department of Computer Science & Engineering Mysuru India

ISBN: (纸本)9798331529635

Stuttering is a speech disorder accompanied by disruptions in the fluency of speaking and is characterized by involuntary repetitions, prolongations, or blocks in speech. The early detection and recommendation for personalized therapies are considered to be very important in effective treatment. Deep learning and machine learning-based techniques like ANN's and CNN's have held much promise as solutions in the implementation of an automatic stuttering detection and therapy suggestion system. The paper is interested in a number of methods followed for detection of stuttering and focuses on the integration of wav2vec for learning speech representation and BERT for classification based on text for stuttering events. We review existing models and techniques;compare the performances, and an experimental evaluation on the approach presented, using wav2vec and BERT. Our results show that deep learning models, especially language and speech pre-trained models, significantly improve the stuttering detection accuracy. © 2024 IEEE.

关键词： Contrastive Learning

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Efficient mapping of complex groundwater systems associated with braided rivers using small coil surface nuclear magnetic resonance

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Geophysics 2025年第3期90卷 J1-J12页

作者： Vang, Mathias Østbjerg Grombacher, Denys Larsen, Jakob Juul Wilson, Scott Department of Geoscience Aarhus Denmark Aarhus University HydroGeophysics Group Department of Electrical and Computer Engineering Aarhus Denmark Lincoln Agritech Ltd Lincoln New Zealand

Braided river systems are a substantial source of groundwater recharge in New Zealand. These rivers flow from high relief toward the sea across alluvial plain aquifers, across which the primary groundwater recharge occurs. These aquifers are often used for agricultural irrigation, and overexploitation can occur if there is limited knowledge of aquifer recharge mechanisms. To understand these complex recharge patterns, more detailed knowledge of the lithologic controls on the surface water-groundwater exchange is necessary. Here, we develop a case study in which surface nuclear magnetic resonance (SNMR) is used in braided river systems to identify subsurface structures. We find the method to be efficient in identifying permeability contrasts beneath riverbeds and river berms in multiple river settings. We calibrate a local archetype by coincident borehole lithology and SNMR-derived water contents to find the SNMR signature related to lithologic changes. The resulting archetypes are extrapolated to SNMR sites where there is no coinciding borehole information to identify these lithologic transitions at multiple sites. We find that a combined borehole and SNMR survey can map the transitions from aquifers to low-permeability layers efficiently in three case studies. The small 20 m × 20 m SNMR coil enables acquisition on small bars within the river while resolving the thin-saturated units, previously not resolvable with a larger coil size. The laterally constrained inversion improves lateral consistency and the ability to track the features of the braidplain aquifer in all three rivers within the top 10 m. These results demonstrate SNMR as a multisite geophysical method capable of mapping important hydrogeologic layers to provide valuable information on recharge to vulnerable aquifers. © 2025 Society of Exploration Geophysicists. All rights reserved.

关键词： Groundwater resources

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Integrated Colorization and Styling Approach for Enhancing Image Clarity 3

Integrated Colorization and Styling Approach for Enhancing I...

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3rd International Conference on Applied Artificial Intelligence and Computing, ICAAIC 2024

作者： Shalini, Gollapalli Madhu Anand, Rakam Sankeerth, Thalloju Reddy, H. Venkateswara Hariharan, Shanmugasundaram Kukreja, Vinay Vardhaman College of Engineering Department of Computer Science and Engineering Hyderabad India

ISBN: (纸本)9798350375190

Image exploration adopts several dimensionalities with web-based tasks, integrating Streamlit and OpenCV to create an interactive platform for advanced image enhancement. Alongside the transformative tool enabling conversion of black and white images into vibrant compositions, an innovative styling suite offers diverse artistic filters. Notably, the addition of a camera option allows users to capture photos directly within the interface. Through Streamlit's intuitive interface and OpenCV's robust image processing, users experience real-time previews for seamless colorization and filtering. The proposed methodology involves designing a responsive web interface, optimizing Streamlit widgets for file uploads and style selection, and leveraging OpenCV for image processing. Results demonstrate the application's effectiveness, providing immediate insights into style impact. Challenges include processing speed optimization, with future research directions exploring advanced Machine Learning (ML) models for dynamic style transfer. In summary, this research advances interactive image processing, offering a refined platform for creative visual content enhancement. The results obtained is promising leading to effective image colorization and styling, thereby improving the quality of the image. © 2024 IEEE.

关键词： Image enhancement

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A Real Time Prediction Model for Round Winners of Counter Strike:Global Offensive 3

A Real Time Prediction Model for Round Winners of Counter St...

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3rd International Conference on Sentiment Analysis and Deep Learning, ICSADL 2024

作者： Paul, Rosebell Sajeer, Rihan Vaishnav, M.V. Abraham, Savio Vinu Sahajan, Shreya SCMS School of Engineering and Technology Department of Computer Science and Engineering India

ISBN: (纸本)9798350396157

This research study aims to create a full time winner predictor program for Counter-Strike Global Offensive tournaments that can predict the round winner of the game being spectated. The algorithm creates accurate predictions of the round winner. The data required for the prediction has to be extracted from the game servers. The data is collected from the game servers using an API. The prediction can be viewed live by the user with the help of a GUI. The project can provide CS:GO enthusiasts and bettors with real-time insights and predictions for round winners in competitive matches. System aims to enhance the user's understanding of match dynamics and support informed decision making. © 2024 IEEE.

关键词： Forecasting

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Machine Learning (ML) based Electricity Price Prediction with Visualization 3

Machine Learning (ML) based Electricity Price Prediction wit...

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3rd International Conference on Automation, Computing and Renewable Systems, ICACRS 2024

作者： Abisha, D. Kumar, Prasanna D. Christy, Ida J. Evanjalin, Navedha R. Rajesh, P. Haris, Mohamed K. Department of Computer Science and Engineering National Engineering College Kovilpatti India

ISBN: (纸本)9798331532420

Electricity price prediction is crucial for businesses reliant on heavy machinery or computing services, as it aids in cost management and energy optimization. Accurate predictions allow these businesses to forecast annual electricity expenses, enhancing cost efficiency. This project aims to forecast electricity prices using machine learning techniques, specifically the Random Forest regression algorithm. The project utilizes historical data on electricity consumption and other influencing factors, including date and time, holiday indicators, day of the week, week of the year, forecasted and actual wind production, national load, temperature, wind speed, and CO2 intensity. The methodology involves converting string values to numerical values, handling missing data, and normalizing the dataset, identifying key features that significantly impact electricity prices, Splitting the data into training and testing sets, and training the model using the Random Forest regression algorithm and assessing the model's performance with appropriate metrics to ensure accuracy and reliability. The results demonstrate that the Random Forest model effectively predicts electricity prices with high accuracy. Visualizing these predictions in Power BI provides intuitive insights into the factors influencing electricity prices. These findings enable businesses to better anticipate electricity expenses, optimize energy usage, and improve financial planning. The comprehensive approach outlined in this project offers a valuable tool for understanding and forecasting electricity prices, thereby enhancing resource management and cost efficiency. © 2024 IEEE.

关键词： Data Preprocessing Electricity Pricing Energy Cost Optimization Machine Learning Random Forest Regression

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Review on Blockchain-Enabled Techniques for Secure Storage and Sharing of Patient Healthcare Records 2

Review on Blockchain-Enabled Techniques for Secure Storage a...

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2nd International Conference on Emerging Trends in Information Technology and engineering, ic-ETITE 2024

作者： Mahmood, Rasmiya George, Vinodu Lbs College of Engineering Department of Computer Science and Engineering Kasaragod India

ISBN: (纸本)9798350328202

The Patient Medical Health Records (PMHR) have always provided a large amount of research data to medical institutions, and pharmaceutical companies and have contributed to the development in medical research. Lack of confidentiality and integrity produces many security problems with sensitive health information and has a significant negative effect on a patient's life. Therefore, securing medical records is much-needed task for the healthcare system. However, the attack's harmfulness, data vastness and complexity have made data protection a complicated mission. Today's healthcare sector use centralized system to record and manage patient data. These may be less secure and subject to misinformation. Therefore, proper and secure management of patient health records is an important task in entire healthcare field. This include secure storing of health records, sharing among various healthcare professionals. In order to overcome the issues regarding the centralized system, various blockchain enabled approaches have been utilized and most of them could result in enhanced confidentiality, high throughput, low processing time, better access management and finally reduced computational overhead. However, still we need more improvements in assessing and addressing the scalability issues considering the increasing volume of data and the growing number of participants in the healthcare ecosystem. Main contribution of this paper is to review the blockchain enabled techniques in securing patient health records and discuss their underlying mechanisms in order to improve confidentiality, security, throughput, processing time and providing effective access control mechanisms. Finally will conclude with realizing a gap for utilizing methodologies for overcoming scalability issues. © 2024 IEEE.

关键词： Blockchain

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Multimodal Knowledge Graph Completion Using CLIP-Enhanced Hyper-Node Relational Graph Attention Networks 5

Multimodal Knowledge Graph Completion Using CLIP-Enhanced Hy...

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5th International Conference on Electronics and Sustainable Communication Systems, ICESC 2024

作者： Shashank, R.B. Swami, Saurabh B. Pai, Akanksha A Mady, Pranamya Thomas, Sharon Moharir, Minal Rv College of Engineering Department of Computer Science and Engineering Bangalore India

ISBN: (纸本)9798350379945

Knowledge graph (KG) completion is a critical task in Artificial Intelligence (AI) focused on deducing absent connections between entities. The diverse nature of data, encompassing text, images, and numerical information, introduces considerable difficulties in precisely forecasting these missing links. Multi-modal KGs are necessary to fully capture the diverse and rich information associated with entities, enhancing applications in search, recommendation, and AI. This study presents a new framework for completing multi-modal knowledge graphs that integrates textual, visual, and numerical data. Pre-trained Contrastive Language-Image Pre-training (CLIP) embeddings are leveraged for textual and visual information and a dense layer is utilized for numerical data, creating a unified entity representation through a low-rank multi-modal fusion approach. The proposed model employs a Hyper-node Relational Graph Attention Network (HRGAT) to effectively aggregate and process multi-modal information. The research shows that this new approach performs significantly better than current models in certain metrics, such as Mean Rank (MR), particularly excelling in lowering the mean rank of head entities, showcasing the enhanced capability and accuracy of the method proposed in completing multi-modal KGs. © 2024 IEEE.

关键词： Knowledge graph

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