Using normal distribution to retrieve temporal associations by Euclidean distance

Cheruvu, Aravind; Radhakrishna, Vangipuram; Rajasekhar, N.

doi:10.1109/icemis.2017.8273101

Cited by 93 publications

(9 citation statements)

References 16 publications

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“…At the next stage, each centroid starts pulling other data to their cluster based on the Euclidean distance [30]. Each input feature is fed into the distance calculation with centroids.…”

Section: Training Processmentioning

confidence: 99%

K-centroid convergence clustering identification in one-label per type for disease prediction

Hoang,

Delmonte

2024

IJ-AI

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Disease prediction is a high demand field which requires significant support from machine learning (ML) to enhance the result efficiency. The research works on application of K-means clustering supervised classification in disease prediction where each class only has one labeled data. The K-centroid convergence clustering identification (KC3I) system is based on semi-K-means clustering but only requires single labeled data per class for the training process with the training dataset to update the centroid. The KC3I model also includes a dictionary box to index all the input centroids before and after the updating process. Each centroid matches with a corresponding label inside this box. After the training process, each time the input features arrive, the trained centroid will put them to its cluster depending on the Euclidean distance, then convert them into the specific class name, which is coherent to that centroid index. Two validation stages were carried out and accomplished the expectation in terms of precision, recall, F1-score, and absolute accuracy. The last part demonstrates the possibility of feature reduction by selecting the most crucial feature with the extra tree classifier method. Total data are fed into the KC3I system with the most important features and remain the same accuracy.

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“…At the next stage, each centroid starts pulling other data to their cluster based on the Euclidean distance [30]. Each input feature is fed into the distance calculation with centroids.…”

Section: Training Processmentioning

confidence: 99%

K-centroid convergence clustering identification in one-label per type for disease prediction

Hoang,

Delmonte

2024

IJ-AI

View full text Add to dashboard Cite

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“…The integration of solar-powered EV charging infrastructure with the existing electrical grid introduces additional layers of complexity. The bidirectional flow of energy, wherein EVs can potentially return energy to the grid, opens new avenues for grid support services such as peak load management and frequency regulation [6][7][8][9][10][11]. This bidirectional capability necessitates a re-examination of grid infrastructure and the development of smart grid technologies that can seamlessly accommodate the additional energy flows.…”

Section: *Corresponding Authormentioning

confidence: 99%

Integration Challenges and Solutions for Solar-Powered Electric Vehicle Charging Infrastructure: From Panel to Battery

Lalitha,

Kaliyaperumal,

Nijhawan

et al. 2024

E3S Web Conf.

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The integration of solar power with electric vehicle (EV) charging infrastructure presents a promising avenue to foster sustainable transportation. This study delves into the multifaceted challenges encountered in the synthesis of solar-powered EV charging stations and proffers solutions that span the complete energy transfer chain from photovoltaic panels to EV batteries. Initial concerns address the intermittent nature of solar energy and its impact on the reliability of power delivery. Advanced energy management strategies are explored, incorporating predictive analytics and real-time adjustment mechanisms to enhance the consistency and efficiency of power flow. The second part of the investigation scrutinizes the power electronics interface, emphasizing the need for high-efficiency converters that can operate effectively over varied solar insolation levels. Novel circuit topologies are presented, alongside adaptive control algorithms designed to optimize the power conversion process. Collectively, the findings underscore the potential of solar-powered EV charging infrastructure to not only support the decarbonization of transportation but also to contribute to the stability and efficiency of the power grid. The intersection of power electronics, energy management, and grid integration forms the cornerstone of this endeavor, with each domain providing critical components to the holistic solution. It culminates in a set of recommendations for policy, design, and operation that can accelerate the adoption of this technology.

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“…The cornerstone of enhancing accessibility in multimedia content for differently abled individuals lies in the development and application of adaptive image and video processing techniques [10]. These techniques are engineered to dynamically interpret and transform visual data into alternative forms of sensory input, tailored to the user's specific needs [11]. The essence of adaptivity in this context is the system's ability to modify its behavior based on the content it encounters and the user profiles it interacts with, ensuring a flexible and responsive user experience.…”

Section: Adaptive Image and Video Processing Techniquesmentioning

confidence: 99%

A Review on Enhancing Accessibility Through Image and Video Processing: Solutions for Differently Abled Individuals

Anjimoon,

Sobti

et al. 2024

E3S Web Conf.

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This paper presents innovative methodologies in image and video processing aimed at augmenting accessibility for differently abled individuals. Central to this research is the development of advanced algorithms that enable enhanced interpretation and interaction with multimedia content, thereby empowering users with sensory impairments. The study introduces a multi-layered framework that integrates adaptive filtering, object recognition, and augmented reality, tailored to the needs of users with visual and auditory challenges. Semantic scene analysis is leveraged to provide descriptive audio annotations for the visually impaired, facilitating a comprehensive understanding of visual data. For individuals with hearing impairments, the system incorporates real-time sign language interpretation within videos, utilizing deep learning techniques. The efficacy of these solutions is measured against conventional accessibility tools, demonstrating significant improvements in user engagement and comprehension. A novel contribution of this research is the application of machine learning to calibrate the system according to individual user profiles, ensuring a personalized and intuitive user experience. The scalability of the proposed system is validated through its implementation across various platforms and content formats. The findings suggest that such technological advancements have the potential to significantly reduce the barriers faced by differently abled individuals in accessing multimedia information.

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Using normal distribution to retrieve temporal associations by Euclidean distance

Cited by 93 publications

References 16 publications

K-centroid convergence clustering identification in one-label per type for disease prediction

K-centroid convergence clustering identification in one-label per type for disease prediction

Integration Challenges and Solutions for Solar-Powered Electric Vehicle Charging Infrastructure: From Panel to Battery

A Review on Enhancing Accessibility Through Image and Video Processing: Solutions for Differently Abled Individuals

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