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检索条件"任意字段=2023 International Conference on Algorithms, Computing and Data Processing, ACDP 2023"

共 6946 条记录，以下是4761-4770 订阅

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PerconAI 2023: 2nd Workshop on Pervasive and Resource-Constrained Artificial Intelligence - Welcome and Committees

PerconAI 2023: 2nd Workshop on Pervasive and Resource-Constr...

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IEEE Annual conference on Pervasive computing and Communications Workshops (PerCom)

The PeRConAI workshop aims at promoting the circulation of new ideas and research directions on pervasive and resource-constrained artificial intelligence, serving as a forum for practitioners and researchers working on the intersection between pervasive computing and machine learning, including deep learning and artificial intelligence. The workshop welcomes theoretical and applied research sharing the common objective of investigating solutions advancing towards a truly pervasive and liquid AI. In the long term, we envision a future where every device at the edge of the Internet, regardless of its computing capabilities, will have an active role in the AI process by processing its data and collaborating with other devices to extract knowledge from them.

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A Computational Approach to Measuring the Semantic Divergence of Cognates 20th

A Computational Approach to Measuring the Semantic Diverge...

引用

20th international conference on Computational Linguistics and Intelligent Text processing, CICLing 2019

作者： Uban, Ana-Sabina Cristea [Ciobanu], Alina Dinu, Liviu P. Faculty of Mathematics and Computer Science University of Bucharest Bucharest Romania Human Language Technologies Research Center University of Bucharest Bucharest Romania Data Science Center University of Bucharest Bucharest Romania

ISBN: (纸本)9783031243394

Meaning is the foundation stone of intercultural communication. Languages are continuously changing, and words shift their meanings for various reasons. Semantic divergence in related languages is a key concern of historical linguistics. In this paper we investigate semantic divergence across languages by measuring the semantic similarity of cognate sets in multiple languages. The method that we propose is based on cross-lingual word embeddings. In this paper we implement and evaluate our method on English and five Romance languages, but it can be extended easily to any language pair, requiring only large monolingual corpora for the involved languages and a small bilingual dictionary for the pair. This language-agnostic method facilitates a quantitative analysis of cognates divergence – by computing degrees of semantic similarity between cognate pairs – and provides insights for identifying false friends. As a second contribution, we formulate a straightforward method for detecting false friends, and introduce the notion of "soft false friend" and "hard false friend", as well as a measure of the degree of "falseness" of a false friends pair. Additionally, we propose an algorithm that can output suggestions for correcting false friends, which could result in a very helpful tool for language learning or translation. © 2023, Springer Nature Switzerland AG.

关键词： Semantics

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Prediction of Smart Grid Stability Utilizing Machine Learning algorithms

Prediction of Smart Grid Stability Utilizing Machine Learnin...

引用

Mobile, Intelligent, and Ubiquitous computing conference (MIUCC), international

作者： Ghada Bayomi Alaa Hamdy Mohamed Hasheem. Ahmed.A. Ali Misr International University Cairo AAST Arab Academy for Science Technology & Maritime Transport Egypt Misr International University Cairo Egypt AAST Arab Academy for Science Technology & Maritime Transport Egypt Helwan university Cairo Egypt

The smart grid encountered numerous issues when attempting to continually monitor participant usages, bidirectional data flow, and utility costs as a result of the rise in the use of renewable energy sources. Specialists must detect system failure before it occurs and ascertain whether the smart grid is stable in order to achieve a robust, dependable smart grid. Conventionally in a smart grid, data on consumer demand is gathered and centrally evaluated against current supply conditions where consumers are then given the resulting recommended pricing information so they can decide how much to use. Because the entire traditional process is time-sensitive, dynamically evaluating grid stability becomes not only a concern but also an absolute necessity. Power supply and demand need to be balanced in order to maintain grid stability. Thus, machine learning might significantly aid in classifying whether or not the smart grid is stable by applying contemporary data mining approaches. In this research, different machine learning algorithms will be applied to assess and predict grid stability. The algorithms used in this paper are XG-classifier, Random Forest, Neural Network, KNN, SVM. The experiment results show that the XG-classifier achieved the highest performance yielding Classification accuracy, AUC, F1 and Recall of 97.79%, 96.7%, 95.6%, 95.4%, 94.5% respectively.

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Utilizing Machine Learning algorithms to Optimize Real-Time data Analysis

Utilizing Machine Learning Algorithms to Optimize Real-Time ...

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Smart Generation computing, Communication and Networking (SMART GENCON), international conference on

作者： Vaishali Singh Eesha Gupta D Little Femilin Jana Gopal B. Deshmukh Abhishek Singla K. Gopala Krishna Maharishi School of Engineering and Technology Maharishi University of Information Technology Uttar Pradesh India Department of Computer Science & Engineering Vivekananda Global University Jaipur India Department of Science and Humanities Prince Shri Venkateshwara Padmavathy Engineering College Chennai Department of Computer Engineering Vishwakarma Institute of Information Technology Pune India Centre of Interdisciplinary Research in Business and Technology Chitkara University Institute of Engineering and technology Chitkara University Punjab India Department of Electronics and Communication Engineering Faculty of Engineering and Technology Bangalore Karnataka India

This paper offers a way for optimizing actual-time records analysis the usage of a variety of machine gaining knowledge of algorithms. The algorithms paintings by creating predictive fashions which extract useful information from datasets, follow appropriate preprocessing techniques to make sure the high-quality of the records, and model the information to deduce meaningful capabilities from the data. The predictive models are then used to discover applicable developments, find patterns, and summarize the data for clean analysis. specifically, this paper outlines the technique of using supervised and unsupervised learning algorithms, consisting of Random wooded area, Naïve Bayes, and k-approach clustering, to extract meaningful styles and relationships from facts points in real-time. Additionally, the paper discusses wonderful capacity packages of the proposed technique, including fraud detection and prediction of stock costs. Such capability programs demonstrate the fee of actual-time records analysis for organizations and organizations.

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data sharing in Cloud-Assisted IoT

Data sharing in Cloud-Assisted IoT

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international conference on Science Technology Engineering and Management (ICONSTEM)

作者： T. Primya Swetha M V Ramya S R Taanusri G Ridhanya Rajasekaran Arun Sekar Department of CSE KPR Institute of Engineering and Technology Coimbatore India

Smart gadgets can now communicate from close to a long distance with one another and with the Internet or cloud. Internet of things (IOT) brings a paradigm shift of employing low resource IOT smart system with cloud computing. However, by employing cloud computing, resource-constrained IoT smart devices can gain a number of advantages, Excluding the weight of data processing and storing the data on the network cloud. By implementing it on network edge offers more merits instead of using network cloud in contra to internet of things (IOT) applications which needs high data rates, mobility, and latency-sensitive real-time data processing. In this paper mainly focused on data transfers to cloud and IOT devices form smart data transfer. Here a suggestion that is authenticated search method to look for required information among one's personal or shared data on storage. At last, by evaluating processing time performance of the suggested scheme, outcomes that discussed in the paper, show that our strategy has a chance of working well in IoT applications.

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A benchmark study of protein folding algorithms on nanobodies

A benchmark study of protein folding algorithms on nanobodie...

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Communications, computing and Artificial Intelligence (CCCAI), international conference on

作者： Shibo Liang Ziquan Liang Zecheng Wu Feijuan Huang Xu Wang Yuanzhe Cai Bingding Huang Xin Wang College of Big Data and Internet Shenzhen Technology University Shenzhen China. Shenzhen Institute of Translational Medicine Shenzhen 2nd People’s Hospital Shenzhen China College of Communication Engineering Jilin University Changchun Jilin China

Nanobodies, also known as single-domain or VHH antibodies, are recombinant variable domains derived from heavy-chain-only antibodies. They exhibit desirable characteristics, including small size, high solubility, exceptional stability, rapid blood clearance, and deep tissue penetration, rendering them valuable tools for disease diagnosis and treatment. In recent years, several deep-learning-based methods for protein structure prediction have been developed, requiring only protein sequences as input. Notable examples include AlphaFold2, RoseTTAFold, DeepAb, NanoNet, and tFold, which have demonstrated remarkable performance in protein or antibody/nanobody prediction. In this study, we analyzed 60 nanobody samples with known experimental 3D structures from the Protein data Bank (PDB). The accuracy of these algorithms was assessed using two metrics: RMSD and TM-score. Our findings revealed that NanoNet and tFold, particularly NanoNet, exhibit outstanding performance.

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data Engineering Techniques for Efficient and Accurate Human Physical Activities data Collection: a Summary of the State-of-the-art

Data Engineering Techniques for Efficient and Accurate Human...

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Metrology for Extended Reality, Artificial Intelligence and Neural Engineering (MetroXRAINE), IEEE international conference on

作者： Javidan Abdullayev Andrea Zingoni Department of Information Computing and Technology Western Caspian University Baku Azerbaijan Department of Economics Engineering Business and Society University of Tuscia Viterbo Italy

This contribution presents an overview of engineering techniques to collect human behavior data, highlighting the main research trends and challenges. From our research it emerged that wearable and smartphone sensors are popular for monitoring movement and vital signs, although accuracy can be influenced by environmental factors. Combining multiple sensors improves data accuracy, while machine learning algorithms enable pattern detection and behavior analysis. Non-invasive techniques, such as video monitoring and speech analysis, offer a comprehensive view of behavior. Despite all the positive advancements, there are still remaining challenges that require further research, including the need to enhance sensor accuracy, develop sensor fusion methods, and refine machine learning algorithms for improved data analysis in human activity monitoring.

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Robust Zero Watermarking Algorithm for Medical Images Based on BRISK-FREAK and DCT 11th

Robust Zero Watermarking Algorithm for Medical Images Based ...

引用

11th KES international conference on Innovation in Medicine and Healthcare, InMed 2023

作者： Dong, Fangchun Li, Jingbing Liu, Jing Chen, Yen-Wei School of Information and Communication Engineering Hainan University Haikou570228 China Research Center for Healthcare Data Science Zhejiang Lab Zhejiang Hangzhou311121 China Graduate School of Information Science and Engineering Ritsumeikan University Shiga525-8577 Japan

ISBN: (纸本)9789819933105

With the development of mobile Internet technology, the technology in the field of medical image processing is constantly updated and iterated. Digital watermarking technology plays an important role in the field of medical image processing, providing guarantee for the security of medical image information. Aiming at the poor robustness of medical image watermarking algorithms to geometric attacks, a robust zero-watermarking algorithm for medical images based on BRISK-FREAK and discrete cosine transform (DCT) is proposed. First, Binary Robust Invariant Scalable Kepoints (BRISK) was applied to extract the feature points of the medical image. Then, Fast Retina Keypoint (FREAK) was used to describe the extracted feature points and generate a feature descriptor matrix. Further, DCT was performed on the FREAK descriptor matrix, and the low-frequency part was processed via the perceptual hash algorithm to obtain a 32-bit binary feature vector. In order to enhance the security of the watermark information, the logistic mapping algorithm is utilized to encrypt the watermark before embedding the watermark. Finally, with the help of the zero watermarking technology, the proposed algorithm can be embedded and extract the watermark without changing any of the medical image information. Experimental results show that the algorithm has good robustness under some conventional attacks and geometric attacks. Especially under geometric attacks, the performance of the algorithm is excellent. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Discrete cosine transforms

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Research on the Architecture of Multiple data Center Interconnection based on ODC

Research on the Architecture of Multiple Data Center Interco...

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Cloud computing, Big data Applications and Software Engineering (CBASE), international conference on

作者： Wenyang Yu Ruopeng Li Yi Yu Xianjun Lei Shengtao Sun Aerospace Information Research Institute Chinese Academy of Sciences Beijing China Department of Computer Science and Engineering Yanshan University Qinhuangdao China

With the increasing growth of free and open data from the new generation of Earth-Observation (EO) satellites, Earth Observation has entered the Big data era, and the traditional data management methods face many challenges to keep up with the growth rate of data. The Open data Cube (ODC) is an open-source geospatial data management and analysis tool designed to enable users to process, analyze, and visualize large amounts of satellite imagery and climate data. However, most ODCs are applied independently in current, and due to difficulties with data transfer, and the managing domain restrictions on algorithms, it’s not easy to communicate with each other. This article proposed the data Hub architecture for multiple ODC interconnection. data Hub enables data interconnection and algorithm inter-operation between ODCs and supports the organization of workflow to build Remote Sensing applications based on multiple ODCs. To demonstrate the working mechanism of multiple ODC interconnection, we organized an application that performed the NDVI algorithm on Shennongjia data with the architecture above.

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Power of kernel functions, its benefits, and limitations

Power of kernel functions, its benefits, and limitations

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international Symposium on Advanced computing and Communication (ISACC)

作者： Richa Indu Sushil Chandra Dimri Department of Computer Science and Engineering Graphic Era Deemed to be University Dehradun INDIA

In today's realm of machine learning, the non-linearity of data is the problem often faced during data analysis. A well-known supervised learning algorithm that is known to efficiently handle this problem is the Support Vector Machine. The trick deployed in the background to overcome the problem of non-linearity is the transformation of data from the existing dimension to a higher dimension. To make this conversion of dimensions possible, kernel functions are utilized. These kernel functions are simply mathematical functions, which are available in the literature for a long ago. The present study briefly describes the mathematical background of the kernel functions with examples and a quick summarization of the recent applications of the renowned kernel functions across various domains. Further, the numerous benefits, a few limitations of kernel functions, and future directions are also highlighted in the work.

关键词： Support vector machines Machine learning algorithms data analysis Supervised learning Linearity Machine learning Kernel

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