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

共 6957 条记录，以下是4411-4420 订阅

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Analyzing Encrypted Cloud data With Multiple Keywords Productively For Factories

Analyzing Encrypted Cloud Data With Multiple Keywords Produc...

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Innovations in High Speed Communication and Signal processing (IHCSP), international conference on

作者： Sourabh Sahu Surya Bazal Vivek Anand Department of Electronics and Communication GGITS Bargi Hills Jabalpur

Cloud storage is quickly becoming the norm in today's online infrastructure because of the low administration costs and convenient accessibility. The cloud storage system offers crucial advantages by letting cloud users keep their massive amounts of data in the cloud. Better performance during data retrieval necessitates attending to the privacy and security of data stored in the cloud. The necessary information can be obtained from the cloud using the current improved semantic feature abstraction method E-TFIDF (Enhanced-Term Frequency-Inverse Document Frequency). There was still no solution for the problem of secure cloud-based data access. Current method SEDFS (Searchable Encrypted data File Sharing) system applied along with limited and precise keyword quest; indexing procedure necessary for improvement. The research focuses on SHE (Secured Hybrid Search), a method for conducting encrypted multi-keyword searches on cloud platforms. Using a category-based method, an index list is built for each document depending on its classification. Clearly, when compared to both the current system and the adaptively secure SSE technique, it has been found that the proposed approach significantly improves encryption and decryption throughput in the shortest possible time. In addition, the suggested algorithm shortens the amount of time required to index and conduct a search on a variety of documents. There was also a decrease in the amount of time spent searching and indexing, and an increase in the efficiency with which keywords could be extracted.

关键词： Cloud computing Technological innovation Semantics Signal processing algorithms Signal processing Throughput Production facilities

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Ensemble Machine Learning Based Blockchain Model for Privacy Preserving of Medical data in Fog computing

Ensemble Machine Learning Based Blockchain Model for Privacy...

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Evolutionary algorithms and Soft computing Techniques (EASCT), international conference on

作者： Sheetal V K Vidya Sagar S D Piyush Kumar Pareek Department of Master of Computer Applications Nitte Meenakshi Institute of Technology Bengaluru India Department of Artificial Intelligence and Machine Learning Nitte Meenakshi Institute of Technology Bengaluru India

The advent of COVID-19 highlights the need for big data-driven medical applications, the Internet of Medical Things, and smart healthcare. The biological information collected is strictly private. This enormous quantity of biological information is, however, beyond the capacity of current health care systems. Therefore, cloud computing has become the norm for archiving and sharing data. The combined information is then put to use in a variety of ways, including research and the identification of previously unknown facts. Textual forms (such as test results, prescriptions, and diagnoses) are the norm for biological data. Unfortunately, there are a number of security dangers and assaults that may be made against such data, including infringements on privacy and confidentiality. The security of biological data has come a long way, yet the majority of current methods still cause considerable delays and cannot support real-time replies. To improve the healthcare system, this study suggests a unique fog-enabled blockchain based privacy-preserving approach that makes use of machine learning. The suggested model efficiently carries out Medical Entity Recognition as it is built on four different machine learning models. In terms of detection rate and accuracy, the suggested model is 97% and 98%, respectively, better than the state-of-the-art replicas, as shown by the experiments. The sanitization approach outdoes the state-of-the-art by 8 percentage points when it comes to preserving utilities.

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Towards Minimal Training for Low Resource Languages

Towards Minimal Training for Low Resource Languages

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Sustainable computing and data Communication Systems (ICSCDS), international conference on

作者： Diganta Baishya Rupam Baruah Jorhat Engineering College Assam Science & Technology University Assam India

The advancement of research in the field of natural language processing has made peoples’ daily lives much easier, with numerous applications at the disposal. The traditional methods like the Hidden Markov Model, the CRF classifier, the Naive Bayes classifier, and others are being replaced by neural networks in recent times. However, most of these methods work considerably well only with huge amounts of training data and, hence are not suitable for languages that are poor in terms of trainable resources. The challenge is to make the system work considerably well with minimal training. This paper presents research work to understand the effect of training size for part of speech tagging, which is one of the preliminary tasks for any NLP application. Experiments are conducted to understand the training size required for standard techniques to perform with high accuracy. The results of the experiments conducted for English and Assamese are presented in this paper.

关键词： Training Performance evaluation Neural networks Hidden Markov models Training data Tagging Natural language processing

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Investigation on Multi-objective Optimization of Construction Engineering Based on data Mining Association Rule Algorithm

Investigation on Multi-objective Optimization of Constructio...

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Applied Intelligence and Sustainable computing (ICAISC), international conference on

作者： Shoupu Gu Binzhou Polytechnic Binzhou China

In recent years, with the rapid development of computer technology, people have put forward higher requirements for data mining tools and methods. This paper mainly studied multi-objective optimization problems in the field of construction engineering. By establishing models, information such as building structures and buildings was modeled and processed, and influencing factors were analyzed and predicted. After being quantified, the examples were used to illustrate the application effect, and the model was optimized. The test results showed that the data processing efficiency of the multi-objective optimization model for construction engineering based on the data mining association rule algorithm was around 74% to 77% before optimization, and the data processing efficiency after optimization was between 80% to 90%. At the same time, appropriate index parameter values were selected based on the actual situation to maximize the synergy between engineering projects and optimize economic benefits, thereby achieving the goal of improving construction efficiency and quality.

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Design of Anomaly Detection Algorithm for Intelligent data Acquisition Terminal of Internet of Things Based on Deep Learning

Design of Anomaly Detection Algorithm for Intelligent Data A...

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Internet of Things, Robotics and Distributed computing (ICIRDC), international conference on

作者： Liyou Fang Wuxi Taihu University Wuxi China

Anomaly detection is the identification of items, events, or observed values that do not meet the expected situation or other conditions in the data set. In this paper, an anomaly detection algorithm of IoT (Internet of Things) intelligent data acquisition terminal based on deep learning is proposed. In the case design, the Text-CNN (Text-convolutional Neural Network) is adopted as the algorithm of supervised detection scheme. Split the collected IoT data set to obtain network traffic data of a plurality of different devices, and label these data in categories; Input the data into the established Text-CNN algorithm model through NLP; In the sample category output module, train the model, save the trained model, test it, and output the sample category. The test results show that the algorithm proposed in this chapter is superior to other literature algorithms in anomaly detection, and it is suitable for anomaly detection of complex data, especially high-dimensional data.

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Efficient Prescription data Entry by EMR Software

Efficient Prescription Data Entry by EMR Software

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Automation, computing and Renewable Systems (ICACRS), international conference on

作者： Tanmay Shukla Khushi Suri Pradeep Sudhakaran Department of Computing Technologies SRM Institute of Science and Technology Kattankulathur Chengalpattu District Tamil Nadu India

Efficient and accurate recording of electronic prescription data is of paramount importance in the ever-evolving field of healthcare. As this investigation delves deeper, a groundbreaking solution emerges, adept at bridging the speed gap between traditional handwritten methods and the digital landscape. This innovative technique is founded on a profound understanding of the cognitive processes underpinning manual prescription creation, resulting in a significant overhaul of the digital interface. This approach revolves around the integration of predictive algorithms and tailored templates into the electronic prescription system, with the goal of matching the speed of traditional handwritten methods. This substantial enhancement not only streamlines workflow but also enhances patient care, optimizes pharmacy operations, and provides valuable data for research, promising a transformative impact on the healthcare industry.

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Addressing Challenges in Healthcare Big data Analytics 16th

Addressing Challenges in Healthcare Big Data Analytics

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16th international conference on Multi-disciplinary Trends in Artificial Intelligence, MIWAI 2023

作者： Tirunagari, Santosh Mohan, Senthilkumar Windridge, David Balla, Yashaswini Department of Computer Science Middlesex University The Burroughs London United Kingdom School of Information Technology and Engineering Vellore Institute of Technology Vellore India Neurosciences Department Alder Hey Children’s NHS Foundation Trust Liverpool United Kingdom

ISBN: (纸本)9783031364013

The exponential growth of healthcare data poses significant challenges for clinical researchers who strive to identify meaningful patterns and correlations. The complexity of this data arises from its high dimensionality, sparsity, inaccuracy, incompleteness, longitudinality, and heterogeneity. While conventional pattern recognition algorithms can partially address issues related to high dimensionality, sparsity, inaccuracy, and longitudinality, the problems of incompleteness and heterogeneity remain a persistent challenge, particularly when analyzing electronic health records (EHRs). EHRs often encompass diverse data types, such as clinical notes (text), blood pressure readings (longitudinal numerical data), MR scans (images), and DCE-MRIs (longitudinal video data), and may only include a subset of data for each patient at any given time interval. To tackle these challenges, we propose a kernel-based framework as the most suitable approach for handling heterogeneous data formats by representing them as matrices of equal terms. Our research endeavours to develop methodologies within this framework to construct a decision support system (DSS). To achieve this, we advocate for the incorporation of preprocessing mechanisms to address the challenges of incompleteness and heterogeneity prior to integration into the kernel framework. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Blood pressure

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A Self-Supervised Bearing Failure Detection Method with STFT and Point-wise CNN Feature Extraction 2

A Self-Supervised Bearing Failure Detection Method with STFT...

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2022 2nd international conference on Mechanical Automation and Electronic Information Engineering, MAEIE 2022

作者： Li, Xiaoyu Yang, Jie Shen, Hao Liu, Qing Department of Big Data and Artificial Intelligence China Telecom Corporation Research Institute China

As the most important parts, bearings are widely used in many types of mechanical and electronic equipment. Bearing failure often leads to serious consequences. Therefore, the fault detection of bearings is the key to ensuring the safe and reliable operation of equipment. At present, most feature extraction methods are based on signal processing algorithms. However, for some types of bearings, these feature extraction methods may lose some important information. The bearing vibration data is unbalanced data, which may lead to diagnostic errors. A new self-supervised bearing failure detection method with STFT point-wise CNN feature extraction and auto-encoder is proposed in this paper. Firstly, a point-wise CNN extracts the features from the signal after STFT. Then the auto-encoder compresses and reconstructs the features. Finally, the distance between the auto-encoder input and output is used to judge the signal is abnormal or not. The CWRU bearing vibration signal data set is used as test case. We also proposed the distance ratio as an evaluation index in the experiment. The proposed bearing fault detection method in this paper has a lower data requirement and better effect based on the results. © Published under licence by IOP Publishing Ltd.

关键词： Feature extraction

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What to predict from Twitter data?

What to predict from Twitter Data?

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computing and Information Technology (ICCIT-1441), 2020 international conference on

作者： Mohammed Salemdeeb Shaaban Sahmoud Electrical-Electronics Engineering Istanbul Sabahattin Zaim University Istanbul Turkey Computer Engineering Department Fatih Sultan Mehmet Vakıf University Istanbul Turkey

In the last decade, Twitter data has become one of the most valuable research sources for many areas including health, marketing, security, and politics. Researchers prefer Twitter data since it is completely public and can be easily downloaded using Twitter APIs. The recent intensive use of Twitter data makes it difficult for researchers to follow or analyze its research. In this paper, we summarize most of the predictable patterns, aspects, and attitudes from Twitter data and analyze the performance and feasibility of the algorithms used. Moreover, we describe the current popular Twitter datasets used in various domains and applications. Current challenges and research gaps are discussed, and some recommendations are given for future works from different perspectives.

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Artificial Intelligence in Natural Disaster: data Crawling- and Text Mining-Based Literature Review

Artificial Intelligence in Natural Disaster: Data Crawling- ...

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international conference on Informatics and computing (ICIC)

作者： Zhafran Ramadhan Ariana Yunita Department of Computer Science Universitas Pertamina Jakarta Indonesia

A literature review is an essential part of research. Beginning researchers who would like to conduct research in any field commonly review previous papers to identify trends and gaps in research. However, conducting a literature review is challenging, particularly for novice researchers. First, synthesising previous related works can be time-consuming. Second, accessing expensive databases such as Scopus and Web of Science might be inaccessible for novice researchers in developing countries. Thus, the purpose of this paper is to present a review performed by data crawling ArXiv and using latent Dirichlet allocation (LDA), a topic modelling algorithm. In this paper, we provide an overview of the topic models of natural disasters and artificial intelligence. Papers from 2017–2022 were crawled using Python with specific keywords, and only papers written in English were eligible for further analysis. Then, the papers were pre-processed by lowering the text, removing punctuation and stop words, tokenising, and lemmatising with POS tagging techniques. Pre-processed papers were then trained using the LDA algorithm. For evaluation, coherence metrics were used to determine the optimal number of topics. There were five topics resulting from the topic modelling algorithm. The clusters were qualitatively analysed and labelled as image classification, algorithms, social media analysis, natural language processing, and machine learning. Further findings are also discussed.

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