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The configuration optimization of the plants in vertical farming by 3D modeling 3

The configuration optimization of the plants in vertical far...

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3rd international conference on signal processing and Machine Learning, CONF-SPML 2023

作者： Tan, Kege Zhang, Ying Department of Architecture School of Architecture and Design Southwest Jiaotong University Chengdu611756 China Department Geography Planning and Environment Faculty of Arts and Science Concordia University Montreal Canada

Vertical farming, a terminology in landscape architecture, is designed to cater for the demand of vegetation under the impact of excessive exploid of land, especially in densely-populated areas. The study takes vertical farming in Singapore as an example, applies 3D modeling to investigate the effect of different arrangement patterns on plant light absorption in vertical farms, and proposes some measures. Through the 3D simulation process, each square of the soil for planting can be quantified in sunlight-exposure hour. The experiment result shows that configuration with an array piling up is judicious and feasible for greenhouse growth, especially for commercial usages. Different configurations of plants influence the efficiency of utilizing sunlight, and there is a better layout for a particular site in different usages. For some cities far away from tropical areas, this configuration may alleviate the impact of lacking sunlight exposure. The main advantage of vertical farms is efficiency, as they require less land and water with more yield per acre compared to conventional farms. © Published under licence by IOP Publishing Ltd.

关键词： Tropics

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Text Chunking to Improve Website Classification 1

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3rd international conference on Optimization, Learning Algorithms and Applications (OL2A)

作者： Koufi, Mohamed Zohir Guessoum, Zahia Keziou, Amor Yahiaoui, Itheri Martineau, Chloe Domin, Wandrille Univ Reims CReSTIC EA 3804 Reims France Univ Reims LMR UMR9008 Reims France Olfeo Paris France

ISBN: (数字)9783031530258

ISBN: (纸本)9783031530241;9783031530258

Website classification is a crucial task in various applications such as web search, content filtering, and recommendation systems. Effectively categorizing long web pages into different categories based on their content is essential for providing accurate and personalized user experiences. Traditional transformer-based models, such as BERT and RoBERTa, have significantly advanced the field of natural language processing. However, such models face limitations when handling long sequences due to their fixed-length input restrictions resulting from their quadratic complexity. This paper presents a simple weighted stratified split approach (WSSA), to address the limitations of BERT and RoBERTa, in processing long text sequences for website classification. WSSA consists into chunking web pages into smaller chunks, then a new train chunk dataset is generated by a weighted stratified split following the distribution of the categories in the whole chunk dataset. This train chunk dataset is then used to train the models. Our approach improves the accuracy of BERT and RoBERTa models, surpassing the performance of Longformer and BigBird models. The proposed solution enables efficient processing and data augmentation, with reasonable fine-tuning times for BERT and RoBERTa models. Inference times remain efficient, showcasing the practicality of these models in real-time website classification tasks. The combination of WSSA with the index web page performs exceptionally well, highlighting its effectiveness in addressing the long text sequence limitation and improving transformer-based models for website classification.

关键词： Website Classification NLP Transformers Machine Learning data Splitting

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Software Engineering Classification Model and Algorithm Based on Big data Technology 3

Software Engineering Classification Model and Algorithm Base...

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3rd international conference on Machine Learning and Big data Analytics for IoT Security and Privacy, SPIoT2023

作者： Hu, Tao Department of Intelligent Science and Engineering Yunnan Technology and Business University Yunnan Kunming China

With the development of internet technology, cloud computing is becoming increasingly popular, and it has a wide range of applications in various fields, such as mobile payments and the Internet of Things. The big data model is a very important and valuable set of useful and unique information. This article mainly introduces the establishment of an object-oriented architecture-based machine learning system classification model using big data analysis methods, as well as the use of neural network algorithms to construct machine learning system classification patterns. Through examples, a comparative experiment is conducted to verify the effectiveness of traditional manual annotation modeling methods combined with parallel processing. Its experimental results show that the model has high accuracy, with an accuracy rate above 92% and a recall rate above 94%, and its F1 value is infinitely close to 1, indicating that the average accuracy and precision of the model is very high. © 2023 The Authors. Published by Elsevier B.V.

关键词： Software engineering

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Optimizing Wavelength Division Multiplexing Free-Space Optical Systems: A Comparative Analysis of Predictive Modeling Techniques 3

Optimizing Wavelength Division Multiplexing Free-Space Optic...

引用

3rd international conference on Advanced Electrical Engineering, ICAEE 2024

作者： Rima, Kebaili Samia, Driz Benattou, Fassi University of Djillali Liabes Telecommunications and Digital Signal Processing Laboratory Faculty of Electrical Engineering Sidi Bel Abbes22000 Algeria

ISBN: (纸本)9798350349351

In the ever-evolving landscape of telecommunications, Free-Space Optical (FSO) communication systems are increasingly positioned as promising candidates for next-generation networks. Their immunity to interference and high bandwidth perfectly align with the surging demand for high-speed data transmission. This research dives deep into the world of cutting edge predictive modeling techniques, which are essential for optimizing Wavelength Division Multiplexing FSO systems and addressing fading effects in atmospheric conditions. To rigorously assess the predictive power of various models, we utilize cutting edge OptiSystem simulations. Four Machine Learning (ML) models, including Fine Trees, linear Support Vector Machines (SVMs), boosted trees, and linear regression models incorporating interaction terms, are evaluated to identify the most effective model for accurate performance prediction of the WDM- FSO system, specifically in terms of key metrics like Q-Factor, signal- to Noise Ratio (SNR) and Bit Error Rate (BER). A comprehensive approach is employed. Extensive data is generated through meticulous simulations of a wide range of atmospheric conditions. Further, leveraging rigorous evaluation techniques like Root Mean Squared Error (RMSE), R-squared, Mean Squared Error (MSE), and Mean Absolute Error (MAE), an in depth comparison of the four aforementioned algorithms is performed. This thorough analysis provides valuable insights into the strengths and weaknesses of each model, ultimately guiding the selection of the optimal model for next-generation WDM-FSO systems. © 2024 IEEE.

关键词： Bit error rate

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The Role of AI in Biochips for Early Disease Detection 3

The Role of AI in Biochips for Early Disease Detection

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3rd international conference on Technological Advancements in Computational Sciences, ICTACS 2023

作者： Sukumaran, C. Indhumathi, K. Balamurugan, P. Ambilwade, R.P. Sunthari, P. Mohana Natrayan, L. Government Law College Tamilnadu Thiruchirappalli620023 India Saveetha Engineering College India Vel Tech Multi Tech Dr. Rangarajan Dr. Sakunthala Engineering College Department of Mathematics Chennai India National Defence Academy Pune Department of Computer Science India K S R Institute for Engineering and Technology India Saveetha School of Engineering Simats Department of Mechanical Engineering Tamil Nadu Chennai602105 India

ISBN: (纸本)9798350342338

Improvements in illness diagnosis made possible by combining AI with biochip technology. It highlights the use of AI to improve the sensitivity and efficiency of biochips, allowing for faster and more precise illness diagnosis. Current healthcare uses of biochips are discussed, as well as artificial intelligence methods that may be used to the processing of biochip data. Through a variety of case studies, it shows how AI may be used for real-world tasks including early detection of cancer and other diseases through its capacity to analyze complicated data and perform predictive analysis. data privacy, ethical considerations, and the need for extensive datasets to train AI are all discussed in the study as well as other obstacles to integrating AI with biochips. Furthermore, the legal implications of implementing new healthcare technology are covered. Future research options are suggested, with an emphasis on the importance of multidisciplinary cooperation and the possible effect of AI in biochip design. © 2023 IEEE.

关键词： Artificial Intelligence (AI) Biochips data Privacy Healthcare Technology Predictive Analysis

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Vibrations signal Analysis of Cantilever Beam Using Machine Learning 3rd

Vibrations Signal Analysis of Cantilever Beam Using Machine ...

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3rd international conference on Communication, Networks and Computing, CNC 2022

作者： Sharma, Lali Sharma, Jai Kumar Khatri, Pallavi ITM University Gwalior India

ISBN: (纸本)9783031431449

Vibrations are common phenomena seen in mechanical structures that can be detrimental to many systems. If not monitored, they can cause damage to structures, Vibrometer is sensor used for measuring vibrations of mechanical structures, machines, as well as sound level in an area and will collect lot of data that is to be analyzed to determine the strength of the structures. Machine learning is the basic working technology for prediction and determining the work proposes system aims to predict the health of a structure, converting the cantilever beam vibration reading to graph which is able to give the correct state or health of the structure. This work will cover all the major challenges faced due to natural down fall of quality of structure. The aims to able to check the Structural Health and able predict the condition of the structure. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Machine learning

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Design of Reactive Power Online Monitoring System of Intelligent Distribution Transformer Based on Edge Computing 3

Design of Reactive Power Online Monitoring System of Intelli...

引用

2023 3rd international conference on Power Grid Systems and Green Energy, PGSGE 2023

作者： Huang, Fu Lin, Junhong Zhang, Daolu Wu, Dan Digital Grid Research Institute CSG Guangdong Guangzhou510000 China

Due to the unbalanced development of various regions, in most regions, the distribution transformer of medium voltage line has not realized automatic monitoring, so the staff cannot monitor it in real-time. Therefore, an online monitoring system for the reactive power status of intelligent distribution transformers based on edge computing is designed. The system hardware structure configuration consisting of the master station layer, the edge layer and the acquisition layer is constructed, and the system software functional structure consisting of data processing, online monitoring, and system management is designed. On this basis, the continuous sampling data of the reactive power of the intelligent distribution transformer is collected, and based on edge computing, the reactive power operation characteristics of the intelligent distribution transformer are obtained and matched to judge the reactive state of the intelligent distribution transformer operation, so as to realize online monitoring of the reactive state of the intelligent distribution transformer. The experimental results show that the online monitoring accuracy and efficiency of the intelligent distribution transformer reactive state are high. © Published under licence by IOP Publishing Ltd.

关键词： data handling

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data Extraction and Integration from Unstructured Electronic Health Records 3

Data Extraction and Integration from Unstructured Electronic...

引用

3rd international conference on Technological Advancements in Computational Sciences, ICTACS 2023

作者： Bansal, Anurag Kumar Saraswat, Birendra Sharma, Bhasker Nayan, Sanjeev Kathuria, Kajol 13D Research & Strategy It Department UP India Computer Science & Engineering Raj Kumar Goel Institute of Technology UP Ghaziabad India Computer Science & Engineering Hi-Tech Institute of Engineering & Technology UP Ghaziabad India Ibm India Pvt. Ltd It Department UP India Gl Bajaj Institute of Technology & Management It Department UP Greater Noida India

ISBN: (纸本)9798350342338

Statistics Extraction and Integration from unstructured EHRs pose a giant venture in healthcare. Unstructured EHRs derive much of their cost from the substantial amounts of facts that they contain. However, these records are saved in a ramification of formats, making extracting and integrating with other facts resources difficult. Tons of the existing generation for extracting and integrating unstructured facts is confined by scientific understanding, natural language processing (NLP) techniques, and the capability to discover sequence editions in the records. For instance, that allows you to correctly pick out patterns in an affected person's health records;NLP algorithms must pick out essential medical concepts in free-text descriptions within the EHRs. Additionally, sequence editions, which could range appreciably in period and complexity, require unique interest to discover the correct diagnostic codes appropriately. © 2023 IEEE.

关键词： data Extraction Healthcare Integration Medical Record

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Fast Transmission Technology of Weak signal data in Distribution Network Terminal Based on Carrier Communication

Fast Transmission Technology of Weak Signal Data in Distribu...

引用

Electrical Engineering and Mechatronics Technology (ICEEMT), IEEE international conference on

作者： Mingming Zhang Jin Hu Wenjun Li Guangdong Grid Co Shaoguan Nanxiong Power Supply Shaoguan Guangdong China

At present, the fast transmission mechanism of weak signal data at distribution network terminals is mostly cross processing, and the transmission distance is limited, which leads to the increase of data transmission delay. Therefore, this paper proposes to design and verify the fast transmission technology of weak signal data at distribution network terminals based on carrier communication. According to the actual transmission requirements and standards, carry out distributed transmission data acquisition, use mapping standards as a guide, increase the transmission distance, design GOOSE mapping carrier transmission mechanism, and based on this, build a carrier communication weak signal data fast transmission model, and use UDP priority processing to achieve fast data transmission. The final test results show that: after three stages of measurement, the final data transmission delay is well controlled below 10ms, indicating that the control effect of this method is good. For weak signal data, the transmission speed is fast, and the data transmission delay is within a reasonable range, reducing delay jitter while strengthening the transmission effect, which has practical application value.

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Traffic signal Violation Behavior of Pedestrian at Urban Intersections—A Case Study of New Delhi 3rd

Traffic Signal Violation Behavior of Pedestrian at Urban Int...

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3rd international conference on Structural Engineering and Construction Management, SECON 2022

作者： Haque, Faizanul Kidwai, Farhan Ahmad Department of Civil Engineering Jamia Millia Islamia New Delhi110025 India

ISBN: (纸本)9783031120107

Accidents involving pedestrian at signalized intersections are major safety issue in developing countries such as India. A significant proportion of pedestrian accidents reported at intersections are primarily due to violation behavior of pedestrians. The objective of this study is to analyze pedestrian road crossing behavior and factors affecting their signal violation propensity at urban signalized intersections. data is collected through video recording technique in New Delhi, India. Chi-square and t-test are performed to identify statistically significant explanatory variables. Pedestrian signal violation behavior is modelled using binomial logistic regression. Pedestrian personal attributes and situational factors are found to have significant association with signal violation behavior and the model has significant predictive capability. Interventions focusing on behavioral modifications, such as increasing pedestrian safety awareness would be most effective in reducing violation, though engineering measures might also be useful. The findings would certainly help city planners, policy makers and designers in deeper understanding of pedestrian behavior to improve safety at urban signalized intersections. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Crosswalks

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