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Assessing the Potential Energy Savings in Residential Buildings by City-Scale Application of Cool Materials in a Temperate Oceanic Metropolis 5th

Assessing the Potential Energy Savings in Residential Buildi...

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5th international conference on Building Energy and Environment, COBEE 2022

作者： Zinzi, Michele Falasca, Serena Ding, Lan Augello, Andrea Santamouris, Mattheos Italian National Agency for New Technologies Energy and Sustainable Economic Development Energy Technologies and Renewables Department Smart Energy Division ENEA Rome Italy Department of Physical and Chemical Sciences University of L’Aquila L’Aquila Italy Department of Physics Sapienza University of Rome Rome Italy School of Built Environment University of New South Wales Kensington Australia Department of Architecture Built Environment and Construction Engineering Politecnico di Milano Milan Italy

ISBN: (纸本)9789811998218

The urban overheating is a well-documented phenomenon caused by the synergic action of local and global climate change. Increasing the albedo of cities by means of cool materials is an effective strategy. This paper explores the building cooling saving potential of large-scale application of cool materials through the case of a temperate oceanic metropolis: Melbourne, Australia. A mesoscale model is implemented and validated using the calculation platform WRF, creating reference and modified albedo scenarios of the two hottest summer month. The weather data feeds a dynamic building energy model and the potential energy savings are calculated. Energy savings up to 2.2 kWh/m2 and 31% are calculated, moreover the city scale application over performs the single building application by 0.7 kW/m2. The analysis of spot results show that the urban albedo modification strongly reduces the cooling energy use of buildings in the warmer inner city compared to sea-breeze mitigated coastal areas. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Potential energy

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Service Decomposition method for multimodal data exchange scenarios

Service Decomposition method for multimodal data exchange sc...

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Computer science and Management Technology (ICCSMT), international conference on

作者： Deyou Jiang Lei Huang Hanyu Tian Yujie Xie Xiaoming Li School of Economics and Management Beijing Jiaotong University Beijing China National Engineering Laboratory of Comprehensive Transportation Big Data Application Technology Beijing Jingwei Information Technologies Limited Company Beijing China China Academy of Railway Sciences Institute of Computing Technologies Beijing China

Influenced by the complexity of multimodal data exchange scenarios and their differences in data flow graph construction, the objective basis of existing service decomposition methods is not sufficient, and the granularity of the results is too large and difficult to be decomposed. To address this problem, this paper presents a service decomposition method for multimodal data exchange scenarios. This method reconstructs the differentially constructed data flow diagram by unified rules at the stage of requirement analysis and design, and then completes the fine-grained division of microservices around documents based on service-decomposed rules. The case study shows that the method is more effective than the existing method in the multimodal data exchange scenario.

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IoT and Cloud based Face detection application design for Surveillance systems using Deep Learning 22

IoT and Cloud based Face detection application design for Su...

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6th international conference on Cloud and Big data computing, ICCBDC 2022

作者： Patil, Gouri Bethu, Srikanth Bhavani, Y.V.K. Durga Harikrishna, B. Vijayalakshmi, Adluri B Sankara Babu Department of It Muffakham Jah College of Engg and Tech Telangana Hyderabad500034 India Department of Cse - Aiml and Iot Vnrvjiet Telangana Hyderabad500090 India Department of Cse National Institute of Technology Telangana Warangal506004 India Department of It Vijaya Institute of Technology for Women VijayawadaAP-521108 India Department of Cse Mallareddy Engineering College Telangana Hyderabad500100 India Department of Cse B v Raju Institute of Technology Telangana Hyderabad502313 India Department of Computer Science and Engineering Griet Telangana Hyderabad500090 India

ISBN: (纸本)9781450396578

Semantic Index, Human Action Detection, and Event Detection are video surveillance packages that assist automate surveillance tasks. Video surveillance structures have entered the generation of virtual surveillance structures in which virtual video is used to traverse the course of different virtual facts. advances in storage, telecommunications, and facts compression have enabled the increase of more than one technology in virtual surveillance structures. Using more than one video surveillance fashion in risky conditions extends the competencies of rule implementation organizations. In addition, video surveillance enables the form of a motion to carry out in one-of-a-kind conditions. In addition, spotting a particular man or woman in a video is essential for added security, multimedia, and multimedia packages, including offline, seek, and online monitoring of involved humans within the video. The proposed IoT- Cloud based Face detection application is designed to find a person from a huge size dataset and that can generate more accurate results. This application uses Deep Learning methods to find exactness when classifying the images. IoT here is used for liveness detection by comparing all the images present in cloud systems. For results comparison, we used Haar Cascade and DenseNet architectures. © 2022 ACM.

关键词： Face recognition

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Advanced Stochastic Approaches for Applied computing in Environmental Modeling 14th

Advanced Stochastic Approaches for Applied Computing in En...

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14th international conference on Parallel Processing and Applied Mathematics, PPAM 2022

作者： Todorov, Venelin Dimov, Ivan Ganzha, Maria Paprzycki, Marcin Department of Information Modeling Institute of Mathematics and Informatics Bulgarian Academy of Sciences Acad. Georgi Bonchev Street Block 8 Sofia1113 Bulgaria Department of Parallel Algorithms Institute of Information and Communication Technologies Bulgarian Academy of Sciences Acad. G. Bonchev Street Block 25 A Sofia1113 Bulgaria Polish Academy of Sciences Warszawa Poland

ISBN: (纸本)9783031304415

Mathematical models are used to study and predict the behavior of a variety of complex systems - engineering, physical, economic, social, environmental. Sensitivity studies are nowadays applied to some of the most complicated mathematical models from various intensively developing areas of applications. Sensitivity analysis is a modern promising technique for studying large-scale systems such as ecological systems. The uncertainty in the model input in our case, as in many others, can be due to various reasons: inaccurate measurements or calculation, approximation, data compression, etc. Two kinds of sensitivity analysis have been discussed in the literature: local and global. In the current paper the subject of our study is the global sensitivity analysis performed via the Sobol’ variance-based approach, applied to a specific large-scale air pollution model. The mathematical treatment of the problem of providing global sensitivity analysis consists in evaluating total sensitivity indices which leads to computing multidimensional integrals. We propose a new specific stochastic approach which significantly improves the results by the standard stochastic approaches. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Monte Carlo methods

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MIRLR: An ensemble approach for predicting clinical trial enrollment rates

MIRLR: An ensemble approach for predicting clinical trial en...

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international conference on computing and Networking Technology (ICCNT)

作者： Arti Devi Shashank Uttrani Aryansh Singla Sarthak Jha Preethi Ravula Nataraj Dasgupta Sayee Natarajan Rajeshwari S. Punekar Larry A. Pickett Emilio Lapiello Varun Dutt Applied Cognitive Science Lab Indian Institute of Technology Mandi Mandi India Syneos Health Morrisville USA

ISBN: (数字)9798350370249

ISBN: (纸本)9798350370270

The preclinical and medical sciences are greatly benefiting from clinical trials. However, participant recruiting issues continue to be a global barrier to their success. Insufficient enrollment is a persistent problem that has a large impact, leading to huge expenses and delaying scientific advances. Preventive methods are needed to find and prioritize trials that are likely to fall short of enrollment targets to address this problem. The study’s main aim was to examine the importance of predictive models in predicting the probability of reaching enrollment goals before a trial starts. This study aims to provide a novel prediction framework to determine the feasibility of proposed trials using data-driven approaches and statistical analyses. Embedding algorithms like GloVe and BioBERT convert textual input into vector representations. Subsequently, numerical vectors containing the top 11 features selected through correlation algorithms are combined with the existing dataset from ***. MIRLR, a novel ensemble stacking method is developed based on five models namely, M5P, IBK, Random Forest, Logistic Regression, and REPTree. In MIRLR, each model’s final predictions are combined to create a new dataset. This new dataset is input into the final Random Tree Regressor (RTR) model to forecast the enrollment rate. Results revealed that the MIRLR approach achieved a root mean square error of $\mathbf{0. 3 3 4}$ patients per day on the test dataset and 0.0057 patients per day on the training dataset, following a data split of 70:30. This research showcases the MIRLR’s impressive predictive capabilities, as it performed well across a range of training and test splits in accurately predicting site enrollment rates for clinical trials.

关键词： Training Accuracy Biological system modeling Stacking Clinical trials Predictive models Prediction algorithms data models Vectors Recruitment

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UWSN Survey: Challenges and Application issues

UWSN Survey: Challenges and Application issues

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advances in computing, Communication Control and Networking (ICAC3N), international conference on

作者： Rani Astya C.K. Jha Department of Computer Science Banasthali Vidyapeeth Rajasthan Sharda University India Department of Computer Science Banasthali Vidyapeeth Rajasthan India

In the last few years, research has increased enormously in the field of the underwater domain. It is mainly due to the vast unexplored area of earth covered by river, sea water and needs exploration in multiple dimensions, including offshore and underwater networking environments. Compared to the terrestrial domain, there are many challenges due to seawater’s inherent properties, including viscosity, pressure, turbidity, etc. These all require effective solutions for different useful applications like the study of marine life, sea resources exploration, naval surveillance, etc. in terms of effective communication and routing of data from one end to another, deployment and mobility of nodes, etc. This paper has presented a literature review, comparative study, and feasible tools which can be useful in proving solutions for underwater sensor networking issues.

关键词： Viscosity Natural resources Analytical models Computational modeling Surveillance Media Routing

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Security and Performance Comparison using QR Code through Blockchain Technology

Security and Performance Comparison using QR Code through Bl...

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international conference on Cognitive computing and Information Processing

作者： Sonali S Dash Rajasekar V Computer Science & Engineering AMC Engineering College Bengaluru India

ISBN: (数字)9781665456487

ISBN: (纸本)9781665456494

advances and transitions in healthcare system is constantly changing and acquiring fresh forms. The requirement of modern smart health systems is to protect sensitive patient data from attacks by potential adversaries. The secure data access mechanism ensures that only authorized personnel can access patient's health information. Therefore, in distributed method, it protects data in the healthcare system. Here it provides patients and doctors with an efficient and secure access mechanism based on blockchain data collection. In this health system the proposed system can also protect the privacy of patients. Security analysis depends upon architecture to prevent from attacks and maintain integrity of system. In addition, the implementation of system based Ethereum used for feasibility test of proposed system. By hiding private information in the coverage environment in a way that a third party cannot discover this confidential information. In today's era, with the emergence of new technologies, QR codes have become one of the most popular codes. Provide mechanisms for protecting sensitive information QR codes are the most reliable method of hiding data because they do not use error correction levels and because it is difficult to change the encoded information. The process is divided into different categories, and various statistical parameters are used to evaluate its performance.

关键词： Performance evaluation Trusted computing Privacy Smart contracts Smart healthcare QR codes Computer architecture

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Optical Character Recognition system with Projection Profile based segmentation and Deep Learning Techniques

Optical Character Recognition system with Projection Profile...

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advances in computing, Communication Control and Networking (ICAC3N), international conference on

作者： Arockia Rexi F Lija Jacob Department of Data Science CHRIST University Pune

Optical character recognition is the solution to convert text from printed or scanned documents into editable data. This project is aimed at building a Optical character recognition system that recognizes digital text. A document is first detected using contour-based detection technique without altering the angle of the image and is segmented into lines, once the lines are segmented the words embedded in them are extracted. This segmentation is done using projection profiling method. Characters are then segmented words with vertical projection profiling from the extracted words. These characters are fed into an image recognition model for recognition. The recognition model is CNN based deep learning model. Modified VGG16 architecture is used here to extract maximum features from the images and then classify them. To train the model a dataset is created from a repository of digital character dataset. The dataset consists of images of 153 font variants.

关键词： Deep learning Integrated optics Image segmentation Image recognition Text recognition Optical character recognition Optical fiber networks

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Proposed eLearning framework using open corpus web resources 1

Proposed eLearning framework using open corpus web resources

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1st IEEE international conference on advances in Electrical, computing, Communications and Sustainable technologies, ICAECT 2021

作者： Pattanshetti, Mahantesh K. Jasola, Sanjay Rajput, Akshay Pant, Vishal Department of Computer Science and Engineering Graphic Era Hill University Dehradun India Department of Computer Science and Engineering Graphic Era Deemed University Dehradun India

ISBN: (纸本)9781728157900

Covid-19 has unsettled the traditional teaching-learning model to meet the learning needs of the students in absence of a classroom environment. Knowledge delivery through online classes has been accompanied by low user satisfaction. One possible antidote to supplement a teacher's efforts that merits attention is the utilization of freely available content on the web. Copious volumes of learning content are generated by numerous entities and are easily available for knowledge consumption. Modern search engines do provide access to this trove of data on virtually any topic. However, to consume the content presented by a search engine, a user is required to manually sieve through the content and identify the ones suitable to their needs. To a person possessing domain knowledge of the topic, this approach to knowledge consumption is acceptable. Now, take the case of a class eight student, he/she may not be able to benefit from this search, filter and consume content approach. This is primarily due to the lack of adequate domain and web search expertise on their part. In this paper, approaches to delivering knowledge using web-based resources to the student community taking into consideration the aforementioned concerns are explored. © 2021 IEEE

关键词： Students

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Social Media Mining For Suicidal Ideation

Social Media Mining For Suicidal Ideation

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advances in computing, Communication Control and Networking (ICAC3N), international conference on

作者： Siddhant Satya Rajni Jindal Department of Computer Science and Engineering Delhi Technological University Delhi India

Recent exponential rise of social media impact has resulted in significant changes in how people conduct their lives. Sharing one’s ideas, thoughts, and feelings on a social platform like Facebook, Twitter and Reddit reveal a lot about a person's mental state, which is referred to as online behavior. Online behavior has been the subject of several studies and evaluations. Nowadays, people have begun to express their sentiments more on social media than in person with the introduction of social networking sites. This online accumulating data that users leave behind may be exploited to glean insights about their actions and mental state using machine learning. Different machine learning approaches have been employed, examined, and evaluated for suicidal inclination identification in this study to establish that Machine Learning Algorithms can properly identify a Social Media user's dwelling Suicidal Tendency.

关键词： Machine learning algorithms Social networking (online) Process control Machine learning Predictive models Depression data models

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