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3rd IEEE World Conference on Applied Intelligence and Computing, AIC 2024

作者： Chandok, Vishesh Maurya, Sudhanshu Mirza, Kashish Kumar Yadav, Ajay Thomas Varghese, Sneha Garg, Rachit Symbiosis Institute of Technology Nagpur Campus Pune India Graphic Era Hill University Department of Computer Science & Engineering Uttarakhand Bhimtal Campus India United Institute of Management Department of Computer Application Prayagraj India Lovely Professional University School of Computer Science and Engineering Phagwara India

ISBN: (纸本)9798350384598

An essential feature of the intelligent transportation system is the automatic detection and recognition of license plate information. The e-payment systems for parking and toll collection in traffic control security could be utilized with this application. Numerous algorithms have been created for License Plate Recognition (LPR) and Identification, each with pros and cons depending on the circumstances. computer vision has increased in terms of techniques and developments since deep learning emerged and became popular in several artificial intelligence domains. It is one of the most efficient ways to monitor traffic signals, tax paid for parking, and police monitoring through Automatic LPR. Although it, as a technology, is widely used and developed, much more must be done to improve the system's accuracy. The current science community has excelled in efficiency and problem-solving in recent years. Along with the You Only Look Once (YOLO), targeted at the object detection area and applied to this study, our primary goal is to summarize and analyze several methods and advancements in LPR in the deep learning age. © 2024 IEEE.

关键词： Traffic signals

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Distributed Cloud Simulation Architecture Combining Virtual and Real Environment

Distributed Cloud Simulation Architecture Combining Virtual ...

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2024 International Conference on Interactive Intelligent Systems and Techniques, IIST 2024

作者： Hu, Jiangang Mao, Yukun Li, Junjie Ye, Mengya Chen, Li Wan, Zhangbo Peng, Yuhuai Zhiyuan Research Institute Strategic Research Department Hangzhou China Northeastern University School of Computer Science and Engineering Shenyang China

ISBN: (纸本)9798350374421

With the deep integration of advanced manufacturing technology and the new generation of information technology, human-machine collaborative intelligent manufacturing has emerged as the core technology driving the development of the industrial sector. However, the human-machine collaborative intelligent manufacturing equipment exhibits high complexity and uncertainty in faults. The data silos that exist between equipment, personnel, systems, and platforms contribute to decreased productivity. Furthermore, the prolonged and costly technician training cycles add further obstacles. Therefore, a virtual-real integrated cloud simulation architecture is constructed using LVC (Live, Virtual, Constructive) simulation technology combined with a distributed computing framework. This architecture aims to achieve intelligent human-machine collaboration and real-time virtual-real interaction in industrial production. Real time monitoring of equipment and the entire production process through cloud simulation technology to optimize production processes and efficiency. The architecture supports simulation services such as intelligent task scheduling, human-machine collaborative manufacturing, adaptive flexible manufacturing, and production equipment precision and performance evaluation. The current architecture has just been proposed as a theoretical innovation for intelligent manufacturing cloud simulation solutions, but it has yet to be put into practice. © 2024 IEEE.

关键词： Smart manufacturing

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Wavelength-multiplexed orbital angular momentum meta-holography

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PhotoniX 2024年第1期5卷 276-289页

作者： Jaehyuck Jang Seong-Won Moon Joohoon Kim Jungho Mun Stefan A.Maier Haoran Ren Junsuk Rho Department of Chemical Engineering Pohang University of Science and Technology(POSTECH)Pohang 37673Republic of Korea Department of Materials Science and Engineering Stanford UniversityStanfordCA 94305USA Department of Mechanical Engineering Pohang University of Science and Technology(POSTECH)Pohang 37673Republic of Korea School of Electrical and Computer Engineering Purdue UniversityWest LafayetteIN 47907USA School of Physics and Astronomy Monash UniversityMelbourneVIC 3800Australia Department of Physics Imperial College LondonLondon SW72AZUK Department of Electrical Engineering Pohang University of Science and Technology(POSTECH)Pohang 37673Republic of Korea POSCO-POSTECH-RIST Convergence Research Center for Flat Optics and Metaphotonics Pohang 37673Republic of Korea National Institute of Nanomaterials Technology(NINT) Pohang 37673Republic of Korea

The field of high-bandwidth holography has been extensively studied over the past *** angular momentum(OAM)holography,which utilizes vortex beams with theoretically unbounded OAM modes as information carriers,showcases the large capacitance of hologram ***,OAM holography has been limited to a single wavelength,restricting its potential for full-color holography and *** this study,we propose wavelength and OAM multiplexed holography that utilizes the multiple dimensions of light—wavelength and OAM—to provide a multi-color platform that expands the information capacity of holographic storage *** proposed wavelength-OAM multiplexed holography is physically realized by a metasurface,the state-of-the-art optical element consisting of an array of artificially engineered *** silicon meta-atoms,the constituents of the metasurface,are engineered to possess wavelength selectivity by tailoring the dispersion of polarization *** meta-atoms are used to encode the calculated OAM-preserved phase maps based on our *** sampling grid of the phase map is rotated by 45°,which effectively suppress higher-order diffraction,providing a great strategy for achieving large field-of-view(FOV)*** successfully demonstrate six holographic images that are selectively reconstructed under the illumination of light with specific wavelengths(λ=450,635 nm)and topological charges(l=-2,0,2),without high-order *** work suggests that ultrathin meta-holograms can potentially realize ultrahigh-bandwidth full-color holography and holographic video displays with large FOV.

关键词： Metasurface Meta-holography Wavelength-multiplexed holography OAM holography

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IoT-Enabled WBAN and Machine Learning for Emotion Recognition 4

IoT-Enabled WBAN and Machine Learning for Emotion Recognitio...

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4th International Conference on Technological Advancements in Computational sciences, ICTACS 2024

作者： Mathur, Hritesh Panwar, Harsh Karki, Rahul Jangra, Vivek Yadav, Ramnaresh Kapoor, Anjali Amity University Department of Information Technology Noida India School of Computer Science and Engineering Galgotias University Greater Noida India

ISBN: (纸本)9798350387490

All industries, including computer science and health care and customer service are in dire need of efficient recognition of human feelings. This work is a script for a novel approach to affect recognition which involves both vocal emotion analysis and face emotion detection. Voice sentiment analysis project used machine learning technique to identify the prospects of emotional content in voice data. The model also assists in the classification of emotions with reference to the speech emotional states based on added features of MFCC and chroma. On the other hand, face emotion recognition project by applying deep learning algorithms identifies emotions by face expressions. With the help of convolutional, neural networks (CNNs) determining the happiness, sadness, and anger it analyses the facial features correctly. Combining all of these results in a coherent system that allows for the recognition of feelings through voice and or facial signals. Consequently, as demonstrated in the experimental results provided hereinafter, the accuracy of the presented system in correctly identifying human emotions is significantly higher than when using single models on their own. Therefore this combination creates space for other uses such as healthcare marketing among other fields hence improving user experience while at the same time providing unforeseen analysis of human conduct during an interaction with intelligent devices. © 2024 IEEE.

关键词： Convolutional neural networks

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Deep Learning-Based Approach for Calorie Estimation in Indian Foods 14

Deep Learning-Based Approach for Calorie Estimation in India...

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14th International Conference on Cloud Computing, Data science and engineering, Confluence 2024

作者： Chauhan, Naman Kumar, Aman Teja, Gudi Vishnu Bharati Mohan, G. Prasanna Kumar, R. Kakarla, Yamani Amrita School of Computing Amrita Vishwa Vidyapeetham Department of Computer Science and Engineering Chennai India Kinaxis HalifaxNS Canada

ISBN: (纸本)9798350344837

People in the modern era are increasingly concerned with their diet and food choices to prevent developing chronic diseases like high blood pressure and diabetes. With the increasing usage of smartphones and technology, an application to automatically monitor their diet would be a great boon, especially for the younger generation. This research work proposes automatic food calorie estimation from the food images using deep learning technology. The proposed work has three main phases. In the first phase, the food image is categorised as one of the types, for example, boiled, fried in oil or junk. A novel deep neural network namely MobileNetV2 which is a 15-layer model architecture is used for this purpose. In the second phase, the quantity of the labelled food is estimated. The third and final stage involves computing the calories present in the food using the Google search API. The details of ingredients present in each type of food are used for this purpose. Classification accuracy, food amount calculation precision, and calorie estimation accuracy are used assessing the effectiveness of the proposed approach. The proposed model is producing results with an overall accuracy of 95.21 percent which is better than the existing architecture. © 2024 IEEE.

关键词： Image segmentation

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Exploratory Data Analysis using Autoviz for Machine Learning Classification Problem

Exploratory Data Analysis using Autoviz for Machine Learning...

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2024 International Conference on Emerging Innovations and Advanced Computing, INNOCOMP 2024

作者： Guru Prasad, M.S. Praveen Gujjar, J. Kotiyal, Arnav Prasanna Kumar, H.R. Devadas, Raghavendra M Jahan, Afsar Department of Computer Science and Engg. Dehradun India Faculty of Management Studies Bengaluru India Visvesvaraya Technological University PESITM Dept. of Information Science & Engg. Shivamogga India Gitam School of Technology Department of Computer Science and Engineering Gitam University Bengaluru India Department of Computer Application Dehradun India

ISBN: (纸本)9798350376470

As the field of machine learning continues to advance, the importance of effective exploratory data analysis (EDA) cannot be overstated, especially in the context of classification problems. This research paper introduces an innovative and automated approach to exploratory data analysis using Autoviz, a powerful Python library designed to streamline the visualization process for machine learning datasets. The study aims to demonstrate the efficacy of Autoviz in meaningful insights, facilitating feature understanding, and supporting data preprocessing for classification tasks. Furthermore, the paper discusses how Autoviz assists in feature selection by highlighting the relevance and importance of each feature about the target variable. This feature-centric analysis contributes to optimizing machine learning models by guiding the selection of relevant input features, thereby improving model accuracy and interpretability. Autoviz is a Python library designed to automate the exploratory data analysis (EDA) process for machine learning tasks. It is specifically tailored to generate a variety of visualizations that aid in understanding the structure and characteristics of a dataset. The results indicate that Autoviz significantly reduces the time and effort required for exploratory data analysis while maintaining the quality and depth of insights obtained. The differences between Autoviz and Pycaret is discussed in this paper. It has been observed that AutoViz automates the process of exploring and analyzing data, while PyCaret is designed for the comparison, construction, and tuning of machine learning models. © 2024 IEEE.

关键词： Adversarial machine learning

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Enhancing COVID-19 Diagnosis through Deep Learning on a coupled Edge-Cloud Computing technique using CT Scan Images

Enhancing COVID-19 Diagnosis through Deep Learning on a coup...

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2024 International Conference on Smart Devices, ICSD 2024

作者： Gangadharan, S.M.P. Mohammad, Faz Sunil Reddy, G. Kumar, Kaushal Singh, Jagendra Haridass, R. Liverpool John Moores University Liverpool United Kingdom KCC Institute of Legal and Higher Education Department of Information Technology Greater Noida India SR University School of Computer Science & Artificial Intelligence Warangal India Raj Kumar Goel Institute of Technology Department of Computer Science Engineering Ghaziabad India Bennett University School of Computer Science Engineering & Technology Greater Noida India Karpagam College of Engineering Department of Mechanical Engineering Coimbatore India

ISBN: (纸本)9798350394412

The present research looks at how deep learning models may improve the accuracy of COVID-19 diagnosis using CT scan pictures. The project aims to improve the detection of COVID-19-specific patterns and features by using the capabilities of the VGG19, DenseNet201, and VGG16 models. The training and assessment were based on a collection of 2,521 CT scan images from patients with cardiac problems as well as those who tested positive for COVID-19. To assess the models' diagnostic abilities, performance metrics such as accuracy, precision, recall, and specificity are used. DenseNet201 regularly beats the competition across all key measures, revealing its potential for accurate COVID-19 diagnosis. The suggested edge-cloud architecture further integrates the models into real clinical scenarios. Given the important role of false negatives and false positives in medical diagnostics, the results highlight the need of looking at more than just accuracy. These models are useful diagnostic tools, but they should not be used in place of hands-on medical treatment. This study adds to the increasing body of knowledge at the interface of artificial intelligence and healthcare by providing insights into how deep learning models may be used to improve COVID-19 diagnostic accuracy and perhaps modify medical practices. To fully realize the promise of these findings in enhancing patient care and healthcare systems, more cooperation between machine learning researchers and healthcare practitioners is required. © 2024 IEEE.

关键词： COVID-19

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Predicting Groundwater Quality with Advanced Deep Learning Models: Improving Environmental Management by Addressing Pollution Issues and Obtaining More Accurate Forecasts 8

Predicting Groundwater Quality with Advanced Deep Learning M...

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8th International Conference on Electronics, Communication and Aerospace Technology, ICECA 2024

作者： Selvakumarasamy, K. Mahaveerakannan, R. Kumar, D. N.S Ravi Balamanigandan, R. Kumar, T. Rajesh Saveetha Institute of Medical and Technical Sciences Saveetha School of Engineering Department of Electronics and Communication Engineering Chennai602105 India Saveetha Institute of Medical and Technical Sciences Saveetha School of Engineering Department of Computer Science and Engineering Tamil Nadu Chennai India Sathyabama Institute of Science and Technology School of Electronics Engineering Chennai India

ISBN: (纸本)9798350367904

To ensure clean water for consumption in the future, it is imperative to understand the pollutants and the quality of the existing groundwater. Controlling water pollution and enhancing water management both depend on the ability to predict the water's purity with high accuracy. Previous studies have shown that when it comes to deep learning models, extreme gradient boosting (XGBoost) performs better in terms of accuracy and precision than random forest (RF). This study explores the field of water quality assessment by creating a novel method that combines state-of-the-art deep learning algorithms such as artificial neural networks (ANN), long short-term memory (LSTM), bidirectional LSTM (Bi-LSTM), gated recurrent units (GRU), convolutional neural networks (CNN), recurrent neural networks (RNN), and backpropagation neural networks (BPNN). Utilising deep learning techniques to enhance the index's prediction capacity and accuracy, as well as computing WQI utilising comprehensive water quality metrics, are our main research objectives. Through the use of deep learning models in conjunction with WQI, we hope to uncover patterns and connections in information on water quality that are not visible through conventional means. Plans for groundwater development will benefit from the study's findings, which will also enhance DL applications for better groundwater quality. © 2024 IEEE.

关键词： ANN Bi-LSTM BPNN CNN Deep learning algorithm Forecast for Water Standards GRU LSTM RNN WAWQI

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Detail-Oriented Capsule Network for classification of CT scan images performing the detection of COVID-19

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Materials Today: Proceedings 2023年 80卷 3709-3713页

作者： Modi, Shraddha Guhathakurta, Rajib Praveen, Sheeba Tyagi, Sachin Bansod, Saket Narendra Department of Computer Science & Engineering Government Engineering College Patan Katpur India Sri Venkateswara College of Engineering and Technology Department of IT Andhra Pradesh Chittoor India Department of Computer Science & Engineering Integral University Lucknow226026 India Bharat Institute of Technology School of Pharmacy Meerut India Dr. Ambedkar Institute of Management Studies and Research Deekshabhoomi Nagpur India

COVID-19 is one of the biggest pandemics that the world is facing today, and every day, we are coming up with new challenges in this area. Still, much research is already going on to overcome this pandemic, and we also get succeeded to some extent. Diverse sources such as MRI, CT scanning, blood samples, X-ray image, and many more are available to detect COVID-19. Thus, it can be easily said that through image processing, the classification of COVID-19 can be done. In this study, the COVID-19 detection is done by classifying with the use of a type of convolutional neural network termed a detail-oriented capsule network. Chest CT scan imaging for the prediction of COVID-19 and non-COVID-19 are classified in the present paper using a Detailed Oriented capsule network (DOCN). Accuracy, specificity, and sensitivity are parameters used for model evaluation. The proposed model has achieved 98% accuracy, 81% sensitivity, and 98.4% specificity. © 2021

关键词： Coronavirus

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Enhancing Deep Reinforcement Learning with Scenario-Based Modeling

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SN computer science 2023年第2期4卷 1-13页

作者： Yerushalmi, Raz Amir, Guy Elyasaf, Achiya Harel, David Katz, Guy Marron, Assaf Department of Computer Science and Applied Mathematics Weizmann Institute of Science Rehovot 76100 Israel School of Computer Science and Engineering The Hebrew University of Jerusalem Jerusalem 91904 Israel Software and Information Systems Engineering Ben-Gurion University of the Negev Beer-Sheva 8410501 Israel

Deep reinforcement learning agents have achieved unprecedented results when learning to generalize from unstructured data. However, the “black-box” nature of the trained DRL agents makes it difficult to ensure that they adhere to various requirements posed by engineers. In this work, we put forth a novel technique for enhancing the reinforcement learning training loop, and specifically—its reward function, in a way that allows engineers to directly inject their expert knowledge into the training process. This allows us to make the trained agent adhere to multiple constraints of interest. Moreover, using scenario-based modeling techniques, our method allows users to formulate the defined constraints using advanced, well-established, behavioral modeling methods. This combination of such modeling methods together with ML learning tools produces agents that are both high performing and more likely to adhere to prescribed constraints. Furthermore, the resulting agents are more transparent and hence more maintainable. We demonstrate our technique by evaluating it on a case study from the domain of internet congestion control, and present promising results. © 2023, The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd.

关键词： Deep reinforcement learning Domain expertise Machine learning Rule-based specifications Scenario-based modeling

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