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arXiv 2022年

作者： Lolur, Phalgun Skogh, Mårten Warren, Christopher Biznárová, Janka Osman, Amr Tancredi, Giovanna Wendin, Göran Bylander, Jonas rahm, Martin Department of Chemistry and Chemical Engineering Chalmers University of Technology Gothenburg Sweden Data Science & Modelling Pharmaceutical Science R&D AstraZeneca Gothenburg Sweden Department of Microtechnology and Nanoscience MC2 Chalmers University of Technology Gothenburg Sweden

decoherence and gate errors severely limit the capabilities of state-of-the-art quantum computers. This work introduces a strategy for reference-state error mitigation (rEM) of quantum chemistry that can be straightforwardly implemented on current and near-term devices. rEM can be applied alongside existing mitigation procedures, while requiring minimal post-processing and only one or no additional measurements. The approach is agnostic to the underlying quantum mechanical ansatz and is designed for the variational quantum eigensolver (VQE). Two orders-of-magnitude improvement in the computational accuracy of ground state energies of small molecules (H2, HeH+ and LiH) is demonstrated on superconducting quantum hardware. Simulations of noisy circuits with a depth exceeding 1000 two-qubit gates are used to argue for scalability of the method. © 2022, CC BY-SA.

关键词： Ground state

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Experimental Analysis of radioscopy Images based Pneumonia disease detection using Improved Neural Network Methodology

Experimental Analysis of Radioscopy Images based Pneumonia D...

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Image Processing and Capsule Networks (ICIPCN), International Conference on

作者： Sajja Suneel B.M.S. rani M. Amina Begum Jayendra Gopal Thatipudi Ajay Singh Yadav d. Muthukumaran Department of CSE(Data Science) Institute of Aeronautical Engineering Hyderabad Telangana India Department of ECE Vignan’s Nirula Institute of technology and science for women Department of ECE Oxford College of Engineering Venmani Tamilnadu India Dept. of Computer Science University of North Texas Department of Mathematics SRM Institute of Science and Technology Delhi-NCR Campus Ghaziabad U.P India Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology

ISBN: (数字)9798350367171

ISBN: (纸本)9798350367188

The bacterium Streptococcus pneumonia is the most prevalent causative agent of pneumonia, a severe and potentially fatal lung infection in humans. According to the World Health Organization, pneumonia accounts for one in three deaths in India. Assessing chest radiographs to detect pneumonia typically requires skilled radiologists, making an automatic detection method crucial for rapid treatment, especially in rural areas. This study introduces an Improved Neural Network (INN) learning method, cross-validated against existing Convolutional Neural Networks (CNNs), reflecting the success of deep-learning algorithms in medical image analysis. The research highlights the utility of these methods for disease classification. The features learned by INN and CNN models on large datasets are particularly valuable for image classification tasks. By using feature extractors programmed on trained INN and CNN frameworks, this study compares the performance of various classifiers in identifying normal and abnormal chest radiographs. Statistical results indicate that pre-trained INN models combined with supervised classifier approaches significantly enhance the evaluation of chest radiographs, particularly in detecting pneumonia. This advancement is pivotal for improving diagnosis and treatment outcomes in resource-limited environment.

关键词： Learning systems Pneumonia Neural networks Lung Organizations Feature extraction Convolutional neural networks

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Contrastive Learning for Improved Abstractive Sentence Summarization

Contrastive Learning for Improved Abstractive Sentence Summa...

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Advanced Computing Technologies (ICoACT), International Conference on

作者： Ulligaddala Srinivasarao Karthikeyan r Mohamed Sithik M Faizal Mukthar Hussain S Ohammed Uveise S A Hariharan Shanmugasundaram Department of CSE GITAM (Deemed to be) University Hyderabad India Department of CSE(AI&ML) Vardhaman College of Engineering Hyderabad India Department of CSE School of Computing Vel Tech Rangarajan Dr.Sagunthala R&D Institute of Science and Technology Chennai India Department of CSE Mohamed Sathak Engineering College Ramanathapuram India Artificial Intelligence and Data Science Vardhaman College of Engineering Hyderabad India

ISBN: (数字)9798331509859

ISBN: (纸本)9798331509866

Text summarizing aims to overview the material while maintaining essential details concisely. Lately, summary tasks have been included in the contrastive learning process in addition to visual representation. To optimize their similarities, contrastive learning summarizing algorithms currently concentrate on modelling and semantic similarity in all documents, targets, and data summaries. They do not, however, consider the impact that sentence semantics have on the text. In this study, we propose an rNN technique to develop the model for detecting the salient information. The sentence salience is based on the semantic similarity between the summary sentences of the original content, taking into account the distance of the loss of contrast in the form of soft weights functions. Consequently, our proposed model minimizes the semantic similarity given the textual summaries and possible noise and maximizes the semantic words between summaries and relevant information. Three popular datasets—multi news, CNN/daily Mail, and Booksum datasets achieved the highest accuracy of 0.69% in the news datasets. The experiment outcomes demonstrate that the suggested strategy can considerably raise baseline performance and outperform other contrastive learning techniques.

关键词： Computers Visualization Semantics Noise reduction Noise Contrastive learning Encoding data models data mining Postal services

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Certain Investigation of Optimization Methods of Sensor Nodes in Biomedical recording Systems

Certain Investigation of Optimization Methods of Sensor Node...

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International Conference on Advanced Computing and Communication Systems (ICACCS)

作者： N. Ashokkumar C. Kanmani Pappa Siva Kumar A dhamodharan Srinivasan M. M. Vijay Department of ECE Mohan Babu University Tirupati India Department of ECE Vel Tech Rangarajan Dr.Sagunthala R&D Institute of Science and Technology Chennai Tamilnadu Department of Data Science and Business Systems SRM Institute of Science and Technology Chennai India Department of Computer and Communication Engineering Sri Eshwar College of Engineering Coimbatore Tamilnadu Department of ECE SCAD College of Engineering and Technology Tirunelveli India

Biosensors will monitor a patient's physiological signals (ECG, EEG, etc.) and send an alarm as soon as irregularities are discovered. downsized biomedical sensors are the focus of this article, which explores innovative circuit approaches for miniaturised sensors. In order to demonstrate the proposed biomedical compression engine, ECG compression was applied and assessed. A MIP (mixed-integer programming model) is used to represent and solve the clustering issue based on the power consumption analysis. It follows that an architectural model that may be used to design ECG monitoring systems has been provided, as well as an in-depth study of the ECG monitoring system's value chain and a complete evaluation of relevant literature based on an expert classification. Time synchronisation and signal synthesis are the two stages of the distributed cooperative sensing algorithm's execution of ECG signal gathering. This method uses the mutation probability of each solution to prevent local convergence.

关键词： Wireless sensor networks Electrocardiography Sensor systems Biosensors Synchronization Biomedical monitoring Integrated circuit modeling

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Exploring Meibomian Gland dysfunction Grading: A Comparison of Machine Learning and XAI Approaches

Exploring Meibomian Gland Dysfunction Grading: A Comparison ...

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Image Processing and Computer Applications (ICIPCA), IEEE International Conference on

作者： Congxiao Liang Mini Han Wang Haoyang Liu Kelvin KL Chong Center for Sport Science and Physical Education Beijing Institute of Technology Zhuhai China The Department of Ophthalmology and Visual Sciences Chinese University of Hong Kong Hong Kong China The Faculty of Data Science City University of Macau Macau China The Center of Big Data and AI Zhuhai Institute of Advanced Technology Chinese Academy of Sciences Zhuhai China The R&D Center of AI and Digital Medicine Guangdong Digital Medicine Promotion Science and Technology Co. Ltd Guangdong China Guangdong Medical University Dongguan China Zhuhai Institute of Advanced Technology Chinese Academy of Sciences Zhuhai China Perspective Technology Group Zhuhai China The Faculty of Medicine Chinese University of Hong Kong Hong Kong China

ISBN: (数字)9798350360240

ISBN: (纸本)9798350384161

This research investigates the effectiveness of employing a machine learning methodology and an Explainable Artificial Intelligence (XAI) tool for the assessment of Meibomian Gland dysfunction (MGd). Utilizing resNet34, a convolutional neural network (CNN), trained on a diverse dataset comprising images depicting varying degrees of MGd severity, the machine learning approach endeavors to classify MGd severity into four distinct grades (0, 1, 2, 3) by leveraging extracted characteristic features. Findings reveal comparable accuracy between resNet34 and the XAI tool, with resNet34 achieving a slightly lower accuracy of 99.1% in contrast to the XAI tool's 99.4%. However, resNet34 demonstrates marginally higher precision and F1-score at 98.2% and 98.45%, respectively, suggesting a nuanced advantage in precision-recall equilibrium. Conversely, the XAI tool maintains a commendable precision of 97.2% and an Fl-score of 97.94%, alongside a recall of 98.7%, underscoring its effectiveness in discerning positive instances. Noteworthy is that resNet34 operates as a black-box model, providing limited interpretability, while the XAI tool emphasizes transparency by furnishing clinicians with understandable MGd grading elucidations derived from meibomian gland segmentation and atrophy analysis. This transparency enhances trust and comprehension, which are integral for clinical acceptance and decision-making. Furthermore, the utilization of XAI heatmap generation facilitates data quality enhancement, as evidenced by instances of erroneous artifact identification, emphasizing the imperative nature of artifact elimination for the refinement of MGd grading procedures.

关键词： Heating systems Accuracy Explainable AI data integrity decision making Glands Closed box Computer applications Feature extraction Convolutional neural networks

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Advancing Alzheimer's Nodule detection Through a Comprehensive Multi-Scene deep Learning Framework

Advancing Alzheimer's Nodule Detection Through a Comprehensi...

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International Conference on Advanced Computing and Communication Systems (ICACCS)

作者： C. Premkumar r. rajeshwari r. remya M. S. Cholaathiraj A. reethika S. Bharathidasan Department of Artificial Intelligence and Data Science Bannari Amman Institute of Technology Sathyamangalam India Department of Computer Science and Engineering CMR Institute of Technology Bengaluru India Department of Electronics and Communication Engineering Vel Tech Rangarajan Dr Sagunthala R&D institute of science and Technology Chennai India Department of Artificial Intelligence and Machine Learning Bannari Amman Institute of Technology Sathyamangalam India Department of Electronics and Communication Engineering Sri Ramakrishna Engineering College Coimbatore India Department of Electronics and Communication Engineering Erode Sengunthar Engineering College Erode India

ISBN: (数字)9798350384369

ISBN: (纸本)9798350384376

determining the precise location of Alzheimer's nodules is essential for estimating the risk of brain cancer. Conventional CAd modules, including MrI, PET, and CT, struggle with feature extraction and segmentation due to time limits and complexity. This study proposes an efficient brain nodule detection technique based on the Multi-Scene deep Learning Framework (MSdLF) that makes use of the vesselness filter. A four-channel CNN model is developed using information from two image sequences to enhance radiologists' ability to recognise four-stage nodules. This adaptable model may be used in two different classes. The outcomes demonstrate how well the MSdLF performs while processing substantial volumes of image data for brain nodule recognition, improving with accuracy 98.86% and 98.45% of sensitivity by using decreasing false positives.

关键词： deep learning Training Solid modeling Accuracy Sensitivity Image recognition Magnetic resonance imaging Brain modeling Classification algorithms Alzheimer's disease

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A Novel Architecture for Efficient Mobile Communication based on Scattered and diverse Combination using Prioritize and Postoperative Soft Modulation

A Novel Architecture for Efficient Mobile Communication base...

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International Conference on ent Computing and Control Systems (ICICCS)

作者： J. Sumithra M Mercy Theresa S. Geetha Priya Beulah Jackson V. Samuthira Pandi A. Balamurugan Department of Electronics and Communication Engineering R. M. D Engineering College Tamil Nadu India Department of Data Science and Business Systems SRM Institute of Science and Technology Tamil Nadu India Department of Computer Science and Engineering R. M. D Engineering College Tamil Nadu India Department of Electronics and Communication Engineering Vel Tech Rangarajam Dr. Sagunthala R&D Institute of Science and Technology Tamil Nadu India Department of Electronics and Communication Engineering SRM Institute of Science and Technology Tamil Nadu India Department of Electrical and Electronics Engineering Chennai Institute of Technology Tamil Nadu India

The proposed work suggests a novel approach for the cooperation of geographically scattered individuals and the blending of different points of view. This work ensures that the center on heterogeneous networks in which nodes support both long-range low-rate radio Frequency (rF) interfaces (like cellular) and short-range high-rate rF interfaces (such as IEEE802.11) that communicate over urban rayleigh fading channels. These interfaces are used to connect the devices. The interfaces are what are used to link the various gadgets. This research work proposes and evaluates a wide variety of hierarchically distributed cooperation strategies that can function despite resource restrictions such as limited short-range rF bandwidth. These strategies are contained within the confines of this paradigm, which serves as the framework for proposed work. Post Soft-demodulation Combining, often referred to as PSdC, and Priority Maximum ratio Combining are the two approaches that come highly recommended by our team (PMrC). This work provides evidence that using the offered methods increases throughput, reliability, and signal-to-noise ratio. Moreover, this study found that PMrC and PSdC have the ability to improve throughput performance by more than an order of magnitude across a wide range of communication circumstances.

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revolutionizing Cardiovascular Attack Prediction: A Comprehensive Machine Learning Approach for Accurate and Timely detection

Revolutionizing Cardiovascular Attack Prediction: A Comprehe...

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Expert Clouds and Applications (ICOECA), International Conference on

作者： S. durai d. Jaganathan Prabhu V r. Harini Dept. of CSE Vel Tech Rangarajan Dr.Sagunthala R&D Institute of Science and Technology Avadi Chennai India Department of Artificial Intelligence Madanapalle Institute of Technology & Science Andhra Pradesh India Department of Computer Science and Engineering R.M.K. Engineering College Kavaraipettai Thiruvallur India Artificial Intelligence & Data Science vel Tech Multi Tech Dr.Rangarajan Dr. Sakunthala Engineering College Chennai India

ISBN: (数字)9798350385793

ISBN: (纸本)9798350385809

Cardiac attacks are a leading cause of mortality worldwide, underscoring the critical need for timely and accurate healthcare detection. Predicting cardiovascular attacks is a formidable challenge in clinical data analysis. Machine Learning (ML) proves to be a valuable tool for analyzing vast healthcare data. This innovative approach focuses on employing ML techniques to identify significant features, thereby improving the accuracy of cardiovascular attack prediction. A variety of characteristics and well-known classifiers, including Naïve Bayes (NB), K-Nearest Neighbor (KNN), Support Vector Machine (SVM), random Forest (rF), decision Tree (dT), and Logistic regression (Lr), are used to create the final prediction model. Through collaborative efforts, these algorithms pave the way for binary classification, effectively distinguishing between true and false outcomes. By analyzing their accuracies, this approach enables the possibilities for successful predictions.

关键词： Support vector machines Logistic regression Accuracy Cardiac arrest Nearest neighbor methods Prediction algorithms Bayes methods

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Enhancing Citywide Flow Forecasting through deep Learning and Spatial Temporal Learning

Enhancing Citywide Flow Forecasting through Deep Learning an...

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Integrated Circuits and Communication Systems (ICICACS), IEEE International Conference on

作者： r. Parvathi Swagata Sarkar M. Supraja J P Swagatha d. Kanagajothi Srimathi S Computer Science SRM Arts and Science College Kattankulathur India Department of Artificial Intelligence and Data Science Sri Sairam Engineering College Chennai India Electronics and Communication Engineering Rajarajeshwari College of Engineering Bangalore India Department of IT Panimalar Engineering College Poonamallee India Department of Mathematics Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Department of Bio Technology Saveetha School of Engineering SIMATS Chennai India

Safe traffic management requires citywide flow projections. Transportation, public safety, and municipal planning are substantially affected. It forecasts city intake and outflow using flow data. Traditional methods are inaccurate and less scalable in complex, dynamic metropolitan settings. This study uses deep learning to predict crow traffic. deep learning can handle complicated nonlinear circumstances, making neural network crowd flow prediction more popular. However, deep learning has solved this problem, but it requires plenty of data. data shortages may develop when a city’s service or infrastructure is installed. This research introduces a deep spatial temporal transfer learning system that predicts crowd movement in a data-sparse target city using data from a data-rich source city. Geographic and temporal patterns in crow traffic data may be captured by trained algorithms, enhancing prediction accuracy. We found that our deep learning-based solution is more accurate and robust than prior methods. Crow migration patterns are properly predicted by this research, improving urban planning and management.

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Leveraging Machine Learning for Predictive Maintenance in Supply Chain Management Systems

Leveraging Machine Learning for Predictive Maintenance in Su...

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Science, technology, Engineering and Management (ICSTEM), International Conference on

作者： Bediga Sharan C. Vijai Kingshuk Srivastava Atul Singla Indira Bhati T. Ch. Anil Kumar School of CS &amp AI SR University Warangal Telangana India Department of Commerce and Business Administration Vel Tech Rangarajan Dr.Sagunthala R&D Institute of Science and Technology Chennai India Department of Data Science UPES Dehradun Uttarakhand Lovely Professional University Phagwara India Department of Management MMIM Maharishi Markandeshwar (Deemed to beUniversity). Department of Mechanical Engineering Vignan's Foundation for Science Technology and Research Andhra Pradesh India

ISBN: (数字)9798350376913

ISBN: (纸本)9798350388473

The purpose of this exploratory research is to investigate the incorporation of machine literacy (ML) techniques for prediction conservation in supply chain management (SCM) systems. The successful operation of force chains is essential to the success of an organization in the modern business environment, which is always shifting and transitioning. When it comes to preemptively managing outfit failures, performing in expensive time-outs, and functional dislocations, conventional conservation techniques frequently fall short unexpectedly. To analyze literal data, detector inputs, and external variables, the utilization of machine learning algorithms makes it possible to anticipate conservation requirements. Through the utilization of prophetic analytics, supply chain management systems can identify hidden faults in the outfit, which enables visionary conservation actions. This study assesses various machine learning models and algorithms that have been adapted to forecast conservation circumstances in supply chain management (SCM) systems. Specific variables such as monitoring the status of the outfit and recognizing failure patterns are taken into consideration. In the end, the purpose of the investigation is to provide insight into the actual implementation of machine learning-driven prophetic conservation methods. This will enable organizations to improve their overall performance and optimize their force chain operations.

关键词： Supply chain management Machine learning algorithms Force Organizations Machine learning Predictive models Pattern recognition

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