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Research on the Detection of Abnormal Cervical Cells Based on Improved YOLOv7

Research on the Detection of Abnormal Cervical Cells Based o...

引用

6th international conference on Intelligent computing and Signal Processing (ICSP)

作者： Xuewei Wang Shuang Yuan Lidong Yang Dawei Niu Inner Mongolia Key Laboratory of Pattern Recognition and Intelligent Image Processing Inner Mongolia University of Science and Technology School of Digtial and Intelligence Industry Inner Mongolia China

ISBN: (数字)9798350376548

ISBN: (纸本)9798350376555

Cervical cytologic screening is clinically important for the prevention and diagnosis of cervical cancer. Aiming at the many challenges in the detection of abnormal cervical cells, including the difficult detection of small targets of abnormal cervical cells, complex backgrounds, and uneven cell morphology, a detection algorithm for abnormal cervical cells based on improved YOLOv7 was proposed. The algorithm utilizes Deformable Convolutional Networks v2 (DCNv2) that adaptively de-tunes the scale and receptive field size to more effectively cope with the complexity and diversity of cervical cell images. The experimental results show that the improved YOLOv7 algorithm mAP achieves 57.26%, up to 73.88% accuracy, and 46.90% recall on the cervical abnormal cell dataset. Compared with the benchmark model, they increased by 4.07%, 3.39%, and 5.69%, respectively.

关键词： Accuracy Sensitivity Shape Prevention and mitigation Signal processing algorithms Signal processing Feature extraction Convolutional neural networks Detection algorithms Cervical cancer

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The New Answer to Drug Discovery: Quantum Machine Learning in Preclinical Drug Development

The New Answer to Drug Discovery: Quantum Machine Learning i...

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pattern recognition and Machine Learning (PRML), IEEE international conference on

作者： Yew Kee Wong Yifan Zhou Yan Shing Liang Haichuan Qiu Yu Xi Wu Bin He Basis International School Guangzhou Guangzhou China

The Research & Development (R&D) phase of drug development Drug discovery and development (D&D) is a complex and costly endeavor, typically requiring six to nine years [1] and four hundred to fourteen hundred million USD [2] for the research and development (R&D) phase alone. To transform this process, we propose a novel concept that combines Quantum-based Machine Learning network (QML) and Quantum computing Simulation (QS) to expedite the R&D phase to three to six months and reduce the cost to merely fifty to eighty thousand USD. Our approach takes as inputs the target protein/gene structure and the primary assay [3]. For Hit Generation [3], the QML network generates potential hits [4] based on the molecular structure of the target protein, while the QS filters molecules from the primary assay according to their reaction and binding efficacy with the target protein. Then, for Lead Optimization [3], the resulting molecules from QML and QS are compared, and the ones that are common to both processes are subjected to dozens of molecular variations, while others are only modified slightly. Finally, all optimized molecules undergo multiple rounds of QS filtering with high standards for reaction efficacy and safety, producing a few dozen pre-clinical-trial-ready drugs. Our concept of QML and QS integration can also be applied to other fields, such as agricultural research, genetic editing, and aerospace engineering. Keywords: Quantum Computer, Hit to Lead, Lead Optimization Simulation, Machine Learning.

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Assessment of Grid Integration with renewable Energy sources and Electric Vehicle

Assessment of Grid Integration with renewable Energy sources...

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Applied Artificial Intelligence and computing (ICAAIC), international conference on

作者： P. Sivakumar M. Kaleeswari A. Aswini S. Sabapathi P. Manikandan N. Vijayakumar Department of Electrical and Electronics Engineering Rajalakshmi Engineering College Chennai India Department of Electrical and Electronics Engineering M.P. Nachimuthu M. Jaganathan Engineering College Erode

ISBN: (数字)9798350375190

ISBN: (纸本)9798350375206

To maintain a healthy world, the emission of Greenhouse Gases (GHG) should be minimized. Due to the overall economic crisis in the last few years, the fuel cost for running an automobile, power generation, and operating industries become more complicated. To overcome these issues, new technologies should be adopted. Utilizing Electric Vehicle is one of the most important ways to minimize such effects for increasing overall global warming. When such EV Charging S tations (EVCS) are integrated with the grid, it affects most of the parameters and leads to difficult problems like overvoltage, overloading, harmonics, and instability. This research study deals with analyzing the behavior of such EVCS with the grid and renewable energy integrated system, the IEEE14 bus system with the effective utilization of Electrical Transient and Analysis Program (ETAP).

关键词： Renewable energy sources Power system stability Power system harmonics Prediction algorithms Stability analysis pattern recognition Transient analysis Climate change Greenhouse gases Carbon emissions Electric vehicles Electric vehicle charging PI control Power grids

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Credit Card Fraud Detection Using Symbolic Aggregate Approximation

Credit Card Fraud Detection Using Symbolic Aggregate Approxi...

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Blockchain computing and Applications (BCCA), international conference on

作者： Snit D. Zerea Belsabel T. Woldemichael Feven B. Araya Richard A. Ikuesan College of Technological Innovation Zayed University Abu Dhabi UAE

ISBN: (数字)9798350351538

ISBN: (纸本)9798350351545

In the current digital age, credit card fraud is a cybercrime that costs billions of dollars every year through fraudulent transactions. This study aims to identify fraudulent credit card transactions using Symbolic Aggregate Approximation (SAX) and anomaly discovery through motifs and discords. To do this, credit card data is used as time series data and then divided into equal-length segments. Each segment is then converted into a symbolic representation using a pre-defined threshold. A web-based interface is further developed to load a collection of credit card transaction data, based on the proposed SAX method. The preliminary results show that the proposed approach is capable of identifying fraudulent transactions within a set of transactions. By integrating this approach with other fraud detection mechanisms, a near real-time fraud detection process can be developed. This approach therefore provides a potential for context-independent fraud detection which is a major challenge in the fraud detection domain.

关键词： Machine learning algorithms Costs Aggregates Time series analysis Credit cards Information age Real-time systems Fraud pattern recognition Security

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Image Texture Analysis with Local Binary patterns: A Review 3

Image Texture Analysis with Local Binary Patterns: A Review

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3rd IEEE international Virtual conference on Innovations in Power and Advanced computing Technologies, i-PACT 2021

作者： Srilatha, N. Reddy, V. Lokeswara JNT University Anantapauramu CSE Department Anantapuramu India KSRM College of Engineering CSE Department Kadapa India

ISBN: (纸本)9781665426916

Local binary pattern (LBP) and its changed forms have been widely used in the many applications of different fields such as image processing, computer vision, model recognition and texture analysis etc. In this paper, we did extensive research-based study on LBP and its forms. The pros and cons of standalone LBP methods which used in many applications were evaluated in this study. Finally, we interpret based on their defects and focused the gapes of recent forms of LBP model signifier. © 2021 IEEE.

关键词： Local binary pattern

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Recursive Descent Parser and Context Free Grammar in Examining Indonesian Sentence patterns

Recursive Descent Parser and Context Free Grammar in Examini...

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Smart computing, IoT and Machine Learning (SIML), international conference on

作者： Ega Citra Meilandri Husni Thamrin informatics department universitas muhammadiyah surakarta Surakarta Indonesia

ISBN: (数字)9798350364101

ISBN: (纸本)9798350364118

Language is the most important supporting tool in communication between humans which aims to convey ideas, thoughts, emotions, feelings or messages to other people. Language can be conveyed orally or in writing. Both forms of language have the same goal, namely to facilitate the delivery of messages clearly and effectively to the recipient of the message. In writing effective sentences, the sentences are built according to correct grammatical rules because they will influence the delivery of the message. Therefore, the author proposes research related to checking sentence patterns which is carried out automatically whether the sentence is in accordance with grammar which has the elements of subject, predicate, object, information (SPOK). The aim of this research is to make it easier to examine sentence patterns. This research uses a recursive descent parser algorithm and context free grammar. The context free grammar algorithm is used to group the types of words and sentence patterns to be examined. Meanwhile, the recursive descent parser algorithm is used to examine Indonesian sentence patterns. The results of this research produced a success rate (%) of 67.2% of the 500 sentences that had been entered.

关键词： Triples (Data structure) Production Machine learning Grammar pattern recognition

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Impression-Based Automatic pattern Generation for Product Design Support

Impression-Based Automatic Pattern Generation for Product De...

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international Symposium on soft computing and Intelligent Systems (SCIS)

作者： Ryunosuke Ota Yuichiro Kinoshita Kentaro Go Department of Computer Science and Engineering University of Yamanashi Kofu Yamanashi Japan

This research proposed a method for automatically creating patterns that match the impression desired by users in product design support. An impression rating experiment clarified the relationship between pattern physical aspects and impressions. Impression evaluation models that estimate the impression of patterns are implemented by training convolutional neural networks using experimental scores. patterns are generated by evolutionary computation with impression evaluation models serving as evaluation functions. The validity of the automatically generated patterns is examined by the generation of evolutionary computation and the final generation patterns.

关键词： Training Computational modeling Training data Evolutionary computation Product design Data models Convolutional neural networks

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Feature extraction technique for vision-based indian sign language recognition system: A review

Feature extraction technique for vision-based indian sign la...

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international conference on Computational Methods and Data Engineering, ICMDE 2020

作者： Tyagi, Akansha Bansal, Sandhya AmbalaHaryana India

ISBN: (纸本)9789811568756

Vision-based sign language recognition system is an emerging research area in human-computer interaction (HCI). It has been proved as a powerful communication tool for deaf and mute society, irrespective of geographical differences. Automatic sign language recognition system works broadly in three phases namely image pre-processing, feature extraction and classification of gestures. The output of the feature extraction phase is crucial for various classifiers. This paper presents a comprehensive review of feature extraction techniques used in vision-based sign language recognition system. A taxonomy of currently used techniques for feature extraction has been presented. The paper concludes by presenting future direction in feature extraction technique for Indian sign language (ISL). © Springer Nature Singapore Pte Ltd. 2021.

关键词： soft computing

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Dynamic Edge/Cloud Resource Allocation for Distributed Computation Under Semi-Static Demands

Dynamic Edge/Cloud Resource Allocation for Distributed Compu...

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IEEE international conference on Communications (ICC)

作者： Ippokratis Sartzetakis Panagiotis Pantazopoulos Konstantinos V. Katsaros Vasilis Sourlas Emmanouel Varvarigos

ISBN: (数字)9781728190549

ISBN: (纸本)9781728190556

Edge computing is a recent paradigm where the processing takes place close to the data sources. It therefore reduces latency and saves bandwidth compared to traditional cloud computing. The latter can continue to play a supportive role. Edge-cloud computing provides benefits in many use cases including distributed computation algorithms, where the processing is divided into a number of tasks that are executed in parallel on different equipment. An important relevant challenge is to allocate the appropriate resources to process the data that are continuously generated from user devices. The issue becomes more complicated when we take into account the variations in the volume of the generated data as a function of time. In this paper we present a resource allocation algorithm for distributed computation with emphasis on machine learning algorithms. We consider that the resource requirements vary with time in a semi-static way that exhibits some daily pattern. We distinguish between periodic (expected) variations that occur during the day, and sporadic variations due to unexpected events. We propose an Integer Linear Programming algorithm to allocate the periodic resource requirements. To handle the non-periodic requirements, we consider a suitable prediction algorithm coupled with a reconfiguration algorithm that allocates the predicted required resources. Our results indicate that our proposal outperforms traditional allocation algorithms in terms of resource utilization, monetary cost and achieved accuracy.

关键词： Machine learning algorithms Costs Heuristic algorithms soft sensors Prediction algorithms Approximation algorithms Planning

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Ambient Intelligence Ecosystem for Elderly pattern Detection and Care Using Social Robots

Ambient Intelligence Ecosystem for Elderly Pattern Detection...

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IEEE/ACM Int'l conference on & Int'l conference on Cyber, Physical and Social computing (CPSCom) Green computing and Communications (GreenCom)

作者： Raúl Gómez-Ramos Jaime Duque-Domingo Eduardo Zalama Jaime Gómez-García-Bermejo CARTIF Technological Center ITAP-DISA University of Valladolid Valladolid Spain ITAP-DISA University of Valladolid Valladolid Spain

ISBN: (数字)9798350351637

ISBN: (纸本)9798350351644

This article presents an ambient intelligence system to provide support and security to residents living alone, particularly in supervised domiciles. It confronts the challenges arising from the burgeoning elderly population in Europe, many of whom opt for independent living. The system combines non-intrusive sensors, a social robot, and algorithms geared towards activity monitoring and anomaly detection. Central to its functionality is the monitoring of activities, employing a hierarchical Hidden Markov Model (HMM)-based Human Activity recognition (HAR) system. This system adeptly discerns the Activities of Daily Living (ADLs) performed by users, enabling swift interventions or alerts in instances of emergencies. Furthermore, anomalies such as prolonged periods of inactivity or nocturnal wanderings are promptly identified, triggering requisite responses to ensure user safety and well-being. A key component is the cognitive system, which establishes rules for actions predicated on data gleaned from the HAR module and anomaly detection. This enables interactions with the social robot and interventions within the environment. Validation and performance evaluation in real-world scenarios underscore the system’s efficacy, with high precision, recall, and F1Score metrics reported for the HAR module.

关键词： Performance evaluation Social robots Hidden Markov models Sensor systems Ambient intelligence Human activity recognition Security Older adults Monitoring Anomaly detection

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