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共 563 条记录，以下是201-210 订阅

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4th international conference on Advanced Informatics for computing Research, ICAICR 2020

4th International Conference on Advanced Informatics for Com...

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4th international conference on Advanced Informatics for computing Research, ICAICR 2020

ISBN: (纸本)9789811636523

the proceedings contain 23 papers. the special focus in this conference is on Advanced Informatics for computing Research. the topics include: Internet of things in Healthcare: A Review;inter-Vehicular Communication for Intelligent Collision Avoidance Using machine learning: An Overview;wireless Sensor Networks Source Location Privacy Preservation Mechanism;a Study on Secure Data Aggregation and Routing for Wireless Sensor Networks;comparative Analysis of Internet of things (IoT) Security Models;study of Energy Efficient Routing Protocols for Adhoc-Wireless Sensor Networks;ioT Based Smart Transport Management System;vehicular Ad Hoc Network: Routing Protocols;a Reluctant Checksum Scheme Based on the Inherent Characteristic of Wireless Networks;Optimised Multi Energy System (OMES) for Electric Vehicle;anomaly Based Detection of Cross Site Scripting Attack in Web Applications Using Gradient Boosting Classifier;crypto Key Protection Generated from Images and Chaotic Logistic Maps;an Effıcıent Fault Management in Communication Networks Usıng On Demand Link State Algorıthm;application and Analysis of Phishing Website Detection in machine learning and Neural Networks;modeling of Microstrip Patch Antenna Using Artificial Neural Network Algorithms;Smart PDM: A Novel Smart Meter for Design Measurement and Data Collection for Smart Grid;GSM Based Remote Distribution Transformer Condition Monitoring System;Performance Analysis and Classification of Node Capture Attack Models in WSN;an Efficient IoT Based Framework for Missed Call Messaging Services for Electricity Consumers;A Divide and Conquer Based Data Aggregation Technique for WBANs;open Research Challenges and Blockchain Based Security Solution for 5G Enabled IoT.

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4th international conference on Advanced Informatics for computing Research, ICAICR 2020

4th International Conference on Advanced Informatics for Com...

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4th international conference on Advanced Informatics for computing Research, ICAICR 2020

ISBN: (纸本)9789811636592

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A Comprehensive Survey on Parametric and Non-Parametric machine learning Approaches for Bus Arrival Time Prediction

A Comprehensive Survey on Parametric and Non-Parametric Mach...

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

作者： Chinmayee P Anusha H Patil Bhavika V K Ashwini B P R Sumathi Department of Computer Science and Engineering Siddaganga Institute of Technology Tumakuru India

Public transportation plays a major role in modern society. Bus transportation is vitally important in public transportation, and it reduces more than 60% of private vehicle usage. It reduces fuel exhaustion and traffic burdens. Hence, promoting the usage of public transit as against private has become very important recently. the prediction of bus travel and arrival time for the passenger and service provider improves the reliability of the service by reducing the waiting time and uncertainty for the passengers. Several research works have focused on predicting the travel and arrival time of public transit buses in the past. A review of the existing works using parametric and non-parametric machine learning models for bus travel arrival time is conducted in this article. Parameters such as data sources, the spatial-temporal scales, and the study location are considered for the study. It is observed from the review that parametric models are widely used; especially network models, most models use data sourced from the GPS, spatially; segmented routes are well studied, and temporally, all-day data are the most studied. the existing works are well spread across the globe irrespective of developed and developing countries. Overall, it is observed from the survey that bus arrival time prediction is a well-studied area, but still, there is scope for innovative solutions in the future.

关键词： Uncertainty soft sensors machine learning Predictive models Data models Spatial databases Parametric statistics

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Force of Gravity Oriented Classification Technique in machine learning 4th

Force of Gravity Oriented Classification Technique in Machin...

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4th international conference on Data Management, Analytics and Innovation, ICDMAI 2020

作者： Guha Neogi, Pinaki Prasad Goswami, Saptarsi Department of CSE Meghnad Saha Institute of Technology Kolkata India A.K. Choudhury School of Information Technology University of Calcutta Kolkata India

ISBN: (纸本)9789811556159

the level of impersonation from nature is astonishingly high in the domain of Evolutionary Computation and Computational Intelligence and attempts are being constantly made to develop more number of algorithms, which completely or partially imitate nature and the activities that occur in a particular natural phenomenon. the same can also be employed in various aspects of machine learning such as classification methodologies. this paper puts forward a novel approach for machine learning classification by employing the concept of center of mass and force of gravity. the performance of the proposed force of gravity (Fg)-based classification technique has been evaluated on a number of standard and popular datasets. the efficiency of the proposed method of classification can be perceived when the output obtained is compared with other standard and popular methods of classification such as Logistic Regression, KNN, Decision Tree, and SVM. © 2021, Springer Nature Singapore Pte Ltd.

关键词： Classification (of information)

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Fruit recognition based on near-infrared spectroscopy using deep neural networks 21

Fruit recognition based on near-infrared spectroscopy using ...

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5th international conference on machine learning and soft computing, icmlsc 2021

作者： Ninh, Duy Khanh Doan, thi-Ngoc-Canh Ninh, Chi Khanh Nguyen-thi, thanh Xuan thanh, Nhan Le The University of Danang University of Science and Technology Danang Viet Nam The University of Danang University of Economics Viet Nam The University of Danang Vietnam-Korea University of Information and Communication Technology Danang Viet Nam The University of Danang Danang International Institute of Technology Danang Viet Nam

ISBN: (纸本)9781450387613

Near-infrared (NIR) spectroscopy has been widely used to determine the varieties and chemical properties of agricultural and food products. the major advantage of NIR spectroscopy is that the analysis is carried out in a simple, fast, and non-destructive manner, making it suitable for food applications. As the first step in applying NIR spectroscopy for fruit recognition and analysis in Vietnam, this paper presents deep neural networks (DNNs) based solutions for automatic recognition of several kinds of fruits. We compared two proposed DNN architectures based on Convolutional Neural Network (CNN) and Residual Network (ResNet). Additionally, we proposed feature extraction methods using the first and second derivatives of the Savitzky-Golay (SG) filtered normalized NIR data. Experimental results show that the deep learning approach combined with reasonable feature extraction process can achive the accuracy of approximately 99% for the task of classifying five types of fruits including Apple, Avocado, Dragon Fruit, Guava, and Mango. the ResNet-based model is more compact and has slightly better recognition performance than the CNN-based one. the inclusion of the first and second derivatives of SG-smoothed normalized spectra improves the recognition accuracy of the proposed DNN models by more than 8%. Moreover, the recognition performance of the proposed DNN models surpasses that of traditional classifiers, including k-nearest neighbors, Naive Bayes, and support vector machine. Our proposed methods were proved to be robust against the freshness of fruits, the NIR device's calibration parameters, and the measurement position on the body of fruits. © 2021 ACM.

关键词： Deep neural networks

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An Ingenious Method to Detect COVID in X-Ray Images Using machine learning Techniques

An Ingenious Method to Detect COVID in X-Ray Images Using Ma...

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

作者： Palak Kumari Neha Rani N. Suresh Kumar School of Computing Science and Engineering Galgotias University Greater Noida India

Covid-19 is a fatal disease caused by the Covid-19 virus. It is very big problem for the whole world. the World Health Organization (WHO) has declared a pandemic. In May 2020, more people throughout the world had a favorable experience. the COVID illness is rapidly growing, and we are unable to stop it. We addressed the COVID-19 data science research initiatives employing a number of approaches, including statics, machine learning (ML), modelling, simulation, data visualization, and artificial intelligence (AI). We all suffering from COVID-19. in this case higher value of case comes from negative and lower false positive rate. the global impact of the COVID-19 outbreak was enormous. To tackle the pandemic, many projects have been launched, including those in the field of deep learning. this paper proposes a deep neural network modification based on the Xception model. the model is used to detect COVID-19 using chest X-ray images. Batch normalization and two stacks of two dense layers each are used in the proposed model. the layer addition is intended to avoid overfitting the proposed model. the proposed as a result, we compare the model’s loss, accuracy, and performance speed, and the results show that the quality of the machine learning model has higher prediction accuracy and loss, but it takes longer to execute than traditional machine learning languages. machine learning algorithms in general, and convolutional neural networks (CNNs) in particular, have shown promise in medical picture analysis and categorization. the architecture of this study has been presented for the diagnosis of COVID-19.

关键词： COVID-19 Deep learning machine learning algorithms Pandemics Computational modeling Predictive models Data models

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Design of a predictor for COVID-19 misinformation prediction 4

Design of a predictor for COVID-19 misinformation prediction

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4th international conference on Innovative computing (ICIC)

作者： Chughtai, Muhammad Anzer Hou, Jun Long, Huaqiu Li, Qianmu Ismail, Muhammad Nanjing Univ Sci & Technol Sch Comp Sci & Engn Nanjing Peoples R China Nanjing Vocat Univ Ind Technol Sch Social Sci Nanjing Peoples R China Wuyi Univ Intelligent Mfg Jiangmen Peoples R China

ISBN: (纸本)9781665400916

Due to the increase of social media usage, the online sharing of content has been extremely increased. As a result, the spread of misinformation on social media platforms has also increased. To address this issue, we proposed an approach that predicts the news is fake or real. In our approach, we select the top k ranked features through a filter base algorithm and feed them to the classifier. the main objective of this research is to provide two things. First, to provide an approach, which compares the benchmark performance results of the evolutionary detection approach on the Koirala dataset. the second is to build, publicly available dataset through web scraping for the classification of COVID-19 fake news articles. Our method significantly uplifts the F1-score with 14.88 percent for the same number of features selected 605 for the already existing approach. Also, stated the number of features 5000 on which the approach showed the best results with a margin of F1-score of 20.4 percent, respectively. Similarly, on the self-build dataset, this approach also outshines and achieved 99.66 percent of F1-score, respectively. Our experimental results show that our robust approach by comparing with other classifiers and existing approach, Max-Min Ratio (MMR) along with support vector machine (SVM) outperformed on both of these datasets. Hence, feature selection plays a vital role in the performance of the model rather than deeply tuning and training the classifier.

关键词： Misinformation COVID-19 machine learning NLP Feature Selection

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Experimental Analysis of Tuberculosis Classification Based on Clinical Data Using machine learning Techniques 4th

Experimental Analysis of Tuberculosis Classification Based o...

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4th international conference on soft computing and Data Mining (SCDM)

作者： Yugaswara, Hery Fathurahman, Muhamad Suhaeri Univ YARSI Fac Informat Technol Informat Dept Jakarta 10510 Indonesia

ISBN: (纸本)9783030360566;9783030360559

the early detection of tuberculosis plays a significant rule to reduce the death rate of tuberculosis. However, the early detection of tuberculosis nowadays has a limitation such as it needs long periods of time to acquire accurate diagnosis because it includes many clinical examinations. To overcome this problem a new diagnosis schema is needed. this study evaluates the common machine learning techniques including Logistic Regression, K-Nearest Neighbour, Naive Bayes, Support Vector machine, Random Forest, Neural Network and Linear Discriminant Analysis to diagnose tuberculosis using classification methods based on clinical data. the results show that most of machine learning techniques that use in this study have a good performance in classifying tuberculosis based clinical data. those machine learning techniques have achieved 0.97-0.99 in testing F1-Score.

关键词： Tuberculosis machine learning Classification Early detection

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Vouch augmented Program Courses Recommendation System for E-learning 4th

Vouch augmented Program Courses Recommendation System for E-...

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4th international conference on Smart computing and Informatics, SCI 2020

作者： Rama Narasimham, K.B.V. Prasad, C.V.P.R. Jyothirmai, J. Raghava, M. Career Point University Aalniya India Malla Reddy Engineering College for Women Hyderabad India VNRVJIET Secunderabad India CVR College of Engineering Hyderabad India

ISBN: (纸本)9789811615016

In the realm of remote working and online living, it is imperative for educational institutions to steer their teaching learning process toward virtual classrooms and online evaluations. However, the existing learning management systems offer online video conference tools and facilitate the stakeholders with recommendations based on hard coded rules. As a result, the participating students also are failing to consolidate the theoretical concepts and started shying away from the critical core courses which are highly an unwanted situation for the next generation knowledge building. this paper aspires to propose developing a cognizant system which continually collects the multi-modal data from diversified sources, integrate them with the emotions of the students, intuitions of the torch bearers of various fields and evolve authentic recommendations to the students. the proposed vouch augmented learning management system, which we refer as vLMS, offers a framework that does a deep poll in the background on various data sources, derives the semantic relations into cognizance and offers hard as well as soft recommendations for a student to rediscover himself. Finally, the article presents the detailed architecture, the suitable soft-computing models and the technology stack support for implementing the vLMS framework. © 2021, the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Students

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Fraud404: A Fuzzy Approach for Financial Statement Fraud Detection in Resource-Constrained Settings

Fraud404: A Fuzzy Approach for Financial Statement Fraud Det...

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computing and Information Technology (ICCIT-1441), 2020 international conference on

作者： Sumayyah Albarakati Shomokh Althagafi Reyouf Wasel Doja Alhebshi Shorouq Alansari Computer Science and Artificial Intelligence Department Umm Al-Qura University Makkah Saudi Arabia Cybersecurity Department Umm Al-Qura University Makkah Saudi Arabia

ISBN: (数字)9798350353839

ISBN: (纸本)9798350353846

Organizations, ranging from resource-constrained startups to established institutions, are susceptible to financial statement fraud. Traditional detection methods often lack transparency and struggle with complex fraud schemes. Fraud404, an approach leveraging fuzzy logic, offers a solution applicable across various organizational landscapes. this model analyzes key financial indicators and relationships within statements, effectively capturing subtle patterns and inconsistencies that might signal potential fraud and generate an overall fraudulent degree for the input financial statements. Unlike opaque machine learning methods, Fraud404 prioritizes interpretability, generating clear explanations for its decision-making process and fostering trust within the financial ecosystem. this transparency empowers stakeholders to understand the reasoning behind potential fraud flags, enabling informed decision-making. Additionally, Fraud404 possesses the capability to dynamically calculate the optimal threshold based on the relative importance of precision, recall, and F1-score. this flexibility allows organizations to tailor Fraud404's behavior to their specific risk tolerance and fraud detection priorities. Fraud404 operates efficiently with O(n) time complexity, enabling faster fraud identification. In essence, Fraud404 addresses the limitations of traditional methods by employing fuzzy logic and prioritizing interpretability. this approach, achieving a high F1-score of 0.996 at a threshold of 52.419, offers exceptional performance. At this specific threshold, Fraud404 achieves a precision of 1, indicating that all statements flagged as fraudulent are indeed confirmed frauds, while also maintaining a high recall of 0.992, which indicates that the model successfully identifies a very large proportion of true fraud cases, minimizing the chances of overlooking actual fraudulent activity. By empowering faster and more accurate fraud detection, Fraud404 fosters a more secure a

关键词： Fuzzy logic Industries Decision making Organizations machine learning Fraud Stakeholders Time complexity Protection Information technology

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