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检索条件"任意字段=3rd International Conference on Machine Learning and Data Mining in Pattern Recognition"

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3rd international conference on Smart Innovations in Communications and Computational Sciences, ICSICCS 2020

3rd International Conference on Smart Innovations in Communi...

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3rd international conference on Smart Innovations in Communications and Computational Sciences, ICSICCS 2020

ISBN: (纸本)9789811553448

The proceedings contain 29 papers. The special focus in this conference is on Smart Innovations in Communications and Computational Sciences. The topics include: Multipath routing using improved grey wolf optimizer (igwo)-based ad hoc on-demand distance vector routing (aodv) algorithm on manet;analysis and prediction of fuel oil in a terminal;an android malware detection method based on native libraries;fire early warning system based on precision positioning technology;the construction of learning resource recommendation system based on recognition technology;review of ultra-low-power cmos amplifier for bio-electronic sensor interface;an enhancement of underwater images based on contrast restricted adaptive histogram equalization for image enhancement;hybridization of social spider optimization (sso) algorithm with differential evolution (de) using super-resolution reconstruction of video images;enhanced convolutional neural network (ecnn) for maize leaf diseases identification;facial expression recognition using improved local binary pattern and min-max similarity with nearest neighbor algorithm;visibility improvement of hazy image using fusion of multiple exposure images;comparative analysis of consensus algorithms and issues in integration of blockchain with iot;use of hadoop for sentiment analysis on twitter’s big data;enhanced mobility management model for mobile communications;multi-objective-based lion optimization algorithm for relay selection in wireless cooperative communication networks;embpred30: Assessing 30-days readmission for diabetic patients using categorical embeddings;prediction of different classes of skin disease using machine learning techniques;feature selection and classification based on swarm intelligence approach to detect malware in android platform;preface;trust-based fuzzy bat optimization algorithm for attack detection in manet;transfer learning: Survey and classification.

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Facial expression recognition based on transfer learning and SVM 3

Facial expression recognition based on transfer learning and...

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2021 3rd international conference on Artificial Intelligence and Computer Science, AICS 2021

作者： Yang, Lei Zhang, Haiqing Li, Daiwei Xiao, Fei Yang, Shanglin Department of Software Engineering Chengdu University of Information Technology Chengdu610225 China

The facial expression datasets always have a problem: data with small amount or large amounts of data but also with large noisy. Both problems will affect the facial expression recognition accuracy of the model. A transfer learning method for facial expression recognition is proposed by combining the Convolutional Neural Network (CNN) and Support Vector machine (SVM). SVM have good performance on small data sets and CNN based on transfer learning have better ability of feature extraction for large noisy data set. This method reduces the training time of model and increase the facial expression recognition accuracy. The experimental results show that the accuracy of the proposed method on the CK+ and FER2013 data sets has reached 99.6% and 68.1%. © Journal of Physics: conference Series 2021.

关键词： Support vector machines

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machine learning Algorithms for the Diagnosis of Cardiac Arrhythmia in IoT Environment 3rd

Machine Learning Algorithms for the Diagnosis of Cardiac Arr...

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3rd international conference on Recent Trends in Image Processing and pattern recognition, RTIP2R 2020

作者： Yadav, Samir Kadam, Vinod Jadhav, Shivajirao Dr. Babasaheb Ambedkar Technological University Lonere Raigad India

ISBN: (纸本)9789811604928

Cardiac arrhythmia is very harmful heart disease which can cause severe and even potentially deadly symptoms. Early diagnosis of an arrhythmia can save lives. In the modern health care environment, the use of Internet of Things (IoT) technologies brings the suitability for medical professionals and patients, because they are useful in various medical background. An arrhythmia diagnosis system based on IoT sensors helps and monitors automatic transmission and measurement of Electrocardiogram (ECG) signal data, analyzes this data, and alerts medical professional for an urgency. We are developing a new approach for monitoring and diagnosing cardiac arrhythmia using IoT environment. The concerned ECG signal data is generated by using the IoT sensors and standard online (UC Irvine repository) dataset for predicting severity of cardiac arrhythmia. Also, we used a new fuzzy logic-based neural network classifier algorithm for diagnosing cardiac arrhythmia. The experiments are carried out by various machine learning algorithms on standard UCI Repository dataset as well as on real patients data records collected from multiple Indian hospitals. The proposed machine learning algorithm offers the best accuracy than different machine learning approaches with minimum time complexity. Patients can check their heart condition at their home by using this system. The maintenance and development cost of this system is low due to it is a lightweight and small size system. © 2021, Springer Nature Singapore Pte Ltd.

关键词： Internet of things

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Intelligent machine learning based model for customer churn prediction in business sectors

Intelligent machine learning based model for customer churn ...

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The 3rd international conference on Distributed Sensing and Intelligent Systems (ICDSIS 2022)

作者： N. Metawa S. Mouakket A. A. Ali University of Sharjah Sharjah UAE

In recent times, customer churn prediction (CCP) remains a hot research topic among data analysts and business people. The proper design of CCP models helps to alleviate the financial loss of the company by preventing the customers from leaving the product or services. This study introduces a new intelligent machine learning-based model for customer churn prediction in business sectors. The proposed model is a hybrid of bacterial foraging optimization algorithm with kernel extreme learning machine (BFOA-KELM). The major focus of the BFOA-KELM technique is to identify the presence of customer churns or not in the business sector. The proposed BFOA-KELM method involves the design of effective KELM classification method, which categorizes the input customer data as to 2 class labels such as churn and non-churn. With an intention of enhancing the customer churn classification outcomes, the BFOA is applied to it and consequently it leads to enhanced outcomes. In order to exhibit the improved performance of the BFOA-KELM method, a set of simulations were carried out and the outcomes are investigated in various aspects. The comprehensive comparison study reported the enhanced outcomes of the BFOA-KELM model compared to its existing models.

关键词： consumer behaviour learning (artificial intelligence) optimisation pattern classification

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Utilization of machine learning Algorithms for Prediction of Diseases 3

Utilization of Machine Learning Algorithms for Prediction of...

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

作者： Deeba, Farha Patil, Siddarama R. P.D.A College of Engineering Electronics and Communication Engineering Kalaburagi India

ISBN: (纸本)9781665426916

For analysis of any health related problems or diseases, on time investigation and accuracy of prediction plays a vital role. machine learning technology has become the most significant field that has various applications in the healthcare field. The healthcare professionals are looking forward for most advanced and reliable healthcare systems that assist in providing best possible prognosis and treatment to patients with great accuracy in stipulated amount of time. There are various data mining techniques which are being developed so as to extract useful information from the health dataset collected. The main motto behind the development of prediction system using machine learning algorithms is to find the best possible solution for the health related issues during diagnosis. The sample dataset utilized for the implementation consist a record of about 4922 patients'. The prognosis was carried out based on 132 symptoms for prediction of 42 commonly occurring diseases. The paper also discusses the system design required for prediction of common diseases using ML Algorithms where the doctors/clinicians just need to enter the symptoms with which patient is suffering. The best ML Algorithms found so far for implementation in the field of healthcare are Decision Tree, Random Forest Classifier and Naive Bayes Classifier. The paper presents the comparison results between various algorithms. The system model yields a high accuracy rate of 95.12% for prediction of diseases. © 2021 IEEE.

关键词： Decision trees

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Empirical Analysis of data mining Techniques in Network Intrusion Detection Systems 3rd

Empirical Analysis of Data Mining Techniques in Network Intr...

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3rd international conference on Intelligent Technologies and Applications, INTAP 2020

作者： Soufizadeh, Reza Bagherzadeh, Jamshid Department of Computer Engineering Science and Research Branch Islamic Azad University West Azerbaijan Iran Department of Computer Engineering Urmia University Urmia Iran

ISBN: (纸本)9783030717100

Computer networks have an essential role in modern societies which are developing extensively. Considering that the main goal of the attackers is the ability to access a huge amount of information, Intrusion detection techniques have been attracted attention to the researchers and they believe that to proffer an approach that has an optimization rate both in timing and performance recognition. Moreover, it can be also implemented in commercial devices. A complete analysis of the latest researches in anomaly detection with a high recognition rate of 98% and 2% false positive can be reported. Despite the high rate of attack detection in academic researches, looking at industry solutions that are commercially produced, fewer products can be found that implement smart methods on devices. However, cybersecurity engineers still do not believe in the performance of these new technologies. In order to find out the reason for this contradiction, NSL-KDD and KDDCUP99 data sets with some machine learning approaches will be evaluated and the results will be compared with previous related works in this paper. © 2021, Springer Nature Switzerland AG.

关键词： Intrusion detection

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The Cryptographic Algorithm Identification: Using Deep learning to Empower Smart Grids

The Cryptographic Algorithm Identification: Using Deep Learn...

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Networking Systems of AI (INSAI), international conference on

作者： Mingliang Chen Yayun Zhu Lei Mei Xiaojuan Zhang Sheng Yuan Baiji Hu State Grid Jiangxi Electric Power Co. Ltd. Nanchang China China Electric Power Research Institute Co. Ltd. Beijing China University of Electronic Science and Technology of China Chengdu China

ISBN: (数字)9798350344660

ISBN: (纸本)9798350344677

The smart grid has now been fully implemented with a robust defense system that is built on the primary security principles of "security zoning, network specialization, horizontal isolation, and vertical authentication." The secure cryptographic module plays a pivotal role as one of the key components in the power system, facilitating horizontal isolation and vertical authentication. Nevertheless, assessing secure cryptographic modules within power systems presents several challenges. These include low recognition efficiency of cryptographic algorithms, the difficulty in identifying the characteristics of encryption algorithm variants, and challenges specific to special business scenarios. To address these issues, this paper introduces an automatic cryptographic algorithm recognition method. Leveraging a deep learning framework in conjunction with modules such as data encryption, data enhancement, feature extraction, model construction, and five-fold cross-validation, this method achieves automated recognition of cryptographic algorithms, enhancing the testing capabilities of secure cryptographic modules. The experiments reveal that cryptographic algorithm detection using deep learning offers significant advantages, including a high recognition success rate, strong adaptability, and a high degree of automation. This method proves beneficial in significantly enhancing the efficiency of security evaluation and modification, effectively detecting the ciphertext features of commercial codes, and providing vital support for the secure operation of the smart grid.

关键词： Deep learning machine learning algorithms Passwords Manuals Feature extraction Smart grids Encryption Classification algorithms Cryptography Security

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Smart Crop Cultivation: Harnessing machine and Deep learning for Informed Agricultural Practices

Smart Crop Cultivation: Harnessing Machine and Deep Learning...

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

作者： Smit Meghani Jay Vele Kunal Madrecha Abhijeet Salunke Department of Computer Engineering Sardar Patel Institute of Technology Mumbai India

ISBN: (数字)9798350375190

ISBN: (纸本)9798350375206

Amongst various occupations in India, farming is the most common. It boosts the Indian economy significantly and provides Indians with a wide range of job opportunities. The ideal crop for each type of soil is no longer chosen by farmers. This has an immediate effect on agricultural yields. It is causing serious financial losses for the farmers in the area. Cultivating a certain crop on a particular type of soil now involves several factors to take into account. The optimal crop to plant is determined by taking into account a number of soil specifications. This novel opinion mining method will consider the following parameters: temperature, air humidity, soil pH, nitrogen, potassium, phosphorus, and annual rainfall. The model aids in determining the kind of crop that has to be planted by taking into account the data collected from prior years. In order to support the mission, various algorithms are analyzed and evaluated on multiple fronts, including comparative research and analysis. In addition, the ideal fertilizer will be recommended depending on crop-related environmental factors, temperature, pH level, and soil moisture content.

关键词： Temperature dependence Sentiment analysis Temperature Rain Soil moisture Crops Phosphorus

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Optimizing Potato Disease Detection in Pakistan with machine learning: A Comparative Analysis

Optimizing Potato Disease Detection in Pakistan with Machine...

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Emerging Trends in Electrical, Control, and Telecommunication Engineering (ETECTE), international conference on

作者： Irfan Ahmed Manahal Khalil Safoora Ghulam Jabar Mahmood Khadija Batool Nibtal Khalid Pakiza Arshad Dept of Computer Science University of Sialkot Sialkot Pakistan State Key Laboratory of Blockchain and Data Security School of Cyber Science and Technology College of Computer Science & Technology Zhejiang University Hangzhou China Dept of Computer Science Int. Institute of Science Art & Technology Gujranwala Pakistan

ISBN: (数字)9798331504861

ISBN: (纸本)9798331504878

The agricultural sector of Pakistan depends heavily on the production of potatoes, however diseases like Bacterial Wilt, Late Blight, and Early Blight are posing a growing danger to this industry since they can negatively affect crop productivity and food security. Detecting these illnesses using traditional methods is time-consuming, labor-intensive, and sometimes it requires specialized knowledge which is not easy for farmers. The implementation of a machine learning (ML) framework for illness identification using the methods of K-Nearest Neighbours (KNN), Random Forest (RF), Decision Tree (DT), Naive Bayes (NB), Support Vector machine (SVM), and Logistic Regression (LR) overcomes these challenges. This study utilizes data mining and preprocessing strategies to improve the accuracy of models and adaptability on a dataset of potato plants’ photos gathered across diverse areas in Pakistan. The models are assessed using performance criteria such computational efficiency, accuracy, precision, recall and F1-score. Random Forest model having 97% accuracy outperforms the other ML models in identifying plant disease. The suggested approach provides a practical, affordable, and effective way to identify diseases in a timely manner, allowing farmers to make informed decisions to lower crop losses. The aim of the study is to highlight how ML has the potential to transform Pakistani agriculture, enhancing food security and promoting long-term economic prosperity.

关键词： Support vector machines Productivity Adaptation models Accuracy Biological system modeling Computational modeling Crops Food security Random forests Classification tree analysis

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Enhancement of Degraded CCTV Footage for Forensic Analysis 3rd

Enhancement of Degraded CCTV Footage for Forensic Analysis

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3rd international conference on Innovative Computing and Communication, ICICC 2020

作者： Vinay, A. Lokesh, Aditya Kamath, Vinayaka R. Murty, K.N.B. Natarajan, S. Center for Pattern Recognition and Machine Intelligence PES University Bengaluru India

ISBN: (纸本)9789811551123

Forensic analysis has proved to be one of the most utilitarian tool in investigating crime. Forensic analysis provides evidence/basic information of the said crime through analysis of physical evidence. In this paper, we present a scintillating technique to enhance the image quality of CCTV video to assist in the investigation of criminal cases. This paper, address the issue of lack of a budget surplus for a high-quality image/video capturing device. The problem of enhancing images is addressed by pure image processing methods and machine learning techniques. The paper analyzes both techniques and further concludes that the machine learning approach produces a more efficient result. The applications of this technique can range from simple image filtering/beautification to forensic image processing. © 2021, Springer Nature Singapore Pte Ltd.

关键词： Crime

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