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Rainfall Prediction Using machine learning 2

Rainfall Prediction Using Machine Learning

引用

2nd international conference on Recent Trends in Microelectronics, Automation, Computing, and Communications Systems, ICMACC 2024

作者： Sreenivasu, S.V.N. Rafi, Shaik. Lakshmi, V.V.A.S. Sivanageswara Rao, S. Rajani, Ch. Narasaraopeta Engineering College Dept of CSE Andhra Pradesh Palnadu India

ISBN: (纸本)9798350366570

The title is 'Rainfall Prediction Using machine learning'. The initiative's dataset is written in Python and stored in Microsoft Excel. A wide range of machine learning algorithms are used to discover which strategy generates the best accurate predictions. In many sections of the country, rainfall forecasting is critical for avoiding major natural disasters. This forecast was created using a variety of machine learning approaches, including catboost, xgboost, decision tree, random forest, logistic regression, neural network, and light gbm. It incorporates several components. The Weather Dataset was utilized. The primary goal of the research is to evaluate a variety of algorithms and determine which one performs best. Farmers may greatly profit from growing the appropriate crops based on the amount of water they require. © 2024 IEEE.

关键词： Adversarial machine learning

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Transfer learning based COVID-19 detection Using Radiological Images 2

Transfer learning based COVID-19 detection Using Radiologica...

引用

2nd IEEE international conference on Applied Electromagnetics, signal processing, and Communication, AESPC 2021

作者： Mahapatra, Aurosweta Pahadsingh, Sasmita Kar, T.

ISBN: (纸本)9781665442992

Artificial Intelligence is a process that enables machines to imitate human behaviour. Both machine learning and Deep learning are subsets of AI. The basic difference between ML(machine learning) and DL(Deep learning) is that in machine learning manually defining of features is done to get the desired outcome whereas in deep learning the neural network learns of its own and publishes the result. In the present crisis due to COVID-19 pandemic the contagious power of virus has led to huge encounter of cases on daily basis. This stimulates the need for specialised and accurate methods to detect COVID-19 cases. The contribution of deep learning to this problem has been significant. The application of deep learning concepts has shown its emence importance and utility in medical domain for detection of COVID-19 cases using CT scan and X-Ray images of lungs. Our proposed method compares the accuracy of multiple pretrained models in predicting COVID-19 infected cases for a specific dataset of radiological images using three distinct optimizers for each model. This research aims to determine which model, together with its associated optimizer, is most suitable for identifying COVID-19 infected cases from radiological lungs images. © 2021 IEEE.

关键词： Computerized tomography

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Autonomous Driving Decision Algorithm based on Hierarchical Dueling Double Deep Q-Network 2

Autonomous Driving Decision Algorithm based on Hierarchical ...

引用

2nd international conference on signal processing and Intelligent Computing, SPIC 2024

作者： Qian, Bo Huang, Bowen Shenyang Ligong University College of Information Science and Engineering Shenyang China

ISBN: (纸本)9798350368888

The sparse reward in traditional reinforcement learning will lead to poor training effect of autonomous vehicles. This paper proposes a hierarchical automatic driving decision method based on reinforcement learning. The multi-modal fusion of camera images and radar data ensures the state input of autonomous vehicles. The action space is divided into high-level options and low-level actions. The high-level option network and the underlying action network are built respectively. The vehicle interacts with the environment to learn the optimal driving strategy. Through simulation experiments, compared with DDDQN and DQN algorithms, the average speed of autonomous vehicles based on HDDDQN increases by about 4.48%, the average number of lane changes decreases by about 4.54%, and the average number of overtaking increases by about 18.6 %. Therefore, the safety and driving efficiency of automatic driving are significantly improved. © 2024 IEEE.

关键词： Adversarial machine learning

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Improved extreme learning machine and its application in SAR target recognition 2

Improved extreme learning machine and its application in SAR...

引用

2nd international conference on Image and Video processing, and Artificial Intelligence (IPVAI)

作者： Jian, Chen Fuzhou Univ Coll Elect Engn & Automat Fuzhou 350108 Peoples R China

ISBN: (数字)9781510634107

ISBN: (纸本)9781510634107

In this paper, An improved algorithm for the extreme learning machine is proposed and applied to SAR target *** order to solve the influence of the noise and spatial distribution of the training samples on the calculation of the classification plane, different penalty factors are given to different training samples,and according to this, the "weighted extreme learning machine" is proposed. And then,the kernel function is introduced into the "extreme learning machine" to improve the ability of nonlinear function approximation. Considering that the general training algorithm of the weighted extreme learning machine is slow and consumes a lot of computer memory when the number of training samples is large, a training method based on conjugate gradient algorithm is proposed. The test on "banana benchmark data" shows that the weighted extreme learning machine based on the conjugate gradient method can complete the convergence in the number of iterations far less than the number of samples, and the calculation speed is much faster than the traditional algorithm. Finally, this proposed algorithm is applied to SAR target recognition. The test on MSTAR data set shows that the proposed algorithm is not only extremely fast in SAR target recognition, but also has better recognition performance than support vector machine. general limit learning machine. BP neural network and other algorithms.

关键词： Extreme learning machine Conjugate Gradient Method Synthetic aperture radar Target recognition

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Cuff-Less BP Stratification based on Bio-signals processing using machine learning : AAn Investigative Study 2

Cuff-Less BP Stratification based on Bio-Signals processing ...

引用

2nd international conference on Smart Electronics and Communication, ICOSEC 2021

作者： Shinde, Santosh Rajarajeswari, Pothuraju Koneru Lakshmaiah Education Foundation Department of Computer Science and Engineering AP Vaddeswaram522502 India

ISBN: (纸本)9781665433686

Cuff-Less Blood Pressure Stratification using signal processing with machine learning has gained immense attraction in the past decade among the research community. Blood Pressure, one of the most vital parameter of the human body representing overall well being of an individual. Most of the cardiovascular and cerebrovascular diseases (CCVD) including Hypertension are highly correlated to Blood Pressure. Existing BP measurement approaches are highly inconvenient and intermittent and do not allow continuous measurement of BP. Continuous BP measurement could prove to be significant indicator to most of the medicinal conditions and will lead to breakthrough achievement in the field of medical science. Cuff-Less Blood Pressure estimation hopefully can enable continuous blood pressure measurements in the time to come. A Plethora of methods for Cuff-Less BP Stratification have been experimented out by using Vital Bio-signals such PTT based, nPTT based, machine learning and Deep learning based. Most of these methods leading to satisfactory beliefs that Cuff-Less BP estimation could be possible to the utmost accuracy for the diagnosis of most of the CCVD diseases as well as to monitor the overall well being of humans. However, most of the approaches still needs improvements, needs to be tested on a larger population with varying demographic features and real time application. This paper presents an investigative study of existing Cuff-Less BP Estimation approaches and discusses the merits and opportunities for improvements of the Cuff-Less BP Estimation methods. © 2021 IEEE.

关键词： Blood pressure

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A study of Spoken Word Recognition using Unsupervised learning with reference to Assamese Language 2

A study of Spoken Word Recognition using Unsupervised Learni...

引用

2nd international conference on Innovations in Electronics, signal processing and Communication, IESC 2019

作者： Nath, Dipen Kalita, Sanjib Kr. Mangaldai College Dept. of Computer Science IT MangaldaiAssam India Gauhati University Dept. of Computer Sience Guwahati-14Assam India

ISBN: (纸本)9781728107448

The aim of this paper is to measure the performance of Auto Associative Neural Network (AANN) in terms of recognition rate of spoken words with reference to Assamese language. One AANN models is built in the present study for spoken word recognition using continuous speech database. The recognition is measured by taking Mel Frequency Cepstral Coefficients (MFCC) as the feature vector along with its first and second derivatives. In this study, one database is created by using continuous speech. In the continuous database, there are 240 continuous speech files each around two minutes of duration to train and test the AANN model. AANN is trained using the continuous speech database and the recognition of certain words present within the continuous speech signal is measured. In this study, fifteen native Assamese speakers are used which involves eight Male speakers and seven feMale speakers. It is observed that the performance of AANN is 85.08% in terms of accuracy and the performance of AANN is 93.61% in terms of sensitivity. © 2019 IEEE.

关键词： Neural network models

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2024 2nd international conference on Advances in Computation, Communication and Information Technology, ICAICCIT 2024

2024 2nd International Conference on Advances in Computation...

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2nd international conference on Advances in Computation, Communication and Information Technology, ICAICCIT 2024

ISBN: (纸本)9798331541217

The proceedings contain 245 papers. The topics discussed include: developing an advanced tool for transforming spreadsheet data into interactive dashboards with predictive insights;smart education ecosystems for enhancing learning experiences through deep attention learning and artificial intelligence;recent innovations in patch antenna technology for wearable biomedical devices;wine quality prediction using machine learning techniques;data analytics and artificial intelligence for improving employee retention strategies;hybrid steganographic and encryption approach using color analysis for secure image communication;a robotic arm vehicle using voice recognition for physically challenged people;artificial intelligence based plant disease detection system;comprehensive review for video surveillance based suspicious human activity detection;edge computing in smart cities: enhancing real-time data processing;a comprehensive analysis of various federated learning techniques;cardiovascular disease prediction implementing artificial intelligence and machine learning approaches;and signal processing techniques for enhanced wireless network performance.

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Digital Recognition of Street View House Numbers Based on DCGAN 19

Digital Recognition of Street View House Numbers Based on DC...

引用

2nd international conference on Image and Graphics processing (ICIGP) / 5th international conference on Virtual Reality

作者： Zhong, Juping Gao, Jing Chen, Rongjun Li, Jun Guangdong Polytech Normal Univ Coll Comp Sci Guangzhou 510665 Guangdong Peoples R China Guangdong Hengdian Informat Technol Co Ltd Guangzhou 510635 Guangdong Peoples R China

ISBN: (纸本)9781450360920

Deep learning algorithms have surpassed human resolution in applications such as face recognition and object classification. However, it can only produce very blurred, lack of details of the image. Generative Adversarial Network is a game training of minimax antagonism between generator G and discriminator D, and ultimately achieves Nash equilibrium. We use deep convolutional GAN that recognizes sequence numbers and without split characters. First we use convolution network to extract character features. Second we construct a convolution neural network to recognize digits of natural scene house number. DCGAN is used to improve the resolution of the number of fuzzy houses, so as to extract more abundant data features in data set training. It can better recognize the numbers in the natural street.

关键词： Image processing machine learning deep convolutional generative adversarial networks street view house numbers

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An Overview of Traditional and Recent Trends in Video processing 2

An Overview of Traditional and Recent Trends in Video Proces...

引用

2nd international conference on Smart Systems and Inventive Technology, ICSSIT 2019

作者： Joy, Helen K. Kounte, Manjunath R. School of ECE REVA University Bangalore India

ISBN: (纸本)9781728121192

Video processing is a significant field of research interest in recent years. Before going into the recent advancement of video processing, an overview about the traditional video processing is a matter of interest. Knowing about this, its advantages and limitations help to give a strong base and invoke an insight into the further development of this research area. This paper introduces the concept of video processing in early era followed by hybrid video processing, MJPEG and describes the traditional video processing methodologies in hierarchal order. Also, the paper summarizes the recent trends in video processing with NN, CNN, deep learning and focus on the applications in this area. It focus mainly on the video processing development and its application till current era. © 2019 IEEE.

关键词： Video signal processing

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Mobile Health Solutions for Effective Symptom Detection and Disease Management Using machine learning 2

Mobile Health Solutions for Effective Symptom Detection and ...

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2nd international conference on Recent Trends in Microelectronics, Automation, Computing, and Communications Systems, ICMACC 2024

作者： Choudhary, Santosh Kumar Kumar, Pradeep Vaishnavi, Udipi Patchala, Deekshita Dept. of ECE VNRVJIET Hyderabad500090 India

ISBN: (纸本)9798350366570

People in today's world are so busy with work and other commitments that they rarely have time to see doctors for illnesses that initially seem minor but eventually become life-threatening. In the above situations, where patients do not have easy access to healthcare or face difficulties in utilizing it, this can be very useful in helping them understand the urgency of their health condition. The suggested algorithm makes use of machine learning to forecast diseases based on user-provided symptoms. The flow of the disease diagnosis support system using symptoms is as follows: First, data pre-processing is performed, and several machine learning classification models, including K Nearest Neighbor, are then applied to the data, Random Forest, and Support Vector machine (SVM). The accuracy of each model is calculated to aid in predicting the disease based on the mentioned symptoms. The predicted disease is then sent back to the application, where it is displayed to the user. Our model primarily focuses on seasonal infections and diseases. © 2024 IEEE.

关键词： Adversarial machine learning

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