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Some novel integration techniques to explore the conformable M-fractional Schrödinger-Hirota equation

Journal of Ocean engineering and Science 2022年第4期7卷 337-344页

作者： Asim Zafar M.Raheel M.Asif Kamyar Hosseini Mohammad Mirzazadeh Lanre Akinyemi Department of Mathematics COMSATS University IslamabadVehari CampusPakistan Department of Mathematics and Statistics ISP MultanPakistan Department of Mathematics Rasht BranchIslamic Azad UniversityRashtIran Department of Engineering Sciences Faculty of Technology and EngineeringEast of GuilanUniversity of GuilanVajargah P.C.44891-63157RudsarIran Department of Mathematics Lafayette CollegeEastonPAUSA

The current study deals with exact soliton solutions for Schrödinger-Hirota(SH)equation via two modi-fied integration *** methods are known as the improved(G/G)-expansion method and the Kudryashov *** model is a generalized version of the nonlinear Schrödinger(NLS)equation with higher order dispersion and cubic *** can be considered as a more accurate approximation than the NLS equation in explaining wave propagation in the ocean and optical fibers.A novel deriva-tive operator named as the conformable truncated M-fractional is used to study the above mentioned *** obtained results can be used in describing the Schrödinger-Hirota equation in some better *** the obtained results are verified through symbolic computational ***,the ob-tained results show that the suggested approaches have broaden capacity to secure some new soliton type solutions for the fractional differential equations in an effective *** the end,the results are also explained through their graphical representations.

关键词： Shrödinger-Hirota equation Truncated M-fractional derivative Soliton solutions

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Analyzing User Satisfaction with Mobile Internet Service Providers through Technical Performance and User Experience Evaluation

Analyzing User Satisfaction with Mobile Internet Service Pro...

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2024 International Research Conference on Smart Computing and Systems engineering, SCSE 2024

作者： Pothuwilage, Prasanga Hewa Dissanayaka, Pansujee Liyanage, Uditha Prabhath University of Moratuwa Faculty of Engineering Department of Mathematics Moratuwa Sri Lanka University of Kelaniya Faculty of Science Department of Statistics and Computer Science Kelaniya Sri Lanka

ISBN: (纸本)9798350375688

In today's digital era, the Internet holds a fundamental position in daily life, particularly in Sri Lanka. This study addresses two primary objectives: first, to quantify the technical performance of internet services, and second, to assess customer satisfaction and its alignment with service provider performance. Conducted in the Narthanagala Grama Niladhari Division, specifically the 616C division in Horana, the study utilizes a structured questionnaire to gather customer experience data. Technical data are collected using the "Open Signal"and "Network Cell Info"mobile applications. Statistical techniques are employed to achieve the objectives. Principal Component Analysis reduces the initial 9 dimensions of internet service quality to a singular dimension, with the primary principal component explaining 84.40% of variance. This reduction is validated through the composite index of "Application Based Technical Performance of Mobile Data Service (AppBased_TecPerf)". Factor Analysis and simple linear regression unveil two influential latent factors: Customer-Perceived Technical Performance of Mobile Data Service (Customer_TecPerf) and Customer Service Satisfaction (Customer_Service), collectively accounting for 60.66% of variability across six dimensions. These dimensions include Video Quality Experience, Gaming Experience, Voice & Video Quality Experience on Social Media Apps, Client Rating for Internet Speed at Residence, Client Rating for Satisfaction with Price Fairness, and Client Rating for Customer Service Quality. Reliability analysis confirms the effectiveness of these factors in measuring the Application Based Technical Performance of Mobile Data Service. Regression analysis confirms that the index, AppBased_TecPerf, effectively captures Customer_TecPerf. © 2024 IEEE.

关键词： Factor analysis

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Mathematical modeling and RAMD investigation of cloud infrastructure

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International Journal of Information Technology (Singapore) 2023年第6期15卷 3157-3168页

作者： Saini, Monika Choudhary, Ravi Kumar, Ashish Saini, Dinesh Kumar Department of Mathematics and Statistics Manipal University Jaipur Jaipur 303007 India Department of Computer and Communication Engineering Manipal University Jaipur Jaipur 303007 India

The prominent objective of present study is to investigate the key reliability measures of cloud infrastructure. The proposed cloud infrastructure is configured using five subsystems namely client, network, database, cloud, and services. The concept of cold standby redundancy is adopted at database level. The system may suffer due to hardware and software failures. Initially, reliability, availability, maintainability, and dependability (RAMD) technique are applied to investigate the system’s time dependent reliability measures and then a novel Markov model is developed for availability evaluation of cloud infrastructure. All the failure and repair rates are considered as exponentially distributed. The repair and switch devices are perfect. Sufficient repair facility always available with system to perform all repair and maintenance activities. The Chapman–Kolmogorov differential-difference equations derived for the proposed model to investigate various reliability characteristics of cloud infrastructure. The impact of variation in failure and repair rates observed on system reliability measures. The numerical results for various system characteristics are derived and these may be helpful for maintenance engineers to plan the maintenance strategies as well as for system designers. © 2023, The Author(s), under exclusive licence to Bharati Vidyapeeth's Institute of Computer Applications and Management.

关键词： Availability Cloud infrastructure Maintainability Markovian approach Reliability

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Real-Time Bitcoin Price Prediction Using Hybrid 2D-CNN LSTM Model 9

Real-Time Bitcoin Price Prediction Using Hybrid 2D-CNN LSTM ...

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9th International Conference on Web Research, ICWR 2023

作者： Kazeminia, Saman Sajedi, Hedieh Arjmand, Masoud University of Tehran Department of Computer Engineering Kish Iran University of Tehran Statistics and Computer Science Department of Mathematics Tehran Iran

ISBN: (纸本)9798350399691

Due to the growing importance of the cryptocurrency market, as well as the diversity and expansion of online trading platforms, cryptocurrency technology has piqued the curiosity of a wide range of people, from market traders to researchers and analysts. Reliable price prediction is a necessity since investors face multiple challenges including market volatility, risk management, and market complexity. Therefore, numerous studies have been done using deep learning and machine learning algorithms to demonstrate their functionality and efficiency in this area. In this paper, we employed Bitcoin historical data to make predictions for the next day's closing price using a new hybrid 2D-CNNLSTM model with OPTUNA hyperparameter tuning. The dataset used to train the model was gathered using an automated web scraping technique. With the proposed model, the R2 error achieved 0.98166 and the MAPE was 0.034. Our proposed model is compared with three different models: CNN, LSTM, and GRU. The predicted results show that the proposed hybrid model is efficient for accurately predicting bitcoin prices and reliable for supporting investors to make their informed investment decisions. Additionally, the proposed model has outperformed other commonly used algorithms, namely CNN, LSTM, and GRU in terms of R2, and MAPE. This model is also capable of performing real-time forecasting. © 2023 IEEE.

关键词： Forecasting

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Semantic and spatial‐spectral feature fusion transformer network for the classification of hyperspectral image

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CAAI Transactions on Intelligence Technology 2023年第4期8卷 1308-1322页

作者： Erxin Xie Na Chen Jiangtao Peng Weiwei Sun Qian Du Xinge You Hubei Key Laboratory of Applied Mathematics Faculty of Mathematics and StatisticsHubei UniversityWuhanChina Department of Geography and Spatial Information Techniques Ningbo UniversityNingboChina Department of Electrical and Computer Engineering Mississippi State UniversityMississippi StateMississippiUSA School of Electronic Information and Communications Huazhong University of Science and TechnologyWuhanChina

Recently,transformer‐based networks have been introduced for the classification of hyperspectral image(HSI).Although transformer‐based methods can well capture spectral sequence information,their ability to fuse different types of information contained in HSI is still *** exploit rich spectral,spatial and semantic information in HSI,a novel semantic and spatial‐spectral feature fusion transformer(S3FFT)network is proposed in this *** the proposed S3FFT method,spatial attention and efficient channel attention(ECA)modules are employed for the extraction of shallow spatialspectral ***,a transformer‐based module is designed to extract advanced fused features and to produce the pseudo‐label and class probability of each pixel for semantic feature ***,the semantic,spatial and spectral features are combined by the transformer for *** with traditional deep learning methods and recently transformer‐based methods,the proposed S3FFT shows relatively better results on three HSI datasets.

关键词： image classification machine learning

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Investigating Deep Neural Network Architecture and Feature Extraction Designs for Sensor-based Human Activity Recognition 7

Investigating Deep Neural Network Architecture and Feature E...

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7th International Conference on Internet of Things and Applications, IoT 2023

作者： Ahangarani, Danial Shirazi, Mohammad Ashraf, Navid Sharif University of Technology Department of Computer Engineering Iran Allameh Tabataba'i University Department of Statistics Mathematics and Computer Iran

ISBN: (纸本)9798350369410

The extensive ubiquitous availability of sensors in smart devices and the Internet of Things (IoT) has opened up the possibilities for implementing sensor-based activity recognition. As opposed to traditional sensor time-series processing and hand-engineered feature extraction, in light of deep learning's proven effectiveness across various domains, numerous deep methods have been explored to tackle the challenges in activity recognition, outperforming the traditional signal processing and traditional machine learning approaches. In this work, by performing extensive experimental studies on two human activity recognition datasets, we investigate the performance of common deep learning and machine learning approaches as well as different training mechanisms (such as contrastive learning), and various feature representations extracted from the sensor time-series data and measure their effectiveness for the human activity recognition task. © 2023 IEEE.

关键词： Network architecture

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Weighted least squares estimation of the risk-free rate from derivative prices

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Communications in statistics Case Studies Data Analysis and Applications 2023年第1期9卷 72-105页

作者： Blomvall, Jörgen Söderbäck, Pontus Singull, Martin Department of Management and Engineering Production Economics Linköping University Linkoping Sweden Department of Mathematics Mathematical Statistics Linköping University Linkoping Sweden

This study proposes a method for estimating the interest spread over an OIS-implied spot rate used in market-consistent derivative pricing. Our method generalizes previous proposed ordinary least squares methods in the literature in two ways. First, it utilizes intraday data rather than data from a single time. Second, it is formulated as weighted least squares to counteract heteroscedasticity. Additionally, we present a general methodology to quantify the performance difference between methods when the true value is unknown. We find that our method outperforms previously proposed methods with statistical significance and that the primary improvement is the utilization of intraday data. © 2023 The Author(s). Published with license by Taylor & Francis Group, LLC.

关键词： Big data adaption derivative pricing implied interest rate linear regression weighted least squares

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Hybrid Convolutional Neural Network for Plant Diseases Prediction

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Intelligent Automation & Soft Computing 2023年第5期36卷 2393-2409页

作者： S.Poornima N.Sripriya Adel Fahad Alrasheedi S.S.Askar Mohamed Abouhawwash Department of Information Technology Sri Sivasubramaniya Nadar College of EngineeringChennai603110India Department of Statistics and Operations Research College of ScienceKing Saud UniversityRiyadh11451Saudi Arabia Department of Mathematics Faculty of ScienceMansoura UniversityMansoura35516Egypt Department of Computational Mathematics Scienceand Engineering(CMSE)Michigan State UniversityEast LansingMI48824USA

Plant diseases prediction is the essential technique to prevent the yield loss and gain high production of agricultural *** monitoring of plant health continuously and detecting the diseases is a signiﬁcant for sustainable *** system to monitor the diseases in plant is time consuming and report a lot of *** is high demand for technology to detect the plant dis-eases *** image processing approach and deep learning approach are highly invited in detection of plant *** diseases like late blight,bacterial spots,spots on Septoria leaf and yellow leaf curved are widely found in *** are the main reasons to affects the plants life and *** identify the diseases earliest,our research presents the hybrid method by com-bining the region based convolutional neural network(RCNN)and region based fully convolutional networks(RFCN)for classifying the *** the leaf images of plants are collected and preprocessed to remove noisy data in *** data normalization,augmentation and removal of background noises are *** images are divided as testing and training,training images are fed as input to deep learning ***,we identify the region of interest(RoI)by using selective *** every region,feature of convolutional neural network(CNN)is extracted independently for further classiﬁ*** plants such as tomato,potato and bell pepper are taken for this *** plant input image is analyzed and classify as healthy plant or unhealthy *** the image is detected as unhealthy,then type of diseases the plant is affected will be *** proposed technique achieves 98.5%of accuracy in predicting the plant diseases.

关键词： Disease detection people detection image classiﬁcation deep learning region based convolutional neural network

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Shape from Shading under changing Illumination using a CNN 38

Shape from Shading under changing Illumination using a CNN

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38th International Conference on Image and Vision Computing New Zealand, IVCNZ 2023

作者： Rate, Keetley Green, Richard University of Canterbury Department of Mathematics and Statistics New Zealand University of Canterbury Department of Computer Science and Software Engineering New Zealand

ISBN: (纸本)9798350370515

This paper presents an alternative method to the classical yet foundational problem of recovering the geometry of a 3D object from a single 2D brightness image, i.e., shape-from-shading. Drawing inspiration from traditional approaches and recent work in single-image 3D reconstruction, we employ a convolutional neural network trained on synthetic images of characteristic shapes as well as high-frequency surfaces to address the shape-from-shading problem. Although the resulting network is less accurate than non-deep learning (DL) approaches, it exhibits greater resilience to common pitfalls, such as variations in light source direction and the presence of noise in the input image. © 2023 IEEE.

关键词： Convolutional neural networks

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A multiresolution collocation method and its convergence for Burgers' type equations

A multiresolution collocation method and its convergence for...

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作者： Ahsan, Muhammad Tran, Thanh Siraj-ul-Islam Hussain, Iltaf School of Mathematics and Statistics The University of New South Wales Sydney Australia Department of Basic Sciences University of Engineering and Technology Peshawar Peshawar Pakistan Department of Mathematics University of Swabi Swabi Pakistan

In this article, a hybrid numerical method based on Haar wavelets and finite differences is proposed for shock ridden evolutionary nonlinear time-dependent partial differential equations (PDEs). A linear procedure using Taylor expansions is adopted to linearize the nonlinearity. The Euler difference scheme is used to discretize the time derivative part of the PDE. The PDE is converted into full algebraic form, once the space derivatives are replaced by finite Haar series. Convergence analysis is performed both in space and time, and computational stability of the proposed method is also discussed. Different benchmark cases related to 1-D and 2-D Burgers' type equations are considered to verify the theory. The proposed method is also compared numerically with existing methods in the literature. © 2022 John Wiley & Sons, Ltd.

关键词： Nonlinear equations

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