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23 articles for “Mean Squared Error) MSE)”
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Managing the Resources of LTE Networks using Multi-orthogonal Access based on Deep Learning
Abstract: AbstractOne of the topics discussed in telecommunications systems is joint subcarrier and power allocation in the uplink of an NOMA system that we study. Due to this reason a novel radio resource management framework is presented based on code-domain and a deep learning algorithm for uplink and downlink transmissions, such that the neural network is trained by Bayesian regularization back propagation and the mean squared error )MSE) are the training …
Published in Journal of Telecommunication, Switching Systems and Networks · Vol. 7, Issue 2, 2020 · pp. 19–26 Read article
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Machine Learning Approach to Predict the Performability and Emissions of Diesel Engine Fueled with Doped Biodiesel Blend
Abstract: Enhancing the performability and emission characteristics of diesel engines has been a difficult task in light of growing concerns about global warming and other negative effects, as diesel accounts for 70% of global energy demand. In this study, engine performance and exhaust emissions for various fuel blends were thoroughly evaluated using machine learning techniques to predict engine emission and performance behavior. We focused on biodiesel blend and nanoparticle additive concentration …
Published in International Journal of Mechanical Dynamics and Systems Analysis · Vol. 3, Issue 1, 2025 · pp. 1–12 Read article
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Study of an Improved Quantum Particle Swarm Optimization-Based Framework for Neural Network Optimization in Modelling of Polymer Data
Abstract: The accurate forecasting of polymer viscosity at various physicochemical conditions has been quite critical due to the nonlinear interactions and interrelations between the variables. This paper suggests a better hybrid modelling framework, which involves the use of Artificial Neural Networks (ANN) and more advanced versions of Quantum Particle Swarm Optimization (QPSO) to better predict polymer viscosity. The input parameters taken are, namely, log (shear rate), polymer concentration, NaCl concentration, Ca …
Published in Journal of Polymer & Composites · Vol. 14, Issue 4, 2026 · pp. 282–297 Read article
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A Suboptimal Nonlinear Duty-cycle Modulation Scheme
Abstract: The DCM (duty-cycle modulation) technique is increasingly used in industrial electronics applications, including instrumentation systems, interfacing drivers and signal transmission chains. However, in existing research works related to new applications of DCM technique, the linear approximation policy is used for the sake of structural simplicity and low implementation cost, at the expends of rigorous analysis and low approximation errors. In this paper, a suboptimal nonlinear DCM scheme is developed. It …
Published in Journal of Electronic Design Technology · Vol. 7, Issue 1, 2016 · pp. 22–31 Read article
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Estimation of Semi-Blind Channel for Two-Way Relay by Using CRB: Pilot-based and Random Pilot-based
Abstract: The main purpose of semi-blind channel estimation scheme is for frequency-selective channel estimation, for multi-input and multi-output (MIMO) on relay networks, for using high bandwidth efficient use of spectrum. This scheme motivate to the low complexity of system to allow exchange information users to users by using an intermediate relay node. Because of superposition of signals at the relay node, section receiving signal at the user terminals is affected by …
Published in Current Trends in Signal Processing · Vol. 7, Issue 2, 2017 · pp. 22–31 Read article
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Nonlinear Channel Equaliser using Discrete Gabor Transform
Abstract: ABSTRACTThe adaptive equaliser makes use of adaptive digital filters whose filter coefficients are modified depending on the channel characteristics at the front end of the receiver. The noise introduced in the channel gets nullified and hence the signal-to-noise ratio of the receiver improves. Discrete Gabor Transform (DGT) helps decorrelate input data because of which the convergence speed of the Mean Square Error (MSE) improves considerably. It is found that the …
Published in Journal of Computer Technology & Applications · Vol. 1, Issue 1-3, 2011 · pp. 63–74 Read article
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Deep Learning Enhanced Compressive Sensing for Wireless IoT Data Optimization and Weather Monitoring.
Abstract: This research explores the application of deep learning and compressive sensing in order to optimize data traffic in non-orthogonal multiple access (NOMA)-based wireless internet of things (IoT) networks and weather monitoring. Such a framework would be very effective and overcome pilot attacks and reconstruction losses for secure data transmission. In this regard, a strong communication model has been adopted based on power-domain NOMA for simultaneous wireless transmission by multiple IoT …
Published in International Journal of Satellite Remote Sensing · Vol. 2, Issue 2, 2024 · pp. 20–36 Read article
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An Improved Power Line Interference Reduction Approach Based on Combination of Mirror Extension and IIR filtering Through a Data Driven Mechanism
Abstract: Electrocardiogram (ECG) is a clinical sign monitoring measurement of the cardiac abnormalities. Like other biomedical signals, the ECG signal is also contaminated by various kinds of noise and artifacts such as power line interference, base line wandering, muscle artifacts and electrode artifacts. The present paper proposes a scheme for power line interference (PLI) reduction from ECG signal. It makes use of the concept of mirror method, empirical mode decomposition (EMD) …
Published in Current Trends in Signal Processing · Vol. 7, Issue 2, 2017 · pp. 13–21 Read article
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Zoom Based Image Super-Resolution: Using Two Level DWT as Feature Model
Abstract: AbstractIn this paper we present an algorithm of super-resolution (SR) imaging to reconstruct high-resolution (HR) image from sequence of low-resolution (LR) images of static scene captured at the different camera zoom factor. The resultant HR image is constructed at the resolution of the most zoomed LR image. In the proposed approach algorithm uses LR images of the static scene captured at three distinct zoom-factors. Learning based SR technique is used …
Published in Journal of Communication Engineering & Systems · Vol. 9, Issue 2, 2019 · pp. 95–105 Read article
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Performance of Some Modified Ordinary Ridge Regression Estimators
Abstract: In multiple linear regressions, if the data suffer from severe multicollinearity, then the ordinary least squares (OLS) method become more sensitive to it, and in such a case OLS could yield wrong sign for some of the regression coefficients. Therefore, when such a situation arises, we could use one of the biased regression methods viz., ridge regression, principal component regression, and so on, as an alternative method to OLS. This …
Published in Research & Reviews : Journal of Statistics Read article
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Dynamic Performance Enhancement of Polymer Composites through Metaheuristic machinining optimization
Abstract: This work aims to provide an optimization of meta-heuristic algorithms in order to improve the dynamic behavior of composite materials utilized in various practical engineering tasks. Based on the Comprehensive literature review it has been observed that composite sandwich panels with PVC foam cores accomplished mechanical characteristics superior than those ones that were produced on PU foam core mainly in flexural, compression, and impact tests Thus the study establishes the …
Published in Journal of Polymer & Composites · Vol. 12, Issue 2, 2024 · pp. 114–129 Read article
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Enzyme Stability Prediction using BERT and CNN-A Deep Learning Approach for Enhanced Biocatalysis
Abstract: An important factor in determining the efficacy of industrial enzymes used in various biotechnological applications is their stability. The goal of this study is to develop a predictive model for industrial enzyme stability, which is essential to the efficiency of these enzymes in biotechnological applications. The research takes a comprehensive strategy to comprehend the parameters affecting enzyme stability by combining statistical analysis, deep learning algorithms (BERT and CNN), and molecular …
Published in Research and Reviews : A Journal of Life Sciences · Vol. 14, Issue 2, 2024 · pp. 19–35 Read article
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Harnessing Machine Learning for Stock Movement Prediction: A Review of Current Approaches
Abstract: Stock price prediction is a crucial task in financial analysis, aiding investors and traders in making informed decisions. This study investigates the use of deep learning methods, particularly Long Short-Term Memory (LSTM) networks, for predicting stock prices based on historical market data. The dataset, sourced from Yahoo Finance, consists of time-series stock price data, which is preprocessed, feature-engineered, and visualized to improve prediction accuracy. The model's performance is assessed using …
Published in Journal of Advances in Shell Programming · Vol. 12, Issue 2, 2025 · pp. 29–40 Read article
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Image Compression via Non-separable Discrete Fractional Fourier Transform
Abstract: The main idea behind image compression is to reduce the bandwidth for transmission and required space for storage. Thus, image compression is of great importance. In this paper we present the image compression technique using Non-Separable Discrete Fractional Fourier Transform (NSDFrFT). Numerical simulation results suggested that image compression method using NSDFrFT as transform technique gives better performance for images when compression ratio is high in comparison to DFrFT and JPEG. …
Published in Research & Reviews: Discrete Mathematical Structures · Vol. 2, Issue 3, 2015 · pp. 1–9 Read article
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A Comprehensive survey of robust image quality metrics for satellite imagery
Abstract: Satellite imagery is essential for applications like environmental monitoring, urban development, precision agriculture, defence surveillance, and disaster response. The reliability of these applications is closely tied to the quality of the captured images, which may be compromised by atmospheric effects, sensor imperfections, compression artifacts, and transmission noise. As a result, accurate image quality assessment (IQA) is essential to ensure trustworthy analysis and informed decision-making in satellite-based systems. The distinctive properties …
Published in Research & Reviews : Journal of Space Science & Technology · Vol. 15, Issue 1, 2026 · pp. 7–20 Read article
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House Price Estimation Using Linear Regression: A Machine Learning Perspective
Abstract: House price prediction plays a crucial role in the real estate industry, helping buyers, sellers, and investors make well-informed decisions. Accurate estimation of property values enables stakeholders to assess market trends, plan investments, and minimize financial risks. This study focuses on the application of linear regression, a fundamental and widely used machine learning algorithm, to predict house prices based on multiple influencing factors. These factors include location, property size, number …
Published in Research & Reviews : Journal of Statistics · Vol. 15, Issue 1, 2026 · pp. 21–29 Read article
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Machine Learning-Based Channel Estimation in 5G, Beyond-5G, and 6G Networks: Recent Advances and Future Directions
Abstract: Accurate channel estimation is one of the most fundamental challenges in modern wireless communication systems. In fifth- generation (5G) New Radio (NR) and emerging sixth-generation (6G) networks, precise knowledge of the wireless channel is essential for achieving reliable data transmission, high spectral efficiency, and low Bit Error Rate (BER). Conventional estimation techniques such as Least Squares (LS) and Minimum Mean Square Error (MMSE) rely on mathematical channel models and predefined …
Published in Journal of Telecommunication, Switching Systems and Networks · Vol. 13, Issue 2, 2026 Read article
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Efficient Partial Update Algorithm over Wireless Networks
Abstract: Adaptive partial update algorithm is developed based on incremental method. The proposed algorithm applies in real time changing environment. The proposed algorithm responds to linear estimation with nodes in cooperative manner and less number of computations. The algorithm has powerful advantage is that it requires less number of coefficient and reduced computational and communication complexity in wireless sensor network. It is efficient because it has power of solving distributed estimation …
Published in Current Trends in Signal Processing · Vol. 6, Issue 3, 2016 · pp. 1–8 Read article
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Optimization of Symmetric Linear Phase Low Pass FIR Filter Using Genetic Algorithm
Abstract: In the proposed paper, optimization of symmetric linear phase low pass Finite Impulse Response (FIR) filter using Genetic Algorithm GA is a computational optimization technique; which optimizes the Mean Square Error (MSE) and filter coefficients with the help of fitness function. The optimize results are compared with two different filter designing analysis (FDA) methods viz. least square method and equiripple method
Published in Current Trends in Signal Processing · Vol. 5, Issue 2, 2015 · pp. 9–14 Read article
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Nonlinear Semiconductor Device Modeling using Neural Networks
Abstract: This paper describes the nonlinear semiconductor (transistor) of small and large signal modeling using a single neural network. Multilayer perceptron (MLP) with back-propagation (BP) learning is adopted in this work to model the drain current (ID) and the transconductance (gm) of the transistor. MLP modeling performance in terms of mean square error (MSE) and complexity of the network are illustrated briefly. Artificial neural network (ANN) model outcome for nonlinear function …
Published in Journal of VLSI Design Tools and Technology · Vol. 4, Issue 3, 2014 · pp. 1–6 Read article