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Exploration of soliton structures and modulation instability analysis for the highly dispersive perturbed NLSE with sextic-power law refractive index

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Optical and Quantum Electronics 2025年第1期57卷 1-18页

作者： Abdullah, Eman H. M. Ahmed, Hamdy M. Zaghrout, Afaf A. S. Bahnasy, Amal Ibrahim Ahmed Rabie, Wafaa B. Hashemi, M.S. Bayram, M. Department of Physics and Engineering Mathematics Higher Institute of Engineering El Shorouk Academy Cairo Egypt Department of Mathematics Faculty of Science Al Azhar University Cairo Egypt Department of Physics and Engineering Mathematics Higher Institute of Engineering and Technology Tanta Egypt Department of Mathematics Basic Science Faculty University of Bonab Bonab Iran Computer Engineering Biruni University Istanbul Turkey

This study introduces some novel soliton solutions and other analytic wave solutions for the highly dispersive perturbed nonlinear Schrödinger equation with generalized nonlocal laws and sextic-power law refractive index. This equation models the behavior of complex wave propagation. The improved modified extended tanh function governs the transmission of various solitons with different effects. New optical solitons are constructed as bright solitons, dark solitons, combo bright-dark solitons and singular solitons. Furthermore, exponential solutions, triangular solutions and rational wave solutions are shown. The findings conferred in this article show a crucial advancement in understanding the propagation of waves in non-linear media. The study also includes modulation instability analysis to examine the stability of the derived wave solutions, supplemented by graphical simulations that depict their distinctive features. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024.

关键词： Solitons

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Novel Symmetric/Asymmetric Generalized Multilevel Inverter Based on T-Type With Reduced Switch Count 16

Novel Symmetric/Asymmetric Generalized Multilevel Inverter B...

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16th Annual Power Electronics, Drive Systems, and Technologies Conference, PEDSTC 2025

作者： Seifi, Ali Nadermohammadi, Ali Hosseini, Seyed-Hossein Hagh, Mehrdad Tarafdar Hosseinpour, Majid University of Tabriz Faculty of Electrical and Computer Engineering Tabriz Iran University of Mohaghegh Ardabili Department of Electrical and Computer Engineering Ardabil Iran

ISBN: (纸本)9798331533946

A sinusoidal voltage with excellent quality and total harmonic distortion (THD) is produced using multi-level inverters (MLI). The idea of MLI was introduced by some traditional designs, but their drawbacks - such as the quantity of DC sources and switching components and their low THD - made the inverter more expensive and larger. With fewer switches, the suggested MLI can get around these restrictions. The suggested structure contains less switching devices, drivers, and total standing voltage (TSV) as compared to MLI architectures. The T-Type inverter has been enhanced in the suggested construction. An enhanced H -bridge and two T-type inverters are combined in the suggested configuration. Two symmetrical diodes make up the T-type inverter that makes up the suggested configuration. To prove the superiority of the suggested structure, we compare it with different structures. Lastly, we use nearest level modulation (NLM) to show the simulation results under different loads. © 2025 IEEE.

关键词： Harmonic distortion

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Photovoltaic power one-day and multistep-hourly AI predictions using node-by-node evolved binomial tree structures to form L-transformed PDE modules

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Soft Computing 2025年第5期29卷 2483-2495页

作者： Zjavka, Ladislav Department of Computer Science Faculty of Electrical Engineering and Computer Science VŠB-Technical University of Ostrava 17. Listopadu 15/2172 Ostrava Czech Republic

Photovoltaic (PV) power is generated by two common types of solar components that are primarily affected by fluctuations and development in cloud structures as a result of uncertain and chaotic processes. Local PV forecasting is unavoidable in supply and load planning necessary in integration of smart systems into electrical grids. Intra- or day-ahead modelling of weather patterns based on Artificial Intelligence (AI) allows one to refine available 24 h. cloudiness forecast or predict PV production at a particular plant location during the day. AI usually gets an adequate prediction quality in shorter-level horizons, using the historical meteo- and PV record series as compared to Numerical Weather Prediction (NWP) systems. NWP models are produced every 6 h to simulate grid motion of local cloudiness, which is additionally delayed and usually scaled in a rough less operational applicability. Differential Neural Network (DNN) is based on a newly developed neurocomputing strategy that allows the representation of complex weather patterns analogous to NWP. DNN parses the n-variable linear Partial Differential Equation (PDE), which describes the ground-level patterns, into sub-PDE modules of a determined order at each node. Their derivatives are substituted by the Laplace transforms and solved using adapted inverse operations of Operation Calculus (OC). DNN fuses OC mathematics with neural computing in evolution 2-input node structures to form sum modules of selected PDEs added step-by-step to the expanded composite model. The AI multi- 1…9-h and one-stage 24-h models were evolved using spatio-temporal data in the preidentified daily learning sequences according to the applied input–output data delay to predict the Clear Sky Index (CSI). The prediction results of both statistical schemes were evaluated to assess the performance of the AI models. Intraday models obtain slightly better prediction accuracy in average errors compared to those applied in the second-day-ahead

关键词： Laplace transforms

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Hybrid Reptile-Snake Optimizer Based Channel Selection for Enhancing Alzheimer’s Disease Detection

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Journal of Bionic engineering 2025年第2期22卷 884-900页

作者： Digambar Puri Pramod Kachare Smith Khare Ibrahim Al-Shourbaji Abdoh Jabbari Abdalla Alameen Department of Electronics and Telecommunication Ramrao Adik Institute of TechnologyD.Y.Patil CampusNavi-MumbaiMaharashtra400706India Maersk Mc-Kinney Moller Institute Faculty of EngineeringUniversity of Southern DenmarkOdenseDenmark Department of Electrical and Electronics Engineering Jazan UniversityJazan45142Saudi Arabia Department of Computer Science University of HertfordshireHatfieldUK Department of Computer Engineering and Information College of Engineering in Wadi AlddawasirPrince Sattam Bin Abdulaziz UniversityWadi Alddawasir11991Saudi Arabia

The global incidence of Alzheimer's Disease(AD)is on a swift *** Electroencephalogram(EEG)signals is an effective tool for the identification of AD and its initial Mild Cognitive Impairment(MCI)stage using machine learning *** of AD using EEG involves multi-channel ***,the use of multiple channels may impact the classification performance due to data redundancy and *** this work,a hybrid EEG channel selection is proposed using a combination of Reptile Search Algorithm and Snake Optimizer(RSO)for AD and MCI detection based on decomposition *** Mode Decomposition(EMD),Low-Complexity Orthogonal Wavelet Filter Banks(LCOWFB),Variational Mode Decomposition,and discrete-wavelet transform decomposition techniques have been employed for subbands-based EEG *** extracted thirty-four features from each subband of EEG ***,a hybrid RSO optimizer is compared with five individual metaheuristic algorithms for effective channel *** effectiveness of this model is assessed by two publicly accessible AD EEG *** accuracy of 99.22% was achieved for binary classification from RSO with EMD using 4(out of 16)EEG ***,the RSO with LCOWFBs obtained 89.68%the average accuracy for three-class classification using 7(out of 19)*** performance reveals that RSO performs better than individual Metaheuristic algorithms with 60%fewer channels and improved accuracy of 4%than existing AD detection techniques.

关键词： Alzheimer's Disease Brain disorder Electroencephalogram Reptile Search Algorithm Snake Optimizer Optimization

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A Review on the Applications of Machine Learning and Deep Learning Algorithms for Image Recognition 4

A Review on the Applications of Machine Learning and Deep Le...

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4th International Conference on Sentiment Analysis and Deep Learning, ICSADL 2025

作者： Wanjari, Ketan Verma, Prateek Department of Computer Science and Engineering Faculty of Engineering and Technology Maharashtra Wardha442001 India Department of Artificial Intelligence and Data Science Faculty of Engineering and Technology Maharashtra Wardha442001 India

ISBN: (纸本)9798331523923

Modern image recognition has experienced dramatic improvements because of Machine Learning and Deep Learning algorithms together. This study investigates CNNs and SVMs for recognition enhancement while reviewing image recognition technologies extensively through literature. This paper demonstrates how the applications of healthcare and security systems and social media analysis influence society. New developments in technology have not resolved multiple unresolved obstacles such as data bias as well as computational complexity and privacy concerns together with real-time processing restrictions. The latest image processing techniques include ViTs alongside GANs and Few-Shot Learning but developers need to achieve better results in future improvements. The main goal of this research examines present-day advancements in ML and DL with a review of their capabilities as well as constraints before recommending future study paths to overcome problems encountered today. This paper evaluates both the future potential and benefits alongside drawbacks of ML and DL models applied to image recognition. © 2025 IEEE.

关键词： Adversarial machine learning

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Ultra-High Step-Up Quadratic DC-DC Converter with Soft Switching, High Efficiency, and Cost Effectiveness for DC Microgrid Applications 16

Ultra-High Step-Up Quadratic DC-DC Converter with Soft Switc...

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16th Annual Power Electronics, Drive Systems, and Technologies Conference, PEDSTC 2025

作者： Nadermohammadi, Ali Hayati, Mohammad Mohsen Oshnoei, Arman Hosseini, Seyed Hossein Babaei, Ebrahim Abapour, Mehdi University of Tabriz Faculty of Electrical and Computer Engineering Tabriz Iran University Faculty of Electrical and Computer Engineering Tabriz Iran Aalborg University Aalborg Department of Energy Denmark

ISBN: (纸本)9798331533946

This study presents a novel ultra-high step-up (UHSU) DC-DC topology tailored for applications in DC microgrids. The proposed configuration utilizes a quadraticbased topology, achieving a remarkably high voltage gain by elevating the turn ratio of the coupled inductor (CI). Notable advantages of this design include its capability to achieve ultrahigh voltage amplification, reduced diode reverse recovery losses, minimized voltage stress on switching components, continuous input current flow, and a shared ground connection linking the input and output. Additionally, the converter is equipped with soft-switching functionality for some of semiconductor elements, which significantly boosts efficiency and minimizes energy dissipation. By maintaining low voltage stress on the switches, the system ensures enhanced operational reliability and overall performance. The operational principles of the converter are analyzed in detail, and its effectiveness is highlighted through a comparison with other existing topologies. Finally, the proposed design's performance is confirmed through the fabrication and experimental validation of a 400-W prototype, performing at a switching frequency of 50 k H z, with an input voltage of 30 V and an output voltage of 360 v. © 2025 IEEE.

关键词： coupled inductor DC-DC converter quadratic converter softswitching ultra-high step-up

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A Review on the Applications of Machine Learning and Deep Learning Techniques for Skin Cancer Detection 4

A Review on the Applications of Machine Learning and Deep Le...

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4th International Conference on Sentiment Analysis and Deep Learning, ICSADL 2025

作者： Wanjari, Ketan Verma, Prateek Faculty of Engineering and Technology Department of Computer Science and Engineering Maharashtra Wardha442001 India Faculty of Engineering and Technology Department of Artificial Intelligence and Data Science Maharashtra Wardha442001 India

ISBN: (纸本)9798331523923

Skin cancer is the most commonly reported type of cancer globally and one of the few cancers that can be effectively treated if detected in its early stages. Recent advancements in artificial intelligence (AI) have significantly improved skin cancer diagnosis through Machine Learning (ML) and Deep Learning (DL) models. This review explores ML and DL methodologies, including Random Forest, Convolutional Neural Networks (CNNs), Inception Networks, ResNets, and Support Vector Machines (SVMs), which have been widely used for automated diagnosis. However, existing AI-based systems face several challenges, including the need for balanced datasets, model interpretability issues, computational complexity, and difficulties in generalizing across diverse populations. Moreover, multimodal detection systems and Explainable AI (XAI) present additional challenges in making AI-driven diagnoses more reliable and transparent. This article discusses these challenges while also highlighting emerging techniques that integrate multiple data sources and individualized diagnostic tools to enhance precision. By addressing these challenges, this review aims to provide insights into the practical advancements and future directions of AI in skin cancer detection and treatment. © 2025 IEEE.

关键词： Adversarial machine learning

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Optimizing breast cancer diagnosis: Harnessing the power of nature-inspired metaheuristics for feature selection with soft voting classifiers

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International Journal of Cognitive Computing in engineering 2025年第1期6卷 1-20页

作者： Benghazouani, Salsabila Nouh, Said Zakrani, Abdelali Department of Mathematics and Computer Science Faculty of Sciences Ben M'Sick Hassan II University Casablanca Morocco Department of Computer Science Engineering ENSAM Hassan II University Casablanca Morocco

Breast cancer is a widespread and serious condition that poses a significant threat to women's health globally, contributing significantly to mortality rates. Machine learning tools play a critical role in both the effective management and early detection of this disease. Feature selection (FS) methods are essential for identifying the most impactful features to improve breast cancer diagnosis. These methods reduce data dimensionality, eliminate irrelevant information, enhance learning accuracy, and improve the comprehensibility of results. However, the increasing complexity and dimensionality of cancer data pose substantial challenges to many existing FS methods, thereby reducing their efficiency and effectiveness. To overcome these challenges, numerous studies have demonstrated the success of nature-inspired optimization (NIO) algorithms across various domains. These algorithms excel in mimicking natural processes and efficiently solving complex optimization problems. Building on these advancements, we propose an innovative approach that combines powerful feature selection methods based on NIO techniques with a soft voting classifier. The NIO techniques employed include the Genetic Algorithm, Cuckoo Search, Salp Swarm, Jaya, Flower Pollination, Whale Optimization, Sine Cosine, Harris Hawks, and Grey Wolf Optimization algorithms. The Soft Voting Classifier integrates various machine learning models, including Support Vector Machines, Gaussian Naive Bayes, Logistic Regression, Decision Tree, and Gradient Boosting. These are used to improve the effectiveness and accuracy of breast cancer diagnosis. The proposed approach has been empirically evaluated using a variety of evaluation measures, such as F1 score, precision, recall, accuracy and Area Under the Curve (AUC), for performance comparison with individual machine learning techniques. The results demonstrate that the soft-voting ensemble technique, particularly when combined with feature selection based on the Jaya

关键词： Support vector machines

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The Analysis of Device-to-Device (D2D) Communication System Operating in 5G Underlaying Cellular Networks on Multiple Bands over Nakagami-m Fading Channels

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SN computer Science 2025年第5期6卷 1-15页

作者： Shatnawi, Razan A. Khodeir, Mahmoud A. Al-Mistarihi, Mamoun F. Darabkh, Khalid A. Al-Zuraiqi, Eyad T. Qasaymeh, Mahmoud M. Electrical Engineering Department Faculty of Engineering Jordan University of Science and Technology P.O. Box 3030 Irbid 22110 Jordan Computer Engineering Department School of Engineering The University of Jordan Amman 11942 Jordan Department of Telecommunications Engineering Hijjawi Faculty for Engineering Technology Yarmouk University P.O. Box 566 Irbid 21163 Jordan Computer Engineering and Communication Department Tafila Technical University Tafila 66110 Jordan

In contrast to traditional cellular connection, device-to-device (D2D) communication is a direct connection amidst adjacent mobile users that does not pass through the base station (BS) and does not rely on network infrastructure to meet low power requirements and high data rate. Cellular networks and D2D connections were combined in underlay mode to address power usage concerns. In underlay D2D communication, licensed frequency bands are utilized by both D2D and cellular connections;due to resource sharing, these bands may overlap, which lowers service quality but boosts spectral efficiency. Most existing research exclusively discuss energy efficiency (EE) in the context of single cells. This article has taken into account the entire cellular network with D2D connections on various bands, and stochastic geometric theory has been used in order to obtain closed form expressions for the probability of successful transmission (STP), the average sum rate (ASR), and EE for D2D connection underlaid cellular networks over Nakagami-m fading channels. Results of simulations are used to validate analytical conclusions. Simulation results demonstrate that higher STP, higher ASR, and lower EE all result in enhanced system performance. In addition, as the shape parameter’s value m (Nakagami-m order) increases, the EE, ASR, and STP of D2D users improve, which enhances system performance. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2025.

关键词： Device-to-device communication Energy efficiency Nakagami-m fading channel Stochastic geometry

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Early Parkinson's Disease Detection and Classification using Machine Learning and Neutrosophic Sets

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Neutrosophic Sets and Systems 2025年 82卷 873-886页

作者： Oudah, Hasan H. Metwaly, Ahmed A. Eassa, Mohamed Abdelhafeez, Ahmed Nagm, Ahmad M. Salama, Ahmed S. College of Arts Department of Journalism Ahl Al Bayt University Karbala Iraq Department of Computer Science Faculty of Computers and Informatics Zagazig University Zagazig44519 Egypt Computer Science Department Faculty of Information System and Computer Science October 6 University Giza12585 Egypt Applied Science Research Center Applied Science Private University Amman Jordan Department of Computer Engineering and Electronics Cairo Higher Institute for Engineering Computer Science and Management New Cairo Egypt

Optimizing therapy and rehabilitation for Parkinson's disease (PD) requires early identification and precise evaluation of the illness's course. However, there is disagreement about the best way to use gait analysis to categorize the severity of motor symptoms and identify early-stage Parkinson's disease. The precision of machine learning (ML) models in identifying early and intermediate stages of Parkinson's disease was assessed in this study. Six ML models are used in this study for the prediction of PD. Different metrics are used to evaluate ML models such as accuracy, precision, recall, f1-score, and AUC score. Then we propose a multi-criteria decision-making methodology (MCDM) to evaluate the ML models and select the best one. Two MCDM methods are used such as CRITIC method to compute the criteria weights and the TOPSIS method to rank the alternatives. These methods are used under the bipolar neutrosophic sets (BNSs) to deal with uncertainty information. The results show the support vector machine is the best ML model for the prediction of PD. © 2025, University of New Mexico. All rights reserved.

关键词： Neurodegenerative diseases

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