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LEO: Lagrange elementary optimization

Neural Computing and Applications 2025年 1-33页

作者： Aladdin, Aso M. Rashid, Tarik A. Computer Science Department College of Science Charmo University KR Chamchamal/Sulaimani46023 Iraq Computer Science and Engineering Department AIIC University of Kurdistan Hewler KR Erbil44001 Iraq

Global optimization problems are often addressed using the practical and efficient approach of evolutionary sophistication, which refers to advanced processes inspired by various systems, particularly those rooted in biological systems. However, these problems, like the original evolutionary systems that inspired them, become increasingly complex, particularly in terms of efficiency, scalability, and achieving a balance between exploitation and exploration. To address these challenges, this research introduces the Lagrange elementary optimization (LEO) algorithm, an evolutionary method that is self-adaptive and inspired by the exceptional accuracy of vaccinations, modeled using the albumin quotient of human blood. They develop intelligent agents using their fitness function value after gene crossing. These genes direct the search agents during both exploration and exploitation. The main objective of the LEO algorithm is presented in this paper along with the inspiration and motivation for the concept. To demonstrate its precision, the proposed algorithm is validated against a variety of test functions, including 19 traditional benchmark functions and the CEC-C06 2019 test functions. The results of LEO for 19 classic benchmark test functions are evaluated against DA, PSO, and GA separately, and then two other recent algorithms such as FDO and LPB are also included in the evaluation. In addition, the LEO is tested by ten functions on CEC-C06 2019 with DA, WOA, SSA, FDO, LPB, and FOX algorithms distinctly. The cumulative outcomes demonstrate LEO’s capacity to increase the starting population and move toward the global optimum. Different standard measurements are used to verify and prove the stability of LEO in both the exploration and exploitation phases. Moreover, Statistical analysis supports the findings results of the proposed research. Finally, novel applications in the real world are introduced to demonstrate the practicality of LEO. © The Author(s), under exclus

关键词： Lagrange multipliers

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Design of real time intelligent system for women safety

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Recent Patents on engineering 2024年第3期18卷 77-83页

作者： Kohli, Priyanka Singh, Kawaljeet Sidhu, Brahmaleen Kaur Department of Computer Science & Engineering Punjabi University Patiala India University Computer Centre Punjabi University Patiala India

Background: Even the technology has advanced a lot in these modern times, women's safety is still a massive issue. Women are not safe anywhere. Their safety is an essential and decisive issue in today’s world. They face extreme troubles when they are alone, travelling on lonely roads and at workplace. Education is essential for everyone and especially for women. It is a powerful weapon to end poverty. Government and NGOs have made many rules and regulations but the crime against women has not ended. There are many instances of harassment, abuse, eve-teasing, kidnapping, domestic violence and rape. Women are working equally as men but still their safety is a big challenge. With the boom in the IT and corporate sector, many women are now working and even during nightshifts. Objectives: A Real Time Intelligent System for Women Safety has been proposed to overcome these limitations. Methods: In this paper, many women's safety systems that were proposed or developed have been discussed. Major drawbacks in the current system, devices or applications have been highlighted. Latest technologies like machine learning, artificial intelligence or data science have been ignored. Results: The proposed system will be highly sophisticated with the latest techniques and women will feel safe in all situations. It will predict the safe route while moving alone. Conclusion: Moreover, it will record the evidence and will also suggest the rights of women. © 2024 Bentham science Publishers.

关键词： Intelligent systems

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DEDDR: early detection of diabetic retinopathy from widefield and fundus images using pyspark framework

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Multimedia Tools and Applications 2025年 1-30页

作者： Kotiyal, Bina Pathak, Heman Department of Computer Science and Engineering Graphic Era Hill University Uttarakhand Dehradun248001 India Department of Computer Science Kanya Gurukul Kangri Uttarakhand Dehradun248001 India

This research introduces a Deep Learning Approach for early Detection of Diabetic Retinopathy (DEDDR), leveraging the Pyspark computational framework. DEDDR addresses challenges in manual evaluation, achieving an exceptional accuracy rate of 99.9% in grading diabetic retinopathy. It incorporates advanced techniques, including image preprocessing and nerve segmentation. Strategic class weight balancing ensures equitable model training in the presence of class imbalance. The Retinoactive activation function, a unique creation, facilitates learning from negative values. Rigorous assessment metrics validate DEDDR’s robust capabilities. Notably, a customized loss function further refines model performance. The proposed approach carries profound implications for healthcare, accelerating early diabetic retinopathy detection and averting irreversible vision loss. Ongoing and future work encompasses model optimization, interpretability techniques, and seamless integration into clinical practice for real-world validation and impact assessment. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2025.

关键词： Deep learning

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Optimal Design of a Planning and Contracting Framework to Enable Vehicle to Building and Grid Services via Demand Response

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IEEE Transactions on Transportation Electrification 2025年第3期11卷 7542-7556页

作者： Elsayed, Ahmed Abd Elaziz Saxena, Shivam Farag, Hany E. Z. Electrical Engineering and Computer Science Department Lassonde School of Engineering York University Keele Campus Toronto ON Canada Department of Electrical and Computer Engineering University of New Brunswick Fredericton NB Canada

Bidirectional electric vehicle (EV) charging enables stored energy to reduce peak loads for buildings (V2B) and the grid (V2G). However, building owners investing in V2B infrastructure while generating revenue from V2G services face challenges in planning and coordinating with EV owners due to uncertainties in their schedules and profit-sharing expectations. Additionally, misaligned building and grid peak times can create conflicts between V2B and V2G goals, which may negatively impact the building electricity bill. Unlike previous studies that used a contract-free approach for aggregating V2B and V2G, resulting in inconsistent participation, this paper proposes a novel planning and contracting framework that enables building owner to determine the optimal contract parameters with EV owners. These parameters include minimum participation time in DR events, minimum arrival State of Charge (SoC), and permitted emergency departure hours. The framework supports V2B aggregation with on-site distributed energy resources for DR and V2G services, ensuring transparency and fairness through shared profits and performance-based penalties, while compensating building electricity bills due to V2G activities. The framework is composed of a tri-stage optimization process that uses Monte Carlo Simulations to generate EV owner profit assessments, select optimal EV candidates based on charger availability, estimate contract parameters with profit/penalty sharing, and assign contracts between power system operators, building owners, and EV owners under multiple virtual scenarios. Simulation results validate the contract parameter assessment and its performance via a three day case study with real-world data sets that demonstrates net revenue generation of $209 for each EV owner and $950 for building owners during DR events with a Return on Investment (ROI) of 134.5% 130.7% for the EVs and Building owners, respectively. © 2015 IEEE.

关键词： Vehicle-to-grid

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Feature Extraction From Single-Channel EEG Using Tsfresh and Stacked Ensemble Approach for Sleep Stage Classification

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International Journal of e-Collaboration 2023年第3期19卷 1-20页

作者： Radhakrishnan, B.L. Ezra, Kirubakaran Jebadurai, Immanuel Johnraja Department of Computer Science and Engineering Karunya Institute of Technology and Sciences Coimbatore India Department of Computer Science and Engineering GRACE College of Engineering Thoothukudi India

The smart world under Industry 4.0 is witnessing a notable spurt in sleep disorders and sleep-related issues in patients. Artificial intelligence and IoT are taking a giant leap in connecting sleep patients remotely with healthcare providers. The contemporary single-channel-based monitoring devices play a tremendous role in predicting sleep quality and related issues. Handcrafted feature extraction is a time-consuming job in machine learning-based automatic sleep classification. The proposed single-channel work uses Tsfresh to extract features from both the EEG channels (Pz-oz and Fpz-Cz) of the SEDFEx database individually to realise a single-channel EEG. The adopted mRMR feature selection approach selected 55 features from the extracted 787 features. A stacking ensemble classifier achieved 95%, 94%, 91%, and 88% accuracy using stratified 5-fold validation in 2, 3, 4, and 5 class classification employing healthy subjects data. The outcome of the experiments indicates that Tsfresh is an excellent tool to extract standard features from EEG signals. © 2023 IGI Global. All rights reserved.

关键词： Extraction

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Deep reinforcement learning for online scheduling of photovoltaic systems with battery energy storage systems

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Intelligent and Converged Networks 2024年第1期5卷 28-41页

作者： Yaze Li Jingxian Wu Yanjun Pan the Department of Electrical Engineering University of ArkansasFayettevilleAR 72701USA the Department of Computer Science and Computer Engineering University of ArkansasFayettevilleAR 72701USA

A new online scheduling algorithm is proposed for photovoltaic(PV)systems with battery-assisted energy storage systems(BESS).The stochastic nature of renewable energy sources necessitates the employment of BESS to balance energy supplies and demands under uncertain weather *** proposed online scheduling algorithm aims at minimizing the overall energy cost by performing actions such as load shifting and peak shaving through carefully scheduled BESS charging/discharging *** scheduling algorithm is developed by using deep deterministic policy gradient(DDPG),a deep reinforcement learning(DRL)algorithm that can deal with continuous state and action *** of the main contributions of this work is a new DDPG reward function,which is designed based on the unique behaviors of energy *** new reward function can guide the scheduler to learn the appropriate behaviors of load shifting and peak shaving through a balanced process of exploration and *** new scheduling algorithm is tested through case studies using real world data,and the results indicate that it outperforms existing algorithms such as Deep *** online algorithm can efficiently learn the behaviors of optimum non-casual off-line algorithms.

关键词： photovoltaic(PV) battery energy storage system(BESS) Markov decision process(MDP) deep deterministic policy gradient(DDPG)

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Improving Prediction Efficiency of Machine Learning Models for Cardiovascular Disease in IoST-Based Systems through Hyperparameter Optimization

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computers, Materials & Continua 2024年第9期80卷 3485-3506页

作者： Tajim Md.Niamat Ullah Akhund Waleed M.Al-Nuwaiser Department of Computer Science and Engineering(CSE) Daffodil International UniversityDhaka1216Bangladesh Graduate School of Science and Engineering Saga UniversitySaga8408502Japan Computer Science Department College of Computer and Information SciencesImam Mohammad Ibn Saud Islamic University(IMSIU)Riyadh11623Saudi Arabia

This study explores the impact of hyperparameter optimization on machine learning models for predicting cardiovascular disease using data from an IoST(Internet of Sensing Things)*** distinct machine learning approaches were implemented and systematically evaluated before and after hyperparameter *** improvements were observed across various models,with SVM and Neural Networks consistently showing enhanced performance metrics such as F1-Score,recall,and *** study underscores the critical role of tailored hyperparameter tuning in optimizing these models,revealing diverse outcomes among *** Trees and Random Forests exhibited stable performance throughout the *** enhancing accuracy,hyperparameter optimization also led to increased execution *** representations and comprehensive results support the findings,confirming the hypothesis that optimizing parameters can effectively enhance predictive capabilities in cardiovascular *** research contributes to advancing the understanding and application of machine learning in healthcare,particularly in improving predictive accuracy for cardiovascular disease management and intervention strategies.

关键词： Internet of sensing things(IoST) machine learning hyperparameter optimization cardiovascular disease prediction execution time analysis performance analysis wilcoxon signed-rank test

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A Deep Learning based Ultra-Lightweight Architecture for Atrial Fibrillation Detection Using Single-Lead ECG Recordings

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IEEE Access 2025年 13卷 86474-86486页

作者： Khan, Abbas Mughal, Muhammad Rizwan Irtaza, Syed Ali Khan, Mohsin Tahir, Madiha Ali, Ahtisham Saeed, Zoraize Ghulam Ishaq Khan Institute of Engineering Science and Technology Pakistan Sultan Qaboos University Electrical and Computer Engineering Department Muscat Oman Institute of Space Technology Electrical Engineering and Computer Science Department Islamabad Pakistan FAST National University of Computer and Emerging Sciences Islamabad Pakistan

In this paper, we have proposed a novel deep-learning model to process electrocardiogram (ECG) signals from single-lead ECG device. This is achieved by using a hybrid of CNN (convolutional neural network) and LSTM (long short-term memory) neural netwrok, where CNN extracts localized features from ECG signals, while the LSTM network extract temporal relationships. Moreover, a filtering-based method is proposed to generate synthetic data to overcome the data imbalance posed by the publicly available PhysioNet/CinC Challenge 2017 dataset. Specifically, three distinct ECG filters: Neurokit, BioSPPy, and EngZeeMod are used to perform data augmentation. As depicted by the simulation results, the proposed model exhibits superior performance with an F1 score of 99.56% providing a gain of 102.25 dB in terms of model size and performance gain of 1.06% outperforming existing state-of-the-art method enabling cost-effective real-time diagnosis with an inference time of 0.48×10-3 secs. Atrial fibrillation (A-Fib), a common cardiac arrhythmia, is linked to serious complications like heart failure and stroke, which early diagnosis could prevent. However, its paroxysmal and often asymptomatic nature complicates detection. In this paper, we propose a novel deep-learning model for single-lead ECG signal analysis, combining a convolutional neural network (CNN) to extract localized features and a long short-term memory (LSTM) network to capture temporal dependencies. To address data imbalance in the PhysioNet/CinC Challenge 2017 dataset, we employ a filtering-based approach with Neurokit, BioSPPy, and EngZeeMod for synthetic data generation. Simulation results show the model achieves a 99.56% F1 score, a 102.25 dB gain in efficiency, a 1.06% performance gain over the state-of-the-art method, and real-time diagnosis with a 0.48×10-3 second inference time. These simulation results confirm the superior performance of the proposed model. © 2013 IEEE.

关键词： Electrocardiography

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IRS-enabled NOMA communication systems:A network architecture primer with future trends and challenges

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Digital Communications and Networks 2024年第5期10卷 1503-1528页

作者： Haleema Sadia Ahmad Kamal Hassan Ziaul Haq Abbas Ghulam Abbas Muhammad Waqas Zhu Han Faculty of Electrical Engineering and Telecommunications and Networking(TeleCoN)Research Center Ghulam Ishaq Khan Institute of Engineering and Technology(GIKI)Topi 23640Pakistan Faculty of Computer Science and Engineering GIKITopi 23640Pakistan Department of Computer Engineering College of Information TechnologyUniversity of Bahrain32028Bahrain School of Engineering Edith Cowan UniversityPerth WA6027Australia Department of Electrical and Computer Engineering University of HoustonHoustonTX 77004USA Department of Computer Science and Engineering Kyung Hee UniversitySeoul 446-701South Korea

Non-Orthogonal Multiple Access(NOMA)has already proven to be an effective multiple access scheme for5th Generation(5G)wireless *** provides improved performance in terms of system throughput,spectral efficiency,fairness,and energy efficiency(EE).However,in conventional NOMA networks,performance degradation still exists because of the stochastic behavior of wireless *** combat this challenge,the concept of Intelligent Reflecting Surface(IRS)has risen to prominence as a low-cost intelligent solution for Beyond 5G(B5G)*** this paper,a modeling primer based on the integration of these two cutting-edge technologies,i.e.,IRS and NOMA,for B5G wireless networks is *** in-depth comparative analysis of IRS-assisted Power Domain(PD)-NOMA networks is provided through 3-fold ***,a primer is presented on the system architecture of IRS-enabled multiple-configuration PD-NOMA systems,and parallels are drawn with conventional network configurations,i.e.,conventional NOMA,Orthogonal Multiple Access(OMA),and IRS-assisted OMA *** by this,a comparative analysis of these network configurations is showcased in terms of significant performance metrics,namely,individual users'achievable rate,sum rate,ergodic rate,EE,and outage ***,for multi-antenna IRS-enabled NOMA networks,we exploit the active Beamforming(BF)technique by employing a greedy algorithm using a state-of-the-art branch-reduceand-bound(BRB)*** optimality of the BRB algorithm is presented by comparing it with benchmark BF techniques,i.e.,minimum-mean-square-error,zero-forcing-BF,and ***,we present an outlook on future envisioned NOMA networks,aided by IRSs,i.e.,with a variety of potential applications for 6G wireless *** work presents a generic performance assessment toolkit for wireless networks,focusing on IRS-assisted NOMA *** comparative analysis provides a solid foundation for the dev

关键词： Intelligent reflecting surface Non-orthogonal multiple access 6G Beamforming Sum rate Energy efficiency

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A Deep Learning Approach to Industrial Corrosion Detection

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computers, Materials & Continua 2024年第11期81卷 2587-2605页

作者： Mehwash Farooqui Atta Rahman Latifa Alsuliman Zainab Alsaif Fatimah Albaik Cadi Alshammari Razan Sharaf Sunday Olatunji Sara Waslallah Althubaiti Hina Gull Department of Computer Engineering(CE) College of Computer Science and Information Technology(CCSIT)Imam Abdulrahman Bin Faisal UniversityDammam31441Saudi Arabia Department of Computer Science(CS) College of Computer Science and Information Technology(CCSIT)Imam Abdulrahman Bin Faisal UniversityDammam31441Saudi Arabia Department of Computer Information Systems(CIS) College of Computer Science and Information Technology(CCSIT)Imam Abdulrahman Bin Faisal UniversityDammam31441Saudi Arabia

The proposed study focuses on the critical issue of corrosion,which leads to significant economic losses and safety risks worldwide.A key area of emphasis is the accuracy of corrosion detection *** recent studies have made progress,a common challenge is the low accuracy of existing detection *** models often struggle to reliably identify corrosion tendencies,which are crucial for minimizing industrial risks and optimizing resource *** proposed study introduces an innovative approach that significantly improves the accuracy of corrosion detection using a convolutional neural network(CNN),as well as two pretrained models,namely YOLOv8 and *** leveraging advanced technologies and methodologies,we have achieved high accuracies in identifying and managing the hazards associated with corrosion across various industrial *** advancement not only supports the overarching goals of enhancing safety and efficiency,but also sets a new benchmark for future research in the *** results demonstrate a significant improvement in the ability to detect and mitigate corrosion-related concerns,providing a more accurate and comprehensive solution for industries facing these *** CNN and EfficientNetB0 exhibited 100%accuracy,precision,recall,and F1-score,followed by YOLOv8 with respective metrics of 95%,100%,90%,and 94.74%.Our approach outperformed state-of-the-art with similar datasets and methodologies.

关键词： Deep learning YOLOv8 EfficientNetB0 CNN corrosion detection Industry 4.0 sustainability

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