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Wavelet-Enhanced Deep Learning Ensemble for Accurate Stock Market Forecasting: A case study of Nifty 50 Index

IEEE Access 2025年 13卷 87036-87047页

作者： Singh, Priya Jha, Manoj Patel, Harshita Maulana Azad National Institute of Technology Department of Mathematics Bioinformatics & Computer Applications Bhopal 462003 India Vellore Institute of Technology School of Computer Science Engineering and Information Systems Vellore India

Portfolio theory underpins portfolio management, a much-researched yet uncharted field. Stock market prediction is a challenging and essential endeavour in financial research, owing to the nonlinear, volatile, and stochastic characteristics of financial time series data. Conventional statistical techniques often fall short to encapsulate complex interdependencies, resulting in diminished predictive accuracy. This research proposes an ensemble model that integrates Long Short-Term Memory (LSTM), Convolutional Neural Networks (CNN), and Temporal Convolutional Networks (TCN) for effective stock market prediction. The Nifty 50 index dataset is utilized for the empirical evidences. Wavelet-based denoising is utilised as a preprocessing measure to mitigate the intrinsic noise in stock market data. The model’s efficacy is assessed utilising error metrics, such as Mean Squared Error (MSE), Root Mean Squared Error (RMSE), Mean Absolute Error (MAE), and R-squared (R²). The five-fold cross-validation is utilized to establish the robustness of the models. Furthermore, we ascertain the statistical significance of performance enhancements by parametric t-tests, including normality assessments via the Shapiro-Wilk test. Moreover, current state-of-the-art models advocates in favour of proposed study. © 2013 IEEE.

关键词： CNN Ensemble learning LSTM Random Forest Statistical Analysis Stock Market TCN

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An upgraded variant of backtracking search algorithm and its application to COVID-19 CT images segmentation

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Multimedia Tools and Applications 2025年 1-57页

作者： Debnath, Sanjib Nama, Sukanta Chakraborty, Sanjoy Saha, Apu Kumar Mirjalili, Seyedali Department of Computer Science & Engineering National Institute of Technology Agartala Tripura Agartala799046 India Department of Science & Humanities Gomati District Polytechnic Fulkumari Tripura Udaipur799013 India Department of Information Technology Bir Bikram Memorial College Tripura Agartala India Department of Mathematics National Institute of Technology Agartala Tripura Agartala799046 India Center for Artificial Intelligence Research and Optimization Torrens University Australia Brisbane Australia Faculty of Electrical Engineering and Computer Science VŠB-TU Ostrava Ostrava Czech Republic University Research and Innovation Center Obuda University Budapest Hungary Department of Computer Science & Engineering The ICFAI University Tripura Tripura 799210 India

To improve the performance of the original backtracking search algorithm (BSA), this work combines BSA with a centralized population initialization and an elitism-based local escape operator and proposes a new, improved variant of BSA called CiBSA. BSA's performance is improved by centralized population initialization regarding search capability, convergence speed, and capacity to jump out of the local optimum. The suggested algorithm's elitism-based local escape operator method prevents BSA searching slowness and enhances convergence rates and local search efficiency. The performance of the proposed algorithm has been tested on a set of 54 test suites. An extensive comparison with other recent meta-heuristics algorithms (MA) demonstrates that both methodologies substantially assist BSA in boosting the quality of its solutions and speeding up the convergence rate. The proposed CiBSA is then applied to a real-world problem: image segmentation using Kapur's entropy function. The proposed CiBSA is successfully applied to the segmentation of COVID-19 CT images using Kapur's entropy for grey-scale images. Considering the performance matrices Peak Signal-to-Noise Ratio (PSNR), Structural Similarity Index (SSIM), Feature Similarity Index (FSIM), and Intersection over Union (IoU), the suggested CiBSA outperformed conventional MA algorithms. In COVID-19 CT image segmentation trials, CiBSA has superior segmentation capability and adaptability at varied threshold levels than other approaches. As a result, CiBSA has a lot of potential for enhancing COVID-19 diagnosis accuracy. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Signal to noise ratio

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Artificial Neural Network-Based Inverter Control and MPPT for Enhanced Performance of Hybrid Microgrids With PV and Battery Storage 15

Artificial Neural Network-Based Inverter Control and MPPT fo...

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15th International Renewable Energy Congress, IREC 2025

作者： Rajab, Z. Elrashid, M. Elhashane, M. Gomez-Romero, J. Khalil, A. Seliman, A. Mohamed, F. Benghazi University Faculty if Engineering Electrical and Electronic Department Libya University of Granada Department of Computer Science and Artificial Intelligence Spain Technical University of Denmark Dtu Engineering Technology Denmark Omar Al-Mukhtar University Faculty of Engineering Electrical Department Libya Authority of Natural Science Research and Technology Tripoli Libya

ISBN: (纸本)9798331516772

Today's power system network has become more complex, with greater responsibilities and challenges in providing a secure, reliable, and high-quality energy supply to communities. A smaller entity of the electrical network, known as a Microgrid (MG), has become more popular in recent times to enhance the reliability and security of energy supply during any energy crisis in the utility network. Microgrids (MG) can provide clean energy through renewable resources such as photovoltaic (PV) systems, wind, and energy storage, offering an effective solution for power supply in both remote and urban residential communities. To ensure the stable operation of a standalone microgrid based on a photovoltaic system, it is essential to implement various mechanisms and control strategies simultaneously for optimal efficiency. This paper introduces a complete model of a hybrid microgrid consisting of a PV source, an Artificial Neural Network (ANN)-based Maximum Power Point Tracking (MPPT) system, a storage unit, a DC to AC converter with an ANN controller, and both DC and AC loads. The performance of the controller under various ambient conditions and load variations is tested. The ANN controller demonstrates good performance in different situations. © 2025 IEEE.

关键词： Hybrid power

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Wastewater treatment plant site selection using advanced decision tree machine learning and remote sensing techniques

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Environmental science and Pollution Research 2025年第1期32卷 234-247页

作者： Thangarasu, Thenmozhi Aldehim, Ghadah Alruwais, Nuha Kandasamy, Anguraj Department of Electronics and Communication Engineering Government College of Engineering Tamil Nadu Salem India Department of Information Systems College of Computer and Information Sciences Princess Nourah Bint Abdulrahman University P.O. Box 84428 Riyadh11671 Saudi Arabia Department of Computer Science and Engineering College of Applied Studies and Community Services King Saud University Saudi Arabia P.O.Box 22459 Riyadh11495 Saudi Arabia Department of Electronics & Communication Engineering Sona College of Technology Tamil Nadu Salem India

Wastewater treatment plants in Coimbatore South are under pressure from rapid urbanization, inadequate infrastructure, and industrial pollution, leading to environmental and public health concerns. This study aimed to identify suitable locations for wastewater treatment plants using a combination of machine learning, remote sensing, and GIS-based multicriteria decision analysis (MCDA). Several datasets were analysed, with the analytical hierarchy process (AHP) assigning weights to factors such as slope (18.51) and elevation (31.39), which were found to be crucial in site selection. The study classified the suitability of sites into five categories, with the western region being the most favourable due to its low elevation (147 to 200 m), gentle slopes (0–3%), and substantial land availability (approximately 309.00 sq. km). Overall, the site suitability analysis revealed that 14.48% (110.2 sq. km) of the area falls within "Very High Favourable Zones," while 11.21% (85.3 sq. km) is categorized as "High Favourable Zones." Moderate and low favourable zones account for 9.63% (73.3 sq. km) and 24.04% (182.9 sq. km), respectively. The remaining 40.57% (308.7 sq. km) is considered "Very Low Favourable Zones." These results can guide urban planning decisions, highlighting the importance of factors such as land availability, population growth, wastewater volume, and flood vulnerability. Integrating GIS technology with decision-making processes enhances the strategic placement of urban utilities, ensuring long-term sustainability for Coimbatore South's wastewater management. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2024.

关键词： Site selection

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Opposition-based multi-objective ant colony optimization framework for sustainable retrofitting: time–cost–energy–risk trade-offs

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Asian Journal of Civil engineering 2025年第5期26卷 2223-2239页

作者： Pokkuluri, Kiran Sree Chauhan, Trupti Ravindra Macedo, Víctor Daniel Jiménez Manjunath, T.C. Bharadwaj, Manish Sethi, Krushna Chandra Department of Computer Science and Engineering Shri Vishnu Engineering College for Women Bhimavaram India Department of Design Manipal School of Architecture and Planning Manipal Academic of Higher Education Manipal Karnataka Udupi India Mechanical Engineering Faculty Michoacan University of Saint Nicholas of Hidalgo Morelia Mexico Department of Computer Science and Engineering Rajarajeswari College of Engineering Bengaluru India Department of Management Studies Atal Bihari Vajpayee Indian Institute of Information Technology and Management Gwalior India Civil Engineering Department Centurion University of Technology and Management Gajapati India

The increasing emphasis on sustainable retrofitting of buildings has brought attention to the need for optimizing trade-offs among critical objectives, such as time, cost, energy consumption, and risk. This study proposes an opposition-based multi-objective ant colony optimization (OB-MOACO) framework to address these challenges effectively. The framework incorporates opposition-based learning to enhance solution exploration and convergence in multi-objective optimization problems, specifically tailored for sustainable retrofitting projects. The study develops a time–cost–energy–risk trade-off (TCERT) model with four objectives: minimizing retrofitting time, cost, energy consumption, and risks. The model integrates constraints related to budget, project timelines, and energy efficiency to ensure feasibility and sustainability. A detailed case study of retrofitting a mixed-use building is presented, encompassing 11 aspects such as structural reinforcement, energy efficiency enhancement, and accessibility improvements. Results demonstrate the superiority of OB-MOACO over conventional methods like NSGA-III, MODE, and MOPSO in achieving Pareto-optimal solutions. Key performance metrics, including hypervolume (HV) and inverted generational distance (IGD), highlight the model's efficiency in balancing competing objectives. The study contributes to advancing sustainable retrofitting practices by providing an innovative, cost-effective, and energy-efficient decision-making framework. Implications for practitioners and policymakers are discussed, alongside recommendations for future research. © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2025.

关键词： Multi-objective optimization Opposition-based ant colony optimization Pareto optimization Sustainable retrofitting Time–cost–energy–risk trade-off

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Multi-Mode Inflow Prediction for Urban Transportation systems 14th

Multi-Mode Inflow Prediction for Urban Transportation System...

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14th International Conference on Frontier Computing, FC 2024

作者： Hung, Chih-Chieh Hung, Min-Hsien Chang, Jia-Wei Department of Management Information Systems National Chung Hsing University Taichung Taiwan Department of Computer Science and Information Engineering National Taichung University of Science and Technology Taichung Taiwan

ISBN: (纸本)9789819623945

Traffic congestion poses significant challenges to modern cities, leading to increased energy use, pollution, and long commute times. Optimizing public transit systems and encouraging their use is an effective solution. However, public transit integrates various modes like subways, buses, and shared bikes, requiring transfers between modes. Therefore, understanding the relationships between modes and accurately predicting passenger flows for each mode is crucial. Accurate predictions enable better capacity allocation, scheduling, and seamless transfers between modes, enhancing accessibility and convenience to attract more riders. Existing multi-modal prediction methods typically focus on station-to-region or region-to-region flows, overlooking station-to-station predictions that can better capture inter-modal relationships and provide fine-grained insights for traffic management. This study proposes a multi-modal framework for MRT and YouBike data that employs a spatial-temporal module to effectively model the spatiotemporal dependencies in the data. Evaluations on real-world datasets demonstrate the framework outperforms benchmark methods. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Traffic congestion

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Superconductivity in Bi1.6Pb0.4Sr2Ca2Cu3O10+δ ceramics with cobalt ferrite (CoFe2O4) nanoparticle addition

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Ceramics International 2025年第12期51卷 16134-16142页

作者： Tran, Duc H. Pham, An T. Binh, Nguyen Thanh Anh, Do Thi Kim Nam, Nguyen Hoang Hong, Nguyen T.M. Cuong, Le Viet Pham, Phong V. Huy, Nguyen Quang Huong, Tran Thi Man, Nguyen K. Lim, Kean Pah Kechik, Mohd Mustafa Awang Chen, Soo Kien Le, Tien Faculty of Physics University of Science Vietnam National University Hanoi100000 Viet Nam Nano and Energy Center University of Science Vietnam National University Hanoi100000 Viet Nam Faculty of Engineering Physics and Nanotechnology VNU University of Engineering and Technology Hanoi100000 Viet Nam University of Science Vietnam National University Hanoi100000 Viet Nam Faculty of Electronics and Computer Engineering University of Economics - Technology for Industries Hanoi100000 Viet Nam Faculty of Electronic Materials and Devices School of Materials Science and Engineering Hanoi University of Science and Technology Hanoi100000 Viet Nam Department of Physics Faculty of Science Universiti Putra Malaysia Selangor Serdang43400 Malaysia University of Science and Technology of Hanoi Vietnam Academy of Science and Technology 18 Hoang Quoc Viet Cau Giay Hanoi100000 Viet Nam

This study explores the use of cobalt ferrite (CoFe2O4) nanoparticles as artificial pinning centers to investigate flux pinning characteristics in the Bi1.6Pb0.4Sr2Ca2Cu3O10+δ ceramics. Polycrystalline samples with the composition (Bi1.6Pb0.4Sr2Ca2Cu3O10+δ)1−x(CoFe2O4)x, where x ranges from 0.000 to 0.010, are synthesized based on the solid-state formation. Structural and surface analyses reveal that the samples predominantly comprise superconducting phases, with volume fractions varying with x. An increase in x leads to a drop in the critical temperature (Tc). However, an improvement in the critical current density (Jc) is observed at x = 0.002, owing to the addition of CoFe2O4. The collective pinning theory indicates a shift from a single-vortex to small bundle vortex pinning at the crossover field (Bsb) in all samples. The dependence of Bsb on temperatures is consistent with δl pinning, attributed to changes in the mean free path of the charge carrier. Geometries of artificial pinning centers are identified as 2D using the Dew–Hughes model. CoFe2O4 nanoparticles are primarily located at the grain boundaries, filling voids and supporting pinning through grain boundaries. © 2025 Elsevier Ltd and Techna Group S.r.l.

关键词： Flux pinning

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MOCBOA:Multi-Objective Chef-Based Optimization Algorithm Using Hybrid Dominance Relations for Solving engineering Design Problems

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computer Modeling in engineering & sciences 2025年第4期143卷 967-1008页

作者： Nour Elhouda Chalabi Abdelouahab Attia Abdulaziz S.Almazyad Ali Wagdy Mohamed Frank Werner Pradeep Jangir Mohammad Shokouhifar Computer Science Department University of M’silaIchebiliaM’sila28000Algeria Computer ScienceDepartment Mohamed El Bachir El IbrahimiUniversity of Bordj BouArreridjBordj BouArreridj34000Algeria Department of Computer Engineering College of Computer and Information SciencesKing Saud UniversityRiyadh11543Saudi Arabia Operations Research Department Faculty of Graduate Studies for Statistical ResearchCairo UniversityGiza12613Egypt Applied Science Research Center Applied Science Private UniversityAmman11931Jordan Faculty of Mathematics Otto-von-Guericke UniversityMagdeburg39016Germany University Centre for Research and Development Chandigarh UniversityMohali140413India Department of Electrical and Computer Engineering Shahid Beheshti UniversityTehran14399Iran DTU AI and Data Science Hub(DAIDASH) Duy Tan UniversityDa Nang550000Vietnam

Multi-objective optimization is critical for problem-solving in engineering,economics,and *** study introduces the Multi-Objective Chef-Based Optimization Algorithm(MOCBOA),an upgraded version of the Chef-Based Optimization Algorithm(CBOA)that addresses distinct *** approach is unique in systematically examining four dominance relations—Pareto,Epsilon,Cone-epsilon,and Strengthened dominance—to evaluate their influence on sustaining solution variety and driving convergence toward the Pareto *** comparison investigation,which was conducted on fifty test problems from the CEC 2021 benchmark and applied to areas such as chemical engineering,mechanical design,and power systems,reveals that the dominance approach used has a considerable impact on the key optimization measures such as the hypervolume *** paper provides a solid foundation for determining themost effective dominance approach and significant insights for both theoretical research and practical applications in multi-objective optimization.

关键词： Multi-objective optimization chef-based optimization algorithm(CBOA) pareto dominance epsilon dominance cone-epsilon dominance strengthened dominance

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Using Local Gradient Compensation in Federated Learning for Improving Privacy and Performance 14th

Using Local Gradient Compensation in Federated Learning for ...

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14th International Conference on Frontier Computing, FC 2024

作者： Chang, Jia-Wei Chen, Wei-Cheng Hung, Chih-Chieh Department of Computer Science and Information Engineering National Taichung University of Science and Technology Taichung Taiwan Department of Management Information Systems National Chung Hsing University Taichung Taiwan

ISBN: (纸本)9789819623945

Federated learning (FL) has emerged as a leading approach for decentralized model training, preserving data privacy by exchanging only model parameters. However, recent studies have exposed vulnerabilities, revealing that attackers can reconstruct private data from shared model parameters, compromising user privacy. To address this, various defense methods have been proposed, with gradient perturbation being a lightweight yet effective strategy. However, it often incurs a trade-off with model performance. In this paper, we introduce a lightweight defense mechanism tailored for Federated Stochastic Gradient Descent (FedSGD). Our method leverages maximum gradient values to gauge noise scale, while also preserving and compensating for previous noise iterations to mitigate performance impacts. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Federated learning

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DFT analysis of the physical properties of direct band gap semiconducting double perovskites A2BIrCl6 (A = Cs, Rb;B = Na, K) for solar cells and optoelectronic applications

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RSC Advances 2025年第17期15卷 13643-13661页

作者： Tarekuzzaman, Md. Shahadath, Nazmul Montasir, Mufrat Alsalmi, O. Mia, M.H. Al-Dmour, Hmoud Rasheduzzaman, Md. Hasan, Md. Zahid Materials Research and Simulation Lab Department of Electrical and Electronic Engineering International Islamic University Chittagong Kumira Chittagong4318 Bangladesh Department of Physics College of Science Umm Al-Qura University Makkah21955 Saudi Arabia Department of Textile Engineering Northern University Bangladesh Uttara Dhaka Bangladesh Department of Computer and Communication Engineering International Islamic University Chittagong Kumira Chattogram4318 Bangladesh Department of Physics Faculty of Science Mutah University karak61710 Jordan

Double perovskite-based optoelectronic devices are gaining attention due to their unique characteristics, including a simple and stable crystal structure. This study employs density functional theory (DFT) with the full-potential linearized augmented plane-wave (FP-LAPW) method to investigate the structural, electronic, optical, mechanical, and thermodynamic properties of A2BIrCl6 (A = Cs, Rb;B = Na, K) double perovskite halides. The primary aim is to assess their potential applicability in optoelectronic devices and renewable energy technologies. The cubic stability of the predicted compounds was confirmed through the Goldsmith tolerance factor, octahedral factor, and a new tolerance factor. Additionally, to confirm their thermodynamic stability, we assessed the formation energy, binding energy, and phonon dispersion curves. We used the TB-mBJ potential to accurately predict the optoelectronic properties. The calculations of the electronic band structure indicated that the examined double perovskites exhibit a direct band gap semiconducting nature, with the following band gap values: 1.927 eV for Cs2NaIrCl6 1.991 eV for Cs2KIrCl6, 2.025 eV for Rb2NaIrCl6, and 2.102 eV for Rb2KIrCl6. The A2BIrCl6 (A = Cs, Rb;B = Na, K) compounds demonstrate impressive optical properties, including low reflectivity and high light absorption coefficients (104 cm−1) in the visible spectrum. Their spectral response extends from the visible to the UV range, making them ideal candidates for applications in solar cells and optoelectronic devices. The mechanical stability of the titled compounds was confirmed through the Born-Huang stability conditions based on their stiffness constants. The brittle nature of all the examined perovskites is confirmed by Pugh's ratio, Cauchy pressure, and Poisson's ratio. Finally, the Helmholtz free energy (F), internal energy (E), entropy (S), and specific heat capacity (Cv) are calculated based on the phonon density of states. © 2025 The Royal Society of C

关键词： Opacity

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