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Enhanced Stock Market Trend Prediction on the Indonesia Stock Exchange Using Improved Bacterial Foraging optimization and Elitist whale optimization algorithms

Enhanced Stock Market Trend Prediction on the Indonesia Stoc...

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Integrated Intelligence and Communication Systems (ICIICS), International Conference on

作者： Deepak Kumar Priyanka Pramod Pawar Mohan Kumar Meesala Piyush Kumar Pareek Santosh Reddy Addula K S Shwetha Department of Information Technology University of the Cumberlands Williamsburg USA Department of AIML Nitte Meenakshi Institute of Technology Bengaluru India Department of Computer Science and Engineering Dayananda Sagar College of Engineering Bangalore India

ISBN: (数字)9798331504960

ISBN: (纸本)9798331504977

It was difficult to find low-risk firms in 2022 due to the large number of 825 listed on the Indonesia Stock Exchange (IDX). Essential concerns in financial works include stock price forecasting and price movement forecast. The stock market research has shown encouraging outcomes in the past. We still don't have a consensus on the finest stock value forecasting classical due to the fact that each study report has different architecture and uses different stock issuers. This paper presents a new method for optimizing bacterial foraging that uses optimal deep learning to forecast stock market trends. For the prediction process, the given model employs a multiplicative long short term memory (MLSTM) perfect, with IBFO serving as the model's fine-tuner. The research paper presents a modified whale optimization technique for feature selection. This research proposes an elitist whale optimization algorithm with the nonlinear parameter (EWOANP) to fix the issue where the whale optimization procedure does not strike a balance among exploration and exploitation and instead falls into the local optimum. To improve the odds of evading the local optimum, the shrinking encircling mechanism employs an elitist tactic based on random Cauchy mutation. Using the random Cauchy mutation as a starting point, the method cleverly selects the population with the best mutation solutions to move on to the next iteration. To strike a compromise among the two, the logarithmic spiral mechanism makes use of a nonlinear parameter. There are 2,588,451 entries in the dataset representing 727 IDX firms' open, highest, lowest, closed, and volume transactions. Various stock time records are used to examine the trial findings using current methodologies in terms of distinct metrics.

关键词： Microorganisms Spirals Predictive models Feature extraction Market research Prediction algorithms whale optimization algorithms Stock markets Forecasting Tuning

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whale optimization Algorithm-Based Channel Parameter Estimation for Ranging Applications in Closely Spaced Multipath Environment

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IEEE TRANSACTIONS ON ANTENNAS AND PROPAGATION 2025年第4期73卷 2684-2689页

作者： Liang, Zhiwei Lyu, Yue Wang, Wei An, Yisheng Changan Univ Sch Informat Engn Xian 710064 Shaanxi Peoples R China

Range-based positioning using wireless signal has gained remarkable attention. Accurate estimation of channel parameters including delay and complex amplitude is essential for positioning applications. However, multipath propagation of wireless signal significantly impacts the performance of channel estimation, particularly the estimation accuracy degrades in closely spaced multipath environment. In this communication, we propose a whale optimization algorithm (WOA)-based channel estimator to extract channel parameter in closely spaced multipath environment. We derive the fitness function based on the maximum likelihood criterion. Both the simulation and measurement-based validation for closely spaced multipath environment reveal that the proposed method outperforms several state-of-the-art methods including as space-alternating generalized expectation-maximization (SAGE), gray wolf optimizer (GWO), and salp swarm algorithm (SSA). The results conclusively demonstrate that the WOA is well-suited for dense multipath parameter estimation.

关键词： Channel estimation Distance measurement Vectors whales whale optimization algorithms Parameter estimation Spirals Frequency response Delays Autoregressive processes Channel parameter estimation positioning ranging performance whale optimization algorithm (WOA)

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Near-Global-Optimal Resource Allocation for Uplink SCMA Systems Based on whale optimization Algorithm

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IEEE TRANSACTIONS ON WIRELESS COMMUNICATIONS 2025年第2期24卷 1089-1103页

作者： Soltanpour, Meysam Zhang, Haixia Shandong Key Lab Intelligent Commun Sensing Comp I Jinan 250061 Shandong Peoples R China Shandong Univ Sch Control Sci & Engn Jinan 250061 Shandong Peoples R China

This paper investigates a joint resource allocation (RA) algorithm to maximize the average sum rate (ASR) of the sparse code multiple access (SCMA) random model-based networks. The ASR optimization problem turns out to be non-convex mixed-integer nonlinear programming (MINLP) problem which is challenging to address. The previous works strongly depend on the upper/lower bound of ASR, successive convex approximation (SCA) methods, initial assignment definition, post-processing procedures, and generating/training the data sets, thus cannot be applied. Given the above challenges, considering the ability of the whale optimization algorithm (WOA) in solving the non-convex MINLP NP-hard problems, we first construct the resource element assignment (REA) matrix utilizing the binary WOA (BWOA) through the reshaping the pattern of the arrangement of non-zero elements (PANs) and converting the structural constraints. Then we enhance the continuous WOA (CWOA) to mine the power allocation (PA) coefficients by using an acceleration factor that improves the exploration and exploitation phases. We also develop a Kuhn-Munkres (KM)-based algorithm as an REA benchmark which needs no for post-processing requirements. Finally, our proposed algorithms are compared with the state-of-the-art works in the literature. Results show that SCMA CWOA-BWOA algorithm (CBWOA) provides more robust optimality in massive connectivity situations with a larger number of users. Besides, the CBWOA method guarantees the structural constraints by increasing the number of search agents (NSAs) through the exploitation phase.

关键词： Resource management Uplink Interference whale optimization algorithms Vectors Clustering algorithms Training Measurement Industrial Internet of Things Wireless networks SCMA whale optimization algorithm non-convex MINLP resource allocation metaheuristic

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Integrating Two-Tier optimization Algorithm With Convolutional Bi-LSTM Model for Robust Anomaly Detection in Autonomous Vehicles

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IEEE ACCESS 2025年 13卷 6820-6833页

作者： Alotaibi, Moneerah Alohali, Manal Abdullah Mahmood, Khalid Alhashmi, Asma A. Alqurni, Jehad Saad Alotaibi, Sultan Refa Alzahrani, Ahmad A. Issaoui, Imene Shaqra Univ Coll Sci & Humanities Dawadmi Dept Comp Sci Shaqra 11961 Saudi Arabia Princess Nourah Bint Abdulrahman Univ Coll Comp & Informat Sci Dept Informat Syst POB 84428 Riyadh 11671 Saudi Arabia King Khalid Univ Appl Coll Mahayil Dept Informat Syst Abha 61421 Saudi Arabia Northern Border Univ Coll Sci Dept Comp Sci Ar Ar 91431 Saudi Arabia Imam Abdulrahman bin Faisal Univ Coll Educ Dept Educ Technol Dammam 31441 Saudi Arabia Saudi Elect Univ Coll Comp & Informat Dept Comp Sci Riyadh 11673 Saudi Arabia Umm Al Qura Univ Coll Comp Dept Comp Sci & Artificial Intelligence Mecca 21955 Saudi Arabia Qassim Univ Appl Coll Unit Sci Res Buraydah 51452 Saudi Arabia

Industrial development has changed vehicles of traditional into autonomous vehicles (AVs). AVs play a significant role since they are measured as a vital module of smart cities. The AV is an advanced automobile efficient in preserving secure driving by evading collisions formed by drivers. In contrast with traditional vehicles, which are fully coordinated and functioned by humans, AVs gather information regarding the exterior environment utilizing sensors to guarantee secure navigation. AVs decrease environmental effects since they regularly employ electricity to function rather than fossil fuel, thus diminishing greenhouse gasses. However, AVs might be exposed to cyber-attacks, causing dangers to human life. Machine learning (ML) and deep learning (DL) based anomaly recognition has progressed as a new study track in autonomous driving. ML and DL-based anomaly detection scholars have focused on improving accuracy as a typical classification task without aiming at mischievous information. This article develops an improved whale optimization algorithm-based feature selection using explainable artificial intelligence for robust anomaly detection (IWOAFS-XAIAD) technique in autonomous driving. The major aim of the IWOAFS-XAIAD technique is an endwise XAI structure to construe and imagine the anomaly recognition classifications prepared by AI models securing autonomous driving systems. Initially, the IWOAFS-XAIAD technique utilizes the Z-score data normalization method to convert input data into a compatible layout. Besides, the IWOAFS-XAIAD technique employs an improved whale optimization algorithm (IWOA)-based feature subset selection to pick an optimum set of features. An attention mechanism with the CNN-BiLSTM (CNN-BiLSTM-A) model is employed for anomaly detection and classification. Moreover, the catch-fish optimization algorithm (CFOA) selects the hyperparameters connected to the CNN-BiLSTM-A model. Finally, utilizing the SHAP XAI method, the IWOAFS-XAIAD technique

关键词： Anomaly detection Artificial intelligence Feature extraction Autonomous vehicles Accuracy Transportation Classification algorithms whale optimization algorithms Safety Data models INDEX TERMS Explainable artificial intelligence anomaly detection improved whale optimization algorithm autonomous vehicles catch fish optimization

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UAV-NavS: Three-Dimensional Navigation System of Multiple Unmanned Aerial Vehicles Using Hybrid optimization Algorithm

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IEEE ACCESS 2025年 13卷 20247-20259页

作者： Digra, Monia Jain, Upma Dewangan, Ram Kishan Suyal, Himanshu Jain Dept CSE FET Bengaluru 560069 India Graph Era Dept Comp Sci & Engn Dehra Dun 248002 India Bennett Univ Sch Comp Sci Engn & Technol Greater Noida 201310 India

This paper proposes a hybrid approach for multiple Unmanned Aerial Vehicle navigation. This is an NP-hard problem since the robots must find the optimal safe path without colliding with other robots and obstacles in a three-dimensional search space. The proposed approach enhances the exploration capabilities of the whale optimization algorithm. Then, it hybridises this improved whale optimization algorithm with the sine cosine algorithm to improve the overall exploitation capabilities. The efficiency of the proposed hybrid approach is compared with other meta-heuristic algorithms for multi-UAV navigation. Results obtained through simulation ensure the validity of the proposed approach.

关键词： Navigation Autonomous aerial vehicles Three-dimensional displays Robots Metaheuristics Heuristic algorithms whale optimization algorithms Trajectory Polynomials NP-hard problem UAV whale optimization algorithm sine cosine algorithm navigation deterministic meta-heuristic hybrid algorithm

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A Triaxial Magnetometer Calibration Method With Improved Beluga whale optimization Algorithm

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IEEE SENSORS JOURNAL 2025年第6期25卷 9522-9530页

作者： Li, Zhuoxuan Li, Yuguo Luo, Ming Dong, Chenxu Ding, Xuezhen Ocean Univ China Coll Marine Geosci Qingdao 266100 Peoples R China

The triaxial magnetometer is a vector magnetic field sensor widely used in various fields. Due to limitations in manufacturing and installation processes, a nonorthogonal error, a scale factor error, and a zero-bias error can occur. These sensor errors significantly affect the accuracy of magnetic field measurements, so calibration is essential to ensure reliable data. This article proposes a novel error calibration method for triaxial magnetometers based on an improved Beluga whale optimization (BWO) algorithm. In addition, three synthetic simulations, with and without noise, and with different initial search spaces and population sizes are designed to validate the proposed method. Ideally, the minimum absolute error between the calibrated and theoretical results is 10(-13) nT. Furthermore, when the calibration method is applied to the field experiment data, the total geomagnetic field fluctuation is reduced from 600 to 9 nT, which is about 66 times smaller. Both simulation and experimental results further confirm the effectiveness and practicality of the proposed algorithm.

关键词： Magnetometers Calibration Vectors Magnetic sensors Magnetic field measurement optimization Sensitivity Magnetic susceptibility whale optimization algorithms Noise Beluga whale optimization (BWO) calibration model parameter estimation triaxial magnetometers

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Real-Time Road Surface Unevenness Recognition for ITS Using BWO-BiGRU-Transformer With Adaptive CKF

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IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS 2025年

作者： Cao, Xianteng Niu, Huawei Chen, Zhengqing Hunan Univ State Key Lab Bridge Safety & Resilience Changsha 410082 Peoples R China Hunan Univ State Key Lab Adv Design & Mfg Technol Vehicle Changsha 410082 Peoples R China

The recognition of the road surface unevenness can provide an important support for optimizing the maintenance and management strategies of local intelligent transportation networks. This study proposes a BiGRU-Transformer model optimized with Beluga whale optimization (BWO) and combined with the Cubature Kalman Filter (CKF) for road surface unevenness recognition in intelligent transportation systems. The model addresses the limitations of existing methods in time series data processing and nonlinear state estimation. It uses BiGRU to model short-term vehicle vibration dependencies and incorporates the Transformer's self-attention mechanism to capture long-term and global dynamics. BWO optimizes the global hyperparameters of the model, avoiding local optima and improving accuracy. CKF updates the noise matrix in real-time, enhancing prediction and generalization through recursive nonlinear state estimation. Experimental results demonstrate significant improvements in performance, including recognition accuracy, noise immunity, and convergence speed, when applied to real road datasets. The proposed model outperforms existing methods, offering enhanced road condition identification and providing effective real-time decision support for intelligent transportation systems, thereby improving management efficiency and responsiveness.

关键词： Roads Neural networks Vibrations Vehicle dynamics optimization Mathematical models Accuracy whale optimization algorithms Feature extraction Training Road surface unevenness BiGRU-Transformer Intelligent transportation Cubature Kalman Filter Beluga whale optimization

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A Hybrid Heuristic Algorithm for Optimizing the Optimal Landing Time Window Problem in Intelligent Air Transportation Systems

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IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS 2025年

作者： Lu, Yong Chen, Shikang Pan, Xiuqin Minzu Univ China Sch Informat Engn Beijing 100081 Peoples R China

The Aircraft Landing Problem (ALP) involves optimizing the scheduling of flight arrivals and departures while simultaneously managing airport resources to maximize flight utilization, minimize delays, reduce costs, and enhance overall operational efficiency. However, existing algorithms for solving ALP often face challenges in converging optimally and effectively handling penalty values associated with timing violations. To address these issues, this paper proposes a novel hybrid heuristic algorithm, AGWOA, designed to enhance convergence rates and effectively manage penalties in ALP. AGWOA integrates Artificial Bee Colony (ABC) and Genetic Algorithm (GA) techniques into the whale optimization Algorithm (WOA), leveraging their complementary strengths to strengthen global search efficiency and refine local constraint handling. This integration accelerates convergence and significantly mitigates penalty costs. Moreover, AGWOA incorporates an adaptive coefficient to facilitate improved convergence, along with a Fast Convergence Update Mechanism (FCUM) to guide the algorithm toward optimal solutions more efficiently. Experimental results conducted on public datasets demonstrate that AGWOA outperforms existing advanced algorithms in both convergence speed and penalty minimization. Specifically, AGWOA achieves a 4.05% decrease in penalty costs compared to baseline algorithms. These results underscore AGWOA's effectiveness in overcoming the challenge of slow convergence and its competitive advantage over other methods in optimizing ALP. The proposed algorithm offers a promising solution for real-world ALP applications, significantly enhancing airline operational efficiency and optimizing resource management.

关键词： Heuristic algorithms Aircraft Convergence Mathematical models Airports whale optimization algorithms Genetic algorithms Spirals whales Resource management ALP WOA heuristic algorithm problem optimization intelligent air transportation systems

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Optimizing Energy Consumption and Latency in IoT Through Edge Computing in Air-Ground Integrated Network With Deep Reinforcement Learning

IEEE OPEN JOURNAL OF VEHICULAR TECHNOLOGY

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IEEE OPEN JOURNAL OF VEHICULAR TECHNOLOGY 2025年 6卷 412-425页

作者： That, Vitou Chhea, Kimchheang Lee, Jung-Ryun Chung Ang Univ Dept Intelligent Energy Ind Convergence Seoul 156756 South Korea Chung Ang Univ Dept Intelligent Energy Ind Seoul 156756 South Korea Chung Ang Univ Sch Elect & Elect Engn Seoul 156756 South Korea

With the increasing computational demands of Internet of Things (IoT) applications, air-ground integrated networks (AGIN), leveraging the capabilities of Unmanned Aerial Vehicles (UAVs) and High-Altitude Platform (HAP), provides an essential solution to these challenges. In this paper, we propose a framework that facilitates local computing at IoT devices and offers the flexibility to offload tasks to aerial platforms when necessary. Specifically, we formulate a multi-objective optimization model aiming at simultaneously minimizing energy consumption and reducing task latency by adjusting control variables such as transmit power, offloading decisions, and UAV placement in a distributed network of IoT devices. Our proposed framework employs Deep Deterministic Policy Gradient (DDPG) techniques to dynamically optimize network operations, allowing for efficient real-time adjustments to network conditions and task demands. The performance of the proposed algorithm is compared to traditional algorithms, including the whale optimization Algorithm (WOA), Gradient Search with Barrier, and Bayesian optimization (BO). Simulation results show that this approach significantly minimizes energy consumption and latency, outperforming conventional optimization methods. Additionally, scalability tests confirm that our framework can efficiently integrate an increasing number of IoT devices and UAVs.

关键词： Internet of Things Energy consumption Autonomous aerial vehicles optimization Resource management Heuristic algorithms Servers Delays Vehicular and wireless technologies whale optimization algorithms Internet of Things (IoT) air-ground integrated networks (AGIN) edge computing Deep Deterministic Policy Gradient (DDPG) UAV placements multi-objective optimization

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Research on 3D Localization Technology Based on Hybrid Signal Models and Adaptive Dynamic Weight whale optimization Algorithm

Research on 3D Localization Technology Based on Hybrid Signa...

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Digital Analysis and Processing, Intelligent Computation (DAPIC), International Conference on

作者： Zhengxin Li Yiyang Du Jingchen Ma Chengcheng Jin Zhen Zhang Bijie Power Supply Bureau Guizhou Power Grid Limited Liability Company Bijie China School of Computer and Data Engineering Zhejiang University of Technology Ningbo Institute Ningbo China School of Energy Systems Lappeenranta University of Technology Lappeenranta Finland School of Computer Science and Technology Shenyang Institute of Engineering Shenyang China Jiulan Academy Nanchang University Nanchang China

ISBN: (数字)9798331536169

ISBN: (纸本)9798331536176

This paper investigates a 3D localization technology based on hybrid signal models and the Adaptive Dynamic Weight whale optimization Algorithm (AWOA). By analyzing the physical characteristics of five types of opportunity signals (TOA, TDOA, DFD, AOA, and RSS), mathematical models are constructed to derive the position information. It is found that at least four signal sources are required for accurate localization using TOA and RSS methods, while TDOA and AOA methods need at least three signal sources. DFD requires at least three signal sources to determine the target's direction and velocity. To enhance localization accuracy, a hybrid localization model based on the least squares method is proposed and optimized in real-time using the AWOA. This algorithm, inspired by the hunting behavior of whales, effectively addresses localization errors in non-line-ofsight (NLOS) environments. Experimental results demonstrate that the technology can accurately track the target's trajectory in real-time over a 10-second interval, with the final localization error within an acceptable range. This study provides an effective solution for high-precision 3D localization in complex environments.

关键词： Location awareness Adaptation models Solid modeling Three-dimensional displays Accuracy Heuristic algorithms whale optimization algorithms Real-time systems Robustness optimization

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