The World Wide Web(WWW) comprises a wide range of information, and it is mainly operated on the principles of keyword matching which often reduces accurate information retrieval. Automatic query expansion is one of th...
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The World Wide Web(WWW) comprises a wide range of information, and it is mainly operated on the principles of keyword matching which often reduces accurate information retrieval. Automatic query expansion is one of the primary methods for information retrieval, and it handles the vocabulary mismatch problem often faced by the information retrieval systems to retrieve an appropriate document using the keywords. This paper proposed a novel approach of hybrid coot-based Cat and Mouse Optimization (CMO) algorithm named as hybrid coot-CMO for the appropriate selection of optimal candidate terms in the automatic query expansion process. To improve the accuracy of the Cat and Mouse Optimization (CMO) algorithm, the parameters are tuned with the help of the coot algorithm. The best suitable expanded query is identified from the available expanded query sets also known as candidate query pools. All feasible combinations in this candidate query pool should be obtained from the top retrieved documents. Benchmark datasets such as the GOV2 Test Collection, the Cranfield Collections, and the NTCIR Test Collection are utilized to assess the performance of the proposed hybrid coot-CMO method for automatic query expansion. This proposed method surpasses the existing state-of-the-art techniques using many performance measures such as F-score, precision, and mean average precision (MAP).
Incorporating ground-breaking technologies such as deep learning (DL) has revolutionized predictive modelling in the rapidly evolving landscape of the finance sector. DL approaches, capable of extracting complex patte...
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Incorporating ground-breaking technologies such as deep learning (DL) has revolutionized predictive modelling in the rapidly evolving landscape of the finance sector. DL approaches, capable of extracting complex patterns from vast data collections, become an efficient approach for predicting financial trends. By integrating the complex neural network architecture with comprehensive datasets, including investor sentiment, market indicators, and economic variables, finance experts have introduced prediction models well known for their ability to capture the nuanced dynamics of financial markets with remarkable performance. Incorporating DL approaches within the finance sector provided the basis for more informed decision-making, enabling institutions, investors, and analysts to capitalize on emerging opportunities with greater confidence and precision and navigate market volatility. This study develops a novel quasi-oppositional coot algorithm with a deep learningbased predictive method on the financial sector (QOCODL-PMFC) technique. The QOCODL-PMFC technique aims to perform a prediction process on the financial sector. The QOCODL-PMFC method applies min-max normalization to measure the input dataset into a meaningful format to achieve this. Next, the QOCODL-PMFC method designs the QOCO technique for selecting an optimal set of features. The QOCODL-PMFC technique applies the attention bidirectional gated recurrent unit (ABiGRU) model for the prediction process. The Harris Hawks Optimization (HHO) model is utilized to boost the performance of the ABiGRU network. The simulation evaluation of the QOCODL-PMFC technique is tested under a benchmark finance dataset. The experimental values of the QOCODL-PMFC technique exhibit a minimal MSE of 0.7452 over other models.
Recent growth in magnetic levitation can be attributed to its ability to minimize friction and disturbance in industries, transportation, aerospace, biomedicine, and magnetic bearings. Due to the magnetic levitation s...
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Recent growth in magnetic levitation can be attributed to its ability to minimize friction and disturbance in industries, transportation, aerospace, biomedicine, and magnetic bearings. Due to the magnetic levitation system's nonlinear and unstable nature, control engineers found it exceedingly challenging to design a stabilizing controller. The magnetic levitation system is abbreviated as a maglev system. Using the integral square error criterion, a newly developed metaheuristic algorithm named the coot algorithm is used to optimize the PID controller parameters. The performance of the proposed algorithm is evaluated using simulation and hardware with several kinds of reference trajectories and compared to the performance of other algorithms, such as the genetic algorithm and the whale optimization algorithm. Based on simulation and hardware results, it was determined that the proposed algorithm performed well with less settling time, rise time, and integral square error.
To achieve a low-carbon and economical operation for the power grid, this paper proposes a novel optimal carbon–energy combined flow (OCECF) by considering the influence of aluminum plants. It attempts to minimize th...
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To achieve a low-carbon and economical operation for the power grid, this paper proposes a novel optimal carbon–energy combined flow (OCECF) by considering the influence of aluminum plants. It attempts to minimize the carbon emission of power grid, power loss, and the voltage deviation by taking the reactive power control of aluminum plants into account. Since the presented OCECF is a nonlinear and complex optimization problem, a new metaheuristic algorithm called coot algorithm is employed to acquire a high-quality dispatch solution under various scenarios. The coot algorithm is inspired by the movement of birds on the water surface, which can implement a wide exploration via the random movement and a deep exploitation via the chain movement with the group leaders. The performance of coot algorithm for the presented OCECF is carried out on an IEEE 57-bus system with various power plants, in which the group search optimizer is introduced for performance comparison.
Usually, digital cameras comprise sensor arrays enclosed by Color Filter Arrays (CFAs), mosaics of minute color filters. Thus, every pixel sensor usually records limited spectral data regarding relevant pixels. Demosa...
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Usually, digital cameras comprise sensor arrays enclosed by Color Filter Arrays (CFAs), mosaics of minute color filters. Thus, every pixel sensor usually records limited spectral data regarding relevant pixels. Demosaicing is defined as the procedure of deducing the misplaced data for every pixel, which plays a vital role in recreating high-quality full-color images. Denoising and demosaicing are the major processes in the camera imaging chain for both videos and images. Here, reconstruction errors occur in these points and have undesirable effects on the final outcome, when it is not appropriately managed. The demosaicing process provokes color and spatial correlation of noises, and it is improved by means of imagining a pipeline. This organized noise usually destroys the quality of the image as well as fails to prevent accurate interpretation of an image. During the mitigation of this structured noise on processed data, denoising techniques diminish the texture and information. Therefore, an effectual demosaicing technique is essential for recreating the full-color image from the defective color samples. Thus, in this paper, an effectual video demosaicing model is proposed using an optimized deep learning system. The designed video demosaicing system achieved better performance with a Peak Signal-to-Noise Ratio (PSNR) of 59.74 dB, Second Derivative, like Measure of Enhancement (SDME) of 63.51, and Root Mean Squared Error (RMSE) of 0.3660.
PurposeWire Electrical Discharge Machining (WEDM) is the most commonly used machining method due to its versatility towards complex machining projects, especially in mould-making industries. But the efficiency and sur...
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PurposeWire Electrical Discharge Machining (WEDM) is the most commonly used machining method due to its versatility towards complex machining projects, especially in mould-making industries. But the efficiency and surface finish of this WEDM process is questionable for mass production. Therefore, it is necessary to optimize WEDM process parameters for mouldmaking *** experiments are conducted on AISI P20+Ni material which is prevalent in plastic mould-making industries. By introducing Ultrasonic Vibration (UV) into cutting wire, this study attempts to improve the efficiency and surface finishing of WEDM process. Further, the dynamics of key WEDM parameters such as peak current, servo voltage, pulse on time, and pulse off time on output responses are analyzed through Response Surface Methodology (RSM). Furthermore, the output parameters such as, Material Removal Rate (MRR), Micro Hardness (MH), Surface Roughness (SR), and Recast Layer Thickness (RLT) during UV-assisted WEDM are predicted using a novel hybrid deep learning technique called Deep Neural Network based coot optimization algorithm (DNN+coot).ResultsThe results shown that the proposed DNN+coot algorithm learned better to predict output responses with an accuracy of 98.77% compared to the usual DNN. Finally, the UV-assisted WEDM process parameters are optimized to obtain maximum MRR, MH, and minimum SR, RLT at an average desirability of *** study concludes, the UV-WEDM process as the most effect machining method for AISI P20+Ni with an optimal input settings of 12A peak current, 5.999V of servo voltage, 106.21 mu s pulse on time, and 50.2683 mu s pulse off time.
This article presents the combined frequency and voltage regulation of two area interconnected power systems. In addition to conventional generations, renewable energy sources such as wind plants, solar PV plants, and...
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A Mobile Ad hoc Network (MANET) is a widely used and vibrant network, which is unevenly distributed in the environment. It is a set of self-organized independent mobile nodes interconnected without any centralized inf...
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A Mobile Ad hoc Network (MANET) is a widely used and vibrant network, which is unevenly distributed in the environment. It is a set of self-organized independent mobile nodes interconnected without any centralized infrastructure. However, this topology nature makes the network prompt to various network security attacks. To address this issue, this paper proposes a coot Chimp Optimization algorithm- Deep Q-Network (CChOA-DQN) for detecting the black hole attacks in MANET. Here, the designed CChOA is used for the identification of the optimal route in the MANET for transmitting data, which takes into fitness parameters, such as energy, distance, neighbourhood quality, link quality, and trust. The features are extracted using the Fisher score and augmented using the over-sampling technique, which is further allowed for the detection process using DQN. Also, the weights of the DQN are enhanced using the CChOA algorithmic technique to enhance the detection performance. Additionally, the results gathered from the experiment revealed that CChOA attained high performance with a maximum of 0.983 Mbps throughput, 93.70 % Packet Delivery Ratio (PDR), and minimum end-end delay of 0.096Sec, Residual energy of 0.119 J, and Control overhead of 4473.11. Also, the CChOA-DQN technique achieved the minimum False Positive Rate (FPR) of 0.122, False Negative Rate (FNR) of 0.121, Computation time of 0.153 and Run time of 0.094.
Electric vehicles (EVs) perform a significant part in transportation, which reduces ecological pollution as well as fuel costs and the usage of electric vehicles is raised in future. Despite this, the number of vehicl...
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Electric vehicles (EVs) perform a significant part in transportation, which reduces ecological pollution as well as fuel costs and the usage of electric vehicles is raised in future. Despite this, the number of vehicles available in the real world is greater than the available charging stations. Scheduling the charging period of the huge fleet of EVs creates a major issue owing to charging station conditions. In addition, the charging time of batteries as well as the power restraints makes the system more complex. Hence, the energy-aware EV charge scheduling algorithm is designed based on the proposed coot Feedback Artificial Tree (coot-FAT) approach, which is the amalgamation of the coot algorithm and Feedback Artificial Tree (FAT) approach. The EV is simulated, where the charging stations are installed in a parking area for every user. At that, the EV charge scheduling process is implemented using the developed coot-FAT procedure based on fitness function by contemplating several constraints, including tardiness as well as energy consumption, which is used to effectively minimize tardiness and charging delay of the EVs. Then, the developed method achieved rates of minimum average energy efficiency, minimum tardiness, and the maximum number of charge vehicles is 2024.8 Kw/h, 0.18, and 44, respectively.
作者：
Wang, ZongyaoPeng, QiyangRao, WeiLi, DanNanchang Inst Technol
Sch Informat Engn Nanchang 330099 Peoples R China Fudan Univ
Sch Informat Sci & Technol Key Lab Informat Sci Electromagnet Waves Shanghai 200433 Peoples R China Fudan Univ
Res Ctr Smart Networks & Syst Sch Informat Sci & Technol Shanghai 200433 Peoples R China
Addressing the shortcomings of the Sparrow Search algorithm (SSA), such as low accuracy of convergence and tendency of falling into local optimum, a Multi-strategy Integrated Sparrow Search algorithm (MISSA) is propos...
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Addressing the shortcomings of the Sparrow Search algorithm (SSA), such as low accuracy of convergence and tendency of falling into local optimum, a Multi-strategy Integrated Sparrow Search algorithm (MISSA) is proposed. In this method, by improving the black-winged kite algorithm and applying it to the producer's position update formula, an improved search strategy (ISS) is firstly proposed to enhance search ability. Secondly, a new strategy inspired by the coot algorithm, called the group follow strategy (GFS), is proposed to improve the ability to jump out of the local optimum. Finally, a proposed random opposition-based learning strategy (ROBLS) is applied to the population after each iteration to enhance its diversity. To verify MISSA's effectiveness, extensive testing is conducted on 24 benchmark functions as well as CEC 2017 functions. The experimental results, complemented by Wilcoxon rank-sum tests, conclusively demonstrate that MISSA outperforms SSA and other advanced optimization algorithms, exhibiting superior overall performance.
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