Navigation planning is a critical component in the operation of mobile systems such as robots, autonomous vehicles, and unmanned aerial vehicles (UAVs). As the complexity of these systems increases, the need for robus...
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ISBN:
(数字)9798331512965
ISBN:
(纸本)9798331512972
Navigation planning is a critical component in the operation of mobile systems such as robots, autonomous vehicles, and unmanned aerial vehicles (UAVs). As the complexity of these systems increases, the need for robust and efficient pathfinding algorithms becomes more significant. Traditional heuristic approaches often struggle in dynamic environments, prompting the adoption of metaheuristic algorithms inspired by natural phenomena. This study evaluates five widely used metaheuristic algorithms: Ant Colony Optimization (ACO), Artificial Bee Colony (ABC), Firefly Algorithm (FA), Moth Flame Optimization (MFO), and Tabu Search (TS). The performance of these algorithms is compared across multiple scenarios, including static shortest-path calculations, the Traveling Salesperson Problem (TSP), and randomized environment. Results indicate that ACO consistently identifies optimal paths but incurs relatively high computational time. Tabu Search offers a balanced trade-off between computational efficiency and solution quality, while ABC delivers suboptimal paths with faster execution times. In contrast, MFO and FA exhibit excessive exploration, leading to higher costs and inefficiencies in specific scenarios.
The efficiency issue of logic optimization becomes critical as the scale of VLSI designs grows. Since various algorithms are interleaved during optimization to ensure quality, it is necessary to accelerate those commo...
ISBN:
(纸本)9798350323481
The efficiency issue of logic optimization becomes critical as the scale of VLSI designs grows. Since various algorithms are interleaved during optimization to ensure quality, it is necessary to accelerate those commonly used algorithms for obtaining substantial total speed-up. This paper proposes novel parallel algorithms for AIG refactoring and AND-balancing. Equipped with delicately designed parallel-friendly, data-race-free frameworks and GPU data structures, our algorithms obtain significant speed-up and enable the resyn2 sequence to be fully GPU-parallelized when combined with GPU rewriting. Experiments show that on large AIGs, we achieve average accelerations up to 45.9× over ABC with comparable or better qualities.
Fruits are some of the most nutrient-dense cash crops that can be found on Earth. Because fruits can vary greatly in size, shape, color, and texture, manually classifying and disease-detecting a big amount of fruit is...
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ISBN:
(数字)9798331508685
ISBN:
(纸本)9798331519476
Fruits are some of the most nutrient-dense cash crops that can be found on Earth. Because fruits can vary greatly in size, shape, color, and texture, manually classifying and disease-detecting a big amount of fruit is a time-consuming and error-prone procedure that demands substantial human involvement. As a hybrid ensemble method for identification and classification, this research presents a new multilayer fusion approach that combines rigorous preprocessing, segmentation, and classification of fruit images. Using this method, fruit diseases can be identified and fruits can be categorized. Few data points, different kinds of fruit, different quality standards, and inconsistent disease classifications are some of the problems they found in the existing literature that stem from relying on a single data source. They greatly aggregated and pre-processed multi-fruit data such that the resulting model could be used to large-scale datasets that included both disease recognition and fruit classification. Standard and improved images of a variety of fruits were included in the multi-fruit imagery data set. These included apple, banana, cherry, avocado, grape, blue, guava, mango, pineapple, strawberry, and papaya. Similarly, they standardised the images and built an auto-labeling system using the preexisting picture clusters to reliably place erroneous data into the correct buckets. lastly checked the auto-labeled data for correct class assignment by going over it thoroughly. Python simulations show that the proposed ensemble classifier outperforms all prior methods of fruit categorization and disease identification, with an accuracy of 96.93%.
Lung cancer has the highest rate of cancer-caused deaths, and early-stage diagnosis could increase the survival rate. Lung nodules are one of the most common lung cancer signs, therefore, the development of lung nodul...
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Fuzzy rule-based systems interpret data in low-dimensional domains, providing transparency and interpretability. In contrast, deep learning excels in complex tasks like image and speech recognition but is prone to ove...
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Trailers have been a vital part of the entertainment industry to pique audience interest. The process of trailer generation has been evolving to provide effective trailers to the public. In this paper we propose a nov...
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ISBN:
(数字)9798331531935
ISBN:
(纸本)9798331531942
Trailers have been a vital part of the entertainment industry to pique audience interest. The process of trailer generation has been evolving to provide effective trailers to the public. In this paper we propose a novel approach by utilizing the audio and video modalities to extract key trailer segments that are compiled to create an effective trailer which highlights the impactful scenes of the short film. In contrast to previous work in this domain, the novel approach gives greater emphasis to the auditory features, since auditory features have a significant impact on the film in the horror genre. The dataset is a compilation of 311 short films and their respective trailers from various reputable public sources. Our approach introduces an audio guided visual model that compliments the acoustic features ensuring that the extracted segments are key moments both in terms of audio and video. The results obtained indicate that a significant proportion of predicted segments are deemed highly trailer-worthy.
Metabolomics is a powerful tool for the understanding of biological systems by analysis of metabolites and their related pathways. Prediction of metabolic pathways is still one of the most challenging tasks because of...
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ISBN:
(数字)9798331531935
ISBN:
(纸本)9798331531942
Metabolomics is a powerful tool for the understanding of biological systems by analysis of metabolites and their related pathways. Prediction of metabolic pathways is still one of the most challenging tasks because of the complexity of molecular structures and graph-structured metabolomics data. This article presents a robust framework using Graph Convolutional Networks (GCNs) to address the challenges. The methodology proposed includes first preprocessing through metabolite identification by mass spectrometry, and then it utilizes feature extraction through the RDKit library. The objective of the research is aim to metabolic pathway prediction using machine learning algorithm. Complex patterns and relationships are captured from the SMILES representation through the molecular graphs constructed and passed on for the GCN model to learn structured data. ReLU activation functions have been employed within a three-layer sequential GCN architecture that enables it to deliver highly accurate results while ensuring that they are understandable as well. The proposed sequential GCN Model was evaluated on the KEGG dataset with an accuracy of 98.00%, precision of 92.10%, and recall of 93.02%. The performance of these metrics is well beyond traditional approaches such as KNN, ensemble logistic regression, and other GCN based approaches. Thus, this work brings GCN based approaches closer to revolutionizing metabolic pathway prediction and the advancement of the metabolomics field.
The advancement of human-computer interfaces (HCIs) has significantly impacted accurate emotion detection using electroencephalography signals. Existing methods unveil several limitations, including feature redundancy...
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ISBN:
(数字)9798331529246
ISBN:
(纸本)9798331529253
The advancement of human-computer interfaces (HCIs) has significantly impacted accurate emotion detection using electroencephalography signals. Existing methods unveil several limitations, including feature redundancy and reduced estimation accuracy for new users when utilizing pre-trained models. Therefore, this study emphasizes improved EEG-based emotion recognition by developing a Swarm Intelligence (SI) based model for feature selection. The proposed model integrates a feature extractor, feature selector, and label classifier interface. It employs time-domain (TD) and time-frequency domain (TFD) analyses to establish fundamental information for emotion recognition. Then, an Artificial Bee Colony (ABC) algorithm will be used to select the most discriminative features from the brainwave data. The Long short-term memory (LSTM) is utilized to train and classify emotions. Another swarm-based algorithm, the firefly algorithm, is used for the tuning of hyperparameters of a classification model. The proposed approach demonstrates a significant improvement in accuracy over ABC-LSTM, LSTM, DNN, RF, and NB at 8.71%, 12.7%,20.2%, 22.7%, and 20.1%, respectively. The proposed hybrid ABC-F-LSTM model with precision 96.39, a recall of 98.86, an F-measure of 97.65 and an accuracy of 92.03% performed best against state-of-the-art classifiers and represents a very effective strategy for EEG-based emotion classification.
Water quality is vital for public health and ecosystem sustainability. However, approximately 2 billion people worldwide do not have access to drinking water that is appropriately managed, contributing to significant ...
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Quantum computers, leveraging superposition and entanglement, offer significant qubit efficiency for data processing compared to classical systems. However, encoding classical data into quantum states, given the curre...
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ISBN:
(数字)9798350368741
ISBN:
(纸本)9798350368758
Quantum computers, leveraging superposition and entanglement, offer significant qubit efficiency for data processing compared to classical systems. However, encoding classical data into quantum states, given the current limitations of quantum hardware, often results in higher runtime complexity than classical methods, thus limiting the perceived quantum advantage. Previous quantum data compression methods, primarily based on Amplitude Encoding and mixed-state systems, result in lossy data recovery and necessitate extensive preprocessing. In this work, we propose Quantum Run-Length Encoding (QRLE), a novel lossless quantum data compression method that integrates Basic Encoding with Run-Length Encoding principles. By encoding repeated data sequences with their run lengths, QRLE achieves efficient and accurate data recovery on quantum computers, while exponentially reducing both qubit costs and runtime complexity compared to existing quantum data storage models. We further explore QRLE’s application in image processing, where it significantly optimizes quantum resource utilization over recent quantum image representation techniques. Experiments conducted on both quantum simulators and IBM’s superconducting quantum computer validate the efficiency of QRLE and confirm its compatibility with current quantum hardware.
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