Retrieval Applications Research Articles

Novel Hybrid Model to Estimate Leaf Carotenoids Using Multilayer Perceptron and PROSPECT Simulations

Leaf carotenoids (Cxc) play a crucial role in vegetation as essential pigments responsible for capturing sunlight and protecting leaf tissues. They provide vital insights into a plant physiological status and serve as sensitive indicators of plant stress. However, remote sensing of Cxc at the leaf level has been challenging due to the low Cxc content and weaker absorption features compared to those of chlorophylls in the visible domain. Existing vegetation indices have been widely applied but often lack a solid physical foundation, which limits their applicability and robustness in characterizing Cxc. Yet, physical models can confront this ill-posed problem, though with high operational costs. To address this issue, this study presents a novel hybrid inversion method that combines the multilayer perceptron (MLP) algorithm with PROSPECT model simulations to accurately retrieve Cxc. The effectiveness of the MLP method was investigated through comparisons with the classical PROSPECT model inversion (look-up table [LUT] method), the convolutional neural network (CNN) hybrid model, and the Transformer hybrid model. In the pooled results of six experimental datasets, the MLP method exhibited its robustness and generalization capabilities for leaf Cxc content estimation, with RMSE of 3.12 μg/cm2 and R2 of 0.52. The Transformer (RMSE = 3.14 μg/cm2, R2 = 0.46), CNN (RMSE = 3.42 μg/cm2, R2 = 0.28), and LUT (RMSE = 3.82 μg/cm2, R2 = 0.24) methods followed in descending order of accuracy. A comparison with previous studies using the same public datasets (ANGERS and LOPEX) also demonstrated the performance of the MLP method from another perspective. These findings underscore the potential of the proposed MLP hybrid method as a powerful tool for accurate Cxc retrieval applications, providing valuable insights into vegetation health and stress response.

Deep global semantic structure-preserving hashing via corrective triplet loss for remote sensing image retrieval

With the explosive increase of remote sensing data, how to search for remote sensing data quickly and accurately in a vast dataset is an incredibly critical matter for research subjects. The deep hashing method has become the dominant method for remote sensing image retrieval because of its low-cost storage and high-speed retrieval. However, for the reason of the limitation of fixed convolutional kernels, deep hashing frameworks based on Convolutional Neural Networks (CNNs) fail to obtain the global semantic information well, which leads to the generation of suboptimal solutions. Furthermore, existing hashing methods commonly employ the random sampling strategy or hardest sample mining to build training batches, resulting in bad local minima. To remedy these problems, a novel Deep Global Semantic Structure-preserving Hashing framework via corrective triplet loss (DGSSH) is proposed for remote sensing image retrieval to learn a discriminative and stable embedding space, achieving intra-class confusion and inter-class diversity. Specifically speaking, the feature extraction module based on Swim Transformer architecture is developed to capture global semantic information and multiscale features from remote sensing images. Based on a distribution matching constraint, the corrective triplet loss for deep hashing schemes is designed to reduce the distribution shift caused by the random selection or hardest sample mining. Meanwhile, to reduce the time overhead of the model, the asymmetric learning strategy is employed to perform effective compact representation learning. Numerous experiments have been carried out on three publicly available benchmarks, which indicates that the proposed DGSSH framework could achieve optimal performance for remote sensing image retrieval applications. The source code of our DGSSH framework is hosted at https://github.com/QinLab-WFU/DGSSH.git.

An application of internal multiple prediction and primary reflection retrieval to salt structures

Irregular salt bodies complicate the seismic wavefield propagation and cause multipathing, leading to intricate internal multiples that cannot be easily suppressed during data processing. To investigate the performance of internal multiple removal in salt bodies, we compare two types of Marchenko-based internal multiple removal strategies. One focuses on internal multiple prediction (IMP) based on Marchenko redatuming and seismic interferometry. With the retrieved upgoing and downgoing Green’s functions, internal multiples can be reconstructed using convolutional interferometry. The second method emphasizes primary reflection retrieval (PRR) by capturing primary reflections without adaptive subtraction. It is derived from a modified method based on the Gel’fand-Levitan-Marchenko equation. Testing with the Pluto salt model, we find that for the IMP the critical point is obtaining the purely scattering components of the upgoing and downgoing Green’s functions from the complicated wavefield. Our complex salt model indicates that the predicted internal multiples’ adaptive subtraction also introduces an extra challenge for attenuation quality. We develop a layer-by-layer adaptive subtraction scheme for the IMP to obtain primary reflections. The constant-time truncation operator in the time domain makes the PRR independent of additional model information, which can exclude internal multiples from the primary reflections. However, selecting an appropriate smoothing window for truncation requires further investigation. We evaluate the weaknesses of these methods and enhance each method individually. Our numerical results indicate that these improved schemes can correctly handle internal multiples in salt structure scenarios. In the addition, we study the robustness of the IMP when an erroneous background velocity model is used to estimate the direct arrival and the effects of sampling on the virtual receiver/source lines in IMP.

An Optimal Algorithm for Finding Champions in Tournament Graphs

A tournament graph is a complete directed graph, which can be used to model a round-robin tournament between $n$ players. In this paper, we address the problem of finding a champion of the tournament, also known as Copeland winner, which is a player that wins the highest number of matches. In detail, we aim to investigate algorithms that find the champion by playing a low number of matches. Solving this problem allows us to speed up several Information Retrieval and Recommender System applications, including question answering, conversational search, etc. Indeed, these applications often search for the champion inducing a round-robin tournament among the players by employing a machine learning model to estimate who wins each pairwise comparison. Our contribution, thus, allows finding the champion by performing a low number of model inferences. We prove that any deterministic or randomized algorithm finding a champion with constant success probability requires $\Omega(\ell n)$ comparisons, where $\ell$ is the number of matches lost by the champion. We then present an asymptotically-optimal deterministic algorithm matching this lower bound without knowing $\ell$, and we extend our analysis to three variants of the problem. Lastly, we conduct a comprehensive experimental assessment of the proposed algorithms on a question answering task on public data. Results show that our proposed algorithms speed up the retrieval of the champion up to $13\times$ with respect to the state-of-the-art algorithm that perform the full tournament.

NNFSRR: Nearest Neighbor Feature Selection and Redundancy Removal Method for Nearest Neighbor Search in Microarray Gene Expression Data

INTRODUCTION: Gene expression data analysis is a critical aspect of disease prediction and classification, playing a pivotal role in the field of bioinformatics and biomedical research. High-dimensional gene expression datasets hold a wealth of information, but their effective utilization is hindered by the presence of irrelevant dimensions and noise. The challenge lies in extracting meaningful features from these datasets to enhance the accuracy of disease prediction and classification while maintaining computational efficiency.
 Feature selection is a crucial step in addressing these challenges, as it aims to identify and retain only the most informative characteristics from large high-dimensional microarray datasets. In the context of microarray gene expression data, characterized by its substantial dimensionality, selecting relevant features is essential for efficient nearest neighbor search, a fundamental component of various analytical tasks in bioinformatics and data mining.
 Existing feature selection methods in high-dimensional data often face issues related to the trade-off between search accuracy and computational efficiency. This paper introduces a novel approach, the Nearest Neighbor Feature Selection with Symmetrical Uncertainty-based Redundancy Removal (NNFSRR) method, designed to enhance the classification of microarray gene expression data through feature selection. The NNFSRR method focuses on reducing the dimensionality of the dataset by identifying and removing redundant features, allowing subsequent searches to operate solely on relevant dimensions.
 OBJECTIVES: The primary goal is to evaluate the NNFSRR method's effectiveness in improving nearest neighbor search in microarray gene expression datasets by reducing dimensionality. This method utilizes Symmetrical Uncertainty-based correlation between dimensions for feature selection and aims to enhance accuracy and efficiency compared to existing methods.
 METHODS: The NNFSRR method uses Symmetrical Uncertainty to identify and remove redundant features from microarray gene expression datasets. Reduced datasets are used for nearest neighbor search, improving accuracy and efficiency. Experiments are conducted using real-world datasets, and comparisons with existing methods are made based on search time and accuracy.
 RESULTS: The NNFSRR method demonstrates improved nearest neighbor search performance, outperforming basic brute force methods and existing feature selection techniques. Selected feature sets exhibit strong class associations while minimizing feature correlations, enhancing classification precision.
 CONCLUSION: In conclusion, the NNFSRR method presents a promising approach to address the challenges posed by high-dimensional gene expression data. It effectively reduces dimensionality, improves search accuracy, and enhances the efficiency of nearest neighbor search. Our experimental results demonstrate that this method outperforms existing techniques in terms of search time and accuracy, making it a valuable tool for applications in bioinformatics, data mining, pattern recognition, and biological information retrieval. The NNFSRR method holds the potential to advance our understanding of complex biological processes and support more accurate disease prediction and classification.

Bathymetry Inversion Using Attention-Based Band Optimization Model for Hyperspectral or Multispectral Satellite Imagery

Satellite-derived bathymetry enables the non-contact derivation of large-scale shallow water depths. Hyperspectral satellite images provide more information than multispectral satellite images, making them theoretically more effective and accurate for bathymetry inversion. This paper focuses on the use of hyperspectral satellite images (PRISMA) for bathymetry inversion and compares the retrieval capabilities of multispectral satellite images (Sentinel-2 and Landsat 9) in the southeastern waters of Molokai Island in the Hawaiian Archipelago and Yinyu Island in the Paracel Archipelago. This paper proposes an attention-based band optimization one-dimensional convolutional neural network model (ABO-CNN) to better utilize the increased spectral information from multispectral and hyperspectral images for bathymetry inversion, and this model is compared with a traditional empirical model (Stumpf model) and two deep learning models (feedforward neural network and one-dimensional convolutional neural network). The results indicate that the ABO-CNN model outperforms the above three models, and the root mean square errors of retrieved bathymetry using the PRISMA images are 1.43 m and 0.73 m in the above two study areas, respectively. In summary, this paper demonstrates that PRISMA hyperspectral imagery has superior bathymetry inversion capabilities compared to multispectral images (Sentinel-2 and Landsat 9), and the proposed deep learning model ABO-CNN is a promising candidate model for satellite-derived bathymetry using hyperspectral imagery. With the increasing availability of ICESat-2 bathymetric data, the use of a combination of the proposed ABO-CNN model and the ICEsat-2 data as the training data provides a practical approach for bathymetric retrieval applications.

Design of CSD based bi-orthogonal wavelet filter bank for medical image retrieval

Two-channel wavelet filter-banks (FBs) are universally used in many applications of signal processing. In this work, design of a bi-orthogonal wavelet FB with low complexity is suggested. The proposed design is based on canonical signed digit (CSD) implementation of the bi-orthogonal wavelet FB. CSD technique has been applied to the FB to eliminate the power consuming multipliers and to reduce the number of shifters and adders in the implementation of bi-orthogonal FB. The suggested FB design is suitable for the application of high speed signal processing applications. In addition, the proposed bi-orthogonal wavelet FB is implemented on hardware using Field Programmable Gate Array (FPGA) board Kintex-7 from Xilinx. It is found that the suggested bi-orthogonal wavelet FB attained low digital hardware requirement as compared to well-known rationalized existing bi-orthogonal wavelet FBs. The efficacy of the suggested wavelet FB is demonstrated on medical image retrieval application. NEMA, OASIS, and EXACT09, three publicly accessible medical image datasets, are utilized to measure the efficacy of image retrieval. It is shown that the suggested bi-orthogonal wavelet FB performs better than the bi-orthogonal wavelet FBs existing that are being used.

Comparison of Multiple Machine Learning Algorithms for Music Genre Classification

With the fast advance of the Internet and the continuous improvement of computer technology, speech recognition has been applied in many fields, and speech recognition has broad prospects for development. Music and audio classification technology can add category labels to music based on music content, which is of great significance in the research and application of efficient organization, retrieval and recommendation of music resources. In order to efficiently classify audio from massive online music data and help users to obtain the most suitable music style, a deep learning classification algorithm based on convolutional neural network (CNN) is proposed. To examine its effectiveness, it is compared with traditional machine learning algorithm. First, the original music data set was preprocessed and then feature extraction was carried out to obtain music features and transform them into spectral maps. Traditional machine learning model and deep learning component model were used for simulation experiments. The testing accuracy of the deep learning model is up to 92%, verifying the model's superiority.

A DCT probability histogram-based ROI features for content-based natural and medical image retrieval applications

The effectiveness of a Content-based Image Retrieval (CBIR) system depends on selecting representative and salient visual features. Capturing the salient image features for forming a feature vector is an essential part of the CBIR process, as it can significantly affect retrieval efficiency. Most images contain a visually prominent region that perceives maximum image semantic information, known as Regions-of-Interest (ROIs). This paper suggests a new CBIR scheme that employs a Graph-based Visual Saliency (GBVS) map to extract the ROI of the image. Further, block-level Discrete Cosine Transforms (DCT) and subsequent histograms are computed for the DC and significant AC coefficients of the extracted ROI image. In addition, a low-dimensional feature vector is formed from the modified histograms. Subsequently, six statistical features from the background scene have also been computed for the final feature vector construction. As a result, the final feature vector contains ROI and background information in their proper proportion to improve retrieval performance. Moreover, the performance of the proposed CBIR scheme has been evaluated using one medical, three natural, two objects, one texture, and one natural heterogeneous image database. All these databases collectively contain 53732 different images from 200 image classes. The outcomes of the experiments demonstrate substantial improvement in the retrieval process with some existing related schemes. At the same time, it also shows that the performance of the proposed scheme is robust concerning all kinds of image databases.

Enhanced Detection of Charged N-Glycans in the Brain by Infrared Matrix-Assisted Laser Desorption Electrospray Ionization Mass Spectrometric Imaging.

N-linked glycosylation represents a structurally diverse, complex, co- and posttranslational protein modification that bridges metabolism and cellular signaling. Consequently, aberrant protein glycosylation is a hallmark of most pathological scenarios. Due to their complex nature and non-template-driven synthesis, the analysis of glycans is faced with several challenges, underlining the need for new and improved analytical technologies. Spatial profiling of N-glycans through direct imaging on tissue sections reveals the regio-specific and/or disease pathology correlating tissue N-glycans that serve as a disease glycoprint for diagnosis. Infrared matrix-assisted laser desorption electrospray ionization (IR-MALDESI) is a soft hybrid ionization technique that has been used for diverse mass spectrometry imaging (MSI) applications. Here, we report the first spatial analysis of the brain N-linked glycans by IR-MALDESI MSI, leading to a significant increase in the detection of the brain N-sialoglycans. A formalin-fixed paraffin-embedded mouse brain tissue was analyzed in negative ionization mode after tissue washing, antigen retrieval, and pneumatic application of PNGase F for enzymatic digestion of N-linked glycans. We report a comparative analysis of section thickness on the N-glycan detection using IR-MALDESI. One hundred thirty-six unique N-linked glycans were confidently identified in the brain tissue (with an additional 132 unique N-glycans, not reported in GlyConnect), where more than 50% contained sialic acid residues, which is approximately 3-fold higher than the previous reports. This work demonstrates the first application of IR-MALDESI in N-linked glycan imaging of the brain tissue, leading to a 2.5-fold increase in the in situ total brain N-glycan detection compared to the current gold standard of positive-mode matrix-assisted laser desorption/ionization mass spectrometry imaging. This is also the first report of the application of the MSI toward the identification of sulfoglycans in the rodent brain. Overall, IR-MALDESI-MSI presents a sensitive glycan detection platform to identify tissue-specific and/or disease-specific glycosignature in the brain while preserving the sialoglycans without any chemical derivatization.

TransVCL: Attention-Enhanced Video Copy Localization Network with Flexible Supervision

Video copy localization aims to precisely localize all the copied segments within a pair of untrimmed videos in video retrieval applications. Previous methods typically start from frame-to-frame similarity matrix generated by cosine similarity between frame-level features of the input video pair, and then detect and refine the boundaries of copied segments on similarity matrix under temporal constraints. In this paper, we propose TransVCL: an attention-enhanced video copy localization network, which is optimized directly from initial frame-level features and trained end-to-end with three main components: a customized Transformer for feature enhancement, a correlation and softmax layer for similarity matrix generation, and a temporal alignment module for copied segments localization. In contrast to previous methods demanding the handcrafted similarity matrix, TransVCL incorporates long-range temporal information between feature sequence pair using self- and cross- attention layers. With the joint design and optimization of three components, the similarity matrix can be learned to present more discriminative copied patterns, leading to significant improvements over previous methods on segment-level labeled datasets (VCSL and VCDB). Besides the state-of-the-art performance in fully supervised setting, the attention architecture facilitates TransVCL to further exploit unlabeled or simply video-level labeled data. Additional experiments of supplementing video-level labeled datasets including SVD and FIVR reveal the high flexibility of TransVCL from full supervision to semi-supervision (with or without video-level annotation). Code is publicly available at https://github.com/transvcl/TransVCL.

Librarians’ ICT skills and service delivery in private universities in Nigeria

This paper examined the Information and Communication Technology (ICT) skills of librarians for service delivery in private universities with particular reference to Southern Nigeria. The study adopted the survey research method. Forty-five (45) private university libraries in Southern Nigeria and their librarians constituted the sample size for the study. The study’s samples consisted of 181 librarians in the private universities, hence, total enumeration was adopted. Questionnaire was used for data collection from the respondents. Out of the 181 copies of the questionnaire distributed, 173 were returned and found valid for analysis. Data gathered was analysed using the Statistical Package for Social Sciences (SPSS) Version 21 and the results were presented in frequency count, percentage, mean and chart. Findings of the study revealed that librarians in private universities in Southern Nigeria delivered various library services to their users. The librarians have acquired basic ICT skills and were versatile in its application for Internet searches, information retrieval, social communication and Microsoft-office related activities with the exception of programming skills. Majority of the librarians acquired their ICT skills through self-learning. The finding revealed that the acquisition of ICT skills by librarians in Southern Nigeria private universities is relevant to their delivery of improved library services. The paper recommended that regular acquisition of relevant ICT skills is essential for librarians and university management should support librarians’ ICT capacity development at national and international levels as this would enhance their relevance and get them abreast of the latest development for effective service delivery.

Content-Based remote sensing image retrieval method using adaptive tetrolet transform based GLCM features

This paper proposed a novel texture feature extraction technique for radar remote sensing image retrieval application using adaptive tetrolet transform and Gray level co-occurrence matrix. Tetrolets have provided fine texture information in the radar image. Tetrominoes have been employed on each decomposed radar image and best pattern of tetrominoes has been chosen which represents the better radar image geometry at each decomposition level. All three high pass components of the decomposed radar image at each level and low pass component at the last level are considered as input values for Gray level co-occurrence matrix (GLCM), where GLCM provides the spatial relationship among the pixel values of decomposed components in different directions at certain distances. The GLCMs of decomposed components are computed in (1). (0, π/2, π, 3π/2), (2). (π/4, 3π/4, 5π/4, 7π/4) (3). (0, π/4, π/2, 3π/4, π, 3π/2, 5π/4, 7π/4) directions individually and subsequently a texture feature descriptor is constructed by computing statistical parameters from the corresponding GLCMs. The retrieval performance is validated on two standard radar remote sensing image databases: 20-class satellite remote sensing dataset and 21-class land-cover dataset. The average metrices i.e., precision, recall and F-score are 61.43%, 12.29% and 20.47% for 20-class satellite remote sensing dataset while 21-class land-cover dataset have achieved 67.75%, 9.03% and 15.94% average metrices. The retrieved results show the better accuracy as compared to the other related state of arts radar remote sensing image retrieval methods.

Human Behavior Recognition via Hierarchical Patches Descriptor and Approximate Locality-Constrained Linear Coding.

Human behavior recognition technology is widely adopted in intelligent surveillance, human-machine interaction, video retrieval, and ambient intelligence applications. To achieve efficient and accurate human behavior recognition, a unique approach based on the hierarchical patches descriptor (HPD) and approximate locality-constrained linear coding (ALLC) algorithm is proposed. The HPD is a detailed local feature description, and ALLC is a fast coding method, which makes it more computationally efficient than some competitive feature-coding methods. Firstly, energy image species were calculated to describe human behavior in a global manner. Secondly, an HPD was constructed to describe human behaviors in detail through the spatial pyramid matching method. Finally, ALLC was employed to encode the patches of each level, and a feature coding with good structural characteristics and local sparsity smoothness was obtained for recognition. The recognition experimental results on both Weizmann and DHA datasets demonstrated that the accuracy of five energy image species combined with HPD and ALLC was relatively high, scoring 100% in motion history image (MHI), 98.77% in motion energy image (MEI), 93.28% in average motion energy image (AMEI), 94.68% in enhanced motion energy image (EMEI), and 95.62% in motion entropy image (MEnI).

Efficient discrete cross-modal hashing with semantic correlations and similarity preserving

With its merits in query speed and memory footprint, hashing has elicited considerable monument in cross-media similarity retrieval applications. Many label-dependent supervised hashing methods have been proposed for similarity searching across modality boundaries. However, the current cross-modal hashing (CMH) works are subjected to severe information loss, and their performances may dramatically degrade because of the expensive costs of constructing affinity graphs, inadequate mining of label information, and disregarding label correlations. To facilitate these problems, we propose an Efficient Discrete Cross-Modal Hashing (EDCH) in which an asymmetric model is introduced, which not only conveys external semantic information via embedding high-order labels but also preserves internal modality attributes by introducing binary representations and common subspace. To fully use label semantic information, we integrate the semantic supervised intersection scheme and the category correlations embedding in a shallow framework. Moreover, we elaborately develop an efficient and effective discrete optimization strategy to learn binary representations and a novel mutual linear projection to strengthen the capability and effectiveness of hash functions. Comprehensive experiments are conducted on three representative datasets to evaluate our method. The results validated that our method achieved promising and competitive retrieval performance and surpasses several typical and cutting-edge approaches.

Extraction and normalization of IR indexing terms and phrases in a highly inflectional language

Abstract Term-based indexing of documents is conventionally implemented by stemmers or their corpus-based improvements, both of which encode implicit linguistic information. Terms are directly derived from document content such that a unique indexing approach is available at indexing run-time. For highly inflectional languages where term variation is high, such techniques are more error-prone. The main focus of the current study is the extraction and normalization of single terms and phrases and the proposal of authenticated control of indexing. The proposed approach relies on the use of explicit linguistic knowledge, appropriately encoded in large language resources. Such control guarantees the highest possible expansion factor for indexing terms as well as indexing consistency. Moreover, it offers a framework where different and eventually contradicting indexing criteria can be practiced, conventional and Natural Language Processing (NLP)-based Information Retrieval (IR) applications can be served, while adaptations can be made for tuning to a specific domain or corpus.

Environmentally Friendly Plastic Boats - A Facile Strategy for Cleaning Oil Spills on Water with Excellent Efficiency.

In this report, we demonstrate a novel plastic boat capable of selectively and efficiently collecting spilled oils while floatingon water. The boat has macroscopic openings in its vertical and curved sidewalls. It is easily, quickly, and inexpensively fabricated using an environmentally friendly polymer via a three-dimensional printing technique. Its surface is sequentially coated with nano-ceramic coating liquid and oil, which imparts favorable hydrophobic, oleophilic, and high oil-wettability properties. Using the boat prototype, a small pump system, and an oil boom-like device, we demonstrate that spilled oils with a wide range of viscosities (2.0-1000 cSt at 25-40°C) are rapidly collected from the surface of both pure water and seawater. Remarkably, it efficiently collects oil spills on seawater under wavy conditions, and the retrieved oil does not mix with any drop of water. Moreover, the boat can be scaled up to a large size easily and has a long-term usage. By exhibiting these characteristics, our developed boat is a prominent potential device for practical oil retrieval applications.

ENHANCED AI BASED FEATURE EXTRACTION TECHNIQUE IN MULTIMEDIA IMAGE RETRIEVAL

In the era of rapid technological advancements, the demand for efficient and accurate identification and retrieval of information from multimedia images has seen a substantial increase. To meet this growing demand, artificial intelligence (AI)-based technologies, particularly feature extraction techniques, have gained significant popularity. Feature extraction involves the extraction of salient features from multimedia images, such as edges, lines, curves, textures, and colors, with the aim of representing the data in a more suitable format for analysis. This paper presents an enhanced AI-based feature extraction technique for multimedia image retrieval. The proposed method introduces a novel approach that combines the power of deep learning and evolutionary algorithms in a neuro-symbolic computation framework. Specifically, the renowned VGG16 deep learning algorithm is employed as the initial feature extractor. VGG16 is a state-of-the-art deep convolutional neural network that has demonstrated exceptional performance in various computer vision tasks, including image classification and feature extraction. The primary idea behind this approach is to leverage the capabilities of AI to extract the most discriminative features from the source images using VGG16. These features are then further refined using evolutionary algorithms, which employ a search and optimization process inspired by natural evolution. By iteratively improving the extracted features through the evolutionary algorithms, the method aims to enhance the discriminative power and representational quality of the extracted features. To evaluate the performance of the proposed approach, extensive experiments were conducted. The results demonstrate that the method achieves superior performance in terms of precision, recall, and F-measure when compared to conventional feature extraction techniques. Furthermore, a comprehensive comparison with state-of-the-art AI-based feature extraction techniques further highlights the potential and effectiveness of the proposed approach in multimedia image retrieval applications.

Semantic Web technologies and bias in artificial intelligence: A systematic literature review

Bias in Artificial Intelligence (AI) is a critical and timely issue due to its sociological, economic and legal impact, as decisions made by biased algorithms could lead to unfair treatment of specific individuals or groups. Multiple surveys have emerged to provide a multidisciplinary view of bias or to review bias in specific areas such as social sciences, business research, criminal justice, or data mining. Given the ability of Semantic Web (SW) technologies to support multiple AI systems, we review the extent to which semantics can be a “tool” to address bias in different algorithmic scenarios. We provide an in-depth categorisation and analysis of bias assessment, representation, and mitigation approaches that use SW technologies. We discuss their potential in dealing with issues such as representing disparities of specific demographics or reducing data drifts, sparsity, and missing values. We find research works on AI bias that apply semantics mainly in information retrieval, recommendation and natural language processing applications and argue through multiple use cases that semantics can help deal with technical, sociological, and psychological challenges.

Capacity of Sun-driven lunar swingby sequences and their application in asteroid retrieval

For deep-space mission design, the gravity of the Sun and the Moon can be first considered and utilized. Their gravity can provide the energy change for launching spacecraft and retrieving spacecraft as well as asteroids. Regarding an asteroid retrieval mission, it can lead to the mitigation of asteroid hazards and an easy exploration and exploitation of the asteroid. This paper discusses the application of the Sun-driven lunar swingby sequence for asteroid missions. Characterizing the capacity of this technique is not only interesting in terms of dynamic insights but also non-trivial for trajectory design. The capacity of a Sun-driven lunar swingby sequence is elucidated in this paper with the help of the "Swingby-Jacobi" graph. The capacity can be represented by a range of the Jacobi integral that encloses around 660 asteroids currently cataloged. To facilitate trajectory design, a database of Sun-perturbed Moon-to-Moon transfers, including multi-revolution cases is generated and employed. Massive trajectory options for spacecraft launch and asteroid capture can then be explored and optimized. Finally, a number of asteroid flyby, rendezvous, sample-return, and retrieval mission options enabled by the proposed technique are obtained.