Applied Sciences

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17 pages, 4873 KiB

Open AccessArticle

An Ensemble Approach for Speaker Identification from Audio Files in Noisy Environments

by Syed Shahab Zarin, Ehzaz Mustafa, Sardar Khaliq uz Zaman, Abdallah Namoun and Meshari Huwaytim Alanazi

Appl. Sci. 2024, 14(22), 10426; https://doi.org/10.3390/app142210426 - 13 Nov 2024

Viewed by 387

Abstract

Automatic noise-robust speaker identification is essential in various applications, including forensic analysis, e-commerce, smartphones, and security systems. Audio files containing suspect speech often include background noise, as they are typically not recorded in soundproof environments. To this end, we address the challenges of [...] Read more.

Automatic noise-robust speaker identification is essential in various applications, including forensic analysis, e-commerce, smartphones, and security systems. Audio files containing suspect speech often include background noise, as they are typically not recorded in soundproof environments. To this end, we address the challenges of noise robustness and accuracy in speaker identification systems. An ensemble approach is proposed combining two different neural network architectures including an RNN and DNN using softmax. This approach enhances the system’s ability to identify speakers even in noisy environments accurately. Using softmax, we combine voice activity detection (VAD) with a multilayer perceptron (MLP). The VAD component aims to remove noisy frames from the recording. The softmax function addresses these residual traces by assigning a higher probability to the speaker’s voice compared to the noise. We tested our proposed solution on the Kaggle speaker recognition dataset and compared it to two baseline systems. Experimental results show that our approach outperforms the baseline systems, achieving a 3.6% and 5.8% increase in test accuracy. Additionally, we compared the proposed MLP system with Long Short-Term Memory (LSTM) and Bidirectional LSTM (BiLSTM) classifiers. The results demonstrate that the MLP with VAD and softmax outperforms the LSTM by 23.2% and the BiLSTM by 6.6% in test accuracy. Full article

(This article belongs to the Special Issue Advances in Intelligent Information Systems and AI Applications)

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16 pages, 1371 KiB

Open AccessArticle

An Application Programming Interface (API) Sensitive Data Identification Method Based on the Federated Large Language Model

by Jianping Wu, Lifeng Chen, Siyuan Fang and Chunming Wu

Appl. Sci. 2024, 14(22), 10162; https://doi.org/10.3390/app142210162 - 6 Nov 2024

Viewed by 425

Abstract

The traditional methods for identifying sensitive data in APIs mainly encompass rule-based and machine learning-based approaches. However, these methods suffer from inadequacies in terms of security and robustness, exhibit high false positive rates, and struggle to cope with evolving threat landscapes. This paper [...] Read more.

The traditional methods for identifying sensitive data in APIs mainly encompass rule-based and machine learning-based approaches. However, these methods suffer from inadequacies in terms of security and robustness, exhibit high false positive rates, and struggle to cope with evolving threat landscapes. This paper proposes a method for detecting sensitive data in APIs based on the Federated Large Language Model (FedAPILLM). This method applies the large language model Qwen2.5 and the LoRA instruction tuning technique within the framework of federated learning (FL) to the field of data security. Under the premise of protecting data privacy, a domain-specific corpus and knowledge base are constructed for pre-training and fine-tuning, resulting in a large language model specifically designed for identifying sensitive data in APIs. This paper conducts comparative experiments involving Llama3 8B, Llama3.1 8B, and Qwen2.5 14B. The results demonstrate that Qwen2.5 14B can achieve similar or better performance levels compared to the Llama3.1 8B model with fewer training iterations. Full article

(This article belongs to the Special Issue Advances in Intelligent Information Systems and AI Applications)

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15 pages, 1686 KiB

Open AccessArticle

Artificial Punishment Signals for Guiding the Decision-Making Process of an Autonomous System

by Daniel Cabrera-Paniagua, Rolando Rubilar-Torrealba, Nelson Castro and Joaquín Taverner

Appl. Sci. 2024, 14(17), 7595; https://doi.org/10.3390/app14177595 - 28 Aug 2024

Viewed by 557

Abstract

Somatic markers have been evidenced as determinant factors in human behavior. In particular, the concepts of somatic reward and punishment have been related to the decision-making process; both reward and somatic punishment represent bodily states with positive or negative sensations, respectively. In this [...] Read more.

Somatic markers have been evidenced as determinant factors in human behavior. In particular, the concepts of somatic reward and punishment have been related to the decision-making process; both reward and somatic punishment represent bodily states with positive or negative sensations, respectively. In this research work, we have designed a mechanism to generate artificial somatic punishments in an autonomous system. An autonomous system is understood as a system capable of performing autonomous behavior and decision making. We incorporated this mechanism within a decision model oriented to support decision making on stock markets. Our model focuses on using artificial somatic punishments as a tool to guide the decisions of an autonomous system. To validate our proposal, we defined an experimental scenario using official data from Standard & Poor’s 500 and the Dow Jones index, in which we evaluated the decisions made by the autonomous system based on artificial somatic punishments in a general investment process using 10,000 independent iterations. In the investment process, the autonomous system applied an active investment strategy combined with an artificial somatic index. The results show that this autonomous system presented a higher level of investment decision effectiveness, understood as the achievement of greater wealth over time, as measured by profitability, utility, and Sharpe Ratio indicators, relative to an industry benchmark. Full article

(This article belongs to the Special Issue Advances in Intelligent Information Systems and AI Applications)

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32 pages, 3289 KiB

Open AccessArticle

The Influence of AI and AR Technology in Personalized Recommendations on Customer Usage Intention: A Case Study of Cosmetic Products on Shopee

by Siti Rabiatul Adawiyah, Betty Purwandari, Imairi Eitiveni and Erisva Hakiki Purwaningsih

Appl. Sci. 2024, 14(13), 5786; https://doi.org/10.3390/app14135786 - 2 Jul 2024

Cited by 1 | Viewed by 4998

Abstract

With the rapid growth of Augmented Reality (AR) in e-commerce, it is necessary to conduct in-depth studies related to the integration of AR with Artificial Intelligence (AI) technology to improve personalization in product recommendations. AI and AR are expected to work together, where [...] Read more.

With the rapid growth of Augmented Reality (AR) in e-commerce, it is necessary to conduct in-depth studies related to the integration of AR with Artificial Intelligence (AI) technology to improve personalization in product recommendations. AI and AR are expected to work together, where AI systems analyze consumer data to provide more suitable product recommendations, while AR helps consumers visualize these recommendations in the real world. This research explores the influence of AI and AR technology integration in personalized recommendations on customer usage intention, with a focus on the cosmetics industry on the e-commerce platform Shopee. Adopting the Technology Acceptance Model (TAM) and the Theory of Planned Behavior (TPB), this research uses quantitative methods with the try-on feature of cosmetic products as the main focus. With a total number of 387 respondents, the results of the PLS-SEM analysis revealed factors impacting the intention to use, such as perceived ease of use, perceived usefulness, and users’ perceived trust, on the intention to use personalized recommendations. This research contributes to the understanding of AI–AR technology acceptance in the context of online shopping, particularly in the cosmetics sector. Full article

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21 pages, 1055 KiB

Open AccessArticle

Automatic Translation between Mixtec to Spanish Languages Using Neural Networks

by Hermilo Santiago-Benito , Diana-Margarita Córdova-Esparza , Noé-Alejandro Castro-Sánchez , Teresa García-Ramirez , Julio-Alejandro Romero-González and Juan Terven

Appl. Sci. 2024, 14(7), 2958; https://doi.org/10.3390/app14072958 - 31 Mar 2024

Viewed by 1093

Abstract

This paper introduces a novel method for collecting and translating texts from the Mixtec to the Spanish language. The method comprises four primary steps. First, we collected a Mixtec–Spanish corpus that includes 4568 sentences from educational and religious domain texts. To enhance the [...] Read more.

This paper introduces a novel method for collecting and translating texts from the Mixtec to the Spanish language. The method comprises four primary steps. First, we collected a Mixtec–Spanish corpus that includes 4568 sentences from educational and religious domain texts. To enhance the parallel corpus, we generate synthetic data with GPT-3.5. Second, we cleaned the data with a semi-automatic approach followed by preprocessing and tokenization. In preprocessing, we removed stop words, duplicated sentences, special characters, and numbers and converted them to lowercase. Third, we performed semi-automatic alignment to find the correspondence of Mixtec–Spanish sentences to generate sentence-level aligned texts necessary for translation. Finally, we trained automatic translation models based on recurrent neural networks, bidirectional recurrent neural networks, and Transformers. Our system achieved a BLEU score of 95.66 for Mixtec-to-Spanish translation and 99.87 for Spanish-to-Mixtec translation. We also obtained a translation edit rate (TER) of 0.5 for Spanish-to-Mixtec and a TER of 16.5 for Mixtec-to-Spanish. Our research stands out as a pioneering effort in the field of automatic Mixtec-to-Spanish translation in Mexico, filling a gap identified in the current literature. Full article

(This article belongs to the Special Issue Advances in Intelligent Information Systems and AI Applications)

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23 pages, 10484 KiB

Open AccessArticle

Ball Tracking Based on Multiscale Feature Enhancement and Cooperative Trajectory Matching

by Xiao Han, Qi Wang and Yongbin Wang

Appl. Sci. 2024, 14(4), 1376; https://doi.org/10.3390/app14041376 - 7 Feb 2024

Cited by 1 | Viewed by 1191

Abstract

Most existing object tracking research focuses on pedestrians and autonomous driving while ignoring sports scenes. When general object tracking models are used for ball tracking, there are often problems, such as detection omissions due to small object sizes and trajectory loss due to [...] Read more.

Most existing object tracking research focuses on pedestrians and autonomous driving while ignoring sports scenes. When general object tracking models are used for ball tracking, there are often problems, such as detection omissions due to small object sizes and trajectory loss due to occlusion. To address these challenges, we propose a ball detection and tracking model called HMMATrack based on multiscale feature enhancement and multilevel collaborative matching to improve ball-tracking results from the entire process of sampling, feature extraction, detection, and tracking. It includes a Heuristic Compound Sampling Strategy to deal with tiny sizes and imbalanced data samples; an MNet-based detection module to improve the ball detection accuracy; and a multilevel cooperative matching and automatic trajectory correction tracking algorithm that can quickly and accurately correct the ball’s trajectory. We also hand-annotated SportsTrack, a ball-tracking dataset containing soccer, basketball, and volleyball scenes. Extensive experiments are conducted on the SportsTrack, demonstrating that our proposed HMMATrack model outperforms other representative state-of-the-art models in ball detection and tracking. Full article

(This article belongs to the Special Issue Advances in Intelligent Information Systems and AI Applications)

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24 pages, 4130 KiB

Open AccessArticle

Random Forests Machine Learning Applied to PEER Structural Performance Experimental Columns Database

by Konstantinos G. Megalooikonomou and Grigorios N. Beligiannis

Appl. Sci. 2023, 13(23), 12821; https://doi.org/10.3390/app132312821 - 29 Nov 2023

Cited by 5 | Viewed by 2942

Abstract

Columns play a very important role in structural performance and, therefore, this paper contributes to the critical need for failure mode prediction of reinforced concrete (RC) columns by exploring the capabilities of random forest machine learning (ML) based on a well-known experimental column [...] Read more.

Columns play a very important role in structural performance and, therefore, this paper contributes to the critical need for failure mode prediction of reinforced concrete (RC) columns by exploring the capabilities of random forest machine learning (ML) based on a well-known experimental column database. Known as the PEER structural performance database, it assembles the results of over 400 cyclic, lateral-load tests of reinforced concrete columns. The database describes tests of spiral or circular hoop-confined columns, rectangular tied columns and columns with or without lap splices of longitudinal reinforcement at the critical sections. The efficiency towards the aforementioned goal of supervised ML methods such as random forests using a randomly assigned test set from the Pacific Earthquake Engineering Research Center (PEER) database is examined here. The overall accuracy score for rectangular RC columns is 94% and for circular RC columns is 86%. The latter performances are influenced by the size of the testing and training sets of data and are independent of the number of decision trees in the forest employed in the random forest algorithm. The performances of random forests in postdicting the failure mode of RC columns prove that ML has great promise in revolutionizing the profession of earthquake engineering. Full article

(This article belongs to the Special Issue Advances in Intelligent Information Systems and AI Applications)

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30 pages, 2053 KiB

Open AccessArticle

Determinants of Emotion Recognition System Adoption: Empirical Evidence from Malaysia

by Muhammad Nadzree Mohd Yamin, Kamarulzaman Ab. Aziz, Tan Gek Siang and Nor Azlina Ab. Aziz

Appl. Sci. 2023, 13(21), 11854; https://doi.org/10.3390/app132111854 - 30 Oct 2023

Viewed by 2457

Abstract

Emotion recognition systems (ERS) are an emerging technology with immense potential, exemplifying the innovative utilization of artificial intelligence (AI) within the context of the fourth industrial revolution (IR 4.0). Given that personalization is a key feature of the fifth industrial revolution (IR 5.0), [...] Read more.

Emotion recognition systems (ERS) are an emerging technology with immense potential, exemplifying the innovative utilization of artificial intelligence (AI) within the context of the fourth industrial revolution (IR 4.0). Given that personalization is a key feature of the fifth industrial revolution (IR 5.0), ERS has the potential to serve as an enabler for IR 5.0. Furthermore, the COVID-19 pandemic has increased the relevance of this technology as work processes were adapted for social distancing and the use of face masks. Even in the post-pandemic era, many individuals continue to wear face masks. Therefore, ERS offers a technological solution to address communication challenges in a masked world. The existing body of knowledge on ERS primarily focuses on exploring modalities or modes for emotion recognition, system development, and the creation of applications utilizing emotion recognition functions. However, to enhance the development of impactful ERS, it is essential for researchers and innovators to understand the factors that influence its usage and adoption among the intended users. Therefore, this study presents a framework that combines technology adoption theories to identify the determinants of ERS adoption among Malaysian youth. Data for this study were collected through a survey involving 386 respondents. The findings revealed attitudes, subjective norms, perceived behavioral control, and awareness as significant determinants of ERS adoption. Additionally, the study found that technology aptitude plays a moderating role. These insights can inform the formulation of effective policies and programs to encourage and facilitate the development of innovative ERS solutions. Full article

(This article belongs to the Special Issue Advances in Intelligent Information Systems and AI Applications)

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14 pages, 985 KiB

Open AccessArticle

Estimation of Obesity Levels with a Trained Neural Network Approach optimized by the Bayesian Technique

by Fatma Hilal Yagin, Mehmet Gülü, Yasin Gormez, Arkaitz Castañeda-Babarro, Cemil Colak, Gianpiero Greco, Francesco Fischetti and Stefania Cataldi

Appl. Sci. 2023, 13(6), 3875; https://doi.org/10.3390/app13063875 - 18 Mar 2023

Cited by 11 | Viewed by 4144

Abstract

Background: Obesity, which causes physical and mental problems, is a global health problem with serious consequences. The prevalence of obesity is increasing steadily, and therefore, new research is needed that examines the influencing factors of obesity and how to predict the occurrence [...] Read more.

Background: Obesity, which causes physical and mental problems, is a global health problem with serious consequences. The prevalence of obesity is increasing steadily, and therefore, new research is needed that examines the influencing factors of obesity and how to predict the occurrence of the condition according to these factors. This study aimed to predict the level of obesity based on physical activity and eating habits using the trained neural network model. Methods: The chi-square, F-Classify, and mutual information classification algorithms were used to identify the most critical factors associated with obesity. The models’ performances were compared using a trained neural network with different feature sets. The hyperparameters of the models were optimized using Bayesian optimization techniques, which are faster and more effective than traditional techniques. Results: The results predicted the level of obesity with average accuracies of 93.06%, 89.04%, 90.32%, and 86.52% for all features using the neural network and for the features selected by the chi-square, F-Classify, and mutual information classification algorithms. The results showed that physical activity, alcohol consumption, use of technological devices, frequent consumption of high-calorie meals, and frequency of vegetable consumption were the most important factors affecting obesity. Conclusions: The F-Classify score algorithm identified the most essential features for obesity level estimation. Furthermore, physical activity and eating habits were the most critical factors for obesity prediction. Full article

(This article belongs to the Special Issue Advances in Intelligent Information Systems and AI Applications)

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12 pages, 2241 KiB

Open AccessArticle

Robustness of Contrastive Learning on Multilingual Font Style Classification Using Various Contrastive Loss Functions

by Irfanullah Memon, Ammar ul Hassan Muhammad and Jaeyoung Choi

Appl. Sci. 2023, 13(6), 3635; https://doi.org/10.3390/app13063635 - 13 Mar 2023

Viewed by 1639

Abstract

Font is a crucial design aspect, however, classifying fonts is challenging compared with that of other natural objects, as fonts differ from images. This paper presents the application of contrastive learning in font style classification. We conducted various experiments to demonstrate the robustness [...] Read more.

Font is a crucial design aspect, however, classifying fonts is challenging compared with that of other natural objects, as fonts differ from images. This paper presents the application of contrastive learning in font style classification. We conducted various experiments to demonstrate the robustness of contrastive image representation learning. First, we built a multilingual synthetic dataset for Chinese, English, and Korean fonts. Next, we trained the model using various contrastive loss functions, i.e., normalized temperature scaled cross-entropy loss, triplet loss, and supervised contrastive loss. We made some explicit changes to the approach of applying contrastive learning in the domain of font style classification by not applying any image augmentation. We compared the results with those of a fully supervised approach and achieved comparable results using contrastive learning with fewer annotated images and a smaller number of training epochs. In addition, we also evaluated the effect of applying different contrastive loss functions on training. Full article

(This article belongs to the Special Issue Advances in Intelligent Information Systems and AI Applications)

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17 pages, 2972 KiB

Open AccessArticle

A Convolutional Autoencoder Approach for Boosting the Specificity of Retinal Blood Vessels Segmentation

by Natalia Nikoloulopoulou, Isidoros Perikos, Ioannis Daramouskas, Christos Makris, Povilas Treigys and Ioannis Hatzilygeroudis

Appl. Sci. 2023, 13(5), 3255; https://doi.org/10.3390/app13053255 - 3 Mar 2023

Cited by 6 | Viewed by 1816

Abstract

Automated retina vessel segmentation of the human eye plays a vital role as it can significantly assist ophthalmologists in identifying many eye diseases, such as diabetes, stroke, arteriosclerosis, cardiovascular disease, and many other human illnesses. The fast, automatic and accurate retina vessel segmentation [...] Read more.

Automated retina vessel segmentation of the human eye plays a vital role as it can significantly assist ophthalmologists in identifying many eye diseases, such as diabetes, stroke, arteriosclerosis, cardiovascular disease, and many other human illnesses. The fast, automatic and accurate retina vessel segmentation of the eyes is very desirable. This paper introduces a novel fully convolutional autoencoder for the retina vessel segmentation task. The proposed model consists of eight layers, each consisting of convolutional2D layers, MaxPooling layers, Batch Normalisation layers and more. Our model has been trained and evaluated on DRIVE and STARE datasets with 35 min of training time. The performance of the autoencoder model we introduce is assessed on two public datasets, the DRIVE and the STARE and achieved quite competitive results compared to the state-of-the-art methods in the literature. In particular, our model reached an accuracy of 95.73, an AUC_ROC of 97.49 on the DRIVE dataset, and an accuracy of 96.92 and an AUC ROC of 97.57 on the STARE dataset. Furthermore, our model has demonstrated the highest specificity among the methods in the literature, reporting a specificity of 98.57 on the DRIVE and 98.7 on the STARE dataset, respectively. The above statement can be noticed in the final blood vessel segmentation images produced by our convolutional autoencoder method since the segmentations are more accurate, sharp and noiseless than the result images of other proposed methods. Full article

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27 pages, 2370 KiB

Open AccessArticle

Evaluating Deep Learning Techniques for Natural Language Inference

by Petros Eleftheriadis, Isidoros Perikos and Ioannis Hatzilygeroudis

Appl. Sci. 2023, 13(4), 2577; https://doi.org/10.3390/app13042577 - 16 Feb 2023

Cited by 6 | Viewed by 3175

Abstract

Natural language inference (NLI) is one of the most important natural language understanding (NLU) tasks. NLI expresses the ability to infer information during spoken or written communication. The NLI task concerns the determination of the entailment relation of a pair of sentences, called [...] Read more.

Natural language inference (NLI) is one of the most important natural language understanding (NLU) tasks. NLI expresses the ability to infer information during spoken or written communication. The NLI task concerns the determination of the entailment relation of a pair of sentences, called the premise and hypothesis. If the premise entails the hypothesis, the pair is labeled as an “entailment”. If the hypothesis contradicts the premise, the pair is labeled a “contradiction”, and if there is not enough information to infer a relationship, the pair is labeled as “neutral”. In this paper, we present experimentation results of using modern deep learning (DL) models, such as the pre-trained transformer BERT, as well as additional models that relay on LSTM networks, for the NLI task. We compare five DL models (and variations of them) on eight widely used NLI datasets. We trained and fine-tuned the hyperparameters for each model to achieve the best performance for each dataset, where we achieved some state-of-the-art results. Next, we examined the inference ability of the models on the BreakingNLI dataset, which evaluates the model’s ability to recognize lexical inferences. Finally, we tested the generalization power of our models across all the NLI datasets. The results of the study are quite interesting. In the first part of our experimentation, the results indicate the performance advantage of the pre-trained transformers BERT, RoBERTa, and ALBERT over other deep learning models. This became more evident when they were tested on the BreakingNLI dataset. We also see a pattern of improved performance when the larger models are used. However, ALBERT, given that it has 18 times fewer parameters, achieved quite remarkable performance. Full article

(This article belongs to the Special Issue Advances in Intelligent Information Systems and AI Applications)

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18 pages, 1890 KiB

Open AccessFeature PaperArticle

The Use of Digital Color Imaging and Machine Learning for the Evaluation of the Effects of Shade Drying and Open-Air Sun Drying on Mint Leaf Quality

by Ewa Ropelewska, Kadir Sabanci and Muhammet Fatih Aslan

Appl. Sci. 2023, 13(1), 206; https://doi.org/10.3390/app13010206 - 23 Dec 2022

Cited by 3 | Viewed by 2206

Abstract

The objective of this study was to reveal the usefulness of image processing and machine learning for the non-destructive evaluation of the changes in mint leaves caused by two natural drying techniques. The effects of shade drying and open-air sun drying on the [...] Read more.

The objective of this study was to reveal the usefulness of image processing and machine learning for the non-destructive evaluation of the changes in mint leaves caused by two natural drying techniques. The effects of shade drying and open-air sun drying on the ventral side (upper surface) and dorsal side (lower surface) of leaves were compared. Texture parameters were extracted from the digital color images converted to color channels R, G, B, L, a, b, X, Y, and Z. Models based on image features selected for individual color channels were built for distinguishing mint leaves in terms of drying techniques and leaf side using machine learning algorithms from groups of Lazy, Rules, and Trees. In the case of classification of the images of the ventral side of fresh and shade-dried mint leaves, an average accuracy of 100% and values of Precision, Recall, F-Measure, and MCC of 1.000 were obtained for color channels B (KStar and J48 machine learning algorithms), a (KStar and J48), b (KStar), and Y (KStar). The effect of open-air sun drying was greater. Images of the ventral side of fresh and open-air sun-dried mint leaves were completely correctly distinguished (100% correctness) for more color channels and algorithms, such as color channels R and G (J48), B, a and b (KStar, JRip, and J48), and X and Y (KStar). The classification of the images of the dorsal side of fresh and shade-dried mint leaves provided 100% accuracy in the case of color channel B (KStar) and a (KStar, JRip, and J48). The fresh and open-air sun-dried mint leaves imaged on the dorsal side were correctly classified at an accuracy of 100% for selected textures from color channels a (KStar, JRip, J48), b (J48), and Z (J48). The developed approach may be used in practice to monitor the changes in the structure of mint leaves caused by drying in a non-destructive, objective, cost-effective, and fast manner without the need to damage the leaves. Full article

(This article belongs to the Special Issue Advances in Intelligent Information Systems and AI Applications)

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17 pages, 1850 KiB

Open AccessArticle

A Variable Neighbourhood Search-Based Algorithm for the Transit Route Network Design Problem

by Christina Iliopoulou, Ioannis Tassopoulos and Grigorios Beligiannis

Appl. Sci. 2022, 12(20), 10232; https://doi.org/10.3390/app122010232 - 11 Oct 2022

Cited by 8 | Viewed by 1807

Abstract

The transit route network design problem (TRNDP) has long attracted research attention, with many metaheuristic approaches proposed for its solution. So far, and despite the promising performance of Variable Neighbourhood Search (VNS) variants for vehicle routing problems, the performance of the algorithm on [...] Read more.

The transit route network design problem (TRNDP) has long attracted research attention, with many metaheuristic approaches proposed for its solution. So far, and despite the promising performance of Variable Neighbourhood Search (VNS) variants for vehicle routing problems, the performance of the algorithm on the TRNDP remains unexplored. In this context, this study develops a VNS-based algorithm for the problem at hand. The performance of the algorithm is tested using benchmark networks used in bus transit network design and compared with some of the most recent and efficient methods from the literature. Results show that the algorithm yields superior results over existing implementations in short computational times. Full article

(This article belongs to the Special Issue Advances in Intelligent Information Systems and AI Applications)

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Review

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26 pages, 1939 KiB

Open AccessReview

A Survey of Full-Cycle Cross-Modal Retrieval: From a Representation Learning Perspective

by Suping Wang, Ligu Zhu, Lei Shi, Hao Mo and Songfu Tan

Appl. Sci. 2023, 13(7), 4571; https://doi.org/10.3390/app13074571 - 4 Apr 2023

Cited by 3 | Viewed by 3885

Abstract

Cross-modal retrieval aims to elucidate information fusion, imitate human learning, and advance the field. Although previous reviews have primarily focused on binary and real-value coding methods, there is a scarcity of techniques grounded in deep representation learning. In this paper, we concentrated on [...] Read more.

Cross-modal retrieval aims to elucidate information fusion, imitate human learning, and advance the field. Although previous reviews have primarily focused on binary and real-value coding methods, there is a scarcity of techniques grounded in deep representation learning. In this paper, we concentrated on harmonizing cross-modal representation learning and the full-cycle modeling of high-level semantic associations between vision and language, diverging from traditional statistical methods. We systematically categorized and summarized the challenges and open issues in implementing current technologies and investigated the pipeline of cross-modal retrieval, including pre-processing, feature engineering, pre-training tasks, encoding, cross-modal interaction, decoding, model optimization, and a unified architecture. Furthermore, we propose benchmark datasets and evaluation metrics to assist researchers in keeping pace with cross-modal retrieval advancements. By incorporating recent innovative works, we offer a perspective on potential advancements in cross-modal retrieval. Full article

(This article belongs to the Special Issue Advances in Intelligent Information Systems and AI Applications)

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