{"title":"Content moderation assistance through image caption generation","authors":"Liam Kearns","doi":"10.1016/j.iswa.2025.200489","DOIUrl":"10.1016/j.iswa.2025.200489","url":null,"abstract":"<div><div>The rapid growth in digital media creation has led to an increased challenge in content moderation. Manual and automated moderation are susceptible to risks associated with a slower response time and false positives arising from unpredictable user inputs respectively. Image caption generation has been suggested as a viable content moderation tool, but there is a lack of real world deployment in this context. In this work, a collaborative approach is taken, where a machine learning model is used to assist human moderators in the approval and rejection of media within a scavenger hunt game. The proposed model is trained on the Flickr30k and MS Coco datasets to generate captions for images. The results demonstrate a 13% reduction in review times, indicating that human–machine collaboration contributes to mitigating the risk of unsustainable review backlog growth. Furthermore, fine-tuning the model led to a 28% reduction in review times when compared to the untuned model. Notably, this paper contributes to knowledge by demonstrating caption generation as a viable content moderation tool in addition to its sensitivity to accurate captions, whereby false positives risk a deterioration in moderator response time.</div></div>","PeriodicalId":100684,"journal":{"name":"Intelligent Systems with Applications","volume":"25 ","pages":"Article 200489"},"PeriodicalIF":0.0,"publicationDate":"2025-02-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143436567","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Vu Hong Son Pham , Van Nam Nguyen , Nghiep Trinh Nguyen Dang
{"title":"Innovative hybrid algorithm for efficient routing of limited capacity vehicles","authors":"Vu Hong Son Pham , Van Nam Nguyen , Nghiep Trinh Nguyen Dang","doi":"10.1016/j.iswa.2025.200491","DOIUrl":"10.1016/j.iswa.2025.200491","url":null,"abstract":"<div><div>This study addresses the critical challenges posed by the capacitated vehicle routing problem (CVRP), particularly in the logistics of cement transportation under capacity constraints. Existing algorithms, including grey wolf optimizer (GWO) and whale optimization algorithm (WOA), exhibit significant limitations such as imbalanced exploration and exploitation, inefficiency in refining solutions, and inadequate adaptability to dynamic routing conditions. These limitations hinder their ability to provide comprehensive solutions that optimize time, cost, and environmental sustainability. To address these critical challenges, this research proposes an enhanced hybrid metaheuristic algorithm, mGWOA, designed to overcome the limitations of existing approaches by combining the GWO's strong exploitation capabilities and the WOA's exploratory strengths. By integrating opposition-based learning (OBL) to expand the search space and mutation techniques to escape local optima, the mGWOA is tailored to provide more flexible, adaptive, and efficient solutions for the complex and dynamic requirements of the CVRP. The mGWOA framework leverages the exploratory advantages of WOA, the exploitative strengths of GWO, and the diversity-promoting features of OBL and mutation to address the complexities of CVRP. Through computational evaluations in various scenarios, including five case studies ranging from small to large, the algorithm demonstrates its superior ability to generate high-quality solutions, especially as the customer base expands. The results underscore the potential of mGWOA as a robust and adaptive approach to solving CVRP, minimizing time and cost, and contributing to sustainable logistics operations. By bridging existing knowledge gaps, this research provides an innovative global optimization framework, offering practical applications for CVRP and other engineering challenges.</div></div>","PeriodicalId":100684,"journal":{"name":"Intelligent Systems with Applications","volume":"25 ","pages":"Article 200491"},"PeriodicalIF":0.0,"publicationDate":"2025-02-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143421817","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Taoufiq El Moussaoui, Chakir Loqman, Jaouad Boumhidi
{"title":"Decoding legal processes: AI-driven system to streamline processing of the criminal records in Moroccan courts","authors":"Taoufiq El Moussaoui, Chakir Loqman, Jaouad Boumhidi","doi":"10.1016/j.iswa.2025.200487","DOIUrl":"10.1016/j.iswa.2025.200487","url":null,"abstract":"<div><div>In Morocco, the manual process of feeding the criminal records database has become more challenging as the number of judgments has increased. This operation is carried out in two stages. The court clerk classifies the judgments as convictions or non-convictions, then extracts the guilty personal details and case information from those that present a conviction to feed the criminal records database. The current process has several drawbacks such as prolonged processing times, potential errors, and data confidentiality concerns. In this paper, we present a novel Arabic decision support legal system designed to assist in feeding the criminal records database. The system comprises two key components. The first component is a CNN-based judgment classifier that classifies judgments into convictions and non-convictions, while the second component is a legal entities extractor that can efficiently extract 11 entities from judgments classified as conviction. Both models were trained on purpose-built Arabic legal corpora created based on 4966 Arabic verdicts issued from the Moroccan courts. The judgment classifier achieves an accuracy of 96.6% on the judicial decision corpus, 98% on the Khaleej dataset, and 96.27% on the ECHR dataset. The legal entities extractor achieves 98.42%, 93.72%, and 93.5% F-scores on the legal entities corpus, the ANERCorp dataset, and the CONLL2003 respectively, outperforming prior research. These results highlight the potential of the system in improving the operation of feeding the criminal records database. Furthermore, the creation of these Arabic legal corpora provides valuable resources for enhancing legal document classification and domain-specific NER models in Arabic.</div></div>","PeriodicalId":100684,"journal":{"name":"Intelligent Systems with Applications","volume":"25 ","pages":"Article 200487"},"PeriodicalIF":0.0,"publicationDate":"2025-02-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143396056","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Van Quang Nghiem, Huy Hoang Nguyen, Minh Son Hoang
{"title":"LEAF-YOLO: Lightweight Edge-Real-Time Small Object Detection on Aerial Imagery","authors":"Van Quang Nghiem, Huy Hoang Nguyen, Minh Son Hoang","doi":"10.1016/j.iswa.2025.200484","DOIUrl":"10.1016/j.iswa.2025.200484","url":null,"abstract":"<div><div>Advances in Unmanned Aerial Vehicles (UAVs) and deep learning have spotlighted the challenges of detecting small objects in UAV imagery, where limited computational resources complicate deployment on edge devices. While many high-accuracy deep learning solutions have been developed, their large parameter sizes hinder deployment on edge devices where low latency and efficient resource use are essential. To address this, we propose LEAF-YOLO, a lightweight and efficient object detection algorithm with two versions: LEAF-YOLO (standard) and LEAF-YOLO-N (nano). Using Lightweight-Efficient Aggregating Fusion along with other blocks and techniques, LEAF-YOLO enhances multiscale feature extraction while reducing complexity, targeting small object detection in dense and varied backgrounds. Experimental results show that both LEAF-YOLO and LEAF-YOLO-N outperform models with fewer than 20 million parameters in accuracy and efficiency on the Visdrone2019-DET-val dataset, running in real-time (<span><math><mo>></mo></math></span>30 FPS) on the Jetson AGX Xavier. LEAF-YOLO-N achieves 21.9% AP<span><math><msub><mrow></mrow><mrow><mo>.</mo><mn>50</mn><mo>:</mo><mo>.</mo><mn>95</mn></mrow></msub></math></span> and 39.7% AP<span><math><msub><mrow></mrow><mrow><mo>.</mo><mn>50</mn></mrow></msub></math></span> with only 1.2M parameters. LEAF-YOLO achieves 28.2% AP<span><math><msub><mrow></mrow><mrow><mo>.</mo><mn>50</mn><mo>:</mo><mo>.</mo><mn>95</mn></mrow></msub></math></span> and 48.3% AP<span><math><msub><mrow></mrow><mrow><mo>.</mo><mn>50</mn></mrow></msub></math></span> with 4.28M parameters. Furthermore, LEAF-YOLO attains 23% AP<span><math><msub><mrow></mrow><mrow><mo>.</mo><mn>50</mn></mrow></msub></math></span> on the TinyPerson dataset, outperforming models with <span><math><mo>≥</mo></math></span> 20 million parameters, making it suitable for UAV-based human detection.</div></div>","PeriodicalId":100684,"journal":{"name":"Intelligent Systems with Applications","volume":"25 ","pages":"Article 200484"},"PeriodicalIF":0.0,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143136329","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
William Villegas-Ch , Alexandra Maldonado Navarro , Araceli Mera-Navarrete
{"title":"Using Generative Adversarial Networks for the synthesis of emotional facial expressions in virtual educational environments","authors":"William Villegas-Ch , Alexandra Maldonado Navarro , Araceli Mera-Navarrete","doi":"10.1016/j.iswa.2025.200479","DOIUrl":"10.1016/j.iswa.2025.200479","url":null,"abstract":"<div><div>The generation of emotional facial expressions using Generative Adversarial Networks (GANs) has been widely researched, achieving significant advances in creating high-quality images. However, one of the main challenges remains the accurate transmission of complex and negative emotions, such as anger or sadness, due to the difficulty of correctly capturing the facial micro gestures that characterize these emotions. Traditional GAN architectures, such as StyleGAN and DCGAN, have proven highly effective in synthesizing positive emotions such as joy. However, they have limitations regarding more subtle emotions, leading to a higher rate of false negatives. In response to this problem, we propose fine-tuning the GAN discriminator. This tuning optimizes the discriminator’s ability to identify the minor details in facial expressions by using perceptual losses, allowing for better differentiation between the generated emotions, reducing classification errors, and improving precision in difficult-to-represent emotions. The results obtained in this study demonstrate a significant improvement in the system’s precision, especially in complex emotions. Precision for the anger emotion increased from 85.7% to 89.1%, and the number of false negatives was reduced from 16 to 10. Overall, precision for complex emotions exceeded 85%, substantially improving traditional solutions. These results demonstrate the potential of fine-tuning the GAN architecture for applications requiring more faithful and effective emotional interaction, significantly improving user experience across multiple domains.</div></div>","PeriodicalId":100684,"journal":{"name":"Intelligent Systems with Applications","volume":"25 ","pages":"Article 200479"},"PeriodicalIF":0.0,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143303226","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Kevin B. de Carvalho , Hiago de O.B. Batista , Leonardo A. Fagundes-Junior , Iure Rosa L. de Oliveira , Alexandre S. Brandão
{"title":"Q-learning global path planning for UAV navigation with pondered priorities","authors":"Kevin B. de Carvalho , Hiago de O.B. Batista , Leonardo A. Fagundes-Junior , Iure Rosa L. de Oliveira , Alexandre S. Brandão","doi":"10.1016/j.iswa.2025.200485","DOIUrl":"10.1016/j.iswa.2025.200485","url":null,"abstract":"<div><div>The process of path planning plays a crucial role in enabling self-directed movement, particularly for unmanned aerial vehicles. This involves accommodating diverse priorities, such as route length, safety, and energy efficiency. Traditional techniques, including geometric and dynamic programming, have historically been employed to address this challenge. However, recent years have testified to an increasing prevalence of artificial intelligence methodologies such as reinforcement learning. This study introduces a novel approach to offline path planning in static environments, utilizing Q-learning as its foundation. The method optimizes three pivotal factors: path length, safety, and energy consumption. By effectively balancing exploration and exploitation, this technique enables an autonomous agent to efficiently navigate from any initial point to a specified destination on the map. To evaluate the proposed strategy’s effectiveness, extensive simulations are conducted across diverse environments. A comparative analysis with three established strategies showcases the algorithm’s proficiency in generating feasible routes. The user can freely tailor the system’s priorities by modifying each of their weights prior to training. Additionally, scalability tests reveal the algorithm’s swift convergence, achieving stability within just 35 s for larger environments spanning up to 40 × 40 units. To further validate the proposed approach, both simulations and real-world experiments are employed, collectively demonstrating its performance and applicability.</div></div>","PeriodicalId":100684,"journal":{"name":"Intelligent Systems with Applications","volume":"25 ","pages":"Article 200485"},"PeriodicalIF":0.0,"publicationDate":"2025-01-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143303445","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Lafta Alkhazraji , Ayad R. Abbas , Abeer S. Jamil , Zahraa Saddi Kadhim , Wissam Alkhazraji , Sabah Abdulazeez Jebur , Bassam Noori Shaker , Mohammed Abdallazez Mohammed , Mohanad A. Mohammed , Basim Mohammed Al-Araji , Abdulkareem Z. Mohmmed , Wasiq Khan , Bilal Khan , Abir Jaafar Hussain
{"title":"Employing the concept of stacking ensemble learning to generate deep dream images using multiple CNN variants","authors":"Lafta Alkhazraji , Ayad R. Abbas , Abeer S. Jamil , Zahraa Saddi Kadhim , Wissam Alkhazraji , Sabah Abdulazeez Jebur , Bassam Noori Shaker , Mohammed Abdallazez Mohammed , Mohanad A. Mohammed , Basim Mohammed Al-Araji , Abdulkareem Z. Mohmmed , Wasiq Khan , Bilal Khan , Abir Jaafar Hussain","doi":"10.1016/j.iswa.2025.200488","DOIUrl":"10.1016/j.iswa.2025.200488","url":null,"abstract":"<div><div>Addiction and adverse effects resulting from schizophrenia are rapidly becoming a global issue, necessitating the development of advanced approaches that can provide support to psychiatrists and psychologists to understand and replicate the hallucinations and imagery experienced by patients. Such approaches can also be useful for promoting interest in human artwork, particularly surrealist images. Accordingly, in the present, a stacking ensemble Deep Dream model was developed that aids psychiatrists and psychologists in addressing the challenge of mimicking hallucinations. The dream-like images generated in the present study possess an aesthetic quality reminiscent of surrealist art. For model development, a series of five pre-trained Convolutional Neural Network (CNN) architectures—VGG-19, Inception v3, VGG-16, Inception-ResNet-V2, and Xception were stacked in an ensemble learning approach to create Deep Dream images whereby the upper hidden layers of the architectures were activated, and the models were trained via the Adam optimizer. Performance of the proposed model was evaluated across three octaves to amplify the maximum possible patterns and features of the base image. The resulting dream-like images contain shapes that reflect elements from the ImageNet dataset on which the above pre-trained models were trained. Each of the base images was manipulated to generate various dreamed images, each one with three octaves, which were finally combined to construct the final image with its loss. Final Deep Dream image showed a loss of 47.5821, while still retaining some features from the base image.</div></div>","PeriodicalId":100684,"journal":{"name":"Intelligent Systems with Applications","volume":"25 ","pages":"Article 200488"},"PeriodicalIF":0.0,"publicationDate":"2025-01-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143136328","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Ahmed Rashad Sayed , Mohamed Helmy Khafagy , Mostafa Ali , Marwa Hussien Mohamed
{"title":"Exploring the VAK model to predict student learning styles based on learning activity","authors":"Ahmed Rashad Sayed , Mohamed Helmy Khafagy , Mostafa Ali , Marwa Hussien Mohamed","doi":"10.1016/j.iswa.2025.200483","DOIUrl":"10.1016/j.iswa.2025.200483","url":null,"abstract":"<div><div>Adaptive learning systems focus on improving the performance of educational processes by adapting them to different students. One of the factors which require this adaptation is the preferred way of students to learn, which is at times considered as a blend of visual, auditory, kinesthetic, (VAK) etc. Knowing such things, not only helps the teacher to improve the delivery of the content, but also assists in improving assessment methods to suit each student. The primary motivation of this research is to analyze students’ engagement characteristics in Virtual Learning Environments (VLE) and determine their prevalent instructional preference and learning style and recommend the best learning assessment tools. To accomplish this goal, we have proposed an integrated system which encompasses the use of machine learning (ML) algorithms. This hybrid model is aimed at linking various activities to VAK model of learning and hence place students in their various class learning preferences derived from their activities and the patterns created during the learning processes. We used the Open University Learning Analytics Dataset (OULAD)to assess the efficiency of the proposed system. Multiple tests were performed by different machine learning classifiers, mainly in predicting learning style and recommending an assessment methodology. Our results show that the Random Forest algorithm achieved the highest accuracy with 98 %.This research shows how machine learning techniques embedded in learning analytics could expand the functionalities of VLEs toward greater personalization and effectiveness, with every student receiving the best educational experience that suits their learning styles.</div></div>","PeriodicalId":100684,"journal":{"name":"Intelligent Systems with Applications","volume":"25 ","pages":"Article 200483"},"PeriodicalIF":0.0,"publicationDate":"2025-01-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143136765","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Yourui Huang , Quanzeng Liu , Tao Han , Tingting Li , Hongping Song
{"title":"Integrated multi-strategy sand cat swarm optimization for path planning applications","authors":"Yourui Huang , Quanzeng Liu , Tao Han , Tingting Li , Hongping Song","doi":"10.1016/j.iswa.2025.200486","DOIUrl":"10.1016/j.iswa.2025.200486","url":null,"abstract":"<div><div>An integrated multi-strategy sand cat swarm optimization algorithm is proposed to address the shortcomings of the sand cat swarm algorithm, such as inefficient solutions, insufficient optimization accuracy, and a tendency to fall into local optimal solutions. The algorithm introduces an improved circle chaotic mapping to balance the population distribution, water wave dynamic convergence factor to maintain population diversity, and a lens opposition-based learning to enhance the global optimization capability. Additionally, the golden sine strategy is incorporated to improve the local search ability. Experiments on 23 test functions demonstrate the new algorithm's optimal average performance on 18 of them. It was further applied to 9 2D path planning instances and 2 3D path planning instances, all of which were able to find the shortest path. The results show that the improved algorithm is less prone to local optimization, exhibits high stability, and can effectively solve path planning problems.</div></div>","PeriodicalId":100684,"journal":{"name":"Intelligent Systems with Applications","volume":"25 ","pages":"Article 200486"},"PeriodicalIF":0.0,"publicationDate":"2025-01-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143136764","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Application of computer vision algorithm in ceramic surface texture analysis and prediction","authors":"Yao Tian, Feifei Zhu","doi":"10.1016/j.iswa.2025.200482","DOIUrl":"10.1016/j.iswa.2025.200482","url":null,"abstract":"<div><div>The recognition and prediction of ceramic surface texture is a key step to improve the quality of ceramic products. The existing ceramic texture feature recognition methods are easily interfered by many factors, resulting in poor texture recognition effect, and the prediction results of texture generation do not match the actual situation. In order to improve the surface quality of ceramic products, this paper proposes a ceramic surface texture recognition analysis and texture generation prediction method based on computer vision algorithm. In this method, laser lines are used to scan along the radial direction of ceramic, and the position of laser stripe is located by straight line detection algorithm. After obtaining the position of laser stripe, the characteristics on laser stripe and the images around laser stripe are detected, so as to judge the quantity and state of ceramic surface texture. From the experimental analysis results, it can be seen that the computer vision algorithm proposed in this paper has good performance in ceramic surface texture recognition analysis and texture generation prediction, which can further improve the quality of subsequent ceramic products.</div></div>","PeriodicalId":100684,"journal":{"name":"Intelligent Systems with Applications","volume":"25 ","pages":"Article 200482"},"PeriodicalIF":0.0,"publicationDate":"2025-01-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143136763","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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