Big Data and Cognitive Computing最新文献_第3页

Autonomous Vehicles: Evolution of Artificial Intelligence and the Current Industry Landscape 自动驾驶汽车：人工智能的发展和当前的产业格局

Big Data and Cognitive Computing Pub Date : 2024-04-07 DOI: 10.3390/bdcc8040042

Divya Garikapati, Sneha Sudhir Shetiya

{"title":"Autonomous Vehicles: Evolution of Artificial Intelligence and the Current Industry Landscape","authors":"Divya Garikapati, Sneha Sudhir Shetiya","doi":"10.3390/bdcc8040042","DOIUrl":"https://doi.org/10.3390/bdcc8040042","url":null,"abstract":"The advent of autonomous vehicles has heralded a transformative era in transportation, reshaping the landscape of mobility through cutting-edge technologies. Central to this evolution is the integration of artificial intelligence (AI), propelling vehicles into realms of unprecedented autonomy. Commencing with an overview of the current industry landscape with respect to Operational Design Domain (ODD), this paper delves into the fundamental role of AI in shaping the autonomous decision-making capabilities of vehicles. It elucidates the steps involved in the AI-powered development life cycle in vehicles, addressing various challenges such as safety, security, privacy, and ethical considerations in AI-driven software development for autonomous vehicles. The study presents statistical insights into the usage and types of AI algorithms over the years, showcasing the evolving research landscape within the automotive industry. Furthermore, the paper highlights the pivotal role of parameters in refining algorithms for both trucks and cars, facilitating vehicles to adapt, learn, and improve performance over time. It concludes by outlining different levels of autonomy, elucidating the nuanced usage of AI algorithms, and discussing the automation of key tasks and the software package size at each level. Overall, the paper provides a comprehensive analysis of the current industry landscape, focusing on several critical aspects.","PeriodicalId":505155,"journal":{"name":"Big Data and Cognitive Computing","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-04-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140733059","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Generating Synthetic Sperm Whale Voice Data Using StyleGAN2-ADA 使用 StyleGAN2-ADA 生成合成抹香鲸声音数据

Big Data and Cognitive Computing Pub Date : 2024-04-03 DOI: 10.3390/bdcc8040040

E. Kopets, Tatiana Shpilevaya, Oleg Vasilchenko, Artur Karimov, D. Butusov

{"title":"Generating Synthetic Sperm Whale Voice Data Using StyleGAN2-ADA","authors":"E. Kopets, Tatiana Shpilevaya, Oleg Vasilchenko, Artur Karimov, D. Butusov","doi":"10.3390/bdcc8040040","DOIUrl":"https://doi.org/10.3390/bdcc8040040","url":null,"abstract":"The application of deep learning neural networks enables the processing of extensive volumes of data and often requires dense datasets. In certain domains, researchers encounter challenges related to the scarcity of training data, particularly in marine biology. In addition, many sounds produced by sea mammals are of interest in technical applications, e.g., underwater communication or sonar construction. Thus, generating synthetic biological sounds is an important task for understanding and studying the behavior of various animal species, especially large sea mammals, which demonstrate complex social behavior and can use hydrolocation to navigate underwater. This study is devoted to generating sperm whale vocalizations using a limited sperm whale click dataset. Our approach utilizes an augmentation technique predicated on the transformation of audio sample spectrograms, followed by the employment of the generative adversarial network StyleGAN2-ADA to generate new audio data. The results show that using the chosen augmentation method, namely mixing along the time axis, makes it possible to create fairly similar clicks of sperm whales with a maximum deviation of 2%. The generation of new clicks was reproduced on datasets using selected augmentation approaches with two neural networks: StyleGAN2-ADA and WaveGan. StyleGAN2-ADA, trained on an augmented dataset using the axis mixing approach, showed better results compared to WaveGAN.","PeriodicalId":505155,"journal":{"name":"Big Data and Cognitive Computing","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-04-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140748444","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

From Traditional Recommender Systems to GPT-Based Chatbots: A Survey of Recent Developments and Future Directions 从传统推荐系统到基于 GPT 的聊天机器人：最新发展和未来方向概览

Big Data and Cognitive Computing Pub Date : 2024-03-27 DOI: 10.3390/bdcc8040036

T. M. Al-Hasan, A. Sayed, Fayal Bensaali, Yassine Himeur, Iraklis Varlamis, G. Dimitrakopoulos

{"title":"From Traditional Recommender Systems to GPT-Based Chatbots: A Survey of Recent Developments and Future Directions","authors":"T. M. Al-Hasan, A. Sayed, Fayal Bensaali, Yassine Himeur, Iraklis Varlamis, G. Dimitrakopoulos","doi":"10.3390/bdcc8040036","DOIUrl":"https://doi.org/10.3390/bdcc8040036","url":null,"abstract":"Recommender systems are a key technology for many applications, such as e-commerce, streaming media, and social media. Traditional recommender systems rely on collaborative filtering or content-based filtering to make recommendations. However, these approaches have limitations, such as the cold start and the data sparsity problem. This survey paper presents an in-depth analysis of the paradigm shift from conventional recommender systems to generative pre-trained-transformers-(GPT)-based chatbots. We highlight recent developments that leverage the power of GPT to create interactive and personalized conversational agents. By exploring natural language processing (NLP) and deep learning techniques, we investigate how GPT models can better understand user preferences and provide context-aware recommendations. The paper further evaluates the advantages and limitations of GPT-based recommender systems, comparing their performance with traditional methods. Additionally, we discuss potential future directions, including the role of reinforcement learning in refining the personalization aspect of these systems.","PeriodicalId":505155,"journal":{"name":"Big Data and Cognitive Computing","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-03-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140373709","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A Comparative Study for Stock Market Forecast Based on a New Machine Learning Model 基于新机器学习模型的股市预测比较研究

Big Data and Cognitive Computing Pub Date : 2024-03-26 DOI: 10.3390/bdcc8040034

Enrique González-Núñez, Luis A. Trejo, Michael Kampouridis

{"title":"A Comparative Study for Stock Market Forecast Based on a New Machine Learning Model","authors":"Enrique González-Núñez, Luis A. Trejo, Michael Kampouridis","doi":"10.3390/bdcc8040034","DOIUrl":"https://doi.org/10.3390/bdcc8040034","url":null,"abstract":"This research aims at applying the Artificial Organic Network (AON), a nature-inspired, supervised, metaheuristic machine learning framework, to develop a new algorithm based on this machine learning class. The focus of the new algorithm is to model and predict stock markets based on the Index Tracking Problem (ITP). In this work, we present a new algorithm, based on the AON framework, that we call Artificial Halocarbon Compounds, or the AHC algorithm for short. In this study, we compare the AHC algorithm against genetic algorithms (GAs), by forecasting eight stock market indices. Additionally, we performed a cross-reference comparison against results regarding the forecast of other stock market indices based on state-of-the-art machine learning methods. The efficacy of the AHC model is evaluated by modeling each index, producing highly promising results. For instance, in the case of the IPC Mexico index, the R-square is 0.9806, with a mean relative error of 7×10−4. Several new features characterize our new model, mainly adaptability, dynamism and topology reconfiguration. This model can be applied to systems requiring simulation analysis using time series data, providing a versatile solution to complex problems like financial forecasting.","PeriodicalId":505155,"journal":{"name":"Big Data and Cognitive Computing","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-03-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140377755","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Two-Stage Method for Clothing Feature Detection 服装特征检测的两阶段方法

Big Data and Cognitive Computing Pub Date : 2024-03-26 DOI: 10.3390/bdcc8040035

Xinwei Lyu, Xinjia Li, Yuexin Zhang, Wenlian Lu

引用次数: 0

Cancer Detection Using a New Hybrid Method Based on Pattern Recognition in MicroRNAs Combining Particle Swarm Optimization Algorithm and Artificial Neural Network 基于微粒群优化算法和人工神经网络的微 RNA 模式识别的新型混合方法可用于癌症检测

Big Data and Cognitive Computing Pub Date : 2024-03-19 DOI: 10.3390/bdcc8030033

Sepideh Molaei, Stefano Cirillo, Giandomenico Solimando

{"title":"Cancer Detection Using a New Hybrid Method Based on Pattern Recognition in MicroRNAs Combining Particle Swarm Optimization Algorithm and Artificial Neural Network","authors":"Sepideh Molaei, Stefano Cirillo, Giandomenico Solimando","doi":"10.3390/bdcc8030033","DOIUrl":"https://doi.org/10.3390/bdcc8030033","url":null,"abstract":"MicroRNAs (miRNAs) play a crucial role in cancer development, but not all miRNAs are equally significant in cancer detection. Traditional methods face challenges in effectively identifying cancer-associated miRNAs due to data complexity and volume. This study introduces a novel, feature-based technique for detecting attributes related to cancer-affecting microRNAs. It aims to enhance cancer diagnosis accuracy by identifying the most relevant miRNAs for various cancer types using a hybrid approach. In particular, we used a combination of particle swarm optimization (PSO) and artificial neural networks (ANNs) for this purpose. PSO was employed for feature selection, focusing on identifying the most informative miRNAs, while ANNs were used for recognizing patterns within the miRNA data. This hybrid method aims to overcome limitations in traditional miRNA analysis by reducing data redundancy and focusing on key genetic markers. The application of this method showed a significant improvement in the detection accuracy for various cancers, including breast and lung cancer and melanoma. Our approach demonstrated a higher precision in identifying relevant miRNAs compared to existing methods, as evidenced by the analysis of different datasets. The study concludes that the integration of PSO and ANNs provides a more efficient, cost-effective, and accurate method for cancer detection via miRNA analysis. This method can serve as a supplementary tool for cancer diagnosis and potentially aid in developing personalized cancer treatments.","PeriodicalId":505155,"journal":{"name":"Big Data and Cognitive Computing","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-03-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140228141","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

AI-Generated Text Detector for Arabic Language Using Encoder-Based Transformer Architecture 使用基于编码器的变换器架构的阿拉伯语人工智能文本检测器

Big Data and Cognitive Computing Pub Date : 2024-03-18 DOI: 10.3390/bdcc8030032

Hamed Alshammari, Ahmed El-Sayed, Khaled Elleithy

{"title":"AI-Generated Text Detector for Arabic Language Using Encoder-Based Transformer Architecture","authors":"Hamed Alshammari, Ahmed El-Sayed, Khaled Elleithy","doi":"10.3390/bdcc8030032","DOIUrl":"https://doi.org/10.3390/bdcc8030032","url":null,"abstract":"The effectiveness of existing AI detectors is notably hampered when processing Arabic texts. This study introduces a novel AI text classifier designed specifically for Arabic, tackling the distinct challenges inherent in processing this language. A particular focus is placed on accurately recognizing human-written texts (HWTs), an area where existing AI detectors have demonstrated significant limitations. To achieve this goal, this paper utilized and fine-tuned two Transformer-based models, AraELECTRA and XLM-R, by training them on two distinct datasets: a large dataset comprising 43,958 examples and a custom dataset with 3078 examples that contain HWT and AI-generated texts (AIGTs) from various sources, including ChatGPT 3.5, ChatGPT-4, and BARD. The proposed architecture is adaptable to any language, but this work evaluates these models’ efficiency in recognizing HWTs versus AIGTs in Arabic as an example of Semitic languages. The performance of the proposed models has been compared against the two prominent existing AI detectors, GPTZero and OpenAI Text Classifier, particularly on the AIRABIC benchmark dataset. The results reveal that the proposed classifiers outperform both GPTZero and OpenAI Text Classifier with 81% accuracy compared to 63% and 50% for GPTZero and OpenAI Text Classifier, respectively. Furthermore, integrating a Dediacritization Layer prior to the classification model demonstrated a significant enhancement in the detection accuracy of both HWTs and AIGTs. This Dediacritization step markedly improved the classification accuracy, elevating it from 81% to as high as 99% and, in some instances, even achieving 100%.","PeriodicalId":505155,"journal":{"name":"Big Data and Cognitive Computing","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-03-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140234550","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Machine Learning Approaches for Predicting Risk of Cardiometabolic Disease among University Students 预测大学生心血管代谢疾病风险的机器学习方法

Big Data and Cognitive Computing Pub Date : 2024-03-13 DOI: 10.3390/bdcc8030031

Dhiaa Musleh, Ali Alkhwaja, Ibrahim Alkhwaja, Mohammed Alghamdi, Hussam Abahussain, Mohammed Albugami, Faisal Alfawaz, Said El-Ashker, M. Al-Hariri

{"title":"Machine Learning Approaches for Predicting Risk of Cardiometabolic Disease among University Students","authors":"Dhiaa Musleh, Ali Alkhwaja, Ibrahim Alkhwaja, Mohammed Alghamdi, Hussam Abahussain, Mohammed Albugami, Faisal Alfawaz, Said El-Ashker, M. Al-Hariri","doi":"10.3390/bdcc8030031","DOIUrl":"https://doi.org/10.3390/bdcc8030031","url":null,"abstract":"Obesity is increasingly becoming a prevalent health concern among adolescents, leading to significant risks like cardiometabolic diseases (CMDs). The early discovery and diagnosis of CMD is essential for better outcomes. This study aims to build a reliable artificial intelligence model that can predict CMD using various machine learning techniques. Support vector machines (SVMs), K-Nearest neighbor (KNN), Logistic Regression (LR), Random Forest (RF), and Gradient Boosting are five robust classifiers that are compared in this study. A novel “risk level” feature, derived through fuzzy logic applied to the Conicity Index, as a novel feature, which was previously unused, is introduced to enhance the interpretability and discriminatory properties of the proposed models. As the Conicity Index scores indicate CMD risk, two separate models are developed to address each gender individually. The performance of the proposed models is assessed using two datasets obtained from 295 records of undergraduate students in Saudi Arabia. The dataset comprises 121 male and 174 female students with diverse risk levels. Notably, Logistic Regression emerges as the top performer among males, achieving an accuracy score of 91%, while Gradient Boosting lags with a score of 72%. Among females, both Support Vector Machine and Logistic Regression lead with an accuracy score of 87%, while Random Forest performs least optimally with a score of 80%.","PeriodicalId":505155,"journal":{"name":"Big Data and Cognitive Computing","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-03-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140247702","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Proposal of a Service Model for Blockchain-Based Security Tokens 基于区块链的安全代币服务模式提案

Big Data and Cognitive Computing Pub Date : 2024-03-12 DOI: 10.3390/bdcc8030030

Keundug Park, H. Youm

引用次数: 0

The Distribution and Accessibility of Elements of Tourism in Historic and Cultural Cities 历史文化名城旅游要素的分布和可达性

Big Data and Cognitive Computing Pub Date : 2024-03-11 DOI: 10.3390/bdcc8030029

Wei-Lng Hsu, Yi-Jheng Chang, Lin Mou, Juan-Wen Huang, Hsin-Lung Liu

{"title":"The Distribution and Accessibility of Elements of Tourism in Historic and Cultural Cities","authors":"Wei-Lng Hsu, Yi-Jheng Chang, Lin Mou, Juan-Wen Huang, Hsin-Lung Liu","doi":"10.3390/bdcc8030029","DOIUrl":"https://doi.org/10.3390/bdcc8030029","url":null,"abstract":"Historic urban areas are the foundations of urban development. Due to rapid urbanization, the sustainable development of historic urban areas has become challenging for many cities. Elements of tourism and tourism service facilities play an important role in the sustainable development of historic areas. This study analyzed policies related to tourism in Panguifang and Meixian districts in Meizhou, Guangdong, China. Kernel density estimation was used to study the clustering characteristics of tourism elements through point of interest (POI) data, while space syntax was used to study the accessibility of roads. In addition, the Pearson correlation coefficient and regression were used to analyze the correlation between the elements and accessibility. The results show the following: (1) the overall number of tourism elements was high on the western side of the districts and low on the eastern one, and the elements were predominantly distributed along the main transportation arteries; (2) according to the integration degree and depth value, the western side was easier to access than the eastern one; and (3) the depth value of the area negatively correlated with kernel density, while the degree of integration positively correlated with it. Based on the results, the study put forward measures for optimizing the elements of tourism in Meizhou’s historic urban area to improve cultural tourism and emphasize the importance of the elements.","PeriodicalId":505155,"journal":{"name":"Big Data and Cognitive Computing","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-03-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140254207","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0