Modern Intelligent Times最新文献

{"title":"The Role of AI in Co-design and Production Process in the Design Industry: A Prompt Investigation in the Creative Industry","authors":"Kung Wong Lau","doi":"10.53964/mit.2024002","DOIUrl":"https://doi.org/10.53964/mit.2024002","url":null,"abstract":"","PeriodicalId":166011,"journal":{"name":"Modern Intelligent Times","volume":"9 12","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-04-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140653359","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}

{"title":"Enhancing On-demand Transportation: Prioritizing Travel Costs with Hybrid Evolutionary Algorithms","authors":"Sonia Nasri, H. Bouziri, Wassila Aggoune Mtalaa","doi":"10.53964/mit.2024001","DOIUrl":"https://doi.org/10.53964/mit.2024001","url":null,"abstract":"Objective: Enhancing sustainability in on-demand transportation is a challenging task by offering flexible and optimized mobility solutions. This study focuses on on-demand mobility systems. We introduce a novel evolutionary computing method designed to address a bi-objective customized on-demand transportation problem, aiming to minimize total travel cost and total waiting time. Methods: The method incorporates specific optimization techniques, along with an efficient dominance sorting approach, using an intelligent candidate list to reduce computational time. Results: Comparative results demonstrate the effectiveness of this hybrid computing method, particularly when prioritizing total travel cost from the service provider’s perspective. Conclusion: These findings present a promising framework for decision-makers, empowering them to navigate favorable compromises between conflicting objectives and make informed choices aligned with their preferences and customer-oriented constraints.","PeriodicalId":166011,"journal":{"name":"Modern Intelligent Times","volume":" 33","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-04-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140687420","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}

{"title":"A System for Identifying Deficiency Elements in Apple Leaf Disease Based on Deep Learning","authors":"Linping Zheng, Wanyi Li, Nanjian Li, Canming Huang, Jinyi Cheng, Yun Kuang, Xiaoyun Guo, Zou Ling","doi":"10.53964/mit.2023006","DOIUrl":"https://doi.org/10.53964/mit.2023006","url":null,"abstract":"Background: Plant diseases and pests were natural disasters that seriously threaten agricultural production. Leaf diseases posed a significant threat to the overall productivity and quality of apple orchards. Currently, the diagnosis of plant diseases in apple orchards mainly relied on manual labor, which was both time-consuming and costly. It took a considerable amount of time to train an expert capable of accurately identifying apple diseases and pests, and different crop diseases vary. However, with the use of AI technology, training could be completed in just a few hours to a day, transforming computers into experts in disease and pest identification. Although China became a major apple cultivation nation, the development data from 2021 revealed that traditional advantageous production areas were undergoing dynamic adjustments due to optimization of industrial structure, leading to a decrease in cultivated land. Conversely, the demand for apples in the Chinese market continued to rise, even as the country's population structure gradually trended towards an aging population. This situation underscored the urgent need to replace the laborious and high-cost task of identifying apple foliar diseases in the cultivation process with intelligent agricultural technological solutions. Taking into account the aforementioned factors of the natural environment and socio-cultural considerations and addressing the issue of reduced apple yields due to the high impact of foliar diseases, we initiated study on disease prevention and control during the cultivation process. Methods: To overcome the objective impact of environmental factors, the system extracted images and performed operations such as enhancement and segmentation. Subsequently, a convolutional neural network was employed to process the enhanced images, extracting features such as color, shape, and texture of apple foliar lesions, which were then summarized into histograms using mathematical methods. These histograms served as the primary reference for distinguishing various diseases. Python was chosen as the programming language for the system, and the recognition system was operated by integrating frontend technologies, backend database design, and other relevant techniques. Results: Based on a substantial number of experimental results, the apple leaf disease and nutrient deficiency recognition system could accurately and with extremely high precision identifying major diseases commonly occurring in apple plants, including apple scab, leaf spot disease, rust disease, gray mold disease, and brown spot disease. Moreover, the system was capable of proposing remedies tailored to the specific pain points of each disease. It not only detected apple leaf diseases but also performs nutrient deficiency detection. This holds significant importance in enhancing both the yield and quality of apples. Conclusion: For the commonly occurring foliar diseases in cultivation, the system exhibited the capability ","PeriodicalId":166011,"journal":{"name":"Modern Intelligent Times","volume":"2 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-12-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139158205","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}

{"title":"The Decision of Product Market Forecasting and Delisting Based on PSO-BP","authors":"Yafei Wang, Yong Luo, Jing Chang, Z. Chen","doi":"10.53964/mit.2023004","DOIUrl":"https://doi.org/10.53964/mit.2023004","url":null,"abstract":"Objective: Aiming to construct a product sales forecasting model with higher forecasting accuracy to make guidance on product delisting and maximize product sales revenue. Methods: This paper uses the particle swarm optimization (PSO) algorithm to optimize the back propagation (BP) neural network of the sales model, and selects the iPhone 11 cell phone sale record as the data set for experimental simulation of the sales forecasting model. The delisting model makes forecasting on the sales revenue of the same cell phone with different memory in a future period. A reasonable product delisting time scale is set, and the delisting time point is determined in conjunction with the revenue situation. Results: The empirical results show that all evaluation indexes of the forecasting accuracy of the PSO-BP neural network forecasting method used in this paper can be improved by more than 20%. Meanwhile, when cell phones with a different memory of the same model are sold in the same market, the cell phones with the medium level of memory have a longer life cycle than those with large and small memory, and the total revenue obtained from selling the cell phones with the middle level of memory is higher. Conclusion: Some insights can be drawn from the study. The PSO-BP neural network forecasting method not only has higher forecasting accuracy than the BP network, but also has better generalization performance and robustness. Since the lifecycle of cell phones with medium memory and the revenue obtained from sales are higher, companies should produce more cell phones with that memory size to maximize corporate revenue. The forecasting results provide a powerful guide for enterprises to make a reasonable dynamic adjustment. These insights of this paper provide theoretical guidance for enterprises to make product retirement decisions.","PeriodicalId":166011,"journal":{"name":"Modern Intelligent Times","volume":"34 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134305950","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}

{"title":"Cross-modal Person Re-identification Based on Hybrid Learning Networks","authors":"Guangpu Zhu, Haifeng Sang","doi":"10.53964/mit.2023003","DOIUrl":"https://doi.org/10.53964/mit.2023003","url":null,"abstract":"Objective: Person re-identification (cross-modal person re-identification, Re-ID) aims to retrieve query identities from a gallery of non-overlapping camera views. It has many practical applications such as missing person search, forensics, etc. Much pedestrian Re-ID research mainly focuses on single-modal retrieval tasks, where both indoor and outdoor pedestrian images are from red, green, and blue (RGB) modalities. However, the security monitoring system will automatically switch from the visible state to the infrared state in dark situations. In order to meet the needs of practical applications, research on cross-modal pedestrian Re-ID based on IR-RGB has attracted more and more attention. The features of different modality images essentially lack modality sharing information. Methods: Most of the existing methods use the network to extract shared features to deal with differences, but they ignore the unique feature information that is beneficial to recognition in these two modality images. In this paper, a hybrid learning network is proposed to solve the problem of learning the unique and common features of the two modalities by mixing the single-modal branch and the cross-modal branch to learn from each other. At the same time, the graph convolution attention mechanism and granular feature learning are used to process the features extracted by the cross-modal branch and the single-modal branch, so as to enrich the information input by different branch classifiers, thereby improving the discriminability of features. Results: And achieved 59.68% Rank-1 accuracy and 58.28% mean average precision average mean precision on the cross-modal data set SYSU-MM01. Conclusion: The experimental results also show that this is the effectiveness of the method.","PeriodicalId":166011,"journal":{"name":"Modern Intelligent Times","volume":"26 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-04-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115836501","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}

{"title":"1-dimensional Embedding Approach for Adolescent Psychosis Prediction from EEG Brain Waves using Support Vector Machines and Deep Learning","authors":"E. Nsugbe","doi":"10.53964/mit.2023002","DOIUrl":"https://doi.org/10.53964/mit.2023002","url":null,"abstract":"Objective: Schizophrenia is a form of psychosis which is prevalent within the population and affects both adults and adolescents, where it has also been noted that the conventional means towards diagnosing the disorder involve a high degree of subjectivity, can yield false positive results and are overall difficult to replicate. The use of electroencephalography (EEG) brain waves has been seen to carry promise as a means towards a non-invasive diagnosis of brain states via the acquisition of the bioelectrical manifestation of neuronal activities from across the skull. The acquisition of EEG signals involves a high number of electrode channels alongside the extraction of a relatively broad number of signal features which ultimately yields a high dimensional vector that scales upwards with the addition of data from subsequent subjects. Methods: As a means around dealing with this issue, this paper proposes a combination of signal decomposition prior to feature extraction, alongside dimensionality reduction, to obtain a low dimensional representation and embedding which is sufficient to allow for an improved prediction performance with a low dimensional projection capable of distinguishing between schizophrenic and non-schizophrenic adolescents. Results: The results from the pilot carried out, showed that a combination of a metaheuristic signal decomposition, linear dimensionality reduction and the long short-term memory deep learning model provided the best classification performance for the recognition of adolescent schizophrenia from a 1-dimensional embedding representation with a prediction accuracy of 88%. Conclusion: Further work in this area would now look into the application of probabilistic modelling as a means towards inferring an associated stage of the schizophrenia psychosis where predicted to be present in an adolescent subject.","PeriodicalId":166011,"journal":{"name":"Modern Intelligent Times","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-04-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127839587","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}

{"title":"A Review of the Consensus Achievement Strategies in the Context of Swarm Robotics","authors":"Theviyanthan Krishnamohan","doi":"10.53964/mit.2023001","DOIUrl":"https://doi.org/10.53964/mit.2023001","url":null,"abstract":"Swarm robotics applies swarm intelligence to robotics to solve real-life problems that cannot be solved by single, monolithic robots. This review first studies swarm robotics in depth and analyzes the different types of tasks carried out by swarms of robots. Then, the consensus-achievement behavior is elaborated on and its relation to the collective perception scenario is reviewed. The collective perception scenario is expounded in detail and the three discreet consensus-achievement strategies, namely the direct modulation of majority-based decision, direct modulation of voter-based decision, and direct comparison are discussed apropos of the collective perception scenario. This review then compares these strategies and explores their shortcoming such as the vulnerability to Byzantine robots.","PeriodicalId":166011,"journal":{"name":"Modern Intelligent Times","volume":"46 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-03-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133535635","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}