Human-Centric Intelligent Systems最新文献

{"title":"Newtonian Physics Informed Neural Network (NwPiNN) for Spatio-Temporal Forecast of Visual Data","authors":"Anurag Dutta, K. Lakshmanan, Sanjeev Kumar, A. Ramamoorthy","doi":"10.1007/s44230-024-00071-5","DOIUrl":"https://doi.org/10.1007/s44230-024-00071-5","url":null,"abstract":"","PeriodicalId":303535,"journal":{"name":"Human-Centric Intelligent Systems","volume":"22 18","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-05-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140968478","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":"Irrigation with Artificial Intelligence: Problems, Premises, Promises","authors":"Hanyu Wei, Wen Xu, Byeong Kang, R. Eisner, Albert Muleke, Daniel Rodriguez, P. deVoil, Victor Sadras, Marta Monjardino, M. T. Harrison","doi":"10.1007/s44230-024-00072-4","DOIUrl":"https://doi.org/10.1007/s44230-024-00072-4","url":null,"abstract":"","PeriodicalId":303535,"journal":{"name":"Human-Centric Intelligent Systems","volume":"114 2","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-05-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140985717","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":"An Enhanced Location-Aided Ant Colony Routing for Secure Communication in Vehicular Ad Hoc Networks","authors":"Raghu Ramamoorthy","doi":"10.1007/s44230-023-00059-7","DOIUrl":"https://doi.org/10.1007/s44230-023-00059-7","url":null,"abstract":"","PeriodicalId":303535,"journal":{"name":"Human-Centric Intelligent Systems","volume":"67 25","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-01-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139534747","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":"Efficient Feature Selection in High Dimensional Data Based on Enhanced Binary Chimp Optimization Algorithms and Machine Learning","authors":"Farid Ayeche, Adel Alti","doi":"10.1007/s44230-023-00048-w","DOIUrl":"https://doi.org/10.1007/s44230-023-00048-w","url":null,"abstract":"Abstract Feature selection with the highest performance accuracy is the biggest win for multidimensional data. The Chimpanzee Optimization Algorithm (ChOA) serves as a crucial technique for dealing with multidimensional global optimization issues. However, ChOA often lacks fast convergence and good selection of sensitive attributes leading to poor performance. To address these issues, most significant features were selected using two variants of ChOA called BChimp1 and BChimp2 (BChimp1 and BChimp are available at : https://www.mathworks.com/matlabcentral/fileexchange/133267-binary-chimpoptimization-algorithm-forfeatures-selection . September 22, 202). BChimp1 selects the optimal solution from the four best possible solutions and it applies a stochastic crossover on four moving solutions to deeply speed-up convergence level. BChimp2 uses the sigmoid function to select the significant features. Then, these features were trained using six-well known classifiers. The proposed techniques tend to select the most significant features, speed up the convergence rate and decrease training time for high-dimensional data. 23 standard datasets with six well-known classifiers were employed to assess the performance of BChimp1 and BChimp2. Experimental results validate the efficiency of BChimp1 and BChimp2 in enhancing accuracy by 83.83% and 82.02%, and reducing dimensionality by 42.77% and 72.54%, respectively. However, time-evaluation results of BChimp1 and BChimp2 in all datasets showed fast convergence and surpassed current optimization algorithms such as PSO, GWA, GOA, and GA.","PeriodicalId":303535,"journal":{"name":"Human-Centric Intelligent Systems","volume":"20 20","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-11-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135972863","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":"Machine Learning-Based Screening Solution for COVID-19 Cases Investigation: Socio-Demographic and Behavioral Factors Analysis and COVID-19 Detection","authors":"K. M. Aslam Uddin, Farida Siddiqi Prity, Maisha Tasnim, Sumiya Nur Jannat, Mohammad Omar Faruk, Jahirul Islam, Saydul Akbar Murad, Apurba Adhikary, Anupam Kumar Bairagi","doi":"10.1007/s44230-023-00049-9","DOIUrl":"https://doi.org/10.1007/s44230-023-00049-9","url":null,"abstract":"Abstract The COVID-19 pandemic has unleashed an unprecedented global crisis, releasing a wave of illness, mortality, and economic disarray of unparalleled proportions. Numerous societal and behavioral aspects have conspired to fuel the rampant spread of COVID-19 across the globe. These factors encompass densely populated areas, adherence to mask-wearing protocols, inadequate awareness levels, and various behavioral and social practices. Despite the extensive research surrounding COVID-19 detection, an unfortunate dearth of studies has emerged to meticulously evaluate the intricate interplay between socio-demographic and behavioral factors and the likelihood of COVID-19 infection. Thus, a comprehensive online-based cross-sectional survey was methodically orchestrated, amassing data from a substantial sample size of 500 respondents. The precisely designed survey questionnaire encompassed various variables encompassing socio-demographics, behaviors, and social factors. The Bivariate Pearson’s Chi-square association test was deftly employed to unravel the complex associations between the explanatory variables and COVID-19 infection. The feature importance approach was also introduced to discern the utmost critical features underpinning this infectious predicament. Four distinct Machine Learning (ML) algorithms, specifically Decision Tree, Random Forest, CatBoost, and XGBoost, were employed to accurately predict COVID-19 infection based on a comprehensive analysis of socio-demographic and behavioral factors. The performance of these models was rigorously assessed using a range of evaluation metrics, including accuracy, recall, precision, ROC-AUC score, and F1 score. Pearson’s Chi-square test revealed a statistically significant association between vaccination status and COVID-19 infection. The use of sanitizer and masks, the timing of infection, and the interval between the first and second vaccine doses were significantly correlated with the likelihood of contracting the COVID-19 virus. Among the ML models tested, the XGBoost classifier demonstrated the highest classification accuracy, achieving an impressive 97.6%. These findings provide valuable insights for individuals, communities, and policymakers to implement targeted strategies aimed at mitigating the impact of the COVID-19 pandemic.","PeriodicalId":303535,"journal":{"name":"Human-Centric Intelligent Systems","volume":"10 9","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136233704","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":"CSI-Based Location Independent Human Activity Recognition Using Deep Learning","authors":"Fahd Abuhoureyah, Yan Chiew Wong, Ahmad Sadhiqin Bin Mohd Isira, Mohammed Nasser Al-Andoli","doi":"10.1007/s44230-023-00047-x","DOIUrl":"https://doi.org/10.1007/s44230-023-00047-x","url":null,"abstract":"Abstract Human Activity Recognition (HAR) is widely used in various applications, from smart homes and healthcare to the Internet of Things (IoT) and virtual reality gaming. However, existing HAR technologies suffer from limitations such as location dependency, sensitivity to noise and interference, and lack of flexibility in recognizing diverse activities and environments. In this paper, we present a novel approach to HAR that addresses these challenges and enables real-time classification and absolute location-independent sensing. The approach is based on an adaptive algorithm that leverages sequential learning activity features to simplify the recognition process and accommodate variations in human activities across different people and environments by extracting the features that match the signal with the surroundings. We employ the Raspberry Pi 4 and Channel State Information (CSI) data to extract activity recognition data, which provides reliable and high-quality signal information. We propose a signal segmentation method using the Long Short-Term Memory (LSTM) algorithm to accurately determine the start and endpoint of human activities. Our experiments show that our approach achieves a high accuracy of up to 97% in recognizing eight activities and mapping activities associated with environments that were not used in training. The approach represents a significant advancement in HAR technology and has the potential to revolutionize many domains, including healthcare, smart homes, and IoT.","PeriodicalId":303535,"journal":{"name":"Human-Centric Intelligent Systems","volume":"30 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135804339","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":"Future Food Production Prediction Using AROA Based Hybrid Deep Learning Model in Agri-Sector","authors":"Swathi Baswaraju, V. Uma Maheswari, krishna Keerthi Chennam, Arunadevi Thirumalraj, M. V. V. Prasad Kantipudi, Rajanikanth Aluvalu","doi":"10.1007/s44230-023-00046-y","DOIUrl":"https://doi.org/10.1007/s44230-023-00046-y","url":null,"abstract":"Abstract Policymaking and administration of national tactics of action for food security rely heavily on advances in models for accurate estimation of food output. In several fields, including food science and engineering, machine learning (ML) has been established to be an effective tool for data investigation and modelling. There has been a rise in recent years in the application of ML models to the tracking and forecasting of food safety. In our analysis, we focused on two sources of food production: livestock production and agricultural production. Livestock production was measured in terms of yield, number of animals, and sum of animals slaughtered; crop output was measured in terms of yields and losses. An innovative hybrid deep learning model is proposed in this paper by fusing a Dense Convolutional Network (DenseNet) with a Long Short-Term Memory (LSTM) to do production analysis. The hybridised algorithm, or A-ROA for short, combines the Arithmetic Optimisation Algorithm (AOA) and the Rider Optimisation Algorithm (ROA) to determine the ideal weight of the LSTM. The current investigation focuses on Iran as a case study. Therefore, we have collected FAOSTAT time series data on livestock and farming outputs in Iran from 1961 to 2017. Findings from this study can help policymakers plan for future generations' food safety and supply by providing a model to anticipate the upcoming food construction.","PeriodicalId":303535,"journal":{"name":"Human-Centric Intelligent Systems","volume":"24 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135351455","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":"Street Object Detection from Synthesized and Processed Semantic Image: A Deep Learning Based Study","authors":"Parthaw Goswami, A. B. M. Aowlad Hossain","doi":"10.1007/s44230-023-00043-1","DOIUrl":"https://doi.org/10.1007/s44230-023-00043-1","url":null,"abstract":"Abstract Semantic image synthesis plays an important role in the development of Advanced Driver Assistance System (ADAS). Street objects detection might be erroneous during raining or when images from vehicle’s camera are blurred, which can cause serious accidents. Therefore, automatic and accurate street object detection is a demanding research scope. In this paper, a deep learning based framework is proposed and investigated for street object detection from synthesized and processed semantic image. Firstly, a Conditional Generative Adversarial Network (CGAN) has been used to create the realistic image. The brightness of the CGAN generated image has been increased using neural style transfer method. Furthermore, Enhanced Super-Resolution Generative Adversarial Networks (ESRGAN) based image enhancement concept has been used to improve the resolution of style-transferred images. These processed images exhibit better clarity and high fidelity which is impactful in the performance improvement of object detector. Finally, the synthesized and processed images were used as input in a Region-based Convolutional Neural Network (Faster R-CNN) and a MobileNet Single Shot Detector (MobileNetSSDv2) model separately for object detection. The widely used Cityscape dataset is used to investigate the performance of the proposed framework. The results analysis shows that the used synthesized and processed input improves the performance of the detectors than the unprocessed counterpart. A comparison of the proposed detection framework with related state of the art techniques is also found satisfactory with a mean average precision (mAP) around 32.6%, whereas most of the cases, mAPs are reported in the range of 20–28% for this particular dataset.","PeriodicalId":303535,"journal":{"name":"Human-Centric Intelligent Systems","volume":"16 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-09-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136308021","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":"Distributed Multi-Agent Hierarchy Construction for Dynamic DCOPs in Mobile Sensor Teams","authors":"Brighter Agyemang, Fenghui Ren, Jun Yan","doi":"10.1007/s44230-023-00044-0","DOIUrl":"https://doi.org/10.1007/s44230-023-00044-0","url":null,"abstract":"Abstract Coordinating multiple agents to optimize an objective has several real-world applications. In areas such as disaster rescue, environment monitoring and the like, mobile agents may be deployed to work as a team to achieve a joint goal. Recently, multi-agent problems involving mobile sensor teams have been formalized in the literature as DCOP_MSTs. Under this class of problems, DCOP algorithms are applied to enable agents to coordinate the assignment of their physical locations as they jointly optimize the team objective. In DCOP_MSTs, the environment is dynamic, and agents may leave or join the environment at random times. As a result, a predefined interaction topology or graph may not be useful over the problem horizon. Therefore, there is a need to study methods that could facilitate agent-to-agent interaction in such open and dynamic environments. Existing methods require reconstructing the entire graph upon detecting changes in the environment or assume a predefined interaction graph. In this study, we propose a dynamic multi-agent hierarchy construction algorithm that can be used by DCOP_MST algorithms that require a pseudo-tree for execution. We evaluate our proposed method in a simulated target detection case study to show the effectiveness of the proposed approach in large agent teams.","PeriodicalId":303535,"journal":{"name":"Human-Centric Intelligent Systems","volume":"12 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-09-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135878261","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":"Applications and Techniques of Machine Learning in Cancer Classification: A Systematic Review","authors":"Abrar Yaqoob, Rabia Musheer Aziz, Navneet Kumar verma","doi":"10.1007/s44230-023-00041-3","DOIUrl":"https://doi.org/10.1007/s44230-023-00041-3","url":null,"abstract":"Abstract The domain of Machine learning has experienced Substantial advancement and development. Recently, showcasing a Broad spectrum of uses like Computational linguistics, image identification, and autonomous systems. With the increasing demand for intelligent systems, it has become crucial to comprehend the different categories of machine acquiring knowledge systems along with their applications in the present world. This paper presents actual use cases of machine learning, including cancer classification, and how machine learning algorithms have been implemented on medical data to categorize diverse forms of cancer and anticipate their outcomes. The paper also discusses supervised, unsupervised, and reinforcement learning, highlighting the benefits and disadvantages of each category of Computational intelligence system. The conclusions of this systematic study on machine learning methods and applications in cancer classification have numerous implications. The main lesson is that through accurate classification of cancer kinds, patient outcome prediction, and identification of possible therapeutic targets, machine learning holds enormous potential for improving cancer diagnosis and therapy. This review offers readers with a broad understanding as of the present advancements in machine learning applied to cancer classification today, empowering them to decide for themselves whether to use these methods in clinical settings. Lastly, the paper wraps up by engaging in a discussion on the future of machine learning, including the potential for new types of systems to be developed as the field advances. Overall, the information included in this survey article is useful for scholars, practitioners, and individuals interested in gaining knowledge about the fundamentals of machine learning and its various applications in different areas of activities.","PeriodicalId":303535,"journal":{"name":"Human-Centric Intelligent Systems","volume":"49 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-09-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135981846","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}