自主智能(英文)最新文献_第6页

Moving towards a sustainable world with the circular economy practices concerning the SMEs in Visakhapatnam’s ice-cream industry 在维萨卡帕特南冰淇淋行业的中小企业中，通过循环经济实践走向可持续发展的世界

自主智能(英文) Pub Date : 2023-06-28 DOI: 10.32629/jai.v6i1.676

Mukesh Kondala, S. Nudurupati, Eko Riwayadi, Abhijeet Chavan, Shaik Rajah Asif, N. Gupta

{"title":"Moving towards a sustainable world with the circular economy practices concerning the SMEs in Visakhapatnam’s ice-cream industry","authors":"Mukesh Kondala, S. Nudurupati, Eko Riwayadi, Abhijeet Chavan, Shaik Rajah Asif, N. Gupta","doi":"10.32629/jai.v6i1.676","DOIUrl":"https://doi.org/10.32629/jai.v6i1.676","url":null,"abstract":"The Circular Economy (CE) is getting its attention these days, which has a massive impact on the industries, particularly in the manufacturing segment. The countries worldwide started believing in CE, and its practices got the benefits after thoroughly implementing it to their current practices. The concept is not new, but it came up with a new ideology and new techniques already proven by countries like China and the UK. Different industries show their innovativeness by adapting to the change for the future. We found that the Ice Cream Industry is one of them that adopt change quickly. The paper discusses the introduction of the CE, the current trends, the comparison of the olden style with new style after implementing CE practices, the challenges and barriers in implementing, and the benefits of implementing CE Practices in Visakhapatnam’s dairy industry. We followed a personal interview method for getting first-hand and rich information from the CEOs and operational managers of the company. Also, we followed the case study method to extract how they shifted from traditional manufacturing practices to the current and latest trends in manufacturing. In their manufacturing practices, we aimed to get factual information on the changeover from linear to circular.","PeriodicalId":70721,"journal":{"name":"自主智能(英文)","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-06-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42179163","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: 1

Diabetic retinopathy feature extraction images based on confusion neural network 基于模糊神经网络的糖尿病视网膜病变图像特征提取

自主智能(英文) Pub Date : 2023-06-28 DOI: 10.32629/jai.v6i1.636

M. Ghet, Omar Ismael Al-Sanjary, A. Khatibi

引用次数: 0

Mining timed sequential patterns: The Minits-AllOcc technique 挖掘时序模式:Minits-AllOcc技术

自主智能(英文) Pub Date : 2023-06-19 DOI: 10.32629/jai.v6i1.593

Somayah Karsoum, Clark Barrus, L. Gruenwald, Eleazar Leal

{"title":"Mining timed sequential patterns: The Minits-AllOcc technique","authors":"Somayah Karsoum, Clark Barrus, L. Gruenwald, Eleazar Leal","doi":"10.32629/jai.v6i1.593","DOIUrl":"https://doi.org/10.32629/jai.v6i1.593","url":null,"abstract":"Sequential pattern mining is one of the data mining tasks used to find the subsequences in a sequence dataset that appear together in order based on time. Sequence data can be collected from devices, such as sensors, GPS, or satellites, and ordered based on timestamps, which are the times when they are generated/collected. Mining patterns in such data can be used to support many applications, including transportation recommendation systems, transportation safety, weather forecasting, and disease symptom analysis. Numerous techniques have been proposed to address the problem of how to mine subsequences in a sequence dataset; however, current traditional algorithms ignore the temporal information between the itemset in a sequential pattern. This information is essential in many situations. Though knowing that measurement Y occurs after measurement X is valuable, it is more valuable to know the estimated time before the appearance of measurement Y, for example, to schedule maintenance at the right time to prevent railway damage. Considering temporal relationship information for sequential patterns raises new issues to be solved, such as designing a new data structure to save this information and traversing this structure efficiently to discover patterns without re-scanning the database. In this paper, we propose an algorithm called Minits-AllOcc (MINIng Timed Sequential Pattern for All-time Occurrences) to find sequential patterns and the transition time between itemsets based on all occurrences of a pattern in the database. We also propose a parallel multi-core CPU version of this algorithm, called MMinits-AllOcc (Multi-core for MINIng Timed Sequential Pattern for All-time Occurrences), to deal with Big Data. Extensive experiments on real and synthetic datasets show the advantages of this approach over the brute-force method. Also, the multi-core CPU version of the algorithm is shown to outperform the single-core version on Big Data by 2.5X.","PeriodicalId":70721,"journal":{"name":"自主智能(英文)","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-06-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46544838","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 Hybrid Software Defects Prediction Model for Imbalance Datasets Using Machine Learning Techniques: (S-SVM Model) 基于机器学习技术的失衡数据集混合软件缺陷预测模型(S-SVM模型)

自主智能(英文) Pub Date : 2023-06-16 DOI: 10.32629/jai.v6i1.559

Mohd. Mustaqeem, Tamanna Siddiqui

{"title":"A Hybrid Software Defects Prediction Model for Imbalance Datasets Using Machine Learning Techniques: (S-SVM Model)","authors":"Mohd. Mustaqeem, Tamanna Siddiqui","doi":"10.32629/jai.v6i1.559","DOIUrl":"https://doi.org/10.32629/jai.v6i1.559","url":null,"abstract":"Software defect prediction (SDP) is an essential task for developing quality software, and various models have been developed for this purpose. However, the imbalanced nature of software defect datasets has challenged these models, resulting in decreased performance. To address this challenge, the author has proposed a hybrid machine learning model that combines Synthetic Minority Oversampling Technique (SMOTE) with Support Vector Machine (SVM)—SMOTE-SVM (S-SVM) model. The author has empirically examined SDP using multiple datasets (CM1, PC1, JM1, PC3, KC1, EQ and JDT) from the PROMISE and AEEEM repositories. The experimental study indicates that the S-SVM model involved training and compared with previously developed balanced and imbalanced test datasets using four evaluation metrics: Precision, Recall, F1 score, and Accuracy. For the balanced dataset, the S-SVM model achieved precision values ranging from 70 to 96, recall values ranging from 52 to 94, F1-score values ranging from 67 to 90, and accuracy values ranging from 69 to 98. For the imbalanced dataset, the S-SVM model achieved precision values ranging from 60 to 93, recall values ranging from 64 to 97, F1-score values ranging from 69 to 91, and accuracy values ranging from 67 to 87. The proposed S-SVM model outperforms other models’ ability to classify and predict software defects. Therefore, the hybridisation of SMOTE and SVM improved the model’s ability to categories and predict balanced and imbalanced datasets when sufficient defective and non-defective data is provided.","PeriodicalId":70721,"journal":{"name":"自主智能(英文)","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-06-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49055736","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: 1

Transfer learning model for the motion detection of sports players 运动运动员运动检测的迁移学习模型

自主智能(英文) Pub Date : 2023-06-13 DOI: 10.32629/jai.v6i1.577

Wael Alghamdi

{"title":"Transfer learning model for the motion detection of sports players","authors":"Wael Alghamdi","doi":"10.32629/jai.v6i1.577","DOIUrl":"https://doi.org/10.32629/jai.v6i1.577","url":null,"abstract":"Recognizing and analyzing moving targets is an important research subject since computer vision is employed in so many facets of our daily lives, including intelligent robotics, video surveillance, medical education, sporting events, and the maintenance of our national defense. This is because it may be difficult to properly analyse and keep up with moving materials. The various training postures of an athlete are explored in this study through the examination of a weightlifting video. This article was written to assist coaches in their efforts to improve the performance of their athletes in their respective sports. A technique for extracting essential poses from sports films has been proposed. The classification of different subjects of interest serves as the foundation for this technique. Because of its inadequate edge detection method, the current motion identification system does a bad job of detecting athletes, which is one of the reasons why it does a poor job of identifying motion in general. This flaw is one of the reasons why the system isn’t very strong at detecting athletes. The following was one of the factors that contributed to this outcome: in truth, the situation is currently in this state. The result of the newly developed system outperforms the prior system in terms of tracking recognition accuracy and convergence speed. The system was put to the test. The findings of the system’s study served as the foundation for this decision. Finally, the findings of the categorization reveal that the selection approach tries to separate fundamental postures.","PeriodicalId":70721,"journal":{"name":"自主智能(英文)","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-06-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42852200","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

Designing new student performance prediction model using ensemble machine learning 利用集成机器学习设计新的学生成绩预测模型

自主智能(英文) Pub Date : 2023-05-24 DOI: 10.32629/jai.v6i1.583

Rajan Saluja, Munishwar Rai, R. Saluja

{"title":"Designing new student performance prediction model using ensemble machine learning","authors":"Rajan Saluja, Munishwar Rai, R. Saluja","doi":"10.32629/jai.v6i1.583","DOIUrl":"https://doi.org/10.32629/jai.v6i1.583","url":null,"abstract":"Academic success for students in any educational institute is the primary requirement for all stakeholders, i.e., students, teachers, parents, administrators and management, industry, and the environment. Regular feedback from all stakeholders helps higher education institutions (HEIs) rise professionally and academically, yet they must use emerging technologies that can help institutions to grow at a faster pace. Early prediction of students’ success using trending artificial intelligence technologies like machine learning, early finding of at-risk students, and predicting a suitable branch or course can help both management and students improve their academics. In our work, we have proposed a new student performance prediction model in which we have used ensemble machine learning with stacking of four multi-class classifiers, decision tree, k-nearest neighbor, Naïve Bayes, and One vs. Rest support vector machine classifiers. The proposed model predicts the final grade of a student at the earliest possible time and the suitable stream for a new student. A student dataset of over a thousand students from five different branches of an engineering institute has been taken to test the results. The proposed model compares the four-machine learning (ML) techniques being used and predicts the final grade with an accuracy of 93%.","PeriodicalId":70721,"journal":{"name":"自主智能(英文)","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-05-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41795096","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: 2

Machine learning approach to analyze the impact of demographic and linguistic features of children on their stuttering 用机器学习方法分析儿童人口统计学和语言特征对口吃的影响

自主智能(英文) Pub Date : 2023-05-24 DOI: 10.32629/jai.v6i1.553

Shaikh Abdul Waheed, Mohammed Abdul Matheen, Syed Hussain Hussain, A. K. Lodhi, G.S. Maboobatcha

{"title":"Machine learning approach to analyze the impact of demographic and linguistic features of children on their stuttering","authors":"Shaikh Abdul Waheed, Mohammed Abdul Matheen, Syed Hussain Hussain, A. K. Lodhi, G.S. Maboobatcha","doi":"10.32629/jai.v6i1.553","DOIUrl":"https://doi.org/10.32629/jai.v6i1.553","url":null,"abstract":"This study aims at analyzing the impact of gender and race on the linguistic abilities and stuttering of children. The current article also seeks to check whether children with stuttering disorder and normal children differ in linguistic skills. Parametric methods like t-tests and Analysis of Variance (ANOVA) have been applied to test hypotheses. The p-values that were generated in the parametric tests signify that the gender of the child has an impact on the onset of stuttering. However, the race of children did not affect the onset of stuttering. The regression results of the machine learning part have indicated many findings. The results indicated that a child’s race does not impact the onset of stuttering. Hence, the null hypothesis about race was accepted by signifying that children of any race can adopt stuttering. This finding also suggests that children can face linguistic difficulties irrespective of their race. Another finding is that children with stuttering (CWS) repeat more words than children with not stuttering (CWNS). In addition, CWS repeat more syllables than CWNS. It indicates that the null hypothesis can be accepted by stating that children can suffer from linguistic difficulties irrespective of their race. Another key finding is that there can be a significant difference in the linguistic abilities of male and female children. Another inference is that the p-values indicate a significant difference between linguistic skills among CWS and CWNS. In other words, CWS are more prone to repeat syllables than normal children.","PeriodicalId":70721,"journal":{"name":"自主智能(英文)","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-05-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42502308","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: 2

Can artificial intelligence help a clinical laboratory to draw useful information from limited data sets ? Application to Mixed Connective Tissue Disease 人工智能能帮助临床实验室从有限的数据集中提取有用的信息吗？混合结缔组织病的应用

自主智能(英文) Pub Date : 2023-05-24 DOI: 10.1101/2023.05.23.23290343

D. Bertin, P. Bongrand, N. Bardin

{"title":"Can artificial intelligence help a clinical laboratory to draw useful information from limited data sets ? Application to Mixed Connective Tissue Disease","authors":"D. Bertin, P. Bongrand, N. Bardin","doi":"10.1101/2023.05.23.23290343","DOIUrl":"https://doi.org/10.1101/2023.05.23.23290343","url":null,"abstract":"Diagnosis is a key step of patient management. During decades, refined decision algorithms and numerical scores based on conventional statistic tools were elaborated to ensure optimal reliability. Recently, a number of machine learning tools were developed and applied to process more and more extensive data sets, including up to million of items and yielding sophisticated classification models. While this approach met with impressive efficiency in some cases, practical limitations stem from the high number of parameters that may be required by a model, resulting in increased cost and delay of decision making. Also, information relative to the specificity of local recruitment may be lost, hampering any simplification of universal models. Here, we explored the capacity of currently available artificial intelligence tools to classify patients found in a single health center on the basis of a limited number of parameters. As a model, the discrimination between systemic lupus erythematosus (SLE) and mixed connective tissue disease (MCTD) on the basis of thirteen biological parameters was studied with eight widely used classifiers. It is concluded that classification performance may be significantly improved by a knowledge-based selection of discriminating parameters.","PeriodicalId":70721,"journal":{"name":"自主智能(英文)","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-05-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48268369","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

Financial time series prediction using deep computing approaches 利用深度计算方法进行金融时间序列预测

自主智能(英文) Pub Date : 2023-05-04 DOI: 10.32629/jai.v6i1.558

M. Durairaj, C. Suneetha, B. Mohan

{"title":"Financial time series prediction using deep computing approaches","authors":"M. Durairaj, C. Suneetha, B. Mohan","doi":"10.32629/jai.v6i1.558","DOIUrl":"https://doi.org/10.32629/jai.v6i1.558","url":null,"abstract":"A financial time series is chaotic and non-stationary in nature, and predicting it outcomes is a very complex and challenging task. In this research, the theory of chaos, Long Short-Term Memory (LSTM), and Polynomial Regression (PR) are used in tandem to create a novel financial time series prediction hybrid, Chaos+LSTM+PR. The first step in this hybrid will determine whether or not a financial time series contains chaos. Following that, the chaos in the time series is modeled using Chaos Theory. The modeled time series is fed into the LSTM to obtain initial predictions. The error series obtained from LSTM predictions is fitted by PR to obtain error predictions. The error predictions and initial predictions from LSTM are combined to obtain final predictions. The effectiveness of this hybrid is examined by three types of financial time series (Chaos+LSTM+PR), including stock market indices (S&P 500, Nifty 50, Shanghai Composite), commodity prices (gold, crude oil, soya beans), and foreign exchange rates (INR/USD, JPY/USD, SGD/USD). The results show that the proposed hybrid outperforms ARIMA (autoregressive integrated moving average), Prophet, CART (Classification and Regression Tree), RF (Random Forest), LSTM, Chaos+CART, Chaos+CART, and Chaos+LSTM. The results are also checked for statistical significance.","PeriodicalId":70721,"journal":{"name":"自主智能(英文)","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-05-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43410537","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: 1

Managing Humanitarian Challenges of Disaster Responses and Pandemic Crises: Interface of 4IR Ecosystem 管理灾害应对和流行病危机的人道主义挑战:第四次工业革命生态系统的接口

自主智能(英文) Pub Date : 2023-04-18 DOI: 10.32629/jai.v5i2.550

Arindam Chakrabarty, U. Das, S. Kushwaha, Prathamesh P. Churi

{"title":"Managing Humanitarian Challenges of Disaster Responses and Pandemic Crises: Interface of 4IR Ecosystem","authors":"Arindam Chakrabarty, U. Das, S. Kushwaha, Prathamesh P. Churi","doi":"10.32629/jai.v5i2.550","DOIUrl":"https://doi.org/10.32629/jai.v5i2.550","url":null,"abstract":"The human civilization has witnessed myriads of road-block and crossroads at every facet of its journey. Many a time, it becomes untenable to sustain its existence. The series of health hazards, critical epidemics and even the catastrophic pandemic diseases have been challenging our vivid foundation and perpetuity. The disasters both natural and man-made have attempted massively to destroy, devastate, and ruin our glorious leadership on earth. In all such cases, the society has responded through rendering relief and rescue operations and offering emergency health services to mitigate these humanitarian crises. It is imperative to understand, the response time for such emergencies varies with the nature and intensity of the hazards. It is still difficult to reach the epicenter or the point of occurrence even though services have begun to function towards the outer periphery region. The deployment of medical and non-medical personnel at the critical point in the early hours becomes unsuitable and unwise decision. There are issues of the inadequacy of resources for deployment strategy. In the era of 4IR (4th Industrial Revolution or Industry 4.0), it is emergent to improvise AI induced guided or auto guided devices that can perform various tasks at such unprecedented humanitarian crisis. The introduction of the Internet of Robotic Things (IoRT) protocol embedded with medical based AI i.e. Internet of Medical Robotic Things (IoMRT) would be able to deliver superior performance to minimize loss of life and property. This paper has attempted to explore how the IoMRT system can contribute to society with excellence.","PeriodicalId":70721,"journal":{"name":"自主智能(英文)","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-04-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47427755","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