Journal of Autonomous Intelligence最新文献_第10页

Sentiment analysis and classification of COVID-19 tweets using machine learning classifier 利用机器学习分类器对 COVID-19 微博进行情感分析和分类

Journal of Autonomous Intelligence Pub Date : 2023-12-12 DOI: 10.32629/jai.v7i2.801

Chataparti Suvarna Lakshmi, Sameer Saxena, B. Suresh Kumar

{"title":"Sentiment analysis and classification of COVID-19 tweets using machine learning classifier","authors":"Chataparti Suvarna Lakshmi, Sameer Saxena, B. Suresh Kumar","doi":"10.32629/jai.v7i2.801","DOIUrl":"https://doi.org/10.32629/jai.v7i2.801","url":null,"abstract":"In March of 2020, the World Health Organization identified COVID-19 as a new pandemic and issued a statement to that effect. This fatal virus was able to disperse and propagate throughout several countries all over the world. During the progression of the pandemic, social networking sites like Twitter generated significant and substantial volumes of data that helped improve the quality of decisions pertaining to health care applications. In this paper, we proposed a sentiment classification using various feature extraction and machine leavening techniques for social media dataset. The system has divided into four phase data collection, preprocessing and normalization, feature extraction and feature selection and finally classification. In first phase we collect data from social media sources such as twitter using Twitter API. In second phase the tweets, data was ready for preprocessing and it was sorted into three categories: positive, neutral, and negative. During the third phase, various features were extracted from the tweets by employing a number of widely utilized approaches, including as bag of words (BoW), Term Frequency-Inverse Document Frequency (TF-IDF), Word2Vec, and FastText, to gather feature datasets. These methods were employed to extract distinct datasets for the features. The final phase different machine learning classification algorithms are applied for detection of sentiment using machine learning. In the extensive experimental analysis, the BoW performed better results with modified support vector machine (mSVM) than existing machine learning algorithms. The proposed mSVM performed superiorly to the other classifiers by 98.15% accuracy rate. Once the tweets are correctly classified as COVID-19 tweets, it is further categorized into three sentiments that is positive, negative and neural. Proposed mSVM achieves 93% of accuracy rate for positive sentiment which better as compared to other Machine Learning (ML) classifiers.","PeriodicalId":307060,"journal":{"name":"Journal of Autonomous Intelligence","volume":"16 3","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-12-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139007388","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

Artificial intelligence with machine learning and the enigmatic discovery of HIV cure 人工智能与机器学习以及神秘的艾滋病毒治愈方法的发现

Journal of Autonomous Intelligence Pub Date : 2023-12-11 DOI: 10.32629/jai.v7i2.697

B. Lainjo

{"title":"Artificial intelligence with machine learning and the enigmatic discovery of HIV cure","authors":"B. Lainjo","doi":"10.32629/jai.v7i2.697","DOIUrl":"https://doi.org/10.32629/jai.v7i2.697","url":null,"abstract":"HIV’s complexity has long presented a problem in the quest for a cure. However, the development of machine learning (ML) and artificial intelligence (AI) technology has opened up promising new directions for HIV cure research. This study investigates the impact of AI and ML on the discovery and development of an HIV cure to shed light on their potential role in hastening advancements in this field. The study employs quantitative methodology, and the execution of the methods is achieved by using AI and ML techniques for analysis processes and presenting the study’s findings by utilizing the Kaggle.com HIV dataset, where pertinent features are found for the machine learning algorithm. Additionally, advanced statistical techniques, such as Structural Equation Modeling (SEM), to investigate the causal link between AI and ML utilization and the development of a cure for HIV is utilized. The robustness of the analysis is enhanced by using Penalized Ridge and Lasso Regressions. The study utilizes logistic regression as the machine learning model, and the mean square error is used to evaluate performance. Control variables, including the year, borough, the Uniform Hospitalization Fund (UHF) code, gender, age, race, concurrent diagnoses, percentage linked to care within three months, the prevalence of (People living with HIV/AIDS) PLWDHI, and percentage of viral suppression, deaths, death rate, and HIV-related death rate are all taken into consideration, to ensure a thorough analysis. This study finds that AI and ML are the future of the healthcare sector, providing promising opportunities for finding a cure for HIV and enhancing patient care. Further, the study confirms that new targets for HIV cure research can be found by utilizing AI and ML, and treatment outcomes and individualized treatment plans can also be developed. AI and ML can also enhance clinical trials, boost HIV prevention efforts, and lower the number of new infections.","PeriodicalId":307060,"journal":{"name":"Journal of Autonomous Intelligence","volume":"27 4","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-12-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138979374","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

Outcome-based assessment in India: A method for quantifying course outcome attainment 印度基于成果的评估：量化课程成果实现情况的方法

Journal of Autonomous Intelligence Pub Date : 2023-12-11 DOI: 10.32629/jai.v7i2.1160

S. G., Mrudul Arkadi, Sangeetha K., Shubhangi Suryawanshi, J. A.

{"title":"Outcome-based assessment in India: A method for quantifying course outcome attainment","authors":"S. G., Mrudul Arkadi, Sangeetha K., Shubhangi Suryawanshi, J. A.","doi":"10.32629/jai.v7i2.1160","DOIUrl":"https://doi.org/10.32629/jai.v7i2.1160","url":null,"abstract":"The National Board of Accreditation (NBA), India was established by the AICTE (All India Council of Technical Education) to assess the qualitative competence of the programs offered by engineering institutions. NBA focuses on outcome-based education (OBE). The main principles of OBE are to provide concluding significant outcomes, to expand the opportunities for success, to set high expectations to succeed. Each course is defined with a set of course outcomes. One of the key aspects of OBE is the attainment of course outcomes (CO). At the end of each course, the CO needs to be calculated and evaluated, to verify whether outcome expected has been attained or not. The attainment of the CO proves the efficiency of the teaching and learning process of the course. The course outcome attainment enables the faculties to plan and develop appropriate tools, materials and methodologies to improve the teaching learning process as well as to provide a measure for quality assurance. This paper shows the method to quantify the course outcomes with their target level. Assessment methods and tools are used to identify, collect and prepare data to evaluate the attainment of CO. This method can be applicable to all engineering programs in the line of accrediting their program to the NBA.","PeriodicalId":307060,"journal":{"name":"Journal of Autonomous Intelligence","volume":"14 41","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-12-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138980755","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

Hybrid energy balancer for clustering and routing techniques to enhance the lifetime and energy-efficiency of wireless sensor networks 用于聚类和路由技术的混合能量平衡器，以提高无线传感器网络的寿命和能效

Journal of Autonomous Intelligence Pub Date : 2023-12-07 DOI: 10.32629/jai.v7i2.961

R. K. Krishna, A. K. Lodhi, Zainulabedin Hasan Mohammed, Mohammed Abdul Matheen, Ahmed Sawy Khaled, C. Altaf

{"title":"Hybrid energy balancer for clustering and routing techniques to enhance the lifetime and energy-efficiency of wireless sensor networks","authors":"R. K. Krishna, A. K. Lodhi, Zainulabedin Hasan Mohammed, Mohammed Abdul Matheen, Ahmed Sawy Khaled, C. Altaf","doi":"10.32629/jai.v7i2.961","DOIUrl":"https://doi.org/10.32629/jai.v7i2.961","url":null,"abstract":"Clustering and Routing have been recognized as one of the most proficient methods for the conservation of energy. In addition, efficient routing further enhances the energy-saving capacity of WSNs (Wireless sensor networks). In this work, a hybrid technique is proposed that usages the prominent features as multiple energy-conserving techniques have been combined to develop a configuration that delivers a highly efficient Wireless network that not only saves energy but also transmits data efficiently. The Clusters are designed and Cluster Heads (CH) are designated by maintaining a minimum distance from the basic nodes for quick data transmission from source to destination. The concept of multiple cluster heads is proposed to provide secure and efficient transmission without losing the data packets. Three cluster heads are selected from each cluster so that when the energy in one Cluster head is exhausted the second Cluster head takes over to continue data communication thus increasing the lifetime of the network. The unequal clustering concept is used to avoid the issue of hot spots as well as Energy Balancing. In this clustering, lesser clusters are positioned closer to the base station. Depending on the energy distribution, the Nodes in the cluster are divided into advanced nodes, intermediate nodes, and normal nodes. The two paths routing method is adopted for rapid transmission towards the Base station. Finally, an evaluation of the proposed technique with the existing comparable techniques has been done which shows that the proposed system gives better results in terms of energy consumption, lifetime, and the number of alive nodes.","PeriodicalId":307060,"journal":{"name":"Journal of Autonomous Intelligence","volume":"25 19","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-12-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138591222","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

Emotion sensitive analysis of learners’ cognitive state using deep learning 利用深度学习对学习者的认知状态进行情感敏感分析

Journal of Autonomous Intelligence Pub Date : 2023-12-01 DOI: 10.32629/jai.v7i2.790

S. Aruna, Swarna Kuchibhotla

{"title":"Emotion sensitive analysis of learners’ cognitive state using deep learning","authors":"S. Aruna, Swarna Kuchibhotla","doi":"10.32629/jai.v7i2.790","DOIUrl":"https://doi.org/10.32629/jai.v7i2.790","url":null,"abstract":"The assessment of the state of mind of a student has traditionally been a troublesome task. The advances in deep learning have given analysts new opportunities to try and do therefore. Most state of mind methods focus principally on attention, failing to account for the significance of human emotions. Emotions are significant in laptop vision and a good deal of analysis is conducted exploitation human feelings. Our objective is to propose an emotion-sensitive analysis of individuals’ mental state, specifically focusing on students’ attention levels. This analysis will be carried out in a non-intrusive manner by detecting both head posture and emotions. To achieve this, we employ a multi-task learning approach that utilizes convolutional neural networks (CNNs). These networks are capable of simultaneously identifying facial expressions, locating facial landmarks, and estimating head position, all in real-time. Face alignment is additional assessed by estimating the pinnacle position and face alignment. The estimation of the pinnacle cause and alignment of the face is additional employed by the trainer to live the learner’s span. Experimental results show that the technique will accurately verify students’ emotions with a ninety-four accuracy rate.","PeriodicalId":307060,"journal":{"name":"Journal of Autonomous Intelligence","volume":"97 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138626165","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

Comparative analysis of collaborative filtering recommender system algorithms for e-commerce 电子商务协同过滤推荐系统算法比较分析

Journal of Autonomous Intelligence Pub Date : 2023-11-30 DOI: 10.32629/jai.v7i2.1182

Kapil Saini, Ajmer Singh

{"title":"Comparative analysis of collaborative filtering recommender system algorithms for e-commerce","authors":"Kapil Saini, Ajmer Singh","doi":"10.32629/jai.v7i2.1182","DOIUrl":"https://doi.org/10.32629/jai.v7i2.1182","url":null,"abstract":"Collaborative recommender systems are information filtering systems that seek to predict a user’s rating or preference for an item. They play a vital role in various business use cases, such as personalized recommendations, item ranking and sorting, targeted marketing and promotions, content curation and catalog organization, and feedback analysis and quality control. When evaluating these systems, rating prediction metrics are commonly employed. Efficiency, including the prediction time, is another crucial aspect to consider. In this study, the performance of different algorithms was investigated. The study employed a dataset consisting of e-commerce product ratings and assessed the algorithms based on rating prediction metrics and efficiency. The results demonstrated that each algorithm had its own set of strengths and weaknesses. For the metric of Root Mean Squared Error (RMSE), the BaselineOnly algorithm achieved the lowest mean value. Regarding Mean Absolute Error (MAE), the Singular Value Decomposition with Positive Perturbations Singular Value Decomposition with Positive Perturbations (SVDPP) algorithm exhibited the lowest mean value; Mean Squared Error (MSE) also achieved the lowest mean value. Moreover, the BaselineOnly algorithm showcased superior performance with the lowest mean test times when considering efficiency. Researchers and practitioners can use the findings of this study to select the best algorithm for a particular application. Researchers can develop new algorithms that combine the strengths of different algorithms. Practitioners can also use the findings of this study to tune the parameters of existing algorithms.","PeriodicalId":307060,"journal":{"name":"Journal of Autonomous Intelligence","volume":"37 4","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-11-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139206921","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

Models for estimation of solar irradiance in Zimbabwe: A statistical and machine learning approach 津巴布韦太阳辐照度估算模型：统计和机器学习方法

Journal of Autonomous Intelligence Pub Date : 2023-11-29 DOI: 10.32629/jai.v7i2.1032

K. Chiteka, Rejoice Mwarazi, Rajesh Arora, C. Enweremadu

{"title":"Models for estimation of solar irradiance in Zimbabwe: A statistical and machine learning approach","authors":"K. Chiteka, Rejoice Mwarazi, Rajesh Arora, C. Enweremadu","doi":"10.32629/jai.v7i2.1032","DOIUrl":"https://doi.org/10.32629/jai.v7i2.1032","url":null,"abstract":"The present study focused on the statistical development of solar irradiance predictive models for locations with limited solar irradiance measuring equipment. Multiple linear regression models were developed using both measured and satellite corrected meteorological data. The study chose easy to measure and access meteorological data for analysis and modelling. Multicollinearity and correlation analysis were performed to analyse the relationships among the independent and depended variables. Statistical predictive models were developed, and the prediction accuracy of the developed models was analysed using the coefficient of determination (R2) and the Mean Absolute Percentage Error (MAPE). The results revealed a higher performance of the developed models compared to generic empirical models. The prediction MAPE for the three models developed were respectively 0.117 kWh/m2, 0.132 kWh/m2 and 0.044 kWh/m2 for Hg, Hb and Hd. The models also had R2 values of 0.895, 0.972 and 0.993 respectively for global horizontal irradiance (Hg), direct normal irradiance (Hb) and diffuse irradiance (Hd). The developed models outperformed the generic models by a minimum of 5.74%. The study showed that it is more accurate to predict Global Horizontal Irradiance by summing the predicted component of Hb and Hd.","PeriodicalId":307060,"journal":{"name":"Journal of Autonomous Intelligence","volume":"175 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-11-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139214642","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

IoT intrusion detection system using ensemble classifier and hyperparameter optimization using tuna search algorithm 使用集合分类器和金枪鱼搜索算法优化超参数的物联网入侵检测系统

Journal of Autonomous Intelligence Pub Date : 2023-11-28 DOI: 10.32629/jai.v7i2.962

P. Vijayan, S. Sundar

{"title":"IoT intrusion detection system using ensemble classifier and hyperparameter optimization using tuna search algorithm","authors":"P. Vijayan, S. Sundar","doi":"10.32629/jai.v7i2.962","DOIUrl":"https://doi.org/10.32629/jai.v7i2.962","url":null,"abstract":"The Internet of Things (IoT) is a dynamic and delightful research field in this emerging technology. It can be globally connected with many IoT devices and exchange a large amount of data. However, the threats also developed and misguided the entire network’s behaviour. This article proposes an Intrusion Detection System (IDS) using the proposed ensemble classifier along with the Tuna Swarm Optimization (TSO) to fine-tune the hyperparameters and help to enhance the detection accuracy of attacks that take place in IoT environment. Here, the publicly available message queue telemetry transport (MQTT) network dataset is used to classify the given data into the following categories: SlowlTe, malformed, brute force, flood, DoS, and legitimate. Initially, the dataset is pre-processed to remove possible outliers, then data balancing is performed using the Synthetic Minority Oversampling Technique (SMOTE) technique and features are extracted with the help of Recursive Feature Elimination (RFE). Finally, ensemble classifier along with the optimized parameters using TSO helps in detecting the attacks in IoT attacks. The proposed TSO-ensemble classifier achieved a classification accuracy of 99.12%. In contrast, the classification accuracy of the existing Improved Vulture Starvation-based African Vultures Optimization (IVS-AVOA) and Convolutional Neural Network Long Short-Term Memory (CNN-LSTM) have achieved a classification accuracy of 96.61% and 98.94% respectively.","PeriodicalId":307060,"journal":{"name":"Journal of Autonomous Intelligence","volume":"1 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-11-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139218588","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

Basil plant leaf disease detection using amalgam based deep learning models 利用基于汞齐的深度学习模型检测罗勒植物叶片病害

Journal of Autonomous Intelligence Pub Date : 2023-11-27 DOI: 10.32629/jai.v7i1.1002

Deepak Mane, Mahendra Deore, Rashmi Ashtagi, Sandip Shinde, Yogesh Gurav

{"title":"Basil plant leaf disease detection using amalgam based deep learning models","authors":"Deepak Mane, Mahendra Deore, Rashmi Ashtagi, Sandip Shinde, Yogesh Gurav","doi":"10.32629/jai.v7i1.1002","DOIUrl":"https://doi.org/10.32629/jai.v7i1.1002","url":null,"abstract":"Medicinal plants have been found and utilized in traditional medical practices from ancient times. Many medicinal plants play a vital role in curating many life threatening diseases. Very few of the medicinal herbs are commercially cultivated. Many plant diseases are there which destroys these medicinal plants. Early detection of plant diseases can prevent the huge loss of these medicinal plants. Here, we presented a hybrid model that makes use of SVM along with the traditional convolutional neural network (CNN) for predicting Basil plants leaves diseases. We transformed the conventional CNN model by adding a classification layer Support Vector Machine (SVM) and K-Nearest Neighbor (KNN) after feature extraction and this approach tends to perform better than traditional CNN as we make the dataset balanced by data augmentation and SVN and KNN tend to perform better in case of balanced samples. CNN is used for training, SVM/KNN is used for classification. The advantages of CNN and SVM are used in proposed the CNN and SVM and KNN model. It is assumed that such a combined model would incorporate the benefits of CNN and SVM. Here, we identified the four types of diseases that affect basil plant leaves as Leaf spot, Downy mildew, Fusarium wilt, Fungal, and Healthy. Since there isn’t a standard dataset for basil leaves, we created our own 803 picture data set and used various machine learning techniques to train and evaluate the model. However, over other existing algorithms, our hybrid model i.e., CNN+SVM has produced more accurate results. For five classes of basil plant leaves, the proposed model produced 95.02% accuracy of for leaf diseases.","PeriodicalId":307060,"journal":{"name":"Journal of Autonomous Intelligence","volume":"255 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-11-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139233680","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

Cultural communication based on image processing in multimedia network environment 多媒体网络环境下基于图像处理的文化传播

Journal of Autonomous Intelligence Pub Date : 2023-11-24 DOI: 10.32629/jai.v7i1.1195

Ruolei Chen, Xujia Chen

{"title":"Cultural communication based on image processing in multimedia network environment","authors":"Ruolei Chen, Xujia Chen","doi":"10.32629/jai.v7i1.1195","DOIUrl":"https://doi.org/10.32629/jai.v7i1.1195","url":null,"abstract":"In the process of uploading pictures of cultural products under the multimedia network environment, image processing technology is indispensable for page production and picture design, and image processing plays an indispensable role. Purpose: The use of image processing technology enriches the means of cultural communication, and also improves the visual effect and dissemination rate of cultural communication to a certain extent, and produces the effect of deepening people’s hearts. Methods: This paper firstly focuses on the problems and development trends in cultural communication, and uses image processing technology in the multimedia network environment to analyse the effects of cultural communication, and secondly focuses on the application of multimedia network and image processing in cultural communication. Finally, the four-degree evaluation method is used to evaluate the effect of cultural communication. Results: The final results show that with the use of image processing techniques, the rate of cultural dissemination in various cultural fields can reach 50% to 75%. Conclusion: The research on cultural communication based on image processing can deepen the understanding of cultural communication in the multimedia network environment, expand the application of image processing technology in the field of cultural communication, analyse the role and influence of images in cultural communication, and promote the cross-fertilization of cultural communication and image processing, which is of great practical application and academic value.","PeriodicalId":307060,"journal":{"name":"Journal of Autonomous Intelligence","volume":"2017 26","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-11-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139239518","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