自主智能(英文) Pub Date : 2023-09-06 DOI: 10.32629/jai.v6i3.740

S. Sasi, Srividya Bharadwaj Venkata Subbu, Premkumar Manoharan, L. Abualigah

引用次数: 0

Conditioning and monitoring of grinding wheels: A state-of-the-art review 砂轮的调节和监测：最新进展

自主智能(英文) Pub Date : 2023-09-06 DOI: 10.32629/jai.v6i3.622

Shrinath M. Patil-Mangore, Niranjan L. Shegokar, Nand Jee Kanu

{"title":"Conditioning and monitoring of grinding wheels: A state-of-the-art review","authors":"Shrinath M. Patil-Mangore, Niranjan L. Shegokar, Nand Jee Kanu","doi":"10.32629/jai.v6i3.622","DOIUrl":"https://doi.org/10.32629/jai.v6i3.622","url":null,"abstract":"Grinding wheel condition monitoring is an important step towards the prediction of grinding wheel faulty conditions. It is beneficial to define techniques to minimize the wear of the grinding wheels and finally enhance the life of the grinding wheels. Grinding wheel condition monitoring is done by two techniques such as (i) direct and (ii) indirect. Direct monitoring employs optical sensors and computer vision techniques, and indirect monitoring is done by signal analysis such as acoustic emission (AE), vibration, cutting force, etc. Methods implemented for grinding wheel monitoring in the published research papers are reviewed. The review is compiled in five sections: (a) process parameters measurement, (b) data acquisition systems, (c) signal analysis techniques, (d) feature extraction, and (e) classification methods. In today’s era of Industry 4.0, a large amount of manufacturing data is generated in the industry. So, conventional machine learning techniques are insufficient to analyze real-time conditioning monitoring of the grinding wheels. However, deep learning techniques such as artificial neural network (ANN), convolutional neural network (CNN) have shown prediction accuracy above 99%.","PeriodicalId":70721,"journal":{"name":"自主智能(英文)","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-09-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44457232","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

An improved fuzzy c-means-raindrop optimizer for brain magnetic resonance image segmentation 一种用于脑磁共振图像分割的改进模糊c-均值-雨滴优化器

自主智能(英文) Pub Date : 2023-09-05 DOI: 10.32629/jai.v6i3.973

Bindu Puthentharayil Vikraman, Jabeena A. Afthab

{"title":"An improved fuzzy c-means-raindrop optimizer for brain magnetic resonance image segmentation","authors":"Bindu Puthentharayil Vikraman, Jabeena A. Afthab","doi":"10.32629/jai.v6i3.973","DOIUrl":"https://doi.org/10.32629/jai.v6i3.973","url":null,"abstract":"The performance of healthcare systems, particularly regarding disease diagnosis and treatment planning, depends on the segmentation of medical images. Fuzzy c-means (FCM) is one of the most widely used clustering techniques for image segmentation due to its simplicity and effectiveness. FCM, on the other hand, has the disadvantages of being noise-sensitive, quickly settling on local optimal solutions, and being sensitive to initial values. This paper suggests a fuzzy c-means clustering improved with a nature-inspired raindrop optimizer for lesion extraction in brain magnetic resonance (MR) images to get around this constraint. In the preprocessing stage, the possible noises in a digital image, such as speckles, gaussian, etc., are eliminated by a hybrid filter—A combination of Gaussian, mean, and median filters. This paper presents a comparative analysis of FCM clustering and FCM-raindrop optimization (FCM-RO) approach. The algorithm performance is evaluated for images subjected to various possible noises that may affect an image during transmission and storage. The proposed FCM-RO approach is comparable to other methods now in use. The suggested system detects lesions with a partition coefficient of 0.9505 and a partition entropy of 0.0890. Brain MR images are analyzed using MATLAB software to find and extract malignancies. Image data retrieved from the public data source Kaggle are used to assess the system’s performance.","PeriodicalId":70721,"journal":{"name":"自主智能(英文)","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-09-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45048357","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

Key management and access control based on combination of cipher text-policy attribute-based encryption with Proxy Re-Encryption for cloud data 基于密文-策略属性加密与代理重加密相结合的云数据密钥管理和访问控制

自主智能(英文) Pub Date : 2023-09-04 DOI: 10.32629/jai.v6i3.748

R. M. Naik, H. M. T. Gadiyar, M. B. Kumar, B. K. Jeevitha, G. S. Thyagaraju, U. J. Ujwal, K. Arjun, S. M. Manasa, S. Avinash, J. A. Kumar, T. K. Sowmya, K. P. Uma, A. R. Ramaprasad

{"title":"Key management and access control based on combination of cipher text-policy attribute-based encryption with Proxy Re-Encryption for cloud data","authors":"R. M. Naik, H. M. T. Gadiyar, M. B. Kumar, B. K. Jeevitha, G. S. Thyagaraju, U. J. Ujwal, K. Arjun, S. M. Manasa, S. Avinash, J. A. Kumar, T. K. Sowmya, K. P. Uma, A. R. Ramaprasad","doi":"10.32629/jai.v6i3.748","DOIUrl":"https://doi.org/10.32629/jai.v6i3.748","url":null,"abstract":"In various cloud computing models, the data need to be protected and to access these data in secure manner is important. The cryptographic key which is used to secure these data using both in the encryption as well as in decryption it is mandatory to manage these keys to secure these keys by disclosing in public networks such as any wireless and cloud environment. Utilizing Ciphertext Policy Attribute-based Encryption (CP-ABE), which provides effective data governance and key management, for cloud data encryption. The work based on the combination of Cipher Text-Policy Attribute based Encryption and Proxy Re-Encryption is elaborated in the article (CP-ABE-PRE). The encrypted data should ideally be transformed such that it may be unlocked with new keys, without an intermediate decryption step that would allow the cloud provider to read the plaintext this process is known as data re-encryption. The computational and communication burden on users connecting to the cloud from resource constrained devices can be reduced using the proposed technique. The experimental results show for Cipher Text-Policy Attribute-Based Encryption are compared to the current algorithm (CP-ABE) demonstrate good results in encryption and decryption times. Additionally, the CP-ABE offers crucial distribution and administration options for cloud data. CP-ABE with Proxy Re-Encryption does appear to be highly efficient which proves verifiability and fairness for cloud data users to which also address revocation problem as well as collusion resistant model.","PeriodicalId":70721,"journal":{"name":"自主智能(英文)","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-09-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43099718","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

Novel scientific design of hybrid opposition based—Chaotic little golden-mantled flying fox, White-winged chough search optimization algorithm for real power loss reduction and voltage stability expansion 科学设计基于混沌小金毛狐、白翅白鸦搜索的混合对抗优化算法，降低实际功率损耗，扩大电压稳定性

自主智能(英文) Pub Date : 2023-09-04 DOI: 10.32629/jai.v6i3.680

L. Kanagasabai

{"title":"Novel scientific design of hybrid opposition based—Chaotic little golden-mantled flying fox, White-winged chough search optimization algorithm for real power loss reduction and voltage stability expansion","authors":"L. Kanagasabai","doi":"10.32629/jai.v6i3.680","DOIUrl":"https://doi.org/10.32629/jai.v6i3.680","url":null,"abstract":"In this paper hybrid opposition based—Chaotic little golden-mantled flying fox algorithm and White-winged chough search optimization algorithm (HLFWC) is applied to solve the loss dwindling problem. Key objective of the paper is real power loss reduction, voltage deviation minimization and voltage stability expansion. Proposed little golden-mantled flying fox algorithm is designed based on the deeds of the little golden-mantled flying fox. Maximum classes have single progenies at a period afterwards of prenatal period. This little procreative production means that when populace forfeiture their figures are deliberate to ricochet. In White-winged chough search optimization algorithm magnifying the encumbrance element in a definite assortment will pointedly enlarge the exploration region. In a coiled exploration, the position of any White-winged chough can differ in numerous scopes to cover the exploration region, predominantly in the projected problem. Hybrid opposition based—Chaotic little golden-mantled flying fox algorithm and White-winged chough search optimization algorithm (HLFWC) is accomplished by integrating the actions of little golden-mantled flying fox and White-winged chough. Through the hybridization of both algorithms exploration and exploitation has been balanced throughout the procedure. Proposed hybrid opposition based—Chaotic little golden-mantled flying fox algorithm and White-winged chough search optimization algorithm (HLFWC) is corroborated in IEEE 30 and 57 systems. From the simulation results it has been observed that real power loss reduction, voltage deviation minimization and voltage stability expansion has been achieved.","PeriodicalId":70721,"journal":{"name":"自主智能(英文)","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-09-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43442184","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

Blowfish based encryption model in real cloud environment 真实云环境中基于Blowfish的加密模型

自主智能(英文) Pub Date : 2023-09-04 DOI: 10.32629/jai.v6i3.695

R. Walia, P. Garg, Manish Kumar

引用次数: 0

A precise coronary artery disease prediction using Boosted C5.0 decision tree model 基于boosting C5.0决策树模型的冠状动脉疾病精确预测

自主智能(英文) Pub Date : 2023-09-01 DOI: 10.32629/jai.v6i3.628

Surjeet Dalal, U. Lilhore, Sarita Simaiya, Vivek Jaglan, Anand Mohan, Sachin Ahuja, Akshat Agrawal, Martin Margala, Prasun Chakrabarti

{"title":"A precise coronary artery disease prediction using Boosted C5.0 decision tree model","authors":"Surjeet Dalal, U. Lilhore, Sarita Simaiya, Vivek Jaglan, Anand Mohan, Sachin Ahuja, Akshat Agrawal, Martin Margala, Prasun Chakrabarti","doi":"10.32629/jai.v6i3.628","DOIUrl":"https://doi.org/10.32629/jai.v6i3.628","url":null,"abstract":"In coronary artery disease, plaque builds up in the arteries that carry oxygen-rich blood to the heart. Having plaque in the arteries can constrict or impede blood flow, leading to a heart attack. Shortness of breath and soreness in the chest are common symptoms. Lifestyle modifications, medication, and potentially surgery are all options for treatment. In coronary artery disease, plaque builds up in the arteries that carry oxygen-rich blood to the heart. Having plaque in the arteries can constrict or impede blood flow, leading to a heart attack. Shortness of breath and soreness in the chest are common symptoms. Lifestyle modifications, medication, and potentially surgery are all options for treatment. This paper presents a Hybrid Boosted C5.0 model to predict coronary artery disease more precisely. A Hybrid Boosted C5.0 model is formed by combining the C5.0 decision tree and boosting methods. Boosting is a supervised machine learning method that leverages numerous inadequate models to construct a more robust and powerful model. The proposed model and some well-known existing machine learning models, i.e., decision tree, AdaBoost, and random forest, were implemented using an online coronary artery disease dataset of 6611 patients and compared based on various performance measuring parameters. Experimental analysis shows that the proposed model achieved an accuracy of 91.62% at training and 81.33% at the testing phase. The AUC value achieved in the training and testing phase is 0.957 and 0.88, respectively. The Gini value achieved in the training and testing phase is 0.914 and 0.759, respectively, far better than the proposed method.","PeriodicalId":70721,"journal":{"name":"自主智能(英文)","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47264440","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

Prediction of customer review’s helpfulness based on sentences encoding using CNN-BiGRU model 基于CNN BiGRU模型的句子编码预测客户评论的有用性

自主智能(英文) Pub Date : 2023-08-30 DOI: 10.32629/jai.v6i3.699

Suryanarayan Sharma, Laxman Singh, Rajdev Tiwari

{"title":"Prediction of customer review’s helpfulness based on sentences encoding using CNN-BiGRU model","authors":"Suryanarayan Sharma, Laxman Singh, Rajdev Tiwari","doi":"10.32629/jai.v6i3.699","DOIUrl":"https://doi.org/10.32629/jai.v6i3.699","url":null,"abstract":"The infrastructure of smart cities is intended to save citizens’ time and effort. After COVID-19, one of such available infrastructure is electronic shopping. Online consumer reviews have a big influence on the electronic retail market. A lot of customers save time by deciding which products to buy online by evaluating the products’ quality based on user reviews. The goal of this study is to forecast if reviews based on reviews representation mining will be helpful while making online purchases. Predicting helpfulness is used in this suggested study to determine the usefulness of a review in relation to glove vector encoding of reviews text. Using an encoding-based convolution neural network and a bidirectional gated recurrent unit, the authors of this study constructed a classification model. The suggested model outperformed these baseline models and other state-of-the-art techniques in terms of classification outcomes, reaching the greatest accuracy of 95.81%. We also assessed the effectiveness of our models using the criteria of accuracy, precision, and recall. The outcomes presented in this study indicate how the proposed model has a significant influence on enhancing the producers’ or service providers’ businesses.","PeriodicalId":70721,"journal":{"name":"自主智能(英文)","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-08-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48651486","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

An improved firefly algorithm for the rank generation to optimize the route discovery process in IoV 改进的firefly秩生成算法优化IoV中的路由发现过程

自主智能(英文) Pub Date : 2023-08-28 DOI: 10.32629/jai.v6i3.705

Sumit Kumar, Jaspreet Singh

引用次数: 0

Cross-domain synergy: Leveraging image processing techniques for enhanced sound classification through spectrogram analysis using CNNs 跨域协同:利用图像处理技术，通过使用cnn的频谱图分析来增强声音分类

自主智能(英文) Pub Date : 2023-08-28 DOI: 10.32629/jai.v6i3.678

Valentina Franzoni

{"title":"Cross-domain synergy: Leveraging image processing techniques for enhanced sound classification through spectrogram analysis using CNNs","authors":"Valentina Franzoni","doi":"10.32629/jai.v6i3.678","DOIUrl":"https://doi.org/10.32629/jai.v6i3.678","url":null,"abstract":"In this paper, the innovative approach to sound classification by exploiting the potential of image processing techniques applied to spectrogram representations of audio signals is reviewed. This study shows the effectiveness of incorporating well-established image processing methodologies, such as filtering, segmentation, and pattern recognition, to enhance the feature extraction and classification performance of audio signals when transformed into spectrograms. An overview is provided of the mathematical methods shared by both image and spectrogram-based audio processing, focusing on the commonalities between the two domains in terms of the underlying principles, techniques, and algorithms. The proposed methodology leverages in particular the power of convolutional neural networks (CNNs) to extract and classify time-frequency features from spectrograms, capitalizing on the advantages of their hierarchical feature learning and robustness to translation and scale variations. Other deep-learning networks and advanced techniques are suggested during the analysis. We discuss the benefits and limitations of transforming audio signals into spectrograms, including human interpretability, compatibility with image processing techniques, and flexibility in time-frequency resolution. By bridging the gap between image processing and audio processing, spectrogram-based audio deep learning gives a deeper perspective on sound classification, offering fundamental insights that serve as a foundation for interdisciplinary research and applications in both domains.","PeriodicalId":70721,"journal":{"name":"自主智能(英文)","volume":"1 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-08-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"69961260","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

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