人工智能技术学报(英文)最新文献

{"title":"Implementing Machine Learning-based Autonomic Cyber Defense for IoT-enabled Healthcare Devices","authors":"Murali Dhar, S. M, Roger Norabuena-Figueroa, R. Mahaveerakannan, S. Saraswathi, K. Selvakumarasamy, Sri krishna adithya","doi":"10.37965/jait.2023.0209","DOIUrl":"https://doi.org/10.37965/jait.2023.0209","url":null,"abstract":"Smart homes present a serious challenge for the aged and those with mobility issues due to the environment's inherent danger. Unwary people have the propensity to fall over when bending over in these settings. Here, they show two time-based reasoning models to identify incidents of potentially fatal falls that have not been accounted for (CM-I and CM-II). The ubiquitous use of IoT altimeter watches among the elderly provides a wealth of data that can be used by these algorithms to predict the likelihood of a fall based on categorization criteria. They compared actual and simulated data involving missteps, mishaps, and crashes to gauge the programmers’ performance. Results suggest that using such logic models to help healthcare providers determine if senior people living in smart homes have fallen is a potential field for future study. The CM-II model had the highest prediction accuracy of any model identified in the literature, at 0.98 when compared to the test parameter. Since the number of devices linked to the IoT can be quickly extended in contrast to the number of devices connected to conventional computers, the number of hacks aimed at the IoT has grown dramatically. There is no way to fix the issue that hacked IoT devices create until they figure out how to track down the source of the attacks. Pursuing a deeper understanding of the technologies, protocols, and architecture of IoT systems, as well as the potential consequences of using infected IoT devices, is the overarching goal of this study. There are many Internet of Things (IoT) systems vulnerable to cybercriminal manipulation, so this study also explores a range of machine learning and deep learning-based methods that can be used to detect such compromise.","PeriodicalId":70996,"journal":{"name":"人工智能技术学报(英文)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-06-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48624688","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":"Diagnostic Segmentation Based on Kidney Medical Image","authors":"Shixuemei, Mideth Abisado","doi":"10.37965/jait.2023.0214","DOIUrl":"https://doi.org/10.37965/jait.2023.0214","url":null,"abstract":"Lesion segmentation of medical images is an important component of smart medicine. The development of deep learning technology is followed by rapid advancement in lesion segmentation technology of medical images. Though the present segmentation technology can retain spatial features, insufficient spatial features are retained with low segmentation accuracy. Our proposed PST-UNet model combines transformer with U-shaped structure and better infuses encoder's multi-scale features by using convolution fusion module. PST-UNet model adopts two types of block Swin transform at encoder and decoder ends respectively. Renal lesion data tends to present a normal distribution. Therefore, to preserve more spatial features and enhance the precision of renal lesion segmentation, Swin transformer block and full GELU (Gaussian Error Linear Unit) activation function are introduced at the encoder end. Similarly, at the decoder end, Swin transformer block, full GELU activation function, up-sampling and jumper wires from the convolution fusion module are also introduced.","PeriodicalId":70996,"journal":{"name":"人工智能技术学报(英文)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-06-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44976475","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":"Classification of Benign and Malignancy in Lung Cancer Using Capsule Networks with Dynamic Routing Algorithm on Computed Tomography Images","authors":"Bushara A. R., Vinod Kumar R. S., Kumar S. S.","doi":"10.37965/jait.2023.0218","DOIUrl":"https://doi.org/10.37965/jait.2023.0218","url":null,"abstract":"There is a widespread agreement that lung cancer is one of the deadliest types of cancer, affecting both women and men. As a result, detecting lung cancer at an early stage is crucial to create an accurate treatment plan and forecasting the reaction of the patient to the adopted treatment. For this reason, the development of Convolutional Neural Networks (CNNs) for the task of lung cancer classification has recently seen a trend in attention. CNNs have great potential, but they need a lot of training data and struggle with input alterations. To address these limitations of CNNs, a novel machine-learning architecture of capsule networks has been presented, and it has the potential to completely transform the ares of deep learning. Capsule networks, which are the focus of this work, are interesting because they can withstand rotation and affine translation with relatively little training data. This research optimizes the performance of CapsNets by designing a new architecture that allows them to perform better on the challenge of lung cancer classification. The findings demonstrate that the proposed Capsule Network method outperforms CNNs on the lung cancer classification challenge. CapsNet with a single convolution layer and 32 features (CN-1-32), CapsNet with a single convolution layer and 64 features (CN-1-64), and CapsNet with a double convolution layer and 64 features (CN-2-64) are the three Capsulel networks developed in this research for lung cancer classification. Lung nodules, both benign and malignant, are classified using these networks using CT images. The LIDC-IDRI database was utilized to assess the performance of those networks. Based on the testing results, CN-2-64 network performed the better out of the three networks tested, with a specificity of 98.37%, sensitivity of 97.47% and an accuracy of 97.92%.","PeriodicalId":70996,"journal":{"name":"人工智能技术学报(英文)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-05-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46393519","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":"How to Use Artificial Intelligence to Evaluate Board Efficiency","authors":"Nguyen Thi Thanh Binh, Pin-Yu Huang","doi":"10.37965/jait.2023.0221","DOIUrl":"https://doi.org/10.37965/jait.2023.0221","url":null,"abstract":"The era of the technological revolution brought rapid changes in the way businesses are managed and operated with new methods. This study applies and develops Artificial Neural Networks (ANNs) to predict the ROE (Return on Equity) and ROA (Return on Assets) based on data of the structure of the Board of Directors and managers of 839 Taiwanese electronics firms listed on the Taiwan Stock Exchange for the period 2000 to 2021. The results show that the characteristics of the Board of Directors and managers decide 64.25% of the value of the ROE and 67.05% of the ROA. Empirical results also show that the Board with fewer members is easy to reach a consensus on decisions rather than a larger board, leading to better firm performance. When ROE and ROA are at their worst, board members use their power to protect their wealth. However, independent board members have a negative influence on financial performance. Large company size has always been a strong supporter of high profitability, and a high debt ratio has not yet brought about tax savings.","PeriodicalId":70996,"journal":{"name":"人工智能技术学报(英文)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-05-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41728373","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":"People Recognition via Tongue Print Using Deep and Machine Learning","authors":"Ahmed Shallal Obaid, Mohammed Y. Kamil, B. H. Hamza","doi":"10.37965/jait.2023.0219","DOIUrl":"https://doi.org/10.37965/jait.2023.0219","url":null,"abstract":"The tongue is a unique organ that is well protected inside the mouth and not affected by external factors; it is also difficult to forge. Several biometric systems are widely used for authentication and recognition, such as fingerprints, faces, iris, sound, retina, etc. Traditional biometrics represent a challenge and an obstacle as they can be falsified, duplicates can be made (e.g., iris, face, fingers, signature), or they are expensive and rarely used (e.g., DNA). The increased security measures called for modern biometrics that is more secure, less expensive, and cannot be falsified. As a result, the goal of this paper is to create a system for distinguishing people based on their tongue prints. It will contribute to solving many forensic issues and increasing electronic security because it has features suitable for identification and biometrically distinguishing between people. In this paper, the tongue is located based on the fixed window size method. After tongue localization (ROI), feature extraction using the VGG-16 model, and a classification system that uses both transfer learning and machine learning as VGG-16, XGBoost, KNN, and RF classifiers, extracted features are then trained for personal identification. The dataset consisted of 1085 tongue images of 138 people with a test ratio of 20%, and the results achieved an accuracy of 92%. The process of distinguishing people through tongue prints has proven to be effective and accurate.","PeriodicalId":70996,"journal":{"name":"人工智能技术学报(英文)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-05-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47299252","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 Empirical Model for The Classification of Diabetes and Diabetes_Types Using Ensemble Approaches","authors":"Sushma Jaiswal, Priyanka Gupta, Narasimha Prasad, R. Kulkarni","doi":"10.37965/jait.2023.0220","DOIUrl":"https://doi.org/10.37965/jait.2023.0220","url":null,"abstract":"Diabetes is a hereditary disorder that interferes with human life at all ages. It is challenging for cells to absorb glucose from the bloodstream when an individual has diabetes. The two main subtypes of diabetes are type 1 diabetes and type 2 diabetes. Type 1 diabetes develops when the pancreas cannot make enough insulin, whereas type 2 diabetes spreads due to insulin resistance. Diabetes is a recurrent, and chronic illness that is incurable. In modern healthcare systems, disease detection technology is pervasive. Detecting diabetes in its early stages is crucial for initiating timely treatment and halting disease progression. The proposed method has the potential not only to forecast the likelihood of future diabetes onset but also to identify the specific type of diabetes a person may develop. This paper investigates a potential solution for a diabetes prediction model in light of the continually rising prevalence of diabetes among patients. The proposed framework is designed using two datasets: the Pima Indian dataset, which is used to forecast diabetes, and the Diabetes Type dataset, which is used to identify the type of diabetes mellitus an individual has. This research aims to apply machine learning classifiers and ensemble models, such as Bagging, Voting, Averaging, and Stacking, for diabetes prediction. In this context, SMOTE (Synthetic Minority Oversampling Technique) and hyperparameter adjustment of the algorithms are considered and have substantially improved the findings. The developed heterogeneous ensemble model offers enhanced prediction rates with different performance criteria. Using the bagging technique, Random Forest attains a 96% accuracy rate, resulting in better predictions in the PID dataset. Regarding the Diabetes Type dataset, the Voting Ensemble Model provides a 98.5% accuracy rate. This study highlights that Ensemble learning models are effective in predicting diabetes and can outperform earlier relevant studies.","PeriodicalId":70996,"journal":{"name":"人工智能技术学报(英文)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-05-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44468283","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":"Fruit Tree Disease Recognition Based on Residual Neural Network and Attention Mechanism","authors":"Xiedong Song, Vladimir Y. Mariano","doi":"10.37965/jait.2023.0291","DOIUrl":"https://doi.org/10.37965/jait.2023.0291","url":null,"abstract":"Fruit growing has played a huge role in solving food supply issues in many coutries. However, the yield and quality of fruits can be affected by various diseases, and thus timely and accurate identification of disease conditions is particularly important. Currently, using image recognition and object detection technology to diagnose fruit tree diseases has become a research hotspot in forestry informatization. Convolutional neural networks eliminate the preprocessing of manual feature selection and have high recognition performance. However, it is not easy to train due to the risk of gradient disappearance. In order to achieve better recognition effect, this research addresses the problem of applying small-scale data samples through data enhancement and transfer learning, and it optimizes the model by combining the two main attention mechanisms of SE and CBAM with ResNet50. Through experiments, it is found that the CBAM ResNet50 model has the best effect, improving the application performance of the studied model in actual scenarios.","PeriodicalId":70996,"journal":{"name":"人工智能技术学报(英文)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-05-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48275697","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":"Research on Miniaturization Trend of ChatGPT Technology Model","authors":"Xuemei Shi, Xiaoguang Du, Xiedong Song","doi":"10.37965/jait.2023.0298","DOIUrl":"https://doi.org/10.37965/jait.2023.0298","url":null,"abstract":"Miniaturization and micro-miniaturization are trends in technology models, such as ChatGPT. These trends have the potential to enhance the practicality and professionalism of the model, as well as making them more widely accessible. Consequently, more individuals and organizations can leverage these technologies, and their impacts can be significant. Notably, miniaturization and micro-miniaturization can decrease the size of the model and the computing resources required, and thus resulting the widespread use and development of artificial intelligence technology. Moreover, they can boost the speed of model operation and training efficiency, thereby improving the practicality and efficacy of applications. Ultimately, this trend will have a profound impact on diverse fields, including scientific research, education, coaching, medical care, and daily life.","PeriodicalId":70996,"journal":{"name":"人工智能技术学报(英文)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-05-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48296472","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":"Do Artificial Intelligence Chatbots Have a Writing Style? An Investigation into the Stylistic Features of ChatGPT-4","authors":"M. AlAfnan, Siti Fatimah MohdZuki","doi":"10.37965/jait.2023.0267","DOIUrl":"https://doi.org/10.37965/jait.2023.0267","url":null,"abstract":"Even though Turnitin generates AI (Artificial Intelligence) writing detection reports, these AI reports shall not be used for punitive purposes as Turnitin AI reports accuracy is way below the 98% claimed by Turnitin, as revealed in this study. To assist professors, teachers, and content evaluation stakeholders in their strive to identify AI-generated material, this study examines the stylistic features of case study, business correspondence, and academic writing ChatGPT-4 generated responses by exploring sentence length, paragraph structure, word choice, mood, tense, voice, pronouns, keywords density, lexical density, lexical diversity, and reading ease. The study revealed that ChatGPT-4 case study generated responses are produced in paragraphs of 2 to 3 sentences of 16 to 18 words each. The sentences are mainly formed in imperative mood. The use of the second-person pronoun ‘you’ and the second-person possessive determiner ‘your’ is prevalent. Keywords and lexical density are relatively low, lexical diversity is average, and the reading ease is relatively high. The study also found that ChatGPT-4 business correspondence responses are generated in paragraphs of 2 to 3 sentences of 16 to 20 words each. The sentences are mainly generated in declarative mood thru simple present tense in active voice using third-person singular pronouns. Technical words and abbreviations are used without outlining what they stand for. The keywords density, lexical density, and lexical diversity are high and the reading ease is low. The study also revealed that ChatGPT-4 academic writing generated responses are provided in paragraphs of 3 to 4 sentences of 16 to 19 words each. The sentences are mainly generated in declarative mood using active voice, agentless passive in times, with diverse present tenses. Keywords and lexical densities are high and the lexical diversity is low, which makes the reading ease average difficulty, except for the undefined abbreviations. Noticeably, ChatGPT-4 supports the transgender movement by intentionally using the third-person plural pronoun ‘they’ to refer to a singular.","PeriodicalId":70996,"journal":{"name":"人工智能技术学报(英文)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-05-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41645844","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":"Classification of Breast Masses Using Ultrasound Images by Approaching GAN, Transfer Learning and Deep Learning Techniques","authors":"Sushovan Chaudhury, Kartik Sau","doi":"10.37965/jait.2023.0175","DOIUrl":"https://doi.org/10.37965/jait.2023.0175","url":null,"abstract":"Breast cancer is a common cause of death among women worldwide. Ultrasonic imaging is a valuable diagnostic tool in breast cancer detection. However, the accuracy of computer-aided diagnosis systems for breast cancer classification is limited due to the lack of well-annotated datasets. This study proposes a deep learning-based framework for breast mass classification using ultrasound images, which incorporates a novel data augmentation technique, Generative Adversarial Network (GAN), and Transfer Learning (TL). Automating early tumor identification and classification in breast cancer diagnosis can save lives by improving the accuracy of diagnoses and reducing the need for invasive procedures. However, the limited availability of well-annotated datasets for ultrasound images of breast cancer has hampered the development of accurate computer-aided diagnosis systems. The accuracy of breast mass classification using ultrasound images is limited due to the lack of well-annotated datasets. Conventional data augmentation techniques have limitations in applications with strict guidelines, such as medical datasets. Therefore, there is a need to develop a novel data augmentation technique to improve the accuracy of breast mass classification using ultrasound images. The proposed framework can be extended to other medical imaging applications, where the availability of well-annotated datasets is limited. The GAN-based data augmentation technique and TL-based feature extraction can be used to improve the accuracy of classification models in other medical imaging applications. Additionally, the proposed framework can be used to develop accurate computer-aided diagnosis systems for breast cancer detection in clinical settings. The proposed framework incorporates a deep learning-based approach for breast mass classification using ultrasound images. The framework includes a GAN-based data augmentation technique and TL for feature extraction. The dataset used for training and testing the model is the Breast Ultrasound Images (BUSI) dataset, which includes 1311 images with normal and abnormal breast masses. The proposed framework achieved an accuracy of 99.6% for breast mass classification using ultrasound images, which outperformed existing methods. The GAN-based data augmentation technique and TL-based feature extraction improved the accuracy of the classification model. The results suggest that deep learning algorithms can be effectively applied for breast ultrasound categorization. The proposed framework presents a novel approach for breast mass classification using ultrasound images, which incorporates a GAN-based data augmentation technique and TL-based feature extraction. The results demonstrate that the proposed framework outperforms existing methods and achieves high accuracy in breast mass classification using ultrasound images. This framework can be useful for developing accurate computer-aided diagnosis systems for breast cancer detection.","PeriodicalId":70996,"journal":{"name":"人工智能技术学报(英文)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-05-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45644732","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}