International Journal on Artificial Intelligence Tools最新文献_第3页

Metrics for Domain Shift Characterization: Comparisons and New Directions 域偏移特征的度量：比较与新方向

IF 1.1 4区计算机科学

International Journal on Artificial Intelligence Tools Pub Date : 2024-04-04 DOI: 10.1142/s0218213024500027

N. Nagananda, A. Savakis

引用次数: 0

Abax: Extracting Mathematical Formulas from Chart Images Using Spatial Pixel Information Abax：利用空间像素信息从图表图像中提取数学公式

IF 1.1 4区计算机科学

International Journal on Artificial Intelligence Tools Pub Date : 2024-03-30 DOI: 10.1142/s0218213024500076

Michail S. Alexiou, Nikolaos G. Bourbakis

引用次数: 0

Graph CNN-ResNet-CSOA Transfer Learning Architype for an Enhanced Skin Cancer Detection and Classification Scheme in Medical Image Processing 用于医学图像处理中增强型皮肤癌检测和分类方案的图 CNN-ResNet-CSOA 转移学习架构

IF 1.1 4区计算机科学

International Journal on Artificial Intelligence Tools Pub Date : 2024-03-30 DOI: 10.1142/s021821302350063x

G. N. Balaji, S. A. Sahaaya Arul Mary, Nagesh Mantravadi, Francis H. Shajin

{"title":"Graph CNN-ResNet-CSOA Transfer Learning Architype for an Enhanced Skin Cancer Detection and Classification Scheme in Medical Image Processing","authors":"G. N. Balaji, S. A. Sahaaya Arul Mary, Nagesh Mantravadi, Francis H. Shajin","doi":"10.1142/s021821302350063x","DOIUrl":"https://doi.org/10.1142/s021821302350063x","url":null,"abstract":"<p>Skin cancer is a perilous kind of cancer caused by damaged DNA and it leads to death. This damaged DNA causes uncontrolled proliferation of cells. Even though, the image analysis of lesions is highly difficult due to light reflections from skin surface, fluctuations at color lighting, variety of lesions’ forms and sizes in skin cancer. Because of these issues, automatic recognition of skin cancer accurateness is decreased. Therefore, a Graph Convolutional Neural Network (GCNN) by ResNet 152 Transfer Learning Architype optimized with Chameleon Swarm Optimization Algorithm (GCNN-ResNet 152 TL-CSOA) is proposed at this manuscript for enhancing skin cancer detection with classification in medical image processing. Initially, the input images are taken from International Skin Imaging Collaboration (ISIC) of dermoscopic skin cancer imagery data set. Afterward, the input images are pre-processed utilizing trilateral filter method for removing noise. The pre-processed output is supplied to the process of feature extraction. Here, image features, like morphologic, gray scale statistic and Haralick texture features are extracted by Gray-Level Co-Occurrence Matrix window adaptive approach (GLCM-WAA) technique. After that, the GCNN-ResNet 152 TL classifies the skin cancer images into Actinic Keratosis, Basal Cell Carcinoma, Malignant Melanoma and Squamous Cell Carcinoma. Additionally, GCNN-ResNet 152 TL weight parameters is tuned by Chameleon Swarm Optimization Algorithm (CSOA). The simulation process is executed at Python tool. From simulation, the proposed approach attains 23.34%, 12.03%, 21.42% improved accuracy and 18.23%, 21.23%, 14.56% higher sensitivity compared with existing approaches.</p>","PeriodicalId":50280,"journal":{"name":"International Journal on Artificial Intelligence Tools","volume":null,"pages":null},"PeriodicalIF":1.1,"publicationDate":"2024-03-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140322536","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

IoT Based Wireless Communication System for Smart Irrigation and Rice Leaf Disease Prediction Using ResNeXt-50 基于物联网的无线通信系统，利用 ResNeXt-50 进行智能灌溉和水稻叶病预测

IF 1.1 4区计算机科学

International Journal on Artificial Intelligence Tools Pub Date : 2024-03-30 DOI: 10.1142/s0218213024500040

S. Sangeetha, N. Indumathi, Reena Grover, Rakshit Singh, Renu Mavi

{"title":"IoT Based Wireless Communication System for Smart Irrigation and Rice Leaf Disease Prediction Using ResNeXt-50","authors":"S. Sangeetha, N. Indumathi, Reena Grover, Rakshit Singh, Renu Mavi","doi":"10.1142/s0218213024500040","DOIUrl":"https://doi.org/10.1142/s0218213024500040","url":null,"abstract":"<p>Agriculture not only plays a vital role in human survival but also contributes to the nation’s greater economic development. With the use of technologies like IoT, WSNs, remote sensing, camera surveillance, and many more, precision agriculture is the newest buzzword in the field of technology. Its primary goal is to lessen the labour of farmers while increasing the output of farms. Many machine learning techniques are designed to improve the productivity and quality of the crops, but the improper irrigation and disease prediction of the existing techniques leads to loss of productivity and quality. Hence, the IoT based wireless communication system is designed for smart irrigation and rice leaf prediction using ANN and ResNeXt-50 model. In this designed model, smart irrigation is controlled by collecting the temperature and moisture of the soil in the agricultural field by using the WSN. Likewise, a surveillance camera is placed in the agricultural field to capture the rice leaf to find the disease such as rice blast, leaf smut, brown spot and bacterial blight. These collected data are processed and classified for smart irrigation and rice leaf disease prediction. For evaluating the performance of both the ANN and ResNeXt-50 trained model, the performance metrics such as accuracy, sensitivity, specificity, precision, error etc. The performance metrics for the ANN and ResNeXt-50 model are 0.9427, 0.925, 0.903, 0.86, 0.0573 and 0.967, 0.935, 0.943, 0.939 and 0.033. Thus, the evaluation of the designed model results that the proposed approach is performing better compared to the current techniques.</p>","PeriodicalId":50280,"journal":{"name":"International Journal on Artificial Intelligence Tools","volume":null,"pages":null},"PeriodicalIF":1.1,"publicationDate":"2024-03-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140322543","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Efficient Online Big Data Stream Clustering Using Dual Interactive Wasserstein Generative Adversarial Network 使用双交互式瓦瑟斯坦生成对抗网络进行高效在线大数据流聚类

IF 1.1 4区计算机科学

International Journal on Artificial Intelligence Tools Pub Date : 2024-03-16 DOI: 10.1142/s021821302450009x

S. Matheswaran, N. Nachimuthu, G. Prakash

引用次数: 0

Enhancing Speech Assistive Systems Through a Sequence-to-Vector Representation Approach for Disordered Speech 通过序列到矢量表示法增强语音辅助系统，解决语音紊乱问题

IF 1.1 4区计算机科学

International Journal on Artificial Intelligence Tools Pub Date : 2024-03-12 DOI: 10.1142/s0218213024500143

S.V. Veni, B. Santhi

引用次数: 0

Hybrid Optimized Gated Recurrent Unit with Ridge Classifier for Crop Recommendation for Precise Agriculture Using Fused Feature Selection Concept 混合优化门控循环单元与岭分类器，利用融合特征选择概念为精准农业提供作物建议

IF 1.1 4区计算机科学

International Journal on Artificial Intelligence Tools Pub Date : 2024-03-12 DOI: 10.1142/s021821302450012x

D.A.S.S. Latha, R.P. Kumar

引用次数: 0

Emotion-driven Energy Load Forecasting: An Ensemble Leveraging Insights from News 情感驱动的能源负荷预测：利用新闻洞察力的集合

IF 1.1 4区计算机科学

International Journal on Artificial Intelligence Tools Pub Date : 2024-02-27 DOI: 10.1142/s0218213024500131

C. M. Liapis, A. Karanikola, S. Kotsiantis

引用次数: 0

Sparse Approximate Pseudoinverse Preconditioning for Sparse Supervised Learning Problems with More Features than Samples 针对特征多于样本的稀疏监督学习问题的稀疏近似伪逆预处理

IF 1.1 4区计算机科学

International Journal on Artificial Intelligence Tools Pub Date : 2024-02-23 DOI: 10.1142/s0218213024500118

A.-D.E. Lipitakis, G. Gravvanis, C. Filelis‐Papadopoulos, S. Kotsiantis, D. Anagnostopoulos

引用次数: 0

Hybrid Classifier for Crowd Anomaly Detection with Bernoulli Map Evaluation 利用伯努利图评估人群异常检测的混合分类器

IF 1.1 4区计算机科学

International Journal on Artificial Intelligence Tools Pub Date : 2024-02-21 DOI: 10.1142/s0218213024500088

R. Chaudhary, M. Kumar

引用次数: 0