2022 2nd International Conference on Artificial Intelligence (ICAI)最新文献_第3页

Comparison of Loss functions and Optimizers for Multi-class X-ray Bone Segmentation 多类x线骨分割的损失函数与优化器的比较

2022 2nd International Conference on Artificial Intelligence (ICAI) Pub Date : 2022-03-30 DOI: 10.1109/ICAI55435.2022.9773572

T. Anwar, Seemab Zakir

引用次数: 0

LSTM-based Model for Forecasting of COVID-19 Vaccines in Pakistan 基于lstm的巴基斯坦COVID-19疫苗预测模型

2022 2nd International Conference on Artificial Intelligence (ICAI) Pub Date : 2022-03-30 DOI: 10.1109/ICAI55435.2022.9773668

Saba Bashir, Kinza Rohail, Rizwan Qureshi

引用次数: 0

Covid-19 detection from X-ray images using Customized Convolutional Neural Network 基于自定义卷积神经网络的x射线图像新冠肺炎检测

2022 2nd International Conference on Artificial Intelligence (ICAI) Pub Date : 2022-03-30 DOI: 10.1109/ICAI55435.2022.9773586

Shahzad Shafiq, Luqman Ali, Wasif Khan, Rooh Ullah, Tanveer Ahmed Khan, Fady Alnaiiar

{"title":"Covid-19 detection from X-ray images using Customized Convolutional Neural Network","authors":"Shahzad Shafiq, Luqman Ali, Wasif Khan, Rooh Ullah, Tanveer Ahmed Khan, Fady Alnaiiar","doi":"10.1109/ICAI55435.2022.9773586","DOIUrl":"https://doi.org/10.1109/ICAI55435.2022.9773586","url":null,"abstract":"COVID-19 continues to have a devastating impact on the lives of people all over the world. Various new technologies arose in the research environment to assist mankind in surviving and living a better life. It is important to screen the infected patients in a timely and cost-effective manner to combat this disease and avoid its transmission. To achieve this aim, detection of Covid-19 from radiological evaluation of chest x-ray images using deep learning algorithms is less expensive and easily available option as it ensures fast and efficient diagnosis of the disease. Therefore, this paper presents a novel customized convolutional neural network (CNN) approach for the detection of COVID-19 from chest x-ray images. The performance of the proposed model is evaluated on three different size datasets, created from publicly available datasets. Experimental results show that the proposed model has better performance on Dataset 2. A very large increase or decrease of the number of samples in the dataset degrades the performance of the proposed model. The performance of the CNN model is compared with traditional pretrained networks namely VGG-16, VGG-19, ResNet-50 and Inception-V3. All the models show promising performance on dataset 2 which shows that optimum amount of data is enough for the model to lean features from the input data. Overall, the best validation accuracy of 97.78 was achieved by the proposed model on dataset 2.","PeriodicalId":146842,"journal":{"name":"2022 2nd International Conference on Artificial Intelligence (ICAI)","volume":"43 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-03-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124093943","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

Uncovering Price Puzzle in the Wheat Economy of Pakistan: An Application of Artificial Neural Networks 揭示巴基斯坦小麦经济中的价格之谜:人工神经网络的应用

2022 2nd International Conference on Artificial Intelligence (ICAI) Pub Date : 2022-03-30 DOI: 10.1109/ICAI55435.2022.9773693

Abdul Subhan, Nabila Khurshid, Zarwa Shah

{"title":"Uncovering Price Puzzle in the Wheat Economy of Pakistan: An Application of Artificial Neural Networks","authors":"Abdul Subhan, Nabila Khurshid, Zarwa Shah","doi":"10.1109/ICAI55435.2022.9773693","DOIUrl":"https://doi.org/10.1109/ICAI55435.2022.9773693","url":null,"abstract":"Wheat is at the epicenter of global food security. Extreme wheat price volatility can contribute to broader social risks in terms of food security, human development and have a significant influence on farmers' incomes in the coming years especially in developing countries like Pakistan. Wheat is not only the major staple crop of the country's food security, but it also contributes about 10.3% in agriculture which accounts for 2.2% of domestic GDP. However, the presumable intensification in climate change and macroeconomic instability is reputed as a threat to wheat price stability nationwide. Against this backdrop, this research develops a precise wheat price puzzle forecasting model using the Long- Short Term Memory Recurrent Neural Networks (LSTM-RNN) - an application of Artificial Intelligence. LSTM-RNN are proficient in handling non-linear complex systems owing to their special LSTM nodes. An assessment of the planned framework with a handful of prevailing models is also discussed. Results showed that LSTM-RNN outperformed in terms of accuracy and uncovered that wheat prices will progressively swell and shrink by 2030, which will pose menaces to the whole economy. Moreover, our proposed methodology may be used as a guiding principle for other crops as well, to fortify sustainable agriculture development by 2030.","PeriodicalId":146842,"journal":{"name":"2022 2nd International Conference on Artificial Intelligence (ICAI)","volume":"16 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-03-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127431396","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

Contrastive Self-Supervised Learning: A Survey on Different Architectures 对比自监督学习:不同架构的调查

2022 2nd International Conference on Artificial Intelligence (ICAI) Pub Date : 2022-03-30 DOI: 10.1109/ICAI55435.2022.9773725

Adnan Khan, S. Albarri, Muhammad Arslan Manzoor

{"title":"Contrastive Self-Supervised Learning: A Survey on Different Architectures","authors":"Adnan Khan, S. Albarri, Muhammad Arslan Manzoor","doi":"10.1109/ICAI55435.2022.9773725","DOIUrl":"https://doi.org/10.1109/ICAI55435.2022.9773725","url":null,"abstract":"Self-Supervised Learning (SSL) has enhanced the learning process of semantic representations from images. SSL has reduced the need for annotating or labelling the data by relying less on class labels during the training phase. SSL techniques dependent on Constrative Learning (CL) are acquiring prevalence because of their low dependency on training data labels. Different CL methods are producing state-of-the-art results on datasets which are used as the benchmarks for Supervised Learning. In this survey, we provide a review of CL-based methods including SimCLR, MoCo, BYOL, SwAV, SimTriplet and SimSiam. We compare these pipelines in terms of their accuracy on ImageNet and VOC07 benchmark. BYOL propose basic yet powerful architecture to accomplish 74.30 % accuracy score on image classification task. Using clustering approach SwAV outperforms other architectures by achieving 75.30 % top-1 ImageNet classification accuracy. In addition, we shed light on the importance of CL approaches which can maximise the use of huge amounts of data available today. At last, we report the impediments of current CL methodologies and emphasize the need of computationally efficient CL pipelines.","PeriodicalId":146842,"journal":{"name":"2022 2nd International Conference on Artificial Intelligence (ICAI)","volume":"20 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-03-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129509153","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: 14

EEG Guided Multimodal Lie Detection with Audio-Visual Cues 脑电引导多模态测谎视听线索

2022 2nd International Conference on Artificial Intelligence (ICAI) Pub Date : 2022-03-30 DOI: 10.1109/ICAI55435.2022.9773469

Hamza Javaid, Aniqa Dilawari, Usman Ghani Khan, Bilal Wajid

{"title":"EEG Guided Multimodal Lie Detection with Audio-Visual Cues","authors":"Hamza Javaid, Aniqa Dilawari, Usman Ghani Khan, Bilal Wajid","doi":"10.1109/ICAI55435.2022.9773469","DOIUrl":"https://doi.org/10.1109/ICAI55435.2022.9773469","url":null,"abstract":"Lying is considered a form of deception that defines one of the inevitable parts of human essence. Also, deception or lie detection has numerous applications in criminal and judicial community. Traditional practices of identifying deceit includes the monitoring of physiological signals, transcripts, visual and acoustic information with scientific techniques. In this paper, we propose a multimodal lie detection system that leverage the capabilities of novel deep learning techniques. In particular, the study investigates the importance of visual, acoustic and EEG information of a human subject for deception detection task. On the vision side, the system extracts dense optical flow features from consecutive frames in a video to monitor the facial movements. A two-stream convolution neural network utilize this visual features to detect lie or truth. Speech based deceit identification system extracts frequency distributed spectrograms from audio signals and attention augmented CNN is employed to learn changes in distribution of frequencies in speech. For lie detection with EEG signals, we utilize bidirectional long short term neural network for representation and classification of EEG data. EEG signals are represented as time series data and Bi-directional LSTM is learns the correspondences of past signals and future signals. The study performs multimodal fusion on all modalities for lie detection with best performing classifier. Experiments on Bag-Of-Lies dataset showed that the system outperformed traditional machine learning approaches with a significant difference. When all modalities are combined, the system achieves an accuracy of 83.5% in distinguishing deceptive and truthful samples.","PeriodicalId":146842,"journal":{"name":"2022 2nd International Conference on Artificial Intelligence (ICAI)","volume":"34 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-03-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133157447","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: 4

A GIS Architecture for Medical Disaster Management to Support Modern Healthcare Management System 支持现代医疗卫生管理系统的医疗灾害管理GIS体系结构

2022 2nd International Conference on Artificial Intelligence (ICAI) Pub Date : 2022-03-30 DOI: 10.1109/ICAI55435.2022.9773460

Atif Ali, Sabir Ali Changezi, H. Rizwan, Khurram Shehzad, Muhammad Usama Nazir, Muhammad Imran Naz

引用次数: 1

A Novel FCM and DT based Segmentation and Profiling Approach for Customer Relationship Management 一种新的基于FCM和DT的客户关系管理细分与分析方法

2022 2nd International Conference on Artificial Intelligence (ICAI) Pub Date : 2022-03-30 DOI: 10.1109/ICAI55435.2022.9773772

Faisal Abdullah, Z. Jalil

{"title":"A Novel FCM and DT based Segmentation and Profiling Approach for Customer Relationship Management","authors":"Faisal Abdullah, Z. Jalil","doi":"10.1109/ICAI55435.2022.9773772","DOIUrl":"https://doi.org/10.1109/ICAI55435.2022.9773772","url":null,"abstract":"In the current era of e-businesses, customer relationship management is a decisive process for selecting profitable customers and enhancing customer relationships for the betterment of the organization. However, most customer-oriented organizations face a common problem of categorizing customers, understanding the difference between them, and extracting profitable customers. In this paper, we present an approach to address these issues identifying the future and current values of customers. This helps in identifying and retaining the customers that a firm can most profitably serve. In our proposed system, we purify data through pre-processing and data cleaning, and then three key parameters i.e. recency, frequency, and monetary (RFM) are extracted from data. A Analytical Hierarchical Process is then applied to calculate the weights of RFM. These weighted RFM parameters are used for categorization of customers with the help of a fuzzy-c-mean algorithm. The validity of clusters is checked with Davies-Bouldin Index and finally, classification is done using decision tree and recommendation is given to enhance customer relationships. We evaluated the performance of our proposed system on two publicly available KDD Cup and Instacart datasets and achieved an accuracy rate of 95.5% and 94.3% respectively. The proposed system can be utilized for enhancing marketing strategies and developing new services for valuable customers.","PeriodicalId":146842,"journal":{"name":"2022 2nd International Conference on Artificial Intelligence (ICAI)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-03-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127535111","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