2023 3rd International Conference on Artificial Intelligence (ICAI)最新文献_第2页

Adversarial Attacks on Aerial Imagery : The State-of-the-Art and Perspective 航空图像的对抗性攻击:最新技术和视角

2023 3rd International Conference on Artificial Intelligence (ICAI) Pub Date : 2023-02-22 DOI: 10.1109/ICAI58407.2023.10136660

Syed M. Kazam Abbas Kazmi, Nayyer Aafaq, Mansoor Ahmad Khan, Ammar Saleem, Zahid Ali

{"title":"Adversarial Attacks on Aerial Imagery : The State-of-the-Art and Perspective","authors":"Syed M. Kazam Abbas Kazmi, Nayyer Aafaq, Mansoor Ahmad Khan, Ammar Saleem, Zahid Ali","doi":"10.1109/ICAI58407.2023.10136660","DOIUrl":"https://doi.org/10.1109/ICAI58407.2023.10136660","url":null,"abstract":"In recent years, deep model's feature learning skills have become more compelling, resulting in huge advancements in various artificial intelligence (AI) applications. Specifically, depth and breadth of Computer Vision (CV) have expanded rapidly considering the usage of Deep Neural Networks (DNNs). However, it has been shown in the literature that DNNs are vulnerable to adversarial attacks caused by carefully crafted perturbations through solving complex optimization problems. Although the attacks reveal weaknesses in sophisticated DNN algorithms, they might be seen as an opportunity to address issues in real-world security-critical applications. These attacks represent a paradigm change for circumstances in which vulnerable assets must be concealed from autonomous detection systems onboard drones, Unmanned Aerial Vehicles (UAVs), and satellites. Flying AI-models with strong remote detection and classification capabilities may relay exact target-object kinds on the ground, compromising victim security. The employment of conventional tactics to hide huge stationary and movable assets from autonomous aerial detection has become ineffective for larger areas owing to its cost and applicability. Previous works have explained the broader perspective of adversarial attacks in both digital and physical domains. This is the first effort to characterize the multiplicity of adversarial attacks from the viewpoint of autonomous aerial imaging. In addition to providing a thorough literature review of adversarial attacks on aerial imagery in CV tasks, this paper also offers non-specialists succinct descriptions of technical terms and prospects associated with this direction of study.","PeriodicalId":161809,"journal":{"name":"2023 3rd International Conference on Artificial Intelligence (ICAI)","volume":"5 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-02-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127355563","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: 2

YouFake: A Novel Multi-Modal Dataset for Fake News Classification YouFake:一种新的多模态假新闻分类数据集

2023 3rd International Conference on Artificial Intelligence (ICAI) Pub Date : 2023-02-22 DOI: 10.1109/ICAI58407.2023.10136667

Syeda Arooj Fatima, Adeel Zafar, K. Malik

引用次数: 0

Diagnosing Spinal Abnormalities Using Machine Learning: A Data-Driven Approach 使用机器学习诊断脊柱异常:数据驱动的方法

2023 3rd International Conference on Artificial Intelligence (ICAI) Pub Date : 2023-02-22 DOI: 10.1109/ICAI58407.2023.10136651

Zia Ul Islam Nasir, K. Khan, Momna Asghar

{"title":"Diagnosing Spinal Abnormalities Using Machine Learning: A Data-Driven Approach","authors":"Zia Ul Islam Nasir, K. Khan, Momna Asghar","doi":"10.1109/ICAI58407.2023.10136651","DOIUrl":"https://doi.org/10.1109/ICAI58407.2023.10136651","url":null,"abstract":"Low back pain is a predominant condition which can affects people from different diaspora. The goal of this work is to use machine learning approach to forecast spinal abnormalities. Extratreesclassifier is utilized as a data preprocessing stage to choose the dataset's most prominent features. On a dataset of 310 samples, spinal anomalies are diagnosed using machine learning algorithms like the Support Vector Machine (SVM) and the multilayer perceptron (MLP). The purpose of this study is to determine the most crucial factors that produce backbone abnormalities and to predict them using supervised machine learning techniques. The classification of normal and abnormal spinal patients is investigated in terms of various aspects, including testing and training accuracy, precision, and recall. The observed accuracies for SVM and MLP with 80% training data are 92% and 90%, respectively. The result shows that these models can achieve high accuracy in predicting spinal abnormalities, with the SVM model performing the better. The result suggest that this approach has the potential to significantly improve the efficiency and accuracy of spinal abnormality diagnosis, leading to better patient outcomes.","PeriodicalId":161809,"journal":{"name":"2023 3rd International Conference on Artificial Intelligence (ICAI)","volume":"4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-02-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125464295","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

A non Invasive Brain-Computer-Interface for Service Robotics 服务机器人的非侵入性脑机接口

2023 3rd International Conference on Artificial Intelligence (ICAI) Pub Date : 2023-02-22 DOI: 10.1109/ICAI58407.2023.10136672

F. Ahmed, Hashim Iqbal, Ahmed Nouman, H. F. Maqbool, Saqib Zafar, M. Saleem

引用次数: 0

Automatic Detection of Paediatric Congenital Heart Diseases from Phonocardiogram Signals 利用心音图信号自动检测小儿先天性心脏病

2023 3rd International Conference on Artificial Intelligence (ICAI) Pub Date : 2023-02-22 DOI: 10.1109/ICAI58407.2023.10136668

Zafar Ahmad, Muhammad Zeeshan, Arshad Sohail, Fazal-e-Haq, Muhammad Haris, Misha Urooj Khan, Sayed Shahid Hussain, Muhammad Salman Khan

{"title":"Automatic Detection of Paediatric Congenital Heart Diseases from Phonocardiogram Signals","authors":"Zafar Ahmad, Muhammad Zeeshan, Arshad Sohail, Fazal-e-Haq, Muhammad Haris, Misha Urooj Khan, Sayed Shahid Hussain, Muhammad Salman Khan","doi":"10.1109/ICAI58407.2023.10136668","DOIUrl":"https://doi.org/10.1109/ICAI58407.2023.10136668","url":null,"abstract":"The World Health Organization (WHO) claims that 0.8% to 1.2% of newborns worldwide are affected by congenital heart diseases (CHDs). There are many methods for CHD identification, and the most prevalent is phonocardiography (PCG). It is a non-invasive method that offers crucial knowledge about the sounds (S1, S2, S3, and S4) and beats of the heart. This research study aims to train a binary categorization system using a deep neural network for CHDs by using a combination of local and public datasets. The local dataset (LD) had 583 signals (normal and abnormal PCG), while the public dataset (PD) taken from Michigan University had 23 PCG recordings. Both datasets were down-sampled to 8 kHz. The band pass filter was designed such that it ensured that any signals outside of the 20–650 Hz range were filtered out, allowing only the desired frequencies to be processed. All signals were chunked at a signal duration of 4 seconds. For data augmentation, pitch-shifting was applied and passed to a 1D convolutional neural network (CNN). The best results were achieved for case C, with an accuracy of 98.56 %, precision of 98.57 %, F1 score of 98.56 %, specificity of 98.0 %, and sensitivity of 99.0%.","PeriodicalId":161809,"journal":{"name":"2023 3rd International Conference on Artificial Intelligence (ICAI)","volume":"405 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-02-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130279787","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

ISA: Internet of Medical Things (IoMT) in Smart Healthcare and its Applications: A Review 智能医疗中的医疗物联网(IoMT)及其应用综述

2023 3rd International Conference on Artificial Intelligence (ICAI) Pub Date : 2023-02-22 DOI: 10.1109/ICAI58407.2023.10136661

Fahad Majeed, Maria Nazir, J. Schneider

引用次数: 0

Investigating the Energy Production through Sustainable Sources by Incorporating Multifarious Machine Learning Methodologies 结合多种机器学习方法，通过可持续资源调查能源生产

2023 3rd International Conference on Artificial Intelligence (ICAI) Pub Date : 2023-02-22 DOI: 10.1109/ICAI58407.2023.10136677

Umer Javaid, R. Usman, Ali Javaid

{"title":"Investigating the Energy Production through Sustainable Sources by Incorporating Multifarious Machine Learning Methodologies","authors":"Umer Javaid, R. Usman, Ali Javaid","doi":"10.1109/ICAI58407.2023.10136677","DOIUrl":"https://doi.org/10.1109/ICAI58407.2023.10136677","url":null,"abstract":"Artificial Intelligence (AI) has the potential to revolutionize the way we predict and manage energy generation from solar and wind sources. It can greatly enhance the accuracy and scalability of energy prediction in solar and wind power systems. The implementation of advanced machine learning techniques can optimize the performance of these renewable energy systems and predict the variability of energy production. AI-powered algorithms can analyze data from various sources such as weather forecasts and historical energy production data to predict energy generation with increased precision, allowing for optimization of the system's operation. Furthermore, AI can also predict energy demand and adjust energy production, accordingly, resulting in reduced energy waste and increased system efficiency. As the demand for renewable energy continues to rise, the integration of AI in this field becomes increasingly crucial in ensuring a sustainable and dependable energy future. In this research, we investigate the utilization of AI techniques for evaluating the generation of hydrogen from solar and wind power. The results of this study will provide insight into the potential of AI tools for forecasting hydrogen production from solar and wind energy. The amount of hydrogen produced from solar energy is up to 93.3 × 103kg/km2whereas estimated production from wind is 6.7 kg/day. The comparison of the performance of the different machine learning models used in the study will help to identify the most effective method for forecasting hydrogen production in this context. Additionally, the study aims to contribute to the growing body of knowledge on the application of AI in the field of renewable energy and its potential to improve the efficiency, scalability, and reliability of energy systems.","PeriodicalId":161809,"journal":{"name":"2023 3rd International Conference on Artificial Intelligence (ICAI)","volume":"75 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-02-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134485623","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

Intelligent System for the Diagnosis of Schizophrenia featuring Brain Textures from EEG 基于脑电结构特征的精神分裂症智能诊断系统

2023 3rd International Conference on Artificial Intelligence (ICAI) Pub Date : 2023-02-22 DOI: 10.1109/ICAI58407.2023.10136624

Sumair Aziz, Muhammad Umar Khan, Muhammad Faraz, Siddhant Sharma, Awadia Gareeballah, Gabriel Axel Montes

{"title":"Intelligent System for the Diagnosis of Schizophrenia featuring Brain Textures from EEG","authors":"Sumair Aziz, Muhammad Umar Khan, Muhammad Faraz, Siddhant Sharma, Awadia Gareeballah, Gabriel Axel Montes","doi":"10.1109/ICAI58407.2023.10136624","DOIUrl":"https://doi.org/10.1109/ICAI58407.2023.10136624","url":null,"abstract":"Schizophrenia (ScZ) is a harmful disorder of the brain often associated with anxiety, depression and sociopsychological problems. An accurate and timely diagnosis of SZ proves helpful in the efficient cure of the disease. This research presents a novel pattern recognition framework for the accurate diagnosis of SZ using non-invasive electroencephalography (EEG). The raw dataset contains 19 channel EEGs collected from fourteen patients. Each EEG recording was segmented into 60-second segments to increase the number of observations and increase the diagnosis system performance. These segmented EEG observations were preprocessed by passing them through Fast-Independent component analysis (Fast-K'A), followed by band pass filter, and Empirical Mode Decomposition (EMD). EMD splits the input signal into Intrinsic mode functions (IMFs). After manual analysis, only the first two IMFs were added together to form a reconstructed preprocessed signal. Next, novel Brain Texture features were extracted from each channel of preprocessed EEG signal. Brain texture features from each channel were serially fused to form a final feature vector. These features were used to train and test a broad range of machine learning classification methods and the best performance was obtained via Fine k-Nearest Neighbors (FKNN). The proposed framework achieved 94.9% accuracy using 10-fold cross-validation outperforming the existing techniques.","PeriodicalId":161809,"journal":{"name":"2023 3rd International Conference on Artificial Intelligence (ICAI)","volume":"21 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-02-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133778828","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 Intelligent Technique For The Effective Prediction Of Monkeypox Outbreak 一种有效预测猴痘暴发的智能技术

2023 3rd International Conference on Artificial Intelligence (ICAI) Pub Date : 2023-02-22 DOI: 10.1109/ICAI58407.2023.10136662

Azka Mir, A. Rehman, Sabeen Javaid, Tahir Muhammad Ali

{"title":"An Intelligent Technique For The Effective Prediction Of Monkeypox Outbreak","authors":"Azka Mir, A. Rehman, Sabeen Javaid, Tahir Muhammad Ali","doi":"10.1109/ICAI58407.2023.10136662","DOIUrl":"https://doi.org/10.1109/ICAI58407.2023.10136662","url":null,"abstract":"Monkey pox is a viral disease that spreads from animals especially monkey to human beings. Monkey pox outbreak has been increasing at a concerning rate. The outbreak of monkey pox has infected several people around the world. The extent and intensity of the disease can be determined by the occurrence of the symptoms. The objective of this paper is to predict monkeypox virus so that outbreak can be administered before monkeypox looms as a viral health hazard. The monkeypox case has been classified as confirmed, discarded and suspected. This paper uses a supervised machine learning model to predict the status of monkey pox case. To diagnose monkeypox virus case, clinical parameters are required. The selected dataset contains the parameters of monkey pox virus from April 2022 onwards. It is necessary to predict the monkey pox outbreak before it effects more valuable lives. For the purpose of this paper, supervised machine learning techniques have been used to determine the performance of the dataset through experimental analysis. The experiment has been performed using various classifiers such as Decision tree, Naïve Bayes etc. to compare the accuracy rate. After the comparative analysis of the resulting accuracy percentage of different classifiers, we have proposed the model with the classifier with the highest accuracy. Our proposed model has achieved an accuracy rate of 93.51% using K-NN classifier with k=5 neighbors. Rapid miner platform is used for the application of the machine learning tools and techniques for the purpose of this research. This paper highlights the effective machine learning steps for the development of highly accurate model using machine learning techniques on monkey pox outbreak dataset.","PeriodicalId":161809,"journal":{"name":"2023 3rd International Conference on Artificial Intelligence (ICAI)","volume":"56 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-02-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127689243","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

Multi-Speaker Diarization using Long-Short Term Memory Network 基于长短期记忆网络的多说话人分组

2023 3rd International Conference on Artificial Intelligence (ICAI) Pub Date : 2023-02-22 DOI: 10.1109/ICAI58407.2023.10136670

Nayyer Aafaq, Usama Qamar, Sohaib Ali Khan, Z. Khan

引用次数: 0