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Artificial intelligence (AI) has undergone major advances over the past decades, propelled by key innovations in machine learning and the availability of big data and computing power. This paper surveys the historical progress of AI from its origins in logic-based systems like the Logic Theorist to recent deep learning breakthroughs like Bidirectional Encoder Representations from Transformers (BERT), Generative Pretrained Transformer 3 (GPT-3) and Large Language Model Meta AI (LLaMA). The early rule-based systems using handcrafted expertise gave way to statistical learning techniques and neural networks trained on large datasets. Milestones like AlexNet and AlphaGo established deep learning as a dominant AI approach. Transfer learning enabled models pre-trained on diverse corpora to excel at specialised downstream tasks. The scope of AI expanded from niche applications like playing chess to multifaceted capabilities in computer vision, natural language processing and dialogue agents. However, current AI still needs to catch up to human intelligence in aspects like reasoning, creativity, and empathy. Addressing limitations around real-world knowledge, biases, and transparency remains vital for further progress and aligning AI with human values. This survey provides a comprehensive overview of the evolution of AI and documents innovations that shaped its advancement over the past six decades. [ABSTRACT FROM AUTHOR]

A catastrophic epidemic of Severe Acute Respiratory Syndrome-Coronavirus, commonly recognised as COVID-19, introduced a worldwide vulnerability to human community. All nations around the world are making enormous effort to tackle the outbreak towards this deadly virus through various aspects such as technology, economy, relevant data, protective gear, lives-risk medications and all other instruments. The artificial intelligence-based researchers apply knowledge, experience and skill set on national level data to create computational and statistical models for investigating such a pandemic condition. In order to make a contribution to this worldwide human community, this paper recommends using machine-learning and deep-learning models to understand its daily accelerating actions together with predicting the future reachability of COVID-19 across nations by using the real-time information from the Johns Hopkins dashboard. In this work, a novel Exponential Smoothing Long-Short-Term Memory Networks Model (ESLSTM) learning model is proposed to predict the virus spread in the near future. The results are evaluated using RMSE and R-Squared values. [ABSTRACT FROM AUTHOR]

Research on machine deep learning with fuzzy neural network (FNN) is one hot topic in the Artificial Intelligent (AI) domain. In order to support the application of the IoT (Internet of Things) and make use of these image data to get perfect image reasonably and efficiently, it is necessary to fuse these sensed data, therefore the multiple-sensors' image aggregation becomes a key technology. In this paper, novel FNN-based machine deep learning approach for image aggregation in application of the IoT is proposed. When this approach is done, dynamic learning from eigenvalue transition example can improve traditional learning approach based on static eigenvalue of example. And the neural network is used to be demonstrated its unique superiority of image understanding. FNN-based machine deep learning approach can learn from dynamic eigenvalues, the change of data can be learned and the varieties of the eigenvalue can be understood and remembered. The relative experiments have shown the designed approach for image aggregation is fast and effective, and it can be adapted for the many image applications of the IoT. [ABSTRACT FROM AUTHOR]

Recent advancements in the field of Artificial Intelligence (AI) have provided an opportunity to create autonomous devices, robots, and machines characterised particularly with the ability to make decisions and perform tasks without human mediation. One of these devices, Unmanned Aerial Vehicles (UAVs) or drones are widely used to perform tasks like surveillance, search and rescue, object detection and target tracking, and many more. Efficient real-time object detection in aerial videos is an urgent need, especially with the increasing use of UAV in various fields. The sensitivity in performing said tasks demands that drones must be efficient and reliable. This paper presents our research progress in the development of applications for the identification and detection of person using the convolutional neural networks (CNN) YOLO-v2 based on the camera of drone. The position and state of the person are determined with deep-learning-based computer vision. The person detection results show that YOLO-v2 detects and classifies object with a high level of accuracy. For real-time tracking, the tracking algorithm responds faster than conventionally used approaches, efficiently tracking the detected person without losing it from sight. [ABSTRACT FROM AUTHOR]

Deep Learning Technique (DLT) is the sub-branch of Machine Learning (ML) which assists to learn the data in multiple levels of representation and abstraction and shows impressive performance on many Artificial Intelligence (AI) tasks. This paper presents a new method to analyse the healthcare data using DLT algorithms and associated mathematical formulations. In this study, we have first developed a DLT to programme two types of deep learning neural networks, namely: (a) a two-hidden layer network, and (b) a three-hidden layer network. The data was analysed for predictability in both of these networks. Additionally, a comparison was also made with simple and multiple Linear Regression (LR). The demonstration of successful application of this method is carried out using the dataset that was constructed based on 2014 Medicare Provider Utilization and Payment Data. The results indicate a stronger case to use DLTs compared to traditional techniques like LR. Furthermore, it was identified that adding more hidden layers to neural network constructed for performing deep learning analysis did not have much impact on predictability for the dataset considered in this study. Therefore, the experimentation described in this article sets up a case for using DLTs over the traditional predictive analytics. The investigators assume that the algorithms described for deep learning is repeatable and can be applied for other types of predictive analysis on healthcare data. The observed results indicate, the accuracy obtained by DLT was 40% more accurate than the traditional multivariate LR analysis. [ABSTRACT FROM AUTHOR]