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Abstract The domain of Machine learning has experienced Substantial advancement and development. Recently, showcasing a Broad spectrum of uses like Computational linguistics, image identification, and autonomous systems. With the increasing demand for intelligent systems, it has become crucial to comprehend the different categories of machine acquiring knowledge systems along with their applications in the present world. This paper presents actual use cases of machine learning, including cancer classification, and how machine learning algorithms have been implemented on medical data to categorize diverse forms of cancer and anticipate their outcomes. The paper also discusses supervised, unsupervised, and reinforcement learning, highlighting the benefits and disadvantages of each category of Computational intelligence system. The conclusions of this systematic study on machine learning methods and applications in cancer classification have numerous implications. The main lesson is that through accurate classification of cancer kinds, patient outcome prediction, and identification of possible therapeutic targets, machine learning holds enormous potential for improving cancer diagnosis and therapy. This review offers readers with a broad understanding as of the present advancements in machine learning applied to cancer classification today, empowering them to decide for themselves whether to use these methods in clinical settings. Lastly, the paper wraps up by engaging in a discussion on the future of machine learning, including the potential for new types of systems to be developed as the field advances. Overall, the information included in this survey article is useful for scholars, practitioners, and individuals interested in gaining knowledge about the fundamentals of machine learning and its various applications in different areas of activities.

Abstract Predicting and understanding the behavior of dynamic systems have driven advancements in various approaches, including physics-based models and data-driven techniques like deep neural networks. Chaotic systems, with their stochastic nature and unpredictable behavior, pose challenges for accurate modeling and forecasting, especially during extreme events. In this paper, we propose a novel deep learning framework called Attractor-Inspired Deep Learning (AiDL), which seamlessly integrates actual statistics and mathematical models of system kinetics. AiDL combines the strengths of physics-informed machine learning and data-driven methods, offering a promising solution for modeling nonlinear systems. By leveraging the intricate dynamics of attractors, AiDL bridges the gap between physics-based models and deep neural networks. We demonstrate the effectiveness of AiDL using real-world data from various domains, including catastrophic weather mechanics, El Niño cycles, and disease transmission. Our empirical results showcase AiDL’s ability to substantially enhance the modeling of extreme events. The proposed AiDL paradigm holds promise for advancing research in Time Series Prediction of Extreme Events and has applications in real-world chaotic system transformations.

Abstract Human Activity Recognition (HAR) is widely used in various applications, from smart homes and healthcare to the Internet of Things (IoT) and virtual reality gaming. However, existing HAR technologies suffer from limitations such as location dependency, sensitivity to noise and interference, and lack of flexibility in recognizing diverse activities and environments. In this paper, we present a novel approach to HAR that addresses these challenges and enables real-time classification and absolute location-independent sensing. The approach is based on an adaptive algorithm that leverages sequential learning activity features to simplify the recognition process and accommodate variations in human activities across different people and environments by extracting the features that match the signal with the surroundings. We employ the Raspberry Pi 4 and Channel State Information (CSI) data to extract activity recognition data, which provides reliable and high-quality signal information. We propose a signal segmentation method using the Long Short-Term Memory (LSTM) algorithm to accurately determine the start and endpoint of human activities. Our experiments show that our approach achieves a high accuracy of up to 97% in recognizing eight activities and mapping activities associated with environments that were not used in training. The approach represents a significant advancement in HAR technology and has the potential to revolutionize many domains, including healthcare, smart homes, and IoT.

Abstract Automatic Arabic handwritten recognition is one of the recently studied problems in the field of Machine Learning. Unlike Latin languages, Arabic is a Semitic language that forms a harder challenge, especially with the variability of patterns caused by factors such as the writer’s age. Most studies have focused on adults, with only one recent study on children. Moreover, many recent machine-learning methods have focused on using Convolutional Neural Networks (CNNs), a powerful class of neural networks that can extract complex features from images. In this paper, we propose a convolutional neural network (CNN) model that recognizes children’s handwriting with an accuracy of 91% on the Hijja dataset, a recent dataset built by collecting images of Arabic characters written by children, and 97% on the Arabic Handwritten Character Dataset. The results showed a good improvement over the proposed model from the Hijja dataset authors, yet it revealed a bigger challenge to solve for children’s Arabic handwritten character recognition. Moreover, we proposed a new approach using multiple models instead of a single model based on the number of strokes in a character and merged Hijja with AHCD, which achieved an average prediction accuracy of 96%.

Abstract In the study of social relationship inference, social relationship link inference aims to infer whether there is a social relationship between users. Most previous works applied the unsupervised graph random walk sampling, which had sampling bias and lost much information. In this paper, a Social Relationship Link Inference Based on Graph Convolutional Networks (SLiGCN) is proposed, which learns the spatiotemporal information of check-ins and the impact of related users’ trajectories. It firstly employs the end-to-end supervised learning and applies a recurrent neural network to extract spatiotemporal sequence features from trajectories. Then the graph convolutional network fuses the features of neighboring nodes and employs the fully connected network to infer social relationship links. Finally, it is evaluated with AUC on three real-world datasets. The experiment results show that, compared with baseline models, it not only avoids hand-crafted feature construction that requires much prior knowledge, but also achieves 10% improvement on average.