Internet of Things (IoT) devices often operate with limited resources while interacting with users and their environment, generating a wealth of data. Machine learning models interpret such sensor data, enabling accurate predictions and informed decisions. However, the sheer volume of data from billions of devices can overwhelm networks, making traditional cloud data processing inefficient for IoT applications. This paper presents a comprehensive survey of recent advances in models, architectures, hardware, and design requirements for deploying machine learning on low-resource devices at the edge and in cloud networks. Prominent IoT devices tailored to integrate edge intelligence include Raspberry Pi, NVIDIA’s Jetson, Arduino Nano 33 BLE Sense, STM32 Microcontrollers, SparkFun Edge, Google Coral Dev Board, and Beaglebone AI. These devices are boosted with custom AI frameworks, such as TensorFlow Lite, OpenEI, Core ML, Caffe2, and MXNet, to empower ML and DL tasks (e.g., object detection and gesture recognition). Both traditional machine learning (e.g., random forest, logistic regression) and deep learning methods (e.g., ResNet-50, YOLOv4, LSTM) are deployed on devices, distributed edge, and distributed cloud computing. Moreover, we analyzed 1000 recent publications on “ML in IoT” from IEEE Xplore using support vector machine, random forest, and decision tree classifiers to identify emerging topics and application domains. Hot topics included big data, cloud, edge, multimedia, security, privacy, QoS, and activity recognition, while critical domains included industry, healthcare, agriculture, transportation, smart homes and cities, and assisted living. The major challenges hindering the implementation of edge machine learning include encrypting sensitive user data for security and privacy on edge devices, efficiently managing resources of edge nodes through distributed learning architectures, and balancing the energy limitations of edge devices and the energy demands of machine learning.

Brain tumors are a leading cause of death globally, with numerous types varying in malignancy, and only 12% of adults diagnosed with brain cancer survive beyond five years. This research introduces a hyperparametric convolutional neural network (CNN) model to identify brain tumors, with significant practical implications. By fine-tuning the hyperparameters of the CNN model, we optimize feature extraction and systematically reduce model complexity, thereby enhancing the accuracy of brain tumor diagnosis. The critical hyperparameters include batch size, layer counts, learning rate, activation functions, pooling strategies, padding, and filter size. The hyperparameter-tuned CNN model was trained on three different brain MRI datasets available at Kaggle, producing outstanding performance scores, with an average value of 97% for accuracy, precision, recall, and F1-score. Our optimized model is effective, as demonstrated by our methodical comparisons with state-of-the-art approaches. Our hyperparameter modifications enhanced the model performance and strengthened its capacity for generalization, giving medical practitioners a more accurate and effective tool for making crucial judgments regarding brain tumor diagnosis. Our model is a significant step in the right direction toward trustworthy and accurate medical diagnosis, with practical implications for improving patient outcomes.

This systematic review details recent advancements in the field of Explainable Artificial Intelligence (XAI) from 2014 to 2024. XAI utilises a wide range of frameworks, techniques, and methods used to interpret machine learning (ML) black-box models. We aim to understand the technical advancements in the field and future directions. We followed the PRISMA methodology and selected 30 relevant publications from three main databases: IEEE Xplore, ACM, and ScienceDirect. Through comprehensive thematic analysis, we categorised the research into three main topics: ‘model developments’, ‘evaluation metrics and methods’, and ‘user-centred and XAI system design’. Our results uncover ‘What’, ‘How’, and ‘Why’ these advancements were developed. We found that 13 papers focused on model developments, 8 studies focused on the XAI evaluation metrics, and 12 papers focused on user-centred and XAI system design. Moreover, it was found that these advancements aimed to bridge the gap between technical model outputs and user understanding.

This study investigates the intention of Saudi Arabian users to adopt mobile health (m-health) applications through the lens of the United Theory of Acceptance and Use of Technology (UTAUT) framework. The research highlights the growing importance of m-health solutions in Saudi Arabia, especially in the context of the country's Vision 2030 development agenda and the accelerated adoption of e-health/m-health technologies during the COVID-19 pandemic. The key findings indicate that health information seeking, and social influence are significant factors driving users' intentions to adopt m-health applications, while Performance Expectancy (PE) is not a primary driver. Additionally, Effort Expectancy (EE) positively influences users' behavioral intentions. Improving health information features in these applications could facilitate broader adoption. This research contributes to the existing literature on information systems and m-health adoption by shedding light on the critical factors influencing user intentions in a developing context. However, the study does not account for all potential technological and external variables that could affect adoption behavior. Future research, particularly qualitative or mixed-method studies, should explore the impact of age on m-health adoption, as the current findings primarily reflect younger users.

The integration of data science plays a vital role in the progression of mobile applications, as it enhances their overall effectiveness and provides users with more benefits. Data science tools have the potential to assist organizations in enhancing their brand marketing endeavors and providing personalized offers, therefore contributing to the overall improvement of consumer acquisition. Consequently, the primary objective of this research was to investigate the influence of the personalized offers aspect on users of mobile commerce applications in Saudi Arabia, in addition to the core elements of the technology acceptance model (TAM). The result of this study indicates that perceived usefulness exhibits the greatest degree of impact in influencing customer attitudes toward usage, with personalized offers following closely. The research further reveals that personalized offers exert a substantial favorable influence on customers’ attitudes towards their use. Additionally, the research reveals that personalized offers significantly influence individuals’ behavioral intentions concerning the use of m-commerce applications. This research offers significant insights that might inform future academic research in the field. Moreover, it provides app developers and enterprises with novel knowledge and comprehension to devise tactics that enhance the ongoing advancement of mobile commerce applications.

Wearable technologies have developed relatively quickly in the modern years. This development has impacted the management of personal health and wellness to a considerable extent. The purpose of this study is to understand to what extent the interest of individuals in health and fitness, coupled with their general orientation towards technology, affects the acceptance of wearable technologies and related applications in the Saudi Arabian environment. Utilizing the UTAUT model, the research examines six hypotheses, revealing that health information- seeking behavior (β = 0.316, p < 0.001) and personal innovativeness in IT (β = 0.273, p < 0.001) are strong predictors of behavioral intention (BI) to adopt wearables. Performance expectancy (β = 0.186, p = 0.030) also significantly influences BI, while facilitating conditions (β = 0.267, p < 0.001) and BI (β = 0.528, p < 0.001) are crucial for actual use behavior (UB). These findings provide valuable insights for healthcare practitioners promoting wearable fitness devices, as they underscore the importance of enhancing public understanding of exercise benefits and active living. These would have significant implications for healthcare practitioners when promoting wearable fitness gadgets to the general population. In that respect, healthcare professionals could play significant roles in enhancing the general population’s understanding of physical exercise benefits and an active life by disseminating information and knowledge gained from these equipment and gadgets. Wearable technology companies can subsequently learn from and integrate such valuable inputs. At the same time, designers and marketing strategists design plans to satisfy the unique needs and tastes of the Saudi market. The results may further explain how personal health concerns and technical trends interact in embracing new, creative, health-focused solutions.