This paper explores the concept of smart cities and the role of the Internet of Things (IoT) and machine learning (ML) in realizing a data-centric smart environment. Smart cities leverage technology and data to improve the quality of life for citizens and enhance the efficiency of urban services. IoT and machine learning have emerged as key technologies for enabling smart city solutions that rely on large-scale data collection, analysis, and decision-making. This paper presents an overview of smart cities’ various applications and discusses the challenges associated with implementing IoT and machine learning in urban environments. The paper also compares different case studies of successful smart city implementations utilizing IoT and machine learning technologies. The findings suggest that these technologies have the potential to transform urban environments and enable the creation of more livable, sustainable, and efficient cities. However, significant challenges remain regarding data privacy, security, and ethical considerations, which must be addressed to realize the full potential of smart cities.

The electrocardiogram (ECG) is one of the most extensively employed signals used in the diagnosis and prediction of cardiovascular diseases (CVDs). The ECG signals can capture the heart’s rhythmic irregularities, commonly known as arrhythmias. A careful study of ECG signals is crucial for precise diagnoses of patients’ acute and chronic heart conditions. In this study, we propose a two-dimensional (2-D) convolutional neural network (CNN) model for the classification of ECG signals into eight classes; namely, normal beat, premature ventricular contraction beat, paced beat, right bundle branch block beat, left bundle branch block beat, atrial premature contraction beat, ventricular flutter wave beat, and ventricular escape beat. The one-dimensional ECG time series signals are transformed into 2-D spectrograms through short-time Fourier transform. The 2-D CNN model consisting of four convolutional layers and four pooling layers is designed for extracting robust features from the input spectrograms. Our proposed methodology is evaluated on a publicly available MIT-BIH arrhythmia dataset. We achieved a state-of-the-art average classification accuracy of 99.11%, which is better than those of recently reported results in classifying similar types of arrhythmias. The performance is significant in other indices as well, including sensitivity and specificity, which indicates the success of the proposed method.

Deep learning is an obvious method for the detection of disease, analyzing medical images and many researchers have looked into it. However, the performance of deep learning algorithms is frequently influenced by hyperparameter selection, the question of which combination of hyperparameters are best emerges. To address this challenge, we proposed a novel algorithm for Adaptive Hyperparameter Tuning (AHT) that automates the selection of optimal hyperparameters for Convolutional Neural Network (CNN) training. All of the optimal hyperparameters for the CNN models were instantaneously selected and allocated using a novel proposed algorithm Adaptive Hyperparameter Tuning (AHT). Using AHT, enables CNN models to be highly autonomous to choose optimal hyperparameters for classifying medical images into various classifications. The CNN model (Deep-Hist) categorizes medical images into basic classes: malignant and benign, with an accuracy of 95.71%. The most dominant CNN models such as ResNet, DenseNet, and MobileNetV2 are all compared to the already proposed CNN model (Deep-Hist). Plausible classification results were obtained using large, publicly available clinical datasets such as BreakHis, BraTS, NIH-Xray and COVID-19 X-ray. Medical practitioners and clinicians can utilize the CNN model to corroborate their first malignant and benign classification assessment. The recommended Adaptive high F1 score and precision, as well as its excellent generalization and accuracy, imply that it might be used to build a pathologist’s aid tool.

The COVID-19 data is critical to support countries and healthcare organizations for effective planning and evidence-based practices to counter the pressures of cost reduction, improved coordination, and outcome and produce more with less. Several COVID-19 datasets are published on the web to support stakeholders in gaining valuable insights for better planning and decision-making in healthcare. However, the datasets are produced in heterogeneous proprietary formats, which create data silos and make data discovery and reuse difficult. Further, the data integration for analysis is difficult and is usually performed by the domain experts manually, which is time-consuming and error-prone. Therefore, an explicit, flexible, and widely acceptable methodology to represent, store, query, and visualize COVID-19 data is needed. In this paper, we have presented the design and development of the Linked Open COVID-19 Data system for structuring and transforming COVID-19 data into semantic format using explicitly developed ontology and publishing on the web using Linked Open Data (LOD) principles. The key motivation of this research is the evaluation of LOD technology as a potential option and application of the available Semantic Web tools (i.e., Protégé, Excel2RDF, Fuseki, Silk, and Sgvizler) for building LOD-based COVID-19 information systems. We have also underpinned several use-case scenarios exploiting the LOD format of the COVID-19 data, which could be used by applications and services for providing relevant information to the end-users. The effectiveness of the proposed methodology and system is evaluated using the system usability scale and descriptive statistical methods and results are found promising.

The variety of e-commerce and online business environments makes spotting fraud or fake reviews challenging. The goal of this research is to create methods that could ensure the authenticity of reviews. Numerous endeavors have already been made by the research community to address this problem. Unfortunately, these efforts were confined to a specific number of languages, such as English, Arabic, and a few others. Roman Urdu is being used across the subcontinent. For this task, this language hasn't been properly investigated. On the other hand, Transformer-based models have demonstrated tremendous success over the past few years for a variety of Natural Language Processing applications. Transformer-based models, however, have not been investigated for the goal of examining fake Roman Urdu reviews. This study compares multiple transfer-based models, including BERT_base and BERT_large with five deep learning models Simple RNN, LSTM, Bidirectional LSTM, GRU, and Bidirectional GRU aims to close this research gap. The evaluation was conducted using frequently applied metrics, i.e., ROC-Accuracy, F_1, Precision, and Recall. The BERT_large with Bi-LSTM model produced the best results (ROC-ACC=0.94 and F_1=0.94). Results from a previous study using the same corpus were improved by 6% in this investigation.

In this article, the researcher explores an automated approach for detecting a brain tumor using MRI scans of the brain. In underdeveloped countries, many people are dying due to the slow detection process and other negligence of radiologists. People suffer from these diseases due to the slow process of recognition. Since the number of patients is greater than that of radiologists, there is the possibility of human error, which can cause serious damage. The detection of tumors from magnetic resonance imaging (MRI) data is an important manual task, specifically in terms of the time that the radiologist performs. In this study, the researchers sought to study state‐of‐the‐art techniques to detect normal brain and brain tumors from MRI using machine learning techniques. The main objective of this study is to develop a novel automated technique for brain tumor detection. Through the worldwide consideration of practical literature, it is clear that traditional approaches are insufficient to resolve all uncertainties and problems. Therefore, a novel approach to examining MRI must be adapted. This study proposes two different novel techniques: one that uses ensemble classification and the other that makes use of the deep learning model of YOLOv3. In ensemble classification, two classification algorithms are used which are support vector machine (SVM) and K‐nearest neighbors (KNNs). The YOLOv3 model is used to detect and outline tumor locations in the images. This study used an open‐source dataset and data collected from hospitals in Lahore, Pakistan. The ensemble classifier achieved an overall accuracy of 80.50%, while the YOLOv3 model achieved higher performance with 97.80% accuracy, 97.40% precision, 98.18% recall, and a mean intersection over union (IoU) score of 0.65. These results confirm that YOLOv3 is a useful technique for identifying brain tumors.