Purpose: With the development of emerging engineering technology and industrialization, there are greater changes in the life style of people in smart urban cities; therefore, there is also more chance of various health problems in urban areas. The life style of persons in metro urban areas with the expansive volume of population is similarly influenced by different application and administration frameworks. These are affecting the human health system up to an extended extent and there are more health-related issues and health hazard concerns that can be identified in urban areas. The purpose of this paper is to present an analytical study on various aspects of the smart health care system in a smart perspective by analyzing them with respect to emerging engineering technologies such as mobile network, cloud computing, Internet of Things (IoT), big data analytics and ubiquitous computing. This paper also carries out a detailed survey of health issues and improved solutions in automated systems using these technologies. Second, the paper also presents a novel health care system using smart and safe ambulances and their appropriate control at traffic points with safety and security features in a smart city, so that the valuable life of patients can be saved in time by immediate treatment in nearest hospital or health care units. Design/methodology/approach: In this paper, an analytical survey was conducted for improvement in the health care sector using computer technology and IoT-based various modern health care applications. An idea of Smart Health Care Hospital using sensors, mobile agent smart vehicle configuration and safety traffic control for ambulance was proposed. Findings: A simulation was carried out to see the performance of a safety mechanism in the proposed approach. Comparative analysis was carried out with other approaches to know the execution time, response time and probable delay due to the implementation of this approach. Originality/value: It is an original research work with motivation inspired from current emergent technology to apply in the health care system.

Recently, it has proven difficult to make an immediate remote diagnosis of any coronary illness, including heart disease, diabetes, etc. The drawbacks of cloud computing infrastructures, such as excessive latency, bandwidth, energy consumption, security, and privacy concerns, have lately been addressed by Fog computing with IoT applications. In this study, an IoT-Fog-Cloud integrated system, called a Fog-empowered framework for real-time analysis in heart patients using ENsemble Deep learning (FRIEND), has been introduced that can instantaneously facilitate remote diagnosis of heart patients. The proposed system was trained on the combined dataset of Long-Beach, Cleveland, Switzerland, and Hungarian heart disease datasets. We first tested the model with eight basic ML approaches, including the decision tree, logistic regression, random forest, naive Bayes, k-nearest neighbors, support vector machine, AdaBoost, and XGBoost approaches, and then applied ensemble methods including bagging classifiers, weighted averaging, and soft and hard voting to achieve enhanced outcomes and a deep neural network, a deep learning approach, with the ensemble methods. These models were validated using 16 performance and 9 network parameters to justify this work. The accuracy, PPV, TPR, TNR, and F1 scores of the experiments reached 94.27%, 97.59%, 96.09%, 75.44%, and 96.83%, respectively, which were comparatively higher when the deep neural network was assembled with bagging and hard-voting classifiers. The user-friendliness and the inclusion of Fog computing principles, instantaneous remote cardiac patient diagnosis, low latency, and low energy consumption, etc., are advantages confirmed according to the achieved experimental results.

Wireless sensor networks (WSNs) have various applications which include zone surveillance, environmental monitoring, event tracking where the operation mode is long term. WSNs are characterized by low-powered and battery-operated sensor devices with a finite source of energy. Due to the dense deployment of these devices practically it is impossible to replace the batteries. The finite source of energy should be utilized in a meaningful way to maximize the overall network lifetime. In the space domain, there is a high correlation among sensor surveillance constituting the large volume of the sensor network topology. Each consecutive observation constitutes the temporal correlation depending on the physical phenomenon nature of the sensor nodes. These spatio-temporal correlations can be efficiently utilized in order to enhance the maximum savings in energy uses. In this paper, we have proposed a Spatial and Temporal Correlation-based Data Redundancy Reduction (STCDRR) protocol which eliminates redundancy at the source level and aggregator level. The estimated performance score of proposed algorithms is approximately 7.2 when the score of existing algorithms such as the KAB (K-means algorithm based on the ANOVA model and Bartlett test) and ED (Euclidian distance) are 5.2, 0.5, respectively. It reflects that the STCDRR protocol can achieve a higher data compression rate, lower false-negative rate, lower false-positive rate. These results are valid for numeric data collected from a real data set. This experiment does not consider non-numeric values.

Across all countries, both developing and developed, women face the greatest risk of breast cancer. Patients who have their breast cancer diagnosed and staged early have a better chance of receiving treatment before the disease spreads. The automatic analysis and classification of medical images are made possible by today’s technology, allowing for quicker and more accurate data processing. The Internet of Things (IoT) is now crucial for the early and remote diagnosis of chronic diseases. In this study, mammography images from the publicly available online repository The Cancer Imaging Archive (TCIA) were used to train a deep transfer learning (DTL) model for an autonomous breast cancer diagnostic system. The data were pre-processed before being fed into the model. A popular deep learning (DL) technique, i.e., convolutional neural networks (CNNs), was combined with transfer learning (TL) techniques such as ResNet50, InceptionV3, AlexNet, VGG16, and VGG19 to boost prediction accuracy along with a support vector machine (SVM) classifier. Extensive simulations were analyzed by employing a variety of performances and network metrics to demonstrate the viability of the proposed paradigm. Outperforming some current works based on mammogram images, the experimental accuracy, precision, sensitivity, specificity, and f1-scores reached 97.99%, 99.51%, 98.43%, 80.08%, and 98.97%, respectively, on the huge dataset of mammography images categorized as benign and malignant, respectively. Incorporating Fog computing technologies, this model safeguards the privacy and security of patient data, reduces the load on centralized servers, and increases the output.

Waste management is recognized as a crucial issue in modern civilization, requiring substantial effort and resources while significantly impacting various societal aspects. In sustainable cities striving to eliminate carbon emissions, implementing effective waste management strategies is prioritized. Tackling the three interconnected challenges in trash management, including preventing overflow, tracking bin locations, and designing efficient garbage collection routes, is complex. Current methods often provide incomplete solutions for all three aspects simultaneously. To overcome these difficulties, a smart waste management framework was proposed for environmentally friendly cities, combining the Internet of Things (IoT), long-range (LoRa) technology, and Deep Learning techniques. The proposed system utilized ultrasonic sensors equipped with a LoRa connection to facilitate the real-time monitoring of bin status. Prompt intervention to prevent overflow scenarios was facilitated. Integrating the Floyd-Warshall algorithm enhanced the garbage collection route efficiency by considering the bin fill levels and their exact locations. Deployment was made affordable and straightforward using inexpensive IoT components, including LoRa modules, facilitating smooth data transfer. In addition, incorporating RecycleCnn, implemented using Python with TensorFlow and Keras frameworks, enhanced the proposed framework by enabling automatic garbage classification with a 98% accuracy rate. This classification system facilitated the categorization of garbage into specific groups, improved recycling initiatives, and advocated for sustainable waste management methods. The proposed system used Arduino UNO microcontrollers, ultrasonic sensors, and LoRaWAN technologies to provide a precise and effective method for assessing garbage levels and controlling waste distribution. This holistic approach to intelligent waste management seeks to provide cleaner, pollution-free urban environments by addressing problems arising from ineffective garbage collection methods. The proposed framework addressed trash management and recycling challenges while laying the foundation for sustainable development projects in smart towns like Khandagiri and Pokhariput. Also, it provided a comprehensive approach to garbage collection, categorization, and management.

In high-dimensional data analysis, Feature Selection (FS) is one of the most fundamental issues in machine learning and requires the attention of researchers. These datasets are characterized by huge space due to a high number of features, out of which only a few are significant for analysis. Thus, significant feature extraction is crucial. There are various techniques available for feature selection; among them, the filter techniques are significant in this community, as they can be used with any type of learning algorithm and drastically lower the running time of optimization algorithms and improve the performance of the model. Furthermore, the application of a filter approach depends on the characteristics of the dataset as well as on the machine learning model. Thus, to avoid these issues in this research, a combination of feature reduction (CFR) is considered designing a pipeline of filter approaches for high-dimensional microarray data classification. Considering four filter approaches, sixteen combinations of pipelines are generated. The feature subset is reduced in different levels, and ultimately, the significant feature set is evaluated. The pipelined filter techniques are Correlation-Based Feature Selection (CBFS), Chi-Square Test (CST), Information Gain (InG), and Relief Feature Selection (RFS), and the classification techniques are Decision Tree (DT), Logistic Regression (LR), Random Forest (RF), and k-Nearest Neighbor (k-NN). The performance of CFR depends highly on the datasets as well as on the classifiers. Thereafter, the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) method is used for ranking all reduction combinations and evaluating the superior filter combination among all.