High glucose levels during pregnancy cause Gestational Diabetes Mellitus (GDM). The risks include cesarean deliveries, long-term type 2 diabetes, fetal macrosomia, and infant respiratory distress syndrome. These risks highlight the need for accurate GDM prediction. This research proposes a novel fusion model for early GDM prediction. It uses conventional Machine Learning (ML) and advanced Deep Learning (DL) algorithms. Subsequently, it combines the strengths of both ML and DL algorithms using various ensemble techniques. It incorporates a meta-classifier that further reinforces its robust prediction performance. The dataset is split into training and testing sets in a 70/30 ratio. The initial steps involve exploratory analysis and data preprocessing techniques such as iterative imputation and feature engineering. Subsequently, oversampling is applied to the training set to address class imbalance which ensures the model learns effectively. The testing set remains imbalanced to maintain the credibility of the model’s performance evaluation. The fusion model achieves an accuracy of 98.21%, precision of 97.72%, specificity of 98.64%, recall of 97.47%, F1 score of 97.59%, and an Accuracy Under the Curve (AUC) of 99.91%. The model exhibits efficiency with an average processing time of 0.06 s to predict GDM. These results outperform the previous studies using the same GDM prediction dataset and demonstrate the model's superior performance. Additionally, Explainable Artificial Intelligence (XAI) techniques are utilized to interpret the model’s decisions. They highlight the most influential features in GDM prediction and ensures transparency. The proposed fusion model can facilitate proactive GDM prediction to elevate GDM management and maternal–fetal health outcomes.

In the era of web 2.0, online forums, blogs and Twitter are becoming primary sources for sharing views, opinions and comments about different topics. Classifying these views, opinions and comments is known as sentiment analysis which is an active research area. Sentiment analysis has vast applications in different fields of life, such as marketing, e-commerce and business. Under the umbrella of sentiment analysis, sentiment quantification that deals with estimating relative frequency of class of interest is being investigated by researchers nowadays. In sentiment quantification, exploring effect of new features and comparison of diverse types of classifiers to assess their effectiveness needs further investigation. In this paper, we explore diverse feature sets and classifiers for sentiment quantification. In addition, empirical performance analysis of conventional machine learning techniques, ensemble-based methods and state-of-the-art deep learning algorithms on basis of features set, is performed. The computed results show that the diverse features sets affect the performance of classifiers in sentiment quantification. The results also confirm that the deep learning techniques perform better than the conventional machine learning algorithms.

The recent surge in Internet of Things (IoT) applications and smart devices has led to a substantial rise in the data generation. One of the major issues involved is to meet strict quality of service (QoS) requirements for computing these applications in terms of execution time, cost and in an energy-efficient manner. To extract useful information, fast processing and analysis of data is needed. Consequently, moving all the data to centralized cloud data centers would lead to high processing times, increased cost and energy consumption and more bandwidth usage; thus, processing of applications with strict latency requirements becomes challenging. The addition of fog layer between cloud and IoT devices has provided promising solutions to such issues. However, efficient employment of computing resources in the hybrid infrastructure of fog and cloud nodes is of great significance and demands an optimal scheduling strategy. Toward this direction, a novel Pareto-based algorithm in fog computing, namely energy-efficient time and cost (ETC) constraint scheduling algorithm, is introduced in this paper for scheduling workflow applications. ETC attempts to optimize monetary cost along with time and energy objectives. Improved multi-objective differential evolution (I-MODE) meta-heuristic is introduced and incorporated with deadline-aware stepwise frequency scaling approach that is based on our previously proposed energy makespan multi-objective optimization (EM-MOO) algorithm. Synthetic and real-world application workflows are used to conduct evaluation of the proposed work with existing well-known algorithms from the literature. The experimental results for synthetic workflows reveal that the proposed algorithm lessens energy utilization by 14–21%, execution time by almost 25% and cost consumption by 22–27%, while for real-world application workflows, energy consumption is reduced by 12–24%, execution time by 14–16% and cost consumption by 23–29%.

Fetal movements are spontaneous actions performed by the fetus in the womb. Monitoring fetal movements is vital for assessing fetal health and detecting potential complications during pregnancy. Unmonitored fetal activity can indicate potential adverse effects such as fetal distress, restricted growth, or placental insufficiency. Regular monitoring helps detect issues early and ensures fetal health. Fetal movements involve monitoring the frequency and force with which the baby kicks as well as the location of the kicks in the abdominal area. Monitoring of these parameters for a longer period gives details about fetal activity and its health. This paper presents a novel fetus monitoring belt system designed to measure and analyze fetal kicks, its’ intensity, frequency, and the position where it occurred in abdomen. The system utilizes high accuracy thin film pressure sensors strategically placed within a flexible and breathable belt to cover the entire abdominal area. The sensors operate by detecting changes in resistance caused by applied force due to fetus kicks and translate these changes into measurable electrical signals. The belt system integrates with a mobile application to offer real-time data visualization and analysis. Additionally, it tracks the temperature and position of the expecting mother using integrated sensors. This innovative system enables home-based monitoring and reduces the need for frequent visits to healthcare facilities and enhances maternal convenience. The belt’s high sensitivity is capable of detecting forces ranging from 1.47 to 98.1 newtons. The belt is flexible and comfortable to wear. The fetus monitoring belt system can store and track data over time, which makes it a practical tool for continuous as well as remote monitoring. The fetus monitoring belt system offers convenience, accuracy, and comprehensive monitoring capabilities. The performance of the belt is evaluated by perceived fetus kicks and movements by volunteers and the comparison results proved significant accuracy of proposed belt system.

Sentiment analysis has been one of the most active research areas in the past decade due to its vast applications. Sentiment quantification, a new research problem in this field, extends sentiment analysis from individual documents to an aggregated collection of documents. Sentiment analysis has been widely researched, but sentiment quantification has drawn less attention despite offering a greater potential to enhance current business intelligence systems. In this research, to perform sentiment quantification, a framework based on feature engineering is proposed to exploit diverse feature sets such as sentiment, content, and part of speech, as well as deep features including word2vec and GloVe. Different machine learning algorithms, including conventional, ensemble learners, and deep learning approaches, have been investigated on standard datasets of SemEval2016, SemEval2017, STS-Gold, and Sanders. The empirical-based results reveal the effectiveness of the proposed feature sets in the process of sentiment quantification when applied to machine learning algorithms. The results also reveal that the ensemble-based algorithm AdaBoost outperforms other conventional machine learning algorithms using a combination of proposed feature sets. The deep learning algorithm RNN, on the other hand, shows optimal results using word embedding-based features. This research has the potential to help diverse applications of sentiment quantification, including polling, trend analysis, automatic summarization, and rumor or fake news detection.

The integration of the Internet of Vehicles (IoV) with the Intelligent Transportation System (ITS) plays a significant role in ensuring safe and efficient traffic management. However, communication signals may encounter natural or artificial obstacles that result in uncovered road segments, where connectivity is lost. Even minor collisions in these unprotected areas can escalate into major incidents or traffic bottlenecks, especially when vehicles are traveling at high speeds on highways. Therefore, it is crucial to promptly inform the ITS of any adverse events occurring in these unnoticed areas. The importance of Store-Carry-Forward (SCF) techniques increases in such scenarios when uncovered patches exist and intervehicle distances exceed the communication range. Store-Carry-Forward (SCF) is a technique where vehicles temporarily store data and forward it when a connection is available. To address improved coverage and reduced latency using SCF, we propose a beacon-based Store-Carry-Forward Scheme to Deliver Emergency Messages via vehicle-to-vehicle (V2V) Communication for Highways (SCF-EMD). The proposed method aims to minimize congestion and delays in disseminating information about adverse events. It involves selecting high-mobility vehicles to travel across both covered and uncovered highway segments. The scheme integrates V2V and vehicle-to-infrastructure (V2I) communication to ensure rapid delivery of emergency alerts to ITS. SCF-EMD demonstrated significant improvements over existing schemes in Network Simulator $$(NS-3)$$ , achieving over $$10\%$$ , $$16\%$$ and $$5\%$$ gains in information coverage, end-to-end delay, and packet delivery ratio (PDR), respectively.