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For achieving the profitable human activity recognition (HAR) method, this paper solves the HAR problem under wireless body area network (WBAN) using a developed ensemble learning approach. The purpose of this study is,to solve the HAR problem under WBAN using a developed ensemble learning approach for achieving the profitable HAR method. There are three data sets used for this HAR in WBAN, namely, human activity recognition using smartphones, wireless sensor data mining and Kaggle. The proposed model undergoes four phases, namely, “pre-processing, feature extraction, feature selection and classification.” Here, the data can be preprocessed by artifacts removal and median filtering techniques. Then, the features are extracted by techniques such as “t-Distributed Stochastic Neighbor Embedding”, “Short-time Fourier transform” and statistical approaches. The weighted optimal feature selection is considered as the next step for selecting the important features based on computing the data variance of each class. This new feature selection is achieved by the hybrid coyote Jaya optimization (HCJO). Finally, the meta-heuristic-based ensemble learning approach is used as a new recognition approach with three classifiers, namely, “support vector machine (SVM), deep neural network (DNN) and fuzzy classifiers.” Experimental analysis is performed.

The proposed HCJO algorithm was developed for optimizing the membership function of fuzzy, iteration limit of SVM and hidden neuron count of DNN for getting superior classified outcomes and to enhance the performance of ensemble classification.

The accuracy for enhanced HAR model was pretty high in comparison to conventional models, i.e. higher than 6.66% to fuzzy, 4.34% to DNN, 4.34% to SVM, 7.86% to ensemble and 6.66% to Improved Sealion optimization algorithm-Attention Pyramid-Convolutional Neural Network-AP-CNN, respectively.

The suggested HAR model with WBAN using HCJO algorithm is accurate and improves the effectiveness of the recognition.

In wireless sensor networks, improving the network lifetime is considered as the prime objective that needs to be significantly addressed during data aggregation. Among the traditional data aggregation techniques, cluster-based dominating set algorithms are identified as more effective in aggregating data through cluster heads. But, the existing cluster-based dominating set algorithms suffer from a major drawback of energy deficiency when a large number of communicating nodes need to collaborate for transferring the aggregated data. Further, due to this reason, the energy of each communicating node is gradually decreased and the network lifetime is also decreased. To increase the lifetime of the network, the proposed algorithm uses two sets: Dominating set and hit set.

The proposed algorithm uses two sets: Dominating set and hit set. The dominating set constructs an unequal clustering, and the hit set minimizes the number of communicating nodes by selecting the optimized cluster head for transferring the aggregated data to the base station. The simulation results also infer that the proposed optimized unequal clustering algorithm (OUCA) is greater in improving the network lifetime to a maximum amount of 22% than the existing cluster head selection approach considered for examination.

In this paper, lifetime of the network is prolonged by constructing an unequal cluster using the dominating set and electing an optimized cluster head using hit set. The dominator set chooses the dominator based on the remaining energy and its node degree of each node. The optimized cluster head is chosen by the hit set to minimize the number of communicating nodes in the network. The proposed algorithm effectively constructs the clusters with a minimum number of communicating nodes using the dominating and hit set. The simulation result confirms that the proposed algorithm prolonging the lifetime of the network efficiently when compared with the existing algorithms.

The proposed algorithm effectively constructs the clusters with a minimum number of communicating nodes using the dominating and hit sets. The simulation result confirms that the proposed algorithm is prolonging the lifetime of the network efficiently when compared with the existing algorithms.

Whereas a human operator is hard to observe the networking infrastructure and its functions on a continuous basis, wireless sensor network (WSN) nodes must overcome faults and route the perceived data to the sink/base stations (BS). The main target of this research article is to ensure the fault-tolerance (FT) capability, especially for transmission of sensed data to its destination without failure. Thus, through this paper, a fuzzy-based subordinate support (FSS) system is introduced as an additional feature to the existing optimized mobile sink improved energy efficient Power-Efficient Gathering in Sensor Information Systems (PEGASIS)-based (OMIEEPB) routing protocol, which lacks focus on ensuring the FT capabilities to the selected leaders of the corresponding chain. The central focus of FSS is to prevent the incident of fault, especially to the cluster heads.

WSNs encounter several issues owing to random events or different causes such as energy exhaustion, negative influences of the deployed region, signal interference, unbalanced supply routes, instability of motes due to misalignments and collision, which ultimately intends the failure of the network. Throughout the past investigation periods, researchers gain an understanding of fault-tolerant strategies that may improve the data integrity or reliability, precision, energy efficiency, the life expectancy of the system and reduce/prevent the failure of deployed components. If that is the case, the maximum chances of data packets (sensed) needed to be transferred reliably and accurately to the sink node or BS are degraded.

The FSS system utilizes the fuzzy logic concepts that have been proved to be beneficial since it permits several parameters to be combined effectively and evaluated. Here, near-point, residual energy, total operation time and past average processing time are considered as vital parameters. Moreover, the FSS system ensures the key performance activities of the network, such as optimization of response time, enhancing the data transmission reliability and accuracy. Simulation-based experiments are carried out through the Mannasim framework. After several experimental executions, the outcome of the proposed system is analyzed through elaborated comparison with the advanced existing systems.

Finally, it has been observed that the FSS system not only enhanced the FT features to OMIEEPB but also assures the improved accuracy level (>95%) with optimized response time (<0.09 s) during data communication between leaders and the normal nodes.

Despite numerous positive aspects of digital contact tracing, the implied nature of contact tracing is still viewed with skepticism. Those in favor of contact tracing often undermine various risks involved with it, while those against it often undermine its positive benefits. However, unless the government and the app makers can convince a significant section of the population to use digital contact apps, desired results cannot be achieved. This study aims to focus on analyzing the perception of citizens belonging to developing countries about digital contact tracing.

For this study, data were collected from Twitter. Tweets containing hashtag and the word “contact tracing” were crawled using Python library Tweepy. Tweets across the top five developing countries (India, Brazil, South Africa, Argentina and Columbia) with high COVID-19 cases were collected for this study. After eliminating tweets of other languages, we selected 50,000 unique English tweets for this study. Using the machine learning algorithm, we have detected the sentiment of all the tweets belonging to each country. Structural topic modeling was performed for the tweets to understand the concerns shared by citizens of the developing countries about digital contact tracing.

The study was conducted in two parts. Study 1 results show that Indians and Brazilians citizens record more negative sentiments toward “digital contact tracing” than other major developing countries. Surprisingly, the citizens of India and Brazil also records more positive sentiments about contact tracing. This shows the polarized nature of the population of both countries while dealing with digital contact tracing. Overall, only 33.3% of total tweets were positively related to contact tracing, while 53.7% of the total tweets were neutral. Study 2 results show that factors such as the reliability of the contact tracing apps, contact tracing may lead to unnecessary panic, invasion of privacy and data misuse as the prominent reasons why the citizens of the five countries feel pessimistic about contact tracing.

After the COVID-19 strikes, numerous studies were conducted to analyze and suggest the best possible way of implementing digital contact tracing to curb COVID. However, only a handful of studies were conducted examining how the general public perceives the concept of digital contact tracing, especially pertaining to developing countries. This study fills that gap.

The construction industries at present are focusing on designing sustainable concrete with less carbon footprint. Considering this aspect, a Fibre-Reinforced Geopolymer Concrete (FGC) was developed with 8 and 10 molarities (M). At elevated temperatures, concrete experiences deterioration of its mechanical properties which is in some cases associated with spalling, leading to the building collapse.

In this study, six geopolymer-based mix proportions are prepared with crimped steel fibre (SF), polypropylene fibre (PF), basalt fibre (BF), a hybrid mixture consisting of (SF + PF), a hybrid mixture with (SF + BF), and a reference specimen (without fibres). After temperature exposure, ultrasonic pulse velocity, physical characteristics of damaged concrete, loss of compressive strength (CS), split tensile strength (TS), and flexural strength (FS) of concrete are assessed. A polynomial relationship is developed between residual strength properties of concrete, and it showed a good agreement.

The test results concluded that concrete with BF showed a lower loss in CS after 925 °C (i.e. 60 min of heating) temperature exposure. In the case of TS, and FS, the concrete with SF had lesser loss in strength. After 986 °C and 1029 °C exposure, concrete with the hybrid combination (SF + BF) showed lower strength deterioration in CS, TS, and FS as compared to concrete with PF and SF + PF. The rate of reduction in strength is similar to that of GC-BF in CS, GC-SF in TS and FS.

Performance evaluation under fire exposure is necessary for FGC. In this study, we provided the mechanical behaviour and physical properties of SF, PF, and BF-based geopolymer concrete exposed to high temperatures, which were evaluated according to ISO standards. In addition, micro-structural behaviour and linear polynomials are observed.

This study aims to characterize the properties of natural cellulose fiber from the pseudo-stems of the curcuma zedoaria plant.

The fiber was extracted using the biological retting process (cold-water retting). The intrinsic fiber properties obtained were used to evaluate the possibility of using fiber for textile applications.

The average length of a curcuma zedoaria fiber was 34.77 cm with a fineness value of 6.72 Tex. A bundle of curcuma zedoaria fibers was comprised of many elementary fibers. Curcuma zedoaria had an irregular cross-section, with the lumen having a varied oval shape. Curcuma zedoaria fibers had tenacity and elongation value of 3.32 gf/denier and 6.95%, respectively. Curcuma zedoaria fibers had a coefficient of friction value of 0.46. Curcuma zedoaria fibers belong to a hygroscopic fiber type with a moisture regain value of 10.29%.

Extraction and Characterization of Curcuma zedoaria Pseudo-stems Fibers for Textile Application.