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Abstract Polypharmacy involves an individual using many medications at the same time and is a frequent healthcare technique used to treat complex medical disorders. Nevertheless, it also presents substantial risks of negative medication responses and interactions. Identifying and addressing adverse effects caused by polypharmacy is crucial to ensure patient safety and improve healthcare results. This paper introduces a new method using Graph Convolutional Networks (GCN) to identify polypharmacy side effects. Our strategy involves developing a medicine interaction graph in which edges signify drug-drug intuitive predicated on pharmacological properties and hubs symbolize drugs. GCN is a well-suited profound learning procedure for graph-based representations of social information. It can be used to anticipate the probability of medicate unfavorable impacts and to memorize important representations of sedate intuitive. Tests were conducted on a huge dataset of patients’ pharmaceutical records commented on with watched medicate unfavorable impacts in arrange to approve our strategy. Execution of the GCN show, which was prepared on a subset of this dataset, was evaluated through a disarray framework. The perplexity network shows the precision with which the show categories occasions. Our discoveries demonstrate empowering advance within the recognizable proof of antagonistic responses related with polypharmaceuticals. For cardiovascular system target drugs, GCN technique achieved an accuracy of 94.12%, precision of 86.56%, F1-Score of 88.56%, AUC of 89.74% and recall of 87.92%. For respiratory system target drugs, GCN technique achieved an accuracy of 93.38%, precision of 85.64%, F1-Score of 89.79%, AUC of 91.85% and recall of 86.35%. And for nervous system target drugs, GCN technique achieved an accuracy of 95.27%, precision of 88.36%, F1-Score of 86.49%, AUC of 88.83% and recall of 84.73%. This research provides a significant contribution to pharmacovigilance by proposing a data-driven method to detect and reduce polypharmacy side effects, thereby increasing patient safety and healthcare decision-making.

In the present era of smart grids, accurate prediction of energy uses is becoming increasingly essential to guarantee optimal energy efficiency. This study contributes to the field by utilizing advanced machine learning techniques to perform predictions of energy consumption using the data from Internet of Things (IoT) devices. Specifically, the approach utilizes regression neural network (RNN) structures, such as long short-term memory (LSTM) and gated recurrent units (GRUs). The data from IoT sensors are more extensive and detailed than those of conventional smart meters, allowing for the development of more complex models of energy use patterns. This study utilizes Adam-optimized LSTM, RNN, and GRU models, along with stochastic gradient descent, to evaluate their performance in addressing the complexity of time-series data in energy forecasting on different network configurations. Result of the analysis indicates that LSTM models, which are run with the Adam optimizer, are more accurate in terms of predictions compared with the other models. This conclusion is supported by the test results of these models that are within the lowest root mean square error and mean absolute error scores. All the models under the analysis exhibit signs of overfitting based on the performance indicators for the training and the testing data. This notion implies that regularization should be utilized to ensure the improved generalizability of the models. These findings show that deep learning can have a lasting influence in improving energy consumption management systems to meet sustainability and energy efficiency requirements. These observations are beneficial for the gradual improvements of smart grids.

The rapid emergence and growth in the number of digital communication platforms have resulted in a previously unimaginable volume of text data suitable for sentiment analysis. Public sentiment extraction and interpretation definitive in various areas of marketing, politics, and customer service sectors. Existing approaches prove inadequate to address language complexity and subtlety. Consequently, more advanced analytical tools are required. Therefore, this study employs a BERT model in performing sentiment analysis on textual data associated with ChatGPT, an AI conversational tool OpenAI develops. The datasets utilized in this research are three, which are Data Collection, ChatGPT Sentiment Analysis Dataset, and ChatGPT App Reviews Dataset. They range from topics such as tweets to comprehensive reviews and thus provide diverse backgrounds for testing the model’s applicability. The BERT model is chosen for its strong processing abilities, especially due to its capacity to understand text nuances. It applies sentiment analysis in six training epochs, and the results indicated a model competent in accurate sentiment classification, irrespective of language and sentiment uniqueness. The overall performance was ousttanding, as evidenced by the model’s accuracy and its classification precision in both datasets. These findings have the potential to make the BERT model dominant in all sentiment analysis tasks. The analysis would prove instrumental in providing in-depth public opinion and sentiments made possible by the model’s credible results. In this stutdy, it is clear that BERT models can significantly improve machine learning towards better understanding and interacting in human language. These results also provide a basis for further research into these modalities across real-time applications or further modification that would develop the model to be exposed to different kinds of data.

A metaheuristic approach based on the nature-inspired and well-known Grey Wolf Optimization algorithm (GWO) was employed in this study to design an approach for retrieving strong designs of $8\times 8$ substitution boxes (S-boxes). The GWO was developed as a novel metaheuristic based on inspiration from grey wolves and how they hunt. The ability of the GWO to quickly explore the search space for the near/optimal feature subsets that maximize any given fitness function (in consideration of its distinctive hierarchical structure) aids in the construction of strong S-boxes that can satisfy the required criteria. However, when tackling optimization problems, GWO may experience the problem of premature convergence. Therefore, a variant of GWO called Crossover Grey Wolf Optimizer (XGWO) has been proposed in this study. The performance of the proposed novel approach was evaluated using numerous cryptographic performance metrics, including bijective property, bit independence, strict avalanche, linear probability, and I/O XOR distribution and the result was contrasted with a couple of existing S-box creation techniques. Overall, the results of the experiment showed that the suggested S-box design had adequate cryptographic features.

Except for critical basic implementations seen in historical records, the sheer quantity of similar frameworks, platforms, and apps available may be deceptive. Striking a balance between 3D certified displays and incorporated complicated features continues to be difficult. Geometrically precise point clouds and meshes are the result of cutting-edge methodology (computer vision, reverse engineering, and digital photogrammetry), cutting-edge technology (LiDAR, laser scanners, and unmanned aerial vehicles), and random research approaches. They are a by-product of the procedure. As the precision, size, and complexity of these things rise, the capabilities of present technology to manage them deteriorate. In this paper we propose a method for Development of a low-cost 3d visualization platform, capable of interactively perform tasks. Moreover, we seek to verify the methodological aspect of implementing steps and simplified algorithms in a virtual reality.

To reduce frequent sensor recharging and replacement due to resource constraint, it becomes imperative to increase the management of energy and network’s quality of service. To this end, this article provides a new wireless body sensor network architecture with two sink nodes and multiple energy management and quality-of-service algorithms. The first algorithm is the normal data avoidance algorithm that is responsible for decreasing the energy usage by avoiding the transmission of normal data. Duplicate data avoidance algorithm avoid transmitting duplicate data thus saving the bandwidth and battery life. Past knowledge-based weighted routing algorithm oversees taking the ideal direction to transmit information, hence improving quality of service. Furthermore, sleep scheduling is integrated to further improve the battery life. In addition, in our proposed model, linear programming which is based on mathematical models was used to model the network lifetime maximization and continuous data transmission minimization. Through simulation in Castalia-based OMNeT++ demonstrates that our proposed work outperforms the works of quasi-sleep-preempt-supported with regard to network lifetime and delay with 50% and 30% improvement, respectively, moreover, it improves the work of critical data with respect to packet drop and throughput with 30% and 75% improvement, respectively.

Abstract Wireless Body Area Network (WBAN) is an important element of future smart healthcare services in smart cities. For this reason, many research works have been undergone in WBAN. The significant research issue in the WBAN environment is Quality of Service (QoS) provisioning. In literature, some of the works have been focused on QoS aspect of WBAN. However, they have only marginal impact due to ineffective network design and the usage of a single sink node. Also, most of the research works have been tested within a single BAN and present intra‐BAN communication. In practical, an effective multi‐BAN communication system is emerging due to the need of large‐scale healthcare system. This paper addresses these issues and designs a novel QoS‐aware Clustered Triad Layer (QC‐TriL) architecture for multi‐BAN environment. The novel QC‐TriL uses dual sink nodes in each BAN and both sinks are deployed in the optimal positions. To determine the optimal position, Type‐II Fuzzy Logic (T2FL) is proposed with different criteria. Next, we form clusters based on the positions of dual sink nodes. QC‐TriL adopts criticality‐aware routing by presenting Delay‐aware One‐Hop Transmission and Fused Rank Scheme (FuRank) for critical data and normal data routing respectively. The data transmission is organized by a novel 1D‐Dragonfly topology‐based Priority Dual TDMA (PD‐TDMA) scheduling protocol. Finally, multi‐BAN communication is enabled with an optimal route selected by QoS‐aware Emperor Penguin Colony (QoS‐EPC) algorithm. The QC‐TriL multi‐BAN environment is modeled in OMNeT++ simulator and evaluated in terms of energy consumption, packet delivery ratio, throughput, and delay. The experiments show promising results for the multi‐BAN environment.

Diabetic retinopathy is one of the leading diseases affecting eyes. Lack of early detection and treatment can lead to total blindness of the diseased eyes. Recently, numerous researchers have attempted producing automatic diabetic retinopathy detection techniques to supplement diagnosis and early treatment of diabetic retinopathy symptoms. In this manuscript, a new approach has been proposed. The proposed approach utilizes the feature extracted from the fundus image using a local extrema information with quantized Haralick features. The quantized features encode not only the textural Haralick features but also exploit the multiresolution information of numerous symptoms in diabetic retinopathy. Long Short-Term Memory network together with local extrema pattern provides a probabilistic approach to analyze each segment of the image with higher precision which helps to suppress false positive occurrences. The proposed approach analyzes the retina vasculature and hard-exudate symptoms of diabetic retinopathy on two different public datasets. The experimental results evaluated using performance matrices such as specificity, accuracy, and sensitivity reveal promising indices. Similarly, comparison with the related state-of-the-art researches highlights the validity of the proposed method. The proposed approach performs better than most of the researches used for comparison.