60 research outputs found
Reliable and Resilient Communication in Duty Cycled Software Defined Wireless Sensor Networks
No full textReliable and resilient network communication with flexible management is one of the significant issues in wireless sensor networks (WSNs). Due to the substantial packet loss, energy usage, and inadequate security of WSNs, the reliable data delivery is necessary when using multi-hop data communication. This paper employs the software-defined networking (SDN) concept to provide flexible and effective management with reliability in network communication. Therefore, it proposes a reliable and resilient communication in duty cycled software defined wireless sensor networks that addresses two parts. First, it considers the four attributes with their probability distributions to provide reliability and resilience in data plane communication. The attributes are direct trust, recommended trust, signal to interference noise ratio, and residual energy. Second, the SDN controller computes those attributes along with the Expected Duty Cycled Wake-ups (EDC) to make it more reliable and assigns communication strategies to each node through the reliable nodes. It also restricts the number of forwarding nodes for each node in order to minimize packet duplication. The simulation results indicate that, when compared to state-of-the-art protocols, the proposed protocol greatly enhances the reliability and resilience of the network
MOTiFS: Monte Carlo Tree Search Based Feature Selection
No full textGiven the increasing size and complexity of datasets needed to train machine learning algorithms, it is necessary to reduce the number of features required to achieve high classification accuracy. This paper presents a novel and efficient approach based on the Monte Carlo Tree Search (MCTS) to find the optimal feature subset through the feature space. The algorithm searches for the best feature subset by combining the benefits of tree search with random sampling. Starting from an empty node, the tree is incrementally built by adding nodes representing the inclusion or exclusion of the features in the feature space. Every iteration leads to a feature subset following the tree and default policies. The accuracy of the classifier on the feature subset is used as the reward and propagated backwards to update the tree. Finally, the subset with the highest reward is chosen as the best feature subset. The efficiency and effectiveness of the proposed method is validated by experimenting on many benchmark datasets. The results are also compared with significant methods in the literature, which demonstrates the superiority of the proposed method
Deep Learning for Molecular Thermodynamics
No full textThe methods used in chemical engineering are strongly reliant on having a solid grasp of the thermodynamic features of complex systems. It is difficult to define the behavior of ions and molecules in complex systems and to make reliable predictions about the thermodynamic features of complex systems across a wide range. Deep learning (DL), which can provide explanations for intricate interactions that are beyond the scope of traditional mathematical functions, would appear to be an effective solution to this problem. In this brief Perspective, we provide an overview of DL and review several of its possible applications within the realm of chemical engineering. DL approaches to anticipate the molecular thermodynamic characteristics of a broad range of systems based on the data that are already available are also described, with numerous cases serving as illustrations
Blockchain and Internet of Things in smart cities and drug supply management: Open issues, opportunities, and future directions
No full textBlockchain-based drug supply management (DSM) requires powerful security and privacy procedures for high-level authentication, interoperability, and medical record sharing. Researchers have shown a surprising interest in Internet of Things (IoT)-based smart cities in recent years. By providing a variety of intelligent applications, such as intelligent transportation, industry 4.0, and smart financing, smart cities (SC) can improve the quality of life for their residents. Blockchain technology (BCT) can allow SC to offer a higher standard of security by keeping track of transactions in an immutable, secure, decentralized, and transparent distributed ledger. The goal of this study is to systematically explore the current state of research surrounding cutting-edge technologies, particularly the deployment of BCT and the IoT in DSM and SC. In this study, the defined keywords “blockchain”, “IoT”, drug supply management”, “healthcare”, and “smart cities” as well as their variations were used to conduct a systematic search of all relevant research articles that were collected from several databases such as Science Direct, JStor, Taylor & Francis, Sage, Emerald insight, IEEE, INFORMS, MDPI, ACM, Web of Science, and Google Scholar. The final collection of papers on the use of BCT and IoT in DSM and SC is organized into three categories. The first category contains articles about the development and design of DSM and SC applications that incorporate BCT and IoT, such as new architecture, system designs, frameworks, models, and algorithms. Studies that investigated the use of BCT and IoT in the DSM and SC make up the second category of research. The third category is comprised of review articles regarding the incorporation of BCT and IoT into DSM and SC-based applications. Furthermore, this paper identifies various motives for using BCT and IoT in DSM and SC, as well as open problems and makes recommendations. The current study contributes to the existing body of knowledge by offering a complete review of potential alternatives and finding areas where further research is needed. As a consequence of this, researchers are presented with intriguing potential to further create decentralized DSM and SC apps as a result of a comprehensive discussion of the relevance of BCT and its implementation.© 2023 The Author(s). Published by Elsevier B.V. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).fi=vertaisarvioitu|en=peerReviewed
Supervised Learning Based Classification of Cardiovascular Diseases
No full textDetecting cardiovascular disease (CVD) in the early stage is a difficult and crucial process. The objective of this study is to test the capability of machine learning (ML) methods for accurately diagnosing the CVD outcomes. For this study, the efficiency and effectiveness of four well renowned ML classifiers, i.e., support vector machine (SVM), logistics regression (LR), naive Bayes (NB), and decision tree (J48), are measured in terms of precision, sensitivity, specificity, accuracy, Matthews correlation coefficient (MCC), correctly and incorrectly classified instances, and model building time. These ML classifiers are applied on publically available CVD dataset. In accordance with the measured result, J48 performs better than its competitor classifiers, providing significant assistance to the cardiologists
Do Governance, Foreign Direct Investment and Human Capital Matter to Bolster Trade Liberalization? Fresh Insight from Developing Countries
No full textThis study aims to explore the impact of governance, foreign direct investment and human capital on trade liberalization in developing countries (lower income, middle income and upper middle income). The study employed fixed effect for the period of 2000 to 2019. Results show governance, foreign direct investment and human capital are highly significant with trade liberalization in the case of lower-income countries. In the case of middle-income countries, empirical findings demonstrate governance and foreign direct investment are highly significant with a negative sign, while human capital has positive on trade liberalization. In the case of upper-middle-income countries, results show human capital and foreign direct investment affect positively, while governance has a negative effect on trade liberalization. On the behalf of results it is suggested that in the countries where human capital is high, most of the inflows of foreign direct investment happen. It means that the government can develop human resources to attract more foreign direct investments. The governments of developing countries should also concentrate on education, including training facilities and other quality educational facilities for human skill development
Impact of Monetary Policy on Inflation and Investment in Pakistan: A Time Series Analysis
No full textPurpose: This study aims to explore the impact of monetary policy on inflation and investment in Pakistan.
Methodology: Our study employs the Autoregressive distributed lag model (ARDL) over the time of 1972 to 2019.
Findings: The empirical findings show that in the long-run impact of money supply has significant and positive on investment and other variables trade, foreign direct investment, gross domestic saving, services are also positively associated with the investment. While other variables interest rate and exchange rate negatively linked with investment. Empirical findings of the second econometric model show the core variable money supply has a significant and positive on inflation including other variables foreign direct investment, exchange rate, exports and government expenditures on education but other variables interest rate, gross domestic saving and agriculture output negatively linked with inflation.
Implications: The study indicates that a stable monetary policy should be introduced to improve a country's economic development. Monetary policy should be used to build an agreeable environment of uncertainty that draws both domestic and outside investors to promote economic growth. Economic growth can be accomplished by encouraging efficient monetary policy steps for inflation stability and attractive interest rates
Comparison of Effectiveness of Topical Steroid Versus Systemic Steroid in Nasal Polypi
No full textBackground : To compare the effectiveness of topical steroid and oral steroid in reducing size of polyps and improving patency of nasal air way.Methods: In this randomized controlled trial a total of 140 patients were taken with bilateral nasal polypi. They were divided in 2 groups. Group A (70 ) were given topical steroids and Group B (70) were given oral steroids. Patients aged above 15 years of both genders with bilateral nasal polypi of stage 2 and 3 were included. Anterior rhinoscopy was done with the help of head-light. Patency was noted of both nostrils using nasal speculum. Endoscopic staging was done at the same time. Size was assessed with rigid endoscope as it can diagnose small polyps in the middle meatus. Both groups were examined again in follow up by anterior rhinoscopy and then by endoscope to see the stage of polyps. Chi square test were applied for comparison of efficacy. p value ≤0.05 was considered significant.Results: Mean+SD of age (yrs) of patient in Group A and Group B were 43.71+10.756 and 42.77+12.238 respectively. Chi-square test was applied to compare the efficacy (right nostril and left nostril) in both the groups which was statistically significant (p-value 0.009 and 0.005) which showed that topical steroid was more effective than systemic steroid in treating nasal polyps.Conclusion: Topical steroid is significantly effective than systemic steroid in treating nasal polyps
Esophageal Atresia: Management and Outcome in Resource Limited Settings
No full textBackground: Esophageal Atresia (EA) with or without associated tracheo-esophageal fistula (TEF) is one of the common congenital anomaly that can be life threatening if left unattended. In low and middle income countries like Pakistan, the management and outcome of such type of cases depends upon many factors related to resource limitation.
Objective: To prospectively evaluate the management and outcome of esophageal atresia at Children Hospital, Pakistan Institute of Medical Sciences (PIMS), Islamabad, Pakistan.
Study design, settings and duration: An Observational- descriptive study was conducted at the Department of Pediatric Surgery, The Children Hospital, PIMS, Islamabad from October 2017 to August 2018.
Methodology: Consecutive patients diagnosed with esophageal atresia were included in the study. Demographic data, investigations, procedure performed and outcome were collected on a pre designed proforma and results were analysed.
Results: Total 140 consecutive patients of esophageal atresia were enrolled in study. Out of 140 patients, 79 (56.4%) were male and 61 (43.6%) were female. Mean age at presentation of esophageal atresia was 5.5 days (ranged from 1-30 days). Mean weight was 2.43 kg. Regarding type of esophageal atresia, 10 (7.1%) patients had type A, 97 (69.3%) had type C, 1 (0.7%) had type E and 1 (0.7%) had type F esophageal atresia. Nine patients with type A underwent cervical esophagostomy along with feeding gastrostomy. Right thoracotomy was performed in 98 cases. End to end esophageal anastomosis was possible in 76 patients. Twenty one patients had long gap EA for which cervical esophagostomy and feeding gastrostomy was done. Sepsis was the main complication post operatively (29.3%) followed by pneumonia (14.3%), Anastomotic leak (7.9%) and surgical site-infection (2.1%). Overall mortality was 57.9% (81/140) with pre operative mortality of 21.4% (30/140) and post operative mortality of 36.5% (51/140). Low birth weight and post operative sepsis, anastomotic leak and pneumonia had statistically significant relationship with mortality using SPSS version 21.
Conclusion: With the improvement of medical facilities, better survival rates of patients with esophageal atresia can be achieved
Monitoring the impact of COVID-19 lockdown on the production of nitrogen dioxide (NO2) pollutants using satellite imagery : a case study of South Asia
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