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A pandemic disease, COVID-19, has caused trouble worldwide by infecting millions of people. The studies that apply artificial intelligence (AI) and machine learning (ML) methods for various purposes against the COVID-19 outbreak have increased because of their significant advantages. Although AI/ML applications provide satisfactory solutions to COVID-19 disease, these solutions can have a wide diversity. This increase in the number of AI/ML studies and diversity in solutions can confuse deciding which AI/ML technique is suitable for which COVID-19 purposes. Because there is no comprehensive review study, this study systematically analyzes and summarizes related studies. A research methodology has been proposed to conduct the systematic literature review for framing the research questions, searching criteria and relevant data extraction. Finally, 264 studies were taken into account after following inclusion and exclusion criteria. This research can be regarded as a key element for epidemic and transmission prediction, diagnosis and detection, and drug/vaccine development. Six research questions are explored with 50 AI/ML approaches in COVID-19, 8 AI/ML methods for patient outcome prediction, 14 AI/ML techniques in disease predictions, along with five AI/ML methods for risk assessment of COVID-19. It also covers AI/ML method in drug development, vaccines for COVID-19, models in COVID-19, datasets and their usage and dataset applications with AI/ML.

This book delves into the dynamic realm of ensemble methods, offering a comprehensive exploration of its evolution, methodologies, and diverse applications. Chapters are gathered from the collective wisdom of researchers, practitioners, and innovators who have pioneered this ever-evolving domain. This book serves as a compendium, bringing together theoretical foundations, cutting-edge advancements, and practical insights, catering to both seasoned experts and those venturing into the intricate world of ensemble learning. Each chapter encapsulates the essence of collaboration among diverse models, unveiling the intricacies of ensemble techniques, their fusion strategies, and their impact across industries. From boosting algorithms to bagging, stacking, and beyond, this book illuminates the nuances of ensemble learning, illustrating how these techniques amplify predictive accuracy, enhance generalization, and fortify models against the complexities of real-world data. The editors hope this book will serve as a guiding beacon for enthusiasts, researchers, and practitioners navigating the intricate landscape of ensemble machine learning, fostering innovation, and paving the way for future breakthroughs.

This edited book is a collection of quality research articles reporting research advances in the area of deep learning, IoT and urban computing. It describes new insights based on deep learning and IoT for urban computing and is useful for architects, engineers, policymakers, facility managers, academicians, and researchers who are interested in expanding their knowledge of the applications of deep learning trends involving urban computing.

Over the past decades, Saudi Arabia’s economic development has strongly depended on oil revenues fueled by the rise of oil prices and the strong global market demands for crude oils. However, the country can no longer depend on oil revenues in the face of the dynamic global market, and hence, the Saudi government’s Vision 2030 seeks to reduce this dependence and diversify the economy’s sources of income. Motivated by this, this study aims to investigate the impact of growth factors: financial innovation (FI), nonoil trade openness (TO), nonoil gross capital formation (GCF), and human capital (CH) development on the nonoil economic growth in Saudi Arabia. The goal of this investigation is to examine the dynamic symmetrical and nonsymmetrical impact of these growth factors on nonoil economic growth and policymaking in Saudi Arabia. To achieve this, this study utilizes the distributed lag symmetric and asymmetric (ARDL and NARD) approaches to assess the short- and long-term symmetric relationships among these growth variables with nonoil economic growth as well as the stationarity, cointegration, and directionality among variables with the theory of “ceteris paribus” in the error correction model (ECM), and Granger causality framework to analyze time-series data from 1980 to 2020. The findings of this study revealed that the FI, TO, GCF, and CH have an impact on the nonoil economic growth in the short and long terms. Additionally, in the long term, the NARDL technique showed that the positive adjustments of HC, FI, TO, and GCF boost the development, which have very significant effects on the nonoil GDP. They also indicate that negative movements have more influence than positive movements in FI. Meanwhile, mixed directional causation results were observed in the short-run analyses. Overall, the findings of this study provide significant insights, empirical recommendations, and implications for policymakers striving to achieve sustainable nonoil trade economic growth in Saudi Arabia and the region.

Energy consumption has become a common problem since days. Addressing the energy related problem is a challenging task. There are various strategies present to minimize this problem. One among them is using cloud computing infrastructure and VM setup. Virtual Machine consolidation is a viable solution to mitigate energy related issues of data centres. In recent times, we have seen various learning approaches which are used in managing the cloud data resources well. Among the approaches, Virtual Machine consolidation technique gives the viable solution for energy related issues by mitigating them. We have also delved with reinforcement learning algorithm to tackle the virtual machines. In this implementation we make use of different RL algorithms such as SARSA, Q-learning etc. and finds out the best suited algorithm. Furtherly, we will execute the model on using the algorithm chosen to build the model. The inputs we take are VM numbers, power utilization, scalability of VMs, CPU utilization time etc. and finds out what percentage of these values we are getting as an output which highlights the effectiveness of our approach, improvement in energy efficiency and service reduction etc.

Experiment was conducted to evaluate the effect of weaning age on growth performance, feed intake, feed efficiency (FE) and blood metabolites in Nili-Ravi male buffalo (Bubalus bubalis) calves. Twenty-four male buffalo calves were assigned to one of the three treatment groups: continuous milk feeding (CMF), limited milk feeding (LMF) and early weaning (EW), and weaned off milk at 12, 10 and 8 weeks of age, respectively. For the first 3 days after birth, calves in all three treatments were fed colostrum, and were then moved to individual milk feeding at 10% of BW for the next 6 weeks. Thereafter, the provision of milk to the CMF group was gradually tapered to zero through week 12, using week 6 intakes as a base. The LMF calves were fed milk at 7.5%, 5.0%, 3.5%, and 1.5% of BW during weeks 7 to 10, respectively. Lastly, calves in the EW group were fed milk at 5.0% and 2.5% of BW at weeks 7 and 8, respectively. Calf starter (CS) feed was also provided ad libitum from weeks 2 to 12 and individual intakes were recorded on a daily basis. Blood samples were taken from weeks 6 to 12, on a weekly basis; whereas, the BW, heart girth, withers height and hip width were measured at the start of experiment and later on a weekly basis. Weight gain, average daily gain, and body measurements were the same across all three groups. Milk intake was lower (P < 0.05), whereas CS intake was greater (P < 0.05) in the EW calves compared with the other treatment groups. Dry matter intake was greater (P < 0.05) in the EW and LMF calves compared with the CMF calves. The FE was greater (P < 0.05) in the CMF calves compared with the LMF and EW treatment groups. Blood glucose concentration was similar among the treatments; however, blood urea nitrogen was greater (P < 0.05) in the EW calves compared with the CMF and LMF groups. Plasma concentration of non-esterified fatty acids was higher (P < 0.05) in the EW calves compared with the CMF calves. In light of these results, it is evident that buffalo calves can be successfully weaned as early as 8 weeks of age without negatively affecting their growth performance.

This research was carried out in a greenhouse located south of Baghdad, Yusufiyah, Abu Halan area, in fall season at 20/10/2021, on Ruby gem strawberry cultivar. To find out the effect of spraying with growth regulator Benzyl adenine BA and seaweed extract on strawberry runners and some vegetative and fruiting characteristics. The study included implementation of a factorial experiment with two factors, first factor is spraying of growth regulator BA with three concentrations: 0, 50, 100 PPM as B0, B1, B2, respectively and second factor is seaweed extract spray with four concentrations: 0, 2, 4, 6 mg.L as A0, A1, A2, A3, respectively. treatments were distributed randomly in a randomized complete block design (RCBD) experiment. As experiment included 12 treatments with three replications and 12 plants for each experimental unit, thus the number of plants included in the experiment was 432 plants. The results showed that, seaweed extract spray at 6 mg/L.(A3) significantly increased in leaves number of 15.40 leaf.plant-1 and highest runner’s number of 4.77 runner.plant-1, highest leaf chlorophyll content of 0.825 %, highest leaf nitrogen content of 1.908 %, highest leaf phosphor content of 0.570 % and highest leaf potassium content of 1.87 %.

Attention deficit hyperactivity disorder (ADHD) is a widespread neuropsychiatric disorder in both children and adolescents, which is associated with social isolation and poor academic performance. Complement proteins are regarded as a major player in inflammation and disease development for several neuropsychiatric diseases such as schizophrenia and bipolar diseases. As clarified by previous data, increased levels of complement molecules and other immunological markers as cytokines were demonstrated in these disorders. Limited studies have investigated complement proteins particularly terminal complement complex or membrane attack complex (C5b-9) among ADHD patients. The present research aims to elucidate the association between C5b-9 complex protein and ADHD. This is a cross-sectional study. Sera were collected from Al-Hussain Teaching Medical City in Holy Karbala, Iraq, during 2019-2020. Sera were tested for C5-b9 using commercial kits by enzyme-linked immunosorbent assay (ELISA). In 90 participants included in the study, a significant increment in C5b-9 levels among ADHD patients (P=0.019) was observed. Patients with positive C5b-9 levels had a 2.76 times higher risk of developing ADHD than control subjects. The diagnostic utility for C5b-9 was statistically significant with 71.11% sensitivity, 55.6% specificity, and a high negative predictive value (97.3%). The study concluded elevation of the C5b-9 terminal complements complex levels in ADHD patients, which could point to the association of complement proteins as inflammatory markers with the ADHD disease process.