Research on the Effect of Online Marketing Based on Multimodel Fusion and Artificial Intelligence in the Context of Big Data

Traditional online marketing methods use a single model to predict the advertising conversion rate, but the prediction results are not accurate, and users are not satisfied with the recommendation results. Therefore, this paper proposes an online marketing method based on multimodel fusion and artificial intelligence algorithms under the background of big data. First, it introduces big data technology and analyzes the characteristics of network advertising marketing model (RTB). Second, combined with multitask learning and fusion technology to improve the single model in advertising conversion rate prediction effect, prediction results to further improve the accuracy of results. Then, tF-IDF technology in artificial intelligence algorithm is used to measure the importance of advertising words in online marketing and calculate the contribution degree. Finally, according to XGBoost technology, the multitask fusion model of online marketing effect is classified. Experiments are used to analyze the effect of online marketing. Experimental results show that the proposed method can improve the accuracy of advertising conversion rate prediction and online sales of goods.

First International Conference on Artificial Intelligence, Computational Electronics and Communication System (AICECS 2021)

Preface Welcome to AICECS 2021 The First International conference on Artificial Intelligence, Computational Electronics and Communication System (AICECS) was held in the serene premises of Manipal Institute of Technology, Manipal Academy of Higher Education (Institute of Eminence, declared by Government of India) Manipal in the coastal region of Mangalore. Due to the impact of COVID-19, we organized this conference in virtual mode. AICECS attracted people working in diverse fields: Artificial Intelligence, Computational Electronics and Communication System. The conference was designed to create a platform for researchers from academia and industry, practicing engineers, and students. AICECS 2021 invited full-length original research contributions from science, engineering professionals from industries, R&D organizations, academic institutions, government departments, and research scholars from across the world. The manuscripts were required to contribute original research ideas, developmental ideas, analysis, findings, results, etc. A series of keynote presentations and technical paper presentations were planned to foster vigorous exchange of research findings and ideas among the participants. Each submission has been carefully reviewed at least by a minimum of two reviewers, from a committee composed of 106 members from various institutes. The AICECS 2021 Conference received 149 submissions, from various institutes across the globe, 78 were selected for full presentation at the conference. Acceptance and publication were judged based on the relevance to the conference track, clarity of presentation, originality and accuracy of results and proposed solutions. We would like to thank all authors and conference committee members for their enthusiastic participation and contribution. List of Editers, Disclaimer, About the Institute, Committees, Few Snapshots of Inaugural Function of AICECS 2021 and Keynote Speakers are available in this pdf.

International acquisitions on efficiency and productivity of the Serbian cement industry

PurposeThis paper explores the impact of international acquisitions on the efficiency and productivity of the cement industry in an emerging economy.Design/methodology/approachThe data envelopment analysis (DEA) and Malmquist index (MI) are used to calculate the partial efficiency and productivity of individual inputs (materials, labour and fixed assets), as well as the total factor efficiency and productivity during the period 2000–2018. DEA and MI are combined with bootstrapping to perform succinct statistical inferences for determining the accuracy of results. In this paper we apply the input-oriented CCR DEA Window model. With respect to the level of analysis, data was collected from individual companies and then aggregated data at the industry level.FindingsThe research results show that international acquisitions positively affect efficiency of the cement industry in the long term. Efficiency of capital is lower in the short period after acquisitions. Additionally, international acquisitions positively affect partial productivity, as well as total factor productivity of the cement industry.Practical implicationsThe results of the study may be significant for managers and policy makers to design appropriate strategies for the improvement of the cement industry performance over time.Originality/valueResearch in emerging economies related to subject matter is limited, and this is one of the earliest research studies which explore change in efficiency and productivity at the level of Serbian cement industry.

The Research on Rejoining of the Oracle Bone Rubbings Based on Curve Matching

The rejoining of oracle bone rubbings is a fundamental topic for oracle research. However, it is a tough task to reassemble severely broken oracle bone rubbings because of detail loss in manual labeling, the great time consumption of rejoining, and the low accuracy of results. To overcome the challenges, we introduce a novel CFDA&CAP algorithm that consists of the Curve Fitting Degree Analysis (CFDA) algorithm and the Correlation Analysis of Pearson (CAP) algorithm. First, the orthogonalization system is constructed to extract local features based on the curve features analysis. Second, the global feature descriptor is depicted by using coordinate points sequences. Third, we screen candidate curves based on the features as well as the CFDA algorithm, so the search range of the candidates is narrowed down. Finally, image recommendation libraries for target curves are generated by adopting the CAP algorithm, and the rank for each target matching curve generates simultaneously for result evaluation. With experiments, the proposed method shows a good effect in rejoining oracle bone rubbings automatically: (1) it improves the average accuracy rate of curve matching up to 84%, and (2) for a low-resource task, the accuracy of our method has 25% higher accuracy than that of other methods.

Adoption of Blockchain Based Smart Application in Machine Learning

In recent years, the emergence of blockchain technology (BT) has become a novel, most disruptive, and trending technology. The redistributed database in BT emphasizes data security and privacy. Also, the consensus mechanism makes positive that data is secured and bonafide. Still, it raises new security issues like majority attacks and double-spending. To handle the said problems, data analytics is required on blockchain-based secure knowledge. Analytics on these data raises the importance of arising technology Machine Learning (ML). ml involves the rational quantity of data to create precise selections. data reliability and its sharing are terribly crucial in ml to enhance the accuracy of results. the combination of those two technologies (ML and BT) provide give highly precise results. in this paper, present gift a detailed study on ml adoption we BTbased present applications additional resilient against attacks. There area unit varied ancient ML techniques, for example, Support Vector Machines (SVM), clustering, bagging, and Deep Learning (DL) algorithms like Convolutional Neural Network (CNN) and Long STM (LSTM) are often used to analyze the attacks on a blockchain-based network. Further, we tend to embody however each the technologies are often applied in many sensible applications like unmanned Aerial Vehicle (UAV), sensible Grid (SG), healthcare, and sensible cities. Then, future analysis problems and challenges are explored. At last, a case study is presented with a conclusion. Keywords: Blockchain, machine learning, smart grid, data security and privacy, data analytics, smart applications.

Gender-Specific Outcomes after Percutaneous Left Atrial Appendage Closure - A Nationwide Readmission Database Analysis

Background: Thromboembolism-associated stroke is the most feared complication of Atrial fibrillation (AF). Percutaneous left atrial appendage closure (pLAAC) is indicated for stroke prevention in patients with AF who can’t tolerate long-term anticoagulation. We aim to study gender differences in peri-procedural and readmissions outcomes in pLAAC patients. Methods: Using the national readmission database from January 2016 to December 2018, AF patients undergoing the pLAAC procedure were identified. We used multivariate logistic regression analyses and time-to-event Cox regression analyses to conduct the study. Propensity matching with the Greedy method was done for the accuracy of results. Result: 28,819 patients were included in our study. Among them 11,946 (41.5%) were women and 16,873 (58.6%) were men. The mean age of overall population was 76.1 ± 8.5 years, with women ~ 1 year older than men. The overall rate of complications was higher in women (8.6% vs 6.6%, P<0.001), primarily driven by bleeding-related complications i.e., Major bleed (OR: 1.32 95% CI: 1.03-1.69, p=0.029), blood transfusion (OR: 1.45, 95% CI: 1.06-1.97, p=0.019) and cardiac tamponade (OR: 1.80, 95% CI: 1.13-2.89, p=0.014). Women had two times higher peri-procedural ischemic stroke. There was no difference in peri-procedural mortality. Women remained at 20% and 13% higher risk for readmission at 30 days and 6 months of discharge. Conclusion: Women had higher peri-procedural complications and were at higher risk of readmissions at 30 days and six months. However, there was no difference in mortality during the index hospitalization. Further studies are necessary to determine causality.

An Expert System for COVID-19 Infection Tracking in Lungs Using Image Processing and Deep Learning Techniques

The proposed method introduces algorithms for the preprocessing of normal, COVID-19, and pneumonia X-ray lung images which promote the accuracy of classification when compared with raw (unprocessed) X-ray lung images. Preprocessing of an image improves the quality of an image increasing the intersection over union scores in segmentation of lungs from the X-ray images. The authors have implemented an efficient preprocessing and classification technique for respiratory disease detection. In this proposed method, the histogram of oriented gradients (HOG) algorithm, Haar transform (Haar), and local binary pattern (LBP) algorithm were applied on lung X-ray images to extract the best features and segment the left lung and right lung. The segmentation of lungs from the X-ray can improve the accuracy of results in COVID-19 detection algorithms or any machine/deep learning techniques. The segmented lungs are validated over intersection over union scores to compare the algorithms. The preprocessed X-ray image results in better accuracy in classification for all three classes (normal/COVID-19/pneumonia) than unprocessed raw images. VGGNet, AlexNet, Resnet, and the proposed deep neural network were implemented for the classification of respiratory diseases. Among these architectures, the proposed deep neural network outperformed the other models with better classification accuracy.

Experimental and Numerical Study of Pressure Intensity in Detachable Joints of D Series Pumps

The object of study is a single-stage double-suction axially split volute casing centrifugal pump. Background. In the design process of pumps, the problems associated with ensuring the tightness of the axial joint of a pump casing being under the effect of mechanical and temperature loads, are being solved. During the study of axial joint tightness, numerical calculation methods are used to estimate the pressure intensity on the contacting surfaces. A detachable joint under the external load satisfies the criterions of the tightness if the pressure intensity on the sealing surfaces is higher than values of the specific pressure prescribed by regulations. However, inability to experimentally determine the pressure intensity on the contacting surfaces has so far prevented to assess the accuracy of results obtained by the numerical calculation methods. Objective. In order to verify the numerical results obtained by mathematical models, an experiment was carried out using a special Prescale film that registers the magnitude of the contact pressure on the joint of the specimen model of the flange fragment. Based on the experimental results, an analysis of the pressure intensity distribution was conducted. Methods. To conduct the experiment, there was developed a method for determining the pressure intensity on the contacting surfaces according to the proposed scheme of the specimen of the flange fragment. Results. A comparative analysis of the solving results obtained for the contact problem on the finite-element models of the flange fragment and the zone of the pump casing joint in the discharge chamber area showed a good coincidence of the results. Analysis of results obtained experimentally on the specimen of flange fragment and results of the numerical calculation on the flange fragment model also showed a good agreement. Conclusions. Therefore, results of the calculation of the pressure intensity in the detachable joints on mathematical models have been experimentally confirmed.

Applying Bayesian Belief Networks to Assess Alpine Grassland Degradation Risks: A Case Study in Northwest Sichuan, China

Grasslands are crucial components of ecosystems. In recent years, owing to certain natural and socio-economic factors, alpine grassland ecosystems have experienced significant degradation. This study integrated the frequency ratio model (FR) and Bayesian belief networks (BBN) for grassland degradation risk assessment to mitigate several issues found in previous studies. Firstly, the identification of non-encroached degraded grasslands and shrub-encroached grasslands could help stakeholders more accurately understand the status of different types of alpine grassland degradation. In addition, the index discretization method based on the FR model can more accurately ascertain the relationship between grassland degradation and driving factors to improve the accuracy of results. On this basis, the application of BBN not only effectively expresses the complex causal relationships among various variables in the process of grassland degradation, but also solves the problem of identifying key factors and assessing grassland degradation risks under uncertain conditions caused by a lack of information. The obtained result showed that the accuracies based on the confusion matrix of the slope of NDVI change (NDVIs), shrub-encroached grasslands, and grassland degradation indicators in the BBN model were 85.27, 88.99, and 74.37%, respectively. The areas under the curve based on the ROC curve of NDVIs, shrub-encroached grasslands, and grassland degradation were 75.39% (P &lt; 0.05), 66.57% (P &lt; 0.05), and 66.11% (P &lt; 0.05), respectively. Therefore, this model could be used to infer the probability of grassland degradation risk. The results obtained using the model showed that the area with a higher probability of degradation (P &gt; 30%) was 2.22 million ha (15.94%), with 1.742 million ha (78.46%) based on NDVIs and 0.478 million ha (21.54%) based on shrub-encroached grasslands. Moreover, the higher probability of grassland degradation risk was mainly distributed in regions with lower vegetation coverage, lower temperatures, less potential evapotranspiration, and higher soil sand content. Our research can provide guidance for decision-makers when formulating scientific measures for alpine grassland restoration.