bluuebunny/crossref_metadata_2025_split huggingface dataset:
Numerical Simulation on the Sloshing Characteristics of Gasliquid Flow in Cargo Tank and Anti-sloshing Methods
Authors: Zhang Qiong , Zhu Hanhua , Wei Wei 
Published: January 2021

Abstract
With the rapid development of society, the demand for liquid carrier ships and vehicles is increasing rapidly. Under the action of external load, the liquid in the tank would move relative to different degrees and produce hydrodynamic pressure on the local part of the liquid bulkhead, which will cause local damage or complete destruction of the structure, and then lead to leakage or other safety accidents. In this paper, computational fluid dynamics method is used to simulate the sloshing characteristics of the liquid in the cargo tank, and the instantaneous characteristic data of the free surface displacement fluctuation of the sloshing liquid are monitored. Through the pressure acquisition system, the hydrodynamic pressure distribution acting on the side wall of the cargo tank under external excitation is obtained. Through the free surface displacement fluctuation and the maximum hydrodynamic pressure acting on the side wall, the severity of liquid sloshing in cargo tank under external excitation is evaluated. The effects of different baffle combinations on liquid sloshing in cargo tanks are studied and discussed. The effects of baffles with holes on liquid sloshing are also discussed.
An Algorithmic Study to Maximize 5G Network Throughput Based on the Markov Decision Process
Authors: Xiao Zheng 
Published: January 2021

Abstract
In this article, the network throughput optimization problem is investigated based on the theory of Markov decision process, combining the device-to-device direct selection problem with the finite stage discount MDP model problem. First, models for the device-to-device communication selection using MDP are built; second, the optimal mode selection strategy is derived using a finite stage backward iterative algorithm; and finally, the given mode selection strategy is evaluated by conducting a large number of simulation experiments. The results show that the MDP-based mode selection method proposed in this article has better performance in maximizing throughput and can yield better mode selection strategies with the advantage of obtaining larger system throughput.
AGV Path Planning based on Improved Dijkstra Algorithm
Authors: Sun Yinghui , Fang Ming , Su Yixin 
Published: January 2021

Abstract
Aiming at the path planning problem of Automated Guided Vehicle (AGV) in intelligent storage, an improved Dijkstra algorithm that combines eight-angle search method and Dijkstra algorithm for path optimization is proposed. The grid method is used to model the storage environment, and the improved Dijkstra algorithm is used to optimize the route of the AGV. The simulation test of the AGV path planning process with Matlab shows that the AGV can effectively avoid obstacles by using the traditional Dijkstra algorithm and the improved Dijkstra algorithm, and then search for a collision-free optimized path from the start point to the end point; and the traditional Dijkstra algorithm In comparison, the path length planned by the improved Dijkstra algorithm is shorter and the turning angle is less, indicating that the improved algorithm is correct, feasible and effective, and has a strong global search ability.
A New Strategy using Date Integration for Identifying Interactions between Genes and Environment
Authors: Zhou Jiabin , Tian Sucai , Wang Jiaqi , Guo Yixing 
Published: January 2021

Abstract
The detection of gene-environment (GE) interactions is very important for dealing with complex diseases. Although some methods have been proposed to detect the interactions between genes and the environment, however, in the case of rare variants, these methods are limited. In this paper, we extend the existing ADA method and proposed a combined strategy (i-ADA) to identify the gene-environment interaction for rare variants. Firstly, we use Levene test to test the equality of variances under different sites. Secondly, based on the first-sep P-values and combined with the ADA method, we design a novel interaction test. Finally, we use the GAW17 dataset to illustrate the practicality of the proposed method.
Piecewise Multi-Parameter Tangent/Cotangent Function and Its Application in Chaotic Pseudorandom Bit Generator
Authors: Wu Qi 
Published: January 2021

Abstract
To increase the number of parameters of 1-dimensional discrete chaotic mappings, employing translation & scaling, this letter creatively designs a piecewise 5-parameters Tangent/Cotangent Function. Both bifurcation diagram and Lyapunov exponent spectrum illustrate that the proposed function seems to be inappropriate for cryptographic applications, due to its narrow chaotic area. However, after applying it to devising chaotic pseudorandom bit generator, it outperforms skew tent mapping, whose chaotic area is full. It might be applied to some other fields as well, such as cryptographic hash functions, which will possibly be paid attention to in the future.
An Optimized Method of Packet Loss Rate under Backup Path Protection in OBS Network
Authors: Meng Aodong , Chen Yucheng , Dai Jiahao , Chen Yongqun 
Published: January 2021

Abstract
In order to calculate the packet loss rate in OBS network, this paper proposes a method, which performs good in both accuracy and rapidity, based on Erlang B formula to estimate the packet loss probability in OBS network using backup path protection mechanism. This method can be used to optimize the design of OBS network protocol, and it also provides an accurate and efficient approximate calculation method of packet loss rate in OBS network and can be conductive to the study of using copy transmission to enhance the network survivability. Based on the formula of Erlang B, this paper briefly introduces the derivation process and principle of this method. The basic experimental principles of simulation and estimation processes are designed in this paper, and the experimental results of the proposed method under a typical network topology (ARPA network) with no protection mechanism and 1 and 2 backup paths protection mechanisms are analyzed to verify its rapidity and accuracy. The results of experiments show the accuracy and efficiency of this method.
Performance Analysis of Arbitration Scheduling Algorithm Based on RR Optimization for Network-on-chips
Authors: Ma Ruofei , Bao Liyong , Ding Hongwei , Guan Zheng 
Published: January 2021

Abstract
The RR arbitration algorithm commonly used in the current Network-on-chips cannot meet the requirements for low power consumption and rapid response under high load conditions, with the continuous development of microelectronics technology. In order to solve this problem, based on the idea of non-competitive transmission and parallel optimization, this paper improves on the RR algorithm to obtain a MAC layer arbitration scheduling algorithm suitable for the Network-on-chips. By optimizing the service and query transfer process in parallel, the algorithm reduces the queue length of information grouping in the system and shortens the query period of the system. The algorithm model is established by using Markov process and probability generating function, mathematically analyze the first-order characteristics of the system, and obtain an accurate solution. The simulation model of the algorithm is established based on the simulation platform to verify the accuracy of the precise solution. After optimization, the system’s average queue length and average cycler have been greatly improved.
Optimization Research and Defect Object Detection of Aeroengine Blade Boss Based on YOLOv4
Authors: Chen Wei , Zhong Xintong , Zhang Jing 
Published: January 2021

Abstract
This paper proposes an object detection algorithm based on YOLOv4 (You Only Look Once) aiming at detecting the defects of the blade boss inside the aeroengine. The algorithm uses transfer learning to load the weights of the pre-trained model trained on the coco public data set. In order to adapt to the characteristics of small targets and complex structures better in the detection of bosses, the size of the bounding boxes is adjusted through the cluster analysis. In the meantime, the original data set is enhanced through the Mosaic method. The experimental results show that the improved YOLOv4 model increased the detection accuracy by 15.85%, the recall rate by 21%, with an average intersection ratio of 0.75, and the detection performance is significantly better than the data applying the SSD object detection algorithm in the same data set.
Optimization and Implementation Performance of Sequence Alignment on the Intel Xeon Phi-based Heterogeneous System
Authors: Chen Shaolong , Luo Zhenzhen , Yang Wei , Wang Wenle 
Published: January 2021

Abstract
The heterogeneous system based on different architectures becomes a convenient solution in the high performance computing research when facing the expanding sequence data in bioinformatics analysis. Intel Xeon Phi-based cluster is one of the most utilized heterogeneous systems in recent years. Without accurate results from sequence alignment, the remaining two steps in the variant analysis, variant calling and variant annotation, cannot achieve the correct consequence. However, most sequence aligners are developed facing the multicore system and cannot take advantage of Intel Xeon Phi-based cluster. This paper explored the implementation modes on the Intel Xeon Phi-based heterogeneous system, including native, offload and symmetric modes. We indicate that native mode cannot take advantage of Intel Xeon Phi-based cluster through the evaluation of scalability of various modes under sequence alignment. Although offload mode owns a promising future, it is not easy to enhance performance without comprehensive coding ability. Finally, the symmetric mode could provide a low complexity solution that supports significant improvements in performance.
The Optimization of the Running Technique using Video Analysis Method
Authors: Dobre A G , Gheorghe C 
Published: January 2021

Abstract
Systematic and continuous monitoring of athletic performance for the purpose of developing incrementally adaptive optimized training solutions is often presented as the future of more efficient training systems. This is a shared view in the specialty literature, with authors such as Lyle J (2005, p. 21) stating that “operational training implies the existence of a continuous monitoring process, not only within a competition but also within the training and preparation”. The periodic analysis of key performance parameters with a great effect on performance during a sports contest is a process based on a coherent, self-adaptive and continuously developing system, being the result of a a systemic analysis mechanism that focuses on the analytical evaluation, data collection and analytics, interpreting, and reporting the information obtained as a result of the research. Our approach is to apply and expand on the video analysis method by focusing on certain key questions: how much, how well, how precise but with what costs, with what effort, with what efficiency. Eliminating the “traditional” methods prompt the conclusions that using advanced mechatronic devices render us capable continuous monitoring, recording, and developing comparison solutions that enable concrete interventions on improving the technical execution of various athletic events and more.
Cardiovascular Diseases Prediction using various Machine Learning Techniques
Authors: Aradhana S , Jankisharan P , Virendra SK , Ashish M 
Published: January 2021

Abstract
Prediction of cardiovascular diseases is one of the crucial aspects which every healthcare organization has to do with. Prediction of cardiovascular disease well in time may save the life of patients. In this paper, we applied various machine learning techniques for cardiovascular disease or heart disease prediction. In the urge to provide an efficient technique for the prediction of cardiovascular diseases, we have used UCI heart disease data. This data set is dedicated to classification problems related to cardiovascular disease patients. To predict this disease various machine learning techniques like KNN, Logistic Regression, Random Forest, Decision Tree, and naive Bayes have been tested on Data Set. It has been found from the conducted experiments that the results obtained by the Gaussian Naïve Bayes have given superior results compared to other techniques.
Preeminent Development Boards to Design Sustainable Integrated Model of a Smart Healthcare System under IoT
Authors: Dalal Priya , Aggarwal Gaurav , Tejasvee Sanjay 
Published: January 2021

Abstract
Conventional healthcare systems were just passive with the approach of smart healthcare services. Smart healthcare has been tended to consequently decrease the death and casualty rates the world over. The gratefulness lies in the court of the Internet and ICTs. IoT (Internet of Things) helps healthcare administrations by providing dynamic, inescapable, and intellectual services. Due to deal with medical issues and various unexpected diseases, a suitable, incorporated, smart, well-groomed, modern, and sustainable healthcare monitoring with wellness service deliverance arrangement is required under the IoT platform. A variety of development-boards have been created and are currently being utilized in the field of healthcare. These IoT based development boards reduce endeavors, cost, and time in a feasible way. Patients care better in IoT based integrated healthcare services system. Several companies have made-up these development-boards and these are being used by several organizations to fulfill the requirement of healthcare services for many purposes. This review paper provides an insight on aspects, dimensions, a prototype of sustainable integrated smart healthcare architecture with explicit clarification of significant development boards to design and implement a sustainable smart healthcare system that empowers strong connectivity among numerous devices and human beings in the recent digital era.
A comparative study of new and existing segmentation techniques
Authors: Sarma Rituparna , Gupta Yogesh Kumar 
Published: January 2021

Abstract
Image segmentation is the most crucial part in image processing techniques. Numerous segmentation techniques are used to segment digital images into smaller regions called segments, consisting of sets of pixels in order to analyze important information from the images. Segmentation simplifies the process of information retrieval from the region of interest (ROI).It helps in converting the digital image into more relevant information and easier to analyze. This paper presents a comparative analysis of existing segmentation techniques and its modification to form new segmentation techniques to overcome some of the drawbacks of the existing image segmentation approaches.
Machine Learning in liver disease diagnosis: Current progress and future opportunities
Authors: Tanwar Neha , Rahman Khandakar Faridar 
Published: January 2021

Abstract
There has been a rapid growth in the use of automatic decision-making systems and tools in the medical domain. By using the concepts of big data, deep learning, and machine learning, these systems extract useful information from large medical datasets and help physicians in making accurate and timely decisions regarding predictions and diagnosis of diseases. In this regard, this study provides an extensive review of the progress of applying Artificial Intelligence in forecasting and detecting liver diseases and then summarizes related limitations of the studies followed by future research.
Hurricane Damage Detection using Machine Learning and Deep Learning Techniques: A Review
Authors: Kaur Swapandeep , Gupta Sheifali , Singh Swati 
Published: January 2021

Abstract
Hurricane is one of the most disastrous natural disasters causing immense harm to the ecosystem and economic system worldwide. It is also known as a tropical cyclone. Heavy rainfall and high winds accompanying hurricane inflict damage to property as well as loss of human life. Hence, appropriate steps need to be taken to mitigate the damage caused by the disaster. Recently, social media platforms are used that help in providing immediate relief to the people affected by the disaster. Since, the difficulty arises in analysis of high volume data of social media, satellite imagery is also being used for damage detection due to its ability to cover large spatial and temporal areas. But manual damage detection is error prone. Therefore, machine learning and deep learning which automated methods are used for detection of damage. This paper includes the use of machine learning and deep learning for detection of damage caused by natural disasters with a special focus on hurricane damage.
Blockchain and IoT based Vehicle Tracking System for Industry 4.0 Applications
Authors: Chauhan Harsha , Kumar Devashish , Gupta Deepali , Gupta Sheifali , Verma Vishal 
Published: January 2021

Abstract
Vehicle automation is one of the main applications of industry 4.0 especially for tourism companies who provide their vehicles to migrants for traveling purposes. It is difficult to maintain trust among the travel agency and the customers. Although there are many existing solutions in the research that addresses the trust issues, but most of the solutions are based on centralized architecture such as cloud computing where the data is prone to various security threats. Motivated by the aforementioned discussion, the authors have proposed a vehicle tracking system based on the integration of blockchain and IoT in this paper. The proposed system will increase the trust among the entities by providing them transparency in tracking details of the vehicle. As the trip details are immutable and decentralized in nature, so, blockchain is used to store the details. Also, the system will generate the trip summary that contains the route detail, the number of kilometres travelled and the total cost of the trip. Tools such as Ethereum, Remix IDE, MetaMask, and RinkeyBy have been used to measure the performance of the system.
EMT: Ensemble Meta-Based Tree Model for Predicting Student Performance in Academics
Authors: Kumar Mukesh , Mehta Gaurav , Nayar Nandini , Sharma Abha 
Published: January 2021

Abstract
Data Mining is a field in which hidden information is extracted from a large database by using some algorithms implementation. These algorithms are further divided into some categories like classification, clustering, association rule mining etc according to information we want to extract. Data mining is a field which is widely spread over different areas like telecommunication, marketing, operation, hospitals, hotel industry, education etc. Predicting the academic’s performance and progress of the students has revealed the attention of the young researchers. To facilitate the task of building an academic prediction model, historical student academic dataset is used. In this paper, the contributions are exhibited in two different folds. In the first fold, the main aim is to build the prediction model by different families of the Machine Learning Techniques on the selected dataset for consideration. In the second fold, implementations of different ensemble meta-based model are presented by combining with different classification algorithms of Machine Learning Techniques. Different ensemble meta-based model taken into consideration for implementation are Bagging, AdaBoostM1, RandomSubSpace. The implementation results demonstrate that the ensemble meta-based technique (AdaBoostM1) gained a superior accuracy performance with MultilayerPerceptron Machine Learning technique reaching up to 80.33%.
A Review Paper on Breast Cancer Detection Using Deep Learning
Authors: Priyanka Kumar Sanjeev 
Published: January 2021

Abstract
Breast Cancer is most popular and growing disease in the world. Breast Cancer is mostly found in the women. Early detection is a way to control the breast cancer. There are many cases that are handled by the early detection and decrease the death rate. Many research works have been done on the breast cancer. The Most common technique that is used in research is machine learning. There are many previous researches that conducted through the machine learning. Machine learning algorithms like decision tree, KNN, SVM, naïve bays etc. gives the better performance in their own field. But now days, a new developed technique is used to classify the breast cancer. The new developed technique is deep learning. Deep learning is used to overcome the drawbacks of machine learning. A deep learning technique that is mostly used in data science is Convolution neural network, Recurrent neural network, deep belief network etc. deep learning algorithms gives the better results as compared to machine learning. It extracts the best features of the images. In our research, CNN is used to classify the images. Basically our research is based on the images and CNN is most popular technique to classify the images. In present paper, reviews of all authors are conducted.
The Impact of COVID-19 Epidemic on Indian Economy Unleashed By Machine Learning
Authors: Garg Kamal Deep , Gupta Manik , Kumar Munish 
Published: January 2021

AbstractThe outbreak of the Corona Virus (COVID-19) that has begun in December 2019 drastically affected the world. Endemic Coronavirus (COVID-19) is rapidly growing across the globe. SARS-CoV-2 is the virus name that causes a highly contagious and deadly disease COVID-19. It also entered India by the end of January 2020 and has significantly influenced India. More than two million people worldwide have been confirmed to have been contaminated with this virus as of the date (29 July 2020), and more than 7, 24,000 have died of this disease. The governments of most countries, including India, have already taken several measures to reduce the spread of COVID-19, such as lockdown, social distancing, closure of shopping malls, gyms, schools, universities, religious gatherings, etc. This lockdown has affected every Indian sector, such as the Economy, Retail Sector, Tourism Industry, etc. This paper aims to explore to what extent a 2020 epidemic like Covid-19 had impacted the Indian economy using a machine learning approach. The statistical data from esteemed and trustworthy information sources were gathered to realize the impact of the Corona Virus on the Indian economy. Based on this trusted data, analysis has been performed using the various regression models.
A Study on Tree Rings: Dendrochronology using Image Processing
Authors: Divya K , Kaur Sukhvir 
Published: January 2021

Abstract
This paper outlines an introduction to dendrochronology. The tree rings are being identified through image processing and statistical simulations. The study of the tree rings is known as dendrochronology but the techniques do need to be modified for better performance. Image analyses convert the tree ring into digital data using imaging tools. This method involves the scaling, piling, width calculation. Technology is required to evaluate the factors of different tree ring pattern. In this review paper, literature survey has been provided which deals with the contribution of different researchers, which methodologies they preferred and what were their limitations. And two attributes are being calculated named MSE and PSNR, as these obtainable values indicates to work further with the enhanced image. Also, researcher can get idea what all the attributes are important to implement this technique.