bluuebunny/crossref_metadata_2025_split huggingface dataset:
Optimization Analysis of Max-Link Secure Relay Selection in Buffer-Aided Cooperative Networks
Authors: Zhang Yajun , Wu Jun , Wang Bing 
Published: January 2021

Abstract
Cooperative communication technology has been proved to be an useful approach to accomplish physical layer security. In recent years, more and more attention has been paid to the collaboration network with buffer relay. This paper studies the safety performance of the maximum link level options. Compared with the existing buffer-assisted cooperative security schemes, max-Link secure relay selection (Max-Link-SRS) does not require the eavesdropper’s channel state information (CSI) and has a lower complexity. Based on markov chain theory, a new closed form expression of secure interrupt probability (SOP) is derived. Numerical simulation results confirm our analysis and demonstrate that the functioning of max-Link-SRS is superior to the previously reported buffer-less aided cooperative security scheme. It provides a significant coding obtain at a smaller buffer size, while ensuring diversity gain is close to the upper limit of the double number of relays at a bigger buffer zone.
Optimizing Allocation of Sea Area Utilization Based on Genetic Algorithm
Authors: Wang Xiaowen , Kang Minjie 
Published: January 2021

Abstract
Optimal allocation of sea area utilization solves the problem of quantity allocation and spatial distribution of sea area used by various departments. It is a necessary method to coordinate the conflicts between different departments and realize the dual goals of development and ecological protection. This paper presents an optimal allocation method of sea area utilization based on genetic algorithm. The basic principle and solving process of optimal allocation of sea area utilization based on genetic algorithm are described. The fitness evaluation model, genetic operator and constraint conditions are designed.
Optimization Technology of Optical Fiber Communication Network Based on Service Classification
Authors: Zhang Ning , Chen Bingjun , Yang Yansong , Sun Huijuan , Chen Ming , Chen Tingting , Chen Xiaodan , Xiao Mingming , Luo Yang 
Published: January 2021

Abstract
Optical fiber has huge bandwidth, as a physical medium, it has good anti electromagnetic interference characteristics and excellent security performance, which is very suitable for the establishment of backbone transmission network. Wavelength division multiplexing(WDM) network based on optical fiber has been widely used. Using optical fiber for information transmission can give full play to the nearly unlimited transmission capacity of optical fiber. With the advent of the information age, data service has become the mainstream business, replacing the original telephone voice service. The great development of data transmission service further promotes the continuous development of WDM network technology. Among all kinds of services in optical network, IP service accounts for the largest proportion. In optical networks, switching is an important technology. Network performance is directly related to switching technology. With the development of optical communication technology, a variety of optical switching devices are introduced into WDM optical network, such as optical packet switching, optical burst switching and so on. This makes WDM layer have the ability of dynamic path establishment and removal similar to IP layer. In order to improve the performance of the optical network and reduce the blocking rate of the network, in WDM optical network, the services are designed in different levels. For some very important services, the highest priority is given, and the optical path channel with better survivability and higher fault tolerance is selected. For general services, it can be designed as a lower level service, and choose the appropriate optical path channel for transmission. The experimental results show that this hierarchical service design method can make more effective use of network resources, reduce the network blocking rate, and improve the overall performance of the network.
Optimization of a Comprehensive Sequence Forecasting Framework Based on DAE-LSTM Algorithm
Authors: Zhao Yonghe , Ren Xiaochen , Zhang Xingyi 
Published: January 2021

Abstract
Sales forecasting is a multivariate time series forecasting problem. The main challenges of the forecasting task are the high-dimensional influence variables with noise and the complex time series relationships. In this paper, we propose a DAE-LSTM algorithm combined with denoising autoencoder(DAE) and long short-term memory(LSTM) to deal with this problem. The DAE is a non-linear dimensionality reduction method that enhances data robustness. The LSTM is a deep learning algorithm suitable for dealing with multivariate time series problem. Combining the advantages of the above two algorithms, DAE-LSTM is a new multivariate time series prediction algorithm, which transforms unsupervised DAE into supervised data feature extraction method, and adds feature selection function to LSTM algorithm. In addition, in order to build a comprehensive forecasting framework, the hyperparameters of the model are determined by Bayesian optimization method(BOM) and the parameters of the model are solved by adaptive moment estimation(ADAM) optimization algorithm. In the training process, due to the adaptability of the BOM and ADAM, the forecasting model has few parameters that need to be set artificially, only including dimensional scale and time steps determined by data. Finally, the proposed forecasting framework is applied to forecast the sales of online retailers on JD.COM. In comparison with other machine learning algorithms, including multiple linear regression(MLR), support vector regression(SVR), artificial neural network(ANN) and LSTM, the proposed DAE-LSTM achieves the best prediction accuracy.
An Optimal Design of High Output Power CMOS Class E Power Amplifier with Broadband Matching for RFID Applications
Authors: Zahid Muhammad Noaman , Jiang Jianliang , Lu Heng , Khan Shahrukh , Zhang Hengli 
Published: January 2021

Abstract
In the radio frequency frontend the power amplifiers are most essential block for reliable wireless communication. Power amplifiers are used for amplification and enhance the input signal to the desired power level at output. The class-E power amplifiers are used in many RF portable electronic devices that need low power consumption, high gain, and high efficiency. In this article, we present an optimum design of a complementary metal-oxide semiconductor (CMOS) class-E power amplifier (PA) that achieves high efficiency and high gain simultaneously for RF based electronic article surveillance (EAS) system. The simulated analysis and implementation of a class-E RF power amplifier with an appropriate output impedance matching network are presented. The proposed CMOS class-E PA has 41.7 dBm output power with 63% of power efficiency at frequency of 7.7 MHz to 8.7MHz.
Automation of detection of social network mental disorders – A review
Authors: Singh Anju , Singh Jaspreet 
Published: January 2021

Abstract
Recent years have seen a growing association between social media use and mental disorders. Experts opine that excessive use of social media sites has reached alarming levels, leading to Social Network Mental Disorders (SNMD). The Social Networking Sites (SNS) usage generates massive amount of complex data, which is difficult to analyze, find patterns within and make predictions manually, in turn making it difficult to detect SNMD in the SNS users. The paper summarizes the studies involved in the detection of mental disorders due to prolonged use of social networking sites and provides means of understanding an accurate predictive platform using data mining techniques to build machine-learning framework complementary to the conventional detection methods. We present an in-depth survey of the proposals that included the subtypes of SNMD in the SNS users – Cyber relationship addiction, Net compulsion, Information overload addiction, Cyber sex addiction and Computer addiction. The researchers bring forth models that integrate data mining techniques, Natural Language and computer vision programming tools, with social media and behaviour sciences, providing promising results. Data mining approaches to detect SNMD despite being challenging, are effective and can be used as an efficient tool. The challenges and issues related to automate the detection process is also analyzed in the paper. Thus we present that the automation of detection of SNMD in SNS users has potential to improve the existing health care systems.
RETRACTED: Analysis and identification of body features in Alzheimer’s patients for gait recognition and gait attractiveness
Authors: Abhilash  , Kaur Sukhkirandeep 
Published: January 2021

Abstract
Alzheimer’s infection is a degenerative mind malady and the most well-known reason for dementia [1, 2]. There has been a lot of past research on which substantial highlights are generally significant in gait investigation. The essential objective of the examination revealed here was to adopt a diagnostic strategy to addressing these inquiries, with regards to recognizing the highlights that are generally significant for gait acknowledgment and gait allure assessment. We dissected the relative movements from various body sections to a root marker of 30 guys by the fixed root strategy. The lower left arm, hips and lower legs were distinguished as significant highlights for gait acknowledgment. The lower legs were perceived as significant highlights for gait allure assessment. Three diverse extraction strategies were applied to accomplish gait marks. In extra, stride appeal was broke down by means of Principle component analysis and linear regression strategy.
Biomedical image classification using deep convolutional neural networks – overview
Authors: Bharath Simha Reddy M , Rana Pooja 
Published: January 2021

Abstract
Deep Learning is an advanced area of machine learning which gained much interest in the past decades. It has been widely used in a variety of applications and has proved to be an effective machine learning method for many complicated issues. Especially when it comes to the medical field, the classification of biomedical images is a complex task to identify and classify the images manually by the doctors. So, Deep Learning is a key to enhance the classification of biomedical images using various architectures. The biomedical picture classification aims to identify and classify biomedical characteristics efficiently, which have significant advantages to numerous study and development fields. In this paper, the framework focused on the different architectures that were used to classify the medical images along with their performances.
Fog Computing Serverless Architecture for Real Time Unpredictable Traffic
Authors: Kaur Navjeet , Mittal Ayush 
Published: January 2021

Abstract
Fog computing is a middle layer in the cloud-IoT model. The additional layer developed to offload the traffic from the cloud by sharing its workload. The offloading of tasks in Fog computing is done by deploying intelligent nodes near the edge of a network to process data locally. The smart nodes are also called Fog nodes, which act as a mini cloud within an area of the network. The Fog network comprises various Fog nodes and a Fog manager which handles their communication and allocates each incoming request to optimal capacity Fog node. The Fog manager at the Fog layer is busier in managing the network than providing the solution to different applications, which should be its primary responsibility. The vast responsibilities list of a Fog manager degrades its efficiency to provide solutions on time. This problem is targeting through this paper, and the proposed work provides a solution through a serverless approach at the Fog layer.
On Numerical Implementation of the Laplace Equation-Based Image Inpainting
Authors: Thanh Le Thi , Hoang Thanh Dang Ngoc , Hue Nguyen Minh , Thuy Linh Ha Nguyen , Surya Prasath V. B. 
Published: January 2021

Abstract
In the current study, we focus on the development of a numerical algorithm for the Laplace equation-based image inpainting problem. A software program to implement the proposed algorithm is also developed. In experiments, the proposed method recovered corrupted images effectively. It can also remove defects as well as unnecessary objects successfully. The program provides an effective, convenient, and easy solution to perform the image inpainting in practical tasks. It also provides a useful feature to assess image quality after processing with both full reference and blind image quality assessment.
A Statistical Analysis On Covid-2019 To Distinguish Between Myths And Facts With Data Visualization
Authors: Konar Karthik , Kabli Nirali 
Published: January 2021

Abstract
Covid-2019 pandemic which has become the world’s worst nightmare, with no cure in sight and a lot of misinformation available on the internet, one can have a very tough time in separating fact from fiction. From non-veg meats, garlic water and weird self test, one will find a lot of myths related to coronavirus infection. Therefore to separate myths from facts we conducted a survey to know what actually people think about the virus. The main objectives of our survey was:(1)To describe the level of knowledge of the participants regarding COVID-19.(2)To determine participants’ point of view related to the COVID-19 disaster.(3)Provide useful information to the government of India.(4)To distinguish between myths and facts related to COVID-19 Disaster.
Development of Association Rule Mining Model for Gender Classification
Authors: Tiwari Meena , Shanthi V. , Mishra Ashish 
Published: January 2021

AbstractFingers have unique and developmental properties for biometric authentication systems. This is because fingerprints have the following characteristics: workable, distinctive (clear), durable, accurate and reliable, and accept security and identity worldwide. Fingers are seen as legal proof of this evidence in courts around the world. SVM support machines, NN is a neural network, FCM is a fingerprint classification system used in various modes, such as the Fuzzy C method, which is widely used to adopt a model. Gender determination from fingerprints is an important step in reducing the list of offensive searches in anthropology, as few machine-based methods have been requested for gender acceptance and correction. Several researchers have carried out gender fingerprint analysis and obtained competitive results. This article explains the difference between the genders using communication methods and classification methods. It is recommended that competent studies be combined with different methods and methods with comparative indicators to predict results. This allows researchers to conduct a comprehensive study and further research on models for the management of gender-specific mines.
Detection of Pneumonia using ML & DL in Python
Authors: Sharma A , Negi M , Goyal A , Jain R , Nagrath P 
Published: January 2021

Abstract
Pneumonia is form of a respiratory infection that affects the lungs. In these acute respiratory diseases, human lungs which are made up of small sacs called alveoli which in air in normal and healthy people but in pneumonia these alveoli get filled with fluid or “pus” one of the major step of phenomena detection and treatment is getting the chest X-ray of the (CXR). Chest X-ray is a major tool in treating pneumonia, as well as many decisions taken by doctor are dependent on the chest X-ray. Our project is about detection of Pneumonia by chest X-ray using Convolutional Neural Network. This paper written by us is an efficient approach towards classifying chest X- rays into pneumonia and no pneumonia X-rays. We have taken this approach as the most used radiography method produces errors. So, we have used CNN and batch normalization from keras to develop this model, and calculated accuracy using confusion matrix. We were successful in doing so with the help of “Python” and “OpenCV”, both of which are freely available and are open source tools and can be used by anyone. Pneumonia day, states that by the year 2030, 11 million children who are under the age of 5 year.
Technology assisted farming: Implications of IoT and AI
Authors: Aggarwal N , Singh D 
Published: January 2021

Abstract
With the advancement of technologies, things became intelligent with the capabilities of self-communication between them. Internet of Things (IoT) connected daily household things to the Internet and make them able to make decisions like the human mind. Sensors collect the real atmospheric data and with the help of Artificial intelligence (AI) algorithms analysis of data takes place so that devices behave more smartly. The present article discusses how IoT revolutionized the agricultural community. According to the study, it is analyzed that 70% population is dependent on agriculture for their livelihood in India, but the status of agriculture is no more concealed from society. With the involvement of technology, it becomes easy to predict temperature, rainfall, humidity, the need for fertilizers, water requirements, etc. The introduction of modern agriculture techniques using IoT & AI is revolutionizing the traditional agriculture methodologies and are making farming a profitable venture also.
Efficient Mobile Sink Path Scheduling for Clustered based Wireless Sensor Network
Authors: Ghorpade Naveen , Vijaykarthik P. 
Published: January 2021

Abstract
WSN (Wireless Sensor Network) has found wide range of adoption in different sector and it is primary component of much real time application such as IoT, Big Data etc. Meanwhile modern application needs scalable real time data access and low latency since they are heterogeneous; however achieving the low latency for real time data causes the energy overhead in sensor device. Moreover, two technique i.e. clustering technique and Mobile sink helps in achieving the scalability and reducing the energy consumption in sensor device, further this causes the energy overhead in rendezvous node and CH (Cluster Head). In order to address this issue several optimization technique were presented for selection of rendezvous node and optimal cluster, the main drawbacks of these techniques are that they are applicable only for the homogeneous network. Hence, to overcome this issue, this research work presents technique EMSPS (Efficient Mobile Sink Path scheduling) technique for heterogeneous WSN, this technique is cluster based. Furthermore, EMSPS introduces the multiple-objective function such as network traffic for CH and rendezvous node selection, coverage time, radio selection strength, connection time and connectivity. EMSPS is evaluated considering the various parameter such as loss of connectivity, first node death, total node death and network lifetime, further comparison has been done with the state-of-art technique by considering the above parameters. Nevertheless, EMSPS provides the satisfactory trade-off between the energy and latency requirement.
Automatic Deep Neural Network Hyper-Parameter Optimization for Maize Disease Detection
Authors: Bansal Subodh , Kumar Anuj 
Published: January 2021

Abstract
Deep Convolutional Neural Networks (DCNNs) have proved to be very useful for image classification. These need to be optimized for the dataset in hand, for its optimal use. DCNNs have many attached hyper parameters. These hyper-parameters are fine-tuned for optimizing the DCNN model. The present research uses the Bayesian Optimization technique for fine-tuning the hyper-parameters for AlexNet DCNN model. The model is trained and tested on Maize (corn) disease sub-dataset of the Plant Village dataset. The trained DCNN model with optimized parameters achieved the accuracy of 96.05%.
The efficient consensus algorithm for land record management system
Authors: Yadav A S , Shikha S , Gupta S , Kushwaha D S 
Published: January 2021

Abstract
This research presents a scalable property/land registration framework. It is based on blockchain technology, where all registry offices come together on a single framework based on blockchain to improve the registration process. This paper mainly focuses on the problems in the land record and revenue sectors. In this paper, we explore the use of blockchain technology in current business scenarios. We discuss issues such as data security, privacy, and, more significantly, the absence of a shared forum amongst the involved organizations. This research proposes an efficient consensus mechanism to evaluate the performance of a blockchain-based implementation of a land revenue & recording automation system. The proposed Modified Round Robin Consensus Algorithm (MRRCA) reduced the 30% - 40% message overhead in committing a transaction to the network and thus adding the block to the blockchain.
Reconstruction of hyperspectral images from RGB images
Authors: Pushparaj Pratish , Dahiya Nishthavan , Dabas Mayank 
Published: January 2021

Abstract
This paper accounts for the problem of construction of hyper-spectral (hs) images from RGB-images, i.e recovery of the whole spectral details/signature from a three-channel RGB image. The dataset used in this paper consists of ‘clean’ images, that are images without noise. There are 450 clean images along with correlative 450 hyperspectral images and all the pages are in PNG (.png) format. We approached this problem using 3 models Convhs_5, Enhanced-ResNet and Dense-HSCNN (D-HSCNN). These models increase in complexity from Convhs_5 to Dense-HSCNN. In the evaluation phase, Convhs_5 achieved average results with not so much clarity, whereas the best model came out to be Enhanced-ResNet with satisfying results, however, due to insufficiency of computational power/resources, we were not able to get the expected results from the Dense-HSCNN model. Whilst having more number of images in the dataset and more computational power can reduce the loss in all of the models furthermore.
Holistic Approach in Deep Learning and its Applications
Authors: Yadav Sahil , Choudhary Karan , Singh Tanishq , Yadvanshi Dhruv , Goyal Monika 
Published: January 2021

Abstract
Deep learning has become a territory fundamental to scientists in the past barely any years. Convolutional Neural Organization is a significant learning approach that is thoroughly utilized for managing complex issues. It beats the prerequisites of standard Artificial Insight moves close. Critical learning, a technique with its establishment in fake neural systems, is making beginning late as a remarkable asset for computer based intelligence, promising to reshape the conceivable predetermination of man-made reasoning. Getting data and important encounters from unusual, high-dimensional and heterogeneous biomedical data remains a key test in changing clinical administrations. Moreover we will investigate the difficulties in enormous information assessment.
Image Sizing using Removal or Insertion of Seams based on Energy Function
Authors: Suhandas  , Santhosh Kumar G , Aravind K U 
Published: January 2021

Abstract
The nature of the image plays a significant role in any image sizing algorithm. The technique of improving or condensing an image while preserving its key image quality is challenging. The resolutions of various display devices vary from one another in order to fit the image according to the resolution image sizing plays a vital role. In order to size the image a operator is introduced which will remove or insert the seam from an images based on the resolution of the device used. A seam in an image is related to the pixel path from up to down or right to left, the seam vector to be deleted for image size is defined by the image energy attribute. The Seam removal or insertion operator can be applied to an image and aspect ratio can be adjusted accordingly by continuously removing or adding a seam in the desired region of image. The collection and order of seams decides the picture quality, as described by the energy function. By storing the order of seams in an image that can continuously change to fit a given size in real time, multi-sized images can be produced.