scholarly journals Scene Classification for Sports Video Summarization Using Transfer Learning

Sensors ◽  
2020 ◽  
Vol 20 (6) ◽  
pp. 1702 ◽  
Author(s):  
Muhammad Rafiq ◽  
Ghazala Rafiq ◽  
Rockson Agyeman ◽  
Gyu Sang Choi ◽  
Seong-Il Jin
Keyword(s):  
Data Augmentation ◽  
Video Summarization ◽  
Performance Comparison ◽  
Practical Implementation ◽  
Scene Classification ◽  
Advertising Agencies ◽  
Sports Video ◽  
Performance Results ◽  
Implementation Gaps

This paper proposes a novel method for sports video scene classification with the particular intention of video summarization. Creating and publishing a shorter version of the video is more interesting than a full version due to instant entertainment. Generating shorter summaries of the videos is a tedious task that requires significant labor hours and unnecessary machine occupation. Due to the growing demand for video summarization in marketing, advertising agencies, awareness videos, documentaries, and other interest groups, researchers are continuously proposing automation frameworks and novel schemes. Since the scene classification is a fundamental component of video summarization and video analysis, the quality of scene classification is particularly important. This article focuses on various practical implementation gaps over the existing techniques and presents a method to achieve high-quality of scene classification. We consider cricket as a case study and classify five scene categories, i.e., batting, bowling, boundary, crowd and close-up. We employ our model using pre-trained AlexNet Convolutional Neural Network (CNN) for scene classification. The proposed method employs new, fully connected layers in an encoder fashion. We employ data augmentation to achieve a high accuracy of 99.26% over a smaller dataset. We conduct a performance comparison against baseline approaches to prove the superiority of the method as well as state-of-the-art models. We evaluate our performance results on cricket videos and compare various deep-learning models, i.e., Inception V3, Visual Geometry Group (VGGNet16, VGGNet19), Residual Network (ResNet50), and AlexNet. Our experiments demonstrate that our method with AlexNet CNN produces better results than existing proposals.

Direct ammonia fueled solid oxide fuel cells: A comprehensive review on challenges, opportunities and future outlooks

2020 ◽  
pp. 70-91 ◽  
Author(s):  
Molla Asmare ◽  
Mustafa Ilbas
Keyword(s):  
Electric Power ◽  
Energy Demand ◽  
Renewable Energy Sources ◽  
Clean Energy ◽  
Performance Comparison ◽  
Optimum Operating ◽  
Practical Implementation ◽  
Human Society ◽  
Energy Access ◽  
Green Fuel

Nowadays, the most decisive challenges we are fronting are perfectly clean energy making for equitable and sustainable modern energy access, and battling the emerging alteration of the climate. This is because, carbon-rich fuels are the fundamental supply of utilized energy for strengthening human society, and it will be sustained in the near future. In connection with this, electrochemical technologies are an emerging and domineering tool for efficiently transforming the existing scarce fossil fuels and renewable energy sources into electric power with a trivial environmental impact. Compared with conventional power generation technologies, SOFC that operate at high temperature is emerging as a frontrunner to convert the fuels chemical energy into electric power and permits the deployment of varieties of fuels with negligible ecological destructions. According to this critical review, direct ammonia is obtained as a primary possible choice and price-effective green fuel for T-SOFCs. This is because T-SOFCs have higher volumetric power density, mechanically stable, and high thermal shocking resistance. Also, there is no sealing issue problem which is the chronic issues of the planar one. As a result, the toxicity of ammonia to use as a fuel is minimized if there may be a leakage during operation. It is portable and manageable that can be work everywhere when there is energy demand. Besides, manufacturing, onboard hydrogen deposition, and transportation infrastructure connected snags of hydrogen will be solved using ammonia. Ammonia is a low-priced carbon-neutral source of energy and has more stored volumetric energy compared with hydrogen. Yet, to utilize direct NH3 as a means of hydrogen carrier and an alternative green fuel in T-SOFCs practically determining the optimum operating temperatures, reactant flow rates, electrode porosities, pressure, the position of the anode, thickness and diameters of the tube are still requiring further improvement. Therefore, mathematical modeling ought to be developed to determine these parameters before planning for experimental work. Also, a performance comparison of AS, ES, and CS- T-SOFC powered with direct NH3 will be investigated and best-performed support will be carefully chosen for practical implementation and an experimental study will be conducted for verification based on optimum parameter values obtained from numerical modeling.

Femtocell Selection Scheme for Reducing Unnecessary Handover and Enhancing Downlink QoS in Cognitive Femtocell Networks

2017 ◽  
Author(s):  
Hoang Nhu Dong ◽  
Hoang Nam Nguyen ◽  
Hoang Trong Minh ◽  
Takahiko Saba
Keyword(s):  
Cellular Networks ◽  
Estimation Methods ◽  
Mobility Prediction ◽  
Capacity Estimation ◽  
Femtocell Networks ◽  
Selection Scheme ◽  
Indoor Communications ◽  
Performance Results ◽  
Cognitive Femtocell

Femtocell networks have been proposed for indoor communications as the extension of cellular networks for enhancing coverage performance. Because femtocells have small coverage radius, typically from 15 to 30 meters, a femtocell user (FU) walking at low speed can still make several femtocell-to-femtocell handovers during its connection. When performing a femtocell-to-femtocell handover, femtocell selection used to select the target handover femtocell has to be able not only to reduce unnecessary handovers and but also to support FU’s quality of service (QoS). In the paper, we propose a femtocell selection scheme for femtocell-tofemtocell handover, named Mobility Prediction and Capacity Estimation based scheme (MPCE-based scheme), which has the advantages of the mobility prediction and femtocell’s available capacity estimation methods. Performance results obtained by computer simulation show that the proposed MPCE-based scheme can reduce unnecessary femtocell-tofemtocell handovers, maintain low data delay and improve the throughput of femtocell users. DOI: 10.32913/rd-ict.vol3.no14.536

scholarly journals Nodule Detection with Convolutional Neural Network Using Apache Spark and GPU Frameworks

2021 ◽  
Vol 11 (6) ◽  
pp. 2838
Author(s):  
Nikitha Johnsirani Venkatesan ◽  
Dong Ryeol Shin ◽  
Choon Sung Nam
Keyword(s):  
Neural Network ◽  
Radiation Dose ◽  
Convolutional Neural Network ◽  
Model Performance ◽  
Performance Comparison ◽  
Apache Spark ◽  
Training Time ◽  
Learning Framework ◽  
Proposed Model

In the pharmaceutical field, early detection of lung nodules is indispensable for increasing patient survival. We can enhance the quality of the medical images by intensifying the radiation dose. High radiation dose provokes cancer, which forces experts to use limited radiation. Using abrupt radiation generates noise in CT scans. We propose an optimal Convolutional Neural Network model in which Gaussian noise is removed for better classification and increased training accuracy. Experimental demonstration on the LUNA16 dataset of size 160 GB shows that our proposed method exhibit superior results. Classification accuracy, specificity, sensitivity, Precision, Recall, F1 measurement, and area under the ROC curve (AUC) of the model performance are taken as evaluation metrics. We conducted a performance comparison of our proposed model on numerous platforms, like Apache Spark, GPU, and CPU, to depreciate the training time without compromising the accuracy percentage. Our results show that Apache Spark, integrated with a deep learning framework, is suitable for parallel training computation with high accuracy.

scholarly journals Popularity and performance of bioinformatics software: the case of gene set analysis

2021 ◽  
Vol 22 (1) ◽  
Author(s):  
Chengshu Xie ◽  
Shaurya Jauhari ◽  
Antonio Mora
Keyword(s):  
Comprehensive Evaluation ◽  
Gene Set Analysis ◽  
Tool Selection ◽  
Functional Interpretation ◽  
Selection Procedures ◽  
Gene Set ◽  
And Performance ◽  
Reported Data ◽  
Performance Results

Abstract Background Gene Set Analysis (GSA) is arguably the method of choice for the functional interpretation of omics results. The following paper explores the popularity and the performance of all the GSA methodologies and software published during the 20 years since its inception. "Popularity" is estimated according to each paper's citation counts, while "performance" is based on a comprehensive evaluation of the validation strategies used by papers in the field, as well as the consolidated results from the existing benchmark studies. Results Regarding popularity, data is collected into an online open database ("GSARefDB") which allows browsing bibliographic and method-descriptive information from 503 GSA paper references; regarding performance, we introduce a repository of jupyter workflows and shiny apps for automated benchmarking of GSA methods (“GSA-BenchmarKING”). After comparing popularity versus performance, results show discrepancies between the most popular and the best performing GSA methods. Conclusions The above-mentioned results call our attention towards the nature of the tool selection procedures followed by researchers and raise doubts regarding the quality of the functional interpretation of biological datasets in current biomedical studies. Suggestions for the future of the functional interpretation field are made, including strategies for education and discussion of GSA tools, better validation and benchmarking practices, reproducibility, and functional re-analysis of previously reported data.

scholarly journals Model of the omni-channel Interaction in the Web 4.0 environment

2018 ◽  
Vol 44 ◽  
pp. 00023
Author(s):  
Anna Chernikova ◽  
Sergey Krasnov ◽  
Sergey Sergeyev
Keyword(s):  
Global Markets ◽  
Entire Group ◽  
Practical Implementation ◽  
Channel Interaction ◽  
Complex Topology ◽  
Scientific Approach ◽  
On Line ◽  
Cloud Servers ◽  
The Web

The practical implementation of the concept “Industry 4.0” entirely depends on the level of coordination between businesses, organizations and the entire group of consumers. The dominating comprehension of such interaction as a net of complex topology where nodes act as objects that have an interface a surface for contacts, results in a neuro-net-concept or the Web 4.0. Within the framework of the Russian national program “National Technological Initiative”, the development of this direction is considered as one of the main priorities. The quality of implementation of the system interaction in this paradigm is greatly influenced by algorithms used in the cloud servers of the nodes included into the structure of the Web 4.0. Besides, operation of businesses in the conditions of global markets will take place in the situation of strict competition and solutions forming the basis of the software have to be based on the scientific approach with the use of the methods of optimization and mathematical models. As the competition in the commercial sphere turned into the contest between the economic efficiency of on-line platforms, this work proposes a particular implementation of the algorithm of omni-channel interaction formed on the basis of a mathematical model.

scholarly journals Enhancing the operational efficiency of quantum random number generators

2021 ◽  
Vol 13 (2) ◽  
pp. 10-18
Author(s):  
Botond L. Márton ◽  
Dóra Istenes ◽  
László Bacsárdi
Keyword(s):  
Random Number ◽  
Statistical Tests ◽  
Practical Implementation ◽  
Random Numbers ◽  
Random Number Generators ◽  
Original Source ◽  
Negative Effect ◽  
Quantum Phenomena ◽  
The Right

Random numbers are of vital importance in today’s world and used for example in many cryptographical protocols to secure the communication over the internet. The generators producing these numbers are Pseudo Random Number Generators (PRNGs) or True Random Number Generators (TRNGs). A subclass of TRNGs are the Quantum based Random Number Generators (QRNGs) whose generation processes are based on quantum phenomena. However, the achievable quality of the numbers generated from a practical implementation can differ from the theoretically possible. To ease this negative effect post-processing can be used, which contains the use of extractors. They extract as much entropy as possible from the original source and produce a new output with better properties. The quality and the different properties of a given output can be measured with the help of statistical tests. In our work we examined the effect of different extractors on two QRNG outputs and found that witg the right extractor we can improve their quality.

scholarly journals A comparative analysis of non­reference assessment methods

Connectivity ◽  
2020 ◽  
Vol 148 (6) ◽  
Author(s):  
V. V. Grebenyuk ◽  
◽  
O. A. Dibrivnyy ◽  
O. V. Nehodenko
Keyword(s):  
Comparative Analysis ◽  
Image Quality ◽  
Image Quality Assessment ◽  
Excellent Result ◽  
Video Quality ◽  
Mean Value ◽  
Performance Comparison ◽  
Quality Level ◽  
Reference Methods

A comparative analysis of functions to assess image quality in the absence of a sample: no-reference (NR) measure or NR-type methods. The availability of NR-methods is very important for assessing the quality of streaming video such as television, game streaming, online conferences, web-chatting, etc. (because on the side of the recipient of the video there is no standard for quality comparison) and assessing the results of transformations aimed at improving video, and choosing the parameters of these transformations (brightness change, semitone and others). The human visual system (HVS) is able to visually assessing video quality, but If required to visually assess the quality of dozens or hundreds of videos or ranking them by quality level it will be needed a huge amount of time. Six types of experiments were performed to analyze the correlation of calculated quantitative estimates with visual assessments of the quality of the tested video files. Three of them are fundamentally new: comparing video after gamma correction and changing the contrast with different parameters, as well as blurring, which may be the result of defocusing the camcorder. A hybrid method (or reduced-reference (RR) measure) and a full-reference (FR) measure or FR-type method were also added for comparison. It has been experimentally shown that none of the studied non-reference methods of image quality assessment is universal, and the calculated assessment cannot be converted into a quality scale without taking into account the factors influencing the distortion of image quality. Moreover, all NR-type methods could not cope with the experiment of changing the contrast, believing that the best result is the most contrasting image but the original. Instead, the reference methods showed an excellent result (except one, which showed partial ineffectiveness). Also, it has been shown performance comparison between methods. It is shown that most of the studied methods calculate local estimates for each frame, and their arithmetic mean value is an estimate of the quality of the entire video file. If the video is dominated by large areas of uniform evaluation, methods of this type may give incorrect quality evaluations that do not coincide with the visual evaluations.

scholarly journals Strategi Peningkatan Kompetensi Aparatur Sipil Negara Melalui Peningkatan E-Literasi dan Edukasi Kebijakan Publik

2020 ◽  
Vol 14 (1) ◽  
pp. 27-36
Author(s):  
Apandi Apandi
Keyword(s):  
Public Policy ◽  
Human Resources ◽  
Research Method ◽  
Public Services ◽  
Improve Performance ◽  
The Public ◽  
Secondary Sources ◽  
Low Levels ◽  
Performance Results

E-literacy and education are important components in improving the quality of human resources needed in an organization. The purpose of this study is to reveal the importance of e-literacy and public policy education. The State Civil Apparatus (ASN) requires e-literacy and education to improve performance results so that public services can be maximized. This becomes interesting research in the development of science-based on the real events that occur in Indonesia. Where so far, e-literacy and education is only intended for the public, especially millennial generation. The research method used is the study of literature where data is obtained through various secondary sources that are relevant to theoretically strengthen research results. The findings explain that e-literacy and education are effective and efficient strategies in overcoming budget constraints and low levels of ASN competence. With e-literacy and public policy education, it is hoped that the professionalism of the ASN bureaucracy will increase and the quality of public services will be better.

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