2020 International Conference on Emerging Trends in Smart Technologies (ICETST)最新文献

Modeling POS Tagging for the Urdu Language 乌尔都语词性标注的建模

2020 International Conference on Emerging Trends in Smart Technologies (ICETST) Pub Date : 2020-03-01 DOI: 10.1109/ICETST49965.2020.9080721

Zarmeen Nasim, Shaukat R. Abidi, Sajjad Haider

引用次数: 1

A Novel Neuro-Fuzzy GmpptAlgorithm based on Generalized Global Sliding Mode Concept for Variable-Shaded Photovoltaic System 基于广义全局滑模概念的变遮阳光伏系统神经模糊gmppp算法

2020 International Conference on Emerging Trends in Smart Technologies (ICETST) Pub Date : 2020-03-01 DOI: 10.1109/ICETST49965.2020.9080754

Waleed Ahmad, U. Khan, Z. Khan, I. Haq, Zaheer Alam, Rashid Khan

引用次数: 0

Mechanics of Digital Mathematics Games for Learning of Problem-Solving: An Extensive Literature Review 学习解决问题的数字数学游戏机制:广泛的文献综述

2020 International Conference on Emerging Trends in Smart Technologies (ICETST) Pub Date : 2020-03-01 DOI: 10.1109/ICETST49965.2020.9080715

Nadira Dayo, Unaeza Alvi, Muhammad Mujtaba Asad

{"title":"Mechanics of Digital Mathematics Games for Learning of Problem-Solving: An Extensive Literature Review","authors":"Nadira Dayo, Unaeza Alvi, Muhammad Mujtaba Asad","doi":"10.1109/ICETST49965.2020.9080715","DOIUrl":"https://doi.org/10.1109/ICETST49965.2020.9080715","url":null,"abstract":"Research suggests that well-designed digital games support the learning of problem-solving skills. Though, the literature is replete with various types and purposes of digital games, it is important to understand and analyze the game mechanics that have more educational value. Recently educators are exploring how well-designed digital mathematics games can be used for holistic learning of students i.e. knowledge, skills, and attitude and the specific mechanics of digital games that support the learning of mathematics problem-solving. In the current review paper, the digital games for learning of mathematics problem-solving and the effects of digital mathematics games on domains of problem-solving that is achievement, skills and attitude are reviewed extensively. Furthermore, the evidences of the different mechanics of digital games including goals and rewards, rules and instructions, aesthetics, interaction, challenge and feedback are reviewed with the purpose to understand the game mechanics that foster learning of mathematics problem-solving. Finally, the direction for future research are given.","PeriodicalId":204493,"journal":{"name":"2020 International Conference on Emerging Trends in Smart Technologies (ICETST)","volume":"12 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116027611","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Electricity Theft Detection using Empirical Mode Decomposition and K-Nearest Neighbors 基于经验模态分解和k近邻的窃电检测

2020 International Conference on Emerging Trends in Smart Technologies (ICETST) Pub Date : 2020-03-01 DOI: 10.1109/ICETST49965.2020.9080727

Sumair Aziz, Syed Zohaib Hassan Naqvi, Muhammad Umar Khan, Taimoor Aslam

{"title":"Electricity Theft Detection using Empirical Mode Decomposition and K-Nearest Neighbors","authors":"Sumair Aziz, Syed Zohaib Hassan Naqvi, Muhammad Umar Khan, Taimoor Aslam","doi":"10.1109/ICETST49965.2020.9080727","DOIUrl":"https://doi.org/10.1109/ICETST49965.2020.9080727","url":null,"abstract":"Electricity theft is a criminal practice of stealing electricity. In the country like Pakistan where the consumption is more than the production, the electricity theft can be hazardous for the economy. During the year 2017–18, there was a loss of 53 billion Rs. to the economy due to electricity theft. A novel system for the detection of electricity thefts is designed. The dataset provided by State Grid Corporation of China (SGCC) was used which contained two classes i.e. normal and theft. The dataset comprised of data collected for 1,035 days. The dataset included various missing and erroneous values. Preprocessing techniques such as interpolation was used to get the missing values and for the breakdown of signal, empirical mode decomposition was employed. After that the features were extracted from the signals of both classes. After a number of experiments combinations of features were found that gave maximum accuracy. K-nearest neighbors (KNN) classifier was used because of its advantage that it is very fast and simple. System was able to detect the electricity theft with accuracy of 91.0%. The system is very reliable and can be helpful in reducing the losses due to electricity theft. It is a very easy to use system as well as cost efficient.","PeriodicalId":204493,"journal":{"name":"2020 International Conference on Emerging Trends in Smart Technologies (ICETST)","volume":"61 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124701777","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 31

Evaluating the Optimized Mutation Analysis Approach in Context of Model-Based Testing 基于模型测试的优化突变分析方法评价

2020 International Conference on Emerging Trends in Smart Technologies (ICETST) Pub Date : 2020-03-01 DOI: 10.1109/ICETST49965.2020.9080737

Fozia Mehboob, A. Rauf, R. Qazi

引用次数: 2

Pattern Analysis for Classification of Power Quality Disturbances 电能质量扰动分类的模式分析

2020 International Conference on Emerging Trends in Smart Technologies (ICETST) Pub Date : 2020-03-01 DOI: 10.1109/ICETST49965.2020.9080722

Sumair Aziz, Muhammad Umar Khan, Abdullah, A. Usman, Areeba Mobeen

引用次数: 7

Training of Artificial Neural Network Using New Initialization Approach of Particle Swarm Optimization for Data Classification 基于粒子群初始化方法的人工神经网络数据分类训练

2020 International Conference on Emerging Trends in Smart Technologies (ICETST) Pub Date : 2020-03-01 DOI: 10.1109/ICETST49965.2020.9080707

A. Ashraf, W. Bangyal, Hafiz Tayyab Rauf, Sobia Pervaiz, J. Ahmad

{"title":"Training of Artificial Neural Network Using New Initialization Approach of Particle Swarm Optimization for Data Classification","authors":"A. Ashraf, W. Bangyal, Hafiz Tayyab Rauf, Sobia Pervaiz, J. Ahmad","doi":"10.1109/ICETST49965.2020.9080707","DOIUrl":"https://doi.org/10.1109/ICETST49965.2020.9080707","url":null,"abstract":"Artificial neural network (ANN) has a wide variety of practice for the solution of problems in the area of data classification. Back propagation algorithm is a famous neural network (NN) traditional training approach. Hence, this classical training technique has many drawbacks like stuck in the local minima and maximum number of iterations required. Particle Swam Optimization (PSO) has been widely applied for the solutions of data classification problems. Population initialization is a vital factor in PSO algorithm, which considerably influences the diversity and convergence during the PSO's process. In this paper, the training of the ANN has been implemented with new initialization technique by using low discrepancies sequence, Torus termed as TO-PSO. In this paper, a detailed comparative performance analysis for the training of neural network is observed on nine benchmark data sets taken from UCI repository. The Results demonstrate that training of ANN with proposed initialization technique offer efficient and best substitute to traditional training approaches of the NN, which gives the solution of problems related to the data classification. Furthermore, the performance of TO-PSO has been compared with back propagation algorithm (BPA), standard PSO-NN and two other initialization approaches Sobol based PSO (SO-PSONN) and Halton based PSO (H-PSONN) for the training of ANN. The experimental results show that the proposed approach outperforms than BPA, traditional PSONN, SO-PSONN and H-PSONN in terms of converging speed and better accuracy Moreover, the outcomes of our work present a foresight that how the proposed initialization technique can be used as an efficient alternative to standard training approaches for the data classification problems.","PeriodicalId":204493,"journal":{"name":"2020 International Conference on Emerging Trends in Smart Technologies (ICETST)","volume":"32 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114513675","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 2

Rapid Aircraft Classification in Satellite Imagery using Fully Convolutional Residual Network 基于全卷积残差网络的卫星图像飞机快速分类

2020 International Conference on Emerging Trends in Smart Technologies (ICETST) Pub Date : 2020-03-01 DOI: 10.1109/ICETST49965.2020.9080734

S. Khan, Syed Irteza Ali Khan, Zain UI Abideen, Muhammad Salman Khan, S. Anwar

{"title":"Rapid Aircraft Classification in Satellite Imagery using Fully Convolutional Residual Network","authors":"S. Khan, Syed Irteza Ali Khan, Zain UI Abideen, Muhammad Salman Khan, S. Anwar","doi":"10.1109/ICETST49965.2020.9080734","DOIUrl":"https://doi.org/10.1109/ICETST49965.2020.9080734","url":null,"abstract":"Advancement in high-performance computing technology has paved way for development of Deep Learning algorithms for computer vision to provide unprecedented performance both in terms of accuracy and speed. Image recognition, a subfield of computer vision, is one of the key application areas in which deep learning based Convolutional Neural Networks (CNN have achieved ground-breaking performance). Majority of the algorithms of object classification in CNN are focused on street view imagery that is of high resolution and have small size. The problem with satellite imagery is that it has objects in small size and images are especially in large size. There are two main objectives of this research: time reduction of processing large dimensions satellite images, and achieving acceptable accuracy of classifying small aircrafts. For this purpose, ResNet-50 has been modified such that it can process high-resolution satellite imagery of large dimension in one go instead of processing it in small patches sequentially, without affecting the accuracy of object classification. ResNet-50 with sliding-window scanning technique and the proposed model trained on satellite imagery are compared. The proposed method reduces the processing time by 99.9% by keeping the accuracy at the same level.","PeriodicalId":204493,"journal":{"name":"2020 International Conference on Emerging Trends in Smart Technologies (ICETST)","volume":"178 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121980508","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Soft Computing Technique based Nonlinear Sliding Mode Control for Stand-Alone Photovoltaic System 基于软计算技术的单机光伏系统非线性滑模控制

2020 International Conference on Emerging Trends in Smart Technologies (ICETST) Pub Date : 2020-03-01 DOI: 10.1109/ICETST49965.2020.9080708

A. Rehman, L. Khan, Nagmash Ali, Zaheer Alam, Z. Khan, M. A. Khan

{"title":"Soft Computing Technique based Nonlinear Sliding Mode Control for Stand-Alone Photovoltaic System","authors":"A. Rehman, L. Khan, Nagmash Ali, Zaheer Alam, Z. Khan, M. A. Khan","doi":"10.1109/ICETST49965.2020.9080708","DOIUrl":"https://doi.org/10.1109/ICETST49965.2020.9080708","url":null,"abstract":"Energy production capability of a photovoltaic (PV) system is extensively depends upon the ambient temperature (T) and solar irradiance (Ee), In order to adapt the ever increasing interest in energy, the PV array must be operated at the maximum power point (MPP). However, due to varying climatic conditions, there is a low energy efficiency problem. In this research article, a robust and efficient nonlinear sliding mode control (SMC) based maximum power point tracking (MPPT) technique is designed to extract maximum power from the PV array. This study uses artificial feed-forward neural network (AFNN) to generate the reference voltage for MPPT using non-inverting DC-DC Buck-Boost converter. Asymptotically convergence is ensures using Lyapunov stability criteria. The MATLAB/SIMULINK platform is used to design, simulate and test the performance of the proposed technique. To further validate the proposed control technique in terms of efficiency, tracking speed and robustness, results are compared with the non-linear backstepping (B) technique under continuous conditions of environment, faults and parametric uncertainties.","PeriodicalId":204493,"journal":{"name":"2020 International Conference on Emerging Trends in Smart Technologies (ICETST)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129785575","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Climate Smart Agriculture: A Survey and Taxonomy 气候智慧型农业:调查与分类

2020 International Conference on Emerging Trends in Smart Technologies (ICETST) Pub Date : 2020-03-01 DOI: 10.1109/ICETST49965.2020.9080695

M. Gulzar, G. Abbas, M. Waqas

{"title":"Climate Smart Agriculture: A Survey and Taxonomy","authors":"M. Gulzar, G. Abbas, M. Waqas","doi":"10.1109/ICETST49965.2020.9080695","DOIUrl":"https://doi.org/10.1109/ICETST49965.2020.9080695","url":null,"abstract":"Due to the disruption in the cycles of rainfall, increasing atmospheric temperatures and a rise in CO2 emissions, food security is at a risk globally. This has posed severe threats to food availability, quality, quantity and livelihoods of the stakeholders in the agricultural industry. Thus, an integration of smart and environment friendly systems is inevitable to sustain the natural resources, livelihoods, food production and distribution to thereby ensure food security and mitigate climate change risks. To that end, Smart Agriculture (SA) and Climate Smart Agriculture (CSA) augment the existing agricultural systems to adapt to and mitigate changing climatic conditions while ensuring resource utilization optimization. Through the use of latest technologies, such as the Internet of Things (IoT), Artificial Intelligence, Geo-informatics and Big Data analytics, SA and CSA have been widely studied to determine optimal crop health and increase food safety. However, there is a need to analyze the role of SA and CSA in reducing the impact of climate change on food security. This paper presents an analytical review of SA and CSA along with a thorough CSA architectural taxonomy. We also highlight the limitations in the existing literature, and present recommendations to address the open issues.","PeriodicalId":204493,"journal":{"name":"2020 International Conference on Emerging Trends in Smart Technologies (ICETST)","volume":"17 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127535965","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 3