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Predicting trends in air pollution in Delhi using data mining 利用数据挖掘预测德里空气污染趋势
2016 1st India International Conference on Information Processing (IICIP) Pub Date : 2016-08-01 DOI: 10.1109/IICIP.2016.7975379
Shweta Taneja, N. Sharma, Kettun Oberoi, Yash Navoria
{"title":"Predicting trends in air pollution in Delhi using data mining","authors":"Shweta Taneja, N. Sharma, Kettun Oberoi, Yash Navoria","doi":"10.1109/IICIP.2016.7975379","DOIUrl":"https://doi.org/10.1109/IICIP.2016.7975379","url":null,"abstract":"Over the past years the development and urbanization in Delhi has led to increase in air pollution. This has led to study and research in this area. We have used data mining to analyze the existing trends in air pollution in Delhi and make prediction about the future. The data mining techniques used are linear regression and multilayer perceptron. We have seen the trends of various air pollutants like sulphur dioxide(SO2), nitrogen dioxide (NO2), particulate matter (PM), carbon monoxide (CO), ozone (O3). By using the above techniques we have observed that there will be an increase in amount of PM10 by 45.9% in coming years. However amount of CO and NO2 may show slight increase due to increasing number of 2 wheelers on road. The other pollutants like SO2 may show decrease due to usage of non sulphur fuel and stringent pollution control measures.","PeriodicalId":220344,"journal":{"name":"2016 1st India International Conference on Information Processing (IICIP)","volume":"13 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123004250","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: 32
Stepping towards dynamic measurement for object oriented software 面向对象软件的动态测量
2016 1st India International Conference on Information Processing (IICIP) Pub Date : 2016-08-01 DOI: 10.1109/IICIP.2016.7975323
Ashu Jain, A. Chug
{"title":"Stepping towards dynamic measurement for object oriented software","authors":"Ashu Jain, A. Chug","doi":"10.1109/IICIP.2016.7975323","DOIUrl":"https://doi.org/10.1109/IICIP.2016.7975323","url":null,"abstract":"Software metrics are very helpful in measuring the different aspects of software like cohesion, coupling, polymorphism, inheritance etc. The objective of measuring software metrics are quality assurance, defect prediction, maintainability prediction, cost estimation, debugging, etc. Many authors proposed the use of static metrics for the software maintainability prediction (SMP) and were successful, but static metrics don't take into account the run-time behavior of software. Hence, to capture this dynamic behavior, dynamic metrics are necessary to be evaluated. This paper presents the empirical investigation of dynamic metrics for SMP and also compares them with static metrics. Six machine learning algorithms are used to build the prediction models for both the static and dynamic metrics. The performance of all models is compared using prevalent accuracy measures. Results show that dynamic metrics perform better than static metrics, and can be used as a sound alternative for SMP.","PeriodicalId":220344,"journal":{"name":"2016 1st India International Conference on Information Processing (IICIP)","volume":"5 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127690526","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
Speed control of hybrid electric vehicle using PSO based fractional order PID controller 基于粒子群算法的分数阶PID控制器的混合动力汽车速度控制
2016 1st India International Conference on Information Processing (IICIP) Pub Date : 2016-08-01 DOI: 10.1109/IICIP.2016.7975363
P. Kaur, Vikash Kumar, Richa Sharma
{"title":"Speed control of hybrid electric vehicle using PSO based fractional order PID controller","authors":"P. Kaur, Vikash Kumar, Richa Sharma","doi":"10.1109/IICIP.2016.7975363","DOIUrl":"https://doi.org/10.1109/IICIP.2016.7975363","url":null,"abstract":"In this paper, a conventional Proportional-Integral-Derivative (PID) and fractional-order PID controller have been designed for the speed control of hybrid electric vehicle. The parameters of both PID and FOPID controller are optimized using Practical Swarm Optimization (PSO) method taking Integral of Absolute Error (IAE) as the fitness function. The performance of the implemented controller is analyzed in terms of transient and steady state specifications. A performance comparison is carried out among the implemented controllers in the presence of vehicle parameter variations.","PeriodicalId":220344,"journal":{"name":"2016 1st India International Conference on Information Processing (IICIP)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130197935","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: 4
Improved and distributive code assignment technique for interference-free CDMA network 无干扰CDMA网络的改进分配式码分配技术
2016 1st India International Conference on Information Processing (IICIP) Pub Date : 2016-08-01 DOI: 10.1109/IICIP.2016.7975321
Imam Khursheed, Ankush Karda, Shweta R. Malwe
{"title":"Improved and distributive code assignment technique for interference-free CDMA network","authors":"Imam Khursheed, Ankush Karda, Shweta R. Malwe","doi":"10.1109/IICIP.2016.7975321","DOIUrl":"https://doi.org/10.1109/IICIP.2016.7975321","url":null,"abstract":"Code assignment In the CDMA network has been a major area of study concerned with more spatial use of the codes and reduced interferences in the network. Some of the major code assignment techniques that have been proposed so far are mainly RCA, TCA, PCA and hybrid TCA-PCA. Each of these code assignment techniques face some interferences after the code assignment. In this paper, we have discussed about the best improvements one can perform in-order to reduce the interferences faced in the network to zero while also showing the trade-off one would face in terms of number of codes used in the network. It has been shown that the code requirement of i-PCA will vary from (2(Δ) +3) to (2(2(Δ) +3)) and 2(Δ) +1 to 3(Δ) +1 for i-TCA-PCA Hybrid. The second part of paper discusses a new distributive algorithm which is based on the principles of deadlock free algorithm. Using this an increased throughput in terms of memory and total time of assignment has been achieved.","PeriodicalId":220344,"journal":{"name":"2016 1st India International Conference on Information Processing (IICIP)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130507485","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
An efficient PDE-Based nonlinear filter adapted to Rician noise for restoration and enhancement of magnetic resonance images 一种有效的基于偏微分方程的非线性滤波器用于磁共振图像的恢复和增强
2016 1st India International Conference on Information Processing (IICIP) Pub Date : 2016-08-01 DOI: 10.1109/IICIP.2016.7975339
R. B. Yadav, Subodh Srivastava, R. Srivastava
{"title":"An efficient PDE-Based nonlinear filter adapted to Rician noise for restoration and enhancement of magnetic resonance images","authors":"R. B. Yadav, Subodh Srivastava, R. Srivastava","doi":"10.1109/IICIP.2016.7975339","DOIUrl":"https://doi.org/10.1109/IICIP.2016.7975339","url":null,"abstract":"In the present manuscript, a PDE based nonlinear filter adapted to Rician noise is proposed for removal of Rician noise from MR images. The proposed method is casted into a variational framework. The introduced filter consists of two terms wherein the first term is a data fidelity term and the second term is a prior function. The first term is obtained by minimizing the negative log likelihood of Rician pdf. Since the solution of the first term is ill-posed in nature and hence a prior function is introduced which is a nonlinear anisotropic diffusion based filter. To balance the trade off between data fidelity term and prior a regularization parameter has been introduced. The performance analysis and comparative study of the proposed method with other standard methods is presented for Brain Web dataset at varying noise levels in terms of PSNR and SSIM. From the simulation results, it is observed that the proposed method is performing better noise removal in comparison to other methods.","PeriodicalId":220344,"journal":{"name":"2016 1st India International Conference on Information Processing (IICIP)","volume":"78 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130793692","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: 4
An adaptive decision threshold scheme for the matched filter method of spectrum sensing in cognitive radio using artificial neural networks 基于人工神经网络的认知无线电频谱感知匹配滤波自适应决策阈值方案
2016 1st India International Conference on Information Processing (IICIP) Pub Date : 2016-08-01 DOI: 10.1109/IICIP.2016.7975334
Atchutananda Surampudi, K. Kalimuthu
{"title":"An adaptive decision threshold scheme for the matched filter method of spectrum sensing in cognitive radio using artificial neural networks","authors":"Atchutananda Surampudi, K. Kalimuthu","doi":"10.1109/IICIP.2016.7975334","DOIUrl":"https://doi.org/10.1109/IICIP.2016.7975334","url":null,"abstract":"Spectrum sensing methods in Cognitive Radio have been one of the key areas of research. To maximize the Probability of Detection for a given Probability of False Alarm in varying environmental conditions has been a challenging task. Making the decision threshold adaptive to the channel conditions is one of the ideas that become an optimal solution for the same. Here we propose an Adaptive Threshold scheme for Matched Filter based detection over an Additive White Gaussian Noise channel by implementing Artificial Neural Networks. Predictive analysis and experiential learning become viable components in achieving high performance even during extreme fading conditions.","PeriodicalId":220344,"journal":{"name":"2016 1st India International Conference on Information Processing (IICIP)","volume":"29 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124084662","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: 16
A robust approach for automatic skin cancer disease classification 一种可靠的皮肤癌疾病自动分类方法
2016 1st India International Conference on Information Processing (IICIP) Pub Date : 2016-08-01 DOI: 10.1109/IICIP.2016.7975301
S. Singh, A. S. Jalal
{"title":"A robust approach for automatic skin cancer disease classification","authors":"S. Singh, A. S. Jalal","doi":"10.1109/IICIP.2016.7975301","DOIUrl":"https://doi.org/10.1109/IICIP.2016.7975301","url":null,"abstract":"Skin cancers particularly malignant melanoma is lethal and difficult to identify in last stages. The variation of stages (Squamous Cell Carcinoma, Actinic Keratosis Cell, Basal cell and Malignant Melanoma) of Skin Cancer is highly ambiguous and difficult to recognize. To clearly recognize the stage of Skin Cancer is primarily important for effective treatment, which cause in increasing the survival rate from Skin cancer. In this work, we propose a methodology to reduce the probability of false diagnosis. In the proposed methodology, the data set is first preprocessed using K-mean Clustering algorithm. This preprocessing helps to increase the rate of reorganization by removing all irrelevant texture. The preprocessed data is then used to extract the features. The classification results illustrate that the proposed method can considerably improve in classification of Skin cancer disease. The computed accuracy of classification for this algorithm is achieved up to 94.4%.","PeriodicalId":220344,"journal":{"name":"2016 1st India International Conference on Information Processing (IICIP)","volume":"155 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116873219","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: 7
HCAB: HealthCare analysis and data archival using big data tool HCAB:使用大数据工具的医疗保健分析和数据存档
2016 1st India International Conference on Information Processing (IICIP) Pub Date : 2016-08-01 DOI: 10.1109/IICIP.2016.7975353
Priyanka Dhaka, R. Johari
{"title":"HCAB: HealthCare analysis and data archival using big data tool","authors":"Priyanka Dhaka, R. Johari","doi":"10.1109/IICIP.2016.7975353","DOIUrl":"https://doi.org/10.1109/IICIP.2016.7975353","url":null,"abstract":"Today, healthcare industry is going through a phase which is seeing an ever increasing rise in number of doctors, patients, diseases and medicines. The healthcare data that existed in few megabytes in the past has seen an exponential rise to petabytes and exabytes in the recent years and billions of dollars are being spend not only in the storage of data but also in maintaining datasets worldwide. Since there is a large increment in datasets, it is becoming difficult to handle it with traditional data mining and diagnosis tools, so it is becoming a necessity to use big data tools and techniques in healthcare industry in order to manage, analyse and extract right amount of data for right patients at right time for right living and care. This paper puts forth the ideas and strategies of using data mining techniques such as clustering that is applied on the data which is extracted from world healthcare datasets and is deployed into a big data tool like MongoDB for effective analysis of healthcare data.","PeriodicalId":220344,"journal":{"name":"2016 1st India International Conference on Information Processing (IICIP)","volume":"19 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128573139","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: 8
Feature extraction in enhancing speech signal for mobile communication 特征提取在增强移动通信语音信号中的应用
2016 1st India International Conference on Information Processing (IICIP) Pub Date : 2016-08-01 DOI: 10.1109/IICIP.2016.7975374
U. Purushotham, K. Suresh
{"title":"Feature extraction in enhancing speech signal for mobile communication","authors":"U. Purushotham, K. Suresh","doi":"10.1109/IICIP.2016.7975374","DOIUrl":"https://doi.org/10.1109/IICIP.2016.7975374","url":null,"abstract":"Mobile phones are considered as smart phones if it has additional characteristics other than regular wireless voice transmission. Smartness does not mean compromising with quality of voice under noisy conditions. This paper discusses about the methodology for improving the quality of voice signal under adverse noise conditions. Voice signal affected by real time noise signals is passed through amplitude estimator and from there on, it is processed in cepstral and multiband domain to extract the features of speech signal. The extracted speech is used to reconstruct the original signal which is free from real noise.","PeriodicalId":220344,"journal":{"name":"2016 1st India International Conference on Information Processing (IICIP)","volume":"348 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132975998","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
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