Pub Date : 2020-01-01DOI: 10.1504/ijcaet.2020.10025501
N. Arunkumar, P. Shakeel, V. Venkatraman
Traditional ayurvedic doctors examine the state of the body by analysing the wrist pulse from the patient. Mysteriously, the characteristics of the pulses vary corresponding to the various changes in the body. The three pulses acquired from the wrist are named as vata, pitta and kapha. Ayurveda says that when there is imbalance in these three doshas, one will have disease. Two different diseases will have different patterns in their pulse characteristics. Thus, the wrist pulse signal serves as a tool to analyse the health status of a patient. In the earlier work, we have standardised the signals for normal persons and then classified the diabetic cases using approximate entropy (ApEn) (Arunkumar and Sirajudeen, 2011) and later enhanced the results using sample entropy. In the present work, sample entropy (SampEn) is being used to classify for the acute arthritis cases.
{"title":"Automatic identification of acute arthritis from ayurvedic wrist pulses","authors":"N. Arunkumar, P. Shakeel, V. Venkatraman","doi":"10.1504/ijcaet.2020.10025501","DOIUrl":"https://doi.org/10.1504/ijcaet.2020.10025501","url":null,"abstract":"Traditional ayurvedic doctors examine the state of the body by analysing the wrist pulse from the patient. Mysteriously, the characteristics of the pulses vary corresponding to the various changes in the body. The three pulses acquired from the wrist are named as vata, pitta and kapha. Ayurveda says that when there is imbalance in these three doshas, one will have disease. Two different diseases will have different patterns in their pulse characteristics. Thus, the wrist pulse signal serves as a tool to analyse the health status of a patient. In the earlier work, we have standardised the signals for normal persons and then classified the diabetic cases using approximate entropy (ApEn) (Arunkumar and Sirajudeen, 2011) and later enhanced the results using sample entropy. In the present work, sample entropy (SampEn) is being used to classify for the acute arthritis cases.","PeriodicalId":38492,"journal":{"name":"International Journal of Computer Aided Engineering and Technology","volume":"2 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2020-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"88785278","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}
Pub Date : 2019-09-30DOI: 10.34218/ijcet.10.5.2019.001
Puja Sinha, P. Srivastava, Sumeet Kumar
{"title":"SOFTWARE DEVELOPMENT EFFORT ESTIMATION USING SOFT COMPUTING METHODS","authors":"Puja Sinha, P. Srivastava, Sumeet Kumar","doi":"10.34218/ijcet.10.5.2019.001","DOIUrl":"https://doi.org/10.34218/ijcet.10.5.2019.001","url":null,"abstract":"","PeriodicalId":38492,"journal":{"name":"International Journal of Computer Aided Engineering and Technology","volume":"21 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2019-09-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"77826006","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}
Pub Date : 2019-09-30DOI: 10.34218/ijcet.10.5.2019.002
Kuruganty Seetha Ram Babu, A. Satyanarayana, A. Kumar
{"title":"INVENTORY HOLDING PERIOD ANALYSIS OF SAP IMPLEMENTED COMPANIES","authors":"Kuruganty Seetha Ram Babu, A. Satyanarayana, A. Kumar","doi":"10.34218/ijcet.10.5.2019.002","DOIUrl":"https://doi.org/10.34218/ijcet.10.5.2019.002","url":null,"abstract":"","PeriodicalId":38492,"journal":{"name":"International Journal of Computer Aided Engineering and Technology","volume":"199 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2019-09-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"74251344","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}
Pub Date : 2019-09-30DOI: 10.34218/ijcet.10.5.2019.004
Rubin Zou
{"title":"COOPERATIVE POSITIONING OR UAV SWARMS BY FUSING IMU/UWB/GPS WITH FEDERAL KALMAN FILTER","authors":"Rubin Zou","doi":"10.34218/ijcet.10.5.2019.004","DOIUrl":"https://doi.org/10.34218/ijcet.10.5.2019.004","url":null,"abstract":"","PeriodicalId":38492,"journal":{"name":"International Journal of Computer Aided Engineering and Technology","volume":"28 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2019-09-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"79154902","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}
Pub Date : 2019-09-30DOI: 10.34218/ijcet.10.5.2019.005
R. Biswas, P. Talwar
{"title":"MODEL BASED SOFTWARE PROCESS","authors":"R. Biswas, P. Talwar","doi":"10.34218/ijcet.10.5.2019.005","DOIUrl":"https://doi.org/10.34218/ijcet.10.5.2019.005","url":null,"abstract":"","PeriodicalId":38492,"journal":{"name":"International Journal of Computer Aided Engineering and Technology","volume":"13 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2019-09-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"90889053","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}
Pub Date : 2019-09-30DOI: 10.34218/ijcet.10.5.2019.003
N SanjayK, K. Shaila, R. VenugopalK.
The physical conditions of the area of interest is being collected at the central location using a set of dedicated sensors that forms a network is referred to as Wireless Sensor Network. A dynamic environment is required for a secure multi-hop communication between nodes of the heterogeneous Wireless Sensor Network. One such solution is to employ autonomic based learning in a MAC Layer of the UWB TxRx. Over a time period the autonomic based network learns from the previous experience and adapts to the environment significantly. Exploring the Autonomicity would help us in evading the congestion of about 30% in a typical UWB-WSNs. Simulation results showed an improvement of 5% using Local Automate Collision Avoidance Scheme (LACAS-UWB) compared to LACAS. Key words: Autonomic Network Architecture, Dynamic Environment, LACAS, Stocastic Model, Ultra Wide-band, Wireless Sensor Networks. Cite this Article: Sanjay K N, Shaila K, Venugopal K R, Congestion Control for a Ultra-Wideband Dynamic Sensor Network Using Autonomic Based Learning, International Journal of Computer Engineering and Technology 10(5), 2019, pp. 20-37. http://www.iaeme.com/IJCET/issues.asp?JType=IJCET&VType=10&IType=5 Congestion Control for a Ultra-Wideband Dynamic Sensor Network Using Autonomic Based Learning http://www.iaeme.com/IJCET/index.asp 21 editor@iaeme.com 1. INTRODUCTION In collaborative space-timing task requires a low cost integrated sensing, communicating and computing nodes that involves an innovative technology in wireless networking, array processing and microelectronics. In the areas of embedded systems, networking, multi-agent systems and pervasive computing WSN finds a significant consideration due to the real time scenarios like environment monitoring, disaster relief. With the combination of large number of static sensor nodes the distributive sensing would be achieved in WSNs [1]. The unique functioning of WSNs can be characterized and the effective use of the communication protocol itself is mandatory and demands for the cross-layer design. One such approach is to combine the Local Automate and Autonomic Network Architecture at the MAC level of the network [2]. This leads to self-healing network that are energy efficient MAC with self-organizing and fault-tolerant routing protocols that involves distributed algorithms. MAC rules have been developed to minimize interference and packet collisions that includes [4], [5], [6]: optimizing the channel access, packet transmission and retransmission methods; packets lengths; modulation and coding; transmission powers; etc. are few of the well-known algorithms that are used till date [8], [9]. These techniques, are not well-suited to the WSNs due to the addressing issues. Thus, a serious paradigm shift in MAC designs is required. Therefore, the decentralized character of a typical WSNs has to be rolled out that complicates with any number of attempts that are required to attain a network wide synchronization [10], [11].
我们的工作考虑了大量的超小型自主超宽带设备，其中传感器节点配备了集成传感器，数据处理能力和短距离无线电通信。节点之间的数据通信是Sanjay K N, Shaila K, Venugopal K R http://www.iaeme.com/IJCET/index.asp 22 editor@iaeme.com转发到专门的网关节点。对于传感器网络，有两种可选的路由方法，即平面多跳和聚类[20-22]。节点间的数据通信通过专用网关节点实现，采用扁平、多跳和集群路由方式。为了使成本最小化，传感器数据可以在节点或附近节点的集群内进行组合和压缩，并减少数据包的有效载荷[23]。在无线传感器网络中，如何管理数据包的开销是一个很大的挑战。现有的方法主要关注路由和目的地识别问题[25]。一个关键的问题仍然存在，那就是MAC头或MAC地址的开销。在目前的方法中，使用的唯一标识符是相同的大小或大于数据包的有效载荷，显示能源消耗的重要来源。蜂窝系统中可用的空闲空间包含地址不可知和存在于数据包中的地址。这种在传感器网络中进行MAC寻址的方法节省了能量[26-30]。ANA架构由两层协调组成，即较低的任务执行层和较高的任务分配层，如图1所示。虚线表示的是Autonomic Network Architecture的细节，它不同于现有采用响应方法的多跳协调分层架构[31]。•自配置:传感器网络通过任务分配和执行方案的过程自适应动态变化的环境。 自优化:系统在拓扑、传播、干扰等方面的配置都是自主的，能够持续适应流量轮廓和网络环境。自我修复:任务分配系统对网络故障具有鲁棒性，其中任务执行能够自我修复意外的机器人地层损坏。自我保护:任务性能允许系统路由和协商复杂的不可预见的障碍。·节点任务分配:极端情况下，由于工作量少或不需要工作，节点的移动将受到限制。因此，一般系统性能会受到任务干扰的不利影响。为了尽量减少物理干扰，在我们的工作中，节点是动态分布的。·动态变化节点的复杂性:现有节点往往没有充分利用传感器输入，而传感器输入可能提供有用的数据来协调行为并选择最合适的行动。·最小节点的联盟形成:令人兴奋的多智能体编队方案需要复杂的规划，特殊的编队方案需要复杂的调度、明确的谈判和精确的联盟成本评估。因此，它们可能无法在大规模传感器网络中实时运行。·资源受限节点的合作:只能从传感器节点获得局部的、不确定的、通信和传感能力有限的环境信息。基于自主学习的超宽带动态传感器网络拥塞控制http://www.iaeme.com/IJCET/index.asp 23 editor@iaeme.com图1。自主网络架构动机无线传感器网络设计的重点是保证其在特定能量和复杂性约束下长期存在。MAC在这个设计中起着重要的作用，因为它控制着每个节点的活动和睡眠状态。因此，MAC协议需要可靠性、寿命、公平性、可扩展性和延迟等主要设计因素[6]。为了在网络中实现良好的数据传输可靠性，拥塞是需要考虑的最重要的因素。由于无线传感器网络中拥塞的增加，导致传感器节点上能量的大量耗散。这将导致数据包丢失，并造成不公平和不可靠的数据包流[11]。在许多情况下，节点在不更换能源的情况下长时间不间断地工作。因此，优化传感器节点的能量消耗是wsn的关键问题[15]。在中间节点和局部控制中心的帮助下，传感器网络中的所有节点将数据传输到汇聚节点[18]。这增加了中间节点发生拥塞的可能性。在为任何应用设计wsn时都必须解决这些问题[19-20]。
{"title":"CONGESTION CONTROL FOR A ULTRA-WIDEBAND DYNAMIC SENSOR NETWORK USING AUTONOMIC BASED LEARNING","authors":"N SanjayK, K. Shaila, R. VenugopalK.","doi":"10.34218/ijcet.10.5.2019.003","DOIUrl":"https://doi.org/10.34218/ijcet.10.5.2019.003","url":null,"abstract":"The physical conditions of the area of interest is being collected at the central location using a set of dedicated sensors that forms a network is referred to as Wireless Sensor Network. A dynamic environment is required for a secure multi-hop communication between nodes of the heterogeneous Wireless Sensor Network. One such solution is to employ autonomic based learning in a MAC Layer of the UWB TxRx. Over a time period the autonomic based network learns from the previous experience and adapts to the environment significantly. Exploring the Autonomicity would help us in evading the congestion of about 30% in a typical UWB-WSNs. Simulation results showed an improvement of 5% using Local Automate Collision Avoidance Scheme (LACAS-UWB) compared to LACAS. Key words: Autonomic Network Architecture, Dynamic Environment, LACAS, Stocastic Model, Ultra Wide-band, Wireless Sensor Networks. Cite this Article: Sanjay K N, Shaila K, Venugopal K R, Congestion Control for a Ultra-Wideband Dynamic Sensor Network Using Autonomic Based Learning, International Journal of Computer Engineering and Technology 10(5), 2019, pp. 20-37. http://www.iaeme.com/IJCET/issues.asp?JType=IJCET&VType=10&IType=5 Congestion Control for a Ultra-Wideband Dynamic Sensor Network Using Autonomic Based Learning http://www.iaeme.com/IJCET/index.asp 21 editor@iaeme.com 1. INTRODUCTION In collaborative space-timing task requires a low cost integrated sensing, communicating and computing nodes that involves an innovative technology in wireless networking, array processing and microelectronics. In the areas of embedded systems, networking, multi-agent systems and pervasive computing WSN finds a significant consideration due to the real time scenarios like environment monitoring, disaster relief. With the combination of large number of static sensor nodes the distributive sensing would be achieved in WSNs [1]. The unique functioning of WSNs can be characterized and the effective use of the communication protocol itself is mandatory and demands for the cross-layer design. One such approach is to combine the Local Automate and Autonomic Network Architecture at the MAC level of the network [2]. This leads to self-healing network that are energy efficient MAC with self-organizing and fault-tolerant routing protocols that involves distributed algorithms. MAC rules have been developed to minimize interference and packet collisions that includes [4], [5], [6]: optimizing the channel access, packet transmission and retransmission methods; packets lengths; modulation and coding; transmission powers; etc. are few of the well-known algorithms that are used till date [8], [9]. These techniques, are not well-suited to the WSNs due to the addressing issues. Thus, a serious paradigm shift in MAC designs is required. Therefore, the decentralized character of a typical WSNs has to be rolled out that complicates with any number of attempts that are required to attain a network wide synchronization [10], [11]. ","PeriodicalId":38492,"journal":{"name":"International Journal of Computer Aided Engineering and Technology","volume":"92 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2019-09-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"76832774","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}
Pub Date : 2019-08-30DOI: 10.34218/ijcet.10.4.2019.004
T. Asfaw
{"title":"PREDICTION OF DIABETES MELLITUS USING MACHINE LEARNING TECHNIQUES ","authors":"T. Asfaw","doi":"10.34218/ijcet.10.4.2019.004","DOIUrl":"https://doi.org/10.34218/ijcet.10.4.2019.004","url":null,"abstract":"","PeriodicalId":38492,"journal":{"name":"International Journal of Computer Aided Engineering and Technology","volume":"62 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2019-08-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"83836688","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}
Pub Date : 2019-08-30DOI: 10.34218/ijcet.10.4.2019.003
S. Bahuguna
Audio steganography is one of the important methods to transit hidden information by modifying an audio signal in an imperceptible manner. In audio steganography, determining the best possible position for embedding the secret message with minimum alteration of the cover audio remains a challenging issue. In this technique we hide secrete text or audio information in a host message such that no one except the intended receiver is aware of its existence and prevents unauthorised access. Various techniques have been used for embedding information in digital audio. Least significant bit (LSB) technique is most commonly used technique for secure data transfer. This paper presents replacement of bits of samples to hide a sequence of bits of secrete text. The bits of samples are selected in a circular manner from LSB to MSB or vice versa. The results reveal satisfactory performance of the system and may be used for secure movement of data.
{"title":"AUDIO STEGANOGRAPHY TECHNIQUE USING CIRCULAR BIT REPLACEMENT","authors":"S. Bahuguna","doi":"10.34218/ijcet.10.4.2019.003","DOIUrl":"https://doi.org/10.34218/ijcet.10.4.2019.003","url":null,"abstract":"Audio steganography is one of the important methods to transit hidden information by modifying an audio signal in an imperceptible manner. In audio steganography, determining the best possible position for embedding the secret message with minimum alteration of the cover audio remains a challenging issue. In this technique we hide secrete text or audio information in a host message such that no one except the intended receiver is aware of its existence and prevents unauthorised access. Various techniques have been used for embedding information in digital audio. Least significant bit (LSB) technique is most commonly used technique for secure data transfer. This paper presents replacement of bits of samples to hide a sequence of bits of secrete text. The bits of samples are selected in a circular manner from LSB to MSB or vice versa. The results reveal satisfactory performance of the system and may be used for secure movement of data.","PeriodicalId":38492,"journal":{"name":"International Journal of Computer Aided Engineering and Technology","volume":"11 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2019-08-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"87440869","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}
Pub Date : 2019-07-30DOI: 10.34218/ijcet.10.4.2019.001
B. Srinivas, J. Prasad
{"title":"GA BASED DIMENSIONALITY REDUCTION IN HYPERSPECTRAL IMAGE SEGMENTATION FRAMEWORK ","authors":"B. Srinivas, J. Prasad","doi":"10.34218/ijcet.10.4.2019.001","DOIUrl":"https://doi.org/10.34218/ijcet.10.4.2019.001","url":null,"abstract":"","PeriodicalId":38492,"journal":{"name":"International Journal of Computer Aided Engineering and Technology","volume":"23 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2019-07-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"73856125","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}
Pub Date : 2019-07-30DOI: 10.34218/ijcet.10.4.2019.002
T. Asfaw
Breast cancer is one of the greatest common diseases among women in Africa and worldwide. Accurate and early diagnosis is very significant phase in therapy and action. However, it is not an easy one due to some doubts in detection of breast cancer. Machine learning helps us to extract information and knowledge from this the basis of past experiences and detect hard-to-perceive pattern from large and noisy dataset. This paper compares and analysis the performance of machine learning algorithms, namely Decision Tree (DT), Logistic Regression (LR), Naïve Bayes (NB), and K-Nearest Neighbors (KNN) for detecting breast cancer. The data set used for comparison was from UCI Wisconsin original breast cancer data set. The result outcome shows that Logistic Regression performs better and classification accuracy is 96.93%.
{"title":"COMPARATIVE ANALYSIS OF CLASSIFICATION APPROACHES FOR BREAST CANCER","authors":"T. Asfaw","doi":"10.34218/ijcet.10.4.2019.002","DOIUrl":"https://doi.org/10.34218/ijcet.10.4.2019.002","url":null,"abstract":"Breast cancer is one of the greatest common diseases among women in Africa and worldwide. Accurate and early diagnosis is very significant phase in therapy and action. However, it is not an easy one due to some doubts in detection of breast cancer. Machine learning helps us to extract information and knowledge from this the basis of past experiences and detect hard-to-perceive pattern from large and noisy dataset. This paper compares and analysis the performance of machine learning algorithms, namely Decision Tree (DT), Logistic Regression (LR), Naïve Bayes (NB), and K-Nearest Neighbors (KNN) for detecting breast cancer. The data set used for comparison was from UCI Wisconsin original breast cancer data set. The result outcome shows that Logistic Regression performs better and classification accuracy is 96.93%.","PeriodicalId":38492,"journal":{"name":"International Journal of Computer Aided Engineering and Technology","volume":"21 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2019-07-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"88679486","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}
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