It is a familiar fact that conservation and preservation of network energy is one of the primary objectives of the sensor nodes in a wireless sensor network. This becomes even more important when we are talking about Wireless Body Area Network (WBAN). In this case, the sensor nodes are working either very close to or inside a human body. Hence performance is a very important task here. In this project we aim to reduce the consumption of energy while a transmission is made. We tend to strategically toggle between working/non-working status of a sensor node while it is being involved or not involved in the transmission process. This was, we are able to increase the network time by a very good amount. Other deceptive parameters are also to be calculated. With the advancement in technology, we now have access to wearable physiological monitoring system. In this concept, an individual will wear a fabric in which a collection of sensors will be embedded. All these sensors will be connected to a central monitoring system. Sensors will continuously send data to these central monitoring systems. Hence, wireless sensors are now being used as wearable gadgets. But the limitation here is that they have very limited amount of energy. And when it comes in medical terms, every fault in an instrument can be a factor in determining the cause of a healthy life or an unnoticed illness. Hence, it becomes very important to work on these sensors and give them a long lifetime so that their monitoring does not get affected. There are many ways we can achieve this. Good amount of research has been done in this domain. We here are working on an algorithm in which a sensor node will be strategically switched on and off based upon its usage. This way, only the appropriate amount of energy will be used by the sensor and overall energy of the complete system or network will be preserved on a larger extent.
{"title":"A Hop To Hop Energy Efficient Transmission for WBAN (Wireless Body Area Network)","authors":"Er. Pinki Rani, Er. Rajnish Kansal","doi":"10.21090/ijaerd.84169","DOIUrl":"https://doi.org/10.21090/ijaerd.84169","url":null,"abstract":"It is a familiar fact that conservation and preservation of network energy is one of the primary objectives of the sensor nodes in a wireless sensor network. This becomes even more important when we are talking about Wireless Body Area Network (WBAN). In this case, the sensor nodes are working either very close to or inside a human body. Hence performance is a very important task here. In this project we aim to reduce the consumption of energy while a transmission is made. We tend to strategically toggle between working/non-working status of a sensor node while it is being involved or not involved in the transmission process. This was, we are able to increase the network time by a very good amount. Other deceptive parameters are also to be calculated. With the advancement in technology, we now have access to wearable physiological monitoring system. In this concept, an individual will wear a fabric in which a collection of sensors will be embedded. All these sensors will be connected to a central monitoring system. Sensors will continuously send data to these central monitoring systems. Hence, wireless sensors are now being used as wearable gadgets. But the limitation here is that they have very limited amount of energy. And when it comes in medical terms, every fault in an instrument can be a factor in determining the cause of a healthy life or an unnoticed illness. Hence, it becomes very important to work on these sensors and give them a long lifetime so that their monitoring does not get affected. There are many ways we can achieve this. Good amount of research has been done in this domain. We here are working on an algorithm in which a sensor node will be strategically switched on and off based upon its usage. This way, only the appropriate amount of energy will be used by the sensor and overall energy of the complete system or network will be preserved on a larger extent.","PeriodicalId":13720,"journal":{"name":"International Journal of Advance Research, Ideas and Innovations in Technology","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2017-05-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"89069080","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 : 2017-05-18DOI: 10.20546/ijcmas.2017.610.257
Ramanjali Thirri, T. Singh
Laboratory evaluation of eleven Bt cotton cultivars expressing both Cry1Ac and Cry2Ab endotoxins (BT-II) and non Bt cotton on weight of first, second, third and fourth instar larvae of Spodoptera litura. Different plant parts were used i.e. leaves, squares and bolls at 60, 75, 90 and 125 days after sowing of the crop for bioassay. First instar larvae fed on leaves and bolls shows hundred per cent mortality. The final weight of the each instar was reduced at seven days after feeding when compared to non Bt cotton. Reduced in weight was more in case of first, second and third instar than fourth instar larvae. Among leaves, squares and bolls reduction in weight of all four instars was more on leaves followed by squares and bolls
{"title":"Effect of BG-II Cotton Hybrids and Non Bt Cotton on Weight of Different Instars of Spodoptera Litura (Fab.)","authors":"Ramanjali Thirri, T. Singh","doi":"10.20546/ijcmas.2017.610.257","DOIUrl":"https://doi.org/10.20546/ijcmas.2017.610.257","url":null,"abstract":"Laboratory evaluation of eleven Bt cotton cultivars expressing both Cry1Ac and Cry2Ab endotoxins (BT-II) and non Bt cotton on weight of first, second, third and fourth instar larvae of Spodoptera litura. Different plant parts were used i.e. leaves, squares and bolls at 60, 75, 90 and 125 days after sowing of the crop for bioassay. First instar larvae fed on leaves and bolls shows hundred per cent mortality. The final weight of the each instar was reduced at seven days after feeding when compared to non Bt cotton. Reduced in weight was more in case of first, second and third instar than fourth instar larvae. Among leaves, squares and bolls reduction in weight of all four instars was more on leaves followed by squares and bolls","PeriodicalId":13720,"journal":{"name":"International Journal of Advance Research, Ideas and Innovations in Technology","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2017-05-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"88402916","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}
Face detection and recognition is an important paradigm when we consider the biometric based systems. Among various biometric elements, the face is the most reliable one and can be easily observed even from a distance as compared to iris or fingerprint which needs to be closely observed to use them for any kind of detection and recognition. Challenges faced by face detection algorithms often involve the presence of facial features such as beards, mustaches, and glasses, facial expressions, and occlusion of faces like surprised or crying. Another problem is illumination and poor lighting conditions such as in video surveillance cameras image quality and size of an image as in passport control or visa control. Complex backgrounds also make it extremely hard to detect faces. In this research work, a number of methods and research paradigms pertaining to face detection and recognition is studied at length and evaluate various face detection and recognition methods, provide a complete solution for image-based face detection and recognition with higher accuracy, a better response rate as an initial step for video surveillance.
{"title":"A New Hybrid Approach on Face Detection and Recognition","authors":"Saloni Dwivedi, Nitika Gupta","doi":"10.31219/osf.io/r7984","DOIUrl":"https://doi.org/10.31219/osf.io/r7984","url":null,"abstract":"Face detection and recognition is an important paradigm when we consider the biometric based systems. Among various biometric elements, the face is the most reliable one and can be easily observed even from a distance as compared to iris or fingerprint which needs to be closely observed to use them for any kind of detection and recognition. Challenges faced by face detection algorithms often involve the presence of facial features such as beards, mustaches, and glasses, facial expressions, and occlusion of faces like surprised or crying. Another problem is illumination and poor lighting conditions such as in video surveillance cameras image quality and size of an image as in passport control or visa control. Complex backgrounds also make it extremely hard to detect faces. In this research work, a number of methods and research paradigms pertaining to face detection and recognition is studied at length and evaluate various face detection and recognition methods, provide a complete solution for image-based face detection and recognition with higher accuracy, a better response rate as an initial step for video surveillance.","PeriodicalId":13720,"journal":{"name":"International Journal of Advance Research, Ideas and Innovations in Technology","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2017-05-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"85530241","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}
Cloud computing is a globalized concept and there are no borders within the cloud. Computers used to process and store user data can be located anywhere on the globe, depending on where the capacities that are required are available in the global computer networks used for cloud computing. Because of the attractive features of cloud computing, many organizations are using cloud storage for storing their critical information. The data can be stored remotely in the cloud by the users and can be accessed using thin clients as and when required. One of the major issue in the cloud today is data security in cloud computing. Storage of data in the cloud can be risky because of use of the Internet by cloud-based services which means less control over the stored data. One of the major concern in the cloud is how do we grab all the been ts of the cloud while maintaining security controls over the organizations' assets. Our aim is to propose a more reliable, decentralized lightweight key management technique for cloud systems which provides more e client data security and key management in cloud systems. Our proposed technique provides better security against Byzantine failure, server colluding and data modi cation attacks. Keywords: Cloud security; key management; server colluding attacks; Byzantine failure;
{"title":"Data Security in Cloud Computing","authors":"Vimal Kumar, Sivadon Chaisiri, R. Ko","doi":"10.1049/PBSE007E","DOIUrl":"https://doi.org/10.1049/PBSE007E","url":null,"abstract":"Cloud computing is a globalized concept and there are no borders within the cloud. Computers used to process and store user data can be located anywhere on the globe, depending on where the capacities that are required are available in the global computer networks used for cloud computing. Because of the attractive features of cloud computing, many organizations are using cloud storage for storing their critical information. The data can be stored remotely in the cloud by the users and can be accessed using thin clients as and when required. One of the major issue in the cloud today is data security in cloud computing. Storage of data in the cloud can be risky because of use of the Internet by cloud-based services which means less control over the stored data. One of the major concern in the cloud is how do we grab all the been ts of the cloud while maintaining security controls over the organizations' assets. Our aim is to propose a more reliable, decentralized lightweight key management technique for cloud systems which provides more e client data security and key management in cloud systems. Our proposed technique provides better security against Byzantine failure, server colluding and data modi cation attacks. Keywords: Cloud security; key management; server colluding attacks; Byzantine failure;","PeriodicalId":13720,"journal":{"name":"International Journal of Advance Research, Ideas and Innovations in Technology","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2017-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"90736330","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 : 2016-11-26DOI: 10.24996/ijs.2017.58.4b.20
N. Sivakumar, Bavithra
In this work we introduce another class of normal operator which is (K-N) quasi n normal operator and given some basic properties. The relation between this operator with another types of normal operators are discussed. Here the results are given by using the conditions of (K-N) quasi normal operators.
{"title":"On the Class of (K-N) Quasi-n-Normal Operators on Hilbert Space","authors":"N. Sivakumar, Bavithra","doi":"10.24996/ijs.2017.58.4b.20","DOIUrl":"https://doi.org/10.24996/ijs.2017.58.4b.20","url":null,"abstract":"In this work we introduce another class of normal operator which is (K-N) quasi n normal operator and given some basic properties. The relation between this operator with another types of normal operators are discussed. Here the results are given by using the conditions of (K-N) quasi normal operators.","PeriodicalId":13720,"journal":{"name":"International Journal of Advance Research, Ideas and Innovations in Technology","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2016-11-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"76814145","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}
Now a days,social networking are more popular.for example,twitter,Facebook etc.social networking are used forevent detection in real time.Real time events are traffic detection,earthquake monitoring.In this paper,we use the the twitter for real time traffic event detection.Firstly,the system extract the tweets from twitter and apply the text mining techniques on that tweets.those techniques are tokenization, stop-word removing,stemming.after that classify that on the basis of class label i.e traffic event or no traffic event.In this paper, we present an online method for detection of real-traffic events in Twitter data.
{"title":"Twitter Stream Analysis for Traffic Detection in Real Time","authors":"Rucha Kulkarni, Sayali Dhanawade, S. Raut","doi":"10.21090/ijaerd.76454","DOIUrl":"https://doi.org/10.21090/ijaerd.76454","url":null,"abstract":"Now a days,social networking are more popular.for example,twitter,Facebook etc.social networking are used forevent detection in real time.Real time events are traffic detection,earthquake monitoring.In this paper,we use the the twitter for real time traffic event detection.Firstly,the system extract the tweets from twitter and apply the text mining techniques on that tweets.those techniques are tokenization, stop-word removing,stemming.after that classify that on the basis of class label i.e traffic event or no traffic event.In this paper, we present an online method for detection of real-traffic events in Twitter data.","PeriodicalId":13720,"journal":{"name":"International Journal of Advance Research, Ideas and Innovations in Technology","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2016-10-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"87076937","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}
Hazard is a situation or thing that has the potential to harm people's, property or the environment. Hazardous area cause many people health. So we must to prevent from it. We are develop hazard reporting system to prevent from hazard prob- lem. Important task of the Reporting is Data Collection.The Geo-spatial Data is used to Indicate the Data along with the geographic component.This means that the data set have loca- tion information tied to them such as geographical data in the form of coordinates,address,city,or ZIP code.User report to the organization by using the same data and organization solve that problem.
{"title":"Hazards Reporting based on Real-Time Field Data Collection using Personal Mobile Phone.","authors":"Jayanti Khutwad, B. Konde, A. Deokate, A.A.Kadam","doi":"10.21090/ijaerd.78931","DOIUrl":"https://doi.org/10.21090/ijaerd.78931","url":null,"abstract":"Hazard is a situation or thing that has the potential to harm people's, property or the environment. Hazardous area cause many people health. So we must to prevent from it. We are develop hazard reporting system to prevent from hazard prob- lem. Important task of the Reporting is Data Collection.The Geo-spatial Data is used to Indicate the Data along with the geographic component.This means that the data set have loca- tion information tied to them such as geographical data in the form of coordinates,address,city,or ZIP code.User report to the organization by using the same data and organization solve that problem.","PeriodicalId":13720,"journal":{"name":"International Journal of Advance Research, Ideas and Innovations in Technology","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2016-10-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"73717115","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 : 2016-10-10DOI: 10.1007/978-1-4842-3069-5_5
Aparna Priyadarsini Khadanga, S. K. Nayak
{"title":"Natural Language Processing","authors":"Aparna Priyadarsini Khadanga, S. K. Nayak","doi":"10.1007/978-1-4842-3069-5_5","DOIUrl":"https://doi.org/10.1007/978-1-4842-3069-5_5","url":null,"abstract":"","PeriodicalId":13720,"journal":{"name":"International Journal of Advance Research, Ideas and Innovations in Technology","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2016-10-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"87595426","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}
Data mining is process to analyses number of data sets and then extracts the meaning of data. It helps to predict the patterns and future trends, allowing business in decision making. Data mining applications are able to give the answer of business questions which can take much time to resolve traditionally. High amount of data that can be generated for the prediction of disease is analyzed traditionally and is too complicated along with voluminous to be processed. Data mining provides methods and techniques for transformation of the data into useful information for decision making. These techniques can make process fast and take less time to predict the heart disease with more accuracy. The healthcare sector assembles enormous quantity of healthcare data which cannot be mined to uncover hidden information for effectual decision making. However, there is a plenty of hidden information in this data which is untapped and not being used appropriately for predictions. It becomes more influential in case of heart disease that is considered as the predominant reason behind death all over the world. In medical field, Data Mining provides several methods which are widely used in the medical and clinical decision support systems which should be helpful for diagnosis and predicting of various diseases. These data mining techniques can be used in heart diseases takes less time and make the process much faster for the prediction system to predict diseases with good accuracy to improve their health. In this paper we survey different papers in which one or more algorithms of data mining used for the prediction of heart disease. By Applying data mining techniques to heart disease data which requires to be processed, we can get effective results and achieve reliable performance which will help in decision making in healthcare industry. It will help the medical practitioners to diagnose the disease in less time and predict probable complications well in advance. Identify the major risk factors of Heart Disease categorizing the risk factors in an order which causes damages to the heart such as diabetes, high blood cholesterol, obesity, hyper tension, smoking, poor diet, stress, etc. Data mining techniques and functions are used to identify the level of risk factors which helps the patients to take precautions in advance to save their life.
{"title":"Prediction of Heart Disease using Data Mining Techniques","authors":"Era Singh Kajal, Nishika","doi":"10.21090/ijaerd.030137","DOIUrl":"https://doi.org/10.21090/ijaerd.030137","url":null,"abstract":"Data mining is process to analyses number of data sets and then extracts the meaning of data. It helps to predict the patterns and future trends, allowing business in decision making. Data mining applications are able to give the answer of business questions which can take much time to resolve traditionally. High amount of data that can be generated for the prediction of disease is analyzed traditionally and is too complicated along with voluminous to be processed. Data mining provides methods and techniques for transformation of the data into useful information for decision making. These techniques can make process fast and take less time to predict the heart disease with more accuracy. The healthcare sector assembles enormous quantity of healthcare data which cannot be mined to uncover hidden information for effectual decision making. However, there is a plenty of hidden information in this data which is untapped and not being used appropriately for predictions. It becomes more influential in case of heart disease that is considered as the predominant reason behind death all over the world. In medical field, Data Mining provides several methods which are widely used in the medical and clinical decision support systems which should be helpful for diagnosis and predicting of various diseases. These data mining techniques can be used in heart diseases takes less time and make the process much faster for the prediction system to predict diseases with good accuracy to improve their health. In this paper we survey different papers in which one or more algorithms of data mining used for the prediction of heart disease. By Applying data mining techniques to heart disease data which requires to be processed, we can get effective results and achieve reliable performance which will help in decision making in healthcare industry. It will help the medical practitioners to diagnose the disease in less time and predict probable complications well in advance. Identify the major risk factors of Heart Disease categorizing the risk factors in an order which causes damages to the heart such as diabetes, high blood cholesterol, obesity, hyper tension, smoking, poor diet, stress, etc. Data mining techniques and functions are used to identify the level of risk factors which helps the patients to take precautions in advance to save their life.","PeriodicalId":13720,"journal":{"name":"International Journal of Advance Research, Ideas and Innovations in Technology","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2016-02-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"90045903","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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