Face recognition has drawn dramatic attention due to the advancement of pattern recognition technologies. Face recognition systems have reached a level of maturity under certain conditions but still the performance of face recognition algorithms are easily affected by external and internal variations. Thus many well-known algorithms have been proposed to overcome these challenging problems. Here we are trying to use one sample face image of individual for training the whole system which will not only reduce labouring effort for the collection and also reduce cost for storing and processing them. One sample per person face recognition (OSPP) is considered as a challenging problem in face recognition community and lack of samples leads to performance deterioration. Here face recognition is performed by application of the swarm optimization algorithms [12] . It was found out that the underlying foraging principle and the swarm optimization can be integrated into evolutionary computational algorithms to provide a better search strategy for finding optimal feature vectors for face recognition. Finally, it is believed that the particle swarm optimization may be useful for the development of face recognition system. A meta-heuristic algorithm PSO is used that makes few or no assumptions about the problem being optimized and can search very large spaces of candidate solutions and also used for classifying purpose. Keywords: One sample per person, particle swarm optimization, meta-heuristic algorithm, face recognition, intra-class variety model I. Introduction The fundamental task of face recognition technology is to recognize a human face. Here we consider the images (training set) of different subject and then extract the features from them. the next step includes training the system for classification purpose. The same architecture is followed and one sample per person is considered for the training purpose and for classification purpose Meta-heuristic algorithm like PSO is used. Particle Swarm Optimisation is an algorithm capable of optimising a nonlinear and multidimensional problem which usually reaches good solutions efficiently while requiring minimal parameterisation. The basic concept of the algorithm is to create a swarm of particles which could move in the space around the problem space searching for their goal, the place which best suits their needs given by a fitness function. OSPP face recognition algorithm is based on PSO. If we consider a neutral image of each subject as a particle, each particle has a specific mode of direction (mode of direction has been discus in proposed model). In each iteration every particle moves towards optimal solution based on the cost function. after a specific amount of iterations, the particle holding best cost function will be the optimal point (solution). Particle swarm optimisation is chosen to find out the optimum combination of the basis and variety in terms of the minimum L2 distance relative to the
{"title":"OSPP Face Recognition Using Meta-Heuristic Algorithm","authors":"Syed ArshiRahaman","doi":"10.9790/0661-1903036165","DOIUrl":"https://doi.org/10.9790/0661-1903036165","url":null,"abstract":"Face recognition has drawn dramatic attention due to the advancement of pattern recognition technologies. Face recognition systems have reached a level of maturity under certain conditions but still the performance of face recognition algorithms are easily affected by external and internal variations. Thus many well-known algorithms have been proposed to overcome these challenging problems. Here we are trying to use one sample face image of individual for training the whole system which will not only reduce labouring effort for the collection and also reduce cost for storing and processing them. One sample per person face recognition (OSPP) is considered as a challenging problem in face recognition community and lack of samples leads to performance deterioration. Here face recognition is performed by application of the swarm optimization algorithms [12] . It was found out that the underlying foraging principle and the swarm optimization can be integrated into evolutionary computational algorithms to provide a better search strategy for finding optimal feature vectors for face recognition. Finally, it is believed that the particle swarm optimization may be useful for the development of face recognition system. A meta-heuristic algorithm PSO is used that makes few or no assumptions about the problem being optimized and can search very large spaces of candidate solutions and also used for classifying purpose. Keywords: One sample per person, particle swarm optimization, meta-heuristic algorithm, face recognition, intra-class variety model \u2028 I. Introduction The fundamental task of face recognition technology is to recognize a human face. Here we consider the images (training set) of different subject and then extract the features from them. the next step includes training the system for classification purpose. The same architecture is followed and one sample per person is considered for the training purpose and for classification purpose Meta-heuristic algorithm like PSO is used. Particle Swarm Optimisation is an algorithm capable of optimising a nonlinear and multidimensional problem which usually reaches good solutions efficiently while requiring minimal parameterisation. The basic concept of the algorithm is to create a swarm of particles which could move in the space around the problem space searching for their goal, the place which best suits their needs given by a fitness function. OSPP face recognition algorithm is based on PSO. If we consider a neutral image of each subject as a particle, each particle has a specific mode of direction (mode of direction has been discus in proposed model). In each iteration every particle moves towards optimal solution based on the cost function. after a specific amount of iterations, the particle holding best cost function will be the optimal point (solution). Particle swarm optimisation is chosen to find out the optimum combination of the basis and variety in terms of the minimum L2 distance relative to the ","PeriodicalId":91890,"journal":{"name":"IOSR journal of computer engineering","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2017-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"74854497","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 recognized as a hottest technology which has a significant impact on IT field in the nearby future. Cloud computing is an Internet based computing. It provides the services to the organizations like storage, applications and servers. Cloud computing is on demand and pay per use service. That means customers pay providers based on usage. Data Integrity is the major issue in cloud computing. To provide the integrity various methods have been proposed by the researchers. In this paper we will proposed a model which will provide the data integrity using Elgamal Algorithm and SHA-2 Algorithm. The Proposed model shows that the data which is uploaded on cloud is secured if the hash key matches with the local keys which are stored on the system.
{"title":"Data Integrity Verification in Cloud Computing","authors":"G. Gupta","doi":"10.9790/0661-1903052327","DOIUrl":"https://doi.org/10.9790/0661-1903052327","url":null,"abstract":"Cloud computing is recognized as a hottest technology which has a significant impact on IT field in the nearby future. Cloud computing is an Internet based computing. It provides the services to the organizations like storage, applications and servers. Cloud computing is on demand and pay per use service. That means customers pay providers based on usage. Data Integrity is the major issue in cloud computing. To provide the integrity various methods have been proposed by the researchers. In this paper we will proposed a model which will provide the data integrity using Elgamal Algorithm and SHA-2 Algorithm. The Proposed model shows that the data which is uploaded on cloud is secured if the hash key matches with the local keys which are stored on the system.","PeriodicalId":91890,"journal":{"name":"IOSR journal of computer engineering","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2017-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"86462176","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}
V. Kamat, Terence Johnson, Rudresh Chodankar, Rama Harmalkar, G. Naik, Prajyot Narulkar
Document clustering is a process of grouping data object having similar properties. Bisecting kmeans is a top down clustering approach wherein all the documents are considered as single cluster. That cluster is then partitioned into two sub-clusters using k-means clustering algorithm, so k is considered as 2. Sum of square errors (SSE) of both the clusters are calculated. The cluster which has SSE greater, that cluster is split. This process is repeated until the desired number of clusters are obtained. Divisive Hierarchical Bisecting Min–Max Clustering Algorithm is similar to bisecting k-means clustering algorithm with a slight modification. To obtain a certain number of clusters. The main cluster is divided into two clusters using Min-Max algorithm. A cluster is selected in order to split it furthers. This process is repeated until the desired number of clusters are obtained. Divisive Hierarchical Bisecting Min–Max Clustering Algorithm is similar to bisecting k-means clustering algorithm with a slight modification. To obtain a certain number of clusters. The main cluster is divided into two clusters using Min-Max algorithm. A cluster is selected in order to split it furthers. This process is repeated until desired numbers of clusters are obtained.
{"title":"Document Clustering Using Divisive Hierarchical Bisecting Min Max Clustering Algorithm","authors":"V. Kamat, Terence Johnson, Rudresh Chodankar, Rama Harmalkar, G. Naik, Prajyot Narulkar","doi":"10.9790/0661-1903066670","DOIUrl":"https://doi.org/10.9790/0661-1903066670","url":null,"abstract":"Document clustering is a process of grouping data object having similar properties. Bisecting kmeans is a top down clustering approach wherein all the documents are considered as single cluster. That cluster is then partitioned into two sub-clusters using k-means clustering algorithm, so k is considered as 2. Sum of square errors (SSE) of both the clusters are calculated. The cluster which has SSE greater, that cluster is split. This process is repeated until the desired number of clusters are obtained. Divisive Hierarchical Bisecting Min–Max Clustering Algorithm is similar to bisecting k-means clustering algorithm with a slight modification. To obtain a certain number of clusters. The main cluster is divided into two clusters using Min-Max algorithm. A cluster is selected in order to split it furthers. This process is repeated until the desired number of clusters are obtained. Divisive Hierarchical Bisecting Min–Max Clustering Algorithm is similar to bisecting k-means clustering algorithm with a slight modification. To obtain a certain number of clusters. The main cluster is divided into two clusters using Min-Max algorithm. A cluster is selected in order to split it furthers. This process is repeated until desired numbers of clusters are obtained.","PeriodicalId":91890,"journal":{"name":"IOSR journal of computer engineering","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2017-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"74284579","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}
{"title":"Performance Analysis of Adaptive Approach for Congestion Control In Wireless Sensor Networks","authors":"S. Kumari, Dr.C.S. Lamba, Ajay Kumar","doi":"10.9790/0661-1903047178","DOIUrl":"https://doi.org/10.9790/0661-1903047178","url":null,"abstract":"","PeriodicalId":91890,"journal":{"name":"IOSR journal of computer engineering","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2017-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"87976183","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}
Certificateless aggregate signature reduces nsignatures on n distinct messages from n distinct users into a compact single length signature. Recently Deng et al proposed CLAS Scheme which is an improvement of Hou et al scheme and claims that their scheme is secure against type I type II adversary but unfortunately it is found insecure by against the“Honest but Curious” attack by adversary II. In this paper, we demonstrate that Deng et al proposed CLAS scheme is insecure against type II adversary and suggest an improved CLAS scheme.
{"title":"Cryptanalysis and Further Improvement of a Certificateless Aggregate Signature Scheme","authors":"Pankaj Kumar, Vishnu Sharma, Vinod Kumar, Ankush Kumar","doi":"10.9790/0661-1903067175","DOIUrl":"https://doi.org/10.9790/0661-1903067175","url":null,"abstract":"Certificateless aggregate signature reduces nsignatures on n distinct messages from n distinct users into a compact single length signature. Recently Deng et al proposed CLAS Scheme which is an improvement of Hou et al scheme and claims that their scheme is secure against type I type II adversary but unfortunately it is found insecure by against the“Honest but Curious” attack by adversary II. In this paper, we demonstrate that Deng et al proposed CLAS scheme is insecure against type II adversary and suggest an improved CLAS scheme.","PeriodicalId":91890,"journal":{"name":"IOSR journal of computer engineering","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2017-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"83339858","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}
In this paper, a comparison between novel adaptive Radial Basis Function Neural Networks (RBFNN) algorithm and conventional RBFNN is conducted. Both algorithms are used to forecast electrical load demand in Jordan. The Same forecasting features are used in both algorithms. Most of the forecasting models need to be adjusted after a period of time, because the change in the system parameters. The data used in this paper is real data measured by National Electrical Power co. (Jordan). The data is divided into two sets. Set for a training and the other for testing. The results illustrated that the adaptive RBFNN model outperformed conventional RBFNN. The proposed adaptive RBFNN model can enhance the reliability of the conventional RBFNN after embedding the network in the system. This is achieved by introducing an adaptive algorithm that allows the change of the weights of the RBFNN after the training process is completed, which will eliminate the need to retrain the RBFNN model again.
{"title":"Comparison between Adaptive and Conventional RBFNN Based Approach for Short-Term Load Forecasting","authors":"Eyad K. Almaita","doi":"10.9790/0661-1903043340","DOIUrl":"https://doi.org/10.9790/0661-1903043340","url":null,"abstract":"In this paper, a comparison between novel adaptive Radial Basis Function Neural Networks (RBFNN) algorithm and conventional RBFNN is conducted. Both algorithms are used to forecast electrical load demand in Jordan. The Same forecasting features are used in both algorithms. Most of the forecasting models need to be adjusted after a period of time, because the change in the system parameters. The data used in this paper is real data measured by National Electrical Power co. (Jordan). The data is divided into two sets. Set for a training and the other for testing. The results illustrated that the adaptive RBFNN model outperformed conventional RBFNN. The proposed adaptive RBFNN model can enhance the reliability of the conventional RBFNN after embedding the network in the system. This is achieved by introducing an adaptive algorithm that allows the change of the weights of the RBFNN after the training process is completed, which will eliminate the need to retrain the RBFNN model again.","PeriodicalId":91890,"journal":{"name":"IOSR journal of computer engineering","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2017-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"86544676","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}
Statistical process control (SPC) is one of the important approaches used in quality management. SPC can be applied in plants to obtain good quality and high standard products that have become very popular in many industries. Fuzzy process capability analysis by using X-R control charts gives more realistic results, developed with fuzzy theory. Fuzzy control charts are more sensitive than SPC. Therefore, fuzzy control charts lead to producing better-quality products. In this study, total color difference parameter (ΔE) was studied using fuzzy observation on a calcite grinding plant products. For this purpose, color parameters of the grinding plant products were evaluated using triangular fuzzy number (TFN) and fuzzy process capability indices (PCIs). The results show that the mill plant seems to be under control. Therefore, on a randomly selected sample used in the fuzzy statistical process control work was chosen and other color parameters such as whiteness index (WI), saturation index (SI), hue angle (H), browning index (BI) and yellowness index (YI) and particle size properties, XRF, XRD, FTIR, TGA-DTA and SEM were then determined on the calcite sample.
{"title":"Fuzzy Statistical Process Control of a Calcite Grinding Plant Using Total Color Difference Parameter (ΔE)","authors":"M. Uçurum","doi":"10.9790/3021-0705010722","DOIUrl":"https://doi.org/10.9790/3021-0705010722","url":null,"abstract":"Statistical process control (SPC) is one of the important approaches used in quality management. SPC can be applied in plants to obtain good quality and high standard products that have become very popular in many industries. Fuzzy process capability analysis by using X-R control charts gives more realistic results, developed with fuzzy theory. Fuzzy control charts are more sensitive than SPC. Therefore, fuzzy control charts lead to producing better-quality products. In this study, total color difference parameter (ΔE) was studied using fuzzy observation on a calcite grinding plant products. For this purpose, color parameters of the grinding plant products were evaluated using triangular fuzzy number (TFN) and fuzzy process capability indices (PCIs). The results show that the mill plant seems to be under control. Therefore, on a randomly selected sample used in the fuzzy statistical process control work was chosen and other color parameters such as whiteness index (WI), saturation index (SI), hue angle (H), browning index (BI) and yellowness index (YI) and particle size properties, XRF, XRD, FTIR, TGA-DTA and SEM were then determined on the calcite sample.","PeriodicalId":91890,"journal":{"name":"IOSR journal of computer engineering","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2017-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"73544413","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}
{"title":"Comparative Study On The Effect Of Temperature On Octane Number Rating Of Reformate From Nigerian Heavy Treated Naphtha Samples.","authors":"I. Otaraku, I. Egun","doi":"10.9790/3021-0705013641","DOIUrl":"https://doi.org/10.9790/3021-0705013641","url":null,"abstract":"","PeriodicalId":91890,"journal":{"name":"IOSR journal of computer engineering","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2017-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"79992048","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 : 2017-05-01DOI: 10.9790/0661-190302102106
Himanshu Sharma
Cloud Computing is a rapidly developing technology in the IT Sector. It provides an on demand service to its users using the Internet. It provides the users with the resources as per their demand and also enables the users to pay as per their usage of resources. The users can access the cloud anytime using the Internet. Such features eliminate the requirement of carrying documentation for the users as the cloud can be accessed globally and remotely by the user. But, this introduces the possibility of security breaches or intruder attacks while the user interacts with the cloud database. In this paper, some of the security concerns while using a Cloud based Online Judge will be addressed using new approaches. An Online Judge is a system which takes a code, compiles it, checks for compilation errors, and executes the code by giving it some hidden inputs and capturing the output produced after it. It checks for runtime errors and finally matches the output with the expected output from the solution. The Online Judge system is very important for grading of coding assignments and for programming contests as it can include the constraints on the solutions for source code limit and various resources such as time and space requirements. It also removes the subjective nature in the grading system of coding assignments. The Online judge faces various security issues at authorization level and access rights of users. There is also possibility of a user running a malicious code on the judge thus harming the server or other resources. In this paper we address such issues.
{"title":"Secure Online Judge in Cloud Environment","authors":"Himanshu Sharma","doi":"10.9790/0661-190302102106","DOIUrl":"https://doi.org/10.9790/0661-190302102106","url":null,"abstract":"Cloud Computing is a rapidly developing technology in the IT Sector. It provides an on demand service to its users using the Internet. It provides the users with the resources as per their demand and also enables the users to pay as per their usage of resources. The users can access the cloud anytime using the Internet. Such features eliminate the requirement of carrying documentation for the users as the cloud can be accessed globally and remotely by the user. But, this introduces the possibility of security breaches or intruder attacks while the user interacts with the cloud database. In this paper, some of the security concerns while using a Cloud based Online Judge will be addressed using new approaches. An Online Judge is a system which takes a code, compiles it, checks for compilation errors, and executes the code by giving it some hidden inputs and capturing the output produced after it. It checks for runtime errors and finally matches the output with the expected output from the solution. The Online Judge system is very important for grading of coding assignments and for programming contests as it can include the constraints on the solutions for source code limit and various resources such as time and space requirements. It also removes the subjective nature in the grading system of coding assignments. The Online judge faces various security issues at authorization level and access rights of users. There is also possibility of a user running a malicious code on the judge thus harming the server or other resources. In this paper we address such issues.","PeriodicalId":91890,"journal":{"name":"IOSR journal of computer engineering","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2017-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"81097152","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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