Pub Date : 2022-06-09DOI: 10.18535/ijecs/v11i06.4675
Shireen Shireen, Dr. Shaheen Ayyub
As different types of dynamic networks are developed by easy means of devices, people start using them for various means. Such vulnerable networks are easy places to attack and perform malicious activities. This work develops a model that can generate a path from source to destination in a dynamic node environment without prior information. Path generation artificial immune genetic algorithms will be used, as this algorithms find a good path in a short time. In order to detect the malicious activity, such nodes need to be identified. Hence identification of attackers nodes is done by trust model where Adamic Adar trust function finds the mutual trust value of node as epr past performance of nodes.
{"title":"Artificial Immune Algorithm and Adamic Adar based Wireless Sensor Network Optimization","authors":"Shireen Shireen, Dr. Shaheen Ayyub","doi":"10.18535/ijecs/v11i06.4675","DOIUrl":"https://doi.org/10.18535/ijecs/v11i06.4675","url":null,"abstract":"As different types of dynamic networks are developed by easy means of devices, people start using them for various means. Such vulnerable networks are easy places to attack and perform malicious activities. This work develops a model that can generate a path from source to destination in a dynamic node environment without prior information. Path generation artificial immune genetic algorithms will be used, as this algorithms find a good path in a short time. In order to detect the malicious activity, such nodes need to be identified. Hence identification of attackers nodes is done by trust model where Adamic Adar trust function finds the mutual trust value of node as epr past performance of nodes.","PeriodicalId":231371,"journal":{"name":"International Journal of Engineering and Computer Science","volume":"203 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-06-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133040385","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 : 2022-05-29DOI: 10.18535/ijecs/v11i05.4672
Neethu Jayaram
This paper discusses the carbon footprint caused by computer resources, cloud computing and the damage it causes to nature. The growing need of data centers and the resulting environmental problems like emission of harmful gases and production of heat are a relevant field of study. Efficient use of computing resources without harming the nature on pay-as-you-go basis is a concern. This paper discusses how to improve the above with green computing. Keywords – Cloud Computing, Green Cloud Computing, Carbon Emission, Sustainable Energy, Eco-Friendly;
{"title":"Green Cloud Computing","authors":"Neethu Jayaram","doi":"10.18535/ijecs/v11i05.4672","DOIUrl":"https://doi.org/10.18535/ijecs/v11i05.4672","url":null,"abstract":"This paper discusses the carbon footprint caused by computer resources, cloud computing and the damage it causes to nature. The growing need of data centers and the resulting environmental problems like emission of harmful gases and production of heat are a relevant field of study. Efficient use of computing resources without harming the nature on pay-as-you-go basis is a concern. This paper discusses how to improve the above with green computing.\u0000Keywords – Cloud Computing, Green Cloud Computing, Carbon Emission, Sustainable Energy, Eco-Friendly;\u0000 \u0000 ","PeriodicalId":231371,"journal":{"name":"International Journal of Engineering and Computer Science","volume":"354 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-05-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115924697","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 : 2022-04-30DOI: 10.18535/ijecs/v11i04.4670
Raj Kumar Yaduwanshi Raj, Prof. Manorama Malviya
Easy access, simulation of IOT network increases its application and demands in different area. As many of IOT networks are vulnerable in nature and attracts intruders to take advantage of weak security. This paper has developed a model that can detect the IOT network intrusion. In this work feature optimization was done by use of artificial immune system algorithm. AIS reduces the dimension of the dataset by applying affinity check and cloning steps. Selected features were further use for the traiing of neural network. Trained neural network predict the class of IOT network session (Normal / Malicious). Experiment was done on real dataset of IOT session and result shows that rpopsoed model has improved the detection accuracy as compared o existing models.
{"title":"Network Intrusion Detection by Artificial Immune System and Neural Network","authors":"Raj Kumar Yaduwanshi Raj, Prof. Manorama Malviya","doi":"10.18535/ijecs/v11i04.4670","DOIUrl":"https://doi.org/10.18535/ijecs/v11i04.4670","url":null,"abstract":"Easy access, simulation of IOT network increases its application and demands in different area. As many of IOT networks are vulnerable in nature and attracts intruders to take advantage of weak security. This paper has developed a model that can detect the IOT network intrusion. In this work feature optimization was done by use of artificial immune system algorithm. AIS reduces the dimension of the dataset by applying affinity check and cloning steps. Selected features were further use for the traiing of neural network. Trained neural network predict the class of IOT network session (Normal / Malicious). Experiment was done on real dataset of IOT session and result shows that rpopsoed model has improved the detection accuracy as compared o existing models.","PeriodicalId":231371,"journal":{"name":"International Journal of Engineering and Computer Science","volume":"61 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-04-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126577375","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 : 2022-03-28DOI: 10.18535/ijecs/v11i03.4663
Shubhi Jain, Anu Sharma, Rupal Gupta
Nowadays Business Intelligence applications are trending in different fields such as Healthcare, LifeSciences, ERP, Marketing, Retail, food industry, travel and transport industry etc. This paper mainly focuses on how we can use different ETL functionalities in order to ease the daily routine task used in different healthcare industries.
{"title":"Business Intelligence In Healthcare Industry","authors":"Shubhi Jain, Anu Sharma, Rupal Gupta","doi":"10.18535/ijecs/v11i03.4663","DOIUrl":"https://doi.org/10.18535/ijecs/v11i03.4663","url":null,"abstract":"Nowadays Business Intelligence applications are trending in different fields such as Healthcare, LifeSciences, ERP, Marketing, Retail, food industry, travel and transport industry etc. This paper mainly focuses on how we can use different ETL functionalities in order to ease the daily routine task used in different healthcare industries.","PeriodicalId":231371,"journal":{"name":"International Journal of Engineering and Computer Science","volume":"73 2","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-03-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"120918086","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 : 2022-03-17DOI: 10.18535/ijecs/v11i03.4664
N. V. Anyakora, C. Ajinomoh, A. S. Ahmed, I. Mohammed-Dabo, S. Ejeh, H. Abba, J. Okoro
Contemporary studies on the use of paved drying bed (PDB) indicate a decline in knowledge and technology-gap on the performance of this infrastructure. In consequence therefore, environmental pollution arising from untreated sludge is on the increase, especially in developing countries. In this work, model equation was developed with the existing data from field experiment using Polymath 5.1 software. The process parameters considered were temperature, relative humidity, wind speed, sun intensity and drying rate. The multiple linear regression results showed that wind speed optimised the response with the highest factor coefficient of 0.12. The developed model was well validated with a correlation coefficient of 0.97, thus could serve as a useful tool for sludge treatment plant operators in evaluation and assessment of the performance of PDB.
{"title":"Modelling Of Sludge Drying Parameters in a Paved Drying Bed","authors":"N. V. Anyakora, C. Ajinomoh, A. S. Ahmed, I. Mohammed-Dabo, S. Ejeh, H. Abba, J. Okoro","doi":"10.18535/ijecs/v11i03.4664","DOIUrl":"https://doi.org/10.18535/ijecs/v11i03.4664","url":null,"abstract":"Contemporary studies on the use of paved drying bed (PDB) indicate a decline in knowledge and technology-gap on the performance of this infrastructure. In consequence therefore, environmental pollution arising from untreated sludge is on the increase, especially in developing countries. In this work, model equation was developed with the existing data from field experiment using Polymath 5.1 software. The process parameters considered were temperature, relative humidity, wind speed, sun intensity and drying rate. The multiple linear regression results showed that wind speed optimised the response with the highest factor coefficient of 0.12. The developed model was well validated with a correlation coefficient of 0.97, thus could serve as a useful tool for sludge treatment plant operators in evaluation and assessment of the performance of PDB.","PeriodicalId":231371,"journal":{"name":"International Journal of Engineering and Computer Science","volume":"51 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-03-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115991726","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 : 2022-02-01DOI: 10.18535/ijecs/v11i02.4650
Song Yu, Li Min, Duan Weidong, He Yujie, Gou Yao, Wu Zhaoqing, Lv Yilong
To solve the problem that the feature maps generated by feature extraction network of traditional weakly supervised learning object detection algorithm is not strong in feature, and the mapping relationship between feature space and classification results is not strong, which restricts the performance of object detection, a weakly supervised object detection algorithm based on strong representation learning is proposed in this paper. Due to enhance the representation ability of feature maps, the algorithm weighted the channels of feature maps according to the importance of each channel, to strengthen the weight of crucial feature maps and ignore the significance of secondary feature maps. Meanwhile, a Gaussian Mixture distribution model with better classification performance was used to design the object instance classifier to enhance further the representation of the mapping between feature space and classification results, and a large-margin Gaussian Mixture (L-GM) loss was designed to increase the distance between sample categories and improve the generalization of the classifier. For verifying the effectiveness and advancement of the proposed algorithm, the performance of the proposed algorithm is compared with six classical weakly supervised target detection algorithms on VOC datasets. Experiments show that the weakly supervised target detection algorithm based on strong representation learning has outperformed other classical algorithms in average accuracy (AP) and correct location (CorLoc), with increases of 1.1%~14.6% and 2.8%~19.4%, respectively.
{"title":"Strong Representation Learning for Weakly Supervised Object Detection","authors":"Song Yu, Li Min, Duan Weidong, He Yujie, Gou Yao, Wu Zhaoqing, Lv Yilong","doi":"10.18535/ijecs/v11i02.4650","DOIUrl":"https://doi.org/10.18535/ijecs/v11i02.4650","url":null,"abstract":"To solve the problem that the feature maps generated by feature extraction network of traditional weakly supervised learning object detection algorithm is not strong in feature, and the mapping relationship between feature space and classification results is not strong, which restricts the performance of object detection, a weakly supervised object detection algorithm based on strong representation learning is proposed in this paper. Due to enhance the representation ability of feature maps, the algorithm weighted the channels of feature maps according to the importance of each channel, to strengthen the weight of crucial feature maps and ignore the significance of secondary feature maps. Meanwhile, a Gaussian Mixture distribution model with better classification performance was used to design the object instance classifier to enhance further the representation of the mapping between feature space and classification results, and a large-margin Gaussian Mixture (L-GM) loss was designed to increase the distance between sample categories and improve the generalization of the classifier. For verifying the effectiveness and advancement of the proposed algorithm, the performance of the proposed algorithm is compared with six classical weakly supervised target detection algorithms on VOC datasets. Experiments show that the weakly supervised target detection algorithm based on strong representation learning has outperformed other classical algorithms in average accuracy (AP) and correct location (CorLoc), with increases of 1.1%~14.6% and 2.8%~19.4%, respectively.","PeriodicalId":231371,"journal":{"name":"International Journal of Engineering and Computer Science","volume":"1545 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128062159","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 : 2022-01-20DOI: 10.36227/techrxiv.18461786.v1
J. Goyal
The research paper involves model based Intrusion detection through data mining techniques using the NSL-KDD dataset. The approach involves building of classification model and hybrid model which are created using classification techniques and, combining both classification and clustering techniques respectively. Classification model can detect known attacks effectively whereas hybrid models can detect unknown or new attacks also. The comparison of the results of different models is done over different performance evaluation parameters.
{"title":"Model Based Intrusion Detection using Data Mining Techniques with Feature Reduction","authors":"J. Goyal","doi":"10.36227/techrxiv.18461786.v1","DOIUrl":"https://doi.org/10.36227/techrxiv.18461786.v1","url":null,"abstract":"The research paper involves model based Intrusion detection through data mining techniques using the NSL-KDD dataset. The approach involves building of classification model and hybrid model which are created using classification techniques and, combining both classification and clustering techniques respectively. Classification model can detect known attacks effectively whereas hybrid models can detect unknown or new attacks also. The comparison of the results of different models is done over different performance evaluation parameters.","PeriodicalId":231371,"journal":{"name":"International Journal of Engineering and Computer Science","volume":"5 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-01-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126902868","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 : 2021-12-16DOI: 10.18535/ijecs/v10i12.4640
D. Senduraja, Mr.Pradeep Kumar.A
Optical Burst Switching (OBS) is a promising paradigm for high speed transmission of data. In OBS, a key problem is to schedule bursts with minimum loss. Single method is not sufficient to improve performance. So, our performance model includes some feasible methods to improve OBS performance without significantly increasing the implementation complexity. The methods are addition of simple fiber delay lines (FDLs), increasing random extra offset time, window based channel scheduling (WBS) and Burst Delay Feedback scheduling (BDFS). Additional FDLs can only eliminate the negative impact caused by the variation of the offset time between control packets and data bursts. The random extra offset time approach does not require any additional hardware in the nodes. WBS provides better throughput improvement when FDLs are used in the nodes to compensate the processing time. Finally Burst Delay Feedback Scheduling in addition with these methods can significantly improve OBS throughput and reduce transmission delay.
{"title":"Efficient Predictable Probe of Optical Burst Switched For Wireless Feeler Bond","authors":"D. Senduraja, Mr.Pradeep Kumar.A","doi":"10.18535/ijecs/v10i12.4640","DOIUrl":"https://doi.org/10.18535/ijecs/v10i12.4640","url":null,"abstract":"Optical Burst Switching (OBS) is a promising paradigm for high speed transmission of data. In OBS, a key problem is to schedule bursts with minimum loss. Single method is not sufficient to improve performance. So, our performance model includes some feasible methods to improve OBS performance without significantly increasing the implementation complexity. The methods are addition of simple fiber delay lines (FDLs), increasing random extra offset time, window based channel scheduling (WBS) and Burst Delay Feedback scheduling (BDFS). Additional FDLs can only eliminate the negative impact caused by the variation of the offset time between control packets and data bursts. The random extra offset time approach does not require any additional hardware in the nodes. WBS provides better throughput improvement when FDLs are used in the nodes to compensate the processing time. Finally Burst Delay Feedback Scheduling in addition with these methods can significantly improve OBS throughput and reduce transmission delay.","PeriodicalId":231371,"journal":{"name":"International Journal of Engineering and Computer Science","volume":"23 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133379200","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 : 2021-12-15DOI: 10.18535/ijecs/v10i12.4641
Mr. Dinesh Prabhu. M, D. Senduraja
In Wireless sensor Network, several researchers have provided different routing protocol for sensor networks, particularly routing protocols depending on clusters protocols. Reliability of nodes is necessary parameter in effective sensor networks. We use MAC protocol for controlling the network packets. This is because the usage of cluster based routing has several merits like minimized control messages, re-usability of bandwidth and enhanced power control. Different cluster based routing protocol is proposed by many researchers for the purpose of reducing the consumption energy in wireless sensor networks. Those techniques reduces the energy consumption but with several disadvantages like lack of QoS, inefficient transmission, etc., To overcome those problems, modified QoS enhanced base station controlled in Mistrial Approach (flooding Technique) for wireless sensor networks is proposed in this work. Here we reduce the number of retransmission and detect the overlay packets in networks using proposed approach. Simulation results show the better energy consumption, Maximum Life time & Efficient Bandwidth is achieved by flooding management when compared to the conventional techniques
{"title":"MAC chastised Dynamism Efficient in Wireless Device Lattice Spending Mistralapproach","authors":"Mr. Dinesh Prabhu. M, D. Senduraja","doi":"10.18535/ijecs/v10i12.4641","DOIUrl":"https://doi.org/10.18535/ijecs/v10i12.4641","url":null,"abstract":"In Wireless sensor Network, several researchers have provided different routing protocol for sensor networks, particularly routing protocols depending on clusters protocols. Reliability of nodes is necessary parameter in effective sensor networks. We use MAC protocol for controlling the network packets. This is because the usage of cluster based routing has several merits like minimized control messages, re-usability of bandwidth and enhanced power control. Different cluster based routing protocol is proposed by many researchers for the purpose of reducing the consumption energy in wireless sensor networks. Those techniques reduces the energy consumption but with several disadvantages like lack of QoS, inefficient transmission, etc., To overcome those problems, modified QoS enhanced base station controlled in Mistrial Approach (flooding Technique) for wireless sensor networks is proposed in this work. Here we reduce the number of retransmission and detect the overlay packets in networks using proposed approach. Simulation results show the better energy consumption, Maximum Life time & Efficient Bandwidth is achieved by flooding management when compared to the conventional techniques","PeriodicalId":231371,"journal":{"name":"International Journal of Engineering and Computer Science","volume":"14 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122888107","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 : 2021-12-15DOI: 10.18535/ijecs/v10i12.4642
M. S, D. Senduraja
In energy limited wireless sensor networks, both local quantization andmultihop transmission are essential to save transmission energy and thus prolong the network lifetime. The goal is to maximize the network lifetime, defined as the estimation task cycles accomplished before the network becomes nonfunctional.The network lifetime optimization problem includes three components: Optimizing source coding at each sensor node, optimizing source throughput at each sensor node.Optimizing multihop routing path. Source coding optimization can be decoupled from source throughput and multihop routing path optimization and is solved by introducing a concept of equivalent 1-bit Mean Square Error (MSE) function. Based on optimal source coding, multihop routing path optimization is formulated as a linear programming problem, which suggests a new notion of character based routing. It is also seen that optimal multihop routing improves the network lifetime bound significantly compared with single-hop routing for heterogeneous networks. Furthermore, the gain is more significant when the network is denser since there are more opportunities for multihop routing. Also the gain is more significant when the observation noise variances are more diverse.
{"title":"Grid Lifespan Enlargement for Assessment in Multihop Wireless Detector Facilities","authors":"M. S, D. Senduraja","doi":"10.18535/ijecs/v10i12.4642","DOIUrl":"https://doi.org/10.18535/ijecs/v10i12.4642","url":null,"abstract":"In energy limited wireless sensor networks, both local quantization andmultihop transmission are essential to save transmission energy and thus prolong the network lifetime. The goal is to maximize the network lifetime, defined as the estimation task cycles accomplished before the network becomes nonfunctional.The network lifetime optimization problem includes three components: Optimizing source coding at each sensor node, optimizing source throughput at each sensor node.Optimizing multihop routing path. Source coding optimization can be decoupled from source throughput and multihop routing path optimization and is solved by introducing a concept of equivalent 1-bit Mean Square Error (MSE) function. Based on optimal source coding, multihop routing path optimization is formulated as a linear programming problem, which suggests a new notion of character based routing. It is also seen that optimal multihop routing improves the network lifetime bound significantly compared with single-hop routing for heterogeneous networks. Furthermore, the gain is more significant when the network is denser since there are more opportunities for multihop routing. Also the gain is more significant when the observation noise variances are more diverse.","PeriodicalId":231371,"journal":{"name":"International Journal of Engineering and Computer Science","volume":"7 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128713538","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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