Journal of Computational Science and Intelligent Technologies最新文献

Improving The Performances of WSN Using Data Scheduler and Hierarchical Tree 利用数据调度和层次树改进WSN的性能

Journal of Computational Science and Intelligent Technologies Pub Date : 2021-08-16 DOI: 10.53409/mnaa/jcsit/2202

Jayamma R

{"title":"Improving The Performances of WSN Using Data Scheduler and Hierarchical Tree","authors":"Jayamma R","doi":"10.53409/mnaa/jcsit/2202","DOIUrl":"https://doi.org/10.53409/mnaa/jcsit/2202","url":null,"abstract":"Users of data-intensive implementation needs intelligent services and schedulers that will provide models and strategies to optimize their data transfer jobs. Normally sensor nodes are connected to consecutive sensor nodes depending on frequent transmission. To enhance end-to-end data flow parallelism for throughput optimization in high speed WSNs. The major objective is to maximize the WSNs throughput, minimizing the model overhead, avoiding disputation among users and using minimum number of end-system resources. Data packets are broadcasted from sender node to target node. Though, all nodes operate concurrently in various communications, the analysis shows that more packet latencies are occurred and priority-based transmission tasks are performed. Then the proposed Bearing parallelism-based Data Scheduler (BPDS) is used for data scheduling to enhance the end-to-end throughput input parameter. Sensor nodes are fast working node, it verifies each and every node before allocating packet transmission for that node. Busy resources are monitored to inform the nodes that are in processing, based on the schedule it allocates various paths to particular node and monitors the node capacity. Sampling algorithm supports for fixing threshold value, based on the values, they are further allocated to communicate between channels. It assigns the routing path with minimum resources and reduces end to end delay, to improve throughput, and network lifetime.","PeriodicalId":125707,"journal":{"name":"Journal of Computational Science and Intelligent Technologies","volume":"136 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-08-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122918476","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 3

An Analysis of Deep Learning Techniques in Neuroimaging 神经影像学中的深度学习技术分析

Journal of Computational Science and Intelligent Technologies Pub Date : 2021-04-16 DOI: 10.53409/mnaa/jcsit/2102

Narmatha C, Hayam Alatawi, H. Alatawi

引用次数: 3

Performance Analysis of Emotion Classification Using Multimodal Fusion Technique 基于多模态融合技术的情绪分类性能分析

Journal of Computational Science and Intelligent Technologies Pub Date : 2021-04-16 DOI: 10.53409/mnaa/jcsit/2103

Chettiyar Vani Vivekanand

{"title":"Performance Analysis of Emotion Classification Using Multimodal Fusion Technique","authors":"Chettiyar Vani Vivekanand","doi":"10.53409/mnaa/jcsit/2103","DOIUrl":"https://doi.org/10.53409/mnaa/jcsit/2103","url":null,"abstract":"As the central processing unit of the human body, the human brain is in charge of several activities, including cognition, perception, emotion, attention, action, and memory. Emotions have a significant impact on human well-being in their life. Methodologies for accessing emotions of human could be essential for good user-machine interactions. Comprehending BCI (Brain-Computer Interface) strategies for identifying emotions can also help people connect with the world more naturally. Many approaches for identifying human emotions have been developed using signals of EEG for classifying happy, neutral, sad, and angry emotions, discovered to be effective. The emotions are elicited by various methods, including displaying participants visuals of happy and sad facial expressions, listening to emotionally linked music, visuals, and, sometimes, both of these. In this research, a multi-model fusion approach for emotion classification utilizing BCI and EEG data with various classifiers was proposed. The 10-20 electrode setup was used to gather the EEG data. The emotions were classified using the sentimental analysis technique based on user ratings. Simultaneously, Natural Language Processing (NLP) is implemented for increasing accuracy. This analysis classified the assessment parameters as happy, neutral, sad, and angry emotions. Based on these emotions, the proposed model’s performance was assessed in terms of accuracy and overall accuracy. The proposed model has a 93.33 percent overall accuracy and increased performance in all emotions identified.","PeriodicalId":125707,"journal":{"name":"Journal of Computational Science and Intelligent Technologies","volume":"55 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-04-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127636573","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A Novel Intrusion Detection System in WSN using Hybrid Neuro-Fuzzy Filter with Ant Colony Algorithm 基于蚁群算法的神经模糊混合滤波的WSN入侵检测系统

Journal of Computational Science and Intelligent Technologies Pub Date : 1900-01-01 DOI: 10.53409/mnaa.jcsit1101

Sarah Salaheldin Lutfi, Evans Ga Usa. Aysik Consulting Services, Mahmoud Lutfi Ahmed

{"title":"A Novel Intrusion Detection System in WSN using Hybrid Neuro-Fuzzy Filter with Ant Colony Algorithm","authors":"Sarah Salaheldin Lutfi, Evans Ga Usa. Aysik Consulting Services, Mahmoud Lutfi Ahmed","doi":"10.53409/mnaa.jcsit1101","DOIUrl":"https://doi.org/10.53409/mnaa.jcsit1101","url":null,"abstract":"With the wide application of wireless sensor networks in military and environmental monitoring, security issues have become increasingly prominent. Data exchanged over wireless sensor networks is vulnerable to malicious attacks due to the lack of physical defense equipment. Therefore, corresponding schemes of intrusion detection are urgently needed to defend against such attacks. A new method of intrusion detection using Hybrid Neuro-Fuzzy Filter with Ant Colony Algorithm (HNF-ACA) is proposed in this study, which has been able to map the network status directly into the sensor monitoring data received by base station, accordingly that base station can sense the abnormal changes in network.The hybridized Sugeno-Mamdani based fuzzy interference system is implemented in both the NF filters to obtain more efficient noise removal system. The Modified Mutation Based Ant Colony Algorithm technique improves the accuracy of determining the membership values of input trust values of each node in fuzzy filters. To end, the proposed method was tested on the WSN simulation and the results showed that the intrusion detection method in this work can effectively recognise whether the abnormal data came from a network attack or just a noise than the existing methods.","PeriodicalId":125707,"journal":{"name":"Journal of Computational Science and Intelligent Technologies","volume":"24 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125323171","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 20

A Proficient Adaptive K-means based Brain Tumor Segmentation and Detection Using Deep Learning Scheme with PSO 基于PSO的深度学习方案的高效自适应k均值脑肿瘤分割与检测

Journal of Computational Science and Intelligent Technologies Pub Date : 1900-01-01 DOI: 10.53409/mnaa.jcsit20201302

A. T, C. G, S. M

{"title":"A Proficient Adaptive K-means based Brain Tumor Segmentation and Detection Using Deep Learning Scheme with PSO","authors":"A. T, C. G, S. M","doi":"10.53409/mnaa.jcsit20201302","DOIUrl":"https://doi.org/10.53409/mnaa.jcsit20201302","url":null,"abstract":"Determining the size of the tumor is a significant obstacle in brain tumour preparation and objective assessment. Magnetic Resonance Imaging (MRI) is one of the non-invasive methods that has emanated without ionizing radiation as a front-line diagnostic method for brain tumour. Several approaches have been applied in modern years to segment MRI brain tumours automatically. These methods can be divided into two groups based on conventional learning, such as support vector machine (SVM) and random forest, respectively hand-crafted features and classifier method. However, after deciding hand-crafted features, it uses manually separated features and is given to classifiers as input. These are the time consuming activity, and their output is heavily dependent upon the experience of the operator. This research proposes fully automated detection of brain tumor using Convolutional Neural Network (CNN) to avoid this problem. It also uses brain image of high grade gilomas from the BRATS 2015 database. The suggested research performs brain tumor segmentation using clustering of k-means and patient survival rates are increased with this proposed early diagnosis of brain tumour using CNN.","PeriodicalId":125707,"journal":{"name":"Journal of Computational Science and Intelligent Technologies","volume":"186 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124314519","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 5

Hybrid Convolutional Neural Network with PSO Based Severe Dengue Prognosis Method in Human Genome Data 基于PSO的混合卷积神经网络预测人类基因组数据中的重症登革热

Journal of Computational Science and Intelligent Technologies Pub Date : 1900-01-01 DOI: 10.53409/mnaa.jcsit1104

Mohammed Mustafa, R. E. Ahmed, S. Eljack

引用次数: 16

A New Framework for Anomaly Detection in NSL-KDD Dataset using Hybrid Neuro-Weighted Genetic Algorithm 基于混合神经加权遗传算法的NSL-KDD数据异常检测新框架

Journal of Computational Science and Intelligent Technologies Pub Date : 1900-01-01 DOI: 10.53409/mnaa.jcsit1105

P. Muneeshwari, M. Kishanthini

{"title":"A New Framework for Anomaly Detection in NSL-KDD Dataset using Hybrid Neuro-Weighted Genetic Algorithm","authors":"P. Muneeshwari, M. Kishanthini","doi":"10.53409/mnaa.jcsit1105","DOIUrl":"https://doi.org/10.53409/mnaa.jcsit1105","url":null,"abstract":"There are an increasing number of security threats to the Internet and computer networks. For new kinds of attacks constantly emerging, a major challenge is the development of versatile and innovative security-oriented approaches. Anomaly-based network intrusion detection techniques are in this sense a valuable tool for defending target devices and networks from malicious activities. With testing dataset, this work was able to use the NSL-KDD data collection, the binary and multiclass problems. With that inspiration, data mining techniques are used to offer an automated platform for network attack detection. The system is based on the Hybrid Genetic Neuro-Weighted Algorithm (HNWGA).In this weighted genetic algorithm is used for the selection of features and in this work a neuro-genetic fuzzy classification algorithm has been proposed which is used to identify malicious users by classifying user behaviors. The main benefit of this proposed framework is that it reduces the attacks by highly accurate detection of intruders and minimizes false positives. The evaluation of the performance is performed in NSL-KDD dataset. The experimental result shows of that the proposed work attains better accuracy when compared to previous methods. Such type of IDS systems are used in the identification and response to malicious traffic / activities to improve extremely accuracy.","PeriodicalId":125707,"journal":{"name":"Journal of Computational Science and Intelligent Technologies","volume":"25 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116774869","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 12

Imaging Modalities used in Prostate Cancer Detection 用于前列腺癌检测的成像方式

Journal of Computational Science and Intelligent Technologies Pub Date : 1900-01-01 DOI: 10.53409/mnaa/jcsit/2105

Rajesh M N, Chandrasekar B S

引用次数: 0

A Deep Web Data Extraction Framework Enhancement Method 一种深度网络数据提取框架增强方法

Journal of Computational Science and Intelligent Technologies Pub Date : 1900-01-01 DOI: 10.53409/mnaa/jcsit/e202203013342

Salar Faisal Noori, Bazeer Ahamed B

引用次数: 0

Classification of Alzheimer's disease from MRI Images using CNN based Pre-trained VGG-19 Model 使用基于CNN的预训练VGG-19模型从MRI图像中分类阿尔茨海默病

Journal of Computational Science and Intelligent Technologies Pub Date : 1900-01-01 DOI: 10.53409/mnaa.jcsit20201205

Manimurugan S

{"title":"Classification of Alzheimer's disease from MRI Images using CNN based Pre-trained VGG-19 Model","authors":"Manimurugan S","doi":"10.53409/mnaa.jcsit20201205","DOIUrl":"https://doi.org/10.53409/mnaa.jcsit20201205","url":null,"abstract":"Determining the size of the tumor is a significant obstacle in brain tumour preparation and objective assessment. Magnetic Resonance Imaging (MRI) is one of the non-invasive methods that has emanated without ionizing radiation as a front-line diagnostic method for brain tumour. Several approaches have been applied in modern years to segment MRI brain tumours automatically. These methods can be divided into two groups based on conventional learning, such as support vectormachine (SVM) and random forest, respectively hand-crafted features and classifier method. However, after deciding hand-crafted features, it uses manually separated features and is given to classifiers as input. These are the time consuming activity, and their output is heavily dependent upon the experience of the operator. This research proposes fully automated detection of brain tumor using Convolutional Neural Network (CNN) to avoid this problem. It also uses brain image of high grade gilomas from the BRATS 2015 database. The suggested research performs brain tumor segmentation using clustering of k-means and patient survival rates are increased with this proposed early diagnosis of brain tumour using CNN.","PeriodicalId":125707,"journal":{"name":"Journal of Computational Science and Intelligent Technologies","volume":"13 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127975589","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 5