International Journal of Intelligent Systems and Applications in Engineering最新文献_第10页

Exploiting Artificial Immune System to Optimize Association Rules for Word Sense Disambiguation 利用人工免疫系统优化词义消歧关联规则

International Journal of Intelligent Systems and Applications in Engineering Pub Date : 2021-12-26 DOI: 10.18201/ijisae.2021473638

Mohd. Shahid Husain

引用次数: 0

Effectiveness of Logarithmic Entropy Measures for Pythagorean Fuzzy Sets in diseases related to Post COVID Implications under TOPSIS Approach TOPSIS方法下勾股模糊集的对数熵测度在新冠肺炎后相关疾病中的有效性

International Journal of Intelligent Systems and Applications in Engineering Pub Date : 2021-12-26 DOI: 10.18201/ijisae.2021473637

Yazar Adı Yazar Soyadı, Anjali Naithani

{"title":"Effectiveness of Logarithmic Entropy Measures for Pythagorean Fuzzy Sets in diseases related to Post COVID Implications under TOPSIS Approach","authors":"Yazar Adı Yazar Soyadı, Anjali Naithani","doi":"10.18201/ijisae.2021473637","DOIUrl":"https://doi.org/10.18201/ijisae.2021473637","url":null,"abstract":"Following the second wave of Covid-19 infections in India, individuals are now arriving to hospitals with a variety of symptoms, not simply for mucormycosis, a fungal infection. The most common symptoms are extreme tiredness, drowsiness, body and joint pain, mental fog, and fever, but pneumonia, collapsed lungs, heart attacks, and strokes have all been reported. Pythagorean fuzzy sets (PFSs) proposed by Yager [42] offers a novel technique to characterize uncertainty and ambiguity with greater precision and accuracy. The idea was developed specifically to describe uncertainty and ambiguity mathematically and to provide a codified tool for dealing with imprecision in real-world circumstances. This article addresses novel logarithmic entropy measures under PFSs. Additionally, numerical illustration is utilized to ascertain the strength and validity of the proposed entropy measures. Application of the measures is used in detecting diseases related to Post COVID 19 implications through TOPSIS method. Comparison of the suggested measures with the existing ones is also demonstrated. © 2021, Ismail Saritas. All rights reserved.","PeriodicalId":14067,"journal":{"name":"International Journal of Intelligent Systems and Applications in Engineering","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-12-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49573678","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

Clustering Method Based on Artificial Algae Algorithm 基于人工藻类算法的聚类方法

International Journal of Intelligent Systems and Applications in Engineering Pub Date : 2021-12-26 DOI: 10.18201/ijisae.2021473632

Khaleel Ibrahim Anwer, S. Servi

引用次数: 3

Punjabi Emotional Speech Database:Design, Recording and Verification 旁遮普语情感语音数据库:设计、记录和验证

International Journal of Intelligent Systems and Applications in Engineering Pub Date : 2021-12-26 DOI: 10.18201/ijisae.2021473641

K. Kaur

引用次数: 7

ILSHR Rumor Spreading Model by Combining SIHR and ILSR Models in Complex Networks 基于SIHR和ILSR模型的复杂网络谣言传播模型

International Journal of Intelligent Systems and Applications in Engineering Pub Date : 2021-12-08 DOI: 10.5815/ijisa.2021.06.05

Adel Angali, Musa Mojarad, Hassan Arfaeinia

引用次数: 0

Data Analysis for the Aero Derivative Engines Bleed System Failure Identification and Prediction 航空衍生发动机排气系统故障识别与预测的数据分析

International Journal of Intelligent Systems and Applications in Engineering Pub Date : 2021-12-08 DOI: 10.5815/ijisa.2021.06.02

Khalid Salmanov, Hadi Harb

引用次数: 1

Heuristic-based Approach for Dynamic Consolidation of Software Licenses in Cloud Data Centers 基于启发式的云数据中心软件许可动态整合方法

International Journal of Intelligent Systems and Applications in Engineering Pub Date : 2021-12-08 DOI: 10.5815/ijisa.2021.06.01

Leila Helali, Mohamed Nazih Omri

{"title":"Heuristic-based Approach for Dynamic Consolidation of Software Licenses in Cloud Data Centers","authors":"Leila Helali, Mohamed Nazih Omri","doi":"10.5815/ijisa.2021.06.01","DOIUrl":"https://doi.org/10.5815/ijisa.2021.06.01","url":null,"abstract":"Since its emergence, cloud computing has continued to evolve thanks to its ability to present computing as consumable services paid by use, and the possibilities of resource scaling that it offers according to client’s needs. Models and appropriate schemes for resource scaling through consolidation service have been considerably investigated,mainly, at the infrastructure level to optimize costs and energy consumption. Consolidation efforts at the SaaS level remain very restrained mostly when proprietary software are in hand. In order to fill this gap and provide software licenses elastically regarding the economic and energy-aware considerations in the context of distributed cloud computing systems, this work deals with dynamic software consolidation in commercial cloud data centers 𝑫𝑺𝟑𝑪. Our solution is based on heuristic algorithms and allows reallocating software licenses at runtime by determining the optimal amount of resources required for their execution and freed unused machines. Simulation results showed the efficiency of our solution in terms of energy by 68.85% savings and costs by 80.01% savings. It allowed to free up to 75% physical machines and 76.5% virtual machines and proved its scalability in terms of average execution time while varying the number of software and the number of licenses alternately.","PeriodicalId":14067,"journal":{"name":"International Journal of Intelligent Systems and Applications in Engineering","volume":"225 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-12-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"77123966","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: 2

A Novel Ant Colony Based DBN Framework to Analyze the Drug Reviews 基于蚁群的DBN框架药物评论分析

International Journal of Intelligent Systems and Applications in Engineering Pub Date : 2021-12-08 DOI: 10.5815/ijisa.2021.06.03

Nazia Tazeen, K. Rani

{"title":"A Novel Ant Colony Based DBN Framework to Analyze the Drug Reviews","authors":"Nazia Tazeen, K. Rani","doi":"10.5815/ijisa.2021.06.03","DOIUrl":"https://doi.org/10.5815/ijisa.2021.06.03","url":null,"abstract":"Nowadays, big data is directing the entire advanced world with its function and applications. Moreover, to make better decisions from the ever emerging big data belonging to the respective organizations, deep learning (DL) models are required. DL is also widely used in the sentiment classification tasks considering data from social networks.Furthermore, sentiment classification signifies the best way to analyze the big data and make decisions accordingly. Analyzing the sentiments from big data applications is quite challenging task and also requires more time for the execution process. Therefore, to analyze and classify big data emerging from social networks in a better way, DL models are utilized. DL techniques are being used among the researchers to get high end results. A novel Ant Colonybased Deep Belief Neural Network (AC-DBN) framework is proposed in this research. Drug review tweets are opted to perform sentiment classification by using the proposed framework in python environment. A model fitness function is initiated in the DL framework and is observed that it is attaining high accuracy with low computation time. Additionally, the obtained results attained from the proposed framework are validated with existing methods for evaluating the efficiency of the proposed AC-DBN approach.","PeriodicalId":14067,"journal":{"name":"International Journal of Intelligent Systems and Applications in Engineering","volume":"9 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-12-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"87311758","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

Normalized Statistical Algorithm for Afaan Oromo Word Sense Disambiguation 阿法安奥罗莫语词义消歧的归一化统计算法

International Journal of Intelligent Systems and Applications in Engineering Pub Date : 2021-12-08 DOI: 10.5815/ijisa.2021.06.04

A. Abafogi

{"title":"Normalized Statistical Algorithm for Afaan Oromo Word Sense Disambiguation","authors":"A. Abafogi","doi":"10.5815/ijisa.2021.06.04","DOIUrl":"https://doi.org/10.5815/ijisa.2021.06.04","url":null,"abstract":"Language is the main means of communication used by human. In various situations, the same word can mean differently based on the usage of the word in a particular sentence which is challenging for a computer to understand as level of human. Word Sense Disambiguation (WSD), which aims to identify correct sense of a given ambiguity word, is a long-standing problem in natural language processing (NLP). As the major aim of WSD is to accurately understand the sense of a word in particular context, can be used for the correct labeling of words in natural language applications. In this paper, I propose a normalized statistical algorithm that performs the task of WSD for Afaan Oromo language despite morphological analysis The propose algorithm has the power to discriminate ambiguous word’s sense without windows size consideration, without predefined rule and without utilize annotated dataset for training which minimize a challenge of under resource languages. The proposed system tested on 249 sentences with precision, recall, and F-measure. The overall effectiveness of the system is 80.76% in F-measure, which implies that the proposed system is promising on Afaan Oromo that is one of under resource languages spoken in East Africa. The algorithm can be extended for semantic text similarity without modification or with a bit modification. Furthermore, the forwarded direction can improve the performance of the proposed algorithm.","PeriodicalId":14067,"journal":{"name":"International Journal of Intelligent Systems and Applications in Engineering","volume":"77 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-12-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"83887724","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

Rice Leaf Disease Recognition using Local Threshold Based Segmentation and Deep CNN 基于局部阈值分割和深度CNN的水稻叶片病害识别

International Journal of Intelligent Systems and Applications in Engineering Pub Date : 2021-10-08 DOI: 10.5815/ijisa.2021.05.04

Anam Islam, Redoun Islam, S. Haque, S. Islam, Mohammad Ashik Iqbal Khan

{"title":"Rice Leaf Disease Recognition using Local Threshold Based Segmentation and Deep CNN","authors":"Anam Islam, Redoun Islam, S. Haque, S. Islam, Mohammad Ashik Iqbal Khan","doi":"10.5815/ijisa.2021.05.04","DOIUrl":"https://doi.org/10.5815/ijisa.2021.05.04","url":null,"abstract":"Timely detection of rice diseases can help farmers to take necessary action and thus reducing the yield loss substantially. Automatic recognition of rice diseases from the rice leaf images using computer vision and machine learning can be beneficial over the manual method of disease recognition through visual inspection. During the recent years, deep learning, a very popular and efficient machine learning algorithm, has shown great promise in image classification task. In this paper, a segmentation-based method using deep neural network for classifying rice diseases from leaf images has been proposed. Disease-affected regions of the rice leaves have been segmented using local segmentation method and the Convolutional Neural Network (CNN) has been trained with those images. Proposed method has been applied on three different datasets including the one created by us which consists of the rice leaf images collected from Bangladesh Rice Research Institute (BRRI). Three state-of-the-art CNN architectures VGG, ResNet and DenseNet, used in the proposed method, have been trained with these three datasets for classifying the diseases. Classification performance of the proposed method using the said three CNN architectures for the three datasets have been analyzed and compared. These results show that this model is quite promising in classifying rice leaf diseases. Outcome of this research is an enhancement in the performance of rice disease classification which is quite significant for the viability of this work to be transformed into a real-time application for the farmers.","PeriodicalId":14067,"journal":{"name":"International Journal of Intelligent Systems and Applications in Engineering","volume":"9 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-10-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"79766153","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: 15