International Journal of Machine Learning and Networked Collaborative Engineering最新文献

{"title":"IJMLNCE Editorial Note Volume No 04, Issue No 02","authors":"V. K. Solanki, LeDuc Anh","doi":"10.30991/ijmlnce.2020v04i02","DOIUrl":"https://doi.org/10.30991/ijmlnce.2020v04i02","url":null,"abstract":"","PeriodicalId":338210,"journal":{"name":"International Journal of Machine Learning and Networked Collaborative Engineering","volume":"19 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-10-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121825430","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":"IJMLNCE Editorial Note Volume No 04, Issue No 01","authors":"V. K. Solanki","doi":"10.30991/ijmlnce.2020v04i01","DOIUrl":"https://doi.org/10.30991/ijmlnce.2020v04i01","url":null,"abstract":"","PeriodicalId":338210,"journal":{"name":"International Journal of Machine Learning and Networked Collaborative Engineering","volume":"48 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-08-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121768086","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":"Revealing Brain Tumor Using Cross-Validated NGBoost Classifier","authors":"S. Dutta, P. Bandyopadhyay","doi":"10.21203/rs.3.rs-47048/v1","DOIUrl":"https://doi.org/10.21203/rs.3.rs-47048/v1","url":null,"abstract":"\u0000 Brain is the most complicated and delicate anatomical structure in human body. Statistics proves that, among various brain ailments, brain tumor is most fatal and in many cases they become carcinogenic. Brain tumor is characterized by abnormal and uncontrolled growth of brain cells, and takes up space within the cranial cavity and varies in shape, size, position and characteristics viz., can be benign or malignant, which makes the detection of brain tumor very critical and challenging. The vital information a neurologist or neurosurgeon needs to have is the precise size and location of tumor in the brain and whether it is causing any swelling or compression of the brain that may need urgent attention. This paper exploits ensemble strategy based Machine Learning (ML) algorithms for reveling brain tumors. NGBoost algorithm along with 5-fold stratified cross-validation scheme is proposed as classifier model that automatically detects patients with brain tumors. The proposed method is implemented with necessary fine-tuning of parameters which is compared against ensemble based baseline classifiers such as AdaBoost, Gradient Boost, Random Forest and Extra Trees Classifier. Experimental study implies that proposed method outperforms baseline models with significantly improved efficiency. The interfering features those have impact on brain tumor classification are ranked and this ranking is retrieved from the best classifier model.","PeriodicalId":338210,"journal":{"name":"International Journal of Machine Learning and Networked Collaborative Engineering","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-07-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123736283","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":"The mechanism for Predictive Load Control in the Implementation Framework through Genetic Intelligence","authors":"T. Pushpalatha, S. Nagaprasad","doi":"10.30991/ijmlnce.2019v03i04.002","DOIUrl":"https://doi.org/10.30991/ijmlnce.2019v03i04.002","url":null,"abstract":"Cloud Storage is a pay-per-use range of resources. The consumer wants to ensure that all requirements met in a limited time for optimal performance in cloud applications that are every day. Load balancing is also crucial, and one of the essential cloud computing issues. It is also called the NP-full load balancing problem since load balancing is harder with increasing demand. This paper provides a genetic algorithm (GA) framework for cloud load. Depending on population initialization duration, the urgent need for the proposal considered. The idea behind the emphasis is to think about the present world. Real-World Scenario structures have other targets that our algorithms can combine. Cloud Analyst models the suggested method. A load-balancing algorithm based on the forecasts of the end -to - end Cicada method given in this paper. The simulator for cloud services or Cloud Sim can be used as a simulator to achieve a low computing requirement algorithm and a better workload balance. A simulation of cloud services is feasible. The result indicates the possibility of offering a quantitative workload balancing approach that can help manage workloads through the usage of computer resources. The next generation of cloud computing would make the network scalable and use available resources effectively. Load balancing, a significant problem in the cloud storage, and distributed workload over \u0000  \u0000Several nodes to ensure that no single resource is overloaded. This can be seen as a question of efficiency, and its solution must adapt to the environment and styles of work to the right balance of load. This article introduces a new approach to genetic algorithm (GA) power loads. When trying to reduce the complexity of a particular task, the algorithm handles the cloud computing fee. A software analyst model evaluated the proposed method of load balancing. Results from simulations for a standard sample program show that the suggested algorithms outperform current methods like FCFS, Round Robing (RR), and local search algorithms Stochastic Hill Climbing (SHC).","PeriodicalId":338210,"journal":{"name":"International Journal of Machine Learning and Networked Collaborative Engineering","volume":"58 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-01-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117312478","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":"IJMLNCE Editorial Note Volume No 03, Issue No 04","authors":"Shivani Agarwal, Manju Khari, P. Tanwar","doi":"10.30991/ijmlnce.2019v03i04","DOIUrl":"https://doi.org/10.30991/ijmlnce.2019v03i04","url":null,"abstract":"","PeriodicalId":338210,"journal":{"name":"International Journal of Machine Learning and Networked Collaborative Engineering","volume":"2013 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-01-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125673997","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":"Volume No 02 Issue No 04 : 2018","authors":"","doi":"10.30991/ijmlnce.2018v02i04","DOIUrl":"https://doi.org/10.30991/ijmlnce.2018v02i04","url":null,"abstract":"","PeriodicalId":338210,"journal":{"name":"International Journal of Machine Learning and Networked Collaborative Engineering","volume":"70 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-12-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127270157","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":"IJMLNCE Editorial Note Volume No 03, Issue No 03","authors":"V. K. Solanki, Vicente Garcia Diaz","doi":"10.30991/ijmlnce.2019v03i03","DOIUrl":"https://doi.org/10.30991/ijmlnce.2019v03i03","url":null,"abstract":"","PeriodicalId":338210,"journal":{"name":"International Journal of Machine Learning and Networked Collaborative Engineering","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-12-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133479817","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":"Joint Spatial Geometric and Max-margin Classifier Constraints for Facial Expression Recognition Using Nonnegative Matrix Factorization","authors":"Thanh Trong Phan, D. V. Thang","doi":"10.30991/ijmlnce.2019v03i03.001","DOIUrl":"https://doi.org/10.30991/ijmlnce.2019v03i03.001","url":null,"abstract":"Based on the constrained non-negative matrix factor algorithm, the article presents a new approach to facial recognition recognition. Our proposed method incorporated two tasks in an automatic expression analysis system: facial feature extraction and classification into expressions. To obtain local and geometric structure information in the data as much as possible, we amalgamate max-margin relegation into the constrained NMF optimization, resulting in a multiplicative updating algorithm is additionally proposed for solving optimization quandary. Experimental results on JAFFE dataset demonstrate that the effectiveness of the proposed method with improved performances over the conventional dimension reduction methods.","PeriodicalId":338210,"journal":{"name":"International Journal of Machine Learning and Networked Collaborative Engineering","volume":"4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-11-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130494593","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":"Study of Energy Efficient Algorithms for Cloud Computing based on Virtual Machine Migration Techniques","authors":"S. K. Sen, Sharmistha Dey, R. Bag","doi":"10.30991/IJMLNCE.2019V03I02.003","DOIUrl":"https://doi.org/10.30991/IJMLNCE.2019V03I02.003","url":null,"abstract":"Green cloud is a catchphrase in today’s IT industry and hence energy efficiency in cloud computing is one of the most significant parameters to follow nowadays to evaluate the efficiency of the cloud service. It is a driving force for adaptability of a cloud computing service in recent era. For a highly commercial service like cloud, maintaining the QoS parameters and keeping the service availability and service quality highly optimized to get the competitive advantage, cloud data centers are almost available on a 24x7 basis ; which in turn is a reson for high power consumption. So it is very much necessary to maintain a balance between power and quality of the service. One feasible solution for achieving energy efficiency is Virtual Machine migration technique in real time or when they are in turned off condition. This paper discusses about several VM Migration techniques and analyses their perspectives.","PeriodicalId":338210,"journal":{"name":"International Journal of Machine Learning and Networked Collaborative Engineering","volume":"41 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-07-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129744516","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":"IJMLNCE Editorial Note Volume No 03, Issue No 02","authors":"V. K. Solanki, V. G. Díaz","doi":"10.30991/ijmlnce.2019v03i02","DOIUrl":"https://doi.org/10.30991/ijmlnce.2019v03i02","url":null,"abstract":"","PeriodicalId":338210,"journal":{"name":"International Journal of Machine Learning and Networked Collaborative Engineering","volume":"11 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-07-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126456425","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}