International Journal of Innovative Computing Information and Control最新文献

{"title":"Drone Aerial Image Identification of Tropical Forest Tree Species using the Mask R-CNN","authors":"Robiah Hamzah, Mohammad Faizuddin Md. Noor","doi":"10.11113/ijic.v12n2.381","DOIUrl":"https://doi.org/10.11113/ijic.v12n2.381","url":null,"abstract":"Tropical forests have a wide variety of species and support environmental activities. The drone's image resolution is 90% more accurate than satellite data. It boosted productivity, safety, and the capacity to make better decisions by comparing archived and prospective images. Labeling tree species in heavily forested locations is labor-intensive, time-consuming, and expensive. This research seeks to design a new model for classifying tree species based on drone imagery, then test and assess its effectiveness. This study shows that drone technology can diminish productivity per hectare compared to conventional ground approaches. The study shows drones are more productive than ground approaches. The approach is feasible since it targets commercial timber species in the forest's higher stratum. Drones are cheaper than satellite data, therefore they're being used more in forest management and deep learning. Drones allow flexible, high-resolution data collection. This research uses Mask R-CNN to recognize and segment trees. This study uses high-resolution RGB images of tropical forests. The mAP, recall, and precision all performed well. Our suggested method yields a solid prediction model for detecting tree species, validated by 75% of ground truth data. This strategy can help plan and execute forest inventory, as shown. This initiative's success may lead to the first phase of a forest inventory, affecting the region's logging and forest management.","PeriodicalId":50314,"journal":{"name":"International Journal of Innovative Computing Information and Control","volume":null,"pages":null},"PeriodicalIF":1.0,"publicationDate":"2022-11-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"77253904","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":"Classifying Sarcoma Cancer Using Deep Neural Networks Based on Multi-Omics Data","authors":"Nur Sabrina Azmi, Azurah A Samah, Hairudin Abdul Majid, Zuraini Ali Shah, H. Hashim, Nuraina Syaza Azman, Ezzeddin Kamil Mohamed Hashim","doi":"10.11113/ijic.v12n1.360","DOIUrl":"https://doi.org/10.11113/ijic.v12n1.360","url":null,"abstract":"The challenge in classifying cancer may lead to inaccurate classification of cancers, especially sarcoma cancer since it consists of rare types of cancer. It is hard for the clinician to confirm the patient's condition because an accurate diagnosis can only be made by the specialist pathology.  Therefore, instead of a single omics is used to identify the disease marker, an approach of integrating these omics to represent multi-omics brings more advantages in detecting and presenting the phenotype of the cancers. Nowadays, the advancement of computational models especially deep learning offered promising approaches in solving high-level omics of data with faster processing speed. Hence, the purpose of this study is to classify cancer and non-cancerous patients using Stacked Denoising Autoencoder (SDAE) and One-dimensional Convolutional Neural Network (1D CNN) to evaluate which algorithm classifies better using high correlated multi-omics data. The study employed both computational models to fit multi-omics dataset. Sarcoma omics datasets used in this study was obtained from the Multi-Omics Cancer Benchmark TCGA Pre-processed Data of ACGT Ron Shamir Lab repository. From the results, the accuracy obtained for the SDAE was 50.93% and 52.78% for the 1D CNN. The result show 1D CNN model outperformed SDAE in classifying sarcoma cancer.","PeriodicalId":50314,"journal":{"name":"International Journal of Innovative Computing Information and Control","volume":null,"pages":null},"PeriodicalIF":1.0,"publicationDate":"2022-03-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"72541406","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":"A Systematic Literature Review of Failure Prediction in Production Environment Using Machine Learning Technique","authors":"Hanafi Majid, Syahid Anuar","doi":"10.11113/ijic.v12n1.348","DOIUrl":"https://doi.org/10.11113/ijic.v12n1.348","url":null,"abstract":"Context: Process continuity is one of the fundamental quality attributes of a production environment. The accurate prediction of a process failure is a significant challenge for the effective management of the production delivery process. \u0000Objective: The primary aim of this paper is to present a systematic review of studies related to the prediction of failure of production environment using machine learning techniques. Several research questions are identified and investigated in this review, with the goal of providing a comprehensive summary, analyses, and discuss variously viewpoints concerning failure prediction measurements, datasets, metrics, measures of evaluation, individual models and also with the ensemble models. \u0000Method: The study employs the usual systematic literature review methodology and is limited to the most widely used digital database libraries for computer science from January 2016 to May 2021. \u0000Results: We examine 40 relevant research published in peer-reviewed journals and conference proceedings. The findings indicate that there is just a small amount of activity in the region of the production environment using failure prediction compared with other service quality attributes. SVM, RF, DT, LR, and LSTM were the most commonly used ML techniques employed in the selected primary studies, and the most accurate is the prediction model using ANN. The majority of studies concentrated on regression problems and used supervised kinds of machine learning. Individual and ensemble prediction models were used in the majority of investigations, with the number of studies using each type being nearly equal. \u0000 Conclusion: According to the findings of this comprehensive literature analysis, ensemble models outperformed individual models in terms of accuracy prediction and have been found to be helpful models for predicting the fault or unexpected events. However, their use is rather infrequent, and there is a pressing need to put these and other models to use in the real world to a large number of datasets with a diverse collection of datasets in order to improve the accuracy and consistency of the findings.","PeriodicalId":50314,"journal":{"name":"International Journal of Innovative Computing Information and Control","volume":null,"pages":null},"PeriodicalIF":1.0,"publicationDate":"2022-02-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"89040076","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":"A Conceptual Design for COMBI Dengue Prevention based on an Integrated Psychology and Persuasive Technology Models","authors":"Masitah Ghazali, Afzan Rosli, Noraini Ibrahim, Habel Hisham","doi":"10.11113/ijic.v12n1.340","DOIUrl":"https://doi.org/10.11113/ijic.v12n1.340","url":null,"abstract":"Dengue prevention is the best way to prevent dengue outbreaks, as the tagline goes, “prevention is better than cure”. But the challenges lie on sustaining the preventive activity among the community, which commonly only takes place periodically, i.e. when they are dengue outbreaks, with the presence of health officers under the Communication for Behavioral Impact (COMBI) campaign. In this study, a behaviour change model based on the Transtheoretical Model (TTM) and trigger elements derived from the Fogg Behaviour Model (FBM) is proposed to sustain a community in carrying out preventive activities to prevent dengue. Furthermore, the intervention strategy is added to connect the TTM and FBM. In addition, an interview with the community leader, from the community which used to be a hotspot for dengue, and a survey with its residents are performed to give further insights into the development of the proposed model.","PeriodicalId":50314,"journal":{"name":"International Journal of Innovative Computing Information and Control","volume":null,"pages":null},"PeriodicalIF":1.0,"publicationDate":"2022-02-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"82697752","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":"A Novel Approach to Decision Making Based on Type-ii Generalized Fermatean Bipolar Fuzzy Soft Sets","authors":"M. Palanikumar, Aiyared Iampan","doi":"10.24507/ijicic.18.03.769","DOIUrl":"https://doi.org/10.24507/ijicic.18.03.769","url":null,"abstract":"This research article is devoted to Type-II generalized Fermatean bipolar fuzzy soft sets. Type-II generalized Fermatean bipolar fuzzy soft sets approaches are a great point of view for decision making, which is a new generalization of bipolar fuzzy soft sets and generalized fuzzy soft sets. We indicate an algorithm to solve the decision making real life problem based on a soft set model. We discussed the similarity measure between Type-II generalized Fermatean bipolar fuzzy soft sets. Suppose that there are four patients in a hospital with certain symptoms of corona viruses and the universal set contains COVID-19, severe acute respiratory syndrome, middle east respiratory syndrome, usually mild respiratory illness and the set of a parameter is the set of certain symptoms of corona viruses represented by fever, cough, difficulty breathing or shortness of breath, loss of speech or mobility, or confusion and chest pain. Also, we communicate with interact Type-II generalized Fermatean bipolar fuzzy soft sets that can be applied to detecting whether the person is more affecting from a corona disease or not. © 2022 ICIC International.","PeriodicalId":50314,"journal":{"name":"International Journal of Innovative Computing Information and Control","volume":null,"pages":null},"PeriodicalIF":1.0,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"68958254","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":"Innovative Computing: Proceedings of the 4th International Conference on Innovative Computing (IC 2021)","authors":"","doi":"10.1007/978-981-16-4258-6","DOIUrl":"https://doi.org/10.1007/978-981-16-4258-6","url":null,"abstract":"","PeriodicalId":50314,"journal":{"name":"International Journal of Innovative Computing Information and Control","volume":null,"pages":null},"PeriodicalIF":1.0,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"84695625","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":"Innovative Computing: Proceedings of the 5th International Conference on Innovative Computing (IC 2022)","authors":"","doi":"10.1007/978-981-19-4132-0","DOIUrl":"https://doi.org/10.1007/978-981-19-4132-0","url":null,"abstract":"","PeriodicalId":50314,"journal":{"name":"International Journal of Innovative Computing Information and Control","volume":null,"pages":null},"PeriodicalIF":1.0,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"73568469","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":"A Review: Deep Learning for 3D Reconstruction of Human Motion Detection","authors":"Junzi Yang, A. W. Ismail","doi":"10.11113/ijic.v12n1.353","DOIUrl":"https://doi.org/10.11113/ijic.v12n1.353","url":null,"abstract":"3D reconstruction of human motion is an important research topic in VR/AR content creation, virtual fitting, human-computer interaction and other fields. Deep learning theory has made important achievements in human motion detection, recognition, tracking and other aspects, and human motion detection and recognition is an important link in 3D reconstruction. In this paper, the deep learning algorithms in recent years, mainly used for human motion detection and recognition, are reviewed, and the existing methods are divided into three types: CNN-based, RNN-based and GNN-based. At the same time, the main stream data sets and frameworks adopted in the references are summarized. The content of this paper provides some references for the research of 3D reconstruction of human motion.","PeriodicalId":50314,"journal":{"name":"International Journal of Innovative Computing Information and Control","volume":null,"pages":null},"PeriodicalIF":1.0,"publicationDate":"2021-12-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"90046149","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":"PubMed Text Data Mining Automation for Biological Validation on Lists of Genes and Pathways","authors":"Hui Wen Nies, Z. Zakaria, Weng Howe Chan, Izyan Izzati Kamsani, Nor Shahida Hasan","doi":"10.11113/ijic.v12n1.313","DOIUrl":"https://doi.org/10.11113/ijic.v12n1.313","url":null,"abstract":"Abstract — A prognostic cancer marker is helpful in oncology to identify the abnormal cancer cells from the collected sample. This marker can be used as an indicator to determine a disease outcome, cancer treatment, and drug discovery. Identifying cancer markers is also beneficial to improve cancer patients’ survival rate in receiving the treatment decision-making. Cancer markers can be determined by testing every gene or pathway in the wet lab manually or using the text mining automation method. The use of text mining techniques effectively investigates hidden information and gathers new knowledge from many existing sources. Unfortunately, querying relevant text to excavate important information is a challenging task. PubMed text data mining is one of the applications that help explore potential cancer markers as the trend of scientific articles in PubMed is steadily increased. Besides, it can support biologists to concentrate on the identified small set of genes or pathways. PubMed identifiers (PMIDs) are then obtained as evidence to ascertain the relationship between diseases and genes (or pathways) used as biological validation. Thus, this technique can discover the biological relationship between disease and genes or pathways. Therefore, the PubMed text data mining automation is invented to link to the websites for saving time instead of manually. \u0000Keywords — PubMed, text data mining, biological validation, cancer markers, diseases, genes, pathways.","PeriodicalId":50314,"journal":{"name":"International Journal of Innovative Computing Information and Control","volume":null,"pages":null},"PeriodicalIF":1.0,"publicationDate":"2021-11-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"74523391","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 Evaluation of Support Vector Machine Kernels in Intrusion Detection System for Wireless Sensor Network","authors":"Muhammad Amir Hamzah, S. H. Othman","doi":"10.11113/ijic.v12n1.334","DOIUrl":"https://doi.org/10.11113/ijic.v12n1.334","url":null,"abstract":"Wireless sensor network is very popular in the industrial application due to its characteristics of infrastructure-less wireless network and self-configured for physical and environmental conditions monitoring. However, the dynamic environments of wireless network expose WSN to network vulnerabilities. Intrusion Detection System (IDS) has been used to mitigate the vulnerability issue of network. Researches towards the efficiency improvement of WSN-IDS has been extensively done because the rapid growth of technologies influence the growth of network attacks. Implementation Support Vector Machine (SVM) was found to be one of the optimum algorithms for the improvement of WSN-IDS. Yet, classification efficiency of SVM is based on the kernel function used because different kernel gives different SVM architecture. Linear classification of SVM has limitation to maximize the margin due to the dynamic environment of wireless network which consist of nonlinear data. Since maximizing the margin is the primary goal of SVM, it is crucial to implement the optimum kernel in the classification of nonlinear data. Each SVM model in this research use different kernels which are Linear, RBF, Polynomial and Sigmoid kernels. Further, NSL-KDD dataset was used for the experiment of this research. Performance of each kernel were evaluated based on the experimental result obtained and it was found that RBF kernel provides the best classification accuracy with the score of 91%. Finally, discussion based on the findings was made.","PeriodicalId":50314,"journal":{"name":"International Journal of Innovative Computing Information and Control","volume":null,"pages":null},"PeriodicalIF":1.0,"publicationDate":"2021-11-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"82042116","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}