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.
{"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":"23 1","pages":""},"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}
Nur Sabrina Azmi, Azurah A Samah, Hairudin Abdul Majid, Zuraini Ali Shah, H. Hashim, Nuraina Syaza Azman, Ezzeddin Kamil Mohamed Hashim
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.
对癌症进行分类的挑战可能会导致癌症的分类不准确，尤其是肉瘤癌症，因为它由罕见的癌症类型组成。临床医生很难确认病人的病情，因为准确的诊断只能由专业病理学做出。因此，代替单一组学来识别疾病标志物，整合这些组学来代表多组学的方法在检测和呈现癌症表型方面更具优势。如今，计算模型特别是深度学习的进步为解决高水平数据组学提供了有前途的方法，处理速度更快。因此，本研究的目的是利用堆叠去噪自编码器(SDAE)和一维卷积神经网络(1D CNN)对癌症和非癌症患者进行分类，并利用高相关的多组学数据评估哪种算法分类效果更好。该研究采用了两种计算模型来拟合多组学数据集。本研究中使用的肉瘤组学数据集来自ACGT Ron Shamir实验室存储库的多组学癌症基准TCGA预处理数据。从结果来看，SDAE的准确率为50.93%，1D CNN的准确率为52.78%。结果表明，1D CNN模型在肉瘤癌分类上优于SDAE模型。
{"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":"98 ","pages":""},"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}
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. Objective: 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. Method: 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. Results: 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. 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.
{"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":"104 1","pages":""},"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}
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.
{"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":"49 1","pages":""},"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}
Pub Date : 2022-01-01DOI: 10.1007/978-981-16-4258-6
{"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":"8 1","pages":""},"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}
Pub Date : 2022-01-01DOI: 10.1007/978-981-19-4132-0
{"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":"56 1","pages":""},"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}
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.
{"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":"46 3 1","pages":""},"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}
Hui Wen Nies, Z. Zakaria, Weng Howe Chan, Izyan Izzati Kamsani, Nor Shahida Hasan
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. Keywords — PubMed, text data mining, biological validation, cancer markers, diseases, genes, pathways.
{"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":"9 1","pages":""},"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}
Automatic skull identification systems play a vital role for forensic law authorities to recognize victim identity. Motivated by potential applications of these kinds of systems, this research aims to apply a pre-trained deep convolutional neural network (CNN) for face skull recognition. Basically, the unknown skull image is fed to a pre-trained CNN network to extract a 1D feature vector, and then it will be matched with photos at database agencies to identify the closest match. To validate the proposed skull recognition system, it has been applied for a total of 13 skulls, and the reported results indicated a good was achieved. In addition, various CNN architectures were investigated, including shallow, medium, and deep CNN models. The best performance was reported from the shallow CNN model with a 92% recognition rate.
{"title":"Convolutional Neural Network for Skull Recognition","authors":"Hussein Samma, Bader Lahasan","doi":"10.11113/ijic.v12n1.347","DOIUrl":"https://doi.org/10.11113/ijic.v12n1.347","url":null,"abstract":"Automatic skull identification systems play a vital role for forensic law authorities to recognize victim identity. Motivated by potential applications of these kinds of systems, this research aims to apply a pre-trained deep convolutional neural network (CNN) for face skull recognition. Basically, the unknown skull image is fed to a pre-trained CNN network to extract a 1D feature vector, and then it will be matched with photos at database agencies to identify the closest match. To validate the proposed skull recognition system, it has been applied for a total of 13 skulls, and the reported results indicated a good was achieved. In addition, various CNN architectures were investigated, including shallow, medium, and deep CNN models. The best performance was reported from the shallow CNN model with a 92% recognition rate. ","PeriodicalId":50314,"journal":{"name":"International Journal of Innovative Computing Information and Control","volume":"33 1","pages":""},"PeriodicalIF":1.0,"publicationDate":"2021-11-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"87230250","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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