Pub Date : 2023-01-01DOI: 10.1504/ijnt.2023.10059562
Rijwan Khan, Ke Huang, Mohammad Shabaz, Xianming Sun, Bichuan Sun
{"title":"Implementation of intrusion detection system and improvement utilising genetic algorithm","authors":"Rijwan Khan, Ke Huang, Mohammad Shabaz, Xianming Sun, Bichuan Sun","doi":"10.1504/ijnt.2023.10059562","DOIUrl":"https://doi.org/10.1504/ijnt.2023.10059562","url":null,"abstract":"","PeriodicalId":14128,"journal":{"name":"International Journal of Nanotechnology","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135955947","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"材料科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Studying the impact of anti-oxidant extract of different vegetables on the formation of PAHs in rabbit meat","authors":"Rabia Siddique, Ameer Fawad Zahoor, Sajjad Ahmad, Hamad Ahmad, Abid Hussain","doi":"10.1504/ijnt.2023.10059574","DOIUrl":"https://doi.org/10.1504/ijnt.2023.10059574","url":null,"abstract":"","PeriodicalId":14128,"journal":{"name":"International Journal of Nanotechnology","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135955972","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"材料科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-01-01DOI: 10.1504/ijnt.2023.134016
Pankaj Agarwal, Deepthi Gorijavolu, G. Hanumat Sastry, Venkatadri Marriboyina, D. Vijendra Babu, G.K. Kishore
Modern agriculture system involves an automation technology (AT) which supports formers to do their activities at maximum extent. This paper proposes a wireless smart automation (WSA) influencing IoT system that supports mobile user interface for real time crop field monitoring and controlling. It reduces effect of manual interpretation for agriculture farming and field monitoring. It deploys a dedicated global server for user control accessibility anywhere around the world. Thereby, the drones are operated from smart mobile which have unique IP connectivity for secure authentication Arduino UNO microcontroller. The major advantage of the proposed system allows continuous crop field monitoring and ensures limited usage of pesticides and fertilisers. Additionally, user can monitor their farm field by using unified Android mobile app and thus, ensure delay difference between turn ON and OFF state by 2 seconds irrespective of any load conditions at any time.
{"title":"Real time crop field monitoring system using agriculture IoT systems","authors":"Pankaj Agarwal, Deepthi Gorijavolu, G. Hanumat Sastry, Venkatadri Marriboyina, D. Vijendra Babu, G.K. Kishore","doi":"10.1504/ijnt.2023.134016","DOIUrl":"https://doi.org/10.1504/ijnt.2023.134016","url":null,"abstract":"Modern agriculture system involves an automation technology (AT) which supports formers to do their activities at maximum extent. This paper proposes a wireless smart automation (WSA) influencing IoT system that supports mobile user interface for real time crop field monitoring and controlling. It reduces effect of manual interpretation for agriculture farming and field monitoring. It deploys a dedicated global server for user control accessibility anywhere around the world. Thereby, the drones are operated from smart mobile which have unique IP connectivity for secure authentication Arduino UNO microcontroller. The major advantage of the proposed system allows continuous crop field monitoring and ensures limited usage of pesticides and fertilisers. Additionally, user can monitor their farm field by using unified Android mobile app and thus, ensure delay difference between turn ON and OFF state by 2 seconds irrespective of any load conditions at any time.","PeriodicalId":14128,"journal":{"name":"International Journal of Nanotechnology","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136209412","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"材料科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-01-01DOI: 10.1504/ijnt.2023.10056476
Akash Kumar Bhoi, R. Jhaveri, V. Joshi, Rakesh Kumar, Gaurav Dhiman
{"title":"The combined study of improved fuzzy optimisation techniques with the analysis of the upgraded facility location centre for the Covid-19 vaccine by fuzzy clustering algorithms","authors":"Akash Kumar Bhoi, R. Jhaveri, V. Joshi, Rakesh Kumar, Gaurav Dhiman","doi":"10.1504/ijnt.2023.10056476","DOIUrl":"https://doi.org/10.1504/ijnt.2023.10056476","url":null,"abstract":"","PeriodicalId":14128,"journal":{"name":"International Journal of Nanotechnology","volume":null,"pages":null},"PeriodicalIF":0.5,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"66787207","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"材料科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-01-01DOI: 10.1504/ijnt.2023.10059554
S. Vimal, Neha Singh, Gaurav Dhiman, Deepali Virmani
{"title":"Multi to binary class size based imbalance handling technique in wireless sensor networks","authors":"S. Vimal, Neha Singh, Gaurav Dhiman, Deepali Virmani","doi":"10.1504/ijnt.2023.10059554","DOIUrl":"https://doi.org/10.1504/ijnt.2023.10059554","url":null,"abstract":"","PeriodicalId":14128,"journal":{"name":"International Journal of Nanotechnology","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135955949","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"材料科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-01-01DOI: 10.1504/ijnt.2023.10059568
Cunhong Li, Chunmeng Lu, Yanfang Ma
{"title":"Design of IoT aided prevention and control platform for major public health emergencies","authors":"Cunhong Li, Chunmeng Lu, Yanfang Ma","doi":"10.1504/ijnt.2023.10059568","DOIUrl":"https://doi.org/10.1504/ijnt.2023.10059568","url":null,"abstract":"","PeriodicalId":14128,"journal":{"name":"International Journal of Nanotechnology","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135956263","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"材料科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-01-01DOI: 10.1504/ijnt.2023.134005
Muhammad Umer, Saima Sadiq, Arif Mehmood, Imran Ashraf, Gyu Sang Choi, Sadia Din
{"title":"Analysing behavioural and academic attributes of students using educational data mining","authors":"Muhammad Umer, Saima Sadiq, Arif Mehmood, Imran Ashraf, Gyu Sang Choi, Sadia Din","doi":"10.1504/ijnt.2023.134005","DOIUrl":"https://doi.org/10.1504/ijnt.2023.134005","url":null,"abstract":"","PeriodicalId":14128,"journal":{"name":"International Journal of Nanotechnology","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136209174","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"材料科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-01-01DOI: 10.1504/ijnt.2023.134040
P. Nancy, G. Murugesan, Abu Sarwar Zamani, Karthikeyan Kaliyaperumal, Malik Jawarneh, Surendra Kumar Shukla, Samrat Ray, Abhishek Raghuvanshi
The fatality rate has risen in recent years due to an increase in the number of encephaloma tumours in each age group. Because of the complicated structure of tumours and the involution of noise in magnetic resonance (MR) imaging data, physical identification of tumours becomes a difficult and time-consuming operation for medical practitioners. As a result, recognising and locating the tumour's location at an early stage is crucial. Cancer tumour areas at various levels may be followed and prognosticated using medical scans, which can be utilised in concert with segmentation and relegation techniques to provide a correct diagnosis at an early time. This paper aims to develop image processing and machine learning based framework for early and accurate detection of brain tumour. This framework includes image preprocessing, image segmentation, feature extraction, and classification using the support vector machine (SVM), K-nearest neighbour (KNN), and Naïve Bayes algorithms. Image preprocessing is performed using Gaussian Elimination, image enhancement using histogram equalisation, image segmentation using k-means and feature extraction performed using PCA algorithm. For performance comparison, parameters like: accuracy, sensitivity and specificity are used. Experimental results have shown that the KNN is getting better accuracy for classification of brain tumour related images. KNN is performing admirably in terms of accuracy. In terms of specificity, both SVM and KNN perform similarly well. KNN outperforms other algorithms in terms of sensitivity. Accuracy of KNN classifier is around 98% in brain tumour image classification.
{"title":"Detection of brain tumour using machine learning based framework by classifying MRI images","authors":"P. Nancy, G. Murugesan, Abu Sarwar Zamani, Karthikeyan Kaliyaperumal, Malik Jawarneh, Surendra Kumar Shukla, Samrat Ray, Abhishek Raghuvanshi","doi":"10.1504/ijnt.2023.134040","DOIUrl":"https://doi.org/10.1504/ijnt.2023.134040","url":null,"abstract":"The fatality rate has risen in recent years due to an increase in the number of encephaloma tumours in each age group. Because of the complicated structure of tumours and the involution of noise in magnetic resonance (MR) imaging data, physical identification of tumours becomes a difficult and time-consuming operation for medical practitioners. As a result, recognising and locating the tumour's location at an early stage is crucial. Cancer tumour areas at various levels may be followed and prognosticated using medical scans, which can be utilised in concert with segmentation and relegation techniques to provide a correct diagnosis at an early time. This paper aims to develop image processing and machine learning based framework for early and accurate detection of brain tumour. This framework includes image preprocessing, image segmentation, feature extraction, and classification using the support vector machine (SVM), K-nearest neighbour (KNN), and Naïve Bayes algorithms. Image preprocessing is performed using Gaussian Elimination, image enhancement using histogram equalisation, image segmentation using k-means and feature extraction performed using PCA algorithm. For performance comparison, parameters like: accuracy, sensitivity and specificity are used. Experimental results have shown that the KNN is getting better accuracy for classification of brain tumour related images. KNN is performing admirably in terms of accuracy. In terms of specificity, both SVM and KNN perform similarly well. KNN outperforms other algorithms in terms of sensitivity. Accuracy of KNN classifier is around 98% in brain tumour image classification.","PeriodicalId":14128,"journal":{"name":"International Journal of Nanotechnology","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136209181","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"材料科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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