Australian Journal of Engineering and Innovative Technology最新文献

{"title":"Study and Prediction of Covid-19 Cases and Vaccinations Using Machine Learning in Bangladesh","authors":"","doi":"10.34104/ajeit.022.01300139","DOIUrl":"https://doi.org/10.34104/ajeit.022.01300139","url":null,"abstract":"Coronavirus disease 2019 (COVID-19) is a contagious disease caused by a virus, the severe acute respiratory syndrome coronavirus 2(SARS-CoV-2). This first known case was identified in Wuhan, China in December 2019. The first case of COVID-19 was detected on 8th March 2020 in Bangladesh. Subsequently, COVID-19 cases have been increasing at an alarming rate in Bangladesh because of the high-density population with limited resources. However, to protect against this type of disaster there is an urgent need for high-quality data and forecasting for national preparedness and action plans, which is currently unavailable. Therefore, we developed a machine learning-based project that provides us with trend analysis and predicts the likely number of cases and deaths in upcoming days, Distributed Bangladesh in different zone according to the number of cases to increase people's awareness, finding the correlation between coronavirus and any other disease. For this investigation, we applied linear regression, polynomial regression, Extra Tree Classifier, Decision Tree Regression, and chi-square test machine learning algorithm with high and best accuracy.","PeriodicalId":375342,"journal":{"name":"Australian Journal of Engineering and Innovative Technology","volume":"21 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121629587","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":"GIS Based Mapping and Assessment of Noise Pollution in Gazipur City, Bangladesh","authors":"","doi":"10.34104/ajeit.022.01070115","DOIUrl":"https://doi.org/10.34104/ajeit.022.01070115","url":null,"abstract":"Noise pollution is an extensive concern for urban areas around the world. Gazipur is one of the significant hubs of the garment industry in Bangladesh. The purpose of the study was to address the highest sources of noise pollution in this City and identify the public perceptions of noise pollution. Noise levels have been measured from 38 locations of the City from 8.00 am to 6.00 pm including industrial, mixed, commercial, residential, and sensitive zones on both working and non-working days. ArcGIS based assessment using IDW spatial interpolation technique was used for the mapping of the measured noise levels across the study area. The highest average noise level (100.9 dB) was observed at Joydebpur Junction in the period of the evening (4.00 pm to 6.00 pm) and the lowest average noise level (47.5 dB) was observed in front of the Banghabandhu Satellite Ground Station in the period of the afternoon (12.00 pm to 2.00 pm). Heavy industrialization, vehicles horn, construction sites, traffic noise, loudspeakers, expired engines, loud talking, and outdoor parties are the extensive reasons for the increase in noise pollution in Gazipur City. Study showed that the noise level in every location was higher than the standard levels. Survey data indicated that city dwellers endure noise pollution headaches, irritable moods, sleeplessness, bad temper, and stress. This study strongly suggests that proper implementation of the existing laws helps to mitigate and reduce the noise pollution situation in the study area.","PeriodicalId":375342,"journal":{"name":"Australian Journal of Engineering and Innovative Technology","volume":"16 5","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132148737","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":"An Effective Fake News Detection on Social Media and Online News Portal by Using Machine Learning","authors":"","doi":"10.34104/ajeit.022.0950106","DOIUrl":"https://doi.org/10.34104/ajeit.022.0950106","url":null,"abstract":"In today's world, misinformation is a major problem. Fake news is a characteristic that is influencing our publication, explicitly in the political world. Because there are only a limited amount of resources (such as datasets and distributed writing) available, the emerging research field of counterfeit news is experiencing difficulties. Yet, profound learning procedures' new forward leaps in muddled regular language handling errands make them a potential response for distinguishing counterfeit news from legitimate assets. We propose in this paper a fake news recognizable proof model that utilizes man-made intelligence methods. We explored eight different machine courses of action methods. For correlation, we chose some notable grouping AI models, including Strategic Relapse (LR), Choice Tree Arrangement (DTC), Inclination Supporting Classifier (GBC), Arbitrary Backwoods Classifier (RFC), Direct SVC (SVC), Inactive Forceful Classifier (Dad), K Neighbors Classifier (KNC), and Multinomial NB (MNB). Trial assessment yields the best exhibition utilizing the Direct Help Vector Classifier (Straight SVC) as a classifier, with a precision of 96%.","PeriodicalId":375342,"journal":{"name":"Australian Journal of Engineering and Innovative Technology","volume":"24 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125460925","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":"Assessment of the Irrigation Water Supply and Demand in Eastern Afghanistan: A Case Study of Behsud Canal in Nangarhar Province","authors":"","doi":"10.34104/ajeit.021.090094","DOIUrl":"https://doi.org/10.34104/ajeit.021.090094","url":null,"abstract":"The study was carried out in one of the districts of Nangarhar province located in Eastern Afghanistan between latitude 34.25o N and longitude 70.50o E. Nangarhar has huge ground and surface water resources. There are 78 irrigation canals in the province having an overall length of 365 km. Behsud Canal is located in the Behsud district of Nangarhar province of Afghanistan. The results showed that the highest crop water requirements were found for the month of July in Behsud Canal. Similarly, water application efficiency ranged recorded from 34% to 65.2%. Average evapotranspiration was found to be 49% in the present canal. The irrigation water demand increased with the increased size of the agriculture field for the production of crops in the Behsud Canal of Nangarhar province.","PeriodicalId":375342,"journal":{"name":"Australian Journal of Engineering and Innovative Technology","volume":"60 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-09-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123117737","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":"Application of IoT for Developing Sustainable and Smart Farming","authors":"","doi":"10.34104/ajeit.021.078089","DOIUrl":"https://doi.org/10.34104/ajeit.021.078089","url":null,"abstract":"Traditional farming is labour-intensive, and the need to constantly check crops may be a strain on farmers. On another side, agricultural yield space is shrinking by the day. So, without a question, managing a huge amount of food that is legally right for us is a very difficult problem for any human being. There may always be a footprint available to meet the high demand. By achieving the idea of smart farming based on new technology by using the Internet of Things (IoT), the authors have presented a strategy in this study work by which a farmer may manage water irrigation, detect the total amount of brightness, and monitor the moisture level of soil and current status of crops using IoT. By utilizing such a technology, the farmer would obtain an auto lighting system, an auto water irrigation system, prohibit external vehicles, and conserve electricity by utilizing real-time data obtained from various types of sensors and utilizing a Wi-Fi system. The suggested system's hardware is all directly linked to the NodeMCU ESP8266. An algorithm has been created to manage the entire project. The solar panel will supply the entire system's necessary electric power, allowing us to save money, conserve electricity, and make the total system more environmentally friendly. This work’s suggested system can identify meteorological conditions that are beneficial to agriculture. This proposed concept has exceptional performance potential as an interface between sensors as input and the IoT as an output medium. The suggested system is compared to other existing systems in a variety of ways.","PeriodicalId":375342,"journal":{"name":"Australian Journal of Engineering and Innovative Technology","volume":"248 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-08-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116402360","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":"Students Performance Measurement and Prediction Based on Academic Features Through the Machine Learning","authors":"Akash Kumar Pal, Shahrin Sumona, Md. Atikur Rashid, M. A. R. Hasan, Mithun Kumar, Md. Anwar Hossain","doi":"10.34104/ajeit.022.065077","DOIUrl":"https://doi.org/10.34104/ajeit.022.065077","url":null,"abstract":"The education sector plays a vital role in achieving long-term economic and national development. During the last decades, due to the availability of resources and technology, efficient and higher standards in education have become easier to attain. The amount of data in educational institutions is growing rapidly. Through the data mining and machine learning methodologies, it has become easier to look into data from a different perspective and extract various information from the data. In our research, we use various algorithms to find out the correlation between features and predict students’ performance using their academic records. We try to find out the factors which affect and influence the performance of students. We implemented both unsupervised and supervised learning algorithms in our research. K-mean clustering has been used as an unsupervised learning method to group the students and find out the dependencies between the features. For prediction purposes, we use classification techniques like KNN and Linear Regression to predict students’ performance. Our research not only aims into finding useful information but also provides insight into students’ preferred teaching methods, potentiality, and performance. This information can guide the students for their future and guide them to their preferred fields according to their skill sets.","PeriodicalId":375342,"journal":{"name":"Australian Journal of Engineering and Innovative Technology","volume":"13 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133115584","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":"Evaluation of Impact of Soil Properties on Strength of Flood Levees in Indus River Basin of Pakistan","authors":"","doi":"10.34104/ajeit.022.052064","DOIUrl":"https://doi.org/10.34104/ajeit.022.052064","url":null,"abstract":"Flood bunds are earthen embankments constructed along rivers to control the floods. Historically speaking, floods in Indus River Basin (IRB) in Pakistan have been posing serious threats and damages to infrastructure and human lives. A long infrastructure of flood bunds (about 6807km) exists parallel to the main river to agricultural crops, human settlements, livestock, industries, etc. from the hydro-disasters arising from the floods in the country. Punjab Province has been severely hit by heavy floods causing severe loss to human lives, infrastructure, crops, livestock, etc. A study was carried out to evaluate the “health” of flood bunds by investigating their geotechnical, geometrical, geochemical, and hydraulic properties to ascertain strength against breaching during floods. A length of about 731 km of river Chenab in Punjab Province of Pakistan was selected to investigate the vulnerability of flood embankments at 78 sites of 38 flood bunds which were found critical after the 2014 flood in Pakistan. Different field activities including density test, measurement of geometrical parameters, collection of undisturbed/disturbed soil samples, physical inspections, and collection of ground-water samples from bore holes were performed. Laboratory tests including soil texture, Atterberg’s limits, compaction, unconfined compressive strength, permeability, direct shear, etc. were performed in laboratories of Irrigation Research Institute, Lahore, Pakistan. An Index called river embankment breaching vulnerability index (REBVI) has been prescribed and calculated to evaluate the strength of the flood embankments against breaching. Cluster analysis has been carried out for different indicators keeping in view the value of REBVI. On the basis of REBVI and Cluster analysis, it was concluded that 11 out of 78 sites are showing high strength against the breaching action, while 35 sites indicated low potential against the breaching action. In other words, it has been found that the consistency of the embankment is very high for 11 sites and high for 35 flood bund sites. The consistency of the embankments is medium, low, and very low at the remaining 16, 12, and 4 flood bund sites due to moderate vulnerability, high probability of embankment breaching, and very high vulnerability respectively.","PeriodicalId":375342,"journal":{"name":"Australian Journal of Engineering and Innovative Technology","volume":"5 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-06-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128516427","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":"Data Encryption Using Image Processing: A Brief Study","authors":"Mahmud Rahman Parag, Tania Akter Setu","doi":"10.34104/ajeit.022.045051","DOIUrl":"https://doi.org/10.34104/ajeit.022.045051","url":null,"abstract":"We can see considerable growth in the field of science and technology over the past few decades. This growth has also brought an enormous threat to the data used by users. Encryption and decryption of data play an important role in safeguarding the data. Many authors have contributed much outstanding research on unauthorized access to user data. In this journal, I have proposed an algorithm for data encryption using images. My main aim is to provide a new and highly customizable approach to protecting data using images and eliminating the use of plaintext. In my research, I will use images to encrypt and decrypt data rather than using plaintext.","PeriodicalId":375342,"journal":{"name":"Australian Journal of Engineering and Innovative Technology","volume":"249 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-05-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122704470","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":"Landslide Affected Areas and Challenges Imposed in North Eastern Region of India: An Appraisal","authors":"","doi":"10.34104/ajeit.022.032044","DOIUrl":"https://doi.org/10.34104/ajeit.022.032044","url":null,"abstract":"The Earth Observation (EO) data of various Spatio-temporal resolutions are used to study potential sources of landslide hazards in the North Eastern Region (NER) of India. The studies are made in all the eight states of NER, namely Arunachal Pradesh, Assam, Manipur, Meghalaya, Mizoram, Nagaland, Sikkim, and Tripura on different project modes during the past decade. All types of landslides of various magnitudes and dimensions are triggered by rainfall, earthquakes, or by combined effects in this tectonically active region. Anthropogenic factors also play a role in places, particularly in urban areas. The NER is one of the most seismically active regions in the world, and it falls in the highest seismic risk zone, (Zone V), in the seismic zoning map of India. The region also receives much rainfall because of its geographic and climatic conditions. We report how the physiography, as well as the local geology, plays the main role in the failure of the rock formation and over-burden causing the landslide hazards, which create severe communication disruption and sometimes casualties. Fragile, soft, and weathered rock formations along the steep slope make the terrain much more susceptible to landslides. Deep-seated landslides disrupt transportation networks for a longer duration while shallow slides, mostly affect the urban life for a short duration. The reported causalities associated with landslide hazards are much high in the NER compared to other parts of India.","PeriodicalId":375342,"journal":{"name":"Australian Journal of Engineering and Innovative Technology","volume":"37 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-04-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127113569","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":"Bangla Handwritten Characters Recognition Using Convolutional Neural Network","authors":"Md Anwar Hossain, M. A. R. Hasan, A. Abadin, Nafiul Fatta","doi":"10.34104/ajeit.022.027031","DOIUrl":"https://doi.org/10.34104/ajeit.022.027031","url":null,"abstract":"In the Bangla language, there are 50 complex-shaped characters and working with this huge amount of characters with an appropriate set of features is a tough problem to recognize handwritten characters. Moreover, ambiguity and precision errors are common in handwritten words. Furthermore, among a large number of complex-shaped letters, some are quite similar in shape, making handwritten Bangla characters difficult to recognize. In this work, we proposed a convolutional neural network-based approach for recognizing the handwritten Bangla alphabet. In character recognition, the convolutional neural network (CNN) outperforms most of the other models. However, to guarantee a satisfactory performance, CNNs usually need a great number of samples. Bangla handwriting recognition has been a hot topic for several years, but due to the similarity of many Bangla characters, it's difficult to achieve good results. By training and testing on Bangla character datasets, the model gets a 90.22% validation accuracy for Bangalekha isolated dataset and 93.22% validation accuracy for the Ekush dataset.","PeriodicalId":375342,"journal":{"name":"Australian Journal of Engineering and Innovative Technology","volume":"10 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-03-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114153915","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}