Pub Date : 2021-12-18DOI: 10.1109/ICCIT54785.2021.9689836
Faisal Bin Ashraf, Abdul Matin, Md. Shafiur Raihan Shafi, Muhammad Usama Islam
k-means is the most widely used clustering algorithm which is an unsupervised technique that needs assumptions of centroids to begin the process. Hence, the problem is NP-hard and needs careful consideration and optimization to get a better quality of clusters of data. In this work, a meta-heuristic based genetic algorithm is proposed to optimize the centroid initialization process. The proposed method includes tournament selection, probability-based mutation, and elitism that leads to finding the optimal centroids for the clusters of a given dataset. Nine different and diversified datasets were used to test the performance of the proposed method in terms of the davies-bouldin index and it performed better in all the datasets than the standard k-means and minibatch k-means algorithm.
{"title":"An Improved K-means Clustering Algorithm for Multi-dimensional Multi-cluster data Using Meta-heuristics","authors":"Faisal Bin Ashraf, Abdul Matin, Md. Shafiur Raihan Shafi, Muhammad Usama Islam","doi":"10.1109/ICCIT54785.2021.9689836","DOIUrl":"https://doi.org/10.1109/ICCIT54785.2021.9689836","url":null,"abstract":"k-means is the most widely used clustering algorithm which is an unsupervised technique that needs assumptions of centroids to begin the process. Hence, the problem is NP-hard and needs careful consideration and optimization to get a better quality of clusters of data. In this work, a meta-heuristic based genetic algorithm is proposed to optimize the centroid initialization process. The proposed method includes tournament selection, probability-based mutation, and elitism that leads to finding the optimal centroids for the clusters of a given dataset. Nine different and diversified datasets were used to test the performance of the proposed method in terms of the davies-bouldin index and it performed better in all the datasets than the standard k-means and minibatch k-means algorithm.","PeriodicalId":166450,"journal":{"name":"2021 24th International Conference on Computer and Information Technology (ICCIT)","volume":"25 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133686801","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 : 2021-12-18DOI: 10.1109/ICCIT54785.2021.9689865
Md. Abdus Siddique, Md Imran Sarker, R. Ghosh, K. Gosh
Music lyrics have a broad scope of impacts on our day-to-day life. The connection between music and the cerebrum has been extensively studied as far as feeling and intellectual interaction. From school children to strict adherents, the audience has the right to taste great music. For example, men presented with physically rough verses tend to more generalized perspectives toward ladies. Listening to particularly toxic or nontoxic songs can affect our mood. Music recommendation system follows different features based on the user’s historical data. The listener’s mode could be improved if the recommendation system filters out toxicity. In this study, we classify lyrics in terms of toxicity and nontoxicity from different music genres and artists using machine learning (ML) algorithms. The toxicity and nontoxicity have been measured using high valence and low valence. From the results, we found that Random Forest (RF) is a much more effective toxicity classification classifier, giving an overall accuracy of 93%.
{"title":"Toxicity Classification on Music Lyrics Using Machine Learning Algorithms","authors":"Md. Abdus Siddique, Md Imran Sarker, R. Ghosh, K. Gosh","doi":"10.1109/ICCIT54785.2021.9689865","DOIUrl":"https://doi.org/10.1109/ICCIT54785.2021.9689865","url":null,"abstract":"Music lyrics have a broad scope of impacts on our day-to-day life. The connection between music and the cerebrum has been extensively studied as far as feeling and intellectual interaction. From school children to strict adherents, the audience has the right to taste great music. For example, men presented with physically rough verses tend to more generalized perspectives toward ladies. Listening to particularly toxic or nontoxic songs can affect our mood. Music recommendation system follows different features based on the user’s historical data. The listener’s mode could be improved if the recommendation system filters out toxicity. In this study, we classify lyrics in terms of toxicity and nontoxicity from different music genres and artists using machine learning (ML) algorithms. The toxicity and nontoxicity have been measured using high valence and low valence. From the results, we found that Random Forest (RF) is a much more effective toxicity classification classifier, giving an overall accuracy of 93%.","PeriodicalId":166450,"journal":{"name":"2021 24th International Conference on Computer and Information Technology (ICCIT)","volume":"53 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130106170","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 : 2021-12-18DOI: 10.1109/ICCIT54785.2021.9689905
M. Hasan, Muhammad Usama Islam, M. Sadeq
The technological explosion has paved the way for agriculture to flourish exponentially thus contributing to better yield of crops through the aid of machine learning, the Internet of things, mechanical systems in agriculture. In our research work, we have investigated various types of dry beans followed by a deep neural network based approach to classify the beans automatically. The results shows that our approach had an accuracy of 93.44%, and an F-1 score of 94.57%, with the dataset that consisted of 7 varieties of dry beans. Our results, which performed substantially better in comparison to traditional machine learning approaches aided us to devise further research scopes in the field of agricultural machine learning.
{"title":"A Deep Neural Network for Multi-class Dry Beans Classification","authors":"M. Hasan, Muhammad Usama Islam, M. Sadeq","doi":"10.1109/ICCIT54785.2021.9689905","DOIUrl":"https://doi.org/10.1109/ICCIT54785.2021.9689905","url":null,"abstract":"The technological explosion has paved the way for agriculture to flourish exponentially thus contributing to better yield of crops through the aid of machine learning, the Internet of things, mechanical systems in agriculture. In our research work, we have investigated various types of dry beans followed by a deep neural network based approach to classify the beans automatically. The results shows that our approach had an accuracy of 93.44%, and an F-1 score of 94.57%, with the dataset that consisted of 7 varieties of dry beans. Our results, which performed substantially better in comparison to traditional machine learning approaches aided us to devise further research scopes in the field of agricultural machine learning.","PeriodicalId":166450,"journal":{"name":"2021 24th International Conference on Computer and Information Technology (ICCIT)","volume":"83 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134071986","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 : 2021-12-18DOI: 10.1109/ICCIT54785.2021.9689779
Sabiha Nasrin, T.I.M. Fahim Sahryer, A. B. M. A. Al Islam, Jannatun Noor
We conduct experiments on the different public clouds provided in this paper to bring out a comparative study, to help the developers understand the diversity of the platforms. This study will help them choose a suitable platform for their desired application. The contemporary and future usage of this diverse nature of cloud computing is hugely demanding. Thus, we elaborate our study on serverless cloud computing to suffice the demand. Serverless prevents a great deal of unessential consumption of power and is a pay-as-you-go service. This technology has added a great impact on software and application development. Although the major obstacle to this development field is that there is not enough documentation on how the big companies provide this facility and how their architecture is built. The comparative study on this diverse platform is missing in the literature. Therefore, our research is based on the on-demand serverless use cases and comparative study with necessary measures. This can be effective and efficient to use for further serverless implementation. Hence, we and others can follow our research for understanding the technical complexity.
{"title":"Feature and Performance Based Comparative Study on Serverless Frameworks","authors":"Sabiha Nasrin, T.I.M. Fahim Sahryer, A. B. M. A. Al Islam, Jannatun Noor","doi":"10.1109/ICCIT54785.2021.9689779","DOIUrl":"https://doi.org/10.1109/ICCIT54785.2021.9689779","url":null,"abstract":"We conduct experiments on the different public clouds provided in this paper to bring out a comparative study, to help the developers understand the diversity of the platforms. This study will help them choose a suitable platform for their desired application. The contemporary and future usage of this diverse nature of cloud computing is hugely demanding. Thus, we elaborate our study on serverless cloud computing to suffice the demand. Serverless prevents a great deal of unessential consumption of power and is a pay-as-you-go service. This technology has added a great impact on software and application development. Although the major obstacle to this development field is that there is not enough documentation on how the big companies provide this facility and how their architecture is built. The comparative study on this diverse platform is missing in the literature. Therefore, our research is based on the on-demand serverless use cases and comparative study with necessary measures. This can be effective and efficient to use for further serverless implementation. Hence, we and others can follow our research for understanding the technical complexity.","PeriodicalId":166450,"journal":{"name":"2021 24th International Conference on Computer and Information Technology (ICCIT)","volume":"204 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133095510","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 : 2021-12-18DOI: 10.1109/ICCIT54785.2021.9689826
Shehzan Haider Chowdhury, Murshed Al Amin, A. M. Rahman, M. A. Amin, A. Ali
Assessing the rehabilitation exercises are essential in the recovery and treatment of various musculoskeletal conditions following surgery. According to reports, over 90% of all rehabilitative exercise sessions are conducted in a home environment. As the number of patients grows, this method becomes prohibitively expensive. Providing technology support for home-based rehabilitation is an excellent approach to address this. The patient remains at home and does the exercises in front of the camera, with the footage or data being sent to the physician for comments on the exercises. In this paper, we propose two machine learning-based models to assess the quality of exercises where the data is captured by such kinect 3D sensors. The proposed models consist of a long short-term memory(LSTM) network which uses the time series skeletal data to predict the quality of the exercises. The first model uses the predefined features proposed by the physicians. For the second model, we extract features using graph convolutional network(GCN) on the skeletal data where each node represents a body part or joint in the body and the edges represent the connection between the body parts. We conclude that LSTM is more accurate at predicting the results when GCN features are used.
{"title":"Assessment of Rehabilitation Exercises from Depth Sensor Data","authors":"Shehzan Haider Chowdhury, Murshed Al Amin, A. M. Rahman, M. A. Amin, A. Ali","doi":"10.1109/ICCIT54785.2021.9689826","DOIUrl":"https://doi.org/10.1109/ICCIT54785.2021.9689826","url":null,"abstract":"Assessing the rehabilitation exercises are essential in the recovery and treatment of various musculoskeletal conditions following surgery. According to reports, over 90% of all rehabilitative exercise sessions are conducted in a home environment. As the number of patients grows, this method becomes prohibitively expensive. Providing technology support for home-based rehabilitation is an excellent approach to address this. The patient remains at home and does the exercises in front of the camera, with the footage or data being sent to the physician for comments on the exercises. In this paper, we propose two machine learning-based models to assess the quality of exercises where the data is captured by such kinect 3D sensors. The proposed models consist of a long short-term memory(LSTM) network which uses the time series skeletal data to predict the quality of the exercises. The first model uses the predefined features proposed by the physicians. For the second model, we extract features using graph convolutional network(GCN) on the skeletal data where each node represents a body part or joint in the body and the edges represent the connection between the body parts. We conclude that LSTM is more accurate at predicting the results when GCN features are used.","PeriodicalId":166450,"journal":{"name":"2021 24th International Conference on Computer and Information Technology (ICCIT)","volume":"9 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115608225","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 : 2021-12-18DOI: 10.1109/ICCIT54785.2021.9689845
Ahsanul Islam, Md Rakibul Hasan, Md. Zakir Hossain, M. Hasan
One of the key challenges of digital signal processing is to estimate sinusoidal components of an unknown signal. Researchers and engineers have been adopting various methods to analyze noisy signals and extract essential features of a given signal. Singular spectrum analysis (SSA) has been a popular and effective tool for extracting sinusoidal components of an unknown noisy signal. The process of singular spectrum analysis includes embedding time series into a Hankel matrix. The eigenvalue distribution of the Hankel matrix exhibits significant properties that can be used to estimate an unknown signal’s rhythmic components and frequency response. This paper proposes a method that utilizes the Hankel matrix’s eigenvalue distribution to estimate sinusoidal components from the frequency spectrum of a noisy signal. Firstly, an autoregressive (AR) model has been utilized for simulating time series employed to observe eigenvalue distributions and frequency spectrum. Nevertheless, the approach has been tested on real-life speech data to prove the applicability of the proposed mechanism on spectral estimation. Overall, results on both simulated and real data confirm the acceptability of the proposed method. This study suggests that eigenvalue distribution can be a helpful tool for estimating the frequency response of an unknown time series. Since the autoregressive model can be used to model various real-life data analyses, this study on eigenvalue distribution and frequency spectrum can be utilized in those real-life data. This approach will help estimate frequency response and identify rhythmic components of an unknown time series based on eigenvalue distribution.
{"title":"The Eigenvalue Distribution of Hankel Matrix: A Tool for Spectral Estimation From Noisy Data","authors":"Ahsanul Islam, Md Rakibul Hasan, Md. Zakir Hossain, M. Hasan","doi":"10.1109/ICCIT54785.2021.9689845","DOIUrl":"https://doi.org/10.1109/ICCIT54785.2021.9689845","url":null,"abstract":"One of the key challenges of digital signal processing is to estimate sinusoidal components of an unknown signal. Researchers and engineers have been adopting various methods to analyze noisy signals and extract essential features of a given signal. Singular spectrum analysis (SSA) has been a popular and effective tool for extracting sinusoidal components of an unknown noisy signal. The process of singular spectrum analysis includes embedding time series into a Hankel matrix. The eigenvalue distribution of the Hankel matrix exhibits significant properties that can be used to estimate an unknown signal’s rhythmic components and frequency response. This paper proposes a method that utilizes the Hankel matrix’s eigenvalue distribution to estimate sinusoidal components from the frequency spectrum of a noisy signal. Firstly, an autoregressive (AR) model has been utilized for simulating time series employed to observe eigenvalue distributions and frequency spectrum. Nevertheless, the approach has been tested on real-life speech data to prove the applicability of the proposed mechanism on spectral estimation. Overall, results on both simulated and real data confirm the acceptability of the proposed method. This study suggests that eigenvalue distribution can be a helpful tool for estimating the frequency response of an unknown time series. Since the autoregressive model can be used to model various real-life data analyses, this study on eigenvalue distribution and frequency spectrum can be utilized in those real-life data. This approach will help estimate frequency response and identify rhythmic components of an unknown time series based on eigenvalue distribution.","PeriodicalId":166450,"journal":{"name":"2021 24th International Conference on Computer and Information Technology (ICCIT)","volume":"28 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114180163","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}
People of this modern era are very much dependable on online reviews when it is the matter of purchasing any product. It is vital to bring out information from the huge amount of accessible text reviews. People of almost every age often visit restaurants. In today’s world food review is the fundamental requirement for visiting restaurants. But selecting a restaurant based on reviews is not quite an easy task. Deciding whether a food is worth having or not can be difficult. Several factors including the price, quality, taste, quantity can influence the actual worth of a food. From the perspective of a consumer, it is a dilemma to select a food appropriately. Food quality prediction can be a challenging task due to the high number of reviews that should be considered for the accurate prediction. Most people nowadays select restaurants based on their preferred food’s review. But the reviews present on the social platforms are mostly broad. People don’t find it useful to read the whole review. Therefore, a model which is capable of accepting reviews as input and is able to predict the food quality as output can become a great solution to this problem. So in this study, we have introduced a method which will be able to classify long Bengali food reviews into precise classes such as Good, Bad and Best using LSTM. The whole dataset which was used in our experiment has been collected from Facebook food review groups. Among them 80% was used for model training and 20% data used for the validation. Our model was able to classify 1000 Bengali review with 98% training and 80% validation accuracy.
{"title":"Classification of Food Reviews from Bengali Text using LSTM","authors":"Md. Muhaiminul Islam, Tazrina Haque Mohana, Lamia Rukhsara","doi":"10.1109/ICCIT54785.2021.9689847","DOIUrl":"https://doi.org/10.1109/ICCIT54785.2021.9689847","url":null,"abstract":"People of this modern era are very much dependable on online reviews when it is the matter of purchasing any product. It is vital to bring out information from the huge amount of accessible text reviews. People of almost every age often visit restaurants. In today’s world food review is the fundamental requirement for visiting restaurants. But selecting a restaurant based on reviews is not quite an easy task. Deciding whether a food is worth having or not can be difficult. Several factors including the price, quality, taste, quantity can influence the actual worth of a food. From the perspective of a consumer, it is a dilemma to select a food appropriately. Food quality prediction can be a challenging task due to the high number of reviews that should be considered for the accurate prediction. Most people nowadays select restaurants based on their preferred food’s review. But the reviews present on the social platforms are mostly broad. People don’t find it useful to read the whole review. Therefore, a model which is capable of accepting reviews as input and is able to predict the food quality as output can become a great solution to this problem. So in this study, we have introduced a method which will be able to classify long Bengali food reviews into precise classes such as Good, Bad and Best using LSTM. The whole dataset which was used in our experiment has been collected from Facebook food review groups. Among them 80% was used for model training and 20% data used for the validation. Our model was able to classify 1000 Bengali review with 98% training and 80% validation accuracy.","PeriodicalId":166450,"journal":{"name":"2021 24th International Conference on Computer and Information Technology (ICCIT)","volume":"5 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122581902","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 : 2021-12-18DOI: 10.1109/ICCIT54785.2021.9689840
Ibrahim Al Azhar, Sohel Ahmed, Md Saiful Islam, Aisha Khatun
Authorship Attribution is the task of determining the author of an unknown text using one’s writing patterns. It is a well-established task for high-resource languages like English, but it is challenging for low-resource languages like Bengali. In this paper, we propose a Bi-directional Long Short Term Memory(Bi-LSTM) model with self-attention mechanism to address this problem. GloVe embedding vectors encode the semantic and syntactic knowledge of words, which are then fed into the Bi-LSTM models. Moreover, attention mechanism enhances the model’s ability to learn the complex linguistics patterns through learnable parameters, which gives lower weights to common words and higher weights to keywords that capture an author’s stylistic components. It improves performance extract contextual features. We evaluate our model on multiple datasets and experiment with various architectures. Our proposed model outperforms the state-of-the-art model by 12.14%-20.24% in the BAAD6 author dataset, 1.05% - 7.34% in the BAAD16 author dataset, with best performance accuracy of 97.99%. The experimental results demonstrate that the Bi-LSTM model’s attention mechanism notably boosts performance. (The source code are shared as free tools at https://github.com/IbrahimAlAzhar/AuthorshipAttribution)
{"title":"Identifying Author in Bengali Literature by Bi-LSTM with Attention Mechanism","authors":"Ibrahim Al Azhar, Sohel Ahmed, Md Saiful Islam, Aisha Khatun","doi":"10.1109/ICCIT54785.2021.9689840","DOIUrl":"https://doi.org/10.1109/ICCIT54785.2021.9689840","url":null,"abstract":"Authorship Attribution is the task of determining the author of an unknown text using one’s writing patterns. It is a well-established task for high-resource languages like English, but it is challenging for low-resource languages like Bengali. In this paper, we propose a Bi-directional Long Short Term Memory(Bi-LSTM) model with self-attention mechanism to address this problem. GloVe embedding vectors encode the semantic and syntactic knowledge of words, which are then fed into the Bi-LSTM models. Moreover, attention mechanism enhances the model’s ability to learn the complex linguistics patterns through learnable parameters, which gives lower weights to common words and higher weights to keywords that capture an author’s stylistic components. It improves performance extract contextual features. We evaluate our model on multiple datasets and experiment with various architectures. Our proposed model outperforms the state-of-the-art model by 12.14%-20.24% in the BAAD6 author dataset, 1.05% - 7.34% in the BAAD16 author dataset, with best performance accuracy of 97.99%. The experimental results demonstrate that the Bi-LSTM model’s attention mechanism notably boosts performance. (The source code are shared as free tools at https://github.com/IbrahimAlAzhar/AuthorshipAttribution)","PeriodicalId":166450,"journal":{"name":"2021 24th International Conference on Computer and Information Technology (ICCIT)","volume":"182 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127545219","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 : 2021-12-18DOI: 10.1109/ICCIT54785.2021.9689851
Md. Rakibul Haque, Azmain Yakin Srizon, Md. Al Mamun
With the introduction of high-resolution hyperspectral sensors, hyperspectral images have become one of the paramount mediums of collecting information from remote places. Owing to the enormous dimension of spectral bands and high correlation between the bands, proper classification of hyperspectral images suffers seriously. Furthermore, proper exploration of merged spectral and spatial features remains challenging for traditional approaches. Keeping the above challenges in mind, we have proposed a properly tuned Stacked AutoEncoder(SAE) and a novel Convolutional neural network (CNN) architecture that simultaneously considers the output of all the convolutional blocks. We have used a benchmark hyperspectral dataset called KSC center. Experimental results have shown that our method has achieved an average accuracy of 99.50%, surpassing other state-of-the-art approaches significantly.
{"title":"Exploring Spectral and Spatial Features Using a Hybrid Approach Combining Stacked AutoEncoder and a Novel Convolutional Neural Network for Hyperspectral Image Classification","authors":"Md. Rakibul Haque, Azmain Yakin Srizon, Md. Al Mamun","doi":"10.1109/ICCIT54785.2021.9689851","DOIUrl":"https://doi.org/10.1109/ICCIT54785.2021.9689851","url":null,"abstract":"With the introduction of high-resolution hyperspectral sensors, hyperspectral images have become one of the paramount mediums of collecting information from remote places. Owing to the enormous dimension of spectral bands and high correlation between the bands, proper classification of hyperspectral images suffers seriously. Furthermore, proper exploration of merged spectral and spatial features remains challenging for traditional approaches. Keeping the above challenges in mind, we have proposed a properly tuned Stacked AutoEncoder(SAE) and a novel Convolutional neural network (CNN) architecture that simultaneously considers the output of all the convolutional blocks. We have used a benchmark hyperspectral dataset called KSC center. Experimental results have shown that our method has achieved an average accuracy of 99.50%, surpassing other state-of-the-art approaches significantly.","PeriodicalId":166450,"journal":{"name":"2021 24th International Conference on Computer and Information Technology (ICCIT)","volume":"53 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124107765","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 : 2021-12-18DOI: 10.1109/ICCIT54785.2021.9689802
Sayeda Islam Nahid, Mohammad Monirujjaman Khan
This paper suggests the design and execution of a Toxic gas sensor and temperature monitoring device using the Internet of Things (IoT). For many years, people have experienced severe health problems such as weariness, dizziness, shortness of breath, respiratory disorders, and even death as a result of hazardous gas exposure in the workplace. Working areas that employ a variety of chemicals, such as the textile and garment industry, and sites such as mines have poisonous fumes in the air that are detrimental to the human body. Furthermore, extremes in temperature can weaken both the body and the intellect, causing them to lose focus. As a result, it is critical to understand the to improve safety, reduce the amount of harmful gas in the surrounding air. This device detects dangerous chemicals in the air, including as methane, hydrogen, and carbon monoxide. It also keeps track of the ambient temperature. Excessive concentrations of poisonous gas in the air, as well as unusually hot or low temperatures, will set off the alarm and inform anyone nearby by audio and visual clues. The technology also refreshes the data on the web server and mobile app, allowing users to access it from any location in the world at any time.
{"title":"Toxic Gas Sensor and Temperature Monitoring in Industries using Internet of Things (IoT)","authors":"Sayeda Islam Nahid, Mohammad Monirujjaman Khan","doi":"10.1109/ICCIT54785.2021.9689802","DOIUrl":"https://doi.org/10.1109/ICCIT54785.2021.9689802","url":null,"abstract":"This paper suggests the design and execution of a Toxic gas sensor and temperature monitoring device using the Internet of Things (IoT). For many years, people have experienced severe health problems such as weariness, dizziness, shortness of breath, respiratory disorders, and even death as a result of hazardous gas exposure in the workplace. Working areas that employ a variety of chemicals, such as the textile and garment industry, and sites such as mines have poisonous fumes in the air that are detrimental to the human body. Furthermore, extremes in temperature can weaken both the body and the intellect, causing them to lose focus. As a result, it is critical to understand the to improve safety, reduce the amount of harmful gas in the surrounding air. This device detects dangerous chemicals in the air, including as methane, hydrogen, and carbon monoxide. It also keeps track of the ambient temperature. Excessive concentrations of poisonous gas in the air, as well as unusually hot or low temperatures, will set off the alarm and inform anyone nearby by audio and visual clues. The technology also refreshes the data on the web server and mobile app, allowing users to access it from any location in the world at any time.","PeriodicalId":166450,"journal":{"name":"2021 24th International Conference on Computer and Information Technology (ICCIT)","volume":"13 1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124275278","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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