Pub Date : 2022-12-17DOI: 10.1109/ICCIT57492.2022.10055001
Md Momenul Haque, S. Paul, Rakhi Rani Paul, Mirza A. F. M. Rashidul Hasan, Sultan Fahim, S. Islam
South Asia countries like Bangladesh, India, and Pakistan have a large number of fuel filling stations that use centralized payment transaction systems. In some cases, this fuel filling station uses the hand cash payment system which is not secured and time-consuming. Each transaction takes more than five minutes to process. For that reason, in some cases, customers face the huge hassle of standing in a long line and waiting for their turn. Not only that, there are high possibilities of fraud activities and robbery being occur for large amounts of the payment transaction. To solve this problem we propose a blockchain-based payment transaction method for fuel filling stations. Here we use the decentralized open ledger infrastructure and proof-of-work to approve each transaction block. Every transaction between the customer and the filling station authority is completed through a digital wallet which is fully secured, fast, and transparent. Comparing to the bank payment transaction system our proposed method is decentralized and has low transaction fees applied in every transaction. This transaction process is free from third-party involvement and all transactions are immutable. For that reason no issues of customer trust and safe from fraud activities in a large number of payment transactions. Our proposed payment transaction method can play an important part to handle large amounts of transactions and provide transaction security for increasing the number of fuel filling stations in South Asia's most populated country.
{"title":"A Blockchain-Based Secure Payment System for Vehicle Fuel Filling Station","authors":"Md Momenul Haque, S. Paul, Rakhi Rani Paul, Mirza A. F. M. Rashidul Hasan, Sultan Fahim, S. Islam","doi":"10.1109/ICCIT57492.2022.10055001","DOIUrl":"https://doi.org/10.1109/ICCIT57492.2022.10055001","url":null,"abstract":"South Asia countries like Bangladesh, India, and Pakistan have a large number of fuel filling stations that use centralized payment transaction systems. In some cases, this fuel filling station uses the hand cash payment system which is not secured and time-consuming. Each transaction takes more than five minutes to process. For that reason, in some cases, customers face the huge hassle of standing in a long line and waiting for their turn. Not only that, there are high possibilities of fraud activities and robbery being occur for large amounts of the payment transaction. To solve this problem we propose a blockchain-based payment transaction method for fuel filling stations. Here we use the decentralized open ledger infrastructure and proof-of-work to approve each transaction block. Every transaction between the customer and the filling station authority is completed through a digital wallet which is fully secured, fast, and transparent. Comparing to the bank payment transaction system our proposed method is decentralized and has low transaction fees applied in every transaction. This transaction process is free from third-party involvement and all transactions are immutable. For that reason no issues of customer trust and safe from fraud activities in a large number of payment transactions. Our proposed payment transaction method can play an important part to handle large amounts of transactions and provide transaction security for increasing the number of fuel filling stations in South Asia's most populated country.","PeriodicalId":255498,"journal":{"name":"2022 25th International Conference on Computer and Information Technology (ICCIT)","volume":"61 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117319413","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 : 2022-12-17DOI: 10.1109/ICCIT57492.2022.10055134
Satirtha Paul Shyam, C. M. A. Rahman, H. Rashid
Compared to edge-based models, region-based active contour models (ACM) have demonstrated superior performance in a number of areas, including noise tolerance, back- ground complexity and inhomogeneity correction, initialization resilience, and speed of curve evolution. However, combining both of their credentials with suitable and relevant parameters exhibits promising potential in enhancing segmentation performance. Therefore, this work reports an effective fusion of optimized Difference of Gaussian (DoG) edge estimation, with the region scalable fitting ( RSF) m odel t o c apitalize o n t heir a ttributes. A locally computed edge entropy image is also used as a weight to the energy functional to infuse local edge information in the energy functional. With the integration of relevant edge and region based feature descriptors, the proposed model thereby, outperforms the established ACMs in terms of iteration time, noise tolerance, initial contour convergence, inhomogeneity suppression and segmentation accuracy.
{"title":"A Faithful DoG is All you Need","authors":"Satirtha Paul Shyam, C. M. A. Rahman, H. Rashid","doi":"10.1109/ICCIT57492.2022.10055134","DOIUrl":"https://doi.org/10.1109/ICCIT57492.2022.10055134","url":null,"abstract":"Compared to edge-based models, region-based active contour models (ACM) have demonstrated superior performance in a number of areas, including noise tolerance, back- ground complexity and inhomogeneity correction, initialization resilience, and speed of curve evolution. However, combining both of their credentials with suitable and relevant parameters exhibits promising potential in enhancing segmentation performance. Therefore, this work reports an effective fusion of optimized Difference of Gaussian (DoG) edge estimation, with the region scalable fitting ( RSF) m odel t o c apitalize o n t heir a ttributes. A locally computed edge entropy image is also used as a weight to the energy functional to infuse local edge information in the energy functional. With the integration of relevant edge and region based feature descriptors, the proposed model thereby, outperforms the established ACMs in terms of iteration time, noise tolerance, initial contour convergence, inhomogeneity suppression and segmentation accuracy.","PeriodicalId":255498,"journal":{"name":"2022 25th International Conference on Computer and Information Technology (ICCIT)","volume":"89 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131160724","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}
Sentiment analysis is one of the core fields of Natural Language Processing(NLP). Numerous machine learning and deep learning algorithms have been developed to achieve this task. Generally, deep learning models perform better in this task as they are trained on massive amounts of data. This, however, also poses a disadvantage as collecting sufficient amounts of data is a challenge and training with this data requires devices with high computational power. Word embedding is a vital step in applying machine learning models for NLP tasks. Different word embedding techniques affect the performance of machine learning algorithms. This paper evaluates GloVe, CountVectorizer, and TF-IDF embedding techniques with multiple machine learning models and proves that the right combination of embedding technique and machine learning model(TF-IDF+Logistic Regression: 87.75% accuracy) can achieve nearly the same performance or more as deep learning models (LSTM: 87.89%).
{"title":"Performance Evaluation of Different Word Embedding Techniques Across Machine Learning and Deep Learning Models","authors":"Tanmoy Mazumder, Shawan Das, Md. Hasibur Rahman, Tanjina Helaly, Tanmoy Sarkar Pias","doi":"10.1109/ICCIT57492.2022.10055572","DOIUrl":"https://doi.org/10.1109/ICCIT57492.2022.10055572","url":null,"abstract":"Sentiment analysis is one of the core fields of Natural Language Processing(NLP). Numerous machine learning and deep learning algorithms have been developed to achieve this task. Generally, deep learning models perform better in this task as they are trained on massive amounts of data. This, however, also poses a disadvantage as collecting sufficient amounts of data is a challenge and training with this data requires devices with high computational power. Word embedding is a vital step in applying machine learning models for NLP tasks. Different word embedding techniques affect the performance of machine learning algorithms. This paper evaluates GloVe, CountVectorizer, and TF-IDF embedding techniques with multiple machine learning models and proves that the right combination of embedding technique and machine learning model(TF-IDF+Logistic Regression: 87.75% accuracy) can achieve nearly the same performance or more as deep learning models (LSTM: 87.89%).","PeriodicalId":255498,"journal":{"name":"2022 25th International Conference on Computer and Information Technology (ICCIT)","volume":"42 11-12","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132496730","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 : 2022-12-17DOI: 10.1109/ICCIT57492.2022.10055683
Wasi Mashrur, Shahriar Bin Salim, Sunjida Sultana, Md. Soyaeb Hasan, Md. Akhter Uz Zaman, K. M. Zahidur Rahman, Md Rafiqul Islam
In this paper, the impact of Extended Back Gate (EBG) length on GaAs based DG-JLMOSFET is simulated to analyze its superior behaviors in contrast with conventional DG- JLMOSFETs. For determining the optimal performance of EBG in DG-JLMOSFET, the back gate is extended symmetrically from gate towards source and drain sides for several distinct lengths ranging from 10 nm to 20 nm. For both top and back gates HfO2 is taken as the gate oxide material and the oxide thickness is considered as 1 nm. For a fixed channel length of 10 nm, the suggested model displays that when gate length is increased the impact of the drain voltage on the drain current is diminished resulting significant decrease in OFF-state current with a larger Ion/Ioff ratio of ~ 109. In fact, this leads to a reduced drain induced barrier lowering. Moreover, numerous simulated results from SILVACO ATLAS TCAD offers larger drain current as well as lower subthreshold swing of 67.5 mV/Dec for the proposed model. Due to its superior performance over traditional DG-JLMOSFET, the proposed structure can be deployed effectively in the near future.
{"title":"Effect of Extended Back Gate in GaAs Based DG- JLMOSFET","authors":"Wasi Mashrur, Shahriar Bin Salim, Sunjida Sultana, Md. Soyaeb Hasan, Md. Akhter Uz Zaman, K. M. Zahidur Rahman, Md Rafiqul Islam","doi":"10.1109/ICCIT57492.2022.10055683","DOIUrl":"https://doi.org/10.1109/ICCIT57492.2022.10055683","url":null,"abstract":"In this paper, the impact of Extended Back Gate (EBG) length on GaAs based DG-JLMOSFET is simulated to analyze its superior behaviors in contrast with conventional DG- JLMOSFETs. For determining the optimal performance of EBG in DG-JLMOSFET, the back gate is extended symmetrically from gate towards source and drain sides for several distinct lengths ranging from 10 nm to 20 nm. For both top and back gates HfO2 is taken as the gate oxide material and the oxide thickness is considered as 1 nm. For a fixed channel length of 10 nm, the suggested model displays that when gate length is increased the impact of the drain voltage on the drain current is diminished resulting significant decrease in OFF-state current with a larger Ion/Ioff ratio of ~ 109. In fact, this leads to a reduced drain induced barrier lowering. Moreover, numerous simulated results from SILVACO ATLAS TCAD offers larger drain current as well as lower subthreshold swing of 67.5 mV/Dec for the proposed model. Due to its superior performance over traditional DG-JLMOSFET, the proposed structure can be deployed effectively in the near future.","PeriodicalId":255498,"journal":{"name":"2022 25th International Conference on Computer and Information Technology (ICCIT)","volume":"12 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132518637","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 : 2022-12-17DOI: 10.1109/ICCIT57492.2022.10055404
Tithi Paul
A list of components can be arranged in a certain order using a sorting algorithm, which is a fundamental concept in computer science. The temporal complexity of the two fundamental and widely used sorting algorithms, Bubble sort and Insertion sort is $mathcal{O}left( {{N^2}} right)$, where N is the total number of items. When it comes to sorting a specific amount of items, it is superior. However, by adding more parts to its quadratic complexity, it loses efficiency. Because of this, it is less frequently employed in computer science’s practical and real-world applications, despite being widely utilized as a subroutine in other areas. Numerous extension techniques for the insertion sort and bubble sort algorithms have been put out in the literature, but none of them tries to combine the two to create a combination algorithm like ours. The bubble and insertion sort method was modified in this study, and its computational complexity was estimated to be $mathcal{O}(Nsqrt N )$. The technique begins by dividing the input array into a few pieces, sorting each of the blocks using a modified bubble sort, and then merging all of the blocks together using a modified insertion sort. The suggested bubble and insertion sort outperform traditional bubble and insertion sorting as well as all other sorting algorithms with a computational complexity of $mathcal{O}left( {{N^2}} right)$.
一个组件列表可以使用排序算法按照一定的顺序排列，这是计算机科学中的一个基本概念。冒泡排序(Bubble sort)和插入排序(insert sort)这两种基本且广泛使用的排序算法的时间复杂度为$mathcal{O}left( {{N^2}} right)$，其中N为项目总数。当涉及到分类特定数量的物品时，它是优越的。然而，通过增加二次复杂度的部分，它失去了效率。正因为如此，尽管在其他领域作为子例程被广泛使用，但它在计算机科学的实际和实际应用中较少使用。文献中已经提出了许多插入排序和冒泡排序算法的扩展技术，但没有一个试图将两者结合起来创建像我们这样的组合算法。本文对气泡插入排序方法进行了改进，估计其计算复杂度为$mathcal{O}(Nsqrt N )$。该技术首先将输入数组分成几个部分，使用修改后的冒泡排序对每个块进行排序，然后使用修改后的插入排序将所有块合并在一起。建议的气泡和插入排序优于传统的气泡和插入排序以及所有其他排序算法，计算复杂度为$mathcal{O}left( {{N^2}} right)$。
{"title":"Enhancement of Bubble and Insertion Sort Algorithm Using Block Partitioning","authors":"Tithi Paul","doi":"10.1109/ICCIT57492.2022.10055404","DOIUrl":"https://doi.org/10.1109/ICCIT57492.2022.10055404","url":null,"abstract":"A list of components can be arranged in a certain order using a sorting algorithm, which is a fundamental concept in computer science. The temporal complexity of the two fundamental and widely used sorting algorithms, Bubble sort and Insertion sort is $mathcal{O}left( {{N^2}} right)$, where N is the total number of items. When it comes to sorting a specific amount of items, it is superior. However, by adding more parts to its quadratic complexity, it loses efficiency. Because of this, it is less frequently employed in computer science’s practical and real-world applications, despite being widely utilized as a subroutine in other areas. Numerous extension techniques for the insertion sort and bubble sort algorithms have been put out in the literature, but none of them tries to combine the two to create a combination algorithm like ours. The bubble and insertion sort method was modified in this study, and its computational complexity was estimated to be $mathcal{O}(Nsqrt N )$. The technique begins by dividing the input array into a few pieces, sorting each of the blocks using a modified bubble sort, and then merging all of the blocks together using a modified insertion sort. The suggested bubble and insertion sort outperform traditional bubble and insertion sorting as well as all other sorting algorithms with a computational complexity of $mathcal{O}left( {{N^2}} right)$.","PeriodicalId":255498,"journal":{"name":"2022 25th International Conference on Computer and Information Technology (ICCIT)","volume":"82 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131877992","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 : 2022-12-17DOI: 10.1109/ICCIT57492.2022.10054677
Missba Khanam, K. Moria
In this paper, an attention U-net-based deep learning method for the semantic segmentation of the corpus callosum (CC) from brain Magnetic Resonance Imaging (MRI) scans is proposed and implemented. Most neurological analyses benefit greatly from the structural data that can be obtained from the segmentation of brain MRI images. The proposed technique has a deep supervised encoder-decoder architecture and a redesigned attention network. Slice by slice, the model analyzes an entire MRI image to determine the ideal mask for corpus callosum. The model was trained using the ABIDE and OASIS datasets, and its performance was analyzed for different test samples using a standard measure of dice coefficient, yielding a dice accuracy of 93.5%. Visual samples of predicted CC from brain MRI are given and contrasted with the original ground truth to help understand how well the model performs. The findings demonstrate that the suggested approach is one of the best segmentation techniques, as it achieved very competitive CC segmentation performance even with a single model.
{"title":"Segmentation of Corpus Callosum using Attention U-Net Architecture for MRI Scan","authors":"Missba Khanam, K. Moria","doi":"10.1109/ICCIT57492.2022.10054677","DOIUrl":"https://doi.org/10.1109/ICCIT57492.2022.10054677","url":null,"abstract":"In this paper, an attention U-net-based deep learning method for the semantic segmentation of the corpus callosum (CC) from brain Magnetic Resonance Imaging (MRI) scans is proposed and implemented. Most neurological analyses benefit greatly from the structural data that can be obtained from the segmentation of brain MRI images. The proposed technique has a deep supervised encoder-decoder architecture and a redesigned attention network. Slice by slice, the model analyzes an entire MRI image to determine the ideal mask for corpus callosum. The model was trained using the ABIDE and OASIS datasets, and its performance was analyzed for different test samples using a standard measure of dice coefficient, yielding a dice accuracy of 93.5%. Visual samples of predicted CC from brain MRI are given and contrasted with the original ground truth to help understand how well the model performs. The findings demonstrate that the suggested approach is one of the best segmentation techniques, as it achieved very competitive CC segmentation performance even with a single model.","PeriodicalId":255498,"journal":{"name":"2022 25th International Conference on Computer and Information Technology (ICCIT)","volume":"46 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125618959","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 : 2022-12-17DOI: 10.1109/ICCIT57492.2022.10055777
S. Saha, R. Parvin, P. Ng, M. Khoo, Xinying Chew
The assessment of variables that influence the estimation, control, and regulation of the quality of analytical testing processes is increasingly being done using computer simulation. The quality management of manufacturing firms is introduced as a data mining application. For quality control and production management, quality factor analysis is crucial. Numerous studies have investigated the variable sampling interval (VSI) chart for the process average. Despite being significantly more widely used than the median chart, when faced with extremes or unforeseen data sets that cast doubt on the normality assumption, the mean ($bar X$ ) chart is less resistant. The median chart, however, is more effective than the process average chart when outliers or extreme values are present in the process data being monitored. Since practitioners may believe that process shifts could have happened in the dataset because of the extreme values, incorrect inferences may be drawn. To solve this challenge, the variable sampling interval (VSI) median chart is proposed in this study. The VSI feature is used to enhance the performance of the median chart. The average time to signal (ATS) and expected average time to signal (EATS) criteria are used to evaluate the performance of the proposed charts. Based on the ATS and EATS criteria, the results show that the proposed VSI median chart outperforms the Shewhart (SH) median chart in detecting all sizes of shifts.
{"title":"A proposed variable sampling interval median chart for identifying out-of-control signals in process control","authors":"S. Saha, R. Parvin, P. Ng, M. Khoo, Xinying Chew","doi":"10.1109/ICCIT57492.2022.10055777","DOIUrl":"https://doi.org/10.1109/ICCIT57492.2022.10055777","url":null,"abstract":"The assessment of variables that influence the estimation, control, and regulation of the quality of analytical testing processes is increasingly being done using computer simulation. The quality management of manufacturing firms is introduced as a data mining application. For quality control and production management, quality factor analysis is crucial. Numerous studies have investigated the variable sampling interval (VSI) chart for the process average. Despite being significantly more widely used than the median chart, when faced with extremes or unforeseen data sets that cast doubt on the normality assumption, the mean ($bar X$ ) chart is less resistant. The median chart, however, is more effective than the process average chart when outliers or extreme values are present in the process data being monitored. Since practitioners may believe that process shifts could have happened in the dataset because of the extreme values, incorrect inferences may be drawn. To solve this challenge, the variable sampling interval (VSI) median chart is proposed in this study. The VSI feature is used to enhance the performance of the median chart. The average time to signal (ATS) and expected average time to signal (EATS) criteria are used to evaluate the performance of the proposed charts. Based on the ATS and EATS criteria, the results show that the proposed VSI median chart outperforms the Shewhart (SH) median chart in detecting all sizes of shifts.","PeriodicalId":255498,"journal":{"name":"2022 25th International Conference on Computer and Information Technology (ICCIT)","volume":"47 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122959771","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 : 2022-12-17DOI: 10.1109/ICCIT57492.2022.10056111
Saifuddin Mahmud, M. Ferdous, R. Sourave, Mohammad Insanur Rahman Shuvo, Jong-Hoon Kim
Routine inspections and emergency response are unavoidable needs for power plants, oil refineries, iron works, and industrial units, as they directly influence output and safety. By utilizing autonomous robots, they can be improved. With the exception of facilities located in hazardous areas, such as off-shore factories, where dispatching people might be impossible, accidents caused by human mistakes can be prevented by autonomous inspections and diagnosis of facilities (pumps, tanks, boilers, and so on). Furthermore, if any disaster or accident happens in the plant victims should get immediate assistance. Autonomous robots can enable quick emergency assistance for victims once they are detected. The primary obstacles in robot-assisted inspection operations and victim detection are identifying various types of gauges and reading them, detecting the actual victims in any lighting condition, and taking appropriate actions. This study describes a unique robot vision system for plant inspection and victim detection system that may be used to enhance the frequency of routine checks, hence minimizing equipment faults and accidents (explosions or fires caused by gas leaks) caused by human mistakes or degradation and detecting victims to provide an immediate response. This suggested system can conduct facility inspections by detecting and reading a variety of gauges and finding victims, and it issues reports if any anomalies are discovered. Furthermore, this system can respond to unforeseen anomalous events that are potentially harmful to people and execute specific activities such as valve control if necessary.
{"title":"An Essential Robot Vision System for Robot Assisted Plant Disaster Prevention and Response Missions","authors":"Saifuddin Mahmud, M. Ferdous, R. Sourave, Mohammad Insanur Rahman Shuvo, Jong-Hoon Kim","doi":"10.1109/ICCIT57492.2022.10056111","DOIUrl":"https://doi.org/10.1109/ICCIT57492.2022.10056111","url":null,"abstract":"Routine inspections and emergency response are unavoidable needs for power plants, oil refineries, iron works, and industrial units, as they directly influence output and safety. By utilizing autonomous robots, they can be improved. With the exception of facilities located in hazardous areas, such as off-shore factories, where dispatching people might be impossible, accidents caused by human mistakes can be prevented by autonomous inspections and diagnosis of facilities (pumps, tanks, boilers, and so on). Furthermore, if any disaster or accident happens in the plant victims should get immediate assistance. Autonomous robots can enable quick emergency assistance for victims once they are detected. The primary obstacles in robot-assisted inspection operations and victim detection are identifying various types of gauges and reading them, detecting the actual victims in any lighting condition, and taking appropriate actions. This study describes a unique robot vision system for plant inspection and victim detection system that may be used to enhance the frequency of routine checks, hence minimizing equipment faults and accidents (explosions or fires caused by gas leaks) caused by human mistakes or degradation and detecting victims to provide an immediate response. This suggested system can conduct facility inspections by detecting and reading a variety of gauges and finding victims, and it issues reports if any anomalies are discovered. Furthermore, this system can respond to unforeseen anomalous events that are potentially harmful to people and execute specific activities such as valve control if necessary.","PeriodicalId":255498,"journal":{"name":"2022 25th International Conference on Computer and Information Technology (ICCIT)","volume":"11 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128462267","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 : 2022-12-17DOI: 10.1109/ICCIT57492.2022.10055067
Md. Tauhiduzzaman Khan, Shabnam Ghaffarzadegan, Z. Feng, T. Hasan
Dietary habits play a significant role in public health and well-being. Monitoring dietary activities is thus essential for maintaining a healthy lifestyle and preventing many widespread diseases, such as diabetes, obesity, and hypertension. In this work, we present a low-cost wearable neckband for automatic diet activity monitoring. The $5 fabric-based device, comprising an electret microphone, a Bluetooth radio module, and a rechargeable Lithium-ion battery, can wirelessly transmit audio to a smart device in real-time. The classification algorithm processes the audio stream in 3s segments and extracts short-time spectral, waveform, and energy-based acoustic features. We compute various statistical functions from the acoustic features to obtain segmental feature vectors, which are subsequently used for machine learning. We perform an experimental evaluation using an in-house dataset collected using the neckband. We compare the performance of different classifiers in distinguishing between drinking, chewing solid foods, and other non-dietary activities. An averaged class-wise F-measure of 81.25% is achieved using the proposed wearable device and a Random Forest (RF) based classifier.
{"title":"A Fabric-based Inexpensive Wearable Neckband for Accurate and Reliable Dietary Activity Monitoring","authors":"Md. Tauhiduzzaman Khan, Shabnam Ghaffarzadegan, Z. Feng, T. Hasan","doi":"10.1109/ICCIT57492.2022.10055067","DOIUrl":"https://doi.org/10.1109/ICCIT57492.2022.10055067","url":null,"abstract":"Dietary habits play a significant role in public health and well-being. Monitoring dietary activities is thus essential for maintaining a healthy lifestyle and preventing many widespread diseases, such as diabetes, obesity, and hypertension. In this work, we present a low-cost wearable neckband for automatic diet activity monitoring. The $5 fabric-based device, comprising an electret microphone, a Bluetooth radio module, and a rechargeable Lithium-ion battery, can wirelessly transmit audio to a smart device in real-time. The classification algorithm processes the audio stream in 3s segments and extracts short-time spectral, waveform, and energy-based acoustic features. We compute various statistical functions from the acoustic features to obtain segmental feature vectors, which are subsequently used for machine learning. We perform an experimental evaluation using an in-house dataset collected using the neckband. We compare the performance of different classifiers in distinguishing between drinking, chewing solid foods, and other non-dietary activities. An averaged class-wise F-measure of 81.25% is achieved using the proposed wearable device and a Random Forest (RF) based classifier.","PeriodicalId":255498,"journal":{"name":"2022 25th International Conference on Computer and Information Technology (ICCIT)","volume":"53 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128588139","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 : 2022-12-17DOI: 10.1109/ICCIT57492.2022.10055308
Ruhul Amin, Mohammad Shamsul Islam, Redwanul Islam Arif, Ashraful Islam, Md. Monir Hossain
Over time, how we used to keep track of our academic and work certificates has led to problems in terms of security and authenticity. The academic and experience certificate that a person gets over the course of their life are kept by centralized administrations with little to no connection with others. It becomes challenging to gather all these certificates from multiple institutions, arrange them together, and apply for a job. Because certificate forgery is so common, companies have a hard time getting official certifications, hurting the relationship between academia and business. The job market and educational institutions must be more efficient and open. Therefore, we made a blockchain-based integrated education-industry cooperative employment system where educational institutions and businesses can upload information and permit recruiters to use it. The recruiter can post job openings, and applicants looking for a job can apply by generating their CV. In our proposed method, we chose Hyperledger Fabric because of its ability to manage document permissions, transaction speed, scalability, no transaction fees, and other properties.
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