Pub Date : 2023-04-07DOI: 10.1109/I2CT57861.2023.10126166
A. P, Vimala H S, J Shreyas
The Internet of Things connects millions of devices in the areas of smart cities, e-health, transportation, and the military to fulfill a variety of human needs. To offer these services, a large amount of data must be transmitted to the IoT network servers. But the node processing power, buffer size, and server capacity limitations on IoT networks have a negative influence on throughput, latency, and energy consumption. Additionally, the IoT network’s performance is decreased by congestion caused by the high network traffic that results from the high volume of data. In order to handle congestion challenges in IoT networks, unique congestion control strategies—such as the queue management strategy—must be created. In this study, a novel Priority Queue-based Token Bucket Algorithm (PQTBA) is suggested as a means of controlling congestion in IoT networks. The PQTBA uses a preemptive/non-preemptive technique with a discretionary rule to categorize network traffic into priority groups in accordance with real-time requirements. Our proposed work performs con-siderably better than the most recent techniques in terms of throughput, packet loss ratio, and energy consumption.
{"title":"PQTBA: Priority Queue based Token Bucket Algorithm for congestion control in IoT network","authors":"A. P, Vimala H S, J Shreyas","doi":"10.1109/I2CT57861.2023.10126166","DOIUrl":"https://doi.org/10.1109/I2CT57861.2023.10126166","url":null,"abstract":"The Internet of Things connects millions of devices in the areas of smart cities, e-health, transportation, and the military to fulfill a variety of human needs. To offer these services, a large amount of data must be transmitted to the IoT network servers. But the node processing power, buffer size, and server capacity limitations on IoT networks have a negative influence on throughput, latency, and energy consumption. Additionally, the IoT network’s performance is decreased by congestion caused by the high network traffic that results from the high volume of data. In order to handle congestion challenges in IoT networks, unique congestion control strategies—such as the queue management strategy—must be created. In this study, a novel Priority Queue-based Token Bucket Algorithm (PQTBA) is suggested as a means of controlling congestion in IoT networks. The PQTBA uses a preemptive/non-preemptive technique with a discretionary rule to categorize network traffic into priority groups in accordance with real-time requirements. Our proposed work performs con-siderably better than the most recent techniques in terms of throughput, packet loss ratio, and energy consumption.","PeriodicalId":150346,"journal":{"name":"2023 IEEE 8th International Conference for Convergence in Technology (I2CT)","volume":"75 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-04-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125581063","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}
The current research focuses on examining the negative impacts of blue light on human eyes. With the increasing usage of digital devices such as laptops, smartphones, and televisions, individuals are spending most of their time in front of screens. This prolonged screen time puts immense strain on the eyes, and blue light with wavelengths between 415 nm and 455 nm is a significant contributor to eye strain and damage. To understand the extent of damage, we considered various parameters such as the size of the screen, light intensity, and luminous intensity. We used a TCS34725 RGB sensor to measure the blue light emissions reaching the human eye and established a relationship between sensor outputs and light intensity. To classify the data, we utilized both KNN and Naïve Bayes algorithms for efficient analysis and quicker results.
{"title":"Eye Health Monitoring System","authors":"Krishi Godhani, Adit Patel, Harsh Shah, Achal Mehta, Devlina Adhikari","doi":"10.1109/I2CT57861.2023.10126343","DOIUrl":"https://doi.org/10.1109/I2CT57861.2023.10126343","url":null,"abstract":"The current research focuses on examining the negative impacts of blue light on human eyes. With the increasing usage of digital devices such as laptops, smartphones, and televisions, individuals are spending most of their time in front of screens. This prolonged screen time puts immense strain on the eyes, and blue light with wavelengths between 415 nm and 455 nm is a significant contributor to eye strain and damage. To understand the extent of damage, we considered various parameters such as the size of the screen, light intensity, and luminous intensity. We used a TCS34725 RGB sensor to measure the blue light emissions reaching the human eye and established a relationship between sensor outputs and light intensity. To classify the data, we utilized both KNN and Naïve Bayes algorithms for efficient analysis and quicker results.","PeriodicalId":150346,"journal":{"name":"2023 IEEE 8th International Conference for Convergence in Technology (I2CT)","volume":"45 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-04-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123163045","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 : 2023-04-07DOI: 10.1109/I2CT57861.2023.10126491
P. Ashok, B. T. Sundari
A new computing method using stochastic-based numbers is gaining importance as an approximate computing method to save area, energy, and computation time based on the accuracy required. This works uses stochastic computing, which is suitable for enhancing the efficiency of neural network. Herein we focus on developing activation functions that are essential parameters in the design of neural networks. The activation function in stochastic computing is typically a threshold function that maps the input bits to a binary output based on a probability distribution. This paper presents the development of modified activation functions tanh and COS using SC-based arithmetic components. Two different types of stochastic number generators (SNGs) have been used. Error analysis has been done based on the computation using two SNGs. Also, accuracy measurement is performed using error analysis for these complex functions mentioned above.
{"title":"Implementation of stochastic computing in activation functions using stochastic arithmetic components","authors":"P. Ashok, B. T. Sundari","doi":"10.1109/I2CT57861.2023.10126491","DOIUrl":"https://doi.org/10.1109/I2CT57861.2023.10126491","url":null,"abstract":"A new computing method using stochastic-based numbers is gaining importance as an approximate computing method to save area, energy, and computation time based on the accuracy required. This works uses stochastic computing, which is suitable for enhancing the efficiency of neural network. Herein we focus on developing activation functions that are essential parameters in the design of neural networks. The activation function in stochastic computing is typically a threshold function that maps the input bits to a binary output based on a probability distribution. This paper presents the development of modified activation functions tanh and COS using SC-based arithmetic components. Two different types of stochastic number generators (SNGs) have been used. Error analysis has been done based on the computation using two SNGs. Also, accuracy measurement is performed using error analysis for these complex functions mentioned above.","PeriodicalId":150346,"journal":{"name":"2023 IEEE 8th International Conference for Convergence in Technology (I2CT)","volume":"4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-04-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125163226","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 : 2023-04-07DOI: 10.1109/I2CT57861.2023.10126126
Ankit Bansal, Rishabh Sharma, Vikrant Sharma, A. Jain, V. Kukreja
Powdery mildew is a fungal disease that affects kiwi fruit plants and leads to a reduction in yield and quality. Early detection and classification of the disease can help farmers to take necessary measures to curb the transmission of the infection. In this research, we conducted binary and multi-classification of kiwi powdery mildew disease (KPMD) using 12000 images of kiwi fruit. The binary classification was done using two classes, healthy and inf, while multi-classification was done using four different classes of powdery mildew. An integrated CNN and LSTM model was developed for multi-classification, which resulted in an accuracy of 92.14% in binary classification and 95.91% in multi-classification. The results were analyzed based on various performance parameters, and the proposed model demonstrated encouraging results in terms of accuracy for both detection and classification. The analysis also revealed that the proposed model had the highest accuracy for detecting powdery mildew in its terminal severity level. This research provides a useful tool for the early detection and classification of kiwi powdery mildew disease, which can assist farmers in preventing the spread of the disease and improving crop yield and quality.
{"title":"An Automated Approach for Accurate Detection and Classification of Kiwi Powdery Mildew Disease","authors":"Ankit Bansal, Rishabh Sharma, Vikrant Sharma, A. Jain, V. Kukreja","doi":"10.1109/I2CT57861.2023.10126126","DOIUrl":"https://doi.org/10.1109/I2CT57861.2023.10126126","url":null,"abstract":"Powdery mildew is a fungal disease that affects kiwi fruit plants and leads to a reduction in yield and quality. Early detection and classification of the disease can help farmers to take necessary measures to curb the transmission of the infection. In this research, we conducted binary and multi-classification of kiwi powdery mildew disease (KPMD) using 12000 images of kiwi fruit. The binary classification was done using two classes, healthy and inf, while multi-classification was done using four different classes of powdery mildew. An integrated CNN and LSTM model was developed for multi-classification, which resulted in an accuracy of 92.14% in binary classification and 95.91% in multi-classification. The results were analyzed based on various performance parameters, and the proposed model demonstrated encouraging results in terms of accuracy for both detection and classification. The analysis also revealed that the proposed model had the highest accuracy for detecting powdery mildew in its terminal severity level. This research provides a useful tool for the early detection and classification of kiwi powdery mildew disease, which can assist farmers in preventing the spread of the disease and improving crop yield and quality.","PeriodicalId":150346,"journal":{"name":"2023 IEEE 8th International Conference for Convergence in Technology (I2CT)","volume":"28 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-04-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131757884","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 : 2023-04-07DOI: 10.1109/I2CT57861.2023.10126416
Prashasthi K Kanchan, Sahana S, Shreya K Loni, R. Raksha, T. Babu
Conversations between humans and machines have become an increasingly common occurrence in today’s digital age. As technology progresses, automated systems are becoming more adept at comprehending and reacting to human language through advancements in language analysis and computational linguistics. While this has brought many benefits, such as improved customer service and faster access to information, there are also some potential drawbacks to consider. Though technology is advancing rapidly, they still lack in many aspects such as understanding human emotions, intentions, empathy, flexibility, privacy and security. The main challenge here is how the machine responds relevantly and quickly in a continuous conversation with humans. This research is all about developing an android application that helps vocally impaired people to book appointments easily using a bidirectional LSTM model, text-to-speech and speech-to-text to have a natural conversation with human.
{"title":"Vocals - An App for Vocally Impaired using NLP Conversational Model","authors":"Prashasthi K Kanchan, Sahana S, Shreya K Loni, R. Raksha, T. Babu","doi":"10.1109/I2CT57861.2023.10126416","DOIUrl":"https://doi.org/10.1109/I2CT57861.2023.10126416","url":null,"abstract":"Conversations between humans and machines have become an increasingly common occurrence in today’s digital age. As technology progresses, automated systems are becoming more adept at comprehending and reacting to human language through advancements in language analysis and computational linguistics. While this has brought many benefits, such as improved customer service and faster access to information, there are also some potential drawbacks to consider. Though technology is advancing rapidly, they still lack in many aspects such as understanding human emotions, intentions, empathy, flexibility, privacy and security. The main challenge here is how the machine responds relevantly and quickly in a continuous conversation with humans. This research is all about developing an android application that helps vocally impaired people to book appointments easily using a bidirectional LSTM model, text-to-speech and speech-to-text to have a natural conversation with human.","PeriodicalId":150346,"journal":{"name":"2023 IEEE 8th International Conference for Convergence in Technology (I2CT)","volume":"38 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-04-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131961440","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 : 2023-04-07DOI: 10.1109/I2CT57861.2023.10126331
Kshitij G. Saha, Dhruv Jyoti Garodia, Prarthana Kalal, Devang Saraogi, V. R. Badri Prasad
White blood cell classification is a task that is given paramount importance in the field of pathology in order to accurately diagnose a plethora of ailments and diseases. Blood cell classification is essential for the calculation of the differential count of blood cells in a particular blood sample. Physicians derive a medical prognosis based on blood reports in which differential count plays a key role. To reduce the cost and difficulty of this task as well as to improve accuracy in cell classification, image processing and deep learning have proven to be viable alternatives to conventional pathological methods. To tackle this problem through image classification, we have successfully conducted a comparative study of accuracy metrics of various Convolution Neural Network models including the very popular Alexnet, VGG, ResNet, Inception, Densenet and EfficientNet. With multiple methods of preprocessing the dataset and deploying these well-known image classification models, the most accurate and computationally inexpensive deep learning model was identified for WBC classification.
{"title":"Facilitating Leucocyte Count Using Deep Learning: A Paradigm Shift","authors":"Kshitij G. Saha, Dhruv Jyoti Garodia, Prarthana Kalal, Devang Saraogi, V. R. Badri Prasad","doi":"10.1109/I2CT57861.2023.10126331","DOIUrl":"https://doi.org/10.1109/I2CT57861.2023.10126331","url":null,"abstract":"White blood cell classification is a task that is given paramount importance in the field of pathology in order to accurately diagnose a plethora of ailments and diseases. Blood cell classification is essential for the calculation of the differential count of blood cells in a particular blood sample. Physicians derive a medical prognosis based on blood reports in which differential count plays a key role. To reduce the cost and difficulty of this task as well as to improve accuracy in cell classification, image processing and deep learning have proven to be viable alternatives to conventional pathological methods. To tackle this problem through image classification, we have successfully conducted a comparative study of accuracy metrics of various Convolution Neural Network models including the very popular Alexnet, VGG, ResNet, Inception, Densenet and EfficientNet. With multiple methods of preprocessing the dataset and deploying these well-known image classification models, the most accurate and computationally inexpensive deep learning model was identified for WBC classification.","PeriodicalId":150346,"journal":{"name":"2023 IEEE 8th International Conference for Convergence in Technology (I2CT)","volume":"30 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-04-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134570081","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 : 2023-04-07DOI: 10.1109/I2CT57861.2023.10126204
Shiyona Dash, Deepjyoti Kalita, K. B. Mirza
Recent research works have increasingly focused on gaining a better understanding of visual perception from brain activity. This work was partially motivated by functional Magnetic Resonance Imaging (fMRI) based studies on the neurobiology of "mental images" and Brain-Computer Interface (BCI) devices. The ultimate objective is to recreate thoughts from brain activity using generative AI models. It is crucial to extract and enumerate the differences between visual perception (when a stimulus is present) and visual imagery (the recall of the stimulus after that) by the brain. In this work, we determine that it is possible to detect changes in brain activity due to differences in Visual Perception and Imagery even while using EEG signal features recorded with limited channels. The first step in this process was doing a spatiotemporal-based feature estimation on the EEG data for seven people across all channels and trials. Results indicate that Alpha Band power, an essential characteristic in the posterior electrodes and indicating a parieto-occipital origin, significantly differed across the different channels.
{"title":"Analysis of differences in EEG Signal features between Visual Imagery and Perception","authors":"Shiyona Dash, Deepjyoti Kalita, K. B. Mirza","doi":"10.1109/I2CT57861.2023.10126204","DOIUrl":"https://doi.org/10.1109/I2CT57861.2023.10126204","url":null,"abstract":"Recent research works have increasingly focused on gaining a better understanding of visual perception from brain activity. This work was partially motivated by functional Magnetic Resonance Imaging (fMRI) based studies on the neurobiology of \"mental images\" and Brain-Computer Interface (BCI) devices. The ultimate objective is to recreate thoughts from brain activity using generative AI models. It is crucial to extract and enumerate the differences between visual perception (when a stimulus is present) and visual imagery (the recall of the stimulus after that) by the brain. In this work, we determine that it is possible to detect changes in brain activity due to differences in Visual Perception and Imagery even while using EEG signal features recorded with limited channels. The first step in this process was doing a spatiotemporal-based feature estimation on the EEG data for seven people across all channels and trials. Results indicate that Alpha Band power, an essential characteristic in the posterior electrodes and indicating a parieto-occipital origin, significantly differed across the different channels.","PeriodicalId":150346,"journal":{"name":"2023 IEEE 8th International Conference for Convergence in Technology (I2CT)","volume":"9 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-04-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131294182","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 : 2023-04-07DOI: 10.1109/I2CT57861.2023.10126261
Dhvani Shah, Chinmay Shokeen, Shantanu Khanzode, P. Kale, Divyaprabha Kn
A significant amount of online shopping has been observed during the pandemic. Surveys suggest that 20-30% more businesses are moving online in response to the pandemic. The goal was to develop an apparel e-commerce website that is also tailored to the individual using Natural Language Processing and Collaborative Filtering algorithms, thereby recommending items based on what they choose. In the current system, the market uses a collaborative filtering and content-based filtering, hybrid model. We aimed to resolve the cold start problem that persists in many of the current e-commerce websites. The recommendation engine is based on the input given by the pre-trained Natural Language Processing model, where the items from the dataset are segregated into broad themes based on the product description. These themes are then put into clusters based on their similarity. The recommendation engine uses collaborative filtering and picks up similar products from the clusters formed to provide the users with appropriate recommendations.
{"title":"Recommendation System using NLP and Collaborative Filtering","authors":"Dhvani Shah, Chinmay Shokeen, Shantanu Khanzode, P. Kale, Divyaprabha Kn","doi":"10.1109/I2CT57861.2023.10126261","DOIUrl":"https://doi.org/10.1109/I2CT57861.2023.10126261","url":null,"abstract":"A significant amount of online shopping has been observed during the pandemic. Surveys suggest that 20-30% more businesses are moving online in response to the pandemic. The goal was to develop an apparel e-commerce website that is also tailored to the individual using Natural Language Processing and Collaborative Filtering algorithms, thereby recommending items based on what they choose. In the current system, the market uses a collaborative filtering and content-based filtering, hybrid model. We aimed to resolve the cold start problem that persists in many of the current e-commerce websites. The recommendation engine is based on the input given by the pre-trained Natural Language Processing model, where the items from the dataset are segregated into broad themes based on the product description. These themes are then put into clusters based on their similarity. The recommendation engine uses collaborative filtering and picks up similar products from the clusters formed to provide the users with appropriate recommendations.","PeriodicalId":150346,"journal":{"name":"2023 IEEE 8th International Conference for Convergence in Technology (I2CT)","volume":"188 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-04-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132081594","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 : 2023-04-07DOI: 10.1109/I2CT57861.2023.10126308
A. T. Reddy, K. Premchand, A. J. Rani, Ch. Navadeep, B. Aravind
A slot-loaded antipodal vivaldi antenna for Radar and tracking applications is presented in this paper. To enhance the bandwidth, the antenna is loaded with rectangular slots to increase the electrical length of the antenna and proper impedance matching. The proposed antenna is analyzed, designed, and simulated using HFSS-2015 software, and is found to be operating efficiently in the required frequency range of 5-20 GHz with an enhanced bandwidth and unidirectional radiation patterns. The reported antenna is having the compact dimensions of 20 x 40 x 0.8 mm which are essential for radar and tracking applications. To optimize the low frequency, namely 6-10 GHz, parametric studies on various antenna parameters are conducted. A moderate gain of 8.5 dB at 20 GHz of the proposed antenna is reported.
本文提出了一种用于雷达和跟踪应用的缝隙加载对足维瓦尔第天线。为了提高带宽，在天线上加载矩形槽，以增加天线的电长度和适当的阻抗匹配。利用HFSS-2015软件对该天线进行了分析、设计和仿真，结果表明，该天线在5- 20ghz所需频率范围内高效工作，具有增强的带宽和单向辐射方向图。报道的天线具有20 x 40 x 0.8毫米的紧凑尺寸，这对于雷达和跟踪应用至关重要。为了优化低频，即6-10 GHz，对天线的各种参数进行了参数化研究。报道了该天线在20 GHz时的中等增益为8.5 dB。
{"title":"Antipodal Vivaldi Antenna with Rectangular Loaded Slots for Radar and Tracking Applications","authors":"A. T. Reddy, K. Premchand, A. J. Rani, Ch. Navadeep, B. Aravind","doi":"10.1109/I2CT57861.2023.10126308","DOIUrl":"https://doi.org/10.1109/I2CT57861.2023.10126308","url":null,"abstract":"A slot-loaded antipodal vivaldi antenna for Radar and tracking applications is presented in this paper. To enhance the bandwidth, the antenna is loaded with rectangular slots to increase the electrical length of the antenna and proper impedance matching. The proposed antenna is analyzed, designed, and simulated using HFSS-2015 software, and is found to be operating efficiently in the required frequency range of 5-20 GHz with an enhanced bandwidth and unidirectional radiation patterns. The reported antenna is having the compact dimensions of 20 x 40 x 0.8 mm which are essential for radar and tracking applications. To optimize the low frequency, namely 6-10 GHz, parametric studies on various antenna parameters are conducted. A moderate gain of 8.5 dB at 20 GHz of the proposed antenna is reported.","PeriodicalId":150346,"journal":{"name":"2023 IEEE 8th International Conference for Convergence in Technology (I2CT)","volume":"28 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-04-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131763364","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 : 2023-04-07DOI: 10.1109/I2CT57861.2023.10126309
Taj Sapra, Shubhama, S. Meena
The world has seen an exponential rise in machine learning and artificial intelligence since the 1990s. We apply machine learning models to solve various real life problems like regression and classification. However, class imbalance is a very common issue faced for classification problems in machine learning. In this study, we propose new greedy resampling techniques to solve the problem of class imbalance. We shall also compare the results of these techniques with the Synthetic Minority Over-sampling Technique (SMOTE).
{"title":"A Novel approach to Handle Imbalanced Dataset in Machine Learning","authors":"Taj Sapra, Shubhama, S. Meena","doi":"10.1109/I2CT57861.2023.10126309","DOIUrl":"https://doi.org/10.1109/I2CT57861.2023.10126309","url":null,"abstract":"The world has seen an exponential rise in machine learning and artificial intelligence since the 1990s. We apply machine learning models to solve various real life problems like regression and classification. However, class imbalance is a very common issue faced for classification problems in machine learning. In this study, we propose new greedy resampling techniques to solve the problem of class imbalance. We shall also compare the results of these techniques with the Synthetic Minority Over-sampling Technique (SMOTE).","PeriodicalId":150346,"journal":{"name":"2023 IEEE 8th International Conference for Convergence in Technology (I2CT)","volume":"5 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-04-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121832114","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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