Pub Date : 2021-08-26DOI: 10.1109/SPIN52536.2021.9565961
Priyadharshini Shanmugasundaram, Aakash Sinha
Reinforcement learning, a sub-field of Machine Learning has been garnering lot of research attention lately. It helps create intelligent agents that can incrementally learn optimal strategies for challenging environments by interacting with it. Such agents are best suited for solving problems like traffic congestion, which demand solutions that eater to dynamic changes in the traffic throughput. Intelligent transportation systems which use deep reinforcement learning can adapt to varying traffic demands and learn to maintain reduced congestion. In this paper, we propose a solution approach to use Double Deep Q Networks for traffic signal control of varied traffic flows in an isolated intersection. To improve the stability of our proposed method we have used target networks, delayed updates and experience replay mechanisms. We evaluate the performance of our method on different time-varying traffic flows and find that our method learns a robust and optimal strategy which reduces vehicle waiting time and queue length significantly. Our method achieved superior performance compared to traditional traffic signal control strategies. The method has been trained and evaluated through simulations of road networks created on Simulation of Urban Mobility (SUMO).
{"title":"Intelligent Traffic Control using Double Deep Q Networks for time-varying Traffic Flows","authors":"Priyadharshini Shanmugasundaram, Aakash Sinha","doi":"10.1109/SPIN52536.2021.9565961","DOIUrl":"https://doi.org/10.1109/SPIN52536.2021.9565961","url":null,"abstract":"Reinforcement learning, a sub-field of Machine Learning has been garnering lot of research attention lately. It helps create intelligent agents that can incrementally learn optimal strategies for challenging environments by interacting with it. Such agents are best suited for solving problems like traffic congestion, which demand solutions that eater to dynamic changes in the traffic throughput. Intelligent transportation systems which use deep reinforcement learning can adapt to varying traffic demands and learn to maintain reduced congestion. In this paper, we propose a solution approach to use Double Deep Q Networks for traffic signal control of varied traffic flows in an isolated intersection. To improve the stability of our proposed method we have used target networks, delayed updates and experience replay mechanisms. We evaluate the performance of our method on different time-varying traffic flows and find that our method learns a robust and optimal strategy which reduces vehicle waiting time and queue length significantly. Our method achieved superior performance compared to traditional traffic signal control strategies. The method has been trained and evaluated through simulations of road networks created on Simulation of Urban Mobility (SUMO).","PeriodicalId":343177,"journal":{"name":"2021 8th International Conference on Signal Processing and Integrated Networks (SPIN)","volume":"60 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-08-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123456000","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 : 2021-08-26DOI: 10.1109/SPIN52536.2021.9566114
Abadhan Ranganath, P. Sahu, M. Senapati
The Diagnosis of Corona Virus Disease (COVID) manually from a Chest X-Ray (CX-R) is time-consuming and may be inaccurate. In this paper, a new feature extraction method called the "Pivot Distribution Count (PDC)" method has been proposed, which finds the white spots in COVID infected lungs. The state of art method called "Gliding Box Method (GBM)" and a recently developed technique called Pixel Range Calculation (PRC) method have been applied for comparing the results obtained from texture features from the Chest X-Ray (CX-R) images with that of the proposed method. For carrying out the experiment Chest X-Ray dataset from the Kaggle database has been used. From the experimental result, it is observed that the PDC and PRC method has got the maximum detection rate of 100%, whereas, GBM detects COVID with a detection rate of 56%. For Non-COVID samples, the PDC method outperforms the other two methods with an accuracy of 96%.
{"title":"Detection of COVID from Chest X-Ray Images using Pivot Distribution Count Method","authors":"Abadhan Ranganath, P. Sahu, M. Senapati","doi":"10.1109/SPIN52536.2021.9566114","DOIUrl":"https://doi.org/10.1109/SPIN52536.2021.9566114","url":null,"abstract":"The Diagnosis of Corona Virus Disease (COVID) manually from a Chest X-Ray (CX-R) is time-consuming and may be inaccurate. In this paper, a new feature extraction method called the \"Pivot Distribution Count (PDC)\" method has been proposed, which finds the white spots in COVID infected lungs. The state of art method called \"Gliding Box Method (GBM)\" and a recently developed technique called Pixel Range Calculation (PRC) method have been applied for comparing the results obtained from texture features from the Chest X-Ray (CX-R) images with that of the proposed method. For carrying out the experiment Chest X-Ray dataset from the Kaggle database has been used. From the experimental result, it is observed that the PDC and PRC method has got the maximum detection rate of 100%, whereas, GBM detects COVID with a detection rate of 56%. For Non-COVID samples, the PDC method outperforms the other two methods with an accuracy of 96%.","PeriodicalId":343177,"journal":{"name":"2021 8th International Conference on Signal Processing and Integrated Networks (SPIN)","volume":"6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-08-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123638736","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 : 2021-08-26DOI: 10.1109/SPIN52536.2021.9566069
Yamuna Shanker Kumawat, Rajat Arora, Sanjay. D. Mehta
This paper presents a method to detect and correct single event upsets likely to occur in the configuration memory of SRAM-based FPGAs especially in a spaceborne scenario, specifically addressing the Virtex-5QV FPGAs. As compared to DWC or TMR techniques, the scrubbing approach is instead preferred; the internal type being superior due to self-contained configuration interfaces. A Finite State Machine based controller is used to control the detection of memory upsets and subsequently to effect the scrubbing process. Internal Configuration Access Port primitive is used to read the configuration memory frames and an Error Correction Code primitive used to detect & locate the single bit error location inside a frame. Hardware implementation of the proposed technique is carried out and the simulation results presented. Pulsing diagrams indicate successful SEU detection and subsequent scrubbing through the PRGRAM_B pin of the FPGA, that may be invoked by telecommand on-board.
{"title":"In Orbit Single Event Upset Detection and Configuration Memory Scrubbing of Virtex-5QV FPGA","authors":"Yamuna Shanker Kumawat, Rajat Arora, Sanjay. D. Mehta","doi":"10.1109/SPIN52536.2021.9566069","DOIUrl":"https://doi.org/10.1109/SPIN52536.2021.9566069","url":null,"abstract":"This paper presents a method to detect and correct single event upsets likely to occur in the configuration memory of SRAM-based FPGAs especially in a spaceborne scenario, specifically addressing the Virtex-5QV FPGAs. As compared to DWC or TMR techniques, the scrubbing approach is instead preferred; the internal type being superior due to self-contained configuration interfaces. A Finite State Machine based controller is used to control the detection of memory upsets and subsequently to effect the scrubbing process. Internal Configuration Access Port primitive is used to read the configuration memory frames and an Error Correction Code primitive used to detect & locate the single bit error location inside a frame. Hardware implementation of the proposed technique is carried out and the simulation results presented. Pulsing diagrams indicate successful SEU detection and subsequent scrubbing through the PRGRAM_B pin of the FPGA, that may be invoked by telecommand on-board.","PeriodicalId":343177,"journal":{"name":"2021 8th International Conference on Signal Processing and Integrated Networks (SPIN)","volume":"20 9","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-08-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"120870012","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 : 2021-08-26DOI: 10.1109/SPIN52536.2021.9565963
Palak Khurana, Shakshi Sharma, Anjali Goyal
Heart diseases are the leading cause of deaths nowadays. Due to the high severity of the problem, it has attracted several researchers around the globe. Researchers have considered the heart diagnosis as a classification problem where meaningful patterns are detected using data mining techniques. This paper presents an evaluation of various supervised learning algorithms and feature selection techniques for heart disease prediction. The performance of six machine learning classifiers (Naïve Bayes, Decision Tree, Logistic Regression, Random Forest, Support Vector Machine, k-Nearest Neighbour) and five feature selection techniques (Chi-Square, Gain Ratio, Information Gain, One-R and RELIEF) have been investigated on the benchmark dataset obtained from UCI Machine Learning Repository, Cleveland. The experimental results show that machine learning classifiers can achieve prediction accuracy up to 82.81% for heart disease prediction. The feature selection techniques further improve the classification performance and achieve prediction accuracy up to 83.41%.
{"title":"Heart Disease Diagnosis: Performance Evaluation of Supervised Machine Learning and Feature Selection Techniques","authors":"Palak Khurana, Shakshi Sharma, Anjali Goyal","doi":"10.1109/SPIN52536.2021.9565963","DOIUrl":"https://doi.org/10.1109/SPIN52536.2021.9565963","url":null,"abstract":"Heart diseases are the leading cause of deaths nowadays. Due to the high severity of the problem, it has attracted several researchers around the globe. Researchers have considered the heart diagnosis as a classification problem where meaningful patterns are detected using data mining techniques. This paper presents an evaluation of various supervised learning algorithms and feature selection techniques for heart disease prediction. The performance of six machine learning classifiers (Naïve Bayes, Decision Tree, Logistic Regression, Random Forest, Support Vector Machine, k-Nearest Neighbour) and five feature selection techniques (Chi-Square, Gain Ratio, Information Gain, One-R and RELIEF) have been investigated on the benchmark dataset obtained from UCI Machine Learning Repository, Cleveland. The experimental results show that machine learning classifiers can achieve prediction accuracy up to 82.81% for heart disease prediction. The feature selection techniques further improve the classification performance and achieve prediction accuracy up to 83.41%.","PeriodicalId":343177,"journal":{"name":"2021 8th International Conference on Signal Processing and Integrated Networks (SPIN)","volume":"34 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-08-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127688091","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 : 2021-08-26DOI: 10.1109/SPIN52536.2021.9566125
Shubham Saxena, Malek Souilem, W. Dghais, J. N. Tripathi, H. Shrimali
This paper presents a study on non-linear modeling, reported in the state-of-the-art in the last decade for I/O drivers. The study includes the IBIS-like modeling techniques including package parasitics. The IBIS-like model has been analyzed mathematically and validated using 28 nm CMOS technology of TSMC foundry. For validation purposes, the predriver circuit and the I/O buffers have been simulated with 0.9 V of VDD. The IBIS-like nonlinear models have been created using Simulink® and the results have been compared with the Electronic Design Automation (EDA) tools. The Simulink® results show a Normalized Mean Square Error (NMSE) of - 51.91 dB with 1.63 sec of CPU time for the case of pull-up current, -49.42 dB with 474.34 msec of CPU time for the case of pulldown current response. In the case of output voltage response, the NMSE is - 48.33 dB and 2.12 sec of CPU time.
{"title":"A Study on an IBIS-like Model to Ensure Signal/Power Integrity for I/O Drivers","authors":"Shubham Saxena, Malek Souilem, W. Dghais, J. N. Tripathi, H. Shrimali","doi":"10.1109/SPIN52536.2021.9566125","DOIUrl":"https://doi.org/10.1109/SPIN52536.2021.9566125","url":null,"abstract":"This paper presents a study on non-linear modeling, reported in the state-of-the-art in the last decade for I/O drivers. The study includes the IBIS-like modeling techniques including package parasitics. The IBIS-like model has been analyzed mathematically and validated using 28 nm CMOS technology of TSMC foundry. For validation purposes, the predriver circuit and the I/O buffers have been simulated with 0.9 V of VDD. The IBIS-like nonlinear models have been created using Simulink® and the results have been compared with the Electronic Design Automation (EDA) tools. The Simulink® results show a Normalized Mean Square Error (NMSE) of - 51.91 dB with 1.63 sec of CPU time for the case of pull-up current, -49.42 dB with 474.34 msec of CPU time for the case of pulldown current response. In the case of output voltage response, the NMSE is - 48.33 dB and 2.12 sec of CPU time.","PeriodicalId":343177,"journal":{"name":"2021 8th International Conference on Signal Processing and Integrated Networks (SPIN)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-08-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126566458","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 : 2021-08-26DOI: 10.1109/SPIN52536.2021.9566101
Naman Sood, Naveen Nandakumar, R. S
Colorization of images is one of the preliminary steps of image analysis and documentation. Autocolorization is an automated process of converting a single-channeled image into a complete colorized 3 channel RGB image. There has been extensive research gap in the field since the dawn of deep learning. This document is a model for a statistical-learning driven approach to approach Autocolorization through building an Encoder-decoder model with Convolutional neural networks. Learning Models: Keras, openCV, Numpy and Tensorflow. A direct function to convert grayscale into coloured images that can be coupled with various software or sensors. The results obtained provide a visualization of autocolorization with different regularization techniques and optimizers.
{"title":"Automatic Colorization of images using Auto-encoders","authors":"Naman Sood, Naveen Nandakumar, R. S","doi":"10.1109/SPIN52536.2021.9566101","DOIUrl":"https://doi.org/10.1109/SPIN52536.2021.9566101","url":null,"abstract":"Colorization of images is one of the preliminary steps of image analysis and documentation. Autocolorization is an automated process of converting a single-channeled image into a complete colorized 3 channel RGB image. There has been extensive research gap in the field since the dawn of deep learning. This document is a model for a statistical-learning driven approach to approach Autocolorization through building an Encoder-decoder model with Convolutional neural networks. Learning Models: Keras, openCV, Numpy and Tensorflow. A direct function to convert grayscale into coloured images that can be coupled with various software or sensors. The results obtained provide a visualization of autocolorization with different regularization techniques and optimizers.","PeriodicalId":343177,"journal":{"name":"2021 8th International Conference on Signal Processing and Integrated Networks (SPIN)","volume":"4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-08-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125419286","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 : 2021-08-26DOI: 10.1109/SPIN52536.2021.9566144
A. P., Jayalakshmi N. S., V. K. Jadoun
Electromagnetic (EM) radiations are emitted by almost all electrically energized devices such as laptops, smartphones, wireless routers, Wi-Fi, etc. The radiations emitted from the antennas of these electrically charged bodies, when exposed to human tissue without any electromagnetic compatibility protection, can cause severe damage. The presence of electrically charged components is nevertheless increasing day by day and, in turn, increasing the presence of electromagnetic radiation. In present days, electromagnetic radiations consist of radio waves in higher percentages and are present abundantly everywhere. These electromagnetic radiations to be a source of renewable source of energy. The primary aim of this work is to fabricate an electromagnetic collector to energize a battery by absorbing surrounding natural radiation. The gatherer should assemble free power from pretty much anything, including PCs, cell phones, overhead electrical cables, iceboxes, or even the outflows from Wi-Fi or cell phone. This is achieved by the process of wireless power transfer (WPT). One harvester might not be useful, but several such harvesters put together with power electronic converters can generate high output power. A case study is carried out, and the same is demonstrated using a simulation study on the MATLAB/Simulink platform.
{"title":"Electricity from Air: An efficient way of Wireless Power Tapping","authors":"A. P., Jayalakshmi N. S., V. K. Jadoun","doi":"10.1109/SPIN52536.2021.9566144","DOIUrl":"https://doi.org/10.1109/SPIN52536.2021.9566144","url":null,"abstract":"Electromagnetic (EM) radiations are emitted by almost all electrically energized devices such as laptops, smartphones, wireless routers, Wi-Fi, etc. The radiations emitted from the antennas of these electrically charged bodies, when exposed to human tissue without any electromagnetic compatibility protection, can cause severe damage. The presence of electrically charged components is nevertheless increasing day by day and, in turn, increasing the presence of electromagnetic radiation. In present days, electromagnetic radiations consist of radio waves in higher percentages and are present abundantly everywhere. These electromagnetic radiations to be a source of renewable source of energy. The primary aim of this work is to fabricate an electromagnetic collector to energize a battery by absorbing surrounding natural radiation. The gatherer should assemble free power from pretty much anything, including PCs, cell phones, overhead electrical cables, iceboxes, or even the outflows from Wi-Fi or cell phone. This is achieved by the process of wireless power transfer (WPT). One harvester might not be useful, but several such harvesters put together with power electronic converters can generate high output power. A case study is carried out, and the same is demonstrated using a simulation study on the MATLAB/Simulink platform.","PeriodicalId":343177,"journal":{"name":"2021 8th International Conference on Signal Processing and Integrated Networks (SPIN)","volume":"62 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-08-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125798570","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 : 2021-08-26DOI: 10.1109/SPIN52536.2021.9565947
Divyam Dalal, U. Keshwala
The article presents the design and analysis of EMG (Electromyography) controlled prosthetic hand prototype. The data acquisition and processing of the EMG data has been carried out by PC. The performance of the prototype has been analyzed by observing the characteristics of EMG signal at periodic muscle movement. With the advances in 3D printing technology, commercial and affordable prosthetics hand can be designed.
{"title":"Design and analysis of EMG controlled anthropomorphic Prosthetic hand","authors":"Divyam Dalal, U. Keshwala","doi":"10.1109/SPIN52536.2021.9565947","DOIUrl":"https://doi.org/10.1109/SPIN52536.2021.9565947","url":null,"abstract":"The article presents the design and analysis of EMG (Electromyography) controlled prosthetic hand prototype. The data acquisition and processing of the EMG data has been carried out by PC. The performance of the prototype has been analyzed by observing the characteristics of EMG signal at periodic muscle movement. With the advances in 3D printing technology, commercial and affordable prosthetics hand can be designed.","PeriodicalId":343177,"journal":{"name":"2021 8th International Conference on Signal Processing and Integrated Networks (SPIN)","volume":"16 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-08-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128198132","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 : 2021-08-26DOI: 10.1109/SPIN52536.2021.9566027
Shresth Gupta, Anurag Singh, Abhishek Sharma
Mean Arterial Pressure (MAP) is defined as central pressure in the arteries of a person during a single cardiac cycle. It is regarded as an important bio-marker of blood perfusion in vital organs as compared to systolic blood pressure (SBP). The actual MAP can be determined by manual monitoring and complex calculations limited to occasional monitoring status. Growing personalized health care monitoring devices have already evinced a variety of health parameters to track on a daily basis with the additional advantage of continuous, noninvasive, and unobstructed measurement. This work proposes a direct strategy for the estimation of mean arterial pressure without using the systolic and diastolic BP values. By exploring 13 significant morphological features from a single PPG signal which are most related to the target MAP are derived such as Pulse Interval, Inflection Ratio etc. The estimation is performed using LSTM network with an architecture having 2- LSTM layers followed by a dropout and dense layer. With 942 subjects of UCI repository dataset our model achieves a remarkable mean absolute error of 1.48, standard deviation of 2.36 and pearson correlation coefficient of 0.96 which is better as compared to the existing works and even chalked up the British Hypertension Society (BHS) benchmark with grade A.
{"title":"Photoplethysmogram Based Mean Arterial Pressure Estimation Using LSTM","authors":"Shresth Gupta, Anurag Singh, Abhishek Sharma","doi":"10.1109/SPIN52536.2021.9566027","DOIUrl":"https://doi.org/10.1109/SPIN52536.2021.9566027","url":null,"abstract":"Mean Arterial Pressure (MAP) is defined as central pressure in the arteries of a person during a single cardiac cycle. It is regarded as an important bio-marker of blood perfusion in vital organs as compared to systolic blood pressure (SBP). The actual MAP can be determined by manual monitoring and complex calculations limited to occasional monitoring status. Growing personalized health care monitoring devices have already evinced a variety of health parameters to track on a daily basis with the additional advantage of continuous, noninvasive, and unobstructed measurement. This work proposes a direct strategy for the estimation of mean arterial pressure without using the systolic and diastolic BP values. By exploring 13 significant morphological features from a single PPG signal which are most related to the target MAP are derived such as Pulse Interval, Inflection Ratio etc. The estimation is performed using LSTM network with an architecture having 2- LSTM layers followed by a dropout and dense layer. With 942 subjects of UCI repository dataset our model achieves a remarkable mean absolute error of 1.48, standard deviation of 2.36 and pearson correlation coefficient of 0.96 which is better as compared to the existing works and even chalked up the British Hypertension Society (BHS) benchmark with grade A.","PeriodicalId":343177,"journal":{"name":"2021 8th International Conference on Signal Processing and Integrated Networks (SPIN)","volume":"16 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-08-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130071198","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 : 2021-08-26DOI: 10.1109/SPIN52536.2021.9566022
S. Veni, A. Krishna Sameera, V. Samuktha, R. Anand
The necessity for monitoring food calorie intake is becoming imperative, in order to prevent obesity and adopt healthy food habits. This work aims in aiding dieticians, physicians, and patients to measure their daily calorie intake by manually capturing multiple fruit images and by feeding them to the calorie measurement system which utilizes Adaptive Neuro- Fuzzy Inference System (ANFIS). This classifier is used for identification and classification of fruit type. The mass of acquired fruits is estimated using image processing techniques to calculate the relative calories present, according to the food portion nutrition tables. Our system displays the type of each of the fruits present in the multiple fruit dataset, as well as their corresponding calories present in it and the total calories of fruits in the multiple fruit image. The results obtained are shown to have better calorie estimation of fruits by utilizing ANFIS classifier and color histogram feature extraction techniques.
{"title":"A Robust Approach Using Fuzzy Logic for the Calories Evaluation of Fruits","authors":"S. Veni, A. Krishna Sameera, V. Samuktha, R. Anand","doi":"10.1109/SPIN52536.2021.9566022","DOIUrl":"https://doi.org/10.1109/SPIN52536.2021.9566022","url":null,"abstract":"The necessity for monitoring food calorie intake is becoming imperative, in order to prevent obesity and adopt healthy food habits. This work aims in aiding dieticians, physicians, and patients to measure their daily calorie intake by manually capturing multiple fruit images and by feeding them to the calorie measurement system which utilizes Adaptive Neuro- Fuzzy Inference System (ANFIS). This classifier is used for identification and classification of fruit type. The mass of acquired fruits is estimated using image processing techniques to calculate the relative calories present, according to the food portion nutrition tables. Our system displays the type of each of the fruits present in the multiple fruit dataset, as well as their corresponding calories present in it and the total calories of fruits in the multiple fruit image. The results obtained are shown to have better calorie estimation of fruits by utilizing ANFIS classifier and color histogram feature extraction techniques.","PeriodicalId":343177,"journal":{"name":"2021 8th International Conference on Signal Processing and Integrated Networks (SPIN)","volume":"282 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-08-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133924765","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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