Pub Date : 2023-04-05DOI: 10.1109/PCEMS58491.2023.10136045
K. Rawat, D. Kumar, Roushan Kumar, D. Bharadwaj, Vedaant Soti, Gopal Rathore, Debdyuti Biswas, Poornima Singh
Electric vehicles are the upcoming trend as well as the future of the automotive sector. It is a new concept in the world of go-karts, that allows the use of electronic driving assistance systems, something that the combustion engine category does not offer. A chassis was designed as per the standard of federation of motor sports clubs of India by improving the existing chassis structure of the kart used in auto India racing championship season 4 by team dirt marshalls, the structural optimization was done on SOLIDWORKS and tested on ANSYS. We also designed some basic advanced driver assistance systems like obstacle detection system which is a level 0 Advanced Driver Assistance System on TINKERCAD and an anti-lock braking system which is a level 1 Advanced Driver Assistance System on MATLAB to aid the driver. It was observed that the structural changes reduced the chassis’ weight by 2 kg and increased its flexibility without compromising the safety of the driver and the employment of an Anti-lock Braking System reduced the stopping distance by almost 5 m and time by almost 1 s along with improving steering control. The systems were designed and simulated on various softwares and validated in season 6 of Indian Karting Race, and it can be concluded that employment of the systems improved the overall performance of the vehicle.
{"title":"Structural optimisation of go kart chassis with basic electronic driver assistance systems","authors":"K. Rawat, D. Kumar, Roushan Kumar, D. Bharadwaj, Vedaant Soti, Gopal Rathore, Debdyuti Biswas, Poornima Singh","doi":"10.1109/PCEMS58491.2023.10136045","DOIUrl":"https://doi.org/10.1109/PCEMS58491.2023.10136045","url":null,"abstract":"Electric vehicles are the upcoming trend as well as the future of the automotive sector. It is a new concept in the world of go-karts, that allows the use of electronic driving assistance systems, something that the combustion engine category does not offer. A chassis was designed as per the standard of federation of motor sports clubs of India by improving the existing chassis structure of the kart used in auto India racing championship season 4 by team dirt marshalls, the structural optimization was done on SOLIDWORKS and tested on ANSYS. We also designed some basic advanced driver assistance systems like obstacle detection system which is a level 0 Advanced Driver Assistance System on TINKERCAD and an anti-lock braking system which is a level 1 Advanced Driver Assistance System on MATLAB to aid the driver. It was observed that the structural changes reduced the chassis’ weight by 2 kg and increased its flexibility without compromising the safety of the driver and the employment of an Anti-lock Braking System reduced the stopping distance by almost 5 m and time by almost 1 s along with improving steering control. The systems were designed and simulated on various softwares and validated in season 6 of Indian Karting Race, and it can be concluded that employment of the systems improved the overall performance of the vehicle.","PeriodicalId":330870,"journal":{"name":"2023 2nd International Conference on Paradigm Shifts in Communications Embedded Systems, Machine Learning and Signal Processing (PCEMS)","volume":"3 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-04-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121022868","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 : 2023-04-05DOI: 10.1109/PCEMS58491.2023.10136112
Kondapalli Vinay Kumar, Kunigiri Anil Teja, Reddy Teja Bhargav, V. Satpute, Cheggoju Naveen, V. Kamble
Facial recognition is one of the most fascinating and interesting research areas. It has attracted the attention of many scientists and researchers for its amazing applications in identity authentication, policing, healthcare, marketing, and security. There are different face recognition algorithms available that give very good results but at the cost of huge data. Humans can recognize a person just by seeing a person once but this is not the case for computers they need enormous amounts of data just to recognize a person. In the case of a small dataset, only one algorithm stands out which is one-shot learning. In the case of ‘‘One-shot’’ learning, the model learns from a single input image. The thought is to train a CNN model with an enormous dataset of individuals with different faces, expressions, and lighting conditions specified model once given a single image of an individual will be recognized properly. For this, we tend to use the ‘‘Siamese neural network’’ to be told the similarity between faces.
{"title":"One-Shot Face Recognition","authors":"Kondapalli Vinay Kumar, Kunigiri Anil Teja, Reddy Teja Bhargav, V. Satpute, Cheggoju Naveen, V. Kamble","doi":"10.1109/PCEMS58491.2023.10136112","DOIUrl":"https://doi.org/10.1109/PCEMS58491.2023.10136112","url":null,"abstract":"Facial recognition is one of the most fascinating and interesting research areas. It has attracted the attention of many scientists and researchers for its amazing applications in identity authentication, policing, healthcare, marketing, and security. There are different face recognition algorithms available that give very good results but at the cost of huge data. Humans can recognize a person just by seeing a person once but this is not the case for computers they need enormous amounts of data just to recognize a person. In the case of a small dataset, only one algorithm stands out which is one-shot learning. In the case of ‘‘One-shot’’ learning, the model learns from a single input image. The thought is to train a CNN model with an enormous dataset of individuals with different faces, expressions, and lighting conditions specified model once given a single image of an individual will be recognized properly. For this, we tend to use the ‘‘Siamese neural network’’ to be told the similarity between faces.","PeriodicalId":330870,"journal":{"name":"2023 2nd International Conference on Paradigm Shifts in Communications Embedded Systems, Machine Learning and Signal Processing (PCEMS)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-04-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125819568","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 : 2023-04-05DOI: 10.1109/PCEMS58491.2023.10136104
Dilip Kumar Vallabhadas, M. Sandhya, Soumyadip Sarkar, Y. R. Chandra
In this paper multimodal biometric system is developed using two traits iris and fingerprint. The features generated by iris and fingerprint images are fused at the feature level. The generated fused feature vector template cannot be stored directly on the server, if stored directly can lead to various privacy and security concerns. So, these templates are encrypted in such a way that even when applying any operations on the templates, the templates should be in encrypted form. So, the operations need to be performed in the encrypted domain without decrypting it, and the final result, when decrypted should again give back the correct result as if the operations are performed on the original data. Fully Homomorphic encryption (FHE) scheme is designed to satisfy the above conditions. FHE is used to compute the hamming distance between the reference and probe template in an encrypted domain. To improve accuracy rotational invariant technique is used, which solves rotational inconsistency problems. The computational speed is increased by using a batching scheme to reduce the number of operations during homomorphic multiplication. We have conducted our experiment on the IITD and CASIA dataset. The best EER is obtained in CASIA dataset of 0.01% with a computational time of 0.0152 sec per template.
{"title":"Multimodal biometric authentication using Fully Homomorphic Encryption","authors":"Dilip Kumar Vallabhadas, M. Sandhya, Soumyadip Sarkar, Y. R. Chandra","doi":"10.1109/PCEMS58491.2023.10136104","DOIUrl":"https://doi.org/10.1109/PCEMS58491.2023.10136104","url":null,"abstract":"In this paper multimodal biometric system is developed using two traits iris and fingerprint. The features generated by iris and fingerprint images are fused at the feature level. The generated fused feature vector template cannot be stored directly on the server, if stored directly can lead to various privacy and security concerns. So, these templates are encrypted in such a way that even when applying any operations on the templates, the templates should be in encrypted form. So, the operations need to be performed in the encrypted domain without decrypting it, and the final result, when decrypted should again give back the correct result as if the operations are performed on the original data. Fully Homomorphic encryption (FHE) scheme is designed to satisfy the above conditions. FHE is used to compute the hamming distance between the reference and probe template in an encrypted domain. To improve accuracy rotational invariant technique is used, which solves rotational inconsistency problems. The computational speed is increased by using a batching scheme to reduce the number of operations during homomorphic multiplication. We have conducted our experiment on the IITD and CASIA dataset. The best EER is obtained in CASIA dataset of 0.01% with a computational time of 0.0152 sec per template.","PeriodicalId":330870,"journal":{"name":"2023 2nd International Conference on Paradigm Shifts in Communications Embedded Systems, Machine Learning and Signal Processing (PCEMS)","volume":"5 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-04-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126565371","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 : 2023-04-05DOI: 10.1109/PCEMS58491.2023.10136121
Dhruvi Thakkar, Pragati Agrawal
The main cause of death in the world is heart disease. Accurate detection of heart illness is critical for competently managing cardiac patients prior to a cardiac arrest. Moreover, the volume of information composes manual prediction and analysis taxing and time-consuming. The early diagnosis of people in hazard level for the disease is essential for avoiding its growth. A Deep Learning (DL) approach is better to predict heart disease. Deep Convolutional Neural Network (Deep CNNs) is widely used for medical decision support to accurately detecting and diagnosing various diseases. Because of their capability to identify the relations and concealed designs in health care data, DCNNs have been exceedingly successful for designing health support systems. The Min-max normalization technique is developed in this stage of preprocessing. In addition, the Kumar-Hassebrook and Dice coefficients are used in the feature selection process. This method uses embedded feature selection to choose a subset of structures, which are considerably related with a heart disease. Bootstrap is a broadly applied and really powerful analytical tool for data quantification. A Light Spectrum optimization (LSO)-based technique has attained maximum values of accuracy, sensitivity, and specificity of 95 %, 94.9 %, and 93.8 % for 90% of learning set.
{"title":"Hybrid feature selection and Optimized Deep CNN for Heart disease Prediction","authors":"Dhruvi Thakkar, Pragati Agrawal","doi":"10.1109/PCEMS58491.2023.10136121","DOIUrl":"https://doi.org/10.1109/PCEMS58491.2023.10136121","url":null,"abstract":"The main cause of death in the world is heart disease. Accurate detection of heart illness is critical for competently managing cardiac patients prior to a cardiac arrest. Moreover, the volume of information composes manual prediction and analysis taxing and time-consuming. The early diagnosis of people in hazard level for the disease is essential for avoiding its growth. A Deep Learning (DL) approach is better to predict heart disease. Deep Convolutional Neural Network (Deep CNNs) is widely used for medical decision support to accurately detecting and diagnosing various diseases. Because of their capability to identify the relations and concealed designs in health care data, DCNNs have been exceedingly successful for designing health support systems. The Min-max normalization technique is developed in this stage of preprocessing. In addition, the Kumar-Hassebrook and Dice coefficients are used in the feature selection process. This method uses embedded feature selection to choose a subset of structures, which are considerably related with a heart disease. Bootstrap is a broadly applied and really powerful analytical tool for data quantification. A Light Spectrum optimization (LSO)-based technique has attained maximum values of accuracy, sensitivity, and specificity of 95 %, 94.9 %, and 93.8 % for 90% of learning set.","PeriodicalId":330870,"journal":{"name":"2023 2nd International Conference on Paradigm Shifts in Communications Embedded Systems, Machine Learning and Signal Processing (PCEMS)","volume":"2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-04-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131271698","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 : 2023-04-05DOI: 10.1109/PCEMS58491.2023.10136105
Jubilee Gogoi, Sanghamitra Nath
Stress identification is an important problem in speech processing, which aims to convey special attention to the listeners. Stressed words or syllables result in changing the meaning of a sentence. In the case of tonal languages, stress identification is essential to understand how stress over words may affect the tone. This work is an attempt to identify the effects of stress on tones in Mizo and intonation in Assamese. It also aims to analyze the co-articulatory effects of stress and tones in Mizo and stress and intonation in Assamese with the help of an audio dataset. Although a few works are available to identify the effects of tones and stress, for Indian languages especially, in North East Indian languages which are extremely low in resources, to the best of our knowledge, no such work is available. For our work, we have considered one tonal language, i.e., Mizo or Lushai, spoken in and around the state of Mizoram, and one non-tonal language, i.e., Assamese, spoken in and around the state of Assam in India.
{"title":"Analysing Word Stress and its effects on Assamese and Mizo using Machine Learning","authors":"Jubilee Gogoi, Sanghamitra Nath","doi":"10.1109/PCEMS58491.2023.10136105","DOIUrl":"https://doi.org/10.1109/PCEMS58491.2023.10136105","url":null,"abstract":"Stress identification is an important problem in speech processing, which aims to convey special attention to the listeners. Stressed words or syllables result in changing the meaning of a sentence. In the case of tonal languages, stress identification is essential to understand how stress over words may affect the tone. This work is an attempt to identify the effects of stress on tones in Mizo and intonation in Assamese. It also aims to analyze the co-articulatory effects of stress and tones in Mizo and stress and intonation in Assamese with the help of an audio dataset. Although a few works are available to identify the effects of tones and stress, for Indian languages especially, in North East Indian languages which are extremely low in resources, to the best of our knowledge, no such work is available. For our work, we have considered one tonal language, i.e., Mizo or Lushai, spoken in and around the state of Mizoram, and one non-tonal language, i.e., Assamese, spoken in and around the state of Assam in India.","PeriodicalId":330870,"journal":{"name":"2023 2nd International Conference on Paradigm Shifts in Communications Embedded Systems, Machine Learning and Signal Processing (PCEMS)","volume":"7 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-04-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126494509","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 : 2023-04-05DOI: 10.1109/PCEMS58491.2023.10136107
Pramod Kumar Gautam, Manisha Bharti, N. Paras
In this paper, The performance of a nanotube (NT) junctionless field effect transistor (JLFET) is studied in the sub-5 nm range. We demonstrate the effect of quantum confinement effects which leads to direct source to drain tunneling in the 5nm NT JLFET. Lateral band to band tunneling(L-BTBT) along with Direct source to drain tunneling which affects the OFF state performance of the device is also studied in this paper. The inclusion of high dielectric(high-k) spacers and the core gate to the device improve the performance. Lastly we have shown the effect of diameter of core gate on the device and inclusion of the hetero structures such Si-Ge also helps in achieving better performance in the device.
本文研究了纳米管(NT)无结场效应晶体管(JLFET)在亚5nm范围内的性能。我们证明了量子约束效应在5nm NT JLFET中导致直接源极到漏极隧穿的影响。本文还研究了影响器件关闭状态性能的横向带到带隧道效应(L-BTBT)和直接源到漏隧道效应。高介电(高k)间隔片和核心栅极的加入提高了器件的性能。最后，我们展示了芯栅直径对器件的影响，以及硅锗等异质结构的加入也有助于器件获得更好的性能。
{"title":"Investigation of the Direct Source to Drain Tunneling in 5 nm Nanotube Junctionless Field Effect Transistor","authors":"Pramod Kumar Gautam, Manisha Bharti, N. Paras","doi":"10.1109/PCEMS58491.2023.10136107","DOIUrl":"https://doi.org/10.1109/PCEMS58491.2023.10136107","url":null,"abstract":"In this paper, The performance of a nanotube (NT) junctionless field effect transistor (JLFET) is studied in the sub-5 nm range. We demonstrate the effect of quantum confinement effects which leads to direct source to drain tunneling in the 5nm NT JLFET. Lateral band to band tunneling(L-BTBT) along with Direct source to drain tunneling which affects the OFF state performance of the device is also studied in this paper. The inclusion of high dielectric(high-k) spacers and the core gate to the device improve the performance. Lastly we have shown the effect of diameter of core gate on the device and inclusion of the hetero structures such Si-Ge also helps in achieving better performance in the device.","PeriodicalId":330870,"journal":{"name":"2023 2nd International Conference on Paradigm Shifts in Communications Embedded Systems, Machine Learning and Signal Processing (PCEMS)","volume":"22 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-04-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122292940","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 : 2023-04-05DOI: 10.1109/PCEMS58491.2023.10136044
Devashree R. Patrikar, M. Parate
Events that do not confront normal behavior are called anomalies and they are extremely arduous to recognize. The recent approaches that deploy a reconstruction approach for anomaly detection, predominantly emphasize minimizing the reconstruction error of training data. These techniques cannot assure larger reconstruction errors in the event of an anomaly. In our work, we propose to systematize the issue of abnormal event detection within a regime of future frame prediction. Provided a set of input video frames $i_1, i_2, i_3 ldots i_n$, our next-frame prediction model predicts a new frame $i_{n+1}$ instead of reconstructing the same frame $i_{n+1}$. By extending the contributions of Convolutional Neural Networks (CNNs) and Long Short Term Memory (LSTM), we propose ConvolutionalLSTM (C-LSTM) as a predictor to predict the next frame. To scrutinize the capability of the prediction model, we determine the intensity loss between the actual frame and the predicted frame. The larger error between the predicted frame and ground truth facilitates the detection of anomalous events that do not confront the expectation. This paper mainly emphasizes how well the model predicts the future frame and provides a new baseline for abnormal event detection.
{"title":"Anomaly Detection by Predicting Future Frames using Convolutional LSTM in Video Surveillance","authors":"Devashree R. Patrikar, M. Parate","doi":"10.1109/PCEMS58491.2023.10136044","DOIUrl":"https://doi.org/10.1109/PCEMS58491.2023.10136044","url":null,"abstract":"Events that do not confront normal behavior are called anomalies and they are extremely arduous to recognize. The recent approaches that deploy a reconstruction approach for anomaly detection, predominantly emphasize minimizing the reconstruction error of training data. These techniques cannot assure larger reconstruction errors in the event of an anomaly. In our work, we propose to systematize the issue of abnormal event detection within a regime of future frame prediction. Provided a set of input video frames $i_1, i_2, i_3 ldots i_n$, our next-frame prediction model predicts a new frame $i_{n+1}$ instead of reconstructing the same frame $i_{n+1}$. By extending the contributions of Convolutional Neural Networks (CNNs) and Long Short Term Memory (LSTM), we propose ConvolutionalLSTM (C-LSTM) as a predictor to predict the next frame. To scrutinize the capability of the prediction model, we determine the intensity loss between the actual frame and the predicted frame. The larger error between the predicted frame and ground truth facilitates the detection of anomalous events that do not confront the expectation. This paper mainly emphasizes how well the model predicts the future frame and provides a new baseline for abnormal event detection.","PeriodicalId":330870,"journal":{"name":"2023 2nd International Conference on Paradigm Shifts in Communications Embedded Systems, Machine Learning and Signal Processing (PCEMS)","volume":"4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-04-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128294420","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 : 2023-04-05DOI: 10.1109/PCEMS58491.2023.10136108
Pavan Mohan Neelamraju, Pranav Pothapragada, G. Rana, D. Chaturvedi, Rupesh Kumar
Dipole antennae are commonly used radio frequency devices. They gained good prominence as a result of their efficiency, consistent performance and flexibility. Different optimization strategies such as particle swarm optimization, differential evolution and Machine Learning algorithms have been utilized in the past to design dipole antennae. This helps in creating a complete device profile and increases its efficacy. Due to the complexity of modern antennas in terms of topology and performance requirements, standard antenna design approaches are tedious and cannot be guaranteed to produce effective results. Out of the strategies that are widely being utilized, Machine Learning (ML) algorithms evolved rapidly due to their capabilities in extrapolating the dimensional and material profiles of the device. Antenna design optimization still faces several difficulties, even though machine learning-based design optimization complements traditional antenna design methodologies. The effectiveness and optimization capabilities of available ML approaches to address a wide range of antenna design problems, considering the increasingly strict specifications of current antennas, are the fundamental difficulties in antenna design optimization which need to be focused on. In our current work, the capability of ML algorithms in elucidating minor trends in device profiles is tested. A bootstrap aggregation model is proposed, concatenating Linear Regression, Support Vector Regression and Decision Tree Regression algorithms. The concatenated model was used to optimize the parameters of reflection coefficient, directivity, efficiency and operating frequency, depending on the feed length, dipole radius and dipole length of the antenna.
{"title":"Machine Learning based Low-Scale Dipole Antenna Optimization using Bootstrap Aggregation","authors":"Pavan Mohan Neelamraju, Pranav Pothapragada, G. Rana, D. Chaturvedi, Rupesh Kumar","doi":"10.1109/PCEMS58491.2023.10136108","DOIUrl":"https://doi.org/10.1109/PCEMS58491.2023.10136108","url":null,"abstract":"Dipole antennae are commonly used radio frequency devices. They gained good prominence as a result of their efficiency, consistent performance and flexibility. Different optimization strategies such as particle swarm optimization, differential evolution and Machine Learning algorithms have been utilized in the past to design dipole antennae. This helps in creating a complete device profile and increases its efficacy. Due to the complexity of modern antennas in terms of topology and performance requirements, standard antenna design approaches are tedious and cannot be guaranteed to produce effective results. Out of the strategies that are widely being utilized, Machine Learning (ML) algorithms evolved rapidly due to their capabilities in extrapolating the dimensional and material profiles of the device. Antenna design optimization still faces several difficulties, even though machine learning-based design optimization complements traditional antenna design methodologies. The effectiveness and optimization capabilities of available ML approaches to address a wide range of antenna design problems, considering the increasingly strict specifications of current antennas, are the fundamental difficulties in antenna design optimization which need to be focused on. In our current work, the capability of ML algorithms in elucidating minor trends in device profiles is tested. A bootstrap aggregation model is proposed, concatenating Linear Regression, Support Vector Regression and Decision Tree Regression algorithms. The concatenated model was used to optimize the parameters of reflection coefficient, directivity, efficiency and operating frequency, depending on the feed length, dipole radius and dipole length of the antenna.","PeriodicalId":330870,"journal":{"name":"2023 2nd International Conference on Paradigm Shifts in Communications Embedded Systems, Machine Learning and Signal Processing (PCEMS)","volume":"27 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-04-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116659784","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 : 2023-04-05DOI: 10.1109/PCEMS58491.2023.10136116
Khizar Baig Mohammed, Sai Venkat Boyapati, Manasa Datta Kandimalla, Madhu Babu Kavati, Sumalatha Saleti
DNA is widely considered the blueprint of life. The instructions required for all life forms, to evolve, breed, and thrive are found in DNA. Deoxyribonucleic acid, more commonly known as DNA, is among the most essential chemicals in living cells. A biological macro-molecule is DNA, also known as deoxyri-bonucleic acid. Life’s blueprint is encoded by it. Sequencing of DNA has exponentially progressed due to the immense increase in data production in today’s world. By means of this paper, we intend to evaluate the evolution of DNA Sequencing methods and perform a comparative analysis of modern-day DNA sequencing techniques to the ones of the past. We also illuminate the potential of machine learning in this domain by taking an exploratory and predicting the DNA Sequence using a Multinomial Naive Bayes classifier.
{"title":"A Comparative Analysis of the Evolution of DNA Sequencing Techniques along with the Accuracy Prediction of a Sample DNA Sequence Dataset using Machine Learning","authors":"Khizar Baig Mohammed, Sai Venkat Boyapati, Manasa Datta Kandimalla, Madhu Babu Kavati, Sumalatha Saleti","doi":"10.1109/PCEMS58491.2023.10136116","DOIUrl":"https://doi.org/10.1109/PCEMS58491.2023.10136116","url":null,"abstract":"DNA is widely considered the blueprint of life. The instructions required for all life forms, to evolve, breed, and thrive are found in DNA. Deoxyribonucleic acid, more commonly known as DNA, is among the most essential chemicals in living cells. A biological macro-molecule is DNA, also known as deoxyri-bonucleic acid. Life’s blueprint is encoded by it. Sequencing of DNA has exponentially progressed due to the immense increase in data production in today’s world. By means of this paper, we intend to evaluate the evolution of DNA Sequencing methods and perform a comparative analysis of modern-day DNA sequencing techniques to the ones of the past. We also illuminate the potential of machine learning in this domain by taking an exploratory and predicting the DNA Sequence using a Multinomial Naive Bayes classifier.","PeriodicalId":330870,"journal":{"name":"2023 2nd International Conference on Paradigm Shifts in Communications Embedded Systems, Machine Learning and Signal Processing (PCEMS)","volume":"21 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-04-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123892128","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 : 2023-04-05DOI: 10.1109/PCEMS58491.2023.10136090
Sandeep Mandia, Faisel Mushtaq, Kuldeep Singh, R. Mitharwal, A. Panthakkan
Online education has increased tremendously due to vast availability of internet recently. Student emotion and engagement is directly related to learning goals and productivity. The existing computer vision based student engagement analysis techniques require two steps for engagement detection. In this paper, single step student affect state detection method is proposed using recent deep learning algorithms. Also a learning centered affect state dataset is curated from public databases. The YOLO-v5 deep learning algorithm is trained on the curated database to detect the affect states. The experimental results show that the proposed one step method is able to detect the affect states reliably. The proposed method also performs inference on an edge device with limited compute resource. The proposed method achieved 0.996, 0.921, 0.96, and 0.777 values of overall precision, recall, mAP@0.5, and mAP@0.5-0.95 respectively.
{"title":"YOLO-v5 Based Single Step Student Affect State Detection System","authors":"Sandeep Mandia, Faisel Mushtaq, Kuldeep Singh, R. Mitharwal, A. Panthakkan","doi":"10.1109/PCEMS58491.2023.10136090","DOIUrl":"https://doi.org/10.1109/PCEMS58491.2023.10136090","url":null,"abstract":"Online education has increased tremendously due to vast availability of internet recently. Student emotion and engagement is directly related to learning goals and productivity. The existing computer vision based student engagement analysis techniques require two steps for engagement detection. In this paper, single step student affect state detection method is proposed using recent deep learning algorithms. Also a learning centered affect state dataset is curated from public databases. The YOLO-v5 deep learning algorithm is trained on the curated database to detect the affect states. The experimental results show that the proposed one step method is able to detect the affect states reliably. The proposed method also performs inference on an edge device with limited compute resource. The proposed method achieved 0.996, 0.921, 0.96, and 0.777 values of overall precision, recall, mAP@0.5, and mAP@0.5-0.95 respectively.","PeriodicalId":330870,"journal":{"name":"2023 2nd International Conference on Paradigm Shifts in Communications Embedded Systems, Machine Learning and Signal Processing (PCEMS)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-04-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127363060","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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