Pub Date : 2022-08-26DOI: 10.1109/ASIANCON55314.2022.9908975
D. Tripathi, S. Biswas, S. Reshmi, Arpita Nath Boruah, B. Purkayastha
Diabetes is an incurable disease which is due to a high level of sugar in the blood over a long period of time. Hence, early prediction is required to reduce its severity significantly. Now-a-days Machine Learning (ML) community has been working on diabetes prediction and much research has been done for decades for its prediction. Keeping in view of its severity, this paper proposes a model, named Diabetes Expert System using Machine Learning Analytics (DESMLA) to explore the diabetes data to predict the disease more effectively. The Diabetes Dataset (DD) is imbalanced in nature; therefore, the DESMLA model uses the 5 most prominent oversampling techniques namely SMOTE, Borderline SMOTE, ADASYN SMOTE, K-Means SMOTE and Gaussian SMOTE to get rid of this class imbalance problem of the diabetes dataset. DESMLA model also performs feature selection to determine only the significant features for diabetes prediction as DD may contain some irrelevant and redundant features. DESMLA shows the comparison between filter and wrapper approaches for feature selection. From the experimental results, it is observed that DESMLA with wrapper approach produces better performance than that of filter approach. The performance improvement of DESMLA with class imbalance treatment and feature selection is observed which is promising and significant.
{"title":"Diabetes Prediction Using Machine Learning Analytics: Ensemble Learning Techniques","authors":"D. Tripathi, S. Biswas, S. Reshmi, Arpita Nath Boruah, B. Purkayastha","doi":"10.1109/ASIANCON55314.2022.9908975","DOIUrl":"https://doi.org/10.1109/ASIANCON55314.2022.9908975","url":null,"abstract":"Diabetes is an incurable disease which is due to a high level of sugar in the blood over a long period of time. Hence, early prediction is required to reduce its severity significantly. Now-a-days Machine Learning (ML) community has been working on diabetes prediction and much research has been done for decades for its prediction. Keeping in view of its severity, this paper proposes a model, named Diabetes Expert System using Machine Learning Analytics (DESMLA) to explore the diabetes data to predict the disease more effectively. The Diabetes Dataset (DD) is imbalanced in nature; therefore, the DESMLA model uses the 5 most prominent oversampling techniques namely SMOTE, Borderline SMOTE, ADASYN SMOTE, K-Means SMOTE and Gaussian SMOTE to get rid of this class imbalance problem of the diabetes dataset. DESMLA model also performs feature selection to determine only the significant features for diabetes prediction as DD may contain some irrelevant and redundant features. DESMLA shows the comparison between filter and wrapper approaches for feature selection. From the experimental results, it is observed that DESMLA with wrapper approach produces better performance than that of filter approach. The performance improvement of DESMLA with class imbalance treatment and feature selection is observed which is promising and significant.","PeriodicalId":429704,"journal":{"name":"2022 2nd Asian Conference on Innovation in Technology (ASIANCON)","volume":"75 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-08-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124670081","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-08-26DOI: 10.1109/ASIANCON55314.2022.9908934
M. Sivachitra, S. Dinesh, B. Gowtham, K. S. Vinothraja, S. Eraianbu
An environment is a habitat where humans, plants, and animals all live together. Cleaning is an important part of the day-to-day routine. We have to maintain the place clean so that we can walk around the streets feeling fresh. A filthy environment leads to a deterioration of society, the emergence of diseases, and a slew of other issues. Humans are currently using pulling machines for cleaning which is usually done when there is no traffic on the roadways. But during bad conditions like pandemics, if the humans are directly involved in the cleaning process, there are high possibilities of getting diseases. The usage of remote-controlled cleaners will assist sanitation workers in preventing the transmission of diseases. The risk of getting affected by the diseases is reduced when the machine-controlled cleaner is remotely operated by the sanitizing workers. There are several cleaners available on the markets which can operate automatically, but they are mainly used to clean house floors. This paper aims to design and build a remote-controlled cleaner with sanitizer and cleaner that can safely clean roads and public places.
{"title":"Remote-Controlled Multipurpose Road Cleaner","authors":"M. Sivachitra, S. Dinesh, B. Gowtham, K. S. Vinothraja, S. Eraianbu","doi":"10.1109/ASIANCON55314.2022.9908934","DOIUrl":"https://doi.org/10.1109/ASIANCON55314.2022.9908934","url":null,"abstract":"An environment is a habitat where humans, plants, and animals all live together. Cleaning is an important part of the day-to-day routine. We have to maintain the place clean so that we can walk around the streets feeling fresh. A filthy environment leads to a deterioration of society, the emergence of diseases, and a slew of other issues. Humans are currently using pulling machines for cleaning which is usually done when there is no traffic on the roadways. But during bad conditions like pandemics, if the humans are directly involved in the cleaning process, there are high possibilities of getting diseases. The usage of remote-controlled cleaners will assist sanitation workers in preventing the transmission of diseases. The risk of getting affected by the diseases is reduced when the machine-controlled cleaner is remotely operated by the sanitizing workers. There are several cleaners available on the markets which can operate automatically, but they are mainly used to clean house floors. This paper aims to design and build a remote-controlled cleaner with sanitizer and cleaner that can safely clean roads and public places.","PeriodicalId":429704,"journal":{"name":"2022 2nd Asian Conference on Innovation in Technology (ASIANCON)","volume":"8 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-08-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131730767","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-08-26DOI: 10.1109/ASIANCON55314.2022.9909307
M. Rani, G. Andurkar
In recent years due to our busy routine, we don’t want to waste time communicating with handicapped people, so we are proposing CNN-primarily based totally gesture popularity system. To resource characteristic extraction, Preprocessing techniques including morphological filters, contour construction, polygonal approximation, and segmentation. are employed in the training process and testing, and the outcomes are in comparison to current architectures and procedures. To ensure that the system is stable for the provided technique, all generated metrics and convergence graphs created at some stage in evaluation are analyzed and disputed. We evolved our project, which utilizes the Raspberry Pi, that's one of the nice methods for photo processing and video recording, to gather real-time hand gestures as entering and forecast signal languages in written form.
{"title":"Human Gesture Recognition Using CNN","authors":"M. Rani, G. Andurkar","doi":"10.1109/ASIANCON55314.2022.9909307","DOIUrl":"https://doi.org/10.1109/ASIANCON55314.2022.9909307","url":null,"abstract":"In recent years due to our busy routine, we don’t want to waste time communicating with handicapped people, so we are proposing CNN-primarily based totally gesture popularity system. To resource characteristic extraction, Preprocessing techniques including morphological filters, contour construction, polygonal approximation, and segmentation. are employed in the training process and testing, and the outcomes are in comparison to current architectures and procedures. To ensure that the system is stable for the provided technique, all generated metrics and convergence graphs created at some stage in evaluation are analyzed and disputed. We evolved our project, which utilizes the Raspberry Pi, that's one of the nice methods for photo processing and video recording, to gather real-time hand gestures as entering and forecast signal languages in written form.","PeriodicalId":429704,"journal":{"name":"2022 2nd Asian Conference on Innovation in Technology (ASIANCON)","volume":"42 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-08-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131849731","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-08-26DOI: 10.1109/ASIANCON55314.2022.9909322
Nitesh Soni, M. Barai
The main barrier to the widespread adoption of electric vehicles is the low mileage on one charge. A lot of kinetic energy gets wasted on the wheels of the vehicle in the form of heat during braking. Regenerative braking is the method of recovering the kinetic energy from the motor during braking. This paper presents the study of regenerative braking of BLDC motor targeting electric vehicle (EV) applications. The induced back electromotive force (EMF) during braking at the motor terminal is used as a source to recharge the battery. This method of regeneration improves the mileage of an EV and reduces braking time as well. However, the battery can be charged with this back EMF if its magnitude is higher than the battery voltage. A boosting action is performed to boost up the level of this back EMF without using any dedicated DC-DC boost converter or an ultra-capacitor. A three phase two level VSI in the closed loop with BLDC motor load is designed and implemented in MATLAB/Simulink environment. The generations of control signals for two level VSI with BLDC motor in closed loop operation are carried out to perform trapezoidal commutation. The energy recovery operation is verified by charging the battery during the braking of BLDC Motor. Simulation results are presented to illustrate the regenerative braking of the BLDC motor.
{"title":"Performance Study of Regenerative Braking of BLDC Motor targeting Electric Vehicle Applications","authors":"Nitesh Soni, M. Barai","doi":"10.1109/ASIANCON55314.2022.9909322","DOIUrl":"https://doi.org/10.1109/ASIANCON55314.2022.9909322","url":null,"abstract":"The main barrier to the widespread adoption of electric vehicles is the low mileage on one charge. A lot of kinetic energy gets wasted on the wheels of the vehicle in the form of heat during braking. Regenerative braking is the method of recovering the kinetic energy from the motor during braking. This paper presents the study of regenerative braking of BLDC motor targeting electric vehicle (EV) applications. The induced back electromotive force (EMF) during braking at the motor terminal is used as a source to recharge the battery. This method of regeneration improves the mileage of an EV and reduces braking time as well. However, the battery can be charged with this back EMF if its magnitude is higher than the battery voltage. A boosting action is performed to boost up the level of this back EMF without using any dedicated DC-DC boost converter or an ultra-capacitor. A three phase two level VSI in the closed loop with BLDC motor load is designed and implemented in MATLAB/Simulink environment. The generations of control signals for two level VSI with BLDC motor in closed loop operation are carried out to perform trapezoidal commutation. The energy recovery operation is verified by charging the battery during the braking of BLDC Motor. Simulation results are presented to illustrate the regenerative braking of the BLDC motor.","PeriodicalId":429704,"journal":{"name":"2022 2nd Asian Conference on Innovation in Technology (ASIANCON)","volume":"36 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-08-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132280974","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-08-26DOI: 10.1109/ASIANCON55314.2022.9909326
Kishore K. Singh, S. Barde
Multi-Biometrics is a useful technique for the identification of a person. A person has many characteristics that help to identify him. Due to the limitations of unimodal biometrics, multimodal biometrics is being used to obtain more precise results. We propose a method for identifying individuals that integrates facial and palmprint modalities, we apply the Gaussian filter for features extraction and the Harris method for corner detection. We have calculated our result at two fusion levels matching score and decision level. Matching score calculated by the PCA classifier for the face performed on palm modalities. At the decision level, we find out the result by the sum rule fusion and fuzzy fusion that justify and show the accuracy.
{"title":"Face and Palm Identification by the Sum-Rule and Fuzzy Fusion","authors":"Kishore K. Singh, S. Barde","doi":"10.1109/ASIANCON55314.2022.9909326","DOIUrl":"https://doi.org/10.1109/ASIANCON55314.2022.9909326","url":null,"abstract":"Multi-Biometrics is a useful technique for the identification of a person. A person has many characteristics that help to identify him. Due to the limitations of unimodal biometrics, multimodal biometrics is being used to obtain more precise results. We propose a method for identifying individuals that integrates facial and palmprint modalities, we apply the Gaussian filter for features extraction and the Harris method for corner detection. We have calculated our result at two fusion levels matching score and decision level. Matching score calculated by the PCA classifier for the face performed on palm modalities. At the decision level, we find out the result by the sum rule fusion and fuzzy fusion that justify and show the accuracy.","PeriodicalId":429704,"journal":{"name":"2022 2nd Asian Conference on Innovation in Technology (ASIANCON)","volume":"41 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-08-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133909829","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-08-26DOI: 10.1109/ASIANCON55314.2022.9908730
Abhinav Anand, R. Pandey
In this paper a second-generation voltage conveyor (VCII) based comparator and its application as a pulse width modulator has been proposed. A dual terminal comparator is implemented using VCII which is used to chop the modulating signal into discrete components and the output of the comparator serves as modulated signal. Spice simulation results using 0.18-μm CMOS technology and ±0.90 V voltage supply are provided to demonstrate the validity of the theoretical analysis and functionality of the circuit. The results of this work illustrate the potential application of VCII in signal conditioning.
{"title":"Second Generation Voltage Conveyer based Comparator and its application as Pulse Width Modulator","authors":"Abhinav Anand, R. Pandey","doi":"10.1109/ASIANCON55314.2022.9908730","DOIUrl":"https://doi.org/10.1109/ASIANCON55314.2022.9908730","url":null,"abstract":"In this paper a second-generation voltage conveyor (VCII) based comparator and its application as a pulse width modulator has been proposed. A dual terminal comparator is implemented using VCII which is used to chop the modulating signal into discrete components and the output of the comparator serves as modulated signal. Spice simulation results using 0.18-μm CMOS technology and ±0.90 V voltage supply are provided to demonstrate the validity of the theoretical analysis and functionality of the circuit. The results of this work illustrate the potential application of VCII in signal conditioning.","PeriodicalId":429704,"journal":{"name":"2022 2nd Asian Conference on Innovation in Technology (ASIANCON)","volume":"17 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-08-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133353390","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}
This work describes a voice recognition system that does not need an intermediate phonetic representation to convert audio input to text. The system is based on a mix of the the Connectionist Temporal Classification goal function and deep bidirectional LSTM recurrent neural network architecture . A new method is proposed in which the network is taught to reduce the likelihood of an arbitrary transcription loss function being encountered. without the aid of any lexicons or models, this allows for a direct optimization of WER. The system has a WER (word error rate) of 22 percent, 20 percent with simply a lexicon of authorized terms, 9 percent using a trigram language model. The error rate drops to 7 percent when the network is used in conjunction with a baseline system.
{"title":"Reinforcement Learning for Speech Recognition using Recurrent Neural Networks","authors":"Imad Burhan Kadhim, Mahdi Fadil Khaleel, Zuhair Shakor Mahmood, Ali Nasret Najdet Coran","doi":"10.1109/ASIANCON55314.2022.9908930","DOIUrl":"https://doi.org/10.1109/ASIANCON55314.2022.9908930","url":null,"abstract":"This work describes a voice recognition system that does not need an intermediate phonetic representation to convert audio input to text. The system is based on a mix of the the Connectionist Temporal Classification goal function and deep bidirectional LSTM recurrent neural network architecture . A new method is proposed in which the network is taught to reduce the likelihood of an arbitrary transcription loss function being encountered. without the aid of any lexicons or models, this allows for a direct optimization of WER. The system has a WER (word error rate) of 22 percent, 20 percent with simply a lexicon of authorized terms, 9 percent using a trigram language model. The error rate drops to 7 percent when the network is used in conjunction with a baseline system.","PeriodicalId":429704,"journal":{"name":"2022 2nd Asian Conference on Innovation in Technology (ASIANCON)","volume":"62 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-08-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133644808","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 following article presents an inner round architecture for the AES Encryption Scheme suitable for implementation on FPGAs and as ASICs. The uniformity between the encryption and decryption hardware makes them suitable for implementation as separate or co-existing blocks as required. The modular approach of our architecture allows for different encryption/decryption core configurations providing a compact, scalable implementation that is suitable for applications that may demand compact yet high performant hardware. The architecture employs a combinational S-Box forming a crucial step in the parallel operation of the hardware. For an operating frequency of 278.5 MHz, the hardware achieves a high throughput of about 3.5 gigabits per second (GBps).
{"title":"An Inner Round Pipeline Architecture Hardware Core for AES","authors":"Archit Jain, Divyanshu Jain, Arpan Katiyar, Gurjit Kaur","doi":"10.1109/ASIANCON55314.2022.9909114","DOIUrl":"https://doi.org/10.1109/ASIANCON55314.2022.9909114","url":null,"abstract":"The following article presents an inner round architecture for the AES Encryption Scheme suitable for implementation on FPGAs and as ASICs. The uniformity between the encryption and decryption hardware makes them suitable for implementation as separate or co-existing blocks as required. The modular approach of our architecture allows for different encryption/decryption core configurations providing a compact, scalable implementation that is suitable for applications that may demand compact yet high performant hardware. The architecture employs a combinational S-Box forming a crucial step in the parallel operation of the hardware. For an operating frequency of 278.5 MHz, the hardware achieves a high throughput of about 3.5 gigabits per second (GBps).","PeriodicalId":429704,"journal":{"name":"2022 2nd Asian Conference on Innovation in Technology (ASIANCON)","volume":"33 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-08-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122328044","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}
Cryptocurrencies are becoming a well-known and commonly acknowledged kind of substitute trade money. Most monetary businesses now include cryptocurrency. Accordingly, cryptocurrency trading is widely regarded as the most of prevalent and capable types of lucrative investments. However, because this financial sector is already known for its extreme volatility and quick price changes, over brief periods of time. For such constantly changing nature of crypto trends and price, it has become a necessary part for traders and crypto enthusiast to get a detailed analysis before investing. Also, the construction of a precise and dependable forecasting model is regarded vital for portfolio management and optimization. In this paper we propose a web system, which will help to understand cryptocurrency in a more statistical way. Proposed system focuses mainly on four coins : Bitcoin, Ethereum, Dogecoin and Shiba Inu performing analysis and forecasting on all the four coins. System will also do statistical comparison between the coins. Analysis and comparison is carried out using python libraries and modules whereas LSTM and ARIMA are used for forecasting. Extensive research was conducted using real-time and historical information, on four key cryptocurrencies, two of which had the greatest market capitalization, notably Bitcoin and Ethereum, while the other, Dogecoin and Shiba Inu, that had a significant growth in market capitalization over the previous year. In comparison to old fully-connected deep neural networks, the suggested model may employ mixed crypto data more proficiently, minimizing overfitting and computing costs.
{"title":"Cryptocurrency Analysis and Forecasting","authors":"Payal Pagariya, Sadhvee Shinde, Rupali Shivpure, Sakshi Patil, Ashwini Jarali","doi":"10.1109/ASIANCON55314.2022.9909168","DOIUrl":"https://doi.org/10.1109/ASIANCON55314.2022.9909168","url":null,"abstract":"Cryptocurrencies are becoming a well-known and commonly acknowledged kind of substitute trade money. Most monetary businesses now include cryptocurrency. Accordingly, cryptocurrency trading is widely regarded as the most of prevalent and capable types of lucrative investments. However, because this financial sector is already known for its extreme volatility and quick price changes, over brief periods of time. For such constantly changing nature of crypto trends and price, it has become a necessary part for traders and crypto enthusiast to get a detailed analysis before investing. Also, the construction of a precise and dependable forecasting model is regarded vital for portfolio management and optimization. In this paper we propose a web system, which will help to understand cryptocurrency in a more statistical way. Proposed system focuses mainly on four coins : Bitcoin, Ethereum, Dogecoin and Shiba Inu performing analysis and forecasting on all the four coins. System will also do statistical comparison between the coins. Analysis and comparison is carried out using python libraries and modules whereas LSTM and ARIMA are used for forecasting. Extensive research was conducted using real-time and historical information, on four key cryptocurrencies, two of which had the greatest market capitalization, notably Bitcoin and Ethereum, while the other, Dogecoin and Shiba Inu, that had a significant growth in market capitalization over the previous year. In comparison to old fully-connected deep neural networks, the suggested model may employ mixed crypto data more proficiently, minimizing overfitting and computing costs.","PeriodicalId":429704,"journal":{"name":"2022 2nd Asian Conference on Innovation in Technology (ASIANCON)","volume":"167 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-08-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121806240","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-08-26DOI: 10.1109/ASIANCON55314.2022.9908828
S. Jothi, S. Anita, S. Sivakumar
Parkinson’s Disease (PD) is a kind of neurodegenerative disorder. There is an imperative need for identifying the early stage of disease as it keeps on affecting the human mid-brain. The incipient level of the disorder is identified with the help of sixteen volume rendering image slices (VRIS) which are taken from a Single Photon Emission Computed Tomography (SPECT) image as a novel tool. These image slices are selected on account of striated intake from the striatum. The shape and texture attributes of segmented VRIS and Striatal Binding Ratio (SBR) values are considered as a feature set for the analysis. These two different features (attribute) are synthesized to identify the difference between Healthy Control (HC) and the early stage of Parkinson’s disease (EPD). The various classifier models like Extreme Learning Machine (ELM), Support Vector Machine (SVM) and Artificial Neural Network (ANN) with different kernel functions are solely designed for the study the impact of single and multi-features to identify EPD. The performance of the present work is investigated and found that the Polynomial ELM offers an appreciated outcome with reference to the accuracy of 99.3%. The outcome has been compared with the previous work to underline the efficacy of the present work. Hence, the present work could be of a great aid to the experts in neurology to protect the neurons from the impairment.
{"title":"Early stage of Parkinson’s Disease Identification Using Advanced Image Processing Techniques","authors":"S. Jothi, S. Anita, S. Sivakumar","doi":"10.1109/ASIANCON55314.2022.9908828","DOIUrl":"https://doi.org/10.1109/ASIANCON55314.2022.9908828","url":null,"abstract":"Parkinson’s Disease (PD) is a kind of neurodegenerative disorder. There is an imperative need for identifying the early stage of disease as it keeps on affecting the human mid-brain. The incipient level of the disorder is identified with the help of sixteen volume rendering image slices (VRIS) which are taken from a Single Photon Emission Computed Tomography (SPECT) image as a novel tool. These image slices are selected on account of striated intake from the striatum. The shape and texture attributes of segmented VRIS and Striatal Binding Ratio (SBR) values are considered as a feature set for the analysis. These two different features (attribute) are synthesized to identify the difference between Healthy Control (HC) and the early stage of Parkinson’s disease (EPD). The various classifier models like Extreme Learning Machine (ELM), Support Vector Machine (SVM) and Artificial Neural Network (ANN) with different kernel functions are solely designed for the study the impact of single and multi-features to identify EPD. The performance of the present work is investigated and found that the Polynomial ELM offers an appreciated outcome with reference to the accuracy of 99.3%. The outcome has been compared with the previous work to underline the efficacy of the present work. Hence, the present work could be of a great aid to the experts in neurology to protect the neurons from the impairment.","PeriodicalId":429704,"journal":{"name":"2022 2nd Asian Conference on Innovation in Technology (ASIANCON)","volume":"8 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-08-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117149512","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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