Innovation is the key for an enterprise to accomplish development or improvement in developing countries like India. Open Innovation which accentuates the coordination of inward and outside assets in an enterprise has achieved another point of view in innovative improvements. To advance and guarantee the execution of the open Innovation, an appraisal structure and the assessment markers are required. This Paper throws light on measurements taken from literary works and created execution markers, also can be called as performance indicators by ordering viewpoints or opinions from both scholastic specialists and industry specialists. The investigation/ study took the turn towards streaming of viewpoints into an Analytic Hierarchy Process for information/data examination through thorough analysis. The orderly dimension included “Innovation execution,” “Innovation abuse” and “Innovation investigation” and among all the sub-measurements for open development execution, “Worker inclusion” was huge. This investigation recognized three critical markers that are recommended by scholarly and industry specialists: “The level of fruitful cross-departmental staff interest in new item development”, “The level of motivating force/compensate framework usage for development”, and “The level of development sharing among representatives”.
{"title":"A Study on Indicators for Open Innovation Performance in Food Processing SMEs in India through AHP Approach","authors":"Supriya Lamba Sahdev, Gurinder Singh, Navleen Kaur","doi":"10.1109/ICCAKM50778.2021.9357767","DOIUrl":"https://doi.org/10.1109/ICCAKM50778.2021.9357767","url":null,"abstract":"Innovation is the key for an enterprise to accomplish development or improvement in developing countries like India. Open Innovation which accentuates the coordination of inward and outside assets in an enterprise has achieved another point of view in innovative improvements. To advance and guarantee the execution of the open Innovation, an appraisal structure and the assessment markers are required. This Paper throws light on measurements taken from literary works and created execution markers, also can be called as performance indicators by ordering viewpoints or opinions from both scholastic specialists and industry specialists. The investigation/ study took the turn towards streaming of viewpoints into an Analytic Hierarchy Process for information/data examination through thorough analysis. The orderly dimension included “Innovation execution,” “Innovation abuse” and “Innovation investigation” and among all the sub-measurements for open development execution, “Worker inclusion” was huge. This investigation recognized three critical markers that are recommended by scholarly and industry specialists: “The level of fruitful cross-departmental staff interest in new item development”, “The level of motivating force/compensate framework usage for development”, and “The level of development sharing among representatives”.","PeriodicalId":165854,"journal":{"name":"2021 2nd International Conference on Computation, Automation and Knowledge Management (ICCAKM)","volume":"264 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-01-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116048662","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 : 2021-01-19DOI: 10.1109/iccakm50778.2021.9357720
Rohit Agarwal
Different state-of-the-art document classification models are based on bag of words model such as Support Vector Machine, Naive Bayes and Neural Network. These models do not contain the word's semantic meaning. In any document, meaning of the words can be demonstrated by their presence and vicinity of particular words. Bag of Phrases is one technique by which author can preserve the vicinity of the words. This model is proficient to distinguish the capability of phrases in document classification. In this paper author proposes Semi-Supervised Hierarchical Latent Dirichlet Allocation (SSHLDA) model which uses the outstanding theme to isolate the phrases from the corpus. The proposed model incorporates the phrases in vector space model for document classification. Experiment performs on the organic document with Bag of Phrase technique and show the effective classification. When compare with state-of-the-models.
{"title":"Phrases based Document Classification from Semi Supervised Hierarchical LDA","authors":"Rohit Agarwal","doi":"10.1109/iccakm50778.2021.9357720","DOIUrl":"https://doi.org/10.1109/iccakm50778.2021.9357720","url":null,"abstract":"Different state-of-the-art document classification models are based on bag of words model such as Support Vector Machine, Naive Bayes and Neural Network. These models do not contain the word's semantic meaning. In any document, meaning of the words can be demonstrated by their presence and vicinity of particular words. Bag of Phrases is one technique by which author can preserve the vicinity of the words. This model is proficient to distinguish the capability of phrases in document classification. In this paper author proposes Semi-Supervised Hierarchical Latent Dirichlet Allocation (SSHLDA) model which uses the outstanding theme to isolate the phrases from the corpus. The proposed model incorporates the phrases in vector space model for document classification. Experiment performs on the organic document with Bag of Phrase technique and show the effective classification. When compare with state-of-the-models.","PeriodicalId":165854,"journal":{"name":"2021 2nd International Conference on Computation, Automation and Knowledge Management (ICCAKM)","volume":"94 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-01-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134442410","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 : 2021-01-19DOI: 10.1109/iccakm50778.2021.9357750
R. Kiruthiga, D. Akila
In this paper, Heterogeneous Fair Resource Allocation and Scheduling (HFRAS) for cloud based Big Data Streams, is proposed. In this algorithm, a weight value is determined for the user for each of the requested resource, based on the resource priorities. Then each task is assigned a task priority index (TPI) based on this weight value, task arrival time and expected end time (EET). The requested tasks are divided into various priority queues based on the TPI of the tasks assigned. Then tasks are sorted in the ascending order of TPI and scheduled in which the Dominant Resource Share (DRS) is determined for each user. Experimental results have shown that HFRAS attains lesser execution time, minimum response delay and maximum CPU utilization, when compared to the existing algorithm.
{"title":"Heterogeneous Fair Resource Allocation and Scheduling for Big Data Streams in Cloud Environments","authors":"R. Kiruthiga, D. Akila","doi":"10.1109/iccakm50778.2021.9357750","DOIUrl":"https://doi.org/10.1109/iccakm50778.2021.9357750","url":null,"abstract":"In this paper, Heterogeneous Fair Resource Allocation and Scheduling (HFRAS) for cloud based Big Data Streams, is proposed. In this algorithm, a weight value is determined for the user for each of the requested resource, based on the resource priorities. Then each task is assigned a task priority index (TPI) based on this weight value, task arrival time and expected end time (EET). The requested tasks are divided into various priority queues based on the TPI of the tasks assigned. Then tasks are sorted in the ascending order of TPI and scheduled in which the Dominant Resource Share (DRS) is determined for each user. Experimental results have shown that HFRAS attains lesser execution time, minimum response delay and maximum CPU utilization, when compared to the existing algorithm.","PeriodicalId":165854,"journal":{"name":"2021 2nd International Conference on Computation, Automation and Knowledge Management (ICCAKM)","volume":"70 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-01-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124332664","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 : 2021-01-19DOI: 10.1109/ICCAKM50778.2021.9357745
Ravinder Kumar, V. Kawin Singh, K. Harish, RK Bhavish
COVID-19 pandemic has created a huge disturbance in Supply chain management (SCM) all over the globe. Especially the manufacturing and demand-supply pattern of small and medium enterprises (SMEs) got affected very badly. For uninterrupted production and SCM in such scenario, Intelligent Automation (IA) may be used as a futuristic tool by SMEs. The purpose of this paper is to study the importance of intelligent automation in COVID-19 shadow by literature review and case study. In this study we have observed that many authors have stressed on importance of automation but SMEs lack knowledge on intelligent automation. SMEs have intention to adopt IA, but there have been research gaps in implementing IA and the impact of COVID. Automation can be adopted in phased manner. This paper explores the impact of IA in manufacturing sector especially SMEs. The findings of this study will make an impact on adapting IA in multi sectors SMEs.
{"title":"Importance Of Intelligent Automation In Post COVID Era: A Study","authors":"Ravinder Kumar, V. Kawin Singh, K. Harish, RK Bhavish","doi":"10.1109/ICCAKM50778.2021.9357745","DOIUrl":"https://doi.org/10.1109/ICCAKM50778.2021.9357745","url":null,"abstract":"COVID-19 pandemic has created a huge disturbance in Supply chain management (SCM) all over the globe. Especially the manufacturing and demand-supply pattern of small and medium enterprises (SMEs) got affected very badly. For uninterrupted production and SCM in such scenario, Intelligent Automation (IA) may be used as a futuristic tool by SMEs. The purpose of this paper is to study the importance of intelligent automation in COVID-19 shadow by literature review and case study. In this study we have observed that many authors have stressed on importance of automation but SMEs lack knowledge on intelligent automation. SMEs have intention to adopt IA, but there have been research gaps in implementing IA and the impact of COVID. Automation can be adopted in phased manner. This paper explores the impact of IA in manufacturing sector especially SMEs. The findings of this study will make an impact on adapting IA in multi sectors SMEs.","PeriodicalId":165854,"journal":{"name":"2021 2nd International Conference on Computation, Automation and Knowledge Management (ICCAKM)","volume":"7 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-01-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128523157","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 : 2021-01-19DOI: 10.1109/ICCAKM50778.2021.9357760
Faiza Hashim, S. Harous
Blockchain is a peer-to-peer (P2P) decentralized digital data base of distributed, secured, and immutable transaction between any untrusted parties without the involvement of trusted third party via an agreement though smart contract algorithm. These characteristics of blockchain are brought together with precision medicine to achieve the overall interoperability of medical health records within trusted environment. The focus of this paper is to present a review of integrating the precision medicine with blockchain technology to overcome the challenges in the field. This research also investigates the role of blockchain technology to resolve the issues of trust, ownership, and transparency in precision medicine field.
{"title":"Precision Medicine Blockchained: A Review","authors":"Faiza Hashim, S. Harous","doi":"10.1109/ICCAKM50778.2021.9357760","DOIUrl":"https://doi.org/10.1109/ICCAKM50778.2021.9357760","url":null,"abstract":"Blockchain is a peer-to-peer (P2P) decentralized digital data base of distributed, secured, and immutable transaction between any untrusted parties without the involvement of trusted third party via an agreement though smart contract algorithm. These characteristics of blockchain are brought together with precision medicine to achieve the overall interoperability of medical health records within trusted environment. The focus of this paper is to present a review of integrating the precision medicine with blockchain technology to overcome the challenges in the field. This research also investigates the role of blockchain technology to resolve the issues of trust, ownership, and transparency in precision medicine field.","PeriodicalId":165854,"journal":{"name":"2021 2nd International Conference on Computation, Automation and Knowledge Management (ICCAKM)","volume":"104 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-01-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115719709","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 : 2021-01-19DOI: 10.1109/ICCAKM50778.2021.9357718
Sivasankari Manickavasagam, R. Anandan
Ocean Data monitoring and prediction is widely studied using various techniques. The remote sensing data is available for parameters like Sea Surface Temperature (SST), Chlorophyll, and Salinity etc. The goal is to utilize the data and train the system, so that it will be useful in predicting the future data. The paper aims at processing the remote sensing information primarily focused on SST and Chlorophyll parameters using the Downscaling technique and Random Forest methodology. The mean value of the Spatial Distribution is calculated using the multivariate regression model, whose input is the course and the fine resolution data. The temperature data is gotten from INCOIS (Indian National Centre for Ocean Information Services) Site using AVHRR Sensor (Advanced Very High Resolution Radiometer). OCx algorithm is used to process the Chlorophyll data and the images are pre-processed using Gnomonic Projection. We process the pixel data using the prediction model and the outcome is measured in terms of Accuracy and AUC (Area under the Curve) of ROC Curve. The prediction model is compared with Nearest Neighbor (kNN) and Logistic Regression (LR), via the standard parameters Precision, Recall and Accuracy wherein the accuracy of our model stands at 0.943 which is significantly better than the other two (kNN and LR).
{"title":"Processing of Remote Sensing Ocean Parameter Using Downscaling and Machine Learning Techniques","authors":"Sivasankari Manickavasagam, R. Anandan","doi":"10.1109/ICCAKM50778.2021.9357718","DOIUrl":"https://doi.org/10.1109/ICCAKM50778.2021.9357718","url":null,"abstract":"Ocean Data monitoring and prediction is widely studied using various techniques. The remote sensing data is available for parameters like Sea Surface Temperature (SST), Chlorophyll, and Salinity etc. The goal is to utilize the data and train the system, so that it will be useful in predicting the future data. The paper aims at processing the remote sensing information primarily focused on SST and Chlorophyll parameters using the Downscaling technique and Random Forest methodology. The mean value of the Spatial Distribution is calculated using the multivariate regression model, whose input is the course and the fine resolution data. The temperature data is gotten from INCOIS (Indian National Centre for Ocean Information Services) Site using AVHRR Sensor (Advanced Very High Resolution Radiometer). OCx algorithm is used to process the Chlorophyll data and the images are pre-processed using Gnomonic Projection. We process the pixel data using the prediction model and the outcome is measured in terms of Accuracy and AUC (Area under the Curve) of ROC Curve. The prediction model is compared with Nearest Neighbor (kNN) and Logistic Regression (LR), via the standard parameters Precision, Recall and Accuracy wherein the accuracy of our model stands at 0.943 which is significantly better than the other two (kNN and LR).","PeriodicalId":165854,"journal":{"name":"2021 2nd International Conference on Computation, Automation and Knowledge Management (ICCAKM)","volume":"17 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-01-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125153113","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 : 2021-01-19DOI: 10.1109/iccakm50778.2021.9357717
Luqman Ali, S. Harous, N. Zaki, Wasif Khan, F. Alnajjar, Hamad Al Jassmi
Detection of cracks at the earliest stage is crucial, as these are the primary indicators of infrastructure's health. Manual inspection is often carried out for infrastructure inspection which requires in-depth knowledge of domain, which is time-consuming, labor intensive. The in-accessibility of infrastructure in manual inspection make it more challenging and complex. Therefore, various efficient and fast image-based automatic techniques have been introduced in the literature for concrete crack detection task. This paper aims to evaluate the performance six hand-crafted features based traditional approaches in comparison with deep Convolutional Neural Networks (CNN's) for concrete crack detection using different performance metrics. The dataset is obtained by combing data from two publicly available datasets and consists of 40000 crack and non-crack images. Extensive experiments are conducted demonstrating that Random Forest and KNN classifier performs better with 98% accuracy with Area Under the Curve 0.99 as compared to the other classifiers using handcrafted features as well it is faster than deep convolutional neural networks. The computational time for the DCNN is larger than all other classifier but it has the capability to extract feature from images automatically.
{"title":"Performance Evaluation of different Algorithms for Crack Detection in Concrete Structures","authors":"Luqman Ali, S. Harous, N. Zaki, Wasif Khan, F. Alnajjar, Hamad Al Jassmi","doi":"10.1109/iccakm50778.2021.9357717","DOIUrl":"https://doi.org/10.1109/iccakm50778.2021.9357717","url":null,"abstract":"Detection of cracks at the earliest stage is crucial, as these are the primary indicators of infrastructure's health. Manual inspection is often carried out for infrastructure inspection which requires in-depth knowledge of domain, which is time-consuming, labor intensive. The in-accessibility of infrastructure in manual inspection make it more challenging and complex. Therefore, various efficient and fast image-based automatic techniques have been introduced in the literature for concrete crack detection task. This paper aims to evaluate the performance six hand-crafted features based traditional approaches in comparison with deep Convolutional Neural Networks (CNN's) for concrete crack detection using different performance metrics. The dataset is obtained by combing data from two publicly available datasets and consists of 40000 crack and non-crack images. Extensive experiments are conducted demonstrating that Random Forest and KNN classifier performs better with 98% accuracy with Area Under the Curve 0.99 as compared to the other classifiers using handcrafted features as well it is faster than deep convolutional neural networks. The computational time for the DCNN is larger than all other classifier but it has the capability to extract feature from images automatically.","PeriodicalId":165854,"journal":{"name":"2021 2nd International Conference on Computation, Automation and Knowledge Management (ICCAKM)","volume":"63 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-01-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122766576","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 paper provides a detailed understanding about what exactly Bitcoin (BTC) is. It also answers the question about where and how to use bitcoin as a consumer. The paper elaborates about how bitcoin can be earned and generated along with an ease to understand explanation about its technology. The objective of the paper is to identify whether BTC is still a feasible asset to make an investment in as compared with other existing securities and assets for both short term and long-term investors in the global market. The purpose is to figure out if bitcoin has potential in 2020 and coming years and will it yield high returns even after it has seen a price drop in 2019. Bitcoin became popular when its price peaked to almost $20000 in 2017. Global investors not only look at BTC in short term speculative prospective but also as a long-term investment asset hence it is called as the Digital Gold. BTC is also considered to be highly volatile in nature which is why studying the risk factor involved in investing in it becomes very important. Both mean variance approach and correlation approaches are used to evaluate the risk involved in BTC as an asset and comparison is made with other popular global assets to see if investing in Bitcoin is an ideal option or not.
{"title":"Bitcoin: An Investment Management Tool-Comparison between risk and average returns of different financial assets with BTC","authors":"Navleen Kaur, Supriya Lamba Sahdev, Gurinder Singh, Ashna Garg","doi":"10.1109/ICCAKM50778.2021.9357722","DOIUrl":"https://doi.org/10.1109/ICCAKM50778.2021.9357722","url":null,"abstract":"The paper provides a detailed understanding about what exactly Bitcoin (BTC) is. It also answers the question about where and how to use bitcoin as a consumer. The paper elaborates about how bitcoin can be earned and generated along with an ease to understand explanation about its technology. The objective of the paper is to identify whether BTC is still a feasible asset to make an investment in as compared with other existing securities and assets for both short term and long-term investors in the global market. The purpose is to figure out if bitcoin has potential in 2020 and coming years and will it yield high returns even after it has seen a price drop in 2019. Bitcoin became popular when its price peaked to almost $20000 in 2017. Global investors not only look at BTC in short term speculative prospective but also as a long-term investment asset hence it is called as the Digital Gold. BTC is also considered to be highly volatile in nature which is why studying the risk factor involved in investing in it becomes very important. Both mean variance approach and correlation approaches are used to evaluate the risk involved in BTC as an asset and comparison is made with other popular global assets to see if investing in Bitcoin is an ideal option or not.","PeriodicalId":165854,"journal":{"name":"2021 2nd International Conference on Computation, Automation and Knowledge Management (ICCAKM)","volume":"124 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-01-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131597260","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 : 2021-01-19DOI: 10.1109/iccakm50778.2021.9357755
Shahina C.A. Fathimath, Saifudeen Kabeer
Diagnosing and controlling the spread of infectious diseases such as COVID-19 is crucial to managing epidemics. One common measure taken to reduce spreading is to detect infected individuals and trace their primary contacts so as to then selectively isolate any individuals likely to have been infected. The devices, called simply the “Smart Access Card System”, aim to provide contact tracing, diagnosing early symptoms, and helping to maintain social distancing. It can continuously collect the location and contacts of their owners by using sensor tag and Internet of Things (IoT) technology. Proposed mobile gadget technology might be beneficial in any future diseases spread also.
{"title":"Smart Access Card system to mitigate the Covid-19 Outbreak","authors":"Shahina C.A. Fathimath, Saifudeen Kabeer","doi":"10.1109/iccakm50778.2021.9357755","DOIUrl":"https://doi.org/10.1109/iccakm50778.2021.9357755","url":null,"abstract":"Diagnosing and controlling the spread of infectious diseases such as COVID-19 is crucial to managing epidemics. One common measure taken to reduce spreading is to detect infected individuals and trace their primary contacts so as to then selectively isolate any individuals likely to have been infected. The devices, called simply the “Smart Access Card System”, aim to provide contact tracing, diagnosing early symptoms, and helping to maintain social distancing. It can continuously collect the location and contacts of their owners by using sensor tag and Internet of Things (IoT) technology. Proposed mobile gadget technology might be beneficial in any future diseases spread also.","PeriodicalId":165854,"journal":{"name":"2021 2nd International Conference on Computation, Automation and Knowledge Management (ICCAKM)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-01-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130686121","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 : 2021-01-19DOI: 10.1109/iccakm50778.2021.9357770
Souvik Pal, G. Suseendran, D. Akila, R. Jayakarthik, T. Jabeen
The human brain usually generates brain wave signals used for medical research to study the state of the human body. Most common diseases like seizures, insomnia, or other diseases such as brain tumors can be diagnosed using brain wave signals captured with a device's help. Apart from these, nowadays, many devices are invented that operates with brain signals for people with disabilities. And now, in this paper, we will use brain signals for authenticating high-security devices using a network since secondary damage cannot be caused in brain wave signals like generated in a fingerprint, iris, face, etc. Brain wave serves as high-security biometric data. However, brain signals can also be hacked once captured by a malicious personality [14]. Here we will encrypt Brain wave signal with an advanced FFT architecture that incorporates the Cordic system in it. His method enhances high-security transmission of Brain signals over the network for authenticating a high-security device.
{"title":"Advanced FFT architecture based on Cordic method for Brain signal Encryption system","authors":"Souvik Pal, G. Suseendran, D. Akila, R. Jayakarthik, T. Jabeen","doi":"10.1109/iccakm50778.2021.9357770","DOIUrl":"https://doi.org/10.1109/iccakm50778.2021.9357770","url":null,"abstract":"The human brain usually generates brain wave signals used for medical research to study the state of the human body. Most common diseases like seizures, insomnia, or other diseases such as brain tumors can be diagnosed using brain wave signals captured with a device's help. Apart from these, nowadays, many devices are invented that operates with brain signals for people with disabilities. And now, in this paper, we will use brain signals for authenticating high-security devices using a network since secondary damage cannot be caused in brain wave signals like generated in a fingerprint, iris, face, etc. Brain wave serves as high-security biometric data. However, brain signals can also be hacked once captured by a malicious personality [14]. Here we will encrypt Brain wave signal with an advanced FFT architecture that incorporates the Cordic system in it. His method enhances high-security transmission of Brain signals over the network for authenticating a high-security device.","PeriodicalId":165854,"journal":{"name":"2021 2nd International Conference on Computation, Automation and Knowledge Management (ICCAKM)","volume":"99 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-01-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130950035","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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