Pub Date : 2021-09-23DOI: 10.1109/CCGE50943.2021.9776428
Manish Sharma, Harsh Malhotra, S. Panda, S. Malhotra
The research paper reports presents two port multi-input-multi-output (MIMO) antenna were RF Substrate Rogers RTDuroid5880 is used with bell-shaped radiator printed on top plane and rectangular slotted ground with chamfered edges. The antenna is very compact in size with dimensions $24text{mm}times 14text{mm}$. The antenna resonates at 28GHz with −10dB bandwidth of 27.14GHz-29.88GHz. This bandwidth is suitable for 5G 28GHz band for high speed applications useful for Internet-of-Things (IoT) which can be implemented for smart cities. The MIMO antenna provides good isolation and diversity performance. The antenna also offers maximum gain of 4.89dBi with desired radiation pattern. Some of the challenges in deployment of 5G technology is also discussed.
{"title":"Single Band 5G mmWave Two Port MIMO Antenna with Omnidirectional for High Speed Wireless Applications","authors":"Manish Sharma, Harsh Malhotra, S. Panda, S. Malhotra","doi":"10.1109/CCGE50943.2021.9776428","DOIUrl":"https://doi.org/10.1109/CCGE50943.2021.9776428","url":null,"abstract":"The research paper reports presents two port multi-input-multi-output (MIMO) antenna were RF Substrate Rogers RTDuroid5880 is used with bell-shaped radiator printed on top plane and rectangular slotted ground with chamfered edges. The antenna is very compact in size with dimensions $24text{mm}times 14text{mm}$. The antenna resonates at 28GHz with −10dB bandwidth of 27.14GHz-29.88GHz. This bandwidth is suitable for 5G 28GHz band for high speed applications useful for Internet-of-Things (IoT) which can be implemented for smart cities. The MIMO antenna provides good isolation and diversity performance. The antenna also offers maximum gain of 4.89dBi with desired radiation pattern. Some of the challenges in deployment of 5G technology is also discussed.","PeriodicalId":130452,"journal":{"name":"2021 International Conference on Computing, Communication and Green Engineering (CCGE)","volume":"30 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-09-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117181446","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 : 2021-09-23DOI: 10.1109/CCGE50943.2021.9776473
D. Vasanthi, T. Sivasakthi, V. Abarna, R. Arthi
Lately, the necessity of car rental services around the world have been increased and growing quicky as cars have become the most convenient modes of transportation. If any urgent trip is ahead people can rely on the car rental service as they provide us immediate transportation. It is really time -consuming and difficult to find cars using traditional methods before a stipulated price and time. The refore, using online websites it is easy to reserve your car at the stipulated price and time, it also makes the listed cars accessible by just a simple reservation which comes handy. This website allows a user not only to book a car but also to host a car to earn from renting. Only verified users can book a car for rental and verification is done using a Driving Lice nse. The cars are tabulated by obtaining the location from the user. Filters can be applied to the listing like price the listing will be shown with the owner's name and details for contact purpose. Building a web application with React JS and Node JS the website a lot faster, increases productive ness, and also helpful for SEO. MongoDB database is a database with no schema and with good scalability, hence the management of data becomes handy, which helps the user to avoid delay and hardship in the process.
{"title":"Design and Development of Car RentalWebsite Using Mern Stack","authors":"D. Vasanthi, T. Sivasakthi, V. Abarna, R. Arthi","doi":"10.1109/CCGE50943.2021.9776473","DOIUrl":"https://doi.org/10.1109/CCGE50943.2021.9776473","url":null,"abstract":"Lately, the necessity of car rental services around the world have been increased and growing quicky as cars have become the most convenient modes of transportation. If any urgent trip is ahead people can rely on the car rental service as they provide us immediate transportation. It is really time -consuming and difficult to find cars using traditional methods before a stipulated price and time. The refore, using online websites it is easy to reserve your car at the stipulated price and time, it also makes the listed cars accessible by just a simple reservation which comes handy. This website allows a user not only to book a car but also to host a car to earn from renting. Only verified users can book a car for rental and verification is done using a Driving Lice nse. The cars are tabulated by obtaining the location from the user. Filters can be applied to the listing like price the listing will be shown with the owner's name and details for contact purpose. Building a web application with React JS and Node JS the website a lot faster, increases productive ness, and also helpful for SEO. MongoDB database is a database with no schema and with good scalability, hence the management of data becomes handy, which helps the user to avoid delay and hardship in the process.","PeriodicalId":130452,"journal":{"name":"2021 International Conference on Computing, Communication and Green Engineering (CCGE)","volume":"15 3 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-09-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124465058","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 : 2021-09-23DOI: 10.1109/CCGE50943.2021.9776356
S. V. Raut, D. M. Yadav
Epilepsy is a chronic nontransmissible brain disease that affects all ages people. Worldwide epilepsy burden is about 50 million making it a common neurological disease (WHO). Generally, Epilepsy is detected using history and EEG analysis. But this method is time and data-consuming as EEG signals appear to be normal after some time in the conversions. This paper proposed a methodology for the detection of Epilepsy by integrating the fMRI and EEG analysis. Features (mean, standard deviation, and power spectral density) are extracted and provided to the SVM classifier. SVM classifies the data with 94.44% of accuracy. The proposed method is found to have more accuracy than SCA, DCM, and DeepID existing methodologies. Further, accuracy can be improved by increasing the number of subjects and features.
{"title":"Detection of Epileptic Seizure using EEG- fMRI Integration","authors":"S. V. Raut, D. M. Yadav","doi":"10.1109/CCGE50943.2021.9776356","DOIUrl":"https://doi.org/10.1109/CCGE50943.2021.9776356","url":null,"abstract":"Epilepsy is a chronic nontransmissible brain disease that affects all ages people. Worldwide epilepsy burden is about 50 million making it a common neurological disease (WHO). Generally, Epilepsy is detected using history and EEG analysis. But this method is time and data-consuming as EEG signals appear to be normal after some time in the conversions. This paper proposed a methodology for the detection of Epilepsy by integrating the fMRI and EEG analysis. Features (mean, standard deviation, and power spectral density) are extracted and provided to the SVM classifier. SVM classifies the data with 94.44% of accuracy. The proposed method is found to have more accuracy than SCA, DCM, and DeepID existing methodologies. Further, accuracy can be improved by increasing the number of subjects and features.","PeriodicalId":130452,"journal":{"name":"2021 International Conference on Computing, Communication and Green Engineering (CCGE)","volume":"21 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-09-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126809123","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 : 2021-09-23DOI: 10.1109/CCGE50943.2021.9776371
Sanika Shirsat, S. Kedar
The most commonly found diseases in humanbeing is Lung diseases, which include Lung Cancer, Pneumonia and from 2020 Covid. It is essential that the lung diseases to be diagnosed timely. There are many machine learning and image processing models that have being developed to serve this purpose. The already existing algorithms serving this purpose are vanilla neural network, capsule network, and VGG. Here, Convolutional Neural Network i.e., CNN algorithm is used for lung diseases prediction based on images of Chest X-Ray. The tools used for implementation areSpyder, Keras and TensorFlow. The Kaggle repository dataset is used for the proposed model. The model yields 93% of mean accuracy. It will predict if the diseases arelung cancer, Pneumonia, covid or non.
{"title":"Lungs Diseases Prediction based on Convolutional Neural Network","authors":"Sanika Shirsat, S. Kedar","doi":"10.1109/CCGE50943.2021.9776371","DOIUrl":"https://doi.org/10.1109/CCGE50943.2021.9776371","url":null,"abstract":"The most commonly found diseases in humanbeing is Lung diseases, which include Lung Cancer, Pneumonia and from 2020 Covid. It is essential that the lung diseases to be diagnosed timely. There are many machine learning and image processing models that have being developed to serve this purpose. The already existing algorithms serving this purpose are vanilla neural network, capsule network, and VGG. Here, Convolutional Neural Network i.e., CNN algorithm is used for lung diseases prediction based on images of Chest X-Ray. The tools used for implementation areSpyder, Keras and TensorFlow. The Kaggle repository dataset is used for the proposed model. The model yields 93% of mean accuracy. It will predict if the diseases arelung cancer, Pneumonia, covid or non.","PeriodicalId":130452,"journal":{"name":"2021 International Conference on Computing, Communication and Green Engineering (CCGE)","volume":"26 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-09-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126832917","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 : 2021-09-23DOI: 10.1109/CCGE50943.2021.9776373
Rashmi Patil, Sreepathi Bellary
Melanoma is a potentially fatal type of skin cancer in these melanocytes develop uncontrollably. Malignant melanoma is another name for melanoma. Melanoma rates in Australia and New Zealand are the highest in the world. Melanoma is anticipated to strike one in every 15 white New Zealanders at some point in their lives. Invasive melanoma was the third most prevalent malignancy in both men and women in 2012. Melanoma can strike adults of any age, but it is extremely uncommon in youngsters. Melanoma is hypothesised to start as an uncontrolled proliferation of genetically transformed melanocytic stem cells. Early diagnosis of melanoma in Dermoscopy pictures boosts the survival percentage substantially. Melanoma detection, on the other hand, is extremely difficult. As a result, automatic identification of skin cancer is extremely beneficial to pathologists' accuracy. This paper offers an ensemble deep learning strategy for accurately classifying the kind of melanoma at an early stage. The proposed model distinguishes between lentigo maligna, superficial spreading and nodular melanoma, allowing for early detection of the virus and prompt isolation and treatment to prevent the disease from spreading further. The deep layer architectures of the convolutional neural network (CNN) and the shallow structure of the pixel-based multilayer perceptron (MLP) are neural network algorithms that represent deep learning (DL) technique and the classical non-parametric machine learning method. Two methods that have diverse behaviours, were combined in a simple and successful means for the classification of very fine melanoma type detection utilising a rule-based decision fusion methodology. On dataset retrieved from https://dermnetnz.org/, the efficiency of ensemble MLP-CNN classifier was examined. In compared to state-of-the-art approaches, experimental outcomes reveal that the proposed technique is worthier in terms of diagnostic accuracy
{"title":"Ensemble Learning for Detection of Types of Melanoma","authors":"Rashmi Patil, Sreepathi Bellary","doi":"10.1109/CCGE50943.2021.9776373","DOIUrl":"https://doi.org/10.1109/CCGE50943.2021.9776373","url":null,"abstract":"Melanoma is a potentially fatal type of skin cancer in these melanocytes develop uncontrollably. Malignant melanoma is another name for melanoma. Melanoma rates in Australia and New Zealand are the highest in the world. Melanoma is anticipated to strike one in every 15 white New Zealanders at some point in their lives. Invasive melanoma was the third most prevalent malignancy in both men and women in 2012. Melanoma can strike adults of any age, but it is extremely uncommon in youngsters. Melanoma is hypothesised to start as an uncontrolled proliferation of genetically transformed melanocytic stem cells. Early diagnosis of melanoma in Dermoscopy pictures boosts the survival percentage substantially. Melanoma detection, on the other hand, is extremely difficult. As a result, automatic identification of skin cancer is extremely beneficial to pathologists' accuracy. This paper offers an ensemble deep learning strategy for accurately classifying the kind of melanoma at an early stage. The proposed model distinguishes between lentigo maligna, superficial spreading and nodular melanoma, allowing for early detection of the virus and prompt isolation and treatment to prevent the disease from spreading further. The deep layer architectures of the convolutional neural network (CNN) and the shallow structure of the pixel-based multilayer perceptron (MLP) are neural network algorithms that represent deep learning (DL) technique and the classical non-parametric machine learning method. Two methods that have diverse behaviours, were combined in a simple and successful means for the classification of very fine melanoma type detection utilising a rule-based decision fusion methodology. On dataset retrieved from https://dermnetnz.org/, the efficiency of ensemble MLP-CNN classifier was examined. In compared to state-of-the-art approaches, experimental outcomes reveal that the proposed technique is worthier in terms of diagnostic accuracy","PeriodicalId":130452,"journal":{"name":"2021 International Conference on Computing, Communication and Green Engineering (CCGE)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-09-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126998602","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 : 2021-09-23DOI: 10.1109/CCGE50943.2021.9776420
S. Patil, Rane Charushila Vijay
Secrete information is embedded in some cover medium through steganography. The contents of information as well as existence of information must be undetectable to attackers. Steganography is normally done by slightly altering the pixel values of cover image. We have used texture synthesis process for embedding the data instead of changing pixel values. Correlation at joining edges of patches to be stitched is considered for suitable patch selection. Energy of candidate patches is the parameter used to verify uniqueness of candidate patches and to identify patch in data extraction process. Along with energy, other parameters like mean as well as mean, variance, kurtosis and skewness combined are experimented. The data extraction rate in presence of different stego attacks is observed.
{"title":"Spatial Domain Texture Synthesis for Data Embedding","authors":"S. Patil, Rane Charushila Vijay","doi":"10.1109/CCGE50943.2021.9776420","DOIUrl":"https://doi.org/10.1109/CCGE50943.2021.9776420","url":null,"abstract":"Secrete information is embedded in some cover medium through steganography. The contents of information as well as existence of information must be undetectable to attackers. Steganography is normally done by slightly altering the pixel values of cover image. We have used texture synthesis process for embedding the data instead of changing pixel values. Correlation at joining edges of patches to be stitched is considered for suitable patch selection. Energy of candidate patches is the parameter used to verify uniqueness of candidate patches and to identify patch in data extraction process. Along with energy, other parameters like mean as well as mean, variance, kurtosis and skewness combined are experimented. The data extraction rate in presence of different stego attacks is observed.","PeriodicalId":130452,"journal":{"name":"2021 International Conference on Computing, Communication and Green Engineering (CCGE)","volume":"51 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-09-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129019183","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 : 2021-09-23DOI: 10.1109/CCGE50943.2021.9776374
Monali Gulhane, T. Sajana
People are now suffering from a variety of diseases as a result of the environment in which they live and their lifestyle choices. As a result, the goal of predicting disease at an earlier stage becomes increasingly critical. However, making an accurate prediction based on symptoms becomes too tough for doctors to do. The task of accurately predicting disease is one of the most difficult. Data mining is critical in overcoming this difficulty because it may be used to forecast the sickness. Every year, a great amount of data is generated in the field of medicine. Due to the extreme increase in the rate of information being collected in the health and medical industries, it has been possible to conduct precise analyses of medical data, which now has resulted in better patient outcomes. When disease data is used as a starting point, data mining can be used to identify hidden patterns in the huge number of medical data that currently exists. On the basis of the patient's symptoms, we suggested a generic disease prediction model. In ability to implement credible illness predictions, we apply machine learning methods such as convolutional neural networks (CNNs) for disease prediction. Disease symptom datasets are essential for disease forecasting purposes. In this general disease prediction model, the individual's lifestyle behaviour as well as examination data are taken into consideration for reliable disease prediction. It has been demonstrated that the accuracy of generalized predictive modeling that used the CNN algorithm is 98.7 percent, which really is better than those of the present technique. In addition, the time and memory requirements for existing mechanism are higher than those for CNN. When general disease is expected, this method is qualified to determine the threat related to institutional disease, which can be stronger or weaker than the previously mentioned of general disease.
{"title":"A Machine Learning based Model for Disease Prediction","authors":"Monali Gulhane, T. Sajana","doi":"10.1109/CCGE50943.2021.9776374","DOIUrl":"https://doi.org/10.1109/CCGE50943.2021.9776374","url":null,"abstract":"People are now suffering from a variety of diseases as a result of the environment in which they live and their lifestyle choices. As a result, the goal of predicting disease at an earlier stage becomes increasingly critical. However, making an accurate prediction based on symptoms becomes too tough for doctors to do. The task of accurately predicting disease is one of the most difficult. Data mining is critical in overcoming this difficulty because it may be used to forecast the sickness. Every year, a great amount of data is generated in the field of medicine. Due to the extreme increase in the rate of information being collected in the health and medical industries, it has been possible to conduct precise analyses of medical data, which now has resulted in better patient outcomes. When disease data is used as a starting point, data mining can be used to identify hidden patterns in the huge number of medical data that currently exists. On the basis of the patient's symptoms, we suggested a generic disease prediction model. In ability to implement credible illness predictions, we apply machine learning methods such as convolutional neural networks (CNNs) for disease prediction. Disease symptom datasets are essential for disease forecasting purposes. In this general disease prediction model, the individual's lifestyle behaviour as well as examination data are taken into consideration for reliable disease prediction. It has been demonstrated that the accuracy of generalized predictive modeling that used the CNN algorithm is 98.7 percent, which really is better than those of the present technique. In addition, the time and memory requirements for existing mechanism are higher than those for CNN. When general disease is expected, this method is qualified to determine the threat related to institutional disease, which can be stronger or weaker than the previously mentioned of general disease.","PeriodicalId":130452,"journal":{"name":"2021 International Conference on Computing, Communication and Green Engineering (CCGE)","volume":"23 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-09-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131839561","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 : 2021-09-23DOI: 10.1109/CCGE50943.2021.9776417
AnshulVarshav Borawake, Minal Shahakar
Developing mobile application compatible for both Android and iOS, hence a cross-platform development approach, as developers faced a challenge previously learning development specific language for Android and iOS. Compared to other hybrid mobile application frameworks, React Native has faster development time, wide market search and easy third party integration. It is also time and cost efficient for single codebase nature. Getting to the bottom of the solution for the underlying problem, this paper utilizes React Native framework to create an efficient hybrid mobile application “Embankment Protection App” capable of provisioning crowd sourced solutions pertaining to embankment surveys. The framework has been created for Android and iOS, the produced results reflects adequate experience for users on both the platforms. The framework develops truly native apps and does not compromise much with user experiences regardless of the platform. The programming language used for the solution of this research paper is a combination of Javascript.
{"title":"Embankment Protection - React Native Application Cross-Platform Application for protection of embankments by crowd sourced data","authors":"AnshulVarshav Borawake, Minal Shahakar","doi":"10.1109/CCGE50943.2021.9776417","DOIUrl":"https://doi.org/10.1109/CCGE50943.2021.9776417","url":null,"abstract":"Developing mobile application compatible for both Android and iOS, hence a cross-platform development approach, as developers faced a challenge previously learning development specific language for Android and iOS. Compared to other hybrid mobile application frameworks, React Native has faster development time, wide market search and easy third party integration. It is also time and cost efficient for single codebase nature. Getting to the bottom of the solution for the underlying problem, this paper utilizes React Native framework to create an efficient hybrid mobile application “Embankment Protection App” capable of provisioning crowd sourced solutions pertaining to embankment surveys. The framework has been created for Android and iOS, the produced results reflects adequate experience for users on both the platforms. The framework develops truly native apps and does not compromise much with user experiences regardless of the platform. The programming language used for the solution of this research paper is a combination of Javascript.","PeriodicalId":130452,"journal":{"name":"2021 International Conference on Computing, Communication and Green Engineering (CCGE)","volume":"32 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-09-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133108920","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 : 2021-09-23DOI: 10.1109/CCGE50943.2021.9776382
N. Kishore, Priya Raina, N. Nayar, Mukesh Thakur
Digital signatures are widely used to check the authenticity of the identity of the signatory of the message/document and the integrity of the message sent. They are also used by the receiver for ensuring non-repudiation by the sender. They play an important role in making day-to-day processes electronic and paperless. Digital signatures are based on public key infrastructure (PKI). The message digest (hash) of the file is signed by the sender using a private key and appended to the file. The recipient extracts the signature, decrypting it with the sender's public key, and verifies if the received digest matches its own hash calculations. However, complex calculations for secure signatures imply that digital signatures are time consuming for large files. Hashing is the basic security mechanism used in digital signatures that is performed by all the parties and consumes most of the time. This paper presents a solution to this problem by using parallel hashing to achieve fast digital signatures, discussing two possible approaches. The first one uses only parallel hashing, keeping the rest of the algorithm the same as the reference algorithm based on RSA. The second approach parallelizes the entire reference algorithm. Both were implemented using the OpenMP framework, and the experimental results show a significant decline in the execution time in both the cases.
{"title":"Fast Implementation of Digital Signatures Using Parallel Techniques","authors":"N. Kishore, Priya Raina, N. Nayar, Mukesh Thakur","doi":"10.1109/CCGE50943.2021.9776382","DOIUrl":"https://doi.org/10.1109/CCGE50943.2021.9776382","url":null,"abstract":"Digital signatures are widely used to check the authenticity of the identity of the signatory of the message/document and the integrity of the message sent. They are also used by the receiver for ensuring non-repudiation by the sender. They play an important role in making day-to-day processes electronic and paperless. Digital signatures are based on public key infrastructure (PKI). The message digest (hash) of the file is signed by the sender using a private key and appended to the file. The recipient extracts the signature, decrypting it with the sender's public key, and verifies if the received digest matches its own hash calculations. However, complex calculations for secure signatures imply that digital signatures are time consuming for large files. Hashing is the basic security mechanism used in digital signatures that is performed by all the parties and consumes most of the time. This paper presents a solution to this problem by using parallel hashing to achieve fast digital signatures, discussing two possible approaches. The first one uses only parallel hashing, keeping the rest of the algorithm the same as the reference algorithm based on RSA. The second approach parallelizes the entire reference algorithm. Both were implemented using the OpenMP framework, and the experimental results show a significant decline in the execution time in both the cases.","PeriodicalId":130452,"journal":{"name":"2021 International Conference on Computing, Communication and Green Engineering (CCGE)","volume":"61 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-09-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126654742","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 : 2021-09-23DOI: 10.1109/CCGE50943.2021.9776424
Manjusha A. Kanawade, Mrunmai M. Ranade
Cogeneration plants concurrently produce electricity and heat energy. In sugar industry bagasse can be utilized efficiently for generation of thermal and electrical energy. The present study includes optimal scheduling of boiler and generator units for generation of steam and electricity. The mixed integer linear programming (MILP) mathematical formulation is proposed to determine optimal planning. The existing sugar industry under consideration does not sell electricity to grid or other utility. The optimal planning and scheduling of sugar industry components in view of power export indicates reduction in annual cost of sugar industry. The proposed MILP model can be helpful for planner of sugar industry to consider power export option in the existing sugar industry. The study shows clear benefit and efficient utilization of boiler and generator units of the industry after satisfying the thermal and electrical demands.
{"title":"Optimization of Cogeneration in Sugar industry by Mixed integer linear programming Method","authors":"Manjusha A. Kanawade, Mrunmai M. Ranade","doi":"10.1109/CCGE50943.2021.9776424","DOIUrl":"https://doi.org/10.1109/CCGE50943.2021.9776424","url":null,"abstract":"Cogeneration plants concurrently produce electricity and heat energy. In sugar industry bagasse can be utilized efficiently for generation of thermal and electrical energy. The present study includes optimal scheduling of boiler and generator units for generation of steam and electricity. The mixed integer linear programming (MILP) mathematical formulation is proposed to determine optimal planning. The existing sugar industry under consideration does not sell electricity to grid or other utility. The optimal planning and scheduling of sugar industry components in view of power export indicates reduction in annual cost of sugar industry. The proposed MILP model can be helpful for planner of sugar industry to consider power export option in the existing sugar industry. The study shows clear benefit and efficient utilization of boiler and generator units of the industry after satisfying the thermal and electrical demands.","PeriodicalId":130452,"journal":{"name":"2021 International Conference on Computing, Communication and Green Engineering (CCGE)","volume":"111 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-09-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123435839","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}
京公网安备 11010802042870号
京ICP备2023020795号-1
扫码关注我们
求助内容：
应助结果提醒方式：