Pub Date : 2020-11-06DOI: 10.1109/PDGC50313.2020.9315853
B. Kumar, U. Tiwari, Santosh Kumar
A collection of sensor nodes are available in wireless sensor network for gathering the distinguish data from environment. This sensing process consumes more energy of the network which effects the whole network life time. So energy usage in efficient manner is the main issue to maintaining the network. Clustering is the process used for reducing the energy consumption. K-means is the post popular clustering algorithm to form the clusters. In this paper, propose energy efficient clustering i.e quad clustering based on K-means algorithm. This approach improves the performance of wireless sensor network in terms of network lifetime. As simulation shows the proposed work is better than single cluster in case of distance coverage as well as energy consumption.
{"title":"Energy Efficient Quad Clustering based on K-means Algorithm for Wireless Sensor Network","authors":"B. Kumar, U. Tiwari, Santosh Kumar","doi":"10.1109/PDGC50313.2020.9315853","DOIUrl":"https://doi.org/10.1109/PDGC50313.2020.9315853","url":null,"abstract":"A collection of sensor nodes are available in wireless sensor network for gathering the distinguish data from environment. This sensing process consumes more energy of the network which effects the whole network life time. So energy usage in efficient manner is the main issue to maintaining the network. Clustering is the process used for reducing the energy consumption. K-means is the post popular clustering algorithm to form the clusters. In this paper, propose energy efficient clustering i.e quad clustering based on K-means algorithm. This approach improves the performance of wireless sensor network in terms of network lifetime. As simulation shows the proposed work is better than single cluster in case of distance coverage as well as energy consumption.","PeriodicalId":347216,"journal":{"name":"2020 Sixth International Conference on Parallel, Distributed and Grid Computing (PDGC)","volume":"27 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126836511","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}
According to a 2016 report by the Indian Ministry of Human Resource Development, there were 39,658 student hostels across India. In recent years, owing to the growing number of students residing in such hostels, there has been an interest in helping students know more about these hostels by providing them with information and reviews from residing students. We aim to categorize these based on various aspects and give greater insights about them using applications of aspect based sentiment analysis. We have used a neural network based approach to pre-process the texts and propose two models, one for aspect extraction and classification and the other for sentiment polarity analysis. Further, we have presented an extensive evaluation of our models and have achieved an accuracy of more than 75% on both the models.
{"title":"Aspect Based Sentiment Analysis of Student Housing Reviews","authors":"Aniket Mukherjee, Shiv Jethi, Akshat Jain, Ankit Mundra","doi":"10.1109/PDGC50313.2020.9315324","DOIUrl":"https://doi.org/10.1109/PDGC50313.2020.9315324","url":null,"abstract":"According to a 2016 report by the Indian Ministry of Human Resource Development, there were 39,658 student hostels across India. In recent years, owing to the growing number of students residing in such hostels, there has been an interest in helping students know more about these hostels by providing them with information and reviews from residing students. We aim to categorize these based on various aspects and give greater insights about them using applications of aspect based sentiment analysis. We have used a neural network based approach to pre-process the texts and propose two models, one for aspect extraction and classification and the other for sentiment polarity analysis. Further, we have presented an extensive evaluation of our models and have achieved an accuracy of more than 75% on both the models.","PeriodicalId":347216,"journal":{"name":"2020 Sixth International Conference on Parallel, Distributed and Grid Computing (PDGC)","volume":"242 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115657525","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 : 2020-11-06DOI: 10.1109/PDGC50313.2020.9315771
Anushikha Gupta, Mala Kalra
The Security is a vital aspect of cloud service as it comprises of data that belong to multiple users. Cloud service providers are responsible for maintaining data integrity, confidentiality and availability. They must ensure that their infrastructure and data are protected from intruders. In this research work Intrusion Detection System is designed to detect malicious server by using Cuckoo Search (CS) along with Artificial Intelligence. CS is used for feature optimization with the help of fitness function, the server's nature is categorized into two types: normal and attackers. On the basis of extracted features, ANN classify the attackers which affect the networks in cloud environment. The main aim is to distinguish attacker servers that are affected by DoS/DDoS, Black and Gray hole attacks from the genuine servers. Thus, instead of passing data to attacker server, the server passes the data to the genuine servers and hence, the system is protected. To validate the performance of the system, QoS parameters such as PDR (Packet delivery rate), energy consumption rate and total delay before and after prevention algorithm are measured. When compared with existing work, the PDR and the delay have been enhanced by 3.0 %and 21.5 %.
{"title":"Intrusion Detection and Prevention system using Cuckoo search algorithm with ANN in Cloud Computing","authors":"Anushikha Gupta, Mala Kalra","doi":"10.1109/PDGC50313.2020.9315771","DOIUrl":"https://doi.org/10.1109/PDGC50313.2020.9315771","url":null,"abstract":"The Security is a vital aspect of cloud service as it comprises of data that belong to multiple users. Cloud service providers are responsible for maintaining data integrity, confidentiality and availability. They must ensure that their infrastructure and data are protected from intruders. In this research work Intrusion Detection System is designed to detect malicious server by using Cuckoo Search (CS) along with Artificial Intelligence. CS is used for feature optimization with the help of fitness function, the server's nature is categorized into two types: normal and attackers. On the basis of extracted features, ANN classify the attackers which affect the networks in cloud environment. The main aim is to distinguish attacker servers that are affected by DoS/DDoS, Black and Gray hole attacks from the genuine servers. Thus, instead of passing data to attacker server, the server passes the data to the genuine servers and hence, the system is protected. To validate the performance of the system, QoS parameters such as PDR (Packet delivery rate), energy consumption rate and total delay before and after prevention algorithm are measured. When compared with existing work, the PDR and the delay have been enhanced by 3.0 %and 21.5 %.","PeriodicalId":347216,"journal":{"name":"2020 Sixth International Conference on Parallel, Distributed and Grid Computing (PDGC)","volume":"84 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121865498","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 : 2020-11-06DOI: 10.1109/PDGC50313.2020.9315755
Randhir Kumar, Rakesh Tripathi
Today healthcare industries are maintaining COVID-19 patients' information electronically which includes patients' diagnostic reports, patients' private information, and doctor prescriptions. However, the COVID-19, patient sensitive information is currently stored in centralized or third-party storage model. One of the key challenge of centralized storage model is the preserving privacy of patient information and transparency in the system. The privacy risk include illegitimate access to sensitive information of patient such as identification details access and misutilization of patient information and their clinical records. To overcome this challenge, we proposed a distributed on-chain and off-chain storage model using consortium blockchain and interplanetary file systems (IPFS). The proposed framework though maintaining patient privacy makes it easier for legitimate entities like healthcare providers (e.g., physicians and clinical staffs) to access clinical data of COVID-19 patients'.
{"title":"A Secure and Distributed Framework for sharing COVID-19 patient Reports using Consortium Blockchain and IPFS","authors":"Randhir Kumar, Rakesh Tripathi","doi":"10.1109/PDGC50313.2020.9315755","DOIUrl":"https://doi.org/10.1109/PDGC50313.2020.9315755","url":null,"abstract":"Today healthcare industries are maintaining COVID-19 patients' information electronically which includes patients' diagnostic reports, patients' private information, and doctor prescriptions. However, the COVID-19, patient sensitive information is currently stored in centralized or third-party storage model. One of the key challenge of centralized storage model is the preserving privacy of patient information and transparency in the system. The privacy risk include illegitimate access to sensitive information of patient such as identification details access and misutilization of patient information and their clinical records. To overcome this challenge, we proposed a distributed on-chain and off-chain storage model using consortium blockchain and interplanetary file systems (IPFS). The proposed framework though maintaining patient privacy makes it easier for legitimate entities like healthcare providers (e.g., physicians and clinical staffs) to access clinical data of COVID-19 patients'.","PeriodicalId":347216,"journal":{"name":"2020 Sixth International Conference on Parallel, Distributed and Grid Computing (PDGC)","volume":"46 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125015864","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 : 2020-11-06DOI: 10.1109/PDGC50313.2020.9315821
Nidhi Kundu, Geeta Rani, V. Dhaka
Crop diseases are a major cause of degrading the quality and reducing the number of agricultural products. Hence, there is a strong need for the early diagnosis of the disease. The effectiveness of deep learning techniques in pattern matching and image processing motivated the authors to design an automatic tool for the detection of diseases in bell pepper plants. In this manuscript, the authors present the comparative analysis of different deep learning models applied for plant disease classification. They applied the deep learning models namely VGG16, VGG19, ResNet50, ResNet101, ResNet152, InceptionResNetV2, DenseNet121 on the publicly available dataset of the bell pepper plant. The experimental results prove that the model ‘DenseNet’ requires less training time and gives the highest validation accuracy among all the above-stated models. It achieves a training accuracy of 97.49% and the testing accuracy of 96.87% in classifying the bell pepper plants into healthy and diseased categories.
{"title":"A Comparative Analysis of Deep Learning Models Applied for Disease Classification in Bell Pepper","authors":"Nidhi Kundu, Geeta Rani, V. Dhaka","doi":"10.1109/PDGC50313.2020.9315821","DOIUrl":"https://doi.org/10.1109/PDGC50313.2020.9315821","url":null,"abstract":"Crop diseases are a major cause of degrading the quality and reducing the number of agricultural products. Hence, there is a strong need for the early diagnosis of the disease. The effectiveness of deep learning techniques in pattern matching and image processing motivated the authors to design an automatic tool for the detection of diseases in bell pepper plants. In this manuscript, the authors present the comparative analysis of different deep learning models applied for plant disease classification. They applied the deep learning models namely VGG16, VGG19, ResNet50, ResNet101, ResNet152, InceptionResNetV2, DenseNet121 on the publicly available dataset of the bell pepper plant. The experimental results prove that the model ‘DenseNet’ requires less training time and gives the highest validation accuracy among all the above-stated models. It achieves a training accuracy of 97.49% and the testing accuracy of 96.87% in classifying the bell pepper plants into healthy and diseased categories.","PeriodicalId":347216,"journal":{"name":"2020 Sixth International Conference on Parallel, Distributed and Grid Computing (PDGC)","volume":"47 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125664591","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 : 2020-11-06DOI: 10.1109/PDGC50313.2020.9315756
Gaurav Gambhir, J. K. Mandal
The paper presents shared memory implementation of chaotic RGB LSB steganography technique, The proposed technique involves hiding the secret information into RGB components of the cover image. Chaotic logistic map has been used to generate highly random numbers for enhancing the security of embedded information. Encryption and decryption process is parallelized using OpenMP API in multicore environment, and results show significant speed up and highly scalable results even with large amount of data.
{"title":"Multi-core Implementation of Chaotic RGB-LSB Steganography Technique","authors":"Gaurav Gambhir, J. K. Mandal","doi":"10.1109/PDGC50313.2020.9315756","DOIUrl":"https://doi.org/10.1109/PDGC50313.2020.9315756","url":null,"abstract":"The paper presents shared memory implementation of chaotic RGB LSB steganography technique, The proposed technique involves hiding the secret information into RGB components of the cover image. Chaotic logistic map has been used to generate highly random numbers for enhancing the security of embedded information. Encryption and decryption process is parallelized using OpenMP API in multicore environment, and results show significant speed up and highly scalable results even with large amount of data.","PeriodicalId":347216,"journal":{"name":"2020 Sixth International Conference on Parallel, Distributed and Grid Computing (PDGC)","volume":"8 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134279819","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 : 2020-11-06DOI: 10.1109/PDGC50313.2020.9315788
Shikhar Vaish, Shreyam, Sunita Singhal
A* algorithm performs well as a Best First Search method, which would not give the shortest path in certain scenarios. Its accuracy depends on the heuristic function and has slow processing speed in the real world. RRT performs slower than A* and Dijkstra's algorithm gives correct output but shows us a slow runtime performance unsuitable for the real-world. This paper uses Dijkstra's algorithm using the priority queue for testing and proposes an approach that can be applied to any path planning algorithm. Experimental results show that the proposed approach performs 51% faster than A* on game datasets and 14% faster on extremely dense map datasets.
{"title":"Segmented Approach to Path Planning","authors":"Shikhar Vaish, Shreyam, Sunita Singhal","doi":"10.1109/PDGC50313.2020.9315788","DOIUrl":"https://doi.org/10.1109/PDGC50313.2020.9315788","url":null,"abstract":"A* algorithm performs well as a Best First Search method, which would not give the shortest path in certain scenarios. Its accuracy depends on the heuristic function and has slow processing speed in the real world. RRT performs slower than A* and Dijkstra's algorithm gives correct output but shows us a slow runtime performance unsuitable for the real-world. This paper uses Dijkstra's algorithm using the priority queue for testing and proposes an approach that can be applied to any path planning algorithm. Experimental results show that the proposed approach performs 51% faster than A* on game datasets and 14% faster on extremely dense map datasets.","PeriodicalId":347216,"journal":{"name":"2020 Sixth International Conference on Parallel, Distributed and Grid Computing (PDGC)","volume":"42 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130513023","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 : 2020-11-06DOI: 10.1109/PDGC50313.2020.9315817
Gitanjali Wadhwa, Mansi Mathur
Most common cancer in females is found to be Breast cancer which is a widespread disease. One out of eight females worldwide are affected by this cancer only. We can detect this cancer by detecting malignancy from breast tissues. There are various types of computer-aided techniques and approaches which are used by doctors for detecting cancer. The major objective of this paper is to build a well-defined model for the recognition of breast cancer by expending various parameters. Different types of machine learning and deep learning methodologies are used for the classification of malignant and benign tissues. In this we are using a dataset that obtains 569 samples with 30 features, this dataset is majorly called the Wisconsin dataset. Many techniques are implemented on this dataset we are using deep convolutional neural network (CNN) and Machine learning methodology (KNN) for the diagnosis and training purpose and then compare the results of both the techniques. Deep convolutional NN is implemented on the google platform called the Google Colab on the other side KNN is implemented on the Anaconda Spyder platform. The best accuracy achieved from KNN is 96.49%. To improve the performance and accuracy we implemented CNN on the same dataset and then achieved 99.41% accuracy. Deep learning is extensively useful in getting the best and optimal results in other performance matrics such as precision, recall, F1-score and AVC-ROC - 98.64%,97.61 %, 98.08%, 97.61% respectively.
{"title":"A Convolutional Neural Network Approach for The Diagnosis of Breast Cancer","authors":"Gitanjali Wadhwa, Mansi Mathur","doi":"10.1109/PDGC50313.2020.9315817","DOIUrl":"https://doi.org/10.1109/PDGC50313.2020.9315817","url":null,"abstract":"Most common cancer in females is found to be Breast cancer which is a widespread disease. One out of eight females worldwide are affected by this cancer only. We can detect this cancer by detecting malignancy from breast tissues. There are various types of computer-aided techniques and approaches which are used by doctors for detecting cancer. The major objective of this paper is to build a well-defined model for the recognition of breast cancer by expending various parameters. Different types of machine learning and deep learning methodologies are used for the classification of malignant and benign tissues. In this we are using a dataset that obtains 569 samples with 30 features, this dataset is majorly called the Wisconsin dataset. Many techniques are implemented on this dataset we are using deep convolutional neural network (CNN) and Machine learning methodology (KNN) for the diagnosis and training purpose and then compare the results of both the techniques. Deep convolutional NN is implemented on the google platform called the Google Colab on the other side KNN is implemented on the Anaconda Spyder platform. The best accuracy achieved from KNN is 96.49%. To improve the performance and accuracy we implemented CNN on the same dataset and then achieved 99.41% accuracy. Deep learning is extensively useful in getting the best and optimal results in other performance matrics such as precision, recall, F1-score and AVC-ROC - 98.64%,97.61 %, 98.08%, 97.61% respectively.","PeriodicalId":347216,"journal":{"name":"2020 Sixth International Conference on Parallel, Distributed and Grid Computing (PDGC)","volume":"28 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114373337","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 : 2020-11-06DOI: 10.1109/PDGC50313.2020.9315781
Deepak Sarvate, A. Bhati, Rahul Srivastava, VS Choudhary, RV Raghavan
The article aims to investigate the effect of shifting of spectral reflectance on colorimetric parameters due to solar exposure of the commercially available artificial fabric-based vegetation. The spectral reflectance of the control (samples at time t0) and exposed samples (time t0+t) are measured and analyzed in the visible region using a spectrophotometer. The CIE XYZ color coordinates are computed from the measured spectral reflectance. The XYZ represents the area under the multiplied spectral reflectance, illuminant and observer function. The XYZ parameters are computed for D65 illuminant and 10o observer function. The change in the XYZ with wavelength is discussed to correlate the deviation of the XYZ with color fading. The L*a*b and sRGB values are derived from the XYZ to visualize the color change. The work finds a range of applications in color based process automation, object discrimination and remote sensing for change analysis.
{"title":"Color Fading: Variation of Colorimetric Parameters with Spectral Reflectance","authors":"Deepak Sarvate, A. Bhati, Rahul Srivastava, VS Choudhary, RV Raghavan","doi":"10.1109/PDGC50313.2020.9315781","DOIUrl":"https://doi.org/10.1109/PDGC50313.2020.9315781","url":null,"abstract":"The article aims to investigate the effect of shifting of spectral reflectance on colorimetric parameters due to solar exposure of the commercially available artificial fabric-based vegetation. The spectral reflectance of the control (samples at time t0) and exposed samples (time t0+t) are measured and analyzed in the visible region using a spectrophotometer. The CIE XYZ color coordinates are computed from the measured spectral reflectance. The XYZ represents the area under the multiplied spectral reflectance, illuminant and observer function. The XYZ parameters are computed for D65 illuminant and 10o observer function. The change in the XYZ with wavelength is discussed to correlate the deviation of the XYZ with color fading. The L*a*b and sRGB values are derived from the XYZ to visualize the color change. The work finds a range of applications in color based process automation, object discrimination and remote sensing for change analysis.","PeriodicalId":347216,"journal":{"name":"2020 Sixth International Conference on Parallel, Distributed and Grid Computing (PDGC)","volume":"104 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114489039","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 : 2020-11-06DOI: 10.1109/PDGC50313.2020.9315833
A. Mahajan, Jhanvi Patel, Mittal Parmar, Gomes Luis Abrantes Joao, Kishori Shekokar, S. Degadwala
An electroencephalogram (EEG) is one of the ancillary methods to record the signals generated by the electrical activity of the brain. Conventionally, neurologists scrutinize these EEG signals to identify neurological abnormalities such as epilepsy. Such a way of observation is too time-consuming and requires proficiency. Therefore, a computer-aided diagnosis (CAD) system is needed to discriminate the class of these EEG signals automatically. This paper employs long short-term memory (LSTM) for the analysis of EEG signals. Herein, the LSTM model having only three layers is presented. This model achieved 98.5% accuracy to differentiate between non-seizures and seizures only in 30 epochs. Less number of layers and epochs are the main attraction of this work, which makes this model useful for real-time detection purpose.
{"title":"3-Layer LSTM Model for Detection of Epileptic Seizures","authors":"A. Mahajan, Jhanvi Patel, Mittal Parmar, Gomes Luis Abrantes Joao, Kishori Shekokar, S. Degadwala","doi":"10.1109/PDGC50313.2020.9315833","DOIUrl":"https://doi.org/10.1109/PDGC50313.2020.9315833","url":null,"abstract":"An electroencephalogram (EEG) is one of the ancillary methods to record the signals generated by the electrical activity of the brain. Conventionally, neurologists scrutinize these EEG signals to identify neurological abnormalities such as epilepsy. Such a way of observation is too time-consuming and requires proficiency. Therefore, a computer-aided diagnosis (CAD) system is needed to discriminate the class of these EEG signals automatically. This paper employs long short-term memory (LSTM) for the analysis of EEG signals. Herein, the LSTM model having only three layers is presented. This model achieved 98.5% accuracy to differentiate between non-seizures and seizures only in 30 epochs. Less number of layers and epochs are the main attraction of this work, which makes this model useful for real-time detection purpose.","PeriodicalId":347216,"journal":{"name":"2020 Sixth International Conference on Parallel, Distributed and Grid Computing (PDGC)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116840877","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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