Pub Date : 2023-05-31DOI: 10.55524/ijircst.2023.11.3.14
Dr. A. Ranganathan, M. Kranthikumar, D. Sudheerbabu, N. Badulla, T. Tejaswini, D. Nagarjuna, Y. Durgaprasad
Flexible pavements need more attention in selection of Resources and preparation of mixes now a day’s temperature is the main criteria which affect the mix quality, strength and durability. Rapid changes in temperature now a day’s big problem to face worst situations, Durability point is a big factor which affects the life period of the pavement surface and its components, Nominal mixes which consists of inert material doesn’t gives better Durability in severe traffic and climate conditions Today’s flexible pavements are Required to perform better as they are facing increased volume of traffic, increased loads and increased variations in daily or seasonal temperature over what has been Challenged in the past. In addition, the performance of bituminous roads is to be identified that they are poor in high drainage situations. Present scenario on using various additives for better drainage is not satisfying the expected results. However, the additive that is to be used for modification of mix or binder should satisfy both the strength, durability requirements as well as economical aspects. Plastics are using extensively in all over world and developing country like India. As these are non-biodegradable there is a major problem posed to the society with regard to the management of these solid wastes. Even, the reclaimed polyethylene originally made of HDPE has been observed to modify bitumen. In the present study, an attempt has been made to use HDPE and CRUMB RUBBER as admixtures in nominal bitumen mix to overcome the problem of resistance to weathering actions and repetitive wheel load
{"title":"Analysis of Bituminous Concrete Mixes Using H.D.P.E & Crumb Rubber as Admixtures","authors":"Dr. A. Ranganathan, M. Kranthikumar, D. Sudheerbabu, N. Badulla, T. Tejaswini, D. Nagarjuna, Y. Durgaprasad","doi":"10.55524/ijircst.2023.11.3.14","DOIUrl":"https://doi.org/10.55524/ijircst.2023.11.3.14","url":null,"abstract":"Flexible pavements need more attention in selection of Resources and preparation of mixes now a day’s temperature is the main criteria which affect the mix quality, strength and durability. Rapid changes in temperature now a day’s big problem to face worst situations, Durability point is a big factor which affects the life period of the pavement surface and its components, Nominal mixes which consists of inert material doesn’t gives better Durability in severe traffic and climate conditions Today’s flexible pavements are Required to perform better as they are facing increased volume of traffic, increased loads and increased variations in daily or seasonal temperature over what has been Challenged in the past. In addition, the performance of bituminous roads is to be identified that they are poor in high drainage situations. Present scenario on using various additives for better drainage is not satisfying the expected results. However, the additive that is to be used for modification of mix or binder should satisfy both the strength, durability requirements as well as economical aspects. Plastics are using extensively in all over world and developing country like India. As these are non-biodegradable there is a major problem posed to the society with regard to the management of these solid wastes. Even, the reclaimed polyethylene originally made of HDPE has been observed to modify bitumen. In the present study, an attempt has been made to use HDPE and CRUMB RUBBER as admixtures in nominal bitumen mix to overcome the problem of resistance to weathering actions and repetitive wheel load","PeriodicalId":218345,"journal":{"name":"International Journal of Innovative Research in Computer Science and Technology","volume":"73 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116311982","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-05-26DOI: 10.55524/ijircst.2023.11.3.11
M. R. Kumar, M. Rao, G. Jyothi, SK. Durshid, D. Kumar, B. Mahesh, G. Rajesh
A 8x8 multiple input multiple output antenna is developed for the applications of MM wave. this proposed model has 8 ports on the single structure of antenna system. The proposed design gives a triple bands k-band (14.6 at-22dB), ku-band (19.6 at -28dB) and ka-band (27.6 at -27dB) which this ka-band wii be act as mm wave band for the applications of MM wave. The proposed antenna having the dimensions of 64mm x 32mm x 1.6 mm having thickness 1.6mm and the Fr4 substrate has been used for designing. The proposed antenna is designed, measured and tested
针对毫米波应用，研制了一种8x8多输入多输出天线。该模型在天线系统的单一结构上有8个端口。该设计给出了3个频段:k波段(22db时为14.6)、ku波段(-28dB时为19.6)和ka波段(-27dB时为27.6)，其中ka波段可作为毫米波应用的毫米波波段。天线尺寸为64mm x 32mm x 1.6mm，厚度为1.6mm，采用Fr4衬底进行设计。对所提出的天线进行了设计、测量和测试
{"title":"Mutual Coupling Reduction Using 8x8 MIMO Antenna for MM Wave Applications","authors":"M. R. Kumar, M. Rao, G. Jyothi, SK. Durshid, D. Kumar, B. Mahesh, G. Rajesh","doi":"10.55524/ijircst.2023.11.3.11","DOIUrl":"https://doi.org/10.55524/ijircst.2023.11.3.11","url":null,"abstract":"A 8x8 multiple input multiple output antenna is developed for the applications of MM wave. this proposed model has 8 ports on the single structure of antenna system. The proposed design gives a triple bands k-band (14.6 at-22dB), ku-band (19.6 at -28dB) and ka-band (27.6 at -27dB) which this ka-band wii be act as mm wave band for the applications of MM wave. The proposed antenna having the dimensions of 64mm x 32mm x 1.6 mm having thickness 1.6mm and the Fr4 substrate has been used for designing. The proposed antenna is designed, measured and tested","PeriodicalId":218345,"journal":{"name":"International Journal of Innovative Research in Computer Science and Technology","volume":"8 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126768513","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-05-22DOI: 10.55524/ijircst.2023.11.3.3
Anu Sharma, Vivek Kumar
Social network has increased surprising consideration in the most recent decade. Social network deals with enormous volume of composite as well as unstructured data and they are very hard to handle. Due to expanding dimensions and demand, one of the encouraging and interesting research field becomes social network. Data Mining affirms to get knowledge by discovery patterns among data. We have discussed social media mining and Social Media analytics. We have insights on the social media effect of our lives, some facts and reports from various sources. We have Integrated this growing research field of social networks with Machine Learning with one simple example of sentiment analysis of Twitter data using Machine Learning. We have also proposed the algorithms to improve the social media analytics results using Machine Learning. In this paper, we will exhibit how machine learning will utilizing for social networking systems like Twitter. In this procedure, a framework is proposed that will collect the tweets messages from the and we will inspect the item’s input to show the positive, negative, or nonpartisan tweets, for this this purpose we have proposed new machine learning algorithms Naive Bayes, maximum entropy to find these outputs. Our proposed Model will help new researchers, companies, Industries, business community, practitioners, new integrated application designers, and the global community to solve the new research problem and may reducing design failure rate of 80% by large through social media mining and networks.
{"title":"Machine Learning Prospects: Insights for Social Media Data Mining and Analytics","authors":"Anu Sharma, Vivek Kumar","doi":"10.55524/ijircst.2023.11.3.3","DOIUrl":"https://doi.org/10.55524/ijircst.2023.11.3.3","url":null,"abstract":"Social network has increased surprising consideration in the most recent decade. Social network deals with enormous volume of composite as well as unstructured data and they are very hard to handle. Due to expanding dimensions and demand, one of the encouraging and interesting research field becomes social network. Data Mining affirms to get knowledge by discovery patterns among data. We have discussed social media mining and Social Media analytics. We have insights on the social media effect of our lives, some facts and reports from various sources. We have Integrated this growing research field of social networks with Machine Learning with one simple example of sentiment analysis of Twitter data using Machine Learning. We have also proposed the algorithms to improve the social media analytics results using Machine Learning. In this paper, we will exhibit how machine learning will utilizing for social networking systems like Twitter. In this procedure, a framework is proposed that will collect the tweets messages from the and we will inspect the item’s input to show the positive, negative, or nonpartisan tweets, for this this purpose we have proposed new machine learning algorithms Naive Bayes, maximum entropy to find these outputs. Our proposed Model will help new researchers, companies, Industries, business community, practitioners, new integrated application designers, and the global community to solve the new research problem and may reducing design failure rate of 80% by large through social media mining and networks.","PeriodicalId":218345,"journal":{"name":"International Journal of Innovative Research in Computer Science and Technology","volume":"9 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132702463","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-05-20DOI: 10.55524/ijircst.2023.11.3.12
S. Sinha, Treya Sharma
With the exponential growth of digital media platforms and the vast amount of available movie content, users are often overwhelmed when selecting movies that match their preferences. Recommender systems have emerged as an effective solution to assist users in discovering relevant and enjoyable movies. Among these systems, content-based recommendation approaches have gained popularity due to their ability to recommend items based on the content characteristics of movies, such as genres, actors, directors, and plot summaries. The first stage of our system involves the collection and preprocessing of movie metadata from various sources, including genres, actors, directors, and plot summaries. Feature extraction techniques are applied to transform the textual information into meaningful representations that capture the essential characteristics of each movie. Next, a content-based filtering algorithm is employed to compute similarity scores between the user's movie preferences and the extracted features of the available movies. The proposed approach contributes to the advancement of movie recommendation systems and has the potential to enhance user engagement and satisfaction in movie selection.
{"title":"Content-Based Movie Recommendation System: An Enhanced Approach to Personalized Movie Recommendations","authors":"S. Sinha, Treya Sharma","doi":"10.55524/ijircst.2023.11.3.12","DOIUrl":"https://doi.org/10.55524/ijircst.2023.11.3.12","url":null,"abstract":"With the exponential growth of digital media platforms and the vast amount of available movie content, users are often overwhelmed when selecting movies that match their preferences. Recommender systems have emerged as an effective solution to assist users in discovering relevant and enjoyable movies. Among these systems, content-based recommendation approaches have gained popularity due to their ability to recommend items based on the content characteristics of movies, such as genres, actors, directors, and plot summaries. The first stage of our system involves the collection and preprocessing of movie metadata from various sources, including genres, actors, directors, and plot summaries. Feature extraction techniques are applied to transform the textual information into meaningful representations that capture the essential characteristics of each movie. Next, a content-based filtering algorithm is employed to compute similarity scores between the user's movie preferences and the extracted features of the available movies. The proposed approach contributes to the advancement of movie recommendation systems and has the potential to enhance user engagement and satisfaction in movie selection.","PeriodicalId":218345,"journal":{"name":"International Journal of Innovative Research in Computer Science and Technology","volume":"308 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133662969","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
This paper describes the development of a system for detecting driver drowsiness whose goal is to alert drivers of their sleepy state to prevent traffic accidents. It is essential that drowsiness detection in a driving environment be conducted in a non-intrusive manner and that the driver not be troubled by alerts when they are not sleepy. We make use of the MediaPipe Facemesh framework to extract facial features and the Binary Classification Neural Network to precisely detect drowsy states in our solution to this open problem. The solution that minimize false positives is created to determine whether or not the driver exhibits sleepiness symptoms. The approach extracts numerical features from images using deep learning techniques, which are then added to a fuzzy logic-based system. This system typically achieve 91% accuracy on training data and 92% accuracy on test data. The fuzzy logic-based approach, however, stands out because it doesn't raise erroneous alerts (percentage of correctly identified footage where the driver is not tired). Although the findings are not particularly satisfying, the recommendations offered in this study are promising and may be used as a strong platform for future work.
{"title":"Real Time Prevention of Driver Fatigue Using Deep Learning and MediaPipe","authors":"Swapnil Dalve, Ishwar Ramdasi, Ganesh Kothawade, Yash Khadke, Manasi Wete","doi":"10.55524/ijircst.2023.11.3.2","DOIUrl":"https://doi.org/10.55524/ijircst.2023.11.3.2","url":null,"abstract":"This paper describes the development of a system for detecting driver drowsiness whose goal is to alert drivers of their sleepy state to prevent traffic accidents. It is essential that drowsiness detection in a driving environment be conducted in a non-intrusive manner and that the driver not be troubled by alerts when they are not sleepy. We make use of the MediaPipe Facemesh framework to extract facial features and the Binary Classification Neural Network to precisely detect drowsy states in our solution to this open problem. The solution that minimize false positives is created to determine whether or not the driver exhibits sleepiness symptoms. The approach extracts numerical features from images using deep learning techniques, which are then added to a fuzzy logic-based system. This system typically achieve 91% accuracy on training data and 92% accuracy on test data. The fuzzy logic-based approach, however, stands out because it doesn't raise erroneous alerts (percentage of correctly identified footage where the driver is not tired). Although the findings are not particularly satisfying, the recommendations offered in this study are promising and may be used as a strong platform for future work.","PeriodicalId":218345,"journal":{"name":"International Journal of Innovative Research in Computer Science and Technology","volume":"209 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128164475","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-05-05DOI: 10.55524/ijircst.2023.11.3.10
R. S, Guganesh R, Harinee V, S. S
A microstrip patch antenna is a popular choice of antenna for use in the L band frequency range. They also have good radiation characteristics, making them ideal for receiving and transmitting low-power radio frequency signals. The proposed compact antenna is meant for GPS applications covering the L1 band. The antenna is designed involving the Rogers’s substrate having a dielectric constant of 2.2. The design is based on circular microstrip patch structure with inset feed technique. The selection of the dielectric material and its thickness is very crucial in designing microstrip patch antenna. This paper also explains how antenna performance is improved by varying the thickness of the substrate. The radiation pattern, return loss, gain, directivity, VSWR and efficiency are obtained using EM Simulation and the results are compared for various designs structures. It is inferred that as the thickness of the substrate increases, the performance of the antenna also gets better.
{"title":"Analysing the Impact of Substrate Thickness on Antenna Performance for GPS Application","authors":"R. S, Guganesh R, Harinee V, S. S","doi":"10.55524/ijircst.2023.11.3.10","DOIUrl":"https://doi.org/10.55524/ijircst.2023.11.3.10","url":null,"abstract":"A microstrip patch antenna is a popular choice of antenna for use in the L band frequency range. They also have good radiation characteristics, making them ideal for receiving and transmitting low-power radio frequency signals. The proposed compact antenna is meant for GPS applications covering the L1 band. The antenna is designed involving the Rogers’s substrate having a dielectric constant of 2.2. The design is based on circular microstrip patch structure with inset feed technique. The selection of the dielectric material and its thickness is very crucial in designing microstrip patch antenna. This paper also explains how antenna performance is improved by varying the thickness of the substrate. The radiation pattern, return loss, gain, directivity, VSWR and efficiency are obtained using EM Simulation and the results are compared for various designs structures. It is inferred that as the thickness of the substrate increases, the performance of the antenna also gets better.","PeriodicalId":218345,"journal":{"name":"International Journal of Innovative Research in Computer Science and Technology","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129310526","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-05-04DOI: 10.55524/ijircst.2023.11.3.7
Venkateswaran Radhakrishnan
The healthcare industry provides medical devices such as pharmaceuticals. The third-party vendor can also pose a risk to the organization. Cyber security if they are not properly vetted and do not have adequate security measures in place. These will help to mitigate and other cyber security risks, healthcare organizations should implement a range of security measures. Regular security assessments in healthcare organizations should conduct a regular security assessment to identify vulnerable in their systems and network. Distributed dental or services attacks where criminals overload the healthcare system. Health care system servers with traffic, causing them to crash and preventive. Lag mate users from accessing the system's network, stealing data, and causing damage to the system. Ensure that these tools are updated regularly to protect against the latest threats. Regularly check the backup data and critical data and store them in a secure location. Monitoring network activity to detect and respond to any potential security incidents conduct regularly.
{"title":"Review Analysis of Cyber Security in Healthcare System: A Systematic Approach of Modern Development","authors":"Venkateswaran Radhakrishnan","doi":"10.55524/ijircst.2023.11.3.7","DOIUrl":"https://doi.org/10.55524/ijircst.2023.11.3.7","url":null,"abstract":"The healthcare industry provides medical devices such as pharmaceuticals. The third-party vendor can also pose a risk to the organization. Cyber security if they are not properly vetted and do not have adequate security measures in place. These will help to mitigate and other cyber security risks, healthcare organizations should implement a range of security measures. Regular security assessments in healthcare organizations should conduct a regular security assessment to identify vulnerable in their systems and network. Distributed dental or services attacks where criminals overload the healthcare system. Health care system servers with traffic, causing them to crash and preventive. Lag mate users from accessing the system's network, stealing data, and causing damage to the system. Ensure that these tools are updated regularly to protect against the latest threats. Regularly check the backup data and critical data and store them in a secure location. Monitoring network activity to detect and respond to any potential security incidents conduct regularly.","PeriodicalId":218345,"journal":{"name":"International Journal of Innovative Research in Computer Science and Technology","volume":"7 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121924785","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-05-04DOI: 10.55524/ijircst.2023.11.3.9
S. Sinha, Aakarsh Sharma
This paper presents the development of a stress detector using facial expression analysis in Python, utilizing the Deep Face library. Also, after detecting whether the person is in stress or not, it allows the user to inform about his stress to the preferred his/her family member by sending an automated WhatsApp message and showing some remedies to reduce stress.
{"title":"Stress Alarm Raiser Based on Facial Expressions","authors":"S. Sinha, Aakarsh Sharma","doi":"10.55524/ijircst.2023.11.3.9","DOIUrl":"https://doi.org/10.55524/ijircst.2023.11.3.9","url":null,"abstract":"This paper presents the development of a stress detector using facial expression analysis in Python, utilizing the Deep Face library. Also, after detecting whether the person is in stress or not, it allows the user to inform about his stress to the preferred his/her family member by sending an automated WhatsApp message and showing some remedies to reduce stress.","PeriodicalId":218345,"journal":{"name":"International Journal of Innovative Research in Computer Science and Technology","volume":"66 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128092851","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-05-03DOI: 10.55524/ijircst.2023.11.3.6
Aditya Tripathi, Amit Kumar Sharma
Data mining is another term for knowledge discovery in databases (KDD). It's an interdisciplinary field that focuses on rooting meaningful knowledge from data in all sectors similar as health, education, and business. Currently, with the covid epidemic affecting everyone and rising coronavirus cases causing nursing home beds, oxygen, vaccines and individuals to be denied by hospitals, the health structure of the elderly is in the spotlight. There's a wealth of information accessible in the medical world about these diseases. Data booby-trapping concepts may be used to prize meaningful styles from this type of material in order to prognosticate unborn followings. This study emphasizes on several mining approaches that will be applied in the therapy assiduity to achieve the stylish results.
{"title":"Techniques for Data Mining Prediction in the Health Care Sector","authors":"Aditya Tripathi, Amit Kumar Sharma","doi":"10.55524/ijircst.2023.11.3.6","DOIUrl":"https://doi.org/10.55524/ijircst.2023.11.3.6","url":null,"abstract":"Data mining is another term for knowledge discovery in databases (KDD). It's an interdisciplinary field that focuses on rooting meaningful knowledge from data in all sectors similar as health, education, and business. Currently, with the covid epidemic affecting everyone and rising coronavirus cases causing nursing home beds, oxygen, vaccines and individuals to be denied by hospitals, the health structure of the elderly is in the spotlight. There's a wealth of information accessible in the medical world about these diseases. Data booby-trapping concepts may be used to prize meaningful styles from this type of material in order to prognosticate unborn followings. This study emphasizes on several mining approaches that will be applied in the therapy assiduity to achieve the stylish results.","PeriodicalId":218345,"journal":{"name":"International Journal of Innovative Research in Computer Science and Technology","volume":"67 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122586545","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-05-01DOI: 10.55524/ijircst.2023.11.3.1
Md Momenul Haque, S. Paul, Rakhi Rani Paul, Mursheda Nusrat Della, Md. Kamrul Islam, Sultan Fahim
Emotion recognition is a crucial task in human-computer interaction, psychology, and neuroscience. Electroencephalogram (EEG)-based multi-class emotion recognition is a novel approach that aims to identify and classify human emotions by analysing EEG signals. Traditional methods of emotion recognition often face challenges in accurately identifying and classifying human emotions due to their complexity and subjectivity. EEG-based emotion recognition provides a direct and objective measure of three emotional states (positive, neutral, and negative), making it a promising tool for emotion recognition. The proposed hybrid LSTM approach combines the strengths of different traditional machine learning algorithms: Gaussian Naive Bayes (GNB), Support Vector Machine (SVM), Logistic Regression (LR), and Decision Tree (DT). The approach was tested on the EEG brainwave dataset, and LSTM achieved an accuracy of 95%, while the proposed hybrid LSTM-GNB, LSTM-SVM, LSTM-LR, and LSTM-DT models achieved 65%, 96%, 97%, and 96% accuracy, respectively. The contribution of this study is the development of a hybrid LSTM approach that combines the strengths of two different algorithms, resulting in higher accuracy for multi-class emotion recognition using EEG signals. The results demonstrate the potential of the hybrid LSTM approach for real-world applications such as emotion-based human-computer interaction and mental health diagnosis.
{"title":"EEG-Based Multi-Class Emotion Recognition using Hybrid LSTM Approach","authors":"Md Momenul Haque, S. Paul, Rakhi Rani Paul, Mursheda Nusrat Della, Md. Kamrul Islam, Sultan Fahim","doi":"10.55524/ijircst.2023.11.3.1","DOIUrl":"https://doi.org/10.55524/ijircst.2023.11.3.1","url":null,"abstract":"Emotion recognition is a crucial task in human-computer interaction, psychology, and neuroscience. Electroencephalogram (EEG)-based multi-class emotion recognition is a novel approach that aims to identify and classify human emotions by analysing EEG signals. Traditional methods of emotion recognition often face challenges in accurately identifying and classifying human emotions due to their complexity and subjectivity. EEG-based emotion recognition provides a direct and objective measure of three emotional states (positive, neutral, and negative), making it a promising tool for emotion recognition. The proposed hybrid LSTM approach combines the strengths of different traditional machine learning algorithms: Gaussian Naive Bayes (GNB), Support Vector Machine (SVM), Logistic Regression (LR), and Decision Tree (DT). The approach was tested on the EEG brainwave dataset, and LSTM achieved an accuracy of 95%, while the proposed hybrid LSTM-GNB, LSTM-SVM, LSTM-LR, and LSTM-DT models achieved 65%, 96%, 97%, and 96% accuracy, respectively. The contribution of this study is the development of a hybrid LSTM approach that combines the strengths of two different algorithms, resulting in higher accuracy for multi-class emotion recognition using EEG signals. The results demonstrate the potential of the hybrid LSTM approach for real-world applications such as emotion-based human-computer interaction and mental health diagnosis.","PeriodicalId":218345,"journal":{"name":"International Journal of Innovative Research in Computer Science and Technology","volume":"9 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126429210","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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