The capacity to talk smoothly is typically affected by stuttering, a neuro-developmental speech disorder where the flow of speech is disrupted by involuntary pauses and repetition of sounds. Stuttering can be cured by identifying the type of stutter and providing proper speech guidance. Many approaches have been taken to classify stuttered speech via a computer aided process including Deep Learning models. But most of the works rely heavily on a large number of audio features to be extracted manually. Also, many past works use the UCLASS dataset that is much older and lacks in quality. This paper proposes a Deep Learning model using Bidirectional LSTM and Attention to classify five types of stuttering events – Block, Prolongation, Word Repetition, Sound Repetition and Interjection, by utilizing only Mel-spectrogram audio feature. The model is trained and tested on the SEP-28k and latest annotations of the FluencyBank dataset to evaluate the performance and achieves an overall 75% accuracy.
{"title":"An Integrated Usage of Bidirectional LSTM and Computer-based Cognitive Attention to Categorize Speech Stutters","authors":"Krishna Basak, Vineet Sharma, Sarangh Ramesh Kv, Nilamadhab Mishra","doi":"10.1109/ICAIS56108.2023.10073818","DOIUrl":"https://doi.org/10.1109/ICAIS56108.2023.10073818","url":null,"abstract":"The capacity to talk smoothly is typically affected by stuttering, a neuro-developmental speech disorder where the flow of speech is disrupted by involuntary pauses and repetition of sounds. Stuttering can be cured by identifying the type of stutter and providing proper speech guidance. Many approaches have been taken to classify stuttered speech via a computer aided process including Deep Learning models. But most of the works rely heavily on a large number of audio features to be extracted manually. Also, many past works use the UCLASS dataset that is much older and lacks in quality. This paper proposes a Deep Learning model using Bidirectional LSTM and Attention to classify five types of stuttering events – Block, Prolongation, Word Repetition, Sound Repetition and Interjection, by utilizing only Mel-spectrogram audio feature. The model is trained and tested on the SEP-28k and latest annotations of the FluencyBank dataset to evaluate the performance and achieves an overall 75% accuracy.","PeriodicalId":164345,"journal":{"name":"2023 Third International Conference on Artificial Intelligence and Smart Energy (ICAIS)","volume":"24 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-02-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123485512","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 : 2023-02-02DOI: 10.1109/ICAIS56108.2023.10073773
B. Gopi, J. Premalatha, R. Kalaivani, D. Ravikumar
Landslides are one of the most devastating natural disasters that can strike a region. They are caused by the movement of large amounts of earth, rock, and other material down a slope. Landslides are caused by rain, snow, and other precipitation that causes soil to become saturated and unable to support the loads that are placed on it. Landslides can also be triggered by earthquakes or human activities such as mining, construction, and quarrying. Internally generated Internet of Things network and system acquisition generation Landslides were detected using humidity sensors, accelerometers, and vibration sensors, as well as GPS and a siren to inform people. You may charge a little price for this sensor, and if the fee surpasses the basic cost, you can approximately watch people in preparation of an imminent landslide, and big losses are avoided. The microcontroller collects and updates statistics from websites using the MQTT protocol. These telemetry flights can assist folks become aware of an oncoming crisis and have a better understanding of the situation.
{"title":"Cloud based Landslide Detection and Alerting Nearby People by using IoT Technology","authors":"B. Gopi, J. Premalatha, R. Kalaivani, D. Ravikumar","doi":"10.1109/ICAIS56108.2023.10073773","DOIUrl":"https://doi.org/10.1109/ICAIS56108.2023.10073773","url":null,"abstract":"Landslides are one of the most devastating natural disasters that can strike a region. They are caused by the movement of large amounts of earth, rock, and other material down a slope. Landslides are caused by rain, snow, and other precipitation that causes soil to become saturated and unable to support the loads that are placed on it. Landslides can also be triggered by earthquakes or human activities such as mining, construction, and quarrying. Internally generated Internet of Things network and system acquisition generation Landslides were detected using humidity sensors, accelerometers, and vibration sensors, as well as GPS and a siren to inform people. You may charge a little price for this sensor, and if the fee surpasses the basic cost, you can approximately watch people in preparation of an imminent landslide, and big losses are avoided. The microcontroller collects and updates statistics from websites using the MQTT protocol. These telemetry flights can assist folks become aware of an oncoming crisis and have a better understanding of the situation.","PeriodicalId":164345,"journal":{"name":"2023 Third International Conference on Artificial Intelligence and Smart Energy (ICAIS)","volume":"91 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-02-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124678135","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 : 2023-02-02DOI: 10.1109/ICAIS56108.2023.10073876
Dhanashree A. Kulkarni, Mithra Venkatesan, A. Kulkarni
In modern communication systems there are heterogeneous service request from the applications like mobile devices, virtual reality, automatic driving cars, IoT devices. These devices have different QoS requirements in which network slicing enabler plays a vital role in 5G. Network Slicing unfolds a new paradigm for the providers as well as for the users. In this context the resource management has gained importance in the field of networking. Since a huge data is been generated by these devices, it is very difficult to deliver high performance with resource utilization. In such situation these traditional monitoring techniques will not be able to handle such a huge data. Towards this, the researchers have started applying with Deep learning techniques with the network monitoring system. This paper focuses on the work done towards one of the key components of network analysis (i.e.) traffic prediction. This study has reviewed the articles, which have proposed the deep learning techniques for traffic prediction towards resource management in network slicing.*CRITICAL: Do Not Use Symbols, Special Characters, Footnotes, or Math in Paper Title or Abstract. (Abstract)
{"title":"Traffic Prediction with Network Slicing in 5G: A Survey","authors":"Dhanashree A. Kulkarni, Mithra Venkatesan, A. Kulkarni","doi":"10.1109/ICAIS56108.2023.10073876","DOIUrl":"https://doi.org/10.1109/ICAIS56108.2023.10073876","url":null,"abstract":"In modern communication systems there are heterogeneous service request from the applications like mobile devices, virtual reality, automatic driving cars, IoT devices. These devices have different QoS requirements in which network slicing enabler plays a vital role in 5G. Network Slicing unfolds a new paradigm for the providers as well as for the users. In this context the resource management has gained importance in the field of networking. Since a huge data is been generated by these devices, it is very difficult to deliver high performance with resource utilization. In such situation these traditional monitoring techniques will not be able to handle such a huge data. Towards this, the researchers have started applying with Deep learning techniques with the network monitoring system. This paper focuses on the work done towards one of the key components of network analysis (i.e.) traffic prediction. This study has reviewed the articles, which have proposed the deep learning techniques for traffic prediction towards resource management in network slicing.*CRITICAL: Do Not Use Symbols, Special Characters, Footnotes, or Math in Paper Title or Abstract. (Abstract)","PeriodicalId":164345,"journal":{"name":"2023 Third International Conference on Artificial Intelligence and Smart Energy (ICAIS)","volume":"5 3","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-02-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"120836778","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 : 2023-02-02DOI: 10.1109/ICAIS56108.2023.10073845
C. Kavitha, N. Sridevi, D. Dhivagar
Heart rate (HR) and Heart rate variability (HRV) have received a great deal of attention that promises to change the dimension of awareness of health and fitness while swimming. HRV is very useful to understand physiological and psychological status of an individual. The variation in HR, provides a reliable information about the role of Autonomic Nervous System (ANS). HRV is very convenient to understand the overall physiological status of an individual. Due to individuality of the HRV, regular monitoring HRV is useful to understand training adaptation, load, recovery, overtraining. The study provides a brief concept on HR and HRV in swimming individual. Although RR intervals are highly individual centric but due to same practice pattern or same type of physical activity, the swimmer group has very small quartile range. A significance difference in RR intervals between control group and swimmer group may come from two different effects of the nervous system. Either it indicates a significant increase in parasympathetic tone due to normal training adaptation or a sign of overtraining that has caused increase in parasympathetic tone. High HRV denotes good indication of positive adaptation, good cardiovascular efficiency. Low HRV score indicates deterioration in VO2max.
{"title":"Detection and Security in Falls with IoT Server","authors":"C. Kavitha, N. Sridevi, D. Dhivagar","doi":"10.1109/ICAIS56108.2023.10073845","DOIUrl":"https://doi.org/10.1109/ICAIS56108.2023.10073845","url":null,"abstract":"Heart rate (HR) and Heart rate variability (HRV) have received a great deal of attention that promises to change the dimension of awareness of health and fitness while swimming. HRV is very useful to understand physiological and psychological status of an individual. The variation in HR, provides a reliable information about the role of Autonomic Nervous System (ANS). HRV is very convenient to understand the overall physiological status of an individual. Due to individuality of the HRV, regular monitoring HRV is useful to understand training adaptation, load, recovery, overtraining. The study provides a brief concept on HR and HRV in swimming individual. Although RR intervals are highly individual centric but due to same practice pattern or same type of physical activity, the swimmer group has very small quartile range. A significance difference in RR intervals between control group and swimmer group may come from two different effects of the nervous system. Either it indicates a significant increase in parasympathetic tone due to normal training adaptation or a sign of overtraining that has caused increase in parasympathetic tone. High HRV denotes good indication of positive adaptation, good cardiovascular efficiency. Low HRV score indicates deterioration in VO2max.","PeriodicalId":164345,"journal":{"name":"2023 Third International Conference on Artificial Intelligence and Smart Energy (ICAIS)","volume":"39 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-02-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117231723","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 : 2023-02-02DOI: 10.1109/ICAIS56108.2023.10073682
K. C, Vivek Karthick Perumal, M. Vivek Kumar, J. Muralidharan
A power, delay efficient error acquiescent adder is proposed. In recent VLSI expertise, the manifestation of all categories of faults has developed foreseeable. By embracing an emergent perception in VLSI strategy, fault-tolerant adder (FTA) is suggested. The FTA is talented to comfort the harsh constraint on exactitude, and at the identical period accomplish marvelous enhancements in together the power ingestion and speediness enactment. For any transportable uses anywhere the power ingestion and speed are the utmost significant limit, one must diminish the power feeding and upsurge the speed as ample as probable. In this technique certain amendments are suggested to predictable adders to significantly decrease its power feeding. The amendments to the conservative building comprise the elimination of carry generation from LSB to MSB. With this the adder works at high speed with low power consumption.
{"title":"Design of Power and Delay Efficient Fault Tolerant Adder","authors":"K. C, Vivek Karthick Perumal, M. Vivek Kumar, J. Muralidharan","doi":"10.1109/ICAIS56108.2023.10073682","DOIUrl":"https://doi.org/10.1109/ICAIS56108.2023.10073682","url":null,"abstract":"A power, delay efficient error acquiescent adder is proposed. In recent VLSI expertise, the manifestation of all categories of faults has developed foreseeable. By embracing an emergent perception in VLSI strategy, fault-tolerant adder (FTA) is suggested. The FTA is talented to comfort the harsh constraint on exactitude, and at the identical period accomplish marvelous enhancements in together the power ingestion and speediness enactment. For any transportable uses anywhere the power ingestion and speed are the utmost significant limit, one must diminish the power feeding and upsurge the speed as ample as probable. In this technique certain amendments are suggested to predictable adders to significantly decrease its power feeding. The amendments to the conservative building comprise the elimination of carry generation from LSB to MSB. With this the adder works at high speed with low power consumption.","PeriodicalId":164345,"journal":{"name":"2023 Third International Conference on Artificial Intelligence and Smart Energy (ICAIS)","volume":"4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-02-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127904105","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 : 2023-02-02DOI: 10.1109/ICAIS56108.2023.10073702
Ramanamma P, M. Jayanthi, Anuj M A, A. Dharmik Sai Reddy, Devu Maheswar Reddy, G. Pavan Kalyan Reddy
The interest for reasonable Energy age and utilization is expanding step by step as the human populace is relying more upon electronic gadgets for their everyday life. Hence, the need of a full-evidence and monetarily practical power age and circulation framework requests a specific attention. This task proposes usage of human loco motion energy, which albeit extractible goes principally to squander. This demo offers a model that utilizes human strolling, hopping and running as a wellspring of energy and stores it for fundamental use. Such a model is able in a demography that of a nation like India which has such a colossal walker populace. This framework represents a technique for collecting this human headway energy with the utilization of piezoelectric sensor and exhibits a request with the put away energy i.e., to charge a cell phone safely utilizing RFID. The ground response force (GRF) applied from the foot, when switched over completely to voltage by piezoelectric sensors is sufficiently able to control up a gadget. Advanced effort prompts aperiodic voltage develop which with legitimate hardware can be utilized to charge a capacity battery. The power delivered by this method can likewise be used in fundamental application, for example, road lighting, notice sheets, rec centres and different areas of public space. It likewise advances efficient power energy and climate cordial methodology towards energy age. In this undertaking we will give the essential idea and configuration restraints of this model and a fundamental execution of the equal.
{"title":"Footsteps Based Sustainable Energy Generation and Consumption System","authors":"Ramanamma P, M. Jayanthi, Anuj M A, A. Dharmik Sai Reddy, Devu Maheswar Reddy, G. Pavan Kalyan Reddy","doi":"10.1109/ICAIS56108.2023.10073702","DOIUrl":"https://doi.org/10.1109/ICAIS56108.2023.10073702","url":null,"abstract":"The interest for reasonable Energy age and utilization is expanding step by step as the human populace is relying more upon electronic gadgets for their everyday life. Hence, the need of a full-evidence and monetarily practical power age and circulation framework requests a specific attention. This task proposes usage of human loco motion energy, which albeit extractible goes principally to squander. This demo offers a model that utilizes human strolling, hopping and running as a wellspring of energy and stores it for fundamental use. Such a model is able in a demography that of a nation like India which has such a colossal walker populace. This framework represents a technique for collecting this human headway energy with the utilization of piezoelectric sensor and exhibits a request with the put away energy i.e., to charge a cell phone safely utilizing RFID. The ground response force (GRF) applied from the foot, when switched over completely to voltage by piezoelectric sensors is sufficiently able to control up a gadget. Advanced effort prompts aperiodic voltage develop which with legitimate hardware can be utilized to charge a capacity battery. The power delivered by this method can likewise be used in fundamental application, for example, road lighting, notice sheets, rec centres and different areas of public space. It likewise advances efficient power energy and climate cordial methodology towards energy age. In this undertaking we will give the essential idea and configuration restraints of this model and a fundamental execution of the equal.","PeriodicalId":164345,"journal":{"name":"2023 Third International Conference on Artificial Intelligence and Smart Energy (ICAIS)","volume":"4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-02-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128470456","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 : 2023-02-02DOI: 10.1109/ICAIS56108.2023.10073900
Sagar Ramesh Pujar, Raghavendra Vijay Patil, Vivek Sharma S, Srikanth M S
The provision of a highly secure service is by far the most important responsibility of any cloud computing network. Users are able to entrust cloud data centers with their most sensitive data and computing operations since this phase in the cloud computing process is built on trust between users and cloud services providers. However, with the proliferation of collaborative cloud computing comes a significant obstacle in the form of the question of how to provide instant responses to a large number of client enquiries. In order to provide highly dependable services in a timely manner, tens of millions of customers' expectations must be met, and the underlying service platform must be able to efficiently and swiftly fulfil tens of thousands of service requirements automatically. The basic need for setting up a reliable and interactive cloud infrastructure is to use trust systems that are not only lightweight and speedy but also high-speed and low-cost. This paper proposes a novel and concurrent computing architecture for confidence that is centered on large data processing, and it is intended for usage in a world that relies on secure cloud infrastructure. Second, it is suggested that a distributed and scalable perceptive infrastructure for the operation of large virtual machines be built using remote monitoring agents. This infrastructure would be built using remote monitoring agents. After that, a technique for the calculation of confidence that is adaptable, lightweight, and parallel is provided for big, controlled data sets. According to what is currently known, this article is the first one to employ a disruptive and parallel computing method together with a significantly accelerated rate of confidence measurement. This enables the confidence calculation framework to be suitable for application in a large-scale cloud setting. The intended system's efficiency and effectiveness were evaluated based on the outcomes of the success review and experimental research.
{"title":"Large Data Processing for Cloud Service Collaborative Authenticity Computing Model","authors":"Sagar Ramesh Pujar, Raghavendra Vijay Patil, Vivek Sharma S, Srikanth M S","doi":"10.1109/ICAIS56108.2023.10073900","DOIUrl":"https://doi.org/10.1109/ICAIS56108.2023.10073900","url":null,"abstract":"The provision of a highly secure service is by far the most important responsibility of any cloud computing network. Users are able to entrust cloud data centers with their most sensitive data and computing operations since this phase in the cloud computing process is built on trust between users and cloud services providers. However, with the proliferation of collaborative cloud computing comes a significant obstacle in the form of the question of how to provide instant responses to a large number of client enquiries. In order to provide highly dependable services in a timely manner, tens of millions of customers' expectations must be met, and the underlying service platform must be able to efficiently and swiftly fulfil tens of thousands of service requirements automatically. The basic need for setting up a reliable and interactive cloud infrastructure is to use trust systems that are not only lightweight and speedy but also high-speed and low-cost. This paper proposes a novel and concurrent computing architecture for confidence that is centered on large data processing, and it is intended for usage in a world that relies on secure cloud infrastructure. Second, it is suggested that a distributed and scalable perceptive infrastructure for the operation of large virtual machines be built using remote monitoring agents. This infrastructure would be built using remote monitoring agents. After that, a technique for the calculation of confidence that is adaptable, lightweight, and parallel is provided for big, controlled data sets. According to what is currently known, this article is the first one to employ a disruptive and parallel computing method together with a significantly accelerated rate of confidence measurement. This enables the confidence calculation framework to be suitable for application in a large-scale cloud setting. The intended system's efficiency and effectiveness were evaluated based on the outcomes of the success review and experimental research.","PeriodicalId":164345,"journal":{"name":"2023 Third International Conference on Artificial Intelligence and Smart Energy (ICAIS)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-02-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130000021","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 : 2023-02-02DOI: 10.1109/ICAIS56108.2023.10073675
R. Balamurugan, A. A. Kumar, A. Kalaimaran, V. Sathish
The most plentiful form of renewable energy is solar energy. Windstorms and constant soling significantly impair effectiveness. Consequently, it is crucial to clean the panel on a regular basis and properly. The majority of the components require hand cleaning. This kind of cleaning is inconsistent and might harm the workers' health. Solar panel cleaning systems that are permanently installed and fully automated with or without water can address this issue. It contains a brush to remove the dust and water/ chemical solution in addition to have gentle cleaning on the solar panels. In solar power plants, business buildings, and homes, the proposed technique may be installed directly onto the panels. This technique allows a multiple row cleaning. By eliminating any type of dust, this approach aims to boost the efficiency of solar panels. The proposed work comprises a cloud server powered by the internet of things (IoT) to enable online status tracking from anywhere in the world.
{"title":"Integrated IoT System for Automatic Dust Cleaning of Solar Panels","authors":"R. Balamurugan, A. A. Kumar, A. Kalaimaran, V. Sathish","doi":"10.1109/ICAIS56108.2023.10073675","DOIUrl":"https://doi.org/10.1109/ICAIS56108.2023.10073675","url":null,"abstract":"The most plentiful form of renewable energy is solar energy. Windstorms and constant soling significantly impair effectiveness. Consequently, it is crucial to clean the panel on a regular basis and properly. The majority of the components require hand cleaning. This kind of cleaning is inconsistent and might harm the workers' health. Solar panel cleaning systems that are permanently installed and fully automated with or without water can address this issue. It contains a brush to remove the dust and water/ chemical solution in addition to have gentle cleaning on the solar panels. In solar power plants, business buildings, and homes, the proposed technique may be installed directly onto the panels. This technique allows a multiple row cleaning. By eliminating any type of dust, this approach aims to boost the efficiency of solar panels. The proposed work comprises a cloud server powered by the internet of things (IoT) to enable online status tracking from anywhere in the world.","PeriodicalId":164345,"journal":{"name":"2023 Third International Conference on Artificial Intelligence and Smart Energy (ICAIS)","volume":"21 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-02-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131083253","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 : 2023-02-02DOI: 10.1109/ICAIS56108.2023.10073677
Priyanka Gourabathuni, Ramya Sree Pothineni, K. Yelavarti
Emotion classification remains a challenging problem in affective computing. One of the most crucial areas of study in the field of brain wave research is the classification of emotions. Classifying the types of emotions accurately is one of the major issues with the analysis of brainwave emotion. EEG signals used for real-time emotion identification are crucial for affective computing and human-computer interaction. These signals can be produced by the user while engaging in a variety of cognitive, affective, and physical tasks, representing the functionality of the brain. The resulting emotional state produced gives valuable insights on the attitudes and actions of participants in specific situations. The main objective of this research work is to classify the emotions using EEG signals. The process is divided into two steps. The first step is feature extraction and the next step is classification. The feature extraction is performed by using DWT and the selection is done by using L1 norm. The algorithms used to perform signal classification are LSTM, GRU and DNN.
{"title":"Classification of Emotions using EEG Signals","authors":"Priyanka Gourabathuni, Ramya Sree Pothineni, K. Yelavarti","doi":"10.1109/ICAIS56108.2023.10073677","DOIUrl":"https://doi.org/10.1109/ICAIS56108.2023.10073677","url":null,"abstract":"Emotion classification remains a challenging problem in affective computing. One of the most crucial areas of study in the field of brain wave research is the classification of emotions. Classifying the types of emotions accurately is one of the major issues with the analysis of brainwave emotion. EEG signals used for real-time emotion identification are crucial for affective computing and human-computer interaction. These signals can be produced by the user while engaging in a variety of cognitive, affective, and physical tasks, representing the functionality of the brain. The resulting emotional state produced gives valuable insights on the attitudes and actions of participants in specific situations. The main objective of this research work is to classify the emotions using EEG signals. The process is divided into two steps. The first step is feature extraction and the next step is classification. The feature extraction is performed by using DWT and the selection is done by using L1 norm. The algorithms used to perform signal classification are LSTM, GRU and DNN.","PeriodicalId":164345,"journal":{"name":"2023 Third International Conference on Artificial Intelligence and Smart Energy (ICAIS)","volume":"25 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-02-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130868389","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 : 2023-02-02DOI: 10.1109/ICAIS56108.2023.10073796
H. V, L. J., S. A, N. Divya Bharathi, Shrikant Upadhyay, Venkatesh R
In healthcare WSN applications, data loss due to congestion may trigger a "death alert" for a crucial patient. Because of this, a system must be designed to either prevent or reduce congestion. This study presents an energy-efficient and reliable multi-path data transmission protocol for healthcare Wireless Sensor Networks (WSN). Spare data and sensitive data packets are sent through a route with little transmission interference when the system is jammed. The recommended technique assesses the danger of congestion at intermediate nodes and adjusts their transmission rate to prevent congestion. Each node's buffer is partitioned to make data transport fair and efficient. The protocol's high reliability is maintained through hop-by-hop loss recovery and acknowledgement. Simulations are used to test the recommended method's functionality. In terms of energy economy, reliability, and end-to-end delivery ratio, it exceeds existing healthcare congestion management algorithms. This study evaluates and compares the routing techniques. They present a concept for developing an energy-efficient routing protocol. This approach designs quick, compact, more energy-efficient routes than existing ones. NS2 is used to run and test the proposed system. The proposed method beats the current protocol in terms of average delay, energy savings, and packet delivery ratio.
{"title":"Energy Efficient Data Management in Health Care","authors":"H. V, L. J., S. A, N. Divya Bharathi, Shrikant Upadhyay, Venkatesh R","doi":"10.1109/ICAIS56108.2023.10073796","DOIUrl":"https://doi.org/10.1109/ICAIS56108.2023.10073796","url":null,"abstract":"In healthcare WSN applications, data loss due to congestion may trigger a \"death alert\" for a crucial patient. Because of this, a system must be designed to either prevent or reduce congestion. This study presents an energy-efficient and reliable multi-path data transmission protocol for healthcare Wireless Sensor Networks (WSN). Spare data and sensitive data packets are sent through a route with little transmission interference when the system is jammed. The recommended technique assesses the danger of congestion at intermediate nodes and adjusts their transmission rate to prevent congestion. Each node's buffer is partitioned to make data transport fair and efficient. The protocol's high reliability is maintained through hop-by-hop loss recovery and acknowledgement. Simulations are used to test the recommended method's functionality. In terms of energy economy, reliability, and end-to-end delivery ratio, it exceeds existing healthcare congestion management algorithms. This study evaluates and compares the routing techniques. They present a concept for developing an energy-efficient routing protocol. This approach designs quick, compact, more energy-efficient routes than existing ones. NS2 is used to run and test the proposed system. The proposed method beats the current protocol in terms of average delay, energy savings, and packet delivery ratio.","PeriodicalId":164345,"journal":{"name":"2023 Third International Conference on Artificial Intelligence and Smart Energy (ICAIS)","volume":"35 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-02-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128834803","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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