Pub Date : 2023-02-02DOI: 10.1109/ICAIS56108.2023.10073869
Y. Vijaya Lakshmi, K. Naveena, M. Ramya, N. Pravallika, T. Sindhu, V. Namitha
Medical photographs are considered sensitive and crucial data in medical informatics systems. To communicate medical photos via an insecure network, a trustworthy encryption solution must be created. Encryption is the most effective way for ensuring image secrecy since it eliminates the risk of data loss. The two types of encryptions are symmetric encryption, which uses a single key for both encryption and decryption and gives just confidentiality, making it less safe, and asymmetric encryption, which uses two keys for encryption and decryption and provides confidentiality, non-repudiation, and authentication, making it more secure. It is now simpler than ever for complete strangers to access the most sensitive information recorded on your computer due to hacking activities and privacy intrusions. As a result, asymmetric encryption outperforms symmetric encryption. Despite the fact that there are several security alternatives accessible, such as free anti-malware software for home users and cloud anti-virus for organizations, these attempts to secure data Some large corporations and government agencies already use encryption software to secure data, but it is also available and becoming more readily available to the general public. The RSA algorithm is an asymmetric encryption technique that safeguards the picture before transferring it. It is one of the most extensively used encryption tools.
{"title":"Medical Image Encryption using Enhanced Rivest Shamir Adleman Algorithm","authors":"Y. Vijaya Lakshmi, K. Naveena, M. Ramya, N. Pravallika, T. Sindhu, V. Namitha","doi":"10.1109/ICAIS56108.2023.10073869","DOIUrl":"https://doi.org/10.1109/ICAIS56108.2023.10073869","url":null,"abstract":"Medical photographs are considered sensitive and crucial data in medical informatics systems. To communicate medical photos via an insecure network, a trustworthy encryption solution must be created. Encryption is the most effective way for ensuring image secrecy since it eliminates the risk of data loss. The two types of encryptions are symmetric encryption, which uses a single key for both encryption and decryption and gives just confidentiality, making it less safe, and asymmetric encryption, which uses two keys for encryption and decryption and provides confidentiality, non-repudiation, and authentication, making it more secure. It is now simpler than ever for complete strangers to access the most sensitive information recorded on your computer due to hacking activities and privacy intrusions. As a result, asymmetric encryption outperforms symmetric encryption. Despite the fact that there are several security alternatives accessible, such as free anti-malware software for home users and cloud anti-virus for organizations, these attempts to secure data Some large corporations and government agencies already use encryption software to secure data, but it is also available and becoming more readily available to the general public. The RSA algorithm is an asymmetric encryption technique that safeguards the picture before transferring it. It is one of the most extensively used encryption tools.","PeriodicalId":164345,"journal":{"name":"2023 Third International Conference on Artificial Intelligence and Smart Energy (ICAIS)","volume":"58 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":"123859719","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/ICAIS56108.2023.10073898
Vazeer Ali Mohammed, Mehmood Ali Mohammed, Murtuza Ali Mohammed, J. Logeshwaran, Nasmin Jiwani
At present, various electronic devices are used to monitor human heart rates. However, its functions are to avoid predicting the problems caused by heart rate variability in advance and analyzing its implications. It makes it difficult to diagnose problems caused by heart rate variability. A human should have an average heart rate of 72. At the same time, the newborn's heart should beat between 120 and 160 beats per minute. A baby born with autism spectrum disorder may have a lower-than-average heart rate. Complete blockage of the heart at birth is rare. Abnormal heart rate leads to heart block. So, there is a high chance of the child's death due to permanent heart blockage at any time. Most heart diseases in children with Autism Spectrum Disorder (ASD) are present at birth. A significant congenital disability is a hole in the heart. Many people do not realize that having holes in the heart is a common occurrence. Before the baby is born, tiny holes form in the muscular wall that divides the heart into the right and left halves. This paper proposed Machine Learning-Based Evaluation to identify the Heart Rate Variability Response in Children with Autism Spectrum Disorder with Autism Spectrum Disorder. The reasons for this are yet to be identified. However, 70 per cent of perforations resolve spontaneously before or after birth. Exceptionally, Children with Autism Spectrum Disorder with perforations that do not close properly may require surgery or a perforator brace, depending on the location and size of the perforation.
{"title":"Machine Learning-based Evaluation of Heart Rate Variability Response in Children with Autism Spectrum Disorder","authors":"Vazeer Ali Mohammed, Mehmood Ali Mohammed, Murtuza Ali Mohammed, J. Logeshwaran, Nasmin Jiwani","doi":"10.1109/ICAIS56108.2023.10073898","DOIUrl":"https://doi.org/10.1109/ICAIS56108.2023.10073898","url":null,"abstract":"At present, various electronic devices are used to monitor human heart rates. However, its functions are to avoid predicting the problems caused by heart rate variability in advance and analyzing its implications. It makes it difficult to diagnose problems caused by heart rate variability. A human should have an average heart rate of 72. At the same time, the newborn's heart should beat between 120 and 160 beats per minute. A baby born with autism spectrum disorder may have a lower-than-average heart rate. Complete blockage of the heart at birth is rare. Abnormal heart rate leads to heart block. So, there is a high chance of the child's death due to permanent heart blockage at any time. Most heart diseases in children with Autism Spectrum Disorder (ASD) are present at birth. A significant congenital disability is a hole in the heart. Many people do not realize that having holes in the heart is a common occurrence. Before the baby is born, tiny holes form in the muscular wall that divides the heart into the right and left halves. This paper proposed Machine Learning-Based Evaluation to identify the Heart Rate Variability Response in Children with Autism Spectrum Disorder with Autism Spectrum Disorder. The reasons for this are yet to be identified. However, 70 per cent of perforations resolve spontaneously before or after birth. Exceptionally, Children with Autism Spectrum Disorder with perforations that do not close properly may require surgery or a perforator brace, depending on the location and size of the perforation.","PeriodicalId":164345,"journal":{"name":"2023 Third International Conference on Artificial Intelligence and Smart Energy (ICAIS)","volume":"115 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":"124120428","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/ICAIS56108.2023.10073714
D. Sabapathi, Yogesh Shivaji Pawar, Sumagna Patnaik, E. Sivanantham, D. K. Prabhu, N. Prakash
Load forecasting plays a vital role in generation and distribution sectors in the power system. This helps to obtain optimum load scheduling which helps to predict future consumption to increase reliability in the system. The demand side management helps to optimize the consumption of energy based upon the priority of the consumers. The load forecasting helps to predict the usage of power through the priority scheduling of the loads which helps to minimize and maximize the operating cost. The optimization technique plays a versatile role in the load scheduling based on demand side management in the industrial sectors. The combination of advanced technologies with communication infrastructure makes the system more reliable and smarter. The demand side management is achieved through shifting the loads from peak hours to non-peak hours. Thus, to enhance the automatic scheduling of loads in the industrial sector is achieved by the neuro-fuzzy controller and deep learning techniques.
{"title":"Management in Industrial Sectors using Neuro-Fuzzy Controller and Deep Learning","authors":"D. Sabapathi, Yogesh Shivaji Pawar, Sumagna Patnaik, E. Sivanantham, D. K. Prabhu, N. Prakash","doi":"10.1109/ICAIS56108.2023.10073714","DOIUrl":"https://doi.org/10.1109/ICAIS56108.2023.10073714","url":null,"abstract":"Load forecasting plays a vital role in generation and distribution sectors in the power system. This helps to obtain optimum load scheduling which helps to predict future consumption to increase reliability in the system. The demand side management helps to optimize the consumption of energy based upon the priority of the consumers. The load forecasting helps to predict the usage of power through the priority scheduling of the loads which helps to minimize and maximize the operating cost. The optimization technique plays a versatile role in the load scheduling based on demand side management in the industrial sectors. The combination of advanced technologies with communication infrastructure makes the system more reliable and smarter. The demand side management is achieved through shifting the loads from peak hours to non-peak hours. Thus, to enhance the automatic scheduling of loads in the industrial sector is achieved by the neuro-fuzzy controller and deep learning techniques.","PeriodicalId":164345,"journal":{"name":"2023 Third International Conference on Artificial Intelligence and Smart Energy (ICAIS)","volume":"1995 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":"125553640","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/ICAIS56108.2023.10073742
Madhwaraj Kango Gopal, A. V, Adarsh Ishwar Hegde, Adarsh Jaiswal
IoT (Internet of Things) is a concept that has been used extensively due to its acceptance and benefits if offers while used across several domains. Good fresh air with the right mix of particulates is what is needed today to ward off respiratory disorders. An important concern that needs to be addressed is how to provide this fresh air in today’s living environment. In this work, an IoT-based approach has been used to effectively increase the efficiency and overall air quality within indoor spaces. Several parameters like using temperature sensor to sense temperature, humidity sensor to sense humidity, Smoke sensor to collect data from the different gases, and many more other sensors are used to collect data from dust and air particulates. Fuzzy logic algorithms are used to check whether the levels of the gases will cause discomfort or breathing difficulties in the people who are currently within the confines of the area. An automated air ventilation and filtration system is used to either increase or decrease the airflow of the system to mitigate the air quality degradation and bring the system back to an equilibrium state therefore maintaining fresh air circulation.
{"title":"An IoT-based Approach to Air Circulation within a Specific Room Environment","authors":"Madhwaraj Kango Gopal, A. V, Adarsh Ishwar Hegde, Adarsh Jaiswal","doi":"10.1109/ICAIS56108.2023.10073742","DOIUrl":"https://doi.org/10.1109/ICAIS56108.2023.10073742","url":null,"abstract":"IoT (Internet of Things) is a concept that has been used extensively due to its acceptance and benefits if offers while used across several domains. Good fresh air with the right mix of particulates is what is needed today to ward off respiratory disorders. An important concern that needs to be addressed is how to provide this fresh air in today’s living environment. In this work, an IoT-based approach has been used to effectively increase the efficiency and overall air quality within indoor spaces. Several parameters like using temperature sensor to sense temperature, humidity sensor to sense humidity, Smoke sensor to collect data from the different gases, and many more other sensors are used to collect data from dust and air particulates. Fuzzy logic algorithms are used to check whether the levels of the gases will cause discomfort or breathing difficulties in the people who are currently within the confines of the area. An automated air ventilation and filtration system is used to either increase or decrease the airflow of the system to mitigate the air quality degradation and bring the system back to an equilibrium state therefore maintaining fresh air circulation.","PeriodicalId":164345,"journal":{"name":"2023 Third International Conference on Artificial Intelligence and Smart Energy (ICAIS)","volume":"18 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":"125077905","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}
Pub Date : 2023-02-02DOI: 10.1109/ICAIS56108.2023.10073689
Dhruv Kolhatkar, Devika Verma
Over the past few years, research interest in the sub-domain of question answering has tremendously increased. Yet, most of the work on QA and more generally, on natural language processing has been predominantly limited to the English language. In contrast, with each passing year, the number of people with access to the internet is exponentially increasing, especially those residing in South Asian countries whose primary language is not English. With this in mind, the survey’s aim is to recognize, review and analyze the various question-answering datasets that exist for resource-scare Indic languages such as Hindi, Urdu, Tamil, and Marathi. It also intends to shed light on the state-of-the-art of Indic question-answering itself, in terms of methods used, best-performing models, and evaluation metrics. The review also includes multilingual benchmarks which have been recently published.
{"title":"Indic Language Question Answering: A Survey","authors":"Dhruv Kolhatkar, Devika Verma","doi":"10.1109/ICAIS56108.2023.10073689","DOIUrl":"https://doi.org/10.1109/ICAIS56108.2023.10073689","url":null,"abstract":"Over the past few years, research interest in the sub-domain of question answering has tremendously increased. Yet, most of the work on QA and more generally, on natural language processing has been predominantly limited to the English language. In contrast, with each passing year, the number of people with access to the internet is exponentially increasing, especially those residing in South Asian countries whose primary language is not English. With this in mind, the survey’s aim is to recognize, review and analyze the various question-answering datasets that exist for resource-scare Indic languages such as Hindi, Urdu, Tamil, and Marathi. It also intends to shed light on the state-of-the-art of Indic question-answering itself, in terms of methods used, best-performing models, and evaluation metrics. The review also includes multilingual benchmarks which have been recently published.","PeriodicalId":164345,"journal":{"name":"2023 Third International Conference on Artificial Intelligence and Smart Energy (ICAIS)","volume":"28 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":"121673844","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/ICAIS56108.2023.10073812
A. Lakshmi, V. Krishnaveni, G. Vinuthna, A. L. Goud
Many developments have happened in the recent times by applying new technologies in various fields. Precising to agriculture, use of these technologies not only save time and energy but also bring advancements to various processes. Using agricultural technologies for irrigation and fertilizer sensing eases work to farmers one of which including use of Solar Power for automatic water pumping to conserve energy. Fertilizers content in soil causing soil and water pollution cannot be neglected. Hence, this system has been proposed to know if fertilizers are being used in required amounts. A Solar based water pumping is also present additionally to pump water based on soil moisture. A RTC is used to keep track of soil moisture thus pumping water over a fixed interval of time.
{"title":"Fertilizer Sensing and Solar based RTC Water Pumping","authors":"A. Lakshmi, V. Krishnaveni, G. Vinuthna, A. L. Goud","doi":"10.1109/ICAIS56108.2023.10073812","DOIUrl":"https://doi.org/10.1109/ICAIS56108.2023.10073812","url":null,"abstract":"Many developments have happened in the recent times by applying new technologies in various fields. Precising to agriculture, use of these technologies not only save time and energy but also bring advancements to various processes. Using agricultural technologies for irrigation and fertilizer sensing eases work to farmers one of which including use of Solar Power for automatic water pumping to conserve energy. Fertilizers content in soil causing soil and water pollution cannot be neglected. Hence, this system has been proposed to know if fertilizers are being used in required amounts. A Solar based water pumping is also present additionally to pump water based on soil moisture. A RTC is used to keep track of soil moisture thus pumping water over a fixed interval of time.","PeriodicalId":164345,"journal":{"name":"2023 Third International Conference on Artificial Intelligence and Smart Energy (ICAIS)","volume":"206 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":"122428996","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}
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