Pub Date : 2021-07-01DOI: 10.1109/ICSCC51209.2021.9528236
B. K. Jeemon, Shahana T K
Space time block coding (STBC) is a popular technique to improve diversity gain of conventional OFDM systems. Vector OFDM (VOFDM) is a transmission technology that exploits signal space dimension to reduce the effect of spectral nulls on OFDM subcarriers. Space time block coded vector OFDM (STBC VOFDM) tries to extract advantages of both these techniques, thereby improving the reliability of the communication system. This paper illustrates the characteristics of STBC VOFDM systems with maximum likelihood (ML) detection in an i.i.d (independent and identically distributed) multipath complex Rayleigh channel with D channel taps. The expression for diversity gain in STBC VOFDM for most vector blocks is derived as 2{min(M, D)}, where M denotes the number of elements in each vector block and D denotes the number of channel taps. It can be observed that the diversity order in STBC VOFDM has improved by a factor of 2 when compared with VOFDM.
{"title":"Space Time Block Coded Vector OFDM with ML Detection","authors":"B. K. Jeemon, Shahana T K","doi":"10.1109/ICSCC51209.2021.9528236","DOIUrl":"https://doi.org/10.1109/ICSCC51209.2021.9528236","url":null,"abstract":"Space time block coding (STBC) is a popular technique to improve diversity gain of conventional OFDM systems. Vector OFDM (VOFDM) is a transmission technology that exploits signal space dimension to reduce the effect of spectral nulls on OFDM subcarriers. Space time block coded vector OFDM (STBC VOFDM) tries to extract advantages of both these techniques, thereby improving the reliability of the communication system. This paper illustrates the characteristics of STBC VOFDM systems with maximum likelihood (ML) detection in an i.i.d (independent and identically distributed) multipath complex Rayleigh channel with D channel taps. The expression for diversity gain in STBC VOFDM for most vector blocks is derived as 2{min(M, D)}, where M denotes the number of elements in each vector block and D denotes the number of channel taps. It can be observed that the diversity order in STBC VOFDM has improved by a factor of 2 when compared with VOFDM.","PeriodicalId":382982,"journal":{"name":"2021 8th International Conference on Smart Computing and Communications (ICSCC)","volume":"19 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124309709","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-07-01DOI: 10.1109/ICSCC51209.2021.9528257
Shubham Dadhich, A. Dwivedi, G. Mathur
This paper presents TCAD modelling of the 6,13-bis(triisopropylsilylethynyl) Pentacene OTFT. The model is based on defect description and charge reproduction and recombination. This model incorporates metal-semiconductor-insulator interface and contact barrier, field-dependent mobility in TIPS pentacene film. It consists of ‘hopping mobility model’ and ‘multiple trapping and release model’. It describes deep, tail DOS both, and not only matches electrical behavior but also gives a panorama of charge injection, carrier transportation. This model can be used for simulation of other structures also.
{"title":"Surface Charge Based Modeling of TIPS-Pentacene TFT","authors":"Shubham Dadhich, A. Dwivedi, G. Mathur","doi":"10.1109/ICSCC51209.2021.9528257","DOIUrl":"https://doi.org/10.1109/ICSCC51209.2021.9528257","url":null,"abstract":"This paper presents TCAD modelling of the 6,13-bis(triisopropylsilylethynyl) Pentacene OTFT. The model is based on defect description and charge reproduction and recombination. This model incorporates metal-semiconductor-insulator interface and contact barrier, field-dependent mobility in TIPS pentacene film. It consists of ‘hopping mobility model’ and ‘multiple trapping and release model’. It describes deep, tail DOS both, and not only matches electrical behavior but also gives a panorama of charge injection, carrier transportation. This model can be used for simulation of other structures also.","PeriodicalId":382982,"journal":{"name":"2021 8th International Conference on Smart Computing and Communications (ICSCC)","volume":"112 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117244553","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-07-01DOI: 10.1109/ICSCC51209.2021.9528258
Animesh Shukla
Application of the core concepts of Machine Learning and Statistics for predicting whether the customer would leave the services of the bank in future or not. Machine learning model is trained by considering the data of 10,000 customers of the bank. Statistical Techniques are applied so as to investigate the data in depth and infer the relationships between different features or variables of data. The web application uses the trained model in the backend to predict the probability of the customer leaving the bank. Hence, the website can prove to be extremely useful for the bank managers and decision makers of the bank to get an idea of those customers who are likely to leave the services of the bank in future and can retain them by formulating some new policies.
{"title":"Application of Machine Learning and Statistics in Banking Customer Churn Prediction","authors":"Animesh Shukla","doi":"10.1109/ICSCC51209.2021.9528258","DOIUrl":"https://doi.org/10.1109/ICSCC51209.2021.9528258","url":null,"abstract":"Application of the core concepts of Machine Learning and Statistics for predicting whether the customer would leave the services of the bank in future or not. Machine learning model is trained by considering the data of 10,000 customers of the bank. Statistical Techniques are applied so as to investigate the data in depth and infer the relationships between different features or variables of data. The web application uses the trained model in the backend to predict the probability of the customer leaving the bank. Hence, the website can prove to be extremely useful for the bank managers and decision makers of the bank to get an idea of those customers who are likely to leave the services of the bank in future and can retain them by formulating some new policies.","PeriodicalId":382982,"journal":{"name":"2021 8th International Conference on Smart Computing and Communications (ICSCC)","volume":"65 5","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"120907921","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-07-01DOI: 10.1109/ICSCC51209.2021.9528142
Kiran S Parakkal, P. Rahul, R. John, Swathi Madhavan, S. Reshmi
Zoo is a place where animals are cared and looked after by a team of officials. It will also provide entertainment to tourists and the animal lovers to study animal behavior. It is used for learning purposes as well as for tourist revenue. It serves and takes care of many wild animals with the help of zoo administrators. The work of those zoo administrators is very difficult as they are dealing with wild animals in person. In this paper, we proposed an architecture to ease the animal administrator’s daily job by effectively collaborating the Internet of Things (Iot) and Artificial Intelligence (AI). Here we propose a novel and intelligent method for the health prediction of animals by a supervised machine learning algorithm. In addition to that, our architecture involves automatic feeding, cage temperature control, health monitoring of animals, real-time monitoring, and identification of virus infected animals. That helps to make the zoo expenses low. Moreover, in this era of the pandemic, the virus infected animals need to be separated from other animals as well as from the administrators to avoid spreading of the diseases. We also proposed an effective method by using RFID tag to identify virus infected animal and break the chain to prevent the spreading. So, our aim is to uplift the motto one world one health.
{"title":"Management System Using Internet of Things and Artificial Intelligence","authors":"Kiran S Parakkal, P. Rahul, R. John, Swathi Madhavan, S. Reshmi","doi":"10.1109/ICSCC51209.2021.9528142","DOIUrl":"https://doi.org/10.1109/ICSCC51209.2021.9528142","url":null,"abstract":"Zoo is a place where animals are cared and looked after by a team of officials. It will also provide entertainment to tourists and the animal lovers to study animal behavior. It is used for learning purposes as well as for tourist revenue. It serves and takes care of many wild animals with the help of zoo administrators. The work of those zoo administrators is very difficult as they are dealing with wild animals in person. In this paper, we proposed an architecture to ease the animal administrator’s daily job by effectively collaborating the Internet of Things (Iot) and Artificial Intelligence (AI). Here we propose a novel and intelligent method for the health prediction of animals by a supervised machine learning algorithm. In addition to that, our architecture involves automatic feeding, cage temperature control, health monitoring of animals, real-time monitoring, and identification of virus infected animals. That helps to make the zoo expenses low. Moreover, in this era of the pandemic, the virus infected animals need to be separated from other animals as well as from the administrators to avoid spreading of the diseases. We also proposed an effective method by using RFID tag to identify virus infected animal and break the chain to prevent the spreading. So, our aim is to uplift the motto one world one health.","PeriodicalId":382982,"journal":{"name":"2021 8th International Conference on Smart Computing and Communications (ICSCC)","volume":"11 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128094015","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 focuses on developing a prediction model to predict heart stroke using the parameters, namely, age, hypertension, previous heart disease status, average body glucose level, BMI, and smoking status. The prediction model is developed using a support vector machine (SVM) algorithm. Further, the SVM algorithm with various decision boundaries like linear, quadratic, and cubic are also produced. The performance prediction results show that the linear and quadratic SVM has performed better in predicting the heart stoke with greater accuracy values. This is true for both the male and female databases during training and testing.
{"title":"Prediction of Heart Stroke Using Support Vector Machine Algorithm","authors":"Harshita Puri, Jhanavi Chaudhary, Kulkarni Rakshit Raghavendra, Rh Mantri, Kishore Bingi","doi":"10.1109/ICSCC51209.2021.9528241","DOIUrl":"https://doi.org/10.1109/ICSCC51209.2021.9528241","url":null,"abstract":"This paper focuses on developing a prediction model to predict heart stroke using the parameters, namely, age, hypertension, previous heart disease status, average body glucose level, BMI, and smoking status. The prediction model is developed using a support vector machine (SVM) algorithm. Further, the SVM algorithm with various decision boundaries like linear, quadratic, and cubic are also produced. The performance prediction results show that the linear and quadratic SVM has performed better in predicting the heart stoke with greater accuracy values. This is true for both the male and female databases during training and testing.","PeriodicalId":382982,"journal":{"name":"2021 8th International Conference on Smart Computing and Communications (ICSCC)","volume":"9 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128032249","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 focuses on developing a weather prediction model to predict temperature and humidity. Further, a classification model is also extended to predict the weather condition using the expected model’s output. The proposed hybrid model can predict the temperature and humidity and forecast future weather conditions. The prediction and classification models are created using neural networks and k-nearest neighbors, respectively. The prediction model’s results have shown the best ability for both the output variables (temperature and humidity) with R2 values close to one and MSE values close to zero. Further, the classification model’s results also showed better execution in classifying the weather conditions with the highest accuracy values.
{"title":"Weather Prediction and Classification Using Neural Networks and k-Nearest Neighbors","authors":"Rh Mantri, Kulkarni Rakshit Raghavendra, Harshita Puri, Jhanavi Chaudhary, Kishore Bingi","doi":"10.1109/ICSCC51209.2021.9528115","DOIUrl":"https://doi.org/10.1109/ICSCC51209.2021.9528115","url":null,"abstract":"This paper focuses on developing a weather prediction model to predict temperature and humidity. Further, a classification model is also extended to predict the weather condition using the expected model’s output. The proposed hybrid model can predict the temperature and humidity and forecast future weather conditions. The prediction and classification models are created using neural networks and k-nearest neighbors, respectively. The prediction model’s results have shown the best ability for both the output variables (temperature and humidity) with R2 values close to one and MSE values close to zero. Further, the classification model’s results also showed better execution in classifying the weather conditions with the highest accuracy values.","PeriodicalId":382982,"journal":{"name":"2021 8th International Conference on Smart Computing and Communications (ICSCC)","volume":"39 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133001202","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-07-01DOI: 10.1109/ICSCC51209.2021.9528101
Yusra Meraj, E. Khan
The zero memory set partitioned embedded block (ZM-SPECK) technique is an embedded and memory efficient image compression algorithm. However, it is computationally complex due to the repetitive significance checking of sets and coefficients in each and every bit plane. To overcome this limitation, it is proposed to parallelize the algorithm over smaller blocks to reduce the overall encoding and decoding times of ZM-SPECK algorithm. The proposed approach called block based parallel ZM-SPECK (BPZM-SPECK) decomposes the wavelet transformed image into independent nonoverlapping spatial blocks utilizing the unique child-parent relationships in spatial orientation trees (in wavelet domain) and concurrently encodes every single bits in each bit plane of a block. The experimental results show significant improvement in computation time over the existing ZM-SPECK algorithm.
{"title":"A Block Based Parallel ZM-SPECK Algorithm","authors":"Yusra Meraj, E. Khan","doi":"10.1109/ICSCC51209.2021.9528101","DOIUrl":"https://doi.org/10.1109/ICSCC51209.2021.9528101","url":null,"abstract":"The zero memory set partitioned embedded block (ZM-SPECK) technique is an embedded and memory efficient image compression algorithm. However, it is computationally complex due to the repetitive significance checking of sets and coefficients in each and every bit plane. To overcome this limitation, it is proposed to parallelize the algorithm over smaller blocks to reduce the overall encoding and decoding times of ZM-SPECK algorithm. The proposed approach called block based parallel ZM-SPECK (BPZM-SPECK) decomposes the wavelet transformed image into independent nonoverlapping spatial blocks utilizing the unique child-parent relationships in spatial orientation trees (in wavelet domain) and concurrently encodes every single bits in each bit plane of a block. The experimental results show significant improvement in computation time over the existing ZM-SPECK algorithm.","PeriodicalId":382982,"journal":{"name":"2021 8th International Conference on Smart Computing and Communications (ICSCC)","volume":"17 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129514574","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-07-01DOI: 10.1109/ICSCC51209.2021.9528161
Ayes Chinmay, H. K. Pati
The growth of Wireless Local Area Network (WLAN) deployment specifically over the existing infrastructure is increasing tremendously. Such an infrastructure-based WLAN, commonly known as Wireless Fidelity (WiFi) network, needs to support voice service since in general it contributes a significant portion of the traffic supporting personal communication. In this context, Voice over Internet Protocol (VoIP) over WiFi or Voice over WiFi (VoWiFi) is one amongst the very prominent solutions. To ensure Quality of Service (QoS) for VoWiFi calls, it is essential to develop an adequate call admission control (CAC) policy. Such policy requires VoWiFi cell capacity. In this paper, we have derived analytical models to find capacity of the IEEE 802.11ac standard Access Point (AP) providing VoWiFi service. To analyze WLAN AP capacity for VoWiFi service, we have used DCF Inter-frame Spacing (DIFS) for sensing channel status before sending data from one station to another and Short Inter-frame Spacing (SIFS) is used for acknowledgement, Request To Send (RTS) and Clear To Send (CTS) frames. Further, we have used the compressed RTP (cRTP) protocol to optimize VoIP call bandwidth. Using our proposed analytical model, we have estimated VoWiFi cell capacity using different voice codecs like G.729 and G.723.1.
{"title":"VoWiFi Cell Capacity Estimation Using Fifth Generation WLAN Standard","authors":"Ayes Chinmay, H. K. Pati","doi":"10.1109/ICSCC51209.2021.9528161","DOIUrl":"https://doi.org/10.1109/ICSCC51209.2021.9528161","url":null,"abstract":"The growth of Wireless Local Area Network (WLAN) deployment specifically over the existing infrastructure is increasing tremendously. Such an infrastructure-based WLAN, commonly known as Wireless Fidelity (WiFi) network, needs to support voice service since in general it contributes a significant portion of the traffic supporting personal communication. In this context, Voice over Internet Protocol (VoIP) over WiFi or Voice over WiFi (VoWiFi) is one amongst the very prominent solutions. To ensure Quality of Service (QoS) for VoWiFi calls, it is essential to develop an adequate call admission control (CAC) policy. Such policy requires VoWiFi cell capacity. In this paper, we have derived analytical models to find capacity of the IEEE 802.11ac standard Access Point (AP) providing VoWiFi service. To analyze WLAN AP capacity for VoWiFi service, we have used DCF Inter-frame Spacing (DIFS) for sensing channel status before sending data from one station to another and Short Inter-frame Spacing (SIFS) is used for acknowledgement, Request To Send (RTS) and Clear To Send (CTS) frames. Further, we have used the compressed RTP (cRTP) protocol to optimize VoIP call bandwidth. Using our proposed analytical model, we have estimated VoWiFi cell capacity using different voice codecs like G.729 and G.723.1.","PeriodicalId":382982,"journal":{"name":"2021 8th International Conference on Smart Computing and Communications (ICSCC)","volume":"54 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115478918","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-07-01DOI: 10.1109/ICSCC51209.2021.9528295
Deepak Sreedharan, M. S. Subodh Raj, S. N. George, S. Ashok
A worldwide pandemic, COVID-19 has been caused by a newly discovered strain of coronavirus SARS-Cov-2. Its common symptoms are high fever, coughing, and shortness of breath. With the rising number of COVID-19 cases, manual detection of infectious individuals at public spaces is a hectic task. Artificial Intelligence (AI) based detection systems can be deployed at public places like airports, railway stations, etc. for continuous monitoring of potential infectious individuals and screening based on common symptoms exhibited. In this paper, a new algorithm is developed for detecting repetitive coughing action which is the main symptom in COVID-19 cases, and thus detecting people with COVID-19 based on it. The performance of the proposed system is tested on an existing sneeze-cough dataset and also on a real-time dataset. The evaluation shows that the proposed method has superior performance over the state-of-the-art methods.
{"title":"A Novel Cough Detection Algorithm for COVID-19 Surveillance at Public Places","authors":"Deepak Sreedharan, M. S. Subodh Raj, S. N. George, S. Ashok","doi":"10.1109/ICSCC51209.2021.9528295","DOIUrl":"https://doi.org/10.1109/ICSCC51209.2021.9528295","url":null,"abstract":"A worldwide pandemic, COVID-19 has been caused by a newly discovered strain of coronavirus SARS-Cov-2. Its common symptoms are high fever, coughing, and shortness of breath. With the rising number of COVID-19 cases, manual detection of infectious individuals at public spaces is a hectic task. Artificial Intelligence (AI) based detection systems can be deployed at public places like airports, railway stations, etc. for continuous monitoring of potential infectious individuals and screening based on common symptoms exhibited. In this paper, a new algorithm is developed for detecting repetitive coughing action which is the main symptom in COVID-19 cases, and thus detecting people with COVID-19 based on it. The performance of the proposed system is tested on an existing sneeze-cough dataset and also on a real-time dataset. The evaluation shows that the proposed method has superior performance over the state-of-the-art methods.","PeriodicalId":382982,"journal":{"name":"2021 8th International Conference on Smart Computing and Communications (ICSCC)","volume":" 2","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"120826757","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-07-01DOI: 10.1109/ICSCC51209.2021.9528099
Parag R Ghorpade, A. Gadge, A. Lende, Hitesh Chordiya, G. Gosavi, A. Mishra, B. Hooli, Yashwant S. Ingle, N. Shaikh
Floods are the most frequently occurring natural disasters and result in loss of human life, destruction of livelihoods, which in turn, affects the national economies. There are several studies and novel modi operandi to design flood forecasting systems efficaciously. The authors witness and address the recent shift towards data-driven methods for flood prediction. The machine learning-based models trained using climatic parameters' historical data are increasingly useful for forecasting tasks. This paper's main objective is to demonstrate the recent advancements in the flood forecasting field using machine learning algorithms. The authors reviewed some prominent algorithms used for flood forecasting, which various professionals can use to develop their solutions.
{"title":"Flood Forecasting Using Machine Learning: A Review","authors":"Parag R Ghorpade, A. Gadge, A. Lende, Hitesh Chordiya, G. Gosavi, A. Mishra, B. Hooli, Yashwant S. Ingle, N. Shaikh","doi":"10.1109/ICSCC51209.2021.9528099","DOIUrl":"https://doi.org/10.1109/ICSCC51209.2021.9528099","url":null,"abstract":"Floods are the most frequently occurring natural disasters and result in loss of human life, destruction of livelihoods, which in turn, affects the national economies. There are several studies and novel modi operandi to design flood forecasting systems efficaciously. The authors witness and address the recent shift towards data-driven methods for flood prediction. The machine learning-based models trained using climatic parameters' historical data are increasingly useful for forecasting tasks. This paper's main objective is to demonstrate the recent advancements in the flood forecasting field using machine learning algorithms. The authors reviewed some prominent algorithms used for flood forecasting, which various professionals can use to develop their solutions.","PeriodicalId":382982,"journal":{"name":"2021 8th International Conference on Smart Computing and Communications (ICSCC)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123755619","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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