Due to the huge explanation of artificial intelligence, machine learning technology is being used in various areas of our day-to-day life. In Dhaka city, there are lots of Restaurants. Sometimes many of us face a scenario where we go to a restaurant and order some food but the food is not that good or the price of the food is very high as compared to other restaurants. Apart from this, another major problem is the location of the restaurant. We cannot find the best restaurants around us based on our preferences. This research proposed a model by using a machine learning algorithm that will be able to suggest a suitable restaurant based on the user’s criteria. We have collected Dhaka city’s restaurant’s data from various websites, then we have used Weight-based score calculation and cosine similarity matrix to build our machine learning model. This recommendation system will also suggest similar restaurants based on the user’s selected restaurants.
{"title":"Restaurant Recommendation System in Dhaka City using Machine Learning Approach","authors":"Taufiq Ahmed, Lubna Akhter, Fazle Rabby Talukder, Hasan-Al-Monsur, Hasibur Rahman, A. Sattar","doi":"10.1109/SMART52563.2021.9676197","DOIUrl":"https://doi.org/10.1109/SMART52563.2021.9676197","url":null,"abstract":"Due to the huge explanation of artificial intelligence, machine learning technology is being used in various areas of our day-to-day life. In Dhaka city, there are lots of Restaurants. Sometimes many of us face a scenario where we go to a restaurant and order some food but the food is not that good or the price of the food is very high as compared to other restaurants. Apart from this, another major problem is the location of the restaurant. We cannot find the best restaurants around us based on our preferences. This research proposed a model by using a machine learning algorithm that will be able to suggest a suitable restaurant based on the user’s criteria. We have collected Dhaka city’s restaurant’s data from various websites, then we have used Weight-based score calculation and cosine similarity matrix to build our machine learning model. This recommendation system will also suggest similar restaurants based on the user’s selected restaurants.","PeriodicalId":356096,"journal":{"name":"2021 10th International Conference on System Modeling & Advancement in Research Trends (SMART)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122267694","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 : 2021-12-10DOI: 10.1109/SMART52563.2021.9676247
Nishant Agnihotri, S. Prasad
The modern and fast paced lifestyle in today’s real world lead to high prevalence of mental and psychological disorders like stress, Anxiety and depression in people around us worldwide. The disorder is a result of mood swings and occurrence of oscillations in person’s mind in two states-mania and depression. A complex brain disorder that have affected millions of people across the world is Bipolar Disorder. These conditions led to increase mental health precautions and care using Machine Learning Techniques(ML) for diagnosis and treatment of disease. Using ML, we study patterns in human behavior regularly, identify their symptoms and risk factors to develop a prediction modal. Dataset is visualized to extract meaningful predictions and optimizing therapies. The paper presents commonly used ML Algorithms like Logistic Regression, Support Vector Machine, K-Nearest Neighbor, Naïve Bayes and Decision Trees to study their properties and performance that act as a guide to select the appropriate modal. These modal can bridge the gap between Therapist and patients to revel their problems and embarrassment to expose their illness. This is the key task in selecting the features from dataset and applying the appropriate modal.
{"title":"Predicting the Symptoms of Bipolar Disorder in Patients using Machine Learning","authors":"Nishant Agnihotri, S. Prasad","doi":"10.1109/SMART52563.2021.9676247","DOIUrl":"https://doi.org/10.1109/SMART52563.2021.9676247","url":null,"abstract":"The modern and fast paced lifestyle in today’s real world lead to high prevalence of mental and psychological disorders like stress, Anxiety and depression in people around us worldwide. The disorder is a result of mood swings and occurrence of oscillations in person’s mind in two states-mania and depression. A complex brain disorder that have affected millions of people across the world is Bipolar Disorder. These conditions led to increase mental health precautions and care using Machine Learning Techniques(ML) for diagnosis and treatment of disease. Using ML, we study patterns in human behavior regularly, identify their symptoms and risk factors to develop a prediction modal. Dataset is visualized to extract meaningful predictions and optimizing therapies. The paper presents commonly used ML Algorithms like Logistic Regression, Support Vector Machine, K-Nearest Neighbor, Naïve Bayes and Decision Trees to study their properties and performance that act as a guide to select the appropriate modal. These modal can bridge the gap between Therapist and patients to revel their problems and embarrassment to expose their illness. This is the key task in selecting the features from dataset and applying the appropriate modal.","PeriodicalId":356096,"journal":{"name":"2021 10th International Conference on System Modeling & Advancement in Research Trends (SMART)","volume":"61 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130909816","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}
Routing is imperative for communication which paves the pathway for data dissemination amongst various autonomous systems. The routing features, functionality, architecture, and algorithm of a routing protocol define the efficacy of data distribution. The argumentation of routing protocol decides the fate of the data packets during their voyage from source to destination. Robustness, stability, and scalability are the vital attributes of a routing protocol. Its behavior also defines the role and features of routing protocol in an intra- autonomous or inter-autonomous system. This study conducts a comparative analysis of several Interior Gateway Protocols (IGP), in order to determine the efficient protocol in terms of multiple metrics. The experimental setup involves the single network topology consisting of eight different networks used by various IGPs. The implementation is performed using Graphic Network Simulator (GNS3). The results vouch for OSPFv2. The findings provide guidelines for selecting the correct protocols for an effective, stable, and scalable network.
{"title":"An Efficient Model of IGP for Network-based Communication: A Comparison","authors":"Vidhu Baggan, Srishti Priya Chaturvedi, Jyoti Snehi, Manish Snehi","doi":"10.1109/SMART52563.2021.9676272","DOIUrl":"https://doi.org/10.1109/SMART52563.2021.9676272","url":null,"abstract":"Routing is imperative for communication which paves the pathway for data dissemination amongst various autonomous systems. The routing features, functionality, architecture, and algorithm of a routing protocol define the efficacy of data distribution. The argumentation of routing protocol decides the fate of the data packets during their voyage from source to destination. Robustness, stability, and scalability are the vital attributes of a routing protocol. Its behavior also defines the role and features of routing protocol in an intra- autonomous or inter-autonomous system. This study conducts a comparative analysis of several Interior Gateway Protocols (IGP), in order to determine the efficient protocol in terms of multiple metrics. The experimental setup involves the single network topology consisting of eight different networks used by various IGPs. The implementation is performed using Graphic Network Simulator (GNS3). The results vouch for OSPFv2. The findings provide guidelines for selecting the correct protocols for an effective, stable, and scalable network.","PeriodicalId":356096,"journal":{"name":"2021 10th International Conference on System Modeling & Advancement in Research Trends (SMART)","volume":"65 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130944954","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 : 2021-12-10DOI: 10.1109/SMART52563.2021.9676271
Prince Roy, Rajneesh Kumar
Cloud computing uses the “pay as you go model” to provide on-demand services to its users, especially data storage, computing power, network, and others. In recent years, Cloud Technology, a decentralized network has become one of the optimal solutions to store and process large amounts of data. Cloud Networks store and process data by achieving security in terms of authentication, integrity and privacy, a great challenge in today’s world. This paper had proposed a Multilevel level hybrid security framework to preserve security with the use of session key generation, cryptography algorithms, chain of hashes, and storing data with the use of decentralized approaches such as BlockChain. This framework preserves all the security services and mitigates the number of passive and active attacks such as modification, fabrication, session hijacking, network jamming, DOS attack, and attempts to modify or manipulate paths for gaining access.
{"title":"A Hybrid Security Framework to Preserve Multilevel Security on Public Cloud Networks","authors":"Prince Roy, Rajneesh Kumar","doi":"10.1109/SMART52563.2021.9676271","DOIUrl":"https://doi.org/10.1109/SMART52563.2021.9676271","url":null,"abstract":"Cloud computing uses the “pay as you go model” to provide on-demand services to its users, especially data storage, computing power, network, and others. In recent years, Cloud Technology, a decentralized network has become one of the optimal solutions to store and process large amounts of data. Cloud Networks store and process data by achieving security in terms of authentication, integrity and privacy, a great challenge in today’s world. This paper had proposed a Multilevel level hybrid security framework to preserve security with the use of session key generation, cryptography algorithms, chain of hashes, and storing data with the use of decentralized approaches such as BlockChain. This framework preserves all the security services and mitigates the number of passive and active attacks such as modification, fabrication, session hijacking, network jamming, DOS attack, and attempts to modify or manipulate paths for gaining access.","PeriodicalId":356096,"journal":{"name":"2021 10th International Conference on System Modeling & Advancement in Research Trends (SMART)","volume":"4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133498730","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 : 2021-12-10DOI: 10.1109/SMART52563.2021.9676205
N. S. Devi, K. Sharmila
The most talked about topic of interest in the medical realm as of today, is the debate on the impact that COVID-19 vaccine has on individuals, and their response in encountering the virus. While there are quite a few vaccine variants that have been developed, there has always been a lingering ambiguity in declaring that an individual can be completely immune to the virus. There have been many studies whilom this cognition of analysing the sentiment perception of vaccines, however the data utilization from various sources and the apropos implementation using the language processing methodologies have lagged a great deal. This paper pivots on the data drawn from social media platforms, and optimizes the sentiments using the Natural Language processing Toolkit (NLTK). The process of word embedding, with TFIDF vectorizer commingled with data unsheathing through fine-grained sentiment analysis and machine learning algorithms such as Linear SVC, SVM and Naïve bayes on the covid19 dataset have aided in stratifying the public tweet sentiments based on their polarity, precision, recall, f1-score value and support. The simulations have been implemented using the lexicon, rubric-based analytical tool VADER (Valence Aware Dictionary and sentiment Reasoner) incorporated in Python specifically for optimized extraction of sentiments from data.
到目前为止，医学领域最受关注的话题是关于COVID-19疫苗对个人的影响以及他们在遇到病毒时的反应的辩论。虽然已经开发出了相当多的疫苗变体，但在宣布个人可以完全免疫该病毒方面，一直存在一种模棱两可的说法。在分析疫苗情绪感知的认知方面已经有很多研究，但是从各种来源的数据利用和使用语言处理方法的适当实施已经落后了很多。本文以社交媒体平台的数据为基础，使用自然语言处理工具包(NLTK)对情感进行优化。在covid - 19数据集上，通过细粒度情感分析和机器学习算法(如线性SVC、支持向量机和Naïve贝叶斯)，将TFIDF矢量器与数据挖掘相结合的词嵌入过程有助于根据极性、精度、召回率、f1得分值和支持度对公共推文情绪进行分层。模拟是使用Python中包含的词典、基于规则的分析工具VADER (Valence Aware Dictionary and sentiment Reasoner)来实现的，该工具专门用于优化从数据中提取情感。
{"title":"Fine Grainded Sentiment Analysis on COVID-19 Vaccine","authors":"N. S. Devi, K. Sharmila","doi":"10.1109/SMART52563.2021.9676205","DOIUrl":"https://doi.org/10.1109/SMART52563.2021.9676205","url":null,"abstract":"The most talked about topic of interest in the medical realm as of today, is the debate on the impact that COVID-19 vaccine has on individuals, and their response in encountering the virus. While there are quite a few vaccine variants that have been developed, there has always been a lingering ambiguity in declaring that an individual can be completely immune to the virus. There have been many studies whilom this cognition of analysing the sentiment perception of vaccines, however the data utilization from various sources and the apropos implementation using the language processing methodologies have lagged a great deal. This paper pivots on the data drawn from social media platforms, and optimizes the sentiments using the Natural Language processing Toolkit (NLTK). The process of word embedding, with TFIDF vectorizer commingled with data unsheathing through fine-grained sentiment analysis and machine learning algorithms such as Linear SVC, SVM and Naïve bayes on the covid19 dataset have aided in stratifying the public tweet sentiments based on their polarity, precision, recall, f1-score value and support. The simulations have been implemented using the lexicon, rubric-based analytical tool VADER (Valence Aware Dictionary and sentiment Reasoner) incorporated in Python specifically for optimized extraction of sentiments from data.","PeriodicalId":356096,"journal":{"name":"2021 10th International Conference on System Modeling & Advancement in Research Trends (SMART)","volume":"3 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133309045","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 : 2021-12-10DOI: 10.1109/SMART52563.2021.9676327
V. B. Gladshiya, K. Sharmila
Predictive analytics is one of the paramount fields in data analytics which plays an eminent role of analyzing the data and predicting the feature probabilities. In Predictive analytics Machine learning algorithms are the necessitated part to analyze the data in an accurate approach. Using the machine learning algorithms the models can be constructed for prediction according to the data. These machine models can be used to analyze various data of different sectors. In the education field, the success of the students can be predicted using the machine learning models by detecting or uncovering the risk of the students. The benefits for detecting student issues and learning difficulties early a unique opportunity to address the causal factors on time in order to prevent student failure and drop out tendencies. [2]( Taiwo Olapeju Olaleye, Olufunke Rebecca Vincent, 2020). This paper exposes a prediction algorithm developed based on the classification techniques for identifying the risk of the students on a real time foot with student’s data sets on various aspects.
{"title":"A HML-EVC Model for Analyzing the Risk of the Students to Predict the Success Probability in the Field of Education","authors":"V. B. Gladshiya, K. Sharmila","doi":"10.1109/SMART52563.2021.9676327","DOIUrl":"https://doi.org/10.1109/SMART52563.2021.9676327","url":null,"abstract":"Predictive analytics is one of the paramount fields in data analytics which plays an eminent role of analyzing the data and predicting the feature probabilities. In Predictive analytics Machine learning algorithms are the necessitated part to analyze the data in an accurate approach. Using the machine learning algorithms the models can be constructed for prediction according to the data. These machine models can be used to analyze various data of different sectors. In the education field, the success of the students can be predicted using the machine learning models by detecting or uncovering the risk of the students. The benefits for detecting student issues and learning difficulties early a unique opportunity to address the causal factors on time in order to prevent student failure and drop out tendencies. [2]( Taiwo Olapeju Olaleye, Olufunke Rebecca Vincent, 2020). This paper exposes a prediction algorithm developed based on the classification techniques for identifying the risk of the students on a real time foot with student’s data sets on various aspects.","PeriodicalId":356096,"journal":{"name":"2021 10th International Conference on System Modeling & Advancement in Research Trends (SMART)","volume":"44 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133261284","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 : 2021-12-10DOI: 10.1109/SMART52563.2021.9676320
Vineet Saxena, R. Dwivedi, Ashok Kumar
Crop classification is main area of our planet for understanding the agricultural cover. Studies via satellite imagery are often limited to public data with low revisit rates and/or coarse spatial resolution. However, a recent surge in satellite data from new-aerospace companies provides daily imagery with relatively high spatial resolution. With high revisit rates in satellite image capture enable the incorporation of temporal information into crop classification schemes. With high cadence temporal information just now becoming available, there is plenty of room to explore the data and methods for classification [60].Crop mapping methodology is used for the monitoring of various crop types. These methodology is depend on a large space of satellite imagery and different time series data values which is use in supervised classifiers such as Support Vector Machines (SVMs) and Random Forest (RF)[1]. These classifiers are applied at three unique degrees of crop terminology order and compare the result with accuracy and execution time. SVM gives ideal execution and demonstrates essentially better than RF for the least level of the classification. The significance of information factors such as Near Infrared (NIR), vegetation red-edge, and Short-Wave Infrared (SWIR) multispectral groups, and the Normalized Difference Vegetation (NDVI) and Plant Senescence Reflectance (PSRI) are used during cutting edge crop phenology stages and crop mapping [2].
{"title":"Analysis of Machine Learning Algorithms for Crop Mapping on Satellite Image Data","authors":"Vineet Saxena, R. Dwivedi, Ashok Kumar","doi":"10.1109/SMART52563.2021.9676320","DOIUrl":"https://doi.org/10.1109/SMART52563.2021.9676320","url":null,"abstract":"Crop classification is main area of our planet for understanding the agricultural cover. Studies via satellite imagery are often limited to public data with low revisit rates and/or coarse spatial resolution. However, a recent surge in satellite data from new-aerospace companies provides daily imagery with relatively high spatial resolution. With high revisit rates in satellite image capture enable the incorporation of temporal information into crop classification schemes. With high cadence temporal information just now becoming available, there is plenty of room to explore the data and methods for classification [60].Crop mapping methodology is used for the monitoring of various crop types. These methodology is depend on a large space of satellite imagery and different time series data values which is use in supervised classifiers such as Support Vector Machines (SVMs) and Random Forest (RF)[1]. These classifiers are applied at three unique degrees of crop terminology order and compare the result with accuracy and execution time. SVM gives ideal execution and demonstrates essentially better than RF for the least level of the classification. The significance of information factors such as Near Infrared (NIR), vegetation red-edge, and Short-Wave Infrared (SWIR) multispectral groups, and the Normalized Difference Vegetation (NDVI) and Plant Senescence Reflectance (PSRI) are used during cutting edge crop phenology stages and crop mapping [2].","PeriodicalId":356096,"journal":{"name":"2021 10th International Conference on System Modeling & Advancement in Research Trends (SMART)","volume":"79 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130287081","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 : 2021-12-10DOI: 10.1109/SMART52563.2021.9676310
K. Sharmila, R. Devi, J. Jebathangam, C. Shanthi, P. B. Devi
Biometric modalities have been explored in recent times with great scope of use. This is due to the various challenges encountered with the failure to identify individuals and their departmental changes that they may exhibit. In-order to counter and account for such changes, the biometric modalities are usually registered and compared to their respective matches in the database. There have been various algorithmic methods that have evolved in order to identify the change in the modalities. However, the previous work of study incorporated the commingling of various biometric modalities such as the fingerprint, face, iris, voice and signature. Nonetheless, the proposed indagation focuses on combining the fingerprint biometric modality to that of an audio wave using binary phase shift keying method. This is a unique approach which aids in identifying the pixel transformation through the shift in the bits of the chosen image, and also evinces a digital modulation in the entailed audio wave. The binary shift keying method has been elaborated with other approaches such as voice and signal processing techniques, but this paper focusses on identifying the behavioral change in the pixel bits from the original image to the binary image through the original and modified audio waves. The simulation has been implemented in MATLAB and the outcome has been successfully procured.
{"title":"Amalgamation of Fingerprint with Audio Wave Using Binary Phase Shift Keying Technique To Indagate Change","authors":"K. Sharmila, R. Devi, J. Jebathangam, C. Shanthi, P. B. Devi","doi":"10.1109/SMART52563.2021.9676310","DOIUrl":"https://doi.org/10.1109/SMART52563.2021.9676310","url":null,"abstract":"Biometric modalities have been explored in recent times with great scope of use. This is due to the various challenges encountered with the failure to identify individuals and their departmental changes that they may exhibit. In-order to counter and account for such changes, the biometric modalities are usually registered and compared to their respective matches in the database. There have been various algorithmic methods that have evolved in order to identify the change in the modalities. However, the previous work of study incorporated the commingling of various biometric modalities such as the fingerprint, face, iris, voice and signature. Nonetheless, the proposed indagation focuses on combining the fingerprint biometric modality to that of an audio wave using binary phase shift keying method. This is a unique approach which aids in identifying the pixel transformation through the shift in the bits of the chosen image, and also evinces a digital modulation in the entailed audio wave. The binary shift keying method has been elaborated with other approaches such as voice and signal processing techniques, but this paper focusses on identifying the behavioral change in the pixel bits from the original image to the binary image through the original and modified audio waves. The simulation has been implemented in MATLAB and the outcome has been successfully procured.","PeriodicalId":356096,"journal":{"name":"2021 10th International Conference on System Modeling & Advancement in Research Trends (SMART)","volume":"34 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126475027","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}
Many of the applications and services that we use daily are powered by Artificial Intelligence. The Artificial Intelligence plays important role in our lives now-a-days. With the passing of every minute huge amount of digital data is produced from different sources. This data must be carefully monitored and also requires to be manipulated along with the results and actions that are generated. The release of such products depends on the time parameter which becomes crucial due to more complex software programmes being developed. The product released must be carefully examined to serve all business requirements. Artificial Intelligence becomes important in software testing because one can get more accurate results in less time. The following paper will throw light on the important pillars of Artificial Intelligence with reference to its applications in Software Testing. The software testing results are much better produced by applications of Artificial Intelligence, as per the findings. Further testing driven by Artificial Intelligence will create better quality assurance in times to come. There will be reduction in time by using Artificial Intelligence based software testing thereby increasing efficiency of the organization to develop much more sophisticated software for the market. The approach of applying Artificial Intelligence in software testing will help to create smarter automated testing for complex software applications.
{"title":"Application of Artificial Intelligence in Software Testing","authors":"Priyank Singhal, Shakti Kundu, Harshita Gupta, Harsh Jain","doi":"10.1109/SMART52563.2021.9676244","DOIUrl":"https://doi.org/10.1109/SMART52563.2021.9676244","url":null,"abstract":"Many of the applications and services that we use daily are powered by Artificial Intelligence. The Artificial Intelligence plays important role in our lives now-a-days. With the passing of every minute huge amount of digital data is produced from different sources. This data must be carefully monitored and also requires to be manipulated along with the results and actions that are generated. The release of such products depends on the time parameter which becomes crucial due to more complex software programmes being developed. The product released must be carefully examined to serve all business requirements. Artificial Intelligence becomes important in software testing because one can get more accurate results in less time. The following paper will throw light on the important pillars of Artificial Intelligence with reference to its applications in Software Testing. The software testing results are much better produced by applications of Artificial Intelligence, as per the findings. Further testing driven by Artificial Intelligence will create better quality assurance in times to come. There will be reduction in time by using Artificial Intelligence based software testing thereby increasing efficiency of the organization to develop much more sophisticated software for the market. The approach of applying Artificial Intelligence in software testing will help to create smarter automated testing for complex software applications.","PeriodicalId":356096,"journal":{"name":"2021 10th International Conference on System Modeling & Advancement in Research Trends (SMART)","volume":"38 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122733973","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}
The COVID-19 epidemic spread quickly across nations and country to country counter with lockdowns, about 1.6 billion students remain at home worldwide. Under these situations, and in order to keep student positive on current and for coming challenges. Firstly, we are going to do comparative study on 1st and 2nd wave of corona. Secondly, we are making a website that will give a complete detail about the Coronavirus disease and will tell us about the increasing, death and decreasing cases all over India and will show a map on particular state that will be searched and also make a graph on the particular set of data for the particular searched state. In order to make our website more attractive we are going to use CSS, Html and Python language in developing the website. At last, we will come to know briefly about COVID – 19 (Coronavirus) and also about the cases that are increasing and who all have recovered from it.
{"title":"Comparative Analysis of the 1st and 2nd Wave of COVID–19 and Visualizing the Increasing and Decreasing of COVID–19","authors":"Saloni Gupta, Deependra Rastogi, Kartavya Chauhan, Shivani Sharma","doi":"10.1109/SMART52563.2021.9676279","DOIUrl":"https://doi.org/10.1109/SMART52563.2021.9676279","url":null,"abstract":"The COVID-19 epidemic spread quickly across nations and country to country counter with lockdowns, about 1.6 billion students remain at home worldwide. Under these situations, and in order to keep student positive on current and for coming challenges. Firstly, we are going to do comparative study on 1st and 2nd wave of corona. Secondly, we are making a website that will give a complete detail about the Coronavirus disease and will tell us about the increasing, death and decreasing cases all over India and will show a map on particular state that will be searched and also make a graph on the particular set of data for the particular searched state. In order to make our website more attractive we are going to use CSS, Html and Python language in developing the website. At last, we will come to know briefly about COVID – 19 (Coronavirus) and also about the cases that are increasing and who all have recovered from it.","PeriodicalId":356096,"journal":{"name":"2021 10th International Conference on System Modeling & Advancement in Research Trends (SMART)","volume":"529 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132670843","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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