Pub Date : 2021-11-26DOI: 10.1109/iccica52458.2021.9697123
Mahendra Gaikwad
Network-on-chip architecture is a new paradigm shift for designing IP core based system on chip and also referred as network based communication subsystem which is recently looked as an innovative approach to provide a highly scalable, high computational and communication performance. Energy consumption of network based communication subsystems is becoming the valuable parameter in the design of system which further needs to be optimized. In the recent development of IP core architecture, it is necessary to propose new approach for design methodologies to minimize the communication energy for network based communication subsystems. We have addressed the Rectangular Perfect Difference Network topology for network based communication subsystems for providing optimum bandwidth utilization with lesser number of routing hops and at the most two hops in the communication to achieve the best energy performance. In this paper, we propose Rectangular PDN topology for network based communication subsystems for minimization of communication energy using the mathematical representation of Perfect Difference Set (PDS). We have proposed the analytical model with lower energy consumption for chordal Ring Perfect Difference Network Topology and Rectangular Perfect Difference Network Topology. The proposed analytical model for network based communication subsystems using Perfect Difference Network topology results is simulated and validated for different Network topology having order of n=7. The link energy model and router energy model are validated against simulation results for Rectangular PDN topology of network based communication subsystems. The overall average energy consumption for transfer of data through router from one IP to another IP for Rectangular PDN Topology for network n=7 for perfect difference set of {0, 1, 3} having order δ=2; is compared with overall average energy consumption for 2X2 CLICHÉ architecture
{"title":"Energy Performance of Network on Chip Architecture for Rectangular Perfect Difference Network Topology","authors":"Mahendra Gaikwad","doi":"10.1109/iccica52458.2021.9697123","DOIUrl":"https://doi.org/10.1109/iccica52458.2021.9697123","url":null,"abstract":"Network-on-chip architecture is a new paradigm shift for designing IP core based system on chip and also referred as network based communication subsystem which is recently looked as an innovative approach to provide a highly scalable, high computational and communication performance. Energy consumption of network based communication subsystems is becoming the valuable parameter in the design of system which further needs to be optimized. In the recent development of IP core architecture, it is necessary to propose new approach for design methodologies to minimize the communication energy for network based communication subsystems. We have addressed the Rectangular Perfect Difference Network topology for network based communication subsystems for providing optimum bandwidth utilization with lesser number of routing hops and at the most two hops in the communication to achieve the best energy performance. In this paper, we propose Rectangular PDN topology for network based communication subsystems for minimization of communication energy using the mathematical representation of Perfect Difference Set (PDS). We have proposed the analytical model with lower energy consumption for chordal Ring Perfect Difference Network Topology and Rectangular Perfect Difference Network Topology. The proposed analytical model for network based communication subsystems using Perfect Difference Network topology results is simulated and validated for different Network topology having order of n=7. The link energy model and router energy model are validated against simulation results for Rectangular PDN topology of network based communication subsystems. The overall average energy consumption for transfer of data through router from one IP to another IP for Rectangular PDN Topology for network n=7 for perfect difference set of {0, 1, 3} having order δ=2; is compared with overall average energy consumption for 2X2 CLICHÉ architecture","PeriodicalId":327193,"journal":{"name":"2021 International Conference on Computational Intelligence and Computing Applications (ICCICA)","volume":"73 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116086645","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}
Data transfer is a way to attain the goal of data monetization. While the data is being transferred, securing the records and information of users is the prime concern which needs to be taken care of. There is a strong necessity to find out a new, safe and reliable process in which information of customers should be transferred. This research paper provides a smart and secured method to transfer data from one organization to different organizations for data monetization. It focuses on achieving efficient transfer of data with the permission of the person whose credentials are getting shared, leading to economic growth of both the dealers. It also focuses on how different organizations can use data of a single organization at same time for data monetization without actually accessing the data with the help of the proposed methodology. Proposed methodology is time saving for the different organizations as insights helps to target the relevant people from the same domain.
{"title":"Smart Data Transfer For Data Monetization","authors":"Aditi Prakash Mukte, Ritesh Pravin Jaiswal, Sanket Anil Dambhare, Urvashi Agrawal, R. Agrawal","doi":"10.1109/iccica52458.2021.9697182","DOIUrl":"https://doi.org/10.1109/iccica52458.2021.9697182","url":null,"abstract":"Data transfer is a way to attain the goal of data monetization. While the data is being transferred, securing the records and information of users is the prime concern which needs to be taken care of. There is a strong necessity to find out a new, safe and reliable process in which information of customers should be transferred. This research paper provides a smart and secured method to transfer data from one organization to different organizations for data monetization. It focuses on achieving efficient transfer of data with the permission of the person whose credentials are getting shared, leading to economic growth of both the dealers. It also focuses on how different organizations can use data of a single organization at same time for data monetization without actually accessing the data with the help of the proposed methodology. Proposed methodology is time saving for the different organizations as insights helps to target the relevant people from the same domain.","PeriodicalId":327193,"journal":{"name":"2021 International Conference on Computational Intelligence and Computing Applications (ICCICA)","volume":"51 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125698000","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 : 2021-11-26DOI: 10.1109/iccica52458.2021.9697114
Divyam Sheth, A. R. Gupta, L. D'mello
This paper describes an application that performs a semantic search on an employee database. It helps Human Resources employees to target relevant people for their events and trainings. Syntactic or lexical searching involves keyword matching but does not match synonyms and other contextually related data. By using regular keyword search, a document either contains the given word or not, and there is no middle ground. Semantic Search allows the matching of data contextually linked with the search term. High dimensional vectors, also known as embeddings, are generated for a complete sentence and are then used for searching. Under the hood, Google’s Universal Sentence Encoder. The Universal Sentence Encoder encodes text into high dimensional vectors that can be used for text classification, semantic similarity, clustering, and other natural language tasks to provide better performance of the model as compared to a custom trained Convolution Neural Network which also requires more training data.
{"title":"Using Universal Sentence Encoder for Semantic Search of Employee Data","authors":"Divyam Sheth, A. R. Gupta, L. D'mello","doi":"10.1109/iccica52458.2021.9697114","DOIUrl":"https://doi.org/10.1109/iccica52458.2021.9697114","url":null,"abstract":"This paper describes an application that performs a semantic search on an employee database. It helps Human Resources employees to target relevant people for their events and trainings. Syntactic or lexical searching involves keyword matching but does not match synonyms and other contextually related data. By using regular keyword search, a document either contains the given word or not, and there is no middle ground. Semantic Search allows the matching of data contextually linked with the search term. High dimensional vectors, also known as embeddings, are generated for a complete sentence and are then used for searching. Under the hood, Google’s Universal Sentence Encoder. The Universal Sentence Encoder encodes text into high dimensional vectors that can be used for text classification, semantic similarity, clustering, and other natural language tasks to provide better performance of the model as compared to a custom trained Convolution Neural Network which also requires more training data.","PeriodicalId":327193,"journal":{"name":"2021 International Conference on Computational Intelligence and Computing Applications (ICCICA)","volume":"10 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130952097","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 : 2021-11-26DOI: 10.1109/iccica52458.2021.9697232
Akash Kothare, Shridhara Chaube, Yash Moharir, Gaurav Bajodia, S. Dongre
Synthetic data is superficial data generated using various machine learning techniques. The respective synthetic data generated can be used to preserve privacy, test systems, or create training data for machine learning algorithms. Synthetic data generation is critical as the need for specific data is huge in today's world, for example, synthetic data can be used to practice various data science tasks and techniques, while maintaining the anonymity of the samples generated. We used an open-source engine named Faker (v5.6.1) and Gaussian copula to create a platform that can generate datasets, based on user requirements as well as available resources. The user can also perform a variety of machine learning algorithms and differentiate their performance either over the generated dataset or a predefined dataset.
{"title":"SynGen: Synthetic Data Generation","authors":"Akash Kothare, Shridhara Chaube, Yash Moharir, Gaurav Bajodia, S. Dongre","doi":"10.1109/iccica52458.2021.9697232","DOIUrl":"https://doi.org/10.1109/iccica52458.2021.9697232","url":null,"abstract":"Synthetic data is superficial data generated using various machine learning techniques. The respective synthetic data generated can be used to preserve privacy, test systems, or create training data for machine learning algorithms. Synthetic data generation is critical as the need for specific data is huge in today's world, for example, synthetic data can be used to practice various data science tasks and techniques, while maintaining the anonymity of the samples generated. We used an open-source engine named Faker (v5.6.1) and Gaussian copula to create a platform that can generate datasets, based on user requirements as well as available resources. The user can also perform a variety of machine learning algorithms and differentiate their performance either over the generated dataset or a predefined dataset.","PeriodicalId":327193,"journal":{"name":"2021 International Conference on Computational Intelligence and Computing Applications (ICCICA)","volume":"39 14","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133007794","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 : 2021-11-26DOI: 10.1109/iccica52458.2021.9697144
D. Singh, S. Kediya, R. Mahajan, P. Asthana
The research article aims to know the role of Management Information System in Food grains (Soyabean and Tuwar) in Eastern Maharashtra. Indian Government in its market liberalization plan emphasized on the priority to the development of a market information system (MIS) which could be utilized by traders as well as to deliver frequent information by media on current market price and availability.In collaboration with NIC, the IT project department has created several vital software programs to assist farmers. This study aims at management information systems in the context of food grains (soyabean and tuwar) in Eastern Maharashtra. To ensure that the research design aligns with the research objectives, the researcher has made sure that the instruments used in the study are objective oriented such as Measure of central tendency and Z statistic. The result of the study suggests that because of technical complexity, end-users underestimate the agricultural information system's utility. Because of lack of agricultural knowledge, assistance for people information financing as a key priority in cultivation may dwindle. Farmers should have easier access to public information by increased funding for public information. More interactive information sources might persuade traditional farmers to embrace more modern farming techniques.
{"title":"Management Information System in context of Food grains: An Empirical Study at Eastern Maharashtra","authors":"D. Singh, S. Kediya, R. Mahajan, P. Asthana","doi":"10.1109/iccica52458.2021.9697144","DOIUrl":"https://doi.org/10.1109/iccica52458.2021.9697144","url":null,"abstract":"The research article aims to know the role of Management Information System in Food grains (Soyabean and Tuwar) in Eastern Maharashtra. Indian Government in its market liberalization plan emphasized on the priority to the development of a market information system (MIS) which could be utilized by traders as well as to deliver frequent information by media on current market price and availability.In collaboration with NIC, the IT project department has created several vital software programs to assist farmers. This study aims at management information systems in the context of food grains (soyabean and tuwar) in Eastern Maharashtra. To ensure that the research design aligns with the research objectives, the researcher has made sure that the instruments used in the study are objective oriented such as Measure of central tendency and Z statistic. The result of the study suggests that because of technical complexity, end-users underestimate the agricultural information system's utility. Because of lack of agricultural knowledge, assistance for people information financing as a key priority in cultivation may dwindle. Farmers should have easier access to public information by increased funding for public information. More interactive information sources might persuade traditional farmers to embrace more modern farming techniques.","PeriodicalId":327193,"journal":{"name":"2021 International Conference on Computational Intelligence and Computing Applications (ICCICA)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130523790","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 : 2021-11-26DOI: 10.1109/iccica52458.2021.9697280
J. Saivijayalakshmi, N. Ayyanathan
India always remains a major Tourist destination, given its diverse culture, geography, history and also being the oldest civilization in the world. In view of India’s enormous potential for growth in Tourism, its imperative that we need a reliable and accurate Tourism demand forecasting solution. We reviewed various research papers based on Time-series & Regression methods. They are simple to compute values and also bring out forecasting tentative data of foreign tourist arrivals. Our tourism growth potential demanded more accurate forecasting which called for exploring other methods. We found "Deep Learning Techniques", are highly useful. Time series methods such as Holtwinter, Auto Regressive Integrated Moving Average and Long-short term memory (LSTM) are used to predict accurately foreign Tourist Visitors to India. Based on our analysis, the best model for predicting Tourist arrivals to India from foreign countries is LSTM, compared with traditional techniques.
{"title":"Comparative Performance Analysis of Deep Learning Technique with Statistical models on forecasting the Foreign Tourists arrival pattern to India","authors":"J. Saivijayalakshmi, N. Ayyanathan","doi":"10.1109/iccica52458.2021.9697280","DOIUrl":"https://doi.org/10.1109/iccica52458.2021.9697280","url":null,"abstract":"India always remains a major Tourist destination, given its diverse culture, geography, history and also being the oldest civilization in the world. In view of India’s enormous potential for growth in Tourism, its imperative that we need a reliable and accurate Tourism demand forecasting solution. We reviewed various research papers based on Time-series & Regression methods. They are simple to compute values and also bring out forecasting tentative data of foreign tourist arrivals. Our tourism growth potential demanded more accurate forecasting which called for exploring other methods. We found \"Deep Learning Techniques\", are highly useful. Time series methods such as Holtwinter, Auto Regressive Integrated Moving Average and Long-short term memory (LSTM) are used to predict accurately foreign Tourist Visitors to India. Based on our analysis, the best model for predicting Tourist arrivals to India from foreign countries is LSTM, compared with traditional techniques.","PeriodicalId":327193,"journal":{"name":"2021 International Conference on Computational Intelligence and Computing Applications (ICCICA)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129759755","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 : 2021-11-26DOI: 10.1109/iccica52458.2021.9697160
Shivanee Jaiswal, Joel Marvin Tellis, Rishi Kabra, Swati Mali
In the current COVID-19 pandemic, it has become extremely important to detect the affected patients as soon as possible and isolate them in order to break the chain of the spreading virus. Testing in large numbers at laboratories has overwhelmed their resources. Furthermore, the diagnosis report often takes more than a day to be returned. All this adds up to the incapability of our healthcare infrastructure to test all the possibly infected patients. Radiologists across the world have used chest X-rays to detect chest diseases. X-rays being readily available in far less time than RT-PCR reports make them an easy and quick alternative in comparison to current testing methods. However, examining a vast number of X-rays in an already overwhelmed healthcare facility may still lead to delays in determining the presence of the disease. In addition, it would require expertise and profound knowledge about the much recently explored COVID-19 virus in order to make an accurate assessment of the X-rays. In this study, to find solutions to these problems, we have made use of deep learning for the detection of coronavirus. The proposed system uses three different Convolutional Neural Network (CNN) models to detect COVID-19 from pre-processed chest X-ray images with reliable accuracy and hence provide an alternative for people to be aware of being infected rather than wait days for results.
{"title":"COVID-19 Detection From Chest X-Ray Using Deep Learning and Contrast Enhancement","authors":"Shivanee Jaiswal, Joel Marvin Tellis, Rishi Kabra, Swati Mali","doi":"10.1109/iccica52458.2021.9697160","DOIUrl":"https://doi.org/10.1109/iccica52458.2021.9697160","url":null,"abstract":"In the current COVID-19 pandemic, it has become extremely important to detect the affected patients as soon as possible and isolate them in order to break the chain of the spreading virus. Testing in large numbers at laboratories has overwhelmed their resources. Furthermore, the diagnosis report often takes more than a day to be returned. All this adds up to the incapability of our healthcare infrastructure to test all the possibly infected patients. Radiologists across the world have used chest X-rays to detect chest diseases. X-rays being readily available in far less time than RT-PCR reports make them an easy and quick alternative in comparison to current testing methods. However, examining a vast number of X-rays in an already overwhelmed healthcare facility may still lead to delays in determining the presence of the disease. In addition, it would require expertise and profound knowledge about the much recently explored COVID-19 virus in order to make an accurate assessment of the X-rays. In this study, to find solutions to these problems, we have made use of deep learning for the detection of coronavirus. The proposed system uses three different Convolutional Neural Network (CNN) models to detect COVID-19 from pre-processed chest X-ray images with reliable accuracy and hence provide an alternative for people to be aware of being infected rather than wait days for results.","PeriodicalId":327193,"journal":{"name":"2021 International Conference on Computational Intelligence and Computing Applications (ICCICA)","volume":"18 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121373943","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 use of Artificial Intelligence (AI) in solving real- time problems are increasing day by day with the increase in the availability of data and computation power. It is now substantial to use AI-based tools and techniques in space science. Asteroids, rocky objects that orbit around the sun, often produce an array of effects that cause harm to humans and biodiversity on earth. Such effects can cause wind blast, overpressure shock, thermal radiation, cratering, seismic shaking, ejecta deposition, tsunami, and many more. With the availability of data on asteroid parameters and nature, it provides an opportunity to use Machine Learning (ML) to address this problem and reduce the risk. This paper presents a thorough study on the impact of Potentially Hazardous Asteroids (PHAs) and proposes a supervised machine learning method to detect whether an asteroid with specific parameters is hazardous or not. We compare manifold classification algorithms that were implemented on the data. Random forest gave the best performance in terms of accuracy (99.99%) and average F1- score (99.22%).
{"title":"Supervised Classification for Analysis and Detection of Potentially Hazardous Asteroid","authors":"Vedant Bahel, Pratik Bhongade, Jagrity Sharma, Samiksha Shukla, Mahendra Gaikwad","doi":"10.1109/iccica52458.2021.9697222","DOIUrl":"https://doi.org/10.1109/iccica52458.2021.9697222","url":null,"abstract":"The use of Artificial Intelligence (AI) in solving real- time problems are increasing day by day with the increase in the availability of data and computation power. It is now substantial to use AI-based tools and techniques in space science. Asteroids, rocky objects that orbit around the sun, often produce an array of effects that cause harm to humans and biodiversity on earth. Such effects can cause wind blast, overpressure shock, thermal radiation, cratering, seismic shaking, ejecta deposition, tsunami, and many more. With the availability of data on asteroid parameters and nature, it provides an opportunity to use Machine Learning (ML) to address this problem and reduce the risk. This paper presents a thorough study on the impact of Potentially Hazardous Asteroids (PHAs) and proposes a supervised machine learning method to detect whether an asteroid with specific parameters is hazardous or not. We compare manifold classification algorithms that were implemented on the data. Random forest gave the best performance in terms of accuracy (99.99%) and average F1- score (99.22%).","PeriodicalId":327193,"journal":{"name":"2021 International Conference on Computational Intelligence and Computing Applications (ICCICA)","volume":"26 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124994043","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}
In the recent era, everybody are dealing with the digital data. In such scenario individual one heavily depend on credit card. Therefore, the demand of online transactions and usage of e-commerce sites are rising at the rapid rate. The online payments are the main cause of increasing crime rate heavily. Hence, it is the huge challenge for banks and IT professional to identify and resolve such a critical problems. This critical issue can be tackle with the help of machine learning. This articles mainly emphasis on various data mining algorithms such as like C4.5, CART algorithms, J48, Naïve Bayes algorithm, EM algorithm, Apriori algorithm, SVM and so on and also inform the accuracy and precision of the result. The machine learning finds the genuine and non-genuine transition using learning pattern matching and classification technique. The machine learning also normalized the data, identify the anomalies in transaction and provide appropriate results.
{"title":"Analysis on Credit Card Fraud Detection and Prevention using Data Mining and Machine Learning Techniques","authors":"Puninder Kaur, Avinash Sharma, J. Chahal, Taruna Sharma, Vidhu Kiran Sharma","doi":"10.1109/iccica52458.2021.9697172","DOIUrl":"https://doi.org/10.1109/iccica52458.2021.9697172","url":null,"abstract":"In the recent era, everybody are dealing with the digital data. In such scenario individual one heavily depend on credit card. Therefore, the demand of online transactions and usage of e-commerce sites are rising at the rapid rate. The online payments are the main cause of increasing crime rate heavily. Hence, it is the huge challenge for banks and IT professional to identify and resolve such a critical problems. This critical issue can be tackle with the help of machine learning. This articles mainly emphasis on various data mining algorithms such as like C4.5, CART algorithms, J48, Naïve Bayes algorithm, EM algorithm, Apriori algorithm, SVM and so on and also inform the accuracy and precision of the result. The machine learning finds the genuine and non-genuine transition using learning pattern matching and classification technique. The machine learning also normalized the data, identify the anomalies in transaction and provide appropriate results.","PeriodicalId":327193,"journal":{"name":"2021 International Conference on Computational Intelligence and Computing Applications (ICCICA)","volume":"115 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117130378","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 : 2021-11-26DOI: 10.1109/iccica52458.2021.9697259
{"title":"2021 - International Conference on Computational Intelligence and Computing Applications (ICCICA) [Title page]","authors":"","doi":"10.1109/iccica52458.2021.9697259","DOIUrl":"https://doi.org/10.1109/iccica52458.2021.9697259","url":null,"abstract":"","PeriodicalId":327193,"journal":{"name":"2021 International Conference on Computational Intelligence and Computing Applications (ICCICA)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115255001","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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