Day by day the air pollution becomes serious concern in India as well as in overall world. Proper... more Day by day the air pollution becomes serious concern in India as well as in overall world. Proper or accurate prediction or forecast of Air Quality or the concentration level of other Ambient air pollutants such as Sulfur Dioxide, Nitrogen Dioxide, Carbon Monoxide, Particulate Matter having diameter less than 10µ, Particulate Matter having diameter less than 2.5µ, Ozone, etc. is very important because impact of these factors on human health becomes severe. This literature review focuses on the various techniques used for prediction or modelling of Air Quality Index (AQI) and forecasting of future concentration levels of pollutants that may cause the air pollution so that governing bodies can take the actions to reduce the pollution.
International Scientific Journal of Engineering and Management
Internet traffic classification is a fundamental task for network services and management. There ... more Internet traffic classification is a fundamental task for network services and management. There are good machine learning models to identify the class of traffic. However, finding the most discriminating features to have efficient models remains essential. In this paper, we use interpretable machine learning algorithms such as random forest and gradient boosting to find the most discriminating features for internet traffic classification. This paper aims to overcome these challenges by proposing machine learning classification mechanism.
The telecom industry is going through a massive digital transformation with the adoption of ML, A... more The telecom industry is going through a massive digital transformation with the adoption of ML, AI, feedback-based automation and advanced analytics to handle the next generation of applications and services. AI concepts are not new; the algorithms used by Machine Learning and Deep Learning are being currently implemented in various industries and technology verticals. With growing data and an immense volume of information over 5G, the ability to predict data proactively, swiftly and with accuracy, is critically important. Data-driven decision-making will be vital in future communication networks due to the traffic explosion and Artificial Intelligence (AI) will accelerate the 5G network performance. Mobile operators are looking for a programmable solution that will allow them to accommodate multiple independent tenants on the same physical infrastructure and 5G networks allow for end-to-end network resource allocation using the concept of Network Slicing (NS). Network Slicing will play a vital role in enabling a multitude of 5G applications, use cases, and services. Network slicing functions will provide end-to-end isolation between slices with an ability to customize each slice based on the service demands (bandwidth, coverage, security, latency, reliability, etc).
In materials science, traditional experimental and computational approaches require the investmen... more In materials science, traditional experimental and computational approaches require the investment of enormous amounts of time and resources, and the experimental conditions limit the use of these methods. Sometimes, traditional approaches may not yield satisfactory results for the desired purpose. Therefore, it is essential to develop a new approach to accelerate experimental progress and avoid unnecessary waste of time and resources.
This file contains 5 years of daily time series data for several measures of traffic on the stati... more This file contains 5 years of daily time series data for several measures of traffic on the statistical forecasting teaching notes website whose alias is statforecasting.com. The variables have complex seasonality that is keyed to the day of the week and to the academic calendar. The patterns you see here are similar in principle to what you would see in other daily data with day-of-week and time-of-year effects. Some good exercises are to develop a 1-day-ahead forecasting model, a 7-day ahead forecasting model, and an entire-next-week forecasting model (i.e., next 7 days) for unique visitors. I.
Metal–organic frameworks (MOFs) are a widely investigated class of crystalline solids with tunabl... more Metal–organic frameworks (MOFs) are a widely investigated class of crystalline solids with tunable structures that make it possible to impart specific chemical functionality tailored for a given application. However, the enormous number of possible MOFs that can be synthesized makes it difficult to determine which materials would be the most promising candidates, especially for applications governed by electronic structure properties that are often computationally demanding to simulate and time-consuming to probe experimentally. Here, we have developed the first publicly available quantum-chemical database for MOFs (the “QMOF database”), which consists of properties derived from density functional theory (DFT) for over 14,000 experimentally synthesized MOFs. Throughout this study, we demonstrate how this new database can be used to identify MOFs with targeted electronic structure properties. As a proof-of-concept, we use the QMOF database to evaluate the performance of several machi...
This data was scraped from mtsamples.com. The data contains sample medical transcriptions from v... more This data was scraped from mtsamples.com. The data contains sample medical transcriptions from various medical specialities. The idea is to use these transcriptions and classify them to the medical specialties based on various NLP techniques. The extensive growth of data in the health domain has increased the utility of NLP in health. A vast amount of data in the form of text are generated by medical departments through medical prescriptions. We tried to perform different comparative analysis using classification algorithms and advanced techniques like BERT.
One key issue is that medical information is presented as free-form text and, therefore, requires a time commitment from clinicians to manually extract meaningful information. Natural language processing (NLP) methods can be used to extract relevant information and perform classification methods on it.
The BERT model has arisen to be popular in recent years. It can cope with NLP tasks such as supervised text classification with better feature engineering techniques. In this paper, we also tried to cover the concept of BERT and its application for this particular problem statement.
Day by day the air pollution becomes serious concern in India as well as in overall world. Proper... more Day by day the air pollution becomes serious concern in India as well as in overall world. Proper or accurate prediction or forecast of Air Quality or the concentration level of other Ambient air pollutants such as Sulfur Dioxide, Nitrogen Dioxide, Carbon Monoxide, Particulate Matter having diameter less than 10µ, Particulate Matter having diameter less than 2.5µ, Ozone, etc. is very important because impact of these factors on human health becomes severe. This literature review focuses on the various techniques used for prediction or modelling of Air Quality Index (AQI) and forecasting of future concentration levels of pollutants that may cause the air pollution so that governing bodies can take the actions to reduce the pollution.
International Scientific Journal of Engineering and Management
Internet traffic classification is a fundamental task for network services and management. There ... more Internet traffic classification is a fundamental task for network services and management. There are good machine learning models to identify the class of traffic. However, finding the most discriminating features to have efficient models remains essential. In this paper, we use interpretable machine learning algorithms such as random forest and gradient boosting to find the most discriminating features for internet traffic classification. This paper aims to overcome these challenges by proposing machine learning classification mechanism.
The telecom industry is going through a massive digital transformation with the adoption of ML, A... more The telecom industry is going through a massive digital transformation with the adoption of ML, AI, feedback-based automation and advanced analytics to handle the next generation of applications and services. AI concepts are not new; the algorithms used by Machine Learning and Deep Learning are being currently implemented in various industries and technology verticals. With growing data and an immense volume of information over 5G, the ability to predict data proactively, swiftly and with accuracy, is critically important. Data-driven decision-making will be vital in future communication networks due to the traffic explosion and Artificial Intelligence (AI) will accelerate the 5G network performance. Mobile operators are looking for a programmable solution that will allow them to accommodate multiple independent tenants on the same physical infrastructure and 5G networks allow for end-to-end network resource allocation using the concept of Network Slicing (NS). Network Slicing will play a vital role in enabling a multitude of 5G applications, use cases, and services. Network slicing functions will provide end-to-end isolation between slices with an ability to customize each slice based on the service demands (bandwidth, coverage, security, latency, reliability, etc).
In materials science, traditional experimental and computational approaches require the investmen... more In materials science, traditional experimental and computational approaches require the investment of enormous amounts of time and resources, and the experimental conditions limit the use of these methods. Sometimes, traditional approaches may not yield satisfactory results for the desired purpose. Therefore, it is essential to develop a new approach to accelerate experimental progress and avoid unnecessary waste of time and resources.
This file contains 5 years of daily time series data for several measures of traffic on the stati... more This file contains 5 years of daily time series data for several measures of traffic on the statistical forecasting teaching notes website whose alias is statforecasting.com. The variables have complex seasonality that is keyed to the day of the week and to the academic calendar. The patterns you see here are similar in principle to what you would see in other daily data with day-of-week and time-of-year effects. Some good exercises are to develop a 1-day-ahead forecasting model, a 7-day ahead forecasting model, and an entire-next-week forecasting model (i.e., next 7 days) for unique visitors. I.
Metal–organic frameworks (MOFs) are a widely investigated class of crystalline solids with tunabl... more Metal–organic frameworks (MOFs) are a widely investigated class of crystalline solids with tunable structures that make it possible to impart specific chemical functionality tailored for a given application. However, the enormous number of possible MOFs that can be synthesized makes it difficult to determine which materials would be the most promising candidates, especially for applications governed by electronic structure properties that are often computationally demanding to simulate and time-consuming to probe experimentally. Here, we have developed the first publicly available quantum-chemical database for MOFs (the “QMOF database”), which consists of properties derived from density functional theory (DFT) for over 14,000 experimentally synthesized MOFs. Throughout this study, we demonstrate how this new database can be used to identify MOFs with targeted electronic structure properties. As a proof-of-concept, we use the QMOF database to evaluate the performance of several machi...
This data was scraped from mtsamples.com. The data contains sample medical transcriptions from v... more This data was scraped from mtsamples.com. The data contains sample medical transcriptions from various medical specialities. The idea is to use these transcriptions and classify them to the medical specialties based on various NLP techniques. The extensive growth of data in the health domain has increased the utility of NLP in health. A vast amount of data in the form of text are generated by medical departments through medical prescriptions. We tried to perform different comparative analysis using classification algorithms and advanced techniques like BERT.
One key issue is that medical information is presented as free-form text and, therefore, requires a time commitment from clinicians to manually extract meaningful information. Natural language processing (NLP) methods can be used to extract relevant information and perform classification methods on it.
The BERT model has arisen to be popular in recent years. It can cope with NLP tasks such as supervised text classification with better feature engineering techniques. In this paper, we also tried to cover the concept of BERT and its application for this particular problem statement.
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Papers by Debmalya Ray
good machine learning models to identify the class of traffic. However, finding the most
discriminating features to have efficient models remains essential. In this paper, we use
interpretable machine learning algorithms such as random forest and gradient boosting to find the
most discriminating features for internet traffic classification. This paper aims to overcome these
challenges by proposing machine learning classification mechanism.
The extensive growth of data in the health domain has increased the utility of NLP in health. A vast amount of data in the form of text are generated by medical departments through medical prescriptions.
We tried to perform different comparative analysis using classification algorithms and advanced techniques like BERT.
One key issue is that medical information is
presented as free-form text and, therefore, requires a time commitment from clinicians to manually extract meaningful information. Natural language processing (NLP) methods can be used to extract
relevant information and perform classification
methods on it.
The BERT model has arisen to be popular in recent years. It can cope with NLP tasks such as supervised text classification with better feature engineering techniques. In this paper, we also tried to cover the
concept of BERT and its application for this
particular problem statement.
good machine learning models to identify the class of traffic. However, finding the most
discriminating features to have efficient models remains essential. In this paper, we use
interpretable machine learning algorithms such as random forest and gradient boosting to find the
most discriminating features for internet traffic classification. This paper aims to overcome these
challenges by proposing machine learning classification mechanism.
The extensive growth of data in the health domain has increased the utility of NLP in health. A vast amount of data in the form of text are generated by medical departments through medical prescriptions.
We tried to perform different comparative analysis using classification algorithms and advanced techniques like BERT.
One key issue is that medical information is
presented as free-form text and, therefore, requires a time commitment from clinicians to manually extract meaningful information. Natural language processing (NLP) methods can be used to extract
relevant information and perform classification
methods on it.
The BERT model has arisen to be popular in recent years. It can cope with NLP tasks such as supervised text classification with better feature engineering techniques. In this paper, we also tried to cover the
concept of BERT and its application for this
particular problem statement.