The dataset contains 1120 images divided equally into two classes, where 560 images are of Smoking (smokers) and remaining 560 images belong to NotSmoking (non-smokers) class. The dataset is curated by scanning through various search engines by entering multiple keywords that include cigarette smoking, smoker, person, coughing, taking inhaler, person on the phone, drinking water etc. We tried to consider versatile images in both classes for creating a certain degree of inter-class confusion in order to better train the model. For instance, Smoking class contains images of smokers from multiple angles and various gestures. Moreover, the images in NotSmoking class consists of images of non-smokers with slightly similar gestures as that of smoking images such as people drinking water, using inhaler, holding the mobile phone, coughing etc. The dataset can be used by the prospective researchers to propose deep learning algorithms for automated detection and screening of smoker towards ensuring the green environment and performing surveillance in smart cities. All images in the dataset are preprocessed and resized to a resolution of 250×250. We considered 80% of the data for training and validation purposes and 20% for the testing.

Please cite this article if you use this dataset in your research: A. Khan, S. Khan, B. Hassan, and Z. Zheng, “CNN-Based Smoker Classification and Detection in Smart City Application,” Sensors, vol. 22, no. 3, pp. 892, 2022.

Annual data on the proportion of adults in Great Britain who smoke cigarettes, cigarette consumption, the proportion who have never smoked cigarettes and the proportion of smokers who have quit by sex and age over time.

The smoking prevalence in the United States was forecast to continuously decrease between 2024 and 2029 by in total *** percentage points. After the ****** consecutive decreasing year, the smoking prevalence is estimated to reach ***** percent and therefore a new minimum in 2029. Shown is the estimated share of the adult population (15 years or older) in a given region or country, that smoke on a daily basis. According to the WHO and World bank, smoking refers to the use of cigarettes, pipes or other types of tobacco.The shown data are an excerpt of Statista's Key Market Indicators (KMI). The KMI are a collection of primary and secondary indicators on the macro-economic, demographic and technological environment in up to *** countries and regions worldwide. All indicators are sourced from international and national statistical offices, trade associations and the trade press and they are processed to generate comparable data sets (see supplementary notes under details for more information).Find more key insights for the smoking prevalence in countries like Canada and Mexico.

Comparing the *** selected regions regarding the smoking prevalence , Myanmar is leading the ranking (***** percent) and is followed by Serbia with ***** percent. At the other end of the spectrum is Ghana with **** percent, indicating a difference of ***** percentage points to Myanmar. Shown is the estimated share of the adult population (15 years or older) in a given region or country, that smoke on a daily basis. According to the WHO and World bank, smoking refers to the use of cigarettes, pipes or other types of tobacco.The shown data are an excerpt of Statista's Key Market Indicators (KMI). The KMI are a collection of primary and secondary indicators on the macro-economic, demographic and technological environment in up to *** countries and regions worldwide. All indicators are sourced from international and national statistical offices, trade associations and the trade press and they are processed to generate comparable data sets (see supplementary notes under details for more information).

This data shows the percentage of adults (age 18 and over) who are current smokers.

Smoking is the single biggest cause of preventable death and illnesses, and big inequalities exist between and within communities. Smoking is a major risk factor for many diseases, such as lung cancer, chronic obstructive pulmonary disease (COPD, bronchitis and emphysema) and heart disease. It is also associated with cancers in other organs.

Smoking is a modifiable lifestyle risk factor. Preventing people from starting smoking is important in reducing the health harms and inequalities.

This data is based on the Office for National Statistics (ONS) Annual Population Survey (APS). The percentage of adults is not age-standardised. In this dataset particularly at district level there may be inherent statistical uncertainty in some data values. Thus as with many other datasets, this data should be used together with other data and resources to obtain a fuller picture.

Data source: Public Health England, Public Health Outcomes Framework (PHOF) indicator 92443 (Number 15). This data is updated annually.

This dataset contains the data on 144 daily smokers each rating 44 preparatory activities for quitting smoking (e.g., envisioning one's desired future self after quitting smoking, tracking one's smoking behavior, learning about progressive muscle relaxation) on their perceived ease/difficulty and required completion time. Since becoming more physically active can make it easier to quit smoking, some activities were also about becoming more physically active (e.g., tracking one's physical activity behavior, learning about what physical activity is recommended, envisioning one's desired future self after becoming more physically active). Moreover, participants provided a free-text response on what makes some activities more difficult than others.

Study

The data was gathered during a study on the online crowdsourcing platform Prolific between 6 September and 16 November 2022. The Human Research Ethics Committee of Delft University of Technology granted ethical approval for the research (Letter of Approval number: 2338).

In this study, daily smokers who were contemplating or preparing to quit smoking first filled in a prescreening questionnaire and were then invited to a repertory grid study if they passed the prescreening. In the repertory grid study, participants were asked to divide sets of 3 preparatory activities for quitting smoking into two subgroups. Afterward, they rated all preparatory activities on the perceived ease of doing them and the perceived required time to do them. Participants also provided a free-text response on what makes some activities more difficult than others.

The study was pre-registered in the Open Science Framework (OSF): https://osf.io/cax6f. This pre-registration describes the study setup, measures, etc. Note that this dataset contains only part of the collected data: the data related to studying the perceived difficulty of preparatory activities.

The file "Preparatory_Activity_Formulations.xlsx" contains the formulations of the 44 preparatory activities used in this study.

Data

This dataset contains three types of data:

- Data from participants' Prolific profiles. This includes, for example, the age, gender, weekly exercise amount, and smoking frequency.

- Data from a prescreening questionnaire. This includes, for example, the stage of change for quitting smoking and whether people previously tried to quit smoking.

- Data from the repertory grid study. This includes the ratings of the 44 activities on ease and required time as well as the free-text responses on what makes some activities more difficult than others.

There is for each data file a file that explains each data column. For example, the file "prolific_profile_data_explanation.xlsx" contains the column explanations for the data gathered from participants' Prolific profiles.

Each data file contains a column called "rand_id" that can be used to link the data from the data files.

In the case of questions, please contact Nele Albers (n.albers@tudelft.nl) or Willem-Paul Brinkman (w.p.brinkman@tudelft.nl).

Behavioral Risk Factors - Tobacco

2011-2015 BRFSS Survey Data

By Health [source]

About this dataset

The Centers for Disease Control and Prevention (CDC) developed the State Tobacco Activities Tracking and Evaluation (STATE) System to monitor modifiable behavioral risk factors of chronic diseases and other leading causes of death. Specifically, this dataset focuses on tobacco topics such as cigarette smoking status, cigarette smoking prevalence according to demographics, cigarette smoking frequency, and quit attempts from BRFSS surveys from participating states across the United States.

This dataset includes columns such as Year, LocationAbbr, LocationDesc, TopicType, TopicDesc, MeasureDesc DataSource Response Data_Value_Unit Data_Value_Type Data_Value_Footnote_Symbol Data_Value_Std _Err Sample-Size Gender Race Age Education GeoLocation DisplayOrder which record the information collected by state BRFSS surveys. The collection of data is extremely important in understanding trends in tobacco use across different races gender education levels locations etc which results in more effective public health interventions aimed at reducing harm caused by cigarette use

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How to use the dataset

This dataset contains information about modifiable risk factors for chronic diseases and other leading causes of death related to cigarette smoking. It can be used to identify trends in tobacco-related behavioral risks, as well as analyze the different associated factors.

In order to use this dataset effectively, it is important to understand the columns and what each represents: ⦁ Year – This column shows the year of the survey data. ⦁ LocationAbbr – Abbreviation of the location from which the data originates.
⦁ LocationDesc – Description of the location from which the data originated.
⦁ TopicType - Type of topic that is being examined in regards to tobacco-related behavior. ⦁ TopicDesc - Description of the specific topic being examined in terms of cigarette smoking behavior risks.
⦁ MeasureDesc - Further description provided on how a measure was identified or calculated for each topic/question asked in relation to cigarette smoking behaviors and risk factors studied.
⦁ DataSource - Source(s) where responses were collected when applicable (e.g., interviews, mailed questionnaires).
⦁ Response – The response associated with a given measure when applicable (e.g., “yes” or “no”). ⦁ Data_Value_Unit– The unit used by any numeric measures given, such as percentages or percents (%)

Research Ideas

• Creating an online smoking cessation program to educate people on the long-term effects of smoking and the risks associated with it.
• Investigating differences in smoking habits by demographic factors such as race, gender, education level, age and location in order to plan public health interventions that most effectively target high-risk populations.
• Developing a mobile app that tracks cigarette consumption levels over time, allowing users to monitor their progress towards quitting and/or reductions in their nicotine addiction

Acknowledgements

If you use this dataset in your research, please credit the original authors. Data Source

License

Unknown License - Please check the dataset description for more information.

Columns

File: rows.csv | Column name | Description | |:-------------------------------|:-----------------------------------------------| | YEAR | Year of the survey (Integer) | | LocationAbbr | Abbreviation of the location (String) | | LocationDesc | Description of the location (String) | | TopicType | Type of topic (String) | | TopicDesc | Description of the topic (String) | | MeasureDesc | Description of the measure (String) | | DataSource | Source of the data (String) | | Response | Response to the survey question (String) | | Data_Value_Unit | Unit of the data value (String) | | Data_Value_Type | Type of the data value (String) | | Data_Value_Footnote_Symbol | Symbol of the data value footnote (String) ...

project use R for graph :

https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F16731800%2F9ea39c2ead4bbbaf665a04d32f5cd292%2Fgraph1.gif?generation=1708719099584294&alt=media" alt="">

Tobacco smoking stands as a significant global health crisis, affecting millions worldwide and leading to severe health complications and premature deaths. This issue has persisted for decades, with an estimated 100 million people succumbing prematurely due to smoking-related causes throughout the 20th century, predominantly in affluent nations. However, a decline in the global smoking rate signals a positive shift in global health, potentially enabling millions to enjoy longer, healthier lives.

Annually, smoking is responsible for approximately 8 million premature deaths. These figures highlight the urgent need for effective measures to combat this epidemic. The World Health Organization (WHO) and the Institute for Health Metrics and Evaluation (IHME) provide critical data on the mortality rates associated with tobacco use, emphasizing the gravity of the situation. According to the latest WHO estimates as of November 2023, over 8 million people die each year due to tobacco use, with more than 7 million of these deaths directly linked to smoking. Additionally, around 1.3 million nonsmokers die from exposure to second-hand smoke. The IHME's Global Burden of Disease study further supports these findings, estimating that 8.7 million deaths annually can be attributed to tobacco use, including 7.7 million from smoking and 1.3 million from second-hand smoke exposure, alongside an additional 56,000 deaths from chewing tobacco.

The impact of smoking on mortality is disproportionately higher among men, who account for 71% of premature deaths due to smoking. This disparity underscores the need for targeted interventions that address the specific risks and behaviors associated with smoking among different demographics.

Understanding the vast death toll from tobacco use requires a comprehensive approach that encompasses all forms of tobacco consumption, including smoking and chewing tobacco. The data indicate that the vast majority of tobacco-related deaths are due to smoking, with figures from the IHME suggesting that smoking-related deaths constitute more than 99.9% of all tobacco-use deaths. This emphasizes the critical importance of focusing public health efforts on reducing smoking rates to mitigate the overall impact of tobacco on global health.

The interactive charts and studies provided by organizations like the WHO and IHME offer valuable insights into the global and regional dynamics of smoking-related health issues. These resources allow for a detailed examination of smoking trends and their health consequences, facilitating evidence-based policy-making and public health strategies aimed at reducing smoking prevalence and its associated health burden.

Efforts to combat smoking must take into account the various factors that contribute to its prevalence, including societal norms, economic factors, and the addictive nature of nicotine. Public health campaigns, legislative measures, and support programs for those trying to quit smoking are essential components of a comprehensive strategy to address this issue.

Furthermore, research into the health effects of smoking and the mechanisms by which it contributes to diseases such as cancer, heart disease, and respiratory illnesses is crucial for developing effective treatments and prevention strategies. By understanding the full scope of smoking's impact on health, researchers and policymakers can better target interventions to reduce smoking rates and improve public health outcomes.

In conclusion, the global health crisis posed by tobacco smoking is a multifaceted issue that requires concerted efforts from governments, public health organizations, and communities worldwide. The declining trend in smoking rates offers hope, but the continued high prevalence of smoking-related deaths underscores the need for ongoing action. Through research, public health initiatives, and policy interventions, it is possible to further reduce smoking rates and alleviate the tremendous health burden it imposes on societies around the globe.

This data shows the percentage of adults (age 18 and over) who are current smokers. Smoking is the single biggest cause of preventable death and illnesses, and big inequalities exist between and within communities. Smoking is a major risk factor for many diseases, such as lung cancer, chronic obstructive pulmonary disease (COPD, bronchitis and emphysema) and heart disease. It is also associated with cancers in other organs. Smoking is a modifiable lifestyle risk factor. Preventing people from starting smoking is important in reducing the health harms and inequalities. This data is based on the Office for National Statistics (ONS) Annual Population Survey (APS). The percentage of adults is not age-standardised. In this dataset particularly at district level there may be inherent statistical uncertainty in some data values. Thus as with many other datasets, this data should be used together with other data and resources to obtain a fuller picture. Data source: Office for Health Improvement and Disparities (OHID) Public Health Outcomes Framework (PHOF) indicator 92443 (Number 15). This data is updated annually.

This data shows the percentage of adults (age 18 and over) who are current smokers. Smoking is the single biggest cause of preventable death and illnesses, and big inequalities exist between and within communities. Smoking is a major risk factor for many diseases, such as lung cancer, chronic obstructive pulmonary disease (COPD, bronchitis and emphysema) and heart disease. It is also associated with cancers in other organs. Smoking is a modifiable lifestyle risk factor. Preventing people from starting smoking is important in reducing the health harms and inequalities. This data is based on the Office for National Statistics (ONS) Annual Population Survey (APS). The percentage of adults is not age-standardised. In this dataset particularly at district level there may be inherent statistical uncertainty in some data values. Thus as with many other datasets, this data should be used together with other data and resources to obtain a fuller picture. Data source: Office for Health Improvement and Disparities (OHID) Public Health Outcomes Framework (PHOF) indicator 92443 (Number 15). This data is updated annually.

US States Tobacco Use Prevalence

1996-2010 Survey Data

By Health [source]

About this dataset

This dataset from the Centers for Disease Control and Prevention (CDC) provides state-based surveillance information related to tobacco use among American adults from 1996 to 2010. It contains data on modifiable risk factors for chronic diseases and other leading causes of death obtained from annual BRFSS surveys conducted in participating states.

The dataset focuses on key topics such as cigarette smoking status, prevalence by demographics, frequency, and quit attempts. The metrics collected are important indicators of public health efforts in tobacco prevention, control and cessation programs at the state level.

With this dataset you can explore how people perceive smoking differently across geographical areas as well as their socio-economic backgrounds like gender identity, race or ethnicity, educational level or life stage. Analyzing this data will give us valuable insights into the impact of tobacco consumption in our society today and help create more effective public health interventions tailored to local needs

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How to use the dataset

This dataset can be used to study the prevalence of tobacco use in different US states in the period 1996-2010. The dataset contains information on cigarette smoking status, prevalence by demographics, frequency, and quit attempts.

In order to begin exploring this dataset it is recommended that one first understand the column headers and their corresponding values. This can be done by familiarizing oneself with the included data dictionary that defines each column's name and description.

Next it is recommended to familiarize oneself with the data types contained in the columns. Depending on what type of query you are wanting to make some columns may need conversion from one type to another for better results when performing a query. Some common types found within this dataset include integers (whole numbers), strings (text) and floats (decimals).

Once you have familiarized yourself with both the columns and data types it is now a good time to start considering which questions you want answer related to tobacco use in US states during this period of time. Consider which variables might provide valuable insights into your analysis such as age, gender, race etc., as well as other variables such as location or year that could add more complexity or context understanding into your analysis. Assuming that your desired questions have been determined you can begin querying your data using methods supported by whichever language or platform you are choosing work with such us SQL or Python Pandas Dataframes etc.. This will allow manipulation of all relevant variables according get useful insights out of them related back tobaccos use in US states during this specific period.

Finally when doing an analysis on any given topic its helpful no compare ones findings between multiple datasets if possible so consider obtaining any other datasets relevant top toxins use over a similar timespan which could be compared against these findings if available

Research Ideas

• Identifying and targeting high-risk locations for tobacco use prevention efforts by analyzing the prevalence of different forms of tobacco use in different states.
• Examining patterns of tobacco use among different demographic groups (gender, age, race, etc.) to design better tailored interventions for tobacco cessation.
• Comparing quit attempt rates with smoking frequency and prevalence across states to understand the effectiveness of smoke-free laws and policies that have been enacted in recent years

Acknowledgements

If you use this dataset in your research, please credit the original authors. Data Source

License

See the dataset description for more information.

Columns

File: rows.csv | Column name | Description | |:-------------------------------|:-----------------------------------------------| | YEAR | Year of survey (Integer) | | LocationAbbr | Abbreviation of the state (String) | | LocationDesc | Full name of the state (String) | | TopicType | Type of topic (String) | | TopicDesc | Description of the topic (String) | | MeasureDesc | Description of ...

Data taken from the Health and Wellbeing Survey in 2014 showing people in Plymouth who smoke. It shows the number of people in each Plymouth ward that have answered the Health and Wellbeing Survey. People who say they smoke and the numbers reported in what ward including the total people in the ward. Also includes the area ward code in geo data and percentage that represents each in the ward.

The following image types have been added as no-fire, no-smoke class: 1- fog 2- cloud 3- people smoking 4- sun and sunlight

the dataset may need some changes, as some images may not be in the correct folder.

This dataset has been gathered from 12 Kaggle datasets, including:

Dani215. (n.d.). Fire dataset [Data set]. Kaggle. https://www.kaggle.com/datasets/dani215/fire-dataset/data Amrzish Minha. (2023). Forest-Fire, Smoke, and Non-Fire Image Dataset [Data set]. Kaggle. https://www.kaggle.com/datasets/amerzishminha/forest-fire-smoke-and-non-fire-image-dataset NakendraPrasathK. (2020). Cloud image classification dataset [Data set]. Kaggle. https://www.kaggle.com/datasets/nakendraprasathk/cloud-image-classification-dataset Thaslim V S. (2023). Fog detection dataset [Data set]. Kaggle. https://www.kaggle.com/datasets/thaslimvs/fog-detection-dataset/data Kapadnis, S. (2023). Smoker detection [image] classification dataset [Data set]. Kaggle. https://www.kaggle.com/datasets/sujaykapadnis/smoking Pitroda, K. (2023). Sun and moon images [Data set]. Kaggle. https://www.kaggle.com/datasets/khushipitroda/sun-and-moon-images Yessica Tuteja. (2023). Foggy Cityscapes images [Data set]. Kaggle. https://www.kaggle.com/datasets/yessicatuteja/foggy-cityscapes-image-dataset Ayoub, A. (2025). Fire_data [Data set]. Kaggle. https://www.kaggle.com/datasets/anhalayoub/fire-data Saha, B. (2022). Howard-Cloud-X [Data set]. Kaggle. https://www.kaggle.com/datasets/imbikramsaha/howard-cloudx Bhathena, J. (2021). Weather image recognition dataset [Data set]. Kaggle. https://www.kaggle.com/datasets/jehanbhathena/weather-dataset Lee, H. (2019). Cigarette smoker detection [Data set]. Kaggle. https://www.kaggle.com/datasets/vitaminc/cigarette-smoker-detection

The percentage of people aged 18 and over with SMI, identified on GP systems, who are current smokers Current version updated: Mar-16 Next version due: TBC

This dataset contains three smoking related indicators.

Rates of self reported four-week smoking quitters

Smoking quit rates per 100,000 available from the HNA.

- These quarterly reports present provisional results from the monitoring of the NHS Stop Smoking Services (NHS SSS) in England. This report includes information on the number of people setting a quit date and the number who successfully quit at the 4 week follow-up. Data for London presented with England comparator. PCT level data available from NHS.

Number of Deaths Attributable to Smoking per 100,000 population by borough

Deaths attributable to smoking, directly age-sex standardised rate for persons aged 35 years +. Causes of death considered to be related to smoking are: various cancers, cardiovascular and respiratory diseases, and diseases of the digestive system.

Numbers of adults smoking by borough

Prevalence of smoking among persons aged 18 years and over.
- Population who currently smoke, are ex-smokers, or never smoked by borough. This includes cigarette, cigar or pipe smokers. Data by age is also provided for London with a UK comparator.

Relevant links: http://www.hscic.gov.uk/Article/1685

http://www.apho.org.uk/default.aspx?QN=HP_DATATABLES

Number and percentage of persons being current smokers, by age group and sex.

Prevalence of daily or occasional smoking and vaping in people aged 16 years and over.

This report contains results from the latest survey of secondary school pupils in England in years 7 to 11 (mostly aged 11 to 15), focusing on smoking, drinking and drug use. It covers a range of topics including prevalence, habits, attitudes, and wellbeing. This survey is usually run every two years, however, due to the impact that the Covid pandemic had on school opening and attendance, it was not possible to run the survey as initially planned in 2020; instead it was delivered in the 2021 school year. In 2021 additional questions were also included relating to the impact of Covid. They covered how pupil's took part in school learning in the last school year (September 2020 to July 2021), and how often pupil's met other people outside of school and home. Results of analysis covering these questions have been presented within parts of the report and associated data tables. It includes this summary report showing key findings, excel tables with more detailed outcomes, technical appendices and a data quality statement. An anonymised record level file of the underlying data on which users can carry out their own analysis will be made available via the UK Data Service later in 2022 (see link below).

The information is from the "National Health Interview Survey" of the Ministry of Health and Welfare, which collects information on smoking behavior from the public through telephone interviews. For more information, please visit the "Tobacco Hazard Prevention Information Website" of the National Health Administration (http://tobacco.hpa.gov.tw/).The definition of "daily smoking rate" is the ratio of individuals who have smoked more than 100 cigarettes from the past to present and have used tobacco daily in the last 30 days. The formula for calculation is: Number of respondents aged 15 and above who answered "smoked more than 100 cigarettes so far" and "used tobacco daily in the last 30 days" / Number of valid completed interviews of individuals aged 15 and above * 100%.

Annual data on the proportion of adults in Great Britain who use e-cigarettes, by different characteristics such as age, sex and cigarette smoking status.