SYSTEM FOR PREDICTING SUSTAINABLE DEVELOPMENT GOALS TRENDS

Abstract

ABSTRACT SYSTEM FOR PREDICTING SUSTAINABLE DEVELOPMENT GOALS TRENDS The invention presents a system [100] for predicting sustainable development goals trends of the present disclosure includes a database [102] that stores input data, a pre-processing module [104] that cleans and transforms the data into a suitable format for analysis, and an AI/ML module [106] that processes the data to predict SDG trends. The AI/ML module [106] receives the cleaned data, classifies the data into distinct categories, and trains at least one model to analyze uncertainty within the data using regression modelling. The trained model is then deployed to predict future SDG trends and generate a visual representation of these predictions through a visualization tool [108]. Refer to Figure 1 and Figure 2A

Specification

Description:"SYSTEM FOR PREDICTING SUSTAINABLE DEVELOPMENT GOALS TRENDS"
FIELD OF THE INVENTION
[0001] The present invention relates to a field of predictive analytics and sustainability. Specifically, a system and method for predicting Sustainable Development Goals (SDGs) trends at the state level within India. The system leverages advanced artificial intelligence (AI) and machine learning (ML) techniques to generate insights, incorporate uncertainty, and provide accurate forecasting of SDG trends while accounting for regional variability and local conditions.
BACKGROUND OF THE INVENTION
[0002] The need for efficient, reliable tools to monitor and forecast progress towards the United Nations' Sustainable Development Goals (SDGs) has become increasingly critical. However, current systems for tracking SDG progress often face significant limitations, particularly in the context of India, where the complexity of regional diversity and local conditions must be considered. Existing solutions frequently rely on outdated data, lack the capacity to incorporate real-time or region-specific information, and fail to provide actionable predictions for future trends in SDG achievements.
[0003] Traditional SDG tracking tools often use static data sources that do not account for the dynamic nature of social, economic, and environmental factors affecting sustainable development. This shortcoming becomes particularly apparent when considering India's vast geographical and socio-economic diversity. The progress of SDGs can vary significantly across different states, making it essential for any tracking system to adapt to these variations and provide tailored insights. Without the capability to track these differences accurately, efforts to achieve SDGs may be misdirected, leading to inefficiencies in resource allocation and missed opportunities for targeted intervention.
[0004] The absence of forecasting capabilities in existing SDG tracking systems further exacerbates the challenge. Without predictive models, policymakers and stakeholders are unable to make informed decisions about future trends, which can hinder long-term strategic planning. The inability to assess future SDG trends in real-time means that decision-makers are often operating with a significant lag, making it difficult to address emerging challenges and optimize resources effectively.
[0005] Moreover, the current lack of tools that integrate real-time data from various sources, such as IoT devices, social media platforms, and market analytics services, limits the scope and accuracy of SDG monitoring. Inadequate consideration of local aspects and regional differences further diminishes the effectiveness of these systems, as they fail to provide a comprehensive, nuanced view of the progress toward SDG achievement in different parts of India.
[0006] In summary, existing solutions to track the progress of SDGs are hindered by outdated information, an inability to adapt to regional variations, and a lack of predictive capabilities. There is a pressing need for a system that can integrate real-time data, account for regional diversity, and predict SDG trends based on comprehensive, up-to-date information. This invention seeks to address these limitations by providing an AI-driven, machine learning-based system that dynamically updates and forecasts SDG trends, enabling more informed decision-making and more effective resource allocation at the state level.
SUMMARY OF THE INVENTION
[0007] In view of the foregoing disadvantages inherent in the prior art, the general purpose of the present disclosure is to provide a system for predicting sustainable development goals trends, to include all advantages of the prior art, and to overcome the drawbacks inherent in the prior art.
[0008] Some of the objects of the present disclosure, which at least one embodiment herein satisfies, are as follows:
[0009] An object of the present disclosure is to ameliorate one or more problems of the prior art or to at least provide a useful alternative. An object of the present disclosure is to provide a system for predicting sustainable development goals trends.
[0010] Another object of the present disclosure is to provide a system for predicting sustainable development goals trends that is capable of tracking and forecasting progress towards Sustainable Development Goals (SDGs) by providing a predictive system capable of both short-term and long-term projections of SDG trends.
[0011] Another object of the present disclosure is to provide a system for predicting sustainable development goals trends that is capable of providing a state-specific predictive model that incorporates local variations in socioeconomic and environmental factors, ensuring that the system delivers highly granular and localized insights for each region, particularly within India.
[0012] Another object of the present disclosure is to provide a system for predicting sustainable development goals trends that integrates real-time data from multiple sources, such as IoT devices, social media platforms, and market analytics services, enabling continuous and up-to-date tracking of SDG progress.
[0013] Another object of the present disclosure is to provide a system for predicting sustainable development goals trends that offers an interactive, dynamic visualization of SDG trends through trend graphs and comparative analytics, providing policymakers, researchers, and stakeholders with concise, actionable insights that enhance decision-making.
[0014] Another object of the present disclosure is to provide a system for predicting sustainable development goals trends that is capable of generating both predictive and prescriptive insights, allowing users to project future outcomes and take proactive measures to optimize SDG progress based on current and forecasted trends.
[0015] Another object of the present disclosure is to provide a system for predicting sustainable development goals trends that offers a comprehensive approach to SDG monitoring that accounts for regional differences in development challenges and progress, providing state-level insights that guides localized interventions and strategies.
[0016] Other objects and advantages of the present disclosure will be more apparent from the following description, which is not intended to limit the scope of the present disclosure.
[0017] In view of the above objects, in one aspect, the current disclosure provides a system for predicting sustainable development goals trends that is a robust and novel phishing-resistant authentication tool.
[0018] The system for predicting sustainable development goals trends of the present disclosure includes a database that stores input data, a pre-processing module that cleans and transforms the data into a suitable format for analysis, and an AI/ML module that processes the data to predict SDG trends. The AI/ML module receives the cleaned data, classifies the data into distinct categories, and trains at least one model to analyze uncertainty within the data using regression modelling. The trained model is then deployed to predict future SDG trends and generate a visual representation of these predictions through a visualization tool. The AI/ML module uses machine learning algorithms to train the model, applying techniques such as linear regression, support vector regression, or decision tree regression. The system also includes a feedback module that refines the data cleaning, feature engineering, and classification process, ensuring more accurate predictions over time. Additionally, the system incorporates cross-validation methods, such as K-fold cross-validation, to validate the trained model, further enhancing its reliability. The model dynamically updates based on new data inputs, ensuring that the SDG trend predictions remain current and relevant. The system allows for comprehensive aggregation of the input data, using methods like weighted averages, sum of squares, and principal component analysis (PCA) to calculate a composite score.
[0019] In one embodiment, a method for operating the system is disclosed. The method comprising several steps: cleaning and transforming the input data, receiving the data for classification, training the AI/ML model, deploying the trained model to predict SDG trends, and generating a visual representation of these predictions. The database is also configured to receive data from various external sources, such as Internet of Things (IoT) devices, social media platforms, and market analytics services, further enriching the system's ability to predict and track SDG progress.
BRIEF DESCRIPTION OF DRAWING
[0020] The foregoing summary, as well as the following detailed description of various embodiments, is better understood when read in conjunction with the drawings provided herein. For the purposes of illustration, there are shown in the drawings exemplary embodiments; however, the presently disclosed subject matter is not limited to the specific methods and instrumentalities disclosed.
[0021] Figure 1 illustrates a block diagram of a system for predicting sustainable development goals trends, in accordance with an embodiment of the present invention; and
[0022] Figures 2A and 2B illustrate flow charts of a system for predicting sustainable development goals trends, in accordance with an embodiment of the present invention.
[0023] Like reference numerals refer to like parts throughout the description of several views of the drawing.
DETAILED DESCRIPTION OF THE INVENTION
[0024] Embodiments are provided so as to thoroughly and fully convey the scope of the present disclosure to the person skilled in the art. Numerous details are set forth, relating to specific components, and methods, to provide a complete understanding of embodiments of the present disclosure. It will be apparent to the person skilled in the art that the details provided in the embodiments should not be construed to limit the scope of the present disclosure. In some embodiments, well-known processes, well- known apparatus structures, and well-known techniques are not described in detail.
[0025] The terminology used, in the present disclosure, is only for the purpose of explaining a particular embodiment and such terminology shall not be considered to limit the scope of the present disclosure. As used in the present disclosure, the forms "a," "an," and "the" may be intended to include the plural forms as well, unless the context clearly suggests otherwise. The terms "comprises," "comprising," "including," and "having," are open-ended transitional phrases and therefore specify the presence of stated features, integers, steps, operations, elements, modules, units and/or components, but do not forbid the presence or addition of one or more other features, integers, steps, operations, elements, components, and/or groups thereof. The particular order of steps disclosed in the method and process of the present disclosure is not to be construed as necessarily requiring their performance as described or illustrated. It is also to be understood that additional or alternative steps may be employed.
[0026] The following detailed description should be read with reference to the drawings, in which similar elements in different drawings are identified with the same reference numbers. The drawings, which are not necessarily to scale, depict illustrative embodiments and are not intended to limit the scope of the disclosure.
[0027] It is to be understood that both the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the invention, as claimed. In this application, the use of the singular includes the plural, the word "a" or "an" means "at least one", and the use of "or" means "and/or", unless specifically stated otherwise. Furthermore, the use of the term "including", as well as other forms, such as "includes" and "included", is not limiting. Also, terms such as "element" or "component" encompass both elements and components comprising one unit and elements or components that comprise more than one unit unless specifically stated otherwise.
[0028] Furthermore, the term "module", as used herein, refers to logic embodied in hardware or firmware, or to a collection of software instructions, written in a programming language, such as, Java, C, C++, python, or assembly. One or more software instructions in the modules can be embedded in firmware, such as in an EPROM. The modules described herein can be implemented as either software and/or hardware modules and can be stored in any type of non-transitory computer-readable medium or other storage device. Some non-limiting examples of non-transitory computer-readable media include CDs, DVDs, BLU-RAY, flash memory, and hard disk drives.
[0029] Referring to FIG. 1, an exemplary block diagram of a system [100] for predicting sustainable development goals trends, is shown, in accordance with the exemplary implementations of the present disclosure. The system [100] comprises a database [102], a pre-processing module [104], an artificial intelligence (AI)/machine learning (ML) module [106], a visualization tool [108], and a feedback module [110]. Also, all of the components/ units of the system [100] are assumed to be connected to each other unless otherwise indicated below. As shown in the figures all units shown within the system [100] should also be assumed to be connected to each other. Also, in FIG. 1 only a few units are shown, however, the system [100] may comprise multiple such units or the system [100] may comprise any such numbers of said units, as required to implement the features of the present disclosure.
[0030] Further, in accordance with the present disclosure, it is to be acknowledged that the functionality described for the various the components/units can be implemented interchangeably. While specific embodiments may disclose a particular functionality of these units for clarity, it is recognized that various configurations and combinations thereof are within the scope of the disclosure. The functionality of specific units as disclosed in the disclosure should not be construed as limiting the scope of the present disclosure. Consequently, alternative arrangements and substitutions of units, provided they achieve the intended functionality described herein, are considered to be encompassed within the scope of the present disclosure.
[0031] The system [100] comprises the database [102] is configured to store input data sourced from multiple platforms, including but not limited to IoT devices, social media, and market analytics services. The input data may include various types of environmental, economic, or social data, essential for predicting SDG trends. The database [102] is the central repository where all input data is stored. The database [102] serves as the foundation for all data analysis and is crucial for ensuring that the system has access to accurate, comprehensive, and real-time data.
[0032] Further, the pre-processing module [104] is configured to clean and transform the raw input data from the database [102] into a format suitable for further analysis. The pre-processing module [104] includes handling missing data, performing feature engineering, and transforming variables to ensure data consistency and quality. In one implementation, the pre-processing module [104] applies advanced techniques to address any gaps or inconsistencies within the input data, thereby improving the quality and reliability of the data used by the subsequent modules.
[0033] The system [100] further comprises the AI/ML module [106]. Further, the AI/ML module [106] is communicatively coupled with both the database [102] and the pre-processing module [104]. The AI/ML module [106] is responsible for receiving the pre-processed data and classifying the data into distinct categories based on specific criteria. The classifications are crucial for understanding the underlying trends and patterns in the data, particularly with regard to the SDGs. For instance, the data may be classified into categories such as environmental sustainability, social development, or economic growth, depending on the focus of the SDGs being addressed.
[0034] Once the data is classified, the AI/ML module [106] trains at least one machine learning model based on the categorized input data. The model is configured to aggregate data from multiple sources and analyze any uncertainties within the data using regression modeling techniques. The models are designed to predict SDG trends by examining the relationships between various data points and the associated uncertainties, allowing for more accurate forecasting of future trends. As part of the model training process, the system [100] is configured to use advanced aggregation methods, such as weighted averages, sum of squares, and principal component analysis (PCA), to calculate a composite score based on the categorized input data. Such methods help to synthesize large volumes of complex data into a single, actionable metric that can be used to assess the likelihood of achieving specific SDGs.
[0035] As illustrated in FIG. 2A, to ensure the accuracy and reliability of the trained models, the AI/ML module [106] is configured to validate such models using cross-validation techniques, including K-fold cross-validation. This helps to assess the generalization ability of the models and reduce overfitting, ensuring that the predictions made by the system are based on robust and well-trained models.
[0036] Furthermore, the AI/ML module [106] deploys the trained model to predict trends for achieving sustainable development goals. This includes generating forecasts or actionable insights that are based on the latest available data, thus enabling decision-makers to track the progress of SDG initiatives over time. In one embodiment, the AI/ML module [106] also performs continuous updates to the trained models, adapting to new data inputs to ensure that the predictions remain relevant and timely. Additionally, the AI/ML module [106] applies various regression modeling techniques, such as linear regression, support vector machines (SVM), or decision tree regression, to evaluate the relationships between input data and SDG trends. The use of regression modeling allows the system [100] to estimate the effect of different variables on the predicted trends and to identify the most influential factors in the achievement of SDGs.
[0037] Moreover, to facilitate the understanding of these trends, the system [100] also includes a visualization tool [108]. The visualization tool [108] generates a visual representation of the predicted SDGs trends, providing users with easy-to-interpret graphics or dashboards. The visualizations displays key performance indicators, trends over time, and potential future outcomes, allowing stakeholders to assess progress toward SDGs and make data-driven decisions.
[0038] The system [100] also includes a feedback module [110] that is designed to refine the data cleaning, feature engineering, and classification processes. The feedback module [110] allows the system [100] to learn from past predictions, improving system's performance over time by incorporating insights and corrections into the model training process. This ensures that the system remains adaptive and capable of incorporating new developments in SDG-related data.
[0039] In an alternative embodiment, the AI/ML module [106] is configured to dynamically update the trained model based on new input data. The continuous updating process allows the system [100] to evolve and adjust its predictions as new data becomes available, ensuring that the predictions remain accurate and reflective of the most current trends. The system [100] may include communication modules that ensure data can be exchanged seamlessly between the database [102], pre-processing module [104], AI/ML module [106], and visualization tool [108]. This infrastructure may include mediums such as Wi-Fi, Bluetooth, or cellular networks, facilitating smooth, real-time data transfer
[0040] As illustrated in FIG. 2B, a method [200] for operating the system [100] for predicting sustainable development goals trends is disclosed, Further, the method [200] for operating the system [100] for predicting sustainable development goals trends involves a series of systematic steps designed to optimize advertising effectiveness. At step [202], cleaning and transforming, via a pre-processing module [104], an input data contained within a database [102] into a format suitable for analysis. At step [204], receiving, via an artificial intelligence (AI) module/ machine learning (ML) module communicatively coupled with the database [102] and the pre-processing module [104], the input data. At step [206], classifying, via the AI/ML module [106], the input data into distinct categories. At step [208], training, via the AI/ML module [106], at least one model based on the classified input data. Further, the at least one model is configured to aggregate data and analyse uncertainty within the input data using regression modelling. At step, [210], deploying, via the AI/ML module [106], the trained model to predict a sustainable development goals (SDGs) trend. At step [212], generating, via the AI/ML module [106], a visual representation of the predicted SDGs trend using a visualization tool [108].
[0041] While considerable emphasis has been placed herein on the specific features of the preferred embodiment, it will be appreciated that many additional features can be added and that many changes can be made in the preferred embodiment without departing from the principles of the disclosure. These and other changes in the preferred embodiment of the disclosure will be apparent to those skilled in the art from the disclosure herein, whereby it is to be distinctly understood that the foregoing descriptive matter is to be interpreted merely as illustrative of the disclosure and not as a limitation.
[0042] While the invention has been described in connection with what is presently considered to be the most practical and various embodiments, it is to be understood that the invention is not to be limited to the disclosed embodiments, but on the contrary, is intended to cover various modifications and equivalent arrangements.
[0043] The embodiments described above are intended only to illustrate and teach one or more ways of practicing or implementing the present invention, not to restrict its breadth or scope. The actual scope of the invention, which embraces all ways of practicing or implementing the teachings of the invention, is defined only by the following claims and their equivalents.
, Claims:I/WE CLAIM:
1. A system [100] for predicting sustainable development goals trends, comprising:
a database [102] containing an input data;
a pre-processing module [104] configured to clean and transform the input data into a format suitable for analysis;
an artificial intelligence (AI)/machine learning (ML) module [106] communicatively coupled with the database [102] and the pre-processing module [104], wherein the AI/ML module [106] is configured to:
receive the input data from the pre-processing module [104],
classify the input data into distinct categories,
train at least one model based on the classified input data, wherein the at least one model is configured to aggregate data and analyse uncertainty within the input data using regression modelling,
deploy the trained model to predict a sustainable development goals (SDGs) trend, and
generate a visual representation of the predicted SDGs trend using a visualization tool [108].
2. The system [100] as claimed in claim 1, wherein the pre-processing module [104] configured to handle missing data, and perform feature engineering.

3. The system [100] as claimed in claim 1, wherein the AI/ML module [106] is configured to apply machine learning algorithms to train the at least one model.

4. The system [100] as claimed in claim 1, further comprising a feedback module [110] configured to refine the data cleaning, feature engineering, and classification process.

5. The system [100] as claimed in claim 1, wherein the AI/ML module [106] is configured to validate the trained model using a cross-validation module comprising K-fold cross-validation techniques.

6. The system [100] as claimed in claim 1, wherein the AL/ML module is configured to calculate a composite score based at least on the categorized input data by using aggregation methods such as weighted averages, sum of squares, and principal component analysis (PCA)

7. The system [100] as claimed in claim 1, wherein the regression modelling involves at least one of a linear regression, support vector, or decision tree regression.

8. The system [100] as claimed in claim 1, wherein the AI/ML module [106] is configured to dynamically update the at least one trained model based at least on new inputs.

9. A method [200] for operating the machine learning based fake currency detection system [100] as claimed in claim 1, wherein the method [200] comprising:

cleaning and transforming, via a pre-processing module [104], an input data contained within a database [102] into a format suitable for analysis, at step [202];
receiving, via an artificial intelligence (AI) module/ machine learning (ML) module communicatively coupled with the database [102] and the pre-processing module [104], the input data, at step [204];
classifying, via the AI/ML module [106], the input data into distinct categories, at step [206];
training, via the AI/ML module [106], at least one model based on the classified input data, wherein the at least one model is configured to aggregate data and analyse uncertainty within the input data using regression modelling, at step [208];
deploying, via the AI/ML module [106], the trained model to predict a sustainable development goals (SDGs) trend, at step [210]; and
generating, via the AI/ML module [106], a visual representation of the predicted SDGs trend using a visualization tool [108], at step [212].
10. The method [200] for operating the system [100] as claimed in claim 1, wherein the database [102] is configured to receive data from IoT devices, social media platforms, and market analytics services.

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