1. A system for applying machine learning and carbon dating in archaeological analysis, comprising: a data input module for receiving radiocarbon dating data and associated archaeological information; a processing module for analyzing the radiocarbon dating data using machine learning algorithms; and an output module for generating age estimates, confidence intervals, and visualizations based on the analysis.

2. The system of claim 1, wherein said data input module accepts various types of radiocarbon dating data, including but not limited to, raw radiocarbon measurements, calibrated age ranges, and contextual information related to the samples.

3. The system of claim 1, wherein said processing module employs supervised or unsupervised machine learning techniques, such as regression, classification, clustering, or deep learning, to analyze the radiocarbon dating data.

4. The system of claim 1, wherein said processing module uses artificial intelligence techniques to identify patterns, trends, and relationships within the radiocarbon dating data and associated archaeological information.

5. The system of claim 1, further comprising a user interface, enabling users to interact with the system, input data, adjust analysis parameters, and interpret results.

6. The system of claim 1, wherein said processing module performs predictive modeling to estimate the most probable age ranges of archaeological samples based on the input data.

7. A method for applying machine learning and carbon dating in archaeological analysis, comprising: receiving radiocarbon dating data and associated archaeological information through a data input module; analyzing the radiocarbon dating data using machine learning algorithms in a processing module; and generating age estimates, confidence intervals, and visualizations based on the analysis through an output module.

8. The method of claim 7, further comprising accepting various types of radiocarbon dating data, including but not limited to, raw radiocarbon measurements, calibrated age ranges, and contextual information related to the samples.

9. The method of claim 7, wherein said processing module employs supervised or unsupervised machine learning techniques, such as regression, classification, clustering, or deep learning, to analyze the radiocarbon dating data.

10. The method of claim 7, further comprising using artificial intelligence techniques to identify patterns, trends, and relationships within the radiocarbon dating data and associated archaeological information. MACHINE LEARNING AND ARTIFICIAL INTELLIGENCE IN ARCHAEOLOGICAL ANALYSIS Abstract The present invention relates to a system for applying machine learning and carbon dating in archaeological analysis may be included in embodiments of the present disclosure. This system may include a data input module for receiving radiocarbon dating data and associated archaeological information. Additionally, embodiments may include a system for applying machine learning and carbon dating in archaeological research. A processing module that utilises machine learning techniques to perform an analysis of the data obtained from radiocarbon dating may also be included in embodiments. A module for producing age estimates, confidence intervals, and visualisations based on the study may also be included in certain embodiments. , Claims:Claims :

1. A system for applying machine learning and carbon dating in archaeological analysis, comprising: a data input module for receiving radiocarbon dating data and associated archaeological information; a processing module for analyzing the radiocarbon dating data using machine learning algorithms; and an output module for generating age estimates, confidence intervals, and visualizations based on the analysis.

2. The system of claim 1, wherein said data input module accepts various types of radiocarbon dating data, including but not limited to, raw radiocarbon measurements, calibrated age ranges, and contextual information related to the samples.

3. The system of claim 1, wherein said processing module employs supervised or unsupervised machine learning techniques, such as regression, classification, clustering, or deep learning, to analyze the radiocarbon dating data.

4. The system of claim 1, wherein said processing module uses artificial intelligence techniques to identify patterns, trends, and relationships within the radiocarbon dating data and associated archaeological information.

5. The system of claim 1, further comprising a user interface, enabling users to interact with the system, input data, adjust analysis parameters, and interpret results.

6. The system of claim 1, wherein said processing module performs predictive modeling to estimate the most probable age ranges of archaeological samples based on the input data.

7. A method for applying machine learning and carbon dating in archaeological analysis, comprising: receiving radiocarbon dating data and associated archaeological information through a data input module; analyzing the radiocarbon dating data using machine learning algorithms in a processing module; and generating age estimates, confidence intervals, and visualizations based on the analysis through an output module.

8. The method of claim 7, further comprising accepting various types of radiocarbon dating data, including but not limited to, raw radiocarbon measurements, calibrated age ranges, and contextual information related to the samples.

9. The method of claim 7, wherein said processing module employs supervised or unsupervised machine learning techniques, such as regression, classification, clustering, or deep learning, to analyze the radiocarbon dating data.

10. The method of claim 7, further comprising using artificial intelligence techniques to identify patterns, trends, and relationships within the radiocarbon dating data and associated archaeological information.

Description:MACHINE LEARNING AND ARTIFICIAL INTELLIGENCE IN ARCHAEOLOGICAL ANALYSIS
Field of the Invention
[0001] The present invention relates generally to use machine learning algorithms to analyse archaeological data and make predictions about the age, origin, or significance of artifacts. More particularly, the system and method for applying machine learning and carbon dating in archaeological analysis.
Background
[0002] The background description includes information that may be useful in understanding the present invention. It is not an admission that any of the information provided herein is prior art or relevant to the presently claimed invention, or that any publication specifically or implicitly referenced is prior art.
[0003] Archaeological analysis involves the systematic study of material remains left behind by past human societies in order to reconstruct and interpret their behavior, beliefs, and interactions with their environment. A key aspect of this process is the documentation and analysis of the artifacts, structures, and features that archaeologists uncover through excavation and survey.
[0004] To begin the process of archaeological analysis, researchers must first establish the context of the site or area being studied. This involves documenting the location, size, and layout of the site, as well as any visible geological or environmental features that may have influenced its use or development over time.
[0005] Once the site context has been established, archaeologists will then begin the process of recording and analyzing the artifacts and other material remains found within it. This typically involves the careful excavation and removal of layers of soil and sediment in order to reveal the underlying layers of human activity and construction. Following patent documents disclose machine learning techniques.
[0006] CN111476885A (By: GUANGZHOU OUKE INFORMATION TECHNOLOGY, GUANGDONG INSTITUTE OF CULTURAL RELICS & ARCHAEOLOGY) The embodiment of the invention discloses an archaeological multivariate data fusion method, device and equipment and a storage medium, and the method comprises the steps: receiving a point cloud datapacket transmitted by a control end, wherein the point cloud data packet is formed after the point cloud data collected by different collection points is preprocessed by the control end; processing the point cloud data packet to generate a block point cloud; performing fusion processing on different block point clouds to generate complete point cloud data, and generating a three-dimensional modelaccording to the complete point cloud data. According to the scheme, the point cloud data collected by the multi-terminal equipment can be fused, the point cloud data processing efficiency is improved, and the fusion effect is good.
[0007] CN115095739A (By: ZHENGZHOU HENGZHENG ELECTRONIC TECHNOLOGY) The invention relates to the technical field of data analysis devices, in particular to a ruin archaeological result data analysis system which comprises an all-terrain mobile robot and a U-shaped frame, the U-shaped frame is fixedly installed on the upper surface of the all-terrain mobile robot, and a lifting device is arranged at the top of the all-terrain mobile robot. A lifting column used in cooperation with the lifting device is arranged above the all-terrain mobile robot, a fixing table is fixedly installed at the top of the lifting column, and a rotating device is arranged in the fixing table. According to the invention, the all-terrain mobile robot drives the camera to move at the site of the ruins for photographing, the photographed picture is transmitted to the microprocessor after being processed by the image processor, and the microprocessor analyzes and processes data and then transmits the data to an archaeological research center through the communication module, so that online transmission of the data is realized; a traditional manual shooting mode is replaced, the labor intensity of archaeological personnel is reduced, meanwhile, timeliness is achieved, and the period is shortened.
[0008] AU2021101464A4 (By: ) DETECTION OF ARCHAEOLOGICAL SURVEY SITES THROUGH GEOSPATIAL IMAGES USING MACHINE LEARNING Abstract The field of archaeological research is gaining improved attention in recent years. But still manually processing this hepatic process involves more complications. The technologies of modem geomatic have been in use for several years in the numerous scopes in the archeological field that includes the conversion into digital, three dimensional repositories, documentation, restoration, and system of web geography. The laser scanner development in recent times and UAV (Unmanned Aerial Vehicle) implementations and GIS (Geographical Information System) technologies lead to more developmental opportunities in the field of cultural heritage. The settlement of the semipermanent and the permanent characteristic features of landscapes specifically plains that are sedimentary, semiarid and the arid region is known as the artificial mounds. Their prominent shape makes them easily visible. These regions are made up of debris accumulation that includes sherds from pottery and mud bricks and these take the responsibility for the distinctive color and texture of the surface. This invention is intended in detecting archaeological survey sites via geospatial images using a machine learning algorithm. The deep learning approach arises from the machine learning algorithm that is involved in the detection of the archaeological surveying site through the combination of Convolutional Neural Network (CNN) with Residual Neural Network (RNN).
[0009] As artifacts and features are uncovered, they are carefully catalogued, photographed, and measured in order to create a detailed record of their appearance and location within the site. Researchers may also use specialized tools and techniques such as stratigraphy, radiocarbon dating, and microscopic analysis to further refine their understanding of the chronology and context of the site.
[00010] Throughout the process of archaeological analysis, researchers must be careful to document and analyze their findings in a systematic and objective manner. This requires a thorough understanding of the principles of archaeological research and the ability to critically evaluate the evidence and interpret it in light of broader cultural and historical contexts.
[00011] Ultimately, the goal of archaeological analysis is to reconstruct the lives and experiences of past human societies in as much detail as possible. This requires a combination of scientific rigor, analytical skill, and creative interpretation in order to transform the physical remains of the past into a meaningful and compelling narrative of human history.
[00012] There are several limitations to the techniques used in archaeological analysis, which can impact the accuracy and completeness of the information that archaeologists are able to gather from material remains. Here are some of the key limitations, which includes preservation bias, etc. Thus, a further development in this area of technology is required.

Summary
[00013] The present invention relates generally to use machine learning algorithms to analyse archaeological data and make predictions about the age, origin, or significance of artifacts. More particularly, the system and method for applying machine learning and carbon dating in archaeological analysis.
[00014] The following presents a simplified summary of various aspects of this disclosure in order to provide a basic understanding of such aspects. This summary is not an extensive overview of all contemplated aspects, and is intended to neither identify key or critical elements nor delineate the scope of such aspects. Its purpose is to present some concepts of this disclosure in a simplified form as a prelude to the more detailed description that is presented later.
[00015] The following paragraphs provide additional support for the claims of the subject application.
[00016] Embodiments of the present disclosure may include a system for applying machine learning and carbon dating in archaeological analysis, including a data input module for receiving radiocarbon dating data and associated archaeological information. Embodiments may also include a processing module for analyzing the radiocarbon dating data using machine learning algorithms. Embodiments may also include an output module for generating age estimates, confidence intervals, and visualizations based on the analysis.
[00017] In some embodiments, the data input module accepts various types of radiocarbon dating data, including but not limited to, raw radiocarbon measurements, calibrated age ranges, and contextual information related to the samples. In some embodiments, the processing module employs supervised or unsupervised machine learning techniques, such as regression, classification, clustering, or deep learning, to analyze the radiocarbon dating data.
[00018] In some embodiments, the processing module uses artificial intelligence techniques to identify patterns, trends, and relationships within the radiocarbon dating data and associated archaeological information. In some embodiments, the system may include a user interface, enabling users to interact with the system, input data, adjust analysis parameters, and interpret results. In some embodiments, the processing module performs predictive modeling to estimate the most probable age ranges of archaeological samples based on the input data.
[00019] Embodiments of the present disclosure may also include a method for applying machine learning and carbon dating in archaeological analysis, including receiving radiocarbon dating data and associated archaeological information through a data input module. Embodiments may also include analyzing the radiocarbon dating data using machine learning algorithms in a processing module. Embodiments may also include generating age estimates, confidence intervals, and visualizations based on the analysis through an output module.
[00020] In some embodiments, the method may include accepting various types of radiocarbon dating data, including but not limited to, raw radiocarbon measurements, calibrated age ranges, and contextual information related to the samples. In some embodiments, the processing module employs supervised or unsupervised machine learning techniques, such as regression, classification, clustering, or deep learning, to analyze the radiocarbon dating data. In some embodiments, the method may include using artificial intelligence techniques to identify patterns, trends, and relationships within the radiocarbon dating data and associated archaeological information.
Brief Description of the Drawings
[00021] The features and advantages of the present disclosure would be more clearly understood from the following description taken in conjunction with the accompanying drawings in which:
[00022] FIG. 1 is a block diagram illustrating a system for applying machine learning and carbon dating in archaeological analysis, according to some embodiments of the present disclosure.
[00023] FIG. 2 is a flowchart illustrating a method for applying machine learning and carbon dating in archaeological analysis, according to some embodiments of the present disclosure.
Detailed Description
[00024] In the following detailed description of the invention, reference is made to the accompanying drawings that form a part hereof, and in which is shown, by way of illustration, specific embodiments in which the invention may be practiced. In the drawings, like numerals describe substantially similar components throughout the several views. These embodiments are described in sufficient detail to claim those skilled in the art to practice the invention. Other embodiments may be utilized and structural, logical, and electrical changes may be made without departing from the scope of the present invention. The following detailed description is, therefore, not to be taken in a limiting sense, and the scope of the present invention is defined only by the appended claims and equivalents thereof.
[00025] The use of the terms “a” and “an” and “the” and “at least one” and similar referents in the context of describing the invention (especially in the context of the following claims) are to be construed to cover both the singular and the plural, unless otherwise indicated herein or clearly contradicted by context. The use of the term “at least one” followed by a list

of one or more items (for example, “at least one of A and B”) is to be construed to mean one item selected from the listed items (A or B) or any combination of two or more of the listed items (A and B), unless otherwise indicated herein or clearly contradicted by context. The terms “comprising,” “having,” “including,” and “containing” are to be construed as open-ended terms (i.e., meaning “including, but not limited to,”) unless otherwise noted. Recitation of ranges of values herein are merely intended to serve as a shorthand method of referring individually to each separate value falling within the range, unless otherwise indicated herein, and each separate value is incorporated into the specification as if it were individually recited herein. All methods described herein can be performed in any suitable order unless otherwise indicated herein or otherwise clearly contradicted by context. The use of any and all examples, or exemplary language (e.g., “such as”) provided herein, is intended merely to better illuminate the invention and does not pose a limitation on the scope of the invention unless otherwise claimed. No language in the specification should be construed as indicating any non-claimed element as essential to the practice of the invention.
[00026] The present invention relates generally to use machine learning algorithms to analyse archaeological data and make predictions about the age, origin, or significance of artifacts. More particularly, the system and method for applying machine learning and carbon dating in archaeological analysis.
[00027] The present disclosure may be implemented in a number of different ways, and one of those ways is shown in Figure 1, which is a block diagram of a system 100 for applying machine learning and carbon dating in archaeological analysis. The system 100 may, in some implementations, include a data input module 110 that is responsible for receiving radiocarbon dating data and associated archaeological information and a processing module 120 that is responsible for analysing the radiocarbon dating data using machine learning algorithms. In addition, the system 100 may include an output module 130 that, based on the findings of the research, is in charge of the development of age estimates, confidence intervals, and visualisations.
[00028] The data input module 110 in certain implementations may be configured to accept several radiocarbon dating data formats. This may be the case in some implementations. in addition to, but not limited to, calibrated age ranges, raw radiocarbon data, and contextual information relevant to the samples. The processing module 120 in certain implementations may conduct an analysis of the radiocarbon dating data using supervised or unsupervised machine learning methods, such as regression, classification, clustering, or deep learning.
[00029] The processing module 120 may, in some implementations, make use of techniques from the field of artificial intelligence in order to recognise patterns, trends, and correlations included within the radiocarbon dating data and the archaeological information that is linked to it. This is done in order to ensure that the data can be used in conjunction with the archaeological information. This affords users the opportunity to interact with the system 100 in a variety of ways, including the capability to input data, modify analytical settings, and evaluate results. The processing module 120 may, in certain implementations, carry out predictive modelling in order to determine the most likely age ranges of archaeological samples based on the input data. This can be done in order to determine the most likely age ranges of archaeological samples, the processing module 120 may use the input data.
[00030] Figure 2 is a flowchart that explains a method for applying machine learning and carbon dating in archaeological analysis. The current disclosure may be implemented in a number of different ways, and this technique conforms to one of those implementations. In some implementations of the method, step 210 may involve the process of receiving radiocarbon dating data and other archaeological information related with it through a data input module. However, this step may be omitted. At step 220 of the method, it is possible to conduct an analysis of the data obtained from radiocarbon dating by making use of machine learning algorithms while the data are being processed by a module. The age estimates, confidence intervals, and visualisations that are based on the findings of the research may be provided by an output module, which can be utilised at step 230 of the method.
[00031] In certain implementations of the method, it is possible to have it set up such that it will accept a wide range of radiocarbon dating data in its many formats. in addition to, but not limited to, calibrated age ranges, raw radiocarbon data, and contextual information relevant to the samples. The processing module may, depending on the implementation, apply supervised or unsupervised machine learning techniques, such as regression, classification, clustering, or deep learning, while it is doing an analysis of the radiocarbon dating data. There are a variety of other methods of machine learning that might be used. In some implementations of the approach, methods from artificial intelligence may be used to recognise patterns, trends, and correlations within the radiocarbon dating data and the archaeological information that is linked to it.
[00032] A system for applying machine learning and carbon dating in archaeological analysis may be included in embodiments of the present disclosure. This system may include a data input module for receiving radiocarbon dating data and associated archaeological information. Additionally, embodiments may include a system for applying machine learning and carbon dating in archaeological research. A processing module that utilises machine learning techniques to perform an analysis of the data obtained from radiocarbon dating may also be included in embodiments. An output module that may provide age estimates, confidence intervals, and visualisations based on the study may also be included in certain embodiments.
[00033] Raw radiocarbon readings, calibrated age ranges, and contextual information pertaining to the samples are some of the forms of radiocarbon dating data that may be accepted by the data input module in some implementations. Other forms of radiocarbon dating data may also be accepted. Analyzing the radiocarbon dating data may include the use of supervised or unsupervised machine learning methods in some implementations of the processing module. Some examples of these approaches are regression, classification, clustering, and deep learning.
[00034] The processing module may, in certain implementations, make use of methods from artificial intelligence in order to recognise patterns, trends, and correlations included within the radiocarbon dating data and the archaeological information that is linked with it. A user interface is sometimes included as part of a system, which gives users the ability to interact with the system, provide data, modify analytic settings, and understand the findings. The processing module in certain implementations is responsible for carrying out predictive modelling in order to determine the most likely age ranges of archaeological samples based on the data that is supplied.
[00035] A method for applying machine learning and carbon dating in archaeological analysis may also be included in embodiments of the present disclosure. This method may include receiving radiocarbon dating data and associated archaeological information through a data input module. Additionally, this method may include applying machine learning. Analyzing the data from radiocarbon dating using machine learning algorithms in a processing module is another possibility that may be included in embodiments. Moreover, embodiments can include, through an output module, the generation of age estimates, confidence intervals, and other visualisations depending on the results of the study.
[00036] Accepting multiple forms of radiocarbon dating data is a step that may be included in certain implementations of the technique. These steps may include, but are not limited to, accepting raw radiocarbon readings, calibrated age ranges, and contextual information connected to the samples. Analyzing the radiocarbon dating data may include the use of supervised or unsupervised machine learning methods in some implementations of the processing module. Some examples of these approaches are regression, classification, clustering, and deep learning. The approach may, in some implementations, include the use of methods from artificial intelligence in order to recognise patterns, trends, and linkages contained within the radiocarbon dating data and the archaeological knowledge linked with it.
[00037] The described system and method for applying machine learning and carbon dating in archaeological analysis is designed to help archaeologists more accurately and efficiently date material remains and associated archaeological information. The system comprises three key components: a data input module, a processing module, and an output module.
[00038] The data input module is responsible for receiving radiocarbon dating data and associated archaeological information. This can include various types of radiocarbon dating data, such as raw radiocarbon measurements, calibrated age ranges, and contextual information related to the samples. The processing module then employs machine learning algorithms, such as regression, classification, clustering, or deep learning, to analyze the radiocarbon dating data. The algorithms used can be supervised or unsupervised, depending on the nature of the input data and the desired output.
[00039] The processing module uses artificial intelligence techniques to identify patterns, trends, and relationships within the radiocarbon dating data and associated archaeological information. This allows the system to perform predictive modeling to estimate the most probable age ranges of archaeological samples based on the input data. The output module generates age estimates, confidence intervals, and visualizations based on the analysis, which can be used by archaeologists to better understand the chronology of the material remains.
[00040] The system includes a user interface, enabling users to interact with the system, input data, adjust analysis parameters, and interpret results. The method for applying machine learning and carbon dating in archaeological analysis involves the same steps as the system, including receiving radiocarbon dating data and associated archaeological information through a data input module, analyzing the radiocarbon dating data using machine learning algorithms in a processing module, and generating age estimates, confidence intervals, and visualizations based on the analysis through an output module.
[00041] Overall, this system and method offer a promising approach to improving the accuracy and efficiency of archaeological analysis, particularly in relation to dating material remains. By leveraging machine learning and artificial intelligence techniques, archaeologists can more effectively identify and interpret patterns in the radiocarbon dating data and associated archaeological information, ultimately leading to a more comprehensive and accurate understanding of the past.
[00042] Example embodiments herein have been described above with reference to block diagrams and flowchart illustrations of methods and apparatuses. It will be understood that each block of the block diagrams and flowchart illustrations, and combinations of blocks in the block diagrams and flowchart illustrations, respectively, can be implemented by various means including hardware, software, firmware, and a combination thereof. For example, in one embodiment, each block of the block diagrams and flowchart illustrations, and combinations of blocks in the block diagrams and flowchart illustrations can be implemented by computer program instructions. These computer program instructions may be loaded onto a general purpose computer, special purpose computer, or other programmable data processing apparatus to produce a machine, such that the instructions which execute on the computer or other programmable data processing apparatus create means for implementing the functions specified in the flowchart block or blocks.
[00043] Throughout the present disclosure, the term ‘processing means’ or ‘microprocessor’ or ‘processor’ or ‘processors’ includes, but is not limited to, a general purpose processor (such as, for example, a complex instruction set computing (CISC) microprocessor, a reduced instruction set computing (RISC) microprocessor, a very long instruction word (VLIW) microprocessor, a microprocessor implementing other types of instruction sets, or a microprocessor implementing a combination of types of instruction sets) or a specialized processor (such as, for example, an application specific integrated circuit (ASIC), a field programmable gate array (FPGA), a digital signal processor (DSP), or a network processor).
[00044] The term “non-transitory storage device” or “storage” or “memory,” as used herein relates to a random access memory, read only memory and variants thereof, in which a computer can store data or software for any duration.
[00045] Operations in accordance with a variety of aspects of the disclosure is described above would not have to be performed in the precise order described. Rather, various steps can be handled in reverse order or simultaneously or not at all.
[00046] While several implementations have been described and illustrated herein, a variety of other means and/or structures for performing the function and/or obtaining the results and/or one or more of the advantages described herein may be utilized, and each of such variations and/or modifications is deemed to be within the scope of the implementations described herein. More generally, all parameters, dimensions, materials, and configurations described herein are meant to be exemplary and that the actual parameters, dimensions, materials, and/or configurations will depend upon the specific application or applications for which the teachings is/are used. Those skilled in the art will recognize, or be able to ascertain using no more than routine experimentation, many equivalents to the specific implementations described herein. It is, therefore, to be understood that the foregoing implementations are presented by way of example only and that, within the scope of the appended claims and equivalents thereto, implementations may be practiced otherwise than as specifically described and claimed. Implementations of the present disclosure are directed to each individual feature, system, article, material, kit, and/or method described herein. In addition, any combination of two or more such features, systems, articles, materials, kits, and/or methods, if such features, systems, articles, materials, kits, and/or methods are not mutually inconsistent, is included within the scope of the present disclosure.
 

Claims
I/We Claim:
1. A system for applying machine learning and carbon dating in archaeological analysis, comprising: a data input module for receiving radiocarbon dating data and associated archaeological information; a processing module for analyzing the radiocarbon dating data using machine learning algorithms; and an output module for generating age estimates, confidence intervals, and visualizations based on the analysis.

2. The system of claim 1, wherein said data input module accepts various types of radiocarbon dating data, including but not limited to, raw radiocarbon measurements, calibrated age ranges, and contextual information related to the samples.

3. The system of claim 1, wherein said processing module employs supervised or unsupervised machine learning techniques, such as regression, classification, clustering, or deep learning, to analyze the radiocarbon dating data.

4. The system of claim 1, wherein said processing module uses artificial intelligence techniques to identify patterns, trends, and relationships within the radiocarbon dating data and associated archaeological information.

5. The system of claim 1, further comprising a user interface, enabling users to interact with the system, input data, adjust analysis parameters, and interpret results.

6. The system of claim 1, wherein said processing module performs predictive modeling to estimate the most probable age ranges of archaeological samples based on the input data.

7. A method for applying machine learning and carbon dating in archaeological analysis, comprising: receiving radiocarbon dating data and associated archaeological information through a data input module; analyzing the radiocarbon dating data using machine learning algorithms in a processing module; and generating age estimates, confidence intervals, and visualizations based on the analysis through an output module.

8. The method of claim 7, further comprising accepting various types of radiocarbon dating data, including but not limited to, raw radiocarbon measurements, calibrated age ranges, and contextual information related to the samples.

9. The method of claim 7, wherein said processing module employs supervised or unsupervised machine learning techniques, such as regression, classification, clustering, or deep learning, to analyze the radiocarbon dating data.

10. The method of claim 7, further comprising using artificial intelligence techniques to identify patterns, trends, and relationships within the radiocarbon dating data and associated archaeological information.

MACHINE LEARNING AND ARTIFICIAL INTELLIGENCE IN ARCHAEOLOGICAL ANALYSIS
Abstract
The present invention relates to a system for applying machine learning and carbon dating in archaeological analysis may be included in embodiments of the present disclosure. This system may include a data input module for receiving radiocarbon dating data and associated archaeological information. Additionally, embodiments may include a system for applying machine learning and carbon dating in archaeological research. A processing module that utilises machine learning techniques to perform an analysis of the data obtained from radiocarbon dating may also be included in embodiments. A module for producing age estimates, confidence intervals, and visualisations based on the study may also be included in certain embodiments.
, Claims:Claims
I/We Claim:
1. A system for applying machine learning and carbon dating in archaeological analysis, comprising: a data input module for receiving radiocarbon dating data and associated archaeological information; a processing module for analyzing the radiocarbon dating data using machine learning algorithms; and an output module for generating age estimates, confidence intervals, and visualizations based on the analysis.

2. The system of claim 1, wherein said data input module accepts various types of radiocarbon dating data, including but not limited to, raw radiocarbon measurements, calibrated age ranges, and contextual information related to the samples.

3. The system of claim 1, wherein said processing module employs supervised or unsupervised machine learning techniques, such as regression, classification, clustering, or deep learning, to analyze the radiocarbon dating data.

4. The system of claim 1, wherein said processing module uses artificial intelligence techniques to identify patterns, trends, and relationships within the radiocarbon dating data and associated archaeological information.

5. The system of claim 1, further comprising a user interface, enabling users to interact with the system, input data, adjust analysis parameters, and interpret results.

6. The system of claim 1, wherein said processing module performs predictive modeling to estimate the most probable age ranges of archaeological samples based on the input data.

7. A method for applying machine learning and carbon dating in archaeological analysis, comprising: receiving radiocarbon dating data and associated archaeological information through a data input module; analyzing the radiocarbon dating data using machine learning algorithms in a processing module; and generating age estimates, confidence intervals, and visualizations based on the analysis through an output module.

8. The method of claim 7, further comprising accepting various types of radiocarbon dating data, including but not limited to, raw radiocarbon measurements, calibrated age ranges, and contextual information related to the samples.

9. The method of claim 7, wherein said processing module employs supervised or unsupervised machine learning techniques, such as regression, classification, clustering, or deep learning, to analyze the radiocarbon dating data.

10. The method of claim 7, further comprising using artificial intelligence techniques to identify patterns, trends, and relationships within the radiocarbon dating data and associated archaeological information.