View in EDS

Recent Advancements in the Field of Nanotechnology Using Artificial Intelligence, Data Science and Machine Learning.

Saved in:
Bibliographic Details
Title: Recent Advancements in the Field of Nanotechnology Using Artificial Intelligence, Data Science and Machine Learning.
Authors: Chimkode, Suresh1, Mane, Jayant2, Jakhete, Mayur3, Moharil, Umesh4, Sedani, Chetankumar5, Rao, Nandkishore6, Garad, Rohit7, Rupanar, Shobha7, Kakade, Nirmala8, Choudhari, Ramakant9, Bhambere, Vijay10, Modak, Girish11, Paliwal, Roma12, Desai, Smita13, Maniyar, Kamalkishor13 advancedresearch25@gmail.com, Patil, Ganesh13, Mishra, Ekta13, Ghodke, Prasad14, Dixit, Swati13, Yadav, Mahendra15
Source: Journal of Mines, Metals & Fuels. Jul2025, Vol. 73 Issue 7, p1987-1994. 8p.
Subject Terms: *ARTIFICIAL intelligence, *COMPUTER engineering, *ELECTRONIC data processing, *COMPUTER performance, *COMPUTER engineers
Abstract: Rapidly developing technologies like nanotechnology and machine learning (ML) have many practical uses in data processing, computer engineering, materials research, and medicine. By making data processing easier in the synthesis of nanomaterials, characterisation, and optimization of nanoscale features, machine learning (ML) advances nanotechnology. ML algorithms, on the other hand, depend on faster and more efficient processing power, which nanotechnology enhances. A survey of the academic literature offers insights into the fascinating horizons of both nanotechnology and machine learning, despite the fact that both areas' capabilities are still in their infancy. It also suggests that their integration can have transformational effects. Creating instruments for working with nanomaterials and making sure that data collecting for ML models is impartial and moral are two areas of future research. Furthermore, nanochips can boost the performance and power of contemporary computers, which could cut down on the time and expense required to train intricate machine learning models. The goal is to draw attention to how important it is for these two domains to work together, since this can improve computing power, optimize model training, and hasten nanotechnology research and nanomachine development. It is anticipated that a deeper comprehension of this cross-collaboration will enhance computer capabilities to maximize model training, accelerate nanotechnology research, and guide future nanomachines. Major Findings: This information provides a broad overview of nanotechnology, emphasising both the worldwide concerns regarding their application and the importance of these fields in the development of new systems and technologies. These instances highlight machine learning's revolutionary potential in finding nanoparticles with desired characteristics and streamlining time-consuming procedures in nanotechnology [ABSTRACT FROM AUTHOR]
Database: Academic Search Index
FullText Text:
  Availability: 0
CustomLinks:
  – Url: https://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=EBSCO&SrcAuth=EBSCO&DestApp=WOS&ServiceName=TransferToWoS&DestLinkType=GeneralSearchSummary&Func=Links&author=Chimkode%20S
    Name: ISI
    Category: fullText
    Text: Nájsť tento článok vo Web of Science
    Icon: https://imagesrvr.epnet.com/ls/20docs.gif
    MouseOverText: Nájsť tento článok vo Web of Science
Header DbId: asx
DbLabel: Academic Search Index
An: 186815940
RelevancyScore: 1430
AccessLevel: 6
PubType: Academic Journal
PubTypeId: academicJournal
PreciseRelevancyScore: 1430.4833984375
IllustrationInfo
Items – Name: Title
  Label: Title
  Group: Ti
  Data: Recent Advancements in the Field of Nanotechnology Using Artificial Intelligence, Data Science and Machine Learning.
– Name: Author
  Label: Authors
  Group: Au
  Data: <searchLink fieldCode="AR" term="%22Chimkode%2C+Suresh%22">Chimkode, Suresh</searchLink><relatesTo>1</relatesTo><br /><searchLink fieldCode="AR" term="%22Mane%2C+Jayant%22">Mane, Jayant</searchLink><relatesTo>2</relatesTo><br /><searchLink fieldCode="AR" term="%22Jakhete%2C+Mayur%22">Jakhete, Mayur</searchLink><relatesTo>3</relatesTo><br /><searchLink fieldCode="AR" term="%22Moharil%2C+Umesh%22">Moharil, Umesh</searchLink><relatesTo>4</relatesTo><br /><searchLink fieldCode="AR" term="%22Sedani%2C+Chetankumar%22">Sedani, Chetankumar</searchLink><relatesTo>5</relatesTo><br /><searchLink fieldCode="AR" term="%22Rao%2C+Nandkishore%22">Rao, Nandkishore</searchLink><relatesTo>6</relatesTo><br /><searchLink fieldCode="AR" term="%22Garad%2C+Rohit%22">Garad, Rohit</searchLink><relatesTo>7</relatesTo><br /><searchLink fieldCode="AR" term="%22Rupanar%2C+Shobha%22">Rupanar, Shobha</searchLink><relatesTo>7</relatesTo><br /><searchLink fieldCode="AR" term="%22Kakade%2C+Nirmala%22">Kakade, Nirmala</searchLink><relatesTo>8</relatesTo><br /><searchLink fieldCode="AR" term="%22Choudhari%2C+Ramakant%22">Choudhari, Ramakant</searchLink><relatesTo>9</relatesTo><br /><searchLink fieldCode="AR" term="%22Bhambere%2C+Vijay%22">Bhambere, Vijay</searchLink><relatesTo>10</relatesTo><br /><searchLink fieldCode="AR" term="%22Modak%2C+Girish%22">Modak, Girish</searchLink><relatesTo>11</relatesTo><br /><searchLink fieldCode="AR" term="%22Paliwal%2C+Roma%22">Paliwal, Roma</searchLink><relatesTo>12</relatesTo><br /><searchLink fieldCode="AR" term="%22Desai%2C+Smita%22">Desai, Smita</searchLink><relatesTo>13</relatesTo><br /><searchLink fieldCode="AR" term="%22Maniyar%2C+Kamalkishor%22">Maniyar, Kamalkishor</searchLink><relatesTo>13</relatesTo><i> advancedresearch25@gmail.com</i><br /><searchLink fieldCode="AR" term="%22Patil%2C+Ganesh%22">Patil, Ganesh</searchLink><relatesTo>13</relatesTo><br /><searchLink fieldCode="AR" term="%22Mishra%2C+Ekta%22">Mishra, Ekta</searchLink><relatesTo>13</relatesTo><br /><searchLink fieldCode="AR" term="%22Ghodke%2C+Prasad%22">Ghodke, Prasad</searchLink><relatesTo>14</relatesTo><br /><searchLink fieldCode="AR" term="%22Dixit%2C+Swati%22">Dixit, Swati</searchLink><relatesTo>13</relatesTo><br /><searchLink fieldCode="AR" term="%22Yadav%2C+Mahendra%22">Yadav, Mahendra</searchLink><relatesTo>15</relatesTo>
– Name: TitleSource
  Label: Source
  Group: Src
  Data: <searchLink fieldCode="JN" term="%22Journal+of+Mines%2C+Metals+%26+Fuels%22">Journal of Mines, Metals & Fuels</searchLink>. Jul2025, Vol. 73 Issue 7, p1987-1994. 8p.
– Name: Subject
  Label: Subject Terms
  Group: Su
  Data: *<searchLink fieldCode="DE" term="%22ARTIFICIAL+intelligence%22">ARTIFICIAL intelligence</searchLink><br />*<searchLink fieldCode="DE" term="%22COMPUTER+engineering%22">COMPUTER engineering</searchLink><br />*<searchLink fieldCode="DE" term="%22ELECTRONIC+data+processing%22">ELECTRONIC data processing</searchLink><br />*<searchLink fieldCode="DE" term="%22COMPUTER+performance%22">COMPUTER performance</searchLink><br />*<searchLink fieldCode="DE" term="%22COMPUTER+engineers%22">COMPUTER engineers</searchLink>
– Name: Abstract
  Label: Abstract
  Group: Ab
  Data: Rapidly developing technologies like nanotechnology and machine learning (ML) have many practical uses in data processing, computer engineering, materials research, and medicine. By making data processing easier in the synthesis of nanomaterials, characterisation, and optimization of nanoscale features, machine learning (ML) advances nanotechnology. ML algorithms, on the other hand, depend on faster and more efficient processing power, which nanotechnology enhances. A survey of the academic literature offers insights into the fascinating horizons of both nanotechnology and machine learning, despite the fact that both areas' capabilities are still in their infancy. It also suggests that their integration can have transformational effects. Creating instruments for working with nanomaterials and making sure that data collecting for ML models is impartial and moral are two areas of future research. Furthermore, nanochips can boost the performance and power of contemporary computers, which could cut down on the time and expense required to train intricate machine learning models. The goal is to draw attention to how important it is for these two domains to work together, since this can improve computing power, optimize model training, and hasten nanotechnology research and nanomachine development. It is anticipated that a deeper comprehension of this cross-collaboration will enhance computer capabilities to maximize model training, accelerate nanotechnology research, and guide future nanomachines. Major Findings: This information provides a broad overview of nanotechnology, emphasising both the worldwide concerns regarding their application and the importance of these fields in the development of new systems and technologies. These instances highlight machine learning's revolutionary potential in finding nanoparticles with desired characteristics and streamlining time-consuming procedures in nanotechnology [ABSTRACT FROM AUTHOR]
PLink https://erproxy.cvtisr.sk/sfx/access?url=https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=asx&AN=186815940
RecordInfo BibRecord:
  BibEntity:
    Identifiers:
      – Type: doi
        Value: 10.18311/jmmf/2025/49272
    Languages:
      – Code: eng
        Text: English
    PhysicalDescription:
      Pagination:
        PageCount: 8
        StartPage: 1987
    Subjects:
      – SubjectFull: ARTIFICIAL intelligence
        Type: general
      – SubjectFull: COMPUTER engineering
        Type: general
      – SubjectFull: ELECTRONIC data processing
        Type: general
      – SubjectFull: COMPUTER performance
        Type: general
      – SubjectFull: COMPUTER engineers
        Type: general
    Titles:
      – TitleFull: Recent Advancements in the Field of Nanotechnology Using Artificial Intelligence, Data Science and Machine Learning.
        Type: main
  BibRelationships:
    HasContributorRelationships:
      – PersonEntity:
          Name:
            NameFull: Chimkode, Suresh
      – PersonEntity:
          Name:
            NameFull: Mane, Jayant
      – PersonEntity:
          Name:
            NameFull: Jakhete, Mayur
      – PersonEntity:
          Name:
            NameFull: Moharil, Umesh
      – PersonEntity:
          Name:
            NameFull: Sedani, Chetankumar
      – PersonEntity:
          Name:
            NameFull: Rao, Nandkishore
      – PersonEntity:
          Name:
            NameFull: Garad, Rohit
      – PersonEntity:
          Name:
            NameFull: Rupanar, Shobha
      – PersonEntity:
          Name:
            NameFull: Kakade, Nirmala
      – PersonEntity:
          Name:
            NameFull: Choudhari, Ramakant
      – PersonEntity:
          Name:
            NameFull: Bhambere, Vijay
      – PersonEntity:
          Name:
            NameFull: Modak, Girish
      – PersonEntity:
          Name:
            NameFull: Paliwal, Roma
      – PersonEntity:
          Name:
            NameFull: Desai, Smita
      – PersonEntity:
          Name:
            NameFull: Maniyar, Kamalkishor
      – PersonEntity:
          Name:
            NameFull: Patil, Ganesh
      – PersonEntity:
          Name:
            NameFull: Mishra, Ekta
      – PersonEntity:
          Name:
            NameFull: Ghodke, Prasad
      – PersonEntity:
          Name:
            NameFull: Dixit, Swati
      – PersonEntity:
          Name:
            NameFull: Yadav, Mahendra
    IsPartOfRelationships:
      – BibEntity:
          Dates:
            – D: 01
              M: 07
              Text: Jul2025
              Type: published
              Y: 2025
          Identifiers:
            – Type: issn-print
              Value: 00222755
          Numbering:
            – Type: volume
              Value: 73
            – Type: issue
              Value: 7
          Titles:
            – TitleFull: Journal of Mines, Metals & Fuels
              Type: main
ResultId 1