Volume-1 Issue-1, May 2021 – Indian Journal of Data Mining (IJDM) https://www.ijdm.latticescipub.com Exploring Innovation | ISSN: 2582-9246 (Online) | A Periodical Journal | Reg. No: C/1383209 | Published by Lattice Science Publication (LSP) Sat, 19 Aug 2023 07:22:35 +0000 en-US hourly 1 https://wordpress.org/?v=7.0 https://www.ijdm.latticescipub.com/wp-content/uploads/2021/03/cropped-IJDM-WFS-32x32.png Volume-1 Issue-1, May 2021 – Indian Journal of Data Mining (IJDM) https://www.ijdm.latticescipub.com 32 32 A1607051121 https://www.ijdm.latticescipub.com/portfolio-item/a1607051121/ https://www.ijdm.latticescipub.com/portfolio-item/a1607051121/#respond Wed, 11 Aug 2021 11:32:28 +0000 https://www.ijdm.latticescipub.com/?post_type=portfolio&p=372 The Indian Journal of Data Mining (IJDM) has ISSN 2582-9246 (online), an open-access, peer-reviewed, periodical half-yearly international journal, which is published by Lattice Science Publication (LSP) in May and November. The journal aims to publish high-quality peer–reviewed original articles in the area of Data Mining that covers Data Mining, Data Science, Big Data, Data Warehouse, Visualization, Security, Privacy, Big DaaS, Scalable Computing, Cloud Computing, Knowledge Discovery, Integration, Transformation, Information Retrieval, Social Data and Semantics, Mining Functions, Data Regression, Data Classification, Anomaly Detection, Data Clustering, Data Association, Data Cleaning, Feature Selection and Extraction, Data Mining Algorithms, Apriori Decision Tree, Generalized Linear Models, k-Means, Minimum Description Length, Naive Bayes Non-Negative Matrix Factorization, 0-Cluster, Support Vector Machines, Data Preparation, Mining Unstructured Data, Artificial Intelligence, Future Directions and Challenges in Data Mining and Industrial Challenges in Data Mining. #Data Mining #Data Science #Big Data #Data Warehouse #Visualization #Security #Privacy #Big DaaS #Scalable Computing #Cloud Computing #Knowledge Discovery #Integration #Transformation #Information Retrieval #Social Data and Semantics #Mining Functions #Data Regression #Data Classification #Anomaly Detection #Data Clustering #Data Association #Data Regression #Data Cleaning #Feature Selection and Extraction #Data Mining Algorithms #Apriori #Decision Tree #Generalized Linear Models #k-Means #Minimum Description Length #Naive Bayes #Non-Negative Matrix Factorization #0-Cluster #Support Vector Machines #Data Preparation #Mining Unstructured Data #Artificial Intelligence #Future Directions and Challenges in Data Mining #Industrial Challenges in Data Mining #PhD ademic #Scopus #SCI #LatticeScience #Springer, #ScienceDirect #IEEE #Mendeley #Research #Scholarship #UGC #SSRN #LatticeScience #ESCI #Science #Journal #Conference #SSRN #PubLons #PhD #Academic #Scopus #SCI #LatticeScience #Springer, #ScienceDirect #IEEE #Mendeley #Research #Scholarship #UGC #SSRN #LatticeScience #ESCI #Science #Journal #Conference #SSRN #PubLons

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]]> The Indian Journal of Data Mining (IJDM) has ISSN 2582-9246 (online), an open-access, peer-reviewed, periodical half-yearly international journal, which is published by Lattice Science Publication (LSP) in May and November. The journal aims to publish high-quality peer–reviewed original articles in the area of Data Mining that covers Data Mining, Data Science, Big Data, Data Warehouse, Visualization, Security, Privacy, Big DaaS, Scalable Computing, Cloud Computing, Knowledge Discovery, Integration, Transformation, Information Retrieval, Social Data and Semantics, Mining Functions, Data Regression, Data Classification, Anomaly Detection, Data Clustering, Data Association, Data Cleaning, Feature Selection and Extraction, Data Mining Algorithms, Apriori Decision Tree, Generalized Linear Models, k-Means, Minimum Description Length, Naive Bayes Non-Negative Matrix Factorization, 0-Cluster, Support Vector Machines, Data Preparation, Mining Unstructured Data, Artificial Intelligence, Future Directions and Challenges in Data Mining and Industrial Challenges in Data Mining. #Data Mining #Data Science #Big Data #Data Warehouse #Visualization #Security #Privacy #Big DaaS #Scalable Computing #Cloud Computing #Knowledge Discovery #Integration #Transformation #Information Retrieval #Social Data and Semantics #Mining Functions #Data Regression #Data Classification #Anomaly Detection #Data Clustering #Data Association #Data Regression #Data Cleaning #Feature Selection and Extraction #Data Mining Algorithms #Apriori #Decision Tree #Generalized Linear Models #k-Means #Minimum Description Length #Naive Bayes #Non-Negative Matrix Factorization #0-Cluster #Support Vector Machines #Data Preparation #Mining Unstructured Data #Artificial Intelligence #Future Directions and Challenges in Data Mining #Industrial Challenges in Data Mining #PhD ademic #Scopus #SCI #LatticeScience #Springer, #ScienceDirect #IEEE #Mendeley #Research #Scholarship #UGC #SSRN #LatticeScience #ESCI #Science #Journal #Conference #SSRN #PubLons #PhD #Academic #Scopus #SCI #LatticeScience #Springer, #ScienceDirect #IEEE #Mendeley #Research #Scholarship #UGC #SSRN #LatticeScience #ESCI #Science #Journal #Conference #SSRN #PubLons

Privacy Issues in Big Data and Enhanced Privacy Preservation Model
Panakal Harly Mary1, Prajwal Sunil Solanki2, M Lakshmi3

1Panakal Harly Mary, Department of Computer Science Engineering S.R.M. Institute of Science and Technology, Chennai, India 

2Prajwal Sunil Solanki, Department of Computer Science Engineering S.R.M. Institute of Science and Technology, Chennai, India

3Dr. M Lakshmi, Professor Department of Computer Science and Engineering S.R.M. Institute of Science and Technology, Chennai, India

Manuscript received on 05 April 2021 | Revised Manuscript received on 20 April 2021 | Manuscript Accepted on 15 May 2021 | Manuscript published on 30 May 2021 | PP: 36-41 | Volume-1 Issue-1, May 2021 | Retrieval Number: 100.1/ijdm.A1607051121

Open Access | Ethics and Policies | Indexing and Abstracting

© The Authors. Published by Lattice Science Publication (LSP). This is an open-access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)

Abstract: Privacy is an essential factor for humans, but Big Data can expose set patterns from which trends specific to human behavior called personally identifiable information can be easily derived. We try to overcome the privacy issues in Big Data using traditional privacy preservation techniques, and Kanonymity is the most extensively used technique for preserving privacy for data publishing. This paper has investigated the privacy challenges in big data and proposes an enhanced privacy preservation model that protects the data against homogeneity and background knowledge attacks and maintains a balance between data quality and data privacy. The proposed algorithm gets executed with minimum running time. This technique will also aid data mining as it ensures data quality by reducing information loss.

Keywords: Big data, Data privacy, Data anonymization, K Anonymization, Information loss

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]]> https://www.ijdm.latticescipub.com/portfolio-item/a1607051121/feed/ 0 A1606051121 https://www.ijdm.latticescipub.com/portfolio-item/a1606051121/ Wed, 11 Aug 2021 10:12:45 +0000 https://www.ijdm.latticescipub.com/?post_type=portfolio&p=357 The Indian Journal of Data Mining (IJDM) has ISSN 2582-9246 (online), an open-access, peer-reviewed, periodical half-yearly international journal, which is published by Lattice Science Publication (LSP) in May and November. The journal aims to publish high-quality peer–reviewed original articles in the area of Data Mining that covers Data Mining, Data Science, Big Data, Data Warehouse, Visualization, Security, Privacy, Big DaaS, Scalable Computing, Cloud Computing, Knowledge Discovery, Integration, Transformation, Information Retrieval, Social Data and Semantics, Mining Functions, Data Regression, Data Classification, Anomaly Detection, Data Clustering, Data Association, Data Cleaning, Feature Selection and Extraction, Data Mining Algorithms, Apriori Decision Tree, Generalized Linear Models, k-Means, Minimum Description Length, Naive Bayes Non-Negative Matrix Factorization, 0-Cluster, Support Vector Machines, Data Preparation, Mining Unstructured Data, Artificial Intelligence, Future Directions and Challenges in Data Mining and Industrial Challenges in Data Mining. #Data Mining #Data Science #Big Data #Data Warehouse #Visualization #Security #Privacy #Big DaaS #Scalable Computing #Cloud Computing #Knowledge Discovery #Integration #Transformation #Information Retrieval #Social Data and Semantics #Mining Functions #Data Regression #Data Classification #Anomaly Detection #Data Clustering #Data Association #Data Regression #Data Cleaning #Feature Selection and Extraction #Data Mining Algorithms #Apriori #Decision Tree #Generalized Linear Models #k-Means #Minimum Description Length #Naive Bayes #Non-Negative Matrix Factorization #0-Cluster #Support Vector Machines #Data Preparation #Mining Unstructured Data #Artificial Intelligence #Future Directions and Challenges in Data Mining #Industrial Challenges in Data Mining #PhD ademic #Scopus #SCI #LatticeScience #Springer, #ScienceDirect #IEEE #Mendeley #Research #Scholarship #UGC #SSRN #LatticeScience #ESCI #Science #Journal #Conference #SSRN #PubLons #PhD #Academic #Scopus #SCI #LatticeScience #Springer, #ScienceDirect #IEEE #Mendeley #Research #Scholarship #UGC #SSRN #LatticeScience #ESCI #Science #Journal #Conference #SSRN #PubLons

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]]> The Indian Journal of Data Mining (IJDM) has ISSN 2582-9246 (online), an open-access, peer-reviewed, periodical half-yearly international journal, which is published by Lattice Science Publication (LSP) in May and November. The journal aims to publish high-quality peer–reviewed original articles in the area of Data Mining that covers Data Mining, Data Science, Big Data, Data Warehouse, Visualization, Security, Privacy, Big DaaS, Scalable Computing, Cloud Computing, Knowledge Discovery, Integration, Transformation, Information Retrieval, Social Data and Semantics, Mining Functions, Data Regression, Data Classification, Anomaly Detection, Data Clustering, Data Association, Data Cleaning, Feature Selection and Extraction, Data Mining Algorithms, Apriori Decision Tree, Generalized Linear Models, k-Means, Minimum Description Length, Naive Bayes Non-Negative Matrix Factorization, 0-Cluster, Support Vector Machines, Data Preparation, Mining Unstructured Data, Artificial Intelligence, Future Directions and Challenges in Data Mining and Industrial Challenges in Data Mining. #Data Mining #Data Science #Big Data #Data Warehouse #Visualization #Security #Privacy #Big DaaS #Scalable Computing #Cloud Computing #Knowledge Discovery #Integration #Transformation #Information Retrieval #Social Data and Semantics #Mining Functions #Data Regression #Data Classification #Anomaly Detection #Data Clustering #Data Association #Data Regression #Data Cleaning #Feature Selection and Extraction #Data Mining Algorithms #Apriori #Decision Tree #Generalized Linear Models #k-Means #Minimum Description Length #Naive Bayes #Non-Negative Matrix Factorization #0-Cluster #Support Vector Machines #Data Preparation #Mining Unstructured Data #Artificial Intelligence #Future Directions and Challenges in Data Mining #Industrial Challenges in Data Mining #PhD ademic #Scopus #SCI #LatticeScience #Springer, #ScienceDirect #IEEE #Mendeley #Research #Scholarship #UGC #SSRN #LatticeScience #ESCI #Science #Journal #Conference #SSRN #PubLons #PhD #Academic #Scopus #SCI #LatticeScience #Springer, #ScienceDirect #IEEE #Mendeley #Research #Scholarship #UGC #SSRN #LatticeScience #ESCI #Science #Journal #Conference #SSRN #PubLons

An Advanced Remote Brained Lego Robot System with Object In dentification
Syed Umar1, Endal Kachen Mosisa2

1Syed Umar, Professor, Dept. of Computer Science, Wollega University, Nekemte, Ethiopia.

2Endal Kachen Mosisa, Dean & Assistant Professor, Dept. of Mechanical Engineering, Wollega University, Nekemte, Ethiopia.

Manuscript received on 06 April 2021 | Revised Manuscript received on 22 April 2021 | Manuscript Accepted on 15 May 2021 | Manuscript published on 30 May 2021 | PP: 31-35 | Volume-1 Issue-1, May 2021 | Retrieval Number: 100.1/ijdm.A1606051121 

Open Access | Ethics and Policies | Indexing and Abstracting

© The Authors. Published by Lattice Science Publication (LSP). This is an open-access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)

Abstract: Robotics have been better at many things for many decades, and still, robot technology is used on everything from physical therapy to nuclear research modern robots typically run in predictable settings with in-board modules that are preset before being put into use. Traditional and current uses of robotics, on the other hand, call for extremely powerful onboard computation that would not be able to meet the needs of new and future ones. The robot would not have to physically house its own brain in the body. It starts with the whole brains in the mother’s womb and converses with them through radio connections. The brain is nurtured by having developed in the maternal system through the years. The central concept of the use of wireless software connected intelligent systems is that it connects intelligent systems with physical robot bodies. In this paper, the robot may provide a strong virtual environment’s virtual brain that includes a powerful vision device. As far as the authors are concerned, this method can be applicable to the intelligent design of mobile robots that possess both visual and physical sight to express visual action. A significant amount of effort is placed on exploring new ways to use robotic structures. And in the future, there are still plentiful possibilities for future breed of intelligent, adaptive, autonomous, and scalable robotic systems in the Internet of Things era.

Keywords: The Robot Would Not Have To Physically House Its Own Brain In The Body.

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]]> A1605051121 https://www.ijdm.latticescipub.com/portfolio-item/a1605051121/ Wed, 11 Aug 2021 09:47:50 +0000 https://www.ijdm.latticescipub.com/?post_type=portfolio&p=351 The Indian Journal of Data Mining (IJDM) has ISSN 2582-9246 (online), an open-access, peer-reviewed, periodical half-yearly international journal, which is published by Lattice Science Publication (LSP) in May and November. The journal aims to publish high-quality peer–reviewed original articles in the area of Data Mining that covers Data Mining, Data Science, Big Data, Data Warehouse, Visualization, Security, Privacy, Big DaaS, Scalable Computing, Cloud Computing, Knowledge Discovery, Integration, Transformation, Information Retrieval, Social Data and Semantics, Mining Functions, Data Regression, Data Classification, Anomaly Detection, Data Clustering, Data Association, Data Cleaning, Feature Selection and Extraction, Data Mining Algorithms, Apriori Decision Tree, Generalized Linear Models, k-Means, Minimum Description Length, Naive Bayes Non-Negative Matrix Factorization, 0-Cluster, Support Vector Machines, Data Preparation, Mining Unstructured Data, Artificial Intelligence, Future Directions and Challenges in Data Mining and Industrial Challenges in Data Mining. #Data Mining #Data Science #Big Data #Data Warehouse #Visualization #Security #Privacy #Big DaaS #Scalable Computing #Cloud Computing #Knowledge Discovery #Integration #Transformation #Information Retrieval #Social Data and Semantics #Mining Functions #Data Regression #Data Classification #Anomaly Detection #Data Clustering #Data Association #Data Regression #Data Cleaning #Feature Selection and Extraction #Data Mining Algorithms #Apriori #Decision Tree #Generalized Linear Models #k-Means #Minimum Description Length #Naive Bayes #Non-Negative Matrix Factorization #0-Cluster #Support Vector Machines #Data Preparation #Mining Unstructured Data #Artificial Intelligence #Future Directions and Challenges in Data Mining #Industrial Challenges in Data Mining #PhD ademic #Scopus #SCI #LatticeScience #Springer, #ScienceDirect #IEEE #Mendeley #Research #Scholarship #UGC #SSRN #LatticeScience #ESCI #Science #Journal #Conference #SSRN #PubLons #PhD #Academic #Scopus #SCI #LatticeScience #Springer, #ScienceDirect #IEEE #Mendeley #Research #Scholarship #UGC #SSRN #LatticeScience #ESCI #Science #Journal #Conference #SSRN #PubLons

The post A1605051121 appeared first on Indian Journal of Data Mining (IJDM).

]]> The Indian Journal of Data Mining (IJDM) has ISSN 2582-9246 (online), an open-access, peer-reviewed, periodical half-yearly international journal, which is published by Lattice Science Publication (LSP) in May and November. The journal aims to publish high-quality peer–reviewed original articles in the area of Data Mining that covers Data Mining, Data Science, Big Data, Data Warehouse, Visualization, Security, Privacy, Big DaaS, Scalable Computing, Cloud Computing, Knowledge Discovery, Integration, Transformation, Information Retrieval, Social Data and Semantics, Mining Functions, Data Regression, Data Classification, Anomaly Detection, Data Clustering, Data Association, Data Cleaning, Feature Selection and Extraction, Data Mining Algorithms, Apriori Decision Tree, Generalized Linear Models, k-Means, Minimum Description Length, Naive Bayes Non-Negative Matrix Factorization, 0-Cluster, Support Vector Machines, Data Preparation, Mining Unstructured Data, Artificial Intelligence, Future Directions and Challenges in Data Mining and Industrial Challenges in Data Mining. #Data Mining #Data Science #Big Data #Data Warehouse #Visualization #Security #Privacy #Big DaaS #Scalable Computing #Cloud Computing #Knowledge Discovery #Integration #Transformation #Information Retrieval #Social Data and Semantics #Mining Functions #Data Regression #Data Classification #Anomaly Detection #Data Clustering #Data Association #Data Regression #Data Cleaning #Feature Selection and Extraction #Data Mining Algorithms #Apriori #Decision Tree #Generalized Linear Models #k-Means #Minimum Description Length #Naive Bayes #Non-Negative Matrix Factorization #0-Cluster #Support Vector Machines #Data Preparation #Mining Unstructured Data #Artificial Intelligence #Future Directions and Challenges in Data Mining #Industrial Challenges in Data Mining #PhD ademic #Scopus #SCI #LatticeScience #Springer, #ScienceDirect #IEEE #Mendeley #Research #Scholarship #UGC #SSRN #LatticeScience #ESCI #Science #Journal #Conference #SSRN #PubLons #PhD #Academic #Scopus #SCI #LatticeScience #Springer, #ScienceDirect #IEEE #Mendeley #Research #Scholarship #UGC #SSRN #LatticeScience #ESCI #Science #Journal #Conference #SSRN #PubLons

We Care System using Google Analytics
Mukund Sharma1, Anubhav Baranawal2, Vaishnavi Moorthy3

1Mukund Sharma, Student, CSE, SRM Institute of Science and Technology, India. 

2Anubhav Baranawal, Student, CSE, SRM Institute of Science and Technology, India.

3Vaishnavi Moorthy, Professor, CSE, SRM Institute of Science and Technology, India.

Manuscript received on 9 April 2021 | Revised Manuscript received on 25 April 2021 | Manuscript Accepted on 15 May 2021 | Manuscript published on 30 May 2021 | PP: 23-30 | Volume-1 Issue-1, May 2021 | Retrieval Number: 100.1/ijdm.A1605051121

Open Access | Ethics and Policies | Indexing and Abstracting
© The Authors. Published by Lattice Science Publication (LSP). This is an open-access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)

Abstract: Corona Virus began in 2019 in China and has now spread across the world. This virus destroyed many economies and lives along with it. People fought against this virus and developed its vaccine. Now, the same corona virus has modified itself and is spreading across nations. India is also getting affected by it. As this virus became even more deadly than before. And don’t have an effective countermeasure against it. This paper is made to make people understand about the spread of corona and how to stay away from it even in public places where we need to go dust to meet our life requirements. Like in nearby shops where people stand close to one another and unknowingly get affected by coronavirus. This paper describes a system through which anyone inside India belonging from any place can stay away from markets and crowded places. This paper describes a “We Care System” using it we can change the speed of corona affecting Indian society. As we know more than 55% of people getting affected from corona are getting affected during general interactions in public places like shopping malls, shops, etc. This paper will help people to stay away from public places as much as possible. It will help them interact with shops and establish a proper communication between the customer and shopkeeper. So, one can order all the things he needed for his household using this system. At most he just needs to go and collect those items from a trusted shop and come home. This will reduce time spent during shopping to 5-6 minutes. So now no waiting in queues for hours and getting what you want along with staying away from corona. This entire system will not only help you but will also help Indian economy. As all the money will be spent in nearby shops this won’t let Indian economy fall apart and even help India people to rise and think about their own good for a greater cause.

Keywords: This Paper Describes A System Through Which Anyone Inside India

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]]> A1604051121 https://www.ijdm.latticescipub.com/portfolio-item/a1604051121/ Wed, 11 Aug 2021 09:40:26 +0000 https://www.ijdm.latticescipub.com/?post_type=portfolio&p=348 The Indian Journal of Data Mining (IJDM) has ISSN 2582-9246 (online), an open-access, peer-reviewed, periodical half-yearly international journal, which is published by Lattice Science Publication (LSP) in May and November. The journal aims to publish high-quality peer–reviewed original articles in the area of Data Mining that covers Data Mining, Data Science, Big Data, Data Warehouse, Visualization, Security, Privacy, Big DaaS, Scalable Computing, Cloud Computing, Knowledge Discovery, Integration, Transformation, Information Retrieval, Social Data and Semantics, Mining Functions, Data Regression, Data Classification, Anomaly Detection, Data Clustering, Data Association, Data Cleaning, Feature Selection and Extraction, Data Mining Algorithms, Apriori Decision Tree, Generalized Linear Models, k-Means, Minimum Description Length, Naive Bayes Non-Negative Matrix Factorization, 0-Cluster, Support Vector Machines, Data Preparation, Mining Unstructured Data, Artificial Intelligence, Future Directions and Challenges in Data Mining and Industrial Challenges in Data Mining. #Data Mining #Data Science #Big Data #Data Warehouse #Visualization #Security #Privacy #Big DaaS #Scalable Computing #Cloud Computing #Knowledge Discovery #Integration #Transformation #Information Retrieval #Social Data and Semantics #Mining Functions #Data Regression #Data Classification #Anomaly Detection #Data Clustering #Data Association #Data Regression #Data Cleaning #Feature Selection and Extraction #Data Mining Algorithms #Apriori #Decision Tree #Generalized Linear Models #k-Means #Minimum Description Length #Naive Bayes #Non-Negative Matrix Factorization #0-Cluster #Support Vector Machines #Data Preparation #Mining Unstructured Data #Artificial Intelligence #Future Directions and Challenges in Data Mining #Industrial Challenges in Data Mining #PhD ademic #Scopus #SCI #LatticeScience #Springer, #ScienceDirect #IEEE #Mendeley #Research #Scholarship #UGC #SSRN #LatticeScience #ESCI #Science #Journal #Conference #SSRN #PubLons #PhD #Academic #Scopus #SCI #LatticeScience #Springer, #ScienceDirect #IEEE #Mendeley #Research #Scholarship #UGC #SSRN #LatticeScience #ESCI #Science #Journal #Conference #SSRN #PubLons

The post A1604051121 appeared first on Indian Journal of Data Mining (IJDM).

]]> The Indian Journal of Data Mining (IJDM) has ISSN 2582-9246 (online), an open-access, peer-reviewed, periodical half-yearly international journal, which is published by Lattice Science Publication (LSP) in May and November. The journal aims to publish high-quality peer–reviewed original articles in the area of Data Mining that covers Data Mining, Data Science, Big Data, Data Warehouse, Visualization, Security, Privacy, Big DaaS, Scalable Computing, Cloud Computing, Knowledge Discovery, Integration, Transformation, Information Retrieval, Social Data and Semantics, Mining Functions, Data Regression, Data Classification, Anomaly Detection, Data Clustering, Data Association, Data Cleaning, Feature Selection and Extraction, Data Mining Algorithms, Apriori Decision Tree, Generalized Linear Models, k-Means, Minimum Description Length, Naive Bayes Non-Negative Matrix Factorization, 0-Cluster, Support Vector Machines, Data Preparation, Mining Unstructured Data, Artificial Intelligence, Future Directions and Challenges in Data Mining and Industrial Challenges in Data Mining. #Data Mining #Data Science #Big Data #Data Warehouse #Visualization #Security #Privacy #Big DaaS #Scalable Computing #Cloud Computing #Knowledge Discovery #Integration #Transformation #Information Retrieval #Social Data and Semantics #Mining Functions #Data Regression #Data Classification #Anomaly Detection #Data Clustering #Data Association #Data Regression #Data Cleaning #Feature Selection and Extraction #Data Mining Algorithms #Apriori #Decision Tree #Generalized Linear Models #k-Means #Minimum Description Length #Naive Bayes #Non-Negative Matrix Factorization #0-Cluster #Support Vector Machines #Data Preparation #Mining Unstructured Data #Artificial Intelligence #Future Directions and Challenges in Data Mining #Industrial Challenges in Data Mining #PhD ademic #Scopus #SCI #LatticeScience #Springer, #ScienceDirect #IEEE #Mendeley #Research #Scholarship #UGC #SSRN #LatticeScience #ESCI #Science #Journal #Conference #SSRN #PubLons #PhD #Academic #Scopus #SCI #LatticeScience #Springer, #ScienceDirect #IEEE #Mendeley #Research #Scholarship #UGC #SSRN #LatticeScience #ESCI #Science #Journal #Conference #SSRN #PubLons

Curbing Accidents on Mine Site
Amosu C.O.

1Amosu C.O., Department of Mineral and Petroleum Engineering, Yaba College of Technology, Yaba.

Manuscript received on 09 April 2021 | Revised Manuscript received on 25 April 2021 | Manuscript Accepted on 15 May 2021 | Manuscript published on 30 May 2021 | PP: 7-22 | Volume-1 Issue-1, May 2021 | Retrieval Number: 100.1/ijdm.A1604051121

Open Access | Ethics and Policies | Indexing and Abstracting

© The Authors. Published by Lattice Science Publication (LSP). This is an open-access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)

Abstract: Mining accidents occur in the process of mining activities from machine operations and process application. Thousands of miners die from mining accidents each year, especially in the process of mining for minerals from rocks whether at the surface or underground. A review of specific cases was considered to buttress the need for complete and adequate safety against accidents. Generally speaking, surface mining is commonly less hazardous compared to underground mining; and large mines are prone to more accidents than smaller mines. This paper discusses accidents in mining operations, its causes, positions and ways to prevent its occurrence.

Keywords: Accident, Mine, Injuries, Fatalities, Causes, Number, Coal, Year, Pattern, Surface mining, Underground mining.

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]]> A1603051121 https://www.ijdm.latticescipub.com/portfolio-item/a1603051121/ Wed, 11 Aug 2021 09:34:53 +0000 https://www.ijdm.latticescipub.com/?post_type=portfolio&p=346 The Indian Journal of Data Mining (IJDM) has ISSN 2582-9246 (online), an open-access, peer-reviewed, periodical half-yearly international journal, which is published by Lattice Science Publication (LSP) in May and November. The journal aims to publish high-quality peer–reviewed original articles in the area of Data Mining that covers Data Mining, Data Science, Big Data, Data Warehouse, Visualization, Security, Privacy, Big DaaS, Scalable Computing, Cloud Computing, Knowledge Discovery, Integration, Transformation, Information Retrieval, Social Data and Semantics, Mining Functions, Data Regression, Data Classification, Anomaly Detection, Data Clustering, Data Association, Data Cleaning, Feature Selection and Extraction, Data Mining Algorithms, Apriori Decision Tree, Generalized Linear Models, k-Means, Minimum Description Length, Naive Bayes Non-Negative Matrix Factorization, 0-Cluster, Support Vector Machines, Data Preparation, Mining Unstructured Data, Artificial Intelligence, Future Directions and Challenges in Data Mining and Industrial Challenges in Data Mining. #Data Mining #Data Science #Big Data #Data Warehouse #Visualization #Security #Privacy #Big DaaS #Scalable Computing #Cloud Computing #Knowledge Discovery #Integration #Transformation #Information Retrieval #Social Data and Semantics #Mining Functions #Data Regression #Data Classification #Anomaly Detection #Data Clustering #Data Association #Data Regression #Data Cleaning #Feature Selection and Extraction #Data Mining Algorithms #Apriori #Decision Tree #Generalized Linear Models #k-Means #Minimum Description Length #Naive Bayes #Non-Negative Matrix Factorization #0-Cluster #Support Vector Machines #Data Preparation #Mining Unstructured Data #Artificial Intelligence #Future Directions and Challenges in Data Mining #Industrial Challenges in Data Mining #PhD ademic #Scopus #SCI #LatticeScience #Springer, #ScienceDirect #IEEE #Mendeley #Research #Scholarship #UGC #SSRN #LatticeScience #ESCI #Science #Journal #Conference #SSRN #PubLons #PhD #Academic #Scopus #SCI #LatticeScience #Springer, #ScienceDirect #IEEE #Mendeley #Research #Scholarship #UGC #SSRN #LatticeScience #ESCI #Science #Journal #Conference #SSRN #PubLons

The post A1603051121 appeared first on Indian Journal of Data Mining (IJDM).

]]> The Indian Journal of Data Mining (IJDM) has ISSN 2582-9246 (online), an open-access, peer-reviewed, periodical half-yearly international journal, which is published by Lattice Science Publication (LSP) in May and November. The journal aims to publish high-quality peer–reviewed original articles in the area of Data Mining that covers Data Mining, Data Science, Big Data, Data Warehouse, Visualization, Security, Privacy, Big DaaS, Scalable Computing, Cloud Computing, Knowledge Discovery, Integration, Transformation, Information Retrieval, Social Data and Semantics, Mining Functions, Data Regression, Data Classification, Anomaly Detection, Data Clustering, Data Association, Data Cleaning, Feature Selection and Extraction, Data Mining Algorithms, Apriori Decision Tree, Generalized Linear Models, k-Means, Minimum Description Length, Naive Bayes Non-Negative Matrix Factorization, 0-Cluster, Support Vector Machines, Data Preparation, Mining Unstructured Data, Artificial Intelligence, Future Directions and Challenges in Data Mining and Industrial Challenges in Data Mining. #Data Mining #Data Science #Big Data #Data Warehouse #Visualization #Security #Privacy #Big DaaS #Scalable Computing #Cloud Computing #Knowledge Discovery #Integration #Transformation #Information Retrieval #Social Data and Semantics #Mining Functions #Data Regression #Data Classification #Anomaly Detection #Data Clustering #Data Association #Data Regression #Data Cleaning #Feature Selection and Extraction #Data Mining Algorithms #Apriori #Decision Tree #Generalized Linear Models #k-Means #Minimum Description Length #Naive Bayes #Non-Negative Matrix Factorization #0-Cluster #Support Vector Machines #Data Preparation #Mining Unstructured Data #Artificial Intelligence #Future Directions and Challenges in Data Mining #Industrial Challenges in Data Mining #PhD ademic #Scopus #SCI #LatticeScience #Springer, #ScienceDirect #IEEE #Mendeley #Research #Scholarship #UGC #SSRN #LatticeScience #ESCI #Science #Journal #Conference #SSRN #PubLons #PhD #Academic #Scopus #SCI #LatticeScience #Springer, #ScienceDirect #IEEE #Mendeley #Research #Scholarship #UGC #SSRN #LatticeScience #ESCI #Science #Journal #Conference #SSRN #PubLons

Deep Convolutional Generative Modeling for Artificial Microstructure Development of Aluminum-Silicon Alloy CROSSMARK Color horizontal
Akshansh Mishra1, Tarushi Pathak2

1Akshansh Mishra, Centre for Artificial Intelligent Manufacturing Systems, Stir Research Technologies, India.

2Tarushi Pathak, Department of Computer Science and Engineering, SRM Institute of Science and Technology, Kattangulathur, India. 

Manuscript received on 25 March 2021 | Revised Manuscript received on 15 April 2021 | Manuscript Accepted on 15 May 2021 | Manuscript published on 30 May 2021 | PP: 1-6 | Volume-1 Issue-1, May 2021 | Retrieval Number: 100.1/ijdm.A1603051121 | DOI: 10.54105/ijdm.A1603.051121

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Abstract: Machine learning which is a sub-domain of an Artificial Intelligence which is finding various applications in manufacturing and material science sectors. In the present study, Deep Generative Modeling which a type of unsupervised machine learning technique has been adapted for the constructing the artificial microstructure of Aluminium-Silicon alloy. Deep Generative Adversarial Networks has been used for developing the artificial microstructure of the given microstructure image dataset. The results obtained showed that the developed models had learnt to replicate the lining near the certain images of the microstructures.

Keywords: Machine Learning; Deep Generative Modeling; Artificial Microstructure; Generative Adversarial Network
Scope: Visualization

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