Mining Frequent Itemsets with Fuzzy Taxonomic Structures for Cybercrime Investigations | Chapter 9 | Research and Applications Towards Mathematics and Computer Science Vol. 2

 In the sphere of cybercrime investigations, recognizing patterns and associations among various entities is a critical step towards understanding and mitigating criminal activities. Traditional approaches to finding frequent itemsets typically depend exact matching between articles and lack the ability to handle doubt and imprecision in the dossier. To address this challenge, we propose a method for excavating frequent itemsets with fluffy taxonomic structures in cybercrime investigations. Our approach influences the concept of fluffy sets and taxonomies to represent the changeableness and imprecision in the data, individually. We demonstrate the influence of our method using a honest-world dataset of cybercrime occurrence, where we show that our approach can reveal valuable intuitions into the relationships between different bodies involved in cybercrime. Our findings focal point the importance of combining fuzzy and taxonomic structures in the reasoning of cybercrime data, and plan new avenues for future research in this area.

Author(s) Details:

Pratham Batra,
Maharaja Surajmal Institute of Technology, New Delhi, India.

Praveen Arora,
Jagan Institute of Management Studies, New Delhi, India.

Please see the link here: https://stm.bookpi.org/RATMCS-V2/article/view/11112

Fuzzy Logic-Based Medical Decision System for Diagnosing Chronic Obstructive Pulmonary Disease | Chapter 5 | Research Highlights in Disease and Health Research Vol. 7

 This paper suggests a fuzzy logic-located medical decision whole for diagnosing COPD, which exploits fuzzy sets to show the severity of symptoms, record of what happened, and test results, and fuzzy rules and reasoning to create a diagnostic result as manufacturing. The proposed system aims to support a more accurate and nuanced understanding of a patient's condition, which keep help doctors track changes in the patient's condition and adjust situation accordingly. The projected study involves collecting dossier from a sample of COPD-diagnosed patients and evolving and testing the fuzzy rationale-based healing decision system. It is owned by note that fuzzy logic-located medical decision arrangements should be secondhand in conjunction with other healing expertise and tools to guarantee the best possible patient effects. A fuzzy sanity-based medical resolution system for COPD diagnosis has the potential to upgrade the accuracy and efficiency of COPD disease, leading to better patient consequences and quality of life.

Author(s) Details:

Gurpreet Singh Popli,
C K Birla Hospital, New Delhi, India.

Praveen Arora,
Jagan Institute of Management Studies, New Delhi, India.

Please see the link here: https://stm.bookpi.org/RHDHR-V7/article/view/10569

Mining Rules for Head Injury Patients Using Fuzzy Taxonomic Structures | Chapter 12 | Research Highlights in Disease and Health Research Vol. 5

 The paper reviews how to extract rules from databases that contain vague taxonomic structures. While former studies have looked at extracting rules from diversified tables with fluffy data, there hasn't happened much research done specifically in the healthcare subdivision. The paper introduces a new invention that builds upon previous research and is tailored to the healthcare manufacturing. To test the algorithm's influence, it was applied to a sample dataset of patients the one underwent intellect surgery and fell into a trance. By analyzing the data utilizing the algorithm, the study was intelligent to gain important insights into the cases' conditions. When diagnosing victims, doctors rely on information from miscellaneous sources, which can have their own restraints and uncertainties. Therefore, it's important for physicians to weigh all the available news carefully to form the most accurate disease possible. The algorithm found in this study maybe helpful in identifying potential risk determinants or developing more persuasive treatment protocols for akin cases in the future.

Author(s) Details:

Praveen Arora,
Jagan Institute of Management Studies, New Delhi, India.

Please see the link here: https://stm.bookpi.org/RHDHR-V5/article/view/10124

Frequent Itemsets: Fuzzy Data from Multiple Datasets | Chapter 4 | Novel Research Aspects in Mathematical and Computer Science Vol. 5

Data warehousing and data mining processes rely heavily on the implementation of association rule mining. In order to bolster this claim, the study suggests a model that retrieves frequent itemsets from the database that are arranged in the form of star schema tabular database structures and has a fuzzy taxonomic structure at the backend. The study's goal was to create a new method from an existing one that uses fuzzy association rule-based mining in databases using ER models. The focus of the study is on the extraction of linguistic algorithm rules at multilevel structures in the form of tables from various databases in order to comprehend the functionality of these retrieved data item sets. The suggested data mining algorithm's operation is illustrated by an example in the conclusion. It may be used to quickly and easily generate multi-level fuzzy rules that are relevant to association mining techniques.

Author (s) Details:

Praveen Arora,
Jagan Institute of Management Studies, Rohini Sector 5, Near Rithala Metro Station, New Delhi, India.

Sanjive Saxena,
Jagan Institute of Management Studies, Rohini Sector 5, Near Rithala Metro Station, New Delhi, India.

Silky Madan,
Jagan Institute of Management Studies, Rohini Sector 5, Near Rithala Metro Station, New Delhi, India.

Navneet Joshi,
Jagan Institute of Management Studies, Rohini Sector 5, Near Rithala Metro Station, New Delhi, India.

Please see the link here: https://stm.bookpi.org/NRAMCS-V5/article/view/7481