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All Journal International Journal of Advances in Applied Sciences JOIV : International Journal on Informatics Visualization Jurnal Manajemen dan Dinamika Bisnis
Rajawat, Anand Singh
Unknown Affiliation
Computer Science & IT Decision Sciences, Operations Research & Management Earth & Planetary Sciences Economics, Econometrics & Finance Environmental Science Materials Science & Nanotechnology Mathematics Physics

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Dark Web Financial Fraud Identification Using Mathematical Models in Healthcare Domain Rajawat, Anand Singh; Goyal, S.B.; Solanki, Ram Kumar; Gadekar, Amit; Patil, Dipak
JOIV : International Journal on Informatics Visualization Vol 8, No 1 (2024)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62527/joiv.8.1.2600

Abstract

The so-called "dark web" has emerged as the most trustworthy platform for thieves to launch their enterprises. The healthcare industry has become a haven for illegal activities such as the sale of medical gadgets, trafficking in human beings, and the purchase of organs. This is because the sector provides a high level of privacy, which makes it an ideal location for engaging in unlawful operations. In this field of research, linear regression is utilized to uncover previously unknown patterns in customer demand. A vector will be created using a time series of medical equipment purchases to do this. When we look at the data the case firm gave us, we notice that people tend to desire to purchase products in one of three ways. After that, we sort the hospitals into groups according to the course of the trend vector by employing a technique known as "hierarchical clustering," which we apply to the data. According to the research findings, the trend-based clustering method is an excellent way to partition hospitals into subgroups that share similar tendencies. According to our model evaluations, no one model can reliably produce the most accurate forecasts for each cluster when used by itself. Some models can be utilized to make accurate predictions, and these models apply to a wide variety of time series that exhibit various patterns.
A STUDY OF VENDOR-MANAGED INVENTORY (VMI) AND CLOUD-BASED INVENTORY MANAGEMENT TOOLS FOR MSMES IN BOISAR MIDC OF PALGHAR DISTRICT Mohite, Rohit; Chaurasiya, Ravi; Rajawat, Anand Singh; Rodrigues, Kiran
Jurnal Manajemen dan Dinamika Bisnis (JMDB) Vol. 4 No. 2 (2025): JMDB (Jurnal Manajemen dan Dinamika Bisnis)
Publisher : Universitas Dian Nuswantoro

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Abstract

Micro, Small, and Medium Enterprises (MSMEs) are pivotal to India's manufacturing growth, yet they struggle with efficient inventory management due to infrastructural bottlenecks, outdated practices, and limited digital integration. This study explores the adoption potential of two transformative approaches—Vendor-Managed Inventory (VMI) and Cloud-Based Inventory Management Tools—within the industrial cluster of Boisar MIDC, Palghar District. A survey of 380 MSMEs reveals that while awareness of cloud-based systems is growing, actual implementation remains limited. VMI adoption is even lower, hindered by trust gaps and integration challenges. However, early adopters report tangible benefits including reduced lead times, improved collaboration, and operational cost savings. Using path modelling, the study confirms significant positive effects of technology adoption on manufacturing efficiency (β = 0.49) and strategic planning. These findings underscore the need for policy-backed digital training, phased technology rollouts, and stronger vendor partnerships. The study offers a scalable framework for enhancing inventory efficiency across similar industrial clusters in India.
Harvesting insights: exploring machine learning for crop selection and predictive farming Deshmukh, Tanvi; Rajawat, Anand Singh; Potgantwar, Amol
International Journal of Advances in Applied Sciences Vol 14, No 4: December 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijaas.v14.i4.pp999-1009

Abstract

Modern agriculture has undergone a significant evolution, adopting advanced techniques to streamline crop management processes. One such advancement is the integration of machine learning (ML) technology, which shows great promise in optimizing crop selection and enhancing economic returns. Key determinants of crop productivity, including water availability, soil quality, weather conditions, and timely resource allocation, play pivotal roles in the farming ecosystem. Harnessing these factors, ML algorithms facilitate the identification of optimal crop choices and provide continuous monitoring of cultivation processes to anticipate evolving crop needs. This paper investigates various ML techniques employed for crop selection and evaluates their effectiveness in agricultural settings. Through a comparative analysis, we highlight the advantages of these techniques and provide insights into their potential impact on current farming management practices. By leveraging ML for predictive farming, stakeholders can make informed decisions to maximize yields, minimize resource wastage, and promote sustainable agricultural practices. This study contributes to the ongoing discourse on the integration of technology in agriculture and underscores the transformative potential of ML in shaping the future of crop management. We investigate recent papers from the years 2020 to 2024.
Co-Authors Chaurasiya, Ravi Deshmukh, Tanvi Gadekar, Amit Goyal, S.B. Mohite, Rohit Patil, Dipak Potgantwar, Amol Rodrigues, Kiran Solanki, Ram Kumar