Manuscripts

Recent Papers

Research Paper

Exploring the World of Cryptocurrency

Cryptocurrency represents a revolutionary shift in the world of finance, offering a decentralized, digital alternative to traditional currencies. Built on blockchain technology, cryptocurrencies ensure secure, transparent, and immutable transactions without the need for central banks or intermediaries. This journal explores the fundamentals of cryptocurrency, its underlying technology, and the potential benefits it offers, including faster transactions, lower fees, financial inclusion, and enhanced privacy. However, challenges such as price volatility, regulatory uncertainty, and misuse remain significant obstacles. By examining both the promise and the risks associated with cryptocurrencies, this guide provides a comprehensive overview of how digital currencies are shaping the future of finance.

Published by: Dr. Hasith Soysa

Author: Dr. Hasith Soysa

Paper ID: V10I6-1473

Paper Status: published

Published: December 28, 2024

Full Details
Research Paper

An Intelligent Multi-Criteria Optimization Algorithm for Enhancing Digital Marketing Strategies

In the dynamic landscape of digital marketing, organizations face the challenge of simultaneously optimizing multiple interdependent objectives, such as maximizing audience reach, enhancing engagement, and minimizing costs. This study proposes an intelligent multi-criteria optimization algorithm that integrates the Analytic Hierarchy Process (AHP) and Genetic Algorithms (GA) to address these challenges systematically. The AHP framework establishes objective weights based on strategic priorities, while the GA iteratively refines marketing budget allocations across various channels. A simulation conducted on data for five marketing channels demonstrated that the algorithm successfully prioritized budget allocation towards the most effective channel - Social Media - achieving optimal reach (1.0), engagement (0.977), and cost efficiency (0.258). The convergence analysis revealed consistent improvements across generations, underscoring the algorithm’s ability to balance conflicting objectives effectively. Comparative analysis indicated a 15% improvement in overall campaign performance and a 10% reduction in costs compared to traditional single-objective optimization approaches. These findings suggest that the proposed algorithm provides a scalable and adaptable tool for data-driven decision-making in complex digital marketing environments.

Published by: Ndeye Siga Gueye

Author: Ndeye Siga Gueye

Paper ID: V10I6-1501

Paper Status: published

Published: December 27, 2024

Full Details
Research Paper

The Ecology of Forest Fires

Forest fires, while often perceived as destructive forces, play a complex role in the ecology of forest ecosystems. This paper explores the dual nature of wildfires, emphasising their capacity to disrupt biodiversity and impact the environment while also facilitating ecological succession and promoting biological diversity. It examines the effects of forest fires on carbon dioxide emissions and air quality, particularly in regions like California, where human activities have intensified fire occurrences. Additionally, the paper highlights the adaptive responses of flora and fauna to fire, showcasing examples such as the serotiny of Monterey pines and the dependency of wild lupine for the endangered Karner blue butterfly. A case study on forest fires in Latin America underscores the implications of agricultural practices and deforestation on wildfire frequency and intensity. This literature review emphasises the necessity of understanding the ecological benefits of fires while addressing the challenges posed by climate change and deforestation, advocating for a balanced approach to forest management that preserves the integrity of forest ecosystems.

Published by: Ishaani Vats

Author: Ishaani Vats

Paper ID: V10I6-1492

Paper Status: published

Published: December 26, 2024

Full Details
Research Paper

Transforming Urban Traffic with AI: Insights from Singapore and Opportunities in India

Real-time traffic management has become the backbone of modern urban planning, with AI systems at the forefront of optimizing traffic flow and reducing congestion. With urbanization fast-paced worldwide, cities face unprecedented traffic challenges, such as increasing vehicle density, unpredictable congestion patterns, and growing environmental concerns. This paper reviews the AI-based traffic management system implemented in Singapore, a global leader in smart city innovation. Advanced techniques of AI are put forward by Singapore's LTA in managing traffic such that intelligent traffic lights come through predictive analytics, amongst its integration with public transportation means, cutting significant congestion, travel time as well as vehicle emissions levels alongside improving road safety all across. The potential applicability of such systems is also discussed in Indian cities like Mumbai and Bengaluru. With high population density, diverse traffic compositions, and infrastructure constraints, cities are more demanding and require innovative solutions to handle these challenges. AI-based traffic management could be applied to adjust the timing of traffic signals dynamically, optimize public transport efficiency, and reduce emergency response times for transformative changes in urban mobility. However, fragmented data systems, infrastructure limitations, and cost barriers pose huge implementation challenges in India. By comparing Singapore's success to the realities of Indian cities, this research highlights what's needed to adapt and scale AI technologies to meet local needs. It concludes that the integration of AI-driven systems can provide Indian cities with a sustainable path forward regarding traffic congestion, reduced environmental impact, and the quality of life in an urban setting.

Published by: Satheerth P K

Author: Satheerth P K

Paper ID: V10I6-1471

Paper Status: published

Published: December 25, 2024

Full Details
Research Paper

AI-Based Cancer Detection using FRCNN, Random Forest, SVM, and Regression Models

Early detection of cancers, especially in the mouth, throat, and lungs, significantly improves patient survival rates. This paper presents a comprehensive AI-driven approach combining various machine learning (ML) and deep learning (DL) techniques such as Fast Region-based Convolutional Neural Networks (FRCNN), Random Forest (RF), Support Vector Machines (SVM), and Logistic and Linear Regression models to enhance cancer detection capabilities. Each model's strengths are leveraged to create a hybrid system that excels in detecting and classifying cancerous regions in medical images and analyzing patient data. The proposed workflow incorporates automated image analysis, feature selection, classification, and probabilistic risk estimation, enhancing diagnostic accuracy while addressing challenges like data availability, model interpretability, and computational requirements. This integrated AI-based approach demonstrates potential for real-time clinical application and personalized cancer diagnostics.

Published by: Shivam Chattar, Anshul Gaikwad, Het Savsani, Kshanay Nikam, Avishkar Sarnaik, Prof. Priyanka Patil

Author: Shivam Chattar

Paper ID: V10I6-1450

Paper Status: published

Published: December 23, 2024

Full Details
Research Paper

Sustainable Information Retrieval Techniques for Onion Market Instability Prediction using Machine Learning and Deep Learning Approaches

Price is a critical determinant in financial activities, and sudden fluctuations in price often signal market instability. Machine learning offers robust techniques to forecast product prices and address these instabilities effectively. This study examines the application of machine learning models to predict onion prices in India, utilizing data collected from the Ministry of Agriculture, India. Various machine learning algorithms, including K-Nearest Neighbor (KNN), Naïve Bayes, Decision Tree, Neural Network (NN), and Support Vector Machine (SVM), were employed for classification purposes. Their performance was evaluated to identify the most accurate model. Additionally, this research integrates deep learning approaches, specifically Long Short-Term Memory (LSTM) networks, for forecasting onion prices. These methods classify onion prices into three categories: preferable (low), economical (mid), and expensive (high), providing valuable insights to address market volatility.

Published by: Dr. M.K. Jayanthi Kannan, Satyajit Patel

Author: Dr. M.K. Jayanthi Kannan

Paper ID: V10I6-1455

Paper Status: published

Published: December 22, 2024

Full Details
Request a Call
If someone in your research area is available then we will connect you both or our counsellor will get in touch with you.

    [honeypot honeypot-378]

    X
    Journal's Support Form
    For any query, please fill up the short form below. Try to explain your query in detail so that our counsellor can guide you. All fields are mandatory.

      X
       Enquiry Form
      Contact Board Member

        Member Name

        [honeypot honeypot-527]

        X
        Contact Editorial Board

          X

            [honeypot honeypot-310]

            X