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Research Paper

A Comprehensive Analysis of B2B Marketing Strategies

Business-to-business (B2B) marketing is an essential segment of modern commerce, where businesses market their products or services to other organizations rather than directly to individual consumers. B2B marketing strategies are nuanced and significantly differ from Business-to-Consumer (B2C) approaches. This research article aims to explore B2B marketing strategies, their unique characteristics, channels, challenges, and the metrics used to assess success. With a focus on digital transformation, relationship-building, and content creation, this study highlights the evolution of B2B marketing in the digital era. Additionally, it discusses how businesses can create personalized and value-driven strategies to improve customer engagement and foster long-term relationships.

Published by: Aakash V

Author: Aakash V

Paper ID: V10I6-1381

Paper Status: published

Published: December 7, 2024

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Review Paper

Smart EV Charging Station with Grid Green Power and Wireless Charging

The demand for green charging has increased with the rapid growth of electric vehicles (EVs). This paper reviews a smart EV charging station integrating solar and wind power with on-grid electricity to support multiple charging modes: DC fast charging, AC charging, wireless charging, and grid-powered AC charging. An RFID-based system secures access to wireless charging, enhancing usability. This review looks into the design, benefits, and challenges of integrating renewable energy into EV charging, highlighting the system's potential for environmental and operation advantages in sustainable infrastructures.

Published by: Vivek Makane, Aditi Prashant Ozardekar, Varsha V. Nanavare

Author: Vivek Makane

Paper ID: V10I6-1378

Paper Status: published

Published: December 7, 2024

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Research Paper

AI-Driven Portfolio Optimization: Enhancing Investment Strategies Using Machine Learning

With the evolution of complexity in today’s markets and investment opportunities, traditional methods of appropriate multi-asset portfolio optimization have become insufficient, exposing the funds to overly large levels of risk. This paper addresses the question of how decision-making and investment strategies involving portfolios are assisted by artificial intelligence (AI) and, in particular, machine learning (ML). Everything, including processes such as supervised learning, unsupervised learning, and reinforcement learning, is studied in the context of critical tasks within portfolio management such as investment, risk, and trading strategies. A portfolio processed using AI reported an increase of 12% in predictive accuracy and a decrease of 20% in computation time for backtesting simulations. This was mainly due to well-developed noise filtering features that enable the model to operate in unstable market conditions. Even the key results show that in terms of risk-adjusted performance and managing uncertainty in the market, AI-based models are far better than traditional approaches. In addition, this research draws attention to emerging data sources and ethical AI, AI-based models and practices that further increase transparency, and AI methods associated with ethical concerns and data biases. These results demonstrate how something like AI stands to completely revolutionize the concept of investment and the strategies that accompany it, bringing about a form of change that is very much needed in today’s financial system.

Published by: Mariam Karanjia

Author: Mariam Karanjia

Paper ID: V10I6-1379

Paper Status: published

Published: December 6, 2024

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Research Paper

AI-Powered SEO: Revolutionizing Digital Marketing

Artificial Intelligence (AI) has redefined how digital marketing strategies are conceived and implemented, particularly in Search Engine Optimization (SEO). AI-driven tools now enhance SEO processes by offering predictive analytics, content generation, and advanced user behavior tracking. This paper explores the impact of AI on SEO, supported by statistical insights and real-world applications, and highlights the challenges and future trends associated with this technology.

Published by: Aakash V

Author: Aakash V

Paper ID: V10I6-1374

Paper Status: published

Published: December 6, 2024

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Research Paper

Real-Time Health Monitoring System of a Distribution Transformer

All Transformers are vital components of power systems, and their failure can significantly disrupt the balance of the entire system. Damage to transformers is often caused by overcurrent or excessive temperatures. This project aims to monitor the health of distribution transformers in real time to prevent such failures. Key parameters, including current, voltage, temperature, and oil level, are continuously monitored using sensors. The system leverages IoT technology, with a Wi-Fi module transmitting recorded data via the HTTP protocol, allowing remote access from anywhere. By automating this process, the system eliminates human error and enables quick responses to potential issues. Alerts are sent when parameters exceed predefined thresholds, helping prevent critical failures. The solution is scalable, adaptable to varying operational demands, and designed to enhance current monitoring practices. Data visualization and analysis are implemented using platforms such as ThingSpeak, which provides graphical representations of monitored parameters. Notifications are sent through IFTTT protocols, enabling timely and efficient maintenance responses. This real-time monitoring system ensures the reliable operation of distribution transformers, reduces manual intervention and improves overall efficiency. Its cost-effectiveness and versatility make it an ideal solution for modern power systems, offering a proactive approach to transformer health management.

Published by: Aman Kumar Singh, Devika Aggarwal, Aditya Sharma

Author: Aman Kumar Singh

Paper ID: V10I6-1373

Paper Status: published

Published: December 5, 2024

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Research Paper

A Survey on Improved Dehazing System for Underwater Night Light Fog Images Using Machine Learning Algorithm

The absorption of light in water and scattering leads to the degradation of images captured at night under the water. This degradation causes low brightness, diminished color, and distinguishable objects in the image affected by the night light. Many researchers worked to improve the images underwater during daylight using contrast stretching and the white balance method. The majority of investigations were done using white balance to reduce the greenish effect due to absorption of higher wavelength, which is reduced when propagated through water, this survey presents many works like guided filter transmission used as an edge-preserving smoothly operator and also “CLAHE” (“contrast limited adaptive histogram equalization”) & RGB model. These methods helped to change the intensity of the dehazed image and the level of contrast in the image. All these methodologies developed by the previous researchers were on the dehazing of daylight fog images. Hence, there is a scope for developing an improved dehazing of night light fog images.

Published by: Tamanna Pawar, Shridevi Soma

Author: Tamanna Pawar

Paper ID: V10I6-1362

Paper Status: published

Published: December 4, 2024

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