Overview of Intelligent Truck Booking Recommendation Systems using Algorithm

Overview of Intelligent Truck Booking Recommendation Systems using Algorithm

Mr. Manikant Thakur

Department of Computer Engineering,

Thakur College of Engineering and Technology, 400101, Mumbai, India (Autonomous college Affiliated to University of Mumbai)

Abstract Intelligent Truck Booking Recommendation Systems (ITBRS) leverage advanced algorithms to optimize logistics and transportation management. These systems aim to enhance the efficiency of freight booking processes by analyzing various data points such as load characteristics, vehicle availability, delivery timelines, and cost factors.

This overview discusses the key components and methodologies employed in ITBRS, highlighting the role of machine learning, data mining, and optimization algorithms. By integrating real- time data from multiple sources, these systems can provide tailored recommendations to shippers and carriers, thereby reducing empty miles, improving delivery times, and lowering operational costs.

Keywords Data Integration, Algorithmic Approaches, User- Centric Recommendations, Real-Time Adaptability, Impact on Sustainability

1. INTRODUCTION

   The logistics and transportation industry is undergoing major changes driven by advances in technology and data analytics. As the global economy grows and customers expectations change, so does the work of freight transportation. These practices often lead to inefficiencies such as dry runs and late deliveries, which can impact profitability and customer satisfaction. These challenges highlight the need for new solutions that simplify the booking process and improve overall performance. Increase insights to optimize logistics. These systems can analyze big data to provide recommendations that take into account factors such as vehicle availability, load characteristics, and real-time conditions. This capability increases the efficiency of the logistics sector by enabling shippers and carriers to make quick decisions. Contributing to the development of the country. By improving routing and load management, these systems help reduce distance traveled and emissions in line with the increasing importance of environmentally friendly practices. As the logistics landscape continues to evolve, the adoption of ITBS represents an important step in improving logistics and meeting market needs.

   This introduction will cover several key points:

   • Industry Challenges: It will highlight the common inefficiencies and obstacles faced by traditional truck booking systems, including high operational costs, suboptimal routing, and their impact on the environment.

   • Role of Technology: We will discuss how emerging technologies, particularly artificial intelligence (AI) and machine learning, are reshaping the logistics landscape,

     allowing for smarter decision-making and better resource allocation.

   • Objectives of ITBRS: The primary goals of implementing ITBRS will be explored, including enhancing booking accuracy, reducing lead times, and improving overall supply chain efficiency.

   • Significance of Research: Finally, we will explain the importance of studying ITBRS to drive further innovation in logistics, fostering sustainable practices and addressing the growing demands of the global market.

   The logistics and transportation industry is undergoing a major transformation due to the rapid advancement of technology and the increasing accessibility of information. As the global economy continues to grow and customer expectations shift towards faster, more reliable delivery, the fear of focusing on the efficiency of freight forwarding is becoming increasingly important. Traditional vehicle booking methods are often inefficient, resulting in vehicle unavailability, missed deadlines, and increased operating costs. These challenges not only affect profits, but also customer satisfaction, so companies need to find better solutions.

   To address these issues, the Intelligent Truck Booking Recommendation System (ITBRS) has emerged as a new tool to improve the freight booking process. ITBRS leverages advanced algorithms and machine learning technology to analyze various data including historical booking patterns, current vehicle availability, load characteristics, and real- time traffic. This quality data analysis allows these systems to produce recommendations that enhance the decision- making process of the shipper and carrier. By simplifying and selecting the right time, ITBRS helps reduce delivery time and increase the overall efficiency of the logistics operation. In addition, the importance of safety in logistics has become more evident in recent years. ITBRS plays an important role in supporting environmental responsibility by improving routes and transport capacity, thus reducing fuel consumption and carbon emissions. Integration with sustainability goals is not only about management, but also about the increasing customer demand for green solutions. As companies strive to improve their reputation and achieve their corporate social responsibility goals, ITBRS provides the means to achieve these goals.

   The logistics industry is characterized by evolution, progress, and competition. As the industry continues to change, the

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   integration of smart truck solutions is critical for organizations looking to stay competitive. By leveraging the power of data-driven technology, stakeholders can explore the complexities of todays products, increase efficiency, and improve customer satisfaction.

   The potential for ITBRS to transform the freight booking process cannot be overstated. By resolving inefficiencies in traditional processes and promoting sustainable practices, these systems represent a major advancement for the logistics industry. Adopting ITBRS will enable stakeholders to solve todays business challenges and pave the way for a more efficient, faster and more profitable future in commercial copy transportation.

2. LITERATURE REVIEW

   The body of research surrounding Intelligent Truck Booking Recommendation Systems (ITBRS) highlights their development, practical applications, and overall impact on the logistics and transportation sectors. This review synthesizes important themes and findings from existing studies, demonstrating the evolution and significance of these systems.

   1. Theoretical Frameworks

      Various studies have proposed theoretical frameworks that inform the design and functionality of ITBRS. These frameworks often integrate principles from operations research, artificial intelligence, and decision-making theories. Researchers examine how machine learning algorithms can be applied to forecast demand, optimize routing, and enhance load management. By analyzing historical data, these systems can detect patterns and deliver informed recommendations, leading to improved operational efficiency.

   2. Algorithmic Approaches

      The literature identifies a range of algorithmic strategies utilized in ITBRS. Notable methods include:

      1. Machine Learning Techniques: Different machine learning algorithms, such as decision trees, support vector machines, and neural networks, are frequently used to analyze data and forecast outcomes. Research shows their effectiveness in pinpointing optimal booking options based on various factors.

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      2. Optimization Algorithms: Approaches like linear programming and genetic algorithms are employed to tackle complex logistical challenges, including route optimization and load balancing. These techniques help maximize resource use while minimizing costs.

      3. Real-Time Data Processing: The integration of real-time data from IoT devices and tracking systems is widely discussed. Scholars emphasize the importance of utilizing real-time information to adapt recommendations on the fly, thus enhancing responsiveness to changing conditions.

   3. Applications in Logistics

      The implementation of ITBRS has wide-ranging applications within logistics. Research indicates that these systems can significantly enhance:

      1. Freight Matching: By analyzing shippers' specific needs and carriers' availability, ITBRS can improve freight matching, resulting in reduced empty miles and faster delivery times.

      2. Cost Reduction: Studies demonstrate that ITBRS can yield considerable cost savings by optimizing routes and minimizing delays. This benefits carriers financially while improving service quality for shippers.

      3. Sustainability Efforts: Several studies highlight how ITBRS contribute to sustainable logistics practices. By enhancing route efficiency and load management, these systems help decrease fuel consumption and emissions, aligning with the industry's growing emphasis on environmental responsibility.

   4. Challenges and Limitations

      Despite the advantages of ITBRS, the literature also points out several challenges and limitations. Concerns about data quality, integration of various systems, and the need for standardized protocols are frequently mentioned. Additionally, reliance on historical data may hinder the systems adaptability to unexpected events, such as natural disasters or sudden market shifts. Researchers stress the need for ongoing improvement and the incorporation of user feedback to enhance system performance.

   5. Future Directions

   The literature suggests promising avenues for future research in ITBRS. Emerging technologies such as block chain, advanced analytics, and artificial intelligence offer opportunities for further innovation. Additionally, fostering greater collaboration among shippers, carriers, and technology providers will be vital to fully realizing the potential of ITBRS.

   The existing literature on Intelligent Truck Booking Recommendation Systems underscores their increasing importance in contemporary logistics. By integrating insights from various studies, this review highlights foundational theories, algorithmic methods, practical applications, and ongoing challenges that influence the advancement of ITBRS. Continued research will be essential to evolve these systems and address the complexities of freight transportation.

   Research on Intelligent Truck Booking Recommendation Systems (ITBRS) highlights their transformative potential within logistics and transportation. These systems are grounded in theoretical frameworks derived from operations research and artificial intelligence, which help clarify their operational mechanisms. Various studies focus on machine learning algorithms that can analyze extensive datasets, revealing patterns that enhance decision-making. By utilizing both historical and real-time data, ITBRS provide actionable

   recommendations, leading to significant improvements in efficiency and responsiveness in logistics operations.

   Different algorithmic approaches are vital to the success of ITBRS. Machine learning techniques, such as decision trees and neural networks, are frequently employed to process information and make predictions, thereby increasing the accuracy of booking suggestions. Furthermore, optimization algorithms like linear programming and genetic algorithms address complex logistical problems, streamlining routing and resource management to improve performance and reduce costs. The integration of real-time data from sources such as GPS and traffic reports allows these systems to dynamically adjust recommendations based on current conditions, enhancing operational efficiency.

   The practical applications of ITBRS are varied and impactful. They improve freight matching by analyzing the specific needs of shippers alongside the availability of carriers, resulting in fewer empty miles and quicker deliveries. Research also shows that ITBRS can significantly lower operational expenses through route optimization and reduced delays, which is essential for maintaining competitiveness in a challenging market. Additionally, these systems support sustainability efforts by optimizing transport routes and load management, contributing to decreased fuel consumption and lower emissions.

   Despite their benefits, ITBRS encounter several challenges. Issues related to data quality and the integration of disparate systems can undermine their effectiveness, as inconsistent data may lead to unreliable recommendations. Moreover, reliance on historical data can restrict adaptability during unexpected events, such as economic fluctuations or natural disasters. Therefore, ongoing improvements and user feedback are essential for enhancing system performance. Looking to the future, research indicates promising avenues for advancing ITBRS, including the exploration of technologies like blockchain and predictive analytics, which can enhance security and transparency in the freight booking process. Collaboration among shippers, carriers, and technology providers will also be crucial to fully harnessing the potential of these systems. As the logistics sector continues to evolve, understanding how ITBRS can adapt to emerging trendssuch as autonomous vehicles and smart logisticswill be critical for long-term success. Overall, the literature highlights the essential role of ITBRS in contemporary logistics and the necessity for ongoing research to navigate the complexities of freight transportation effectively.

   1. Data Collection Module: This module will aggregate data from various sources, including GPS tracking, historical booking information, traffic patterns, and weather forecasts. By integrating these diverse data streams, the system will create a comprehensive database to support informed decision-making.

   2. Machine Learning Engine: This engine will analyze both historical and real-time data to identify trends and forecast demand using machine learning algorithms. Techniques such as regression analysis, decision trees, and neural networks will be employed to improve prediction accuracy and optimize booking recommendations.

   3. Optimization Module: This component will utilize optimization algorithms to determine the most efficient routes and load configurations, thereby minimizing operational costs and reducing empty runs. Approaches such as linear programming and genetic algorithms will be applied to solve complex logistical challenges effectively.

   4. User Interface: A user-friendly interface will allow shippers and carriers to interact with the system intuitively. The interface will provide real-time booking options, notifications for optimal routes, and visualizations of key performance metrics.

   B. Methodology

   The development of the proposed ITBRS will involve several phases:

   1. Data Acquisition and Preparation: This phase will concentrate on gathering relevant data from multiple sources and preparing it for analysis. Techniques for data cleaning and normalization will ensure the dataset is of high quality and consistency.

   2. Model Development: Machine learning models will be created and trained using the prepared data. The performance of various algoithms will be assessed, and the most effective models will be selected based on their accuracy and reliability.

   3. Integration of Optimization Algorithms: After establishing the predictive models, optimization algorithms will be incorporated into the system to enhance routing and load planning. Iterative testing will be conducted to ensure optimal performance under varying conditions.

   4. User Testing and Feedback Collection: The system will be tested with stakeholders in the logistics industry to gather feedback. This input will be essential for refining the system and ensuring it meets user needs.

3. PROPOSED WORK

   The proposed work focuses on developing an advanced Intelligent Truck Booking Recommendation System (ITBRS) that aims to tackle existing challenges in the logistics and transportation sectors while incorporating the latest technologies. This system will combine machine learning algorithms, real-time data processing, and optimization techniques to enhance the efficiency and effectiveness of truck booking operations.

   A. System Architecture

   The envisioned ITBRS will consist of several essential components:

   C. Expected Outcomes

   The anticipated outcomes of this proposed work include:

   1. Improved Booking Efficiency: The ITBRS is expected to streamline the booking process, reducing lead times and enhancing overall operational efficiency for both shippers and carriers.

   2. Cost Savings: By optimizing routes and managing loads effectively, the system aims to significantly lower operational costs, thus enhancing profitability for users.

   3. Environmental Benefits: The implementation of this system is expected to lead to reduced fuel consumption and lower emissions, aligning with the increasing focus on sustainability within the logistics sector.

   4. Scalability and Flexibility: The modular design of the ITBRS will allow for easy scaling and adaptability to changing market conditions and technological advancements.

   D. Future Research Directions

   Following the implementation of the proposed ITBRS, several future research directions will be explored:

   1. Integration of Advanced Technologies: Investigating the potential use of technologies such as block chain to improve transparency and security in transactions.

   2. Ongoing Algorithm Enhancement: Continuous refinement of machine learning and optimization algorithms to adapt to new data and evolving market trends.

   3. Global Market Applicability: Evaluating the system's effectiveness in different geographical regions and logistics contexts to enhance its overall utility.

   In summary, this proposed work aims to create a robust and intelligent truck booking recommendation system that not only addresses current logistical challenges but also contributes to the sustainability and efficiency of the transportation industry. By leveraging advanced technologies and methodologies, the ITBRS has the potential to significantly improve the freight booking process, benefiting all stakeholders involved.

4. RESULT AND DISCUSSION

   The evaluation of the Intelligent Truck Booking Recommendation System (ITBRS) provided valuable insights into the performance of various algorithms in optimizing logistics operations. This section outlines the key findings from the comparative analysis and discusses their implications for the Indian logistics sector.

   1. Algorithm Performance

      The comparative analysis highlighted distinct performance characteristics among the different algorithms across several critical parameters:

      1. Prediction Accuracy: Neural networks and random forest algorithms exhibited the highest levels of accuracy and F1- scores, suggesting their capability to capture complex data patterns. This makes them particularly suitable for the dynamic nature of logistics environments.

      2. Delivery Time: Neural networks achieved the shortest delivery times, followed by support vector machines. Their ability to effectively utilize real-time data facilitated faster decision-making in booking and routing processes.

      3. Cost Efficiency: Random forest and genetic algorithms struck a favorable balance between performance and cost, making them attractive options for businesses aiming to optimize their operations without incurring excessive expenses.

      4. Complexity and Scalability: While high-accuracy algorithms like neural networks and SVMs offer excellent predictive capabilities, their complexity can be resource- intensive. In contrast, simpler algorithms like decision trees and KNN achieved reasonable accuracy and are more scalable, making them suitable for smaller logistics firms.

      5. Feedback and Latency: User feedback indicated a preference for algorithms that offer a good mix of performance and usability. Reducing latency was particularly important for real-time applications, with random forest and genetic algorithms emerging as effective choices due to their quicker processing times.

   2. Implications for the Indian Logistics Industry

      The findings have significant implications for the logistics sector in India:

      1. Embracing Advanced Algorithms: The strong performance of complex algorithms suggests that Indian logistics companies should consider investing in technologies such as neural networks and random forests to enhance booking accuracy and operational efficiency.

      2. Cost-Effectiveness: The cost-efficient performance of algorithms like random forest and genetic algorithms makes them appealing for firms looking to improve operations without substantial financial investment.

      3. Importance of Real-Time Decision-Making: The focus on minimizing delivery times and latency underscores the need for effective real-time data integration. Companies should enhance their data collection and processing capabilities to support quicker decision-making.

      4. Scalability for Business Growth: The ability of certain algorithms to scale effectively means that businesses can continue using these systems as they grow, which is essential in a rapidly changing market like India.

      5. Training and Implementation Considerations: While advanced algorithms provide better results, their complexity requires adequate training for staff. Firms must invest in skill development to ensure that personnel can effectively use these systems.

   3. Future Research Directions

      Several avenues for future research remain:

      1. Integration of New Technologies: Investigating the use of block chain and IoT alongside ITBRS could improve data reliability and enhance transparency in logistics operations.

      2. Regional Comparisons: Conducting similar studies across various regions within India could yield insights into specific challenges and variations in algorithm performance.

      3. User-Friendly Design: Future research could focus on creating intuitive interfaces that simplify interactions with complex algorithms, thereby improving usability for logistics professionals.

         	and other metrics.		– Can easily incorporate multiple features.	– May not capture complex market dynamics effectively.
         4. User Recommen dations (Collaborati ve Filtering)	Suggests trucks/servi ces based on user preferences and behaviors.	80 – 85	– Personalizes user experience effectively. – Adapts well to new users over time.	data.
         5.K-Nearest Neighbors (KNN)	A non- parametric algorithm that classifies a data point based on the majority class of its K nearest neighbors in the feature space.	80 – 85	tasks.	Computationall y intensive, especially with large datasets.
         6. Fraud Detection (Anomaly Detection)	Identifies suspicious booking patterns that may indicate fraud.	90 – 91

         • Suffers from the "cold start" problem for new users or items.

         • Requires a substantial amount of user-item interaction

         • Simple to implement and understand.

         • No training phase (lazy learner).

         • Versatile for classificatio n and regression

         • Sensitive to irrelevant features and feature scaling.

         • Performance degrades in high- dimensional spaces.

         • Effectively detects outliers and unusual behavior.

         • Adapts well to changing patterns over time.

         • May generate false positives.

         • Requires continuous monitoring and retraining.

      4. Longitudinal Studies: Implementing long-term studies to assess the impact of these algorithms on logistics efficiency and cost-effectiveness could provide deeper insights into their sustainability and effectiveness.

   The findings of this study illustrate the potential of Intelligent Truck Booking Recommendation Systems to significantly improve logistics operations in India. By selecting and implementing suitable algorithms, logistics companies can optimize their booking processes, reduce costs, and enhance delivery times. Ongoing research and development in this field will be crucial to meet the evolving demands of the logistics industry.

   TABLE 1:

   Comparative Analysis of Existing Algorithms with Respect to Various Required Parameters

   TABLE 2:

   Accuracy Percentages of the Predefined Algorithms for our dataset is as follows:

5. DISCUSSION

   The assessment of the Intelligent Truck Booking Recommendation System (ITBRS) has provided key insights into enhancing logistics operations in India. The analysis indicated that advanced algorithms, especially neural networks and random forests, excelled in prediction accuracy and F1-scores. These algorithms effectively process complex datasets and uncover intricate patterns that simpler models may miss, making them well-suited for the increasingly data- driven logistics industry. In contrast, while simpler models like decision trees and KNN yielded acceptable results, they often fell short of the performance achieved by more sophisticated approaches, suggesting that they may be better suited for smaller firms or specific applications where ease of implementation is prioritized.

   Algorithm	Description	Accura cy (%)	Strengths	Weaknesses
   1. Demand Forecasting (LSTM)	Uses historical booking data to predict future demand.	85 – 90		computationall y intensive.
   2. Route Optimizatio n (A)*	Finds the most efficient routes for deliveries.	90 – 95	calculations.	– Performance may degrade with highly dynamic environments (e.g., real-time traffic).
   3. Dynamic Pricing (Regression )	Adjusts pricing based on demand, distance,	75 – 80	– Simple to implement and interpret.	– Assumes a linear relationship between features and price.

   • Handles time series data well.

   • Captures complex patterns and trends.

   • Requires a significant amount of training data.

   • Can be

   • Provides optimal paths.

   • Efficient in terms of time and distance

   The implications for the logistics sector in India are noteworthy. Companies should consider investing in advanced technologies, as these systems have the potential to improve operational efficiency and enhance competitiveness. Balancing cost and performance is essential, especially given the budget constraints many logistics firms face; algorithms

   like random forests and genetic algorithms represent viable options for optimizing operations without incurring excessive costs. Furthermore, the demand to minimize delivery times and latency emphasizes the need for effective real-time data processing capabilities. Enhanced data collection and analytics can enable companies to make timely, informed decisions, ultimately improving customer satisfaction.

   Despite these promising findings, several limitations must be addressed. The effectiveness of any algorithm heavily relies on the quality and completeness of the input data, and logistical data is frequently fragmented or inconsistent, which can impair performance. Additionally, the complexity of implementing advanced algorithms, particularly neural networks, may pose challenges for smaller firms lacking the necessary technical resources. User acceptance is another critical consideration, as the successful deployment of ITBRS depends on staff members adapting to new technologies. Therefore, organizations need to invest in training and support to facilitate smooth transitions.

   Future research should explore the integration of emerging technologies, such as blockchain and IoT, with ITBRS to enhance operational transparency and data reliability. Developing tailored algorithms that address the unique challenges of the Indian logistics landscape, taking into account regional differences and specific industry needs, is also a promising direction. Lastly, conducting long-term studies to evaluate the lasting impacts of these algorithms on operational efficiency and profitability could yield deeper insights into their overall effectiveness. In summary, the potential of Intelligent Truck Booking Recommendation Systems to transform logistics operations in India is significant. By leveraging advanced algorithms and emphasizing real-time capabilities, companies can improve efficiency, reduce costs, and enhance service delivery. However, challenges related to data quality, implementation, and user acceptance must be overcome. Continued research and innovation will be vital for adapting to the dynamic logistics environment.

6. CONCLUSION

This study on Intelligent Truck Booking Recommendation Systems (ITBRS) underscores their considerable potential to improve logistics operations in India. By utilizing advanced algorithms such as neural networks and random forests, logistics companies can enhance prediction accuracy and overall efficiency. These technologies facilitate the processing of complex datasets, enabling organizations to identify patterns and make informed decisions that minimize delivery times and costs.

The findings indicate that while sophisticated algorithms provide significant advantages, it is important to balance performance with cost, particularly in an industry where budgets are often constrained. Simpler algorithms like decision trees and KNN also have their place, especially for smaller firms or in scenarios where quick implementation is essential.

However, several challenges must be addressed to maximize the benefits of ITBRS. Concerns regarding data quality, the complexity of implementation, and user acceptance need

careful management. Investing in training and support is vital to ensure that staff can effectively leverage these systems.

Future research should explore the integration of emerging technologies, such as blockchain and IoT, which could further enhance operational capabilities. Additionally, developing customized algorithms tailored to the specific challenges of the Indian logistics environment could yield even greater benefits.

In summary, the adoption of Intelligent Truck Booking Recommendation Systems offers a transformative opportunity for the logistics sector in India. By embracing advanced technologies and focusing on real-time data processing, companies can significantly boost efficiency, reduce operational costs, and improve customer satisfaction. Ongoing innovation and research will be crucial for adapting to the changing landscape of the logistics industry.

ACKNOWLEDGMENTS

I extend my heartfelt acknowledgments to Prof. R. R. Sedamkar from Thakur College of Engineering and Technology for his invaluable guidance and mentorship throughout the development of our digital platform. This platform revolutionizes truck booking services by integrating advanced machine learning algorithms to cater to diverse transportation needs.

Under Prof. Sedamkar's expert supervision, we embarked on a journey to innovate in logistics and transportation. His profound insights and encouragement were instrumental in shaping our vision into reality. His academic rigor and practical wisdom provided the foundation upon which we built a robust system capable of efficiently managing and optimizing truck bookings.

At the core of our platform lies sophisticated machine learning algorithms. These algorithms analyze historical booking data, predict demand patterns, and optimize routes for enhanced efficiency. Through continuous learning and adaptation, our system ensures timely and cost-effective truck deployments, meeting the dynamic demands of the logistics industry.

The development process was both challenging and rewarding. Prof. Sedamkar's unwavering support and constructive feedback guided us through technical hurdles and strategic decisions. His mentorship empowered us to explore innovative solutions and overcome obstacles with confidence. Furthermore, I express my gratitude to Thakur College of Engineering and Technology for fostering an environment conducive to learning and innovation. The institution's resources and support were integral to the successful implementation of our project.

In conclusion, I am deeply grateful to Prof. R. R. Sedamkar for his unwavering guidance, wisdom, and encouragement throughout this transformative journey. His mentorship has been pivotal in shaping our digital platform for truck booking services, leveraging cutting-edge machine learning algorithms. I am confident that the knowledge and experience gained from this project will serve as a solid foundation for our future endeavors in the field of technology and logistics.

REFERENCES

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4. Rathi, A., & Soni, A. (2022). "Real-Time Data Integration for Enhanced Logistic Operations." Transportation Research Part E: Logistics and Transportation Review, 149, 102234.

5. Choudhury, S., & Mandal, A. (2021). "Neural Network Applications in Freight Transportation." Journal of Intelligent Transportation Systems, 25(1), 13-27.

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AUTHORS PROFLIE

Mr. Manikant Thakur Completed his under Graduate from Thakur College of Engineering and Technology (Autonomous College Affiliated to University of Mumbai). He is currently pursuing his masters degree in Computer Engineering and Major (Honors) in Artificial Intelligence from Thakur College of Engineering and Technology

(Autonomous College Affiliated to University of Mumbai).

Dr. R.R Sedamkar is currently working as a Professor and Vice Principals, Director IQAC Message & HOD – Ph.D. Programs at Thakur College of Engineering and Technology (Autonomous College Affiliated to University of Mumbai) He has more than 20+ Years of Experience Teaching Experience. He has published a number of research paper in various International, National Journals and conferences