This study introduces an innovative methodology for the planning of metro network routes within the urban environment of Chennai, Tamil Nadu, India. A comparative analysis of the modified Ant Colony Optimization (ACO) method (previously developed) with recent breakthroughs in nature-inspired algorithms demonstrates the modified ACO's superiority over modern techniques. By utilizing the modified ACO algorithm, the most efficient routes connecting the origin and destination of the metro route are generated. Additionally, the model is applied to the existing metro network to highlight variations between the model's results and the current network. The Google Maps platform, integrated with Python, handles real-time data, including land utilization, Geographical Information Systems (GIS) data, census information, and points of interest. This processing enables the identification of stops within the city and along the chosen routes. The resulting metro network showcases substantial benefits compared to conventional route planning methods, with noteworthy enhancements in workforce productivity, decreased planning time, and cost-efficiency. This study significantly enhances the efficiency of urban transport systems, specifically in rapidly changing metropolitan settings such as chennai.

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At Freshworks, we are creating a global workplace that enables everyone to find their true potential, purpose, and passion irrespective of their background, gender, race, sexual orientation, religion and ethnicity. We are committed to providing equal opportunity for all and believe that diversity in the workplace creates a more vibrant, richer work environment that advances the goals of our employees, communities and the business.

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At Kinaxis, who we are is grounded in our common belief that people matter. Each one of us plays an important part in accomplishing our work, building our culture and making a global impact. Every day, we're empowered to work together to help our customers make fast, confident planning decisions. This is how we create a better planet – for each other, for our customers and for generations to come. Our cloud-based platform RapidResponse ensures that the products we need – everything from medicine and cars, to day-to-day items like toothpaste – make it to market and into our hands when we need them with minimal ecological footprint.

Weare looking for a new colleague who will join us on the mission of furthergrowth of Hyperautomation at the DPDHL. The ideal candidate will have bigideas, curious mindset and automation part of DNA that fosters an environmentof collaboration and creativity! Asthe RPA DevOps Engineer, you will be a member of global Intelligent AutomationTeam, team responsible for enterprise level technologies such RPA, IntelligentOCR or Business Process Management. In this role you will be responsible forfull automations delivery lifecycle, namely Agile automations development,proactive handling of operations tasks in production and overall acting as theadvocate of RPA technology.

We are looking for Lead Engineer for the Machine Learning (ML) engineering development team. The primary focus will be to gather requirements from Data Scientists and Product teams, and come up with the optimal solution. Then design, implement and monitor these services for different ML use-cases. You will be working with Data Scientists to implement ML algo's, build and deploy API's to serve models. At Freshworks, we are creating a global workplace that enables everyone to find their true potential, purpose, and passion irrespective of their background, gender, race, sexual orientation, religion and ethnicity.

If you are looking to start your career in Machine learning then this is the course for you. This is a course designed in such a way that you will learn all the concepts of machine learning right from basic to advanced levels. For the code explained in each lecture, you can find a GitHub link in the resources section. I am Professional Trainer and consultant for Languages C, C, Python, Java, Scala, Big Data Technologies – PySpark, Spark using Scala Machine Learning & Deep Learning- sci-kit-learn, TensorFlow, TFLearn, Keras, h2o and delivered at corporates like GE, SCIO Health Analytics, Impetus, IBM Bangalore & Hyderabad, Redbus, Schnider, JP Morgan – Singapore & HongKong, CISCO, Flipkart, MindTree, DataGenic, CTS – Chennai, HappiestMinds, Mphasis, Hexaware, Kabbage. I have shared my knowledge that will guide you to understand the holistic approach towards ML.

Here, we shall discuss Why Robot Framework for Acceptance Testing and ATDD. This blog will help you understand more about Robot Framework.