ALFA LASER’s Post

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🚀 Day 34 of #100DaysOfDSA: Finding the Median of Two Sorted Arrays 🚀 Today, I tackled a classic problem : finding the median of two sorted arrays. This problem is particularly interesting due to its requirement for an efficient solution, ideally in O(log(m + n)) time complexity. #100daysofdsa #dsa

is due to an “eavesdropper” (Eve) in the black hole attempting to obtain "which path" information by measuring the long-range fields of the superposed body. https://lnkd.in/dxmsVFmU,

Have you experienced the feeling of not getting into the right room? Despite HCMIU's small size, it is still possible to get lost. Not being able to find where a room is is very frustrating. Because of this, I made HCMIU Map. HCMIU Map is a web app that has a complete map of HCMIU and allows you to find the shortest path between two rooms. You can even find the shortest path that visits multiple rooms. You can experience it here: https://lnkd.in/gRX-in_D Before writing a single line of code, I asked a friend to map the campus. He did it and sent me a Google Sheets file. Then, with the Google Sheets file, I transcribed the data into HTML. I used CSS Grid to impose a grid structure on the map, then I specified whether a cell is a blank space, a room or just part of a corridor. Pathfinding algorithms operate on the graph data structure. I manually coded the graphs for every floor and associated every vertex with a point on the map. This enables pathfinding between two points on the same floor. To find the path between two points on two different floors, the algorithm has to choose the lift. Every floor has four lifts, and the lift chosen can affect the length of the path. After choosing the lift, the algorithm then computes the path between the starting point and the lift, and the path between the destination and the lift, and joins the two paths together.

#Project 8 update on AMDARI OBSERVATIONS FROM THE DISTRIBUTION DATASET AFTER EDA: 1. From the previous posts, we can see that the Distribution Data has a total of 10 columns and 500 Rows. 2. There are two Numerical columns, which are the Electricity Consumption column and the Number of EVs. 3. The rest of the columns are Categorical Data. 4. From the chart as well as the description we can see that the datasets contain no missing values. Moving on to the Geospatial dataset #NanaAFAmdari #WomenInTech #Amdari21DaysDataChallenge #Day21

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🎯 DAY-(67/100) #100daysofdsa journey 🗓 Today I have solved a question :- ✅ Merge two Sorted Arrays Without Extra Space Stay tuned for more updates on my #100DaysOfDSA journey! #100DaysOfCode #DSA #striversa2zdsa #striverssdesheet #buildinpublic

Nesting tables from MityLite. How will these tables help you and your properties?

Exploring Electricity Price Forecasting in the Netherlands: From Baseline Models to N-BEATS and Exogenous Variables. Discover how we utilized Dart and Statsforecast libraries to establish baseline models, then refined with N-BEATS Fine Tuning using Optuna, and finally incorporated exogenous variables for accurate predictions.