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Introduction Hill Climbing Algorithm comes under the heuristic search or informed search strategy which used to solve many types of AI problem such as block-world, water-jug, etc.,  In this article we are going to discuss about the usage, benefits and drawback of this algorithm. But we are going to focus only on How the algorithm works? rather than on How it is applied? This algorithm named according to its working. The algorithm works very similar to the person in the below image who always focuses on higher points to climb the hill (Goal) . Types of Hill Climbing Algorithm Simple Hill Climbing Algorithm Steepest Ascent Hill Climbing Algorithm Stochastic Hill Climbing Algorithm (Simulated Annealing is a topic related to Hill Climbing) General Working The Algorithm generally move up in the direction of higher values. IT breaks while moving up in the loop when it reaches the peak and no neighbour has a higher value. It doesn't maintain any search tree. It only looks for immediate ne...

  Linguistic Analysis Speech Written Language This article focuses on Written Language rather than Speech because speech could be transfigured into written form. Components in analysing Written Language Pronology : Analysing Sound / Pronunciation Morphology : Analysing Structure of Words. Example : books, booked, booking -> book, run, ran, running -> run Syntax : Grammar Semantics : Meaning of Strings and Interaction among them. Now coming to issues, Issues in Syntax "The dog ate my homework." - Who did what 1. Identify POS( Parts of Speech ) Tagging Dog : Noun, Ate : Verb, Homework : Noun Note: So far English POS tagging is up to 95% accurate but it also can be improvised. 2. Identify collocation mother in law, hot dog are single word. 3. Shallow Parsing Parse tree for the sentence "John loves Marry." 4. Anaphora Resolution The dog entered my home. It scared me. Here It refers to The dog in the first sentence. It must be resolved. 5. Preposition adjustm...

 Introduction Lose less Video Compression (LVC) is an Intraframe compression technique. This article deals with Compression Visual Video that contains frames. Hearable Audio in the Video is best omitted and will be dealt in another article. Photo by Markus Spiske on Unsplash Algorithm Read Video and Focus of Visuals. Read Frame. Each frame having Width and Height (Consider  1280 x 720 pixels ). Now each pixel is having having RGB color value ranges from 0 to 255 in the format (R,G,B) Example: (255,0,0) - RED / R. If you store 255,0,0 the same in mp4 file then it will took around 5 bytes But using this technique you are going to replace the 255 with its ascii equivalent. Coding in Python import cv2 import numpy as np cap = cv2.VideoCapture('/content/cartoon.mp4') if (cap.isOpened()== False):   print("Error opening video stream or file") while(cap.isOpened()):   ret, frame = cap.read()     if ret == True:     f=frame[130][120]     print(...

 Introduction Many search algorithm revolve around the internet and some yet to be published so far the sit as a draft in the Researcher's PC. This search methodology doesn't directly going to command the algorithm to move left, right, top, bottom. It just rank the moves based on the previous search experience and the lessons learnt from previous and current searches in order to effectively and efficiently travel and search the remaining part of the tree. Requirements Neural-Net is going to collect information about both the node and path . Yes! you heard it right it collects data about path also. Information that are collected by Neural-Net: Information belonging to the Path Rank ( which depends on the success rate* ) Redirections in path ( For real-time Usage**) Information belongs to Node Value Maximum, Minimum, and Average Branch Factor. Number of Child levels. Nearness to solution.

Introduction The Parallel Line Search Algorithm uses tow parallel lines to navigate to thee nodes that need to be explored. It is optimised variation of Breadth First Search. Many of search algorithm exist but they either work biased like searching left, right, top or bottom. Working Step 1: The algorithm first visit the Root node. Step 2: Then it visits both Left and Right side (unbiased). At first you may think that it works like Bidirectional Search Algorithm  Just wait a minute. Step 3: Increase the Depth and move to next level of the tree. Step 4: Find all Children. Step 5: Visit all Children. Visiting Left Child Visiting Right Child Step 6: The search continues in the same way(i.e., repeat from Step 3 until the solution is found or the entire tree). Step 7: Stop It drastically reduce the amount of required for exploration of nodes.  If branching factor of tree is b and distance of goal node from source is d, then the normal BFS/DFS searching complexity would be O(b^d)....