​

Alpha-beta pruning is a modified version of the minimax algorithm. It is an optimization technique for the minimax algorithm.
As we have seen in the minimax search algorithm that the number of game states it has to examine are exponential in depth of the tree. Since we cannot eliminate the exponent, but we can cut it to half. Hence there is a technique by which without checking each node of the game tree we can compute the correct minimax decision, and this technique is called pruning. This involves two threshold parameter Alpha and beta for future expansion, so it is called alpha-beta pruning. It is also called as Alpha-Beta Algorithm.
Alpha-beta pruning can be applied at any depth of a tree, and sometimes it not only prune the tree leaves but also entire sub-tree.
The two-parameter can be defined as:
Alpha: The best (highest-value) choice we have found so far at any point along the path of Maximizer. The initial value of alpha is -∞.
Beta: The best (lowest-value) choice we have found so far at any point along the path of Minimizer. The initial value of beta is +∞.
The Alpha-beta pruning to a standard minimax algorithm returns the same move as the standard algorithm does, but it removes all the nodes which are not really affecting the final decision but making algorithm slow. Hence by pruning these nodes, it makes the algorithm fast.

Modifications to min-max: There are some heuristic search methods other than alpha-beta pruning method which are used to improve the performance of min-max procedure. They are: 
Greedy hill climbing method 
Artificial immune algorithm 

Mini-max algorithm is a recursive or backtracking algorithm which is used in decision-making and game theory. It provides an optimal move for the player assuming that opponent is also playing optimally.
Mini-Max algorithm uses recursion to search through the game-tree.
Min-Max algorithm is mostly used for game playing in AI. Such as Chess, Checkers, tic-tac-toe, go, and various tow-players game. This Algorithm computes the minimax decision for the current state.
In this algorithm two players play the game, one is called MAX and other is called MIN.
Both the players fight it as the opponent player gets the minimum benefit while they get the maximum benefit.
Both Players of the game are opponent of each other, where MAX will select the maximized value and MIN will select the minimized value.
The minimax algorithm performs a depth-first search algorithm for the exploration of the complete game tree.

Min-Max algorithm : 
Step 1: Set FINAL_VALUE to be minimum as possible. 
Step 2 : If limit of search has been reached, then FINAL_VALUE = 
GOOD_VALUE of the current position. 
Step 3: Else do. 
Step 3.1 : Generate the successors of the position. 
Step 3.2: Recursively call MIN-MAX again with the present position with 
depth incremented by unity. 
Step 4: Evaluate the GOOD_VALUE. 
Step 5 : If GOOD_VALUE > FINAL_VALUE then FINAL_VALUE = GOOD_VALUE.

Example: 
Consider a game which has 4 final states and paths to reach final state are from root to 4 leaves of a perfect binary tree as shown below. Assume you are the maximizing player and you get the first chance to move

https://media.geeksforgeeks.org/wp-content/uploads/minmax.png

Maximizer goes LEFT: It is now the minimizers turn. The minimizer now has a choice between 3 and 5. Being the minimizer it will definitely choose the least among both, that is 3
Maximizer goes RIGHT: It is now the minimizers turn. The minimizer now has a choice between 2 and 9. He will choose 2 as it is the least among the two values.
Being the maximizer you would choose the larger value that is 3. Hence the optimal move for the maximizer is to go LEFT and the optimal value is 3.

class D
{
    public static void main(String[] args){
        try
        {
            String a="hello";
            System.out.println(a.toUpperCase());
        }
        catch(Exception e)
        {
            System.out.println(e);
        }
        finally
        {
            try
            {
                System.out.println(10/0);
            }
            catch(Exception p)
            {
                System.out.println(p);
            }
            finally
            {
                System.out.println("System ended");
            }
        }
    }
}

class D
{
    public static void main(String[] args){
        try
        {
            String a="hello";
            System.out.println(a.toUpperCase());
        }
        catch(Exception e)
        {
            System.out.println(e);
        }
        finally
        {
            try
            {
                System.out.println(10/0);
            }
            catch(Exception p)
            {
                System.out.println(p);
            }
            finally
            {
                System.out.println("System ended");
            }
        }
    }
}

  <Identity Name="Contoso.AssetTracker" 
    Version="1.0.0.0" 
    Publisher="CN=Contoso Software, O=Contoso Corporation, C=US"/>

message Check {
    string rro_fn = 1;
    int64 date_time = 2;
    bytes check_sign = 3;
    int32 local_number = 4;
    enum Type {
        UNKNOWN = 0;
        CHK = 1;
        ZREPORT = 2;
        SERVICECHK = 3;
    }
    Type check_type = 5;
    string id_offline = 6;
    string id_cancel = 7;

}

//Les tableaux 
// Opérateurs + 
$a = [1, 2];
$b = [4, 5, 6];

print_r($a + $b); //Output = 1, 2, 6 
//Prends la valeur du tableaux de droite et si pas d'index prends la valeur de gauche

// Opérateurs == ne prends pas en compte l'ordre
// Opérateurs === prends en compte l'ordre

// Spread operator :
print_r([...$a, ...$b]); // [1, 2, 4, 5, 6]
// Le tableaux n'existe plus et on étend les valeurs 

//&$variable Récupère l'adresse mémoire d'une variable

// Assigner une valeur à un tableaux :
// Ajouter une valeur à l'index suivant
$arr = [1];
$arr[] = 2; //Output = [1, 2]
// Equivalence avec le spread :
$arr = [...$arr, 3]; // Assigne un nouveau tableau et spread l'ancien en assignant une nouvelle valeur
$arr = [-1, ...$arr]; //Ajoute une valeur au début
array_push($arr, 4);


// Return a random value
$arr = ['orrange', 'blue', 'yellow'];
$value = array_rand($arr);
echo $arr[$value];

// Remove duplicates
$arr2 = ['test', 'test', 1, 2, 3];
$res = array_unique($arr2);

.mini-cart-qnt.quantity{display:flex;padding-right:20px}.mini-cart-qnt a{width:20px;cursor:pointer;display:flex;height:35px;align-items:center;justify-content:center}.mini-cart-qnt .btnPlus{border:1px solid;border-left:0}.mini-cart-qnt .btnMinus{border:1px solid;border-right:0px}.mini-cart-qnt input{height:35px;width:50px;border-radius:0;outline:0;-moz-appearance:textfield}.mini-cart-qnt input::-webkit-inner-spin-button,.mini-cart-qnt input::-webkit-outer-spin-button{-webkit-appearance:none;margin:0}

.loader{position:relative}.lodgin-mini-cart-item{background:#00000070;display:none;position:absolute;width:100%;height:100%;z-index:3;justify-content:center;align-items:center}.loader .lodgin-mini-cart-item{display:flex}.loading-mini-cart{position:relative;width:50px;height:50px}.loading-mini-cart:after{content:'';position:absolute;left:0;bottom:0;z-index:5;width:50px;height:50px;border:10px solid #f3f3f3;border-radius:50%;border-top:10px solid #d6952c;-webkit-animation:spin 2s linear infinite;animation:spin 2s linear infinite}@-webkit-keyframes spin{0%{-webkit-transform:rotate(0)}100%{-webkit-transform:rotate(360deg)}}@keyframes spin{0%{transform:rotate(0)}100%{transform:rotate(360deg)}}

.devnet_fsl-free-shipping .fsl-notice,.devnet_fsl-free-shipping .notice,.devnet_fsl-free-shipping .fsl-title,.devnet_fsl-free-shipping .title{width:100%;display:block;text-align:center}.devnet_fsl-free-shipping{width:100%;margin:1rem 0 2rem;padding:1rem 2rem;box-shadow:0 0 2rem -1rem #000;box-sizing:border-box}.devnet_fsl-free-shipping .fsl-title,.devnet_fsl-free-shipping .title{margin:2rem auto;font-size:1.1em}.devnet_fsl-free-shipping .fsl-notice .woocommerce-Price-amount.amount,.devnet_fsl-free-shipping .notice .woocommerce-Price-amount.amount{font-weight:bold}.devnet_fsl-free-shipping .fsl-.progress-bar,.devnet_fsl-free-shipping .progress-bar{width:100%;justify-content:flex-start;margin:1rem 0;border:.0625rem solid #000;border-radius:.5rem;box-shadow:0 .3rem 1rem -0.5rem #000}.devnet_fsl-free-shipping .fsl-.progress-bar .fsl-.progress-amount,.devnet_fsl-free-shipping .fsl-.progress-bar .progress-amount,.devnet_fsl-free-shipping .progress-bar .fsl-.progress-amount,.devnet_fsl-free-shipping .progress-bar .progress-amount{position:relative;display:block;border-radius:.5rem}.devnet_fsl-free-shipping .fsl-.progress-bar span,.devnet_fsl-free-shipping .progress-bar span{display:inline-block;height:100%;border-radius:3px;box-shadow:0 1px 0 rgba(255,255,255,.5) inset;transition:width .4s ease-in-out}.devnet_fsl-free-shipping .fsl-.progress-bar.shine span,.devnet_fsl-free-shipping .progress-bar.shine span{position:relative}.devnet_fsl-free-shipping .fsl-.progress-bar.shine span::after,.devnet_fsl-free-shipping .progress-bar.shine span::after{content:"";opacity:0;position:absolute;top:0;right:0;bottom:0;left:0;background:#fff;border-radius:3px;-webkit-animation:animate-shine 2s ease-out infinite;animation:animate-shine 2s ease-out infinite}.devnet_fsl-free-shipping .fsl-.progress-bar.stripes span,.devnet_fsl-free-shipping .progress-bar.stripes span{background-size:30px 30px;background-image:linear-gradient(135deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);-webkit-animation:animate-stripes 3s linear infinite;animation:animate-stripes 3s linear infinite}.devnet_fsl-free-shipping.qualified-message .title{margin:0;padding:0}@-webkit-keyframes animate-stripes{0%{background-position:0 0}100%{background-position:60px 0}}@keyframes animate-stripes{0%{background-position:0 0}100%{background-position:60px 0}}@-webkit-keyframes animate-shine{0%{opacity:0;width:0}50%{opacity:.5}100%{opacity:0;width:100%}}@keyframes animate-shine{0%{opacity:0;width:0}50%{opacity:.5}100%{opacity:0;width:100%}}.devnet_fsl-no-shadow{border:none;box-shadow:none}.devnet_fsl-label{display:block !important;margin:1rem auto;padding:.3rem .5rem;font-size:.8em;font-weight:bold;text-align:center;box-shadow:0 5px 16px -8px #000}.devnet_fsl-no-animation .shine span.progress-amount{-webkit-animation:none;animation:none}.devnet_fsl-no-animation .shine span.progress-amount::after{-webkit-animation:none;animation:none}.summary .devnet_fsl-label{max-width:-webkit-max-content;max-width:-moz-max-content;max-width:max-content;margin:inherit;margin:.5rem 0 1rem}
.elementor-menu-cart__main .devnet_fsl-free-shipping {padding: 0px 10px;box-shadow: none; margin:20px 0px 10px;}

   <div class="mobile-mneu-container">
                <div class="left-part">
                    <?php the_custom_logo(); ?>
                </div>
                <div class="right-part">
                    <div class="btn-animation btngradienthov">
                        <a href="">
                            <span class="button-text">Get a Quote</span>
                        </a>
                    </div>
                    <div class="mobile-custome-menu">
                        <div id="clickBtn" class="clickBtn toggle-btn">
                            <div class="three-toggle">
                                <span class="short-line"></span>
                                <span class="large-line"></span>
                                <span class="short-line"></span>
                            </div>
                        </div>
                        <div class="bgoverly">
                            <div id="sideBar" class="sidebar">
                                <?php
                                 wp_nav_menu(array(
                                'menu' => 'Mobile Menu',
                                'menu_id' => '-1',
                                 ));
                                 ?>
                            </div>
                        </div>
                    </div>
                </div>
            </div>


  .mobile-mneu-container {
    display: flex !important;
    justify-content: space-between;
    padding: 10px 20px;
  }

  .service-boxes {
    grid-template-columns: 50% 50%;
  }

  .desktop-menu {
    display: none;
  }

  footer .footer-heading {
    font-size: 36px;
  }

  div#clickBtn {
    cursor: pointer;
    position: relative;
    z-index: 9999;
    width: 28px;
  }

  .top_sect_menu {
    display: grid;
    grid-template-columns: 70% 30%;
    align-items: center;
  }

  .mobile-logo {
    text-align: center;
  }

  .sub-menu strong {
    font-weight: 500;
  }

  .mobile-nav .col-md-3,
  .mobile-nav .col-md-2 {
    display: flex;
    align-items: center;
    justify-content: end;
  }

  .bgoverly {
    position: fixed;
    top: 0;
    left: -10000px;
    width: 100%;
    height: 100%;
    background: #0000005e;
    z-index: 1000;
    transition: all 0.75s;
    top: 70px;
  }

  #sideBar.active,
  .bgoverly.active {
    left: 0 !important;
    transition: all 0.25s;
  }

  #sideBar {
    top: 70px;
    margin: 0;
    position: fixed;
    width: 100%;
    height: 100%;
    background: #fff;
    transition: all 0.75s;
    overflow: scroll;
    padding: 20px 19px;
  }

  .mobile-mneu-container .btn-animation.btngradienthov {
    margin-right: 12px;
  }

  .toggle-btn span {
    width: 24px;
    height: 4px;
    background: #000;
    margin: 6px 0;
    display: block;
    transition: 0.5s;
    border-radius: 10px;
  }

  .make-close img {
    width: 28px;
  }

  span.large-line {
    width: 28px;
    margin-left: -4px;
  }

  .three-toggle {
    margin-top: 11px;
  }

  .close {
    text-align: end;
    cursor: pointer;
  }

  .menu-mobile-menu-container ul {
    margin: 0;
    padding: 0;
  }

  .menu-mobile-menu-container li {
    list-style: none;
    margin-bottom: 5px;
    position: relative;
  }

  .menu-mobile-menu-container li a {
    text-decoration: none;
    color: #000;
    font-size: 15px;
    font-weight: 600;
  }

  .menu-mobile-menu-container li .sub-menu li a {
    color: #000;
    font-size: 15px;
    font-weight: 300;
  }

  .menu-mobile-menu-container li .sub-menu li {
    transition: 00.5s all;
    padding: 8px;
  }

  .menu-mobile-menu-container li .sub-menu li:hover {
    border-left: 15px solid #89d501;
    background: rgba(137, 213, 1, 0.1);
  }

  .mobile-contact {
    padding: 14px 46px;
    background-color: #ef6023;
    color: #fff;
    text-decoration: none;
    font-size: 23px;
    font-weight: 600;
    border-radius: 10px;
  }

  .mobile-bottom-sect {
    display: flex;
    align-items: center;
    justify-content: space-between;
    margin-top: 70px;
  }

  .mobile-icons {
    display: flex;
    gap: 2px;
    justify-content: end;
  }

  .mobile-icons a {
    display: flex;
    background-color: #0f513e;
    justify-content: center;
    align-items: center;
    width: 60px;
    height: 60px;
    text-decoration: none;
    color: #fff;
    font-size: 24px;
    border-radius: 100%;
    border: 4px solid white;
  }

  .mobile-nav {
    display: none;
  }

  .menu-item-has-children .sub-menu {
    height: 0;
    overflow: hidden;
    opacity: 0;
    visibility: hidden;
    transition: all 00.5s ease;
    padding-left: 1rem;
  }

  .menu-item-has-children {
    padding: 7px 0;
  }

  li#menu-item-345 {
    padding: 7px 0;
  }

  li#menu-item-346 {
    padding: 7px 0;
  }

  .menu-item-has-children.open .sub-menu {
    height: 100%;
    opacity: 1;
    visibility: visible;
    overflow: visible;
    padding: 10px 0;
  }

  .menu-item-has-children::before {
    content: "";
    background-image: url("https://mmcgbl.com/wp-content/uploads/2024/11/arrow.png");
    background-repeat: no-repeat;
    background-size: contain;
    position: absolute;
    width: 20px;
    height: 20px;
    right: 1px;
    top: 11px;
    transform: rotate(90deg);
    transition: transform 00.5s ease;
    z-index: 9999;
  }

  .icon-box-content p {
    font-size: 13px;
  }

  .icon-box-content h3 {
    font-size: 19px;
  }

  .menu-item-has-children.open::before {
    transform: rotate(180deg);
  }



document.addEventListener("DOMContentLoaded", function () {
  const clickBtn = document.querySelector(".clickBtn");
  const sideBar = document.querySelector(".sidebar");
  const bgOverlay = document.querySelector(".bgoverly");
  const body = document.body;

  const openMenuHTML = `
    <div class="three-toggle">
      <span class="short-line"></span>
      <span class="large-line"></span>
      <span class="short-line"></span>
    </div>`;

  const closeMenuHTML = `
    <div class="make-close">
      <img src="https://mmcgbl.com/ai/wp-content/uploads/2025/07/closeX.webp" alt="close">
    </div>`;
  if (clickBtn && sideBar && bgOverlay) {
    clickBtn.addEventListener("click", function (e) {
      e.stopPropagation();
      const isSidebarOpen = sideBar.classList.contains("active");

      sideBar.classList.toggle("active");
      bgOverlay.classList.toggle("active");
      body.classList.toggle("sidebar-open");

      clickBtn.innerHTML = isSidebarOpen ? openMenuHTML : closeMenuHTML;
    });
  }
  document.addEventListener('click', function(e) {
    if (!sideBar.contains(e.target) && e.target !== clickBtn) {
      sideBar.classList.remove("active");
      bgOverlay.classList.remove("active");
      body.classList.remove("sidebar-open");
      clickBtn.innerHTML = openMenuHTML;
    }
  });
  const menuItems = document.querySelectorAll(".menu-item-has-children");
  menuItems.forEach((item) => {
    const subMenu = item.querySelector(".sub-menu");
    const link = item.querySelector("a");
    if (!subMenu) return;
    item.addEventListener("click", function (event) {
      if (event.target !== link && !link.contains(event.target)) {
        return;
      }
      if (link && (!link.getAttribute("href") || link.getAttribute("href") === "#")) {
        event.preventDefault();
      }

      const isOpen = item.classList.contains("open");
      menuItems.forEach((otherItem) => {
        if (otherItem !== item) {
          otherItem.classList.remove("open");
          const otherSubMenu = otherItem.querySelector(".sub-menu");
          otherSubMenu?.classList.remove("open");
        }
      });
      item.classList.toggle("open", !isOpen);
      subMenu.classList.toggle("open", !isOpen);
    });
    subMenu.addEventListener("click", function(event) {
      event.stopPropagation();
    });
  });
});

Heuristic is a function which is used in Informed Search, and it finds the most promising path. It takes the current state of the agent as its input and produces the estimation of how close agent is from the goal. The heuristic method, however, might not always give the best solution, but it guaranteed to find a good solution in reasonable time. Heuristic function estimates how close a state is to the goal. It is represented by h(n), and it calculates the cost of an optimal path between the pair of states. The value of the heuristic function is always positive.

h(n) <= h*(n)  
Here h(n) is heuristic cost, and h*(n) is the estimated cost. Hence heuristic cost should be less than or equal to the estimated cost.

class G
{
    public static void main(String[] args){
    try
    {
        int a=10, b=0 , c;
        c=a/b;
        System.out.println(c);
        System.out.println("no error found");
    }
    catch(Exception a)
    {
        try
        {
            String p="okkk";
            System.out.println(p.toUpperCase());
            System.out.println("some  error found");
        }
        catch(Exception u)
        {
            System.out.println("big  error found");
        }
    }
    
    }
}

class D
{
    public static void main(String[] args){
        try{
            try{
                int a=10,b=10,c;
                c=a+b;
                System.out.println(c);
                System.out.println("no error found in ap");
            }
            catch(ArithmeticException a )
            {
                System.out.println("some error found in ap function");
            }
            int a[]={10,20,23,44};
            System.out.println(a[3]);
            System.out.println("no error found in array ");
        }
        catch(ArrayIndexOutOfBoundsException a)
        {
            System.out.println("some error found in array function");
        }
    }
}

select * from worker exists join personUser
  where worker.Person == personUser.PersonParty
	&& personUser.User == curUserId();

select DefaultDimension from HcmEmployment where HcmEmployment.Worker == worker.RecId;
//Department = HcmWorkerHelper::getPrimaryDepartmentRecId(HcmWorkerLookup::currentWorker());
OMOperatingUnit OMOperatingUnit;
OMOperatingUnit = HcmWorkerHelper::getPrimaryDepartment(HcmWorkerLookup::currentWorker());
Department = OMOperatingUnit.OMOperatingUnitNumber;
//Info(Department);
//Department = DimensionAttributeValueSetStorage::find(HcmEmployment.DefaultDimension).getDisplayValueByDimensionAttribute(DimensionAttribute::findByName("Department").RecId);
//  Info(Department);
qbdsdimensionAttributeValueSet = purchTable_ds.query().dataSourceTable(tableNum(PurchTable)).addDataSource(tableNum(dimensionAttributeValueSet));

qbdsdimensionAttributeValueSet.addLink(fieldNum(PurchTable,DefaultDimension),
                                       fieldNum(dimensionAttributeValueSet,RecId));
qbdsdimensionAttributeValueSet.joinMode(JoinMode::ExistsJoin);
qbdsdimensionAttributeValueSet.relations(true);
qbdsdimensionAttributeValueSet.fetchMode(QueryFetchMode::One2One);

qbdsdimensionAttributeValueSetItemView = purchTable_ds.query().dataSourceTable(tableNum(PurchTable)).addDataSource(tableNum(dimensionAttributeValueSetItemView));
qbdsdimensionAttributeValueSetItemView.addLink(fieldNum(PurchTable,DefaultDimension),
                                               fieldNum(dimensionAttributeValueSetItemView,DimensionAttributeValueSet));
qbdsdimensionAttributeValueSetItemView.joinMode(JoinMode::ExistsJoin);
qbdsdimensionAttributeValueSetItemView.relations(true);
qbdsdimensionAttributeValueSetItemView.fetchMode(QueryFetchMode::One2One);
qbdsdimensionAttributeValueSetItemView.addRange(fieldNum(dimensionAttributeValueSetItemView,DisplayValue)).value(Department);

qbdsDimensionAttribute = qbdsdimensionAttributeValueSetItemView.addDataSource(tableNum(DimensionAttribute));
qbdsDimensionAttribute.addLink(fieldNum(dimensionAttributeValueSetItemView,DimensionAttribute),
                               fieldNum(DimensionAttribute,RecId));
qbdsDimensionAttribute.joinMode(JoinMode::ExistsJoin);
qbdsDimensionAttribute.relations(true);
qbdsDimensionAttribute.fetchMode(QueryFetchMode::One2One);
qbdsDimensionAttribute.addRange(fieldNum(DimensionAttribute,Name)).value("DEPARTMENT");

@isTest
public class customer360CommunicationsHelperTest {
    @isTest
    static void getCaseAccountContactTest(){
        
        //Creating a test Account
        Account testAccount = new Account(
            Name = 'Test Account'
        );
        insert testAccount;
        
        //Creating a test Individual 
        Individual testIndividual = new Individual (
        	FirstName = 'Test',
            LastName = 'Individual',
            HasOptedOutSolicit = false
        );
        insert testIndividual;
        
        //Creating a test Contact and assigning the Individual to the Contact
        Contact testContact = new Contact(
            FirstName = 'Test',
            LastName = 'Contact',
            Email = 'TestEmail@hotmail.com',
            AlternativeBillingName__c = 'Test Billing Name',
            ContactPreferences__c = 'Email',
            PreferredCommunicationFormat__c = 'Standard',
            IndividualId = testIndividual.Id
        );
        insert testContact;
        
        //Associating the test Case to the Account and Contact
        Case testCase = new Case(
            Subject = 'Test Case',
            AccountId = testAccount.Id,
            ContactId = testContact.Id
        );
        insert testCase;
        
        List<Id> caseId = new List<Id>{testCase.Id};
        
        Test.startTest();
        List<Map<String, String>> result = customer360CommunicationsHelper.getCaseAccountContact(caseId);
        Test.stopTest();
        
        Map<String, String> resultMap = result[0];
        
        System.assertEquals('Test Account', resultMap.get('AccountName'));
        System.assertEquals(testAccount.Id, resultMap.get('AccountId'));
        System.assertEquals(testContact.Id, resultMap.get('ContactId'));
    }

}

public with sharing class customer360CommunicationsHelper {

    @AuraEnabled(cacheable=true)
    public static List<Map<String, String>> getCaseAccountContact(List<Id> caseId) {

        //Creating a new Map String to return the variables
        List<Map<String, String>> caseAccountContactList = new List<Map<String, String>>();
        
        //SOQL querey to retrieved the Desired fields from the Case
        List<Case> relatedCases = [SELECT AccountId, ContactId, Account.Name, Contact.AlternativeBillingName__c, Contact.ContactPreferences__c, Contact.PreferredCommunicationFormat__c, 
                                    Contact.Individual.HasOptedOutSolicit FROM Case WHERE Id IN :caseId LIMIT 1];

        Case relatedCase = relatedCases[0];

        //Checking if there is an AccountId and ContactId on the Case
        if (relatedCase.AccountId != null && relatedCase.ContactId != null) {
            
            //Creating a new Map String to add the fields onto
            Map<String, String> caseAccountContactMap = new Map<String, String>();

            //All the desired fields are then put into the caseAccountContactMap
            caseAccountContactMap.put('AccountName', relatedCase.Account?.Name);
            caseAccountContactMap.put('AccountId', relatedCase.AccountId);
            caseAccountContactMap.put('ContactId', relatedCase.ContactId);
            caseAccountContactMap.put('ContactAlternativeBillingName', String.isBlank(relatedCase.Contact?.AlternativeBillingName__c) ? '' : relatedCase.Contact.AlternativeBillingName__c);
            caseAccountContactMap.put('ContactOpted', relatedCase.Contact?.Individual.HasOptedOutSolicit != null && relatedCase.Contact.Individual.HasOptedOutSolicit ? 'Opt Out' : '');
            caseAccountContactMap.put('ContactPreferences', relatedCase.Contact?.ContactPreferences__c != null ? relatedCase.Contact.ContactPreferences__c : '');
            caseAccountContactMap.put('PreferredCommunicationFormat', relatedCase.Contact?.PreferredCommunicationFormat__c != null ? relatedCase.Contact.PreferredCommunicationFormat__c : '');

            //The caseAccountContactMap is added onto the caseAccountContactMap
            caseAccountContactList.add(caseAccountContactMap);
        }
        return caseAccountContactList;
    }
}

.slds-box{
   background-color: rgb(243,243,243);
}

.slds-accordion_communications{
   --slds-c-accordion-heading-text-color: #0176d3;
   --slds-c-accordion-heading-font-size: 10pt;
   --slds-c-accordion-section-spacing-inline-start: ;
}

import { LightningElement, api, wire } from 'lwc';
import { NavigationMixin } from 'lightning/navigation';
import getCaseAccountContact from '@salesforce/apex/customer360CommunicationsHelper.getCaseAccountContact';

//Columns to show the Account Name and the Alternative Billing Name
const columns = [
    { 
        label: 'Account Name', 
        fieldName: 'AccountId', 
        type: 'url',
        typeAttributes: {
            label: { fieldName: 'AccountName' }, 
            target: '_self' 
        }
    },
    { 
        label: 'Alternative Billing Name', 
        fieldName: 'AlternativeBillingName', 
        type: 'url',
        typeAttributes: {
            label: { fieldName: 'ContactAlternativeBillingName' }, 
            target: '_self'
        } 
    },
];

//URL variables
const ACCOUNT_URL = '/lightning/r/Account/';
const CONTACT_URL = '/lightning/r/Contact/';
const VIEW_URL = '/view';

export default class Customer360Communications extends NavigationMixin(LightningElement) {
    columns = columns;
    @api recordId; 
    contact;

    //Text Variables
    contactPreferencesFormatText = 'Communications format - ';
    contactPreferencesText = 'Communication preference -  ';
    marketingConsentText = 'Marketing consent - ';
    
    @wire(getCaseAccountContact, { caseId: '$recordId' })
    wiredData({ error, data }) {
        if (data) {
            this.caseAccountContactData = data.map(contact => ({
                ...contact,
                AccountId: contact.AccountId ? ACCOUNT_URL + contact.AccountId + VIEW_URL : '', 
                AlternativeBillingName: contact.ContactAlternativeBillingName ? CONTACT_URL + contact.ContactId + VIEW_URL : '',
                strContactURL: CONTACT_URL + contact.ContactId + VIEW_URL,
                strMarketingConsent: this.marketingConsentText + contact.ContactOpted,
                strContactPrefs: this.contactPreferencesText + contact.ContactPreferences,
                strCommsPrefFormat: this.contactPreferencesFormatText + contact.PreferredCommunicationFormat
            }));
            this.contact = this.caseAccountContactData[0];
        } else if (error) {
            console.error('Error fetching data:', error);
        }
    }
}

<template>
    <lightning-layout-item>
        <h3 class="slds-text-heading_small slds-col slds-p-around_small brandColor"><b>Updated Communications Section</b></h3>
        <div class="slds-box">
            <!-- Checking if there is data returned -->
            <template if:true={contact}>
                <!-- Creating a Lightning Accordion Section -->
                <lightning-accordion allow-multiple-sections-open class = "slds-accordion_communications">
                    <lightning-accordion-section  label="Alternative billing name ">
                        <!-- Creating a Lightning DataTable to view that Data Returned -->
                        <lightning-datatable
                            key-field="id"
                            data={caseAccountContactData}
                            columns={columns}
                            hide-checkbox-column="true">
                        </lightning-datatable>
                    </lightning-accordion-section>
                </lightning-accordion>
                <!-- Creating new fields and formatting it in a URL -->
                <p><lightning-formatted-url value={contact.strContactURL} label={contact.strMarketingConsent} target="_self"></lightning-formatted-url></p>
                <p><lightning-formatted-url value={contact.strContactURL} label={contact.strContactPrefs} target="_self"></lightning-formatted-url></p>
                <p><lightning-formatted-url value={contact.strContactURL} label={contact.strCommsPrefFormat} target="_self"></lightning-formatted-url></p>
            </template>
            <!-- Checking if there is no data returned -->
            <template if:false={contact}>
                <div> There is no Communication record.</div>
            </template>
        </div>
    </lightning-layout-item>
</template>

pip install fpdf2
https://pypi.org/project/fpdf2/

class F
{
    public static void main(String[] args){
        try
        {
            int a=10 , b=0 , c;
            c=a+b;
            System.out.println(c);
            
            int d[]={10,20};
            System.out.println(d[1]);
            
            String j = "amkit";
            System.out.println(j.toUpperCase());
           
        }
        catch(ArrayIndexOutOfBoundsException d)
        {
            System.out.println("array error");
        }
        catch(ArithmeticException F)
        {
           System.out.println("ap error");
        }
        catch(NumberFormatException o)
        {
            System.out.println("number format error");
        }
        catch(Exception L)
        {
            System.out.println("some basic error ");
        }
    }
}

In this component we show an overview of the Contact such as the Marketing Consent, Communication Format and the Preferred Communication Format

You are given an m liter jug and a n liter jug. Both the jugs are initially empty. The jugs don’t have markings to allow measuring smaller quantities. You have to use the jugs to measure d liters of water where d is less than n. 

Initial State, Goal State, and Actions:
The initial state is where you start. In the classic scenario, this typically means both jugs are empty.
The goal state is where you want to reach, representing the desired water level, e.g., 4 liters.
Actions are the operations you can perform on the jugs, such as filling, emptying, or pouring water between them.

Let's consider a scenario with a 3-liter jug and a 5-liter jug, where you want to measure 4 liters of water.

Production rules-
Start with both jugs empty (0, 0).
Fill the 3-liter jug (3, 0).
Pour water from the 3-liter jug into the 5-liter jug (0, 3).
Fill the 3-liter jug again (3, 3).
Pour water from the 3-liter jug into the 5-liter jug until it's full (1, 5).
Empty the 5-liter jug (1, 0).
Pour the remaining water from the 3-liter jug into the 5-liter jug (0, 1).
Fill the 3-liter jug (3, 1).
Pour water from the 3-liter jug into the 5-liter jug until it's full (0, 4).

class F
{
    public static void main(String[] args){
        try
        {
            int a=10,b=0,c;
            c=a/b;
            System.out.println(c);
        }
        catch(Exception a)
        {
            
            System.out.println("error found ");
        }
        try
        {
            int a[] ={10,20,30};
            System.out.println(a[3]);
        }
        catch(ArrayIndexOutOfBoundsException b)
        {
            System.out.println("error found ");
        }
        
        
    }
}

Hill climbing algorithm is a local search algorithm which continuously moves in the direction of increasing elevation/value to find the peak of the mountain or best solution to the problem. It terminates when it reaches a peak value where no neighbor has a higher value.
Hill climbing algorithm is a technique which is used for optimizing the mathematical problems. One of the widely discussed examples of Hill climbing algorithm is Traveling-salesman Problem in which we need to minimize the distance traveled by the salesman.
It is also called greedy local search as it only looks to its good immediate neighbor state and not beyond that.
A node of hill climbing algorithm has two components which are state and value.
Hill Climbing is mostly used when a good heuristic is available.
In this algorithm, we don't need to maintain and handle the search tree or graph as it only keeps a single current state.

Algorithm for Simple Hill Climbing:
Step 1: Evaluate the initial state, if it is goal state then return success and Stop.
Step 2: Loop Until a solution is found or there is no new operator left to apply.
Step 3: Select and apply an operator to the current state.
Step 4: Check new state:
If it is goal state, then return success and quit.
Else if it is better than the current state then assign new state as a current state.
Else if not better than the current state, then return to step2.
Step 5: Exit.

Problems in Hill Climbing Algorithm:
1. Local Maximum: A local maximum is a peak state in the landscape which is better than each of its neighboring states, but there is another state also present which is higher than the local maximum.

Solution: Backtracking technique can be a solution of the local maximum in state space landscape. Create a list of the promising path so that the algorithm can backtrack the search space and explore other paths as well.

Hill Climbing Algorithm in AI
2. Plateau: A plateau is the flat area of the search space in which all the neighbor states of the current state contains the same value, because of this algorithm does not find any best direction to move. A hill-climbing search might be lost in the plateau area.

Solution: The solution for the plateau is to take big steps or very little steps while searching, to solve the problem. Randomly select a state which is far away from the current state so it is possible that the algorithm could find non-plateau region.

Hill Climbing Algorithm in AI
3. Ridges: A ridge is a special form of the local maximum. It has an area which is higher than its surrounding areas, but itself has a slope, and cannot be reached in a single move.

Solution: With the use of bidirectional search, or by moving in different directions, we can improve this problem.

Hill Climbing Algorithm in AI

class D
{
    public static void main(String[] args){
        
        try
        {
            int a=10,b=2,c;
            c=a/b;
            System.out.println(c);
        }
        catch(Exception a)
        {
            System.out.println("Any error found");
        }
        finally
        {
            System.out.println("no error found");
        }
        System.out.println("system ended");
    }
}

nileshdev0707@gmail.com
pass:-qwerty123

SELECT Products.ProductName,COUNT(OrderDetails.OrderDetailID) AS NumberOfOrders FROM OrderDetails
LEFT JOIN Products ON Products.ProductID = OrderDetails.ProductID
GROUP BY ProductName;

10:44:51.[952613]	>>	 

10:44:51.[962614]	>>	======================================================================

10:44:51.[962614]	>>	                                 SEARCHING FOR BARCODE: [240116032002]

10:44:51.[972615]	>>	======================================================================

10:44:51.[982615]	>>	 

10:44:52.[002616]	>>	 

10:44:52.[002616]	>>	======================================================================

10:44:52.[012618]	>>	         SEND DATA TO ANALYZER: [771D48E8-EEF1-4CB5-9809-67DA55A909F2]

10:44:52.[022618]	>>	======================================================================

10:44:52.[032619]	>>	 

10:44:52.[142625]	>>	S:	<VT>MSH|^~\&|||||20240116104452||QCK^Q02|49|P|2.3.1||||||ASCII|||<CR>MSA|AA|49|order<SP>accepted.|||0|<CR>ERR|0|<CR>QAK|SR|OK|<CR><FS><CR>

10:44:52.[152627]	>>	 

10:44:52.[162627]	>>	======================================================================

10:44:52.[172627]	>>	         SEND DATA TO ANALYZER: [771D48E8-EEF1-4CB5-9809-67DA55A909F2]

10:44:52.[172627]	>>	======================================================================

10:44:52.[182629]	>>	 

10:44:52.[312637]	>>	S:	<VT>MSH|^~\&|||||20240116104452||DSR^Q03|49|P|2.3.1||||||ASCII|||<CR>MSA|AA|49|order<SP>information.|||0|<CR>ERR|0|<CR>QAK|SR|OK|<CR>QRD|20240116104452|R|D|49|||RD|240116032002|OTH|||T|<CR>QRF||||||RCT|COR|ALL||<CR>DSP|1||2565011461|||<CR>DSP|2|||||<CR>DSP|3||นส.<SP>วรรณนิศา<SP>วรสินวิวัฒน์|||<CR>DSP|4||19891228000000|||<CR>DSP|5||F|||<CR>DSP|6|||||<CR>DSP|7|||||<CR>DSP|8|||||<CR>DSP|9|||||<CR>DSP|10|||||<CR>DSP|11|||||<CR>DSP|12|||||<CR>DSP|13|||||<CR>DSP|14|||||<CR>DSP|15|||||<CR>DSP|16|||||<CR>DSP|17|||||<CR>DSP|18|||||<CR>DSP|19|||||<CR>DSP|20|||||<CR>DSP|21||240116032002|||<CR>DSP|22|||||<CR>DSP|23||20240116104452|||<CR>DSP|24||N|||<CR>DSP|25|||||<CR>DSP|26||Serum|||<CR>DSP|27|||||<CR>DSP|28||ผู้ป่วยนอก|||<CR>DSP|29||FT3|||<CR>DSP|30||TSH|||<CR>DSC||<CR><FS><CR>

10:44:52.[422644]	>>	 




#####
#####

11:28:59.[428298]	>>	R:	<VT>MSH|^~\&|||||20240116113214||ORU^R01|60|P|2.3.1||||0||ASCII|||<CR>PID|24|2565011461|||นส.<SP>วรรณนิศา<SP>วรสินวิวัฒน์|||F|||ผู้ป่วยนอก||||||||||||||||||||<CR>OBR|24|240116032002|25|^|N|20240116104807|20240116104807|20240116104807||1^28||||20240116104807|Serum||ผู้ป่วยนอก||||||||5|||||||||||||||||||||||<CR>OBX|1|NM|FT3|FT3|3.66|pg/mL|-|N|||F||3.660317|20240116113214|||0|<CR><FS><CR>

11:28:59.[438299]	>>	 

11:28:59.[438299]	>>	======================================================================

11:28:59.[448301]	>>	         SEND DATA TO ANALYZER: [B4334B09-2A52-4FDA-AC32-DF924254F00A]

11:28:59.[458301]	>>	======================================================================

11:28:59.[468302]	>>	 

11:28:59.[578307]	>>	S:	<VT>MSH|^~\&|||||20240116112859||ACK^R01|60|P|2.3.1||||0||ASCII|||<CR>MSA|AA|60|get<SP>result.|||0|<CR><FS><CR>

11:28:59.[588309]	>>	 

11:28:59.[598310]	>>	======================================================================

11:28:59.[608310]	>>	SAVE DATA TO LIS: [240116032002] - [B4334B09-2A52-4FDA-AC32-DF924254F00A]

11:28:59.[608310]	>>	======================================================================

11:28:59.[618311]	>>	 

11:28:59.[648312]	>>	Insert LN: 2401160320 Barcode: 240116032002 Anacode: FT3 Test Code: CH059 Result: 3.66 Status:  Result Flag: - complete!!!

11:41:50.[978888]	>>	 

State space search is a problem-solving technique used in Artificial Intelligence (AI) to find the solution path from the initial state to the goal state by exploring the various states. The state space search approach searches through all possible states of a problem to find a solution. It is an essential part of Artificial Intelligence and is used in various applications, from game-playing algorithms to natural language processing.

State space search has several features that make it an effective problem-solving technique in Artificial Intelligence. These features include:

Exhaustiveness:
State space search explores all possible states of a problem to find a solution.

Completeness:
If a solution exists, state space search will find it.

Optimality:
Searching through a state space results in an optimal solution.

Uninformed and Informed Search:
State space search in artificial intelligence can be classified as uninformed if it provides additional information about the problem.

In contrast, informed search uses additional information, such as heuristics, to guide the search process.

digram from nb

class D
{
    public static void main(String[] args){
    String str="heavy";
    
    try
    {
        int a=Integer.parseInt(str);
        System.out.println("error not found");
    }
   catch(NumberFormatException n)
   {
       System.out.println("error found");
   }
   System.out.println("system ended ");
} 
}

$data = DB::table('MASTER_TABLE_NAME as r')
        ->select('r.*')
        ->whereExists(function ($query) {
            $query->select(DB::raw(1))
                ->from('CHILD_TABLE_NAME')
                ->whereRaw('CHILD_TABLE_NAME.ORDER_NO = MASTER_TABLE_NAME.ORDER_NO')
                ->whereRaw('CHILD_TABLE_NAME.PRDMD_NO <> 0'); //This means not equal to 0 use = if  															  //you want equal value
        })
        ->where('r.UNIT_NO', $org_id)
        ->where('r.PM_ID', $_SESSION['section']);

class D
{
    public static void main(String[] args){
        String a= null;
        
        try
        {
            System.out.print(a.toUpperCase());
            System.out.print("error not found");
            
        }
        catch(NullPointerException n )
        {
            System.out.print("error found");
        }
        
        
} 
}

Algorithm: Unify(Ψ1, Ψ2)

Step. 1: If Ψ1 or Ψ2 is a variable or constant, then:
	a) If Ψ1 or Ψ2 are identical, then return NIL. 
	b) Else if Ψ1is a variable, 
		a. then if Ψ1 occurs in Ψ2, then return FAILURE
		b. Else return { (Ψ2/ Ψ1)}.
	c) Else if Ψ2 is a variable, 
		a. If Ψ2 occurs in Ψ1 then return FAILURE,
		b. Else return {( Ψ1/ Ψ2)}. 
	d) Else return FAILURE. 
Step.2: If the initial Predicate symbol in Ψ1 and Ψ2 are not same, then return FAILURE.
Step. 3: IF Ψ1 and Ψ2 have a different number of arguments, then return FAILURE.
Step. 4: Set Substitution set(SUBST) to NIL. 
Step. 5: For i=1 to the number of elements in Ψ1. 
	a) Call Unify function with the ith element of Ψ1 and ith element of Ψ2, and put the result into S.
	b) If S = failure then returns Failure
	c) If S ≠ NIL then do,
		a. Apply S to the remainder of both L1 and L2.
		b. SUBST= APPEND(S, SUBST). 
Step.6: Return SUBST. 

class D
{
    public static void main(String[] args){
        int a=10,b=0,c;
        System.out.println("started ");
        try{
            c=a+b;
            System.out.println("sum will be "+c);
            System.out.println("no error found ");
        }
        catch(Exception e)
        {
            System.out.println("error founded");
        }
        System.out.println("ended");
    }
}

class D
{
    public static void main(String[] args ){
        System.out.println("Started ");
        int a=10,b=0,c;
        try{
            c=a/b;
        }
        catch(Exception e )
        {
            System.out.println("any errror found");
        }
        System.out.println("ended");
    }
}

Knowledge representation and reasoning (KR, KRR) is the part of Artificial intelligence for representing information about the real world so that a computer can understand and can utilize this knowledge to solve the complex real world problems such as diagnosis a medical condition or communicating with humans in natural language.It is also a way which describes how we can represent knowledge in artificial intelligence. Knowledge representation is not just storing data into some database, but it also enables an intelligent machine to learn from that knowledge and experiences so that it can behave intelligently like a human.

Knowledge representation has two entities : 
Facts: Facts are the truth in some relevant world. 
Representation : Representation is the presentation of facts in some chosen formalism. 
For example: 
Fact : Charlie is a dog. 
Representation of fact using mathematical logic : Dog (Charlie) 

app/layout.tsx
✅ copied
Copy
import { YouTubeEmbed } from '@next/third-parties/google'

export default function RootLayout({
  children,
}: {
  children: React.ReactNode
}) {
  return (
    <html lang="en">
      <body>{children}</body>
      <YouTubeEmbed videoid="ogfYd705cRs" height={400} params="controls=0" />
    </html>
  )
}

app/sitemap.ts
✅ copied
Copy
import { MetadataRoute } from 'next'

export default function sitemap(): MetadataRoute.Sitemap {
  return [
    {
      url: 'https://acme.com',
      lastModified: new Date(),
      changeFrequency: 'yearly',
      priority: 1,
    },
    {
      url: 'https://acme.com/blog',
      lastModified: new Date(),
      changeFrequency: 'weekly',
      priority: 0.5,
    },
  ]
}

When based on available data a decision is taken then the process is called as Forward chaining.	Backward chaining starts from the goal and works backward to determine what facts must be asserted so that the goal can be achieved.
2.	Forward chaining is known as data-driven technique because we reaches to the goal using the available data.	Backward chaining is known as goal-driven technique because we start from the goal and reaches the initial state in order to extract the facts.
3.	It is a bottom-up approach.	It is a top-down approach.
4.	It applies the Breadth-First Strategy.	It applies the Depth-First Strategy.
5.	Its goal is to get the conclusion.	Its goal is to get the possible facts or the required data.
6.	Slow as it has to use all the rules.	Fast as it has to use only a few rules.
7.	It operates in forward direction i.e it works from initial state to final decision.	It operates in backward direction i.e it works from goal to reach initial state.
8.	Forward chaining is used for the planning, monitoring, control, and interpretation application.	It is used in automated inference engines, theorem proofs, proof assistants and other artificial intelligence applications.

Forward chaining is a form of reasoning which start with atomic sentences in the knowledge base and applies inference rules (Modus Ponens) in the forward direction to extract more data until a goal is reached.

The Forward-chaining algorithm starts from known facts, triggers all rules whose premises are satisfied, and add their conclusion to the known facts. This process repeats until the problem is solved.

Properties of Forward-Chaining:

It is a down-up approach, as it moves from bottom to top.

It is a process of making a conclusion based on known facts or data, by starting from the initial state and reaches the goal state.

Forward-chaining approach is also called as data-driven as we reach to the goal using available data.

Forward -chaining approach is commonly used in the expert system, such as CLIPS, business, and production rule systems.

For example, suppose that the goal is to conclude the colour of my pet 
Bruno given that he croaks and eats flies, and that the rule base contains 
the following two rules :  
If X croaks and eats flies - Then X is a frog. 
If X is a frog - Then X is red. 





Backward Chaining
A backward chaining algorithm is a form of reasoning, which starts with the goal and works backward, chaining through rules to find known facts that support the goal.

Properties of backward chaining-
  
It is known as a top-down approach.

Backward-chaining is based on modus ponens inference rule.

In backward chaining, the goal is broken into sub-goal or sub-goals to prove the facts true.

It is called a goal-driven approach, as a list of goals decides which rules are selected and used.

Backward -chaining algorithm is used in game theory, automated theorem proving tools, inference engines, proof assistants, and various AI applications.

e backward-chaining method mostly used a depth-first search strategy for proof.

Direct MTApproach 
The Direct Translation approach works by translating the source language directly into the target language, without any intermediate representation. This method often operates at the word or phrase level, using dictionaries and rules to handle lexical, morphological, and syntactic differences between languages. While this approach can result in speedy translations, it can also lead to inaccuracies and difficulties in coping with complex language structures.

Transfer Approach
The Transfer-Based Translation approach involves converting the source language into an intermediate representation that captures its syntactic and semantic structure. This intermediate representation is then used to generate a translation in the target language, subsequently processed through linguistic rules and transformations. Although typically more computationally expensive than direct translation, transfer-based translation can produce higher-quality translations by preserving the structure and meaning of the source text.

Interlingua approach
Lastly, the Interlingua-Based Translation approach translates the source language into an abstract, language-independent representation called "interlingua." The target language translation is then generated from the interlingua. This approach is advantageous for multilingual translation scenarios, as only two translation steps are needed between any pair of languages. However, creating a comprehensive interlingua that can express different language structures accurately is a challenging task.

FROM php:8.2-fpm

# Arguments defined in docker-compose.yml
ARG user
ARG uid

# Install system dependencies
RUN apt-get update && apt-get install -y \
    git \
    curl \
    libpng-dev \
    libonig-dev \
    libxml2-dev \
    libzip-dev \
    zip \
    unzip \
    && docker-php-ext-install zip

# Clear cache
RUN apt-get clean && rm -rf /var/lib/apt/lists/*

# Install PHP extensions
RUN docker-php-ext-install pdo_mysql mbstring exif pcntl bcmath gd

# Get latest Composer
COPY --from=composer:latest /usr/bin/composer /usr/bin/composer

# Create system user to run Composer and Artisan Commands
RUN useradd -G www-data,root -u $uid -d /home/$user $user
RUN mkdir -p /home/$user/.composer && \
    chown -R $user:$user /home/$user

# Set working directory
WORKDIR /var/www

USER $user

 docker exec -it -u 0 6899f3bfdc70056d5bedc8502bd28bb71afd71f7e8ecc6b0f0d9c9496ac6546d bash
 
 # It is the -u 0 that allows you in as a root user

 docker exec -it -u 0 6899f3bfdc70056d5bedc8502bd28bb71afd71f7e8ecc6b0f0d9c9496ac6546d bash
 
 # It is the -u 0 that allows you in as a root user

COMPOSER_PROCESS_TIMEOUT=20000 composer install

Machine translation (MT) is the use of algorithms and artificial intelligence to automatically convert text or speech from one language to another.

In a machine translation task, the input already consists of a sequence of symbols in some language, and the computer program must convert this into a sequence of symbols in another language.

Lexical Analysis and Morphological-
The first phase of NLP is the Lexical Analysis. This phase scans the source code as a stream of characters and converts it into meaningful lexemes. It divides the whole text into paragraphs, sentences, and words.

Syntactic Analysis (Parsing)
Syntactic Analysis is used to check grammar, word arrangements, and shows the relationship among the words.
Example: Agra goes to the Poonam
In the real world, Agra goes to the Poonam, does not make any sense, so this sentence is rejected by the Syntactic analyzer.

Semantic Analysis
Semantic analysis is concerned with the meaning representation. It mainly focuses on the literal meaning of words, phrases, and sentences.

Discourse integration
Discourse describes communication between 2 or more individuals. Discourse integration analyzes prior words and sentences to understand the meaning of ambiguous language.

Pragmatic analysis
Pragmatic analysis attempts to derive the intended—not literal—meaning of language.

Spam Filters: One of the most irritating things about email is spam. Gmail uses natural language processing (NLP) to discern which emails are legitimate and which are spam.

Algorithmic Trading: Algorithmic trading is used for predicting stock market conditions. Using NLP, this technology examines news headlines about companies and stocks and attempts to comprehend their meaning in order to determine if you should buy, sell, or hold certain stocks.

Questions Answering: NLP can be seen in action by using Google Search or Siri Services. A major use of NLP is to make search engines understand the meaning of what we are asking and generate natural language in return to give us the answers.

Summarizing Information: On the internet, there is a lot of information, and a lot of it comes in the form of long documents or articles. NLP is used to decipher the meaning of the data and then provides shorter summaries of the data so that humans can comprehend it more quickly.

Chatbots
Chatbots are a form of artificial intelligence that are programmed to interact with humans in such a way that they sound like humans themselves. Chatbots are created using Natural Language Processing and Machine Learning.

Language Translator-
Google Translate and other translation tools as well as use Sequence to sequence modeling that is a technique in Natural Language Processing. It allows the algorithm to convert a sequence of words from one language to another which is translation.

Sentiment Analysis-
Companies use natural language processing,to understand the general sentiment of the users for their products and services and find out if the sentiment is good, bad, or neutral. 

Natural Language Processing (NLP) is a subfield of artificial intelligence that deals with the interaction between computers and humans in natural language.

It involves the use of computational techniques to process and analyze natural language data, such as text and speech, with the goal of understanding the meaning behind the language.

NLP is used in a wide range of applications, including machine translation, sentiment analysis, speech recognition, chatbots, and text classification. 

The field is divided into  three different parts:

Speech Recognition — The translation of spoken language into text.
Natural Language Understanding (NLU)  — The computer’s ability to understand what we say.
Natural Language Generation  (NLG) — The generation of natural language by a computer.

NLU and NLG are the key aspects depicting the working of NLP devices