diff --git a/clone-graph/Jeehay28.js b/clone-graph/Jeehay28.js
new file mode 100644
index 000000000..4b08eab35
--- /dev/null
+++ b/clone-graph/Jeehay28.js
@@ -0,0 +1,68 @@
+/**
+ * // Definition for a _Node.
+ * function _Node(val, neighbors) {
+ *    this.val = val === undefined ? 0 : val;
+ *    this.neighbors = neighbors === undefined ? [] : neighbors;
+ * };
+ */
+
+/**
+ * @param {_Node} node
+ * @return {_Node}
+ */
+
+// BFS approach
+// Time Complexity: O(N + E), where N is the number of nodes and E is the number of edges.
+// Space Complexity: O(N), due to the clones map and additional storage (queue for BFS, recursion stack for DFS).
+
+var cloneGraph = function (node) {
+  if (!node) {
+    return null;
+  }
+  let clone = new Node(node.val);
+  let clones = new Map();
+  clones.set(node, clone);
+  let queue = [node];
+  while (queue.length > 0) {
+    node = queue.shift();
+    for (const neighbor of node.neighbors) {
+      if (!clones.get(neighbor)) {
+        const temp = new Node(neighbor.val);
+        clones.set(neighbor, temp);
+        queue.push(neighbor);
+      }
+      clones.get(node).neighbors.push(clones.get(neighbor));
+    }
+  }
+
+  return clone;
+};
+
+// DFS approach
+// Time Complexity: O(N + E), where N is the number of nodes and E is the number of edges.
+// Space Complexity: O(N), due to the clones map and the recursion stack.
+
+var cloneGraph = function (node) {
+  if (!node) {
+    return null;
+  }
+
+  let clones = new Map();
+
+  const dfs = (node) => {
+    if (clones.has(node)) {
+      return clones.get(node);
+    }
+    let clone = new Node(node.val);
+    clones.set(node, clone);
+
+    for (neighbor of node.neighbors) {
+      clone.neighbors.push(dfs(neighbor));
+    }
+
+    return clone;
+  };
+
+  return dfs(node);
+};
+
diff --git a/longest-common-subsequence/Jeehay28.js b/longest-common-subsequence/Jeehay28.js
new file mode 100644
index 000000000..3710cea3f
--- /dev/null
+++ b/longest-common-subsequence/Jeehay28.js
@@ -0,0 +1,95 @@
+/**
+ * @param {string} text1
+ * @param {string} text2
+ * @return {number}
+ */
+
+// 🤔
+// Both memoization (top-down) and dynamic programming (bottom-up) have the same time and space complexity of O(m * n). 
+// The difference lies in their implementation:
+// - Memoization uses recursion with a cache to avoid redundant calculations but may incur overhead from recursive calls and stack space.
+// - Dynamic Programming iteratively builds the solution, avoiding recursion overhead and sometimes offering better performance.
+// DP is often preferred when recursion depth or function call overhead is a concern, while memoization can be more intuitive for certain problems.
+
+// 😊 memoization approach
+// Time Complexity: O(m * n), where m is the length of text1, and n is the length of text2
+// Space Complexity: O(m * n)
+// Top-down approach with recursion.
+// Use a cache (or memoization) to store intermediate results.
+
+var longestCommonSubsequence = function (text1, text2) {
+  const memo = new Map();
+
+  const dfs = (i, j) => {
+    const key = `${i},${j}`; // Convert (i, j) into a unique string key
+    if (memo.has(key)) {
+      return memo.get(key);
+    }
+
+    if (i === text1.length || j === text2.length) {
+      memo.set(key, 0);
+    } else if (text1[i] === text2[j]) {
+      memo.set(key, 1 + dfs(i + 1, j + 1));
+    } else {
+      memo.set(key, Math.max(dfs(i + 1, j), dfs(i, j + 1)));
+    }
+
+    return memo.get(key);
+  };
+  return dfs(0, 0);
+};
+
+// 😊 bottom-up dynamic programming approach
+// Time Complexity: O(m * n), where m is the length of text1, and n is the length of text2
+// Space Complexity: O(m * n)
+
+// text1 = "abcde"
+// text2 = "ace"
+
+//      ""   a   c   e
+// ""   0    0   0   0
+// a    0    1   1   1
+// b    0    1   1   1
+// c    0    1   2   2
+// d    0    1   2   2
+// e    0    1   2   3
+
+
+// var longestCommonSubsequence = function (text1, text2) {
+//     const dp = new Array(text1.length + 1)
+//       .fill(0)
+//       .map(() => new Array(text2.length + 1).fill(0));
+  
+//     for (let i = 1; i <= text1.length; i++) {
+//       for (let j = 1; j <= text2.length; j++) {
+//         if (text1[i - 1] === text2[j - 1]) {
+//           dp[i][j] = dp[i - 1][j - 1] + 1;
+//         } else {
+//           dp[i][j] = Math.max(dp[i - 1][j], dp[i][j - 1]);
+//         }
+//       }
+//     }
+  
+//     return dp[text1.length][text2.length];
+//   };
+
+
+// 😱 Time Limit Exceeded!
+// Brute-force Recursion
+// Time Complexity: O(2^(m+n)) (Exponential)
+// Space Complexity: O(m + n) (Recursive Stack)
+
+// var longestCommonSubsequence = function (text1, text2) {
+//   const dfs = (i, j) => {
+//     if (i === text1.length || j === text2.length) {
+//       return 0;
+//     }
+//     if (text1[i] === text2[j]) {
+//       return 1 + dfs(i + 1, j + 1);
+//     }
+//     return Math.max(dfs(i + 1, j), dfs(i, j + 1));
+//   };
+//   return dfs(0, 0);
+// };
+
+
diff --git a/longest-repeating-character-replacement/Jeehay28.js b/longest-repeating-character-replacement/Jeehay28.js
new file mode 100644
index 000000000..94e68fd1a
--- /dev/null
+++ b/longest-repeating-character-replacement/Jeehay28.js
@@ -0,0 +1,32 @@
+/**
+ * @param {string} s
+ * @param {number} k
+ * @return {number}
+ */
+
+// sliding window technique
+// Time complexity: O(n), where n is the length of the string. Both the start and end pointers traverse the string at most once.
+// Space Complexity: O(1), as we only need a fixed amount of extra space for the character frequency map and some variables.
+var characterReplacement = function (s, k) {
+  let longest = 0;
+  let maxCount = 0;
+  const charCount = {};
+  let start = 0;
+
+  for (let end = 0; end < s.length; end++) {
+    const char = s[end];
+    charCount[char] = (charCount[char] || 0) + 1;
+    maxCount = Math.max(charCount[char], maxCount);
+
+    while (end - start + 1 - maxCount > k) {
+      const temp = s[start];
+      charCount[temp] -= 1;
+      start += 1;
+    }
+
+    longest = Math.max(longest, end - start + 1);
+  }
+
+  return longest;
+};
+
diff --git a/number-of-1-bits/Jeehay28.js b/number-of-1-bits/Jeehay28.js
new file mode 100644
index 000000000..d9c08bc57
--- /dev/null
+++ b/number-of-1-bits/Jeehay28.js
@@ -0,0 +1,68 @@
+// 💪 Optimized Approach: Brian Kernighan's Algorithm
+// Brian Kernighan's algorithm is a very efficient way to count the number of set bits in a number.
+// It works by repeatedly turning off the rightmost set bit.
+
+// Time Complexity: O(k), where k is the number of set bits (1s) in the binary representation of n.
+// - Binary representation of 5 : 101
+
+// Space Complexity: O(1)
+
+/**
+ *
+ * @param {number} n - The number whose set bits need to be counted.
+ * @return {number} - The number of set bits (1s) in the binary representation of n.
+ */
+
+var hammingWeight = function (n) {
+  let cnt = 0;
+
+  while (n > 0) {
+    cnt += 1;
+    n = n & (n - 1); // removes the rightmost set bit (1) in the binary representation of n, and the other bits remain unchanged
+  }
+  return cnt;
+};
+
+// 💪 Improved versiion
+// TC: O(log n)
+// SC: O(1)
+// var hammingWeight = function(n) {
+
+//     let cnt = 0;
+
+//     while(n > 0) {
+//         cnt += n % 2;
+//         n = Math.floor(n / 2)
+//     }
+//     return cnt;
+
+// };
+
+// 💪 My own approach
+// Time Complexity: O(log n)
+// Space Complexity: O(log n)
+
+/**
+ * Time Complexity: O(log n)
+ * - The operation `n.toString(2)` converts the number into its binary representation, which takes O(log n) time, where 'n' is the input number.
+ * - The `replaceAll("0", "")` operation goes through the binary string to remove all '0' characters, which is O(log n) as the binary string has a length of log(n).
+ * - The `.length` operation to count the '1' characters is O(log n) as well.
+ * - Overall, the time complexity is O(log n) since we are iterating over the binary string twice (once to convert and once to remove zeros).
+
+ * Space Complexity: O(log n)
+ * - The binary representation of the number is stored as a string, which takes O(log n) space, where log(n) is the length of the binary string.
+ * - Therefore, the space complexity is O(log n) because of the space used to store the binary string during the conversion process.
+ */
+
+/**
+ * @param {number} n
+ * @return {number}
+ */
+
+// var hammingWeight = function (n) {
+//     let binary = n.toString(2).replaceAll("0", "").length;
+
+//     return binary;
+//   };
+
+
diff --git a/sum-of-two-integers/Jeehay28.js b/sum-of-two-integers/Jeehay28.js
new file mode 100644
index 000000000..bc1b91c18
--- /dev/null
+++ b/sum-of-two-integers/Jeehay28.js
@@ -0,0 +1,23 @@
+/**
+ * @param {number} a
+ * @param {number} b
+ * @return {number}
+ */
+
+// Time Complexity: O(log(max(a, b)))
+// Space Complexity: O(1)
+
+var getSum = function (a, b) {
+  // XOR (^): outputs true (or 1) if the inputs are different, and false (or 0) if the inputs are the same.
+  // And (&): compares each bit of two numbers and returns 1 only if both bits are 1; otherwise, it returns 0.
+  // left shitf (<<): moves the bits one position to the left, which is the same as multiplying by 2.
+
+  while (b !== 0) {
+    let carry = (a & b) << 1;
+    a = a ^ b;
+    b = carry;
+  }
+
+  return a;
+};
+