Faeze Ghorbanpour

Transfer Learning: Through my exploration, the conceptual and semantic embedding techniques were the most effective for combining text and image. Huggingface's BERT model for text and ViT model for image achieved the best results.
Multi-modal Learning: After thorough evaluation of various algorithms, we discovered that for short texts, the Contrastive Loss function combined with concatenation proved to be more effective in fusing text and picture vectors. On the other hand, attention-based fusion worked best for lengthy texts.

EDA: During this phase, we carefully analyze the experimental operations, taking into account factors such as the length of the text and the distribution of labels. This helps us ensure that our approach is thorough and accurate, providing the best possible results for our users.
ugmentation: During this phase, there are a variety of techniques available to augment words, documents, and features. These techniques can be utilized to enhance the overall effectiveness of the process without altering the meaning of the original text. It is important to consider these options to ensure the best possible outcome.
Embedding: During this stage, We utilize text embedding algorithms such as Fasttext, Word2vec, and contextual embedding in order to extract essential features.
Algorithms: In order to optimize outcomes, we utilize cutting-edge machine learning, deep learning, and transformer learning models for selection and implementation in our training. Furthermore, we meticulously fine-tune the algorithm parameters to ensure optimal performance.

Security measures: This project has implemented multiple layers of security to guarantee the safety of the blockchain network. These include authentication through the use of the SHA512 hash function, transaction RSA encryption, and a user authentication method.
Network: The functioning of the system is similar to that of a network, with banks, consumers, and servers all serving as nodes. Transactions are then exchanged among these nodes.

Clean Coding: It was placed special emphasis on the importance of clean coding and becoming proficient in its various techniques.
OOD: Throughout the project, we made sure to follow the fundamental principles of Object Oriented Programming and set up a meticulously structured project framework.

Hidden Markov Model: I utilized this statistical learning algorithm to process identifying drivers' location on the map involves. This technique effectively filters out any noise points that may cause inaccuracies in the matching points.
Visualization: Various methods were employed to display both the initial and enhanced GPS coordinates on the map. I showed a demonstration of the precision and efficacy of the outcomes through visual representation.

Clustering: TWithin this section, the quantity of ranking screws assigned to each page in the search elastic is determined. The obtained results are subsequently integrated into the search elastic and employed for conducting page searches.
Page Rank: In order to optimize the search for information, Elastic Search utilizes blog clustering and calculates the appropriate k value by adding their respective squares.