This paper explores a novel approach to brain signal analysis, leveraging advanced techniques such as Morse code encoding/decoding, thought translation, dream conversion, and emotion detection. The primary goal is to enhance the conversion of brain signals into text and images using multi-modal analysis with OpenAI, offering significant benefits in treating stress disorders, PTSD, ADHD, memory loss, and aiding disabled individuals. We propose a methodology for generating synthetic datasets of brain signals and corresponding texts, utilizing machine learning algorithms to predict these mappings. Random Forest (RF) provides best accuracy. This approach is augmented by integrating emotional and stress metrics from devices like Muse and Flowtime. creating a comprehensive system for braincomputer interfacing.

This paper describes the development of a low-cost robotic car designed for automated delivery of items within indoor environments. The system leverages a Raspberry Pi-based platform, integrating line detection algorithms, object detection techniques, and PID control for smooth and accurate navigation. By combining hardware and software solutions, the robotic car can follow predefined paths, detect intersections, and handle multi-point deliveries with real-time adaptability. The design ensures affordability, reliability, and ease of deployment, making it suitable for applications in healthcare, retail, and logistics. Detailed implementation, testing, and validation results are provided to demonstrate the system's effectiveness.

TP53 gene mutations are key drivers in over 50% of human cancers, contributing to uncontrolled cell growth and loss of tumor-suppressing functions. There is a critical need for analyzing the mutations as well as their implications in diagnoses. This project addresses the requirement for a comprehensive computational approach to understand the impact of TP53 mutations across diverse cancer types. The research integrates various techniques, including statistical modeling, survival analysis, supervised classification, deep learning, K-Mer analysis, and rule-based counting. Genetic sequences from the TP53 database are used to identify mutated nucleotides through K-Mer analysis and the Random Forest algorithm while overexpressed TP53 levels are analyzed through a one-sided T-test and p-values on sequences. Transcriptional activity of the protein is forecasted using advanced classifiers on TP53 biophysical simulation data, tumor properties are predicted with germline data, and tumor types are determined through a Sequential Neural Network on TP53 polymorphism data. Furthermore, rule-based counting techniques unravel amino acid category distributions in TP53 DNA. The results demonstrated the prevalence of TP53 mutations among cancer patients, a high accuracy of 97% in identifying tumor types, a strong performance in predicting mutated nucleotides, and the impact of different classifiers on TP53 transcriptional activity. The survival analysis demonstrated TP53's influence on patient outcomes and showed a ~0.1 survival probability at 60 months for individuals with the TP53 gene. This research advances the understanding of TP53 mutations and provides a comprehensive framework for healthcare professionals to utilize for treatment strategies in precision oncology.