Machine Learning Approach for Human Brain Counselling

The number one goal of this research is to decorate existing methodologies for malware detection via developing a robust and scalable version that robotically identifies malware via the analysis of difficult styles inside both records and code, moving beyond traditional signature-primarily based methods. constructing on previous studies that have efficaciously implemented more than a few devices getting to know techniques, this technique will integrate each supervised and unsupervised studying algorithm. especially, category strategies consisting of choice bushes, random forests, and help vector machines, which have validated accuracies starting from 85% to 95%, could be utilized along superior deep getting to know frameworks, which includes neural networks, which have said accuracies exceeding 96% in positive contexts. by means of education these fashions on an in depth and various dataset that consists of both benign and malicious files, this study aims to improve the version's generalization abilities, consequently allowing it to efficiently perceive new, previously unknown malware variants. The overall performance of the proposed model can be rigorously evaluated against installed benchmarks and metrics, consisting of accuracy, precision, bear in mind, and the false tremendous fee, making sure its efficacy in actual-time malware detection eventualities. This multifaceted technique not best seeks to develop the sphere of cybersecurity but also builds on the foundational paintings of others, offering a greater adaptive and proactive way of malware identification that aligns with present day developments in gadget studying and cybersecurity studies

Analysis of Malware Detection Using Various Machine Learning Approach

Vishal Borate,

Dr. Alpana Adsul,

Aditya Gaikwad

et al. 2024

Video HR Interview Bot using Artificial Intelligence

Gayatri Hogale,

Kalyani Pawar,

Sneha Jadhav

et al. 2024

Due to the swift world of digital media today, recruitment processes are becoming increasingly done on the Internet; one needs efficient, scalable, and accessible solutions. The "Video HR Interview Bot" is AI-based; it revolutionizes this old- fashioned procedure of a human resource interview in the sense that it carries out real-time automated video interviews with candidates. Here, it uses NLP, computer vision, and machine learning algorithms to scan through the candidates-not only through their oral but also by non-verbal signs such as facial emotions, body language, and tone of voice. This bot conducts an HR interview in a structured format. It conducts a series of scripted questions based on the-job description and requirements. Applicants communicate with the bot through video, where their responses are recorded and filtered for key competencies, communication skills, and cultural fit. The system provides real-time feedback and scores on various parameters so that the HR teams could focus on those candidates whose scores in the desired parameters are meeting the desired thresholds. Video HR Interview Bot streamlines the initial interview phase, thereby reducing time-to-hire, objectivity enhancement, and unconscious bias in candidate evaluation. Besides, it gives scalability, thus allowing the companies to handle bulk applications without compromising their standards. The system also provides a good data set for the HR practitioner to analyze, such as automated transcripts, sentiment analysis, and video insights, which all lead to a much more complete and well-informed decision-making process. In pursuing the core objectives of the project -that is, the efficiency and accessibility of recruitment while balancing fairness and quality in hiring-it must also help enhance the case of remote and high-volume recruitment

Symptom Checker Framework: Leveraging Machine Learning for Early Diagnosis in Healthcare Systems

Mohan Jadhav,

Prasad Bhat,

Kunal Thakare

et al. 2024

The early and accurate identification of diseases based on symptoms is a critical factor in effective healthcare. In this project, we introduce DignoSmart: Your Personalized Symptom Check, an intelligent, machine-learning-based application designed to assist users in identifying potential health conditions by entering their symptoms. The system utilizes a decision tree algorithm to predict possible diseases based on user input through a chat-like interface. Users begin by providing personal information, such as age and symptoms. The application processes this data and, through a series of follow-up questions, refines its understanding of the user's health condition. These questions, related to symptom duration, pain intensity, and specific issues like back pain, guide the prediction process. The decision tree algorithm, trained on a dataset of symptoms and disease correlations, predicts the most likely condition based on the responses. The system then provides a detailed description of the predicted disease, including key information about the condition and possible causes. Additionally, it offers recommendations for basic precautions and suggests when to seek medical attention. By integrating machine learning techniques and an intuitive user interface, DignoSmart aims to assist users in recognizing potential health problems early and taking appropriate action, thus contributing to better health outcomes..