Abdul Aleem

Lung diseases are among the most important causes of morbidity and mortality worldwide; it require prompt and accurate diagnosis methods. A novel hybrid deep learning framework for integrating you only look once version 8 (YOLOv8), considering real-time detection and vision transformer (ViT-B/16) for global context-based classification of lung diseases in chest X-ray images, is presented. Based on transfer learning and a two-stage detection-classification pipeline, this proposed model is applicable to dealing with inter-image variability, overlapped disease features and lack of annotated medical examples. Our developed hybrid model achieves the highest classification accuracy of 96.8% and 0.98 AUC-ROC on the National Institutes of Health (NIH) Chest X-ray dataset, which consists of over 112,000 images covering 14 diseases, and outperforms its several current state-of-the-art models. In addition, attention heatmaps and bounding box visualizations highly correlate with clinical variables and enhance interpretability. This paper demonstrates the practicability of hybrid vision driven architectures for better medical image analysis and shows their integration into clinical decision-support systems.

Data analysis unlocks the hidden, latent patterns and structures within datasets. Clustering algorithms, the cornerstone of any data analysis, are usually challenged by high-dimensionality, complexity, or large-scale data. This research proposes a hybrid model that merges neural networks and clustering techniques to handle these problems. Neural networks are used for feature extraction and dimensionality reduction; raw data will be transformed into a robust, low-dimensional representation. With these refined features, the performance of clustering algorithms improves in terms of scalability, efficiency, and accuracy. The proposed model is tested on diversified datasets such as the wisconsin breast cancer dataset (WBCD), GEO Dataset, and image and text data benchmarks for which substantial improvements in clustering metrics such as silhouette score, purity, and computational efficiency are reported. The results demonstrate the efficacy of the hybrid approach in optimizing clustering applications across domains, such as bioinformatics, health care, and image analysis.

The conventional technique of recruiting is not efficient and suffers from drawbacks such as time wastage and biasness in evaluation. Pre-employment resume screening is subjective and takes a lot of time; currently available solutions are keyword based. In order to address such issues, this article presents a resume analysis method, implemented with Python, which incorporates usage of Artificial Intelligence and Natural Language Processing in analyses of resumes. The proposed method makes use of machine learning to sort applicants in relation to the job for which they will be applying for based on the required skills. The major tasks involved in the development of this project include data cleaning, converting the data to TF-IDF vectors, performing classification and the development of an interactive front-end using the Streamlit module. The proposed method scans resume, provides likely job types, and suggestions, while guaranteeing that candidates don't go by looks and can be effectively evaluated based on their abilities. The purpose of this research is to increase the accuracy of prediction, and, consequently, the fairness and efficiency of the hiring processes.

A speech impediment is a disability that impairs a person's ability to communicate verbally. One of the most structured languages is employed as a signal language to overcome this difficulty. In order to facilitate communication even in the event that someone is not proficient in sign language, it is practically necessary to have a method or program that can recognize motions in the language. This article goal is to bridge the gap between persons with disabilities, such as the deaf and dumb, and other people. The artwork provided on paper is an attempt (extension) to look at the problems in the Indian Sign Language (ISL) character class For this assessment, the person need to be capable of take pics of hand motions the use of a webcam, and the computer must expect and display the call of the picture that become taken. The obtained image goes via a numerous process, consisting of dilatation, mask operation, and conversion to grayscale, among other pc- imaginative and predictive techniques. The proposed version is trained the use of a Convolutional Neural Network (CNN) to discover the image. At completion, our model's accuracy is more than 95 %.