Mental Health Chatbot Therapist
AI-Aided Design Studio: Enhancing Graphic Design and User Interface with Machine Learning
Futuristic Smart AI Powered Backpack
Sign Language Recognition with Hand Gestures using Deep Learning
A Review on Early Diagnosis of Glaucoma using Machine Learning Techniques
Retinal Imaging for Diabetic Retinopathy Detection through Deep Learning
Enhanced Mental Health Prediction with Deep Neural Networks for Accurate Diagnosis
Enhanced Mental Health Prediction with Deep Neural Networks for Accurate Diagnosis
Advancing Autism Spectrum Disorder Diagnosis through Ensemble Learning
Enhancing Chronic Kidney Disease Prediction Performance through Algorithm Fusion: A Combined KNN - SVM Approach
A Comparative Exploration of Activation Functions for Image Classification in Convolutional Neural Networks
Machine Learning Solutions for the Healthcare Industry: A Review
Fake news is a growing problem on social media and can have significant negative consequences for individuals and society owing to its accessibility, low cost, and quick distribution. It is difficult to automatically identify bogus news that defies the current content-based analysis techniques. One of the key reasons is that current NLP algorithms still lack common sense, which is frequently necessary for understanding how to read the news. Recent research has demonstrated that the propagation patterns of true and fake news differ on social media. With the potential for automatic fake news detection, this study investigates the use of Deep Learning (DL) models to detect fake news on social media. A Neural Network (NN) model was developed using Natural Language Toolkit (NLTK), TensorFlow, and Natural Language Processing (NLP) for textual analysis. The model was trained on a dataset of fake and real news articles, and was able to achieve high accuracy in identifying fake news. The results of this study suggest that DL models can be valuable tools for detecting fake news on social media.
Brain tumor detection is challenging for radiologists. The early detection of brain tumors is critical, and automated techniques are necessary to achieve this goal. In this study, an automated method is proposed to distinguish between malignant and non-cancerous brain Magnetic Resonance Images (MRI). An ensemble technique was proposed that includes two Deep Learning (DL) models, a 6-Convolutional Neural Network (6-CNN) model with an efficient end-to-end, and a pre-trained Residual Network50 (ResNet50) model. The MRI image classification was experimented in two directions: one using the average probabilities of the ensemble model, and the other considering the optimal weights of the ensemble model for a Support Vector Machine (SVM) classifier with different kernels. Two datasets, Harvard and Retrospective Image Registration Evaluation (RIDER), were used to evaluate the performance of the proposed model. The 6-CNN-ResNet-SVM model achieved the highest accuracy of 97.32% for 10-fold validation, with the remaining performance metrics being an Area Under the Curve (AUC) of 0.98%, sensitivity of 93.62%, specificity of 98%, False Negative Rate (FNR) of 0.06, and False Positive Rate (FPR) of 0.00 for the linear kernel. The proposed model can identify tumors more accurately and quickly than existing approaches.
In smart cities, fire can have disastrous effects, destroying property and putting residents' lives in danger, making it difficult to identify fire in real time because of the accuracy and speed constraints of traditional fire detection techniques. To address this issue, an accurate and cost-effective system that can be used in almost any fire detection scenario was developed. A CNN was used to analyze live video from a fire monitoring system to identify fire. An object identification model for deep learning called You Only Look Once (YOLOv8) was used to detect fire. To identify and alert videos from CCTV footage, a dataset of video frames with flames is used. After pre-processing the data, CNN is used to build a Machine Learning (ML) model. The methodology adopted in this study demonstrated the ability to adjust to various situations.
Due to the increased awareness of lung cancer, researchers have created many algorithms that can recognise the disease in its early stages using a variety of Machine Learning (ML) techniques. Clinicians can manage incidental or screen-found ambiguous pulmonary nodules with the help of machine learning-based models for lung cancer prediction. Such methods might be able to lower the variability in nodule classification, enhance decision making, and eventually decrease the proportion of benign nodules that do not need to be followed. This study proposes a novel lung cancer detection method based on Magnetic Resonance Imaging (MRI). Using ML to classify features in MRI scans, this technology is useful for the early detection of lung cancer. The performance was further enhanced using featureselection methodologies. The images were divided into segments using the FBSO feature selection method, and deep learning techniques were used to analyze the three standard datasets, S1, S2 and S3. In this investigation, 98.9% classifier optimality and 96.7% accuracy were attained. This new approach demonstrated excellent dependability and was found to be the most effective classifier system compared with previous studies.
Medical imaging is fundamental to modern precision medicine and the analysis of complex image data requires sophisticated techniques. This review focuses on recent deep-learning techniques for medical image analysis across existing modalities such as X-rays, MRI, CT and ultrasound. The aim of this study was to review the current state-of-the-art methods, highlight proven applications in precision diagnostics and prognosis, analyze key challenges, and identify promising future directions for this rapidly advancing field. Convolutional Neural Networks (CNNs) enable transformative improvements in tasks such as classification, segmentation and detection compared with prior approaches. A wide range of real-world applications across diagnostic, interventional, prognostic and pharmaceutical settings has been presented. The salient challenges concerning model interpretability, multimodal integration, algorithmic robustness, workflow integration, and responsible ethical deployment were discussed. This synergy between medical imaging and artificial intelligence continues to unlock abundant clinically relevant insights latent in images and transforms datadriven precision medicine for patient benefit.
Generative Adversarial Networks (GANs) have emerged as powerful frameworks for generating realistic and diverse data samples in various domains including computer vision, natural language processing and audio synthesis. In this study, a comprehensive overview of GANs, their key components, the training process, and the evolution of the field are presented. Different variations in GAN architectures and their applications across domains are discussed. Furthermore, the challenges and open research directions in GANs, their stability, mode collapse, evaluation metrics, and ethical considerations are analysed. The aim of this review is to provide a comprehensive understanding of GANs and their strengths, limitations, and potential future developments.
