Comparison of Artificial Neural Networks and Multiple Linear Regression Model in the Prediction of Near-Earth Objects Minimum Distance to the Earth
A Comparative Exploration of Activation Functions for Image Classification in Convolutional Neural Networks
Effects of the Covid-19 Pandemic on Anxiety Disorders: A Topic Modelling Study
Advancing Autism Spectrum Disorder Diagnosis through Ensemble Learning
Enhancing Chronic Kidney Disease Prediction Performance through Algorithm Fusion: A Combined KNN - SVM Approach
A Novel Approach using Machine Learning to Detect and Classify Rice Plant Diseases
The Technology Impacts and AI Solutions in Hospitality
Elevating Healthcare: The Synergy of AI and Biosensors in Disease Management
Machine Learning Solutions for the Healthcare Industry: A Review
RNN LSTM-Based Emotion Recognition using EEG Signals
Transforming Homestay Guest Experience with AI-Powered Design Tools
Intelligent System-Based Stun Gloves for Women's Protection
AI for the Detection of Neurological Condition: Parkinson's Disease & Emotions
A Novel Approach for Lung Cancer Detection using Deep Belief Networks
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
Near Earth Objects (NEO), commonly known as asteroids, are always moving in outer space. These objects could carry very important knowledge or harmful substances. Knowing the whereabouts of an asteroid makes observation easier since asteroids are moving and, in a limited time, an asteroid will pass the observable distance from the Earth. This study has compared the minimum distance prediction of near-earth objects and the Earth using artificial neural networks, machine learning, and multiple linear regression techniques. The models used in the study are the Multiple Linear Regression Model, Feed Forward Neural Network, and Support Vector Regression Model. The study has used a secondary dataset provided by the “Center for Near Earth Object Studies” (CNEOS) project of NASA. Using every method, two models were trained for each. Every model 1 contained all the variables and every model 2 contained three dependent variables. For model 2, dependent variables were reduced by the assumptions used in linear regression. Even though the linear assumptions were not used on the neural networks or machine learning algorithms, every model 2 showed a significant accuracy increase after variable reduction. Model performances were assessed by multiple prediction error values and R-squared values.
Activation functions play a crucial role in enabling neural networks to carry out tasks with increased flexibility by introducing non-linearity. The selection of appropriate activation functions becomes even more crucial, especially in the context of deeper networks where the objective is to learn more intricate patterns. Among various deep learning tools, Convolutional Neural Networks (CNNs) stand out for their exceptional ability to learn complex visual patterns. In practice, ReLu is commonly employed in convolutional layers of CNNs, yet other activation functions like Swish can demonstrate superior training performance while maintaining good testing accuracy on different datasets. This paper presents an optimally refined strategy for deep learning-based image classification tasks by incorporating CNNs with advanced activation functions and an adjustable setting of layers. A thorough analysis has been conducted to support the effectiveness of various activation functions when coupled with the favorable softmax loss, rendering them suitable for ensuring a stable training process. The results obtained on the CIFAR-10 dataset demonstrate the favorability and stability of the adopted strategy throughout the training process.
Psychological well-being was drastically affected due to changes in the social environment brought on by the COVID-19 pandemic. This study investigates how the COVID-19 pandemic affected individuals with anxiety disorders using narratives published on social media. Mid-pandemic and pre-pandemic datasets are collected and compiled from post titles published in the “r/Anxiety” subreddit, an internet community for people suffering from anxiety disorders. Then, a topic modelling pipeline based on clustering sentence embeddings in combination with sentiment analysis is applied to uncover trends in user narratives with the associated sentiment. In this study, three clustering algorithms, namely the Gaussian mixture model, spectral clustering, and k-means, were evaluated for their performance in clustering sentence embeddings using an internal evaluation method. The clusters formed reflected symptoms and types of anxiety disorders, demonstrating that unsupervised machine learning techniques, in particular topic modelling, can be used to detect mental health issues in social media data.
Autism Spectrum Disorder (ASD) diagnostics requires specialized clinical expertise, posing accessibility and affordability barriers for many. To widen the availability of precise screening, this paper examines ensemble machine learning models that combine multiple algorithms for improved accuracy and generalizability. Specifically, this paper compares the performance of K-Nearest Neighbor (KNN), Support Vector Machine (SVM), Naive Bayes, and a Voting Classifier that integrates Logistic Regression, Decision Trees, and KNN. The comparison was conducted on separate datasets of pediatric and adult ASD questionnaire responses. The ensemble model significantly outperforms individual techniques, achieving higher accuracy for both pediatrics and adults with balanced sensitivity and specificity maintained across groups, indicating the viability of accessible community-available screening to alleviate diagnostic bottlenecks. Before scale-up, further model optimization for interpretability and testing on more diverse multi-site data are warranted. Overall, findings demonstrate the feasibility of mobile distributed pre-screening systems leveraging optimized ensembles to predict ASD with high precision across ages, opening possibilities for explainable AI to lower costs and widen access compared to in-person evaluation.
Chronic Kidney Disease (CKD) is characterized by impaired kidney function and its ability to maintain overall health. It often progresses slowly over time and can lead to serious complications if left untreated. In order to prevent consequences such as hypertension, anemia, bone fragility, poor nutrition, and neurological dysfunction, early identification of chronic kidney disease (CKD) is imperative. Large-scale datasets are mined for insights in the healthcare industry that help with well-informed decision-making. Machine learning has applications in user authentication, fraud detection, and medical science, demonstrating its adaptability to a range of problems, including the management of chronic kidney disease. Numerous machine learning algorithms and data mining classification techniques are used in the context of forecasting chronic diseases like CKD. The purpose of this study is to create a novel decision-support system for forecasting chronic kidney disease. In this study, the execution times, accuracy, and precision of Random Forest and SVM-KNN fusion are compared. The results indicate that SVM-KNN performs better in prediction accuracy than Random Forest, providing important insights into machine learning methods for CKD prediction.
Agriculture is the most important sector of the Indian economy. Rice cultivation plays an important role in many regions of India. Most farmers in India are fully dependent on rice. Early detection of diseases in rice plants plays an important role in yielding more. This paper proposes a solution to detect and classify rice plant diseases too early using automatic image processing techniques. Automatic detection uses image segmentation and neural networks for classification of plant leaves. It takes the image as input and applies techniques to that image, like pre-processing and segmentation, and then the input is given to the convolutional neural network in order to classify the disease. Most Indian farmers are not well educated to detect the disease of the plant before it gets damaged, which results in less production. Rice production has the main role in Indian economics, so adequate efforts are needed to improve it.
With the help of technology and Artificial Intelligence (AI), the hospitality industry has undergone a considerable change that has affected both the guest experience and the efficiency of operations. Through chatbots and virtual assistants, AIdriven solutions have become essential for improving interactions with guests, making personalized suggestions, and making reservations easier. Predictive analytics, Robotic Process Automation (RPA), and facial recognition technologies have made operations more efficient by improving security, finding the best pricing strategies, and automating routine tasks. Dynamic pricing algorithms look at market conditions and changes in demand in real-time, which has changed how revenue management is done. Contactless technologies, which AI has made simpler, are crucial to keeping things clean. AI-powered surveillance has strengthened safety and security measures. HR analytics and internal communication chatbots help managers streamline work processes and get employees more involved. IoT sensors make predictive maintenance possible, preventing equipment from breaking down by predicting when it needs maintenance. When voice recognition technology is built into a room, guests can use their voices to control amenities. Data analytics and Customer Relationship Management (CRM) systems also use AI to learn about guests' likes, dislikes, habits, and feedback, which helps with targeted marketing and providing more personalized services. Using secondary data, this study concluded that the hospitality industry continues to use AI and technology, and the sector moves forward thanks to the positive feedback loop between new ideas and happy customers. The study's findings show how AI solutions have changed hospitality, including making things better for guests, making things run more smoothly, and always trying to provide the best service possible.
Biosensors integrated with artificial intelligence (AI) hold immense potential for transforming healthcare through rapid, automated diagnostics and precision therapeutics. This paper reviews the convergence of biosensing and AI towards developing smart biomedical systems. The fundamentals, historical evolution, and classification of biosensors are presented, highlighting key applications across infections, chronic illnesses, and environmental monitoring. Core AI concepts, including machine learning, neural networks, computer vision, and natural language processing, are discussed, along with their implementation to augment biosensor functionality, connectivity, point-of-care adoption, and laboratory automation. Promising research directions and real-world case studies applying AI-integrated biosensors for early diagnosis and drug delivery are discussed. The opportunities and challenges in advancing this synergistic technology are contemplated, underscoring the need for cross-disciplinary collaboration, clinical validation, ethical vigilance and supportive policy environments to successfully translate AI-biosensors into practical healthcare solutions.
