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
Emotion, a representation of the human state of mind, plays an important role in day-to-day human life and helps one make good decisions. A typical way to understand human emotion is by observing a person's facial expressions and modulation of speech, and it can be categorized as sad, angry, happy, fearful, and so on. Emotion recognition using Brain Computer Interface (BCI) systems is beneficial for patients suffering from paralysis, autism, and mental retardation who cannot express their emotions like regular people. In this paper, after analyzing several data mining algorithms and various Neural Network models such as Convolution Neural Networks (CNN), Recurrent Neural Networks (RNN), and the Bi-directional RNN it has been proposed that Recurrent Neural Network-Long Short-Term Memory (RNN-LSTM) based emotion recognition using Electroencephalography (EEG) signals provides a better result. The main purpose of this paper is to introduce models which can work better than the existing ones on the K-EmoCon dataset. The metrics used in this paper are valence and arousal. The proposed RNN-LSTM model achieves a valence accuracy of 69.85% and an arousal accuracy of 45.07%. This model improves the accuracy of emotion detection on the K-EmoCon dataset. This approach achieves 4% more accuracy when compared to existing models such as the Convolution-augmented Transformer.
In this paper, an attempt has been made to investigate the benefits of the Amalgamate Architecture Centric Software Development (AACSD) method through an experimental setup using Machine Learning techniques on an E-Commerce product recommender system. The system recommends products based on authorized user reviews. As part of this research, an Ensemble Dynamic Machine Learning Algorithm (EDMLA) was designed and developed with the integration of AACSD to improve performance quality. Performance was evaluated based on parameters such as sensitivity, specificity, and accuracy.
Artificial Intelligence (AI) is playing a very significant and prominent role in product development as per consumer requirements. It can provide valuable tools and equipment and assist designers in their work. Homestay design is a service sector component with regional and cultural overtones. Therefore, this study aims to analyze the design and production of homestays through AI in order to raise the standards of homestay establishments and encourage the growth of rural tourism. Exploratory Factor Analysis (EFA) is used to search for factors mainly responsible for designing any homestay establishment based on geographical location and local culture. Then confirmatory factor analysis (CFA) was applied to confirm how the explored factors are related and affect the homestay design through the structural model. According to the survey findings, visitors are more interested in the floor plan, type, and essence of homestays. The research and analysis in this paper found that people prefer the room layout and style of homestays, so when designing a homestay, it uses three-dimensional modeling technology to simulate the room layout and homestay style.
In recent times, the security of women and girls has become a major concern in urban areas in many countries. To assist with settling this issue, this paper proposes a Global Positioning System (GPS)-based Stun Gloves for women with many safety techniques. The basic operation of this prototype design is that whenever women detect danger, they can simply press the gadget's ON button. When the gadget becomes active, it will track the online position of women using GPS and send an emergency message using the Global System for Mobile Communications (GSM) to the currently enlisted portable number and the police control room. These security Stun Gloves have both an alarm and a shock provider circuit. The heartbeat rate and temperature are also shown on a connected Liquid Crystal Display (LCD). The heartbeat sensor can detect any unusual heart rate patterns and send the woman's current location via GPS to the rescue team or pre-registered mobile number in the form of a Short Message Service (SMS). Likewise, in the case of self-protection, this gadget is incorporated with a shock generator. This safety precaution can be taken by women in case of any emergency crisis, which a female can use against a person. The main benefit of the design of Stun Gloves is that this gadget is compact and easy to use by any woman.
Artificial Intelligence (AI) is widely applied by many researchers in the measurement and analysis of signals and images in clinical medicine and the biological sciences. The role of machine learning in processing biomedical signals and its applications in medicine and healthcare is huge, and it is now in a very advanced stage. Several types of biomedical signals have been analyzed by using Deep Learning (DL), Neural Networks (NN), and Artificial Intelligence on Electrocardiogram (ECG) and Electroencephalogram (EEG) signals by many researchers. Parkinson's disease (PD) is a neurodegenerative disorder that progresses over time and is characterized by rigidity, tremor, postural instability, and non-motor symptoms caused by the loss of dopaminergic neurons in the substantia nigra. This paper analyses the current state of the art of EEG analysis using AI techniques for Parkinson's disease detection and emotion detection.
Machine Learning (ML) has become an increasingly popular tool in the healthcare industry, providing solutions for a wide range of applications, from diagnosis and treatment to drug discovery and population health management. This paper summarizes the current state of Machine Learning in healthcare and highlights key trends and challenges in the field. Topics covered include deep learning algorithms for medical imaging, reinforcement learning for personalized treatment plans, and unsupervised learning for identifying patterns in large healthcare data sets. This paper also discusses the ethical and privacy implications of using Machine Learning in healthcare and the need for robust evaluation and validation of Machine Learning models. Overall, this paper demonstrates the potential of Machine Learning to revolutionize healthcare while also highlighting the need for further research and development in the field.
