Clinical support systems are affected by the issue of high variance in terms of chronic disorder prognosis. This uncertainty is one of the principal causes for the demise of large populations around the world suffering from some fatal diseases such as chronic kidney disease (CKD). Due to this reason, the diagnosis of this disease is of great concern for healthcare systems. In such a case, machine learning can be used as an effective tool to reduce the randomness in clinical decision making. Conventional methods for the detection of chronic kidney disease are not always accurate because of their high degree of dependency on several sets of biological attributes. Machine learning is the process of training a machine using a vast collection of historical data for the purpose of intelligent classification. This work aims at developing a machine-learning model that can use a publicly available data to forecast the occurrence of chronic kidney disease. A set of data preprocessing steps were performed on this dataset in order to construct a generic model. This set of steps includes the appropriate imputation of missing data points, along with the balancing of data using the SMOTE algorithm and the scaling of the features. A statistical technique, namely, the chi-squared test, is used for the extraction of the least-required set of adequate and highly correlated features to the output. For the model training, a stack of supervised-learning techniques is used for the development of a robust machine-learning model. Out of all the applied learning techniques, support vector machine (SVM) and random forest (RF) achieved the lowest false-negative rates and test accuracy, equal to $99.33\%$ and $98.67\%$, respectively. However, SVM achieved better results than RF did when validated with 10-fold cross-validation.

Activity recognition is the process of continuously monitoring a person’s activity and movement. Human posture recognition can be utilized to assemble a self-guidance practice framework that permits individuals to accurately learn and rehearse yoga postures without getting help from anyone else. With the use of deep learning algorithms, we propose an approach for the efficient detection and recognition of various yoga poses. The chosen dataset consists of 85 videos with 6 yoga postures performed by 15 participants, where the keypoints of users are extracted using the Mediapipe library. A combination of Convolutional Neural Network (CNN) and Long Short-Term Memory (LSTM) has been employed for yoga pose recognition through real-time monitored videos as a deep learning model. Specifically, the CNN layer is used for the extraction of features from the keypoints and the following LSTM layer understands the occurrence of sequence of frames for predictions to be implemented. In following, the poses are classified as correct or incorrect; if a correct pose is identified, then the system will provide user the corresponding feedback through text/speech. This paper combines machine learning foundations with data structures as the synergy between these two areas can be established in the sense that machine learning techniques and especially deep learning can efficiently recognize data schemas and make them interoperable.

The swift advancement of electric vehicle (EV) technology enhances the focus on sustainable energy storage and underscores the crucial significance of lithium-ion batteries. This research primarily presents the techniques of forecasting the Remaining Useful Life (RUL) of this battery using advanced Machine Learning (ML) methods such as Random Forest (RF) and Support Vector Machine (SVM). This research centres around the thorough preprocessing of a detailed dataset received from the NASA Ames Prognostics Center of Excellence. The One-way ANOVA method is employed to find the optimum set of features. The exhaustive hyperparameter-tuning (HPT) was performed to boost the performance of the ML models. An important component of this study is its pragmatic methodology, which considered real-time variables such as temperature changes and usage cycles to analyse the effect on battery capacity (cap). The proposed system helped to understand the behaviours of battery deterioration trends more comprehensively. The effectiveness of the system is decided based on the R2 score and Mean Squared Error (MSE). The RF model has shown R2 score of 0.83 and MSE of 1.67. The result enhances lithium-ion battery safety and efficiency by establishing new predictive models. Thus, it provides a better battery management system for electric vehicles. As a result, it promotes the development of more sustainable and economical energy solutions.

The COVID-19 pandemic has posed significant challenges in accurately diagnosing the disease, as severe cases may present symptoms similar to pneumonia. Real-Time Reverse Transcriptase Polymerase Chain Reaction (RT-PCR) is the conventional diagnostic technique; however, it has limitations in terms of time-consuming laboratory procedures and kit availability. Radiological chest images, such as X-rays and Computed Tomography (CT) scans, have been essential in aiding the diagnosis process. In this research paper, we propose a deep learning (DL) approach based on Convolutional Neural Networks (CNNs) to enhance the detection of COVID-19 and its variants from chest X-ray images. Building upon the existing research in SARS and COVID-19 identification using AI and machine learning techniques, our DL model aims to extract the most significant features from the X-ray scans of affected individuals. By employing an explanatory CNN-based technique, we achieved a promising accuracy of up to $97\%$ in detecting COVID-19 cases, which can assist physicians in effectively screening and identifying probable COVID-19 patients. This study highlights the potential of DL in medical imaging, specifically in detecting COVID-19 from radiological images. The improved accuracy of our model demonstrates its efficacy in aiding healthcare professionals and mitigating the spread of the disease.

This paper proposes a novel technique combining the FisherFace-based emotion recognition and the biometric security using the iris features for smart homes. The focus of smart homes is more inclined towards providing comfort to residents rather than treating energy as the foremost concern. The proposed emotion-based smart home system can refresh the user’s mood by detecting a real-time facial expression and adjusting the house environment - lighting, air-conditioning, and music system, accordingly. It is additionally equipped with biometric (iris) security. The unique features are the pattern of ridges in the iris and the pattern made by nerves on the sclera. The Canny edge detection is used to find the ridges along with pseudo coloring processing. The proposed iris bio-metrics security rests on the unique features obtained from the ridges of the iris. Resident authentication is provided through iris recognition, following which emotion recognition occurs. This model presents iris recognition through modified linear binary patterns and Daugman sheet conversion. The face extraction from a live video occurs using the Haar xml files, which retrieve the frontal face. Face emotions are then detected through edge extraction by applying Sobel filters and a combination of several steps - composite mask and eigenvectors followed by FisherFace recognition. A combination of these techniques produces a higher accuracy and recognition rate.