Cardiovascular disease (CVD) is a prevalent and serious condition causing a significant global mortality rate. According to the World Health Organization (WHO), in 2022, CVD claimed the lives of approximately 19.1 million people, accounting for 33% of global fatalities. ECG is widely used for automatic detection of CVD using traditional machine learning; however, it is usually difficult to select optimal features. Addressing this issue, a scalable machine learning-based architecture is proposed for early CVD detection based optimal feature selection. This architecture aims to revolutionize healthcare by enabling timely diagnosis and treatment, reducing CVD-related fatalities. Comprising data collection, storage, and processing components, the system employs machine learning classifiers to predict patients’ heart conditions. Initially feature are extracted from ECG signals then feature selection techniques like FCBF, MrMr, and relief, along with PSO-optimization are used to select optimal features. Extra Tree and Random Forest classifiers trained on the selected features have achieved notable performance rates with accuracy of 100% respectively. Furthermore, a comparison of the proposed method with state of the art on both small and large dataset is provided. The proposed system holds potential to revolutionize patient care and substantially lower CVD-related mortality, enhancing the quality of life for affected individuals. In summary, this architecture offers a promising solution to the pressing issue of CVD and paves the way for advanced healthcare systems.

Driver’s stress detection is a critical research area that helps reduce the likelihood of traffic accidents and driver’s health complexities due to prolonged stress. Previous work in this area is heavily based on traditional machine learning models that classify the driver’s stress levels using handcrafted features extraction techniques. Extracting the best features using these approaches is always a challenging task. Recently, deep learning techniques have emerged for constructing reliable features automatically and classifying the classes with high accuracy. However, large deep learning models face gradient exploding or vanishing problems. Moreover, acquiring a large dataset for training an entire network from scratch is also a challenging task. This paper is based on the deep transfer learning technique to avoid these problems and to reduce computational cost and time. Seven models are proposed for real-world driver’s stress levels detection using Electrocardiogram (ECG) signals. Different Convolutional Neural Network (CNN)-based pre-trained networks are used to classify the driver’s three stress levels. The time-frequency ECG components for the three stress levels are obtained as scalogram images using a normalized Continuous Wavelet Transform (CWT) filter bank and Morse wavelet. Results show that Model 5 based on Xception outperforms the GoogLeNet, DarkNet-53, ResNet-101, InceptionResNetV2, DenseNet-201, and InceptionV3 based models by 11.32%, 11.32%, 9.45%, 7.54%, 5.66%, and 1.88% respectively and achieves 98.11% overall validation accuracy. Ranking estimation using fuzzy logic approach shows that Xception based Model 5 achieves the highest rank for driver’s high and medium stress levels, while DenseNet-201 based Model 4 achieves the highest rank for low-stress level detection among the other models.

Accuracy is the vital indicator in location estimation used in many scenarios, such as warehousing, tracking, monitoring, security surveillance, etc., in a wireless sensor network (WSN). The conventional range-free DV-Hop algorithm uses hop distance to estimate sensor node positions but has limitations in terms of accuracy. To address the issues of low accuracy and high energy consumption of DV-Hop-based localization in static WSNs, this paper proposes an enhanced DV-Hop algorithm for efficient and accurate localization with reduced energy consumption. The proposed method consists of three steps: first, the single-hop distance is corrected using the RSSI value for a specific radius; second, the average hop distance between unknown nodes and anchors is modified based on the difference between actual and estimated distances; and finally, the least-squares approach is used to estimate the location of each unknown node. The proposed algorithm, named Hop-correction and energy-efficient DV-Hop (HCEDV-Hop), is executed and evaluated in MATLAB to compare its performance with benchmark schemes. The results show that HCEDV-Hop improves localization accuracy by an average of 81.36%, 77.99%, 39.72%, and 9.96% compared to basic DV-Hop, WCL, improved DV-maxHop, and improved DV-Hop, respectively. In terms of message communication, the proposed algorithm reduces energy usage by 28% compared to DV-Hop and 17% compared to WCL.

Parkinson’s disease (PD) Dysgraphia is a disorder that affects most PD patients and is characterized by handwriting anomalies caused mostly by motor dysfunctions. Several effective ways to quantify PD dysgraphia analysis have been used, including online handwriting processing. In this research, an integrated approach, using a combination of a convolutional neural network (CNN) and long short-term memory (LSTM) layers along with a Random Forest (RF) classifier, is proposed for dysgraphia classification. The proposed approach uses uniform and normal distributions to randomly initialize the weights and biases of the CNN and LSTM layers. The CNN-LSTM model predictions are paired with the RF classifier to enhance the model’s accuracy and endurance. The suggested method shows promise in identifying handwriting symbols for those with dysgraphia, with the CNN-LSTM model’s accuracy being improved by the RF classifier. The suggested strategy may assist people with dysgraphia in writing duties and enhance their general writing skills. The experimental results indicate that the suggested approach achieves higher accuracy.

Muscle fibers act as a source of force in the human body. The joint actions occur due to contraction of these muscle fibers controlled by central nervous system (CNS). Surface electromyography (EMG) signals produced during any muscular activity can play a vital role in measuring the muscular force under non-fatigued and fatigued conditions. Due to nonlinear, irregular, and continuous characteristics, sEMG signals need to be preprocessed to estimate muscle force correctly. The upper limb Biceps and Triceps muscles controls the movement of the elbow joints. Any disorder in this muscle can lead to difficulty in bending the elbow, turning palms in and out, and movement of arms. This research paper proposes different deep learning-based sEMG-to-force estimation models. The proposed models estimate the force from the sEMG signals acquired from the Biceps and Triceps muscles during maximal and submaximal contractions. The proposed deep learning models are based on hybrid 1D Convolutional Neural Network-Long Short-term Memory (CNN-LSTM) and 1D Convolutional Neural Network-Gated Recurrent Unit (CNN-GRU) approaches along with self-attention mechanism. The dataset contains raw sEMG signals acquired from the human arm’s Biceps and Triceps muscles. These signals are first preprocessed to remove DC offset and high frequency noises. The models are then trained with the pre-processed signals. Results of the proposed deep learning models are compared with the conventional machine learning algorithms including Support Vector regression (SVR) and Gaussian process regression (GPR) algorithms. Different performance metrics including Mean Square Error (MSE), Root Mean Square Error (RMSE), Mean Absolute Error (MAE) and Coefficient of Determination (R ² ) are utilized to measure the performance of the models based on the predicted and actual force. Results shows that the proposed CNN-LSTM and CNN-GRU models achieved better performance (MSE: 0.001, RMSE: 0.023, MAE: 0.017, and R ² : 0.987) and (MSE: 0.001, RMSE: 0.025, MAE: 0.019, and R ² : 0.985), respectively, as compared to GPR (MSE: 0.080, RMSE: 0.282, MAE: 0.162, and R ² : 0.856) and SVR (MSE: 0.008, RMSE: 0.082, MAE: 0.067, and R ² : 0.482). The proposed research study is helpful in developing a rehabilitation control system beneficial for people suffering with muscular disorder.

Document Image Analysis (DIA) is one of the research areas of Artificial Intelligence (AI) that converts document images into machine-readable codes. In DIA systems, Optical Character Recognition (OCR) plays a key role in digitizing document images. The output of an OCR system is further used in many applications including, Natural Language Processing (NLP), Sentiment Analysis, Speech Recognition, and Translation Services. However, standard datasets are an essential requirement for the development, evaluation and comparison of different text recognition techniques. Pashto is one of such low resource languages that lacks availability regarding standard dataset of handwritten text. This paper therefore, addresses the unavailability of standard dataset for the Pashto handwritten text by developing a dataset named Pashto Handwritten Text Imagebase (PHTI). The PHTI is created by collecting handwritten samples from diverse genre of the Pashto language including poetry, religion, short stories, articles, novels, sports, culture and news. The dataset consists of 4,000 scanned images, written by 400 writers including 200 males and 200 females. These 4,000 images are further segmented into 36,082 text-line images. Each text-line image is annotated/ transcribed with UTF-8 codecs. The dataset can be used for many deep learning-based applications including, text recognition, skew detection, gender classification and age-groups classification.