Fall detection and notification systems play an important role in our daily life, since human fall is a major health concern for many communities in today's aging population. There are different approaches used in developing human fall detection systems for elderly and people with special needs such as disable. The three basic approaches include some sort of wearable, non-wearable ambient sensor and vision based systems. This paper proposes a human fall detection system based on the velocity and position of the subject, extracted from Microsoft Kinect Sensor. Initially the subject and floor plane are extracted and tracked frame by frame. The tracked joints of the subject are then used to measure the velocity with respect to the previous location. Fall detection is confirmed using the position of the subject to see if all the joints are on the floor after an abnormal velocity. From the experimental results obtained, our system was able to achieve an average accuracy of 93.94% with a sensitivity of 100% and specificity of 91.3%.

Unintentional falls are a major public health concern for many communities, especially with aging populations. There are various approaches used to classify human activities for fall detection. Related studies have employed wearable, non-invasive sensors, video cameras and depth sensor-based approaches to develop such monitoring systems. The proposed approach in this study uses a depth sensor and employs a unique procedure which identifies the fall risk levels to adapt the algorithm for different people with their physical strength to withstand falls. The inclusion of the fall risk level identification, further enhanced and improved the accuracy of the fall detection. The experimental results showed promising performance in adapting the algorithm for people with different fall risk levels for fall detection.

Given the significant impact of the UN 2015 agenda on sustainable development, the search for eco-friendly and efficient energy solutions has intensified. The renewable based hybrid systems have emerged as feasible solution in this context. This paper explores the potential of utilizing a hybrid energy system (HES) from a distinctive perspective — addressing the challenges of load shedding at the distribution level. The HES configuration in this investigation includes photovoltaic (PV) array, wind turbines (WT), battery storage unit (BSU) and diesel generator system. The research involves simulations, optimization, and sensitivity analysis for a residential community in the southwestern part of Pakistan, which frequently experiences load shedding. Grasshopper optimization algorithm (GOA) is applied to simultaneously minimize the levelized electricity cost (LEC), payback period (PBP), and loss of power supply probability (LPSP). Simulations compare different HES configurations based on the estimation of local renewable energy potential and the load shedding schedule of the utility company. The optimal solution, with $N_{PV}=110$ , $N_{WT}=2$ , and $N_{BSU}=16$ , was selected, resulting in a minimum LEC of 6.64 cents/kWh and a PBP of 7.4 years. These results are validated using the particle swarm optimization algorithm (PSO). The performance of HES is benchmarked against conventional solutions, such as standalone diesel generators, battery-based uninterruptible power supplies (UPS), and combinations of generators and UPS. The findings demonstrate the significant superiority of HES over conventional solutions in terms of reduced LEC, shorter PBP and reduced carbon emissions. Finally, the sensitivity analysis examines the impact of varying component prices, feed-in tariff rates, meteorological conditions, and load demand variations on the LEC and PBP of the HES.

Hand gestures recognition system has massive applications which are mainly utilized in robotics and computer vision specially to control Unmanned Aerial Vehicles (UAV). These methods bypass the presence of electronic control to UAVs and provide an ease to the operators. In this paper, we present a method for 3D hand gestures segmentation and classification by combining MASK-RCNN with Grass Hopper Optimization. We created a private 3D and RGB hand gestures dataset using Intel Kinetic and Intel Real sense d435i camera, then proposed a model for RGB hand gestures to estimate the key points using human kinematics, the key points later then utilize to get the best degree of freedom (DoF). The grass hopper optimization besides minimum distance function was applied to achieve the finest deep features from the 3D hand gestures dataset. The ResNet50 network is used as the backbone to calculate the Overlap Coefficient (OC) for segmentation and the ResNet50, ResNet101 networks to calculate the classification for 3D hand gestures. The classification accuracy achieved on the private dataset is 99.05% and 99.29% on public Microsoft Kinect and Leap Motion dataset where the OC are 88.16%. and 88.19% respectively.

The false-positive breast cancer cases detected by radiologists and Computer-aided Detection (CAD) systems increase the medical cost and patient discomfort due to the unnecessary breast biopsies. These available CAD systems were developed using traditional machine learning techniques for breast cancer diagnosis. A noteworthy progress is happening in cancer diagnosis after the introduction of deep learning in Convolutional Neural Networks (CNNs) for CAD development. This paper compares the performance of three pre-trained Residual Networks (ResNets), i.e., ResNet18, ResNet50, and ResNet101 with increased image input layer size of $512\times 512\times 3$ for the classification of the pre-processed whole mammograms into normal, benign, and malignant categories. INbreast dataset was pre-processed and then these pre-processed whole breast images were segregated into three categories based on the ground truths. Original and modified networks were developed by replacing the last three layers of the selected ResNets to match the output category along with the image input layer. Data augmentation and transfer learning were applied to overcome the overfitting issue due to smaller dataset. The developed models were tested and the attained training and testing accuracies, sensitivity, and specificity were compared to evaluate their performances. It was observed that ResNet50 with an image input layer of size $512\times 512\times 3$ provided best results after five-fold training and the test accuracy was 79.27% with the average sensitivity and specificity of 0.76, and 0.89, respectively for three categories. This experimental work is significant as it proves that the increased image input layer size has a considerable effect in classifying the whole mammograms. Further development will be done with a balanced dataset and other pre-trained deep networks will also be tried.

Hand detection and tracking are key components in many computer vision applications, including hand pose estimation and gesture recognition for human–computer interaction systems, virtual reality, and augmented reality. Despite their importance, reliable hand detection in cluttered scenes remains a challenge. This study explores the use of deep learning techniques for fast and robust hand detection and tracking. A novel algorithm is proposed by combining the Kernelized Correlation Filter (KCF) tracker with the Single-Shot Detection (SSD) method. This integration enables the detection and tracking of hands in challenging environments, such as cluttered backgrounds and occlusions. The SSD algorithm helps reinitialize the KCF tracker when it fails or encounters drift issues due to sudden changes in hand gestures or fast movements. Testing in challenging scenes showed that the proposed tracker achieved a tracking rate of over 90% and a speed of 17 frames per second (FPS). Comparison with the KCF tracker on 17 video sequences revealed an average improvement of 13.31% in tracking detection rate (TRDR) and 27.04% in object detection error (OTE). Additional comparison with MediaPipe hand tracker on 10 hand gesture videos taken from the Intelligent Biometric Group Hand Tracking (IBGHT) dataset showed that the proposed method outperformed the MediaPipe hand tracker in terms of overall TRDR and tracking speed. The results demonstrate the promising potential of the proposed method for long-sequence tracking stability, reducing drift issues, and improving tracking performance during occlusions.