Coronary artery disease (CAD) causes millions of deaths worldwide every year. The earliest possible diagnosis is quite important, as in any diseases, for heart diseases causing such a large amount of death. The diagnosis processes have been more successful thanks to the recent studies in medicine and the rapid improvement in computer sciences. In this study, the goal is to employ machine learning methods to facilitate rapid disease diagnosis without the need to observe negative outcomes. The dataset utilized in this study was obtained from an IEEE DataPort data repository. The dataset consists of two classes. Firstly, new features have been produced by using the features in the dataset. Then, datasets that consist of multiple features have been created by using feature selection algorithms. Three models, specifically Support Vector Machines (SVM), the k-Nearest Neighbor algorithm (kNN), and Decision Tree ensembles (EDT), were trained using custom datasets. A hybrid model has been created and the performances have been compared with the other models by using these models. The best performance has been obtained from SVM and its seven performance criteria in order of accuracy, sensitivity, specificity, F- measurement, Kappa and AUC are 97.82, 0.97, 0.99, 0.98, 0.96 and 0.98%. In summary, when evaluating the performance of the constructed models, it has been demonstrated that these recommended models could aid in the swift prediction of coronary artery disease in everyday life.

Agriculture is one of the most prominent industries which guarantee food requirements and employment throughout the globe due to huge land availability, and atmospheric conditions. But nowadays, security of the available resources are the major concerns due to damage caused by objects inside the agriculture field. There are many traditional algorithms for object detection, but they are not very effective in terms of real time environments. Hence, a deep learning-based object detection model is generated by enhancing YOLOv3. The process involved firstly, k-means clustering was used to identify clusters, followed by modifying the convolutional neural network layers. Additionally, the batch and subdivision values of the actual YOLOv3 model were optimized under the darknet53 framework. The architecture was also configured to detect eleven classes of objects, ensuring that the model could identify a broad range of objects. The experimental results demonstrate that the Delta model achieved a remarkable increase in accuracy from 75.19% to 95.86%. In addition, the model outperformed other models in terms of precision(97%), recall(96%), F1_Score(96%), IoU(80.81%), and mAP(95.86%). Based on these findings, it can be concluded that the delta model offers superior detection capabilities and lower computational complexity compared to conventional methods used in the agriculture field.

The combination of visible and infrared images from different sensors can provide a more detailed and informative image. Visible images capture environmental details and texture, while infrared sensors can detect thermal radiation and create grayscale images that have high contrast. These images are useful for distinguishing between target and background in challenging conditions, such as at night or in inclement weather. When these two types of images are fused, they create high- contrast images with rich texture and target details. In this paper, an effective image fusion technique has been developed, which utilizes Latent Low Rank Representation (LatLRR) method that decomposes the source images into latent low rank and salient parts to capture common and unique information respectively. The proposed network design incorporates the dense network and VGG-19 architectures for deep feature extraction of latent low- rank and salient parts, that minimize distortion while maintaining crucial texture and details in the output. Weighted average fusion strategies are used to combine these latent low-rank and salient parts, and the resulting fused features are used for feature reconstruction to generate a fused low-rank and salient part. These parts are integrated to yield a fused image output. The proposed approach out performs existing state-of-the-art methods on both visual characteristics and objective evaluation metrics.

In the work, the software implementation of the face mask recognition system using the Viola-Jones method and fuzzy logic is performed. The initial images are read from digital video cameras or from graphic files. 
Detection of face, eye and mouth positions in images is performed using appropriate Haar cascades. The confidence of detecting a face and its features is determined based on the set parameters of Haar cascades.
Face recognition in the image is performed based on the results of face and eye detection by means of fuzzy logic using the Mamdani knowledge base. Fuzzy sets are described by triangular membership functions. Face mask recognition is performed based on the results of face recognition and mouth detection by means of fuzzy logic using the Mamdani knowledge base. Comprehensive consideration of the results of different Haar cascades in the detection of face, eyes and mouth allowed to increase the accuracy of recognition face and face mask.
The software implementation of the system was made in Python using the OpenCV, Scikit-Fuzzy libraries and Google Colab cloud platform. The developed recognition system will allow monitoring the presence of people without masks in vehicles, in the premises of educational institutions, shopping centers, etc. In educational institutions, a face mask recognition system can be useful for determining the number of people in the premises and for analyzing their behavior.

The detection of violent behavior in the public environment using video content has become increasingly important in recent years due to the rise of violent incidents and the ease of sharing and disseminating video content through social media platforms. Efficient and effective techniques for detecting violent behavior in video content can assist authorities with identifying potential hazards, preventing crimes, and promoting public safety. Violence detection can also help to mitigate the psychological damage caused by viewing violent content, particularly in vulnerable populations such as infants and victims of violence. We have proposed an algorithm to calculate new descriptors using the magnitude and orientation of optical flow (MOOF) in the video. Descriptors are extracted from MOOF based on four binary histograms each by applying various weighted thresholds. These descriptors are used to train Support Vector Machine (SVM) and classify the video as violent or nonviolent. The proposed algorithm has been tested on the publicly available Hockey Fight Dataset and Violent Flow dataset. The results demonstrate that the proposed descriptors outperform the state-of-the-art algorithms with an accuracy of 91.5% and 78.5% on the Hockey Fight and Violent Flow datasets, respectively.

Image enhancement technology is widely used to improve images and help radiologists make more accurate cancer diagnoses. In this research work presents an integrating approach for contrast enhancement followed by the segmentation of breast cancer from the mammogram images. The proposed method has been effectively utilized the three different algorithms such as differential Evolution (DE) Algorithm, Kernel Based Fuzzy C Means (KFCM) Clustering and Cuckoo Search Optimization (CSO) algorithm. Here an integrating approach introduced, called Optimized Kernel Fuzzy Adaptive Gamma Correction with Weighed Distribution (OKF-AGCWD) based Level Set Method. The performance of proposed method is enhanced over existing level set methods such as image and vision computing (IVC)-2010, IVC-2013, and Expert Systems with Applications (ESA) 2021. The performance metric parameters like F1_score, Sensitivity, Specificity and accuracy are considered to assess the quality of different methods. The simulation was performed on 16 distinct images from the RIDER mammography database. The experimental results were compared with existing level set approaches such as image and vision computing (IVC)2010, IVC2013 and expert systems and applications (ESA)2021 with respect to OKF-AGCWD. The proposed OKF-AGCWD with contextual level set method (CLSM) minimizes boundary leakage problem of mammogram segmented image and improves segmentation accuracy. 

Nowadays iris recognition has become a promising biometric for human identification and authentication. In this case, feature extraction from near-infrared (NIR) iris images under less-constraint environments is rather challenging to identify an individual accurately. This paper extends a texture descriptor to represent the local spatial patterns. The iris texture is first divided into several blocks from which the shape and appearance of intrinsic iris patterns are extracted with the help of block-based Local Binary Patterns (LBPb). The concepts of uniform, rotation, and invariant patterns are employed to reduce the length of feature space. Additionally, the simplicity of the image descriptor allows for very fast feature extraction. The recognition is performed using a supervised machine learning classifier with various distance metrics in the extracted feature space as a dissimilarity measure. The proposed approach effectively deals with lighting variations, blur focuses on misaligned images and elastic deformation of iris textures. Extensive experiments are conducted on the largest and most publicly accessible CASIA-v4 distance image database. Some statistical measures are computed as performance indicators for the validation of classification outcomes. The area under the Receiver Operating Characteristic (ROC) curves is illustrated to compare the diagnostic ability of the classifier for the LBP and its extensions. The experimental results suggest that the LBPb is more effective than other rotation invariants and uniform rotation invariants in local binary patterns for distant iris recognition. The Braycurtis distance metric provides the highest possible accuracy compared to other distance metrics and competitive methods.

UAVs play a crucial role in various applications, but their effective operation relies on precise and reliable positioning systems. Traditional positioning systems face challenges in delivering the required accuracy due to factors such as signal degradation, environmental interference, and sensor limitations. This study proposes the LeGNSS positioning subsystem, which integrates low Earth orbit (LEO) satellite network data with GPS and MEMS-based inertial systems, to enhance UAV positioning accuracy and reliability. The presented in this research LeGNSS system employs sophisticated algorithms for optimal data processing and filtering from various sources. Simulation results demonstrate a 9.02% improvement in positioning estimation accuracy compared to classic GPS/INS integration and a 26.4% improvement compared to the onboard GPS receiver. The integration of inertial and satellite positioning, corrective mechanisms, and optimized filtration has resulted in improved precision of trajectory computations, attenuation of positioning signal anomalies, and a significant decrease in INS inaccuracies. The proposed LeGNSS positioning system presents a solution for precise and reliable UAV positioning in a wide range of applications. By leveraging the unique advantages of LEO satellite networks and advanced data fusion techniques, this system pushes the boundaries of UAV positioning capabilities. The novel integration of multiple data sources and the use of adaptive error correction algorithms set a new standard for accuracy and robustness, paving the way for unprecedented capabilities in fields such as aerial surveying, precision agriculture, infrastructure monitoring, and emergency response. Analysing the impact of complex environmental factors on LeGNSS operation can provide insights into expanding the list of satellite systems or sensors to improve positioning accuracy, particularly in high-latitude regions. The findings of this study contribute to improving the accuracy, reliability, and resilience of UAV positioning systems, with applications in scientific polar research, geomatics data gathering, and other domains. The LeGNSS system has the potential to become a key feature for the next generation of autonomous aerial vehicles, unlocking efficiency, safety, and innovation across industries.