Wasit Journal of Computer and Mathematics Science

the Haar Cascade Classifier is a popular technique for object detection that uses a machine-learning approach to identify objects in images and videos. In the context of face detection, the algorithm uses a series of classifiers that are trained on thousands of positive and negative images to identify regions of the image that may contain a face. The algorithm is a multi-stage process that involves collecting training data, extracting features, training the classifiers, building the cascade classifier, detecting faces in the test image, and post-processing the results to remove false positives and false negatives. The algorithm has been shown to be highly accurate and efficient for detecting faces in images and videos, but it has some limitations, including difficulty in detecting faces under challenging lighting conditions or when the faces are partially occluded. Overall, the Haar Cascade Classifier algorithm remains a powerful and widely-used tool for face detection, but it is important to carefully evaluate its performance in the specific context of each application and consider using more advanced techniques when necessary.

At the level of both individuals and companies, Wireless Sensor Networks (WSNs) get a wide range of applications and uses. Sensors are used in a wide range of industries, including agriculture, transportation, health, and many more. Many technologies, such as wireless communication protocols, the Internet of Things, cloud computing, mobile computing, and other emerging technologies, are connected to the usage of sensors. In many circumstances, this contact necessitates the transmission of crucial data, necessitating the need to protect that data from potential threats. However, as the WSN components often have constrained computation and power capabilities, protecting the communication in WSNs comes at a significant performance penalty. Due to the massive calculations required by conventional public-key and secret encryption methods, information security in this limited context calls for light encryption techniques. In many applications involving sensor networks, security is a crucial concern. On the basis of traditional cryptography, a number of security procedures are created for wireless sensor networks. Some symmetric-key encryption techniques used in sensor network setups include AES, RC5, SkipJack, and XXTEA. These algorithms do, however, have several flaws of their own, including being susceptible to chosen-plaintext assault, brute force attack, and computational complexity.

At the level of both individuals and companies, Wireless Sensor Networks (WSNs) get a wide range of applications and uses. Sensors are used in a wide range of industries, including agriculture, transportation, health, and many more. Many technologies, such as wireless communication protocols, the Internet of Things, cloud computing, mobile computing, and other emerging technologies, are connected to the usage of sensors. In many circumstances, this contact necessitates the transmission of crucial data, necessitating the need to protect that data from potential threats. However, as the WSN components often have constrained computation and power capabilities, protecting the communication in WSNs comes at a significant performance penalty. Due to the massive calculations required by conventional public-key and secret encryption methods, information security in this limited context calls for light encryption techniques. In many applications involving sensor networks, security is a crucial concern. On the basis of traditional cryptography, a number of security procedures are created for wireless sensor networks. Some symmetric-key encryption techniques used in sensor network setups include AES, RC5, SkipJack, and XXTEA. These algorithms do, however, have several flaws of their own, including being susceptible to chosen-plaintext assault, brute force attack, and computational complexity.

Content-based filtering is a recommendation algorithm that analyzes user activity and profile data to provide personalized recommendations for content that matches a user's interests and preferences. This algorithm is widely used by social media platforms, such as Facebook and Twitter, to increase user engagement and satisfaction. The methodology of content-based filtering involves creating a user profile based on user activity and recommending content that matches the user's interests. The algorithm continually updates and personalizes the recommendations based on user feedback, and incorporates strategies to promote diversity and serendipity in the recommendations. While content-based filtering has some limitations, it remains a powerful tool in the arsenal of social media platforms, offering efficient content discovery and personalized user experiences at scale.

Emotion recognition is a hot topic that has received a lot of attention and study,owing to its significance in a variety of fields, including applications needing human-computer interaction (HCI). Extracting features related to the emotional state of speech remains one of the important research challenges.This study investigated the approach of the core idea behind feature extraction is the residual signal of the prediction procedure is the difference between the original and the prediction .hence the visibility of using sets of extracting features from speech single when the statistical of local features were used to achieve high detection accuracy for seven emotions. The proposed approach is based on the fact that local features can provide efficient representations suitable for pattern recognition. Publicly available speech datasets like the Berlin dataset are tested using a support vector machine (SVM) classifier. The hybrid features were trained separately. The results indicated that some features were terrible. Some were very encouraging, reaching 99.4%. In this article, the SVM classifier test results with the same tested hybrid features that published in a previous article will be presented, also a comparison between some related works and the proposed technique in speech emotion recognition techniques.

This article provides an overview of steganography and its use for hiding images in other images. Steganography is a technique that allows users to hide information in plain sight, making it difficult for unauthorized parties to detect or access the information. Spatial domain steganography is a popular technique for hiding images within other images, where the least significant bits of the cover image are modified to embed the secret image. The article discusses the advantages of steganography and its use in various applications such as digital watermarking and secure communication. The article also provides an overview of the various techniques used for spatial domain steganography, and how these techniques can be implemented using programming languages such as Python. Finally, the article concludes by emphasizing the importance of using steganography responsibly and ethically.

The healthcare sector has traditionally been an early use of technological progress and has achieved significant advantages, especially in the field of machine learning like the prediction of diseases. The COVID-19 epidemic is still having an impact on every facet of life and necessitates a fast and accurate diagnosis. Early detection of COVID-19 is exceptionally critical to saving the lives of human beings. The need for an effective, rapid, and precise way to reduce consultants' workload in diagnosing suspected cases has emerged. This paper presents a proposed model that aims to design and implement an automated model to predict COVID-19 with high accuracy in the early stages. The dataset used in this study considers an imbalanced dataset and converted to a balanced one using Synthetic Minority Over Sampling Technique (SMOTE). Filter-based feature selection method and many machine learning algorithms such as K-Nearest Neighbor, Support Vector Machine, Decision Tree, Logistic Regression, and Random Forest (RF) is used in this model. Since the best classification result was achieved by using the RF algorithm, and this algorithm was optimized by tuning the hyperparameters. The optimized RF enhanced the accuracy from 98.0 to 99.5.

Health and safety concerns have grown in recent years due to the increasing frequency bands and the demand for wireless communication apparatus. Electromagnetic (EM) radiation breakthrough from Radio frequency (RF) into the human head is an issue that needs to be addressed. Radiation from RF sources can cause serious biological hazards inside the human body. This study measures the average Specific Absorption Rate in a 7-year-old child's head tissues using the ANSYS HFSS software and varying the distance from the source to the antenna in order to address these issues. SAR levels of phones sold should be below certain standard limits. We have used an internal antenna of a mobile phone It's a planar inverted F-antenna (PIFA) with a connected feeding structure.

Due to its capacity to make wise decisions, deep learning has become extremely popular in recent years. The current generation of deep learning, which heavily rely centralized servers, are unable to offer attributes like operational transparency, stability, security, and reliable data provenance. Additionally, Single point of failure is a problem that deep learning designs are susceptible since they need centralized data to train them. We review the body of research on the application of deep learning to blockchain. We categorize and arrange the literature for developing topic taxonomy based their criteria: Application domain, deep learning-specific consensus mechanisms, goals for deployment and blockchain type. To facilitate meaningful discussions, we list the benefits and drawbacks of the most cutting-edge blockchain-based deep learning frameworks.

Pose estimation involves estimating the position and orientation of objects in a 3D space, and it has applications in areas such as robotics, augmented reality, and human-computer interaction. There are several methods for pose estimation, including model-based, feature-based, direct, hybrid, and deep learning-based methods. Each method has its own strengths and weaknesses, and the choice of method depends on the specific requirements of the application, object being estimated, and available data. Advancements in computer vision and machine learning have made it possible to achieve high accuracy and robustness in pose estimation, allowing for the development of a wide range of innovative applications. Pose estimation will continue to be an important area of research and development, and we can expect to see further improvements in the accuracy and robustness of pose estimation methods in the future.

The first sources of direct current (DC) were invented, DC machines were one of the first types of electro-mechanical machines used. DC machines are more advantageous over AC machines as regards to Speed regulation and versatility. A DC motor is an electrical actuator with a lot of control that is used in a lot of applications, like robotic manipulators, guided vehicles, steel rolling mills, cutting tools, overhead cranes, electrical traction, and other applications. Due to their speed-torque characteristics and ease of control, DC motors are utilized extensively in industries for demanding variable speed applications. In terms of controller design and implementation, the process control industry has seen numerous advancements over the past two decades. In the industry, there is a great demand for automatic controllers that can respond quickly and accurately to perform precise tasks. The feedback loop is an essential component of system control that must be utilized in order to achieve the desired performance in the majority of systems. Numerous control strategies have been developed for various feedback control systems in order to achieve rapid system dynamic response. Controls in a drive system are crucial if the reference speed is to be accurately and quickly tracked, with little or no steady-state error and as little overshoot as possible. This paper presents the Systematic literature review that was conducted as covers pertinent established concepts and techniques related to the DC motor speed control system design, for applications that require actuators with accurate speed characteristics. Simulation and real time implementation results employed for DC motor speed control systems in various literature are analysed and discussed.

The majority of screening instruments for autism spectrum disorder (ASD) rely on subjective questions given to caregivers. Although behavioral observation is more objective, it is also more expensive, takes longer to complete, and requires a high level of competence. Therefore, there is still a dire need to create workable, scalable, and trustworthy systems that can identify ASD risk behaviors. Since there are no known causes of autism, early detection and intense therapy can significantly alter the behavior of children and people with the disorder. Artificial intelligence has made this possible, saving many lives in the process. Utilizing biological pictures, autism spectrum disorder (ASD) can be defined as a mental illness type which can be identified. The neurological condition known as ASD is linked to brain development and affects later appearance of the flask framework, a convolutional neural network (CNN) with transfer learning, and physical impression of the face. Xception, Visual Geometry Group Network (VGG16) the classification job was carried out using the previously trained models. 2,940 face photos made up the dataset utilized for the testing of those models, which was obtained via Kaggle platform. Outputs of the 3 models of deep learning have been evaluated with the use of common measures of assessment, including accuracy, sensitivity and specificity. With a 91% accuracy rate, Xception model had the greatest results. And theVGG16 models came next with (75%).

Monitoring environmental conditions can be done effectively with the help of the Internet of Things (IOT) sensor network. Massive data generated by IOT sensor networks presents technological hurdles in terms of storage, processing, and querying. A Hadoop cloud platform is suggested as a fix for the issue. The data processing platform makes it possible for one node's work to be shared with others employing the time and workload genetic algorithm (TWLGA), which lowers the risk of software and hardware compatibility while simultaneously increasing the efficiency of a single node. For the experiment, a Hadoop cluster platform employing the TWLGA scheduling algorithm is built, and its performance is assessed. The outcomes demonstrate that processing huge volumes of data from the IOT sensor network is acceptable for the Hadoop cloud platform .

Abstract algebra is one of the influential branches in the field of pure Mathematics. This field concentrate on the study of the algebraic structures and discussed the relationships among them. Many studies have been presented various types of algebraic structures some of which independently and some others have been constructed via extending form other algebraic structure in order to investigate some of their properties. In this paper, we established an algebraic structure namely BDM-Algebras and studied some of its properties. Furthermore, we presented the 0-commtativity, sub-algebra and normal sub-algebra of a BDM-Algebras. In addition, we provided BDM-homomorphism and the kernel of BDM-homomorphism with some properties of them. Moreover, we introduced the quotient BDM-Algebras by using the notation of normal ideal of BDM-Algebras. Finally, we introduced the concept of the direct product of BDM-Algebras and some of its properties have been discussed. Some examples are given to illustrated the results.