The Applications of Discrete Wavelet Transform in Image Processing: A Review

This paper reviews the newly published works on applying waves to image processing depending on the analysis of multiple solutions. the wavelet transformation reviewed in detail including wavelet function, integrated wavelet transformation, discrete wavelet transformation, rapid wavelet transformation, DWT properties, and DWT advantages. After reviewing the basics of wavelet transformation theory, various applications of wavelet are reviewed and multi-solution analysis, including image compression, image reduction, image optimization, and image watermark. In addition, we present the concept and theory of quadruple waves for the future progress of wavelet transform applications and quadruple solubility applications. The aim of this paper is to provide a wide-ranging review of the survey found able on wavelet-based image processing applications approaches. It will be beneficial for scholars to execute effective image processing applications approaches

Hydrogen production via catalyst of green laser, molybdenum and ethanol

Electrolysis is an electrochemical process which is known as a green technology. Laser irradiation and the presence of catalyst in water electrolysis are identified as ways of improving the efficiency and increment of hydrogen production. The enhancement of hydrogen production through water electrolysis is obtained by adding molybdenum to increase the current in electrochemical cell and ethanol as an agent in photochemical reaction. In addition, diode pumped solid-state laser green laser at 532 nm is employed with the purpose to compensate the residual electrical field effect. The combination of the three catalysts is found more powerful to cause water splitting, thus produced 5 times greater H2 production in comparison to the action of individual catalyst

Multi-level fusion in ultrasound for cancer detection based on uniform LBP features

Collective improvement in the acceptable or desirable accuracy level of breast cancer image-related pattern recognition using various schemes remains challenging. Despite the combination of multiple schemes to achieve superior ultrasound image pattern recognition by reducing the speckle noise, an enhanced technique is not achieved. The purpose of this study is to introduce a features-based fusion scheme based on enhancement uniform-Local Binary Pattern (LBP) and filtered noise reduction. To surmount the above limitations and achieve the aim of the study, a new descriptor that enhances the LBP features based on the new threshold has been proposed. This paper proposes a multi-level fusion scheme for the auto-classification of the static ultrasound images of breast cancer, which was attained in two stages. First, several images were generated from a single image using the pre-processing method. The median and Wiener filters were utilized to lessen the speckle noise and enhance the ultrasound image texture. This strategy allowed the extraction of a powerful feature by reducing the overlap between the benign and malignant image classes. Second, the fusion mechanism allowed the production of diverse features from different filtered images. The feasibility of using the LBP-based texture feature to categorize the ultrasound images was demonstrated. The effectiveness of the proposed scheme is tested on 250 ultrasound images comprising 100 and 150 benign and malignant images, respectively. The proposed method achieved very high accuracy (98%), sensitivity (98%), and specificity (99%). As a result, the fusion process that can help achieve a powerful decision based on different features produced from different filtered images improved the results of the new descriptor of LBP features in terms of accuracy, sensitivity, and specificity

Fusion scheme of segmentation and classification for breast cancer static ultrasound images

Breast Cancer (BC) is defined as cancer that forms in the ducts of the breast (tubes that convey milk to the nipple) and lobules of the breast tissue. This study aims to develop a Computer-Aided Diagnosis (CAD) approach that provides a multidisciplinary skill for breast ultrasound images that could aid specialists in improving accuracy in disease identification, thus reducing the rate of false-positive and falsenegative results. To achieve this goal and build a fully automatic solution, the main limitations faced with the breast ultrasound image will be highlighted. First, ultrasound images suffer from speckle noise and artefacts. Second, the similarity between the textures inside the Region of Interest (ROI) and the background region, and that will end up with overlapping between the ROI and the backgrounds. Third, the similarity between the texture of the benign images and the malignant images, and this challenge will reduce the accuracy of the diagnosis by decreasing the sensitivity and the specificity of the proposed solution. Fourth, the borders of the ROI are not clear. Finally, applying a traditional segmentation method, i.e., the threshold method, will end up with a number of false-positive cases and false-negative cases, and both will affect the result of the automatic solution. In the segmentation stage, we have proposed a trainable schema based on multi-texture features to avoid problems related to the similarity between the texture of the ROI and the background. It also used to avoid the noise and the artifact by training the schema on good samples including regions with noise and artifacts. The trainable schema has solved the poor border problems by training the schema on blocks with poor borders. Forth, feature extraction stage (for the segmentation stage), an existing schema, a single feature that is Local Binary Pattern (LBP), was employed to describe the cancer region. This study has developed a hybrid model based on a multi descriptor (texture feature) to enable the effective extraction of the ROI. Furthermore, this thesis focuses on proposing a new describer that can help to identify the breast abnormality by enhancing the LBP texture features and the LBP descriptor using a new threshold that can help to identify the important information required for the identification of abnormal cases. Eventually, multi-level fusion for automatic classification of static ultrasound images of breast cancer is a method that makes it possible to diagnose breast diseases quickly and accurately compared to a manual approach. This study has used median and Wiener filters to reduce the speckle noise to enhance the ultra sound image texture. This process has helped to extract a powerful feature that can help to reduce the overlapping between the benign and malignant class. This process, followed by the fusion process, has helped to produce a significant decision based on different features produced from different filtered images. The experimental results show the proposed method can apply LBP based texture feature for categorizing ultrasound images, which registered a higher accuracy of 98.8%, the sensitivity of 98.01%, and specificity of 99.3%

Skin Lesions Classification Using Deep Learning Techniques: Review

Skin cancer is a significant health problem. More than 123,000 new cases per year are recorded. Melanoma is the most popular type of skin cancer, leading to more than 9000 deaths annually in the USA. Skin disease diagnosis is getting difficult due to visual similarities. While Melanoma is the most common form of skin cancer, other pathology types are also fatal. Automatic melanoma screening systems will be useful in identifying those skin cancers more appropriately. Advances in technology and growth in computational capabilities have allowed machine learning and deep learning algorithms to analyze skin lesion images. Deep Convolutional Neural Networks (DCNNs) have achieved more encouraging results, yet faster systems for diagnosing fatal diseases are the need of the hour. This paper presents a survey of techniques for skin cancer detection from images. The paper aims to present a review of existing state-of-the-art and effective models for automatically detecting Melanoma from skin images. The result of classifications and segmentation from the skin lesion images will be processed better using the ensemble deep learning algorithm

Mobile Ad Hoc Network in Disaster Area Network Scenario: A Review on Routing Protocols

Disasters could cause communication systems partially or completely down. In such a case, relief operations need a rapidly deployed communication system to save lives. Exchanging information among the rescue team is a vital factor to make important decisions. Communication system required to be robust to failures, rapidly deployable, easily maintainable to provide better services. Wireless ad-hoc networks could be the choice of establishing communication with the aid of existing infrastructure in a post-disaster case. In order to optimize mobile ad-hoc network performance, address the challenges that could lead to unreliable performance is required. One and most crucial key challenge is routing information from a sender to receiver. Due to the characteristics of a disaster environment such as signal attenuation, communication links exist between rescue crew is short-lived, suffer from frequent route breakage, and may result in unreliable end-to-end services. Many routing protocols have been proposed and evaluated in different network environments. This paper presents the basic taxonomy of Mobile Ad Hoc Networks and the state of the art in routing categorizes (Proactive, Reactive, Geographic-aware and Delay tolerant Networks (DTN)). The comparison of existing routing protocols in Mobile Ad-Hoc Networks indicates that overhead in Proactive and Geographic is competitive with delay in Reactive and DTN routing

Comparison of VPN Protocols at Network Layer Focusing on Wire Guard Protocol

The key point of this paper is to assess and look over the top of the line network layer-based VPN (Virtual Private Network) protocols because data link layer is hardly ever found to be in use in organizations, the reason is because of its exceedingly high charge. Virtual Private Network (VPN) is commonly used in business situations to provide secure communication channels over public infrastructure such as Internet. Virtual Private Networks (VPNs) provide a secure encrypted communication between remote networks worldwide by using Internet Protocol (IP) tunnels and a shared medium like the Internet. The paper follows and sets standards for different types of protocols and techniques, the VPN (Virtual Private Network) architectural feature is made to deliver dependable and safe network that is not in line with regular networks that provide a higher trust and a higher secure channel between user and organization. The current study took place to summaries the usage of existing VPNs protocol and to show the strength of every VPN. Through different studies that have been made by other researchers as well as an extra focus on the state of art protocol, Wire guard. It is also worthy to mention that wire guard compared with other protocols such as IPsec and GRE. The studies show the WireGuard being a better choice in terms of other well-known procedures to inaugurate a secure and trusted VPN

Medical Images Breast Cancer Segmentation Based on K-Means Clustering Algorithm: A Review

Early diagnosis is considered important for medical images of breast cancer, the rate of recovery and safety of affected women can be improved. It is also assisting doctors in their daily work by creating algorithms and software to analyze the medical images that can identify early signs of breast cancer. This review presents a comparison has been done in term of accuracy among many techniques used for detecting breast cancer in medical images. Furthermore, this work describes the imaging process, and analyze the advantages and disadvantages of the used techniques for mammography and ultrasound medical images. K-means clustering algorithm has been                       specifically used to analyze the medical image along with other techniques. The results                        of the K-means clustering algorithm are discussed and evaluated to show the capacity of this technique in the diagnosis of breast cancer and its reliability to identify a malignant from a benign tumor

Management of Wireless Communication Systems Using Artificial Intelligence-Based Software Defined Radio

The wireless communication system was investigated by novel methods, which produce an optimized data link, especially the software-based methods. Software-Defined Radio (SDR) is a common method for developing and implementing wireless communication protocols. In this paper, SDR and artificial intelligence (AI) are used to design a self-management communication system with variable node locations. Three affected parameters for the wireless signal are considered: channel frequency, bandwidth, and modulation type. On one hand, SDR collects and analyzes the signal components while on the other hand, AI processes the situation in real-time sequence after detecting unwanted data during the monitoring stage. The decision was integrated into the system by AI with respect to the instantaneous data read then passed to the communication nodes to take its correct location. The connectivity ratio and coverage area are optimized nearly double by the proposed method, which means the variable node location, according to the peak time, increases the attached subscriber by a while rati

Leukemia Diagnosis using Machine Learning Classifiers Based on Correlation Attribute Eval Feature Selection

Leukemia refers to a disease that affects the white blood cells (WBC) in the bone marrow and/or blood. Blood cell disorders are often detected in advanced stages as the number of cancer cells is much higher than the number of normal blood cells. Identifying malignant cells is critical for diagnosing leukemia and determining its progression. This paper used machine learning with classifiers to detect leukemia types as a result, it can save both patients and physicians time and money. The primary objective of this paper is to determine the most effective methods for leukemia detection. The WEKA application was used to evaluate and analyze five classifiers (J48, KNN, SVM, Random Forest, and Naïve Bayes classifiers). The results were respectively as follows: 83.33%, 87.5%, 95.83%, 88.88%, and 98.61%, with the Naïve Bayes classifier achieving the highest accuracy; however, accuracy varies according to the shape and size of the sample and the algorithm used to classify the leukemia types