AL-Rafidain Journal of Computer Sciences and Mathematics

 Retinal image analysis is crucial for The classification of retinal diseases such as “Age Related Macular Degeneration (AMD)”, “Diabetic Retinopathy (DR)”, “Retinoblastoma”, “Macular Bunker”, “Retinitis Pigmentosa”, and “Retinal Detachment”. The early detection of such diseases is important insofar as it contributes in mitigating future implications. Many approaches have been developed in the literature for auto-detecting of retinal landmarks and pathologies. The current revolution in deep learning techniques has opened the horizon for researchers in the field of ophthalmology. This paper is a comprehensive review of the deep learning techniques applied for the classification of retinal images, pathology, retinal landmarks, and disease classification. This review is based on indicators such as sensitivity, Area under ROC curve, specificity, F score, and accuracy.

In this paper three-dimensional heat conduction equation in cartesian coordinate has been solved in two different methods one of which depends on the separation of variables and the other depends on the integral transform .The results are got and plotted by using Matlab. And the results obtained showed the difference between the two methods that were used in the solution . That difference is evident in the illustrations . According to the results it was concluded that the integral transform method is the best because it has fewer steps to reached to the solution 

Recently, fake and fabricated images that have been manipulated for several purposes, including cosmetic and some for illegal purposes, have spread on social media. And because this is not an easy matter, it has become necessary for researchers in this field to search and investigate the types of images, to verify their authenticity and how to manipulate them. Therefore, the aim of this research is to serve as an assistant to the researcher who wants to enter this field. In this research, a survey of the types of forgery was presented with examples. The most important common methods for detecting forgery were also presented, and the previous studies that contributed to the process of detecting fraud, both traditional detection and deep learning-based detection, were highlighted, while giving the most important strengths and weaknesses of both types. We note from this that detecting the forgery process is a difficult process and takes time to detect the changes that have been made to the image that cannot be detected by the naked eye. In this research, researchers have been urged to go towards deep learning for the purpose of detecting the  forgery of the features that it enjoys.

Predicting heart attacks using machine learning is an important topic. Medical data sets contain different features, some of which are related to the target group for prediction and some are not. In addition, the data sets are excessively unbalanced, which leads to the bias of machine learning models when modeling heart attacks. To model the unbalanced heart attack data set, this paper proposes the hybridization of Particle swarm optimization (PSO), BAT, and Cuckoo Search (CS) to select the features and adopt the precision for minority classes as a fitness function for each swarm to select the influential features. In order to model the data, set in which the features were selected, it was proposed to use the boosting (Catboost) as a classifier for predicting heart attacks. The proposed method to select features has been compared with each of the three swarms, and the Catboost algorithm has been compared to traditional classification algorithms (naive Bayes, decision trees). The study found that the proposed method of hybridization of the results of the (PSO,  BAT, and BCS) algorithms in selecting features is a promising solution in the field of selecting features and increases the accuracy of the system, and that traditional machine learning models are biased in the case of unbalanced data sets and that selecting the important features according to the target class has an impact on the performance of the models, In addition, the definition of hyperparameters reduces the bias of the selected model. The final model achieved an overall accuracy of 96% on the Accuracy scale and 56% on the Precision scale for the minority class

After the availability of Internet infrastructure all over the world, and connectivity is no longer an obstacle over the Internet, cloud computing has emerged as a practical and ideal solution. A huge revolution has taken place in the field of cloud computing, where it is now an industry. However, it faces great difficulties in ensuring data confidentiality and privacy. People hesitate to use it due to the risk of innumerable attacks and security breaches. This article has covered a several directions relayed to cloud computing ideas. This research would focus on traditional and deep-learning based schemes to secure user’s data in the cloud. This study concluded some points about the capabilities of the traditional and deep learning-based scheme. The comparison showed that both of them increased the levels of security and privacy of the cloud. The study conclude that the Deep learning-based method had been implanted to secure clouds’ data in combination with other technique performed better than others.

Melanoma is considered a serious health disease and one of the most dangerous and deadly types of skin cancer, due to its unlimited spread. Therefore, detection of this disease must be early and sound due to the high mortality rate. It is driven by researchers' desire to use computers to obtain accurate diagnostic systems to help diagnose and detect this disease early. Given the growing interest in cancer prediction, we have presented this paper, a systematic review of recent developments, using artificial intelligence focusing on melanoma skin lesion detection, particularly systems designed on neural networks. Using the neural networks for melanoma detection could be part of system of assistance for dermatologists who must make the final decision on whether to recommend a biopsy if at least one of the dermatologist's diagnoses and the support system (a helpful method) indicate melanoma or to investigate if another type of cancerous lesion exists. In the latter situation, the system can be trained to recognize distinct types of cancerous skin lesions. On the other hand, the system is incapable of making final decisions. Given neural networks' evolutionary patterns, updated, changed, and integrated networks are expected to increase the performance of such systems. Based on the decision fusion, theoretical and applied contributions were studied using traditional classification algorithms and multiple neural networks. The period 2018-2021 has been focused on new trends. Also for the detection of melanomas, the most popular datasets and how they're being used to train neural network models were presented. Furthermore, the field of research emphasized in order to promote better the subject during different directions. Finally, a research agenda was highlighted to advance the field towards the new trends.

An element  is known a strongly nil* clean element if a=e₁ - e₁e₂ + n , where e₁,e₂  are idempotents and n is nilpotent, that commute with one another. An ideal I of a ring R is called a strongly nil* clean ideal if each element of I is strongly nil* clean element. We investigate some of its fundamental features, as well as its relationship to the nil clean ideal.

In this work, the homotopy perturbation method (HPM) is used to solve initial value
problems of first order with various types of discontinuities. The numerical results obtained (are
compared) using the traditional HPM, and the integral equation of the nth equation with the
solution numerical obtained using Simpson and Trapezoidal Rules to demonstrate that the
solution results are extremely accurate when compared to the exact solution. The maximum
absolute error, ‖ . ‖ , maximum relative error, maximum residual error, and expected
convergence order are also provided.

Under a perturbed triple product defined on three semisimple commutative Banach algebras with the influence of two homomorphisms defined on two of them and that carry certain characteristics, we proved that the spectral extension property is stable.

With the development of the Internet of Things (IoT) technology, IoT devices are integrated into many of our daily lives, including industrial, security, medical, and personal applications. Many violations of IoT safety have appeared due to the critical physical infrastructure, and network vulnerabilities. Considering the nature of the restricted and limited resources of these devices in terms of size, capacity, and energy, Security is becoming increasingly important. Lightweight cryptography is one of the directions that offer security solutions in resource-constrained environments such as Radio-frequency identification (RFID) and wireless sensor network (WSN).This paper discusses the security issues of these resource-constrained IoT devices and reviews the most prominent Lightweight Bock Cipher suitable for software implementation. Through studying thespecifications and the inner structure for each cipher and their implementation of the performance evaluation on some kind of platform, we provide a design strategies guideline for cryptographic developers to design improved Lightweight Block cipher solutions and compact software implementation for resource-constrained environments.

The most important aspect of human communication is speech. Lengthy media such as speech takes a long time to read and understand. This difficulty is solved by providing a reduced summary with semantics. Speech summarization can either convert speech to text using automated speech recognition (ASR) and then build the summary, or it can process the speech signal directly and generate the summary. This survey will look at a various of recent studies that have used machine and deep learning algorithms to summarize speech. it discusses the speech summarizing literatures in terms of time restrictions, research methodology, and lack of interest in particular databases for literature searches. As newer deep learning approaches were not included in earlier surveys, this is a new survey in this discipline where different approaches with various datasets were explored for speech summarization and evaluated using subjective or objective methods.

Many cases of theft and property trespass in addition to crimes occur in the world after these people break into people's homes and buildings illegally, so this article aims to shed light on most of the smart methods and computer technologies used in identifying people that help to reduce these crimes. Where the diversity of biometric traits was relied upon, such as fingerprint, handprint, ear, face, texture, some deformations characteristic of people, eye, footprints, DNA analysis and other important biometric traits. Also, many intelligent algorithms were used to identify these traits.