Management of Environmental Protection and Sustainability in the Islamic Revolution

Conservation of environmental resources and divine merits does not mean the absence of exploitation or the minimal use of it, but to use it optimally and seamlessly. Therefore, awareness and education in the field of environmental ethics can have a spiritual guarantee in the light of faith and piety;considering the religious teachings and the experiences of the executives and officials of the Islamic Revolution that become more colorful topics such as environmental justice, environmental ethics, environmental degradation, health and optimal consumption. The expansion of morality is considered to be in the context of human-environmental relations as a revolutionary opportunity and an environmental imperative. In fact, the interests of man are in balance with the interests of other beings;as a result, there are solutions to protect the inhumane community against human harm. In this project, we used of library methods, review and integration on the basis of articles and environmental studies. The results of this study indicate that there is a relationship between religious beliefs and revolutionary approaches to the environment and environmental ethics. The mentioned verses and narratives, the leadership’s recommendations all emphasize on environmental protection;and authorities have done this knowledge and information in the best way to raise the level of education. The results of the research show the upward trend in the environment after the Islamic Revolution in the process of approving and drafting laws.

The Evolution of Botanical and Herbal Medicine in Islamic Civilization

Botanical science and medicinal plants are shaped by the translation movement in Islamic civilization and in the fifth century (AH), through the development of specialized media and the commencement of scientific and research trips, its foundations are strengthened. In the sixth and seventh centuries (AH), the development of the first botanical encyclopedias, the introduction of objective observations and practical experiences on theoretical issues, the prosperity of this science was provided by writing comprehensive books on medicinal plants and in the eighth century (AH). Like other intellectual and transcendental sciences, the loss of the past lost due to the decline of the writings and led to a recession. The Muslims played a role in preserving and building the body of knowledge of the Greek, Roman times. In fact, they gained this science from the distant paths. In the history of medicine, Islamic medicine is the science of medicine developed in the Islamic Golden Age, and written in Arabic, the lingua franca of Islamic civilization. In this article, we try to investigate the effect of Islamic scholars on the dynamics of medicinal herbs and the continuation of Muslim researches and innovations in the field of botany and medicinal herbs.

Basis of Halal Lifestyle in Islamic Law

The halal lifestyle in Islamic law is evaluated within the principles of makasidus-sharia,which aim to protect five principal vales of humanity,namely,life,reason,religion,generation and property.The legitimacy of halal life is therefore based on the provisions of the Qur’an and Sunnah that aim to protect these values of all humanity.The similarities between halal and other ethical practices in the context of universal values concerning both Muslims and non-Muslims will provide an opportunity for global recognition of halal life.In this article we investigate how halal lifestyle is to be based according to Islamic law.We first frame the halal life and later lay down its legal basis and finally conclude by illuminating on the halal lifestyle from a universal perspective.

Optimised CNN Architectures for Handwritten Arabic Character Recognition

Handwritten character recognition is considered challenging compared with machine-printed characters due to the different human writing styles.Arabic is morphologically rich,and its characters have a high similarity.The Arabic language includes 28 characters.Each character has up to four shapes according to its location in the word(at the beginning,middle,end,and isolated).This paper proposed 12 CNN architectures for recognizing handwritten Arabic characters.The proposed architectures were derived from the popular CNN architectures,such as VGG,ResNet,and Inception,to make them applicable to recognizing character-size images.The experimental results on three well-known datasets showed that the proposed architectures significantly enhanced the recognition rate compared to the baseline models.The experiments showed that data augmentation improved the models’accuracies on all tested datasets.The proposed model outperformed most of the existing approaches.The best achieved results were 93.05%,98.30%,and 96.88%on the HIJJA,AHCD,and AIA9K datasets.

Performance Comparison of Hyper-V and KVM for Cryptographic Tasks in Cloud Computing

As the extensive use of cloud computing raises questions about the security of any personal data stored there,cryptography is being used more frequently as a security tool to protect data confidentiality and privacy in the cloud environment.A hypervisor is a virtualization software used in cloud hosting to divide and allocate resources on various pieces of hardware.The choice of hypervisor can significantly impact the performance of cryptographic operations in the cloud environment.An important issue that must be carefully examined is that no hypervisor is completely superior in terms of performance;Each hypervisor should be examined to meet specific needs.The main objective of this study is to provide accurate results to compare the performance of Hyper-V and Kernel-based Virtual Machine(KVM)while implementing different cryptographic algorithms to guide cloud service providers and end users in choosing the most suitable hypervisor for their cryptographic needs.This study evaluated the efficiency of two hypervisors,Hyper-V and KVM,in implementing six cryptographic algorithms:Rivest,Shamir,Adleman(RSA),Advanced Encryption Standard(AES),Triple Data Encryption Standard(TripleDES),Carlisle Adams and Stafford Tavares(CAST-128),BLOWFISH,and TwoFish.The study’s findings show that KVM outperforms Hyper-V,with 12.2%less Central Processing Unit(CPU)use and 12.95%less time overall for encryption and decryption operations with various file sizes.The study’s findings emphasize how crucial it is to pick a hypervisor that is appropriate for cryptographic needs in a cloud environment,which could assist both cloud service providers and end users.Future research may focus more on how various hypervisors perform while handling cryptographic workloads.

Split-dose vs same-day reduced-volume polyethylene glycol electrolyte lavage solution for morning colonoscopy

AIM: To compare same-day whole-dose vs split-dose of 2-litre polyethylene glycol electrolyte lavage solution (PEG-ELS) plus bisacodyl for colon cleansing for morning colonoscopy.

A Hybrid Model Using Bio-Inspired Metaheuristic Algorithms for Network Intrusion Detection System

Network Intrusion Detection System(IDS)aims to maintain computer network security by detecting several forms of attacks and unauthorized uses of applications which often can not be detected by firewalls.The features selection approach plays an important role in constructing effective network IDS.Various bio-inspired metaheuristic algorithms used to reduce features to classify network traffic as abnormal or normal traffic within a shorter duration and showing more accuracy.Therefore,this paper aims to propose a hybrid model for network IDS based on hybridization bio-inspired metaheuristic algorithms to detect the generic attack.The proposed model has two objectives;The first one is to reduce the number of selected features for Network IDS.This objective was met through the hybridization of bioinspired metaheuristic algorithms with each other in a hybrid model.The algorithms used in this paper are particle swarm optimization(PSO),multiverse optimizer(MVO),grey wolf optimizer(GWO),moth-flame optimization(MFO),whale optimization algorithm(WOA),firefly algorithm(FFA),and bat algorithm(BAT).The second objective is to detect the generic attack using machine learning classifiers.This objective was met through employing the support vector machine(SVM),C4.5(J48)decision tree,and random forest(RF)classifiers.UNSW-NB15 dataset used for assessing the effectiveness of the proposed hybrid model.UNSW-NB15 dataset has nine attacks type.The generic attack is the highest among them.Therefore,the proposed model aims to identify generic attacks.My data showed that J48 is the best classifier compared to SVM and RF for the time needed to build the model.In terms of features reduction for the classification,my data show that the MFO-WOA and FFA-GWO models reduce the features to 15 features with close accuracy,sensitivity and F-measure of all features,whereas MVO-BAT model reduces features to 24 features with the same accuracy,sensitivity and F-measure of all features for all classifiers.

Arabic Optical Character Recognition:A Review

This study aims to review the latest contributions in Arabic Optical Character Recognition(OCR)during the last decade,which helps interested researchers know the existing techniques and extend or adapt them accordingly.The study describes the characteristics of the Arabic language,different types of OCR systems,different stages of the Arabic OCR system,the researcher’s contributions in each step,and the evaluationmetrics for OCR.The study reviews the existing datasets for the Arabic OCR and their characteristics.Additionally,this study implemented some preprocessing and segmentation stages of Arabic OCR.The study compares the performance of the existing methods in terms of recognition accuracy.In addition to researchers’OCRmethods,commercial and open-source systems are used in the comparison.The Arabic language is morphologically rich and written cursive with dots and diacritics above and under the characters.Most of the existing approaches in the literature were evaluated on isolated characters or isolated words under a controlled environment,and few approaches were tested on pagelevel scripts.Some comparative studies show that the accuracy of the existing Arabic OCR commercial systems is low,under 75%for printed text,and further improvement is needed.Moreover,most of the current approaches are offline OCR systems,and there is no remarkable contribution to online OCR systems.

Ensemble-Based Approach for Efficient Intrusion Detection in Network Traffic

The exponential growth of Internet and network usage has neces-sitated heightened security measures to protect against data and network breaches.Intrusions,executed through network packets,pose a significant challenge for firewalls to detect and prevent due to the similarity between legit-imate and intrusion traffic.The vast network traffic volume also complicates most network monitoring systems and algorithms.Several intrusion detection methods have been proposed,with machine learning techniques regarded as promising for dealing with these incidents.This study presents an Intrusion Detection System Based on Stacking Ensemble Learning base(Random For-est,Decision Tree,and k-Nearest-Neighbors).The proposed system employs pre-processing techniques to enhance classification efficiency and integrates seven machine learning algorithms.The stacking ensemble technique increases performance by incorporating three base models(Random Forest,Decision Tree,and k-Nearest-Neighbors)and a meta-model represented by the Logistic Regression algorithm.Evaluated using the UNSW-NB15 dataset,the pro-posed IDS gained an accuracy of 96.16%in the training phase and 97.95%in the testing phase,with precision of 97.78%,and 98.40%for taring and testing,respectively.The obtained results demonstrate improvements in other measurement criteria.

Efficient Feature Selection and ML Algorithm for Accurate Diagnostics

Machine learning algorithms have been deployed in numerous optimization,prediction and classification problems.This has endeared them for application in fields such as computer networks and medical diagnosis.Although these machine learning algorithms achieve convincing results in these fields,they face numerous challenges when deployed on imbalanced dataset.Consequently,these algorithms are often biased towards majority class,hence unable to generalize the learning process.In addition,they are unable to effectively deal with high-dimensional datasets.Moreover,the utilization of conventional feature selection techniques from a dataset based on attribute significance render them ineffective for majority of the diagnosis applications.In this paper,feature selection is executed using the more effective Neighbour Components Analysis(NCA).During the classification process,an ensemble classifier comprising of K-Nearest Neighbours(KNN),Naive Bayes(NB),Decision Tree(DT)and Support Vector Machine(SVM)is built,trained and tested.Finally,cross validation is carried out to evaluate the developed ensemble model.The results shows that the proposed classifier has the best performance in terms of precision,recall,F-measure and classification accuracy.

Bio-inspired Hybrid Feature Selection Model for Intrusion Detection

Intrusion detection is a serious and complex problem.Undoubtedly due to a large number of attacks around the world,the concept of intrusion detection has become very important.This research proposes a multilayer bioinspired feature selection model for intrusion detection using an optimized genetic algorithm.Furthermore,the proposed multilayer model consists of two layers(layers 1 and 2).At layer 1,three algorithms are used for the feature selection.The algorithms used are Particle Swarm Optimization(PSO),Grey Wolf Optimization(GWO),and Firefly Optimization Algorithm(FFA).At the end of layer 1,a priority value will be assigned for each feature set.At layer 2 of the proposed model,the Optimized Genetic Algorithm(GA)is used to select one feature set based on the priority value.Modifications are done on standard GA to perform optimization and to fit the proposed model.The Optimized GA is used in the training phase to assign a priority value for each feature set.Also,the priority values are categorized into three categories:high,medium,and low.Besides,the Optimized GA is used in the testing phase to select a feature set based on its priority.The feature set with a high priority will be given a high priority to be selected.At the end of phase 2,an update for feature set priority may occur based on the selected features priority and the calculated F-Measures.The proposed model can learn and modify feature sets priority,which will be reflected in selecting features.For evaluation purposes,two well-known datasets are used in these experiments.The first dataset is UNSW-NB15,the other dataset is the NSL-KDD.Several evaluation criteria are used,such as precision,recall,and F-Measure.The experiments in this research suggest that the proposed model has a powerful and promising mechanism for the intrusion detection system.

Email Filtering Using Hybrid Feature Selection Model

Undoubtedly,spam is a serious problem,and the number of spam emails is increased rapidly.Besides,the massive number of spam emails prompts the need for spam detection techniques.Several methods and algorithms are used for spam filtering.Also,some emergent spam detection techniques use machine learning methods and feature extraction.Some methods and algorithms have been introduced for spam detecting and filtering.This research proposes two models for spam detection and feature selection.The first model is evaluated with the email spam classification dataset,which is based on reducing the number of keywords to its minimum.The results of this model are promising and highly acceptable.The second proposed model is based on creating features for spam detection as a first stage.Then,the number of features is reduced using three well-known metaheuristic algorithms at the second stage.The algorithms used in the second model are Artificial Bee Colony(ABC),Ant Colony Optimization(ACO),and Particle Swarm Optimization(PSO),and these three algorithms are adapted to fit the proposed model.Also,the authors give it the names AABC,AACO,and APSO,respectively.The dataset used for the evaluation of this model is Enron.Finally,well-known criteria are used for the evaluation purposes of this model,such as true positive,false positive,false negative,precision,recall,and F-Measure.The outcomes of the second proposed model are highly significant compared to the first one.

Analytical Modeling of a Multi-queue Nodes Network Router

This paper presents the derivation of an analytical model for a multi-queue nodes network router, which is referred to as the multi-queue nodes （mQN） model. In this model, expressions are derived to calculate two performance metrics, namely, the queue node and system utilization factors. In order to demonstrate the flexibility and effectiveness of the mQN model in analyzing the performance of an mQN network router, two scenarios are performed. These scenarios investigated the variation of queue nodes and system utilization factors against queue nodes dropping probability for various system sizes and packets arrival routing probabilities. The performed scenarios demonstrated that the mQN analytical model is more flexible and effective when compared with experimental tests and computer simulations in assessing the performance of an mQN network router.

Integrated Random Early Detection for Congestion Control at the Router Buffer

This paper proposed an Integrated Random Early Detection(IRED)method that aims to resolve the problems of the queue-based AQM and loadbased AQM and gain the benefits of both using indicators from both types.The arrival factor(e.g.,arrival rate,queue and capacity)and the departure factors are used to estimate the congestion through two integrated indicators.The utilized indicators are mathematically calculated and integrated to gain unified and coherent congestion indicators.Besides,IRED is built based on a new dropping calculation approach that fits the utilized congestion indicators while maintaining the intended buffer management criteria,avoiding global synchronization and enhancing the performance.The results showed that IRED,compared to RED,BLUE,ERED,FLRED,EnRED and DcRED,decreased packet delay and loss under various network status.Specifically,the results showed that in heavy and moderate traffic,the proposed IRED method outperformed the state-of-the-art methods in loss and delay by 18% and 10.6%,respectively.

Similarity Analytic Solutions of a 3D-Fractal Nanofluid Uncoupled System Optimized by a Fractal Symmetric Tangent Function

The science of strategy(game theory)is known as the optimal decision-making of autonomous and challenging players in a strategic background.There are different strategies to complete the optimal decision.One of these strategies is the similarity technique.Similarity technique is a generalization of the symmetric strategy,which depends only on the other approaches employed,which can be formulated by altering diversities.One of these methods is the fractal theory.In this investigation,we present a new method studying the similarity analytic solution(SAS)of a 3D-fractal nanofluid system(FNFS).The dynamic evolution is completely given by the concept of differential subordination and majorization.Subordination andmajorization relationships are the sets of observable individualities.Game theory can simplify the conditions under which particular sets combine.We offer an explicit construction for the complex possible velocity,energy and thermal functions of two-dimensional fluid flow(the complex variable is suggested in the open unit disk,where the disk is selected at a constant temperature and concentration with uniform velocity).We establish that whenever the 3D-fractal nanofluid systemis approximated by a fractal function,the solution has the same property,so a class of fractal tangent function gives SAS.Finally,we demonstrate some simulations and examples that give the consequences of this methodology.

Analysis of extreme ultraviolet spectra of laser-produced Cd plasmas

In order to provide detailed information about Cd structure and gain more insight regarding ionization degrees and types of transition,as well as the understanding of the temporal evolution behavior of laser produced Cd plasmas,extreme ultraviolet spectra of laser-produced cadmium(Cd)plasmas have been measured in the 8.4-12 nm region using spatiotemporally resolved laser-produced plasma spectroscopy technique.Spectral features were analyzed by the Hartree-Fock(HF)method with relativistic correlations(HFR)using the Cowan code.The results showed that the 4p-5s resonance transition arrays from Cd^9+to Cd^13+merged to form intense lines in this spectral region.A number of new spectral features from Cd^9+and Cd^10+ions are reported in this study.Based on the assumption of a normalized Boltzmann distribution among the excited states associated with a steady-state collisional-radiative model,the plasma parameters were obtained by comparing the experimental and simulated spectra.As a result,we succeeded in reproducing the synthetic spectra for different time delays,which yielded good agreement with the experiments.The temporal evolution behaviors of electron temperature and electron density of plasma were also analyzed.

Jadwar(Delphinium denudatum Wall.):a medicinal plant

Delphinium denudatum Wall.is one of the important medicinal herbs of traditional Persian medicine and is known as Jadwar.Medicinal plants are the most widely used drugs in traditional Persian medicine and has been used for various diseases since earlier times.The medicinal uses of Delphinium denudatum Wall.date back to over 1,000 years ago.Rhazes(845–925 C.E.)was the first Persian physician and scientist who reported the use of Delphinium denudatum Wall.as a herbal remedy.During the following centuries,the usages of Delphinium denudatum Wall.in the treatment of various diseases has been mentioned in the books and references of traditional Persian medicine for cures to various diseases such as neurologic and psychiatric disease,gastrointestinal disease,fever,pain,and poisoning.According to modern studies,the dried roots of Delphinium denudatum Wall.have antipyretic,antimicrobial,anticonvulsant,hepatoprotective,antioxidant,and pain-relieving properties.Biomolecules from roots of Delphinium denudatum Wall.were also identified as potential cures for central nervous system diseases as well as for the amelioration of morphine addiction.Delphinium denudatum Wall.,with its properties involving the prevention of mitochondrial dysfunction,reduction of oxidative stress,and inflammation and immune dysregulation,can be utilized in curing inflammatory disorders.The effective therapeutic influence of root extract of Delphinium denudatum Wall.against several diseases needs to be confirmed through controlled clinical trials.This article reviews the different features of Delphinium denudatum Wall.and focuses on the well-known therapeutic effects of this herbal drug on various human disorders and animal disease models.

Depression by the Theory of Breathing and Finding Meaning of Life

This study aimed to examine the relationship of finding meaning to life with the level of depression and investigates the effect of using breathing theory in treating or reducing depression in a sample of individuals suffering from depression in the Kuwait Mental Health Center. In order to achieve these objectives, the researcher used the descriptive and analytical approach through an applied questionnaire on the study sample which consist of (380) individuals and the quasi-experimental approach by dividing the study sample individuals into two groups;control group consisted of (190) individuals and experimental group consist of (190) individuals. Also, the researcher developed meaning of life scale and breathing strategy, also used beck depression inventory scale. The study found a set of results, there is a negative relationship between finding meaning in life and depression and there are statistically significant differences between the members of the two groups due to the use of the breathing strategy, in favor of the experimental group. In the light of this result, the study came out with a set of recommendations, the most important of which are holding training and educational courses for depressed patients on ways and methods of finding meaning in life, as these courses have the benefit in reducing the level of depression among patients.

The Impact of Eating Disorders and Self-Disorders on Women’s Behavior in Kuwait

The purpose of this study is to examine the impact of eating disorders and self-disorders (self-esteem, selflessness, self-efficacy, self-concept clarity, and self-compassion) on women’s behavior in Kuwait. This study used a quantitative approach based on a survey questionnaire by the online survey has been used as the main technique for data collection. The survey was sent to a group of 500 women in Kuwait. The survey was administrated through an online survey tool. 212 women completed the full questionnaire, resulting in a response rate of 42.2 percent. The results indicated that eating disorders have a direct effect on women’s behavior in Kuwait. Moreover, self-disorders (self-esteem, selflessness, self-efficacy, self-concept clarity, and self-compassion) have a direct effect on women’s behavior in Kuwait.

Early fault prediction and detection of hydrocephalus shunting system

Trends of various intracranial pressure (ICP) parameters for high pressure hydrocephalus patients are utilized to detect various shunt faults in their early stages, as well as, to monitor the effect of such faults on shunt performance. A method was proposed to predict the time required for ICP to be abnormal and for the valve to reach full blockage condition. Furthermore, an auto valve schedule updating method is proposed and used to temporarily deal with detected faults until the patient is checked up by his/her physician. The proposed algorithms were evaluated using numerical simulation.