Hybrid Cipher System using Neural Network

The objective of this work is to design and implement a cryptography system that enables the sender to send message through any channel (even if this channel is insecure) and the receiver to decrypt the received message without allowing any intruder to break the system and extracting the secret information. In this work, we implement an interaction between the feedforward neural network and the stream cipher, so the secret message will be encrypted by unsupervised neural network method in addition to the first encryption process which is performed by the stream cipher method. The security of any cipher system depends on the security of the related keys (that are used by the encryption and the decryption processes) and their corresponding lengths. In our work, there are two types of keys; the first type is the keystream that is adopted by the stream cipher stage with optimal length (length of the keystream greater or equal the message length); and the second key type is the final weights that are obtained from the learning process within the neural network stage, So we can represent our work as an update or development for using the neural network to enhance the security of stream cipher. As a result for a powerful hybrid design, the resulted cipher system provides a high degree of security which satisfies the data confidentially which is the main goal of the most cryptography systems

A Review of Principal Component Analysis Algorithm for Dimensionality Reduction

Big databases are increasingly widespread and are therefore hard to understand, in exploratory biomedicine science, big data in health research is highly exciting because data-based analyses can travel quicker than hypothesis-based research. Principal Component Analysis (PCA) is a method to reduce the dimensionality of certain datasets. Improves interpretability but without losing much information. It achieves this by creating new covariates that are not related to each other. Finding those new variables, or what we call the main components, will reduce the eigenvalue /eigenvectors solution problem. (PCA) can be said to be an adaptive data analysis technology because technology variables are developed to adapt to different data types and structures. This review will start by introducing the basic ideas of (PCA), describe some concepts related to (PCA), and discussing. What it can do, and reviewed fifteen articles of (PCA) that have been introduced and published in the last three years

A Novel Method for Evaluating the Dynamic Load Factor of An Involute Gear Tooth with Asymmetric Profiles

In this paper a new generation of asymmetric tooth profile gear is considered  to enhance the dynamic behavior and vibroacoustic properties of toothed gear system. This paper presents a non linear dynamic model as a single degree of freedom equation for teeth meshing   gear system which includes   static and dynamic transmission error in order to investigate the influence of time varying mesh stiffness and periodic tooth errors on dynamic load factor for symmetric and asymmetric spur teeth profile.  A new  model of nonlinear time varying mesh stiffness is based on four types of deflections with consideration a small   pressure angle for loaded tooth profile side and high pressure angle for another side. The complicated variation of meshing stiffness as a function of contact point along the mesh cycle is studied. Typical dynamic load factor equations are developed for symmetric and asymmetric tooth gear in single   and double tooth contact by studied symmetrictooth with pressure angle ( 200/200 ) and two pairs of asymmetric teeth (14.50/250 & 200/250 ).  The effect of pressure of asymmetry and static transmitted load   on transmission error and dynamic load factorare studied. The results indicate enhancement  percentage   in transmission error and dynamic load factor for asymmetric teeth profile compare with that symmetric   tooth profile    . Keywords: Asymmetric spur gear, Transmission Error   , Non-linear mesh stiffnes

APPLYING ADAPTIVE FUZZY NEURAL ALGORITHM FOR INTRUSION DETECTION

Many Network applications used as remote login have some ways for detecting the intruders which are classical ways applied by comparison of operations between login user interface and system stored information. The proposed system tried to detect the intrusions happened by the network intruders using new technique called Adaptive Fuzzy Neural Network which have the ability to detect the intrusions at the same time even if the number of users is large. The proposed system consists of two stages, the first stage is for monitoring all events that happen and analyzing them, and the second stage is to detect intrusions. The detection operation combines anomaly intrusion detection and misuse intrusion detection using the Adaptive Fuzzy Neural Network system, which is a suggested method in our paper used to learn the normal network traffic and detect the abnormal traffic

A study of micro structural, magnetic and electrical properties of La-Co-Sm nanoferrites (LCSF) synthesized by sol-gel method

A Lanthanum (La3+) doped Samarium-Cobalt nanoferrites (La_x,Co_0.2,Sm_0.2,Fe_(2-x) O_4, where x=0.0,0.5,1.0) have been synthesized by sol-gel method in citrate media. Obtained spinal ferrites micro structure properties have been investigated by XRD, FTIR, SEM-EDX, and TEM-SAED techniques. All the samples are nano in size with significant hysteresis. Micro structural analysis by XRD confirms the obtained samples showing the single phase cubic spinal structures with an average crystal size found from 12 nm to 25 nm, while the average particles sizes identified from TEM analysis are ranging from 21.5nm-26.8 nm (~23.4nm) and from 20.5 nm to 28(~26.4nm) nm for x=0.5,1.0. The lattice parameter found to be a= 8.402, 8.423, 8.467Å for the respective values of x= 0.0, 0.05, and 1.0. Electrical properties show increase in dc resistivity with increase in La3+ ion concentration. Finally, it was concluded that the doping of Lanthanum ion (La3+) in the ferrites structure is found to influencing the structural and electrical properties without scarifying the ferromagnetic character

Infantile Nephropathic Cystinosis in Sulaimani Pediatric Teaching Hospital: A Retrospective Cohort Study

Cystinosis is a rare metabolic autosomal recessive disorder which characterized by intralysosomal accumulation of cystine. There are three forms; infantile nephropathic is the commonest forms. to evaluate clinical presentations and outcome of infantile cystinosis. A retrospective cohort study conducted in Sulaimani Pediatric Teaching Hospital on 25 patients with infantile cystinosis during May 1, 2014, to June 1, 2017. This study has depended on clinical symptoms and signs, and corneal crystallization for the diagnosis of cystinosis. Gender of the patients was 13 (52%) females and 12 (48%) males. The ages were ranged between (1-12 years) with a mean age of (6.25 years). Eight (32%) patients were from Sulaimani city, but the other 17 (68%) patients were from outside of Sulaimani. Moreover, a 17 (68%) of them were Arabic and the other eight (32%) were Kurdish ethnic groups. The study showed a 20 (80%) positive consanguinity with 19 (76%) positive family history of infantile cystinosis. Additionally, the age of first presentations was between (0.25-2 years) with a mean of (0.8 years). Clinical features included a 100% for polyuria, polydipsia, and failure to thrive. Furthermore, 10 (40%) presented with constipation, 23 (92%) photophobia and 5 (20%) blond hair. Complications included 24 (96%) rickets, 14 (56%) renal insufficiency, 5 (20%) hypothyroidism, 4 (16%) genu valgum, 3 (12%) growth hormone deficiency, and 3 (12%) developed end-stage renal disease. Subsequently, two patients died (8%) due to end-stage renal disease. Finally, there was a statistically significant relationship between both renal insufficiency (P-value = 0.042) and hypothyroidism (P-value < 0.001) with Kurdish ethnicity. Conclusion: Incidence of cystinosis was high among consanguineous parents and those patients who had a positive family history of cystinosis. Furthermore, the delay in diagnosis was due to atypical presentations and unavailability of specific investigations

Proposed intelligence systems based on digital Forensics : Review paper

The field of information security, in general, has seen shifts a traditional approach to an intelligence system. Moreover, an increasing of researchers to focus on propose intelligence systems and framework based on the forensic case studies because of the limitations of traditional methods such as analysis intensive data manually, intelligence visualization to make the evidence more understandable and intelligence system for store data. However, most of these intelligence systems are still facing different limitations. Furthermore, the primary goal of this work analysis popular intelligence system that was used based on forensic. Moreover, propose new algorithms and hybrid model which it's achieved good results in dif-ferent other fields to develop the forensic systems in the future

Water level forecasting using spatiotemporal attention-based long short-term memory network

Bangladesh is in the floodplains of the Ganges, Brahmaputra, and Meghna River delta, crisscrossed by an intricate web of rivers. Although the country is highly prone to flooding, the use of state-of-the-art deep learning models in predicting river water levels to aid flood forecasting is underexplored. Deep learning and attention-based models have shown high potential for accurately forecasting floods over space and time. The present study aims to develop a long short-term memory (LSTM) network and its attention-based architectures to predict flood water levels in the rivers of Bangladesh. The models developed in this study incorporated gauge-based water level data over 7 days for flood prediction at Dhaka and Sylhet stations. This study developed five models: artificial neural network (ANN), LSTM, spatial attention LSTM (SALSTM), temporal attention LSTM (TALSTM), and spatiotemporal attention LSTM (STALSTM). The multiple imputation by chained equations (MICE) method was applied to address missing data in the time series analysis. The results showed that the use of both spatial and temporal attention together increases the predictive performance of the LSTM model, which outperforms other attention-based LSTM models. The STALSTM-based flood forecasting system, developed in this study, could inform flood management plans to accurately predict floods in Bangladesh and elsewhere

National-scale flood risk assessment using GIS and remote sensing-based hybridized deep neural network and fuzzy analytic hierarchy process models : a case of Bangladesh

Assessing flood risk is challenging due to complex interactions among flood susceptibility, hazard, exposure, and vulnerability parameters. This study presents a novel flood risk assessment framework by utilizing a hybridized deep neural network (DNN) and fuzzy analytic hierarchy process (AHP) models. Bangladesh was selected as a case study region, where limited studies examined flood risk at a national scale. The results exhibited that hybridized DNN and fuzzy AHP models can produce the most accurate flood risk map while comparing among 15 different models. About 20.45% of Bangladesh are at flood risk zones of moderate, high, and very high severity. The northeastern region, as well as areas adjacent to the Ganges–Brahmaputra–Meghna rivers, have high flood damage potential, where a significant number of people were affected during the 2020 flood event. The risk assessment framework developed in this study would help policymakers formulate a comprehensive flood risk management system

Global, regional, and national burden of chronic kidney disease, 1990–2017 : a systematic analysis for the Global Burden of Disease Study 2017

Background Health system planning requires careful assessment of chronic kidney disease (CKD) epidemiology, but data for morbidity and mortality of this disease are scarce or non-existent in many countries. We estimated the global, regional, and national burden of CKD, as well as the burden of cardiovascular disease and gout attributable to impaired kidney function, for the Global Burden of Diseases, Injuries, and Risk Factors Study 2017. We use the term CKD to refer to the morbidity and mortality that can be directly attributed to all stages of CKD, and we use the term impaired kidney function to refer to the additional risk of CKD from cardiovascular disease and gout. Methods The main data sources we used were published literature, vital registration systems, end-stage kidney disease registries, and household surveys. Estimates of CKD burden were produced using a Cause of Death Ensemble model and a Bayesian meta-regression analytical tool, and included incidence, prevalence, years lived with disability, mortality, years of life lost, and disability-adjusted life-years (DALYs). A comparative risk assessment approach was used to estimate the proportion of cardiovascular diseases and gout burden attributable to impaired kidney function. Findings Globally, in 2017, 1·2 million (95% uncertainty interval [UI] 1·2 to 1·3) people died from CKD. The global all-age mortality rate from CKD increased 41·5% (95% UI 35·2 to 46·5) between 1990 and 2017, although there was no significant change in the age-standardised mortality rate (2·8%, −1·5 to 6·3). In 2017, 697·5 million (95% UI 649·2 to 752·0) cases of all-stage CKD were recorded, for a global prevalence of 9·1% (8·5 to 9·8). The global all-age prevalence of CKD increased 29·3% (95% UI 26·4 to 32·6) since 1990, whereas the age-standardised prevalence remained stable (1·2%, −1·1 to 3·5). CKD resulted in 35·8 million (95% UI 33·7 to 38·0) DALYs in 2017, with diabetic nephropathy accounting for almost a third of DALYs. Most of the burden of CKD was concentrated in the three lowest quintiles of Socio-demographic Index (SDI). In several regions, particularly Oceania, sub-Saharan Africa, and Latin America, the burden of CKD was much higher than expected for the level of development, whereas the disease burden in western, eastern, and central sub-Saharan Africa, east Asia, south Asia, central and eastern Europe, Australasia, and western Europe was lower than expected. 1·4 million (95% UI 1·2 to 1·6) cardiovascular disease-related deaths and 25·3 million (22·2 to 28·9) cardiovascular disease DALYs were attributable to impaired kidney function. Interpretation Kidney disease has a major effect on global health, both as a direct cause of global morbidity and mortality and as an important risk factor for cardiovascular disease. CKD is largely preventable and treatable and deserves greater attention in global health policy decision making, particularly in locations with low and middle SDI