A Fuzzy Logic based system for Mixed Reality assistance of remote workforce

The recent years have witnessed an increase in the use of augmented and virtual reality systems, changing the way we interact with our environments. Such systems are commonly associated with advertising, entertainment, medicine, training and education. However, with the increasing acceptance and availability of mobile and wearable devices (e.g. head-mounted displays (HMD)), the use of these technologies is moving towards professional and industrial environments, where they would be able to support employees in their daily tasks, increasing customer satisfaction and reducing business costs. This paper presents an innovative Mixed Reality (MR) system to assist field workforce in remote locations. As part of the overall implementation, the MR system uses fuzzy logic mechanisms to improve accuracy in user tracking and object monitoring, allowing the correct representation of users and objects in the Graphical User Interfaces (GUIs), and improving the experience for users

Physical layer authenticated image encryption for Iot network based on biometric chaotic signature for MPFrFT OFDM system

In this paper, a new physical layer authenticated encryption (PLAE) scheme based on the multi-parameter fractional Fourier transform–Orthogonal frequency division multiplexing (MP-FrFT-OFDM) is suggested for secure image transmission over the IoT network. In addition, a new robust multi-cascaded chaotic modular fractional sine map (MCC-MF sine map) is designed and analyzed. Also, a new dynamic chaotic biometric signature (DCBS) generator based on combining the biometric signature and the proposed MCC-MF sine map random chaotic sequence output is also designed. The final output of the proposed DCBS generator is used as a dynamic secret key for the MPFrFT OFDM system in which the encryption process is applied in the frequency domain. The proposed DCBS secret key generator generates a very large key space of (Formula presented.). The proposed DCBS secret keys generator can achieve the confidentiality and authentication properties. Statistical analysis, differential analysis and a key sensitivity test are performed to estimate the security strengths of the proposed DCBS-MP-FrFT-OFDM cryptosystem over the IoT network. The experimental results show that the proposed DCBS-MP-FrFT-OFDM cryptosystem is robust against common signal processing attacks and provides a high security level for image encryption application. © 2023 by the authors

A Linear General Type-2 Fuzzy Logic Based Computing With Words Approach for Realising an Ambient Intelligent Platform for Cooking Recipes Recommendation

This paper addresses the need to enhance transparency in ambient intelligent environments by developing more natural ways of interaction, which allow the users to communicate easily with the hidden networked devices rather than embedding obtrusive tablets and computing equipment throughout their surroundings. Ambient intelligence vision aims to realize digital environments that adapt to users in a responsive, transparent, and context-aware manner in order to enhance users' comfort. It is, therefore, appropriate to employ the paradigm of “computing with words” (CWWs), which aims to mimic the ability of humans to communicate transparently and manipulate perceptions via words. One of the daily activities that would increase the comfort levels of the users (especially people with disabilities) is cooking and performing tasks in the kitchen. Existing approaches on food preparation, cooking, and recipe recommendation stress on healthy eating and balanced meal choices while providing limited personalization features through the use of intrusive user interfaces. Herein, we present an application, which transparently interacts with users based on a novel CWWs approach in order to predict the recipe's difficulty level and to recommend an appropriate recipe depending on the user's mood, appetite, and spare time. The proposed CWWs framework is based on linear general type-2 (LGT2) fuzzy sets, which linearly quantify the linguistic modifiers in the third dimension in order to better represent the user perceptions while avoiding the drawbacks of type-1 and interval type-2 fuzzy sets. The LGT2-based CWWs framework can learn from user experiences and adapt to them in order to establish more natural human-machine interaction. We have carried numerous real-world experiments with various users in the University of Essex intelligent flat. The comparison analysis between interval type-2 fuzzy sets and LGT2 fuzzy sets demonstrates up to 55.43% improvement when general type-2 fuzzy sets are used than when interval type-2 fuzzy sets are used instead. The quantitative and qualitative analysis both show the success of the system in providing a natural interaction with the users for recommending food recipes where the quantitative analysis shows the high statistical correlation between the system output and the users' feedback; the qualitative analysis presents social scienc

Authenticated public key elliptic curve based on deep convolutional neural network for cybersecurity image encryption application

The demand for cybersecurity is growing to safeguard information flow and enhance data privacy. This essay suggests a novel authenticated public key elliptic curve based on a deep convolutional neural network (APK-EC-DCNN) for cybersecurity image encryption application. The public key elliptic curve discrete logarithmic problem (EC-DLP) is used for elliptic curve Diffie–Hellman key exchange (EC-DHKE) in order to generate a shared session key, which is used as the chaotic system’s beginning conditions and control parameters. In addition, the authenticity and confidentiality can be archived based on ECC to share the (Formula presented.) parameters between two parties by using the EC-DHKE algorithm. Moreover, the 3D Quantum Chaotic Logistic Map (3D QCLM) has an extremely chaotic behavior of the bifurcation diagram and high Lyapunov exponent, which can be used in high-level security. In addition, in order to achieve the authentication property, the secure hash function uses the output sequence of the DCNN and the output sequence of the 3D QCLM in the proposed authenticated expansion diffusion matrix (AEDM). Finally, partial frequency domain encryption (PFDE) technique is achieved by using the discrete wavelet transform in order to satisfy the robustness and fast encryption process. Simulation results and security analysis demonstrate that the proposed encryption algorithm achieved the performance of the state-of-the-art techniques in terms of quality, security, and robustness against noise- and signal-processing attacks

Forecasting the Real Estate Housing Prices Using a Novel Deep Learning Machine Model

There is an urgent need to forecast real estate unit prices because the average price of residential real estate is always fluctuating. This paper provides a real estate price prediction model based on supervised regression deep learning with 3 hidden layers, a Relu activation function, 100 neurons, and a Root Mean Square Propagation optimizer (RMS Prop). The model was developed using actual data collected from 28 Egyptian cities between 2014 and 2022. The model can forecast the price of a real estate unit based on 27 different variables. The model is created in two stages: adjusting the parameters to obtain the best ones using a sensitivity k-fold technique, then optimizing the result. 85 percent of the real estate unit data gathered was used in training and developing the model, while the other 15 percent was used in validating and testing. By using a dropout regularization technique of 0.60 on the model layers, the final developed model had a maximum error of 10.58%. After validation, the model had a maximum error of about 9.50%. A graphical user interface (GUI) tool is developed to make use of the final predictive model, which is very simple for real estate developers and decision-makers to use. Doi: 10.28991/CEJ-SP2023-09-04 Full Text: PD

Novel Levenberg–Marquardt based learning algorithm for unmanned aerial vehicles

In this paper, Levenberg–Marquardt inspired sliding mode control theory based adaptation laws are proposed to train an intelligent fuzzy neural network controller for a quadrotor aircraft. The proposed controller is used to control and stabilize a quadrotor unmanned aerial vehicle in the presence of periodic wind gust. A proportional-derivative controller is firstly introduced based on which fuzzy neural network is able to learn the quadrotor's control model on-line. The proposed design allows handling uncertainties and lack of modelling at a computationally inexpensive cost. The parameter update rules of the learning algorithms are derived based on a Levenberg–Marquardt inspired approach, and the proof of the stability of two proposed control laws are verified by using the Lyapunov stability theory. In order to evaluate the performance of the proposed controllers extensive simulations and real-time experiments are conducted. The 3D trajectory tracking problem for a quadrotor is considered in the presence of time-varying wind conditions

A randomized controlled clinical trial evaluating the effect of Trigonella foenum-graecum (fenugreek) versus glibenclamide in patients with diabetes

Background: Herbal medicines long have been used in the management of diabetes mellitus (DM).Objective: This study was conducted to ascertain if fenugreek compared with glibenclamide had any impacts on controlling blood glucose in patients with uncontrolled type II DM on conventional therapy.Methods: A total of 12 patients with uncontrolled DM and on metformin were recruited and divided into two groups. Patients in group 1 received 2 g fenugreek per day, whereas those in group 2 received glibenclamide 5 mg once daily. The impacts of fenugreek on the glycemic control and lipid profile were measured before initiation of the regimen and then after 12 weeks.Results: Only 9 of the 12 study participants completed the study. Fenugreek at 2 g/day caused an insignificant drop in fasting blood glucose (P = 0.63), but the fasting insulin level increased significantly (P = 0.04). The ratio of high- to low-density lipopro- tein was significantly decreased from before to after treatment (P = 0.006). Fenugreek did not cause any notable adverse impacts on hepatic and renal functions throughout the study.Conclusion: Fenugreek could be used as adjuvant therapy to anti-diabetic drugs to control blood glucose, and further studies are needed.Keywords: Trigonella foenum-graecum (fenugreek), glibenclamide, diabetes

Effect of bilateral uterine artery ligation in cases of postpartum hemorrhage on ovarian reserve

Background: Bilateral uterine artery ligation (BUAL) is a fertility-preserving procedure used in women experiencing postpartum hemorrhage (PPH). However, the long-term effects of this procedure on ovarian reserve remain unclear. Aim to investigate the effect of BUAL in cases of PPH on ovarian reserve, Methods: This study was carried out at department of obstetrics and gynecology Tanta university on 40 patients divided into 2 groups: (The study group); included 20 patients underwent cesarean section with successful BUAL for intractable atonic PPH, (The control group); included 20 patients underwent cesarean section without BUAL; during a period between April 2020 and December 2021, Results: There is no-significant difference between study and control group according to AMH (ng/ml), resistivity index (RI) and pulsatility index (PI) of right and left uterine artery and ovarian artery after 6 months of bilateral UAL, Conclusions: Bilateral UAL had no negative effects on ovarian reserve or ovarian blood supply, so this treatment should be used as a fertility preservation technique to avoid hysterectomy in patients experiencing PPH

Comparison of the accuracy of two scoring systems in predicting the outcome of organophosphate intoxicated patients admitted to intensive care unit (ICU)

AbstractIntroductionOrganophosphates(OP) are one of the most common causes of poisoning, especially in developing countries, with high morbidity and mortality. As mortality rate of OP poisoning is still high, early diagnosis and appropriate treatment is often life saving. OP is the main cause of poisoning and death in the poison control centre (PCC), Ain Shams University (ASU) in Egypt.ObjectiveTo compare the accuracy of acute physiology and chronic health evaluation score (APACHE IV) and simplified acute physiology score (SAPS II) in the prediction of mortality of patients with organophosphate poisoning (OPP) who required admission to the Intensive Care Unit (ICU) of PCC of ASU between January 1st, 2009 and December 31st, 2009.MethodsA prospective study conducted by collecting data on consecutive patients with acute OPP admitted to the intensive care unit over 12months. Data required to calculate the patients’ predicted mortality by (APACHE) IV and (SAPS) II scoring systems were collected.ResultsNinety patients were recruited in the study with acute OP toxicity. The observed mortality following acute OP toxicity was 13.3% (12 patients). The area under the receiver operator characteristic (ROC) curves of APACHE IV score was better than SAPS II score (0.921±0.054 SE, 0.807±0.078 SE, respectively). APACHE IV and SAPS II scores were significantly higher in the non-survival than in the survival group (P<0.05).ConclusionAPACHE IV and SAPS II scores calculated within the first 24h are good prognostic indicators among patients with acute OP toxicity that required ICU admission with preference to APACHE IV score. APACHE IV and SAPS II scores above 89, 44, respectively within the first 24h are a predictor of poor outcome in patients with acute OP toxicity.RecommendationApplication of APACHE IV and SAPS II scores is a good predictor of high mortality in OP intoxicated patients which helps in proper allocation of resources