Brain stimulation techniques offer powerful means of modulating the physiology of specific neural structures. In recent years, non-invasive brain stimulation techniques, such as transcranial magnetic stimulation(TMS) ...Brain stimulation techniques offer powerful means of modulating the physiology of specific neural structures. In recent years, non-invasive brain stimulation techniques, such as transcranial magnetic stimulation(TMS) and transcranial direct current stimulation, have emerged as therapeutic tools for neurology and neuroscience. However, the possible repercussions of these techniques remain unclear, and there are few reports on the incisive recovery mechanisms through brain stimulation. Although several studies have recommended the use of non-invasive brain stimulation in clinical neuroscience, with a special emphasis on TMS, the suggested mechanisms of action have not been confirmed directly at the neural level. Insights into the neural mechanisms of non-invasive brain stimulation would unveil the strategies necessary to enhance the safety and efficacy of this progressive approach. Therefore, animal studies investigating the mechanisms of TMSinduced recovery at the neural level are crucial for the elaboration of non-invasive brain stimulation. Translational research done using animal models has several advantages and is able to investigate knowledge gaps by directly targeting neuronal levels. In this review, we have discussed the role of TMS in different animal models, the impact of animal studies on various disease states, and the findings regarding brain function of animal models after TMS in pharmacology research.展开更多
Millions of people worldwide suffer from a toothache due to tooth cavity,and often permanent tooth loss.Dental caries,also known as tooth decay,is a biofilm-dependent infectious disease that damages teeth by minerals ...Millions of people worldwide suffer from a toothache due to tooth cavity,and often permanent tooth loss.Dental caries,also known as tooth decay,is a biofilm-dependent infectious disease that damages teeth by minerals loss and presents a high incidence of clinical restorative polymeric fillings(tooth colored fillings).Until now,restorative polymeric fillings present no bioactivity.The complexity of oral biofilms contributes to the difficulty in developing effective novel dental materials.Nanotechnology has been explored in the development of bioactive dental materials to reduce or modulate the activities of caries-related bacteria.Nano-structured platforms based on calcium phosphate and metallic particles have advanced to impart an anti-caries potential to restorative materials.The bioactivity of these platforms induces prevention of mineral loss of the hard tooth structure and antibacterial activities against caries-related pathogens.It has been suggested that this bioactivity could minimize the incidence of caries around restorations(CARS)and increase the longevity of such filling materials.The last few years witnessed growing numbers of studies on the preparation evaluations of these novel materials.Herein,the caries disease process and the role of pathogenic caries-related biofilm,the increasing incidence of CARS,and the recent efforts employed for incorporation of bioactive nanoparticles in restorative polymer materials as useful strategies for prevention and management of caries-related-bacteria are discussed.We highlight the status of the most advanced and widely explored interaction of nanoparticle-based platforms and calcium phosphate compounds with an eye toward translating the potential of these approaches to the dental clinical reality.展开更多
Microspheres of Ni_(0.5)Co_(0.5)Ga_(x)Fe_(2-x)O_(4)(x≤1.0)microsphere spinel ferrites(NiCoGaFe-MSFs)and carbon spheres were prepared via a hydrothermal technique.The micro structure of microspheres was investigated t...Microspheres of Ni_(0.5)Co_(0.5)Ga_(x)Fe_(2-x)O_(4)(x≤1.0)microsphere spinel ferrites(NiCoGaFe-MSFs)and carbon spheres were prepared via a hydrothermal technique.The micro structure of microspheres was investigated through scanning and transmission electron microscopy(SEM and TEM),respectively,and X-ray diffraction(XRD).The electrical and dielectric properties of NiCoGaFe-MSF at temperatures ranging from20 to 120℃(between 293,1×10^(3)and 393.1×10^(3))for f≤3.0 MHz were systematically studied as a result of 3D graphical drawing of the data obtained from an impedance analyzer.Relevant parameters such as ac/dc conductivity,dielectric loss,dielectric constant,activation energy,dissipation factor and Cole-Cole impedance spectra were extensively evaluated for various Ga ion mole ratios in the substitution where x≤1.0.We notice that the ac conductivity mostly obeys exponential power law rules,which vary significantly with the substitution ratios of Ga ions.Impedance analyses confirm that differences in conduction mechanisms in NiCoGaFe-MSFs are mainly due to grain-to-grain boundaries related to Ga ion substitution ratios.The change in dielectric constant of NiCoGaFe-MSFs is strongly dependent on the substitution rates and results in a normal dielectric distribution with frequency.The tangential loss for all micro spheres is observed to vary with measured temperatures and their frequency dependencies can be attributed to the conduction mechanism similar to Koop’s phenomenological model.It is clearly seen that the formation of semicircles is dominated by all the NiCoGaFe-MSFs and the diameter of the semicircles mostly decreases with increasing temperature,as evidence of a temperaturedependent relaxation mechanism.展开更多
Yttrium(Y)and cerium(Ce)co-doped ZnO nanoparticles(NPs)were synthesized via the simple sol-gel auto-combustion route.The effect of Ce and Y doping on the structure,morphology,optical,Zeta potential,and photocatalytic ...Yttrium(Y)and cerium(Ce)co-doped ZnO nanoparticles(NPs)were synthesized via the simple sol-gel auto-combustion route.The effect of Ce and Y doping on the structure,morphology,optical,Zeta potential,and photocatalytic activities of ZnO NPs was examined by Fourier transform infrared(FTIR)spectrometer,X-ray diffraction(XRD),transmission electron microscope(TEM),UV-vis spectrophotometer,and Zetasizer instrument.XRD data show that the fabricated samples crystallize into a hexagonal wurtzite structure.The dopants Y and Ce affect the crystal structure of ZnO NPs.The crystallite size is reduced with the co-doping effect.TEM results confirm the nano-sized particles of the prepared samples.An increase in optical bandgap values from 3.19 eV for x=0.0 to 3.22,3.24,and 3.25 eV for x=0.01,0.03,and 0.05 samples was confirmed by UV-Vis spectroscopy analysis.Y and Ce co-doped ZnO nanoparticles show significant alteration of zeta potential and photocatalytic properties compared to undoped ZnO NPs.Comparatively,undoped ZnO shows better stability in deionized water as compared to Ce-Y doped ZnO NPs and exhibits high photocatalytic activity(degradation rate,97.92%)for methyl orange(MO)degradation.展开更多
Obstacle removal in crowd evacuation is critical to safety and the evacuation system efficiency. Recently, manyresearchers proposed game theoreticmodels to avoid and remove obstacles for crowd evacuation. Game theoret...Obstacle removal in crowd evacuation is critical to safety and the evacuation system efficiency. Recently, manyresearchers proposed game theoreticmodels to avoid and remove obstacles for crowd evacuation. Game theoreticalmodels aim to study and analyze the strategic behaviors of individuals within a crowd and their interactionsduring the evacuation. Game theoretical models have some limitations in the context of crowd evacuation. Thesemodels consider a group of individuals as homogeneous objects with the same goals, involve complex mathematicalformulation, and cannot model real-world scenarios such as panic, environmental information, crowds that movedynamically, etc. The proposed work presents a game theoretic model integrating an agent-based model to removethe obstacles from exits. The proposed model considered the parameters named: (1) obstacle size, length, andwidth, (2) removal time, (3) evacuation time, (4) crowd density, (5) obstacle identification, and (6) route selection.The proposed work conducts various experiments considering different conditions, such as obstacle types, obstacleremoval, and several obstacles. Evaluation results show the proposed model’s effectiveness compared with existingliterature in reducing the overall evacuation time, cell selection, and obstacle removal. The study is potentially usefulfor public safety situations such as emergency evacuations during disasters and calamities.展开更多
The complex morphological,anatomical,physiological,and chemical mechanisms within the aging brain have been the hot topic of research for centuries.The aging process alters the brain structure that affects functions a...The complex morphological,anatomical,physiological,and chemical mechanisms within the aging brain have been the hot topic of research for centuries.The aging process alters the brain structure that affects functions and cognitions,but the worsening of such processes contributes to the pathogenesis of neurodegenerative disorders,such as Alzheimer's disease.Beyond these observable,mild morphological shifts,significant functional modifications in neurotransmission and neuronal activity critically influence the aging brain.Understanding these changes is important for maintaining cognitive health,especially given the increasing prevalence of age-related conditions that affect cognition.This review aims to explore the age-induced changes in brain plasticity and molecular processes,differentiating normal aging from the pathogenesis of Alzheimer's disease,thereby providing insights into predicting the risk of dementia,particularly Alzheimer's disease.展开更多
Five perovskite ceramics samples of Ba_(1-x)Er_(x)SnO_(3) with x=0.00,0.05,0.10,0.15,and 0.20 were synthesized using the conventional solid-state reaction method.The prepared samples were characterized by X-ray diffra...Five perovskite ceramics samples of Ba_(1-x)Er_(x)SnO_(3) with x=0.00,0.05,0.10,0.15,and 0.20 were synthesized using the conventional solid-state reaction method.The prepared samples were characterized by X-ray diffraction(XRD),Fourier transforms infrared(FTIR),and UV-Vis spectroscopy.The shielding properties against ionizing radiation were also investigated.The XRD analysis shows the perovskite cubic structure as the major phase in all samples.The FTIR reveals a distinctive band around 614-620 cm^(-1) ascribed to the antisymmetric O-Sn-O vibration.UV-Vis spectroscopy was used to determine the bandgap according to diffuse reflectance,and the results display a fixed enhancement in the bandgap from 3.2 to 3.3 eV for all samples.Gamma-ray shielding properties of the synthesized samples were experimentally measured and compared with XCOM computational results.The relative differences(Δ,%)between experimental and theo retical values are low and fall within the 0.446%-7.10%range.The addition of Er leads to the enhanced density,neutron,and gamma shielding features.In contrast,charged particles’shielding parameters gradually reduce with rising Er contents.These results suggest that Ba_(1-x)Er_(x)SnO_(3) samples can be used in different radiation shielding applications.展开更多
With the rapid growth of internet usage,a new situation has been created that enables practicing bullying.Cyberbullying has increased over the past decade,and it has the same adverse effects as face-to-face bullying,l...With the rapid growth of internet usage,a new situation has been created that enables practicing bullying.Cyberbullying has increased over the past decade,and it has the same adverse effects as face-to-face bullying,like anger,sadness,anxiety,and fear.With the anonymity people get on the internet,they tend to bemore aggressive and express their emotions freely without considering the effects,which can be a reason for the increase in cyberbullying and it is the main motive behind the current study.This study presents a thorough background of cyberbullying and the techniques used to collect,preprocess,and analyze the datasets.Moreover,a comprehensive review of the literature has been conducted to figure out research gaps and effective techniques and practices in cyberbullying detection in various languages,and it was deduced that there is significant room for improvement in the Arabic language.As a result,the current study focuses on the investigation of shortlisted machine learning algorithms in natural language processing(NLP)for the classification of Arabic datasets duly collected from Twitter(also known as X).In this regard,support vector machine(SVM),Naive Bayes(NB),Random Forest(RF),Logistic regression(LR),Bootstrap aggregating(Bagging),Gradient Boosting(GBoost),Light Gradient Boosting Machine(LightGBM),Adaptive Boosting(AdaBoost),and eXtreme Gradient Boosting(XGBoost)were shortlisted and investigated due to their effectiveness in the similar problems.Finally,the scheme was evaluated by well-known performance measures like accuracy,precision,Recall,and F1-score.Consequently,XGBoost exhibited the best performance with 89.95%accuracy,which is promising compared to the state-of-the-art.展开更多
Various feature selection algorithms are usually employed to improve classification models’overall performance.Optimization algorithms typically accompany such algorithms to select the optimal set of features.Among t...Various feature selection algorithms are usually employed to improve classification models’overall performance.Optimization algorithms typically accompany such algorithms to select the optimal set of features.Among the most currently attractive trends within optimization algorithms are hybrid metaheuristics.The present paper presents two Stages of Local Search models for feature selection based on WOA(Whale Optimization Algorithm)and Great Deluge(GD).GD Algorithm is integrated with the WOA algorithm to improve exploitation by identifying the most promising regions during the search.Another version is employed using the best solution found by the WOA algorithm and exploited by the GD algorithm.In addition,disruptive selection(DS)is employed to select the solutions from the population for local search.DS is chosen to maintain the diversity of the population via enhancing low and high-quality solutions.Fifteen(15)standard benchmark datasets provided by the University of California Irvine(UCI)repository were used in evaluating the proposed approaches’performance.Next,a comparison was made with four population-based algorithms as wrapper feature selection methods from the literature.The proposed techniques have proved their efficiency in enhancing classification accuracy compared to other wrapper methods.Hence,the WOA can search effectively in the feature space and choose the most relevant attributes for classification tasks.展开更多
The innovation in technologies related to health facilities today is increasingly helping to manage patients with different diseases.The most fatal of these is the issue of heart disease that cannot be detected from a...The innovation in technologies related to health facilities today is increasingly helping to manage patients with different diseases.The most fatal of these is the issue of heart disease that cannot be detected from a naked eye,and attacks as soon as the human exceeds the allowed range of vital signs like pulse rate,body temperature,and blood pressure.The real challenge is to diagnose patients with more diagnostic accuracy and in a timely manner,followed by prescribing appropriate treatments and keeping prescription errors to a minimum.In developing countries,the domain of healthcare is progressing day by day using different Smart healthcare:emerging technologies like cloud computing,fog computing,and mobile computing.Electronic health records(EHRs)are used to manage the huge volume of data using cloud computing.That reduces the storage,processing,and retrieval cost as well as ensuring the availability of data.Machine learning procedures are used to extract hidden patterns and data analytics.In this research,a combination of cloud computing and machine learning algorithm Support vector machine(SVM)is used to predict heart diseases.Simulation results have shown that the proposed intelligent cloud-based heart disease prediction system empowered with a Support vector machine(SVM)-based system model gives 93.33%accuracy,which is better than previously published approaches.展开更多
In current report,the structural,magnetic,and thermoelectric properties of RE doped MgPm_(2)X_(4)(X=S,Se) spinels were investigated.The energy difference in ferromagnetic and antiferromagnetic states reveals the stabi...In current report,the structural,magnetic,and thermoelectric properties of RE doped MgPm_(2)X_(4)(X=S,Se) spinels were investigated.The energy difference in ferromagnetic and antiferromagnetic states reveals the stability of MgPm_(2)(S/Se)_(4) in the ferromagnetic states.The co mputation of enthalpy of formation also ascertains thermodynamic stability of crystal structure.Spin-dependent band structure and density of states analysis reveal ferromagnetic semiconducting character showing different electronic behavior in both spin channels.The room temperature ferromagnetism,spin polarization and Curie temperature are estimated from exchange energies analysis.In addition,exchange constants(N_(0)α and N_(0)β),exchange energy Δ_(x)(pd),crystal ifeld energy,and double exchange mechanism were studied to explore the magnetic response.Likewise,the electrical conductivity,thermal conductivity,Seebeck co-efficient,and power factor show effect on electrons spin and their potential for thermoelectric devices.展开更多
Pt/Eu2O3-CeO2 materials with different Eu concentrations were prepared and applied to toluene destruction,and the remarkable promotion impact of EuOx on Pt/CeO2 can be observed.The characterization results reveal that...Pt/Eu2O3-CeO2 materials with different Eu concentrations were prepared and applied to toluene destruction,and the remarkable promotion impact of EuOx on Pt/CeO2 can be observed.The characterization results reveal that the presence of EuOx significantly enhances the redox property,lattice O concentration,and Ce3+ ratio of the Pt/CeO2 material,which facilitates the dispersion and activity of Pt active sites and thus accelerates the decomposition process of toluene.Among all catalysts,a sample with an Eu content of 2.5 at.%(Pt/EC-2.5)possesses the best catalytic activity with 0.09 vol% of toluene completely destructed at 200 ℃ under a relatively high GHSV of 50000 h^-1.The possible reaction pathway and mechanism of toluene combustion over Pt/Eu2O3-CeO2 samples are presented according to in-situ DRIFTS,which confirms that the toluene oxidation process obeys the Mars-van Krevelen mechanism with aldehydes and ketones as primary organic intermediates.展开更多
Neuroimaging has emerged over the last few decades as a crucial tool in diagnosing Alzheimer’s disease(AD).Mild cognitive impairment(MCI)is a condition that falls between the spectrum of normal cognitive function and...Neuroimaging has emerged over the last few decades as a crucial tool in diagnosing Alzheimer’s disease(AD).Mild cognitive impairment(MCI)is a condition that falls between the spectrum of normal cognitive function and AD.However,previous studies have mainly used handcrafted features to classify MCI,AD,and normal control(NC)individuals.This paper focuses on using gray matter(GM)scans obtained through magnetic resonance imaging(MRI)for the diagnosis of individuals with MCI,AD,and NC.To improve classification performance,we developed two transfer learning strategies with data augmentation(i.e.,shear range,rotation,zoom range,channel shift).The first approach is a deep Siamese network(DSN),and the second approach involves using a cross-domain strategy with customized VGG-16.We performed experiments on the Alzheimer’s Disease Neuroimaging Initiative(ADNI)dataset to evaluate the performance of our proposed models.Our experimental results demonstrate superior performance in classifying the three binary classification tasks:NC vs.AD,NC vs.MCI,and MCI vs.AD.Specifically,we achieved a classification accuracy of 97.68%,94.25%,and 92.18%for the three cases,respectively.Our study proposes two transfer learning strategies with data augmentation to accurately diagnose MCI,AD,and normal control individuals using GM scans.Our findings provide promising results for future research and clinical applications in the early detection and diagnosis of AD.展开更多
Nowadays,there is tremendous growth in biometric authentication and cybersecurity applications.Thus,the efficient way of storing and securing personal biometric patterns is mandatory in most governmental and private s...Nowadays,there is tremendous growth in biometric authentication and cybersecurity applications.Thus,the efficient way of storing and securing personal biometric patterns is mandatory in most governmental and private sectors.Therefore,designing and implementing robust security algorithms for users’biometrics is still a hot research area to be investigated.This work presents a powerful biometric security system(BSS)to protect different biometric modalities such as faces,iris,and fingerprints.The proposed BSSmodel is based on hybridizing auto-encoder(AE)network and a chaos-based ciphering algorithm to cipher the details of the stored biometric patterns and ensures their secrecy.The employed AE network is unsupervised deep learning(DL)structure used in the proposed BSS model to extract main biometric features.These obtained features are utilized to generate two random chaos matrices.The first random chaos matrix is used to permute the pixels of biometric images.In contrast,the second random matrix is used to further cipher and confuse the resulting permuted biometric pixels using a two-dimensional(2D)chaotic logisticmap(CLM)algorithm.To assess the efficiency of the proposed BSS,(1)different standardized color and grayscale images of the examined fingerprint,faces,and iris biometrics were used(2)comprehensive security and recognition evaluation metrics were measured.The assessment results have proven the authentication and robustness superiority of the proposed BSSmodel compared to other existing BSSmodels.For example,the proposed BSS succeeds in getting a high area under the receiver operating characteristic(AROC)value that reached 99.97%and low rates of 0.00137,0.00148,and 3516 CMC,2023,vol.74,no.20.00157 for equal error rate(EER),false reject rate(FRR),and a false accept rate(FAR),respectively.展开更多
The impact of Ga^(3+)and Gd^(3+)co-substitution on different types of behavior of Ni-Co nanospinel ferrites(NSFs),(Ni_(0.5)Co_(0.5))[Ga_(x)Gd_(x)Fe_(2-2x)]O_(4)(0.000≤x≤0.025),was investigated.NSFs were fabricated t...The impact of Ga^(3+)and Gd^(3+)co-substitution on different types of behavior of Ni-Co nanospinel ferrites(NSFs),(Ni_(0.5)Co_(0.5))[Ga_(x)Gd_(x)Fe_(2-2x)]O_(4)(0.000≤x≤0.025),was investigated.NSFs were fabricated through ultrasound irradiation.The structure,composition,and spherical morphology of all products were verified by numerous characterization techniques such as X-ray diffraction(XRD),scanning and transmission electron microscopes(SEM and TEM),and selected area electron diffraction(SAED).Mossbauer spectra are composed of two ferromagnetic sextets and one paramagnetic doublet.These spectra were fitted to extract hyperfine parameters of the doped samples.Both sublattices'hyperfine magnetic field decreases with substitution.The isomer shift values show that the Mossbauer spectra are composed of magnetic Fe^(3+)sextets.The magnetization measurements at variable magnetic field(M-H)were investigated via a vibrating sample magnetometer(VSM)at temperatures(T)from 300 to 10 K.All NSFs disclose ferrimagnetic behavior at both 300 and 10 K at which they are soft and hard,respectively.We determined that the remanence magnetization(M_(r)),saturation magnetization(M_(s)),and magneton number(n_(B))decrease with increasing Ga^(3+)and Gd^(3+)ions contents.The reduction in these values is predominantly recognized to be the impact of cation redistribution and surface spins on tetrahedral(T_(d))and octahedral(O_(h))sites.As the Ga^(3+)and Gd^(3+)contents increase,the coercivity(H_(c))is also found to decrease.展开更多
From the beginning,the process of research and its publication is an ever-growing phenomenon and with the emergence of web technologies,its growth rate is overwhelming.On a rough estimate,more than thirty thousand res...From the beginning,the process of research and its publication is an ever-growing phenomenon and with the emergence of web technologies,its growth rate is overwhelming.On a rough estimate,more than thirty thousand research journals have been issuing around four million papers annually on average.Search engines,indexing services,and digital libraries have been searching for such publications over the web.Nevertheless,getting the most relevant articles against the user requests is yet a fantasy.It is mainly because the articles are not appropriately indexed based on the hierarchies of granular subject classification.To overcome this issue,researchers are striving to investigate new techniques for the classification of the research articles especially,when the complete article text is not available(a case of nonopen access articles).The proposed study aims to investigate the multilabel classification over the available metadata in the best possible way and to assess,“to what extent metadata-based features can perform in contrast to content-based approaches.”In this regard,novel techniques for investigating multilabel classification have been proposed,developed,and evaluated on metadata such as the Title and Keywords of the articles.The proposed technique has been assessed for two diverse datasets,namely,from the Journal of universal computer science(J.UCS)and the benchmark dataset comprises of the articles published by the Association for computing machinery(ACM).The proposed technique yields encouraging results in contrast to the state-ofthe-art techniques in the literature.展开更多
BACKGROUND Clubfoot,or congenital talipes equinovarus,is a widely recognized cause of disability and congenital deformity worldwide,which significantly impacts the quality of life.Effective management of clubfoot requ...BACKGROUND Clubfoot,or congenital talipes equinovarus,is a widely recognized cause of disability and congenital deformity worldwide,which significantly impacts the quality of life.Effective management of clubfoot requires long-term,multidiscip-linary intervention.It is important to understand how common this condition is in order to assess its impact on the population.Unfortunately,few studies have investigated the prevalence of clubfoot in Saudi Arabia.AIM To determine the prevalence of clubfoot in Saudi Arabia via the patient population at King Fahad University Hospital(KFUH).METHODS This was a retrospective study conducted at one of the largest hospitals in the country and located in one of the most densely populated of the administrative regions.RESULTS Of the 7792 births between 2015 to 2023 that were included in the analysis,42 patients were diagnosed with clubfoot,resulting in a prevalence of 5.3 per 1000 live births at KFUH.CONCLUSION The observed prevalence of clubfoot was significantly higher than both global and local estimates,indicating a substantial burden in the study population.展开更多
Currently, the risk factors of pregnancy loss are increasing andare considered a major challenge because they vary between cases. The earlyprediction of miscarriage can help pregnant ladies to take the needed careand ...Currently, the risk factors of pregnancy loss are increasing andare considered a major challenge because they vary between cases. The earlyprediction of miscarriage can help pregnant ladies to take the needed careand avoid any danger. Therefore, an intelligent automated solution must bedeveloped to predict the risk factors for pregnancy loss at an early stage toassist with accurate and effective diagnosis. Machine learning (ML)-baseddecision support systems are increasingly used in the healthcare sector andhave achieved notable performance and objectiveness in disease predictionand prognosis. Thus, we developed a model to help obstetricians predictthe probability of miscarriage using ML. And support their decisions andexpectations about pregnancy status by providing an easy, automated way topredict miscarriage at early stages using ML tools and techniques. Althoughmany published papers proposed similar models, none of them used Saudiclinical data. Our proposed solution used ML classification algorithms tobuild a miscarriage prediction model. Four classifiers were used in this study:decision tree (DT), random forest (RF), k-nearest neighbor (KNN), andgradient boosting (GB). Accuracy, Precision, Recall, F1-score, and receiveroperating characteristic area under the curve (ROC-AUC) were used to evaluatethe proposed model. The results showed that GB overperformed the otherclassifiers with an accuracy of 93.4% and ROC-AUC of 97%. This proposedmodel can assist in the early identification of at-risk pregnant women to avoidmiscarriage in the first trimester and will improve the healthcare sector inSaudi Arabia.展开更多
Diabetes is a serious health condition that can cause several issues in human body organs such as the heart and kidney as well as a serious eye disease called diabetic retinopathy(DR).Early detection and treatment are...Diabetes is a serious health condition that can cause several issues in human body organs such as the heart and kidney as well as a serious eye disease called diabetic retinopathy(DR).Early detection and treatment are crucial to prevent complete blindness or partial vision loss.Traditional detection methods,which involve ophthalmologists examining retinal fundus images,are subjective,expensive,and time-consuming.Therefore,this study employs artificial intelligence(AI)technology to perform faster and more accurate binary classifications and determine the presence of DR.In this regard,we employed three promising machine learning models namely,support vector machine(SVM),k-nearest neighbors(KNN),and Histogram Gradient Boosting(HGB),after carefully selecting features using transfer learning on the fundus images of the Asia Pacific Tele-Ophthalmology Society(APTOS)(a standard dataset),which includes 3662 images and originally categorized DR into five levels,now simplified to a binary format:No DR and DR(Classes 1-4).The results demonstrate that the SVM model outperformed the other approaches in the literature with the same dataset,achieving an excellent accuracy of 96.9%,compared to 95.6%for both the KNN and HGB models.This approach is evaluated by medical health professionals and offers a valuable pathway for the early detection of DR and can be successfully employed as a clinical decision support system.展开更多
BACKGROUND Among diverse profound impacts on patients’quality of life(QoL),end-stage renal disease(ESRD)frequently results in increased levels of depression,anxiety,and stress.Renal replacement therapies such as hemo...BACKGROUND Among diverse profound impacts on patients’quality of life(QoL),end-stage renal disease(ESRD)frequently results in increased levels of depression,anxiety,and stress.Renal replacement therapies such as hemodialysis(HD)and transplantation(TX)are intended to enhance QoL,although their ability to alleviate psychological distress remains uncertain.This research posits the existence of a significant correlation between negative emotional states and QoL among ESRD patients,with varying effects observed in HD and TX patients.AIM To examine the relationship between QoL and negative emotional states(depression,anxiety,and stress)and predicted QoL in various end-stage renal replacement therapy patients with ESRD.METHODS This cross-sectional study included HD or TX patients in the Eastern Region of Saudi Arabia.The 36-item Short Form Survey and Depression Anxiety Stress Scale(DASS)was used for data collection,and correlation and regression analyses were performed.RESULTS The HD and TX transplantation groups showed statistically significant inverse relationships between QoL and DASS scores.HD patients with high anxiety levels and less education scored low on the physical component summary(PCS).In addition,the results of the mental component summary(MCS)were associated with reduced depression.Compared with older transplant patients,TX patients’PCS scores were lower,and depression,stress,and negative working conditions were highly correlated with MCS scores.CONCLUSION The findings of this study revealed notable connections between well-being and mental turmoil experienced by individuals undergoing HD and TX.The PCS of HD patients is affected by heightened levels of anxiety and lower educational attainment,while the MCS of transplant patients is influenced by advancing age and elevated stress levels.These insights will contribute to a more comprehensive understanding of patient support.展开更多
文摘Brain stimulation techniques offer powerful means of modulating the physiology of specific neural structures. In recent years, non-invasive brain stimulation techniques, such as transcranial magnetic stimulation(TMS) and transcranial direct current stimulation, have emerged as therapeutic tools for neurology and neuroscience. However, the possible repercussions of these techniques remain unclear, and there are few reports on the incisive recovery mechanisms through brain stimulation. Although several studies have recommended the use of non-invasive brain stimulation in clinical neuroscience, with a special emphasis on TMS, the suggested mechanisms of action have not been confirmed directly at the neural level. Insights into the neural mechanisms of non-invasive brain stimulation would unveil the strategies necessary to enhance the safety and efficacy of this progressive approach. Therefore, animal studies investigating the mechanisms of TMSinduced recovery at the neural level are crucial for the elaboration of non-invasive brain stimulation. Translational research done using animal models has several advantages and is able to investigate knowledge gaps by directly targeting neuronal levels. In this review, we have discussed the role of TMS in different animal models, the impact of animal studies on various disease states, and the findings regarding brain function of animal models after TMS in pharmacology research.
文摘Millions of people worldwide suffer from a toothache due to tooth cavity,and often permanent tooth loss.Dental caries,also known as tooth decay,is a biofilm-dependent infectious disease that damages teeth by minerals loss and presents a high incidence of clinical restorative polymeric fillings(tooth colored fillings).Until now,restorative polymeric fillings present no bioactivity.The complexity of oral biofilms contributes to the difficulty in developing effective novel dental materials.Nanotechnology has been explored in the development of bioactive dental materials to reduce or modulate the activities of caries-related bacteria.Nano-structured platforms based on calcium phosphate and metallic particles have advanced to impart an anti-caries potential to restorative materials.The bioactivity of these platforms induces prevention of mineral loss of the hard tooth structure and antibacterial activities against caries-related pathogens.It has been suggested that this bioactivity could minimize the incidence of caries around restorations(CARS)and increase the longevity of such filling materials.The last few years witnessed growing numbers of studies on the preparation evaluations of these novel materials.Herein,the caries disease process and the role of pathogenic caries-related biofilm,the increasing incidence of CARS,and the recent efforts employed for incorporation of bioactive nanoparticles in restorative polymer materials as useful strategies for prevention and management of caries-related-bacteria are discussed.We highlight the status of the most advanced and widely explored interaction of nanoparticle-based platforms and calcium phosphate compounds with an eye toward translating the potential of these approaches to the dental clinical reality.
文摘Microspheres of Ni_(0.5)Co_(0.5)Ga_(x)Fe_(2-x)O_(4)(x≤1.0)microsphere spinel ferrites(NiCoGaFe-MSFs)and carbon spheres were prepared via a hydrothermal technique.The micro structure of microspheres was investigated through scanning and transmission electron microscopy(SEM and TEM),respectively,and X-ray diffraction(XRD).The electrical and dielectric properties of NiCoGaFe-MSF at temperatures ranging from20 to 120℃(between 293,1×10^(3)and 393.1×10^(3))for f≤3.0 MHz were systematically studied as a result of 3D graphical drawing of the data obtained from an impedance analyzer.Relevant parameters such as ac/dc conductivity,dielectric loss,dielectric constant,activation energy,dissipation factor and Cole-Cole impedance spectra were extensively evaluated for various Ga ion mole ratios in the substitution where x≤1.0.We notice that the ac conductivity mostly obeys exponential power law rules,which vary significantly with the substitution ratios of Ga ions.Impedance analyses confirm that differences in conduction mechanisms in NiCoGaFe-MSFs are mainly due to grain-to-grain boundaries related to Ga ion substitution ratios.The change in dielectric constant of NiCoGaFe-MSFs is strongly dependent on the substitution rates and results in a normal dielectric distribution with frequency.The tangential loss for all micro spheres is observed to vary with measured temperatures and their frequency dependencies can be attributed to the conduction mechanism similar to Koop’s phenomenological model.It is clearly seen that the formation of semicircles is dominated by all the NiCoGaFe-MSFs and the diameter of the semicircles mostly decreases with increasing temperature,as evidence of a temperaturedependent relaxation mechanism.
文摘Yttrium(Y)and cerium(Ce)co-doped ZnO nanoparticles(NPs)were synthesized via the simple sol-gel auto-combustion route.The effect of Ce and Y doping on the structure,morphology,optical,Zeta potential,and photocatalytic activities of ZnO NPs was examined by Fourier transform infrared(FTIR)spectrometer,X-ray diffraction(XRD),transmission electron microscope(TEM),UV-vis spectrophotometer,and Zetasizer instrument.XRD data show that the fabricated samples crystallize into a hexagonal wurtzite structure.The dopants Y and Ce affect the crystal structure of ZnO NPs.The crystallite size is reduced with the co-doping effect.TEM results confirm the nano-sized particles of the prepared samples.An increase in optical bandgap values from 3.19 eV for x=0.0 to 3.22,3.24,and 3.25 eV for x=0.01,0.03,and 0.05 samples was confirmed by UV-Vis spectroscopy analysis.Y and Ce co-doped ZnO nanoparticles show significant alteration of zeta potential and photocatalytic properties compared to undoped ZnO NPs.Comparatively,undoped ZnO shows better stability in deionized water as compared to Ce-Y doped ZnO NPs and exhibits high photocatalytic activity(degradation rate,97.92%)for methyl orange(MO)degradation.
文摘Obstacle removal in crowd evacuation is critical to safety and the evacuation system efficiency. Recently, manyresearchers proposed game theoreticmodels to avoid and remove obstacles for crowd evacuation. Game theoreticalmodels aim to study and analyze the strategic behaviors of individuals within a crowd and their interactionsduring the evacuation. Game theoretical models have some limitations in the context of crowd evacuation. Thesemodels consider a group of individuals as homogeneous objects with the same goals, involve complex mathematicalformulation, and cannot model real-world scenarios such as panic, environmental information, crowds that movedynamically, etc. The proposed work presents a game theoretic model integrating an agent-based model to removethe obstacles from exits. The proposed model considered the parameters named: (1) obstacle size, length, andwidth, (2) removal time, (3) evacuation time, (4) crowd density, (5) obstacle identification, and (6) route selection.The proposed work conducts various experiments considering different conditions, such as obstacle types, obstacleremoval, and several obstacles. Evaluation results show the proposed model’s effectiveness compared with existingliterature in reducing the overall evacuation time, cell selection, and obstacle removal. The study is potentially usefulfor public safety situations such as emergency evacuations during disasters and calamities.
文摘The complex morphological,anatomical,physiological,and chemical mechanisms within the aging brain have been the hot topic of research for centuries.The aging process alters the brain structure that affects functions and cognitions,but the worsening of such processes contributes to the pathogenesis of neurodegenerative disorders,such as Alzheimer's disease.Beyond these observable,mild morphological shifts,significant functional modifications in neurotransmission and neuronal activity critically influence the aging brain.Understanding these changes is important for maintaining cognitive health,especially given the increasing prevalence of age-related conditions that affect cognition.This review aims to explore the age-induced changes in brain plasticity and molecular processes,differentiating normal aging from the pathogenesis of Alzheimer's disease,thereby providing insights into predicting the risk of dementia,particularly Alzheimer's disease.
基金Funding support from the Deanship of Scientific Research at King Fahd University of Petroleum&Minerals(KFUPM),Saudi Arabia(DF201008)。
文摘Five perovskite ceramics samples of Ba_(1-x)Er_(x)SnO_(3) with x=0.00,0.05,0.10,0.15,and 0.20 were synthesized using the conventional solid-state reaction method.The prepared samples were characterized by X-ray diffraction(XRD),Fourier transforms infrared(FTIR),and UV-Vis spectroscopy.The shielding properties against ionizing radiation were also investigated.The XRD analysis shows the perovskite cubic structure as the major phase in all samples.The FTIR reveals a distinctive band around 614-620 cm^(-1) ascribed to the antisymmetric O-Sn-O vibration.UV-Vis spectroscopy was used to determine the bandgap according to diffuse reflectance,and the results display a fixed enhancement in the bandgap from 3.2 to 3.3 eV for all samples.Gamma-ray shielding properties of the synthesized samples were experimentally measured and compared with XCOM computational results.The relative differences(Δ,%)between experimental and theo retical values are low and fall within the 0.446%-7.10%range.The addition of Er leads to the enhanced density,neutron,and gamma shielding features.In contrast,charged particles’shielding parameters gradually reduce with rising Er contents.These results suggest that Ba_(1-x)Er_(x)SnO_(3) samples can be used in different radiation shielding applications.
文摘With the rapid growth of internet usage,a new situation has been created that enables practicing bullying.Cyberbullying has increased over the past decade,and it has the same adverse effects as face-to-face bullying,like anger,sadness,anxiety,and fear.With the anonymity people get on the internet,they tend to bemore aggressive and express their emotions freely without considering the effects,which can be a reason for the increase in cyberbullying and it is the main motive behind the current study.This study presents a thorough background of cyberbullying and the techniques used to collect,preprocess,and analyze the datasets.Moreover,a comprehensive review of the literature has been conducted to figure out research gaps and effective techniques and practices in cyberbullying detection in various languages,and it was deduced that there is significant room for improvement in the Arabic language.As a result,the current study focuses on the investigation of shortlisted machine learning algorithms in natural language processing(NLP)for the classification of Arabic datasets duly collected from Twitter(also known as X).In this regard,support vector machine(SVM),Naive Bayes(NB),Random Forest(RF),Logistic regression(LR),Bootstrap aggregating(Bagging),Gradient Boosting(GBoost),Light Gradient Boosting Machine(LightGBM),Adaptive Boosting(AdaBoost),and eXtreme Gradient Boosting(XGBoost)were shortlisted and investigated due to their effectiveness in the similar problems.Finally,the scheme was evaluated by well-known performance measures like accuracy,precision,Recall,and F1-score.Consequently,XGBoost exhibited the best performance with 89.95%accuracy,which is promising compared to the state-of-the-art.
基金This research is part of a project funded by Imam Abdulrahman Bin Faisal University,under Grant Number 2020-083-BASRC.
文摘Various feature selection algorithms are usually employed to improve classification models’overall performance.Optimization algorithms typically accompany such algorithms to select the optimal set of features.Among the most currently attractive trends within optimization algorithms are hybrid metaheuristics.The present paper presents two Stages of Local Search models for feature selection based on WOA(Whale Optimization Algorithm)and Great Deluge(GD).GD Algorithm is integrated with the WOA algorithm to improve exploitation by identifying the most promising regions during the search.Another version is employed using the best solution found by the WOA algorithm and exploited by the GD algorithm.In addition,disruptive selection(DS)is employed to select the solutions from the population for local search.DS is chosen to maintain the diversity of the population via enhancing low and high-quality solutions.Fifteen(15)standard benchmark datasets provided by the University of California Irvine(UCI)repository were used in evaluating the proposed approaches’performance.Next,a comparison was made with four population-based algorithms as wrapper feature selection methods from the literature.The proposed techniques have proved their efficiency in enhancing classification accuracy compared to other wrapper methods.Hence,the WOA can search effectively in the feature space and choose the most relevant attributes for classification tasks.
文摘The innovation in technologies related to health facilities today is increasingly helping to manage patients with different diseases.The most fatal of these is the issue of heart disease that cannot be detected from a naked eye,and attacks as soon as the human exceeds the allowed range of vital signs like pulse rate,body temperature,and blood pressure.The real challenge is to diagnose patients with more diagnostic accuracy and in a timely manner,followed by prescribing appropriate treatments and keeping prescription errors to a minimum.In developing countries,the domain of healthcare is progressing day by day using different Smart healthcare:emerging technologies like cloud computing,fog computing,and mobile computing.Electronic health records(EHRs)are used to manage the huge volume of data using cloud computing.That reduces the storage,processing,and retrieval cost as well as ensuring the availability of data.Machine learning procedures are used to extract hidden patterns and data analytics.In this research,a combination of cloud computing and machine learning algorithm Support vector machine(SVM)is used to predict heart diseases.Simulation results have shown that the proposed intelligent cloud-based heart disease prediction system empowered with a Support vector machine(SVM)-based system model gives 93.33%accuracy,which is better than previously published approaches.
基金funded by the Princess Nourah bint Abdulrahman University Researchers Supporting Project(PNURSP2023R29)Princess Nourah bint Abdulrahman University,Riyadh,Saudi Arabiathe Deanship of Scientific Research at King Khalid University,SaudiArabia for funding this work through Large Groups Project(L.R.G.P2/431/44)。
文摘In current report,the structural,magnetic,and thermoelectric properties of RE doped MgPm_(2)X_(4)(X=S,Se) spinels were investigated.The energy difference in ferromagnetic and antiferromagnetic states reveals the stability of MgPm_(2)(S/Se)_(4) in the ferromagnetic states.The co mputation of enthalpy of formation also ascertains thermodynamic stability of crystal structure.Spin-dependent band structure and density of states analysis reveal ferromagnetic semiconducting character showing different electronic behavior in both spin channels.The room temperature ferromagnetism,spin polarization and Curie temperature are estimated from exchange energies analysis.In addition,exchange constants(N_(0)α and N_(0)β),exchange energy Δ_(x)(pd),crystal ifeld energy,and double exchange mechanism were studied to explore the magnetic response.Likewise,the electrical conductivity,thermal conductivity,Seebeck co-efficient,and power factor show effect on electrons spin and their potential for thermoelectric devices.
基金financially supported by the National Key R&D Program of China (2016YFC0204201)the National Natural Science Foundation of China (21677114, 21477095, 21876139)the Fundamental Research Funds for the Central Universities (xjj2017170)~~
文摘Pt/Eu2O3-CeO2 materials with different Eu concentrations were prepared and applied to toluene destruction,and the remarkable promotion impact of EuOx on Pt/CeO2 can be observed.The characterization results reveal that the presence of EuOx significantly enhances the redox property,lattice O concentration,and Ce3+ ratio of the Pt/CeO2 material,which facilitates the dispersion and activity of Pt active sites and thus accelerates the decomposition process of toluene.Among all catalysts,a sample with an Eu content of 2.5 at.%(Pt/EC-2.5)possesses the best catalytic activity with 0.09 vol% of toluene completely destructed at 200 ℃ under a relatively high GHSV of 50000 h^-1.The possible reaction pathway and mechanism of toluene combustion over Pt/Eu2O3-CeO2 samples are presented according to in-situ DRIFTS,which confirms that the toluene oxidation process obeys the Mars-van Krevelen mechanism with aldehydes and ketones as primary organic intermediates.
基金Research work funded by Zhejiang Normal University Research Fund YS304023947 and YS304023948.
文摘Neuroimaging has emerged over the last few decades as a crucial tool in diagnosing Alzheimer’s disease(AD).Mild cognitive impairment(MCI)is a condition that falls between the spectrum of normal cognitive function and AD.However,previous studies have mainly used handcrafted features to classify MCI,AD,and normal control(NC)individuals.This paper focuses on using gray matter(GM)scans obtained through magnetic resonance imaging(MRI)for the diagnosis of individuals with MCI,AD,and NC.To improve classification performance,we developed two transfer learning strategies with data augmentation(i.e.,shear range,rotation,zoom range,channel shift).The first approach is a deep Siamese network(DSN),and the second approach involves using a cross-domain strategy with customized VGG-16.We performed experiments on the Alzheimer’s Disease Neuroimaging Initiative(ADNI)dataset to evaluate the performance of our proposed models.Our experimental results demonstrate superior performance in classifying the three binary classification tasks:NC vs.AD,NC vs.MCI,and MCI vs.AD.Specifically,we achieved a classification accuracy of 97.68%,94.25%,and 92.18%for the three cases,respectively.Our study proposes two transfer learning strategies with data augmentation to accurately diagnose MCI,AD,and normal control individuals using GM scans.Our findings provide promising results for future research and clinical applications in the early detection and diagnosis of AD.
文摘Nowadays,there is tremendous growth in biometric authentication and cybersecurity applications.Thus,the efficient way of storing and securing personal biometric patterns is mandatory in most governmental and private sectors.Therefore,designing and implementing robust security algorithms for users’biometrics is still a hot research area to be investigated.This work presents a powerful biometric security system(BSS)to protect different biometric modalities such as faces,iris,and fingerprints.The proposed BSSmodel is based on hybridizing auto-encoder(AE)network and a chaos-based ciphering algorithm to cipher the details of the stored biometric patterns and ensures their secrecy.The employed AE network is unsupervised deep learning(DL)structure used in the proposed BSS model to extract main biometric features.These obtained features are utilized to generate two random chaos matrices.The first random chaos matrix is used to permute the pixels of biometric images.In contrast,the second random matrix is used to further cipher and confuse the resulting permuted biometric pixels using a two-dimensional(2D)chaotic logisticmap(CLM)algorithm.To assess the efficiency of the proposed BSS,(1)different standardized color and grayscale images of the examined fingerprint,faces,and iris biometrics were used(2)comprehensive security and recognition evaluation metrics were measured.The assessment results have proven the authentication and robustness superiority of the proposed BSSmodel compared to other existing BSSmodels.For example,the proposed BSS succeeds in getting a high area under the receiver operating characteristic(AROC)value that reached 99.97%and low rates of 0.00137,0.00148,and 3516 CMC,2023,vol.74,no.20.00157 for equal error rate(EER),false reject rate(FRR),and a false accept rate(FAR),respectively.
文摘The impact of Ga^(3+)and Gd^(3+)co-substitution on different types of behavior of Ni-Co nanospinel ferrites(NSFs),(Ni_(0.5)Co_(0.5))[Ga_(x)Gd_(x)Fe_(2-2x)]O_(4)(0.000≤x≤0.025),was investigated.NSFs were fabricated through ultrasound irradiation.The structure,composition,and spherical morphology of all products were verified by numerous characterization techniques such as X-ray diffraction(XRD),scanning and transmission electron microscopes(SEM and TEM),and selected area electron diffraction(SAED).Mossbauer spectra are composed of two ferromagnetic sextets and one paramagnetic doublet.These spectra were fitted to extract hyperfine parameters of the doped samples.Both sublattices'hyperfine magnetic field decreases with substitution.The isomer shift values show that the Mossbauer spectra are composed of magnetic Fe^(3+)sextets.The magnetization measurements at variable magnetic field(M-H)were investigated via a vibrating sample magnetometer(VSM)at temperatures(T)from 300 to 10 K.All NSFs disclose ferrimagnetic behavior at both 300 and 10 K at which they are soft and hard,respectively.We determined that the remanence magnetization(M_(r)),saturation magnetization(M_(s)),and magneton number(n_(B))decrease with increasing Ga^(3+)and Gd^(3+)ions contents.The reduction in these values is predominantly recognized to be the impact of cation redistribution and surface spins on tetrahedral(T_(d))and octahedral(O_(h))sites.As the Ga^(3+)and Gd^(3+)contents increase,the coercivity(H_(c))is also found to decrease.
文摘From the beginning,the process of research and its publication is an ever-growing phenomenon and with the emergence of web technologies,its growth rate is overwhelming.On a rough estimate,more than thirty thousand research journals have been issuing around four million papers annually on average.Search engines,indexing services,and digital libraries have been searching for such publications over the web.Nevertheless,getting the most relevant articles against the user requests is yet a fantasy.It is mainly because the articles are not appropriately indexed based on the hierarchies of granular subject classification.To overcome this issue,researchers are striving to investigate new techniques for the classification of the research articles especially,when the complete article text is not available(a case of nonopen access articles).The proposed study aims to investigate the multilabel classification over the available metadata in the best possible way and to assess,“to what extent metadata-based features can perform in contrast to content-based approaches.”In this regard,novel techniques for investigating multilabel classification have been proposed,developed,and evaluated on metadata such as the Title and Keywords of the articles.The proposed technique has been assessed for two diverse datasets,namely,from the Journal of universal computer science(J.UCS)and the benchmark dataset comprises of the articles published by the Association for computing machinery(ACM).The proposed technique yields encouraging results in contrast to the state-ofthe-art techniques in the literature.
文摘BACKGROUND Clubfoot,or congenital talipes equinovarus,is a widely recognized cause of disability and congenital deformity worldwide,which significantly impacts the quality of life.Effective management of clubfoot requires long-term,multidiscip-linary intervention.It is important to understand how common this condition is in order to assess its impact on the population.Unfortunately,few studies have investigated the prevalence of clubfoot in Saudi Arabia.AIM To determine the prevalence of clubfoot in Saudi Arabia via the patient population at King Fahad University Hospital(KFUH).METHODS This was a retrospective study conducted at one of the largest hospitals in the country and located in one of the most densely populated of the administrative regions.RESULTS Of the 7792 births between 2015 to 2023 that were included in the analysis,42 patients were diagnosed with clubfoot,resulting in a prevalence of 5.3 per 1000 live births at KFUH.CONCLUSION The observed prevalence of clubfoot was significantly higher than both global and local estimates,indicating a substantial burden in the study population.
文摘Currently, the risk factors of pregnancy loss are increasing andare considered a major challenge because they vary between cases. The earlyprediction of miscarriage can help pregnant ladies to take the needed careand avoid any danger. Therefore, an intelligent automated solution must bedeveloped to predict the risk factors for pregnancy loss at an early stage toassist with accurate and effective diagnosis. Machine learning (ML)-baseddecision support systems are increasingly used in the healthcare sector andhave achieved notable performance and objectiveness in disease predictionand prognosis. Thus, we developed a model to help obstetricians predictthe probability of miscarriage using ML. And support their decisions andexpectations about pregnancy status by providing an easy, automated way topredict miscarriage at early stages using ML tools and techniques. Althoughmany published papers proposed similar models, none of them used Saudiclinical data. Our proposed solution used ML classification algorithms tobuild a miscarriage prediction model. Four classifiers were used in this study:decision tree (DT), random forest (RF), k-nearest neighbor (KNN), andgradient boosting (GB). Accuracy, Precision, Recall, F1-score, and receiveroperating characteristic area under the curve (ROC-AUC) were used to evaluatethe proposed model. The results showed that GB overperformed the otherclassifiers with an accuracy of 93.4% and ROC-AUC of 97%. This proposedmodel can assist in the early identification of at-risk pregnant women to avoidmiscarriage in the first trimester and will improve the healthcare sector inSaudi Arabia.
文摘Diabetes is a serious health condition that can cause several issues in human body organs such as the heart and kidney as well as a serious eye disease called diabetic retinopathy(DR).Early detection and treatment are crucial to prevent complete blindness or partial vision loss.Traditional detection methods,which involve ophthalmologists examining retinal fundus images,are subjective,expensive,and time-consuming.Therefore,this study employs artificial intelligence(AI)technology to perform faster and more accurate binary classifications and determine the presence of DR.In this regard,we employed three promising machine learning models namely,support vector machine(SVM),k-nearest neighbors(KNN),and Histogram Gradient Boosting(HGB),after carefully selecting features using transfer learning on the fundus images of the Asia Pacific Tele-Ophthalmology Society(APTOS)(a standard dataset),which includes 3662 images and originally categorized DR into five levels,now simplified to a binary format:No DR and DR(Classes 1-4).The results demonstrate that the SVM model outperformed the other approaches in the literature with the same dataset,achieving an excellent accuracy of 96.9%,compared to 95.6%for both the KNN and HGB models.This approach is evaluated by medical health professionals and offers a valuable pathway for the early detection of DR and can be successfully employed as a clinical decision support system.
文摘BACKGROUND Among diverse profound impacts on patients’quality of life(QoL),end-stage renal disease(ESRD)frequently results in increased levels of depression,anxiety,and stress.Renal replacement therapies such as hemodialysis(HD)and transplantation(TX)are intended to enhance QoL,although their ability to alleviate psychological distress remains uncertain.This research posits the existence of a significant correlation between negative emotional states and QoL among ESRD patients,with varying effects observed in HD and TX patients.AIM To examine the relationship between QoL and negative emotional states(depression,anxiety,and stress)and predicted QoL in various end-stage renal replacement therapy patients with ESRD.METHODS This cross-sectional study included HD or TX patients in the Eastern Region of Saudi Arabia.The 36-item Short Form Survey and Depression Anxiety Stress Scale(DASS)was used for data collection,and correlation and regression analyses were performed.RESULTS The HD and TX transplantation groups showed statistically significant inverse relationships between QoL and DASS scores.HD patients with high anxiety levels and less education scored low on the physical component summary(PCS).In addition,the results of the mental component summary(MCS)were associated with reduced depression.Compared with older transplant patients,TX patients’PCS scores were lower,and depression,stress,and negative working conditions were highly correlated with MCS scores.CONCLUSION The findings of this study revealed notable connections between well-being and mental turmoil experienced by individuals undergoing HD and TX.The PCS of HD patients is affected by heightened levels of anxiety and lower educational attainment,while the MCS of transplant patients is influenced by advancing age and elevated stress levels.These insights will contribute to a more comprehensive understanding of patient support.
