Knowledge, Attitude, and Practice of Using Helmets in Children amongst Parents to Prevent Head Injuries: A Cross-Sectional Study in Riyadh, Saudi Arabia

Objectives: The primary objective was to characterize the range of Knowledge, Attitude, and Practice (KAP) of Helmet use in children amongst parents to prevent head injuries and death. Methods: This is a cross-sectional study, done by online survey using a snowball sampling technique, the number of included responses were 386 parents (Male and female) living in Riyadh Aged 21 - 60 years old or above. Results: The study showed that there is a difference in Parents’ belief in the importance of helmet use while riding a Bicycle vs Motorcycle/Quad bike and that was affected by parents’ education level, almost all the people who answered the survey (76.7%) agree that it is important for their children to wear a helmet when riding both a Bicycle and a Motorcycle or Quadbike with a cumulative percentage of (93.8%). And almost all agreed on multiple approaches to help increase helmet use be it by forcing rental shops to give out helmets, forcing sellers to recommend the use of helmets, increasing awareness campaigns, and imposing fines for not wearing helmets. Conclusions: This study is the first to explore Family helmet use while riding Bicycles and Motorcycles/Quad bikes. Although Parent’s belief in the importance of helmet use for their children was high, it is clear that the level of practice is low. With that the risk of head injuries might be high, our findings suggest that safety interventions for increasing pediatric helmet use are needed to increase helmet use and reduce the risk of head injury and hospitalization.

Mental health status among COVID-19 patients survivors of critical illness in Saudi Arabia:A 6-month follow-up questionnaire study

BACKGROUND Psychological assessment after intensive care unit(ICU)discharge is increasingly used to assess patients'cognitive and psychological well-being.However,few studies have examined those who recovered from coronavirus disease 2019(COVID-19).There is a paucity of data from the Middle East assessing the post-ICU discharge mental health status of patients who had COVID-19.AIM To evaluate anxiety and depression among patients who had severe COVID-19.METHODS This is a prospective single-center follow-up questionnaire-based study of adults who were admitted to the ICU or under ICU consultation for>24 h for COVID-19.Eligible patients were contacted via telephone.The patient’s anxiety and depression six months after ICU discharge were assessed using the Hospital Anxiety and Depression Scale(HADS).The primary outcome was the mean HADS score.The secondary outcomes were risk factors of anxiety and/or depression.RESULTS Patients who were admitted to the ICU because of COVID-19 were screened(n=518).Of these,48 completed the questionnaires.The mean age was 56.3±17.2 years.Thirty patients(62.5%)were male.The main comorbidities were endocrine(n=24,50%)and cardiovascular(n=21,43.8%)diseases.The mean overall HADS score for anxiety and depression at 6 months post-ICU discharge was 11.4(SD±8.5).A HADS score of>7 for anxiety and depression was detected in 15 patients(30%)and 18 patients(36%),respectively.Results from the multivariable ordered logistic regression demonstrated that vasopressor use was associated with the development of anxiety and depression[odds ratio(OR)39.06,95% confidence interval:1.309-1165.8;P<0.05].CONCLUSION Six months after ICU discharge,30% of patients who had COVID-19 demonstrated a HADS score that confirmed anxiety and depression.To compare the psychological status of patients following an ICU admission(with vs without COVID-19),further studies are warranted.

Construction of a Computational Scheme for the Fuzzy HIV/AIDS Epidemic Model with a Nonlinear Saturated Incidence Rate

This work aimed to construct an epidemic model with fuzzy parameters.Since the classical epidemic model doesnot elaborate on the successful interaction of susceptible and infective people,the constructed fuzzy epidemicmodel discusses the more detailed versions of the interactions between infective and susceptible people.Thenext-generation matrix approach is employed to find the reproduction number of a deterministic model.Thesensitivity analysis and local stability analysis of the systemare also provided.For solving the fuzzy epidemic model,a numerical scheme is constructed which consists of three time levels.The numerical scheme has an advantage overthe existing forward Euler scheme for determining the conditions of getting the positive solution.The establishedscheme also has an advantage over existing non-standard finite difference methods in terms of order of accuracy.The stability of the scheme for the considered fuzzy model is also provided.From the plotted results,it can beobserved that susceptible people decay by rising interaction parameters.

Patient Satisfaction with Primary Health Care Services in Riyadh City, Saudi Arabia

Patient satisfaction is a crucial measure of healthcare quality and plays a vital role in ensuring effective healthcare systems. This study aims to assess the level of patient satisfaction with primary healthcare services in Riyadh, Saudi Arabia, identify social factors affecting satisfaction, and determine the reasons behind dissatisfaction and how to improve satisfaction. The study employed a cross-sectional observational design and included a random sample of 400 patients from primary healthcare centers in Riyadh. Data were collected using an electronic questionnaire and analyzed using SPSS software. The study found that patients were generally satisfied with the primary healthcare services provided in Riyadh, with high levels of satisfaction reported for booking appointments, triage services, and emergency care. However, some aspects of the healthcare experience, such as long waiting times and the physical design of healthcare centers, need improvement. These findings can be used to inform the development of policies and interventions aimed at enhancing healthcare quality in Saudi Arabia.

Identifying and validating MMP family members(MMP2,MMP9,MMP12,and MMP16)as therapeutic targets and biomarkers in kidney renal clear cell carcinoma(KIRC)

Kidney Renal Clear Cell Carcinoma(KIRC)is a malignant tumor that carries a substantial risk of morbidity and mortality.The MMP family assumes a crucial role in tumor invasion and metastasis.This study aimed to uncover the mechanistic relevance of the MMP gene family as a therapeutic target and diagnostic biomarker in Kidney Renal Clear Cell Carcinoma(KIRC)through a comprehensive approach encompassing both computational and molecular analyses.STRING,Cytoscape,UALCAN,GEPIA,OncoDB,HPA,cBioPortal,GSEA,TIMER,ENCORI,DrugBank,targeted bisulfite sequencing(bisulfite-seq),conventional PCR,Sanger sequencing,and RT-qPCR based analyses were used in the present study to analyze MMP gene family members to accurately determine a few hub genes that can be utilized as both therapeutic targets and diagnostic biomarkers for KIRC.By performing STRING and Cytohubba analyses of the 24 MMP gene family members,MMP2(matrix metallopeptidase 2),MMP9(matrix metallopeptidase 9),MMP12(matrix metallopeptidase 12),and MMP16(matrix metallopeptidase 16)genes were denoted as hub genes having highest degree scores.After analyzing MMP2,MMP9,MMP12,and MMP16 via various TCGA databases and RT-qPCR technique across clinical samples and KIRC cell lines,interestingly,all these hub genes were found significantly overexpressed at mRNA and protein levels in KIRC samples relative to controls.The notable effect of the up-regulated MMP2,MMP9,MMP12,and MMP16 was also documented on the overall survival(OS)of the KIRC patients.Moreover,targeted bisulfite-sequencing(bisulfite-seq)analysis revealed that promoter hypomethylation pattern was associated with up-regulation of hub genes(MMP2,MMP9,MMP12,and MMP16).In addition to this,hub genes were involved in various diverse oncogenic pathways.The MMP gene family members(MMP2,MMP9,MMP12,and MMP16)may serve as therapeutic targets and prognostic biomarkers in KIRC.

Security Analysis in Smart Agriculture: Insights from a Cyber-Physical System Application

Smart agriculture modifies traditional farming practices,and offers innovative approaches to boost production and sustainability by leveraging contemporary technologies.In today’s world where technology is everything,these technologies are utilized to streamline regular tasks and procedures in agriculture,one of the largest and most significant industries in every nation.This research paper stands out from existing literature on smart agriculture security by providing a comprehensive analysis and examination of security issues within smart agriculture systems.Divided into three main sections-security analysis,system architecture and design and risk assessment of Cyber-Physical Systems(CPS)applications-the study delves into various elements crucial for smart farming,such as data sources,infrastructure components,communication protocols,and the roles of different stakeholders such as farmers,agricultural scientists and researchers,technology providers,government agencies,consumers and many others.In contrast to earlier research,this work analyzes the resilience of smart agriculture systems using approaches such as threat modeling,penetration testing,and vulnerability assessments.Important discoveries highlight the concerns connected to unsecured communication protocols,possible threats from malevolent actors,and vulnerabilities in IoT devices.Furthermore,the study suggests enhancements for CPS applications,such as strong access controls,intrusion detection systems,and encryption protocols.In addition,risk assessment techniques are applied to prioritize mitigation tactics and detect potential hazards,addressing issues like data breaches,system outages,and automated farming process sabotage.The research sets itself apart even more by presenting a prototype CPS application that makes use of a digital temperature sensor.This application was first created using a Tinkercad simulator and then using actual hardware with Arduino boards.The CPS application’s defenses against potential threats and vulnerabilities are strengthened by this integrated approach,which distinguishes this research for its depth and usefulness in the field of smart agriculture security.

RoGRUT: A Hybrid Deep Learning Model for Detecting Power Trapping in Smart Grids

Electricity theft is a widespread non-technical issue that has a negative impact on both power grids and electricity users.It hinders the economic growth of utility companies,poses electrical risks,and impacts the high energy costs borne by consumers.The development of smart grids is crucial for the identification of power theft since these systems create enormous amounts of data,including information on client consumption,which may be used to identify electricity theft using machine learning and deep learning techniques.Moreover,there also exist different solutions such as hardware-based solutions to detect electricity theft that may require human resources and expensive hardware.Computer-based solutions are presented in the literature to identify electricity theft but due to the dimensionality curse,class imbalance issue and improper hyper-parameter tuning of such models lead to poor performance.In this research,a hybrid deep learning model abbreviated as RoGRUT is proposed to detect electricity theft as amalicious and non-malicious activity.The key steps of the RoGRUT are data preprocessing that covers the problem of class imbalance,feature extraction and final theft detection.Different advanced-level models like RoBERTa is used to address the curse of dimensionality issue,the near miss for class imbalance,and transfer learning for classification.The effectiveness of the RoGRUTis evaluated using the dataset fromactual smartmeters.A significant number of simulations demonstrate that,when compared to its competitors,the RoGRUT achieves the best classification results.The performance evaluation of the proposed model revealed exemplary results across variousmetrics.The accuracy achieved was 88%,with precision at an impressive 86%and recall reaching 84%.The F1-Score,a measure of overall performance,stood at 85%.Furthermore,themodel exhibited a noteworthyMatthew correlation coefficient of 78%and excelled with an area under the curve of 91%.

Deep Learning Based Efficient Crowd Counting System

Estimation of crowd count is becoming crucial nowadays,as it can help in security surveillance,crowd monitoring,and management for different events.It is challenging to determine the approximate crowd size from an image of the crowd’s density.Therefore in this research study,we proposed a multi-headed convolutional neural network architecture-based model for crowd counting,where we divided our proposed model into two main components:(i)the convolutional neural network,which extracts the feature across the whole image that is given to it as an input,and(ii)the multi-headed layers,which make it easier to evaluate density maps to estimate the number of people in the input image and determine their number in the crowd.We employed the available public benchmark crowd-counting datasets UCF CC 50 and ShanghaiTech parts A and B for model training and testing to validate the model’s performance.To analyze the results,we used two metrics Mean Absolute Error(MAE)and Mean Square Error(MSE),and compared the results of the proposed systems with the state-of-art models of crowd counting.The results show the superiority of the proposed system.

Facies development and sedimentology of the Middle Miocene carbonates of the Raghama Formation, northeastern Saudi Arabia

Ragahama Formation comprises a siliciclastic continental deposits followed by marine carbonates, representing prograding alluvial fans from adjacent high hinterlands seaward into lagoons and fringing reef environments. The present work aimed to document the facies development and sedimentology of the Raghama carbonates exposed along the eastern coastal plain of the Red Sea, northwestern Saudi Arabia. Four stratigraphic sections were measured and sampled(D1–D4) and thin sections and major and trace element analyses were prepared and applied for petrographic and geochemical approaches. The carbonates were subdivided into three successive fore-reef, reef-core, and back-reef depositional facies. Sandy stromatolitic boundstone, microbial laminites, dolomitic ooidal grainstone, bioclastic coralline algal wackestone, sandy bioclastic wackestone, and coral boundstones were the reported microfacies types. Petrographic analysis reveals that the studied carbonates were affected by dissolution, dolomitization, and aggrading recrystallization, which affects both the original micrite matrix and grains or acts as fracture and veinlet filling leading to widespread vuggy and moldic porosity. No evidence of physical compaction, suggesting rapid lithification and recrystallization during early diagenesis and prior to substantial burial and intensive flushing by meteoric waters. Most of the original microstructure of corals were leached and destructed. This is indicated by the higher depletion in Sr and Ca levels and increase in Mg,Na, Fe, and Mn levels, especially in section D1, in comparison with the worldwide carbonates.

Transparent and Accurate COVID-19 Diagnosis:Integrating Explainable AI with Advanced Deep Learning in CT Imaging

In the current landscape of the COVID-19 pandemic,the utilization of deep learning in medical imaging,especially in chest computed tomography(CT)scan analysis for virus detection,has become increasingly significant.Despite its potential,deep learning’s“black box”nature has been a major impediment to its broader acceptance in clinical environments,where transparency in decision-making is imperative.To bridge this gap,our research integrates Explainable AI(XAI)techniques,specifically the Local Interpretable Model-Agnostic Explanations(LIME)method,with advanced deep learning models.This integration forms a sophisticated and transparent framework for COVID-19 identification,enhancing the capability of standard Convolutional Neural Network(CNN)models through transfer learning and data augmentation.Our approach leverages the refined DenseNet201 architecture for superior feature extraction and employs data augmentation strategies to foster robust model generalization.The pivotal element of our methodology is the use of LIME,which demystifies the AI decision-making process,providing clinicians with clear,interpretable insights into the AI’s reasoning.This unique combination of an optimized Deep Neural Network(DNN)with LIME not only elevates the precision in detecting COVID-19 cases but also equips healthcare professionals with a deeper understanding of the diagnostic process.Our method,validated on the SARS-COV-2 CT-Scan dataset,demonstrates exceptional diagnostic accuracy,with performance metrics that reinforce its potential for seamless integration into modern healthcare systems.This innovative approach marks a significant advancement in creating explainable and trustworthy AI tools for medical decisionmaking in the ongoing battle against COVID-19.

Threshold-Based Software-Defined Networking(SDN)Solution for Healthcare Systems against Intrusion Attacks

The healthcare sector holds valuable and sensitive data.The amount of this data and the need to handle,exchange,and protect it,has been increasing at a fast pace.Due to their nature,software-defined networks(SDNs)are widely used in healthcare systems,as they ensure effective resource utilization,safety,great network management,and monitoring.In this sector,due to the value of thedata,SDNs faceamajor challengeposed byawide range of attacks,such as distributed denial of service(DDoS)and probe attacks.These attacks reduce network performance,causing the degradation of different key performance indicators(KPIs)or,in the worst cases,a network failure which can threaten human lives.This can be significant,especially with the current expansion of portable healthcare that supports mobile and wireless devices for what is called mobile health,or m-health.In this study,we examine the effectiveness of using SDNs for defense against DDoS,as well as their effects on different network KPIs under various scenarios.We propose a threshold-based DDoS classifier(TBDC)technique to classify DDoS attacks in healthcare SDNs,aiming to block traffic considered a hazard in the form of a DDoS attack.We then evaluate the accuracy and performance of the proposed TBDC approach.Our technique shows outstanding performance,increasing the mean throughput by 190.3%,reducing the mean delay by 95%,and reducing packet loss by 99.7%relative to normal,with DDoS attack traffic.

Sepsis Prediction Using CNNBDLSTM and Temporal Derivatives Feature Extraction in the IoT Medical Environment

Background:Sepsis,a potentially fatal inflammatory disease triggered by infection,carries significant healthimplications worldwide.Timely detection is crucial as sepsis can rapidly escalate if left undetected.Recentadvancements in deep learning(DL)offer powerful tools to address this challenge.Aim:Thus,this study proposeda hybrid CNNBDLSTM,a combination of a convolutional neural network(CNN)with a bi-directional long shorttermmemory(BDLSTM)model to predict sepsis onset.Implementing the proposed model provides a robustframework that capitalizes on the complementary strengths of both architectures,resulting in more accurate andtimelier predictions.Method:The sepsis prediction method proposed here utilizes temporal feature extraction todelineate six distinct time frames before the onset of sepsis.These time frames adhere to the sepsis-3 standardrequirement,which incorporates 12-h observation windows preceding sepsis onset.All models were trained usingthe Medical Information Mart for Intensive Care III(MIMIC-III)dataset,which sourced 61,522 patients with 40clinical variables obtained from the IoT medical environment.The confusion matrix,the area under the receiveroperating characteristic curve(AUCROC)curve,the accuracy,the precision,the F1-score,and the recall weredeployed to evaluate themodels.Result:The CNNBDLSTMmodel demonstrated superior performance comparedto the benchmark and other models,achieving an AUCROC of 99.74%and an accuracy of 99.15%one hour beforesepsis onset.These results indicate that the CNNBDLSTM model is highly effective in predicting sepsis onset,particularly within a close proximity of one hour.Implication:The results could assist practitioners in increasingthe potential survival of the patient one hour before sepsis onset.

Perception of dental appearance and aesthetic analysis among patients,laypersons and dentists

BACKGROUND Current concepts of beauty are increasingly subjective,influenced by the viewpoints of others.The aim of the study was to evaluate divergences in the perception of dental appearance and smile esthetics among patients,laypersons and dental practitioners.The study goals were to evaluate the influence of age,sex,education and dental specialty on the participants’judgment and to identify the values of different esthetic criteria.Patients sample included 50 patients who responded to a dental appearance questionnaire(DAQ).Two frontal photographs were taken,one during a smile and one with retracted lips.Laypersons and dentists were asked to evaluate both photographs using a Linear Scale from(0-10),where 0 represent(absolutely unaesthetic)and 10 represent(absolutely aesthetic).One-way analysis of variance(ANOVA)and t-test analysis were measured for each group.Most patients in the sample expressed satisfaction with most aspects of their smiles and dental appearance.Among laypersons(including 488 participants),47 pictures“with lips”out of 50 had higher mean aesthetic scores compared to pictures“without lips”.Among the dentist sample,90 dentists’perception towards the esthetic smile and dental appearance for photos“with lips”and“without lips”were the same for 23 out of 50 patients.Perception of smile aesthetics differed between patients,laypersons and dentists.Several factors can contribute to shape the perception of smile aesthetic.AIM To compare the perception of dental aesthetic among patients,laypersons,and professional dentists,to evaluate the impact of age,sex,educational background,and income on the judgments made by laypersons,to assess the variations in experience,specialty,age,and sex on professional dentists’judgment,and to evaluate the role of lips,skin shade and tooth shade in different participants’judgments.METHODS Patients sample included 50 patients who responded to DAQ.Two frontal photographs were taken:one during a smile and one with retracted lips.Laypersons and dentists were asked to evaluate both photographs using a Linear Scale from(0-10),where 0 represent(absolutely unaesthetic)and 10 represent(absolutely aesthetic).One-way ANOVA and t-test analysis were measured for each group.RESULTS Most patients in the sample expressed satisfaction with most aspects of their smiles and dental appearance.Among laypersons(including 488 participants),47 pictures“with lips”out of 50 had higher mean aesthetic scores compared to pictures“without lips”.Whereas among the dentist sample,90 dentists’perception towards the esthetic smile and dental appearance for photos“with lips”and“without lips”were the same for 23 out of 50 patients.Perception of smile aesthetics differed between patients,laypersons and dentists.CONCLUSION Several factors can contribute to shape the perception of smile aesthetic.

Network Security Enhanced with Deep Neural Network-Based Intrusion Detection System

This study describes improving network security by implementing and assessing an intrusion detection system(IDS)based on deep neural networks(DNNs).The paper investigates contemporary technical ways for enhancing intrusion detection performance,given the vital relevance of safeguarding computer networks against harmful activity.The DNN-based IDS is trained and validated by the model using the NSL-KDD dataset,a popular benchmark for IDS research.The model performs well in both the training and validation stages,with 91.30%training accuracy and 94.38%validation accuracy.Thus,the model shows good learning and generalization capabilities with minor losses of 0.22 in training and 0.1553 in validation.Furthermore,for both macro and micro averages across class 0(normal)and class 1(anomalous)data,the study evaluates the model using a variety of assessment measures,such as accuracy scores,precision,recall,and F1 scores.The macro-average recall is 0.9422,the macro-average precision is 0.9482,and the accuracy scores are 0.942.Furthermore,macro-averaged F1 scores of 0.9245 for class 1 and 0.9434 for class 0 demonstrate the model’s ability to precisely identify anomalies precisely.The research also highlights how real-time threat monitoring and enhanced resistance against new online attacks may be achieved byDNN-based intrusion detection systems,which can significantly improve network security.The study underscores the critical function ofDNN-based IDS in contemporary cybersecurity procedures by setting the foundation for further developments in this field.Upcoming research aims to enhance intrusion detection systems by examining cooperative learning techniques and integrating up-to-date threat knowledge.

Dual primary gastric and colorectal cancer:The known hereditary causes and underlying mechanisms

In this editorial,I commented on the paper by Lin et al,published in this issue of the World Journal of Gastrointestinal Oncology.The work aimed at analysing the clinicopathologic characteristics and prognosis of synchronous and metachronous cancers in patients with dual primary gastric and colorectal cancer(CRC).The authors concluded the necessity for regular surveillance for metachronous cancer during postoperative follow-up and reported the prognosis is influenced by the gastric cancer(GC)stage rather than the CRC stage.Although surveillance was recommended in the conclusion,the authors did not explore this area in their study and did not include tests used for such surveillance.This editorial focuses on the most characterized gastrointestinal cancer susceptibility syndromes concerning dual gastric and CRCs.These include hereditary diffuse GC,familial adenomatous polyposis,hereditary nonpolyposis colon cancer,Lynch syndrome,and three major hamartomatous polyposis syndromes associated with CRC and GC,namely Peutz-Jeghers syndrome,juvenile polyposis syndrome,and PTEN hamartoma syndrome.Careful assessment of these syndromes/conditions,including inheritance,risk of gastric and colorectal or other cancer development,genetic mutations and recommended genetic investigations,is crucial for optimum management of these patients.

Scientific Elegance in NIDS: Unveiling Cardinality Reduction, Box-Cox Transformation, and ADASYN for Enhanced Intrusion Detection

The emergence of digital networks and the wide adoption of information on internet platforms have given rise to threats against users’private information.Many intruders actively seek such private data either for sale or other inappropriate purposes.Similarly,national and international organizations have country-level and company-level private information that could be accessed by different network attacks.Therefore,the need for a Network Intruder Detection System(NIDS)becomes essential for protecting these networks and organizations.In the evolution of NIDS,Artificial Intelligence(AI)assisted tools and methods have been widely adopted to provide effective solutions.However,the development of NIDS still faces challenges at the dataset and machine learning levels,such as large deviations in numeric features,the presence of numerous irrelevant categorical features resulting in reduced cardinality,and class imbalance in multiclass-level data.To address these challenges and offer a unified solution to NIDS development,this study proposes a novel framework that preprocesses datasets and applies a box-cox transformation to linearly transform the numeric features and bring them into closer alignment.Cardinality reduction was applied to categorical features through the binning method.Subsequently,the class imbalance dataset was addressed using the adaptive synthetic sampling data generation method.Finally,the preprocessed,refined,and oversampled feature set was divided into training and test sets with an 80–20 ratio,and two experiments were conducted.In Experiment 1,the binary classification was executed using four machine learning classifiers,with the extra trees classifier achieving the highest accuracy of 97.23%and an AUC of 0.9961.In Experiment 2,multiclass classification was performed,and the extra trees classifier emerged as the most effective,achieving an accuracy of 81.27%and an AUC of 0.97.The results were evaluated based on training,testing,and total time,and a comparative analysis with state-of-the-art studies proved the robustness and significance of the applied methods in developing a timely and precision-efficient solution to NIDS.

Enhancing Cancer Classification through a Hybrid Bio-Inspired Evolutionary Algorithm for Biomarker Gene Selection

In this study,our aim is to address the problem of gene selection by proposing a hybrid bio-inspired evolutionary algorithm that combines Grey Wolf Optimization(GWO)with Harris Hawks Optimization(HHO)for feature selection.Themotivation for utilizingGWOandHHOstems fromtheir bio-inspired nature and their demonstrated success in optimization problems.We aimto leverage the strengths of these algorithms to enhance the effectiveness of feature selection in microarray-based cancer classification.We selected leave-one-out cross-validation(LOOCV)to evaluate the performance of both two widely used classifiers,k-nearest neighbors(KNN)and support vector machine(SVM),on high-dimensional cancer microarray data.The proposed method is extensively tested on six publicly available cancer microarray datasets,and a comprehensive comparison with recently published methods is conducted.Our hybrid algorithm demonstrates its effectiveness in improving classification performance,Surpassing alternative approaches in terms of precision.The outcomes confirm the capability of our method to substantially improve both the precision and efficiency of cancer classification,thereby advancing the development ofmore efficient treatment strategies.The proposed hybridmethod offers a promising solution to the gene selection problem in microarray-based cancer classification.It improves the accuracy and efficiency of cancer diagnosis and treatment,and its superior performance compared to other methods highlights its potential applicability in realworld cancer classification tasks.By harnessing the complementary search mechanisms of GWO and HHO,we leverage their bio-inspired behavior to identify informative genes relevant to cancer diagnosis and treatment.

CNN Channel Attention Intrusion Detection SystemUsing NSL-KDD Dataset

Intrusion detection systems(IDS)are essential in the field of cybersecurity because they protect networks from a wide range of online threats.The goal of this research is to meet the urgent need for small-footprint,highly-adaptable Network Intrusion Detection Systems(NIDS)that can identify anomalies.The NSL-KDD dataset is used in the study;it is a sizable collection comprising 43 variables with the label’s“attack”and“level.”It proposes a novel approach to intrusion detection based on the combination of channel attention and convolutional neural networks(CNN).Furthermore,this dataset makes it easier to conduct a thorough assessment of the suggested intrusion detection strategy.Furthermore,maintaining operating efficiency while improving detection accuracy is the primary goal of this work.Moreover,typical NIDS examines both risky and typical behavior using a variety of techniques.On the NSL-KDD dataset,our CNN-based approach achieves an astounding 99.728%accuracy rate when paired with channel attention.Compared to previous approaches such as ensemble learning,CNN,RBM(Boltzmann machine),ANN,hybrid auto-encoders with CNN,MCNN,and ANN,and adaptive algorithms,our solution significantly improves intrusion detection performance.Moreover,the results highlight the effectiveness of our suggested method in improving intrusion detection precision,signifying a noteworthy advancement in this field.Subsequent efforts will focus on strengthening and expanding our approach in order to counteract growing cyberthreats and adjust to changing network circumstances.

Securing the Internet of Health Things with Certificateless Anonymous Authentication Scheme

Internet of Health Things(IoHT)is a subset of Internet of Things(IoT)technology that includes interconnected medical devices and sensors used in medical and healthcare information systems.However,IoHT is susceptible to cybersecurity threats due to its reliance on low-power biomedical devices and the use of open wireless channels for communication.In this article,we intend to address this shortcoming,and as a result,we propose a new scheme called,the certificateless anonymous authentication(CAA)scheme.The proposed scheme is based on hyperelliptic curve cryptography(HECC),an enhanced variant of elliptic curve cryptography(ECC)that employs a smaller key size of 80 bits as compared to 160 bits.The proposed scheme is secure against various attacks in both formal and informal security analyses.The formal study makes use of the Real-or-Random(ROR)model.A thorough comparative study of the proposed scheme is conducted for the security and efficiency of the proposed scheme with the relevant existing schemes.The results demonstrate that the proposed scheme not only ensures high security for health-related data but also increases efficiency.The proposed scheme’s computation cost is 2.88 ms,and the communication cost is 1440 bits,which shows its better efficiency compared to its counterpart schemes.

Superior rectus/levator complex in acquired anophthalmic socket repaired with spheric implant—a computed tomography scan and topographic study

AIM:To determine whether the levator palpebrae superioris(LPS)/superior rectus(SR)muscle complex,can influence the position of the upper lid and fornix in acquired anophthalmic sockets.METHODS:This comparative non-randomized and non-interventional study included retrospective data of 21 patients with unilateral acquired anophthalmic sockets repaired with spheric implants.High-resolution computed tomography(CT)measurements of the LPM/SR muscle complex and clinical topographic position of the upper lid,superior and inferior fornix depth in primary gaze position were evaluated.Demographic data were presented as frequency and percentage proportions and quantitative variables comparing the socket measurements with the normal contralateral orbit was statistically analyzed using non-parametric tests considering P<0.05.RESULTS:The anophthalmic orbits had a significantly shorter LPS length(P=0.01)and significantly thicker SR(P=0.02)than the normal orbit.Lagophthalmos was present in anophthalmic sockets but not in normal orbits(P=0.002),while levator function was normal in both(P>0.05,all comparisons).The superior fornix depth was similar in the anophthalmic socket and the contralateral normal orbit(P=0.192)as well the inferior fornix depth(P=0.351).CONCLUSION:Acquired anophthalmic sockets repaired with spheric implants have shorter LPS,thicker SR,and more lagophthalmos than normal orbits.The relationship of the LPS and SR with other orbital structures,associated with passive or active forces acting in the final position of the lids and external ocular prosthesis should be further investigated.