Irritable Bowel Syndrome among Medical and Non-Medical Northern Border University Students, Kingdom of Saudi Arabia: Across Sectional Study

Background: Irritable Bowel Syndrome (IBS) is a common functional gastrointestinal disorder (FGID), characterized by abdominal pain or discomfort and alteration in bowel habits. Aim of the study: To determine the overall prevalence, prevalence of each type and risk factors of IBS among Northern Border University (NBU) students, Arar, Kingdom of Saudi Arabia. Material and methods: We use cross sectional, descriptive study with multistage cluster probability sample. Using Rome III criteria questionnaire of IBS;which is a self-administrated consists of ten questions assessing the current status of an apparently normal person. The questionnaire is administrated to Northern Border University students. Results: A total of 228 University students of them, 94 (41.2%) males and 134 (58.8%) females were included in the study. The overall prevalence of IBS according to Rome III criteria in northern border University was (32.5%). The disease prevalence was 33.6% in females and 30.9% in males. Among the study participants, the most common type of IBS was the mixed one 12.7%, followed by the constipation predominant type 10.5%, then the diarrhea pre-dominant type 5.7% while the least common was unsubtyped cases (3.5%). Statistically significant increase in prevalence of this disease was found among female students (60.8% vs. 39.2% in males) (p-value < 0.05), the students who experienced psychic stress and irritability (79.7%) (p-value < 0.05) and students who were obese (p-value < 0.001). Conclusion: The results of this study concluded the prevalence rate of 32.5% for IBS among the students studying in Northern Border University. Stress and high body mass index were significantly associated with IBS. In addition, this study concluded that IBS was not significantly associated with socio-demographic characteristics and smoking.

Evaluation of the Coordinated Approach to Child Health (CATCH) Program in Third through Fifth Graders in Northern Illinois

Background: The Coordinated Approach to Child Health (CATCH) is a school-based health education program, grounded in Social Cognitive Theory (SCT), and designed to improve dietary habits and increase physical activity among children and adolescents. The objective of this study was to evaluate the effectiveness of CATCH program, delivered by dietetic interns and Northern Illinois University (NIU) students, to 3rd-5th graders in Northern Illinois, in increasing their nutrition knowledge and healthy choices behavior. Methods: In total, 167 elementary school children in grades 3 - 5 in Northern Illinois participated in a non-experimental program evaluation study. We delivered 6 CATCH lessons throughout the academic year to five elementary schools. Lessons were focused on “Go, Slow, and Whoa” food categories to help children understand healthier food choices. Validated questionnaires from the CATCH Global Foundation were administered in classrooms and online, pre/post intervention, to assess nutritional knowledge and healthy choices. Results: Children in third through fifth grades significantly increased their knowledge about nutrient dense foods, p < 0.001, p < 0.001, p < 0.001, respectively. Fourth and fifth graders exhibited a significant increase in their ability to make healthier food choices, p = 0.03 and p = 0.007, respectively. As grade level increased from third to fifth grade, improvement in nutrition knowledge and adoption of healthy food choices did not increase significantly, p = 0.973 and p = 0.637, respectively. Conclusion: We conclude that children in grades 3 - 5 who participated in the 6 lessons of the CATCH program expanded their nutritional knowledge and 4th and 5th graders improved their ability to make healthier choices. Conducting evaluations of health promotion programs is imperative to determine the impact of the program, as well as to explore possible improvements in content and delivery for future implementation.

AI-Based Helmet Violation Detection for Traffic Management System

Enhancing road safety globally is imperative,especially given the significant portion of traffic-related fatalities attributed to motorcycle accidents resulting from non-compliance with helmet regulations.Acknowledging the critical role of helmets in rider protection,this paper presents an innovative approach to helmet violation detection using deep learning methodologies.The primary innovation involves the adaptation of the PerspectiveNet architecture,transitioning from the original Res2Net to the more efficient EfficientNet v2 backbone,aimed at bolstering detection capabilities.Through rigorous optimization techniques and extensive experimentation utilizing the India driving dataset(IDD)for training and validation,the system demonstrates exceptional performance,achieving an impressive detection accuracy of 95.2%,surpassing existing benchmarks.Furthermore,the optimized PerspectiveNet model showcases reduced computational complexity,marking a significant stride in real-time helmet violation detection for enhanced traffic management and road safety measures.

Nullors, and Nullor Circuits;There Applications in Symbolic Circuit Analysis and Design

The objective in this presentation is to introduce some of the unique properties and applications of nullors in active circuit analysis and designs. The emphasis is to discuss the role nullors can play in symbolic representation of transfer functions. To show this we adopt the topological platform for the circuit analysis and use a recently developed Admittance Method (AM) to achieve the Sum of Tree Products (STP), replacing the determinant and cofactors of the Nodal Admittance Matrix (NAM) of the circuit. To construct a transfer function, we start with a given active circuit and convert all its controlled sources and I/O-ports to nullors. Now, with a solid nullor circuit (passive elements and nullors) we first eliminate the passive elements through AM operations. This produces the STPs. Second, the all-nullor circuit is then used to find the signs or the STPs. Finally, the transfer function (in symbolic, if chosen) is obtained from the ratio between the STPs.

Exploring battery material failure mechanisms through synchrotron X-ray characterization techniques

Rechargeable battery cycling performance and related safety have been persistent concerns.It is crucial to decipher the capacity fading induced by electrode material failure via a range of techniques.Among these,synchrotron-based X-ray techniques with high flux and brightness play a key role in understanding degradation mechanisms.In this comprehensive review,we summarize recent advancements in degra-dation modes and mechanisms that were revealed by synchrotron X-ray methodologies.Subsequently,an overview of X-ray absorption spectroscopy and X-ray scattering techniques is introduced for charac-terizing failure phenomena at local coordination atomic environment and long-range order crystal struc-ture scale,respectively.At last,we envision the future of exploring material failure mechanism.

Optimizing Connections:Applied Shortest Path Algorithms for MANETs

This study is trying to address the critical need for efficient routing in Mobile Ad Hoc Networks(MANETs)from dynamic topologies that pose great challenges because of the mobility of nodes.Themain objective was to delve into and refine the application of the Dijkstra’s algorithm in this context,a method conventionally esteemed for its efficiency in static networks.Thus,this paper has carried out a comparative theoretical analysis with the Bellman-Ford algorithm,considering adaptation to the dynamic network conditions that are typical for MANETs.This paper has shown through detailed algorithmic analysis that Dijkstra’s algorithm,when adapted for dynamic updates,yields a very workable solution to the problem of real-time routing in MANETs.The results indicate that with these changes,Dijkstra’s algorithm performs much better computationally and 30%better in routing optimization than Bellman-Ford when working with configurations of sparse networks.The theoretical framework adapted,with the adaptation of the Dijkstra’s algorithm for dynamically changing network topologies,is novel in this work and quite different from any traditional application.The adaptation should offer more efficient routing and less computational overhead,most apt in the limited resource environment of MANETs.Thus,from these findings,one may derive a conclusion that the proposed version of Dijkstra’s algorithm is the best and most feasible choice of the routing protocol for MANETs given all pertinent key performance and resource consumption indicators and further that the proposed method offers a marked improvement over traditional methods.This paper,therefore,operationalizes the theoretical model into practical scenarios and also further research with empirical simulations to understand more about its operational effectiveness.

Practical guide:Glucagon-like peptide-1 and dual glucosedependent insulinotropic polypeptide and glucagon-like peptide-1 receptor agonists in diabetes mellitus

Practical guide:Glucagon-like peptide-1 and dual glucosedependent insulinotropic polypeptide and glucagon-like peptide-1 receptor agonists in diabetes mellitus common second-line choice after metformin for treating T2DM.Various considerations can make selecting and switching between different GLP-1 RAs challenging.Our study aims to provide a comprehensive guide for the usage of GLP-1 RAs and dual GIP and GLP-1 RAs for the management of T2DM.

Enhanced Object Detection and Classification via Multi-Method Fusion

Advances in machine vision systems have revolutionized applications such as autonomous driving,robotic navigation,and augmented reality.Despite substantial progress,challenges persist,including dynamic backgrounds,occlusion,and limited labeled data.To address these challenges,we introduce a comprehensive methodology toenhance image classification and object detection accuracy.The proposed approach involves the integration ofmultiple methods in a complementary way.The process commences with the application of Gaussian filters tomitigate the impact of noise interference.These images are then processed for segmentation using Fuzzy C-Meanssegmentation in parallel with saliency mapping techniques to find the most prominent regions.The Binary RobustIndependent Elementary Features(BRIEF)characteristics are then extracted fromdata derived fromsaliency mapsand segmented images.For precise object separation,Oriented FAST and Rotated BRIEF(ORB)algorithms areemployed.Genetic Algorithms(GAs)are used to optimize Random Forest classifier parameters which lead toimproved performance.Our method stands out due to its comprehensive approach,adeptly addressing challengessuch as changing backdrops,occlusion,and limited labeled data concurrently.A significant enhancement hasbeen achieved by integrating Genetic Algorithms(GAs)to precisely optimize parameters.This minor adjustmentnot only boosts the uniqueness of our system but also amplifies its overall efficacy.The proposed methodologyhas demonstrated notable classification accuracies of 90.9%and 89.0%on the challenging Corel-1k and MSRCdatasets,respectively.Furthermore,detection accuracies of 87.2%and 86.6%have been attained.Although ourmethod performed well in both datasets it may face difficulties in real-world data especially where datasets havehighly complex backgrounds.Despite these limitations,GAintegration for parameter optimization shows a notablestrength in enhancing the overall adaptability and performance of our system.

Sports Prediction Model through Cloud Computing and Big Data Based on Artificial Intelligence Method

This article delves into the intricate relationship between big data, cloud computing, and artificial intelligence, shedding light on their fundamental attributes and interdependence. It explores the seamless amalgamation of AI methodologies within cloud computing and big data analytics, encompassing the development of a cloud computing framework built on the robust foundation of the Hadoop platform, enriched by AI learning algorithms. Additionally, it examines the creation of a predictive model empowered by tailored artificial intelligence techniques. Rigorous simulations are conducted to extract valuable insights, facilitating method evaluation and performance assessment, all within the dynamic Hadoop environment, thereby reaffirming the precision of the proposed approach. The results and analysis section reveals compelling findings derived from comprehensive simulations within the Hadoop environment. These outcomes demonstrate the efficacy of the Sport AI Model (SAIM) framework in enhancing the accuracy of sports-related outcome predictions. Through meticulous mathematical analyses and performance assessments, integrating AI with big data emerges as a powerful tool for optimizing decision-making in sports. The discussion section extends the implications of these results, highlighting the potential for SAIM to revolutionize sports forecasting, strategic planning, and performance optimization for players and coaches. The combination of big data, cloud computing, and AI offers a promising avenue for future advancements in sports analytics. This research underscores the synergy between these technologies and paves the way for innovative approaches to sports-related decision-making and performance enhancement.

MXene@c-MWCNT Adhesive Silica Nanofiber Membranes Enhancing Electromagnetic Interference Shielding and Thermal Insulation Performance in Extreme Environments

A lightweight flexible thermally stable composite is fabricated by com-bining silica nanofiber membranes(SNM)with MXene@c-MWCNT hybrid film.The flexible SNM with outstanding thermal insulation are prepared from tetraethyl orthosilicate hydrolysis and condensation by electrospinning and high-temperature calcination;the MXene@c-MWCNT_(x:y)films are prepared by vacuum filtration tech-nology.In particular,the SNM and MXene@c-MWCNT_(6:4)as one unit layer(SMC_(1))are bonded together with 5 wt%polyvinyl alcohol(PVA)solution,which exhibits low thermal conductivity(0.066 W m^(-1)K^(-1))and good electromagnetic interference(EMI)shielding performance(average EMI SE_(T),37.8 dB).With the increase in func-tional unit layer,the overall thermal insulation performance of the whole composite film(SMC_(x))remains stable,and EMI shielding performance is greatly improved,especially for SMC_(3)with three unit layers,the average EMI SET is as high as 55.4 dB.In addition,the organic combination of rigid SNM and tough MXene@c-MWCNT_(6:4)makes SMC_(x)exhibit good mechanical tensile strength.Importantly,SMC_(x)exhibit stable EMI shielding and excellent thermal insulation even in extreme heat and cold environment.Therefore,this work provides a novel design idea and important reference value for EMI shielding and thermal insulation components used in extreme environmental protection equipment in the future.

Chemical diversity of essential oils from flowers,leaves,and stems of Rhanterium epapposum Oliv. growing in northern border region of Saudi Arabia

Objective:To evaluate the medicinal uses of Rhanterium epapposum Oliv.(R.epapposum) growing in northern border region of Saudi Arabia,through the chemical diversity of essential oils extracted from its flowers,leaves and stems.Methods:Aerial parts of R.epapposum were collected in April 2014.Air dried flowers,leaves,and stems were separately subjected to hydrodistillation in a Clevenger-type apparatus for 4 h to extract the essential oils.Gas chromatography-mass spectrometry analysis of the essential oils was carried out using an Agilent 6890 gas chromatograph equipped with an Agilent 5973 mass spectrometric detector.Results:A total of 51 compounds representing 76.35%–94.86% of flowers,leaves and stems oils composition were identified.The chemical profiles of the studied fractions revealed the dominance of monoterpenes,regardless of qualitative and quantitative differences observed.Limonene,linalool,4-terpineol and a-cadinol represented the major constituents of flowers oil.Leaves oil was dominated by limonene,sabinene,a-pinene and b-myrcene whereas linalool,ionole,a-cadinol,b-eudesmol,4-terpineol,and aterpineol were the major constituents of stems oil.Conclusions:Essential oils from flowers,leaves and stems of R.epapposum growing in northern border region of Saudi Arabia are considered as a rich source of monoterpenes which have biological activities.

Mineralogy,Chemical Characteristics and Upgrading of Beach Ilmenite of the Top Meter of Black Sand Deposits of the Kafr Al-Sheikh Governorate,Northern Egypt

Egyptian beach ilmenite occurs in a relatively high content in the naturally highly concentrated superficial black sand deposits at specific beach zones in the northern parts of the Nile Delta at Rosetta. Microscopic study shows that the ilmenite occurs as fresh homogeneous black or heterogeneous multicoloured altered grains and exhibits three types （homogeneous, exsolved and altered） of ilmenite varieties. XRD data of ilmenite indicates their association with minor hematite and quartz, whereas leucoxene shows its association with Nb-rutUe, pseudorutile and hematite. Grain size distribution suggests a very fine sand size of 〉89% and 80% and a fine sand size of 10.5% and 18.3% for fresh and altered ilmenites, respectively. The density of fresh, altered ilmenite and leucoxene concentrates varies from 2.70, 2.50 to 2.40 ton/m^3, suggesting a gradual decrease from high grade fresh to leucoxene and consistent with variation in magnetic susceptibility as a consequence of the leaching of iron. Mass magnetic susceptibility reveals 97.6% of ilmenite and 92% of the altered form are obtained at 0.20 and 0.48 ampere. Fresh iimenite exhibits variable TiO2 （47.18%） and Fe2O3^T （46.10%） with minor MnO, MgO and Cr2O3 （1.22, 1.10 and 0.51%）. The altered ilmenite is higher in TiO2 （76.16%） and SiO2 （4.68%） and lower in Fe2O3^T （14.45%）, MnO, MgO and Cr2O3 （0.39, 0.52 and 0.11%） compared with the fresh form. Three concentrates of ilmenites （G1, G2 and G3） were prepared from crude ore using a Reading cross belt magnetic separator under different conditions, revealing a gradual increase of TiO2, SiO2, Al2O3 and CaO accompanied by a decrease of Fe2O3^T, MgO and Cr2O3 with repetition of the separation processes. Several ore dressing techniques were carried out to upgrade the iimenite concentrate.

Dietary Habits and Oral Hygiene Practice amongst Dental Students at the College of Dentistry, Princess Nourah University

The aim of the present study was to assess the dietary habits and oral hygiene practice of dental students in a new dental school. A self-administered structured closed-ended questionnaire on demographic characteristics, medical history, oral hygiene and dietary habits was distributed to dental students. Results showed that One third of students indicated that they don’t consume low pH beverages (soft drinks) at all, while 48.9% drink a soft drink or two a day. Students took varying amount of time to consume their drinks. The majority of participants consumed citric juices, fruits and/or pickles at least once a day. 91.3% of students use either soft (41.8%) or medium (49.5%) toothbrush. Only a fifth (16.9%) of the students brush their teeth after drinking soft drinks and 58.2% brush their teeth after vomiting. In conclusion, young adults need to be aware about their dietary habits & oral hygiene, and also a proper dental health program needs to be applied.

Surplus energy utilization of spent lithium-ion batteries for high-profit organolithiums

It is challenging to efficiently and economically recycle many lithium-ion batteries(LIBs)because of the low valuation of commodity metals and materials,such as LiFePO_(4).There are millions of tons of spent LIBs where the barrier to recycling is economical,and to make recycling more feasible,it is required that the value of the processed recycled material exceeds the value of raw commodity materials.The presented research illustrates improved profitability and economics for recycling spent LIBs by utilizing the surplus energy in lithiated graphite to drive the preparation of organolithiums to add value to the recycled lithium materials.This study methodology demonstrates that the surplus energy of lithiated graphite obtained from spent LIBs can be utilized to prepare high-value organolithiums,thereby significantly improving the economic profitability of LIB recycling.Organolithiums(R-O-Li and R-Li)were prepared using alkyl alcohol(R-OH)and alkyl bromide(R-Br)as substrates,where R includes varying hindered alkyl hydrocarbons.The organolithiums extracted from per kilogram of recycled LIBs can increase the economic value between$29.5 and$226.5 kg^(−1) cell.The value of the organolithiums is at least 5.4 times the total theoretical value of spent materials,improving the profitability of recycling LIBs over traditional pyrometallurgical($0.86 kg^(−1) cell),hydrometallurgical($1.00 kg^(−1) cell),and physical direct recycling methods($5.40 kg^(−1) cell).

Impact of taxes on the 2030 Agenda for Sustainable Development:Evidence from Organization for Economic Cooperation and Development(OECD)countries

Multiple ecological and socioeconomic problems have occurred worldwide,raising the awareness of sustainability.This study aims to examine the impact of taxes on Sustainable Development Goals(SDGs)in the context of Organization for Economic Co-operation and Development(OECD)countries.This research used effective average tax(EAT),tax on personal income(TPI),tax on corporate profits(TCP),and tax on goods and services(TGS)as the variables of taxes,and employed secondary data from 38 OECD countries covering 2000–2021.The study also used Breusch-Pagan Lagrange Multiplier(LM),Pesaran Scaled LM,Bias-Corrected Scaled LM,and Pesaran Cross-sectional dependence(CSD)tests to analyze the existence of crosssectional dependency.Then,we established the stationarity of variables through second-generation panel unit root tests(Cross-sectional Augmented Dickey-Fuller(CADF)and Cross-sectional Im,Pesaran,and Shin(CIPS)),and confirmed the long-run cointegration of the variables by using secondgeneration panel cointegration test(Westerlund cointegration test).The results showed that EAT,TPI,TCP,and TGS are positively associated with SDGs.However,the change in TPI has a smaller effect on SDGs than the change in EAT or TCP or TGS.The result of panel causality indicated that EAT,TPI,and TGS have a unidirectional causal relationship with SDGs.The study also found that TCP has a bi-directional causal relationship with SDGs.Moreover,the finding indicated that the OECD countries need to focus on tax policies to achieve the 2030 Agenda for Sustainable Development.This study is based on the theory of optimal taxation(TOT),which suggests that tax systems should be designed to maximize social welfare.Finally,we suggests the importance of taking a comprehensive approach for the managers and policy-makers when analyzing the impact of taxes on SDGs.

Bulk preparation of free-standing single-iron-atom catalysts directly as the air electrodes for high-performance zinc-air batteries

The keen interest in fuel cells and metal-air batteries stimulates a great deal of research on the development of a cost-efficient and high-performance catalyst as an alternative to traditional Pt to boost the sluggish oxygen reduction reaction(ORR)at the cathode.Herein,we report a facile and scalable strategy for the large-scale preparation of a free-standing and flexible porous atomically dispersed Fe-N-doped carbon microtube(FeSAC/PCMT)sponge.Benefiting from its unique structure that greatly facilitates the catalytic kinetics,mass transport,and electron transfer,our FeSAC/PCMT electrode exhibits excellent performance with an ORR potential of 0.942 V at^(-3) mA cm^(-2).When the FeSAC/PCMT sponge was directly used as an oxygen electrode for liquid-state and flexible solid-state zinc-air batteries,high peak power densities of 183.1 and 58.0 mW cm^(-2) were respectively achieved,better than its powdery counterpart and commercial Pt/C catalyst.Experimental and theoretical investigation results demonstrate that such ultrahigh ORR performance can be attributed to atomically dispersed Fe-N_(5) species in FeSAC/PCMT.This study presents a cost-effective and scalable strategy for the fabrication of highly efficient and flexible oxygen electrodes,provides a significant new insight into the catalytic mechanisms,and helps to realize significant advances in energy devices.

Sea Turtle Foraging Optimization-Based Controller Placement with Blockchain-Assisted Intrusion Detection in Software-Defined Networks

Software-defined networking(SDN)algorithms are gaining increas-ing interest and are making networks flexible and agile.The basic idea of SDN is to move the control planes to more than one server’s named controllers and limit the data planes to numerous sending network components,enabling flexible and dynamic network management.A distinctive characteristic of SDN is that it can logically centralize the control plane by utilizing many physical controllers.The deployment of the controller—that is,the controller placement problem(CPP)—becomes a vital model challenge.Through the advancements of blockchain technology,data integrity between nodes can be enhanced with no requirement for a trusted third party.Using the lat-est developments in blockchain technology,this article designs a novel sea turtle foraging optimization algorithm for the controller placement problem(STFOA-CPP)with blockchain-based intrusion detection in an SDN environ-ment.The major intention of the STFOA-CPP technique is the maximization of lifetime,network connectivity,and load balancing with the minimization of latency.In addition,the STFOA-CPP technique is based on the sea turtles’food-searching characteristics of tracking the odour path of dimethyl sulphide(DMS)released from food sources.Moreover,the presented STFOA-CPP technique can adapt with the controller’s count mandated and the shift to controller mapping to variable network traffic.Finally,the blockchain can inspect the data integrity,determine significantly malicious input,and improve the robust nature of developing a trust relationship between sev-eral nodes in the SDN.To demonstrate the improved performance of the STFOA-CPP algorithm,a wide-ranging experimental analysis was carried out.The extensive comparison study highlighted the improved outcomes of the STFOA-CPP technique over other recent approaches.

DDoS Attack Detection in Cloud Computing Based on Ensemble Feature Selection and Deep Learning

Intrusion Detection System(IDS)in the cloud Computing(CC)environment has received paramount interest over the last few years.Among the latest approaches,Deep Learning(DL)-based IDS methods allow the discovery of attacks with the highest performance.In the CC environment,Distributed Denial of Service(DDoS)attacks are widespread.The cloud services will be rendered unavailable to legitimate end-users as a consequence of the overwhelming network traffic,resulting in financial losses.Although various researchers have proposed many detection techniques,there are possible obstacles in terms of detection performance due to the use of insignificant traffic features.Therefore,in this paper,a hybrid deep learning mode based on hybridizing Convolutional Neural Network(CNN)with Long-Short-Term Memory(LSTM)is used due to its robustness and efficiency in detecting normal and attack traffic.Besides,the ensemble feature selection,mutualization aggregation between Particle Swarm Optimizer(PSO),Grey Wolf Optimizer(PSO),Krill Hird(KH),andWhale Optimization Algorithm(WOA),is used to select the most important features that would influence the detection performance in detecting DDoS attack in CC.A benchmark dataset proposed by the Canadian Institute of Cybersecurity(CIC),called CICIDS 2017 is used to evaluate the proposed IDS.The results revealed that the proposed IDS outperforms the state-of-the-art IDSs,as it achieved 97.9%,98.3%,97.9%,98.1%,respectively.As a result,the proposed IDS achieves the requirements of getting high security,automatic,efficient,and self-decision detection of DDoS attacks.

An Efficient Approach Based on Remora Optimization Algorithm and Levy Flight for Intrusion Detection

With the recent increase in network attacks by threats,malware,and other sources,machine learning techniques have gained special attention for intrusion detection due to their ability to classify hundreds of features into normal system behavior or an attack attempt.However,feature selection is a vital preprocessing stage in machine learning approaches.This paper presents a novel feature selection-based approach,Remora Optimization Algorithm-Levy Flight(ROA-LF),to improve intrusion detection by boosting the ROA performance with LF.The developed ROA-LF is assessed using several evaluation measures on five publicly available datasets for intrusion detection:Knowledge discovery and data mining tools competition,network security laboratory knowledge discovery and data mining,intrusion detection evaluation dataset,block out traffic network,Canadian institute of cybersecu-rity and three engineering problems:Cantilever beam design,three-bar truss design,and pressure vessel design.A comparative analysis between developed ROA-LF,particle swarm optimization,salp swarm algorithm,snake opti-mizer,and the original ROA methods is also presented.The results show that the developed ROA-LF is more efficient and superior to other feature selection methods and the three tested engineering problems for intrusion detection.