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Novel Framework for an Intrusion Detection System Using Multiple Feature Selection Methods Based on Deep Learning

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Tsinghua Science and Technology 2024年第4期29卷 948-958页

作者： A.E.M.Eljialy mohammed Yousuf Uddin Sultan ahmad Department of Information Systems College of Computer Engineering and SciencesPrince Sattam Bin Abdulaziz UniversityAlkharjSaudi Arabia Department of Computer Science College of Computer Engineering and SciencesPrince Sattam Bin Abdulaziz UniversityAlkharjSaudi Arabiaand also with University Center for Research and Development(UCRD)Department of Computer Science and EngineeringChandigarh UniversityPunjabIndia

Intrusion detection systems (IDSs) are deployed to detect anomalies in real time. They classify a network’s incoming traffic as benign or anomalous (attack). An efficient and robust IDS in software-defined networks is an inevitable component of network security. The main challenges of such an IDS are achieving zero or extremely low false positive rates and high detection rates. Internet of Things (IoT) networks run by using devices with minimal resources. This situation makes deploying traditional IDSs in IoT networks unfeasible. Machine learning (ML) techniques are extensively applied to build robust IDSs. Many researchers have utilized different ML methods and techniques to address the above challenges. The development of an efficient IDS starts with a good feature selection process to avoid overfitting the ML model. This work proposes a multiple feature selection process followed by classification. In this study, the Software-defined networking (SDN) dataset is used to train and test the proposed model. This model applies multiple feature selection techniques to select high-scoring features from a set of features. Highly relevant features for anomaly detection are selected on the basis of their scores to generate the candidate dataset. Multiple classification algorithms are applied to the candidate dataset to build models. The proposed model exhibits considerable improvement in the detection of attacks with high accuracy and low false positive rates, even with a few features selected.

关键词： random forest feature selection decision tree software-defined network logistic regression intrusion detection system XGB classifier AdaBoost

Results Involving Partial Differential Equations and Their Solution by Certain Integral Transform

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Computer Modeling in Engineering & Sciences 2024年第2期138卷 1593-1616页

作者： Rania Saadah mohammed Amleh ahmad Qazza Shrideh Al-Omari Ahmet Ocak Akdemir Department of Mathematics Faculty of ScienceZarqa UniversityZarqa13110Jordan Department of Mathematics Faculty of ScienceAl-Balqa Applied UniversityAmman11134Jordan Department of Mathematics Faculty of Science and LettersAgri Ibrahim莈鏴n UniversityAgri04100Turkey

In this study,we aimto investigate certain triple integral transformand its application to a class of partial *** discuss various properties of the new transformincluding inversion, linearity, existence, scaling andshifting, etc. Then,we derive several results enfolding partial derivatives and establish amulti-convolution ***, we apply the aforementioned transform to some classical functions and many types of partial differentialequations involving heat equations,wave equations, Laplace equations, and Poisson equations ***,wedraw some figures to illustrate 3-D contour plots for exact solutions of some selected examples involving differentvalues in their variables.

关键词： ARA transform double ARA transform triple ARA transform partial differential equations integral transform

Enhancing ChatGPT’s Querying Capability with Voice-Based Interaction and CNN-Based Impair Vision Detection Model

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Computers, Materials & Continua 2024年第3期78卷 3129-3150页

作者： Awais ahmad Sohail Jabbar Sheeraz Akram Anand Paul Umar Raza Nuha mohammed Alshuqayran College of Computer and Information Sciences Imam Mohammad Ibn Saud Islamic University(IMSIU)Riyadh11432Saudia Arabia 2 School of Computer Science and EngineeringKyungpook National UniversityDaegu41566South Korea 3 Department of EngineeringManchester Metropolitan UniversityManchesterM156BHUK

This paper presents an innovative approach to enhance the querying capability of ChatGPT,a conversational artificial intelligence model,by incorporating voice-based interaction and a convolutional neural network(CNN)-based impaired vision detection *** proposed system aims to improve user experience and accessibility by allowing users to interact with ChatGPT using voice ***,a CNN-based model is employed to detect impairments in user vision,enabling the system to adapt its responses and provide appropriate *** research tackles head-on the challenges of user experience and inclusivity in artificial intelligence(AI).It underscores our commitment to overcoming these obstacles,making ChatGPT more accessible and valuable for a broader *** integration of voice-based interaction and impaired vision detection represents a novel approach to conversational ***,this innovation transcends novelty;it carries the potential to profoundly impact the lives of users,particularly those with visual *** modular approach to system design ensures adaptability and scalability,critical for the practical implementation of these ***,the solution places the user at its *** responses for those with visual impairments demonstrates AI’s potential to not only understand but also accommodate individual needs and preferences.

关键词： Accessibility in conversational AI CNN-based impair vision detection ChatGPT voice-based interaction recommender system

Impact of metal polluted sewage water on soil nematode assemblages in agricultural settings of Aligarh,India

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Soil Ecology Letters 2024年第1期6卷 61-75页

作者： mohammed F.S.A.Ghanem Shahid Afzal Humira Nesar Zarrin Imran Wasim ahmad Nematode Biodiversity Research Lab Department of ZoologyAligarh Muslim UniversityAligarh 202002India

Sewage water in agriculture threatens human health and soil ecosystems through metal *** show promise as bioindicators of soil health due to their abundance and position in soil food ***-polluted water decreased abundance of certain nematode groups and Sigma Maturity *** pollution positively affected nematode groups with r-selected life *** water has been inappropriately used in agriculture,posing possible threats to human health and the soil ecosystems by its constituent pollutants,especially heavy *** evaluation of its influences on soil biomes needs to consider the response of soil *** soil organisms,nematodes are seen as the best promising candidates for bioindicators of soil *** in,we collected soil samples from fresh water irrigated field from three sites(S1,S2 and S3)and sewage water irrigated distance gradient(5 m-40 m),to assess the influence of metal(Cu,Zn,Cd,Mn,Pb)polluted water on various characteristics of nematode *** results indicated that the heavy metals decreased the abundance of C-p3 nematodes,herbivores,and predatory nematodes as well as sigma maturity index,whereas,C-p1,C-p2,bacterivore and fungivore nematodes abundance and diversity positively responded to the metal ***,nematode genera with r-selected life cycle were positively affected and those with K-selected life cycle were negatively affected by metal *** nematode community has potential to be used as indicator of pollution stress in agricultural soils to check soil health and sustainability.

关键词： heavy metals sewage irigation agriculture nematodes bioindicators

A comparative in vitro study on the effect of SGLT2 inhibitors on chemosensitivity to doxorubicin in MCF-7 breast cancer cells

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Oncology Research 2024年第5期32卷 817-830页

作者： SHAHID KARIM ALANOUD NAHER ALGHANMI MAHA JAMAL HUDA ALKREATHY ALAM JAMAL HIND A.ALKHATABI mohammed BAZUHAIR AFTAB ahmad Department of Clinical Pharmacology Faculty of MedicineKing Abdulaziz UniversityJeddahSaudi Arabia Department of Biochemistry Faculty of ScienceKing Abdulaziz UniversityJeddahSaudi Arabia Department of Biochemistry College of ScienceUniversity of JeddahJeddah21959Saudi Arabia Health Information Technology Department The Applied CollegeKing Abdulaziz UniversityJeddahSaudi Arabia Pharmacovigilance and Medication Safety Unit Center of Research Excellence for Drug Research and Pharmaceutical IndustriesKing Abdulaziz UniversityJeddahSaudi Arabia

Cancer frequently develops resistance to the majority of chemotherapy *** study aimed to examine the synergistic cytotoxic and antitumor effects of SGLT2 inhibitors,specifically Canagliflozin(CAN),Dapagliflozin(DAP),Empagliflozin(EMP),and Doxorubicin(DOX),using in vitro *** precise combination of CAN+DOX has been found to greatly enhance the cytotoxic effects of doxorubicin(DOX)in MCF-7 ***,it was shown that cancer cells exhibit an increased demand for glucose and ATP in order to support their ***,when these medications were combined with DOX,there was a considerable inhibition of glucose consumption,as well as reductions in intracellular ATP and lactate ***,this effect was found to be dependent on the dosages of the *** addition to effectively inhibiting the cell cycle,the combination of CAN+DOX induces substantial modifications in both cell cycle and apoptotic gene *** work represents the initial report on the beneficial impact of SGLT2 inhibitor medications,namely CAN,DAP,and EMP,on the responsiveness to the anticancer properties of *** underlying molecular mechanisms potentially involve the suppression of the function of SGLT2.

关键词： SGLT2 Cancer Cytotoxicity ATP Cell cycle

A Review and Bibliometric Analysis of the Current Studies for the 6G Networks

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Computer Modeling in Engineering & Sciences 2024年第9期140卷 2165-2206页

作者： Qusay M.Salih Md.Arafatur Rahman ahmad Firdaus mohammed Rajih Jassim Hasan Kahtan Jasni Mohamad Zain Ahmed Hussein Ali Faculty of Computing Universiti Malaysia Pahang Al-Sultan Abdullah(UMPSA)PekanPahang26600Malaysia Department of Medical Equipment Technology Engineering Al Salam University CollegeBaghdadIraq School of Mathematics and Computer Science University of WolverhamptonWolverhampton01902UK Department of Production Engineering and Metallurgy University of TechnologyBaghdadIraq Cardiff School of Technologies Cardiff Metropolitan UniversityCardiffCF52YBUK Institute for Big Data Analytics and Artificial Intelligence(IBDAAI) Kompleks Al-KhawarizmiUniversiti Teknologi MARAShah AlamSelangor40450Malaysia Department Computer Science Al-Salam University CollegeBaghdadIraq

The race to develop the next generation of wireless networks,known as Sixth Generation(6G)wireless,which will be operational in 2030,has already *** realize its full potential over the next decade,6G will undoubtedly necessitate additional improvements that integrate existing solutions with cutting-edge ***,the studies about 6G are mainly limited and scattered,whereas no bibliometric study covers the 6G ***,this study aims to review,examine,and summarize existing studies and research activities in *** study has examined the Scopus database through a bibliometric analysis of more than 1,000 papers published between 2017 and ***,we applied the bibliometric analysis methods by including(1)document type,(2)subject area,(3)author,and(4)country of *** study’s results reflect the research 6G community’s trends,highlight important research challenges,and elucidate potential directions for future research in this interesting area.

关键词： 6G bibliometric wireless networks review

A Low Complexity ML-Based Methods for Malware Classification

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Computers, Materials & Continua 2024年第9期80卷 4833-4857页

作者： Mahmoud E.Farfoura ahmad Alkhatib Deema mohammed Alsekait Mohammad Alshinwan Sahar A.El-Rahman Didi Rosiyadi Diaa Salama Abd Elminaam Cybersecurity Department Al-Zaytoonah University of JordanAmman11733Jordan Department of Computer Science and Information Technology Applied CollegePrincess Nourah Bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Faculty of Information Technology Applied Science Private UniversityAmman11931Jordan Computer Systems Program-Electrical Engineering Department Faculty of Engineering-ShoubraBenha UniversityCairo11629Egypt Research Center for Artificial Intelligence and Cybersecurity Electronics and Informatics OrganizationNational Research and Innovation Agency(BRIN)KST Samaun SamadikunBandung40135Republic of Indonesia Jadara Research Center Jadara UniversityIrbid21110Jordan MEU Research Unit Middle East UniversityAmman11831Jordan

The article describes a new method for malware classification,based on a Machine Learning(ML)model architecture specifically designed for malware detection,enabling real-time and accurate malware *** an innovative feature dimensionality reduction technique called the Interpolation-based Feature Dimensionality Reduction Technique(IFDRT),the authors have significantly reduced the feature space while retaining critical information necessary for malware *** technique optimizes the model’s performance and reduces computational *** proposed method is demonstrated by applying it to the BODMAS malware dataset,which contains 57,293 malware samples and 77,142 benign samples,each with a 2381-feature *** the IFDRT method,the dataset is transformed,reducing the number of features while maintaining essential data for accurate *** evaluation results show outstanding performance,with an F1 score of 0.984 and a high accuracy of 98.5%using only two reduced *** demonstrates the method’s ability to classify malware samples accurately while minimizing processing *** method allows for improving computational efficiency by reducing the feature space,which decreases the memory and time requirements for training and *** new method’s effectiveness is confirmed by the calculations,which indicate significant improvements in malware classification accuracy and *** research results enhance existing malware detection techniques and can be applied in various cybersecurity applications,including real-timemalware detection on resource-constrained *** and scientific contribution lie in the development of the IFDRT method,which provides a robust and efficient solution for feature reduction in ML-based malware classification,paving the way for more effective and scalable cybersecurity measures.

关键词： Malware detection ML-based models dimensionality reduction feature engineering

Exploring the Prevalence and Contributing Factors of Depression among Sickle Cell Anemia Patients in Saudi Arabia’s Jazan Region

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International Journal of Mental Health Promotion 2024年第5期26卷 351-359页

作者： Abdullah A.Alharbi ahmad Y.Alqassim mohammed A.Muaddi Najat H.Aldubayyan Rana I.Abuhadi Futon A.Akoor Sarah J.Mobarki Imtenan A.Oberi Ghaidaa H.Alharbi Yara Ajeebi Adnan M.Alkhayat Hafiz Malhan mohammed Bakkar Anwar M.Makeen Family and Community Medicine Department Faculty of MedicineJazan UniversityJazan45142Saudi Arabia Laboratory Department King Fahd Central HospitalMinistry of HealthJazan45142Saudi Arabia Internal Medicine Department Prince Mohammad bin Nasser HospitalMinistry of HealthJazan45142Saudi Arabia Hematology Department King AbdulAziz Medical City for National GuardRiyadh11426Saudi Arabia

Sickle Cell Anemia(SCA)is a prevalent genetic condition in Saudi Arabia,particularly in the Jazan *** study’s purpose was to assess the prevalence of de-pression among individuals with SCA in this area and to identify the factors associated with this *** study involved 391 adult participants,all patients in the hematology center of Prince mohammed bin Nasser Hospital(PMNH).Data was collected through an anonymous interview questionnaire–the Arabic version of the Patient Health Questionnaire-9(PHQ-9)–between March–May *** questionnaire covered socio-demographic information and variables related to the participant’s SCA history,such as pain levels,frequency of pain,hospitalization duration,and frequency of blood *** variables underwent Chi-square and One-way ANOVA testing,followed by multivariate *** study found a 42%prevalence of depression among the SCA *** significantly linked with heightened depression levels included being widowed or divorced,experiencing frequent and severe pain,and longer duration of hospital *** educational level of at least a bachelor’s degree was also a significant *** study reveals a high prevalence of depression among SCA patients in the Jazan *** underscores the need for policymakers to educate both physicians and patients about the importance of mental health in SCA *** study also highlights the need for more research into the specific causes and effects of depressive symptoms in SCA patients to inform the creation of effective management plans.

关键词： Sickle cell anemia prevalence depression public health policy Jazan region Saudi Arabia

Facial Image-Based Autism Detection:A Comparative Study of Deep Neural Network Classifiers

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Computers, Materials & Continua 2024年第1期78卷 105-126页

作者： Tayyaba Farhat Sheeraz Akram Hatoon SAlSagri Zulfiqar Ali Awais ahmad Arfan Jaffar Faculty of Computer Science and Information Technology The Superior UniversityLahore54600Pakistan Intelligent Data Visual Computing Research(IDVCR) Faculty of Computer Science and Information TechnologyThe Superior UniversityLahore54600Pakistan Information Systems Department College of Computer and Information SciencesImam Mohammad Ibn Saud Islamic University(IMSIU)Riyadh12571Saudi Arabia School of Computer Science and Electronic Engineering(CSEE) University of EssexWivenhoe ParkColchesterCO43SQUK

Autism Spectrum Disorder(ASD)is a neurodevelopmental condition characterized by significant challenges in social interaction,communication,and repetitive *** and precise ASD detection is crucial,particularly in regions with limited diagnostic resources like *** study aims to conduct an extensive comparative analysis of various machine learning classifiers for ASD detection using facial images to identify an accurate and cost-effective solution tailored to the local *** research involves experimentation with VGG16 and MobileNet models,exploring different batch sizes,optimizers,and learning rate *** addition,the“Orange”machine learning tool is employed to evaluate classifier performance and automated image processing capabilities are utilized within the *** findings unequivocally establish VGG16 as the most effective classifier with a 5-fold cross-validation ***,VGG16,with a batch size of 2 and the Adam optimizer,trained for 100 epochs,achieves a remarkable validation accuracy of 99% and a testing accuracy of 87%.Furthermore,the model achieves an F1 score of 88%,precision of 85%,and recall of 90% on test *** validate the practical applicability of the VGG16 model with 5-fold cross-validation,the study conducts further testing on a dataset sourced fromautism centers in Pakistan,resulting in an accuracy rate of 85%.This reaffirms the model’s suitability for real-world ASD *** research offers valuable insights into classifier performance,emphasizing the potential of machine learning to deliver precise and accessible ASD diagnoses via facial image analysis.

关键词： Autism Autism Spectrum Disorder(ASD) disease segmentation features optimization deep learning models facial images classification