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Nature Inspired Improved Firefly Algorithm for Node Clustering in WSNs

Computers, Materials & Continua 2020年第8期64卷 753-776页

作者： V.Manikandan M.Sivaram amin salih mohammed V.Porkodi Department of Computer Networking College of Engineering and Computer ScienceLebanese French UniversityErbil44001Iraq College of Engineering and Computer Science Lebanese French UniversityErbil44001Iraq Department of Information Technology College of Engineering and Computer ScienceLebanese French UniversityErbil44001Iraq

Wireless Sensor Networks(WSNs)comprises low power devices that are randomly distributed in a geographically isolated *** energy consumption of nodes is an essential factor to be ***,an improved energy management technique is designed in this investigation to reduce its consumption and to enhance the network’s *** can be attained by balancing energy clusters using a meta-heuristic Firefly algorithm model for network *** improved technique is based on the cluster head selection technique with measurement of the tour length of *** Division Multiple Access(TDMA)scheduler is also improved with the characteristics/behavior of fireflies and also *** last,the development approach shows the progression of the network lifetime,the total number of selected Cluster Heads(CH),the energy consumed by nodes,and the number of packets *** approach is compared with Ad hoc On-Demand Distance Vector(AODV),Dynamic Source Routing(DSR)and Low Energy Adaptive Clustering Hierarchy(LEAH)*** is performed in MATLAB with the numerical outcomes showing the efficiency of the proposed *** energy consumption of sensor nodes is reduced by about 50%and increases the lifetime of nodes by 78%more than AODV,DSR and LEACH *** parameters such as cluster formation,end to end delay,percentage of nodes alive and packet delivery ratio,are also evaluated...The anticipated method shows better trade-off in contrast to existing techniques.

关键词： Cluster head wireless sensor network LEAH TDMA firefly AODV DSR

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Secure Localization Based Authentication (SLA) Strategy for Data Integrity in WNS

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Computers, Materials & Continua 2021年第6期67卷 4005-4018页

作者： V.Manikandan M.Sivaram amin salih mohammed V.Porkodi K.Shankar Assistant Professor Research Research CenterLebanese French UniversityErbil44001Iraq Vice President Lebanese French UniversityErbil44001Iraq Department of Information Technology College of Engineering and Computer ScienceLebanese French UniversityErbil44001Iraq Department of Computer Applications Alagappa UniversityKaraikudi630003India

Wireless Sensor Networks(WSN)has been extensively utilized as a communication model in Internet of Things(IoT).As well,to offer service,numerous IoT based applications need effective transmission over unstable *** ensure reliability,prevailing investigations exploit multiple candidate forwarders over geographic opportunistic routing in ***,these models are affected by crucial denial of service(DoS)attacks,where huge amount of invalid data are delivered intentionally to the receivers to disturb the functionality of ***,secure localization based authentication(SLA)is presented to fight against DoS attack,and to fulfil the need of reliability and *** examining state information,SLA projects a trust model to enhance efficacy of data ***,of the prevailing opportunistic protocols,SLA guarantees data integrity by modelling a trust based authentication,providing protection against DoS attackers and diminishing computational ***,this model acts as a verification strategy to accelerate?attackers and to handle *** strategy helps SLA in eliminating duplicate transmission and by continuous verification that results from conventional opportunistic *** is performed in a MATLAB environment that offers authentic and reliable delivery by consuming approximately 50%of the cost in contrast to other *** anticipated model shows better trade off in comparison to the prevailing ones.

关键词： Wireless sensor networks opportunistic routing secure localization based authentication denial of service computational cost

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Enhanced termination of zinc and cadmium ions from wastewater employing plain and chitosan-modified mxenes: synthesis, characterization,and adsorption performance

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Green Chemical Engineering 2024年第3期5卷 339-347页

作者： Njood R.Azeez Suhaib S.salih mohammed Kadhom Harith N.mohammed Tushar K.Ghosh Department of Chemical Engineering College of Engineering University of Tikrit Department of Environmental Science College of Energy and Environmental Science Al-Karkh University of Science Department of Nuclear Science & Engineering Institute University of Missouri-Columbia

Zinc and cadmium pollutants cause a significant environmental effect that cannot be ignored. Due to their considerable amount in an aqueous environment, industries are seeking suitable adsorbents that are environmentally friendly and inexpensive for removing metals from wastewater before disposing of them in surface waters. This research employed original MXene(MX) and chitosan-modified MXene(CSMX) to extract zinc(Zn(Ⅱ)) and cadmium(Cd(Ⅱ)) metal ions from water-based solutions. The composite material produced was analyzed using techniques such as X-ray Diffraction(XRD), Scanning Electron Microscopy(SEM), Fourier Transform Infrared Spectroscopy(FTIR), and Brunauer-Emmett-Teller(BET). The effects of contact duration, p H of the solution, and initial concentration of metal ions on the adsorption process of Zn(Ⅱ) and Cd(Ⅱ) onto both MX and CSMX composites were investigated. MX and prepared CSMX composite presented a high adsorption capacity for both studied heavy metals, which were 91.55 and 73.82 mg/g for Zn(Ⅱ) and Cd(Ⅱ) onto MX, 106.84 and 93.07 mg/g for Cd(Ⅱ) and Zn(Ⅱ) onto CSMX composite, respectively. Furthermore, the maximum competitive adsorption capacities for Zn(Ⅱ) onto MX and CSMX composites are 77.29 and 93.47 mg/g, and for are Cd(Ⅱ)60.30 and 79.66 mg/g, respectively. Hence, the removal capacities for both single and competitive metal ions were superior to CSMX composite. However, the adsorption capacities after five successive regeneration sequences were only dropped by 13.2% for Zn(Ⅱ) and 17.4% for Cd(Ⅱ) onto the CSMX composite compared to the first cycle. These results confirm that both metals could be efficiently terminated from wastewater, which makes the prepared CSMX composite a favorable candidate adsorbent in practical applications.

关键词： Adsorption Chitosan MXene Zn(II) Cd(II) Wastewater

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Social Media-Based Surveillance Systems for Health Informatics Using Machine and Deep Learning Techniques:A Comprehensive Review and Open Challenges

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Computer Modeling in Engineering & Sciences 2024年第5期139卷 1167-1202页

作者： Samina amin Muhammad Ali Zeb Hani Alshahrani mohammed Hamdi Mohammad Alsulami Asadullah Shaikh Institute of Computing Kohat University of Science and TechnologyKohat26000Pakistan Department of Computer Science College of Computer Science and Information SystemsNajran UniversityNajran61441Saudi Arabia Department of Information System College of Computer Science and Information SystemsNajran UniversityNajran61441Saudi Arabia

Social media(SM)based surveillance systems,combined with machine learning(ML)and deep learning(DL)techniques,have shown potential for early detection of epidemic *** review discusses the current state of SM-based surveillance methods for early epidemic outbreaks and the role of ML and DL in enhancing their ***,every year,a large amount of data related to epidemic outbreaks,particularly Twitter data is generated by *** paper outlines the theme of SM analysis for tracking health-related issues and detecting epidemic outbreaks in SM,along with the ML and DL techniques that have been configured for the detection of epidemic *** has emerged as a promising ML technique that adaptsmultiple layers of representations or features of the data and yields state-of-the-art extrapolation *** recent years,along with the success of ML and DL in many other application domains,both ML and DL are also popularly used in SM *** paper aims to provide an overview of epidemic outbreaks in SM and then outlines a comprehensive analysis of ML and DL approaches and their existing applications in SM ***,this review serves the purpose of offering suggestions,ideas,and proposals,along with highlighting the ongoing challenges in the field of early outbreak detection that still need to be addressed.

关键词： Social media epidemic machine learning deep learning health informatics pandemic

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

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Computers, Materials & Continua 2024年第7期80卷 1457-1490页

作者： Fatma S.Alrayes mohammed Zakariah Syed Umar amin Zafar Iqbal Khan Jehad Saad Alqurni Information Systems Department College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityRiyadh11671Saudi Arabia College of Computer and Information Sciences King Saud UniversityRiyadh11362Saudi Arabia College of Computer and Information Sciences Prince Sultan UniversityRiyadh11586Saudi Arabia Department of Educational Technologies College of EducationImam Abdulrahman Bin Faisal UniversityP.O.Box 1982Dammam31441Saudi Arabia

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 *** DNN-based IDS is trained and validated by the model using the NSL-KDD dataset,a popular benchmark for IDS *** model performs well in both the training and validation stages,with 91.30%training accuracy and 94.38%validation ***,the model shows good learning and generalization capabilities with minor losses of 0.22 in training and 0.1553 in ***,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 *** macro-average recall is 0.9422,the macro-average precision is 0.9482,and the accuracy scores are ***,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 *** 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 *** study underscores the critical function ofDNN-based IDS in contemporary cybersecurity procedures by setting the foundation for further developments in this *** research aims to enhance intrusion detection systems by examining cooperative learning techniques and integrating up-to-date threat knowledge.

关键词： Machine-Learning Deep-Learning intrusion detection system security privacy deep neural network NSL-KDD Dataset

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A gated recurrent unit model to predict Poisson’s ratio using deep learning

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Journal of Rock Mechanics and Geotechnical Engineering 2024年第1期16卷 123-135页

作者： Fahd Saeed Alakbari Mysara Eissa Mohyaldinn mohammed Abdalla Ayoub Ibnelwaleed A.Hussein Ali Samer Muhsan Syahrir Ridha Abdullah Abduljabbar salih Petroleum Engineering Department Universiti Teknologi PETRONAS32610Bandar Seri IskandarPerakMalaysia Institute of Hydrocarbon Recovery Universiti Teknologi PETRONAS32610Bandar Seri IskandarPerakMalaysia Gas Processing Center College of EngineeringQatar UniversityP.O.Box 2713DohaQatar Department of Chemical Engineering College of EngineeringQatar UniversityP.O.Box 2713DohaQatar Mechanical Engineering Department Universiti Teknologi PETRONAS32610Bandar Seri IskandarPerakMalaysia

Static Poisson’s ratio(vs)is crucial for determining geomechanical properties in petroleum applications,namely sand *** models have been used to predict vs;however,the published models were limited to specific data ranges with an average absolute percentage relative error(AAPRE)of more than 10%.The published gated recurrent unit(GRU)models do not consider trend analysis to show physical *** this study,we aim to develop a GRU model using trend analysis and three inputs for predicting n s based on a broad range of data,n s(value of 0.1627-0.4492),bulk formation density(RHOB)(0.315-2.994 g/mL),compressional time(DTc)(44.43-186.9 μs/ft),and shear time(DTs)(72.9-341.2μ s/ft).The GRU model was evaluated using different approaches,including statistical error *** GRU model showed the proper trends,and the model data ranges were wider than previous *** GRU model has the largest correlation coefficient(R)of 0.967 and the lowest AAPRE,average percent relative error(APRE),root mean square error(RMSE),and standard deviation(SD)of 3.228%,1.054%,4.389,and 0.013,respectively,compared to other *** GRU model has a high accuracy for the different datasets:training,validation,testing,and the whole datasets with R and AAPRE values were 0.981 and 2.601%,0.966 and 3.274%,0.967 and 3.228%,and 0.977 and 2.861%,*** group error analyses of all inputs show that the GRU model has less than 5% AAPRE for all input ranges,which is superior to other models that have different AAPRE values of more than 10% at various ranges of inputs.

关键词： Static Poisson’s ratio Deep learning Gated recurrent unit(GRU) Sand control Trend analysis Geomechanical properties

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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

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CNN Channel Attention Intrusion Detection SystemUsing NSL-KDD Dataset

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Computers, Materials & Continua 2024年第6期79卷 4319-4347页

Intrusion detection systems(IDS)are essential in the field of cybersecurity because they protect networks from a wide range of online *** goal of this research is to meet the urgent need for small-footprint,highly-adaptable Network Intrusion Detection Systems(NIDS)that can identify *** 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 ***,maintaining operating efficiency while improving detection accuracy is the primary goal of this ***,typical NIDS examines both risky and typical behavior using a variety of *** the NSL-KDD dataset,our CNN-based approach achieves an astounding 99.728%accuracy rate when paired with channel *** 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 ***,the results highlight the effectiveness of our suggested method in improving intrusion detection precision,signifying a noteworthy advancement in this *** efforts will focus on strengthening and expanding our approach in order to counteract growing cyberthreats and adjust to changing network circumstances.

关键词： Intrusion detection system(IDS) NSL-KDD dataset deep-learning machine-learning CNN channel Attention network security

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Regulating microstructure and composition by carbonizing in-situ grown metal-organic frameworks on cotton fabrics for boosting electromagnetic wave absorption

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Nano Research 2024年第8期17卷 7290-7300页

作者： Jie Jin Hongsen Long Hu Liu Yan Guo Tiantian Bai Ben Bin Xu mohammed A.amin Hua Qiu Mohamed H.Helal Chuntai Liu Changyu Shen Zeinhom M.El-Bahy Zhanhu Guo State Key Laboratory of Structural Analysis Optimization and CAE Software for Industrial EquipmentNational Engineering Research Center for Advanced Polymer Processing TechnologyZhengzhou UniversityZhengzhou 450002China China State Key Laboratory of Powder Metallurgy Central South UniversityChangsha 410000China School of Electronic Engineering North China University of Water Resources and Electric PowerZhengzhou 450046China Complex Conditions of High-end Tire Technology Innovation Center Shuguang Rubber Industry Research&Design Institute Co.LtdGuilin 541004China Integrated Composites Lab Department of Mechanical and Civil EngineeringNorthumbria UniversityNewcastle Upon TyneNE18STUK Department of Chemistry College of ScienceTaif UniversityP.O.Box 11099Taif 21944Saudi Arabia Shaanxi Key Laboratory of Macromolecular Science and Technology School of Chemistry and Chemical EngineeringNorthwestern Polytechnical UniversityXi’an 710072China Department of Chemistry Faculty of Arts and ScienceNorthern Border UniversityRafha 73213Saudi Arabia Department of Chemistry Faculty of ScienceAl-Azhar UniversityNasr City 11884CairoEgypt

High-temperature carbonized metal-organic frameworks(MOFs)derivatives have demonstrated their superiority for promising electromagnetic wave(EMW)absorbers,but they still suffer from limited EMW absorption capacity and narrow *** the advantage of microstructure and chemical composition regulation for the design of EMW absorber,hierarchical heterostructured MoS_(2)/CoS_(2)-Co_(3)O_(4)@cabonized cotton fabric(CF)(MCC@CCF)is prepared by growing ZIF-67 MOFs onto CF surface,chemical etching,and *** from the dual loss mechanism of magnetic-dielectric multicomponent carbonized MOFs,chemical etching and carbonization process can effectively introduce abundant micro-gap structure that can result in better impedance matching and stronger absorption capacity via internal reflection,doped heteroatoms(Mo,N,S)to supply additional dipolar polarization loss,and numerous heterointerfaces among MoS_(2),CoS_(2),Co_(3)O_(4),and CCF that produce promoted conduction loss and interfacial polarization ***,a minimal reflection loss of−52.87 dB and a broadest effective absorption bandwidth of 6.88 GHz were achieved via tunning the sample thickness and filler loading,showing excellent EMW absorption *** research is of great value for guiding the research on MOFs derivatives based EMW absorbing materials.

关键词： metal-organic frameworks hierarchical structure chemical etching sulfurization electromagnetic wave absorption

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Use of Topical Complementary and Alternative Medicines (CAM) in Diabetic Septic Foot Disease in Makkah Region, Western of Saudi Arabia, an Exploratory Study

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Journal of Biosciences and Medicines 2024年第3期12卷 245-258页

作者： Mohamed Elamin salih Hatim Alrizqi mohammed Hussain Abdullah Alshumrani Ismail Alhasani Ali Alkhamisi Hamzah Alsayed Fahad Alqarni Department of Surgery Faculty of Medicine University of Umm AlQura Mecca Saudi Arabia Faculty of Medicine University of Umm AlQura Mecca Saudi Arabia

Diabetes is a chronic condition that is significantly impacted daily by environment. At least one in five Saudis, suffer from diabetes. “Diabetes is a pandemic of unprecedented magnitude spiraling out of control” (Boulton). Saudi Arabia ranks the second highest in the Middle East, and is the seventh in the world for the prevalence of diabetes (WHO). Diabetes mellitus (DM) has been found to be related to high mortality, morbidity accompanied by poor general health and lower quality of life. Current Saudi diabetes patients’ behaviors, practices, and beliefs regarding foot health problems such as ulcers, are not well investigated. Diabetic patients frequently utilize natural remedies and Herbs for self-medication as a part of complementary and alternative Medicine. Aim of study: To explore the widespread use of the alternative remedies in the local communities of the Western Saudi Arabia, the drives behind using and to focus on the potential adverse effects following their usage. Method: A descriptive, cross-sectional study of 386 diabetic patients with feet problems in Makkah Region, West of Saudi Arabia during the period April - June 2023. Results: Out of the 531 participants, only 386 participants reported diabetic foot disorder (DFD). Most of the participant were above 65 years of age group (24.61 %). Male (52.08%) more than female (47.92%) in the research group. All of the participants had diabetic foot disorders at least once. The prevalence of complementary and alternative medicines (CAM) use among them was 67.88%, as solitary or in combination with hospital medicines. Honey headed the list followed by myrrh and black seeds. Acquaintances advice (59.54%) was the most reason for using the alternative medicine. 60.30% of Diabetic foot patient complained of adverse effects during CAM remedies use. Conclusion: Consumption of CAM remedies among diabetic patients for Diabetic Foot Disorder (DFD) is common. Honey headed the list followed by myrrh and black seeds. Addit

关键词： Diabetic Foot Ulcer Diabetes Complementary and Alternative Medicine CAM West of Saudi Arabia

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