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Extraction of cobalt(Ⅱ) from aqueous solution by N,N'-carbonyl difatty amides

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Chinese Chemical Letters 2011年第4期22卷 469-472页

作者： Emad A.Jaffar al-Mulla Khalid waleed S.al-Janabi Department of Chemistry College of ScienceUniversity of KufaAnNajafIraq Department of Chemistry College of EducationBaghdad UniversityBaghdadIraq

The development of economic and environmentally friendly extractants to recover cobalt metal is required due to the increasing demand for this *** this study,solvent extraction of Co（Ⅱ） from aqueous solution using a mixture of N,N＇-carbonyl difatty amides（CDFAs） synthesised from palm oil as the extractant was carried *** effects of various parameters such as acid,contact time,extractant concentration,metal ion concentration and stripping agent and the separation of Co（Ⅱ） from other metal ions such as Fe（Ⅱ）,Ni（Ⅱ）,Zn（Ⅲ） and Cd（Ⅱ） were *** was found that the extraction of Co（Ⅱ） into the organic phase involved the formation of 1：1 ***（Ⅱ） was successfully separated from commonly associated metal ions such as Fe（Ⅱ）,Ni（Ⅱ）,Zn（Ⅲ） and Cd（Ⅱ）.Co（Ⅱ） stripping from the loaded organic phase was studied in aqueous *** results are useful to recover Co（Ⅱ） from aqueous solution utilising（CDFAs） as an extractant.

关键词： N N'-Carbonyl difatty amides Solvent extraction Co(Ⅱ)

Improving Prediction Efficiency of Machine Learning Models for Cardiovascular Disease in IoST-Based Systems through Hyperparameter Optimization

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Computers, Materials & Continua 2024年第9期80卷 3485-3506页

作者： Tajim Md.Niamat Ullah Akhund waleed M.al-Nuwaiser Department of Computer Science and Engineering(CSE) Daffodil International UniversityDhaka1216Bangladesh Graduate School of Science and Engineering Saga UniversitySaga8408502Japan Computer Science Department College of Computer and Information SciencesImam Mohammad Ibn Saud Islamic University(IMSIU)Riyadh11623Saudi Arabia

This study explores the impact of hyperparameter optimization on machine learning models for predicting cardiovascular disease using data from an IoST(Internet of Sensing Things)*** distinct machine learning approaches were implemented and systematically evaluated before and after hyperparameter *** improvements were observed across various models,with SVM and Neural Networks consistently showing enhanced performance metrics such as F1-Score,recall,and *** study underscores the critical role of tailored hyperparameter tuning in optimizing these models,revealing diverse outcomes among *** Trees and Random Forests exhibited stable performance throughout the *** enhancing accuracy,hyperparameter optimization also led to increased execution *** representations and comprehensive results support the findings,confirming the hypothesis that optimizing parameters can effectively enhance predictive capabilities in cardiovascular *** research contributes to advancing the understanding and application of machine learning in healthcare,particularly in improving predictive accuracy for cardiovascular disease management and intervention strategies.

关键词： Internet of sensing things(IoST) machine learning hyperparameter optimization cardiovascular disease prediction execution time analysis performance analysis wilcoxon signed-rank test

URL Phishing Detection Using Particle Swarm Optimization and Data Mining

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Computers, Materials & Continua 2022年第12期73卷 5625-5640页

作者： Saeed M.alshahrani Nayyar Ahmed Khan Jameel almalki waleed al Shehri College of Computing and IT-Shaqra University Shaqra155572Saudi Arabia Department of Computer Science College of Computer in Al-LeithUmm Al-Qura UniversityMakkahSaudi Arabia

The continuous destruction and frauds prevailing due to phishing URLs make it an indispensable area for *** techniques are adopted in the detection process,including neural networks,machine learning,or hybrid techniques.A novel detection model is proposed that uses data mining with the Particle Swarm Optimization technique(PSO)to increase and empower the method of detecting phishing *** selection based on various techniques to identify the phishing candidates from the URL is *** this approach,the features mined from the URL are extracted using data mining *** features are selected on the basis of URL *** classification of these features identified by the data mining rules is done using PSO *** selection of features with PSO optimization makes it possible to identify phishing *** a large number of rule identifiers,the true positive rate for the identification of phishing URLs is maximized in this *** experiments show that feature selection using data mining and particle swarm optimization helps tremendously identify the phishing URLs based on the structure of the URL ***,it can minimize processing time for identifying the phishing website ***,the approach can be beneficial to identify suchURLs over the existing contemporary detecting models proposed before.

关键词： Phishing particle swarm optimization feature selection data mining classification cloud application

IoMT-Based Healthcare Framework for Ambient Assisted Living Using a Convolutional Neural Network

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Computers, Materials & Continua 2023年第3期74卷 6867-6878页

作者： waleed T.al-Sit Nidal A.al-Dmour Taher M.Ghazal Ghassan F.Issa Department of Computer Engineering Mutah UniversityAl-KarakJordanHigher Colleges of TechnologyDubaiUAE Department of Computer Engineering College of EngineeringMutah UniversityJordan School of Information Technology Skyline University CollegeUniversity City SharjahSharjahUAE Center for Cyber Security Faculty of Information Science and TechnologyUKM43600BangiSelangorMalaysia

In the age of universal computing,human life is becoming smarter owing to the recent developments in the Internet of Medical Things(IoMT),wearable sensors,and telecommunication innovations,which provide more effective and smarter healthcare *** has the potential to shape the future of clinical research in the healthcare *** sensors,patients,healthcare providers,and caregivers can connect through an IoMT network using software,information,and communication *** assisted living(Aal)allows the incorporation of emerging innovations into the routine life events of *** learning(ML)teaches machines to learn from human experiences and to use computer algorithms to“learn”information directly instead of relying on a *** the sample size accessible for learning increases,the performance of the algorithms *** paper proposes a novel IoMT-enabled smart healthcare framework for Aal to monitor the physical actions of patients using a convolutional neural network(CNN)algorithm for fast analysis,improved decision-making,and enhanced treatment *** simulation results showed that the prediction accuracy of the proposed framework is higher than those of previously published approaches.

关键词： Smart healthcare system neural network machine learning

Received Power Based Unmanned Aerial Vehicles (UAVs) Jamming Detection and Nodes Classification Using Machine Learning

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Computers, Materials & Continua 2023年第4期75卷 1253-1269页

作者： waleed aldosari Department of Computer Engineering College of Computer Engineering and SciencesPrince Sattam bin Abdulaziz UniversityAl-Kharj11942Saudi Arabia

This paper presents a machine-learning method for detecting jamming UAVs and classifying nodes during jamming attacks onWireless Sensor Networks(WSNs).Jamming is a type of Denial of Service(DoS)attack and intentional interference where a malicious node transmits a high-power signal to increase noise on the receiver side to disrupt the communication channel and reduce performance *** defend and prevent such attacks,the first step is to detect *** current detection approaches use centralized techniques to detect jamming,where each node collects information and forwards it to the base *** a result,overhead and communication costs *** this work,we present a jamming attack and classify nodes into different categories based on their location to the jammer by employing a single node *** a result,we introduced a machine learning model that uses distance ratios and power received as features to detect such ***,we considered several types of jammers transmitting at different power levels to evaluate the proposed metrics using *** a detection accuracy of 99.7%for the k-nearest neighbors(KNN)algorithm and average testing accuracy of 99.9%,the presented solution is capable of efficiently and accurately detecting jamming attacks in wireless sensor networks.

关键词： Jamming attacks machine learning unmanned aerial vehicle(UAV) WSNs

Synthesis, characterization, biological activities and luminescent properties of lanthanide complexes with [2-thiophenecarboxylic acid, 2-(2-pyridinylmethylene)hydrazide] Schiff bases ligand

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Journal of Rare Earths 2016年第10期34卷 986-993页

作者： Abdulaziz M.Ajlouni Qutaiba Abu-Salem Ziyad A.Taha Ahmed K.Hijazi waleed al Momani Department of Applied Chemical Sciences Jordan University of Science and Technology Department of Chemistry Faculty of Science Al al-Bayt University Department of Basic Medical Sciences Faculty of Medicine Yarmouk University

A Schiff base L [2-thiophenecarboxylic acid, 2-（2-pyridinylmethylene）hydrazide] with its lanthanide metal complexes was synthesized. These complexes were characterized by elemental analysis, molar conductivity measurements, spectral analysis（NMR, FT-IR, and UV-Vis）, luminescence and thermal gravimetric analysis. The Schiff base ligand was a tridentate chelate and coordinates to the central lanthanide ion with 1：2 metal：ligand ratio. The conductivity data showed a 1：1 electrolytic nature with a general formula [LnL_2（NO_3）_2]NO_3. The luminescence emission properties for Sm, Tb, and Eu complexes were observed and showed that the ligand L could absorb and transfer energy to Sm（III）, Tb（III） and Eu（III） ions. The complexes possessed a good antibacterial activity against different bacterial strains. In addition, the scavenging activity of the Ln（III） complexes on DPPH was concentration dependant and the complexes were significantly more efficient in quenching DPPH than the free Schiff base ligand.

关键词： hydrazone lanthanides complexes luminescence properties antibacterial activity rare earths

Deep Learning Based Efficient Crowd Counting System

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Computers, Materials & Continua 2024年第6期79卷 4001-4020页

作者： waleed Khalid al-Ghanem Emad Ul Haq Qazi Muhammad Hamza Faheem Syed Shah Amanullah Quadri Department of Computer Engineering College of Computer and Information SciencesKing Saud UniversityRiyadh12372Saudi Arabia Centre of Excellence in Cybercrime and Digital Forensics Naif Arab University for Security SciencesRiyadh14812Saudi Arabia Center of Excellence for Information Assurance(COEIA) King Saud UniversityRiyadh12372Saudi Arabia

Estimation of crowd count is becoming crucial nowadays,as it can help in security surveillance,crowd monitoring,and management for different *** is challenging to determine the approximate crowd size from an image of the crowd’s *** 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 *** 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 *** 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 *** results show the superiority of the proposed system.

关键词： Crowd counting EfficientNet multi-head attention convolutional neural network transfer learning

Precision time protocol attack strategies and their resistance to existing security extensions

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Cybersecurity 2021年第1期4卷 160-176页

作者： waleed alghamdi Michael Schukat School of Computer Science National University of Ireland Galway Ireland

The IEEE 1588 precision time protocol(PTP)is very important for many industrial sectors and applications that require time synchronization accuracy between computers down to microsecond and even nanosecond ***,PTP and its underlying network infrastructure are vulnerable to cyber-attacks,which can stealthily reduce the time synchronization accuracy to unacceptable and even damage-causing levels for individual clocks or an entire network,leading to financial loss or even physical *** security protocol extensions only partially address this *** paper provides a comprehensive analysis of strategies for advanced persistent threats to PTP infrastructure,possible attacker locations,and the impact on clock and network synchronization in the presence of security protocol extensions,infrastructure redundancy,and protocol *** distinguishes between attack strategies and attacker types as described in RFC7384,but further distinguishes between the spoofing and time source attack,the simple internal attack,and the advanced internal *** experiments were conducted to demonstrate the impact of PTP *** analysis shows that a sophisticated attacker has a range of methodologies to compromise a PTP ***,all PTP infrastructure components can host an attacker,making the comprehensive protection of a PTP network against a malware infiltration,as for example exercised by Stuxnet,a very tedious task.

关键词： APT Cyber-attacks IEEE 1588 PTP Security Time synchronization protocols

Severe autoimmune hepatitis triggered by varicella zoster infection

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World Journal of Gastroenterology 2009年第8期15卷 1004-1006页

作者： waleed K al-Hamoudi Gastroenterology and Hepatology Unit (59) Department of Medicine King Saud University PO Box 2925 Riyadh 11461 Saudi Arabia

Autoimmune hepatitis (AIH) is a chronic disease of unknown etiology that is characterized by the presence of circulatory autoantibodies and inflammatory histological changes in the liver. although the pathogenesis of AIH is not known, it is thought that, in a genetically predisposed individual, environmental factors such as viruses can trigger the autoimmune process. Herpes simplex virus, Epstein-Barr virus, measles virus, and hepatitis viruses are thought to play a role in the etiology of AIH. Proteins belonging to these viruses may be similar to the amino acid chains of different autoantigens in the liver, this causes immune cross reactions and liver tissue damage. We report a case of severe AIH following varicella zoster infection in a 23-year-old man, and speculate that, based on the molecular mimicry hypothesis, the liver damage was caused by an immune cross reaction to the viral proteins. Varicella-zoster-induced AIH has not been reported previously.

关键词： Autoimmune liver disease Varicella zoster virus Infection