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Green Synthesis and Catalysis 2022年第2期3卷 190-193页

作者： Sudha S mohamed afzal pasha Department of Chemistry and Biochemistry Jyoti Nivas College AutonomousBengaluru 560095India Department of Studies in Chemistry Jnanabharathi CampusBangalore UniversityBengaluru 560056India

An efficient,catalyst-free and eco-friendly ultrasound-assisted novel one-pot pseudo-five-component synthesis of aryl-bis-[1H-pyrazol-5-ol-4yl]methanes,het(aryl)-bis-[1H-pyrazol-5-ol-4yl]methanes and their 1-phenyl derivatives in water is reported.The products were obtained in excellent yield in shorter duration with high purity,by simple filtration.This synthetic methodology has been optimized and discussed herein.

关键词： Bis pyrozolyl-methanes Ethyl acetoacetate Aromatic/het(aryl)aldehydes Hydrazine/phenyl hydrazine One-pot multi-component synthesis Ultrasound

Amberlite IR-120:一种可重复利用的微波辅助条件下胺类化合物与甲酸N-甲酰化反应的催化剂(英文)

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催化学报 2010年第5期31卷 518-520页

作者： Madhusudana Reddy MVTHUKUR BHOJEGOWDSHA Aatika NIZAMSHA mohamed afzal pasha Department of Studies in Chemistry Central College CampusBangalore University

A rapid and practical green route for the N-formylation of amines with formic acid using Amberlite IR-120 as a catalyst is described.This method provides an efficient and much improved modification over the reported methods in terms of yield,reaction time,and work-up procedure.A wide variety of substituents is tolerated,which is not the case for existing procedures.

关键词： N-formylation Amberlite IR-120 蚁的酸胺微波

高效、可重复使用的多相催化剂SiO_2-NaHSO_4用于水中三组分一锅法合成八氢喹唑啉-2,5-二酮(英文)

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催化学报 2012年第4期33卷 677-680页

作者： Sadeq Hamood Saleh AZZAM Aisha SIDDEKHA Aatika NIZAM mohamed afzal pasha Department of Studies in Chemistry Central College CampusBangalore UniversityBengaluru-560001India Department of Chemistry Smt.V.H.D.Central Institute of Home ScienceBangalore-560001India

An environmentally benign method for the synthesis of octahydro-quinazolin-2,5-diones by the reaction of aromatic aldehydes,dimedone,and urea in the presence of SiO2-NaHSO4 is reported.SiO2-NaHSO4 acts as an efficient,mild,and recyclable heterogeneous catalyst to give excellent yields within a short reaction time in water at 60-80℃.

关键词：化学催化高压高温

采用燃烧技术制备CuO纳米粒子:一种高效且环境友好的用于芳族醛合成芳族腈催化剂(英文)

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催化学报 2013年第4期34卷 704-710页

作者： Belladamadu Siddappa ANANDAKUMAR Muthukur Bhojegowd Madhusudana REDDY Chikka Nagaiah THARAMANI mohamed afzal pasha Gujjarahalli Thimmanna CHANDRAPPA Department of Chemistry Bangalore University Department of Chemistry Ruhr Universit t Bochum

CuO nanoparticles were synthesized using an energy-efficient and rapid solution combustion technique with malic acid employed as a fuel. The combustion-derived CuO nanoparticles were used as catalysts in a one-pot synthesis of aromatic nitriles from aromatic aldehydes and hydroxylamine hydrochloride. The catalyst was characterized by X-ray diffraction, scanning electron microscopy, energy-dispersive X-ray analysis, transmission electron microscopy, and Brunauer-Emmett-Teller surface area analysis. The catalytic activity of the CuO nanoparticles in the synthesis of aromatic nitriles from aromatic aldehydes was evaluated. The present protocol offers the advantages of a clean reaction, simple methodology, short reaction duration (1-2 min), and high yield (85%-98%). The catalytic activity of the CuO nanoparticles was found to be higher than that of bulk CuO powder under the same conditions. The catalyst can also be recovered and reused up to four times with no significant loss of catalytic activity. The present approach is inexpensive and is a convenient technique suitable for industrial production of CuO nanoparticles and nitriles.

关键词： Copper oxide Nanoparticle Solution combustion Nitrile Aldehyde Hydroxyl amine

Meglumine catalyzed one-pot green synthesis of novel 4,7-dihydro-1H-pyrazolo[3,4-b]pyridin-6-amines

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Chinese Chemical Letters 2017年第2期28卷 437-441页

作者： Santhosh Govindaraju Sumaiya Tabassum Riyaz-ur-Rahaman Khan mohamed afzal pasha Department of Studies in Chemistry Central College Campus Bangalore University Bengaluru 560001 India

Meglumine efficiently catalyzes the one-pot,five-component reaction of hydrazine,ethyl acetoacetate,aryl aldehydes,substituted phenylacetonitriles and ammonium acetate in ethanol at room temperature to afford novel 4,7-dihydro-1H-pyrazolo[3,4-b]pyridin-6-amine derivatives.The present approach offers several advantages such as shorter reaction durations,low cost,excellent yields,milder reaction conditions,simple workup procedure and is environment friendly.All the synthesized derivatives are characterized by IR,^1H NMR,^13C NMR,HRMS and CHN analysis.

关键词： One-pot five-component reaction Substituted phenylacetonitriles Meglumine Pyrazolopyridin-6-amines Ethanol

Modeling of Combined Economic and Emission Dispatch Using Improved Sand Cat Optimization Algorithm

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Computers, Materials & Continua 2023年第6期75卷 6145-6160页

作者： Fadwa Alrowais Jaber S.Alzahrani Radwa Marzouk Abdullah mohamed Gouse pasha Mohammed Department of Computer Sciences College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Industrial Engineering College of Engineering at AlqunfudahUmm Al-Qura UniversityMecca24382Saudi Arabia Research Centre Future University in EgyptNew Cairo11845Egypt Department of Computer and Self Development Preparatory Year DeanshipPrince Sattam bin Abdulaziz UniversityAlKharjSaudi Arabia

Combined Economic and Emission Dispatch(CEED)task forms multi-objective optimization problems to be resolved to minimize emission and fuel costs.The disadvantage of the conventional method is its incapability to avoid falling in local optimal,particularly when handling nonlinear and complex systems.Metaheuristics have recently received considerable attention due to their enhanced capacity to prevent local optimal solutions in addressing all the optimization problems as a black box.Therefore,this paper focuses on the design of an improved sand cat optimization algorithm based CEED(ISCOA-CEED)technique.The ISCOA-CEED technique majorly concen-trates on reducing fuel costs and the emission of generation units.Moreover,the presented ISCOA-CEED technique transforms the equality constraints of the CEED issue into inequality constraints.Besides,the improved sand cat optimization algorithm(ISCOA)is derived from the integration of tra-ditional SCOA with the Levy Flight(LF)concept.At last,the ISCOA-CEED technique is applied to solve a series of 6 and 11 generators in the CEED issue.The experimental validation of the ISCOA-CEED technique ensured the enhanced performance of the presented ISCOA-CEED technique over other recent approaches.

关键词： Economic and emission dispatch multi-objective optimization metaheuristics fuel cost minimization sand cat optimization

Binary Oriented Feature Selection for Valid Product Derivation in Software Product Line

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Computers, Materials & Continua 2023年第9期76卷 3653-3670页

作者： Muhammad Fezan afzal Imran Khan Javed Rashid Mubbashar Saddique Heba G.mohamed Department of CS&SE International Islamic UniversityIslamabad44000Pakistan Information Technology Services University of OkaraOkara56300Pakistan Departmet of Computer Science MLC LabOkara56300Pakistan Department of Computer Science&Engineering University of Engineering&Technology LahoreNarowal CampusNarowal51601Pakistan Department of Electrical Engineering College of EngineeringPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia

Software Product Line(SPL)is a group of software-intensive systems that share common and variable resources for developing a particular system.The feature model is a tree-type structure used to manage SPL’s common and variable features with their different relations and problem of Crosstree Constraints(CTC).CTC problems exist in groups of common and variable features among the sub-tree of feature models more diverse in Internet of Things(IoT)devices because different Internet devices and protocols are communicated.Therefore,managing the CTC problem to achieve valid product configuration in IoT-based SPL is more complex,time-consuming,and hard.However,the CTC problem needs to be considered in previously proposed approaches such as Commonality VariabilityModeling of Features(COVAMOF)andGenarch+tool;therefore,invalid products are generated.This research has proposed a novel approach Binary Oriented Feature Selection Crosstree Constraints(BOFS-CTC),to find all possible valid products by selecting the features according to cardinality constraints and cross-tree constraint problems in the featuremodel of SPL.BOFS-CTC removes the invalid products at the early stage of feature selection for the product configuration.Furthermore,this research developed the BOFS-CTC algorithm and applied it to,IoT-based feature models.The findings of this research are that no relationship constraints and CTC violations occur and drive the valid feature product configurations for the application development by removing the invalid product configurations.The accuracy of BOFS-CTC is measured by the integration sampling technique,where different valid product configurations are compared with the product configurations derived by BOFS-CTC and found 100%correct.Using BOFS-CTC eliminates the testing cost and development effort of invalid SPL products.

关键词： Software product line feature model internet of things crosstree constraints variability management

User Behavior Traffic Analysis Using a Simplified Memory-Prediction Framework

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Computers, Materials & Continua 2022年第2期70卷 2679-2698页

作者： Rahmat Budiarto Ahmad A.Alqarni Mohammed YAlzahrani Muhammad Fermi pasha mohamed Fazilmohamed Firdhous Deris Stiawan College of Computer Science&IT Albaha UniversityAlaqiq65779-7738Saudi Arabia Malaysia School of Information Technology Monash UniversityBandar Sunway47500Malaysia Department of Information Technology University of MoratuwaMoratuwa10400Sri Lanka Faculty of Computer Science Universitas SriwijayaIndralaya30662Indonesia

As nearly half of the incidents in enterprise security have been triggered by insiders,it is important to deploy a more intelligent defense system to assist enterprises in pinpointing and resolving the incidents caused by insiders or malicious software(malware)in real-time.Failing to do so may cause a serious loss of reputation as well as business.At the same time,modern network traffic has dynamic patterns,high complexity,and large volumes that make it more difficult to detect malware early.The ability to learn tasks sequentially is crucial to the development of artificial intelligence.Existing neurogenetic computation models with deep-learning techniques are able to detect complex patterns;however,the models have limitations,including catastrophic forgetfulness,and require intensive computational resources.As defense systems using deep-learning models require more time to learn new traffic patterns,they cannot perform fully online(on-the-fly)learning.Hence,an intelligent attack/malware detection system with on-the-fly learning capability is required.For this paper,a memory-prediction framework was adopted,and a simplified single cell assembled sequential hierarchical memory(s.SCASHM)model instead of the hierarchical temporal memory(HTM)model is proposed to speed up learning convergence to achieve onthe-fly learning.The s.SCASHM consists of a Single Neuronal Cell(SNC)model and a simplified Sequential Hierarchical Superset(SHS)platform.The s.SCASHMis implemented as the prediction engine of a user behavior analysis tool to detect insider attacks/anomalies.The experimental results show that the proposed memory model can predict users’traffic behavior with accuracy level ranging from 72%to 83%while performing on-the-fly learning.

关键词： Machine learning memory prediction framework insider attacks user behavior analytics

Optimal Fuzzy Logic Enabled Intrusion Detection for Secure IoT-Cloud Environment

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Computers, Materials & Continua 2023年第3期74卷 6737-6753页

作者： Fatma S.Alrayes Nuha Alshuqayran mohamed K Nour Mesfer Al Duhayyim Abdullah mohamed Amgad Atta Abdelmageed Mohammed Gouse pasha Mohammed Ishfaq Yaseen Department of Information Systems College of Computer and Information SciencesPrincess Nourah Bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Information Systems College of Computer and Information SciencesImam Mohammad Ibn Saud Islamic UniversitySaudi Arabia Department of Computer Sciences College of Computing and Information SystemUmm Al-Qura UniversitySaudi Arabia Department of Computer Science College of Sciences and Humanities-AflajPrince Sattam bin Abdulaziz UniversitySaudi Arabia Research Centre Future University in EgyptNew Cairo11845Egypt Department of Computer and Self Development Preparatory Year DeanshipPrince Sattam bin Abdulaziz UniversityAlKharjSaudi Arabia

Recently,Internet of Things(IoT)devices have developed at a faster rate and utilization of devices gets considerably increased in day to day lives.Despite the benefits of IoT devices,security issues remain challenging owing to the fact that most devices do not include memory and computing resources essential for satisfactory security operation.Consequently,IoT devices are vulnerable to different kinds of attacks.A single attack on networking system/device could result in considerable data to data security and privacy.But the emergence of artificial intelligence(AI)techniques can be exploited for attack detection and classification in the IoT environment.In this view,this paper presents novel metaheuristics feature selection with fuzzy logic enabled intrusion detection system(MFSFL-IDS)in the IoT environment.The presented MFSFL-IDS approach purposes for recognizing the existence of intrusions and accomplish security in the IoT environment.To achieve this,the MFSFL-IDS model employs data pre-processing to transform the data into useful format.Besides,henry gas solubility optimization(HGSO)algorithm is applied as a feature selection approach to derive useful feature vectors.Moreover,adaptive neuro fuzzy inference system(ANFIS)technique was utilized for the recognition and classification of intrusions in the network.Finally,binary bat algorithm(BBA)is exploited for adjusting parameters involved in the ANFIS model.A comprehensive experimental validation of the MFSFL-IDS model is carried out using benchmark dataset and the outcomes are assessed under distinct aspects.The experimentation outcomes highlighted the superior performance of the MFSFL-IDS model over recentapproaches with maximum accuracy of 99.80%.

关键词： Cloud computing security fuzzy logic intrusion detection internet of things metaheuristics

Quantum Particle Swarm Optimization with Deep Learning-Based Arabic Tweets Sentiment Analysis

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Computers, Materials & Continua 2023年第5期75卷 2575-2591页

作者： Badriyya BAl-onazi Abdulkhaleq Q.A.Hassan mohamed K.Nour Mesfer Al Duhayyim Abdullah mohamed Amgad Atta Abdelmageed Ishfaq Yaseen Gouse pasha Mohammed Department of Language Preparation Arabic Language Teaching InstitutePrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of English College of Science and Arts at MahayilKing Khalid UniversityAbha62217Saudi Arabia Department of Computer Sciences College of Computing and Information SystemUmm Al-Qura UniversityMakkah24231Saudi Arabia Department of Computer Science College of Sciences and Humanities-AflajPrince Sattam bin Abdulaziz UniversityAl-Aflaj16828Saudi Arabia Research Centre Future University in EgyptNew Cairo11845Egypt Department of Computer and Self Development Preparatory Year DeanshipPrince Sattam bin Abdulaziz UniversityAlKharj16436Saudi Arabia

Sentiment Analysis(SA),a Machine Learning(ML)technique,is often applied in the literature.The SA technique is specifically applied to the data collected from social media sites.The research studies conducted earlier upon the SA of the tweets were mostly aimed at automating the feature extraction process.In this background,the current study introduces a novel method called Quantum Particle Swarm Optimization with Deep Learning-Based Sentiment Analysis on Arabic Tweets(QPSODL-SAAT).The presented QPSODL-SAAT model determines and classifies the sentiments of the tweets written in Arabic.Initially,the data pre-processing is performed to convert the raw tweets into a useful format.Then,the word2vec model is applied to generate the feature vectors.The Bidirectional Gated Recurrent Unit(BiGRU)classifier is utilized to identify and classify the sentiments.Finally,the QPSO algorithm is exploited for the optimal finetuning of the hyperparameters involved in the BiGRU model.The proposed QPSODL-SAAT model was experimentally validated using the standard datasets.An extensive comparative analysis was conducted,and the proposed model achieved a maximum accuracy of 98.35%.The outcomes confirmed the supremacy of the proposed QPSODL-SAAT model over the rest of the approaches,such as the Surface Features(SF),Generic Embeddings(GE),Arabic Sentiment Embeddings constructed using the Hybrid(ASEH)model and the Bidirectional Encoder Representations from Transformers(BERT)model.

关键词： Sentiment analysis Arabic tweets quantum particle swarm optimization deep learning word embedding