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| 1 | Novel therapeutic targetsin non - small cell lung cancer 显示文摘 | Alamgeer M Canju V Neil WD | 2013 | Curr Opin Pharmacol2013，，： | 1 |
| 2 | Cancer stem ceils in lung cancer : evidence and controversies 显示文摘 | Alamgeer M Peacock CD Matsui W | 2013 | Respirology2013，18，5： | 1 |
| 3 | Alkaloids, flavonoids, polyphenols might be responsible for potent antiarthritic effect of Solanum nigrum显示文摘OBJECTIVE: To evaluate in vitro and in vivo antiarthritic potential of Solanum nigrum (S. nigrum). METHODS: Aqueous methanolic (70∶30) extract of S. nigrum was prepared. The in vitro antiarthritic effect was evaluated in terms of its inhibition of protein denaturation and membrane stabilization. While, formaldehyde, complete Freund's adjuvant (CFA) and Collagen induced arthritis rat models were used to study in vivo antiarthritic activities of S. nigrum at dose level of 200, 400 and 800 mg/kg. RESULTS: The extract exhibited inhibition of protein denaturation and protected red blood cell by stabilizing the membranes in a concentration dependent manner, with maximum effect attained at 800 μg/mL. Moreover, there was a marked reduction in paw edema observed in extract treated animals, when compared to arthritic control animals in all in vivo models and 800 mg/kg dose got maximum reduction of paw edema. In CFA and collagen models, plant extract restored body weight, hematologic parameters, radiographic and histopathoOBJECTIVE: To evaluate in vitro and in vivo antiarthritic potential of Solanum nigrum (S. nigrum). METHODS: Aqueous methanolic (70∶30) extract of S. nigrum was prepared. The in vitro antiarthritic effect was evaluated in terms of its inhibition of protein denaturation and membrane stabilization. While, formaldehyde, complete Freund's adjuvant (CFA) and Collagen induced arthritis rat models were used to study in vivo antiarthritic activities of S. nigrum at dose level of 200, 400 and 800 mg/kg. RESULTS: The extract exhibited inhibition of protein denaturation and protected red blood cell by stabilizing the membranes in a concentration dependent manner, with maximum effect attained at 800 μg/mL. Moreover, there was a marked reduction in paw edema observed in extract treated animals, when compared to arthritic control animals in all in vivo models and 800 mg/kg dose got maximum reduction of paw edema. In CFA and collagen models, plant extract restored body weight, hematologic parameters, radiographic and histopathologic alterations towards normal.CONCLUSION: It could be concluded that S. nigrum holds antiarthritic potential, supporting its traditional use in treatment of rheumatoid arthritis. | Alamgeer Shanila Akhter Ambreen Malik Uttra Umme Habiba Hasan | 2019 | Journal of Traditional Chinese Medicine2019，39，5： | 1 |
| 4 | Deep Learning Enabled Computer Aided Diagnosis Model for Lung Cancer using Biomedical CT Images显示文摘Early detection of lung cancer can help for improving the survival rate of the patients.Biomedical imaging tools such as computed tomography(CT)image was utilized to the proper identification and positioning of lung cancer.The recently developed deep learning(DL)models can be employed for the effectual identification and classification of diseases.This article introduces novel deep learning enabled CAD technique for lung cancer using biomedical CT image,named DLCADLC-BCT technique.The proposed DLCADLC-BCT technique intends for detecting and classifying lung cancer using CT images.The proposed DLCADLC-BCT technique initially uses gray level co-occurrence matrix(GLCM)model for feature extraction.Also,long short term memory(LSTM)model was applied for classifying the existence of lung cancer in the CT images.Moreover,moth swarm optimization(MSO)algorithm is employed to optimally choose the hyperparameters of the LSTM model such as learning rate,batch size,and epoch count.For demonstrating the improved classifier results of the DLCADLC-BCT approach,a set of simulations were executed on benchmark dataset and the outcomes exhibited the supremacy of the DLCADLC-BCT technique over the recent approaches. | Mohammad Alamgeer Hanan Abdullah Mengash Radwa Marzouk Mohamed K Nour Anwer Mustafa Hilal Abdelwahed Motwakel Abu Sarwar Zamani Mohammed Rizwanullah | 2022 | Computers, Materials & Continua2022，，10： | 1 |
| 5 | The Evolution of Therapies in Non-Small Cell Lung Cancer 显示文摘 | Boolell V Alamgeer M Watkins DN | 2015 | Cancers (Basel)2015，7，3： | 1 |
| 6 | Cancer stem cells in lung cancer: Evidence and controversies 显示文摘 | Alamgeer M Peacock CD Matsui W | 2013 | Respirology2013，18，5： | 1 |
| 7 | Changes in aldehyde dehydrogenase-1 expression during neoadjuvant chemotherapy predict outcome in locally advanced breast cancer显示文摘 | Alamgeer M Ganju V Kumar B | 2014 | Breast Cancer Res2014，16，2： | 1 |
| 8 | Cancer stem cells in lung cancer:Ev- idence and controversies 显示文摘 | Alamgeer M Craig P | 2013 | Respirology2013，18，5： | 1 |
| 9 | Deep Learning Empowered Cybersecurity Spam Bot Detection for Online Social Networks显示文摘Cybersecurity encompasses various elements such as strategies,policies,processes,and techniques to accomplish availability,confidentiality,and integrity of resource processing,network,software,and data from attacks.In this scenario,the rising popularity of Online Social Networks(OSN)is under threat from spammers for which effective spam bot detection approaches should be developed.Earlier studies have developed different approaches for the detection of spam bots in OSN.But those techniques primarily concentrated on hand-crafted features to capture the features of malicious users while the application of Deep Learning(DL)models needs to be explored.With this motivation,the current research article proposes a Spam Bot Detection technique using Hybrid DL model abbreviated as SBDHDL.The proposed SBD-HDL technique focuses on the detection of spam bots that exist in OSNs.The technique has different stages of operations such as pre-processing,classification,and parameter optimization.Besides,SBD-HDL technique hybridizes Graph Convolutional Network(GCN)with Recurrent Neural Network(RNN)model for spam bot classification process.In order to enhance the detection performance of GCN-RNN model,hyperparameters are tuned using Lion Optimization Algorithm(LOA).Both hybridization of GCN-RNN and LOA-based hyperparameter tuning process make the current work,a first-of-its-kind in this domain.The experimental validation of the proposed SBD-HDL technique,conducted upon benchmark dataset,established the supremacy of the technique since it was validated under different measures. | Mesfer Al Duhayyim Haya Mesfer Alshahrani Fahd NAl-Wesabi Mohammed Alamgeer Anwer Mustafa Hilal Mohammed Rizwanullah | 2022 | Computers, Materials & Continua2022，，3： | 1 |
| 10 | Apoptosisoflungepithelial cells in response to TNF-alpha requires angiotensin Ⅱ generation de novo 显示文摘 | WangR AlamG ZagariyaA etal | 2000 | J Cell Physiol2000，185，： | 1 |
| 11 | Smart-Fragile Authentication Scheme for Robust Detecting of Tampering Attacks on English Text显示文摘Content authentication,integrity verification,and tampering detection of digital content exchanged via the internet have been used to address a major concern in information and communication technology.In this paper,a text zero-watermarking approach known as Smart-Fragile Approach based on Soft Computing and Digital Watermarking(SFASCDW)is proposed for content authentication and tampering detection of English text.A first-level order of alphanumeric mechanism,based on hidden Markov model,is integrated with digital zero-watermarking techniques to improve the watermark robustness of the proposed approach.The researcher uses the first-level order and alphanumeric mechanism of Markov model as a soft computing technique to analyze English text.Moreover,he extracts the features of the interrelationship among the contexts of the text,utilizes the extracted features as watermark information,and validates it later with the studied English text to detect any tampering.SFASCDW has been implemented using PHP with VS code IDE.The robustness,effectiveness,and applicability of SFASCDW are proved with experiments involving four datasets of various lengths in random locations using the three common attacks,namely insertion,reorder,and deletion.The SFASCDW was found to be effective and could be applicable in detecting any possible tampering. | Mohammad Alamgeer Fahd N.Al-Wesabi Huda G.Iskandar Imran Khan Nadhem Nemri Mohammad Medani Mohammed Abdullah Al-Hagery Ali Mohammed Al-Sharafi | 2022 | Computers, Materials & Continua2022，，5： | 0 |
| 12 | Data Mining with Comprehensive Oppositional Based Learning for Rainfall Prediction显示文摘Data mining process involves a number of steps fromdata collection to visualization to identify useful data from massive data set.the same time,the recent advances of machine learning(ML)and deep learning(DL)models can be utilized for effectual rainfall prediction.With this motivation,this article develops a novel comprehensive oppositionalmoth flame optimization with deep learning for rainfall prediction(COMFO-DLRP)Technique.The proposed CMFO-DLRP model mainly intends to predict the rainfall and thereby determine the environmental changes.Primarily,data pre-processing and correlation matrix(CM)based feature selection processes are carried out.In addition,deep belief network(DBN)model is applied for the effective prediction of rainfall data.Moreover,COMFO algorithm was derived by integrating the concepts of comprehensive oppositional based learning(COBL)with traditional MFO algorithm.Finally,the COMFO algorithm is employed for the optimal hyperparameter selection of the DBN model.For demonstrating the improved outcomes of the COMFO-DLRP approach,a sequence of simulations were carried out and the outcomes are assessed under distinct measures.The simulation outcome highlighted the enhanced outcomes of the COMFO-DLRP method on the other techniques. | Mohammad Alamgeer Amal Al-Rasheed Ahmad Alhindi Manar Ahmed Hamza Abdelwahed Motwakel Mohamed I.Eldesouki | 2023 | Computers, Materials & Continua2023，，2： | 0 |
| 13 | Indoor Electromagnetic Radiation Intensity Relationship to Total Energy of Household Appliances显示文摘The rapid technological developments in the modern era have led to increased electrical equipment in our daily lives,work,and homes.From this standpoint,the main objective of this study is to evaluate the potential relationship between the intensity of electromagnetic radiation and the total energy of household appliances in the living environment within the building by measuring and analyzing the strength of the electric field and the entire electromagnetic radiation flux density of electrical devices operating at frequencies(5 Hz to 1 kHz).The living room was chosen as a center for measurement at 15 homes in three different environmental regions(urban,suburbs,and open areas).The three measurement methods are(Mode 1:people in a sitting position with electrical appliances on.Mode 2:People in a standing position with electrical appliances on.Mode 3:People are in the upright positionwhile turning off the electrical devices)in the living room.These measurement methods and their results reinforce the importance of this research.The results showed that the average electric field strengthmeasured inMode 2 ismuch greater than the two methods,and we also found less electromagnetic radiation in Mode 3 than in the two modes.All results remain within the recommended overall exposure developed by the International Committee for the Prevention of Non-Ionizing Radiation and the International Electrotechnical Commission. | Murad A.A.Almekhlafi Lamia Osman Widaa Fahd N.Al-Wesabi Mohammad Alamgeer Anwer Mustafa Hilal Manar Ahmed Hamza Abu Sarwar Zamani Mohammed Rizwanullah | 2022 | Computers, Materials & Continua2022，，3： | 0 |
| 14 | An Optimal Text Watermarking Method for Sensitive Detecting of Illegal Tampering Attacks显示文摘Due to the rapid increase in the exchange of text information via internet networks,the security and authenticity of digital content have become a major research issue.The main challenges faced by researchers are how to hide the information within the text to use it later for authentication and attacks tampering detection without effects on the meaning and size of the given digital text.In this paper,an efficient text-based watermarking method has been proposed for detecting the illegal tampering attacks on theArabic text transmitted online via an Internet network.Towards this purpose,the accuracy of tampering detection and watermark robustness has been improved of the proposed method as compared with the existing approaches.In the proposed method,both embedding and extracting of the watermark are logically implemented,which causes no change in the digital text.This is achieved by using the third level and alphanumeric strategy of the Markov model as a text analysis technique for analyzing the Arabic contents to obtain its features which are considered as the digital watermark.This digital watermark will be used later to detecting any tampering of illegal attack on the received Arabic text.An extensive set of experiments using four data sets of varying lengths proves the effectiveness of our approach in terms of detection accuracy,robustness,and effectiveness under multiple random locations of the common tampering attacks. | Anwer Mustafa Hilal Fahd N.Al-Wesabi Mohammed Alamgeer Manar Ahmed Hamza Mohammad Mahzari Murad A.Almekhlafi | 2022 | Computers, Materials & Continua2022，，3： | 0 |
| 15 | Intelligent Deep Learning Based Automated Fish Detection Model for UWSN显示文摘An exponential growth in advanced technologies has resulted in the exploration of Ocean spaces.It has paved the way for new opportunities that can address questions relevant to diversity,uniqueness,and difficulty of marine life.Underwater Wireless Sensor Networks(UWSNs)are widely used to leverage such opportunities while these networks include a set of vehicles and sensors to monitor the environmental conditions.In this scenario,it is fascinating to design an automated fish detection technique with the help of underwater videos and computer vision techniques so as to estimate and monitor fish biomass in water bodies.Several models have been developed earlier for fish detection.However,they lack robustness to accommodate considerable differences in scenes owing to poor luminosity,fish orientation,structure of seabed,aquatic plantmovement in the background and distinctive shapes and texture of fishes from different genus.With this motivation,the current research article introduces an Intelligent Deep Learning based Automated Fish Detection model for UWSN,named IDLAFD-UWSN model.The presented IDLAFD-UWSN model aims at automatic detection of fishes from underwater videos,particularly in blurred and crowded environments.IDLAFD-UWSN model makes use of Mask Region Convolutional Neural Network(Mask RCNN)with Capsule Network as a baseline model for fish detection.Besides,in order to train Mask RCNN,background subtraction process using GaussianMixtureModel(GMM)model is applied.This model makes use of motion details of fishes in video which consequently integrates the outcome with actual image for the generation of fish-dependent candidate regions.Finally,Wavelet Kernel Extreme Learning Machine(WKELM)model is utilized as a classifier model.The performance of the proposed IDLAFD-UWSN model was tested against benchmark underwater video dataset and the experimental results achieved by IDLAFD-UWSN model were promising in comparison with other state-of-the-art methods under different aspects with the maximum accuracy of 98%and 97%on the applied blurred and crowded datasets respectively. | Mesfer Al Duhayyim Haya Mesfer Alshahrani Fahd NAl-Wesabi Mohammed Alamgeer Anwer Mustafa Hilal Manar Ahmed Hamza | 2022 | Computers, Materials & Continua2022，，3： | 0 |
| 16 | Modeling of Artificial Intelligence Based Traffic Flow Prediction with Weather Conditions显示文摘Short-term traffic flow prediction (TFP) is an important area inintelligent transportation system (ITS), which is used to reduce traffic congestion. But the avail of traffic flow data with temporal features and periodicfeatures are susceptible to weather conditions, making TFP a challengingissue. TFP process are significantly influenced by several factors like accidentand weather. Particularly, the inclement weather conditions may have anextreme impact on travel time and traffic flow. Since most of the existing TFPtechniques do not consider the impact of weather conditions on the TF, it isneeded to develop effective TFP with the consideration of extreme weatherconditions. In this view, this paper designs an artificial intelligence based TFPwith weather conditions (AITFP-WC) for smart cities. The goal of the AITFPWC model is to enhance the performance of the TFP model with the inclusionof weather related conditions. The proposed AITFP-WC technique includesElman neural network (ENN) model to predict the flow of traffic in smartcities. Besides, tunicate swarm algorithm with feed forward neural networks(TSA-FFNN) model is employed for the weather and periodicity analysis. Atlast, a fusion of TFP and WPA processes takes place using the FFNN modelto determine the final prediction output. In order to assess the enhancedpredictive outcome of the AITFP-WC model, an extensive simulation analysisis carried out. The experimental values highlighted the enhanced performanceof the AITFP-WC technique over the recent state of art methods. | Mesfer Al Duhayyim Amani Abdulrahman Albraikan Fahd N.Al-Wesabi Hiba M.Burbur Mohammad Alamgeer Anwer Mustafa Hilal Manar Ahmed Hamza Mohammed Rizwanullah | 2022 | Computers, Materials & Continua2022，，5： | 0 |
| 17 | Privacy Preserving Image Encryption with Deep Learning Based IoT Healthcare Applications显示文摘Latest developments in computing and communication technologies are enabled the design of connected healthcare system which are mainly based on IoT and Edge technologies.Blockchain,data encryption,and deep learning(DL)models can be utilized to design efficient security solutions for IoT healthcare applications.In this aspect,this article introduces a Blockchain with privacy preserving image encryption and optimal deep learning(BPPIEODL)technique for IoT healthcare applications.The proposed BPPIE-ODL technique intends to securely transmit the encrypted medical images captured by IoT devices and performs classification process at the cloud server.The proposed BPPIE-ODL technique encompasses the design of dragonfly algorithm(DFA)with signcryption technique to encrypt the medical images captured by the IoT devices.Besides,blockchain(BC)can be utilized as a distributed data saving approach for generating a ledger,which permits access to the users and prevents third party’s access to encrypted data.In addition,the classification process includes SqueezeNet based feature extraction,softmax classifier(SMC),and Nadam based hyperparameter optimizer.The usage of Nadam model helps to optimally regulate the hyperparameters of the SqueezeNet architecture.For examining the enhanced encryption as well as classification performance of the BPPIE-ODL technique,a comprehensive experimental analysis is carried out.The simulation outcomes demonstrate the significant performance of the BPPIE-ODL technique on the other techniques with increased precision and accuracy of 0.9551 and 0.9813 respectively. | Mohammad Alamgeer Saud S.Alotaibi Shaha Al-Otaibi Nazik Alturki Anwer Mustafa Hilal Abdelwahed Motwakel Ishfaq Yaseen Mohamed I.Eldesouki | 2022 | Computers, Materials & Continua2022，，10： | 0 |
| 18 | Optimal Resource Allocation Method for Device-to-Device Communication in 5G Networks显示文摘With the rapid development of the next-generation mobile network,the number of terminal devices and applications is growing explosively.Therefore,how to obtain a higher data rate,wider network coverage and higher resource utilization in the limited spectrum resources has become the common research goal of scholars.Device-to-Device(D2D)communication technology and other frontier communication technologies have emerged.Device-to-Device communication technology is the technology that devices in proximity can communicate directly in cellular networks.It has become one of the key technologies of the fifth-generation mobile communications system(5G).D2D communication technology which is introduced into cellular networks can effectively improve spectrum utilization,enhance network coverage,reduce transmission delay and improve system throughput,but it would also bring complicated and various interferences due to reusing cellular resources at the same time.So resource management is one of the most challenging and importing issues to give full play to the advantages of D2D communication.Optimal resource allocation is an important factor that needs to be addressed in D2D communication.Therefore,this paper proposes an optimization method based on the game-matching concept.The main idea is to model the optimization problem of the quality-of-experience based on user fairness and solve it through game-matching theory.Simulation results show that the proposed algorithm effectively improved the resource allocation and utilization as compared with existing algorithms. | Fahd N.Al-Wesabi Imran Khan Saleem Latteef Mohammed Huda Farooq Jameel Mohammad Alamgeer Ali M.Al-Sharafi Byung Seo Kim | 2022 | Computers, Materials & Continua2022，，4： | 0 |
| 19 | IoT with Evolutionary Algorithm Based Deep Learning for Smart Irrigation System显示文摘In India, water wastage in agricultural fields becomes a challengingissue and it is needed to minimize the loss of water in the irrigation process.Since the conventional irrigation system needs massive quantity of waterutilization, a smart irrigation system can be designed with the help of recenttechnologies such as machine learning (ML) and the Internet of Things (IoT).With this motivation, this paper designs a novel IoT enabled deep learningenabled smart irrigation system (IoTDL-SIS) technique. The goal of theIoTDL-SIS technique focuses on the design of smart irrigation techniquesfor effectual water utilization with less human interventions. The proposedIoTDL-SIS technique involves distinct sensors namely soil moisture, temperature, air temperature, and humidity for data acquisition purposes. The sensordata are transmitted to the Arduino module which then transmits the sensordata to the cloud server for further process. The cloud server performs the dataanalysis process using three distinct processes namely regression, clustering,and binary classification. Firstly, deep support vector machine (DSVM) basedregression is employed was utilized for predicting the soil and environmentalparameters in advances such as atmospheric pressure, precipitation, solarradiation, and wind speed. Secondly, these estimated outcomes are fed intothe clustering technique to minimize the predicted error. Thirdly, ArtificialImmune Optimization Algorithm (AIOA) with deep belief network (DBN)model receives the clustering data with the estimated weather data as inputand performs classification process. A detailed experimental results analysisdemonstrated the promising performance of the presented technique over theother recent state of art techniques with the higher accuracy of 0.971. | P.Suresh R.H.Aswathy Sridevi Arumugam Amani Abdulrahman Albraikan Fahd N.Al-Wesabi Anwer Mustafa Hilal Mohammad Alamgeer | 2022 | Computers, Materials & Continua2022，，4： | 0 |
| 20 | Wearable Robots for Human Underwater Movement Ability Enhancement:A Survey显示文摘Underwater robot technology has shown impressive results in applications such as underwater resource detection.For underwater applications that require extremely high flexibility,robots cannot replace skills that require human dexterity yet,and thus humans are often required to directly perform most underwater operations.Wearable robots(exoskeletons)have shown outstanding results in enhancing human movement on land.They are expected to have great potential to enhance human underwater movement.The purpose of this survey is to analyze the state-of-the-art of underwater exoskeletons for human enhancement,and the applications focused on movement assistance while excluding underwater robotic devices that help to keep the temperature and pressure in the range that people can withstand.This work discusses the challenges of existing exoskeletons for human underwater movement assistance,which mainly includes human underwater motion intention perception,underwater exoskeleton modeling and human-cooperative control.Future research should focus on developing novel wearable robotic structures for underwater motion assistance,exploiting advanced sensors and fusion algorithms for human underwater motion intention perception,building up a dynamic model of underwater exoskeletons and exploring human-in-theloop control for them. | Haisheng Xia Muhammad Alamgeer Khan Zhijun Li MengChu Zhou | 2022 | IEEE/CAA Journal of Automatica Sinica2022，9，6： | 0 |
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